ACM and IEEE Computer Society named Kunle Olukotun, a Professor at Stanford University, as the recipient of the ACM-IEEE CS Eckert-Mauchly Award for contributions and leadership in the development of parallel systems, especially multicore and multithreaded processors.

In the early 1990s, Olukotun became a leading designer of a new kind of microprocessor known as a "chip multiprocessor"—today called a "multicore processor." His work demonstrated the performance advantages of multicore processors over the existing microprocessor designs at the time. He included these ideas in a landmark paper presented at the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 1996), entitled "The Case for a Single-Chip Multiprocessor." This paper received the ASPLOS Most Influential Paper Award 15 years later. Olukotun’s multicore design eventually became the industry standard.

His insights on multicore processors and thread-level speculation research laid the foundation for Olukotun's work on fine-grained multithreading, a technique which improves the overall efficiency of computer processors (CPUs). These designs were the basis for Afara WebSystems, a server company Olukotun founded that was eventually acquired by Sun (and later Oracle). Sun Microsystems used Olukotun’s designs as a foundation for its Niagara chips, which were recognized for their outstanding performance and energy efficiency. The Niagara family of chips are now used in all of Oracle's SPARC-based servers.

Later, with Christos Kozyrakis and others, Olukotun was a leader in designing the Transactional Coherence and Consistency (TCC) approach to simplify parallel programming. He was a co-author of the paper, “Transactional Memory Coherence and Consistency,” which was presented at the 2004 International Symposium on Computer Architecture (ISCA) and received the Most Influential Paper Award in 2019. Olukotun is one of only two researchers who have received the Most Influential Paper Award from both ASPLOS and ISCA.

ACM and IEEE Computer Society co-sponsor the Eckert-Mauchly Award, which was initiated in 1979. It recognizes contributions to computer and digital systems architecture and comes with a $5,000 prize. The award was named for John Presper Eckert and John William Mauchly, who collaborated on the design and construction of the Electronic Numerical Integrator and Computer (ENIAC), the pioneering large-scale electronic computing machine, which was completed in 1947.

He will be formally recognized with the Eckert-Mauchly Award during an awards luncheon on Tuesday, June 20th at the International Symposium on Computer Architecture (ISCA 2023).

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Suhr’s dissertation, “Reasoning and Learning in Interactive Natural Language Systems,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Suhr is an Assistant Professor at the University of California, Berkeley. Suhr’s research is focused on natural language processing, machine learning, and computer vision. Suhr received a BS in Computer Science and Engineering from Ohio State University, as well as a PhD in Computer Science from Cornell University.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions,” which established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.

The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

The mathematical object that Jain’s thesis constructs, indistinguishability obfuscation, is considered a theoretical “master tool” in the context of cryptography—not only in helping achieve long-desired cryptographic goals such as functional encryption, but also in expanding the scope of the field of cryptography itself. For example, indistinguishability obfuscation aids in goals related to software security that were previously entirely in the domain of software engineering.

Jain’s dissertation was awarded the Best Paper Award at the ACM Symposium on Theory of Computing (STOC 2021) and was the subject of an article in Quanta Magazine titled “Scientists Achieve Crown Jewel of Cryptography.”

Jain is an Assistant Professor at Carnegie Mellon University. He is interested in theoretical and applied cryptography and its connections with related areas of theoretical computer science. Jain received a BTech in Electrical Engineering, and an MTech in Information and Communication Technology from the Indian Institute of Technology, Delhi. He received a PhD in Computer Science from the University of California, Los Angeles.

Honorable Mentions

Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Suhr’s dissertation, “Reasoning and Learning in Interactive Natural Language Systems,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Suhr is an Assistant Professor at the University of California, Berkeley. Suhr’s research is focused on natural language processing, machine learning, and computer vision. Suhr received a BS in Computer Science and Engineering from Ohio State University, as well as a PhD in Computer Science from Cornell University.

Watt’s dissertation, “Mechanising and Evolving the Formal Semantics of WebAssembly: the Web’s New Low-Level Language,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

Watt is a Research Fellow (postdoctoral) at the University of Cambridge, where he focuses on mechanized formal verification, concurrency, and the WebAssembly language. He received a MEng in Computer Science from Imperial College London and a PhD in Computer Science from the University of Cambridge.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Jain's dissertation established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

Jain’s dissertation was awarded the Best Paper Award at the ACM Symposium on Theory of Computing (STOC 2021) and was the subject of an article in Quanta Magazine titled “Scientists Achieve Crown Jewel of Cryptography.”

Jain is an Assistant Professor at Carnegie Mellon University. He is interested in theoretical and applied cryptography and its connections with related areas of theoretical computer science. Jain received a BTech in Electrical Engineering, and an MTech in Information and Communication Technology from the Indian Institute of Technology, Delhi. He received a PhD in Computer Science from the University of California, Los Angeles.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Watt’s dissertation, “Mechanising and Evolving the Formal Semantics of WebAssembly: The Web’s New Low-Level Language,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

Watt is a Research Fellow (postdoctoral) at the University of Cambridge, where he focuses on mechanized formal verification, concurrency, and the WebAssembly language. He received a MEng in Computer Science from Imperial College London and a PhD in Computer Science from the University of Cambridge.

ACM and the Computer Science Teachers Association (CSTA) selected four high school students from among a pool of graduating high school seniors throughout the US for the ACM/CSTA Cutler-Bell Prize in High School Computing. Eligible students applied for the award by submitting a project/artifact that engages modern technology and computer science. A panel of judges selected the recipients based on the ingenuity, complexity, relevancy, and originality of their projects.

The Cutler-Bell Prize promotes the field of computer science and empowers students to pursue computing challenges beyond the traditional classroom environment. In 2015, David Cutler and Gordon Bell established the award. Cutler is a software engineer, designer, and developer of several operating systems at Digital Equipment Corporation. Bell, an electrical engineer, is researcher emeritus at Microsoft Research.

Each Cutler-Bell Prize recipient receives a $10,000 cash prize. The prize amount is sent to the financial aid office of the institution the student will be attending next year and is then put toward each student’s tuition or disbursed.

These projects illustrate the diverse applications being developed by the next generation of computer scientists.

Okezue Bell, Moravian Academy, Bethlehem, Pennsylvania

With only 1% of the computer science space being Black, the innovation landscape within the field is reflective of that. There are few race- and class-responsible solutions built effectively for communities that have been historically discriminated against. “Fidutam” is a novel, putative first effort to foster a needs-responsive approach to providing financial accounts for unbanked populations. Bell uses state-of-the-art encryption to ensure the safety of user’s data, creating private signatures using a selfie, name and personal data, and location. The solution focuses on financial documentation, which is proven to be the biggest barrier to entry for the unbanked, Bell created Fidutam not only to provide financial access to unbanked individuals but to develop a platform to enable the upward mobilization of the global poor to revive their community’s economy. This increases their share in the global development landscape of computer science and also encourages and enables those whose voices are often underrepresented in CS to penetrate or quality control the field to ensure the existing products have utility in their milieus.

Nathan Elias, Liberal Arts and Sciences Academy, Austin, Texas

In his project, “A Novel Method for Automated Identification and Prediction of Invasive Species Using Deep Learning,” Elias developed InvasiveAI, a service that helps farmers, agricultural workers, and average citizens in the fight against invasive species. He designed an app that utilizes Artificial Intelligence and machine learning methods to accurately detect, predict, and visualize invasive species growth. Using the app, 200 unique invasive plants, wildlife, insects, and pathogens, can be identified. Elias also created a 3D image detection algorithm to identify over 75 invasive species aerially. Elias envisions that InvasiveAI will contribute to the field of computer science by expanding CS’ reach in environmental and citizen science systems, while also furthering advancements in geospatial and AI-based tracking toolkits. This project was inspired by the loss of Elias’ grandfather’s farm in Southern India to the invasive species Kariba.

Hannah Guan, BASIS San Antonio Shavano, San Antonio, Texas

Guan’s project, “Multi-Dimensional Interpretable Interaction Network (MDiiN) for Modeling Aging Heath and Mortality” was inspired by the retired military population of the city of San Antonio. She wanted to create an efficient and affordable system that would be able to diagnose and find remedies for highly pervasive age-related diseases like cancer or Alzheimer’s. Guan’s research can influence elders’ quality and equity of life worldwide. MDiiN is a computation and affordable predictive model that evaluates health risk factors for elders. It’s the first three-dimensional interaction network to uncover high-dimensional interactions among health variables during the aging process. Doctors can use MDiiN to predict the onset of age-related diseases, which would significantly increase the quality and longevity of life across the grid. It’s fast and easy to run, taking less than a second to get results. This research contributes to computer science by strengthening health equality in our society, improving global health security, and leading to tremendous public health benefits.

Sirihaasa Nallamothu, University High School, Normal, Illinois

In her project, Predicting and Identifying Relevant Features of Vasovagal Syncope in Patients with Postural Orthostatic Tachycardia Syndrome (POTS) using machine learning methods and physiological data, was inspired from a TikTok that led Nallamothu down a rabbit hole about POTS. To her surprise, there were no research studies or consumer solutions to predict syncope on real-world data, and she was determined to use her machine learning skills to predict syncopal episodes. Nallamothu is the first person to conduct an IRB research study and collect human subject field data on POTS patients in the real world using non-invasive technologies. She wrote a Python script to extract the 15-minute window signal data of heart rate, blood volumetric pressure, EDA, temperature, and accelerometer data. Nallamothu also uses the concept called “late fusion” in temporal multimodal machine learning. This research is providing a starting point for future research into real-time prediction and integration into a smartwatch, which will help millions who experience vasovagal syncope research a safe and comfortable position before fainting. After completing her research, Nallamothu plans to work toward creating a consumer product and pairing her algorithm with a smart watch.-->{C}

Margo Seltzer is the Canada 150 Research Chair and the Cheriton Family Chair in Computer Science at the University of British Columbia. She is also the Director of the Berkman Center for Internet and Society at Harvard University.

Seltzer earned a PhD degree in Computer Science from the University of California at Berkeley, and an AB degree in Applied Mathematics from Harvard/Radcliffe College. She has authored more than 194 publications on a wide range of topics related to computer systems including systems for capturing and accessing data provenance, file systems, databases, and storage.

Her honors include receiving the UBC CS Awesome Instructor Award, the ACM SIGMOD Systems Award, the USENIX Lifetime Achievement Award, the CRA-E Undergraduate Research Mentoring Award, and the ACM Software System Award (for BerkeleyDB), among many others. She is a Fellow of ACM, the American Academy of Arts and Sciences, and the National Academy of Engineering.

David B. Papworth was employed at Intel Corporation from 1990 to 2020, having served in positions including Principal Processor Architect, and Intel Fellow. He has broad experience in CPU microarchitecture, the software/hardware interface, and is listed as co-inventor on more than 50 issued patents for his work.

Papworth received a Bachelor of Science in Electrical Engineering from the University of Michigan, Ann Arbor. His honors include receiving the Intel Achievement Award for Microarchitecture, and the Intel Achievement Award for producing a microprocessor chip in record time.

Yael Tauman Kalai is a Senior Principal Researcher at Microsoft Research and an Adjunct Professor at the Massachusetts Institute of Technology (MIT). Kalai earned a BSc in Mathematics from the Hebrew University of Jerusalem, an MS in Computer Science and Applied Mathematics from The Weizmann Institute of Science, and a PhD in Computer Science from the Massachusetts Institute of Technology.

Kalai’s honors include the George M. Sprowls Award for Best Doctoral Thesis in Computer Science (MIT, 2007), an IBM PhD Fellowship (2004-2006), an MIT Presidential Graduate Fellowship (2003-2006), and an Outstanding Master’s Thesis Prize (Weizmann Institute of Science, 2001). She is a Fellow of the International Association for Cryptologic Research (IACR). Additionally, Kalai gave an Invited Talk at the International Congress of Mathematics (ICM, 2018).

Robert Melancton Metcalfe is Emeritus Professor of Electrical and Computer Engineering (ECE) after 11 years at The University of Texas at Austin. He has recently become a Research Affiliate in Computational Engineering at his alma mater, the Massachusetts Institute of Technology (MIT) Computer Science & Artificial Intelligence Laboratory (CSAIL). Metcalfe graduated from MIT in 1969 with Bachelor degrees in Electrical Engineering and Industrial Management. He earned a Master’s degree in Applied Mathematics in 1970 and a PhD in Computer Science in 1973 from Harvard University.

Metcalfe’s honors include the National Medal of Technology, IEEE Medal of Honor, Marconi Prize, Japan Computer & Communications Prize, ACM Grace Murray Hopper Award, and IEEE Alexander Graham Bell Medal. He is a Fellow of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the National Inventors, Consumer Electronics, and Internet Halls of Fame.

Jack J. Dongarra has been a University Distinguished Professor at the University of Tennessee and a Distinguished Research Staff Member at the Oak Ridge National Laboratory since 1989. He has also served as a Turing Fellow at the University of Manchester (UK) since 2007. Dongarra earned a B.S. in Mathematics from Chicago State University, an M.S. in Computer Science from the Illinois Institute of Technology, and a Ph.D. in Applied Mathematics from the University of New Mexico.

Dongarra’s honors include the IEEE Computer Pioneer Award, the SIAM/ACM Prize in Computational Science and Engineering, and the ACM/IEEE Ken Kennedy Award. He is a Fellow of ACM, the Institute of Electrical and Electronics Engineers (IEEE), the Society of Industrial and Applied Mathematics (SIAM), the American Association for the Advancement of Science (AAAS), the International Supercomputing Conference (ISC), and the International Engineering and Technology Institute (IETI). He is a member of the National Academy of Engineering and a foreign member of the British Royal Society.

Siddharth Sandipkumar Bhandari is the recipient of the ACM India 2022 Doctoral Dissertation Award for his dissertation titled “Exact Sampling and List Decoding” that develops new techniques and tools in sampling graph colourings and contributes to improved analyses for understanding list-decodability of codes. Siddharth’s doctoral dissertation work was done at Tata Institute of Fundamental Research, Mumbai under the supervision of Prahladh Harsha.

Siddharth’s dissertation makes fundamental contributions to two different areas of theoretical computer science: (a) sampling colourings of graphs and (b) list-decoding error-correcting codes. Sampling a random k-colouring of a given graph is a classic problem in theoretical computer science and statistical physics. The problem of perfect sampling is to construct a polynomial time randomised algorithm that generates a perfect sample from the uniform distribution of k-colourings. Siddharth’s work improves on a two-decade old algorithm for solving this problem using novel techniques that are likely to have wider applicability. In the area of list-decoding, Siddharth’s dissertation focuses on three problems. The first problem is the zero-error capacity of the q/(q 1) channel. Prior work on perfect hashing shows that as the list-size is reduced, the channel capacity decreases from an inverse-polynomial form to an inverse-exponential form, and it has been an open problem to understand where this transition occurs. By showing that the channel follows a coupon-collector like behaviour, Siddharth’s work demonstrates that there is an almost sharp transition near O(q log q). The other two results in this part are related to list-decodability of algebraic codes and lead to a better understanding of the list-decoding of these codes all the way up to capacity.

Pritish Mohapatra shares the Honorable Mention for his dissertation titled “Optimization for and by Machine Learning” that makes significant contributions towards the design of efficient optimization methods for machine learning, including key results for optimizing ranking metrics used in information retrieval systems. Pritish’s doctoral dissertation work was done at International Institute of Information Technology, Hyderabad under the supervision of C. V. Jawahar.

Pritish’s dissertation addresses the problem of making optimization of complicated loss functions efficient and practical. Specifically, he designs a practically efficient optimization algorithm for a category of non-decomposable ranking metrics that greatly improve the feasibility of using such sophisticated metrics for learning in information retrieval tasks. Pritish also provides a concrete theoretical analysis that proves the superiority of the proposed algorithm for optimization, providing a fine balance between the theoretical soundness and practical utility of the algorithms. Pritish’s work also makes an interesting contribution in the use of the classical optimization technique of partial-linearization for efficient learning of large-scale classification models. This holds special significance for learning of models with structured output spaces. Finally, Pritish’s dissertation contributes to the problem of using learning for optimization. He proposes a novel framework that uses a learnable model for doing rounding for combinatorial optimization algorithms. This is based on a key insight that randomized rounding procedures can be visualized as sampling from latent variable models.

Sruthi Sekar shares the Honorable Mention for her dissertation titled “Near-Optimal Non-Malleable Codes and Leakage Resilient Secret Sharing Schemes” that makes fundamental contributions in our understanding of cryptographic primitives such as non-malleable codes, secret sharing schemes and randomness extractors. Sruthi’s doctoral dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Bhavana Kanukurthi.

The goal of non-malleable codes (NMCs) is to enable encoding of messages so that an adversary cannot tamper it into the encoding of a related message. An open problem in this area is to build 1/2-rate NMCs in 2-split state model where a codeword consists of two independently tamperable states. Sruthi’s dissertation makes fundamental contributions to this area. Specifically, she builds on her earlier work to introduce new two-state non-malleable codes for random messages with rate 1/2. This work also introduces a new primitive called "Non-malleable randomness encoders". Her dissertation also shows how to build a near-optimal rate 1/3, 2-state NMC, thereby taking us a step closer to solving the main open problem in this area. In addition, Sruthi’s dissertation introduces a new pseudorandomness primitive called "Adaptive Extractors" and shows their applicability to building constant-rate leakage resilient secret sharing schemes.

Deepika Yadav shares the Honorable Mention for her dissertation titled “Supporting Ongoing Training of Community Health Workers through Mobile-based Solutions in Rural India” that combines the knowledge of Computer System research and HCI to produce systems that are deployed in fields. Her work resulted in deployment of a mobile based training platform for ASHA workers that has been used to train hundreds of ASHA workers. Deepika’s doctoral dissertation work was done at Indraprastha Institute of Information Technology Delhi under the supervision of Pushpendra Singh.

Deepika’s dissertation develops a low-cost mobile-based training platform called “Sangosthi” that allows a geographically distributed group of community health workers (CHWs) to connect over a conference call and receive training in a structured manner. The developed system uses a hybrid architecture to use Interactive Voice Response for facilitating online audio training sessions, enabling CHWs to access training from anywhere through their feature phones. Her work contributes to (i) testing the feasibility and efficacy of a low-cost technology intervention through a controlled field experiment (ii) unpacking the training needs of CHWs in the field and mapping it back to the existing reference material through a large-scale deployment on 500 CHWs, (iii) investigating the potential for peer-to-peer learning models to address the challenge of experts’ availability through a controlled field experiment, and (iv) exploring the potential for automated techniques in this domain by proposing a semi-automated natural language processing approach for curating generated learning content and exposing CHWs and women to Chatbot-based education for the first time. By using a range of mixed methods and field experiments, Deepika’s dissertation expands the focus of HCI4D and mHealth research on CHW competence development in low-resource settings.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation from a degree-awarding institution based in India for each academic year, running from August 1 of one year to July 31 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award is accompanied by a prize of ₹1,00,000 which is shared amongst the recipients. The dissertation(s) will be published in the ACM Digital Library. Financial support for both the award and honorable mention is provided by Tata Consultancy Services (TCS). Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

ACM, the Association for Computing Machinery, has named 57 of its members ACM Fellows for wide-ranging and fundamental contributions in disciplines including cybersecurity, human-computer interaction, mobile computing, and recommender systems among many other areas. The accomplishments of the 2022 ACM Fellows make possible the computing technologies we use every day.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

“Computing’s most important advances are often the result of a collection of many individual contributions, which build upon and complement each other,” explained ACM President Yannis Ioannidis. “But each individual contribution is an essential link in the chain. The ACM Fellows program is a way to recognize the women and men whose hard work and creativity happens inconspicuously but drives our field. In selecting a new class of ACM Fellows each year, we also hope that learning about these leaders might inspire our wider membership with insights for their own work.”

In keeping with ACM’s global reach, the 2022 Fellows represent universities, corporations, and research centers in Canada, Chile, China, France, Germany, Israel, the Netherlands, Spain, Switzerland, and the United States.

Additional information about the 2022 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows website.

Read the news release.

ACM has named 67 Distinguished Members for significant contributions. All of the 2022 inductees are longstanding ACM members and were selected by their peers for work that has spurred innovation, enhanced computer science education, and moved the field forward.

“The ACM Distinguished Members program honors both accomplishment and commitment,” said ACM President Yannis Ioannidis. “Each of these new 67 Distinguished Members have been selected for specific and impactful work, as well as their longstanding commitment to being a part of our professional association. As ACM celebrates its 75th anniversary this year, it is especially fitting to reflect on how our global membership has built our organization into what it is today. Our Distinguished Members are leaders both within ACM and throughout the computing field.”

The 2022 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Canada, China, Finland, France, Germany, Greece, India, Italy, Japan, the Netherlands, New Zealand, Singapore, Taiwan, the United Kingdom, and the United States. ACM Distinguished Members are selected for their contributions in three separate categories: educational, engineering, and scientific. This year’s class of Distinguished Members made advancements in areas including algorithms, computer science education, cybersecurity, data management, energy efficient computer architecture, information retrieval, healthcare information technology, knowledge graph and semantic analysis, mobile computing, and software engineering, among many others.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience and significant achievements in the computing field. To be nominated, a candidate must have at least 15 years of professional experience in the computing field, five years of professional ACM membership in the last 10 years, and must have achieved a significant level of accomplishment or made a significant impact in the field of computing. A Distinguished Member is expected to have served as a mentor and role model by guiding technical career development and contributing to the field beyond the norm.

Read the ACM news release.

ACM, the Association for Computing Machinery named a 16-member team drawn from French, Japanese, and US institutions as recipient of the 2022 ACM Gordon Bell Prize for their project, “Pushing the Frontier in the Design of Laser-Based Electron Accelerators With Groundbreaking Mesh-Refined Particle-In-Cell Simulations on Exascale-Class Supercomputers.”

The members of the team are: Luca Fedeli, France Boillod-Cerneaux, Thomas Clark, Neil Zaїm, and Henri Vincenti, (CEA); Axel Huebl, Kevin Gott, Remi Lehe, Andrew Myers, Weiqun Zhang, and Jean-Luc Vay, (Lawrence Berkeley National Laboratory); Conrad Hillairet, (Arm); Stephan Jaure, (ATOS); Adrien Leblanc, (Laboratoire d’Optique Appliquée, ENSTA Paris); Christelle Piechurski, (GENCI); and Mitsuhisa Sato, (RIKEN).

Particle-in-Cell (PIC) simulation is a technique within high-performance computing used to model the motion of charged particles, or plasma. PIC has applications in many areas, including nuclear fusion, accelerators, space physics, and astrophysics. The very recent introduction of exascale-class computers has expanded the horizons of PIC simulations and makes this year’s winning project especially exciting. According to their abstract, the team presents a first-of-kind mesh-refined (MR) massively parallel PIC code for kinetic plasma simulations optimized on the Frontier, Fugaku, Summit, and Perlmutter supercomputers.

The 2022 ACM Gordon Bell Prize-winning team concludes by noting that, “the use of mesh refinement in large-scale electromagnetic PIC simulations is a first and represents a paradigm shift. The successful modeling with savings between 1.5× and 4× with mesh refinement that is reported in this paper is a landmark steppingstone toward a new era in the modelling of laser-plasma interactions.”

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), which was held in Dallas, Texas.

New York, NY, October 19, 2022 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that Marcin Copik of ETH Zurich of ETH Zurich and Masado Alexander Ishii of the University of Utah are the recipients of the 2022 ACM-IEEE CS George Michael Memorial HPC Fellowships. Shelby Lockhart of the University of Illinois at Urbana-Champaign received an Honorable Mention. 

Marcin Copik
Copik’s research bridges the gap between high-performance programming and serverless computing. He is bringing the Function-as-a-Service (FaaS) programming model into the HPC domain by developing high-performance software and hardware solutions for the serverless stack. By solving the fundamental performance challenges of FaaS, he is building a fast, efficient programming model that brings innovative cloud techniques into HPC data centers, allowing users to benefit from pay-as-you-go billing and helping operators to decrease running costs and their environmental impact.To that end, he has been working on tailored solutions for different levels of the FaaS computing stack, from computing and network devices up to high-level optimizations and efficient system designs. He has also proposed a new design for serverless platforms that applies HPC practices such as low-latency networking, data locality, and efficient communication.

Masado Alexander Ishii
Ishii is the main developer for the University of Utah’s Dendro-KT framework for four-dimensional adaptivity and parallel in time formulations. Given the ever-increasing levels of parallelism in the largest machines, parallelizing across space is not sufficient—and in many cases the inability to parallelize in time is the biggest bottleneck for several important problems. The Dendro-KT framework addresses this problem and also enables the development of high-orders and variable order in time formulation (similar to p-refinement). Working with collaborators, Ishii has also been involved in developing methods and codes for large-scale fluid simulations around complex objects, including a case with multiple complex objects, to evaluate COVID-19 transmission risk in classrooms.

Shelby Lockhart
Lockhart has made contributions in parallel communication, core parallel numerical algorithms, and advancing capabilities of large-scale predictive simulation. Her focus has been on modeling performance in heterogeneous settings, with an eye on redesigning the message communication “under-the-hood” (aspects of the high-performance architecture that are not readily visible) as well as looking at fundamental algorithmic changes in order to significantly improve achievable performance. Among her research highlights, she has provided detailed communication models to drive the selection of message routing, yielding impressive improvements across a range of problem types. She has also presented a strategy for achieving impressive reductions in communication costs in graphic processing unit (GPU) systems by communication through the host, accounting for different data volumes and GPU counts. Additionally, Lockhart’s thesis work on fixed point solvers has made important contributions to the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS) project.

The ACM-IEEE CS George Michael Memorial HPC Fellowship is endowed in memory of George Michael, one of the founders of the SC Conference series. The fellowship honors exceptional PhD students throughout the world whose research focus is on high performance computing applications, networking, storage, or large-scale data analytics using the most powerful computers that are currently available. The Fellowship includes a $5,000 honorarium and travel expenses to attend the SC conference, where the Fellowships are formally presented.

The Association for Computing Machinery and IEEE Computer Society named Ian Foster, a Professor at the University of Chicago and Division Director at Argonne National Laboratory, as the recipient of the 2022 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes pathbreaking achievements in parallel (high performance) computing. Foster is cited for contributions to programming and productivity in computing via the establishment of new programming models and foundational science services.

Foster has pioneered new approaches to the use of distributed computing for accelerating scientific discovery throughout his career, both within supercomputers and over networks. He has repeatedly proposed out-of-the-box ideas that proved transformative for computer science and computational science: large- scale task-parallel programming, on-demand distributed computations ("grid computing"), virtual organizations, universal data transfer, trust fabrics, and cloud management services for data-intensive science. Each has contributed to programmability and productivity in computing.

Select Technical Contributions
High-level task parallelism: Parallelism has traditionally meant data parallelism and low-level message passing. Recognizing that many interesting task-parallel computations were difficult to realize with such tools, Foster and his colleagues developed new programming methods that eased the specification of interacting tasks, enabled the composition of existing programs, used deterministic constructs to avoid race conditions, and scaled to large distributed and parallel computer systems. Tools such as Strand, Swift, and most recently Parsl, developed in collaboration with colleagues including Steve Taylor, Mike Wilde, and Kyle Chard, have been used to develop pioneering implementations of task-parallel program structures.

Grid computing: Noting the opportunities offered by high-speed networks in the 1990s, Foster and colleagues, including Carl Kesselman and the late Steve Tuecke, launched an effort to create a unifying fabric of protocols, software, and policies for remote and coordinated use of computers, data, instruments, and software regardless of location. Together, these innovations came to be known as “grid computing.” Grid computing, in turn, led to numerous technical advances, innovative applications, and improvements in science infrastructure. For example, the climate community uses these methods to distribute climate data used in Intergovernmental Panel on Climate Change (IPCC) assessments, while the physics community aggregates hundreds of thousands of CPUs contributed by hundreds of participants around the world who use the Large Hadron Collider (LHC) particle accelerator.

A universal data fabric: Historically, network engineers have focused on moving bits and high-performance computing experts on managing data. As a result, end-to-end performance between file systems was invariably poor. Foster and colleagues, including Bill Allcock and Raj Kettimuthu, developed new protocols and software for traversing the storage system-network storage system path, harnessing multilevel parallelism within transfers, negotiating protocol parameters, and detecting and recovering from failure. These methods are now used at thousands of sites worldwide and form the foundation for the demilitarized zones (DMZs)—subnetworks that many scientific institutions use to connect to the world.

A universal trust fabric: Determining who is allowed to perform an action on a remote computer is one of the most challenging problems in distributed computing, encompassing cryptography, protocols, infrastructure, and policies. The fact that it is now possible to transfer data from one laboratory to another without any difficulty owes a great deal to work performed by Foster and colleagues, including Rachana Ananthakrishnan, on identity and credential management, secure authentication, distributed authorization—and, above all, the integration of these elements into usable end-to-end systems.

Cloud services for data-intensive science: Foster and Tuecke realized that the emergence of commercial ("public") cloud services offered exciting opportunities to rethink research infrastructure, in particular by offloading responsibility for previously manual processes to cloud-hosted services. The resulting Globus service provides managed identity management, data transfer and replication, data sharing, data publication, and other services to the research community. As of early 2021, Globus is used at 13 national laboratories, 65 countries, and more than 1,500 institutions, has 150,000 registered users and has been used to transfer more than 200 billion files and 1.3 exabytes.

Foster and his team worked with colleagues around the world to implement Globus as an essential data infrastructure element in computing and experimental facilities within projects worldwide and in settings as diverse as US Department of Energy and National Science Foundation supercomputer centers, African universities, and US National Institutes of Health intramural laboratories.

Service to the Field and Mentoring
Foster’s books include "Designing and Building Parallel Programs," the first book published on the web, “The Grid: Blueprint for a New Computing Infrastructure,” (two volumes, edited with Carl Kesselman), and "Big Data and Social Science," (two editions), which communicated advanced data science methods to government statistical agencies.

He has designed and lead numerous successful US and international projects, from early grid initiatives to recent collaborations in network modeling and exascale co-design. He has served as general and/or program committee chair for many conferences (e.g., High Performance Distributed Computing, IEEE Cloud, IEEE eScience) and numerous scientific advisory boards (e.g., SLAC Scientific Policy Committee, UK eScience, NZ eScience Infrastructure, NSF Computing Community Consortium). Foster has also welcomed dozens of students and postdocs into his group at the University of Chicago and Argonne National Laboratory—people who now are in leadership roles in universities, labs, and companies across the world.

Biographical Background
Ian T. Foster is Senior Scientist, Distinguished Fellow, and Director of the Data Science and Learning Division at Argonne National Laboratory, and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. Foster received a BSc Degree in Computer Science from the University of Canterbury, New Zealand, and a PhD in Computer Science from Imperial College, United Kingdom.

He is a fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the British Computer Society (BCS), the Institute of Electronics and Electrical Engineers (IEEE), and a US Department of Energy Office of Science Distinguished Scientists Fellow. He has received the BCS Lovelace Medal and the IEEE Babbage, Goode, and Kanai Awards. He has received honorary doctorates from the University of Canterbury, New Zealand, and CINVESTAV, Mexico.

ACM and IEEE CS co-sponsor the Kennedy Award, which was established in 2009 to recognize substantial contributions to programmability and productivity in computing and significant community service or mentoring contributions. It was named for the late Ken Kennedy, founder of Rice University’s computer science program and a world expert on high performance computing. The Kennedy Award carries a US $5,000 honorarium endowed by IEEE CS and ACM. The award will be formally presented to Foster in November at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC22).

ACM and IEEE Computer Society named Mark Horowitz, a Professor at Stanford University, as the recipient of the 2022 Eckert-Mauchly Award for contributions to microprocessor memory systems. Horowitz was the first to identify the processor to dynamic random-access memory (DRAM) interface as a key bottleneck that required architecture and circuit optimization. He pioneered high-bandwidth DRAM interfaces. In addition, modern DRAM interfaces such as SDDR and LPDDR were strongly influenced by his techniques.

Horowitz was also a major contributor to the DASH and FLASH projects, which explored scalable methods for implementing cache coherency using directories rather than snooping protocols. Today almost all cache-coherent multiprocessors rely on such directory mechanisms either within or across multicores. Horowitz has led research that recognizes that future performance/energy progress after the end of Dennard scaling will require greater use of hardware accelerators, and pioneered work in Smart Memories, an early work customizing memory as well as processors; many of today’s domain-specific architectures build on this concept. He is a Fellow of ACM, IEEE, and the American Academy of Arts and Sciences, and he is a Member of the National Academy of Engineering.

Horowitz will be formally presented with the award at the ACM/IEEE International Symposium on Computer Architecture (ISCA) being held June 18–22 in New York City.

ACM and IEEE Computer Society co-sponsor the Eckert-Mauchly Award, which was initiated in 1979. It recognizes contributions to computer and digital systems architecture and comes with a $5,000 prize. The award was named for John Presper Eckert and John William Mauchly, who collaborated on the design and construction of the Electronic Numerical Integrator and Computer (ENIAC), the pioneering large-scale electronic computing machine, which was completed in 1947.

Manish Raghavan is the recipient of the 2021 ACM Doctoral Dissertation Award for his dissertation "The Societal Impacts of Algorithmic Decision-Making." Raghavan’s dissertation makes significant contributions to the understanding of algorithmic decision making and its societal implications, including foundational results on issues of algorithmic bias and fairness.

Algorithmic fairness is an area within AI that has generated a great deal of public and media interest. Despite being at a very early stage of his career, Raghavan has been one of the leading figures shaping the direction and focus of this line of research.

Raghavan is a Postdoctoral Fellow at the Harvard Center for Research on Computation and Society. His primary interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. Raghavan received a BS degree in Electrical Engineering and Computer Science from the University of California, Berkeley, and MS and PhD degrees in Computer Science from Cornell University.

Honorable Mentions for the 2021 ACM Doctoral Dissertation Award go to Dimitris Tsipras of Stanford University, Pratul Srinivasan of Google Research and Benjamin Mildenhall of Google Research.

Dimitris Tsipras’ dissertation, “Learning Through the Lens of Robustness,” was recognized for foundational contributions to the study of adversarially robust machine learning (ML) and building effective tools for training reliable machine learning models. Tsipras made several pathbreaking contributions to one of the biggest challenges in ML today: making ML truly ready for real-world deployment.

Tsipras is a Postdoctoral Scholar at Stanford University. His research is focused on understanding and improving the reliability of machine learning systems when faced with the real world. Tsipras received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, as well as SM and PhD degrees in computer science from the Massachusetts Institute of Technology (MIT).

Pratul Srinivasan and Benjamin Mildenhall are awarded Honorable Mentions for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem. Srinivasan’s dissertation, "Scene Representations for View Synthesis with Deep Learning," and Mildenhall’s dissertation, “Neural Scene Representations for View Synthesis,” addressed a long-standing open problem in computer vision and computer graphics. That problem, called “view synthesis” in vision and “unstructured light field rendering” in graphics, involves taking just a handful of photographs of a scene and predicting new images from any intermediate viewpoint. NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication.

Srinivasan is a Research Scientist at Google Research, where he focuses on problems at the intersection of computer vision, computer graphics, and machine learning. He received a BSE degree in Biomedical Engineering and BA in Computer Science from Duke University and a PhD in Computer Science from the University of California, Berkeley.

Mildenhall is a Research Scientist at Google Research, where he works on problems in computer vision and graphics. He received a BS degree in Computer Science and Mathematics from Stanford University and a PhD in Computer Science from the University of California, Berkeley.

Éva Tardos is the Jacob Gould Schurman Professor of Computer Science and Chair of the Department of Computer Science at Cornell University. Earlier in her career at Cornell, she held posts including Associate Dean for Diversity & Inclusion, and Diversity Lead for Computing and Information Sciences.

Tardos earned Diploma and Ph.D. degrees in Mathematics from Eӧtvӧs University, Budapest, and later earned a Candidate degree from the Hungarian Academy of Sciences. She has authored more than 100 publications on topics including approximation algorithms, mathematical optimization, algorithms, network planning and design, and theoretical computer science.

Her honors include receiving the IEEE John von Neumann Medal, the Fulkerson Prize, the George B. Dantzig Prize, the Van Wijngaarden Award, and the Gödel Prize. She is a Fellow of ACM, the Institute for Operations Research and the Management Sciences (INFORMS), the American Mathematical Society (AMS), the Society of Industrial and Applied Mathematics (SIAM), and the Game Theory Society. She has also been elected to the National Academy of Engineering, National Academy of Sciences, Hungarian Academy of Sciences, the American Philosophical Society, and the National Academy of Arts and Science.

Carla E. Brodley is Dean of Inclusive Computing, and past Dean of the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, she was a Professor and Chair of the Department of Computer Science at Tufts University. Earlier in her career she served on the faculty of Purdue University. Brodley earned a B.A. Degree in computer science and mathematics from McGill University, and M.S. and Ph.D. degrees in computer science from the University of Massachusetts at Amherst.

Brodley has authored 30 journal articles, 53 conference papers, 7 magazine articles, 3 book chapters, and 26 symposia and workshop chapters. Her honors include receiving a National Science Foundation (NSF) CAREER Award and the Harrold and Notkin Research and Graduate Mentoring Award from the National Center for Women & Information Technology (NCWIT). Brodley was selected as a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and was named an ACM Fellow for applications of machine learning and for increasing the participation of women in computer science.

Pieter Abbeel is a Professor of Computer Science and Electrical Engineering at the University of California, Berkeley and the Co-Founder, President and Chief Scientist at Covariant, an AI robotics company. Abbeel earned a B.S. in Electrical Engineering from Katholieke Universiteit Leuven, as well as M.S. and Ph.D. degrees in Computer Science from Stanford University.

Abbeel’s honors include a Presidential Early Career Award for Scientists and Engineers, a National Science Foundation Early Career Development Program Award, and a Diane McEntyre Award for Excellence in Teaching. Additionally, Abbeel was named a Top Young Innovator Under 35 by the MIT Technology Review and received the Dick Volz Best U.S. Ph.D. Thesis in Robotics and Automation Award. He is a Fellow of IEEE.

ACM named Jack J. Dongarra recipient of the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software to keep pace with exponential hardware improvements for over four decades. Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee. He also holds appointments with Oak Ridge National Laboratory and the University of Manchester.

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize, with financial support provided by Google, Inc. It is named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing.

Dongarra has led the world of high-performance computing through his contributions to efficient numerical algorithms for linear algebra operations, parallel computing programming mechanisms, and performance evaluation tools. For nearly forty years, Moore’s Law produced exponential growth in hardware performance. During that same time, while most software failed to keep pace with these hardware advances, high performance numerical software did—in large part due to Dongarra’s algorithms, optimization techniques, and production-quality software implementations.

These contributions laid a framework from which scientists and engineers made important discoveries and game-changing innovations in areas including big data analytics, healthcare, renewable energy, weather prediction, genomics, and economics, to name a few. Dongarra’s work also helped facilitate leapfrog advances in computer architecture and supported revolutions in computer graphics and deep learning.

Dongarra’s major contribution was in creating open-source software libraries and standards which employ linear algebra as an intermediate language that can be used by a wide variety of applications. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. Dongarra’s libraries also introduced many important innovations including autotuning, mixed precision arithmetic, and batch computations.

As a leading ambassador of high-performance computing, Dongarra led the field in persuading hardware vendors to optimize these methods, and software developers to target his open-source libraries in their work. Ultimately, these efforts resulted in linear algebra-based software libraries achieving nearly universal adoption for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers. These libraries were essential in the growth of the field—allowing progressively more powerful computers to solve computationally challenging problems.

“Today’s fastest supercomputers draw headlines in the media and excite public interest by performing mind-boggling feats of a quadrillion calculations in a second,” explains ACM President Gabriele Kotsis. “But beyond the understandable interest in new records being broken, high performance computing has been a major instrument of scientific discovery. HPC innovations have also spilled over into many different areas of computing and moved our entire field forward. Jack Dongarra played a central part in directing the successful trajectory of this field. His trailblazing work stretches back to 1979, and he remains one of the foremost and actively engaged leaders in the HPC community. His career certainly exemplifies the Turing Award’s recognition of ‘major contributions of lasting importance.’”

“Jack Dongarra's work has fundamentally changed and advanced scientific computing,” said Jeff Dean, Google Senior Fellow and SVP of Google Research and Google Health. “His deep and important work at the core of the world's most heavily used numerical libraries underlie every area of scientific computing, helping advance everything from drug discovery to weather forecasting, aerospace engineering and dozens more fields, and his deep focus on characterizing the performance of a wide range of computers has led to major advances in computer architectures that are well suited for numeric computations.”

Dongarra will be formally presented with the ACM A.M. Turing Award at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

SELECT TECHNICAL CONTRIBUTIONS

For over four decades, Dongarra has been the primary implementor or principal investigator for many libraries such as LINPACK, BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA, and SLATE. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. His software libraries are used, practically universally, for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers.

These libraries embody many deep technical innovations such as:

Autotuning: through his 2016 Supercomputing Conference Test of Time award-winning ATLAS project, Dongarra pioneered methods for automatically finding algorithmic parameters that produce linear algebra kernels of near-optimal efficiency, often outperforming vendor-supplied codes.

Mixed precision arithmetic: In his 2006 Supercomputing Conference paper, “Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy,” Dongarra pioneered harnessing multiple precisions of floating-point arithmetic to deliver accurate solutions more quickly. This work has become instrumental in machine learning applications, as showcased recently in the HPL-AI benchmark, which achieved unprecedented levels of performance on the world’s top supercomputers.

Batch computations: Dongarra pioneered the paradigm of dividing computations of large dense matrices, which are commonly used in simulations, modeling, and data analysis, into many computations of smaller tasks over blocks that can be calculated independently and concurrently. Based on his 2016 paper, “Performance, design, and autotuning of batched GEMM for GPUs,” Dongarra led the development of the Batched BLAS Standard for such computations, and they also appear in the software libraries MAGMA and SLATE.

Dongarra has collaborated internationally with many people on the efforts above, always in the role of the driving force for innovation by continually developing new techniques to maximize performance and portability while maintaining numerically reliable results using state of the art techniques. Other examples of his leadership include the Message Passing Interface (MPI) the de-facto standard for portable message-passing on parallel computing architectures, and the Performance API (PAPI), which provides an interface that allows collection and synthesis of performance from components of a heterogeneous system. The standards he helped create, such as MPI, the LINPACK Benchmark, and the Top500 list of supercomputers, underpin computational tasks ranging from weather prediction to climate change to analyzing data from large scale physics experiments.

ACM and the Computer Science Teachers Association (CSTA) selected four high school students from among a pool of graduating high school seniors throughout the US for the ACM/CSTA Cutler-Bell Prize in High School Computing. Eligible students applied for the award by submitting a project/artifact that engages modern technology and computer science. A panel of judges selected the recipients based on the ingenuity, complexity, relevancy, and originality of their projects.

The Cutler-Bell Prize promotes the field of computer science and empowers students to pursue computing challenges beyond the traditional classroom environment. In 2015, David Cutler and Gordon Bell established the award. Cutler is a software engineer, designer, and developer of several operating systems at Digital Equipment Corporation. Bell, an electrical engineer, is researcher emeritus at Microsoft Research.

Each Cutler-Bell Prize recipient receives a $10,000 cash prize. The prize amount is sent to the financial aid office of the institution the student will be attending next year and is then put toward each student’s tuition or disbursed.

The winning projects illustrate the diverse applications being developed by the next generation of computer scientists.

Harshal Bharatia, Plano Senior High School, Plano, Texas
In his project, Thermocloud: A Smart Collaborative Thermostat, Harshal Bharatia used agile methodology and an interactive design and development cycle. The purpose of this project is to design and construction of a cloud-based collaborative learning thermostat that optimizes the operation of HVAC systems by learning to collaborate and use the best machine learning approach to maximize comfort and energy savings for each system. Using a collaborative cloud-based learning approach across many houses, it learns to adapt the operation of an HVAC system by identifying the best currently-available machine learning approach and uses this approach to maximize comfort and energy savings. With cost-effective hardware, it enables collaboration with other similar thermostats and controls the HVAC system in a reliable fashion. It also supports additional energy-saving features such as multi-story equalizer, blackout mitigation, and user-driven cost versus comfort trade-off. This approach was truly innovative as it was domain agnostic and allowed a very large number of clients to lazily update the route-mapping and the system automatically addressed degradation as a result feedback triggered automatic cluster refinement, model retraining, and strategy selection to improve performance. With more than 34% energy savings, Bharatia patented the novel Thermocloud approach and released it in public-domain at intellicusp.org/thermocloud, as by enabling people to freely save energy, Bharatia hopes the fight against climate change leads to a better tomorrow.

Yash Narayan, The Nueva School, San Mateo, California
Yash Narayan developed DeepWaste. This easy-to-use mobile application utilizes highly-optimized deep learning techniques to classify waste better than humans. A user using DeepWaste can simply point their phone camera to any piece of waste (food, bottle, paper, etc.) and get instantaneous feedback on whether the item is recyclable, compostable, or trash. Narayan was inspired to create DeepWaste after seeing the large volumes of misclassified waste at his local recycling center several years ago. Indeed, DeepWaste solves a critical problem, as inaccurate waste disposal, at the point of disposal is a significant contributor to climate change. When materials that could be recycled or composted are diverted into landfills, they cause the emission of potent greenhouse gases. His project demonstrates the efficacy of an accurate, easy-to-use, scalable solution to augment human performance in waste disposal, accessible right at the point of disposal–an innovative approach applying AI to a large-scale global problem. If DeepWaste improves human waste-disposal accuracy by even 1%, it would be equivalent to removing over 6.5 million gasoline-burning passenger vehicles from the road.

Shoumik Roychowdhury, Westwood High School, Austin, Texas
Shoumik Roychowdhury’s project, XNet: A Novel Machine Learning Model for Fast MRI Reconstruction, took inspiration from his own experience with MRIs. This project's vision is to create an effective and computationally inexpensive Deep Learning (DL) pipeline that can aid medical professionals in capturing MR images faster. The proposed work is a novel generative adversarial network architecture that uses two similar MR images which have been reconstructed from 0.1%, 2%, or 5% sub-sampled k-spaces, as inputs to produce a complete MR image with a peak signal-to-noise ratio (PSNR) equal or higher than full-space reconstructed MR images. This architecture is dubbed X-Net due to the model's shape looking like the letter 'X.' By treating the reconstructed sub-sampled MR images as matrices, X-Net's generator network can convolutionally auto encode information from two different reconstructed sub-sampled MR images, cross-pollinate features and attributes, convolutionally auto decode the new information, and magnify residual information to create an enhanced image. This resultant image is then sent to the pre-trained discriminator network, which determines whether it is real or fake. Next, the loss functions are automatically updated, and consequently, the generator's hyperparameters are automatically tuned. This process is repeated for 10,000 iterations until the model is finally trained. This tuned model is now ready for deployment in the real world, aiding medical professionals in providing fast and sharp MR reconstruction. In the future, Roychowdhury plans to successfully deploy and implement this solution across the healthcare industry, implementing this work in two key manners.

Hiya Shah, Amador Valley High School, Pleasanton, California
The main vision of Hiya Shah’s project, titled “Maji: Water Security,” is a mobile application that determines the real-time water quality of your home’s water pipes by using innovative machine learning and building a large database of water quality data to achieve our goal of decreasing water health consequences. Another vision of Maji is to computationally design an environmentally sustainable (lower energy costs and lower water wastage) forever chemical (PFAS) filtration membrane that can relay data to the mobile application in real-time. To scale Maji beyond her city, Shah is am currently working with the U.S. Environmental Protection Agency (as part of the U.S. President’s Environmental Youth Award) to implement it on the IOS App store for residents across the United States to use. In the immediate future, she seeks to develop the application for the Google Play Store and reach unincorporated areas and the global market by proposing the app to private water suppliers and local government officials for implementation.

“We are proud to support an effort which encourages high school computer science students to develop projects that will advance society,” said Cutler and Bell. “We hope that, whatever careers these students ultimately pursue, they will consider how technology can have a positive impact on the wider world. Beyond challenging the students to stretch their skills and imaginations, developing their own projects gives students confidence.”

"In today's world, computer science is rapidly becoming an essential aptitude for students at all levels and in every area of study," explains ACM President Gabriele Kotsis. "In the coming years, students who have exposure to computer science education in K-12 settings will be at a decided advantage when they enter university or begin their careers. ACM is proud to be a partner with the CSTA in bestowing the Cutler-Bell Prize. Cutler-Bell Prize-winning students are exemplars for their peers. These students demonstrate that they have the vision to use computing as a tool to address pressing problems in society, as well as the technical aptitude to develop a practical plan outlining how they would make their vision a reality. We also congratulate the computer science teachers who guided these students and Cutler and Bell for funding this award."

"Each year, these winning projects showcase the continuing advancements of computer science and the power of high-quality computer science education,” said Jake Baskin, Executive Director of CSTA. “These students and their projects embody CSforGood, and it’s inspiring to see how they are leveraging their computer science skills to solve pressing problems. CSTA is proud to honor their work and thanks Gordon Bell and David Cutler for their continued support of the award.”

ACM, the Association for Computing Machinery, has named 71 members ACM Fellows for wide-ranging and fundamental contributions in areas including algorithms, computer science education, cryptography, data security and privacy, medical informatics, and mobile and networked systems ─ among many other areas. The accomplishments of the 2021 ACM Fellows underpin important innovations that shape the technologies we use every day.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

“Computing professionals have brought about leapfrog advances in how we live, work, and play,” said ACM President Gabriele Kotsis. “New technologies are the result of skillfully combining the individual contributions of numerous men and women, often building upon diverse contributions that have emerged over decades. But technological progress would not be possible without the essential building blocks of individual contributors. The ACM Fellows program honors the creativity and hard work of ACM members whose specific accomplishments make broader advances possible. In announcing a new class of Fellows each year, we celebrate the impact ACM Fellows make, as well as the many technical areas of computing in which they work.”

In keeping with ACM’s global reach, the 2021 Fellows represent universities, corporations, and research centers in Belgium, Canada, China, France, Germany, India, Israel, Italy, and the United States.

The contributions of the 2021 Fellows run the gamut of the computing field―including cloud database systems, deep learning acceleration, high performance computing, robotics, and theoretical computer science ─ to name a few.

Additional information about the 2021 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows site.

News release

ACM has named 63 Distinguished Members for outstanding contributions to the field. All 2021 inductees are longstanding ACM members and were selected by their peers for a range of accomplishments that advance computing as a science and a profession.

"Each year we are excited to recognize a new class of ACM Distinguished Members for their professional achievements, as well as their longstanding membership with ACM,” explains ACM President Gabriele Kotsis. “The Distinguished Members program is a way both to celebrate the trailblazing work of our members, and to underscore how participation with a professional society enhances one’s career growth. This award category also emphasizes how ACM’s worldwide membership is the foundation of our organization."

The 2021 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Bangladesh, Canada, Chile, China, Germany, India, New Zealand, Norway, Qatar, Saudi Arabia, Singapore, Spain, the United Kingdom and the United States. ACM Distinguished Members are selected for their contributions in three separate categories: educational, engineering and scientific. The new class of Distinguished Members made advancements in areas including bioinformatics, computer architecture, computer graphics, data science, human-computer interaction, networking and distributed systems, semantic web research, security, and software engineering, among many other areas.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience and significant achievements in the computing field. To be nominated, a candidate must have at least 15 years of professional experience in the computing field, five years of professional ACM membership in the last 10 years, and have achieved a significant level of accomplishment, or made a significant impact in the field of computing, computer science or information technology. A Distinguished Member is expected to have served as a mentor and role model by guiding technical career development and contributing to the field beyond the norm.

ACM news release

ACM, the Association for Computing Machinery, named a 14-member team, drawn from Chinese institutions, recipients of the 2021 ACM Gordon Bell Prize for their project, Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer.

The members of the winning team are: Yong (Alexander) Liu, Xin (Lucy) Liu, Fang (Nancy) Li, Yuling Yang, Jiawei Song, Pengpeng Zhao, Zhen Wang, Dajia Peng, and Huarong Chen of Zhejiang Lab, Hangzhou and the National Supercomputing Center in Wuxi; Haohuan Fu and Dexun Chen of Tsinghua University, Beijing, and the National Supercomputing Center in Wuxi; Wenzhao Wu of the National Supercomputing Center in Wuxi; and Heliang Huang and Chu Guo of the Shanghai Research Center for Quantum Sciences.

Quantum supremacy is a term used to denote the point at which a quantum device can solve a problem that no classical computer can solve in a reasonable amount of time. Teams at Google and the University of Science and Technology of China in Hefei both claim to have developed devices that have achieved quantum supremacy.

According to the Gordon Bell Prize recipients, determining whether a device has achieved quantum supremacy for a given task (in a specific scenario) begins with sampling the interactions of the different quantum bits (qubits) in a random quantum circuit (RQC). As the number of possible interactions among qubits in a random quantum circuit is staggeringly large, simulating their interactions is a problem well-suited for a high-performance computer. However, the quantum physics behind the entangled qubits requires that the classical binary bits used in a supercomputer store and compute the information with exponentially-increasing complexity.

In their Gordon Bell Prize-winning work, the Chinese researchers introduced a systematic design process that covers the algorithm, parallelization, and architecture required for the simulation. Using a new Sunway Supercomputer, the Chinese team effectively simulated a 10x10x (1+40+1) random quantum circuit (a new milestone for classical simulation of RQC). Their simulation achieved a performance of 1.2 Eflops (one quintillion floating-point operations per second) single-precision, or 4.4 Eflops mixed-precision, using over 41.9 million Sunway cores (processors).

The project far outpaced state-of-the-art approaches to simulating an RQC. For example, the most recent effort, using the Summit supercomputer to simulate a random quantum circuit of the Google Sycamore quantum processor (which has 53 qubits), was estimated to take 10,000 years to perform. By contrast, the Chinese team’s approach employing the Sunway supercomputer takes only 304 seconds for a simulation of similar quantum complexity.

The Chinese team explained that they undertook this challenge because achieving real-time simulation of an RQC using a supercomputer would aid both in the development of quantum devices and in bringing algorithmic and architectural innovations within the traditional supercomputing community.

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented today by former ACM President Cherri M. Pancake and Professor Mark Parsons, Chair of the 2021 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21), which was held in St. Louis, Missouri, and virtually for those who could not attend.

Mert Hidayetoglu of the University of Illinois at Urbana-Champaign and Tirthak Patel of Northeastern University are the recipients of the 2021 ACM-IEEE CS George Michael Memorial HPC Fellowships. Hidayetoglu is recognized for contributions in scalable sparse applications using fast algorithms and hierarchical communication on supercomputers with multi-GPU nodes. Patel is recognized for contributions toward making the current error-prone quantum computing systems more usable and helping HPC programmers solve computationally challenging problems.

Mert Hidayetoglu
Sparse operations are the main computational workload for numerous scientific, AI and graph analytics applications. Most of the time, the costs of computation and communication for distributed sparse operations constitute an overall performance bottleneck.

Hidayetoglu’s research investigates this bottleneck at two specific points: memory accesses and communication. He has proposed and demonstrated two novel techniques: sparse matrix tiling and hierarchical communications. The first technique, sparse matrix tiling, accelerates sparse matrix multiplication on a single GPU by the preprocessing of sparse data access patterns and constructs necessary data structures accordingly. The second technique, hierarchical communications, eliminates the communication bottleneck , which typically dominates end-to-end execution time when large problems at terabyte (TB) scale fit on only hundreds of GPUs. Hidayetoglu’s technique performs sparse communications (depending on the sparsity pattern of the matrix) locally-first to reduce the costly data communication across nodes.

Hidayetoglu has successfully demonstrated these techniques in award-winning applications, including large-scale X-ray imaging at Argonne National Laboratory and accelerated sparse deep neural network inference at IBM. Related papers at SC20 and HPEC20 won the best paper award and the Sparse Challenge championship title, respectively.

Tirthak Patel
Patel's research addresses the challenge of erroneous program executions on quantum computers and provides robust solutions to improve their reliability. Despite rapid progress in quantum computing, prohibitively high noise on existing near-term intermediate-scale quantum (NISQ) computers remains a fundamental roadblock in the wider adoption of quantum computing. Due to the high noise, program executions on existing quantum computers produce erroneous program outputs. Quantum computing programmers largely lack the tools to estimate the correct output from these noisy program executions.

Patel, advised by Devesh Tiwari at Northeastern University, is designing cross-layer system software for extracting meaningful output from erroneous executions on quantum computers. In particular, he first led the effort to benchmark and characterize the performance of different quantum algorithms on IBM quantum computers. Patel leveraged insights gained from this measurement-based experimental effort to inform the design of his novel tools and methods, including VERITAS, QRAFT, UREQA, and DisQ.

For example, his technique VERITAS demonstrates how carefully-designed statistical methods can mitigate errors post-program execution and help programmers deduce the correct program output effectively. Patel's other solution, QRAFT, leverages the reversibility property of quantum operations to deduce the correct program output, even when the program is executed on qubits with relatively high error rates. Both approaches relieve programmers and compilers from the burden of selecting the qubits with the least error rate, a significant departure from existing approaches in this area. Patel's work lowers the barrier to entry into quantum computing for HPC programmers by open-sourcing multiple novel datasets and system software frameworks.

The Association for Computing Machinery and IEEE Computer Society named David Abramson, a Professor at the University of Queensland, as the recipient of the 2021 ACM-IEEE CS Ken Kennedy Award. Abramson is recognized for contributions to parallel and distributed computing tools, with application from quantum chemistry to engineering design. He is also cited for his mentorship and service to the field.

Technical Contributions
Abramson has performed pioneering research in the design, implementation, and application of software tools for parallel and distributed systems. He has conducted foundational research in distributed and parallel middleware, addressing programmer productivity and software correctness, and has influenced multiple generations of researchers. His papers have been cited more than 12,000 times.

Two highly-regarded tools developed by Abramson include Nimrod, a family of software systems that support the execution of distributed parameter sweeps, searches, and workflows; and Guard, a performance tuning and debugging tool.

The Nimrod template is common in many fields and is well suited to execution in distributed environments. Nimrod makes it possible to write concisely complex parameter sweeps—which entail executing an algorithm repeatedly with varying parameters—and supports advanced searches that integrate optimization algorithms, design of experiments methods, and scientific workflows. Additionally, the Nimrod project spawned a family of tools that make it easy to specify complex computational experiments and has resulted in a spinoff commercial product called EnFuzion, which has been widely adopted for power grid and simulation.

Abramson designed Guard with a hybrid debugging scheme that tests new versions of a program against reference versions known to be correct. Guard greatly enhances programmers’ ability to locate and fix errors in new software versions. The technology was licensed to Cray Inc. (now HPE) and is distributed on Cray supercomputers. As a result, it has been deployed at major international supercomputing centers, including the US National Energy Research Scientific Computing Center (NERSC) and the Swiss National Supercomputing Centre (CSCS).

Mentorship
Abramson has been an advisor to two dozen graduate students in computer science, as well as countless undergraduate and high school students.

Among his most important initiatives, Abramson has been an international driver of the PRIME initiative, an NSF-funded University of California San Diego program that enables undergraduate students to take research internships abroad. Inspired by the success of PRIME, he has introduced similar programs for Australian undergraduates to travel abroad for internships, and he has organized travels for Australian students to top research centers in the US and the UK annually for over 12 years. Since 2011, he has run a unique program that supports Australian high school students attending SC, the leading high performance computing conference.

Also in the mentoring arena, Abramson started streaming video HPC seminars that have allowed Australian students to engage with world leaders, and he launched the Early Adopters PhD Workshop at SC09. Distinct from other doctoral showcases, the workshop specifically targets research students from fields outside of computer science who are applying HPC tools in their research.

Service to the Field
Over his career, Abramson has been General Chair, Program Committee Chair, or program committee member of many conferences related to performance and programmer productivity (on average about eight per year), including IPDPS, HiPC, HPC Asia, HPDC, ICPADS, Cluster, SC, CCGrid, Grid and e-Science.

He is currently the Chair of the e-Science Steering Committee and has served in several senior roles in the IEEE/ACM SC series, including Chair of the Technical Papers Committee (2021) and Test of Time Award Committee (2018), Co-chair of the Invited Speakers Committee (2019) and “More than HPC” Plenary Committee (2020).

ACM and IEEE CS co-sponsor the Kennedy Award, which was established in 2009 to recognize substantial contributions to programmability and productivity in computing and significant community service or mentoring contributions. It was named for the late Ken Kennedy, founder of Rice University’s computer science program and a world expert on high performance computing. The Kennedy Award carries a US $5,000 honorarium endowed by IEEE CS and ACM. The award will be formally presented to Abramson in November at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21).

A Professor at the University of Queensland, Abramson is currently the Chair of the e-Science Steering Committee and has served in several senior roles in the IEEE/ACM SC series, including Chair of the Technical Papers Committee (2021) and Test of Time Award Committee (2018), Co-chair of the Invited Speakers Committee (2019) and “More than HPC” Plenary Committee (2020).

Over his career, Abramson has been General Chair, Program Committee Chair, or program committee member of many conferences related to performance and programmer productivity, and has mentored students as an advisor, through international internship programs, and by offering seminars and workshops.

Chuchu Fan is the recipient of 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 verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems.

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 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 and Ralf Jung.

Corrigan-Gibbs’s dissertation, “Protecting Privacy by Splitting Trust,” improved user privacy on the internet using techniques that combine theory and practice. Corrigan-Gibbs first develops a new type of probabilistically checkable proof (PCP), and then applies this technique to develop the Prio system, an elegant and scalable system that addresses a real industry need. Prio is being deployed at several large companies, including Mozilla, where it has been shipping in the nightly version of the Firefox browser since late 2019, the largest-ever deployment of PCPs.

Corrigan-Gibbs’s dissertation studies how to robustly compute aggregate statistics about a user population without learning anything else about the users. For example, his dissertation introduces a tool enabling Mozilla to measure how many Firefox users encountered a particular web tracker without learning which users encountered that tracker or why. The thesis develops a new system of probabilistically checkable proofs that lets every browser send a short zero-knowledge proof that its encrypted contribution to the aggregate statistics is well formed. The key innovation is that verifying the proof is extremely fast.

Corrigan-Gibbs is an Assistant Professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he is also a member of the Computer Science and Artificial Intelligence Lab. His research focuses on computer security, cryptography, and computer systems. Corrigan-Gibbs received his PhD in Computer Science from Stanford University.

Ralf Jung’s dissertation, “Understanding and Evolving the Rust Programming Language,” established the first formal foundations for safe systems programming in the innovative programming language Rust. In development at Mozilla since 2010, and increasingly popular throughout the industry, Rust addresses a longstanding problem in language design: how to balance safety and control. Like C++, Rust gives programmers low-level control over system resources. Unlike C++, Rust also employs a strong “ownership-based” system to statically ensure safety, so that security vulnerabilities like memory access errors and data races cannot occur. Prior to Jung’s work, however, there had been no rigorous investigation of whether Rust’s safety claims actually hold, and due to the extensive use of “unsafe escape hatches” in Rust libraries, these claims were difficult to assess.

In his dissertation, Jung tackles this challenge by developing semantic foundations for Rust that account directly for the interplay between safe and unsafe code. Building upon these foundations, Jung provides a proof of safety for a significant subset of Rust. Moreover, the proof is formalized within the automated proof assistant Coq and therefore its correctness is guaranteed. In addition, Jung provides a platform for formally verifying powerful type-based optimizations, even in the presence of unsafe code.

Through Jung's leadership and active engagement with the Rust Unsafe Code Guidelines working group, his work has already had profound impact on the design of Rust and laid essential foundations for its future.

Jung is a post-doctoral researcher at the Max Planck Institute for Software Systems and a research affiliate of the Parallel and Distributed Operating Systems Group at the Massachusetts Institute of Technology. His research interests include programming languages, verification, semantics, and type systems. He conducted his doctoral research at the Max Planck Institute for Software Systems, and received his PhD, Master's, and Bachelor's degrees in Computer Science from Saarland University.

Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications.” Honorable Mentions go to Henry Corrigan-Gibbs of the Massachusetts Institute of Technology and Ralf Jung of the Max Planck Institute for Software Systems and MIT.

Ralf Jung’s dissertation, “Understanding and Evolving the Rust Programming Language,” established the first formal foundations for safe systems programming in the innovative programming language Rust. In development at Mozilla since 2010, and increasingly popular throughout the industry, Rust addresses a longstanding problem in language design: how to balance safety and control. Like C++, Rust gives programmers low-level control over system resources. Unlike C++, Rust also employs a strong “ownership-based” system to statically ensure safety, so that security vulnerabilities like memory access errors and data races cannot occur. Prior to Jung’s work, however, there had been no rigorous investigation of whether Rust’s safety claims actually hold, and due to the extensive use of “unsafe escape hatches” in Rust libraries, these claims were difficult to assess.

In his dissertation, Jung tackles this challenge by developing semantic foundations for Rust that account directly for the interplay between safe and unsafe code. Building upon these foundations, Jung provides a proof of safety for a significant subset of Rust. Moreover, the proof is formalized within the automated proof assistant Coq and therefore its correctness is guaranteed. In addition, Jung provides a platform for formally verifying powerful type-based optimizations, even in the presence of unsafe code.

Through Jung's leadership and active engagement with the Rust Unsafe Code Guidelines working group, his work has already had profound impact on the design of Rust and laid essential foundations for its future.

Jung is a post-doctoral researcher at the Max Planck Institute for Software Systems and a research affiliate of the Parallel and Distributed Operating Systems Group at the Massachusetts Institute of Technology. His research interests include programming languages, verification, semantics, and type systems. He conducted his doctoral research at the Max Planck Institute for Software Systems, and received his PhD, Master's, and Bachelor's degrees in Computer Science from Saarland University.

Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications.” Honorable Mentions go to Henry Corrigan-Gibbs of the Massachusetts Institute of Technology and Ralf Jung of the Max Planck Institute for Software Systems and MIT.

Fan’s dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems. Her 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 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.

Andrew McGettrick was named recipient of the Karl V. Karlstrom Outstanding Educator Award for his scholarship and tireless volunteer work and contributions, which have fundamentally improved rigorous computer science as a field of professional practice and as an academic pursuit.

Over five decades, McGettrick, a professor at the University of Strathclyde, has consistently made outstanding contributions to computing education. At the University of Strathclyde, he drove key curriculum improvements in Computer Science and Software Engineering. Additionally, his program evaluation initiatives for other universities and colleges improved the quality and rigor of undergraduate, Master’s, and doctoral programs around the world. McGettrick’s work for the UK government, including driving the first benchmarking standard for computing degrees and chairing the five-year revision of the QAA benchmarking standard for Master’s degrees in Computing, was similarly transformative.

McGettrick has played multiple leadership roles within the British Computing Society (BCS) and has served on the ACM Education Board for two decades. With Eric Roberts, he launched ACM’s Education Council, and he served as its Chair from 2007 to 2014. Under his leadership, the council developed numerous curricular volumes including the ACM/IEEE Curriculum Task Force’s Computer Science, Software Engineering, Computer Engineering, and Overview volumes. He recently served on the ACM Education Board’s Data Science Curriculum Task Force and helped launch the Learning at Scale series of annual conferences.

McGettrick was involved in the Committee on European Computing Education and was a co-founder and member of the Steering Committee of the Informatics for All coalition, a multi-organizational advocacy body that collaborates with the European Commission.

McGettrick’s publications include more than 130 research articles, textbooks, and scholarly papers. His white papers have shaped the nature and progress of computing in Europe. He also edited or co-edited numerous influential collections, including Concurrent Programming Software Specification Techniques (1988), Software Engineering – A European Perspective (1993), and Grand Challenges in Computing (2004). McGettrick was the founding editor of Addison-Wesley’s (now Pearson’s) International Computer Science series (~100 books) and co-editor of Taylor and Francis’ Computer Science undergraduate textbook series (20 books to date).