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How to Start a Research Work in Computer Science: A Framework For Beginners

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Research is one of the key factors behind the improvement and evolution of any subject in the world. However, the skills to perform the research are rarely taught in the school or during the undergraduate courses. This paper provides a practical and efficient framework or method called 'Eight-Step Approach to Research', which will guide you to learn 'how to start doing research' in a particular area of computer science. Although this paper is meant for students and researchers in computer science but it should be kept in mind that this methodology can be applied to any research area in any field of study.

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How to Start a Research Work in Computer Science: A Framework For Beginners

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Research is one of the key factors behind the improvement and evolution of any subject in the world. However, the skills to perform the research are rarely taught in the school or during the undergraduate courses. This paper provides a practical and efficient framework or method called ‘Eight-Step Approach to Research’, which will guide you to learn ‘how to start doing research’ in a particular area of computer science. Although this paper is meant for students and researchers in computer science but it should be kept in mind that this methodology can be applied to any research area in any field of study.

Brainstorming ; Computer Science ; Hints ; Research

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  • Computer Science
  • Brainstorming

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  • Dey, Somdip
  • Faculty of Engineering and Physical Sciences
  • Faculty of Humanities
  • School of Arts, Languages and Cultures
  • School of Computer Science

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  • Starting the research process

A Beginner's Guide to Starting the Research Process

Research process steps

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

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how to start research work in computer science

Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

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All researchers need to write or speak about their work, and to have research that is worth presenting. Based on the author's decades of experience as a researcher and advisor, this third edition provides detailed guidance on writing and presentations and a comprehensive introduction to research methods, the how-to of being a successful scientist. Topics include: Development of ideas into research questions; How to find, read, evaluate and referee other research; Design and evaluation of experiments and appropriate use of statistics; Ethics, the principles of science and examples of science gone wrong. Much of the book is a step-by-step guide to effective communication, with advice on: Writing style and editing; Figures, graphs and tables; Mathematics and algorithms; Literature reviews and referees reports; Structuring of arguments and results into papers and theses; Writing of other professional documents; Presentation of talks and posters. Written in an accessible style and including handy checklists and exercises, Writing for Computer Science is not only an introduction to the doing and describing of research, but is a valuable reference for working scientists in the computing and mathematical sciences.

  • Digh A (2021). Writing and speech instruction in an introductory artificial intelligence course, Journal of Computing Sciences in Colleges , 36 :5 , (119-128), Online publication date: 1-Jan-2021 .

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Reviewer: Alexis Leon

This is a comprehensive guide on research methods and how to produce a scientific publication detailing one's research in computer science (CS) and related fields. The author has drawn from his vast experience as adviser, researcher, and referee, and has produced a classic book on the various aspects of scientific research from research methods to writing style to ethics. The book is full of practical advice that can be readily used, checklists and guidelines that will help readers improve their research and writing, and examples that will help readers navigate the right path and avoid common pitfalls. The first five chapters deal with doing the research. The chapters cover a variety of research-related tasks, including research methods, the development of research ideas, the transformation of ideas into research questions, research planning, reading and reviewing related literature, writing the literature review, developing hypotheses, questions and evidence, and, finally, writing the research paper. The author also gives the format and organization of a research paper and explains how to write clearly, concisely, and correctly. Chapters 6 and 7 deal with usage and style and their importance in writing. Chapter 8 explains punctuation and how to effectively use it. These three chapters, through checklists, guidelines, and examples, give the reader a comprehensive tutorial in producing writing that is unambiguous, error free, and of high quality. Chapter 9 explains how to handle the use, presentation, and writing of mathematical proofs and theorems; how to reproduce the mathematical notations, equations, and formulae correctly; and how to deal with special circumstances encountered while incorporating mathematical proofs and explanations into the research paper. Chapter 10 explains how to present algorithms, the different formalisms, the optimum level of detail, the use of figures, and the correct use of notations. Chapter 11 provides clear and concise instructions on how to include graphs, figures, flowcharts, and tables in a way that will enhance the quality of writing by making it more organized, presentable, and readable. How to create graphs, figures, flowcharts, and tables is explained with the help of examples-both good and bad-so that readers can embrace the good and avoid the bad. Chapter 12 deals with other kinds of professional writing like technical reports, grant applications, and nontechnical writing. This chapter illustrates how to produce writing that helps in achieving the objectives of professional writing-recording, informing, persuading, and convincing. Chapter 13 explains editing, which according to the author is the process of making the document ready for publication or examination. This chapter deals with all aspects of editing, including consistency, style, proofreading, and choice of word processor. Chapter 14 covers experimentation-the use of experiments to verify hypotheses. This chapter explains the principles underlying good experimentation and the various steps involved, like identifying benchmarks and establishing baselines, data collection, interpretation of results, ensuring robustness of the experiments, performance of algorithms, coding, and describing experiments. Chapter 15 explains the various statistical principles and methods used in scientific research and how to interpret and present the results. Chapter 16 deals with presentations and talking about research in front of an audience. It covers the content and organization of the presentation, how to prepare and deliver the presentation, the various parts of the presentation, how to manage question time, how to effectively prepare and use slides and posters, and so on. Chapter 17 covers the ethical aspects of research. Issues like copyright, plagiarism, self-plagiarism, misinterpretation, authorship, confidentiality, and conflict of interest are explained and illustrated. The book also contains a set of exercises that will help readers put into practice what they've learned. This is a must-read for those doing research in CS and related fields. It will greatly benefit anyone who is involved in any kind of scientific research, as the examples are only from the CS field. Students, researchers, scientists, and other academicians involved in scientific research will improve both their research methods and writing by reading this book. Online Computing Reviews Service

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How to do good research

by Gregor v. Bochmann, School of Information Technology and Engineering (SITE), University of Ottawa (This text was prepared in October 2009 at the Hunan University of Science and Technology in Xiangtan, China)

Criteria for funding research in Canada : the most important source of funding for university-based research in computer science is the Natural Sciences and Engineering Research Council (NSERC)

  • Discovery Program (unconstrained basic and applied research) – relatively small amounts of money
  • Good researcher (past performance)
  • Good research proposal (innovative, relevant to current state of the art in theory or practice, well explained and justified)
  • Good opportunity for training of researchers at the Master, PhD and post-doctoral levels
  • Collaborative research (with industry involvement) – relatively large amounts of money
  • Additional criteria: potential application in the industrial context
  • Evidence of industrial interest: (1) letters of support explaining relevance; (2) industrial funding: for many programs, the budget is based on matching industry funds

How to do good research : Important points (overview)

  • Choose an interesting area for research
  • Identify an interesting research topic (a problem for which there is no good solution)
  • Have some good idea how to improve the state of the art
  • Apply it to some examples (realistic case studies, if possible)
  • Prove some properties of your approach (logical properties or analytical performance predictions) and show that it is better than the current state of the art
  • Do simulation studies (e.g. for performance) and show that your approach  is better than the current state of the art
  • Build a software tool that supports your approach
  • Do a systematic comparison with other approaches to the same problem and discuss advantages AND disadvantages of your approach
  • Write up your results in some papers which make these results accessible to the interested expert.

How to do good research : more details

  • Relevance for practical applications
  • Area that has not yet been explored thoroughly
  • Area that corresponds to your past experience (unless you want to change fields)
  • You must be familiar in general with your research area. Depending on your past experience, this may require much reading. Read surveys and overview articles.
  • In order to identify your research topic, you have to look around (within the research area) to find a problem that has not yet been solved, or for which the existing solutions could be improved. For this purpose, you have to read more detailed papers. When you think, you have found a good research topic, you have to study all literature related to the problem at hand. This requires much readings. Look in good journals (e.g. ACM or IEEE Transactions) and good conferences (ACM and IFIP conferences are often better than IEEE conferences – check the acceptance rate of conferences – specialized conferences usually have more interesting papers than general-purpose conferences).
  • Google is a very interesting search tool, in particular, if you know the title of the paper you are looking for. I think it is better not to rely simply on Google to do your literature search. Better: identify the relevant GOOD journals and conferences in the area and look through the published articles – read the abstract of those that have an interesting title in relation to your interest – read the whole article superficially if the abstract is promising -  read the article again (thoroughly) if you are interested in the details.
  • Talk to other people knowledgeable in your research area about your readings and your questions; contact the author of a paper to get a copy, or if you have questions after reading the paper in detail.
  • You may find that the problem that you had chosen is already mostly solved. Maybe it would be better for you to find another topic. Go on reading.
  • In the process of doing this, you may find that there is no good survey paper on the area yet. You may write such a paper.
  • Such a “good idea” comes mostly during the reading of some interesting paper, or by noting that for a given situation described in one paper, an approach presented in another paper may be useful. Most of the time, these “good ideas” do not lead to big results, but only to small improvements. Sometimes, during working on such small improvements, some further “good idea” may appear which may lead to more important “improvements”. Then you have to show that your idea “works” (see below).
  • There is no recipee that always leads to a "good idea". You have to be inventive. It also helps to have a critical attitude towards the paper you are reading. Maybe it is not as simple as they say ??
  • In order to check whether a “new idea” works, you should always try it out with some small examples first, and then some more complex one – if possible an example that covers all aspects of the problem.
  • To convince yourself and others that your approach is interesting in practice, it is very useful to apply it to some realistic case study (this may be a prototype implementation or an extensive simulation study)
  • Which of the above three points is more relevant for showing that your idea works depends on the nature of your problem. Often all of these points may be pursued in parallel.
  • The research work under point (4) should be pursued such that at the end a systematic comparison with all other known approaches can be established. Again, a prerequisite is extensive reading in order to be familiar with the existing literature on the topic.
  • How to write a good research paper is addressed below.
  • You may ask the question: Is the number of papers published important for your career ? - In Canada, it is generally considered that it is better to have a few papers in respected journals and conferences than to have many papers in journals and conferences of lower quality. There is no serious research organization that simply counts the number of papers published.

How to write a good research paper

There are a number of interesting articles on the Internet about this topic. I made copies of those articles I found most interesting (among those that I found with Google).

  • Tips for writing technical papers (by Jennifer Widom) – original :  Good remarks about the different parts that should be included in a paper.
  • Writing technical articles (by Henning Schulzrinne) – original :  This is a good overview of the important points. It also contains many links to related documents (in particular, see the list at the end).
  • How to write a paper (by Mike Ashby) – original : A Powerpoint presentation on how to write a paper is several steps. It includes some nice diagrams, tables, and sketches that provide examples of what should be produced during the different steps.
  • How to write a technical paper (by Andrew A. Chien) – original : Explains how to write a technical paper in 5 steps
  • Writing a technical paper (by Michael Ernst) – original : These are some more good tips for writing technical papers.
  • … and here some other related topics
  • Choosing a venue: conference or journal (by Michael Ernst) – original
  • Making a technical poster (by Michael Ernst) – original
  • Reviewing a technical paper (with several links) – original

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Research projects.

The CS department has resources available for research projects. The following is a quick start guide to research projects in Computer Science Department. This quick start guide does not cover all topics and it is recommended that you consult the CS Guide for more information.

This guide is designed to help those beginning a research project by pointing to appropriate sections of the CS Guide for typical start-up tasks. Research projects typically need the following: storage space that can be shared by members of the research group, a web presence (possibly driven by a back-end database), mailing lists, code/document repositories. Here is how each of these are implemented and requested in the Computer Science Department.

  • Project Disk Space - We encourage projects (even single-person projects) to use disk space outside of the user home directory filesystem.  This has several benefits.  First, the quota is separate from any particular project member and can be much larger than we allow for home directories.  Second, project members can be added and removed to change access without moving the files themselves.  Third, users can collaborate and share files without having to give others access to their home directory.  Finally, by keeping projects in separate partitions, CS Staff can manage our storage more efficiently.  For more details, please see the Disk Space page.  To request disk space, use the "Project Disk Space" form link on the left.  Note that if you specify additional project members in the request form, we will automatically create a unix group consisting of you and the listed users and set the setgid flag on the project directory.
  • Project Web Space - To set-up a web page or web site for the project, first request project disk space and then use the "Project Web Space" form to the left to request that a subdirectory of the project space be mapped to a web URL. Project web space will give you the ability to host your research group or project-related content at its own subdomain (e.g. http://project.cs.princeton.edu/ ).  Even if you are only requesting project disk space for the sole purpose of hosting a project web site, we recommend that you choose a subdirectory (e.g., public_html ) within the project disk space.  This will give you the flexibility in the future to also use the project disk space for other purposes. 
  • Project Database - If your project needs a MySQL database (perhaps as a back-end store for a web site), use the "Database" request form at the left and specify a collaborative database.
  • Mailing Lists - Research projects typically create one or more mailing lists to manage their communication.
  • Source Repository - If your group will be collaboratively developing code or writing papers, you may want to request an SVN repository from OIT (requires Princeton OIT authentication).
  • Rack Space for Servers - If you have physical rack-mount servers, they can be housed either in Room 002 of the CS Building or at the University data center at 151 Forrestal .  Contact CS Staff for availability and additional details.
  • Role Accounts / Mail Aliases - please note that we do not create role accounts or provide email aliases.  By properly configuring access control, role accounts should not be necessary.  Email aliases can be mimicked by requesting a mailing list and selecting the "Mail Alias" type in the form.
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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

IT & Computer Science Research Topics

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

  • Algorithms & data structures
  • Artificial intelligence ( AI )
  • Computer networking
  • Database systems
  • Human-computer interaction
  • Information security (IS)
  • Software engineering
  • Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

  • An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
  • A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
  • An evaluation of machine learning algorithms used for recommender systems in streaming services
  • A review of approximation algorithm approaches for solving NP-hard problems
  • An analysis of parallel algorithms for high-performance computing of genomic data
  • The influence of data structures on optimal algorithm design and performance in Fintech
  • A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
  • A comparison of streaming algorithm performance for the detection of elephant flows
  • A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
  • Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
  • Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
  • A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
  • A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
  • Exploring the feasibility of applying information theory to feature extraction in retail datasets.
  • Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

  • Applying deep learning algorithms for speech recognition in speech-impaired children
  • A review of the impact of artificial intelligence on decision-making processes in stock valuation
  • An evaluation of reinforcement learning algorithms used in the production of video games
  • An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
  • An analysis of the ethical and social implications of artificial intelligence-based automated marking
  • The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
  • An examination of the use of artificial intelligence in orthopaedic surgery
  • The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
  • An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
  • A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Research topic idea mega list

Topics & Ideas: Networking

  • An analysis of the impact of 5G technology on internet penetration in rural Tanzania
  • Assessing the role of software-defined networking (SDN) in modern cloud-based computing
  • A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
  • Exploring the influence of cloud computing on security risks in fintech.
  • An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
  • Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
  • An evaluation of the challenges and opportunities in 6G wireless network adoption
  • The role of network congestion control algorithms in improving network performance on streaming platforms
  • An analysis of network coding-based approaches for data security
  • Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

  • An analysis of big data management systems and technologies used in B2B marketing
  • The impact of NoSQL databases on data management and analysis in smart cities
  • An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
  • Exploring the role of data warehousing and business intelligence in global consultancies
  • An analysis of the use of graph databases for data modelling and analysis in recommendation systems
  • The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
  • An examination of the challenges and opportunities of distributed databases in supply chain management
  • Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
  • An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
  • Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

  • An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
  • An exploration of how artificial intelligence is changing human-computer interaction patterns in children
  • An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
  • Assessing the influence of virtual and augmented reality on consumer purchasing patterns
  • An examination of the use of gesture-based interfaces in architecture
  • Exploring the impact of ease of use in wearable technology on geriatric user
  • Evaluating the ramifications of gamification in the Metaverse
  • A systematic review of user experience (UX) design advances associated with Augmented Reality
  • A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
  • Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

  • A bibliometric review of current trends in cryptography for secure communication
  • An analysis of secure multi-party computation protocols and their applications in cloud-based computing
  • An investigation of the security of blockchain technology in patient health record tracking
  • A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
  • A systematic review of secure data storage solutions used for cloud computing in the fintech industry
  • An analysis of intrusion detection and prevention systems used in the healthcare sector
  • Assessing security best practices for IoT devices in political offices
  • An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
  • A comparative study of digital signature schemes adoption in property transfers
  • An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

  • A study of agile software development methodologies and their impact on project success in pharmacology
  • Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
  • A study of the impact of DevOps practices on software development and delivery in the healthcare sector
  • An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
  • A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
  • An investigation of software testing techniques and methodologies for subscription-based offerings
  • A review of software security practices and techniques for protecting against phishing attacks from social media
  • An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
  • Exploring the impact of software development outsourcing on project success in multinational contexts
  • An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

  • An array-based optimization framework for query processing and data analytics (Chen, 2021)
  • Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
  • Embedding constructural documentation in unit tests (Nassif, 2019)
  • PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
  • Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
  • Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
  • Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
  • Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
  • Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
  • Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
  • Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
  • A state-based approach to context modeling and computing (Yue, 2019)
  • A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
  • HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Introduction to Research in Computer Science

Prerequisite: Computer Science 40 and Computer Science 32; consent of instructor.

Defining a CS research problem, finding and reading technical papers, oral communication, technical writing, and independent learning. Course participants work in teams as they apprentice with a CS research group to propose an original research problem and write a research proposal.

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How can I get into computer science research as a high school student?

I am in High-School but want to pursue research in Computer Science, but as you know High-School usually does not offer any such options or tasks.

So I want to explore avenues where I can get these options and tasks as I am very very interested and really want to do it.

I would also like to say that I have good-strong knowledge of C++, C and assorted APIs from that (3 years programming for 5+ hours daily). So I wouldn't call my self too new to programming and think I can handle programming and so on.

Now, these are my questions:

How can I get involved in research?

How can I contact academics to ask them for research position or even an intern position in research for that matter without coming across as a "waste of time"?

  • research-process
  • career-path
  • computer-science

Community's user avatar

  • 12 The fact that you know C++ programming does not qualify you for research, since you lack the necessary theoretical and mathematical background for research. So, first go to the university, get the necessary knowledge and take it from there. Life is not a race. –  Alexandros Commented Apr 1, 2015 at 19:02
  • 2 I know of universities that have outreach programs that support internships for high school students. Maybe check with the CS departments in your local universities? –  Austin Henley Commented Apr 1, 2015 at 19:18
  • 2 @Alexandros I am not saying anything like that, I am certainly not implying programming qualifies me for research just that I am fine with even doing "the dirty work" of programming and implementing techniques and so on during research role if needed. Next, I agree "Life is not a race" but I love CS and it would be brilliant to do research and spend time around those who also love it just as much. –  user26985 Commented Apr 1, 2015 at 19:51
  • 3 Research is a very strong word. I would suggest to start looking at algorithm contests (IOI, ACM-ICPC(university level, but you can practice), codeforces, etc), and get involved there. Maybe you can contact some programming contest team in a local university and join them to learn, you will get a lot of experience in basic CS. If you get notorious on those contests or in those study circles, you will be given lots of opportunities. Good luck! –  chubakueno Commented Apr 2, 2015 at 1:39
  • 3 @Alexandros In my opinion, your comment is overly harsh. Instead, I think it would be better to suggest learning opportunities that take him in the direction of research, at least to get a taste. Example: science fair projects. –  MrMeritology Commented Apr 2, 2015 at 11:54

7 Answers 7

Good on you!

I have a relatively simple suggestion: do a replication study . First, find a collaborator -- a fellow high school student or college student of similar skill and experience. You'll learn more in a team than doing it alone. Second, read a dozen or so research papers (probably conference papers) in the field or sub-field that interests you the most. Pick one , preferably the simplest one you can find. Your goal is to replicate the methods as described in the paper and compare your results to theirs. (Don't pick one where you have access to their code.) Once you have a paper picked out (or a few), recruit an adviser/mentor -- either a college professor or an experienced researcher. You'll want to meet with your adviser/mentor weekly to talk about progress and problems you encounter along the way. Mostly, this weekly meeting holds you and your partner accountable for progress.

In my field (Computational Social Science) there are many simulation models that are simple enough to be replicated from their specification. This varies widely in subfields of Computer Science, so your mileage may vary.

The point to all this is to get you an experience in the realm of research without requiring that you first go through all the preliminaries. By focusing on replicating one paper, you only need to understand the material and methods in this one paper. You aren't trying to break any new ground. Instead, you are following in the footsteps of other researchers. If they have done their job well, then you should be able to replicate their results. Replication is a valuable scientific endeavor in itself.

MrMeritology's user avatar

  • When you mean recruit, do you literally mean pay them? Or what do you mean? –  user26985 Commented Apr 2, 2015 at 15:42
  • @RohanVijjhalwar Sorry for the ambiguity. No, you shouldn't pay the adviser/mentor, or even suggest it. I mean that you should search for good candidates, initiate contact, propose the arrangement, and offer a description of the benefits to the adviser/mentor. By taking the lead in this process, you are showing the adviser/mentor that you have maturity and initiative. –  MrMeritology Commented Apr 2, 2015 at 19:06
  • Yours answers are brilliant - just what I am looking for - :D +1 for your answer - sorry to bother you again but as I am not an involved in academia what is considered a good candidate? –  user26985 Commented Apr 2, 2015 at 19:52
  • @RohanVijjhalwar No worries. I'm happy to help. What you should look for in an adviser/mentor is someone who has some interest/skill in the topic. But, MOST OF ALL, look for someone who loves mentoring and working with up-and-coming students of any age. Start local. Ask for introductions to good candidates. This is called "networking" (professional-style). "I'm looking for a mentor for XYZ project. Do you know anyone who might be interested in working with a young team (me and my colleague)?" –  MrMeritology Commented Apr 2, 2015 at 20:59
  • 1 @FaheemMitha -- generally, experienced people who mentor younger or inexperienced people are motivated by something other than money. They are motivated by altruism in some form. Sometimes, they were mentored themselves when they were younger, and they might want to give back to society to return the favor. There's also a thrill that some people get when they help someone get "a leg up", especially if that student is eager and enterprising. –  MrMeritology Commented May 19, 2015 at 23:36

This is a tough question, you sound like you're very interested but I'd seriously question your familiarity with the body of computer science to be able to meaningfully contribute to a research project. A better option might be to engage in reading publications and identifying areas of knowledge gaps, and work on rectifying those in preparation for a career in research.

At the same time, I really don't want to discourage your enthusiasm. If there is an institute that engages in research in your area, you might want to check out their website and see what types of research the professors are engaged in. Start off by reading about those fields and , once you're comfortable, reach out to the professor with questions and let them know you're interested in research. Start there and see where it goes!

Rubix Rechvin's user avatar

I am in a math department and many math departments have "math circles" or other activities that reach out to high schoolers to show them what mathematics at a research level is, provide them with teaser problems that show some deeper structure that you may find interesting and that can guide you towards current research. You have to expect that it takes a few years to get to where research really is, but at least it provides you with an avenue to talk to professors on a regular basis and get exposed to research.

Let me just assume that computer science departments have similar avenues. Find the closest university to where you live and check its computer science department's web site for outreach activities, or email their undergraduate coordinator for more information. They may have something like our math circles.

Wolfgang Bangerth's user avatar

A researcher is supposed to have a deep understanding of his/her field and a solid grasp of the basics. Unfortunately to even begin to understand stuff at that level, we need a decent high school level general science background. Not to mention that Computer Science is quite interdisciplinary - it includes topics from physics, mathematics, statistics and lots of other knowledge areas.

Coding is a skill, it enables you to do your work quicker - it helps you do research but is very rarely the research itself. Even " the dirty work of coding" needs some basic background knowledge. If you know how to make rubber, doesn't mean you can make a tire without knowing what a tire is ! Making a tire requires knowledge of things like heat tolerances, load capacity, strength etc.

BUT this is not to discourage you, Absolutely not! Rather to know where you stand and what to expect and how to approach people for opportunities.

I have a few suggestions -

Look for freelancing programming opportunities - there quite a few websites _ I personally know of freelancer.com and fiver.com. Here you can work on programming assignments set by people and get paid for it. This sets you up for the next level - why ? If you do a good enough job that people pay you for, then many more will take you seriously ...

Now for pure research oriented opportunities - The best idea is to talk to people who are conducting undergraduate research - why ? students who are say in first or second year of their undergraduate programs would have more or less the same level of knowledge you have. Plus if you actually worked doing freelancing stuff or some-other paid or otherwise serious opportunity, this will give you an extremely positive point to negotiate an opportunity.

Sid5427's user avatar

  • Great answer! It did not discourage me could you tell me if I could be at-least doing a internship in a research environment - maybe university? Would a professor teach me anything or is that something too far? –  user26985 Commented Apr 1, 2015 at 20:44
  • 1 @RohanVijjhalwar One of the biggest differences between life as a high school student and life as a graduate student researcher is the extent to which one learns by being taught as distinct from learning by reading, studying, experimenting, and thinking without anyone teaching you. –  Patricia Shanahan Commented Apr 2, 2015 at 13:10

As you are in very early stages of your probable research career, one thing I want to say is do shopping. Try to consciously ask yourself and others as to exactly what research you want to do and more importantly why. Do not be afraid to drop ideas or say no to potential supervisors or even current supervisors in the middle of your research work if it does not appeal and/or interest you. This is much easier and crucial at this stage of your career to find the right area if you want to flourish and more importantly enjoy your research in the long term.

Ketan's user avatar

Contact and network with people! These two things are key.

From there, prove to them that you are experienced. I did this by showing them my Github & Bitbucket, my iOS apps, my web apps, my websites, compilers/search engines, and my hackathon experiences.

This is what got me my research opportunity at Stanford in Computer Science.

Yours truly,

High School Junior working on Computer Science Research with PhDs at Stanford University this summer

Rolando Cruz's user avatar

I'm not a CS person (policy and politics PhD) but one thing that has not been discussed: Develop a domain interest by reviewing research on a particular topic.

Are you interested in a particular facet of CS? Is there an application of CS to a field you are interested in? Or is there a research questions that you want to apply CS methods to?

Once you read into the literature a bit ( https://scholar.google.com is a good place to start) then you can find CS programs and professors that match with your interested -- and once you've done this, you can contact professors in the subfield of interest to volunteer your skills and ask domain-specific questions.

Nick Cain's user avatar

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how to start research work in computer science

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Write a Research Question

Before you begin writing your research question, it is first important to craft a purpose statement. What can be a purpose of your study?

Examples of a purpose for a quantitative study include:

  • Examining a relationship between students who take computing classes in high school and those who pursue computer science as a major in college,
  • Evaluating the effectiveness of an outreach activity among underrepresented students, or
  • Measuring engagement or interest in computing among middle school students.

Examples for a qualitative study include:

  • Exploring parent stories about helping their students with computing homework or
  • Developing a theory of effective management techniques in a computer lab.

Once you define the purpose of your study, you can then create a clear purpose statement. Purpose statements help you define your research in a straightforward manner. Here is an example of a well-defined purpose statement.

The purpose of this study is to examine the relationship between the completion of an 9-week computational thinking unit among 7th and 8th grade students in a rural middle school and student achievement on mathematics exams.

This purpose statement explicitly answers these questions:

  • What is the intent of the study?
  • What population group is targeted in the study (i.e., age, location, etc.)?
  • What was the intervention (activity or curriculum), including its duration?

After you have decided on the purpose of your study and have written your purpose statement, you can then craft your research question.

Writing a Well-crafted Research Question

Research questions provide an overarching direction for your study to follow. It guides the type of study you will choose, the type of data you will collect, and the type of analysis on the data that you will perform.

Writing good research questions, then, is an important step in framing your study. What makes a good research question? Research questions should be clear, concise, specific, neutral, and focused. They should also be complex enough that the question requires more than just a “yes” or “no” answer. An example of a thorough research question for a quantitative study follows:

Does guardian understanding of computational thinking affect student performance on computational thinking tasks among primary school students in an urban school district?

For a qualitative study, a thorough research question may look like this:

What are the major challenges teachers face when teaching computational thinking to Kindergarten, 1st grade and 2nd grade students in the United States?

Typically, research questions are not the exact question that you will actually ask the participants in your study. They, however, guide those questions.

Research questions should:

  • Define what is being measured
  • Define the population group
  • Be neutral (not assume the intervention being studied is effective or not)
  • Be able to be answered in the timeframe you have planned for the study

Depending on the study length, more than one research question can be appropriate. Your research questions will most likely be related in some way, since they will be designed to support your purpose statement.

Defining What is being Measured

Defining what is being measured is important for narrowing your research. Consider the following questions:

1A: Does participation in a one-week teacher professional development around the Exploring Computer Science curriculum result in improved teaching practices? 1B: Does participation in a one-week teacher professional development around the Exploring Computer Science curriculum result in more frequent use of inquiry-based learning pedagogical methods?

In the example above, Question 1A refers to “improved teaching practices”. “Improved teaching practices” is unclear, since there is no context for “improved” against the status quo. In Question 1B, one teaching practice, inquiry-based learning, is chosen for the study.

Defining the Population Group

Defining the population group is often missing in research questions, but it is very easy to add. Consider the following questions:

2A: Do participants in a week-long Lego Robotics summer camp have an increased likelihood of taking computer science courses at the college level?

2B: Do 11th and 12th grade students in central Illinois who participate in a week-long Lego Robotics summer camp have an increased likelihood of taking computer science courses during their first year of college?

In the Question 2A, we do not know who the participants are or where they are located. As a research question, clearly stating the population group is important for identifying the group that will be targeted in your study. Sometimes this information may be provided in context within preceding paragraphs. However, restating the population group within the research question makes the target of your study clear to your reader.

In our review of hundreds of articles for this site, we have encountered many articles that do not state whether the group is undergraduate students, primary school students, or secondary school students or in which country or setting the study takes place. It is difficult for other researchers to use or build upon research that hasn’t clearly stated the population group. Embedding this into your research question will enable others to know who the participants in your study were.

Writing Neutral Statements

A neutral statement will exclude any pre-conceived bias. Consider these questions:

3A: What elements of AP Computer Science Principles make it a more appealing course to high school-aged girls than AP Computer Science A? 3B: Is the AP Computer Science Principles course more appealing to high school-aged girls than AP Computer Science A? If so, what is seen as different and/or more appealing?

Question A assumes that the AP Computer Science Principles course is more appealing than the AP Computer Science A course for the target population (high school-aged girls). If this has been previously established in prior research and the researchers are making this a follow-up study, then Question 3A may be seen as neutral. However, if this has not been previously established, then Question 3B may be a more appropriately worded research question.

Defining a Scope/Timeframe

Research studies are projects, and just like any project, it is important to manage scope. Scope is based on your timeframe and your resources. Consider the following questions:

4A: Are middle-school girls who participate in a summer camp more likely to pursue careers in computing fields than those who do not participate in the camp? 4B: Are middle-school girls who participate in a summer camp more likely to express interest in computing-related careers than those who do not participate in the camp?

Question 4A implies that girls will be tracked from middle school through college and into their careers. This longitudinal study would take a minimum of seven years, likely more, if you count the years it would take for a 6th grader to start her career. Question 4B is finite and could be evaluated at the end of camp, three months after the camp has ended, or even the following year.

In this example, both questions could be suitable and is entirely dependent on your timeframe for your study as well as your resources.

Additional Examples

Take a look at these examples that illustrate different types of requirements for well-crafted research questions.

Example 5A: How is Scratch used to teach computational thinking? Example 5B: How are Native American high school teachers in North Dakota using Scratch to teach computational thinking?

The 5A research question is very vague. We know nothing about the population group being studied or the intervention other than one computing education tool being used (Scratch) and the concept being taught. The 5B question specifies how Scratch is being used and the population group being targeted.

Example 6A: Does a game design camp make girls interested in computing? Example 6B: What is the impact of a one-week game design camp on the interest levels in computing among 7th and 8th grade girls located in Chicago’s West Side?

Example 6A is very broad. It may be fine if you are planning on writing a book or a 200-page dissertation. For most of us, though, our studies need to be more focused so that we can complete it in 6 months or 1 or 2 years. Example 6B looks at a specific cause (impact of a one-week game design camp), a specific locale (Chicago’s West Side), and a specific group (7th and 8th grade girls). By making your research question(s) well-defined, you are more likely to be able to answer the question in the timeframe for your study.

how to start research work in computer science

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Bachelor's Degree in Computer Science

Why pursue a bachelor's degree in computer science.

The concentration in Computer Science is designed to teach students skills and ideas they will use immediately and in the future. Because information technology affects every aspect of society, graduates with computer science degrees have open to them an enormous variety of careers—engineering, teaching, medicine, law, basic science, entertainment, management, and countless others. 

At Harvard College, students choose a "concentration," which is what we call a major. All prospective undergraduate students, including those intending to study engineering and applied sciences, apply directly to Harvard College . During your sophomore spring you’ll declare a concentration, or field of study. You may choose from 50 concentrations and 49 secondary field (from Harvard DSO website ).

All undergraduates in Computer Science at Harvard are candidates for the Bachelor of Arts degree (A.B.) . With the knowledge that it requires extra course work, you can consider the more intensive  A.B./S.M. option  through a concurrent masters degree.

Learn about our Computer Science concentrators  >

Apply to Harvard College  >

A.B. in Computer Science

The basic degree requirements are eleven to fourteen 4-credit courses in mathematics, theoretical computer science, computer software, and other areas of computer science. Math courses cover linear algebra, single variable calculus and probability/statistics. Students who place out of part or all of the introductory calculus sequence, Mathematics 1ab, reduce their concentration requirements to 11 courses.

Computer Science Secondary Field

A lightweight way of getting official recognition within Harvard for work in two fields is to do one or the other as a secondary field. For Computer Science, this involves taking 4 courses in the secondary field. Learn more about the  computer science secondary field .

A.B./S.M. in Computer Science

Our  AB/SM degree program  is for currently enrolled Harvard College students only. Students who are eligible for  Advanced Standing  on the basis of A.P. tests before entering Harvard may be able to apply for admission to the S.M. program of the Graduate School of Arts and Sciences and graduate in four years with both a bachelor’s and master’s degree (not necessarily in the same field).

Beginning with the class of 2022, students have the opportunity to apply to the Graduate School of Arts and Sciences for a master’s degree pursued concurrently with the bachelor’s degree. As part of the  concurrent degree program , students will be allowed to double-count up to sixteen credits (normally, four courses) for the Bachelor of Arts and the Master of Science. An undergraduate pursuing the concurrent degree must complete both of these degrees by the end of eight terms of residency, or the equivalent.

The Mind, Brain, and Behavior Program (MBB)

Students interested in addressing questions of neuroscience and cognition from the perspective of computer science may pursue a special program of study affiliated with the University-wide Mind, Brain, and Behavior Initiative, that allows them to participate in a variety of related activities. (Similar programs are available through the Anthropology, History and Science, Human Evolutionary Biology, Linguistics, Neurobiology, Philosophy, and Psychology concentrations.) Requirements for this honors-only program are based on those of the computer science Requirements for Honors Eligibility. See the  handbook entry  for more information and also  Frequently Asked Questions about the MBB Track . This is an honors track program: students are eligible for English Honors.

Why study CS at Harvard? What’s different about pursuing CS in a liberal arts setting?

Get the answer to these questions and learn more about CS .

Prerequisites

Learn about the prerequisites for the concentration on our  First-Year Exploration page . Students interested in concentrating in computer science can refer to our Sophomore Advising page  and request to be matched with a Peer Concentration Advisor  (PCA). PCAs serve as peer advisors for pre-concentrators (and current concentrators), providing a valuable perspective and helping students to discover additional resources and opportunities.

Requirements

Learn more about the Computer Science requirements >

View current Computer Science courses . >

View sample plans of study. >

Tags for Computer Science courses. > 

Research Opportunities in Computer Science

As part of your Bio/Biomedical Engineering coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects.  Learn more about research opportunities at Harvard SEA S.

Learn about the research interests of our Computer Science faculty .

Computer Science Career Paths

Learn about potential career paths for students for students concentrating in Computer Science . 

Computer Science & Society

Harvard Computer Science has several programs that allow undergraduate students to think about the broader issues in tech and CS.

Computer Science Clubs and Organizations

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30+ Easy Research Paper Topics in Computer Science: Explore Exciting Ideas for Students

Welcome to the fascinating world of easy research paper topics in computer science.

Whether you’re a student exploring potential subjects for your next assignment or an enthusiast curious about the latest trends.

In this guide is designed to simple project topics and provide insights into choosing the right research paper topic.

What Makes a Good Research Paper Topic?

Table of Contents

A good research paper topic in computer science is one that is relevant, manageable, and interesting. It should be specific enough to delve into deeply but broad enough to find ample research material. Here are some key criteria:

  • Relevance : Choose a topic that is current and has practical applications in today’s world of technology.
  • Manageability : Ensure the topic is not too broad that it becomes overwhelming or too narrow that it lacks research depth.
  • Interest : Select a topic that sparks your curiosity and passion, as it will keep you motivated throughout the research process.

Importance of Choosing the Easy Research Paper Topics in Computer Science Right Topic

The topic you choose can significantly impact the quality and success of your research paper. A well-chosen topic:

  • Facilitates in-depth research and analysis.
  • Engages readers and showcases your understanding of the subject matter.
  • Allows you to contribute to the existing body of knowledge in computer science.

Popular and Easy Research Paper Topics in Computer Science

Let’s explore some popular and easy research paper topics in computer science that cover various aspects:

Machine Learning Basics: Applications and Future Trends

Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data and improve over time without explicit programming. Researching its basics, applications in real-world scenarios, and future trends can provide valuable insights into this rapidly evolving field.

Cybersecurity Threats and Countermeasures Explained

Cybersecurity remains a critical concern as digital threats continue to evolve. Explore different types of cybersecurity threats, such as malware and phishing, and examine effective countermeasures to protect systems and data.

The Impact of Artificial Intelligence on Society

Artificial intelligence has transformed various industries, from healthcare to finance. Investigate how AI influences society , including its benefits, challenges, and ethical considerations.

Programming Languages and Frameworks

Programming languages and frameworks are fundamental tools for software development. Topics like:

  • Introduction to Python : Discuss its versatility, applications in data science, and advantages for beginners.
  • JavaScript Frameworks : Compare popular frameworks like React.js, Angular, and Vue.js in terms of performance, scalability, and community support.
  • Exploring the World of Java : Cover essentials, applications in enterprise systems, and its enduring popularity in software development.

Emerging Technologies and Innovations

Explore cutting-edge technologies that are reshaping the future:

  • Blockchain Technology : Analyze its practical applications beyond cryptocurrencies, such as supply chain management and voting systems.
  • Internet of Things (IoT) : Investigate how IoT devices enhance connectivity and efficiency in smart homes, healthcare, and industrial sectors.
  • Virtual Reality (VR) and Augmented Reality (AR) : Examine their impact on user experiences in gaming, education, and training.

Ethical Issues and Concerns in Computer Science

Discuss ethical dilemmas arising from advancements in technology:

  • Data Privacy : Explore challenges in protecting personal data amid digital transformations and regulatory frameworks.
  • Ethics of AI and Machine Learning : Address concerns regarding bias in algorithms and fairness in decision-making processes.
  • Cybersecurity Ethics : Debate the balance between security measures and individual privacy rights.

Hands-on easy research paper topics in computer science and Case Studies

Engage in practical learning through hands-on projects:

  • Building a Simple Chatbot Using Natural Language Processing : Step-by-step guide on creating a chatbot using Python and NLTK library.
  • Creating a Basic Web Application with React.js : Learn how to build interactive web interfaces using React.js components.
  • Analyzing Big Data with Apache Spark : Practical insights into processing large datasets and performing data analytics.

Future Directions and Trends

Explore emerging trends that shape the future of computer science:

  • Quantum Computing : Explore its potential to revolutionize computation power and solve complex problems exponentially faster than classical computers.
  • 5G Technology : Discuss its impact on mobile computing, IoT connectivity, and the development of smart cities.
  • Edge Computing : Examine how edge computing enhances data processing efficiency and supports real-time applications.

In conclusion, selecting the right research paper topic in computer science is crucial for a successful academic journey. By choosing topics that align with your interests, exploring their practical applications, and staying updated on emerging trends, you can contribute meaningfully to the field. Remember to approach each topic with curiosity and enthusiasm, and enjoy the process of discovering new insights and innovations in computer science.

Summary of Key Points

  • Choosing Your Research Paper Topic : Select a topic that is relevant, manageable, and interesting.
  • Exploring Topics : Dive into areas like machine learning, cybersecurity, programming languages, emerging technologies, and ethical issues.
  • Hands-On Learning : Engage in practical projects and case studies to deepen your understanding.
  • Future Trends : Stay informed about the latest advancements and their potential impact on the future of technology.

Tips for easy Research paper topics in Computer science Success

  • Research Thoroughly : Gather reliable sources and conduct comprehensive research.
  • Stay Organized : Keep track of your findings, notes, and references throughout the research process.
  • Seek Guidance : Consult with professors, mentors, or peers for feedback and guidance.

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Your chance of acceptance, your chancing factors, extracurriculars, what are the top computer science colleges.

I'm really passionate about computer science and want to study it in college. Can anyone tell me which colleges have the best computer science programs overall? I'm open to any suggestions. Thanks!

There are numerous colleges with highly regarded computer science programs. Here are a few top computer science schools in the United States:

1. Massachusetts Institute of Technology (MIT) - MIT's electrical engineering and computer science program is consistently ranked as one of the top programs in the world. They offer a variety of research opportunities, strong industry connections, and a commitment to interdisciplinary study.

2. Stanford University - Stanford's computer science department is renowned for its cutting-edge research, close ties to Silicon Valley, and excellent faculty. Students have access to various research centers, such as the Stanford Artificial Intelligence Laboratory (SAIL) and the Human-Computer Interaction Group.

3. Carnegie Mellon University (CMU) - CMU's School of Computer Science is known for its top-notch faculty, research facilities, and opportunities for interdisciplinary collaboration. They offer a wide range of specializations, including artificial intelligence, human-computer interaction, machine learning, and robotics.

4. University of California, Berkeley (UC Berkeley) - UC Berkeley's Electrical Engineering and Computer Sciences department is highly acclaimed for its research, faculty, and commitment to diversity. Students benefit from access to the multidisciplinary Berkeley Artificial Intelligence Research (BAIR) Lab and a strong connection to Silicon Valley.

5. California Institute of Technology (Caltech) - Caltech's computer science program emphasizes a foundation in mathematics, physics, and engineering. Students have the opportunity to work on cutting-edge research projects in various areas, such as machine learning, computational biology, and quantum computing.

6. University of Illinois at Urbana-Champaign (UIUC) - UIUC's Grainger College of Engineering is home to one of the nation's top computer science departments. They offer numerous research opportunities, leading centers such as the National Center for Supercomputing Applications (NCSA), and a variety of specializations, including algorithms, systems, and computational science.

7. University of Washington (UW) - UW's Paul G. Allen School of Computer Science & Engineering is known for its innovative research and strong ties to major tech companies in the Seattle area. Students can specialize in areas such as artificial intelligence, human-computer interaction, and data science.

8. Princeton University - Princeton's computer science department focuses on both theoretical and practical aspects, offering a rigorous education and access to state-of-the-art research facilities. Students can pursue areas of interest such as algorithms, machine learning, and computational biology.

These are just a few examples of top computer science colleges. Given the variety and strength of the programs available, it's important to consider factors such as location, class sizes, research opportunities, and faculty when selecting a college.

About CollegeVine’s Expert FAQ

CollegeVine’s Q&A seeks to offer informed perspectives on commonly asked admissions questions. Every answer is refined and validated by our team of admissions experts to ensure it resonates with trusted knowledge in the field.

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Marshall Research Scientist Enables Large-Scale Open Science

Headshot of Rahul Ramachandran in front of a blue background. He wears a light blue shirt, dark gray jacket, and glasses.

By Jessica Barnett  

Most people use tools at work, whether it’s a hammer, a pencil, or a computer. Very few seek a doctorate degree in creating new tools for the job.

Using that degree to make it easier for people around the world to access and use the vast amounts of data gathered by NASA? Well, that might just be unheard of if you didn’t know someone like Rahul Ramachandran, a senior research scientist in the Earth Science branch at NASA’s Marshall Space Flight Center.

“My undergrad was in mechanical engineering. I wanted to do industrial engineering, so I came to the U.S. for that, but I didn’t like the field that much,” Ramachandran explained. “It was by chance somebody suggested meteorology.”

That led him to learn about atmospheric science as well, but it was the 1990s and the technology of the time was very limiting. So, Ramachandran set out to learn more about computers and how to better analyze data.

“The limitations effectively prompted me to get a degree in computer science,” he said. “I now had science, engineering, and computer science in my background. Then, over the years, I got more and more interested in the tools and capabilities that can help not only manage data but also how you extract knowledge from these large datasets.”

Fast forward to today, and Ramachandran is an award-winning scientist helping to ensure the vast amounts of data collected by NASA are accessible and searchable for scientists around the world.

“I never would have thought that I could ever get a job working at an agency like NASA,” he said. “You get to work with some of the smartest people in the world, and you get to work on really hard problems. I think that’s what makes it so intellectually stimulating.”

Over the course of his career, he has worked on many different projects focused on scientific data management, designed frameworks for large scale scientific analysis, and developed machine learning applications. Recently, he worked with team members at IBM Research to create a geospatial AI foundation model that could turn NASA satellite data into maps of natural disasters or other environmental changes. He also established the Interagency Implementation and Advanced Concepts Team ( IMPACT ) at NASA, which supports NASA’s  Earth Science Data Systems Program  by collaborating with other agencies and partners to boost the scientific benefits of data collected by NASA’s missions and experiments.

Ramachandran received the 2023 Greg Leptoukh Lecture award for his accomplishments, an honor he attributes in large part to the many collaborators and mentors he’s had over the years.

During his presentation, Ramachandran spoke about the ways in which artificial intelligence can help NASA continue to adapt and support open science.

“We’ve seen what people can do with ChatGPT, which is built on a language foundation model, but there are AI foundation models for science that can be adapted into analyzing scientific data so we can augment what we are doing now in a much more efficient manner,” he said. “It requires a bit of a change in people’s mindset. How do we rethink our processes? How do we rethink a strategy for managing data? How will people search and analyze data information differently? All those things have to be thought of with a different perspective now.”

Such work will have benefits not only for NASA but for those who use the data collected by the agency. Ramachandran said he recently got an email from someone in Africa who was able to use NASA’s data and the geospatial AI foundation model for detecting locust breeding grounds on the continent.

“NASA has produced valuable science data that we make available to the community to use,” Ramachandran said. “I think the future would be that we not only provide the data, but we also provide these AI models that allow the science community to use the data effectively, whether it’s doing basic research or building applications like the locust breeding ground prediction.”

As that future nears, Ramachandran and his team will be ready to help others in the science community find the data they need to learn and build the tools they’ll use for years to come.

Related Terms

  • Open Science

Explore More

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NASA-IBM Collaboration Develops INDUS Large Language Models for Advanced Science Research

how to start research work in computer science

NASA’s Repository Supports Research of Commercial Astronaut Health  

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NASA, IBM Research to Release New AI Model for Weather, Climate

Eshelman ranked No. 1 in pharmacy research funding

It’s the first time since the American Association of Colleges of Pharmacy began tracking that Eshelman has held the top ranking.

Exterior photo of Eshelman School of Pharmacy building in the daytime.

The UNC Eshelman School of Pharmacy is the No. 1 pharmacy school in the nation in NIH research funding and total funding, according to rankings from the American Association of Colleges of Pharmacy.

It’s the first time Eshelman has earned the No. 1 status. The school earned more than $92 million in total grants for fiscal year 2023.

The annual rankings compile data from more than 140 pharmacy schools across the country.

“This is a credit to our faculty, staff and students who have been pursuing rigorous, innovative and impactful scholarship that is being rewarded by funders,” said Eshelman Dean Angela Kashuba. “This is a testament to them as leaders and innovators in pharmacy and pharmaceutical sciences.”

Eshelman’s research enterprise has a culture of collaboration, partnering across the campus, state, nation and globe to advance research, education and practice.

Recently, the NIH awarded $2.4 million to Delesha Carpenter, professor and executive vice chair of the Division of Pharmaceutical Outcomes and Policy, for a project on racial disparities in naloxone access. Juliane Nguyen, professor and vice chair of the Division of Pharmacoengineering and Molecular Pharmaceutics, received a $1.9 million R01 to develop a non-invasive, living drug depot to treat heart attacks.

In April, Eshelman was named the No. 1 pharmacy school in the county, according to U.S. News and World Report .

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Robert L. Ferris named Lineberger executive director

The Carolina alumnus comes to Chapel Hill from the Hillman Cancer Center at the University of Pittsburgh Medical Center.

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Extras needed for film production in Chapel Hill

Men and women ages 18 and older are welcome to apply for work July 8-10.

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Stan Ahalt studies society through a data lens

Data literacy is essential for tomorrow’s workforce, says the UNC School of Data Science and Society dean.

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Alert Carolina sirens test to be held June 18

No action is required as the University conducts a summer test of its Emergency Notification System.

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Hussman researcher promotes truth in the news

Heesoo Jang, who helped create a fact-checking service in South Korea, now focuses on U.S. media.

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Seal the Seasons now top US locally grown food brand

Started by Carolina students in 2015, the flash frozen produce company creates new markets for family farmers.

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Computer scientist wants to protect your texts

Saba Eskandarian designs online messaging applications that let users report abuses while preserving privacy.

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how to start research work in computer science

The University of Tulsa acquires Fab Lab Tulsa

The University of Tulsa has announced the acquisition of Fab Lab Tulsa, which provides access to digital fabrication tools and resources throughout the community through membership and programming. The move is part of TU’s ongoing efforts to promote innovation and aligns with the university’s global reputation in engineering, computer science, and the creative arts. “We […]

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how to start research work in computer science

Unique organizational studies program offers expansive opportunities

At roughly 75 majors, organizational studies is one of the largest majors in The University of Tulsa’s Kendall College of Arts & Sciences. From social sciences, media, and arts to business administration, the program provides students a wide range of knowledge and skills, rather than limiting them to a single discipline. But as a so-called […]

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how to start research work in computer science

From field work to the classroom, Grau mentors women in energy

Anne Grau has been involved in geology for three decades – working for energy leaders such as EOG Resources and Total Energies – and definitely knows what it’s like to be the only woman in the room. “Being a woman in the oil and gas industry often meant I was one woman in 200 at […]

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TU Law celebrates alumna Sara Hill’s historic confirmation to federal bench

The University of Tulsa’s College of Law congratulates alumna Sara Hill (JD ’03) as she becomes the first Native American woman to serve as a federal judge in Oklahoma. This historic appointment marks a significant milestone in the state’s legal landscape. The U.S. Senate on Tuesday voted overwhelmingly to confirm Hill, who fills a vacant […]

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how to start research work in computer science

New faculty member brings expertise and INSPIRE lab to Psychology Department

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how to start research work in computer science

More than 60 years of James Joyce Quarterly

Legend has it that Thomas Staley, former provost of The University of Tulsa, founded the James Joyce Quarterly, fondly known as JJQ, in his garage. Or was it his kitchen table? That was more than 60 years ago, and since then the journal has become an internationally esteemed publication known for its publishing of critical […]

U.S. News ranks ECS graduate programs among Top 50 at private universities

how to start research work in computer science

“This latest data from U.S. News & World Report is more than just an accolade to the university; it is a testament to the faculty’s dedication to academic excellence, innovative research, and impactful teaching,” said Andreas A. Polycarpou, Ph.D. and James R. Sorem Inaugural Dean of the College of Engineering & Computer Science. “It reflects the profound influence on their students and the academic community, highlighting their commitment to fostering an environment of intellectual growth and discovery.”

UTulsa’s petroleum engineering program was ranked No. 2 among private institutions and No. 6 overall. In addition to program rankings, U.S. News presents the latest data on enrollment numbers, job placement rates, faculty statistics, and other imperative indicators to assist prospective students in making informed decisions.

“UTulsa’s College of Engineering & Computer Science is known for its hands-on opportunities, challenging students with real-world problems to set them up for successful careers,” said Mohan Kelkar, Ph.D. and chair of petroleum engineering. “Faculty and students have access to a state-of-the-art drilling simulator and high-tech equipment on the main campus as well as drilling labs and an operational multiphase flow loop on the North Campus . Traditional and transitional energy research is conducted with industry partners, government agencies, and interdisciplinary colleagues from across the college.”

UTulsa’s mechanical engineering program was ranked in the top 50 among private institutions and No. 146 overall, a staggering 20-spot improvement from the previous year.

“The rise in rankings is a testament to our top-tier faculty, cutting-edge facilities, and innovative research in materials science, tribology, and robotics,” said John Henshaw, Ph.D. and chair of mechanical engineering. “We are thrilled that our commitment is starting to reflect what we have always known: UTulsa is an outstanding place to receive a mechanical engineering education.”

Pursuing an advanced degree in engineering enhances students’ skills, allowing them to specialize in their areas of interest while opening doors to leadership roles with higher earning potential. Learn more about what the College of Engineering & Computer Science offers.  

Privacy Overview

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Working together, we can reimagine medicine to improve and extend people’s lives.

Data Science Innovation Fellow, In Silico Immunogenicity Prediction

About the role.

Internal Job Title:  Innovation Postdoctoral Fellow Location: Cambridge, MA, USA  

About the role:

We are thrilled to open applications for our Innovation Postdoctoral Fellowship. This applied 3-year research program is set to change the way we approach drug discovery, offering fellows a unique chance to train in data science and AI for biomedical research. As a talented fellow, you will learn to apply your computational skills to make a difference for patients and reimagine medicine at Novartis.

Drug hunting is a team sport, and you will gain experience in DS&AI for drug discovery as part of a multi-disciplinary team in Biomedical Research. You will drive innovation by deploying cutting-edge data approaches in collaboration with a vibrant and diverse community of over 300 data scientists globally. The program provides a unique platform to work on real-world, biomedical data at scale, rarely accessible in academia. Under the guidance of experienced mentors, you’ll embark on a journey of professional growth, benefiting from a tailored training program with built-in time for a mini-sabbatical in other areas of Novartis and for attending conferences/workshops.  

Biomedical Research is the home of a vibrant postdoctoral community connected through science and events supporting the professional growth of our fellows, including monthly seminars and an annual Research Day Symposium. Seize this chance to be at the forefront of Data science and AI and shape the future of drug discovery!

You are part of a multidisciplinary team of data scientists and immunogenicity experts and use state-of-the-art data science and bioinformatics approaches to contribute to building a computational platform for the identification of potential immunogenic regions on therapeutic proteins.

You use different types of data such as protein and peptide sequences, 3D structures, and proteomics data for the development and validation of the in silico immunogenicity prediction platform that has the potential to assist the design, selection, and screening of biologics candidates with low immunogenicity risk.

Start Date: Winter 2024

Key responsibilities

As a Data Science Innovation Fellow, you will:

•Join a team of enthusiastic data scientists and immunogenicity experts in the Biologics Research Center, and collaborate with other data scientists and expert drug hunters in Biomedical Research

•Utilize internal immunogenicity data and public data to influence the design and development of a robust and innovative in silico immunogenicity platform

•Explore and evaluate if and how we can leverage latest AI methods and combine them with traditional bioinformatics approaches to build a robust platform to assess the immunogenicity risk of biologics

•Bring with you your curiosity, ideas, out of the box thinking and have an impact on our drug discovery and development process

Role requirements:

•PhD in bioinformatics, computational biology, immunoinformatics, statistics, computer science, machine learning, physics, or a related field (PhD students in the last year of their thesis work, are eligible to apply)

•Experience in developing computational models by utilizing and integrating different types of data sets such as protein sequences and 3D structure data

•Proficiency in R, python, commonly used bioinformatics tools and databases for protein sequences and structures analyses, and machine learning frameworks and techniques

•Experience with protein structure modeling, including AI methods such as AlphaFold, RosettaFold

•Familiarity in working with antibody sequences and structures

•Strong publication record or other scientific achievements (i.e. awards, patents, grants)

•Excellent analytical, communication, presentation and organizational skills

•Passion for research and boundless curiosity

How to apply:

Please submit your CV and cover letter by July 29th  for consideration. Please make sure to discuss in the cover letter how this training program will help you fulfill your career goals.

Why Novartis:  Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

You’ll receive:  You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook: https://www.novartis.com/careers/benefits-rewards

Commitment to Diversity and Inclusion / EEO:  The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

The pay rate for this position at commencement of employment is expected to be $82,000 per year; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network

Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Join our Novartis Network: Not the right Novartis role for you? Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up: https://talentnetwork.novartis.com/network

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  1. How To Start A Research Work in Computer Science PDF

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  2. Complete Guide On How To Compose A Computer Science Research Paper

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  3. How to write your first computer science research paper?

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  4. How to Write Computer Science Research Paper Writing: Great Tricks to

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  5. (PDF) Research methods in computer science

    how to start research work in computer science

  6. Computer Science Research Topics (+ Free Webinar)

    how to start research work in computer science

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  4. Day 2: Basics of Scientific Research Writing (Batch 18)

  5. how to get started in undergraduate research

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  1. How to Start a Research Work in Computer Science: A Framework For Beginners

    How to Start a Research Work in Computer Science: A Framework For Beginners Somdip Dey School of Computer Science The University of Manchester Manchester, United Kingdom. [email protected]; [email protected] ABSTRACT Research is one of the key factors behind the improvement and evolution of any subject in the world.

  2. how to start research

    When you are studying any subject, which in this case is computer science and engineering, try to choose the areas or topics in the subject that you really enjoy to read and ponder onto it. Once you selected or chosen the topics, write them down or scribble them onto a piece of paper. 3.2 Step 2: Search Databases.

  3. How to do research in Computer Science

    But it should be a review of first hand (original) works, and not a review of Reviews. vi) Write a synopsis, if you are to do a Ph.D. For just research, you go ahead with your studying papers. vii ...

  4. How to write your first computer science research paper?

    In this video, I provide an overview of the different sections in a research paper and how to get started.

  5. A Step-by-Step Guide to Kickstarting Your First Computer Science

    An 8-Step Process For Conducting Computer Science Research From Scratch. Here are the 8 steps I recommend to go from having zero research experience to having a solid research direction:

  6. How to Start a Research Work in Computer Science: A Framework For

    How to Start a Research Work in Computer Science: A Framework For Beginners. School of Computer Science, The University of Manchester.. Access to files FULL-TEXT.PDF (pdf) Abstract. Research is one of the key factors behind the improvement and evolution of any subject in the world. However, the skills to perform the research are rarely taught ...

  7. A Beginner's Guide to Starting the Research Process

    Step 1: Choose your topic. First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you're interested in—maybe you already have specific research interests based on classes you've taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose.

  8. Writing for Computer Science:

    This is a comprehensive guide on research methods and how to produce a scientific publication detailing one's research in computer science (CS) and related fields. The author has drawn from his vast experience as adviser, researcher, and referee, and has produced a classic book on the various aspects of scientific research from research methods ...

  9. (PDF) A Beginner's Guide to Computer Science Research

    set of steps, which work well for me and. for others who have followed them. 1. Select a subject area that you like. There are so many topics and subject. areas in computer science. Instead of ...

  10. How to do research in computer science

    How to do good research : Important points (overview) Choose an interesting area for research. Identify an interesting research topic (a problem for which there is no good solution) Have some good idea how to improve the state of the art. Show that your idea works: e.g.

  11. How to Execute a Computer Science Research Project

    1. Find a problem. 2. Review the literature. 3. Design your approach. Be the first to add your personal experience. 4. Conduct your research.

  12. PDF Introduction to Research in Computer Science The Research Process

    Focal Theory. This is the stage in the researchwhere the researcher establishes and analyses the nature of the problem and details exactly what is going to be researched. the research focuses from the general to the specific. as the researcher creates hypotheses. examines the arguments of others.

  13. Research Projects

    The CS department has resources available for research projects. The following is a quick start guide to research projects in Computer Science Department. This quick start guide does not cover all topics and it is recommended that you consult the CS Guide for more information. This guide is designed to help those beginning a research project by ...

  14. Computer Science Research Topics (+ Free Webinar)

    Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you've landed on this post, chances are you're looking for a computer science-related research topic, but aren't sure where to start.Here, we'll explore a variety of CompSci & IT-related research ideas and topic thought-starters ...

  15. How to research in Computer Science 101

    Because it is easy to give up easily. People do not believe in their abilities. The do not realize they have more to offer than they think they do, and finally they do not realize that they can ...

  16. How to start research as an undergraduate CS student

    153 6. Presumably you have some coursework as part of your course. You should conduct coursework by addressing research problems where there is an option do so. Where there isn't, you should consider negotiating. - user2768. Dec 6, 2017 at 16:06. 2. You could also conduct a research-based dissertation or at least have a research element.

  17. Introduction to Research in Computer Science

    Course Description. Prerequisite: Computer Science 40 and Computer Science 32; consent of instructor. Defining a CS research problem, finding and reading technical papers, oral communication, technical writing, and independent learning. Course participants work in teams as they apprentice with a CS research group to propose an original research ...

  18. How to Look for Ideas in Computer Science Research

    Else, I consider it a big deficiency and may hurt you significantly in the future after you graduate, e.g., you may have a hard time leading a research program independently as a professor in ...

  19. career path

    Let me just assume that computer science departments have similar avenues. Find the closest university to where you live and check its computer science department's web site for outreach activities, or email their undergraduate coordinator for more information. They may have something like our math circles.

  20. 13 Study Tips for Computer Science Students

    13 computer science student study tips. You can use the 13 tips below to help you succeed as a computer science student: 1. Pursue knowledge outside of courses. While a fair amount of the knowledge you learn comes from classroom instruction, you can also pursue topics in your own time. For example, if you discover you enjoy a topic that only ...

  21. Write a Research Question

    Question A assumes that the AP Computer Science Principles course is more appealing than the AP Computer Science A course for the target population (high school-aged girls). If this has been previously established in prior research and the researchers are making this a follow-up study, then Question 3A may be seen as neutral. However, if this ...

  22. Begin research work : r/AskComputerScience

    Often computer scientists don't have any research experience until they begin gradschool. In fact, a big part of gradschool is teaching you how to do research and be a professional scientist. Your main mentor will be your graduate advisor, along with other professors and graduate student (and postdoc) peers.

  23. How To Become a Research Scientist (With Tips)

    Obtain a bachelor's degree. Complete a master's degree. Gain experience. Pursue certifications. Consider a doctorate. 1. Obtain a bachelor's degree. Aspiring research scientists should start by pursuing a bachelor's degree that's relevant to the field they're most interested in.

  24. Bachelor's Degree in Computer Science

    With the knowledge that it requires extra course work, you can consider the more intensive A.B./S.M. option through a concurrent masters degree. ... Research Opportunities in Computer Science. As part of your Bio/Biomedical Engineering coursework, or perhaps as part of individual research opportunities working with professors, you will have the ...

  25. 30+ Simple & Easy Research Paper Topics in Computer Science

    Summary of Key Points. Choosing Your Research Paper Topic: Select a topic that is relevant, manageable, and interesting.; Exploring Topics: Dive into areas like machine learning, cybersecurity, programming languages, emerging technologies, and ethical issues.; Hands-On Learning: Engage in practical projects and case studies to deepen your understanding. ...

  26. What are the top computer science colleges?

    There are numerous colleges with highly regarded computer science programs. Here are a few top computer science schools in the United States: 1. Massachusetts Institute of Technology (MIT) - MIT's electrical engineering and computer science program is consistently ranked as one of the top programs in the world. They offer a variety of research opportunities, strong industry connections, and a ...

  27. Marshall Research Scientist Enables Large-Scale Open Science

    "NASA has produced valuable science data that we make available to the community to use," Ramachandran said. "I think the future would be that we not only provide the data, but we also provide these AI models that allow the science community to use the data effectively, whether it's doing basic research or building applications like the locust breeding ground prediction."

  28. Eshelman ranked No. 1 in pharmacy research funding

    Eshelman's research enterprise has a culture of collaboration, partnering across the campus, state, nation and globe to advance research, education and practice. Recently, the NIH awarded $2.4 million to Delesha Carpenter, professor and executive vice chair of the Division of Pharmaceutical Outcomes and Policy, for a project on racial ...

  29. U.S. News ranks UTulsa's College of Engineering & Computer Science

    "This latest data from U.S. News & World Report is more than just an accolade to the university; it is a testament to the faculty's dedication to academic excellence, innovative research, and impactful teaching," said Andreas A. Polycarpou, Ph.D. and James R. Sorem Inaugural Dean of the College of Engineering & Computer Science.

  30. Data Science Innovation Fellow, In Silico Immunogenicity ...

    Internal Job Title: Innovation Postdoctoral FellowLocation: Cambridge, MA, USA About the role:We are thrilled to open applications for our Innovation Postdoctoral Fellowship. This applied 3-year research program is set to change the way we approach drug discovery, offering fellows a unique chance to train in data science and AI for biomedical research. As a talented fellow, you will learn to ...