acm dissertation award 2020

Illinois ECE graduate receives prestigious ACM Doctoral Dissertation Award

7/16/2021 Jim Ormond, ACM

Winning dissertation by Chuchu Fan (MS '16, PhD '19) makes foundational contributions to verification of embedded and cyber-physical systems.

Written by Jim Ormond, ACM

ACM, the Association for Computing Machinery, announced that Chuchu Fan (MS '16, PhD '19) received the 2020 ACM Doctoral Dissertation Award for her dissertation “ Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications .” The dissertation makes foundational contributions to the verification of embedded and cyber-physical systems and demonstrates the applicability of the developed verification technologies in industrial-scale systems.

Presented annually to the author(s) of the best doctoral dissertation(s) in computer science and engineering, the Doctoral Dissertation Award is accompanied by a prize of $20,000. The winning dissertations will also be published in the ACM Digital Library as part of the ACM Books Series. Fan is the first PhD graduate from the University of Illinois Urbana-Champaign to receive this award.

Fan’s dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.

Key contributions of her dissertation include the first data-driven algorithms for bounded verification of nonlinear hybrid systems using sensitivity analysis. A groundbreaking demonstration of this work on an industrial-scale problem showed that verification can scale. Her sensitivity analysis technique was patented, and a startup based at the University of Illinois at Urbana-Champaign has been formed to commercialize this approach.

Fan also developed the first verification algorithm for “black box” systems with incomplete models combining probably approximately correct (PAC) learning with simulation relations and fixed point analyses. DryVR, a tool that resulted from this work, has been applied to dozens of systems, including advanced driver assist systems, neural network-based controllers, distributed robotics, and medical devices.

Additionally, Fan’s algorithms for synthesizing controllers for nonlinear vehicle model systems have been demonstrated to be broadly applicable. The RealSyn approach presented in the dissertation outperforms existing tools and is paving the way for new real-time motion planning algorithms for autonomous vehicles.

Fan's PhD research advisor, Illinois ECE Professor Sayan Mitra , commended this outstanding recognition. "Chuchu’s work combining formal methods and statistics provides tools for analyzing systems that were previously beyond our grasp." Mitra added that "working with her was very enjoyable. Every time we would formulate a conjecture, she worked on it ferociously until she emerged with a proof and working code, or an interesting corner case." Over the course of her PhD research Fan also worked closely with a number of other graduate students and faculty, including Zhenqi Huang , Hussein Sibai , Prof. Mahesh Viswanathan of Illinois CS, Prof. Marta Kwiatkowska of Oxford University, and Prof. Scott Smolka of SUNY Stony Brook. 

Fan is the Wilson Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology, where she leads the Reliable Autonomous Systems Lab. Her group uses rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Fan received a BA in Automation from Tsinghua University. She earned her PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.

Honorable Mentions for the 2020 ACM Doctoral Dissertation Award go to Henry Corrigan-Gibbs for his dissertation from Stanford University, and Ralf Jung for his dissertation from Saarland University and the Max Planck Institute for Software Systems.

Read the original announcement from ACM on its website.

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This story was published July 16, 2021.

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Alumni News Brief

Abebe receives acm dissertation award, rediet abebe, s.m. ’16 (applied math), earned the honor for her ph.d. thesis, "designing algorithms for social good".

Rediet Abebe, S.M. ’16 (applied math) has received the 2020 Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining Dissertation Award for her Ph.D. thesis, " Designing Algorithms for Social Good ."

Abebe graduated from Cornell University in December with a Ph.D. in computer science. Currently a junior fellow at the Harvard Society of Fellows, she will join the faculty of the University of California, Berkeley, as an assistant professor of electrical engineering and computer science.

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ACM Symposium on Principles of Distributed Computing

June 17-21, 2024, Nantes, France

2020 Doctoral Dissertation Award

The winners of the 2020 Principles of Distributed Computing Doctoral Dissertation Award are

• Dr. Yi-Jun Chang for his dissertation Locality of Distributed Graph Problems  written under the supervision of Prof. Seth Pettie at the University of Michigan.
• Dr. Yannic Maus for his dissertation The Power of Locality: Exploring the Limits of Randomness in Distributed Computing written under the supervision of Prof. Fabian Kuhn at the University of Freiburg.

The theses of Dr. Chang and Dr. Maus have played a key role in the recent, rapid development of the theory of distributed graph algorithms and network computing. Both of the theses have significantly advanced our understanding of the distributed computational complexity of many key problems (e.g. graph coloring and splitting). In addition, they have made groundbreaking contributions to the development of distributed computational complexity theory in general.

These theses have introduced highly insightful concepts (e.g. the SLOCAL model) and new intriguing graph problems (e.g. hierarchical coloring), they have developed novel proof techniques (e.g. pumping arguments), and they have proved surprising results (e.g. gap theorems and completeness). Put together, they have dramatically changed the way in which researchers working in this field reason about distributed computational complexity. The theses have been a driving force in this research area, and the concepts and ideas introduced in these theses have already led to major breakthroughs.

The work presented in both dissertations has been published in a remarkably large number of papers, has been presented at top conferences, and has received wide recognition, including multiple best paper awards.

Due to the highly significant role these two theses have played in the development of the field of distributed computing, the award committee unanimously selected them as the winners of the 2020 Principles of Distributed Computing Doctoral Dissertation Award, presented at DISC 2020.

The award is sponsored jointly by the ACM Symposium on Principles of Distributed Computing (PODC) and the EATCS Symposium on Distributed Computing (DISC). It is presented annually, with the presentation taking place alternately at PODC and DISC.

2020 Award Committee:

• Faith Ellen, University of Toronto
• Pierre Fraigniaud, CNRS and University Paris Diderot
• David Peleg (chair), The Weizmann Institute
• Jukka Suomela, Aalto University

SIGKDD Awards

2020 sigkdd dissertation award winners.

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. The original call for nomination is available here.

Review Criteria:

• Relevance of the Dissertation to KDD
• Originality of the Main Ideas in the Dissertation
• Significance of Scientific Contributions
• Technical Depth and Soundness of Dissertation (including experimental methodologies, theoretical results, etc.)
• Overall Presentation and Readability of Dissertation (including organization, writing style and exposition, etc.)

Congratulations to all the outstanding students who were nominated and to the winners of this year.

Following are the granted awards, including one winner, one runner-up, and two honorable mentions.

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2021 SIGKDD Doctoral Dissertation Award: Call for Nominations

• Mar 29 2021

ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award

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2024 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award Call for Nominations

Eligibility Eligible dissertations must have been successfully defended and deposited in the previous calendar year.

Each nominated dissertation must be on a topic relevant to computer architecture.  The determination of whether a thesis is within scope for the award will be made by the SIGARCH/TCCA Dissertation Award Committee.  A dissertation can be nominated for both the SIGARCH/TCCA Dissertation Award and the ACM Doctoral Dissertation Award.

Nomination for the award must include:

• English language copy of thesis
• Statement from the advisor limited to two pages addressing why the nominee’s dissertation should receive this award.  This should address the significance of the dissertation, not simply repeat the information in the abstract. Nomination must come from the advisor; self-nomination is not allowed.
• Three letters of support limited to two pages each. Supporting letters should be included from experts in the field who can provide additional insights or evidence of the dissertation’s impact.  (The nominator/advisor may not write a letter of support.)  The letter should also contain the qualification of the endorser and his/her role with respect to the nominee. If a letter writer is supporting more than one nomination, they may be asked to rank those nominations. At least one letter must come from an expert outside the nominee’s university. Additional letters beyond three will not be considered.
• List of publications contributing to thesis
• Member number for nominee (Nominee must be a SIGARCH or TCCA member)
• Suggested citation if the candidate is selected. This should be a concise statement (maximum of 25 words) describing the key technical contribution for which the candidate merits this award. Note that the final wording for awardees will be at the discretion of the SIGARCH/TCCA Dissertation Award Committee.
• Contribute to society and to human well-being, acknowledging that all people are stakeholders in computing
• Be honest and trustworthy
• Be fair and take action not to discriminate
• Respect the work required to produce new ideas, inventions, creative works, and computing artifacts
• Respect privacy
• Honor confidentiality

Please send nominations (preferably electronically) no later than March 1, 2024 to the chair of the SIGARCH/TCCA Dissertation Award Committee, Stefanos Kaxiras at  stefanos.kaxiras@ it.uu.se .  Nominations must be submitted in English. PDF format is preferred for all materials. Late submissions will not be considered.

The committee will consist of an even number of members, initially six (one SIGARCH EC member, one TCCA EC member, four at large SIGARCH or TCCA members). This number can be adjusted in the future to keep workload moderate. The members will serve three-year staggered terms, with the SIGARCH and TCCA ECs each appointing one new member each year. The committee chairperson shall be the more senior of the two EC members; an EC member should not chair the committee in their first year of service.

The SIGARCH/TCCA Dissertation Award Committee will make a recommendation to the SIGARCH Chair and the TCCA Chair by April 1 of each year. Upon approval of the recommendation by the executive boards of ACM SIGARCH and IEEE TCCA, the chair will inform the winner and honorable mentions.

• Murali Annavaram, University of Southern California
• Reetuparna Das, University of Michigan
• Christina Delimitrou, Cornell University
• Stefanos Kaxiras, Uppsala University (Chair)
• Yasuko Eckert, AMD
• Caroline Trippel, Stanford University

Winner: Georgios Tzimpragos, UCSB, for “ Computing with Temporal Operators .” Advisor: Timothy Sherwood Award citation: “ For temporal logic architectures with applications to in-sensor computation and superconducting electronics .”

Honorable Mention: Vidushi Dadu, UCLA, for “ Generalizing Programmable Accelerators for Irregularity .” Advisor: Tony Nowatzki Award citation: “ For broadening the scope of programmable accelerators by systematizing forms of irregularity across domains and exposing specialization primitives within unified task-dataflow execution models. “

Honorable Mention: Udit Gupta, Harvard, for “ Enabling High Performance, Efficient, and Sustainable Deep Learning Systems at Scale .” Advisor: David Brooks and Gu-Yeon Wei Award citation: “ For contributions to hardware/software design of neural recommendation systems, recognition of the sustainability impact of large-scale AI, and development of embodied carbon models . “

Honorable Mention: Gururaj Saileshwar, Georgia Tech, for “ Architecting Secure Processor Caches .” Advisor: Moin Qureshi Award citation: “ For developing principled state-of-the art cache attacks and defenses by exploiting architectural insights . “

Winner: Prakash Murali, Princeton, for “Enabling Practical Quantum Computation: Compiler and Architecture Techniques for Bridging the Algorithms-to-Devices Resource Gap.” Advisor: Margaret Martonosi Award citation: “For cross-layer computer architecture and compilation techniques that facilitate practical quantum hardware and software, bridging the resource gap between quantum applications and hardware.”

Honorable Mention: Akshitha Sriraman, University of Michigan/Carnegie Mellon, for “Enabling Hyperscale Web Services.” Advisor: Thomas Wenisch Award citation: “For contributions enhancing the efficiency and scalability of hardware and software architecture for hyperscale datacenter systems.”

Winner: Dimitrios Skarlatos, UIUC, for “Rethinking Computer Architecture and Operating Systems Abstractions for Good & Evil.” Advisor: Josep Torrellas Award citation: “For contributions to redesigning the abstractions and interfaces that connect hardware and operating systems.”

Honorable Mention: Yatin Manerkar, Princeton University, for “Progressive Automated Formal Verification of Memory Consistency in Parallel Processors.” Advisor: Margaret Martonosi Award citation: “For developing scalable, sound, and all-program hardware verification methods that demonstrate the potential of progressive correctness verification from early-stage architectural design to late-stage implementation.”

Honorable Mention: Hyoukjun Kwon, Georgia Institute of Technology, for “Data- and Communication-centric Approaches to Model and Design Flexible Deep Neural Network Accelerators.” Advisors: Tushar Krishna and Michael Pellauer Award citation: “For developing mechanisms to quantify the relationship between deep neural network mappings, data reuse and communication flows for system design of flexible deep learning accelerators.”

Winner: Caroline Trippel, Princeton University, for “Concurrency and Security Verification in Heterogeneous Parallel Systems.” Advisor: Margaret Martonosi Award citation: “For developing efficient, formal, hardware-aware concurrency verification methods, which resulted in the identification of important correctness and security vulnerabilities.”

Honorable Mention: Mengjia Yan, UIUC, for “Cache-based Side Channels: Modern Attacks and Defenses”. Advisor: Josep Torrellas Award citation: “For introducing secure processor and cache architecture designs that effectively thwart cache-based side channel attacks, including new attacks proposed in the dissertation.”

Honorable Mention: Joseph Earl McMahan, UCSB, for “The ZARF Architecture for Recursive Functions”. Advisor: Timothy Sherwood Award citation: “For introducing a novel approach to software verification and cross-stack hardware design for critical systems by rethinking instruction-set architectures from a mathematical perspective.”

Winner: Yu-Hsin Chen, MIT, for “Architecture Design for Highly Flexible and Energy-Efficient Deep Neural Network Accelerators.” Advisors: Vivienne Sze and Joel Emer Award citation: “For contributions to efficient and flexible dataflows and architectures for deep learning acceleration.”

Honorable Mention: Alexandros Daglis, EPFL, for “Network-Compute Co-Design for Distributed In-Memory Computing.” Advisors: Babak Falsafi and Edouard Bugnion Award citation: “For contributions to network-centric server architecture for in-memory datacenter services.”

Winner: Aasheesh Kolli, University of Michigan, for “Architecting Persistent Memory Systems.” Advisor: Thomas Wenisch Award citation: “For contributions to the semantics and implementation of programming models for persistent memory systems.”

Honorable Mention: Matt Sinclair, UIUC, for “Efficient Coherence and Consistency for Specialized Memory Hierarchies.” Advisor: Sarita Adve Award citation: “For enabling an efficient global address space for heterogeneous systems.”

Honorable Mention: Yuhao Zhu, UT Austin, for “Energy-Efficient Mobile Web Computing.” Advisor: V.J. Reddi Award citation: “For innovations in computer architecture, spanning hardware design, runtime orchestration, and programming language implementation, for making the Web energy-efficient.”

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Chuchu fan receives 2020 acm doctoral dissertation award.

Chuchu Fan's dissertation advances the theory for sensitivity analysis and symbolic reachability.

Credit: MIT

Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for "Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications." The dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems.

Fan earned her Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. She is now an assistant professor at the Massachusetts Institute of Technology.

Honorable Mentions for the 2020 ACM Doctoral Dissertation Award go to Henry Corrigan-Gibbs for "Protecting Privacy by Splitting Trust," and Ralf Jung for "Understanding and Evolving the Rust Programming Language."

Corrigan-Gibbs received his Ph.D. in Computer Science from Stanford University, and is now an assistant professor at MIT.

Jung received his Ph.D. from Saarland University, and is now a post-doctoral researcher at the Max Planck Institute for Software Systems.

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Association for the Advancement of Artificial Intelligence

AAAI/ACM SIGAI Doctoral Dissertation Award

AAAI and ACM SIGAI established the Joint AAAI/ACM SIGAI Doctoral Dissertation Award to recognize and encourage superior research and writing by doctoral candidates in artificial intelligence. The award is presented annually at the AAAI Conference on Artificial Intelligence, and the winner is invited to present a talk at the conference.

Call for Nominations

Past Recipients

2021 aaai/acm sigai dissertation award winner.

Shibani Santurkar , Massachusetts Institute of Technology for her work entitled Machine Learning Beyond Accuracy: A Features Perspective On Model Generalization

2021 AAAI/ACM SIGAI Dissertation Honorable Mention

Bryan Wilder , Harvard University for his work entitled AI for Population Health: Melding Data and Algorithms on Networks

2020 AAAI/ACM SIGAI Dissertation Award Winner

Noam Brown , Carnegie Mellon University for his work entitled Equilibrium Finding for Large Adversarial Imperfect-Information Games

2020 AAAI/ACM SIGAI Dissertation Honorable Mention

David Abel , Brown University for his work entitled A Theory of Abstraction in Reinforcement Learning

Abhishek Das , Georgia Institute of Technology for his work entitled Building Agents that Can See, Talk, and Act

2019 AAAI/ACM SIGAI Dissertation Award Winner

Jiajun Wu , Massachusetts Institute of Technology, USA for his work entitled Learning to See the Physical World

2019 AAAI/ACM SIGAI Dissertation Honorable Mention

Aishwarya Agrawal , Georgia Institute of Technology for her work entitled Visual Question Answering and Beyond

Li Dong, University of Edinburgh , for his work entitled Learning Natural Language Interfaces with Neural Models

Privacy Overview

ACM Special Interest Group on Artificial Intelligence

We promote and support the growth and application of ai principles and techniques throughout computing, joint aaai/acm sigai doctoral dissertation award.

Award nominations open

AAAI and ACM SIGAI are pleased to announce the continuation of the Joint AAAI/ACM SIGAI Doctoral Dissertation Award to recognize and encourage superior research and writing by doctoral candidates in artificial intelligence. The award is presented annually at the AAAI Conference on Artificial Intelligence, and the winner will be invited to present a talk at the conference.

A nominated dissertation must have been successfully defended (but not necessarily finalized) between October 1 of the previous year through September 30 of the current year. Nominations are welcome from any country, but only English language versions will be accepted. Only one nomination may be submitted per Ph.D. granting institution, including large universities. The nominator must be an ACM SIGAI member and/or AAAI member.

Nomination process:

Nominations are due by November 18, 2022. Nominations must be made by the thesis advisor; a complete nomination includes the following:

• The names, affiliations, and contact information of the nominator and the candidate.
• A nomination statement (200-300 words in length) that explains why the candidate should receive this award and addresses the significance of the dissertation. This should not simply repeat the information in the abstract.
• A letter of endorsement from the department head (or dean), which also certifies that this is the only nomination being made from that institution.
• Supporting letters (not less than two, and not more than three) from from experts in the field who can speak to the impact of the dissertation.
• An electronic copy of the submitted dissertation.

Nominations must be sent in an email (with all the materials attached as a single file) to the chair of the award committee, Prof. Bart Selman ( [email protected] ) by November 18, 2022, 11:59 PM anywhere in the world.

Winner of 2021 AAAI/ACM SIGAI Joint Dissertation Award

ACM SIGAI and AAAI are pleased to announce the winner of the 2020 AAAI/ACM SIGAI Dissertation Award is Shibani Santurkar (MIT thesis, currently at Stanford) for her work entitled “Machine Learning Beyond Accuracy: A Features Perspective On Model Generalization”.

Honorable mention: Bryan Wilder (Harvard thesis, currently at CMU) for his work entitled “AI for Population Health: Melding Data and Algorithms on Networks”

Winner of 2020 ACM SIGAI Dissertation Award

ACM SIGAI and AAAI are pleased to announce the winner of the 2020 AAAI/ACM SIGAI Dissertation Award is David Abel , of Brown University for his work entitled A Theory of Abstraction in Reinforcement Learning.

Runner-Up: Abhishek Das , Georgia Institute of Technology for his work entitled Building Agents that Can See, Talk, and Act

Winner of 2019 ACM SIGAI Dissertation Award

ACM SIGAI and AAAI are pleased to announce the winner of the 2019 AAAI/ACM SIGAI Dissertation Award is Jiajun Wu , of the Massachusetts Institute of Technology, for his work entitled Learning to See the Physical World.

Two runners-Up were also honored: Aishwarya Agrawal of the Georgia Institute of Technology for Visual Question Answering and Beyond, and Li Dong of the University of Edinburgh for Learning Natural Language Interfaces with Neural Models. All winners will be honored during AAAI-21.

AI Technology & Industry Review

ACM SIGGRAPH 2020 Doctoral Dissertation Award Goes to MIT CSAIL’s Tzu-Mao Li for PhD Thesis on Differentiable Visual Computing

ACM SIGGRAPH has honoured MIT CSAIL postdoctoral researcher Li Tzu-Mao with its 2020 Doctoral Dissertation Award for his PhD thesis Differentiable Visual Computing.

ACM SIGGRAPH has honoured MIT CSAIL postdoctoral researcher Li Tzu-Mao with its 2020 Doctoral Dissertation Award for his PhD thesis Differentiable Visual Computing . Launched in 2016, The Doctoral Dissertation Award is presented annually and recognizes young researchers who make notable contributions during their doctoral studies. ACM SIGGRAPH says Li’s dissertation provides a foundation for the emerging differentiable computer graphics research field and hails him as a “pioneer of the new field of physically-based differentiable rendering.”

In his 148-page dissertation, Li addresses challenges involved in obtaining and applying derivatives for complex graphics algorithms and investigates the use of derivatives in the context of visual computing. He introduces three tools related to computing and the application of derivatives for computer graphics, image processing and deep learning applications: differentiable image processing, differentiable Monte Carlo ray tracing, and Hessian-Hamiltonian Monte Carlo rendering.

Efficient Automatic Differentiation for Image Processing and Deep Learning

Previous research efforts had to compose programs with limited coarse-grained operators or hand-deriving derivatives. Li’s dissertation extends the image processing language Halide with reverse-mode automatic differentiation, which can automatically optimize the gradient computations and promises automatic generation of the gradients of arbitrary Halide programs with high performance and little effort from programmers.

Differentiable Monte Carlo Ray Tracing through Edge Sampling

Performing 3D rendering requires gradients related to variables such as camera parameters, light sources, geometry, and appearance. The gradient calculation is however challenging because the rendering integral includes visibility terms that are not differentiable. Li’s dissertation proposes the first general-purpose differentiable ray tracer, which solves the full rendering equation while correctly taking geometric discontinuities into account.

Hessian-Hamiltonian Monte Carlo Rendering

This dissertation also demonstrates that the derivatives of light path throughput — especially those that are second-order — can be useful for guiding sampling in forward rendering. It extends the Metropolis Light Transport algorithm by adapting to the local shape of the integrand using second-order Taylor expansion, thereby increasing sampling efficiency. Li is a postdoctoral researcher at the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) who did his PhD in the computer graphics group at MIT CSAIL and a six-month postdoc at UC Berkeley. He received BS and MS degrees in computer science and information engineering from National Taiwan University in 2011 and 2013. SIGGRAPH started in 1969 and has grown into a leading global community of researchers, artists, developers, filmmakers, scientists and businesspersons interested in computer graphics and interactive techniques. It is part of the Association for Computing Machinery (ACM), the world’s first and largest computing society. The paper Differentiable Visual Computing is on the MIT CSAIL website .

Author : Yuqing Li | Editor : Michael Sarazen & Fangyu Cai

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SIGGRAPH Awards

Each year, ACM SIGGRAPH honors its members with ACM SIGGRAPH awards for researchers, practitioners, artists, and educators. In addition, the SIGGRAPH Conference honors contributors from different areas of the conference.

ACM SIGGRAPH 2020 Awards

ACM SIGGRAPH presents awards to recognize exceptional achievements in computer graphics and interactive techniques. The awards are presented at the annual SIGGRAPH North American conference. For more information about the awards and the individuals being honored, please visit the ACM SIGGRAPH awards page .

The Computer Graphics Achievement Award

Kavita Bala Computer Science Department, Cornell University

The Significant New Researcher Award

Alec Jacobson University of Toronto

The Outstanding Doctoral Dissertation Award

Tzu-Mao Li MIT CSAIL

Honorable Mentions

• Yun Raymond Fei , Columbia University
• Mina Konakovic Lukovic , École polytechnique fédérale de Lausanne

The Outstanding Service Award

Thierry Frey Reel FX

The Distinguished Artist Award for Lifetime Achievement in Digital Art

Jeffrey Shaw City University of Hong Kong, Chair Professor of Media Art

ACM SIGGRAPH Practitioner Award

Elizabeth Baron Unity Technologies

ACM SIGGRAPH Distinguished Educator Award

Donald P. (Don) Greenberg Cornell University

ACM SIGGRAPH Academy

• Kavita Bala , Cornell University
• Elizabeth Baron , Unity Technologies
• Eugene Fiume , Simon Fraser University
• Ming C Lin , University of Maryland
• Hanspeter Pfister , Harvard University
• Alla Sheffer , University of British Columbia