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The Essential Guide to Doing Your Research Project

• Zina O'Leary - The Australia and New Zealand School of Government
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Packed with pragmatic guidance for tackling research in the real world, this fourth edition:

• Offers support for diving into a project using digital data, with how-to guidance on conducting online and social media research
• Empowers you to confidently disseminate your work and present with impact
• Helps you map out your research journey and put a plan in place with decision trees in every chapter
• Challenges you to be reflective and critical about the research you consume and undertake

Zina O'Leary's detailed and down-to-earth approach gives you the research skills and momentum you need to successfully complete your research project.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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The writing style is appropriate to all levels and is totally accessible to the students I teach. Overall, this is a very welcome book for research.

Very user-friendly and interactive – an easy read for my students.

I have added this to the Dissertation Module, for the BA Education Studies. A very good guide in completing research.

This text is a stalwart in approaching independent projects and dissertations. The fourth edition keeps it sharp and current.

I so enjoyed reading this book because I felt like Zina was in the room talking to me and I was her student. She clearly understands undertaking research from a student's perspective. She has obviously drawn on a rich and lengthy experience demonstrated by the relevant questions she poses throughout the book and her writing is so easily accessible and understandable, even the bits students often find tricky. All aspects the process are covered, from preliminary thinking to actually getting started to finally sharing your research with others. A book I will definitely include in our reading list for next year.

This a very useful and comprehensive book for students studying research methods and/or dissertations students.

Simple and easy to use for my MSc Students especially those that have never done a dissertation.

Really useful text, accurate, brief and clear direction for students and staff. I do not manage research but supervise researchers and this is an excellent framework for creating excellence in this essential discipline.

Great book for beginner researchers

Content is appropriate for intended student population

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MIT’s top research stories of 2021

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Despite the pandemic’s disruptions, MIT’s research community still found a way to generate a number of impressive research breakthroughs in 2021. In the spirit of reflection that comes with every new orbit around the sun, below we count down 10 of the most-viewed research stories on MIT News from the past year.

We’ve also rounded up the year’s top MIT community-related stories .

10. Giving cancer treatment a recharge . In October, researchers discovered a way to jump-start the immune system to attack tumors. The method combines chemotherapy and immunotherapy to spur immune cells into action. The researchers hope it could allow immunotherapy to be used against more types of cancer.

9. Generating 3D holograms in real-time . Computer scientists developed a deep-learning-based system that allows computers to create holograms almost instantly. The system could be used to create holograms for virtual reality, 3D printing, medical imaging, and more — and it’s efficient enough to run on a smartphone.

8. Creating inhalable vaccines . Scientists at the Koch Institute developed a method for delivering vaccines directly to the lungs through inhalation. The new strategy induced a strong immune response in the lungs of mice and could offer a quicker response to viruses that infect hosts through mucosal surfaces.

7. Assessing Covid-19 transmission risk . Two MIT professors proposed a new approach to estimating the risks of exposure to Covid-19 in different indoor settings. The guidelines suggest a limit for exposure based on factors such as the size of the space, the number of people, the kinds of activity, whether masks are worn, and ventilation and filtration rates.

6. Teaching machine learning models to adapt . Researchers in CSAIL developed a new type of neural network that can change its underlying equations to continuously adapt to new data. The advance could improve models’ decision-making based on data that changes over time, such as in medical diagnosis and autonomous driving.

5. Programming fibers . In June, a team created the first fabric fiber with digital capabilities. The fibers can sense, store, analyze, and infer data and activity after being sewn into a shirt. The researchers say the fibers could be used to monitor physical performance, to detect diseases, and for a variety of medical purposes.

4. Examining the limitations of data visualizations . A collaboration between anthropologists and computer scientists found that coronavirus skeptics have used sophisticated data visualizations to argue against public health orthodoxy like wearing a mask. The researchers concluded that data visualizations aren’t sufficient to convey the urgency of the Covid-19 pandemic because even the clearest graphs can be interpreted through a variety of belief systems.

3. Developing a Covid-detecting face mask . Engineers at MIT and Harvard University designed a prototype face mask that can diagnose the person wearing the mask with Covid-19 in about 90 minutes. The masks are embedded with tiny, disposable sensors that can be fitted into other face masks and could also be adapted to detect other viruses.

2. Confirming Hawking’s black hole theorem . Using observations of gravitational waves, physicists from MIT and elsewhere confirmed a major theorem created by Stephen Hawking in 1971. The theorem states that the area of a black hole’s event horizon — the boundary beyond which nothing can ever escape — will never shrink.

1. Advancing toward fusion energy . In September, researchers at MIT and the MIT spinout Commonwealth Fusion Systems ramped up a high-temperature superconducting electromagnet to a field strength of 20 tesla, the most powerful magnetic field of its kind ever created on Earth. The demonstration was three years in the making and is believed to resolve one of greatest remaining points of uncertainty in the quest to build the world’s first fusion power plant that produces more energy than it consumes.

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Top 10 research topics from 2021.

Find the answers to your biggest research questions from 2021. With collective views of over 3.7 million, researchers explored topics spanning from nutritional immunology and political misinformation to sustainable agriculture and the human-dog bond .

Research Topics:

1. Infectious disease

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2. Nutritional immunology

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3. Music therapy

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4. Political misinformation

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5. Plant science

• 198,000 views
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6. Sustainable agriculture

• 168,000 views
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7. Mental health

• 136,000 views
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8. Aging brains

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Benefits of human-dog interactions

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10. Mood disorders

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December 22, 2021

2021 Research Highlights — Promising Medical Findings

Results with potential for enhancing human health.

With NIH support, scientists across the United States and around the world conduct wide-ranging research to discover ways to enhance health, lengthen life, and reduce illness and disability. Groundbreaking NIH-funded research often receives top scientific honors. In 2021, these honors included Nobel Prizes to five NIH-supported scientists . Here’s just a small sample of the NIH-supported research accomplishments in 2021.

Printer-friendly version of full 2021 NIH Research Highlights

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Advancing COVID-19 treatment and prevention

Amid the sustained pandemic, researchers continued to develop new drugs and vaccines for COVID-19. They found oral drugs that could  inhibit virus replication in hamsters and shut down a key enzyme that the virus needs to replicate. Both drugs are currently in clinical trials. Another drug effectively treated both SARS-CoV-2 and RSV, another serious respiratory virus, in animals. Other researchers used an airway-on-a-chip to screen approved drugs for use against COVID-19. These studies identified oral drugs that could be administered outside of clinical settings. Such drugs could become powerful tools for fighting the ongoing pandemic. Also in development are an intranasal vaccine , which could help prevent virus transmission, and vaccines that can protect against a range of coronaviruses .

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Developments in Alzheimer’s disease research

One of the hallmarks of Alzheimer’s is an abnormal buildup of amyloid-beta protein. A study in mice suggests that antibody therapies targeting amyloid-beta protein could be more effective after enhancing the brain’s waste drainage system . In another study, irisin, an exercise-induced hormone, was found to improve cognitive performance in mice . New approaches also found two approved drugs (described below) with promise for treating AD. These findings point to potential strategies for treating Alzheimer’s. Meanwhile, researchers found that people who slept six hours or less per night in their 50s and 60s were more likely to develop dementia later in life, suggesting that inadequate sleep duration could increase dementia risk.

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New uses for old drugs

Developing new drugs can be costly, and the odds of success can be slim. So, some researchers have turned to repurposing drugs that are already approved for other conditions. Scientists found that two FDA-approved drugs were associated with lower rates of Alzheimer’s disease. One is used for high blood pressure and swelling. The other is FDA-approved to treat erectile dysfunction and pulmonary hypertension. Meanwhile, the antidepressant fluoxetine was associated with reduced risk of age-related macular degeneration. Clinical trials will be needed to confirm these drugs’ effects.

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Making a wireless, biodegradable pacemaker

Pacemakers are a vital part of medical care for many people with heart rhythm disorders. Temporary pacemakers currently use wires connected to a power source outside the body. Researchers developed a temporary pacemaker that is powered wirelessly. It also breaks down harmlessly in the body after use. Studies showed that the device can generate enough power to pace a human heart without causing damage or inflammation.

20210330-crohns.jpg

Fungi may impair wound healing in Crohn’s disease

Inflammatory bowel disease develops when immune cells in the gut overreact to a perceived threat to the body. It’s thought that the microbiome plays a role in this process. Researchers found that a fungus called  Debaryomyces hansenii  impaired gut wound healing in mice and was also found in damaged gut tissue in people with Crohn’s disease, a type of inflammatory bowel disease. Blocking this microbe might encourage tissue repair in Crohn’s disease.

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Nanoparticle-based flu vaccine

Influenza, or flu, kills an estimated 290,000-650,000 people each year worldwide. The flu virus changes, or mutates, quickly. A single vaccine that conferred protection against a wide variety of strains would provide a major boost to global health. Researchers developed a nanoparticle-based vaccine that protected against a broad range of flu virus strains in animals. The vaccine may prevent flu more effectively than current seasonal vaccines. Researchers are planning a Phase 1 clinical trial to test the vaccine in people.

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A targeted antibiotic for treating Lyme disease

Lyme disease cases are becoming more frequent and widespread. Current treatment entails the use of broad-spectrum antibiotics. But these drugs can damage the patient’s gut microbiome and select for resistance in non-target bacteria. Researchers found that a neglected antibiotic called hygromycin A selectively kills the bacteria that cause Lyme disease. The antibiotic was able to treat Lyme disease in mice without disrupting the microbiome and could make an attractive therapeutic candidate.

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Retraining the brain to treat chronic pain

More than 25 million people in the U.S. live with chronic pain. After a treatment called pain reprocessing therapy, two-thirds of people with mild or moderate chronic back pain for which no physical cause could be found were mostly or completely pain-free. The findings suggest that people can learn to reduce the brain activity causing some types of chronic pain that occur in the absence of injury or persist after healing.

2021 Research Highlights — Basic Research Insights >>

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Science Magazine: ARPA-H: Accelerating biomedical breakthroughs

A DARPA-like culture at NIH can drive biomedical and health advances

By: White House Office of Science and Technology Policy (OSTP) Director Dr. Eric Lander, National Institutes of Health Director (NIH) Dr. Francis Collins, OSTP Assistant Director for Biomedical Science Initiatives Dr. Tara Schwetz, and NIH Principal Deputy Director Dr. Lawrence A. Tabak

The biomedical research ecosystem has delivered advances that not long ago would have been inconceivable, exemplified by highly effective COVID-19 vaccines developed by global partners and approved in less than a year. The United States stands at a moment of unprecedented scientific promise and is challenged to ask: What more can we do to accelerate the pace of break-throughs to transform medicine and health? Toward that end, President Biden recently proposed to create a new entity, the Advanced Research Projects Agency for Health (ARPA-H), within the National Institutes of Health (NIH) “to develop breakthroughs—to prevent, detect, and treat diseases like Alzheimer’s, diabetes, and cancer,” requesting $6.5 billion in the fiscal year 2022 budget (1). The idea is inspired by the Defense Advanced Research Projects Agency (DARPA), which follows a flexible and nimble strategy, undeterred by the possibility of failure, and has driven breakthrough advances for the Department of Defense (DOD) for more than 60 years. To design ARPA-H, it is critical to understand what is working well within the biomedical ecosystem, where there are crucial gaps, and the key principles of DARPA’s success.

WHEN IDEAS DON’T FIT MECHANISMS

Progress in medicine and health in recent decades has been driven by two powerful forces: pathbreaking fundamental re- search and a vibrant commercial biotechnology sector. Fundamental research is typically performed in university, nonprofit, and government labs. In the United States, it is mostly funded by the federal government, largely through the NIH. By steadily pursuing important fundamental questions in biology and medicine, scientists have made great progress in discovering the molecular and cellular mechanisms underlying health and disease—often suggesting new ideas for clinical treatment. Such fundamental research is what economists term a public good, in that it produces knowledge available to everyone and thus requires public investment. Some have estimated that every dollar of federal investment yields at least $8 in economic growth, and suggested that every new therapeutic approved by the US Food and Drug Administration (FDA) can be traced, in part, to fundamental discoveries supported by NIH (2, 3). Given its outsized impact, robust federal investment in fundamental research remains crucial to health and to the economy.

The commercial sector is largely focused on research, development, and marketing of specific products, to bring sophisticated therapies and devices to patients. Biotechnology companies have access to abundant capital to develop products—provided they can protect their intellectual property and recoup the costs by generating sufficient profit in a short enough period of time. Currently, more than 8000 medicines are in development, including 1300 for cancer (4, 5).

In many cases, these two components are all that’s needed to drive progress toward clinical benefit—though subsequent regulatory approvals, reimbursement, and adoption in health care systems can also be optimized. It’s becoming clear, though, that some of the most innovative project ideas, which could yield breakthroughs, don’t always fit existing support mechanisms: NIH support for science traditionally favors incremental, hypothesis-driven research, whereas business plans require an expected return on investment in a reasonable time frame that is sufficient to attract investors. As a result, some of the most promising ideas may never mature, representing substantial lost opportunity.

Bold ideas may not fit existing mechanisms because (i) the risk is too high; (ii) the cost is too large; (iii) the time frame is too long; (iv) the focus is too applied for academia; (v) there is a need for complex coordination among multiple parties; (vi) the near-term market opportunity is too small to justify commercial investment, given the expected market size or challenges in adoption by the health care system; or (vii) the scope is so broad that no company can realize the full economic benefit, resulting in underinvestment relative to the potential impact. Evaluations by companies also may not consider the impact of projects on inequities that persist in our health ecosystem. In short, projects with a potentially transformative impact on the ecosystem may not yet be economically compelling or sufficiently feasible for a company to move forward. At the same time, there are no public mechanisms to propel these public goods at rapid speed.

Many such bold ideas involve creating platforms, capabilities, and resources that could be applicable across many diseases. Whereas most NIH proposals are “curiosity-driven,” these ideas are largely “use-driven” research—that is, research directed at solving a practical problem.

DARPA AS AN INSPIRATION

DARPA was launched in the wake of Sputnik with a singular mission: to make pivotal investments in breakthrough technologies for national security. DARPA has played a key role in generating bold advances that have shaped the world—such as the internet, Global Positioning Systems, and self-driving cars—and has contributed to the development of many others, including messenger RNA vaccines. However, failure, especially failing early, and learning from that failure are also hallmarks of DARPA.

DARPA has a distinctive organization and culture that contrasts with traditional approaches in biomedical research. It is a flat and nimble organization whose work is driven by approximately 100 program managers (PMs) and office directors. The PMs are often recruited from industry or top research universities, and they come for limited terms of 3 to 5 years. They typically bring bold, risky ideas, and they are given the independence and sufficient resources to pursue them, mitigating risk through metric-driven accountability and by pursuing multiple approaches to achieve a quantifiable goal.

DARPA can support research at three stages (basic research, applied research, and advanced technology development); can fund efforts in multiple sectors (industry, university, national labs, and consortia across these sectors); can provide the critical mass of funding needed to tackle bold goals; and is empowered to pro mote collaboration and integration across performers. DARPA does not perform its own internal research. Although proposals are reviewed on a competitive basis, PMs have authority to select a portfolio of projects intended to achieve a particular program goal.

DARPA has long encouraged a culture that values a relentless drive for transformative technical results and a willingness to take risks. Notably, it does not focus on merely accelerating ordinary products to the market or making incremental progress, but on creating true breakthroughs. To act in this way, DARPA makes broad use of flexible hiring, procurement, and contracting authorities, provided by law.

Although DARPA is an excellent inspiration for ARPA-H, it is not a perfect model for biomedical and health research. It serves the needs of a single customer, the DOD, and its mission is focused on national security. Its projects typically involve engineered systems. By contrast, health breakthroughs (i) interact with biological systems that are much more complex and more poorly understood than engineered systems, requiring close coupling to a vast body of biomedical knowledge and experience; (ii) interact with a complex world of many customers and users—including patients, hospitals, physicians, biopharma companies, and payers; (iii) interact in complex ways with human behavior and social factors; and (iv) require navigating a complex regulatory landscape. ARPA-H can learn from DARPA, but will need to pioneer new approaches.

DARPA-LIKE APPROACHES AT NIH

NIH has some experience with running large, complex programs using DARPA-like approaches to drive highly managed, use-inspired, breakthrough research. A classic example was the Human Genome Project, aimed at reading out the complete 3 billion–nucleotide human genetic code. When the project began in 1990, the technology to accomplish the goal hadn’t been invented. By driving innovation, it was completed ahead of schedule and ultimately decreased the cost of sequencing a human genome from $3 billion at the outset to $500 today (6). Though estimates vary, it is clear that the overall economic return on investment has been enormous, with notable analyses estimating a nearly 180- fold return (7, 8).

A very recent example is the NIH’s response to the COVID-19 pandemic. Within weeks, NIH created two programs. The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) program is an unprecedented partnership with government, industry, nonprofits, and academia to drive preclinical and clinical therapeutics, developing master protocols for testing prioritized compounds in rigorous randomized clinical trials. These efforts accelerated the development and testing of several of the vaccines that are now being widely used. The Rapid Acceleration of Diagnostics (RADx) program used an “innovation funnel” approach to identify promising ideas for COVID-19 tests and support 32 new technology platforms that collectively are contributing 2 million tests per day, mostly at point of care (9).

Although these programs have been successful, they required bespoke solutions and herculean efforts to get them off the ground. Because NIH lacks a regular framework for such projects, many bold ideas are hard to realize. That’s where ARPA-H can help.

ARPA-H MISSION

ARPA-H should have a clear mission. Building on DARPA’s mission statement, an initial mission could be: “To make pivotal investments in breakthrough technologies and broadly applicable plat- forms, capabilities, resources, and solutions that have the potential to transform important areas of medicine and health for the benefit of all patients and that cannot readily be accomplished through traditional research or commercial activity.”

Notably, ARPA-H’s focus should be broad—ranging from molecular to societal—because breakthrough technologies are needed and are possible at many levels (see the box). When Pres ident Biden challenges researchers to “end cancer as we know it,” many basic scientists naturally think about solutions at the laboratory bench: powerful ways to enlist DNA and RNA readouts, genetic regulation, novel chemistry, and the immune system to prevent, detect, and treat cancers. Technologists think about new sensors and artificial intelligence-–assisted medical decision-making. As importantly, though, there are also opportunities for highly impactful breakthroughs at the macro level to ensure equity in health care access and health outcomes for all patients. Equity considerations (including race, ethnicity, gender/gender identity, sexual orientation, disability, and income level) must be woven throughout the ARPA-H mission—with some projects focused directly on addressing equity and all projects considering equity in their design. Breakthroughs aimed at the most vulnerable groups are not only just and necessary, they will likely improve care for all patients.

ARPA-H’s mission will clearly be different from the mission of the existing NIH Institute and Centers (ICs). For example, the name and mission of the National Center for Advancing Translational Sciences (NCATS), an NIH institute created in 2011, might suggest some overlap. However, NCATS’ primary focus is to support a national network of clinical research centers and a drug screening hub. These two programs account for nearly 90% of its resources. A modestly sized component within NCATS, the Cures Acceleration Network, is aligned with the general directions of ARPA-H.

Similarly, the NIH Common Fund, a program created by law in 2007, is aimed at a different goal than ARPA-H’s use-driven objective: It supports programs to explore new areas of foundational research that cut across multiple ICs—for example, the human microbiome effort. ARPA-H would also be distinct from other existing agencies, such as the Biomedical Advanced Research and Development Authority (BARDA), which focuses on medical coun termeasures for public health security threats.

DESIGNING ARPA-H: A DISTINCT DIVISION, CULTURE, AND ORGANIZATION AT NIH

ARPA-H should be housed as a division within NIH, rather than being a stand-alone entity, for two reasons. First, the goals of ARPA-H fall squarely within NIH’s mission (“to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability”). Second, ARPA-H will need to draw on the vast range of biomedical and health knowledge, expertise, and activities at NIH. Setting up ARPA-H within NIH will ensure scientific collaboration and productivity and avoid unproductive duplication of scientific and administrative effort.

It is important to acknowledge, however, that a DARPA-like approach is radically different from NIH’s standard mechanisms of operation and will require a new way of thinking. The creation of ARPA-H will benefit from transparency, accountability, and a healthy skepticism to ensure that the entity does not become a typical NIH institute.

Taking many features from the DARPA model, ARPA-H needs to have a distinctive culture, organization, authorities, leadership, and autonomy (10, 11). ARPA-H’s organization should be flat, lean, and nimble. The culture should value bold goals with big potential impact over incremental progress. The organization should lure a diverse cohort of extraordinary PMs from industry or leading universities, for limited terms, with the chance to make a huge impact. They should be empowered to take risks, assemble port- folios of projects, make connections across organizations, help clear roadblocks, establish aggressive milestones, monitor progress closely, and take responsibility for the project’s progress and outcomes. Projects should be bounded in time, typically a few years, with longer periods allowed for efforts that are highly complex. ARPA-H should expect that a sizable fraction of its efforts will fail; if not, the organization is being too risk-averse. The best approach is to fail early in the process, by addressing key risks upfront. To determine which risks should be taken and to evaluate proposed programs and projects, ARPA-H should adopt an approach similar to DARPA’s “Heilmeier Catechism,” a set of principles that assesses the challenge, approach, relevance, risk, duration, and metrics of success (12).

The ARPA-H director should have substantial authority and in- dependence to act. To keep the entity vibrant, the director should typically serve a single term of 5 years, with the possibility of a single extension in rare cases. For ARPA-H to accomplish its goals, it will need to be provided by Congress with certain authorities parallel to those provided to DARPA, including the authority to recruit, attract with competitive pay, and quickly hire for a set term extraordinary PMs.

Unlike DARPA’s focus on a single customer, ARPA-H will need to create breakthrough innovations that serve an entire ecosystem and all populations. ARPA-H should have a senior leader responsible for ensuring that issues of equity are considered in all aspects of ARPA-H’s work—from scientific program development to staff recruitment and hiring.

Within the Department of Health and Human Services, it will be important for ARPA-H to collaborate with other key agencies such as the FDA, the Centers for Disease Control and Prevention, BARDA, and the Centers for Medicare and Medicaid Services—to identify critical needs and opportunities and to partner on complex projects that interact, for example, with public health infrastructure or medical regulation.

DARPA should also play a role in advising ARPA-H on its experiences in driving breakthrough innovation and collaborating on specific projects of shared interest. And, it would be valuable to engage science-based agencies and departments, such as the National Science Foundation, the National Institute of Standards and Technology, and the Department of Energy.

It will be critical for ARPA-H to engage with the broader biomedical community, including patients and their care-givers, researchers, industry, and others, to understand the full range of problems and the practical considerations that need to be ad- dressed for all groups and populations.

The potential opportunity is extraordinary. Through bold, ambitious ideas and approaches, ARPA-H can help shape the future of health and medicine by transforming the seemingly impossible into reality. The time to do this is now.

References and Notes

• Remarks by President Biden in Address to a Joint Session of Congress (2021), www.whitehouse.gov/briefing-room/speeches- remarks/2021/04/29/remarks-by-president-biden-in-address-to-a-joint- session-of-congress/ .
• A. A. Toole, Does Public Scientific Research Complement Private Investment in Research and Development in the Pharmaceutical Industry? J. Law Econ. 50, 81–104 (2007). doi:10.1086/508314
• E. Galkina Cleary, J. M. Beierlein, N. S. Khanuja, L. M. McNamee, F. D. Ledley, Contribution of NIH funding to new drug approvals 2010-2016. Proc. Natl. Acad. Sci. U.S.A. 115, 2329–2334 (2018). doi:10.1073/pnas.1715368115 Medline
• G. Long, “The Biopharmaceutical Pipeline: Innovative Therapies in Clinical Development” (The Pharmaceutical Research and Manufacturers of America, 2017).
• “Medicines in Development for Cancer 2020 Report” (The Pharmaceutical Research and Manufacturers of America, 2020).
• DNA Sequencing Costs: Data (2020), www.genome.gov/about-genomics/fact- sheets/DNA-Sequencing-Costs-Data .
• S. Tripp, M. Grueber, “The Economic Impact and Functional Applications of Human Genetics and Genomics” (American Society of Human Genetics, 2021).
• “The Impact of Genomics on the U.S. Economy” (Batelle Technology Partnership Practice, for United for Medical Research 2013).
• “RADx diversifies COVID-19 test portfolio with four new contracts, including one to detect variants” (2021), www.nibib.nih.gov/news-events/newsroom/radx- diversifies-covid-19-test-portfolio-four-new-contracts-including-one-detect-variants .
• A. Prabhakar, “How to Unlock the Potential of the Advanced Research Projects Agency Model” (Day One Project 2021).
• R. E. Dugan, K. J. Gabriel, in Harvard Business Review (Harvard Business Publishing, 2013).
• “The Heilmeier Catechism” (2021), www.darpa.mil/work-with-us/heilmeier- catechism .

Acknowledgements

The authors thank R. Fleurence and A. Hallett for their helpful input in preparation of the manuscript.

Published online 22 June 2021 10.1126/science.abj8547

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A pioneering new imaging drug developed by Purdue chemistry researcher Phil Low will help surgeons find additional cancer lesions. The drug, developed with support from Purdue’s Center for Cancer Research and the Purdue Institute for Drug Discovery, was approved by the FDA in November. Read more

Whitest paint could help combat global warming

Purdue mechanical engineering professor Xiulin Ruan created the world’s whitest paint, which could eventually reduce or even eliminate the need for air conditioning. Its unique concentration of barium sulfate particles with varying sizes enable it to reflect 98.1% of sunlight. Read more

Cracking the code of cellular defense

Imagine the day when any tissue or organ can be repaired or the replacements personalized to the patient. Through the NSF-funded EMBRIO Institute, Purdue’s Weldon School of Biomedical Engineering professor David Umulis believes we can use AI to see how cells defend themselves or repair their damage with the help of biochemical and mechanical inputs and reactions. Read more

‘Self-aware’ algorithm to ward off hacking attempts

Purdue University professor of Nuclear Engineering Hany Abdel-Khalik has come up with a powerful response to hackers attempting to attack our most critical infrastructure. Abdel-Khalik, a CERIAS affiliated researcher, is working to make the computer models that run these cyberphysical systems both self-aware and self-healing. Read more

Pen-side test for bovine respiratory disease may save cattle industry millions

Mohit Verma, assistant professor of agricultural and biological engineering, is leading research on a new on-site kit for testing bovine respiratory disease, which is the most common and costly disease affecting cattle in the world. The new testing kit will help save time, effort, and resources during treatment. Read more

Using remote sensing technologies and techniques in archaeology

An NSF-funded interdisciplinary research project, Remote Observation and Sensing Technologies and Techniques in Archaeo-Anthropology (ROSETTA), led by Sorin Adam Matei, associate dean of research and graduate education in the College of Liberal Arts, combines the strengths of our remote sensing, computational, and socio-humanities scholars to build artificial intelligence-based framework for modeling complex urban constructions. Read more

Purdue co-leads on DOD-funded lead-free adoption project

A new consortium funded by the U.S. Department of Defense has selected Purdue University to co-lead a project to advance adoption of lead-free electronics in defense systems. The project, for which Carol Handwerker, Purdue’s Reinhardt Schuhmann Jr. Professor of Materials Engineering is a principal investigator, will accelerate the transition to lead-free electronics in aerospace, defense, and other high-performance electronics. Read more

Canadian firm secures exclusive rights to Purdue’s rare-earth element separation and purification tech

A Canadian firm, Medallion Resources, acquired the exclusive rights to Purdue University-developed rare-earth element separation and purification technologies. The flagship technology from Purdue is known as ligand-assisted displacement (LAD), developed by Purdue Chemical Engineering professor Linda Wang. Her LAD technology could enable the U.S. to more safely utilize critical resources from domestic sources and aligned nations. Read more

Purdue researchers develop responsive practices for K-6 students with high intensity needs

A team of interdisciplinary researchers at Purdue was awarded $1.6 million from the U.S. Department of Education to develop responsive practices for K-6 students with high intensity needs. The IPE-SHINES project, led by Rose Mason, associate professor of special education in the Department of Educational Studies, addresses a national need for highly skilled Speech-Language pathologists (SLPs) and Board-Certified Behavior Analysts (BCBAs). Read more

‘Marathon of crisis’: Nurses’ mental health in forefront of new study

Purdue University College of Health and Human Sciences professor Karen Foil’s research project, “Nurses’ Psychological Trauma and Cognitive Control in the COVID-19 Pandemic,” sheds light on vital mental health topics such as depression, anxiety, PTSD, and drug and alcohol use during the height of the pandemic. Read more

Purdue planetary researcher plays key roles in Mars rover mission

Purdue planetary scientist Briony Horgan has several key leadership roles for the Mars rover mission. Horgan’s team produced one of the major results on the location that contributed to NASA’s selection of Jezero Crater as the Mars landing site. She is on the rover’s Mastcam-Z camera team — the scientific eyes for Perseverance, and she is one of the tactical science leads working with NASA to plan the next day’s activities for the rover. Read more

Purdue spotlights quantum and work-life research with prestigious awards

Three Purdue professors advancing quantum science and work-life and work-life family research policy were chosen to receive the university’s most prestigious research and scholarship awards in 2021.

Ellen Ernst Kossek, a leading social scientist whose work has shaped the field of work-life and work-family research policy in the U.S. and internationally, received the 2021 Lu Ann Aday Award, the most prestigious award given by the university for exceptional work in the humanities and social science.

Michael J. Manfra, a leading condensed matter experimentalist, received the 2021 Arden L. Bement Jr. Award, the most prestigious award given by the university in pure and applied science and engineering.

Yong Chen, a leading quantum researcher whose work is at the convergence of two fields with successful experiments in both condensed matter physics and atomic, molecular, and optical physics received the 2021 Herbert Newby McCoy Award, the most prestigious award given by the university for outstanding work in the natural sciences.

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The Essential Guide to Doing Your Research Project Fourth Edition

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Packed with pragmatic guidance for tackling research in the real world, this fourth edition:

• Offers support for diving into a project using digital data, with how-to guidance on conducting online and social media research
• Empowers you to confidently disseminate your work and present with impact
• Helps you map out your research journey and put a plan in place with decision trees in every chapter
• Challenges you to be reflective and critical about the research you consume and undertake

Zina O′Leary′s detailed and down-to-earth approach gives you the research skills and momentum you need to successfully complete your research project.

• ISBN-10 1529713463
• ISBN-13 978-1529713466
• Edition Fourth
• Publisher SAGE Publications Ltd
• Publication date May 11, 2021
• Language English
• Dimensions 6.69 x 0.92 x 9.61 inches
• Print length 456 pages
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About the author.

Zina O’Leary is an internationally recognized leader in research methodologies and has a keen interest in the application of research to evidence-based decision-making. Zina is a Senior ­Fellow at the Australia and New Zealand School of Government where she coordinates research and project units for their Executive Masters and Executive Education Programmes. Zina also has an extensive history as a consultant to both government and the private sector and was the research coordinator for the Centre for Environmental Health Development at the World Health Organization Collaborating Centre. She is the author of Researching Real World Problems, The Social Science Jargon Buster, Workplace Research and three books in Sage’s Little Quick Fix Series: Research Questions; Research Proposals; and Presenting Your Research.

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• Publisher ‏ : ‎ SAGE Publications Ltd; Fourth edition (May 11, 2021)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 456 pages
• ISBN-10 ‏ : ‎ 1529713463
• ISBN-13 ‏ : ‎ 978-1529713466
• Item Weight ‏ : ‎ 1.76 pounds
• Dimensions ‏ : ‎ 6.69 x 0.92 x 9.61 inches
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Five of the Most Inspiring and Influential Projects of 2021

They’re changing the world as we know it, thanks to changemakers behind the scenes.

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Despite historic times that put the world on pause, the past few years have brought tremendous innovation, from vaccines and new ways of working to wildlife protection on a scale we haven’t seen before. And these inventions, experiments, and creations were all possible thanks to the indispensable project managers and changemakers who turned ideas into a world-altering reality.

However, bringing to life any ideas — big or small — amidst a global pandemic is downright challenging, to put it lightly. Across the world, teams pivoted quickly to remote working (some overnight), all while juggling professional, personal, and family lives. Living and working through a global pandemic tested the mettle of everyone. But, yet, many teams and leaders, especially those who brought to life the projects profiled here, put their skills to work and persevered to make change.

Each year, Project Management Institute (PMI) releases a list of the most influential projects across industries and regions, a roadmap to others who’d like to transform the world one day. In that spirit, below are five bold, innovative, and collaborative projects from that list, along with some lessons learned to inspire action.

The mRNA Covid-19 Vaccines

As Covid-19 hit its full rampage in early 2020, the health of a global population — and the fate of a global economy — hung in the balance. A vaccine couldn’t come fast enough, and most take a decade to develop, test, and make their way to market. Even the fast-tracked 1967 mumps vaccine took four years from start to finish.

But two teams, and their project managers keeping everything on track, believed they could deliver Covid-19 vaccines in less than a year by using messenger RNA (mRNA). Instead of using the virus or viral proteins — which are expensive to create and difficult to store — mRNA uses the DNA code of a virus to direct a person’s cells to make specific proteins to fight infections.

By the end of 2020, both teams had delivered, hurtling past critical milestones and challenges — and jabs were soon dosed out around the world. Now, researchers are examining how the tech might be used to combat other diseases, including malaria, cancer, and cardiovascular disease.

Lesson for Leaders: When working under a deadline, “power skills” like collaborative leadership, an innovative mindset, and complex problem solving are crucial.

The Galápagos Islands Rewilding

The Galápagos Islands are going wild (again). A coalition of nonprofits and a certain eco-conscious celebrity are joining forces to restore the spectacular array of biodiversity of one of the first UNESCO World Heritage sites, which will also help bolster the economy through ecotourism.

The project includes restoring Floreana Island, home to 54 threatened species. A project led by Re:wild, Gálapagos National Park Directorate and Island Conservation, along with local communities, plans to reintroduce 13 locally extinct species, including the Floreana mockingbird, and to establish a captive breeding program to prevent the extinction of the pink iguana.

The project leaders of the rewilding plan to replicate their success in the Galápagos, and the scale is ambitious. Over the next 10 years, they will launch an unprecedented push across Latin America’s Pacific archipelagos, from Mexico down to Chile. The coalition aims to double the areas under protection and protect at least 30 percent of each country’s waters, while reversing the decline of more than 250 globally threatened species. They’re just getting started.

Lesson for Leaders: When scaling a project, recognize the potential complex factors a team will face in addition to scaling team size. Key considerations include geographic and organizational distribution, skills availability, and compliance.

The Great Work-From-Home Experiment

What began as a necessary adaptation to keep employees safe during the height of the pandemic quickly became an incubator for fresh thinking around office work — or what used to be office work. After more than a year and a half of hunkering down at home, many employees are now simultaneously settled into their routines and craving time together. Employers and project managers are listening and adapting, whether it’s by going completely virtual, welcoming workers back to the office safely, or some combination of the two. Now, reports say that 20 percent of the global workforce could effectively do their jobs from home several days a week, and there could be four times as many workers working from home now than pre-pandemic.

That leaves project leaders translating lessons learned from the great work-from-home experiment into spaces and systems that can flex to employees’ — and employers’ — needs. Doing so requires even bolder actions: closing some of those high-profile HQ locations, redesigning offices for fewer people, improving resources for wellbeing and collaboration, and fundamentally rethinking what being “at work” even means.

Lesson for Leaders: Like never before, empathy has an invaluable place in business. When facing moments of adversity, lead with understanding and compassion. In a rapidly changing world, togetherness, understanding, problem-solving, and solution-finding are among the essential mainstays.

The Tulsa Race Massacre Excavation

It happened a century ago, but the pain of the 1921 Tulsa Race Massacre still runs deep. What started with a young Black shoeshine vendor being accused of assaulting a white elevator operator in Tulsa, Oklahoma, escalated over the next few days into the razing of Greenwood, the city’s most prosperous Black neighborhood.

Few official records of the days’ events exist, and there’s no official count of lives lost during the massacre, though historians believe the number may be as high as 300. And a 2001 government report concluded that city officials had provided firearms and ammunition to white locals, effectively authorizing them to commit violence.

Looking to answer lingering questions, government leaders have launched a wide-reaching program, using methods including survivor interviews and ground-surveying technology to locate and recover the remains of those killed.

The project broke ground with a test excavation in 2020, followed by a full dig this year, and the team says that so far, it’s been successful. The project managers on the team hope to engage the community throughout the process and build trust through transparency so that more survivors and their descendants will come forward, and the world can get a better idea of the full scope of the massacre and its victims.

Lesson for Leaders: Trust is a crucial component in any project especially as communication breakdowns can contribute to project failures. Take time to build trust by contracting relationships, fostering community, and communicating effectively.

The Kenyan National Wildlife Census

The lions, wildebeest, elephants, and leopards wandering the plains of Kenya aren’t just majestic: they’re a revenue stream that can deliver powerful economic benefits. After all, tourism fuels 8 percent of Kenya’s GDP. But poaching, climate change, and a human population explosion are putting many of the country’s most well-known animals — and economic growth — at risk. As a response, three government agencies launched the country’s first systematic census this year to better track where Kenya’s most-threatened species live, and to glean conservation insights for some of its 25,000 species.

Results of the first census — which involved project leaders managing 100 people, camera traps, light aircrafts, helicopters, boats, and four-wheel-drive vehicles across a span of nearly 140,000 square miles — revealed that 14 species are nationally endangered. That includes five, such as the black rhino, that are critically endangered. The team is hoping to protect Kenya’s wildlife for years to come with the help of wildlife officials, a change in conservation policy, and studies on habits and migration patterns.

Lesson for Leaders: Passion is the key to success. Especially when the project you are managing is also a legacy. Be passionate to allow yourself to go “all-in” and strive for a fulfilling goal that will have impact for years, decades to come.

Bold. Innovative. World-Changing.

If you want to learn more about projects like the  Kenyan National Wildlife Census  and  Tulsa Race Massacre Excavation , check out Project Management Institute’s  full list of the  50 Most Influential Projects of 2021 . 

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Cs summer research projects - 2021.

The CS+,  Data+ , and Code+ undergraduate summer programs held an online Plus Summer 2021 Program Expo to showcase student projects leveraging big data, mobile app and web development, and computer science on August 6, 2021. Student teams — over 150 students in all — presented their projects. CS+ presentation videos and project descriptions are available below, or view all presentation videos in the YouTube playlist: CS+ Undergraduate Summer 2021 Research Projects .

Leads: Cynthia Rudin, Sudeepa Roy, Alex Volfovsky

Participating Students:

Sravan Parim I am a first-year from Connecticut planning to pursue a double major in Computer Science and Economics. After my time at Duke, I intend to go to graduate school and pursue a career that involves researching Artificial Intelligence and shaping technology policy with the goal of reducing economic disparity. Outside of class, I am involved with the Duke International Relations Association and Duke Chamber Ensembles. In my free time I also enjoy reading, meditating, and running. Through CS+ I look forward to learning about machine learning and applying it to estimate causal effects.

Haoning Jiang Hi! I'm Haoning, a rising junior majoring in Computer Science with a concentration in AI and Machine Learning. I'm very grateful for the opportunity to work on this project.

Description: The Almost Matching Exactly lab designs software tools for matching in causal inference. However, our code and interfaces are not perfect, and we would love to have users try out the code on applications. A self-motivated team of students can help us to improve, and at the same time, learn how to troubleshoot and apply this type of code to real problems.

Outcomes: This is up to the student, but we expect the students to be troubleshooting code, writing code, and designing cool applications.

Skills: Coding, communication, machine learning, and causal inference.

Lead: Debmalya Panigrahi

Feng Cong I'm Feng Cong, an international student from Singapore. I'm a rising junior majoring in Mathematics and Computer Science. I'm interested in algorithms, optimization, and problem solving, and love playing Minesweeper and Sudoku.

William He I am from Houston majoring in mathematics and computer science. I enjoy studying many topics in math, especially algebra and algorithms. After graduating from Duke, I hope to pursue a Ph.D. in math or computer science.

Grace Tian I'm from San Jose, CA, and I'm a rising junior studying Computer Science and Mathematics. I'm interested in Computer Science research and plan on applying to graduate school after Duke.

Annie Wang I am from Cary, NC and majoring in computer science and math. After Duke, I am interested in pursuing graduate school.

Description: Recent progress in both combinatorial and algebraic techniques in graph algorithms has given us hope that some of the hardest, and longest standing, challenges in the field might finally be within our reach. This includes, for a graph on n vertices and m edges, the following problems:

• find all pairs min-cuts in an undirected graph faster than n-1 max-flows
• find a global min-cut of a directed graph in o(mn) time
• find the reliability of a graph in near-linear time
• find the min-cut in a hypergraph in near-linear time

All these problems have been open for at least two decades, some of them even for more than five decades. But, for each of these problems, there is increasing evidence that we are close to finally solving them. For (a.), Li and Panigrahi recently showed that the problem can be solved if one allows approximations. For (b.), Panigrahi and others recently gave an o(mn) algorithm for vertex connectivity, which takes us halfway to directed connectivity. For (c.), Karger recently showed that the problem is amenable to techniques from randomized connectivity algorithms, and near-linear time algorithms are already known for this category. For (d.), Panigrahi and Zhang recently solved the problem for hypergraphs of fixed rank, thereby extending this beyond graphs for the first time. This project will explore one of these (or related) problems. The project will be theoretical in nature.

Outcomes: If the project leads to a new result, then it will be published in a research paper. Students will also learn about the process of theoretical research.

Skills: Design and analysis of algorithms, formal proofs, and reading theoretical computer science research papers.

Lead: Kristin Stephens-Martinez

Brian Janger My name is Brian Janger and I'm from Houston, Texas. I'm a rising sophomore here at Duke and I plan to complete the IDM in Computer Science and Statistics and potentially minor in Physics. I'm currently an executive for the newly founded Sports Analytics Club and I am interested in creating software and performing analytics for a sports organization or a Big Tech company.

Hao Xu Belle Xu is a sophomore from Vancouver, Canada who is studying Computer Science and Statistical Science. Passionate about both software engineering and data science, Belle enjoys challenging herself by exploring new types of algorithms to solve nuanced data-processing and data standardization problems. After college, she is excited to work on more backend development projects with her like-minded peers.

Manith Luthria I'm a sophomore from Houston, TX. I'm an ECE+CS major. After Duke, I'd like to continue my computer science career. I hope this experience with CS+ will help me learn more about CS research.

Sona Suryadevara My name is Sona Suryadevara, and I am a rising senior with an Interdepartmental major in Computer Science and Neuroscience. I am excited to join the CS 101 Reviewer App project and use my interdisciplinary skills to further develop the app! After I graduate from Duke, I plan to either go to medical school or become a full-time software engineer.

Description: CS101 Reviewer App is a web application that provides an online quiz tool to students enrolled in CS101 at Duke University. It enables students to quiz themselves on CS101 topics with carefully designed questions that check for specific misunderstandings of the content. A recent feature includes an autogenerated quiz that chooses what topics to focus on for the student based on their past performance. This project has the potential to go in different directions. We have ideas to improve the app, such as adding different question types, improving the algorithm that generates the auto-generated quiz, or adding automated hints based on the student's wrong answers. We also have data analysis needs that will inform future features.

Outcomes: If students want to improve the app, they will modify the existing app codebase and produce new code. If they want to do data analysis, they will need to write code to clean and analyze the data and produce a small report describing what they did and explaining their results.

Skills: Web app development for a Python app OR data analysis in either Python or R.

Leads: Kamesh Munagala and Brandon Fain

Xingyu Zhu I am a rising senior from Beijing, China. I am double majoring in mathematics and computer science, and I am broadly interested in the theoretical aspects of computer science, in particular, theoretical machine learning and algorithms. After graduation, I plan to continue pursuing a PhD in related fields, and hopefully stay in academia. Academics unrelated: I am a amateur guitarist and I love riding road bikes.

Zeyu Shen I'm a first year student at Duke University, intending to major in mathematics and computer science, and minoring in either economics or statistics. My main interest and expertise lies in applying mathematics and programming for problem solving. I enjoy brain teasers of any kind and seeking innovative solutions to them.

George Wang I am a rising junior majoring in computer science and mathematics. I am broadly interested in algorithms, the theory and applications of blockchain technology, and machine learning.

Description: Fairness is an emerging concept within algorithm design. This project will consider settings where multiple participants use the solution to a certain optimization routine, and this solution provides them with different utility. The question we ask is how should these routines be designed so that different demographic groups obtain comparable utility. Though this question sounds abstract, we will work with well-defined notions of fairness, and well-defined optimization problems. The research will be largely theoretical and analytical in nature, but there will be opportunity to test out the resulting procedures on datasets.

Outcomes: Algorithmic insights and a research paper.

Skills: Background in design and analysis of algorithms. Please list algorithms and related courses you have taken.

Lead: Rong Ge

Cindy Weng Hello! I'm a junior from Florida studying computer science and statistics. I'm interested in the intersection of these areas and how we can use quantitative methods to understand and learn from data, as well as how our findings may connect to real-world phenomenon.

Tony Wu Hi! I'm Tony Wu, a rising sophomore at Duke. I intend to double major in Computer Science and Math. I'm very interested in Machine Learning and big data, and I hope to work as a Machine Learning Engineer in the future, combining the cutting-edge research from academia with industrial scenarios. I'm excited about working with Prof Ge this summer and exploring self-supervised learning.

Zeping Luo Hi! I am Danny, a rising junior majoring in CS/Stats. I am from Shenyang, China. I am interested in the intersection of Computer Science and Statistics, and am excited to explore the theoretical aspects of ML and understand the "why" behind model performance during this summer research project.

Description: Recently self-supervised learning has become a popular way to do unsupervised learning (learning without labels). In self-supervised learning, the algorithm will hide some information from the input and try to predict the hidden information. Empirically, such learning algorithms are successful in many domains such as natural language processing and image understanding.

Traditionally, unsupervised learning problems are often solved using latent variable models. The goal of this project is to try to understand why self-supervised learning might have a better performance.

Outcomes: The project will start by experimenting self-supervised learning ideas on data generated for some traditional latent variable models, such as HMM (Hidden Markov Model) or topic models. Then the goal is to systematically change the setting to find scenarios where self-supervised learning can outperform traditional latent variable models, and understand why.

Skills: Math: probabilities, calculus, willingness to learn new things. Machine learning: Used or willing to learn standard deep learning packages (e.g. pytorch).

Leads: Bhuwan Dhingra (lead), Ashwin Machanavajjhala, Jun Yang Contributor: Lavanya Vasudevan

Aakash Kothapally Hi everyone! I am a rising sophomore from Cary, North Carolina, and I plan on majoring in computer science and statistics at Duke. I'm interested in applying machine learning methods to real-world problems, and I am excited to do that this summer.

Dev Seth I'm a junior from Indore, India. I am double majoring in Computer Science and Philosophy, with a concentration in AI and Machine Learning. After I graduate, I plan to pursue a PhD and research the problem of intelligence--what it is, how it works, and how we can recreate it artificially.

Isa Mellody Isa Mellody (class of 2024) plans to major in Computer Science with possible minors in Gender Sexuality and Feminist Studies and Theater Studies. She hails from New York City, but is in love with the Durham area. She hopes to use her computer science knowledge to think critically and solve the problems of inequity in the country.

Shuaichen Liao My name is James and I am a first-year student studying cs, math and linguistics. I am originally from Toronto, Canada. I am interested in exploring the intersection between technology and language.

Description: Misinformation about vaccines has led to vaccination hesitancy, which is listed by WHO as a TOP-10 threat to global health. This project aims at developing automated tools that assist health workers and the public in dispelling myths about vaccines. A key observation is that for interventions to be effective, they must address the individuals’ specific concerns and be perceived as credible, which means they must be highly contextualized and personalized.

To help pinpoint the specific concerns, we have created a taxonomy of common misconceptions about vaccines, collected a corpus of articles containing vaccine misinformation, and worked on developing techniques for labeling articles with specific misconceptions. We plan to curate a corpus of intervention articles that help dispel specific misconceptions and are also diverse enough to appeal to individuals with different backgrounds. Putting these techniques together, our ultimate goal is to develop tools/apps that can be used by health workers or deployed alongside web/social media platforms to combat vaccine misinformation.

One specific aim of this summer will be to develop NLP techniques for identifying different categories of vaccine misinformation from text. A secondary aim will be to apply these techniques to a corpus of articles and social media posts and develop visualizations which aid in understanding how vaccine misinformation evolves over time and with the introduction of new vaccines such as for COVID.

Outcomes: Given a small amount of labeled data and a corpus of articles containing vaccine misinformation, we expect students to train multiple machine learning models that classify sentences, paragraphs or whole articles into our taxonomy of common misconceptions about vaccines. Specifically the students will be expected to adapt existing large-scale language models such as BERT for the task. Using the best model they will then develop visualizations and tools which help understand how the misconceptions change over time, both in our corpus and separate collection of social media data. Students will complete a research report on their experiments and also produce an extensible codebase which will aid further research after the summer. If appropriate, the report may also be submitted as a research paper to a conference.

• Ability to survey papers on machine learning and natural language processing
• Running machine learning models in Python, both off-the-shelf implementations and with minor modifications
• Data visualization and preparation in Python

Leads: Jun Yang, Sudeepa Roy, Kristin Stephens-Martinez

Allen Pan My name is Allen and I will be a sophomore this coming Fall semester. I am studying computer science and hope to become a software developer after graduating. I love music, whether singing, playing the guitar, or performing for others! I also enjoy working out and playing basketball. I can't wait for CS+ this summer and the opportunity to create something amazing!

Zachary Zheng I'm Zach and I'm a freshman from Chapel Hill, NC interested in majoring in computer science and economics. After Duke, I would be interested in pursuing a career in data science as well as software engineering or AI/ML. Other than that, I enjoy playing tennis, working out, practicing guitar, and baking in my free time.

Description: The goal of our project is to create an interactive debugger called I-Rex for SQL, which is a ubiquitous query language for accessing and modifying data stored in relational databases. SQL can quickly get complex in practice and it is a challenge for novice to learn and debug. I-Rex allows users to interactively “trace” through highly complex SQL queries (e.g., those involving aggregation, nesting, and correlation), understand how they execute, and debug wrong queries.

As the need for data manipulation and analysis becomes ever more important to more people, tools like I-Rex are sorely needed. We plan to deploy I-Rex in our courses (CompSci 216/316/516) in Fall 2021. We are looking for help to improve the backend so I-Rex supports all of SQL and to make it robust. We are also looking for help on the frontend to improve both usability and effectiveness. Finally, we are also interested in anyone who wants to help evaluate how well I-Rex helps novices learn relational querying.

Outcomes: The desired deliverables include a fully working I-Rex system and a clean codebase with proper documentation. If students make progress on related research problems, there are opportunities for writing research/demonstration papers.

Skills: Knowledge/experience with at least one of following areas; must be able to learn quickly as needed:

• SQL (CompSci 316 or CompSci 516 would suffice) and Python/Java programming
• Frontend design and implementation (e.g., JavaScript, Web frameworks like Flask, Apache)

Lead: Alberto Bartesaghi

Flora Shi Hi, I am Flora. I am from China and currently majoring in computer science and statistics. I want to go to grad school after Duke and imagine myself being a data scientist in the future.

Lucy Zhang I'm a BME major from NJ and my interests lie primarily in regenerative medicine. After Duke, I hope to pursue an PhD in biomaterials or biomedical engineering and integrate what I learn in undergrad into my research and career.

Neelam Runton My name's Neel Runton, and I'm an Electrical and Computer Engineering major from Cary, NC. I'm currently interested in machine learning, specifically computer vision, and after Duke I'd like to work in the overlap between machine learning and computer hardware.

Description: Cryogenic electron microscopes – or cryo-EM for short – allow researchers to peer at the microscopic shape of cellular proteins like never before. These machines blast proteins with a 300,000-volt beam of electrons so that highly sensitive detectors underneath can tease out their shapes based on the interaction that occurs. Being able to “see” proteins – life’s crucial building materials – can help determine how they work. Recognizing protein structure and function is essential for scientists trying to design better drugs to tackle some the world’s most devastating diseases, including HIV, cancer, COVID-19 and Alzheimer’s disease. A 300,000-volt electron beam is, however, extremely damaging to the proteins it is trying to image. To help protect the samples in the machine, researchers cryogenically freeze them to help maintain their integrity and use very low electron doses to prevent structural damage which results in extremely noisy images.

An emerging modality of cryo-EM called cryo-electron tomography (cryo-ET) uses computerized tomography principles to provide an accurate representation of the 3D molecular architecture of entire cells. The mining of the rich information contained in the native cellular environment is hindered by the crowded nature of cells populated by many different molecular species. The accurate detection of individual molecules in 3D is a critical step towards allowing the visualization of these molecular machines at high-resolution. Motivated by recent advances in deep neural network approaches for object detection in natural images and autonomous navigation, this project seeks to apply these methods to detect the position of macromolecules within 3D images of frozen hydrated cells with the ultimate goal of understanding cellular function and disease at the molecular level.

Outcomes: As part of this project, students will write computer code that will take as input 3D volumes of cells and automatically detect the location of multiple molecular species so they can later be extracted and used for high-resolution 3D visualization. Students will carry out the development in a dedicated high-performance computing (HPC) environment and at the end of the project will write a research paper to describe their approach and present results obtained on real datasets.

Skills: Knowledge of Python and background or interest in deep learning, image processing or computer vision.

Lead: Xiaowei Yang

Vineel Vanam My name is Vineel Vanam. I am a Sophomore CS student from Charlotte, NC. After Duke, I am interested in Grad School but I'd want to work for a while first. I'm interested in Software Engineering, specifically back-end design.

Dominic Ritchey I’m a rising sophomore from the Chicagoland area with plans to major in computer science and biomedical engineering. I enjoy studying molecular computing and software engineering. After graduating, I plan on working for a tech company and then pursuing a PhD.

Description: As global Internet traffic grows, more and more content networks depend on IP anycast to serve their global requests from multiple content caches. Unlike DNS-based content load balancing, the anycast network distributes clients' requests at the mercy of the inter-domain routing protocol Border Gateway Protocol (BGP)[3]. Previous work measured the real performance and benefits of the anycast network and observed highly skewed load distribution and sub-optimal load distribution[1,2].

In order to understand what causes the inefficiency of IP anycast, we propose to measure to what extent Network Providers optimize the anycast network in the wild. Unlike previous anycast measurement projects, which focus on application-level performance, we focus on mining the control plane, i.e., BGP prefix configuration parameters of various routers for different anycast service providers.

Outcomes: The ideal deliverables include a project write-up that can lead to a publication at a high-quality networking conference, including but not limited to Internet Measurement Conference, ACM SIGCOMM, and USENIX NSDI.

Skills Required:

• Familiarity with common Linux commands;
• Familiarity with Python programming and requests package; and
• Familiarity with the data processing in Python, such as regex, and pandas.

Any knowledge of BGP and inter-AS routing is preferred; and any previous experience in network measurement is preferred. Students who took a previous offering of CS356 should have sufficient background knowledge to participate in this project.

Co-Leads : Luyao Zhang (Assistant Professor of Economics at DKU), Kartik Nayak, Yulin Liu, and Fan Zhang

Dylan Paul Hi I am Dylan Paul and I am a sophomore from New York majoring in Computer Science with a concentration in AI/ML as well as minoring in Finance. I am very interested in Decentralized Finance as well as machine learning and I hope to work in one of these fields after Duke.

Malika Rawal Hi! My name is Malika Rawal. I'm from Charlotte, NC, and I'm majoring in Economics with a concentration in Finance, minoring in Computer Science, and doing the Markets & Management Certificate. I am interested in fintech, investment strategy, and product management. For fun, I like to do anything outdoors, hang out with friends, and take my new puppy on walks.

Oum Lahade I'm a freshman, from Morrisville, NC, studying Electrical & Computer Engineering and Mathematics. I'm primarily interested in blockchain and the intersection of finance & technology, and after Duke I'd like to work at the cross-section of these fields.

Urjit Banerjee I live in Charlotte, North Carolina and I am currently pursuing a major in Computer Science with a concentration in AI and Machine Learning, as well as a minor in Math. After I graduate from Duke, I am interested in studying AI and Machine Learning at Graduate School. Outside of school, I enjoy art and watching movies!

Description: According to the World Bank’s Global Findex data, 1.7 billion adults remain unbanked. Financial exclusion is a global issue. Besides, financial services, such as loans, insurance, derivatives, and fundraising, are controlled by financial intermediaries. These financial service providers often lack transparency and charge high fees.Distributed Ledger Technology (DLT), often known as blockchain, empowers smart contracts to automate enforceable agreements, enable financial inclusion, and cut out middlemen. Financial services based on the DLT have been gaining momentum since 2015. The total value locked in Decentralized Finance (DeFi) smart contracts has quickly increased from a few million in 2017 to more than 10 billion in 2020. Decentralized platforms such as Ethereum and Polkadot are attracting more and more developers and users. However, all the existing decentralized public blockchains are constrained by long finality time, low scalability, and low throughput. By contrast, the Internet Computer, developed by DFINITY Foundation, provides a tamperproof, scalable, and efficient environment, where software is secured by default. The Internet Computer is a highly fault-tolerant decentralized network protocol that combines the computing power of independent data centers around the world.

Outcomes: In this project, student-teams are guided to build DeFi applications on the Internet Computer, which will then be used to design experiments for research on DeFi. Students will create a live web application on the Internet Computer, present the application design at SciEcon Accelerator Seminar, document products on a project website, and submit a proposal for further publishable research. Example applications include the following:

• Algorithmic Stable Coin: Cryptocurrencies often experience sharp price fluctuations. To solve this problem, stable coins aim at pegging to sovereign currencies, such as USD. Ampleforth is one promising stable coin. In this project, students design an algorithmic stable coin protocol on the Internet Computer by referring to the design of Ampleforth.
• Decentralized Exchange (DEX): DEX allows users to exchange cryptocurrencies directly with each other on the blockchain without trusting an intermediary. Uniswap is the most popular DEX with the most users and the largest trading volume. In this project, students design a DEX on the Internet Computer that inherits the ideal features of Uniswap.
• Decentralized Bank: Decentralized Banks connect borrowers to lenders efficiently by utilizing smart contracts and allowing users to interact without permission. Moreover, the interest rates in decentralized banks are determined algorithmically following the law of supply and demand rather than being controlled by the central bank. Compound has the largest total value locked among all the existing decentralized banks. In this project, students design a decentralized bank on the Internet Computer following the protocol of Compound.

Skills: Students should have some background in design and analysis of algorithms and in programming/software development. In addition, interest and experience in financial tech is helpful.

Neel Gajjar Hi! I am a sophomore from Charlotte, NC studying computer science interested in pursuing graduate school or entering the industry (SWE/Data Science) after Duke. I am really excited to spend this summer with CS+!

Rui Xin I'm a sophomore majoring in Mathematics and Computer Science. I'm interested in Computational Biology and Machine Learning.

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2021 Co-Funded Research Projects

The ODP provided limited co-funding to NIH Institutes, Centers, and Offices, and other agencies in 2021 for extramural prevention grants, NIH-sponsored meetings and workshops, and federal initiatives and projects.

NIH-Funded Grants

Nih-sponsored meetings, workshops, and conferences, federal initiatives and projects.

NASEM Consensus Study Panel on Measuring Sex, Gender Identity, and Sexual Orientation

2021 Research Projects (Old)

We will continue to post projects to this page as they are approved. Please check back periodically for updates. Note: Each research project will involve background reading for the interns provided by their mentors. Each research project will involve a final presentation by the interns. Interns are expected to work collaboratively on the same project and/or data set. This may preclude rising seniors from submitting papers based on such projects to the Regeneron Science Talent Search competition Please feel welcome to research the projects and labs associated with the SIP Research Projects listed below, but PLEASE DO NOT contact any mentors or faculty. Mentors will reach out to admitted interns who have been assigned to their projects during the research preparation weeks of June 7–18, 2021.

Anthropology 

Project code: ANT-01 Title:  Technology and Oral Story Collection of Indian Immigrants in the USA Primary mentor:  Kati Greaney Faculty advisor:  Dr.  Annapurna Pandey Location:  Remote/online Number of interns:  4

Project description: These days, one often hears that we human beings are primarily story tellers. We tell stories about ourselves as well as about others. What these stories tell us is the rich experience human beings have acquired in their life. The world in which we live today is largely created by technology. The mentor and SIP interns will use various tools provided by technology in their digital story telling research. This project will encourage SIP interns to collect stories about the immigrant experience in the United States. For the last three decades the mentor has been working on the Indian diaspora in the Greater Bay Area, California. The mentor has made two films, “Homeland in the Heart” and “Life Giving Ceremony of Jagannath” documenting the involvement of Odia people (people from the state of Odisha) in building a community and developing a sense of belonging to the United States. The mentor would like to broaden the scope of this research by incorporating the experiences of other Indian immigrants.

Tasks: This project will give an opportunity to the SIP interns to collect oral history material about the experiences of immigrant parents, grandparents, and their American-born children. The material will include streaming audio and written transcripts accessible online in digital formats. The mentor and SIP interns will use various available technology tools. The mentor’s aim in this project is to collect interviews of Indian immigrants in the USA. The SIP interns will interview various members of the Indian community and collect their experiences in this country compared to their experience in their homeland that they have left behind. These interviews are a unique source of contemporary history through the experiences of the immigrants. Past studies have shown that this kind of research has revealing consequences for both the researchers as well as the subjects of their research. Students who have experience and interest in film making, video-making technology, and video editing are encouraged to apply.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Field work, statistical data analysis

Applied Artificial Intelligence 

Project code: AAI-01 (EPS) Title:  Machine Learning and Mineral Identification on Mars Primary mentor:  Genesis Berlanga Faculty advisor:  Prof. Quentin Williams Location:  Remote/online Number of interns:  3

Project description: NASA’s Mars rovers take thousands of images and spectra every day. Analyzing this information is a massive task that takes months of work, but with the help of computers, scientists can shorten the time it takes to arrive at exciting results. In order to train a computer to be a geological assistant, the SIP interns will program a computer to automatically identify rocks and minerals found on the surface of the Moon and Mars. The interns will help build neural networks modeled after the circuitry of brain neurons to train the computer to accomplish this task using rocks we find on Earth. This research project will inform future research for Mars rovers like Curiosity or Perseverance, by finding ways to simplify rock and mineral identification while roving the surface of another planet.

Tasks: The SIP interns’ tasks will include: (1) identifying spectra and images of rocks and minerals relevant to the Moon and Mars; (2) programming in Python, MATLAB, or R; and (3) building a neural network that automatically identifies minerals. Computer programming experience is encouraged but not necessary. The mentor will provide training.

Required skills for interns prior to acceptance:  Computer programming Skills interns will acquire/hone:  Computer programming

Special age requirement: Interns must be 16 years old by June 21, 2021.

Project code: AAI-02 (CSE) Title:  Procedural Road Generation for Self-Driving Vehicles Primary mentor:  Golam Md. Muktadir Faculty advisor:  Prof. Luca de Alfaro Location:  Remote/online Number of interns:  4

Project description: In this project, we build algorithms which can automatically generate roads in digital format which can be used in simulation or game engines such as Unity or Unreal Engines. This procedural generation reduces the amount of human effort in designing digital roads. Our goal is to make novel algorithms that can generate roads which are similar to real-world roads. Current work generates roads which either requires a lot of real-world data and human effort, or generates a very limited set of variations. Anything produced by interns in this project is publishable as papers. Interns will learn about self-driving car development, how their simulation is done, and how to code for them! We can even use AI to simulate driving with the roads that will be created!

Tasks: Interns will (1) learn about self-driving car research and development; (2) learn to do Python programming with clean code (extremely valuable); (3) solve geometric problems with coding; and (4) run simulations (optional).

URL:  https://github.com/AugmentedDesignLab/junction-art

Project code: AAI-03 (ELE) Title:  Energy Data Analytics Primary mentor:  Jing Xiong Faculty advisor:  Prof. Yu Zhang Location:  Remote/online Number of interns:  3

Project description: Have you heard of the Smart Grid on the news or from your energy provider? In this next generation of the electric grid, a huge amount of data is generated and exchanged every day: markets, equipments, and power system data which can be used for reliable and efficient planning and operation, predicting states, providing situational awareness, analyzing stability, detecting faults and providing advance warning. Therefore, energy data analytics have a significant role to make the grid more intelligent, efficient, and productive. This summer, the mentor and SIP interns will explore machine learning (ML) and deep learning (DL) models to play with the energy data, build up the pipeline and increase the performance.

Tasks: The SIP interns will: (1) learn to use Python for programming; (2) gain experience in machine learning frameworks and/or deep learning frameworks such as pytorch; (3) gain exposure on how to collect data from online resources; (4) gain exposure to multiple ML/DL techniques for time series data analytics; (5) learn how to read related research papers; and (6) work collaboratively as a team.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, statistical data analysis

Art, Culture, & Stem

Project code: ACS-01 (AST) Title:  Native Skywatchers – Starry, Starry Nights Collaboration: We Are Stardust Primary mentor:  Prof. Annette Lee (St. Cloud State U.) UCSC faculty contact:  Prof. Raja GuhaThakurta Location:  Remote/online Number of interns:  8

Project description: As described by Mi’kmaw elders: “Two-Eyed Seeing is learning to see from one eye with the strengths of Indigenous knowledges and ways of knowing, and from the other eye with the strengths of Western knowledges and ways of knowing, and to use both these eyes for the benefit of all” (Bartlett, Marshall, and Marshall 2012, 336). This collaboration will follow in the spirit and framework of Two-Eyed Seeing, with particular emphasis on the last line ‘use both these eyes for the benefit of all’. It builds on the existing Native Skywatchers research and programming initiative in a unique merger with the Starry, Starry Nights initiative. The goal is to give students a unique opportunity for authentic involvement in science and to weave in cultural relevance equally into the experience to produce the highest level of engagement, excitement, and meaning for all involved. This project lives at the intersection of science, art, and culture.

Tasks: The SIP interns’ tasks will include the following: (1) participating (remotely) in night time observing with the Lick Observatory in California and Keck Observatory telescope in Hawai’i; (2) digital art (recording, video editing, etc.); (3) Citizen Science; (4) creating art; and (5) documenting and sharing multiple views, ways of knowing, and culture.

URL:  https://nativeskywatchers.com

Project code: ACS-02 (CHE) Title:  Project Coffee Art — Intersection of Art, Chemistry, Racial Harmony, and Social Justice Primary Mentor:  Saul Villegas Faculty advisors:  Sudakshina Ghosh (retd.), Prof. Jennifer Parker Location:  Remote/online Number of interns:  3

Project description: Coffee is one of the most enjoyed drinks. In this research project, the SIP interns and mentors are going to investigate the chemistry involved in the journey of the bean from its raw stage to the cup of coffee and explore how coffee grounds can be used to create works of art. The art works created with coffee should reflect racial harmony. The goal of this research project is to give the interns an opportunity to experience the intersection of art, science, and the humanitarian aspects of life. Submission of art works to  The Coffee Art Project  will allow for active participation in social work. The Coffee Art Project is a high-profile art competition that invites artists to interpret the theme of coffee. Their aim is to support and encourage artists by providing them with a platform to showcase their work and promote coffee culture. Artists at all levels can enter one piece of artwork that connects to ‘coffee’ and/or ‘coffee shop’ experience. The exhibition of art works helps them to raise money to support  Project Waterfall  which is committed to bringing clean water to communities that grow our coffee through the Allegra Foundation and other registered charities.

Tasks: The SIP interns will read scientific articles about the chemistry involved in roasting and grinding coffee beans and have a discussion followed by writing short reports. The interns will also study a painting done with coffee and write a brief description based on group discussion. The mentor will guide the interns to explore the possibility of using coffee as a painting medium. The interns will learn to prepare the medium by collecting coffee ground from nearby coffee shops and create artworks using coffee. The SIP interns will learn how to protect their work. An important part of this research project will involve building a website to promote the idea of using coffee as a medium and to display the interns’ artworks. The mentor will help in selecting art works for online exhibitions like Project Coffee Art.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Art appreciation, learning to use an unconventional medium and designing one’s own website, scientific investigation

URL:  https://www.modernobysaulvillegas.com/ ,  http://www.srijoni.com/

Project code: ACS-03 (EEB) Title:  The Algae Society: BioArt & Design Exhibition with UCSC OpenLab Collaborative Research Center Primary mentor:  Rebecca Ora Faculty advisor:  Prof. Jennifer Parker Location:  Remote/online Number of interns:  3

Project description: At a time of ecological and environmental crisis, the political and policy process alone is too slow to guide a deep search for understanding and interventions that will save the planet and its species. In response, and as an interventionary practice, the diverse international group of artists, scientists, designers, and algae that constitute The Algae Society ( http://algaesociety.org/ ) examine the human position in global ecology through the lens of our algal fellow travelers, with a view to ultimately adopting a post-human philosophical position and practice with human and algae as companion species. The SIP interns will help build data visualizations and models for The Algae Society upcoming exhibition at the Cameron Art Museum in Wilmington, North Carolina.

Tasks: The SIP interns’ tasks will include: (1) exploring best practices and techniques for art and science collaborations; (2) create digital art and 3D assets for VR (virtual reality) and AR (Augmented Reality) and film; (3) work with and explore (remotely) The UCSB Algae Herbarium digital archive; and (4) research the effects of climate change on algae and the symbiotic relationship between algae and humans.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Field work

URL:  http://algaesociety.org

Project code: ACS-04 (EEB/CSE) Title:  Neuroscience/Art/Biology/Computer Science — Visualizing Sleep in Northern Elephant Seals Primary mentor:  Jessica Kendall-Bar Faculty advisor:  Prof. Terrie Williams Location:  Remote/online Number of interns:  3

Project description: This research project examines the first recordings of marine mammal sleep in the wild to learn about the natural behavior of northern elephant seals. These seals sleep while holding their breath underwater and their brain oxygen levels plummet to levels which would cause brain damage in humans. Our visualization is aimed at increasing scientific understanding as well as appreciation and admiration for our underwater counterparts. This project will merge computer science, art, and biology to analyze and visualize the behavior and brain activity of these wild animals.

Tasks: The SIP interns will learn to score sleep and behavior, gain familiarity with programming languages such as R, Python, and Javascript for data analysis and visualization, and work collaboratively to design a front-end user interface for interactive data visualization. Depending on individual interests, a given SIP intern may focus primarily on design, biology, or programming tasks, but each intern will gain an overall understanding of the data visualization pipeline from data collection to processing, analysis, visualization prototyping, debugging, and final implementation.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, field work, lab work, statistical data analysis

URL:  https://jessiekb.com

Project code: ACS-05 (ENV) Title:  Cataloguing Mayan Medicinal Plants: Bridging the Gap between Indigenous Medicine and Contemporary Science Primary mentor:  Dr. Andrea Medina (UC Santa Barbara) Secondary mentor:  Juliette Riancho (Pacifica U.) UCSC faculty contact:  Prof. Raja GuhaThakurta Location:  Remote/online Number of interns:  3

Project description: This research project will engage high school students in the foundational process of setting up a medicinal plant reserve in a Maya jungle in the Yucatán peninsula. The SIP interns will assist in researching the active ingredients/chemical compounds of plants used in Maya communities. The research will also include current uses of such compounds (in synthetic forms) in pharmaceutical medications in order to contrast it with their use in today’s Maya medicine. The decipherment of hieroglyphic Maya writing has been a major tool to learn about this culture. The SIP interns will have an opportunity to use glyphs, and to explore their artistic creativity as they help create this repository of knowledge. Current world events, from the pandemic to mega-tourism-projects, are not only directly impacting the way the Maya peoples live, but they are also devastating the environment. Medicinal plants are at great risk of being lost. The aim of the reserve is to protect, reproduce, and expand the possibilities for the continued existence of these medicinal plants.

Tasks: The SIP interns tasks will include: (1) participating (online) in ethnobotanical field research in a Mayan jungle in Yucatán with the help of their primary and secondary mentor who will be in that location; (2) biochemical research; (3) direct (online) and indirect (online) interactions with Maya healers; (4) creating art; and (5) recording repositories of knowledge from diverse cultural paradigms. The SIP interns on this research project are expected to collaborate with the interns on the related ACS-06 (ENV) research project.

Project code: ACS-06 (ENV) Title:  Creativity in Holistic Healing: Designing Access to Mayan Medicinal Plants through Interactive Tools and Guides Primary mentor:  Juliette Riancho (Pacifica U.) Secondary mentor:  Dr. Andrea Medina (UC Santa Barbara) UCSC faculty contact:  Prof. Raja GuhaThakurta Location:  Remote/online Number of interns:  3

Project description: The Yucatán jungle is home to myriad plants native to the Mayan region, many of which contain medicinal and healing properties. The aim of the research project is to create a comprehensive labeling system and interactive profile of the plants found within the Santa Rosa plant reserve project that takes into account scientific and cultural dimensions of healing. The SIP interns will engage in a two-part creative project that promotes accessibility and enhances healing autonomy for visitors of the plant reserve. The interns on this research project will work closely with the interns assigned to the sister project ACS-06 (ENV) in order to create user-friendly labels for the native plants to be displayed throughout the reserve. The interns will also design an interactive, multi-modal healing tool and biochemical map for use by reserve visitors. The aim of this tool is to support users in identifying their various somatic ailments and matching them to plants across the reserve based on their healing properties. A holistic healing philosophy informed by the Maya culture forms the basis of this research project which promotes mindfulness, multiplicity, and reciprocity with nature.

Tasks: The SIP interns tasks will include: (1) conducting research on indigenous healing philosophies and modalities; (2) interacting directly (online) and indirectly (online) with Maya healers; (3) creating a comprehensive labeling system for medicinal plants; (4) match the healing properties of various plants with corresponding somatic illnesses/ailments; and (5) designing an interactive, multi-modal tool for visitors to use within the reserve.

Astronomy & Astrophysics

Project code: AST-01 Title:  What Happens Around Supermassive Black Holes Primary mentor:  Dr. Martin Gaskell Location:  Remote/online Number of interns:  3

Project description: Astronomers now believe that every large galaxy contains a supermassive black hole in its center. Because of the tremendous energy released as the black hole grows by swallowing gas, these black holes can be readily detected as so-called “active galactic nuclei” (AGNs) back to very early times in the Universe. The details of how supermassive black holes form and grow and how this is related to the formation of normal galaxies is one of the central mysteries of contemporary astrophysics. The mentor’s research group is analyzing spectra and spectral variability to try to understand how AGNs produce the intense radiation seen, what the structure of material around the black hole is like, and how supermassive black holes grow.

Tasks: SIP intern involvement in the project will consist of analyzing multi-wavelength spectral observations of relatively nearby actively accreting supermassive black holes to try to understand the emissions and how the black holes grow. This work will involve compiling data sets, applying corrections, making statistical estimates of parameters, and comparing the results with theoretical models of processes going on around black holes.

Required skills for interns prior to acceptance:  Computer programming Skills interns will acquire/hone:  Computer programming, statistical data analysis

URL:  http://campusdirectory.ucsc.edu/cd_detail?uid=mgaskell

Project code: AST-02 Title:  Cosmological Galaxy Simulations Primary mentor:   Clayton Strawn Faculty advisor:  Prof. Joel Primack Location:  Remote/online Number of interns:  3

Project description: Cosmological galaxy simulations have become increasingly meaningful in the last few decades, and mock “observational” tests of simulations can set meaningful constraints on how accurately the physical assumptions built into the simulation emulate the real universe. This project will use mock quasar/galaxy absorption spectra created with the new software TRIDENT to emulate observations of the region directly outside of galaxies proper but within their dark matter halo, the circumgalactic medium (CGM). The CGM is relatively difficult to observe, because gas is not dense enough to form stars, and therefore this region is only detected in absorption, so only by simulating this observed quantity can one evaluate the simulation’s CGM.

Tasks: Interns will be helping to develop software to analyze the circumgalactic medium (CGM) of a variety of simulation codes. We have access to many codes through the AGORA project, and our main task for the next AGORA paper is to analyze the differences caused by the different code implementations of the same galaxy initial conditions. This will include opportunities to contribute to the project github account (linked below), to learn how to remotely access remote supercomputers, where the simulation data will be stored, and to compare and contrast the different codes.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming

URL:  https://github.com/claytonstrawn/quasarscan

Project code: AST-03 Title:  Identifying Exoplanets with Detectable Precession Rates with Dynamical and Light Curve Modeling of Multi-Planet Systems Primary mentor:  Patrick Maragos (U. of the Pacific) Faculty advisors:  Prof. Daniel Jontof-Hutter (U. of the Pacific), Prof. Raja GuhaThakurta Location:  Remote/online Number of interns:  3

Project description: Exoplanet transit surveys like Kepler and TESS have discovered hundreds of compact multi-planet systems, in which several planets are in a transiting configuration. As a planet’s orbit precesses due to the gravity of its neighbors, its transit impact parameter may change by a detectable amount over time. In this research project, the SIP mentor and interns will calculate the rate of change of impact parameter for hypothetical systems using N-body simulations, and use the results to identify systems of potentially detectable changes in impact parameter from Kepler and TESS. Future transit light curves of these systems may constrain the planetary masses or hint at additional undetected planets.

Tasks: The SIP interns will do the following: (1) explore the effect of orbital periods on transit light curve; (2) explore the effects of orbital-to-stellar radius ratio, inclination, eccentricity, and argument of periastron on the transit light curve; (3) write code to convert inputs for the Batman Python package into impact parameter, and measure changes in transit depth and transit duration from Batman output; (4) learn to construct dynamical models of planets with the Rebound software; (5) get a simple simulation running and print out orbital elements over time; (6) write code to measure inclination variations from simulation output, plot inclination and impact parameter variations over time, and give examples of transit curves with Batman; and (7) apply these codes to some known exoplanet systems.

Project code: AST-04 Title:  Supernovae Primary mentor:  Matthew Siebert Faculty advisor:  Prof. Ryan Foley Location:  Remote/online Number of interns:  3

Project description: Type Ia supernovae are exploding stars that are “standard candles” and therefore essential for understanding the expansion history of the Universe. The SIP interns will use a large spectroscopic dataset to explore the diversity of these phenomena. This will allow us to improve these Type Ia supernova events as cosmological distance indicators.

Tasks: The SIP interns will develop software tools for working with spectroscopic supernova data. The interns will also learn a variety of advanced statistical data analysis techniques that are useful for analyzing large datasets like these.

URL:  https://sites.google.com/ucsc.edu/transients/home?authuser=0

Project code: AST-05 Title:  Modeling the Stellar Kinematics of the Thick Disk and Halo of the Andromeda Galaxy Primary mentor:  Kaela McConnell (Yale U.) Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentor:  Chiara Villanueva Location:  Remote/online Number of interns:  3

Project description: The kinematics of the resolved stellar population within a galaxy help us to understand the galaxy’s evolutionary history and formation. For example, there is considerable debate about the external and internal factors that can cause dynamical heating of stellar disks (e.g., molecular clouds, central bar, dwarf satellite bombardment, dark matter substructure). Using Hubble Space Telescope photometric data from the Panchromatic Hubble Andromeda Treasury (PHAT) survey and Keck DEIMOS spectroscopic data from the Spectroscopic and Photometric Landscape of Andromeda’s Stellar Halo (SPLASH) survey, the mentor’s research group has measured the line-of-sight velocities of approximately 10,000 stars in the nearby Andromeda galaxy (M31). This research project will entail detailed analytical and numerical modeling of the kinematics of red giant branch stars in M31’s thick disk and halo.

Tasks: The SIP interns will: (1) make modifications to the existing Python code (stand alone files and Jupyter notebooks) that the mentors have developed to model the kinematics of M31’s thick disk of RGB stars; (2) add a dynamically hot halo component to the model; and (3) use a maximum likelihood framework to fit the model to the mentor’s group’s database of radial velocities of M31 RGB stars from the SPLASH and PHAT surveys.

URL:  https://aas237-aas.ipostersessions.com/Default.aspx?s=04-A2-B8-DC-EE-2B-E5-AF-A5-83-93-E4-D3-99-B4-DC

Project code: AST-06 Title:  Broad Emission Line Sources (BELS) and Rare Emission Lines in Keck Spectra (RELIKS) of the Andromeda and Triangulum Galaxies Primary mentor:  Olivia Gaunt (Wellesley Coll.) Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentor:  Khang Ngo Location:  Remote/online Number of interns:  3

Project description: What fills the space between stars in a galaxy? The tenuous gas and dust that fills this space is referred to as the interstellar medium (ISM). This project focuses on understanding the ionized gas component of the ISM in the disks of the nearby Andromeda (M31) and Triangulum (M33) galaxies. The SIP interns will work with multi slit spectra obtained by the mentoring team using the DEIMOS instrument on the Keck II 10-m telescope. The interns will use Python spectroscopic data analysis techniques to detect and carry out an investigation into the nature of a mysterious set of rare very broad emission lines in M33 and other rare/weak emission lines in both M31 and M33. They interns will explore the ultraviolet and X-ray properties of these rare sources using archival GALEX and Chandra data, respectively.

Tasks: The SIP interns will develop their own Python code and use existing Python code to work with spectroscopic data from the DEIMOS spectrograph on the Keck II telescope. This research project will initially involve processing of the 1D spectroscopic data to remove the effects of the Earth’s atmosphere (airglow) and instrumental signatures, subsequent analysis of the 2D spectra, and the creation and critical analysis/interpretation of a series of data diagnostic plots. The SIP interns on this research project will work closely with the interns on a related research project: AST-15.

URL:  https://www.astro.ucsc.edu/faculty/index.php?uid=pguhatha

Project code: AST-07 Title:  Using the Canada-France-Hawaii Legacy Survey to Study Distant RR Lyrae Stars in the Halo of the Milky Way Galaxy Primary mentor:  Tawny Sit Faculty advisors:  Prof. Raja GuhaThakurta, Prof. Eric Peng (Peking U., China) Location:  Remote/online Number of interns:  3

Project description: Studies of the density profile, substructure, and kinematics of the Milky Way’s extended stellar halo tell us about our Galaxy’s accretion history and dark matter content. It has long been recognized that RR Lyrae stars can serve as useful tracers of the Milky Way’s stellar halo. These stars have a characteristic periodic pattern of brightness variations that distinguish them from other astronomical sources of comparable apparent brightness and color. Moreover, RR Lyrae are excellent standard candles, and they can also be used to measure the chemical abundance of the halo. The Canada-France-Hawaii Legacy Survey (CFHLS) used the 3.6-m Canada-France-Hawaii Telescope and MegaCam imager to obtain a series of deep images in five filters (ugriz) along four lines of sight. In this research project, SIP Interns will use the CFHLS database to study distant RR Lyrae stars.

Tasks: The SIP interns will analyze CFHLS ugriz-band light curves of known RR Lyrae that were discovered in the Pan-STARRS-1 (PS-1) survey, search for new distant RR Lyrae in the CFHLS database that are fainter than the PS-1 detection threshold, separate Milky Way halo RR Lyrae from background quasars on the basis of unfolded light curves, fit empirical RR Lyrae template light curves derived from SDSS observations to CFHLS RR Lyrae candidates, and analyze new CFHLS RR Lyrae in the Bailey diagram (amplitude vs. period).

Project code: AST-08 Title:  Using Hubble Space Telescope Images and Keck Spectra to Search for and Characterize Variable Stars in the Andromeda and Triangulum Galaxies Primary mentor:  Kevin McKinnon Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentors:  Avi Patel (Haverford Coll.), Dr. Monika Soraisam (UIUC/NCSA) Location:  Remote/online Number of interns:  3

Project description: Through the SALVATION survey, the mentor’s research group has been characterizing transient and variable stars (i.e., stars whose brightness changes over time) in the direction of our nearest galactic neighbor, Andromeda. Stars change brightness over time for many reasons (mass transfer from a companion, supernova explosion, physical instabilities/pulsations, etc.), and studying their properties allows us to understand new stellar physics and the environments that produce these interesting objects. While there are many telescopes that are currently measuring brightness fluctuations to detect variables and transients, we have developed another technique to identify possible targets; specifically, we will identify stars that have large offsets between their brightness measurements from the Hubble Space Telescope and those inferred from Keck DEIMOS spectroscopic observations. With data from the PHAT (photometry) and SPLASH (spectroscopy) surveys for millions of stars in the Andromeda (M31) and Triangulum (M33) galaxies, we plan to identify hundreds — if not thousands — of potential variable stars. This will greatly improve our statistics on variables in M31/M33 and allow us to create a catalogue of targets to track in the future.

Tasks: The SIP interns will explore stellar spectroscopic and photometric properties for many different types of stars with a focus on those that change brightness over time (for a number of reasons). The interns’ tasks will include learning the basics of stellar evolution and stellar structure, and learning how to handle and manipulate photometric data from the Hubble Space Telescope and spectroscopic data from the Keck DEIMOS spectrograph. Ultimately, the group will produce an astronomical catalogue of potentially-variable/transient stars in our neighbouring galaxies.

Project code: AST-09 Title:  The Globular Cluster Systems of Virgo Cluster Dwarf Galaxies Primary mentor:  Prof. Eric Peng (Peking U., China) UCSC faculty contact:  Prof. Raja GuhaThakurta Location:  Remote/online Number of interns:  3

Project description: Star clusters are collections of thousands to millions of stars formed together and still bound together by their gravity. The oldest and most massive star clusters are called “globular clusters” (GCs) for their round appearance. GCs can be seen at much greater distances than individual stars, because they shine with the combined luminosity of many stars coming from a relatively small amount of volume. This project will look for GCs around low-luminosity galaxies in the nearest cluster of galaxies, the Virgo cluster. One of the challenges in finding star clusters around nearby galaxies is that the brightness of the galaxy itself gets in the way. In the project, the SIP interns and mentors will model the smooth galaxy light and subtract it from the images in order to better find faint star clusters.

Tasks: The SIP interns will work with images of galaxies in the Virgo cluster from the Next Generation Virgo cluster Survey (NGVS), a deep survey of the entire Virgo cluster with the Canada-France-Hawaii Telescope (CFHT). Using cutout images of the galaxies, the interns will use custom software (IRAF’s ELLIPSE and its modification, ISOFIT) to model galaxy light. The SIP interns will then use the software Source Extractor to find globular star clusters in the images.

URL:   https://www.ngvs-astro.org/

Project code: AST-10 Title:  Using Difference Imaging to Study Photometrically Variable Stars in Star Clusters in the Andromeda Galaxy Primary mentor:  Dr. Monika Soraisam (UIUC/NCSA) Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentor:  Joseph Liu (Santa Clara U.) Location:  Remote/online Number of interns:  3

Project description: The mentor’s research group has been exploring photometrically variable stars in the Andromeda galaxy (M31). Photometrically variable stars are those that undergo variations, often repeated or even strictly periodic variations, in their brightness due to pulsations. Recent large time-domain surveys (e.g., the POMME survey with the Canada-France-Hawaii Telescope and MegaCam imager) have discovered thousands of variable stars in M31. In addition, the Hubble Space Telescope (HST) has been used to carry out a large near UV/optical/near IR imaging survey called PHAT that covers a fraction of the bright disk of M31, and the mentor’s group has led a large Keck DEIMOS spectroscopic survey of M31 stars called SPLASH. The combination of variable star light curve data from time-domain observations, HST brightness and color measurements, and Keck spectra presents a unique opportunity to understand the nature of these variable stars.

Tasks: The SIP interns will cross match variable stars found in one or more of the time-domain surveys with HST PHAT survey photometric data and Keck DEIMOS spectroscopic data. The matched data set can then be used to construct a variety of color-magnitude diagrams (CMDs) and color-color diagrams. The SIP interns will work on new ways to identify variable stars in the PHAT dataset. They will group the variable stars according to CMD location and study systematic trends in light curve properties across and within the different groups.

Project code: AST-11 (a, b) Title:  Milky Way Halo RR Lyrae:  (a)  Measuring the Completeness Fraction and Quasar Contamination Rate of the Photometric NGVS Dataset, and  (b)  Radial Velocity Measurements from Time Series Spectroscopy with the Keck and Lick Telescopes Primary mentors:  (a)  Yuting Feng, and  (b)  Joseph Salinas Faculty advisors:   Prof. Raja GuhaThakurta, Prof. Eric Peng (Peking U., China) Secondary mentor:   Douglas Grion Filho Location:  Remote/online Number of interns:  4

Project description: Despite its static appearance at first glance, the Universe is constantly changing. Monitoring the sky for these changes is time consuming, but doing so allows us to identify unique celestial phenomena. Most images taken of the sky are not suitable for studying the “time-domain” because they are not taken with an appropriate spacing in time (cadence). The Next Generation Virgo Cluster Survey (NGVS), a deep, multi-color imaging survey of the closest cluster of galaxies, adopted an observing strategy that spaced observations for a given field over a time period of hours to years. While not designed for time-domain studies, this observing strategy allows us to look for things in the sky that change in brightness. Part  (a)  of this research project will analyze two different types of variability: (1) RR Lyrae variable stars in the outskirts of our Milky Way galaxy, excellent probes of our Galaxy’s assembly history via the cannibalism of smaller galaxies, and (2) variability of distant quasars caused by stochastic accretion of material onto the supermassive black holes that power them. Part  (b)  of this research project will use spectra obtained by the mentors’ research group to study the dynamics of Milky Way halo RR Lyrae.

Tasks: Part  (a) : Two of the SIP interns will analyze realistic simulated time series photometry of RR Lyrae and quasars derived from well studied known RR Lyrae and quasars. These simulated data will mimic the relevant characteristics (cadence, photometric errors) of the NGVS deep time series imaging dataset. The interns will apply standard data analysis techniques to these simulated data to measure completeness and contamination fractions in the NGVS RR Lyrae sample. Part  (b) : Two of the SIP interns will analyze time series Keck/ESI, Keck/DEIMOS, and Lick Shane/Kast spectra of bright RR Lyrae to monitor radial velocity variations of their photospheres during their pulsation cycle. The interns will also analyze deep Keck/ESI spectra of faint/distant MW halo RR Lyrae discovered in the NGVS dataset.

URL:   https://www.astro.ucsc.edu/faculty/index.php?uid=pguhatha

Project code: AST-12 Title:  Optimizing the Search for Gamma-Ray Bursts at Very High Energy Primary mentor:  Prof. David Williams Location:  Remote/online Number of interns:  3

Project description: Very high-energy (VHE) gamma rays are used to study some of the most powerful systems in the Universe, including pulsars, supernova remnants, and the black holes at the centers of galaxies. They have recently been observed from a few gamma-ray bursts (GRBs), as well. VHE gamma rays are also the most energetic form of electromagnetic radiation observed from astrophysical sources, a trillion times more energetic than optical light and a million times more energetic than X-rays. Using data from the VERITAS VHE gamma-ray telescopes, the interns will investigate ideas for improving the way the data are analyzed, with the goal to develop analysis methods that are more sensitive, in particular for GRBs. These studies will primarily use data from the Crab Nebula, a strong and steady VHE gamma-ray source powered by the Crab Pulsar. If time allows, the methods developed may be applied to some GRBs of interest. Because the project uses data from the VERITAS Collaboration, there will be some limits and constraints on what results can be presented in various public contexts, consistent with the VERITAS Collaboration publication policies.

Tasks: The SIP interns will learn to use computer programs for analyzing the VERITAS data and run the analysis on data sets from one or more of the objects of interest. They will also learn to inspect the output of programs which test the VERITAS data quality in order to remove poor-quality data (usually the result of bad weather) from the sample. They will compare different ways of doing the analysis in order to identify an optimum approach that gives the best (in the sense of most definitive) results. In doing so, the SIP interns will gain familiarity with standard tools used for astrophysics and particle physics data analysis and with working in the Linux computing environment.

URL:   http://scipp.ucsc.edu/~daw/

Project code: AST-13 Title:  Spectroscopy of Milky Way Halo Stars in HALO7D: Dynamics, Dark Matter, Accretion History, and Chemical Enrichment Primary mentor:  Miranda Apfel Faculty advisors:   Prof. Raja GuhaThakurta, Prof. Connie Rockosi Secondary mentor:   Kevin McKinnon Location:  Remote/online Number of interns:  4

Project description: The Milky Way galaxy which we call home is embedded within a vast and massive halo that is composed of mostly dark matter with a (literally!) light frosting of old, chemically anemic stars. The 3D velocities and surface chemical composition of these stars contains information about the accretion history, dark matter content, and chemical enrichment history of our Galaxy. The mentor’s research collaboration is using precise sky positions (astrometry) of these stars from the space-based Gaia mission and Hubble Space Telescope over a 10+ year baseline to measure their proper motions, two components of their velocity. Earlier this year, the group obtained spectra of these stars using the Keck II 10-meter telescope on Mauna Kea, Hawaii and the DEIMOS spectrograph. These stellar spectra will be used to measure the third (line-of-sight or radial) velocity component of these stars and their detailed chemical abundance patterns.

Tasks: Through this research project, the SIP interns will become intimately familiar with astronomical spectra. They will use the Interactive Data Language (IDL) program zspec to measure/vet measurements of stellar radial velocities and learn to recognize and flag residual instrumental and atmospheric artifacts. They will compare the performance of the IDL-based spec2d and Python-based PypeIt data reduction pipelines. Time permitting, the SIP interns will learn about the basics of chemical abundance measurements from stellar spectra.

Project code: AST-14 Title:  Weak CN Stars, Carbon Stars, and Other Exotic Stars in M31, M33, and the LMC Primary mentor:  Douglas Grion Filho Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentors:   Antara Bhattacharya, Stan Rinehart Location:  Remote/online Number of interns:  3

Project description: The Andromeda galaxy (M31), the nearest galaxy larger than our own galaxy, its companion the Triangulum galaxy (M33), and the Large Magellanic Cloud (LMC), a Milky Way dwarf satellite galaxy, serve as an excellent laboratories for the study of stellar populations including rare stars. Carbon stars constitute one such class of rare stars. The distinguishing characteristic of these stars is their atmosphere contains carbonaceous molecules such as CN, CH, and C_2 that make their presence known via broad absorption bands in the spectra of these stars. The mentor’s research group, working with previous SIP interns, has discovered a new class of rare stars called “weak CN” stars in which the CN spectral absorption feature at about 8000 Angstrom is much weaker than in the spectra of carbon stars. The initial discovery/classification of the weak CN stars was based on visual inspection of spectra and the group has since been working on automated classification with the goal of using state-of-the-art machine learning methods. Other rare stars in M31, M33, and the LMC include two classes of emission line stars.

Tasks: The SIP interns will analyze 1D spectra obtained with the DEIMOS spectrograph on the Keck II 10-meter telescope and the Hydra spectrograph on the CTIO Blanco 4-meter telescope. The interns will work with visually-classified and machine-classified populations of rare stars in M31, M33, and the LMC. The interns will use existing Python software and write custom software to analyze and compare these M31, M33, and LMC samples in terms of the following diagnostics: various HST and ground based color-magnitude diagrams (with theoretical stellar tracks overlaid), fraction relative to normal oxygen-rich stars, co-added spectra, kinematics (line-of-sight velocity dispersion and asymmetric drift relative to neutral hydrogen), and others.

Project code: AST-15 Title:  Kinematical, Chemical, and Physical Properties of the Ionized Gas Disks of Andromeda and Triangulum Primary mentor:  Khang Ngo Faculty advisor:  Prof. Raja GuhaThakurta Secondary mentor:  Olivia Gaunt Location:  Remote/online Number of interns:  3

Project description: The interstellar medium (ISM) in a galaxy is the gas and dust that fills the space between stars. This research project focuses on understanding the ionized gas disks of the Local Group disk galaxies Andromeda (M31) and Triangulum (M33) galaxies. The SIP interns will work with multi slit spectra obtained by the mentoring team using the DEIMOS instrument on the Keck II 10-m telescope. The interns will use Python techniques to detect and characterize the spectral emission lines that are commonly associated with the ionized gas ISM component of galaxies. The interns will compare M31 and M33’s ionized gas disk kinematics to the kinematics of their neutral atomic (HI) and molecular (CO) gas disks. They will draw conclusions about the rotational dynamics, chemical composition, and physical properties of M31 and M33’s ionized gas disks.

Tasks: The SIP interns will develop their own Python code and use existing Python code to work with spectroscopic data from the DEIMOS spectrograph on the Keck II telescope. This research project will initially involve processing of the 1D spectroscopic data to remove the effects of the Earth’s atmosphere (airglow) and instrumental signatures. The interns will fit constrained Gaussian mixture models to the spectra, analyze the Doppler shifts of the emission lines as a function of sky position, and analyze emission line ratios in the so-called BPT diagram to constrain the physical and chemical properties of the ionized gas. The SIP interns on this research project will work closely with the interns on a related research project: AST-06.

Project code: AST-16 Title:  Looking for Evidence of Intermediate Mass Black Holes in Star Clusters in the Virgo Cluster Primary mentor:  Vivian Tang Faculty advisors:  Prof. Raja GuhaThakurta, Prof. Piero Madau Secondary mentors:  Yuting Feng, Prof. Eric Peng (Peking U., China) Location:  Remote/online Number of interns:  3

Project description: Research over the last few decades has established that galaxies comparable in mass to the Milky Way host massive central black holes whose mass  M BH  is tightly correlated with their host galaxy mass  M gal . Extrapolating this  M gal – M BH  relation down to star clusters of mass 10 6   M sun  would suggest that star clusters should host intermediate mass black holes:  M BH  ~ 10 3   M sun . However, observational evidence for/against the presence of intermediate mass black holes has remained the subject of vigorous debate. One useful observational signature of intermediate mass black holes in dense star clusters is the temporary light burst caused by a tidal disruption event (TDE): the tidal disruption of an unfortunate star that strayed too close to the black hole’s event horizon. The Next Generation Virgo Cluster Survey (NGVS) has obtained repeat brightness measurements of tens of thousands of star clusters in the Virgo cluster of galaxies.

Tasks: The SIP interns will carry out the following set of theoretical calculations under the close guidance of the primary and secondary mentors: (1) trying out different slopes for the  M gal – M BH  relation to predict the abundance and mass of black holes in Virgo star clusters, (2) probability of TDEs given the density of stars in the vicinity of the central black hole, and (3) simulating the cadence of the NGVS observations on the theoretically predicted set of TDEs.

URL:  http://www.ucolick.org/~pmadau/Research_Highlights.html

Biomolecular Engineering

Project code: BME-01 Title:  SARS-COVID-19 Variants Study Primary mentor:  Gepoliano Chaves Faculty advisor:  Prof. Nader Pourmand Location:  Remote/online Number of interns:  4

Project description: In response to the COVID-19 pandemic, this research group has been exploring the possibility of developing a DNA-sequencing platform to detect the Coronavirus based on a DNA-sequencing method called NGS, Next Generation Sequencing. In this research project, the group will make use of some of the knowledge gained in working on the development of a Coronavirus detection platform to present the SIP interns with basic strategies to study the genome of the virus. These strategies include the design of primers to amplify genomic regions of interest, and the notion of variant call, identifying Single Nucleotide Polymorphisms (SNPs), which can explain the inter-species jumps of viral strains making them prone to infect different species.

Tasks: The SIP interns will work on the following tasks: (1) primer design for genomic region amplification; and (2) identification of Single Nucleotide Polymorphisms in the genome of the Coronavirus. They will run bash and R scripts to identify variants in the viral genome.

Project code: BME-02 Title:  Machine Learning Model for Predicting Breast Cancer Primary mentor:  Sam Teymoori Faculty advisor:  Prof. Benedict Paten Location:  Remote/online Number of interns:  4

Project description: Artificial intelligence, such as machine learning or deep learning, offers incredible value to the genomics industry. By using machine learning techniques, it is possible to analyze a great amount of genomics-related data, which can be found in publicly available research papers. In this project, we will introduce the mathematical concepts underlying the Logistic Regression, and through use of Python, we will make a predictor for malignancy in breast cancer. For this project, we will use the “TCGA expression datasets,” provided by the UCSC genomics lab database, which comprises gene expression data for twenty thousand tumor and normal samples processed using the exact same genomics pipeline so they can therefore be compared to each other.

Tasks: The SIP interns will go through a machine learning model to make a predictor for malignancy in breast cancer. First, the interns will make the pre-process. Second, the interns will make determinations and selections of the appropriate machine learning model. Third, the interns will train the model. Finally, the interns will test the model to calculate the efficacy of their machine learning task.

Project code: BME-04 Title:  Bioinformatics Pipeline to Calculate the Frequency of Viral Variants of Concern Versus Time in a Geographic Location Primary mentor:  Danilo Coelho (Fed. U. of Viçosa, Brazil) Secondary mentor:  Dr. Gepoliano Chaves

Location:  Remote/online Number of interns:  4

Project description: In the past few years, several countries have been exposed to various segregated viral epidemics. Examples include the HIV epidemic in the 1980s, dengue in the 1990s, and SARS in the early and middle 2000s. All these epidemics have hit Brazil, but the most notable viral epidemics in the country have been the Zika virus in 2015–2016 and the current SARS-CoV-2 pandemic. As part of an international effort led by researchers in Brazil and the University of California Santa Cruz to build an international capacity in genomic sequencing analysis and epidemiology, this research project aims to build a pipeline for the calculation of the frequency as a function of time of Variants of Concern (VOC) of viruses endemic to Brazil, such as Zika and SARS-CoV-2.

Tasks: In this research project, the SIP interns will: (1) learn to use statistical analysis techniques that are used in epidemiology; (2) analyze the molecular basis for the establishment of a variant of concern (VOC): (3) analyze the three-dimensional structure of viral proteins that explain increase in viral transmissibility and viral evasion of the immune system; and (4) use informatics to calculate frequency of a VOC a function of time.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, lab work, statistical data analysis

Project code: BME-05 Title:  Building a Web Application for Visualization of SARS-CoV-2 Variants of Concern for Use in the Context of a Middle Income Country Primary mentor:  Prof. Jaime Amorim (Fed. U. of W. Bahia, Brazil) Secondary mentor:  Dr. Gepoliano Chaves

Location:  Remote/online Number of interns:  3

Project description: It is thought that the present COVID-19 pandemic emerged from a “zoonotic jump” from a wild animal to humans in an animal market in the Chinese province of Hubei in 2019. These zoonotic jumps happen as a result of mutations in the genetic material of the virus to provide viral adaptation to its host. Depending on the threat they represent, these mutations can be called variants of concern (VOC) because, at the same time as they provide adaptation, they may threaten human life as is the case right now with the global pandemic. Health policies should take advantage of genomic information that track VOCs that are potentially more infective than others and therefore dangerous to the public. Geographic regions with increases in the frequency of VOC should implement more rigorous isolation policies. In this research project, the SIP interns will be exposed to the process of construction of a web application to visualize the frequency of VOC with time using genomic sequencing data.

Tasks: The SIP interns will: (1) download data from a database; (2) align data to the reference genome; (3) identify variants in the regions they were isolated from; (4) compare the variants of a region to a list of VOC; (5) learn to use bioinformatics to count variants in these regions; and (6) calculate VOC frequency in the geographic region of interest. Because the mentor’s group works on sequencing SARS-CoV-2 in the Brazilian state of Bahia, the SIP mentor and interns will look particularly closely at sequencing data from this state.

Chemistry & Biochemistry

Project code: CHE-01 Title:  Application of Machine Learning in Developing Promising N-Doped Carbon Catalysts for the Oxygen Reduction Reaction Primary mentor:  Mingpeng Chen Faculty advisor:  Prof. Yat Li Location:  Remote/online Number of interns:  3

Project description: The oxygen reduction reaction (ORR) is the cathodic reaction of a fuel cell. It is a four-electron-transfer process that generates a high energy barrier and limits the reaction efficiency. Platinum is well known as the most efficient catalyst for ORR and is often used as the benchmark for ORR. However, its high cost and scarcity has limited its applications. In addition, platinum-based catalysts suffer from problems such as time-dependent drift, CO deactivation, etc. A number of alternative materials have been explored to replace platinum. Among them, nitrogen (N) doped carbons have been demonstrated to be promising materials for ORR owing to its low cost, high abundance, and strong resistance to the poisoning of CO. In this research project, the SIP mentor and interns will employ machine learning to help understand how the concentration of N doping and ligand affect the ORR activity of N-doped carbon.

Tasks: The SIP interns will: (1) understand the background of the oxygen reduction reaction; (2) understand the physical quantities researchers use to describe the activity of ORR catalysts; (3) generate enough structures for machine learning; and (4) use machine learning to train and find physical rules behind these data.

URL:  https://li.chemistry.ucsc.edu/

Computational Media

Project code: CPM-01 Title:  Game-Making Tools Survey Primary mentor:  Jared Pettitt Faculty advisor:  Prof. Nathan Altice Location:  Remote/online Number of interns:  3

Project description: There are many different tools used to produce different kinds of video games, from casual tools like Twine, to professional tools like Unity 3D. These tools have, built into their design, certain affordances or expectations that shape what people tend to make using them. The mentor is working on research regarding game-making software, and this research project is a survey of different game-making tools, by using the tools to produce small games and then evaluating them afterwards. If the SIP interns are interested in making games, either in learning how to use high-level software, or just want to learn how to do it because it seems fun (it is), then they will definitely find this research project interesting to work on!

Tasks: The SIP mentor and interns will be using several design game creation tools to make small games over the course of the summer, while evaluating how using the tool feels and how its design affects what they make using it. There are many tools that do not require any kind of programming or video game knowledge at all, so if the interns are at all interested but feel that you may not know enough to be helpful, they should not worry about that! The SIP interns will be learning, making games with, and evaluating several of the following tools, depending on their programming skill level: Twine, Bitsy, Pico-8, Unity 3D, Unreal Engine, and GameMaker.

Project code: CPM-02 Title: VR Game Development for Psychosocial Support of Preteens with Cleft Lip and/or Palate Primary mentor:  Tiffany Thang Faculty advisor:  Prof. Sri Kurniawan Location:  Remote/online Number of interns:  4

Project description: This research focuses on the development of a virtual reality (VR) game aimed at providing psychosocial support in the realm of self-confidence for preteens with cleft lip and/or palate (CLP). In collaboration with global CLP organization, Smile Train, we will be working on understanding how we can create a VR game that provides supplemental support to existing psychosocial support from psychologists and clinicians working with preteens with CLP. In this project, we will work with psychologists, clinicians and preteens with CLP in understanding how to develop a VR game that helps with developing and practicing self-confidence skills.

Tasks: SIP interns can expect to gain knowledge in virtual reality, CLP, and assistive technologies through conducting literature reviews early in the program. Interns can also expect to learn the process of designing and developing a VR game through storyboarding and wireframing, and will learn how to create a 3D modeled VR environment using Unity and Blender.

Project code: CPM-03 Title:  Conversational UX Design Primary mentor:  Kehua Lei Faculty advisor:  Prof. David Lee Location:  Remote/online Number of interns:  3

Project description: The goal of this research is to explore novel conversational user experience (UX) design. Conversational UX in this project refers to conversational user interfaces (UIs) and interaction modes that support text-based communications. We now have two research directions. The first one is designing richer conversational features to enable large-scale units in chat-based UIs. We developed a platform for students to attend online sessions that provide one-to-many mentorship experiences and full engagement without the typical chaos of group chat. The other direction is building a platform for users to create a chat group with chatbots that have different personalities. This could offer emotional supports to users. This research project will contribute to the field of human-computer interaction, conversational UX design, software testing, and code generation.

Tasks: The SIP interns will do some or all of the following: (1) design conversational features for one of the platforms; (2) create dialogues for real scenarios; (3) design UIs on Figma; (4) conduct usability tests; (5) develop the platform using JavaScript and Angular framework; (6) identify the research contributions of the projects.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, field work, lab work

URL:  https://tech4good.soe.ucsc.edu/

Project code: CPM-04 Title:  Game Generation via Orchestration Primary mentor:  Isaac Karth Faculty advisor:  Prof. Adam M. Smith Location:  Remote/online Number of interns:  3

Project description: The SIP interns will contribute to the development and evaluation of a game generation program, using a new approach for applying generative design to the game orchestration problem [1] using the Game Boy as a target platform. The interns will author training data, evaluate existing software for examples of training data, use automated playtesting software to gather data, assist with the programming of the generative design software, and assist in evaluating the results. This research project will contribute to the fields of software testing, generative design, game design, and code generation. [1] A. Liapis, G. N. Yannakakis, M. J. Nelson, M. Preuss and R. Bidarra, “Orchestrating Game Generation,” in IEEE Transactions on Games, vol. 11, no. 1, pp. 48-68, March 2019, doi: 10.1109/TG.2018.2870876.

Tasks: The SIP interns will do some or all of the following: (1) evaluate existing games for possible training data, (2) author additional training data, (3) gather experimental data on the results of the generation, (4) run machine playtesting experiments, (5) automate the formatting of playtesting reports, and (6) evaluate the results of generation and playtesting. Programming for this project will primarily use JavaScript and ClojureScript, though the training data authoring will primarily occur via GB Studio and there may be a small amount of scripting in Python. Interns can contribute to the project without programming, though prior experience with some kind of programming can be helpful.

URL:  https://designreasoning.soe.ucsc.edu/

Project code: CPM-05 Title:  Automatic Audio Generation for Game Boy Programs Primary mentor:  Tamara Duplantis Faculty advisor:  Prof. Nathan Altice Location:  Remote/online Number of interns:  3

Project description: This research project will contribute to the fields of procedural audio, algorithmic music, digital musical interface design, and game generation. The SIP interns will contribute to the development and evaluation of a generative audio system targeting the Game Boy platform. The project will involve authoring example audio output, designing programs that generate the target audio output, integrating the resultant audio generators with a GB Studio game generation project, and exploring methods for generating procedural audio systems to be run on the Game Boy.

Tasks: The SIP interns will do some or all of the following: (1) author example game audio output using Game Boy tracker software, (2) assist in the design of generative audio programs, (3) integrate audio output into a larger game generation project using JavaScript. This project as a whole will likely involve programming in JavaScript and C; however, the interns’ contributions will be limited to JavaScript. If interns do not have programming experience, they can still contribute substantially to the project if they have a strong musical background.

URL:  https://www.soe.ucsc.edu/departments/computational-media

Project code: CPM-06 Title:  Social Wearables Primary mentor:  Ella Dagan Peled Faculty advisor:  Prof. Katherine Isbister Location:  Remote/online Number of interns:  3

Project description: The focus of the Social Wearables research project is the potential of incorporating computation in things people wear on their bodies as a way to enhance and strengthen in-person social interactions. Many current wearable devices are not focused on the co-located social interaction, and risk having a negative impact on our everyday social life by distracting people and taking their attention from one another. The mentor’s research group uses Research-through-Design methods to leverage state-of-art technologies in order to envision new designs that address our basic need for human connection and support prosocial interaction. This summer’s research will focus on testing and refining activities for a social wearables educational Larp (live action role playing) game camp.

Tasks: The SIP interns will test out activities that the mentor’s research group is developing for middle school age girls who would participate in a STEM camp that include Live action role playing and designing wearable technology. The activities include basic computer programming with Mircrosoft’s makecode and micro:bit hardware. The interns will refine and comment on the activities to help us prepare them for the camp. The SIP interns could also design and develop their own activities for the campers. Additionally, the interns will help with literature review towards writing an academic paper about the educational camp.

URL:  https://setlab.soe.ucsc.edu/projects.php

Project code: CPM-07 Title:  Reinforcement Learning in Video Games Primary mentor:  Batu Aytemiz Faculty advisor:  Prof. Adam M. Smith Location:  Remote/online Number of interns:  3

Project description: The larger research project aims to ensure people don’t feel helpless playing video games. To accomplish this goal, the mentor is building a parkour video game with no combat, and developing an artificial intelligence (AI) system to play the game. The mentor plans to use AI to help the players instead of fighting against them. The SIP interns will assist in the creation of game levels in Unity game engine, and in the collection of data to train the AI network (by playing the game). Optionally, depending on individual interest, the SIP interns can help with AI development or game development.

Tasks: The SIP interns will: (1) develop playable levels in Unity game engine; (2) create datasets by playing the game; create visualizations of the experimental results (optional); and (4) implement game features in Unity game engine.

URL:  https://youtu.be/D7xiy2TY61g?t=2260

Project code: CPM-08 Title:  Scenario Generation for Autonomous Vehicles Primary mentor:  Abdul Jawad Faculty advisor:  Prof. Jim Whitehead Location:  Remote/online Number of interns:  4

Project description: Scenario-based testing of autonomous vehicles (AVs) in virtual environments has become an essential component of vehicle safety validation efforts because it is scalable, cost-effective, and safe. Critical and challenging scenarios for AVs are the key part of scenario-based testing. The recent trend for generating these critical and challenging scenarios is to use game engine-based simulation tools such as Carla (an Unreal game-engine-based simulator), Apollo (a Unity game-engine-based simulator). This research project will explore techniques of generating critical and challenging scenarios in the open-source vehicle simulation tool Carla. Specifically, SIP interns will get involved in programming in C++ and Python. Also, they will explore existing literature in the related fields.

Tasks: In this research project, the SIP interns will: (1) study different approaches to autonomous vehicle safety validation; (2) learn to write programs in Python and C++; (3) learn how to use the Unreal game engine; (4) study state-of-the-art scenario-based safety validation approaches; and (5) read existing literature in related fields.

URL:  https://carla.org/ ;  https://github.com/AugmentedDesignLab/CruzWay

Project code: CPM-09 Title:  SpokeIt: Preparing a Speech Therapy Game for Global Delivery Primary mentor:  Jared Duval Faculty advisor:  Prof. Sri Kurniawan Location:  Remote/online Number of interns:  6

Project description: Therapy is costly, time-consuming, repetitive, and difficult. Games have the power to teach transferable skills, can turn repetitive tasks into engaging mechanics, have been proven to be effective at delivering various forms of therapy, and can be deployed at large scales. Games move us. The SIP interns will work to bring the mentor’s research group’s speech therapy game, SpokeIt, into the world. SpokeIt ( http://SpokeIttheGame.com ) is a speech therapy game for children born with orofacial cleft. It has been in development for five years, has been studied clinically, and features cutting edge tools that can critically listen to speech. SpokeItTheGame has recently partnered with SmileTrain — an organization that has supported over 1.5 million free cleft surgeries — and is gearing up to be deployed around the world (it is nearly ready for launch on the App Store)! The SIP interns will primarily be working on translating SpokeIt for release on Google Play as well as working towards supporting new languages.

Tasks: Depending on the SIP interns’ expertise and interests, there are many opportunities to work on the research project. All interns will be expected to work on polishing existing game content or creating new content as well as analyzing user studies and playtests. Some example tasks include working on animations, sprite sheets, game engine components, art assets, databases, and analyzing user study data. For development, the mentor’s research group works primarily in Xcode, Unity, and Android Studio. The group uses various Adobe applications for design work, such as Illustrator, XD, Photoshop, After Effects, and Character Animator. The SIP interns will work towards translating SpokeIt to work on Android devices and towards adding support for new languages. The interns will need to have access to the Adobe Creative Cloud, an Android device, and an Apple computer.

URL:  https://jareduval.com/

Computer Science/ Computer Engineering

Project code: CSE-01 Title:  Generative Adversarial Networks Primary mentor:  Saeed Kargar Faculty advisor:  Prof. Faisal Nawab Location:  Remote/online Number of interns:  4

Project description: Generative Adversarial Networks (GANs) represent a class of generative models that were introduced by  Goodfellow et al. (2014) . It is one of the most-cited papers in computer science (over 27,000 in February 2021), which proves this method’s popularity and importance in the machine learning and deep learning fields. Yann LeCun, who is a pioneer in the modern revolution in deep neural networks, declared GANs as “the most interesting idea in the last 10 years in machine learning.” [1] They generate/create new data instances that resemble your training data. For example, GANs can create images of human faces that are statistically indistinguishable from real ones even though the faces in the GAN-created images are not real and don’t belong to any real person. GANs are a clever way of training a generative model by training two sub-models: the generator model that is trained to create/generate new instances, and the discriminator model that tries to classify the instances as either real (belong to the training set) or fake (fake/generated). In this method, the generator, which produces the target output, is paired with the discriminator which learns to distinguish between fake and real instances in an interesting way. The generator tries to fool the discriminator, and the discriminator tries to keep from being fooled. There are a large number of interesting applications of GANs to help one develop an intuition for the types of problems where GANs can be useful such as: (1) generate cartoon characters, (2) image-to-image translation, (3) text-to-image translation, (4) semantic-image-to-photo translation, (5) face frontal view generation, (6) generate new human poses, (7) photos to emojis, (8) photograph editing, (9) face aging, (10) super resolution, (11) photo inpainting, (12) video prediction, (13) 3D object generation, and many others.[2]

Tasks: The SIP interns will learn: (1) the concept behind GANs, and (2) how to implement GANs from scratch. The interns will learn various deep learning concepts and tools — e.g., using the Keras library, pre-trained models such as the VGG19 network, and popular online tools such as Google Colab to solve programming problems. Furthermore, they will learn how to read a research paper and implement it. What the SIP interns will learn are: (1) Python programming; (2) machine learning frameworks such as TensorFlow and Keras; (3) data collection from online resources; (4) one of the most advanced topics in deep learning – i.e., GANs; (5) critical reading of research papers; and (6) collaborating effectively in a team research environment.

Required skills for interns prior to acceptance:  Computer programming (preferred) Skills interns will acquire/hone:  Computer programming, statistical data analysis

Project code: CSE-02 Title:  Enabling Collaborative Learning in Real-World Systems Primary mentor:  Harikrishna Kuttivelil Faculty advisor:  Prof. Katia Obraczka Location:  Remote/online Number of interns:  3

Project description: Decentralized collaborative learning is an emerging field of networking and machine learning in which devices can work together, in the absence of any central server or entity, to develop and share machine learning models with one another. However, its application to real-world systems has been hindered by real-world limitations. In this research, interns will be part of a team to identify solutions to these real-world limitations, study and implement network protocols to facilitate collaborative learning using simulations (and hardware if progress allows), and plan and develop systems resembling real-world applications.

Tasks: Interns will contribute to, run, and report on experiments of network simulators. Interns will read and present papers they’ve read that pertain to research on collaborative learning and network systems. Interns will learn and develop network communication protocols, and engage in discussions about application and systems.

Required skills for interns prior to acceptance:  Computer programming (preferred) Skills interns will acquire/hone:  Computer programming, lab work, statistical data analysis

Project code: CSE-03 Title:  Citizen Science Mobile Apps with Integrated Machine Learning Models Primary mentor:  Fahim Hasan Khan Faculty advisor:  Prof. Alex Pang Location:  Remote/online Number of interns:  3

Project description: Citizen science involves the participation of non-scientists in data collection according to specific scientific protocols and in the process of using and interpreting that data. Increasingly, citizen science platforms are going mobile with the growing power of mobile computation. This research involves developing an open-source software platform that allows a domain researcher to quickly create a citizen science mobile app with integrated machine learning (ML) models for collecting data with real-time analysis. The mentor is currently working on creating ML-powered mobile apps and server-side infrastructures of the citizen science platform. This SIP research will develop and test the citizen science mobile apps and use them to collect data. The collected data will be later used for more training and optimizing the ML models.

Tasks: The SIP interns will participate in a research project to develop mobile apps with integrated ML for a citizen science platform and server-side infrastructures. They will learn how to program using Python, do literature reviews on a topic by reading related research papers and work on an academic research project. The interns will have experience working with ML models for object detection and classification using platforms like TensorFlow. They will be acquainted with the mobile app development process and integrating ML models with mobile apps. The interns will participate in testing and data collection using ML-powered citizen science mobile apps. If time permits, they’ll learn about 3D data capturing using mobile devices.

URL:  https://www.soe.ucsc.edu/people/fkhan4

Project code: CSE-04 Title:  Spike Sorting Algorithm: Basis and Implementation Primary mentor:  Jinghui Geng Faculty advisor:  Prof. Mircea Teodorescu Location:  Remote/online Number of interns:  3

Project description: Electrophysiology in neuroscience is a way to study the electrical properties of the cells and tissues in the nervous system. To detect and characterize the neuronal activities, spike sorting algorithm is a common tool used to analyze electrophysiological data, and it is fundamental for brain-computer interface development. This research is part of a project for developing a real-time neuronal signal acquisition and stimulation device. The interns will develop Python wrapper and functions to benchmark and compare several popular sorting algorithms in runtime, resource and accuracy, and build a thresholding application for a small scale embedded system.

Tasks: Interns will be doing the following: (1) understand the neuronal data from extracellular recording, (2) learn the basic techniques in each step of the sorting algorithm, (3) learn Python tools such as Google Colab, Jupyter Notebook, Docker and etc, (4) program in Python to build the benchmark environment and report the result, (5) program in C/C++ or Python to implement the thresholding application on an embedded device.

URL:  https://braingeneers.ucsc.edu/

Project code: CSE-05 Title:  Origami Robot: Modeling and Simulation Primary mentor:  Samira Zare Faculty advisor:  Prof. Mircea Teodorescu Location:  Remote/online Number of interns:  4

Project description: Origami is a newly emerging field in robotics that can help when limited space is available. They have many applications, from solar panels to medical devices. These deployable structures are able to move and change their shapes and structures based on their environment. For instance, solar origami panels can become compact to transfer and then deploy to their final structure. The mentor’s research group designs, models, and develops a dynamical simulation in Autodesk Inventor and uses Python to analyze and understand their movements.

Tasks: The SIP interns’ primary tasks will be learning Autodesk Inventor and how to use Python to analyze the simulation data. Their secondary tasks will be to come up with Origami design ideas and apply a dynamical simulation to understand them. Finding an origami design that can be applied to real-world problems could be challenging. They can be too complex to control and model or too simple to perform any movement.

Project code: CSE-06 Title:  Bio-Inspired Spiking Neural Network Based on Memristors with Short-Term Plasticity for Audiocortical Processing Primary mentor:  Peng Zhou Faculty advisor:  Prof. Sung-Mo Kang Location:  Remote/online Number of interns:  3

Project description: The mentor’s research group is developing a bio-inspired spiking neural network for audiocortical processing based on memristors. Mammals and birds can detect the direction of a sound source based on coincidence detection in spiking neural networks (SNNs) with short-term plasticity (STP). Memristor, the fourth fundamental electrical element, is widely used in the area of neuromorphic computing. Aiming to overcome the bottleneck of the von Neumann architecture and the end of Moore’s law, neuromorphic computing emulates the human brain. It takes advantage of the brain’s high-density, low-power, and parallel computing. The SIP mentor and interns will simulate the memristor as the synapse model and emulate the sound localization spiking neural networks. This research project will be developed in Python and Brian2. It is exciting to emulate the function of actual human/animal brains!

Tasks: The SIP interns will: (1) learn to use Python for programming; (2) understand the concept of the memristor and simulate the memristor; and (3) learn to build spiking neural networks and emulate a sound localization spiking neural network.

URL:   https://nisl.sites.ucsc.edu/

Project code: CSE-07 Title:  RouteMe2: Providing Spatial Contextual Awareness for Assisted Transit Systems Primary mentor:  Fatemeh Mirzaei Faculty advisor:  Prof. Roberto Manduchi Location:  Remote/online Number of interns:  3

Project description: Navigating a public transit system can be confusing for everyone, especially in an unfamiliar environment (e.g., when visiting a new city). One needs to figure out which transportation lines to take to reach a destination, when and where to catch a bus or a train, when to exit, and how to negotiate transfers. For those with visual impairments or cognitive disabilities, these problems become even more daunting. They need to know nearby objects, have a better sense of their location, and make decisions based on this contextual information. The mentor’s RouteMe2 system tracks and navigates users in complex transit hubs and provides spatial contextual awareness for those in need. In this research project, the SIP interns will be helping with creating geometric shapes (tiles) on a map, defining spatial contextual information for tiles, improving the user interface of the system, and testing the system.

Tasks: The SIP interns will participate in a practical scientific study and will become familiar with dealing with real world interesting challenges through their participation in this research project. The interns will learn to: (1) work with the RouteMe2 system; (2) define geometric shapes using Mapbox; (3) define spatial contextual information and associate it with specific tiles; (4) test the system; and (5) improve tile definition.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, field work

URL:   https://vision.soe.ucsc.edu/welcome-ucsc-computer-vision-lab

Project code: CSE-08 Title:  Deep Learning for Face Emotion Detection Primary mentor:  Negin Majidi Faculty advisor:  Prof. Manfred K. Warmuth Location:  Remote/online Number of interns:  3

Project description: Machine learning models have been shown to be powerful in many learning tasks such as pattern recognition, image classification, and face detection. Human emotion is usually displayed by face and felt by brain signals. We can use artificial intelligence systems to detect faces and extract emotions from faces. To build the models, we need to train an algorithm on a dataset and test it on some samples as our test set. In this research project, the SIP mentor and interns will build and train a convolutional neural network using Keras from scratch to detect face emotions. The data set consists of a collection of face images. The goal of the research project is to classify the images based on the emotion in the facial expression into a class. The project will use OpenCV to automatically detect faces in the images and make a bounding box for the faces. Afterwards, various CNNs will be trained to classify the faces into categories.

Tasks: The SIP interns will: (1) learn useful programming tips in Python; (2) work collaboratively on programming tasks; (3) understand the concepts behind deep learning models, particularly CNNs; (4) learn to work with Keras and pre-trained CNN structures; (5) learn how to use Google Colab; and (6) develop familiarity with OpenCV.

Earth & Planetary Sciences

Project code: EPS-02 Title:  Cloud Formation and Haze-Lake Interactions on Titan Primary mentor:  Dr.  Xinting Yu Faculty advisor:  Prof. Xi Zhang Location:  Remote/online Number of interns:  3

Project description: Titan, the largest moon of Saturn, has an active hydrological cycle similar to Earth, but with liquid hydrocarbons instead of liquid water. Hydrocarbon clouds form in Titan’s atmosphere and are then rained down towards Titan’s surface, forming myriads of lakes and seas. Titan’s methane-nitrogen atmosphere also allows the formation of various organic compounds and eventually lead to the formation of complex organics, that forms Titan’s thick haze layers. In this research project, we will investigate the formation of hydrocarbon clouds in Titan’s atmosphere and the interactions between organic hazes and the lake liquids on Titan’ surface. We will use a combination of laboratory-generated data and direct observational data of the Cassini spacecraft to perform theoretical calculations to understand the above phenomena on Titan. Our goal is to better understand these physical phenomena with exotic materials in an exotic environment and to support future spacecraft missions to Titan, such as the Dragonfly rotorcraft mission, and ground-based observations.

Tasks: Intern will perform the following tasks: (1) use Cassini data and previously published literature to compile the existing observed gas-phase species in Titan’s atmosphere and the lake compositions; (2) analyze collected laboratory data to compile the needed input for theoretical calculations; (3) perform theoretical calculations using laboratory-produced data and the above collected observational data and make predictions for future observations on Titan.

URL:  http://www.xintingyu.com

Project code: EPS-03 Title:  Modeling the Effects of Ocean Alkalinity Enhancement on Atmospheric CO2 and Seawater Chemistry Primary mentor:  Ryan Green Faculty advisor:  Prof. Mathis Hain Location:  Remote/online Number of interns:  4

Project description: Humankind will need to remove hundreds of gigatons of carbon dioxide (CO2) from the atmosphere by the end of the twenty-first century to keep global warming within 2 degrees Celsius or less of the constraints of the global carbon budget. However, so far it is unclear if and how this could be achieved. A widely recognized idea is to accelerate the chemical breakdown of rocks, also known as weathering, which ultimately leads to CO2 being locked up in carbonates on the ocean floor. While this artificial acceleration of weathering mimics a natural carbon cycle process, the impacts of such a large process over such a short timescale (geologically speaking), is not well understood. For this research project, the mentor and interns will be designing and running experiments based on different carbon emission scenarios that test the effects of accelerated weathering in the ocean (ocean alkalinity enhancement) on atmospheric CO2 and seawater chemistry.

Tasks: The SIP interns will: (1) understand the basics of ocean carbonate chemistry; (2) learn basic programming using Python and/or C++; (3) build both forward and inverse modeling experiments using a global carbon cycle model; and (4) analyze and present the results using Python.

URL:  https://biogeochemistry.sites.ucsc.edu/

Project code: EPS-04 Title:  Fish Extinctions during Ancient Climate Change Primary mentor:  Prof. Matthew Clapham Location:  Remote/online Number of interns:  3

Project description: The fossil record contains multiple extinction events caused by climate change, a combination of ocean warming, acidification, and oxygen depletion. These extinctions are often selective, where some types of animals are severely affected but others are more resistant. Past selectivity patterns can help predict the types of animals that may be most vulnerable to future climate change. The goal of this research project is to compare the extinction of fishes with other animals in extinctions during the Cretaceous period, in order to determine whether fishes are especially vulnerable to climate change or are more resistant.

Tasks: The SIP interns will compile a database of fossil fish occurrences from the Cretaceous period, collecting information about the age, geographic location, and possibly body size and/or diet/ecology, from published papers. The interns and mentor will use statistical methods to determine extinction selectivity, comparing fish extinctions to extinctions of other animals during multiple time periods, and evaluating the potential effect of body size or ecology on survival.

URL:  https://people.ucsc.edu/~mclapham

Ecology & Evolutionary Biology

Project code: EEB-01 Title:  Should I Stay or Should I Go? Primary mentor:  Matthew Kustra Faculty advisor:  Prof. Suzanne Alonzo Location:  Remote/online Number of interns:  3

Project description: Alternative reproductive tactics are observed in many species, often as two distinct male types, a territory holding male and a sneaker male that sneaks mating opportunities from the territory holding male. The ocellated wrasse ( Symphodus ocellatus ), a Mediterranean fish species, has three alternative male reproductive tactics. Nesting males make nests, chase away sneakers, court females, and provide all parental care. Sneaker males try to join nesting males and females during mating events. Satellite males help the nesting male by chasing away sneakers but will also compete with them by joining mating events between the nesting male and females. The satellite male needs to balance helping the nesting male out, while also sneaking mating opportunities to gain some reproductive success. The mentor’s research group is investigating factors that may influence the satellite male’s decision on when and how often he should sneak. In this research project, the SIP mentor and interns will analyze underwater videos to record data on satellite male sneaking behavior as well as other attributes of the nest.

Tasks: The SIP interns will primarily be analyzing underwater videos of fish mating behavior by recording spawning events as well as nest dynamics leading up to the spawning event. While collecting the data from these videos, the interns will learn how to properly manage collaborative data sets. Towards the end of the summer, the SIP interns will learn how to perform basic statistical analyses and make graphs in the R programming language. Additionally, the interns will learn more about behavior and evolution through paper discussions to put this research experience in the broader context of evolution and behavioral ecology.

URL:  https://mattkustra.wordpress.com/

Electrical Engineering

Project code: ELE-01 Title:  Developing Radiation Detectors for Health and Environmental Science Applications Primary mentor:  Maryam Farahmandzadeh Faculty advisor:  Prof. Shiva Abbaszadeh Location:  Remote/online Number of interns:  5

Project description: Radiation is all around us. Naturally occurring radiation from outer space has been around since before the birth of earth. In addition, man-made radiation like X-rays are extensively used in medical imaging. Regardless of the source of radiation, human senses are not capable of detecting it; it requires specific equipment to detect and produce an observable output. The goal of this project is to design, fabricate and characterize robust and sensitive radiation detectors with applications in medical imaging, high energy physics and many other areas.

Tasks: The SIP interns will work on numerous aspects of radiation detection development, and will learn how to make a semiconductor-based photodetector in a clean environment. The interns will characterize the detector by learning how to quantify the performance of the developed detector. The interns will also have the opportunity to attend group meetings and learn about the latest developments in radiation detection.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Lab work

URL:  https://ril.soe.ucsc.edu/

Project code: ELE-02 Title:  Rehabilitative Robotics Primary mentor:  Stephanie Herrera Faculty advisor:  Prof. Michael Wehner Location:  Remote/online Number of interns:  3

Project description: The superiority of robot-based rehabilitation over manual rehabilitation is now well-known. Lower extremity exoskeletons are an emerging technology to utilize as rehabilitative devices for patients with abnormal gaits from neuromuscular disorders, such as cerebral palsy, sclerosis, or stroke. Because most of the current systems have been designed with rigid structures and stiff actuation, they are not well suited for use in unstructured environments which can jeopardize human safety, comfort, and psychological acceptance. These concerns have begun a shift toward the inclusion of compliant elements to produce soft exoskeletons that are mainly composed of neoprene, elastic polymers, and innovative textiles. These soft technologies allow the exoskeleton to be moldable against the human body, increasing their degrees of freedom without adding bulkiness.

Tasks: The SIP interns will analyze human gait dynamics and understand how to build a simplified model that allows for easier analysis. The interns will review case studies of rigid and soft lower-extremity exoskeletons to determine which mechatronic design, actuation, and control theory performs best while maintaining the patient safety. The interns will also be able to design their own rehabilitative robots making sure they are safe and compatible with the human body.

Required skills for interns prior to acceptance:  Computer programming Skills interns will acquire/hone:  Computer programming, lab work

Project code: ELE-03 Title:  Renewable Energy and Power Systems Primary mentor:  Shourya Bose Faculty advisor:  Prof. Yu Zhang Location:  Remote/online Number of interns:  3

Project description: In order to reduce detrimental effects on the environment, power plants powered by fossil fuels are being phased out across the US and around the world in favor of renewable sources of energy such as solar and wind energy. The intermittent nature of renewable energy (the sun doesn’t always shine, and the wind doesn’t always blow) introduces new challenges in planning and operation of the electric power grid. The goal of this research project is to develop a plan for an electric power grid that contains solar and wind energy components, alongside large-scale batteries and other forms of power generation, so as to meet the electric power demand of consumers.

Tasks: The SIP interns will review models of electric power grids that have renewable sources of energy attached to them. The interns will also review models of electric power demand from homes and industries. Based on the review, the interns will contribute to research about the modeling aspects of an electric power grid simulation which is being carried out by the mentor. At the conclusion of the summer research internship, the SIP interns will have a solid foundational understanding of how the electric power grid works.

URL:  https://people.ucsc.edu/~yzhan419/

Project code: ELE-04 Title:  Nanophotonic Biosensors for Rapid Detection of Coronavirus SARS-CoV-2 Primary mentor:  Ahsan Habib Faculty advisor:  Prof. Ahmet Ali Yanik Location:  Remote/online Number of interns:  4

Project description: The highly transmissible and pathogenic coronavirus SARS-CoV-2 has resulted in a pandemic of the acute respiratory disease called COVID-19 (coronavirus disease 2019). The first step in COVID-19 management is quick and accurate detection of SARS-CoV-2. Current diagnostic techniques rely primarily on polymerase chain reaction (PCR) tests which require the transport of a sample to a specialized laboratory resulting in massive detection delays. This research project aims to develop a label-free optical biosensor for the rapid diagnosis of COVID-19 disease at the point of care outside of the lab.

Tasks: The SIP interns will work on the design of a nanophotonic biosensor. The interns will: (1) learn the basics of nanophotonic resonators; (2) learn about the optical waveguide design software  Lumerical MODE ; and (3) develop a nanophotonic biosensor using the  Lumerical MODE  software.

URL:  https://scholar.google.com/citations?user=shPv4WEAAAAJ&hl=en/

Environmental Studies

Project code: ENV-01 Title:  Improving Coastal Prairie Restoration for Increased Resilience to Drought Primary mentor:  Justin Luong Faculty advisor:  Prof. Michael Loik Location:  Remote/online Number of interns:  4

Project description: Ecological restoration seeks to alleviate loss of unique ecosystems through native plant reintroductions and invasive species control. However, restoration outcomes can be unpredictable and may become more so with climate change. Functional traits can help practitioners select plants based on traits that may survive better in a given environment. Because restoration practitioners are fund limited they cannot collect traits for plants they want to use and most plants have not yet been quantified. The group is interested in understanding if traits development of California native plants are constrained by evolutionary relationships or by climate. If traits did develop with constraints for climate or evolutionary relationship, practitioners can expand trait-based plant selection to unmeasured related species or those in similar climates.

Tasks: The SIP interns will work on analyzing previously collected leaf samples for various leaf functional traits using the free imaging software: ImageJ. Leaf samples were previously collected from numerous plants along the California Coast. Understanding how leaf traits vary along a climatic gradient or within plant families can be important for improving ecological restoration and conservation by informing which species may be best suited for their local environment. The interns will meet regularly on Zoom with the mentor to learn proper measurement techniques and various lab activities. The SIP interns will read a scientific journal article once every 1 to 2 weeks for group discussion. By week 5, students will begin learning the basics of data analysis to report on their findings.

Project code: ENV-02 Title:  Wildlife Conservation — Human-Conflict Assessment Primary mentor:  Dr. Veronica Yovovich Faculty advisor:  Dr.  Prof. Chris Wilmers Location:  Remote/online Number of interns:  3

Project description: Mountain lions hold the dubious distinction of being California’s last top carnivore, and are a vital part of natural ecosystem balance and integrity. Human development threatens their future persistence by encroaching on habitat, killing mountain lions to prevent conflict with our livestock, and disrupting important dynamics. The mentor and her research group will use a variety of techniques to help understand mountain lion ecology and prevent human-mountain lion conflict. This project will involve working with human-wildlife conflict data to better understand patterns in when humans and mountain lions are at odds.

Tasks: The SIP interns can expect to be involved in data processing associated with mountain lion research. The interns will become familiar with data entry, handling, and integrity in Excel, and may also contact the local wildlife management agency to solicit additional data. The exact research question is flexible and will depend on the interns’ interests. For example, the interns could use the conflict data to investigate spatial patterns in where conflicts occur, seasonal patterns, etc.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Statistical data analysis

URL:   http://www.EcologyApplied.com

Project code: ENV-03 Title:  Causes and Impacts of Urban Sprawl Primary mentor:  Nazanin Rezaei Faculty advisor:  Dr.  Prof. Adam Millard-Ball Location:  Remote/online Number of interns:  3

Project description: Urban form is one of the most central topics in the study of urbanism. Due to its negative environmental, social, and economic impacts, urban sprawl is considered as the unfavorable display of urban form. Most definitions of urban sprawl refer to the low-density and unplanned expansion of urban areas into suburbs. Moreover, it is widely recognized that transport infrastructure development plays a major role in shaping urban form. This research project attempts to figure out to what extent transport infrastructure affects urban sprawl.

Tasks: The SIP interns will do background reading on the impacts of transportation on urban sprawl and identify the methods that have been used in the research articles. The interns can expect to be involved in gathering data on highways in different cities around the world. The interns will learn about statistical methods used in this research as well as SQL basics. They may also become familiar with working with open-source data maps.

URL:   https://people.ucsc.edu/~adammb/research.html

Project code: ENV-04 Title:  Extractive Industries, Water Justice, and Social Movements Primary mentor:  James Alejandro Artiga-Purcell Faculty advisor:  Dr.  Prof. Jeff Bury Location:  Remote/online Number of interns:  3

Project description: Extractive industries underlie many of the world’s most pressing social and environmental challenges. One of the most polluting activities on earth, mining for metals, hydrocarbons, and minerals is a major contributor to global water overuse and pollution. The full impact of extractivism on water must also take into account the myriad social relations with and cultural understandings and experiences of water — as a resource and life source. This research project explores mining’s environmental, social, political economic, and cultural impact on water.

Tasks: The SIP interns will help with a literature review and analysis of mining’s socio-ecological impacts on water. This task will entail searching for, reading, and organizing academic and other sources regarding mining-water relations.

Project code: ENV-05 Title:  Understanding Lichens on Redwoods Primary mentor:  Cristina Riani Faculty advisors:  Dr.  Prof. Michael Loik, Prof. Elliott Campbell Location:  Remote/online Number of interns:  4

Project description: Old-growth redwoods can support huge masses of epiphytes (plants that grow on plants) in their complex canopies. However, many epiphyte species that are common on other trees in the same forests (such as Douglas fir) are not found on redwoods or are much more sparse, especially below the canopy. Epiphytic lichens are important environmental indicators and are sensitive to environmental change. It is particularly important to investigate the relationships between lichens and their host tree species as California forests recover from last year’s fire season. The primary goals of this research project are to compare epiphytic lichen composition between redwood and Douglas fir branches and investigate why some lichens don’t establish on redwoods.

Tasks: The SIP interns will help survey epiphytic lichens on fallen branches by individually traveling to various forest sites in the Santa Cruz mountains (they will need to live in or near the Bay Area and be able to travel to the sites). They will take pictures of the lichens, which they will later help identify. In the first week, the interns will learn basic background information needed to identify lichens. They will also help collect samples of redwood substrate (wood, bark, and needles) for leachate treatments that will be applied to lichens on collected branches. The interns will need to coordinate with the mentor to take turns bringing samples to Santa Cruz once a week.

Latin American & Latino Studies

Project code: LAL-01 (ANT) Title:  Immigration and Family Studies — Mixed-Status Family Relationships in Santa Cruz County Primary mentor:  Karina Ruiz Faculty advisor:  Prof. Jessica K. Taft Location:  Remote/online Number of interns:  3

Project description: Santa Cruz county has a history of immigration, and primarily agricultural work continues to draw immigrants. But mixed-status families, ones with at least one undocumented and one documented member, are a new phenomena in terms of both legal and family studies. This study aims to understand what family dynamics are like for members of mixed-status families. Drawing on interviews from the We Belong project, this study will bring new analysis to a larger community-initiated and student-led research project.

Tasks: The SIP interns will learn introductory qualitative analysis. The interns will learn about the grounded theory analytical approach. They will learn about immigrant family experiences through current research and literature. Then, as a team, the SIP interns and mentor will apply these concepts through interview coding, an essential process for qualitative research in the social sciences.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Field work, lab work

URL:   https://webelongproject.sites.ucsc.edu/

Linguistics

Project code: LIN-01 Title:  Tone and Stress in Santiago Laxopa Zapotec Primary mentor:  Myke Brinkerhoff Faculty advisor:  Prof. Junko Ito Location:  Remote/online Number of interns:  3

Project description: Languages are commonly classified as either being a stressed language or a tonal language. This means that a speaker of will use pitch to convey information about a word. In stressed languages pitch is limited to a specific syllable or portion of the word (i.e., English and Spanish). Tonal languages use varying pitch across a word or a different pitch across each syllable to convey Information about the meaning of the word (i.e., Chinese, Japanese, and Thai). It is commonly believed that languages that have both stress and tone is extremely rare or non-existent across the world’s languages. However, it is commonly claimed that the languages in Mesoamerica are an exception to this typological fact, especially the Zapotecan languages of Oaxaca, Mexico. This project will explore the truth of these claims in an endangered Zapotecan language spoken by approximately 1200 people in Santiago Laxopa, Oaxaca, Mexico and a small number of speakers in Santa Cruz County, CA.

Tasks: The SIP interns will be asked to assist with annotating and analyzing audio recordings collected from a native speaker of Santiago Laxopa Zapotec. This will consist of them learning: (i) how to segment audio into meaningful parts, (ii) measure meaningful acoustic information, (iii) how to annotate the audio files, and (iv) how to perform statistical analyses on those measurements. Additionally, interns will learn how to maintain and enter information into a corpus designed to facilitate phonological and phonetic analyses. While completing these tasks the interns will learn what linguistics is and some of the areas of human language that linguists explore.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Field work, lab work, statistical data analysis

URL:  http://zapotec.ucsc.edu

Project code: LIN-02 Title:  Investigating Taste and Perspective in Conversation Primary mentor:  Jack Duff Faculty advisor:  Prof. Pranav Anand Location:  Remote/online Number of interns:  3

Project description: What do you mean when you say a slice of pizza is “tasty,” a day at the beach was “fun,” or a poem is “beautiful”? And what does it mean when a friend disagrees with you? How do we decide who is right – or can you both be right? Words like “tasty” pose big questions about how humans understand truth and personal experience, questions that are important for researchers who study the structure of meaning in language (semantics). It turns out that with the right type of analysis, we can answer these questions! The goal of this research project will be to carefully examine transcripts and recordings of people having these kinds of arguments, and the ways they are resolved. This kind of detailed investigation will help us learn more about how humans talk about truth and opinions.

Tasks: The SIP interns will learn how to use scientific tools to study language. Parts of this will involve reading lots of conversations and writing about them. But just because the mentor’s research group studies words doesn’t mean they don’t use numbers! The interns will also use scientific software to investigate those recordings, and write code to design professional graphs. Throughout the internship, the SIP interns will also get to study language generally. The skills the interns will learn will be useful for anyone interested in linguistics, foreign languages, psychology, or computer science. In order to facilitate remote communication and contributions to the project, the SIP interns must each have daily access to a computer with a webcam, a reliable internet connection, and ideally at least two Gb of free memory. They should have the ability to install new programs on this computer (the mentor will train them in the use of programs like Praat, ELAN, and RStudio), and be comfortable sharing their screen for training purposes.

URL:  https://linguistics.ucsc.edu/about/what-is-linguistics.html

Project code: LIN-03 Title:  Structure of the Korean Nominal Domain Primary mentor:  Nikolas Webster Faculty advisor:  Prof. Ivy Sichel Location:  Remote/online Number of interns:  3

Project description: Korean is a language that is well-known to lack definite/indefinite articles (words like “the” and “a/an” in English); the ‘definiteness’ of a given nominal is instead determined by conversational context. And yet, closer investigation of the distribution of Korean demonstratives (words like “this” and “that”) reveals that Korean demonstratives seem to behave perhaps like definite markers in their own right. Given this, the split between article and article-less languages is perhaps not so clear cut as one might predict. The focus of this research project is a re-evaluation of what it means to propose languages ‘lack a determiner category’ and whether the current classifications used to differentiate the syntactic behavior of article and article-less languages are perhaps inaccurate. Korean is the focus language of interest in this project; however, some aspects of the research may include evaluating some data from other languages as well, particularly ones that have also been noted to be article-less.

Tasks: Interns will assist in finding and sorting relevant research articles, glossing data, preliminary syntactic analysis, and potentially helping with sifting through corpora and linguistic databases with the use of some basic programming. Interns will additionally receive an introduction to the field of linguistics, particularly to the study of syntax, and learn what syntactic data analysis is like and how syntactic research is conducted. Those interested in learning a little bit about Korean nominal structure are especially encouraged to apply. Interns will be taught how to read and romanize Hangeul (the Korean alphabet) as part of the project.

Molecular, Cell, & Developmental Biology

Project code: MCD-01 Title:  Bacteria Primary mentor:  Amanda Carbajal Faculty advisor:  Prof. Manel Camps Location:  Remote/online Number of interns:  3

Project description: Bacteria are evolutionarily old microbes that have developed a myriad of unique and poorly understood genetic manipulations to achieve survival and evolution. They threaten human health in the sense of their success of antibiotic resistance superbugs. Two aspects of bacterial biology include the unique mobile genetic element of a plasmid, an independent genetic tool that can be passed on and used “as needed” through horizontal gene transfer, yet little is known specifically about how plasmids help bacteria. This project is driven by the goal to understand how plasmid biology specifically allows E.Coli to be so successful, so that new target therapies may be developed to target and minimize the superbug phenomena.

Tasks: The SIP interns’ primary task will be to develop a helpful in-house database of the known data on plasmids and biofilm formation mechanisms of action with respect to each species of bacteria. This database will be used to track genetic and mechanistic comparisons among the different strains and how they utilize plasmids. Their secondary tasks will include learning what it means to be a scientist from reading peer-reviewed scientific journals, identifying strong and weak studies, and methods used in the field to achieve the proving of a hypothesis. Students will learn to network, collaborate, communicate and see a project through. Interns will learn about a field that is emerging and few, if any other labs are working on.

URL:  https://https://www.metx.ucsc.edu/research/camps.html

Project code: MCD-02 Title:  Development of Tagged Spliceosome Proteins for Purification Primary mentor:  Hannah Maul-Newby Faculty advisor:  Prof. Melissa Jurica Location:  Remote/online Number of interns:  3

Project description: The central dogma of biology states that the DNA sequence containing genetic information is copied into messenger RNA, which is then read and translated to generate new proteins. Surprisingly, the DNA sequence of human genes also contains regions that do not hold genetic information. These regions, known as introns, are included in the RNA when it is first made, but are then removed before the information is translated to protein. The removal process is called splicing and is carried out by a molecular machine in the cell called the spliceosome. This research group seeks to understand how the spliceosome assembles upon an intron, including to order the required structural rearrangements of its components. In order to address these questions, the research group utilizes an in vitro assembly assay that relies on HeLa cells to provide the splicing machinery. The research project is to manipulate DNA sequences to engineer “tagged” spliceosome components. These tagged-spliceosomal proteins will then be utilized to purify spliceosome complexes.

Tasks: The SIP interns will obtain a basic understanding of splicing, as well as learn basic DNA cloning techniques. Together, the interns will gain an appreciation for how standard techniques are still unlocking basic questions about fundamental processes. The interns will choose an early complex spliceosome protein of interest and design a cloning strategy that the mentor will perform. Specifically, the SIP intern will: (1) learn how to use the spliceosome database and will choose a protein of interest; (2) learn how to utilize a DNA sequence editing tool; (3) design primers and a cloning strategy to engineer a tag into the protein; (4) perform data analysis of results as provided by the mentor; (5) learn how to troubleshoot as required, based on the obtained data; (6) read primary papers from the field; and (7) attend weekly lab meetings with the entire lab. An overarching goal is for the SIP interns to develop an understanding of the logic behind the experiments, and how this work will contribute to the larger body of research.

URL:  https://bio.research.ucsc.edu/people/jurica/index.html

Project code: MCD-03 Title:  What do Neurons Say? Decoding the Brain with Statistical and Computational Methods Primary mentor:  Yufei Si Faculty advisor:  Prof. David Feldheim Location:  Remote/online Number of interns:  3

Project description: “If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” Yet, neuroscientists are making significant progress in understanding the brain, and one of the first steps is to decode the neuronal code that our brain uses. We now know that neurons use electric signals as their language to communicate, and these signals can be recorded using in vivo electrophysiology techniques. However, how do we know what these neurons are talking about with their electric signals? The SIP mentor and interns will find out using statistical and computational methods.

Tasks: The SIP interns will gain a basic understanding of the nervous system, learn about basic ideas of how to study the brain using available techniques, and practice basic computational analysis with existing electrophysiology data. Specifically, the SIP interns will take part in the following: (1) reading primary papers/textbooks from the field and understanding some general concepts of neuroscience; (2) understanding the logic behind the mentor’s project and experiments being performed; (3) learning about basic statistical and computational tools and understanding the analytical methods being used; (4) analyzing existing data as a final practice/project.

URL:  https://feldheimlab.mcdb.ucsc.edu/index.html

Project code: MCD-04 Title:  Identification of Novel cis-Regulatory Elements at the Nkx3.1 Gene Locus Primary mentor:  Dr. Qing Xie Faculty advisor:  Prof. Zhu Wang Location:  Remote/online Number of interns:  3

Project description: Gene expression is precisely controlled in developmental, physiological and pathological processes. At transcriptional level, gene expression is regulated by the integrated action of many small segments of genomic DNA, called cis-regulatory elements. One of the projects in the laboratory is to study the molecular mechanism of transcription regulation of the Nkx3.1, a critical gene for maintaining prostate cell fate and suppressing tumor initiation. SIP interns will take the advantage of the established sequence databases and a bunch of sequence analyzing softwares to intensively examine the DNA sequence at the Nkx3.1 locus and predict the potential cis-regulatory elements.

Tasks: The SIP interns will be guided to study particular parts of Biology 101 and get more comprehensive knowledge of the central dogma. After understanding the concepts of transcription, translation, enhancer, promoter, transcription factor, exon, intron, coding sequence, and UTRs, interns will study to use the UCSC Genome Browser, the JASPAR transcription factor binding profile database and the MEME Motif-based sequence analysis tools. At the end, interns will do the in silicon analysis of the 17 Kb DNA sequence at the Nxk3.1 gene locus to identify binding sites for several important transcription factors in prostate biology.

URL:  https://wanglab.ucsc.edu/research/

Project code: MCD-05 Title:  Engineering Affinity Tagged Spliceosome Proteins Primary mentor:  Angela Amorello Faculty advisor:  Prof. Melissa Jurica Location:  Remote/online Number of interns:  3

Project description: Gene expression occurs when DNA is transcribed into messenger RNA (mRNA), followed by translation of mRNA into proteins that go on to carry out biological processes. To ensure proteins are properly made, gene expression must be tightly regulated. One way eukaryotes accomplish this is by transcribing RNA with intron sequences that interrupt the exon sequences that encode proteins. Therefore, protein cannot be produced until the introns are removed and the exons are joined together in a process called splicing. Splicing is catalyzed by a large RNA and protein complex called the spliceosome. The focus of the mentor’s lab is to understand early recognition of intron sequences by spliceosome components. To study this process, the lab utilizes an  in vitro  spliceosome assembly system which uses HeLa cell extracts. This research project entails engineering an affinity tag on a spliceosome associated helicase to be used for probing protein-protein interactions.

Tasks: The SIP interns will: (1) gain a general understanding of splicing; (2) learn how to design plasmids; (3) learn how to use bioinformatic tools like BLAST; (4) read splicing-related journal articles; (5) learn how to analyze data; and (6) attend weekly lab meetings. The goal is for the SIP interns to gain critical thinking and problem-solving skills as it pertains to this particular subject area and scientific research in general.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Lab work, statistical data analysis

URL:  https://bio.research.ucsc.edu/people/jurica/

Ocean Sciences

Project code: OCS-01 Title:  Marine Mammal Physiology — Pinniped Heart Rate Study Primary mentor:  Ryan Jones Faculty advisor:  Dr. Colleen Reichmuth Location:  Remote/online Number of interns:  3

Project description: This research lab explor es the inner worlds of amphibious marine mammals. Observations and experiments conducted with trained animals in our laboratory allow us to examine the perceptual and cognitive mec hanisms that enable individuals to gather, organize, and use various types of information, and the physiological mechanisms that support behavioral plasticity. Observations made in the field allow us to see how perception and cognition are translated into decisions and actions. Comparative studies in both settings help us to understand how ecological, evolutionary, and life history factors have influenced different marine mammal species.  This project aims to classify and compare resting cardiorespiratory behavior of up to 9 species of pinnipeds (seals, sea lions, and walruses). Data are collected using non-invasive, electrophysiological techniques with trained, cooperating subjects.

Tasks: The SIP interns will assist with remote data analysis by scoring respiratory behavior as well as by maintaining and updating an existing database.

URL:  https://pinnipedlab.ucsc.edu/

Project code: OCS-02 Title:  The Effect of Captivity on the Microbiome of Rice Coral (Montipora capitata) Primary mentor:  Stephan Bitterwolf Faculty advisor:  Prof. Marilou Sison-Mangus Location:  Remote/online Number of interns:  3

Project description: Does captivity alter the microbiome of corals harvested from the field? If so, when and how does the microbiome change? Do corals on Hawaiian reefs also experience microbiome shifts? Our research project aims to answer these questions by examining the microbiome of captive and wild coral fragments over the course of one month. Students involved in this project would learn all the computational skills required to work with DNA sequence databases, determine the types of bacteria found in our database, and measure if/how bacterial species have changed over time.

Tasks: Students involved in this project will: (1) review literature on coral microbiomes; (2) learn the fundamentals of computational analyses of microbiomes; (3) analyze DNA sequences to determine bacterial taxa present in our databases; (4) create presentations of the results for publishing on  YouTube ; (5) complete other tasks related to this project.

URL:  https://www.stephanbitterwolf.com/

Project code: OCS-03 Title:  Marine Mammal Physiology: Growth and Haul Out Patterns of Alaskan Ice Seals Primary mentor:  Madilyn Pardini Faculty advisor:  Dr. Colleen Reichmuth Location:  Remote/online Number of interns:  3

Project description: Nowhere on Earth are the effects of climate change more apparent than in the Arctic. Ice-dependent Arctic and sub-Arctic seals, including ringed (Pusa hispida), bearded (Erignathus barbatus), and spotted (Phoca largha) seals, are important high trophic-level predators that exert top-down control within these ecosystems. Unfortunately, relatively little is known about their basic biology and physiology, leaving management agencies and conservation practitioners with an incomplete understanding of the physiological requirements and limitations of these species, and a weak ability to make predictions about the capacity of ice-dependent seals to respond to rapid environmental change. It is tough to collect physiological data from ice-dependent seals in the wild, which makes information gained from captive individuals vital to the conservation and management of these species. The aim of this project is to work with and study the largest collection of trained ice-dependent seals in the world, in order to obtain valuable information about the biology and physiology of these unique and important species. This research lab collects longitudinal data from the seals in our care to examine health parameters, determine short- and long-term energetic requirements, define thermal strategies and limitations, and describe the molting physiology of each species.

Tasks: The SIP interns will be exposed to various data collection methods used in a managed care research setting and will learn how data collected with trained animals in a laboratory can help to inform policy. The interns will: (1) be given relevant primary literature to read and discuss; (2) learn to use photo analysis software to track growth; (3) analyze video data and record behavioral patterns; and (4) carry out other assignments and projects as assigned.

URL:  http://pinnipedlab.ucsc.edu

Project code: PHY-01 Title:  Multi-Physics Electro-Thermal Simulation of Memristors Primary mentor:  Ali Fares Faculty advisor:  Prof. Nobuhiko Kobayashi Location:  Remote/online Number of interns:  3

Project description: In this project, we will develop a mathematical model to run two physics simulations of memristors, namely thermal and electric conductivity of the devices, which will be implemented via a parallel computing framework, where two separate subprograms controlled by a main program will solve each equation separately and compare results at the end. Memristors, or memory resistors, are resistors whose resistance is based on their voltage history, which enables them to store memory in the form of a variable resistance. They have use in a multitude of fields, namely in the form of programmable memory as well as in the creation of artificial neural networks, where the ability for the memristors resistance to change based on history of voltage and current application can be used to form logic circuits that can “remember” previous interactions, similar to neurons in the brain.

Tasks: SIP Interns will: (1) be provided the equations used to model thermal and electrical properties of memory resistors and will be taught by the mentor what they are describing; (2) be guided through the process of transforming the equations into a form solvable in Python; (3) use the Python libraries ODEINT and GEKKO to solve the equations and get results on the properties of the device; (4) discuss the results and the implications that it has for future research into the thermal and electrical properties of memory resistors. Students should be comfortable programming in a high level programming language, such as Python, and have a decent understanding of some fundamental college math, such as calculus, though most advanced topics, such as differential equations and math models the students will be guided through by the mentor.

Required skills for interns prior to acceptance:  Computer programming, lab work, statistical analysis Skills interns will acquire/hone:  Computer programming, lab work, statistical analysis

URL:  https://nectar.soe.ucsc.edu/

Project code: PHY-02 Title:  Optimizing Protective Silver Mirror Coating to Improve Reflectivity in the Short Wavelength Range Primary mentor:  Soren Tornoe Faculty advisor:  Prof. Nobuhiko Kobayashi Location:  Remote/online Number of interns:  3

Project description: Silver mirrors are excellent for the creation of high-quality telescopes and deep space observation but suffer from severe environmental degradation requiring a protective coating. This thin coating effects how light interacts with the mirror and can be especially problematic for short wavelengths of light. As such, the primary goal of this project for the summer is to effectively model and optimize the optical properties of silver mirrors with protective coatings on them.

Tasks: The SIP interns will model and optimize the optical properties of coated silver mirrors by writing code using a programming language like MATLAB and developing transfer matrices to calculate the reflective properties of the coated mirror. The mentor will teach a basic understanding of light interactions on multilayered thin film structures as well as go over the principles behind creating transfer matrices and why they are an optimal means of calculation for multilayered optics.

Project code: PHY-03 Title:  Optical Properties of Cu-AlOx Nanocomposites Primary mentor:  Greyson Shoop Faculty advisor:  Prof. Nobuhiko Kobayashi Location:  Remote/online Number of interns:  3

Project description: Thin film structures are being researched for a variety of reasons including the ability of changing the chemical or physical properties of materials. One of these properties of importance are the optical properties of thin film materials at the nano scale, how light interacts with these thin film structures. The nanocomposite structure of interest is Cu-AlOx which is a metal-oxide thin film layer which requires precise methods for fabrication. Among the many methods of creating thin films at the nano scale are Atomic Layer Deposition (ALD) and Magnetron Sputtering (SPU) which allows the creation of these nanometer thick thin films of different materials layered on top of one another. These deposition methods allow the fabrication of thin film structures that are hundreds of layers thick but at nano scale thickness. Computational methods are of interest in order to simulate the optical properties of potential thin film structures without having to construct them and perform in person diagnostics.

Tasks: Interns will learn of the irregularities of nano scale materials and the various deposition methods for thin film characterization. Interns will utilize the transfer matrix method to simulate the spectral reflectivity of the materials in question in order to understand optical properties of Cu-AlOx nanocomposite thin films.

Project code: PSY-01 Title:  Reciprocity in Conversation Primary mentor:  Andrew Guydish Faculty advisor:  Prof. Jean E. Fox Tree Location:  Remote/online Number of interns:  3

Project description: Do people work together to create reciprocal balances across conversations? The SIP mentor is interested in conversational dynamics and how people carry and maintain conversations. In particular, the mentor is interested in conversational balance between participants throughout the course of the conversation, and how these balances influence how individuals communicate with one another.

Tasks: The SIP interns will work on numerous aspects of development regarding psychological experiments. The interns will work with the mentor in the development of experiments examining areas of interest pertaining to cognitive psychology (e.g., discussing experimental design, conducting literature reviews on related concepts), work with real data (e.g., transcribing videos, examining transcripts), as well as running participants in psychological experiments under supervision. Through this process, the SIP interns will gain experience in the following: writing APA style annotated bibliographies; processes associated with experimental development; running human participants; analyzing real data in IBM’s SPSS; and development and use of Python algorithms for data parsing and analysis.

URL:  https://guydish.sites.ucsc.edu ,  https://foxtree.sites.ucsc.edu

Project code: PSY-02 Title:  Earworms Primary mentor:  Matt Evans Faculty advisor:  Prof. Nicolas Davidenko Location:  Remote/online Number of interns:  3

Project description: Have you ever had a song get stuck in your head? Probably! Nearly everybody regularly experiences “earworms,” but very little is known about how and why they happen. The scientific literature refers to earworms as Involuntary Musical Imagery (INMI), and empirical research on the phenomenon is quite limited. The mentor and SIP interns will design and implement an experiment using behavioral techniques that will help contribute to the scientific understanding of various aspects of this near-ubiquitous human experience. This project will specifically explore the invasiveness of primed INMI when the participant is intentionally holding different musical imagery in mind.

Tasks: The SIP interns will participate in the full process of designing an experiment, from literature review through pilot data collection and preliminary data analysis. Interns will read scientific articles on a range of topics (such as musical imagery and mind wandering) and discuss them as a group with the mentor to help refine the research question. Interns will then work with the mentor to build the experiment using Matlab, collect pilot data, and perform statistical analyses. Interns can expect to learn programming skills, gain insight into and experience with the complete process of experimental design, and practice performing and interpreting statistical analyses.

Project code: PSY-04 Title:  Immigrant People’s Well-Being Primary mentor:  Daniel Rodriguez Ramirez Faculty advisor:  Prof. Regina Langhout Location:  Remote/online Number of interns:  3

Project description: One’s ability to feel psychologically well is partly shaped by how one experiences belonging and social support, particularly for people who migrated to the US. The psychological well-being of immigrant people is comprised of constructs such as sense of belonging and social capital, which are the focus of this proposed study. The purpose of this research is to better understand the factors that influence immigrants’ well-being, sense of belonging, and social capital from their own stories. Our project’s aim is to gather information about immigrant people’s experiences to inform strategies by which service-providers (e.g., clinics, schools, non-profits) can better mobilize resources for them during times of crises.

Tasks: The SIP interns will learn introductory qualitative research methods. They will develop skills in understanding and writing annotations on research articles about the topic of immigrant people’s well-being. Interns will gain hands-on experience in transcribing interview audio, and then analyzing interviews. Interns will also nurture skills in writing short research reports following APA style. Interns with Spanish language skills are encouraged to apply, as some interviews will be conducted in Spanish, and all interviewees are from Latin America.

URL:  https://psychology.ucsc.edu/about/people/grad-directory.php?uid=drodri37

Project code: PSY-05 Title:  Correctional Officer Abuse of Power Primary mentor:  Jade Moore Faculty advisor:  Prof. Craig Haney Location:  Remote/online Number of interns:  3

Project description: The SIP interns will be investigating how correctional officers’ abuse of power may be impacting Black individuals in prison. Black individuals have historically been discriminated against in many areas of the U.S.’s criminal justice system (policing, sentencing, etc.). The SIP interns will be analyzing different media outlet reports (newspapers, broadcasts, etc.) to investigate how this discriminatory treatment may translate over in correctional institutions. The goals will be to try to establish if Black individuals are more often the targets of force in correctional settings and what kind of information the media has access to when investigating reports of correctional officers’ abuse of force.

Tasks: The SIP interns will read and have discussions on a range of scientific articles focused on the idea of discrimination in the criminal justice system that will contribute to the literature review for the study. The interns will also assist with finding media outlet reports on the topic of correctional officers’ abuse of power. Interns will then work on coding the reports and running a statistical analysis of the findings. Interns can expect to gain insight into the process of developing a literature review, coding newspapers and news broadcasts, and running and analyzing statistical analyses.

Project code: PSY-06 Title:  Criminal Justice in the Media Primary mentor:  Jada Cheek Faculty advisor:  Prof. Craig Haney Location:  Remote/online Number of interns:  3

Project description: Dehumanization of African Americans throughout history has been salient since the inception of the U.S. Dehumanization is the phenomena of seeing a person or group of people as less than human or animal-like. This can lead to discrimination, violence, and even fatal encounters with dehumanized groups. Police brutality against African Americans is a huge issue in the U.S. Dehumanization is just one factor contributing to police violence against Black people. This project will be analyzing the media coverage on the Breonna Taylor and George Floyd murders. The research group will investigate the role of dehumanizing language in the media coverage of these cases, and also look at the differences in the descriptions of Breonna Taylor (a Black woman) vs. George Floyd (a Black man) to analyze for gender differences, as well.

Tasks: The interns will do background readings surrounding the psychological phenomenon of dehumanization and media criminology that will contribute to the literature review of the study. They will help to code newspaper articles and live news transcripts that cover the Breonna Taylor and George Floyd cases, and run and analyze statistical analyses.

Project code: PSY-07 Title:  How do Families Make Meaning out of their Interactions with Robotic Toys? Primary mentor:  Elizabeth Goldman Faculty advisor:  Prof. Su-hua Wang Location:  Remote/online Number of interns:  3

Project description: SIP Interns assigned to this project will receive foundational knowledge in psychology and the research methods used in the field. Additionally, SIP Interns will be trained in how to run a research study remotely online using Zoom. Learning to conduct research online is a valuable skill. It is widely held that many research labs will continue to conduct research online, even when it becomes safe to resume in-person research. As such, conducting online research will become a valuable skill for future jobs and careers. The project examines how children between the ages of 3.5- and 6-years of age and their parents interact with a robotic toy. First, the parent-child dyad will observe the robotic toy following the directions of a person at various levels of responsiveness. Next, the child and the parent will have an opportunity to have a conversation about the toy. Finally, parental beliefs and values surrounding technology will be assessed through a survey, and children will participate in a short interview where they will be asked to share their perceptions of the robotic toy. No prior experience is necessary, Interns should be interested in working with families, robots and learning how to conduct research online!

Tasks: The SIP Interns will help with multiple aspects of the project including: helping recruit participants (e.g., through social media, parent groups, etc.); scheduling families to participate in the research project; running the research study on Zoom; transcribing parent-child conversations; coding data from participants’ Zoom sessions; and analyzing the collected data. Interns will also develop skills in: talking to families about research; naturalistic observation; research methodology; data analysis; how to find and read scientific journal articles; how to write a scientific research proposal; and APA formatting/ APA citations.

URL:  https://elizabethgoldman.weebly.com

Project code: PSY-08 Title:  Spontaneous Politeness Primary mentor:  Elise Duffau Faculty advisor:  Prof. Jean E. Fox Tree Location:  Remote/online Number of interns:  4

Project description: The SIP mentor is interested in expanding on how we communicate politeness with artificial agents. To explore this, a detailed understanding of how people spontaneously use politeness must be examined. The current project will examine how people use different politeness strategies in different settings and how those strategies may or may not be applied to artificial agent communications.

Tasks: Interns will gain experience in the various aspects of psychological experiments. Interns will work with the mentor in learning how to conduct research in cognitive psychology related to the area of interest. Research skills interns will learn and use include: learning how to design experiments and being part of designing experiments this summer; conducting literature reviews; learning how to code data, and then coding data; and analyzing data in SPSS, R or Python. Interns will also gain experience in writing APA format annotated bibliographies.

Project code: PSY-09 Title:  Mind-Controlled Illusory Apparent Motion Primary mentor:  Allison Allen Faculty advisor:  Prof. Nicolas Davidenko Location:  Remote/online Number of interns:  3

Project description: Why do we experience illusions? For psychologists, studying illusions helps to reveal some of the properties and quirks of perception. One such illusion is Illusory Apparent Motion (IAM) where ambiguous apparent motion is elicited by randomly refreshing pixel textures. Previous research using other apparent motion illusions has found that motion ambiguity can be controlled mentally (for example, one can mentally will ambiguous motion to appear in a clockwise, as opposed to a counterclockwise, direction). This research project explores how IAM is similarly susceptible to mental control in different contexts, and the group is running and designing experiments to measure this in the lab.

Tasks: Interns will have the opportunity to learn about a variety of illusions and what each illusion reveals about the nature of the human sensory system. This will be done by reading scientific articles each week and discussing them with the mentor. The interns will also gain hands on experience running participants (supervised) in a laboratory experiment and will learn how to program and analyze data using Matlab.

URL:  http://davidenko.sites.ucsc.edu/

Project code: PSY-10 Title:  Children’s Learning through Collaboration Primary mentor:  Samantha Basch Faculty advisor:  Prof. Su-hua Wang Location:  Remote/online Number of interns:  3

Project description: The mentor’s research focuses on how toddlers and preschoolers learn through collaboration. This summer, the mentor’s research team will study parent-child collaboration during play. The team will study both natural play and structured play, with a special focus on parental question-asking. The hope is the results will shed light on how culture and context shape parent-child collaboration and learning. Interns will also have the opportunity to design their own research proposal in the area of Developmental Psychology.

Tasks: The SIP interns will get experience with the full range of activities that occur in a developmental psychology lab, including scheduling, explaining informed consent, and running experiments. The interns will also learn how to write a project proposal, collect and analyze observational data. These are important skills for any psychologist. Finally, the interns will have the chance to work with other members of the Infant and Child Development Lab on ongoing projects.

URL:  https://suhua.sites.ucsc.edu/

Project code: PSY-11 Title:  Researcher Identity and Bias — How Psychology Researchers use “Positionality Statements” in Published Articles to Reflect on the Influence of Their Social Position, Identity, and Bias During Research Primary mentor:  Daniel Copulsky Faculty advisor:  Prof. Phillip Hammack Location:  Remote/online Number of interns:  3

Project description: Our individual life experiences shape the kind of scientific questions we ask, the way we conduct our research studies, and the way we interpret the results. As social science researchers, we are called on to be reflective about how our own identities can influence and bias our work. “Positionality” is the way that our social positions (like gender, race, and sexual orientation) shape the way we see the world. “Reflexivity” is the process we use to examine how these perspectives shape the research we do, often as an insider or outsider of groups that we do research about. Scholarly articles sometimes include a statement from the authors about their identity, positionality, or reflexivity process. This project studies the use of these “positionality statements” in recently published articles from psychology journals, considering both how common they are and their common themes.

Tasks: Interns will do background reading about research methods in psychology, processes for practicing reflexivity, and the use of positionality statements in journal articles. The research team will work together to develop a method for finding positionality statements in recently published psychology journal articles. Interns will read positionality statements closely, using qualitative analysis methods to identify themes across the statements and to categorize common elements of the texts. Quantitative analysis will be used to measure how common these elements are. The research team will meet daily over Zoom to review readings, project progress, and tasks for the day.

Project code: PSY-12 Title:  Exploring Everyday Helping Primary mentor:  Margie Martinez Faculty advisor:  Prof. Audun Dahl Location:  Remote/online Number of interns:  3

Project description: How do we come to help others? The mentor’s research group examines moral reasoning in relation to one’s actions in everyday contexts. The group is particularly interested in how parent-child interactions influence the development of helping behavior and how this may vary across different cultural backgrounds. This research project will examine how the daily routines of families impact judgments, reasoning, and decisions about helping behaviors. By examining the everyday experiences with helping, the interns and mentor can gain a better understanding of how children and adults come to the moral decision of who and when to help.

Tasks: The SIP interns will be involved in most or all aspects of this research project. The interns may help design research studies, collect data (for instance, through interviews), analyze video recordings or interview transcripts, and/or discuss research articles. The interns may work with data from past or current projects exploring how children and young adults think about helping. The research group will discuss literature relevant to the project and moral development. This research project will provide an opportunity for interns to learn about and contribute to all stages of psychological research.

Project code: PSY-13 Title:  Service-Learning Outcomes of College Students Primary mentor:  Miguel Lopezzi Faculty advisor:  Prof. Regina Langhout Location:  Remote/online Number of interns:  4

Project description: Service-learning classes are important because they help students learn while they are also engaged in the community and giving back. But, not all service-learning courses are the same. Yet, they are often treated the same in the research literature. In this research project, the SIP mentor and interns will work together to figure out differences in the quality of the course design across several service-learning courses to see if these differences help them to understand college student outcomes better. For example, is the quality of the service-learning course design (course materials [e.g., syllabus]) related to certain kinds of civic outcomes (diverse citizenship [e.g., students’ openness to others and their willingness and desire to be agents of change])?

Tasks: The SIP interns will listen to and transcribe (write down or type out), word for word, interviews between the graduate student researcher and professors who teach service-learning classes. The interns will also check the transcriptions for accuracy, and help to categorize the interviews to determine the quality and nature of the service-learning classes. The SIP interns will learn a lot about interviews, how to work with word data, and the quality aspects of service-learning courses. The interns will get to help make important decisions about the quality of the courses.

Project code: PSY-14 Title:  Neural Mechanisms of Perceptual Decision Making Primary mentor:  Wei Dou Faculty advisor:  Prof. Jason Samaha Location:  Remote/online Number of interns:  3

Project description: We frequently need to make timely decisions based on sensory information we perceive from the external environment. And the subjective judgment about our own perceptual processing is a fundamental feature of adaptive behaviors. This project focuses on investigating the neural mechanisms of the perceptual decision making and the subjective confidence of the decision using the electroencephalogram (EEG). The results could shed light on how perceptual decision making and its confidence are supported or implemented by the electrical activity produced by populations of neurons.

Tasks: SIP interns will do background reading about perception, research methods in psychology, neuroscience, and EEG technique. They will gain experience with basic MATLAB programming, and EEG data preprocessing and analysis using MATLAB. Interns will also learn how to conduct literature reviews on relevant topics, design an experiment, and present research papers.

URL:   https://samahalab.ucsc.edu/

Project code: PSY-15 Title:  Brain Activity Underlying Visual Perception Primary mentor:  Audrey Morrow Faculty advisor:  Prof. Jason Samaha Location:  Remote/online Number of interns:  3

Project description: This research project uses cognitive neuroscience approaches to analyze brain activity associated with visual perception. Specifically, this mentor looks at electroencephalography (EEG) data, which is a record of electrical activity from cortical parts of the brain, to assess alpha waves and stimulus-locked event-related potentials (ERPs) from brain areas associated with vision. Alpha power and ERP amplitudes change during visual perception and are associated with changes in performance on perceptual tasks that use stimuli that are difficult to distinguish. The interns will gain an understanding of how these neural patterns are analyzed and what those analyses can tell us about brain activity when we attend to and perceive visual information.

Tasks: The SIP interns on this research project will learn about brain activity underlying attention and perception, and will learn how to organize, analyze, and visualize EEG data by programming in Matlab. The interns will learn about paradigms and techniques used in cognitive neuroscience studies and may help to program new studies that use perceptual detection or discrimination tasks.

Project code: PSY-16 Title:  Online Searching, Memory, and Metacognition Primary mentor:  Dana-Lis Bittner Faculty advisor:  Prof. Benjamin Storm Location:  Remote/online Number of interns:  3

Project description: The internet is a vast and ever-growing source of information and we have come to rely on it more and more as an external memory store in recent years. The mentor is cognizant of the immense benefits that come with having access to all of this information at our fingertips, but wishes to explore potential negative or unwanted impacts of being able to look up information online that easily on our cognition and behavior. Some of the questions that are being explored in the mentor’s lab are “Do we grow dependent on the internet?”, “Does online searching change how we behave or think or assess our own knowledge?”, or “How does prior knowledge change how or whether we search for information online?”. More specifically, this summer, the mentor would like to investigate whether remembering with the internet (using the internet to jog your memory) could actually lead to a phenomenon referred to as “collaborative inhibition”, resulting in someone remembering less of the originally learned material. The mentor feels that the dynamic nature of online search engines and their use in everyday settings, such as work and education, makes for an exciting and highly relevant field of study.

Tasks: The SIP interns will help develop a new research project, as well as potentially work on ongoing research projects. Throughout the process, the interns will develop a conceptual understanding of research within cognitive psychology through hands-on experience with various tasks required to run an experiment. They will delve into the existing literature regarding the internet and online searching as well as its impacts on human cognition and behavior. Interns will also actively be involved in the conceptualization of a new study and the generation of stimulus materials. They will also gain experience with constructing an online survey and coding its logical flow, as well as potentially help build unique websites needed for a study. To gain experience with data cleaning and analysis, the SIP interns will work with data from past and possibly current projects that explore the impact of online searching on behavior, memory, and metacognition. The interns and mentor will discuss literature, theory, implications of predicted findings, ethical considerations about research more generally, and research tasks. This research project provides a great opportunity for the interns to learn about psychological research at every single stage — from reviewing the existing literature, over conceptualizing a new study, to analyzing and interpreting results.

Required skills for interns prior to acceptance:  None Skills interns will acquire/hone:  Computer programming, statistical data analysis, writing literature reviews in APA format, working with software commonly used in psychological research (e.g., Qualtrics, Excel, SPSS, and R)

URL:   https://people.ucsc.edu/~bcstorm/research.html

Project code: PSY-17 Title:  Getting Students Excited About Research: Implementing Social Justice and Communal Values in Research to Motivate Student Interest and Success Primary mentor:  Katherine Quinteros Faculty advisor:  Prof. Rebecca Covarrubias Location:  Remote/online Number of interns:  3

Project description: Science and research fields are often perceived as being sterile and detached from the everyday lives of people. This perception often causes students to leave science fields early on in higher education, particularly racially minoritized students. For this research project, the mentor is interested in whether integrating communal values (e.g., helping each other) and social justice values (e.g., transforming systems) into research assistant descriptions can impact how students perceive the research project. The mentor is also interested in testing differences among racial and ethnic groups to understand which students are benefitted and in what ways.

Tasks: The SIP interns will learn how to: (1) conduct a literature review of scholarly articles related to the project; (2) create an annotated bibliography; (3) develop research questions and hypotheses; (4) design an experiment and survey; and (5) analyze pilot data using SPSS.

URL:   https://rcovarrubias.sites.ucsc.edu

Project code: PSY-18 Title:  Why do Students Cheat? Primary mentor:  Fiona Debernardi Faculty advisor:  Prof. Audun Dahl Secondary mentor:  Talia Waltzer Location:  Remote/online Number of interns:  4

Project description: This research project examines ethical decision-making in academic settings, specifically focusing on students’ decisions to cheat. The SIP interns will help review literature on academic integrity, and assist with documenting and analyzing integrity policies from a wide sample of colleges across the US. The first of its kind in the US, this research project will advance psychological knowledge on cheating by describing theoretical viewpoints of cheating, students’ motives for cheating, strategies for prevention, and whether these insights are actually applied in real schools’ policies. The interns will also assist with analyzing data from real academic misconduct cases at UCSC, advancing our understanding of the contexts that lead students to cheat.

Tasks: To learn more about moral decision making and academic integrity, the SIP interns will assist with literature review, data collection, classifying open-ended data, and data analysis (using spreadsheets) for lab projects focusing on academic integrity. The interns may also assist with organizing and interpreting qualitative and quantitative data from college records. These tasks will involve reading published research papers, academic policies, and sensitive narrative accounts of student experiences with cheating. In addition, the SIP interns may help with website design. The work schedule will involve independent work on the project, daily check-ins with the mentor, group activities, and weekly team meetings to discuss project progress.

URL:   https://aop.ucsc.edu/

Project code: PSY-19 Title:  Social Presence in Online Learning Primary mentor:  Yasmin Chowdhury Faculty advisor:  Prof. Jean E. Fox Tree Location:  Remote/online Number of interns:  3

Project description: With everything being online, it is important to look into computer-mediated interactions and how they differ from in-person interactions. In this research project, the SIP interns and mentor are going to look at social presence in different settings (chat, audio, video) to see how they differ based on different media. Social presence is how physically present a person feels their conversational partner is when interacting with them via different media. The mentor’s research group is particularly interested in seeing if group sizes, level of familiarity, and other factors impact social presence.

Tasks: The SIP interns will read research papers, complete literature reviews, code data, run experimental participants (with supervision), and transcribe interactions. The interns will have bi-weekly meetings with research teams where they will discuss ongoing and upcoming work.

Project code: PSY-20 Title:  Talking, Texting, and Emotions Primary mentor:  Vanessa Oviedo Faculty advisor:  Prof. Jean E. Fox Tree Location:  Remote/online Number of interns:  3

Project description: The mentor’s research interests are in technology assisted communication and modality switching. Specifically, the mentor is interested in the way that people communicate and perceive their interactions when they switch between two communication mediums, such as from text messaging to audio. The current research project is examining differences in emotional communication when people interact via messaging and audio.

Tasks: The SIP interns will work with the mentor in the completion of experiments by reviewing experimental design, conducting APA style literature reviews, and completing APA style annotated bibliographies. The interns will also work with real data in which they will transcribe audio files, examine transcripts, enter and clean quantitative data using SPSS, and code qualitative audio files. In working with the mentor, the SIP interns will learn skills such as designing a research study, completing a literature review, creating research questions and developing hypotheses, entering quantitative and qualitative data, conducting a corpora analysis, and analyzing the final data set.

URL:  https://foxtree.sites.ucsc.edu

Project code: PSY-21 Title:  First Generation College Students Primary mentor:  Andrew Takimoto Faculty advisor:  Prof. Margarita Azmitia Location:  Remote/online Number of interns:  3

Project description: For students who are the first in their families to go to college (first gen students), going to college can be both exciting and stressful. First gen college students can find it difficult to make new friends on campus because they feel stressed that their peers often know more about college than they do. First gen students can also lack the confidence that they will do well in college and graduate. This research project will use survey data collected from first gen students at UCSC to find out if social support (or the lack of it) can help first gen students feel confident that they will do well in college, find a major they feel passionate about, and graduate.

Tasks: The SIP interns will learn to read research articles critically and search for additional articles online and at the UCSC library. The interns will also learn to write an introduction to a research paper using the writing conventions in psychology. The interns will develop hypotheses to test about the role of social support and self-confidence in succeeding in college and will use survey data collected from first gen students attending UCSC to test them. Finally, the SIP interns will develop a presentation of their research project that they will practice in the research group and deliver at the SIP conference at the end of the summer.

Required skills for interns prior to acceptance:  Lab work, statistical data analysis Skills interns will acquire/hone:  Lab work, statistical data analysis

Project code: SOC-01 Title:  College Education, Gender, and Sexuality Primary mentor:  Michelle Parra Faculty advisor:  Prof. Julie Bettie Location:  Remote/online Number of interns:  3

Project description: How does attending college shape girls’ gender and sexual behaviors? Previous research has found that attending college can immerse white girls in cultural contexts where they can experience new gender and sexual freedoms. Little research has examined how pursuing a four-year college degree shapes the ways in which girls of color view their gender and sexualities. This research project utilizes sociology, feminist studies, and ethnic studies to examine the experiences of girls of color within university settings with a particular focus on first-generation Latina college students. The mentor’s research describes the various ways that attending college results in new gender and sexual oppressions and freedoms for Latina college students.

Tasks: The SIP interns will have the opportunity to read literature on gender, sexuality, and education. They will learn how to retrieve scholarly journals and write research summaries (annotations). In addition, the interns will assist the mentor with reviewing (coding) interview data. The interns will also have the opportunity to create a research presentation based on the literature that they read and the interviews that they coded. They can present their research findings in an undergraduate Sexualities course that the mentor is teaching this summer.

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NSF-led National AI Research Resource Pilot awards first round access to 35 projects in partnership with DOE

The U.S. National Science Foundation and the Department of Energy are thrilled to announce the first 35 projects that will be supported with computational time through the National Artificial Intelligence Research Resource (NAIRR) Pilot, marking a significant milestone in fostering responsible AI research across the nation.

"Today marks a pivotal moment in the advancement of AI research as we announce the first round of NAIRR pilot projects. The NAIRR pilot, fueled by the need to advance responsible AI research and broaden access to cutting-edge resources needed for AI research, symbolizes a firm stride towards democratizing access to vital AI tools across the talented communities in all corners of our country," said NSF Director Sethuraman Panchanathan. "While this is only the first step in our NAIRR efforts, we plan to rapidly expand our partnerships and secure the level of investments needed to realize the NAIRR vision and unlock the full potential of AI for the benefit of humanity and society."

The NAIRR Pilot — a result of President Joe Biden's landmark Executive Order on the Safe, Secure and Trustworthy Development and Use of AI — will provide AI researchers and students access to key AI resources and data. Twenty-seven projects will be supported through resources on NSF-funded advanced computing systems, including Delta at the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign; Frontera and Lonestar at the Texas Advanced Computing Center at The University of Texas at Austin; and the Neocortex system at the Pittsburgh Supercomputing Center, a joint center of Carnegie Mellon University and the University of Pittsburgh . An additional eight projects will have access to DOE-supported systems, including the Summit supercomputer at Oak Ridge National Laboratory and the AI Testbed at Argonne National Laboratory.

"Under President Biden's leadership, we are expanding access to critical data and compute so that more and more people can benefit from responsible AI technology," said Assistant to the President for Science and Technology and Director of the White House Office of Science and Technology Policy Arati Prabhakar. "NAIRR will advance research to develop trustworthy technology that strengthens our values and helps us overcome the great challenges of our times."

Projects granted computing allocations in this initial round encompass a diverse range of AI-related areas, including investigations into language model safety and security, privacy and federated models, and privacy-preserving synthetic data generation. Other projects also focus on domain-specific research, such as using AI and satellite imagery to map permafrost disturbances, developing a foundation model for aquatic sciences, securing medical imaging data and using AI for agricultural pest identification.

"DOE Office of Science has decades of experience in cutting-edge AI research and a longstanding commitment to developing world-leading high performance computing resources that are open to the scientific community," said Harriet Kung, acting director of the DOE Office of Science. "We are proud to continue our mission by providing valuable access to some of the fastest computing facilities in the world to the NAIRR Pilot. Innovations developed in collaboration with industry partners are designed to address not only traditional scientific workloads but also the growing demands of AI research at scale. We are excited to see what the future holds for AI in science."

In tandem with the announcement of initial awards, the NAIRR Pilot opened the next opportunity for researchers and educators to apply for access to resources that support AI research, including advanced computing systems; cloud computing platforms; access to foundation models, software and privacy enhancing technology tools, collaborations to train models; and education platforms. This opportunity includes cutting-edge resources contributed by the pilot's nongovernmental partners, including Microsoft, Amazon Web Services, NVIDIA, SambaNova Systems, Cerebras, OpenAI, Anthropic, Groq, EleutherAI, OpenMined, Hugging Face and Vocareum.

In addition, the research community will have the opportunity to access resources from additional NSF advanced computing platforms at the Pittsburgh Supercomputing Center, the National Center for Supercomputer Applications at the University of Illinois Urbana-Champaign, the San Diego Supercomputer Center at the University of California San Diego and the Texas Advanced Computing Center at The University of Texas at Austin, as well as systems at Purdue University, Indiana University and Texas A&M University.

The second opportunity also seeks to connect educators and instructors in universities to computing, data, and software resources that will enable them to train their students through hands-on projects and exercises.

Researchers and educators can apply for access to these resources and view descriptions of the first cohort projects at https://nairrpilot.org/ beginning May 6, 2024.

The NAIRR Pilot embodies a commitment to diversity and collaboration, recognizing that the strength of the U.S. AI ecosystem depends on having a research and education community which reflects the diversity of our nation. By fostering a strong and responsible AI research ecosystem, the pilot aims to empower a wide range of perspectives and technical directions from researchers and educators from U.S. institutions, including those from underrepresented groups, nonprofits and small businesses. 

• Learn more about the specific projects at the NAIRR Pilot portal
• More information about AI at NSF.gov

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Matt Bennett

• Professor of Social Policy , Social Policy

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Matthew Bennett has experience in two main substantive research areas, prosocial behaviour which includes areas of: Cross-national predictors of volunteering, giving, and informal help Neighbourhood effects on volunteering and giving School effects on civic engagement among youths Health outcomes of volunteering and giving among adult and youth populations And intergroup relations and group solidarity, in particular the neighbourhood effects on religious and national identity. Dr Bennett welcomes enquiries from doctoral researchers interested in either of these areas.

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Expertise related to un sustainable development goals.

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

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• third sector Earth & Environmental Sciences 58%
• Allostasis Medicine & Life Sciences 57%
• Local Government Medicine & Life Sciences 49%
• social cohesion Social Sciences 46%
• health service Social Sciences 45%

Collaborations and top research areas from the last five years

Dive into details.

Select a country/territory to view shared publications and projects

Research output

• 6 Commissioned report
• 2 Chapter (peer-reviewed)
• 1 Other report

Research output per year

Insights Into Informal Caregivers’ Well-being: A Longitudinal Analysis of Care Intensity, Care Location, and Care Relationship

Research output : Contribution to journal › Article › peer-review

• caregiver 100%
• Caregivers 99%
• well-being 77%
• Psychology 70%
• caregiving 32%

Variety Is the Spice of Life: Diverse Social Networks Are Associated With Social Cohesion and Well-Being

• Social Networking 100%
• social cohesion 93%
• social network 68%
• well-being 59%
• outgroup 30%

Valuing Carers 2021: England and Wales

Research output : Book/Report › Commissioned report

• health service 100%

Valuing Carers 2021: Northern Ireland

Cycles of caring: transitions in and out of unpaid care.

Projects per year

Centre for Care

Needham, C. , Overton, L. & Bennett, M.

Economic & Social Research Council

1/11/21 → 31/10/26

Project : Research Councils

Achieving closure? Improving outcomes when care homes close

Needham, C. , Bennett, M. , Stokes-Lampard, H. , Roberts, T. , Taylor, B. , Glasby, J. , Allen, K. , Tanner, D. , Skrybant, M. , Topping, A. , Hewison, A. & Kinghorn, P.

1/04/21 → 31/12/24

Project : Other Government Departments

• Home Care Services 100%
• home care 97%
• Costs and Cost Analysis 59%
• search engine 29%

How do differing rates and modes of child welfare service interventions impact upon educational and criminal justice outcomes of vulnerable children? [Transfer from Sheffield]

Bennett, M.

1/12/21 → 31/01/24

Exploring the relationship between ethnic heterogeneity, intergroup relations and stress

2/12/18 → 1/10/20

Sustainable Care: connecting people and systems

Bennett, M. , Needham, C. , Hall, K. & Phillimore, J.

6/11/17 → 31/08/21

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KU Research GO boosts a dozen projects across disciplines

Thu, 03/28/2024.

Mindie Paget

LAWRENCE — Twelve projects have been selected for funding through KU’s 2024 Research Grant Opportunity program. KU Research GO aims to expand the university’s research enterprise by providing seed funding for projects poised to compete for external funding opportunities with application deadlines in the near future. The Office of Research received 50 proposals, which underwent peer review by KU faculty expert panels. Recipients represent STEM, social science, humanities and arts fields in departments and research centers across the university.

Projects are listed alphabetically by principal investigator:

Yoshiaki Azuma, professor, molecular biosciences Role of SUMO Modification in the Structural Organization of Mitotic Chromosomes Required for Accurate Genome Transmission in Mitosis

Elizabeth Corson, Fred Kurata Assistant Professor, chemical & petroleum engineering Recovering Ammonia from Wastewater: Electrolyte Engineering for Selective and Efficient Nitrate Reduction

Kris Ercums, curator of global contemporary & Asian art, Spencer Museum of Art; and Maki Kaneko, associate professor, art history STREET NIHONGA: The Art of Jimmy Tsutomu Mirikitani

Terry Loecke, associate professor and associate director, environmental studies; associate scientist, Kansas Geological Survey Geochemical and Microbial Controls on Methane Emissions During Variable Hydrologic Conditions

Zachary Mohr, associate professor, public affairs & administration Refocusing Election Administration Cost Research to 20/20: Understanding the Cost Structure and Cost Dynamics of Election Administration into the 2020s

A. Townsend Peterson, University Distinguished Professor, ecology & evolutionary biology; and co-applicants Rafe Brown, Jocelyn Colella, Lucas DeCicco, Richard Glor, Dianna Kresja, Ana Motta and Robert Moyle Climate Change Impacts on Terrestrial Vertebrates of the Great Plains: Measuring Biodiversity Change on Continental Scales

Muhammad Raza, associate professor, child language program Family-based Genetic Study of Specific Language Impairment (SLI)

Panying Rong, associate professor, speech-language-hearing: sciences & disorders Toward a Precision Medicine Approach to Improve the Assessment and Management of Progressive Motor Speech Disorders in Individuals with Neurodegenerative Diseases

Joshua Roundy, associate professor, civil, environmental & architectural engineering; and Sam Zipper, assistant scientist, Kansas Geological Survey Hydrologic Stable States Analysis

Maria Velasco, professor, visual art Reclaiming Home: Remembering the Topeka Bottoms

Richard Yi, professor, psychology Neural Correlates of Art-Episodic Future Thinking (ArtEFT)

Hui Zhao, professor, physics & astronomy Controlling Dipolar Excitons in van der Waals Materials for Future Excitonic Devices

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Water Research Hub Earns 5 More Years of Funding

The national alliance for water innovation will continue work to make us water supplies more accessible, affordable, and energy efficient.

Most Americans get their water from traditional sources, like large freshwater reservoirs or groundwater—fresh, underground rivers flowing beneath our feet.

But change is coming.

Climate change, population growth, and increased industrial and agricultural production are several factors (among many) that are stressing U.S. and global freshwater supplies.

“To supply the water needs of the future, it is critical that the United States develop technologies that provide alternative water sources,” said Abhishek Roy, a senior staff scientist at the National Renewable Energy Laboratory (NREL) and member of the National Alliance for Water Innovation (NAWI).

And NAWI is doing exactly that.

A National Hub Earns More Support

First launched in 2019, NAWI is a research hub that brings together a world-class team of partners from industry and academia, as well as the hub’s public membership organization, called the NAWI Alliance . Together, these experts and water treatment stakeholders are working to lower the cost and energy of water purification technologies, including those that can transform nontraditional water sources, like wastewater or salty groundwater, into clean drinking water.

As of April 2024, NAWI has been extended for five more years, so the team can continue to hone water treatment technologies and increase access to clean drinking water for all Americans, all while reducing the energy and emissions associated with water treatment processes. The organization has received $75 million in funding from two U.S. Department of Energy (DOE) offices: the Industrial Efficiency and Decarbonization Office and the Water Power Technologies Office.

“The extension is great news,” said Matthew Ringer, the laboratory program manager for advanced manufacturing at NREL and NAWI’s partnerships director. “NAWI has more than 460 organizations and more than 1,800 members from around the world who are working together to help produce secure, reliable, and affordable water for communities that are most in need.”

NAWI is led by DOE’s Lawrence Berkeley National Laboratory in partnership with NREL, Oak Ridge National Laboratory, and the National Energy Technology Laboratory.

A Master Road Map to More Secure, Affordable Water

In its first five years, NAWI accomplished a lot—with help from its many members, including researchers at NREL. NREL’s Jordan Macknick led an early NAWI win: a master road map that identifies the highest-priority research needs for water desalination (or purification) and where such technologies are already in use. From the beginning, this road map helped NAWI members optimize their investments; it also serves as the foundation—and future guide—for NAWI’s five-year extension. Macknick also leads one of NAWI’s core research areas—the Data Modeling and Analysis topic area —which focuses on analyzing the cost, efficiency, and performance of entire water treatment systems.

This type of research also happens to be one of NREL’s areas of expertise.

For example, NREL’s Kurban Sitterley helped improve an open-source software tool that can assess the technological and economic value of more than 60 different water treatment technologies. Water treatment researchers and facilities can use the tool, called the Water Treatment Technoeconomic Assessment Platform (or WaterTAP), to evaluate technologies that could help reduce their costs and energy consumption while continuing to meet existing and future water demands. (Sitterley also developed a new model for WaterTAP that can evaluate one of the most promising ways to remove forever chemicals and other contaminants from drinking water).

Plus, WaterTAP can help assess how water treatment facilities could use renewable energy resources, like solar, wind, and geothermal, along with batteries to power their facilities without raising their costs. Some community-scale treatment systems could even provide water for disaster relief and recovery missions or remote military deployments.

“This funding extension is essential to continue the maturation of these technologies and improve their cost and performance,” said Scott Struck, a senior integrated water systems research scientist at NREL.

Solutions for Communities With Dwindling or Contaminated Water Supplies

Struck appreciates how much NAWI has advanced water treatment tools, technologies, policies, and planning. But he is especially excited about NAWI’s work with communities. Even in the United States, not all communities have access to uncontaminated drinking water. In one area of the Central Valley of California, the available drinking water contains high levels of arsenic (a carcinogen), making it unsafe to drink. Because the community is small and financially unable to shoulder the costs of a centralized water treatment system, the residents resorted to buying bottled water instead.

But with NAWI’s help, the community could receive a more affordable and sustainable solution: smaller, more modular water treatment technologies that can filter out enough arsenic to achieve drinking water standards. With that, the community could access a local, more affordable water supply.

In NAWI’s next five years—which has been dubbed NAWI 2.0—the organization will pursue more community-based projects to help Americans maintain affordable and effective drinking water even as hotter temperatures or droughts threaten their supplies. NAWI Alliance members will also continue to advance desalination and other novel technologies that can treat unconventional water sources and cut greenhouse gas emissions at the same time.

“NREL is excited that DOE has extended NAWI for another five years,” Ringer said. “We look forward to working with our partners in national labs, academia, and industry to drive solutions for decarbonizing water and wastewater sectors.”

“Working together,” Struck added, “the NAWI community will help secure a more sustainable and resilient water supply.”

Become a thought leader in clean water innovation and join the NAWI Alliance for free today to unite with world-class lab, industry, and academic experts to address some of the greatest water and energy security challenges.

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Undergraduates Showcase Research Projects, Prowess During Events

University of Texas at Dallas students showed their penchant for discovery during Undergraduate Research Week , which was held April 15-19 and sponsored by the Office of Undergraduate Education .

“We are very fortunate to have some of the best opportunities for undergraduates to engage in research here at UT Dallas,” said Dr. Jessica C. Murphy , dean of undergraduate education and Mary McDermott Cook Chair for Undergraduate Education. “Our team in the Office of Undergraduate Education does an excellent job helping students learn more about research opportunities and empowering undergraduates to articulate their experiences as they pursue a career and graduate school.”

A poster competition capped the week with presentations from 15 finalists chosen from nearly 200 entrants. Students presented their work to a panel of industry judges from Brinker International Inc., Doosan Robotics Americas, Trace3, Veritex Community Bank and Walmart Health.

Biology senior Jacob Roy, who also is pursuing a master’s degree in public affairs , placed first in the poster competition for his research in developing a new approach to RNA modulation. Healthcare studies senior Nanditha Niranjan placed second for her work exploring the impact of educating refugees about the U.S. health care system in reducing the cost of health care. Neuroscience and history senior Arlin Khan finished third for her research on the use of vagus nerve stimulation to aid in the recovery of laryngeal nerve damage.

Students learned much more than basic experimental design throughout their experiences. In addition to technical abilities, they picked up a wide range of professional skills necessary to pursue careers beyond graduation.

“Science takes a lot of patience. All of this took multiple semesters of work,” Khan said. “In a world where things are really instantaneous, I think science is one of those things you have to learn to be patient for and let things work out. I also learned critical thinking and problem solving.”

Garth Edwards, executive vice president at Veritex Community Bank, said: “The subjects of the research are so diverse —  some of these subjects are so relevant today, and some are like, ‘We probably need to be thinking more about them.’ It’s so amazing what [the students] are doing. I’m very encouraged.”

In addition to poster presentations, research week included a match day with more than 20 labs and 200 students, resume workshops and a panel discussion.

Media Contact: The Office of Media Relations, UT Dallas, (972) 883-2155, [email protected] .

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