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.

Jennifer Tour Chayes, a professor at the University of California, Berkeley, was named recipient of the ACM Distinguished Service Award for her effective leadership, mentorship, and dedication to diversity during her distinguished career of computer science research, teaching, and institution building.

Chayes’ service to the computing community is broad and sustained, encompassing leadership at both Microsoft Research and the University of California, Berkeley; service to many computing organizations; expanding the diversity of the computing field through mentorship of women, underrepresented racial minorities and other disadvantaged groups; and making important research contributions.

Chayes’ distinguished service includes founding and leading the Theory Group at Microsoft Research and the Microsoft Research New England and New York City Labs. She also had an important role in the development of Microsoft’s Montreal lab.

The MSR labs that Chayes founded had three times the percentage of women compared to corporate labs, and an unusually high percentage of people of color and members of the LGBTQ community. She has mentored more than 100 women in her career, many of whom have gone on to become leaders in their fields. Chayes continues to emphasize diversity as a core value at Berkeley in her position as Associate Provost of the Division of Computing, Data Science, and Society, and Dean of the School of Information.

Additionally, Chayes has an exceptionally strong record of service at the national and international levels to the computing community. Her service includes participation in advisory boards and committees associated with the National Academy of Sciences, the National Research Council, the American Association for the Advancement of Science, and numerous other organizations. She has served on the Turing Award committee and the Heidelberg Laureate Selection Committee. She has served as an Associate Editor for many leading journals in statistical physics, computer science, mathematics, and data science, and has served as a co-organizer of numerous conferences across these fields.

Richard Anderson, a Professor at the University of Washington, was named recipient of the Eugene L. Lawler Award for contributions bridging the fields of Computer Science, Education, and global health.

Anderson, his students and collaborators have developed a range of innovative applications in health, education, the internet, and financial services, benefiting underserved communities around the globe. He is one of the founders of the emerging field of Information and Communications Technologies for Development (ICTD), which seeks to develop and apply computing and information technologies to benefit low-income populations worldwide, particularly in developing countries.

Anderson has also led various projects using technological innovations to drive community-led video instruction and achieve success in education, agriculture, and health practice. For example, Projecting Health employs handheld projectors to show locally-produced videos to groups of women, spurring follow-up conversations on maternal and child health. Projecting Health has led to over 15,000 screenings across 180 villages, reaching an estimated 190,000 residents.

The Open Data Kit (ODK) research project is another exemplar of an open source infrastructure project revolutionizing data collection in developing regions and enabling improved learning, health care, and farming. Anderson provided leadership to the project as it transitioned from a university-led project to a free-standing organization, and continues to conduct research on expanding ODK-X, a platform for building data management applications that are having significant impact on humanitarian response, control of vector borne diseases, and country immunization systems.

Other successful partnerships have included a human milk bank project in South Africa; a mobile health communication platform for maternal and child health in Kenya; and a vaccine cold-chain project in Uganda and Pakistan.

In addition to research excellence and humanitarian projects, Anderson has played a core leadership role in bringing together several communities under the umbrella of ACM COMPASS (Computing and Sustainable Societies), and organizing and championing conferences, workshops, and tutorials, many of them in developing countries (e.g., Pakistan, Ghana, and Ecuador). Anderson has fostered a growing community of researchers, practitioners and students engaged in using computing and information technology for humanitarian causes.

The Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics recognizes an individual or group who has made a significant contribution through the use of computing technology. It is given once every two years, assuming that there are worthy recipients. The award is accompanied by a prize of $5,000.

Chris Hankin was named recipient of the Outstanding Contribution to ACM Award for fundamental contributions to ACM Europe and for bringing a European perspective to critically important ACM committees and activities.

Hankin, a professor at Imperial College London, has been a continuous member of ACM since 1994, and has made significant contributions to the association. He served on the Editorial Board of ACM Computing Surveys from the mid-1990s and acted as co-editor of the Computing Surveys Symposium on Strategic Directions for Research on Programming Languages, held at MIT in 1996 to celebrate the 50th anniversary of ACM. He served with distinction as Editor-in-Chief of ACM Computing Surveys from 2007 to 2013. He joined the Assessment and Search Committee of the Publications Board in 2015 and became Co-chair in 2017.

Hankin was elected to the ACM Europe Council in 2015 with the goal of reinforcing the policy arm of ACM in Europe. He is the co-author of two major policy papers from the Committee: the white paper on cybersecurity and the white paper on automated decision making. The first was referenced by the European Commission’s top scientific advisory group (SAM). In July 2020, he became Chair of the ACM Europe Technology Policy Committee and contributed to the enlargement and restructuring of the group, with the goal of making it the leading technology policy body in Europe.

Hankin served as Chair of the ACM Europe Council from 2017 to 2019, when he made it a priority to strengthen the visibility of ACM with younger generations in Europe. In this direction, he promoted the organization of two highly successful summer schools (organized by Yannis Ioannidis and Fabrizio Gagliardi), which addressed outstanding graduate and senior undergraduate students.

Finally, Hankin co-edited (with Panagiota Fatourou) the first CACM Special Regional Section on Europe in 2019, which offered a representative imprint of some of the most exciting activities on the continent.

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).

Marc Rotenberg was named the recipient of the 2020 ACM Policy Award for long-standing, high-impact leadership on privacy and technology policy.

Rotenberg is founder and President of the Center for AI and Digital Policy. Previously he was President and Executive Director of the Electronic Privacy Information Center (EPIC), a public interest research center he co-founded in 1994. Early in his career, he launched the Public Interest Computer Association, the first organization in the US to help nonprofits use microcomputers. Rotenberg then helped draft key US privacy and computer security laws as counsel to the Senate Judiciary Committee. He was director of the Computer Professionals for Social Responsibility (CPSR) DC office.

He was also the first ACM Director of Public Policy, and a Chair of the ACM Committee on Scientific Freedom and Human Rights. In 2020 he joined the Michael Dukakis Institute for Leadership and Innovation to launch the Center on Artificial Intelligence and Digital Policy. In late 2020, and in collaboration with others, he edited and published Artificial Intelligence and Democratic Values: The AI Social Index 2020.

A leading advocate for privacy and data protection, Rotenberg has testified before the US Congress and European Parliament more than 60 times and has filed over 150 Freedom of Information lawsuits and amicus briefs in pursuit of greater government transparency and corporate accountability. He also edited and published such landmark reports as Privacy and Human Rights: An International Survey of Privacy Laws and Developments and Cryptography and Liberty.

Rotenberg has mentored two generations of public interest attorneys through internships at EPIC, as an adjunct professor at Georgetown Law, and as the author of many textbooks and articles. He is also a leading voice for civil society at the Organisation for Economic Co-operation and Development (OECD), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and elsewhere. He helped draft and gather support for several global declarations, including The Civil Society Seoul Declaration (2008), The Madrid Privacy Declaration (2009) and The Universal Guidelines for AI (2018).

ACM and IEEE Computer Society named Margaret Martonosi, the Hugh Trumbull Adams '35 Professor of Computer Science at Princeton University, as the recipient of the 2021 Eckert-Mauchly Award for contributions to the design, modeling, and verification of power-efficient computer architecture.

Martonosi has made significant contributions in computer architecture and microarchitecture, and her work has led to new fields of research. She has authored more than 175 publications (with 17,000 + citations) on subjects including parallel architectures, memory hierarchies, compilers, and mobile networks.

Power/Thermal Aware Architectures
Martonosi was an early innovator in the design and modeling of power-aware microarchitectures, including using narrow bit-widths, modeling and responding to thermal issues, and performing power estimation, e.g., as embodied in the ubiquitous Wattch tool.

In the area of narrow bit-widths, Martonosi co-authored (with David Brooks) the paper “Dynamically Exploiting Narrow Width Operands to Improve Processor Power and Performance.” Martonosi and Brooks introduced two optimizations which greatly reduced processor power consumption. The paper earned the HPCA Test of Time Award and the optimizations were licensed to Intel. Martonosi developed subsequent microarchitectural proposals that expanded on this work.

In a later (2001) paper with David Brooks “Dynamic Thermal Management for High-Performance Microprocessors,” Martonosi investigated dynamic thermal management as a technique to control CPU power dissipation. Martonosi and Brooks demonstrated that, with appropriate thermal management, a CPU can be designed for a much lower maximum power rating, with minimal performance impact for typical applications. This was the first computer architecture paper to explicitly focus on thermal issues.

In a series of papers, Martonosi was also the first researcher to demonstrate how to use formal control-theoretic approaches to balance power and performance for dynamic voltage and frequency scaling (DVFS).

Power Simulation and Estimation
Martonosi recognized early the need for microarchitecture- and architecture-level power modeling and measurement infrastructure. She was a co-developer (with David Brooks and Vivek Tiwari) of Wattch, an architectural simulator that estimates CPU power consumption, which is used by thousands of researchers today. Wattch broke ground by demonstrating (against conventional wisdom) that accurate early-stage power models could be developed for early-stage microarchitectural design tradeoffs before more detailed computer-aided design (CAD) tools can be used. Martonosi also developed live runtime measurement tools for detailed power assessments of widely used and complex microprocessor systems.

ZebraNet Full-Stack Computing Platform
Martonosi broadened her scope beyond conventional computers to energy issues in mobile sensor networks, where energy fundamentally dictates system lifetime and data-gathering success.

Martonosi’s ZebraNet Wildlife Tracking Project established the new research field of Mobile Sensor Networks. ZebraNet collected thousands of data points on Plains Zebras in Kenya. ZebraNet developed energy-efficient protocols for short-range, pairwise data transfers. Martonosi’s work demonstrated that sparsely deployed mobile sensors could offer high data delivery rates and sensor coverage over large areas, at practical power budgets. ZebraNet provided biologists with never-before-seen animal behavior data. The work resulted in two test-of-time awards and several widely cited papers.

Memory Consistency Model Specification and Verification
Martonosi’s groundbreaking work has demonstrated the potential of fast, early-stage formal methods to verify the correctness of memory consistency model implementation. This work, embodied in the Check suite of verification tools, has had immediate and significant impact.

Modern hardware complexity also presents security challenges, and the Check suite includes efforts to provide rigorous and automated approaches for determining if a microarchitecture is susceptible to specified classes of security exploits. This kind of automatic checking will be fundamental to future information security.

Martonosi will be formally recognized with the ACM-IEEE CS Eckert-Mauchly Award during the ACM/IEEE International Symposium on Computer Architecture (ISCA), which is being held virtually this year from June 14-19.

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.

ACM named Shyamnath Gollakota, University of Washington, the recipient of the 2020 ACM Grace Murray Hopper Award for contributions to the use of wireless signals in creating novel applications, including battery-free communications, health monitoring, gesture recognition, and bio-based wireless sensing. His work has revolutionized and reimagined what can be done using wireless systems and has a feel of technologies depicted in science fiction novels.

Gollakota defined the technology referred to today as ambient backscatter—a mechanism by which an unpowered, battery-less device can harvest existing wireless signals (such as broadcast TV or WiFi) in the environment for energy and use it to transmit encoded data. In addition, he has developed techniques that can use sonar signals from smartphones to support numerous healthcare applications. Examples include detection and diagnosis of breathing anomalies such as apnea, detection of ear infections, and even detection of life-threatening opioid overdoses. These innovations have the potential to transform the way healthcare systems will be designed and delivered in the future, and some of these efforts are now being commercialized for real-world use.

Gollakota also opened up a new field of extremely lightweight mobile sensors and controllers attached to insects, demonstrating how wireless technology can stream video data from the backs of tiny insects. Some observers believe this could be a first step to creating an internet of biological things, in which insects are employed as delivery vehicles for mobile sensors.

Yossi Azar, Tel Aviv University; Andrei Broder, Google Research; Anna Karlin, University of Washington; Michael Mitzenmacher, Harvard University; and Eli Upfal, Brown University, receive the ACM Paris Kanellakis Theory and Practice Award for the discovery and analysis of balanced allocations, known as the power of two choices, and their extensive applications to practice.

Azar, Broder, Karlin, Mitzenmacher and Upfal introduced the Balanced Allocations framework, also known as the power of two choices paradigm, an elegant theoretical work that had a widespread practical impact.

When n balls are thrown into n bins chosen uniformly at random, it is known that with high probability, the maximum load on any bin is bounded by (lg n/lg lg n) (1+o(1)). Azar, Broder, Karlin, and Upfal (STOC 1994) proved that adding a little bit of choice makes a big difference. When throwing each ball, instead of choosing one bin at random, choose two bins at random, and then place the ball in the bin with the lesser load. This minor change brings on an exponential improvement; now with high probability, the maximal load in any bin is bounded by (lg lg n/lg 2)+O(1).

In the same work, they have shown that, if each ball has d choices, then the maximum load drops with high probability to (ln ln n/ ln d)+O(1). These results were greatly extended by Mitzenmacher in his 1996 PhD dissertation, where he removed the sequential setting, and developed a framework for using the power of two choices in queueing systems.

Since bins and balls are the basic model for analyzing data structures, such as hashing or processes like load balancing of jobs in servers, it is not surprising that the power of two choices that requires only a local decision rather than global coordination has led to a wide range of practical applications. These include i-Google's web index, Akamai’s overlay routing network, and highly reliable distributed data storage systems used by Microsoft and Dropbox, which are all based on variants of the power of two choices paradigm. There are many other software systems that use balanced allocations as an important ingredient.

The Balanced Allocations paper and the follow-up work on the power of two choices are elegant theoretical results, and their content had, and will surely continue to have, a demonstrable effect on the practice of computing.

Hector Levesque and Moshe Vardi receive the ACM - AAAI Allen Newell Award.

Hector Levesque, University of Toronto, is recognized for fundamental contributions to knowledge representation and reasoning, and their broader influence within theoretical computer science, databases, robotics, and the study of Boolean satisfiability.

Levesque is recognized for his outstanding contributions to the broad core of logic-inspired artificial intelligence and the impact they have had across multiple sub-disciplines within computer science. With collaborators, he has made fundamental contributions to cognitive robotics, multi-agent systems, theoretical computer science, and database systems, as well as in philosophy and cognitive psychology. These have inspired applications such as the semantic web and automated verification. He is internationally recognized as one of the deepest and most original thinkers within AI and a researcher who has advanced the flame that AI pioneer Alan Newell lit.

On the representation side, Levesque has worked on the formalization of several concepts pertaining to artificial and natural agents including belief, goals, intentions, ability, and the interaction between knowledge, perception and action.

On the reasoning side, his research has focused on how automated reasoning can be kept computationally tractable, including the use of greedy local search methods. He is recognized for his fundamental contributions to the development of several new fields of research including the fields of description logic, the study of tractability in knowledge representation, the study of intention and teamwork, the hardness of satisfiability problems, and cognitive robotics. Levesque has also made fundamental contributions to the development of the systematic use of beliefs, desires, and intentions in the development of intelligent software, where his formalization of many aspects of intention and teamwork has shaped the entire approach to the use of these terms and the design of intelligent agents.

Moshe Vardi, Rice University, is cited for contributions to the development of logic as a unifying foundational framework and a tool for modeling computational systems.

Vardi has made major contributions to a wide variety of fields, including database theory, program verification, finite-model theory, reasoning about knowledge, and constraint satisfaction. He is perhaps the most influential researcher working at the interface of logic and computer science, building bridges between communities in computer science and beyond. With his collaborators he has made fundamental contributions to major research areas, including: 1) investigation of the logical theory of databases, where his focus on the trade-off between expressiveness and computational complexity laid the foundations for work on integrity constraints, complexity of query evaluation, incomplete information, database updates, and logic programming in databases; 2) the automata-theoretic approach to reactive systems, which laid mathematical foundations for verifying that a program meets its specifications, and 3) reasoning about knowledge through his development of epistemic logic.

In database theory, Vardi developed a theory of general data dependencies, finding axiomatizations and resolving their decision problem; introduced two basic notions of measuring the complexity of algorithms for evaluating queries, data-complexity, and query-complexity, which soon became standard in the field; created a logical theory of data updates; and characterized the expressive power of query languages and related them to complexity classes.

In software and hardware verification, Vardi introduced an automata-theoretic approach to the verification of reactive systems that revolutionized the field, using automata on infinite strings and trees to represent both the system being analyzed and undesirable computations of the system. Vardi’s automata-theoretic approach has played a central role over the last 30 years of research in the field and in the development of verification tools.

In knowledge theory, Vardi developed rigorous foundations for reasoning about the knowledge of multi-agent and distributed systems, a problem of central importance in many disciplines; his co- authored book on the subject is the definitive source for this field.

Margo Seltzer, University of British Columbia; Mike Olson, formerly of Cloudera; and Keith Bostic, MongoDB, receive the ACM Software System Award for Berkeley DB, which was an early exemplar of the NoSQL movement and pioneered the “dual-license” approach to software licensing.

Since 1991, Berkeley DB has been a pervasive force underlying the modern internet: it is a part of nearly every POSIX or POSIX-like system, as well as the GNU standard C library (glibc) and many higher-level scripting languages. Berkeley DB was the transactional key/value store for a range of first- and second-generation internet services, including account management, mail and identity servers, online trading platforms and many other software-as-a-service platforms.

As an open source package, Berkeley DB is an invaluable teaching tool, allowing students to see under the hood of a tool that they have grown familiar with by use. The code is clean, well structured, and well documented—it had to be, as it was meant to be consumed and used by an unlimited number of software developers.

As originally created by Seltzer, Olson and Bostic, Berkeley DB was distributed as part of the University of California’s Fourth Berkeley Software Distribution. Seltzer and Bostic subsequently founded Sleepycat Software in 1996 to continue development of Berkeley DB and provide commercial support. Olson joined in 1997, and for 10 years, Berkeley DB was the de facto data store for major web infrastructure. As the first production quality, commercial key/value store, it helped launched the NoSQL movement; as the engine behind Amazon’s Dynamo and the University of Michigan’s SLAPD server, Berkeley DB helped move non-relational databases into the public eye.

Sleepycat Software pioneered the “dual-license” model of software licensing: use and redistribution in Open Source applications was always free, and companies could choose a commercial license for support or to distribute Berkeley DB as part of proprietary packages. This model pointed the way for a number of other open source companies, and this innovation has been widely adopted in open source communities. The open source Berkeley DB release includes all the features of the complete commercial version, and developers building prototypes with open source releases suffer no delay when transitioning to a proprietary product that embeds Berkeley DB.

In summary, Berkeley DB has been one of the most useful, powerful, reliable, and long-lived software packages. The longevity of Berkeley DB’s contribution is particularly impressive in an industry with frequent software system turnover.

ACM named Ayanna Howard, dean of The Ohio State University College of Engineering, as the 2021-2022 ACM Athena Lecturer. Howard is recognized for fundamental contributions to the development of accessible human-robotic systems and artificial intelligence, along with forging new paths to broaden participation in computing through entrepreneurial and mentoring efforts. Her contributions span theoretical foundations, experimental evaluation, and practical applications.

Howard is a leading roboticist, entrepreneur, and educator whose research includes dexterous manipulation, robot learning, field robotics, and human-robot interaction. She is a leader in studying the overtrust that people place in robots in various autonomous decision-making settings. In addition to her stellar research record, Howard has a strong record of service that demonstrates her commitment to advancing the field and broadening participation.

“Ayanna Howard is a trailblazer in vital research areas, including topics such as trust and bias in AI, which will continue to be front-and-center in society in the coming years,” said ACM President Gabriele Kotsis. “The quality of her research has made her a thought leader in developing accessible human-robot interaction systems. Both as an entrepreneur and mentor, Ayanna Howard has worked to increase the participation of women and underrepresented groups in computing. For all these reasons, she is precisely the kind of leader ACM seeks to recognize with the Athena Lecturer Award.”

Initiated in 2006, the ACM Athena Lecturer Award celebrates women researchers who have made fundamental contributions to computer science. The award carries a cash prize of $25,000, with financial support provided by Two Sigma. The Athena Lecturer gives an invited talk at a major ACM conference of her choice.

KEY TECHNICAL CONTRIBUTIONS

Robotic Manipulation
Her doctoral research on dexterous robotic manipulation of deformable objects proposed some of the first ideas on the modeling of deformable objects via physical simulation, such that they could be robustly grasped by robot arms. This work also demonstrated how neural networks could be trained to extract the minimum force required for subsequent deformable object manipulation tasks.

Terrain Classification of Field Robots
Terrain classification is critical for many robots operating in unstructured natural field environments, including navigating the Arctic or determining safe landing locations on the surface of Mars. Howard’s work introduced fuzzy logic methods to model environmental uncertainty that advanced the state of the art in field robotics, including finding evidence of never-before-observed life on Antarctica’s sea floor.

Robotics for Children with Special Needs
Howard studied the ways in which socially-effective robots could improve the access and scalability of services for children with special needs, as well as potentially improve outcomes through the engaging nature of robots. In adapting her contributions to real-world settings for assistive technology for children, her work has also provided first-of-its-kind computer vision techniques that analyze the movement of children to devise therapeutic measures.

Overtrust in Robotics and AI Systems
Howard is a leader in modeling trust among humans, robots and AI systems, including conversational agents, emergency response scenarios, autonomous navigation systems, child-robot interaction, and the use of lethal force. Her work introduced human-robot interaction algorithms that, for the first time, quantified the impact of robot mistakes on human trust in a realistic, simulated, and very high-risk scenario. This work has led to better understanding of the biases and social inequities underlying AI and robotic systems.

BROADENING PARTICIPATION/SERVICE TO THE FIELD

Howard has created and led numerous programs designed to engage, recruit, and retain students and faculty from groups that are historically underrepresented in computing, including several NSF-funded Broadening Participation in Computing initiatives. She was the principal investigator for (PI)/co-PI for Popularizing Computing in the Mainstream, which focused on creating interventions to engage underrepresented groups in the computing field; Advancing Robotics for Societal Impact Alliance , an initiative to provide mentorship to computer science faculty and students at Historically Black Colleges and Universities (HBCUs); and Accessible Robotic Programming for Students with Disabilities, an initiative to engage middle- and high school students with disabilities in robotics-based programming activities. She also led and co-founded efforts to broaden participation in the field through the IEEE Robotics PhD Forum and the CRA-WP Graduate Cohort Workshop for Inclusion, Diversity, Equity, Accessibility, and Leadership Skills.

As part of her service to the field, Howard has held key roles on various editorial boards and conference/program committees. Some of her more high-profile efforts have included co-organizing the AAAI Symposium on Accessible Hands-on AI and Robotics Education, the International Joint Conference on Neural Networks, the International Conference on Social Robotics, and the IEEE Workshop on Advanced Robotics and Its Social Impacts.

ACM named Michael Franz of the University of California, Irvine the recipient of the 2020 ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award. Franz is recognized for the development of just-in-time compilation techniques that enable fast and feature-rich web services on the internet. Every day, millions of people around the world use online applications such as Gmail and Facebook. These web applications would not have been possible without the groundbreaking compilation technique Franz developed in the mid 1990s.

Beginning with the PhD thesis he completed in 1994, Franz has been exploring the use of just-in-time (JIT) dynamic compilation and optimization, focusing not only on static languages such as Java, but also on dynamically-typed languages. Initially such dynamic languages were primarily used in research and academic settings, but that changed when JavaScript was adopted for creating web services. JavaScript enabled the creation of websites that had application-like behavior, rather than the more static websites enabled by HTML. JavaScript, like other dynamic languages, was initially interpreted, and that led to poor performance. By inventing a new compilation technique, developing a JIT compiler for JavaScript based on this new technique, and then collaborating with Mozilla to incorporate it into the Firefox browser, Franz enabled massive growth in the use of JavaScript, now one of the world’s most heavily used programming languages.

“We all use web-based applications every day and they are now so prevalent that we often forget how revolutionary they were when they were first introduced,” said ACM President Gabriele Kotsis. “Whether we’re connecting with friends or colleagues on a social media platform, preparing our taxes using online software, or booking an accommodation at a hotel, we are using a web-based application. Michael Franz’s work certainly fits the Thacker Award’s criteria for ‘leapfrog contributions to computing ideas and technologies.’ Franz displayed foresight in working with Mozilla to implement his ideas on their browser and in making his technology open source so that it could be continually refined and adapted by developers worldwide.”

The idea of JIT dynamic compilation goes back decades and was initially used for a variety of statically-typed-languages. In the 1970s, researchers at the Xerox Palo Alto Research Center used JIT compilation for Smalltalk, a dynamically-typed language. In the 1980s, researchers at Stanford and Sun explored the use of JITs for Self, a dynamically typed, prototype-based language similar to JavaScript. Franz made several important contributions beyond this earlier work that greatly increased the practicality of JIT compilation.

First, rather than optimizing entire functions, he introduced a technique that optimizes only the loops of a program, using a structure called a “trace tree” to represent alternative paths through a loop that are discovered and subsequently translated incrementally. Second, Franz developed a JIT compiler that could be applied in a variety of settings, including those with more limited CPU or memory resources. With these techniques, Franz’s JIT compiler could often achieve performance improvements of 5-10x on JavaScript, which was critical to its wide-ranging adoption and the transformation of web applications. Most websites today use JavaScript, and all browsers include a JavaScript execution engine. Franz’s technology helped make this transformation possible.

“Microsoft is proud to fund the Breakthrough in Computing Award, named after Chuck Thacker, one of the computing field’s true visionaries,” said Eric Horvitz, Microsoft’s Chief Scientific Officer. “Chuck had a magical ability to transform over-the-horizon computing dreams into world-changing realities. Michael Franz’s work on just-in-time compilation is a great choice for the Breakthrough in Computing honor. His work has been transformative, enabling today’s rich web experiences by allowing websites to execute sophisticated, interactive programs nearly instantaneously. Michael Franz’s insights, and his successful application of those insights, have had tremendous real-world impact.”

Biographical Background

Michael Franz is a Chancellor's Professor in the Department of Computer Science at the University of California (UC), Irvine where he also directs the Secure Systems and Software Laboratory. His current research emphasis is in software systems, particularly focusing on compiler, virtual machine, and related system-level techniques for making software safer, or faster, or both.

Franz received a Doctor of Technical Sciences degree in Computer Science and a Diplomingenieur, Informatik-Ing. ETH degree, both from the Swiss Federal Institute of Technology (ETH Zurich). His honors include receiving a Humboldt Research Award from the Alexander von Humboldt Foundation, a National Science Foundation CAREER Award, an IEEE Computer Society Technical Achievement Award, and a Distinguished Mid-Career Faculty Award for Research from the University of California, Irvine. Franz is a Fellow of ACM, the Institute of Electrical and Electronics Engineers (IEEE), the American Association for the Advancement of Science (AAAS), and the International Federation for Information Processing (IFIP).

ACM named Scott Aaronson the recipient of the 2020 ACM Prize in Computing for groundbreaking contributions to quantum computing. Aaronson is the David J. Bruton Jr. Centennial Professor of Computer Science at the University of Texas at Austin.

The goal of quantum computing is to harness the laws of quantum physics to build devices that can solve problems that classical computers either cannot solve, or not solve in any reasonable amount of time. Aaronson showed how results from computational complexity theory can provide new insights into the laws of quantum physics, and brought clarity to what quantum computers will, and will not, be able to do.

Aaronson helped develop the concept of quantum supremacy, which denotes the milestone that is achieved when a quantum device can solve a problem that no classical computer can solve in a reasonable amount of time. Aaronson established many of the theoretical foundations of quantum supremacy experiments. Such experiments allow scientists to give convincing evidence that quantum computers provide exponential speedups without having to first build a full fault-tolerant quantum computer.

“Few areas of technology have as much potential as quantum computation,” said ACM President Gabriele Kotsis. “Despite being at a relatively early stage in his career, Scott Aaronson is esteemed by his colleagues for the breadth and depth of his contributions. He has helped guide the development of this new field, while clarifying its possibilities as a leading educator and superb communicator. Importantly, his contributions have not been confined to quantum computation, but have had significant impact in areas such as computational complexity theory and physics.”

Notable Contributions

Boson Sampling: In the paper “The Computational Complexity of Linear Optics,” Aaronson and co-author Alex Arkhipov gave evidence that rudimentary quantum computers built entirely out of linear-optical elements cannot be efficiently simulated by classical computers.

Aaronson has since explored how quantum supremacy experiments could deliver a key application of quantum computing, namely the generation of cryptographically random bits.

Fundamental Limits of Quantum Computers: In his 2002 paper “Quantum lower bound for the collision problem,” Aaronson proved the quantum lower bound for the collision problem, which was a major open problem for years. This work bounds the minimum time for a quantum computer to find collisions in many-to-one functions, giving evidence that a basic building block of cryptography will remain secure for quantum computers.

Classical Complexity Theory: Aaronson is well-known for his work on “algebrization”, a technique he invented with Avi Wigderson to understand the limits of algebraic techniques for separating and collapsing complexity classes.

Making Quantum Computing Accessible: Beyond his technical contributions, Aaronson is credited with making quantum computing understandable to a wide audience. Through his many efforts, he has become recognized as a leading spokesperson for the field. He maintains a popular blog, Shtetl Optimized, where he explains timely and exciting topics in quantum computing in a simple and effective way. His posts, which range from fundamental theory questions to debates about current quantum devices, are widely read and trigger many interesting discussions.

Aaronson also authored Quantum Computing Since Democritus, a respected book on quantum computing, written several articles for a popular science audience, and presented TED Talks to dispel misconceptions and provide the public with a more accurate overview of the field.

“Infosys is proud to fund the ACM Prize in Computing and we congratulate Scott Aaronson on being this year’s recipient,” said Pravin Rao, COO of Infosys. “When the effort to build quantum computation devices was first seriously explored in the 1990s, some labeled it as science fiction. While the realization of a fully functional quantum computer may still be in the future, this is certainly not science fiction. The successful quantum hardware experiments by Google and others have been a marvel to many who are following these developments. Scott Aaronson has been a leading figure in this area of research and his contributions will continue to focus and guide the field as it reaches its remarkable potential.”

ACM named Alfred Vaino Aho and Jeffrey David Ullman recipients of the 2020 ACM A.M. Turing Award for fundamental algorithms and theory underlying programming language implementation and for synthesizing these results and those of others in their highly influential books, which educated generations of computer scientists. Aho is the Lawrence Gussman Professor Emeritus of Computer Science at Columbia University. Ullman is the Stanford W. Ascherman Professor Emeritus of Computer Science at Stanford University.

Computer software powers almost every piece of technology with which we interact. Virtually every program running our world—from those on our phones or in our cars to programs running on giant server farms inside big web companies—is written by humans in a higher-level programming language and then compiled into lower-level code for execution. Much of the technology for doing this translation for modern programming languages owes its beginnings to Aho and Ullman.

Beginning with their collaboration at Bell Labs in 1967 and continuing for several decades, Aho and Ullman have shaped the foundations of programming language theory and implementation, as well as algorithm design and analysis. They made broad and fundamental contributions to the field of programming language compilers through their technical contributions and influential textbooks. Their early joint work in algorithm design and analysis techniques contributed crucial approaches to the theoretical core of computer science that emerged during this period.

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.

“The practice of computer programming and the development of increasingly advanced software systems underpin almost all of the technological transformations we have experienced in society over the last five decades,” explains ACM President Gabriele Kotsis. “While countless researchers and practitioners have contributed to these technologies, the work of Aho and Ullman has been especially influential. They have helped us to understand the theoretical foundations of algorithms and to chart the course for research and practice in compilers and programming language design. Aho and Ullman have been thought leaders since the early 1970s, and their work has guided generations of programmers and researchers up to the present day.”

“Aho and Ullman established bedrock ideas about algorithms, formal languages, compilers and databases, which were instrumental in the development of today’s programming and software landscape,” added Jeff Dean, Google Senior Fellow and SVP, Google AI. “They have also illustrated how these various disciplines are closely interconnected. Aho and Ullman introduced key technical concepts, including specific algorithms, that have been essential. In terms of computer science education, their textbooks have been the gold standard for training students, researchers, and practitioners.”

A Longstanding Collaboration

Aho and Ullman both earned their PhD degrees at Princeton University before joining Bell Labs, where they worked together from 1967 to 1969. During their time at Bell Labs, their early efforts included developing efficient algorithms for analyzing and translating programming languages.

In 1969, Ullman began a career in academia, ultimately joining the faculty at Stanford University, while Aho remained at Bell Labs for 30 years before joining the faculty at Columbia University. Despite working at different institutions, Aho and Ullman continued their collaboration for several decades, during which they co-authored books and papers and introduced novel techniques for algorithms, programming languages, compilers and software systems.

Influential Textbooks

Aho and Ullman co-authored nine influential books (including first and subsequent editions). Two of their most widely celebrated books include:

The Design and Analysis of Computer Algorithms (1974)
Co-authored by Aho, Ullman, and John Hopcroft, this book is considered a classic in the field and was one of the most cited books in computer science research for more than a decade. It became the standard textbook for algorithms courses throughout the world when computer science was still an emerging field. In addition to incorporating their own research contributions to algorithms, The Design and Analysis of Computer Algorithms introduced the random access machine (RAM) as the basic model for analyzing the time and space complexity of computer algorithms using recurrence relations. The RAM model also codified disparate individual algorithms into general design methods. The RAM model and general algorithm design techniques introduced in this book now form an integral part of the standard computer science curriculum.

Principles of Compiler Design (1977)
Co-authored by Aho and Ullman, this definitive book on compiler technology integrated formal language theory and syntax-directed translation techniques into the compiler design process. Often called the “Dragon Book” because of its cover design, it lucidly lays out the phases in translating a high-level programming language to machine code, modularizing the entire enterprise of compiler construction. It includes algorithmic contributions that the authors made to efficient techniques for lexical analysis, syntax analysis techniques, and code generation. The current edition of this book, Compilers: Principles, Techniques and Tools (co-authored with Ravi Sethi and Monica Lam), was published in 2007 and remains the standard textbook on compiler design.

Biographical Background

Alfred Vaino Aho
Alfred Aho is the Lawrence Gussman Professor Emeritus at Columbia University. He joined the Department of Computer Science at Columbia in 1995. Prior to Columbia, Aho was Vice President of Computing Sciences Research at Bell Laboratories where he worked for more than 30 years. A graduate of the University of Toronto, Aho earned his Master’s and PhD degrees in Electrical Engineering/Computer Science from Princeton University.

Aho’s honors include the IEEE John von Neumann Medal and the NEC C&C Foundation C&C Prize. He is a member of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the Royal Society of Canada. He is a Fellow of ACM, IEEE, Bell Labs, and the American Association for the Advancement of Science.

Jeffrey David Ullman
Jeffrey Ullman is the Stanford W. Ascherman Professor Emeritus at Stanford University and CEO of Gradiance Corporation, an online learning platform for various computer science topics. He joined the faculty at Stanford in 1979. Prior to Stanford, he served on the faculty of Princeton University from 1969 to 1979, and was a member of the technical staff at Bell Labs from 1966 to 1969. A graduate of Columbia University, Ullman earned his PhD in Computer Science from Princeton University.

Ullman’s honors include receiving the IEEE John von Neumann Medal, the NEC C&C Foundation C&C Prize, the Donald E. Knuth Prize, and the ACM Karl V. Karlstrom Outstanding Educator Award. He is a member of the US National Academy of Engineering, the National Academy of Sciences, and the American Academy of Arts and Sciences, and is an ACM Fellow.

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 winner 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.

Sahithi Ankireddy, James B. Conant High School, Hoffman Estates, Illinois
“BEEP... BEEP...BEEP! The jarring noise was accompanied by the neon green waves bouncing up and down every few seconds. Fixated on the heart monitor, I followed the pattern, hoping the 'beep' would continue in order to indicate the survival of the patient—my father.”

Sahithi Ankireddy used the experience of her father’s heart attack to identify ways to detect heart disease faster and easier in those who aren’t deemed “at risk.” Recalling an article she read about the use of artificial intelligence in speeding up the process of diagnosis. In her project, Assistive Heart Disease Diagnostic Tool using Machine Learning and Deep Neural Networks, Ankireddy tested both machine learning models and deep neural networks using a publicly available heart disease database. Through her testing, Ankireddy recognized the Random Forest ML model was the best method for her project. Ankireddy sees her research and assistive heart disease diagnostic tool as helpful in resource-constrained environments. By using this tool, doctors can evaluate more people in less time and provide treatment to patients more quickly. Ankireddy is currently in the process of working with cardiologists to receive feedback on this tool.

Maurice Korish, Rae Kushner Yeshiva High School, Livingston, New Jersey
The United States Census Bureau cites that 9.4 million noninstitutionalized adults have difficulty with at least one daily activity—including eating. While technology exists to support these individuals, it often requires the person using the technology to remain in the same position during the feeding process. Maurice Korish has developed FeedBot to provide independence and a cost-effective solution for disabled people who are unable to properly use their upper limbs. FeedBot implements facial recognition technology to identify the location of an individual’s mouth. This information is then transmitted to a robotic feeding arm, which is also able to be controlled manually with a joystick. Korish has taken advantage of and is building upon open source libraries, and uses Raspberry Pi, to keep this solution low cost. The use of Raspberry Pi also allows for more mobility than a standard computer, providing more comfort and flexibility for the person using FeedBot.

Brian Minnick, Loudoun Valley High School, Purcellville, Virginia
In his project, Controlling a Fully 3D Printed 3D Printer Without Microprocessors, Brian Minnick looks to allow the printer to function without conventional parts. Minnick has created the first fully 3D printed 3D printer to demonstrate self-manufacture, and along with universality, or the ability to make many useful parts, not just duplicates of itself, marks the half-way point in the development of the technologies behind the self-replicating spacecraft. It also contains the first motor controller for a 3D printer that can be built without a microprocessor. Minnick has created this printer as a stepping-stone toward a self-replicating spacecraft.

Emily Yuan, Thomas S. Wootton High School, Rockville, Maryland
In the United States, more than half of violent crimes are not reported. And while most victims of violent crimes seek out medical treatment, the current system they use to report details provides general, unmappable data. Others choose not to share data because of fear. To address these issues, Emily Yuan created Spatial Drilldown, a visual interactive mapping system where users click down on parcels on a map to report incident locations. The goal of this application was to ensure the preservation of privacy. Yuan worked with the CDC research team and nurses from Atlanta Grady Memorial Hospital to test this prototype. Spatial Drilldown provides a novel, interactive technique for collecting crime data, specifically that which can be mapped, and thus, improving the quality of current violence data. Yuan hopes to integrate the application into electronic medical records systems for real use and expand the crime data to help reduce local violence.

“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.”

George Karniadakis of Brown University was awarded the 2021 SIAM/ACM Prize in Computer Science and Engineering at the SIAM Conference on Computational Science and Engineering (CSE 2021).

Karniadakis is the Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering at Brown University.

The prize honors Karniadakis for advancing spectral elements, reduced-order modeling, uncertainty quantification, dissipative particle dynamics, fractional PDEs, and scientific machine learning, while pushing applications to extreme computational scales and mentoring many leaders.

A Fellow of SIAM, Karniadakis's work has been cited more than 53,500 times.

For more information read the SIAM news release.

ACM, the Association for Computing Machinery, has named 95 members ACM Fellows for wide-ranging and fundamental contributions in areas including artificial intelligence, cloud computing, computer graphics, computational biology, data science, human-computer interaction, software engineering, theoretical computer science, and virtual reality, among other areas. The accomplishments of the 2020 ACM Fellows have driven innovations that ushered in significant improvements across many areas of technology, indus.try, and personal life.

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.

"This year our task in selecting the 2020 Fellows was a little more challenging, as we had a record number of nominations from around the world,” explained ACM President Gabriele Kotsis. “The 2020 ACM Fellows have demonstrated excellence across many disciplines of computing. These men and women have made pivotal contributions to technologies that are transforming whole industries, as well as our personal lives. We fully expect that these new ACM Fellows will continue in the vanguard in their respective fields."

Underscoring ACM’s global reach, the 2020 Fellows represent universities, corporations and research centers in Australia, Austria, Canada, China, Germany, Israel, Japan, The Netherlands, South Korea, Spain, Sweden, Switzerland, Taiwan, the United Kingdom, and the United States.

The contributions of the 2020 Fellows run the gamut of the computing field―including algorithms, networks, computer architecture, robotics, distributed systems, software development, wireless systems, and web science―to name a few.

Additional information about the 2020 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows site.

News release

ACM has named 64 Distinguished Members for outstanding contributions to the field. All 2020 inductees are longstanding ACM members and were selected by their peers for a range of accomplishments that have contributed to technologies that move the computing field forward.

"The active participation of ACM members, in our organization, and in the field more broadly, is the foundation of a global scientific society,” explains ACM President Gabriele Kotsis. “With the Distinguished Member designation, ACM celebrates specific contributions of these members and their career growth as reflected in a long-term commitment to the field, as well as their collaboration with peers in supporting a global professional association for the benefit of all."

The 2020 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Canada, China, India, Qatar, Singapore, Spain, Sweden, Taiwan, the United Kingdom and the United States. These innovators have made contributions in a wide range of technical areas including data science, mobile and pervasive computing, artificial intelligence, computer science education, computer engineering, graphics, cybersecurity, and networking, among many other areas.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience as well as 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 and/or information technology. In addition, it is expected that a Distinguished Member serves as a mentor and role model, guiding technical career development and contributing to the field beyond the norm.

ACM news release

ACM, the Association for Computing Machinery, named a nine-member team, drawn from Chinese and American institutions, recipients of the 2020 ACM Gordon Bell Prize for their project, “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning.”

Winning team members include Weile Jia, University of California, Berkeley; Han Wang, Institute of Applied Physics and Computational Mathematics (Beijing, China); Mohan Chen, Peking University; Denghui Lu, Peking University; Lin Lin, University of California, Berkeley and Lawrence Berkeley National Laboratory; Roberto Car, Princeton University; Weinan E, Princeton University; and Linfeng Zhang, Princeton University.

The famed physicist Richard Feynman once said, “If we were to name the most powerful assumption of all, which leads one on and on to an attempt to understand life, it is that all things are made of atoms, and that everything that living things do can be understood in terms of the jiggling and wiggling of atoms.” Molecular dynamics (MD) is a computer simulation method that analyzes how atoms and molecules move and interact during a fixed period of time. MD simulations allow scientists to gain a better sense of how a system (which could include anything from a single cell to a cloud of gas) progresses over time. Practical applications of molecular dynamics include studying large molecules such as proteins for drug development.

Ab initio (meaning in Latin “from the beginning” or “from first principles”) Molecular Dynamics (AIMD) is an approach that differs slightly from Standard Molecular Dynamics (SMD) in how interatomic forces are calculated during the simulation. The level of precision that can be gained through AIMD has made it the preferred simulation method of scientists for more than 35 years. At the same time, while AIMD allows for greater accuracy, the approach requires more computation—and has therefore been limited to the study of small-sized systems (systems that have a maximum size of thousands of atoms).

In their Gordon Bell Prize-winning paper, the team introduced Deep Potential Molecular Dynamics (DPMD). DPMD is a new machine learning-based protocol that can simulate a more than 1 nanosecond-long trajectory of over 100 million atoms per day. While other machine learning-based protocols have been introduced for MD simulations in recent years, the authors contend that their protocol achieves the first efficient MD simulation of 100 million atoms with ab initio accuracy.

As the Gordon Bell Prize recognizes achievement in high performance computing, finalists must demonstrate that their proposed algorithm can scale (run efficiently) on the world’s most powerful supercomputers. The team developed a highly optimized code (GPU Deep MD-Kit), which they successfully ran on the Summit supercomputer. The team’s GPU Deep MD-Kit efficiently scaled up to the entire Summit supercomputer, attaining 91 PFLOPS (1 PFLOP = 1 quadrillion floating operation points per second) in double precision (45.5% of the peak) and 162/275 PFLOPS in mixed-single/half precision.

The Summit supercomputer, developed by IBM for the (US) Oak Ridge National Laboratory, was the first supercomputer to reach exaflop speed (1 quintillion operations per second), and was the world’s fastest supercomputer from November 2018 to June 2020.

In the abstract of their paper, the Gordon Bell Prize winning team wrote, “The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.”

The award was presented by ACM President Gabriele Kotsis and Bronis de Supinski, Chair of the 2020 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC20), which was held virtually for the first time.

The Association for Computing Machinery (ACM) and IEEE Computer Society IEEE-CS) named Vivek Sarkar of the Georgia Institute of Technology as the recipient of the 2020 ACM-IEEE CS Ken Kennedy Award. Sarkar is recognized for "foundational technical contributions to the area of programmability and productivity in parallel computing, as well as leadership contributions to professional service, mentoring, and teaching."

The Kennedy Award recognizes Sarkar’s leadership in several areas. Sarkar has made foundational technical contributions to programmability and productivity in parallel computing, and has developed innovative programming-model, compiler, and runtime technologies for parallel computing that have influenced other researchers, as well as industry products and standards. Sarkar has led open source software projects that have had significant impact on the research community, he has created new pedagogic materials to make parallel programming more accessible to undergraduate students using the Coursera learner community, and has mentored junior colleagues at IBM and several PhD students after moving to academia. He has also demonstrated leadership in community service by serving as program chair and general chair for major conferences in his research area, serving on US Department of Energy’s Advanced Scientific Computing Advisory Committee (ASCAC) advisory committee since 2009, and on the Computing Research Association (CRA) Board of Directors since 2015.

ACM and the IEEE Computer Society 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 $5,000 honorarium endowed by IEEE-CS and ACM.

Kazem Cheshmi of the University of Toronto, Madhurima Vardhan Duke University, and Keren Zhou of Rice University are the recipients of the 2020 ACM-IEEE CS George Michael Memorial HPC Fellowships. Cheshmi is recognized for his work building a Sympiler that automatically generates efficient parallel code for sparse scientific applications on supercomputers. Vardhan is recognized for her work developing a memory-light massively parallel computational fluid dynamic algorithm using routine clinical data to enable high-fidelity simulations at ultrahigh resolutions. Zhou is recognized for his work developing performance tools for GPU-accelerated applications. The Fellowships are jointly presented by ACM and the IEEE Computer Society.

Kazem Cheshmi
In mathematics, a matrix is a grid (represented in a table of rows and columns) that is used to store, track and manipulate various kinds of data. In computer science, matrices are especially used in graphics, where an image is represented as a matrix in which each datapoint on the matrix table would directly correspond to the color and/or intensity of a given pixel. Matrix computations have a wide range of practical uses. For example, a 3D graphics programmer would hold all the datapoints related to an image as elements of the matrix and might make matrix computations to cause the image to rotate or scale. Matrix computations also play an essential role in computer vision, a branch of AI in which a computer learns to identify an image.

Historically, mathematicians would develop algorithms for matrix computations, and software engineers would write programs to make the algorithms run on powerful parallel computers. However, the emergence of massive datasets has meant that traditional approaches to matrix computation are often inadequate for the enormous matrices, requiring complex algorithms that are increasingly used today in areas such as data analytics, machine learning, and high performance computing.

To address this problem, Cheshmi has developed Sympiler, a domain-specific compiler (a program that translates the source code from a programming language to a code the computer can understand). Cheshmi’s Sympiler generates high performance codes for sparse numerical methods and can process complex matrix computations derived from massive datasets. Sympiler is extended to nonlinear optimization algorithms and performs faster than existing nonlinear optimization tools and is scalable to some of the most powerful high performance computers. Cheshmi’s work was also accepted to SIGGRAPH 2020, where he demonstrated how he is using Sympiler in robotics and graphics applications.

Madhurima Vardhan
Despite recent advances, cardiovascular disease (CVD) remains the leading cause of deaths worldwide. In the field of high performance computing, some researchers develop algorithms that are processed on powerful supercomputers to create visual simulations of complex biological processes. These simulations can be useful tools to help researchers better understand how to treat disease. Currently, a form of simulation called a computational fluid dynamic (CFD) simulation is used in health clinics to provide noninvasive diagnosis of CVDs.

However, existing state-of-the-art CFD simulations do not provide high-fidelity real-time diagnosis of CVDs. These limitations stem from a variety of factors, including problems with model accuracy based on the patient images that comprise the datasets; the extensive memory requirements of these kinds of simulations; the long runtimes on high performance computers that are required for these kinds of simulations; and teaching physicians how to effectively use these simulations.

To address these problems, Vardhan is developing a new kind CFD algorithm using routine patient image datasets that can develop high-fidelity simulations at ultra-high resolutions. Her algorithm is memory-light (that is, using less memory than existing algorithms), and massively parallel (proven to scale on supercomputers). As part of her PhD work, she also completed a study to determine how physicians interact with simulation data, and how physician behavior might be modified in treatment planning.

Keren Zhou
In the last 10 years, graphics processing units (GPUs) have become a critical component in high performance computing systems. For example, five of the top 10 supercomputers in the world today use GPUs to accelerate the performance of applications in various domains. These systems must be designed to avoid common GPU performance problems, and identifying specific performance problems can be challenging.

Working with his advisor John Mellor-Crummey and others, Zhou has taken the lead in developing performance tools for GPU-accelerated supercomputing to help programmers detect program inefficiencies and provide optimization advice.

Their work has already been well received in academia and industry. Zhou and his colleagues have published three papers in top-tier conference proceedings. They are also collaborating with GPU vendors, including AMD, Intel, and NVIDIA; they have submitted a collection of bug reports and offered advice about how to improve their GPU hardware and software measurement interfaces.

About the ACM-IEEE CS George Michael Memorial HPC Fellowship
The ACM-IEEE CS George Michael Memorial HPC Fellowship is endowed in memory of George Michael, one of the founding fathers 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 is presented at SC20, which is being held virtually this year.