ACM has named Bob Metcalfe as recipient of the 2022 ACM A.M. Turing Award for the invention, standardization, and commercialization of Ethernet.

Metcalfe is an Emeritus Professor of Electrical and Computer Engineering (ECE) at The University of Texas at Austin and a Research Affiliate in Computational Engineering at the Massachusetts Institute of Technology (MIT) Computer Science & Artificial Intelligence Laboratory (CSAIL).

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. The award is named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing.

Invention of The Ethernet

In 1973, while a computer scientist at the Xerox Palo Alto Research Center (PARC), Metcalfe circulated a now-famous memo describing a “broadcast communication network” for connecting some of the first personal computers, PARC’s Altos, within a building. The first Ethernet ran at 2.94 megabits per second, which was about 10,000 times faster than the terminal networks it would replace.

Although Metcalfe’s original design proposed implementing this network over coaxial cable, the memo envisioned “communication over an ether,” making the design adaptable to future innovations in media technology including legacy telephone twisted pair, optical fiber, radio (Wi-Fi), and even power networks, to replace the coaxial cable as the “ether.” That memo laid the groundwork for what we now know today as Ethernet.

Metcalfe’s Ethernet design incorporated insights from his experience with ALOHAnet, a pioneering computer networking system developed at the University of Hawaii. Metcalfe recruited David Boggs (d. 2022), a co-inventor of Ethernet, to help build a 100-node PARC Ethernet. That first Ethernet was then replicated within Xerox to proliferate a corporate internet.

In their classic 1976 Communications of the ACM article, “ Ethernet: Distributed Packet Switching for Local Computer Networks ,” Metcalfe and Boggs described the design of Ethernet. Metcalfe then led a team that developed the 10Mbps Ethernet to form the basis of subsequent standards.

Standardization and Commercialization

After leaving Xerox in 1979, Metcalfe remained the chief evangelist for Ethernet and continued to guide its development while working to ensure industry adoption of an open standard. He led an effort by Digital Equipment Corporation (DEC), Intel, and Xerox to develop a 10Mbps Ethernet specification—the DIX standard. The IEEE 802 committee was formed to establish a local area network (LAN) standard. A slight variant of DIX became the first IEEE 802.3 standard, which is still vibrant today.

As the founder of his own Silicon Valley Internet startup, 3Com Corporation, in 1979, Metcalfe bolstered the commercial appeal of Ethernet by selling network software, Ethernet transceivers, and Ethernet cards for minicomputers and workstations. When IBM introduced its personal computer (PC), 3Com introduced one of the first Ethernet interfaces for IBM PCs and their proliferating clones.

Today, Ethernet is the main conduit of wired network communications around the world, handling data rates from 10 Mbps to 400 Gbps, with 800 Gbps and 1.6 Tbps technologies emerging. Ethernet has also become an enormous market, with revenue from Ethernet switches alone exceeding $30 billion in 2021, according to the International Data Corporation.

Metcalfe insists on calling Wi-Fi by its original name, Wireless Ethernet, for old times’ sake.

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.

Avrim Blum, Toyota Technological Institute at Chicago; Irit Dinur, Weizmann Institute; Cynthia Dwork, Harvard University; Frank McSherry, Materialize Inc.; Kobbi Nissim, Georgetown University; and Adam Davison Smith, Boston University, receive the ACM Paris Kanellakis Theory and Practice Award for their fundamental contributions to the development of differential privacy.

Differential privacy is a definition and framework for reasoning about privacy in statistical databases. While the privacy of individuals contributing to a dataset has been a long-standing concern, prior to the Kanellakis recipients’ work, computer scientists only knew how to mitigate several specific privacy attacks via a disparate set of techniques. The foundation for differential privacy emerged in the early 2000’s from several key papers. At the ACM Symposium on the Principles of Database Systems (PODS 2003) Dinur and Nissim presented a paper which showed that any technique that allows reasonably accurate answers to a large number of queries is inherently non-private.

Later, a sequence of papers by Dwork and Nissim at the International Conference on Cryptology (Crypto 2004); as well as Blum, Dwork, McSherry, and Nissim at the ACM Symposium on the Principles of Database Systems (PODS 2005); and Dwork, McSherry, Nissim, and Smith at the Theory of Cryptology Conference (TCC 2006) further defined and studied the notion of differential privacy.

These separate but related papers formed a definition of differential privacy which captures the kind of privacy needed in statistical settings, where individual information must be protected while still allowing for discovery of common trends. These fundamental works created a vibrant and multidisciplinary area of research, leading to practical deployments of Differential Privacy in industry and by the U.S. Census Bureau, among other applications.

The authors also showed that their definition includes post-processing and composition properties that facilitate design, analysis, and applications of differentially private algorithms. The Laplace and the Gaussian noise mechanisms, which show differentially private analogs of statistical query learning algorithms, also grew out of the Kanellakis recipients’ work on differential privacy.

Raluca Ada Popa, University of California, Berkeley, is the recipient of the 2021 ACM Grace Murray Hopper Award for the design of secure distributed systems. The systems protect confidentiality against attackers with full access to servers while maintaining full functionality.

Popa’s fundamental work of building secure systems focuses on protecting the confidentiality of data stored on remote servers. Cloud computing makes sensitive data more accessible to hackers and insiders, despite the common “faulty” assumption that parts of the server–say the databaseor operating system–are inaccessible and can be “trusted." Popa’s research provides confidentiality guarantees where servers only need to store encrypted data, processing it without decrypting. Thus, hackers see only encrypted data.

Computing on encrypted data, possible in theory, has been prohibitively inefficient in practice. Popa addresses this by replacing generality with building systems for a broad set of applications with common traits, and developing encryption schemes tailored to these application archetypes. In SQL databases, for example, Popa extracts a few primitive operations that support most queries, utilizes encryption schemes that efficiently support these primitives, and thus can perform most computations on encrypted databases.

Popa, as the senior researcher, has designed an astonishing number of prototype systems in different application domains, providing functionality over encrypted data. In Opaque, DORY, Metal, and CryptDB, she showed how the utilization of cryptographic schemes that efficiently support a few carefully identified primitive operations enables performant encrypted databases and file systems. The Helen and Senate prototypes she and her students contributed enable multiple organizations to collaboratively train a machine-learning model or perform data analytics over their combined encrypted data. In Delphi and MUSE, machine learning models execute on the client’s input, without revealing the data to the model provider or leaking the model to the client.

Xavier Leroy, Collège de France; Sandrine Blazy, University of Rennes 1, IRISA; Zaynah Dargaye, Nomadic Labs; Jacques-Henri Jourdan, CNRS, Laboratoire Méthodes Formelles; Michael Schmidt, AbsInt Angewandte Informatik; Bernhard Schommer, Saarland University, and AbsInt Angewandte Informatik GmbH; and Jean-Baptiste Tristan, Boston College, receive the ACM Software System Award for the development of CompCert, the first practically useful optimizing compiler targeting multiple commercial architectures that has a complete, mechanically checked proof of its correctness.

CompCert, initiated in 2005, is a compiler for the C programming language and the first industrial-strength compiler with a mechanically checked proof of correctness. It can be used with most computer architectures including PowerPC, ARM, RISC-V and x86 (32 and 64 bits) architectures.

When it was introduced, CompCert represented a major advance over other production compilers, because it did not experience miscompilation issues since it is formally verified using machine-assisted mathematical proofs. The code it produces is proved to behave exactly as specified by the semantics of the source C program. This level of confidence in the correctness of the compilation process enables CompCert to meet the highest levels of software assurance.

Today, CompCert continues as a research project at Inria, the French National Institute for Research in Digital Science and Technology and is available under commercial and noncommercial licenses (source code openly available for noncommercial use). Other researchers build on CompCert, and multiple corporations use it for safety-critical applications.

Carla Gomes of Cornell University receives the ACM - AAAI Allen Newell Award for establishing and nurturing the field of computational sustainability and for foundational contributions to artificial intelligence..

Gomes is a leader in AI, particularly in reasoning, optimization, and the integration of learning and reasoning. She is the driving force behind the new subfield of computational sustainability, embodying the values of multidisciplinary research and social impact. Her research advances core computer science and AI while establishing rich connections to other disciplines.

Gomes has played a key role in advancing the integration of methods from AI and operations research. With collaborators, she pioneered randomized restarts and algorithm portfolios for combinatorial solvers. This work has had a tremendous practical impact on solvers for satisfiability (SAT), mixed integer programming (MIP), and satisfiability modulo theories (SMT). Gomes discovered and characterized heavy-tailed runtime distributions and backdoor variables in combinatorial search, explaining the large runtime variations of combinatorial solvers. She also introduced XOR-streamlining, a novel strategy for model counting that was a key step to further advances in efficient probabilistic inference.

Inspired by her early work on experiment design for nitrogen management and wildlife-corridor design, Gomes conceived an ambitious vision for computational sustainability: a highly interdisciplinary research area which incorporates computational thinking to solve critical sustainability challenges.

As the lead principal investigator (PI) of two National Science Foundation (NSF) Expeditions Awards, Gomes has grown Computational Sustainability into a robust and vibrant subfield. She has shown that addressing challenges in sustainability often leads to transformative research in computer science, in addition to having a significant practical impact. Gomes and her collaborators developed a framework for computing the high-dimensional Pareto frontier of ecological and socio-economic tradeoffs of hydro dam expansion in the Amazon Rain Forest.

Gomes also pioneered the use of AI in materials discovery. Together with her team, she developed Deep Reasoning Networks, a novel computational paradigm integrating deep learning with constraint reasoning over rich prior knowledge. This framework was used to solve the crystal-structures phase-mapping problem, which led to the discovery of new solar fuel materials for sustainable energy storage.

Dame Wendy Hall, Regius Professor of Computer Science at the University of Southampton, receives the ACM Presidential Award. Hall is recognized for her technical contributions that have significantly influenced the development of the Semantic Web and the field of Web Science; her leadership and impact in shaping the science and engineering policy agenda internationally; her advocacy and leadership in promoting informatics education throughout Europe through the Informatics for All coalition and other international groups; and her committed and inspired work to strengthen ACM’s geographically diverse footprint and establishing and fostering regional councils to promote ACM activities in China, India, and Europe.

Hall is one of the first computer scientists to undertake serious research in multimedia and hypermedia. The influence of her work has been significant in many areas including digital libraries, the development of the Semantic Web, and the emerging research discipline of Web Science. With Sir Tim Berners-Lee, Sir Nigel Shadbolt, and Daniel J Weitzner, she co-founded the Web Science Research Initiative and is the Managing Director of the Web Science Trust, which has a global mission to support the development of research, education and thought leadership in Web Science. Since 2014, she has served as a Commissioner for the Global Commission on Internet Governance.

Hall has also helped shape science and engineering policy and education. In 2018, she helped found the Informatics for All coalition, and serves as the chair of its steering committee. Informatics for All aims to establish informatics as a fundamental and independent subject in school for students at all levels throughout Europe.

As the first ACM President from outside North America, Hall initiated the establishment of ACM Councils in Europe, India and China, extending the organization’s scope to a borderless audience. She also focused on the education of upcoming computer science generations, promoting gender diversity and nurturing talent in computing from all corners of the world.

Judy Brewer receives the ACM Policy Award for her leadership of the Web Accessibility Initiative and development of multiple web accessibility standards, which have been adopted globally and improved accessibility for millions worldwide.

Brewer leads the development of standards and strategies for inclusive web design, providing web developers with tools necessary to bring the power and the promise of the World Wide Web to millions of people who otherwise might have been excluded from this vital component of modern life. She is based at the Massachusetts Institute of Technology, where she is a Principal Research Scientist in the Computer Science and Artificial Intelligence Laboratory.

In the late 1990s, although web design was flourishing, accessibility was not. Millions of new users uploaded image maps, frames, and other features that proved problematic at best and prohibitive at worst for users with auditory, cognitive, motor, neurological, physical, speech and visual disabilities. Under Brewer’s direction, WAI develops the Web Content Accessibility Guidelines (WCAG), which provide developers with a set of criteria to judge the accessibility of the sites they are building. WCAG also inspired the development of numerous evaluation tools capable of reviewing web pages to identify potential barriers such as non-navigable menu structures and images without alternative textual descriptions. The WCAG specifications and these tools provide a baseline for accessible web design, and for accessibility of web-based technologies such as real-time communications and virtual reality.

Under Brewer’s leadership, WAI has also worked extensively with government agencies, industry, and disability organizations, leading to the adoption of WCAG as an international standard (ISO/IEC 40500:2012), and the alignment of WCAG with Section 508 of the Rehabilitation Act in the US and European Mandate 376 requiring accessible technology procurement and development in all EU countries. The WCAG standards have been translated into twenty-three languages and have been adopted by dozens of countries. Nearly every government around the world that has requirements for digital accessibility cites WCAG. WAI has led the evolution of the web from its early days of featuring expensive and unscalable alternative text-only designs to a modern reality of highly accessible layouts designed to seamlessly work on both desktops and smartphones.

Brewer has led these efforts for more than 20 years, participating on numerous committees of the US Access Board, the US National Council on Disability, the US Federal Communications Commission (FCC), the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) standards bodies, industry groups, and many others. She has published widely on accessibility; testified before US Congress and government organizations in Korea, Denmark, China, and other countries; given numerous international keynote presentations; and has been honored by numerous groups concerned with accessibility.

Erik Altman, Research Scientist at IBM Research, receives the ACM Distinguished Service Award for leadership in the computer architecture communities, and for contributions to ACM organizational development.

Erik Altman has demonstrated excellence, both as a computer architecture research scientist at IBM and as a driver of positive change within the Association for Computing Machinery and the IEEE Computer Society. For example, as chair of the ACM Special Interest Group on Microarchitecture (SIGMICRO), Altman drove the recognition of important contributions by co-founding and chairing the Micro Hall of Fame award. He also helped establish a successful oral history project and worked to improve computer architecture conferences.

As chair of ACM's Special Interest Group (SIG) Governing Board, he worked to better coordinate the activities of the more than three dozen SIGs. He tested ways to engage and educate the larger ACM community with daily news feeds, paving the way for ACM's successful TechNews. He contributed to ACM's financial well-being by serving on the Executive Committee as Secretary-Treasurer for two years. He has also been a long-term member of the ACM Investment Committee, which oversees a $100M+ fund.

As editor-in-chief of IEEE Computer Society’s Micro journal for four years, Altman drove the creation of important special themed issues. He helped establish the IEEE Rau Award for substantial contributions to microarchitectures. As a member of the IEEE Computer Society's Magazine Operations Committee, he has worked to improve the financial health of several IEEE magazines.

He has also served as program chair, vice-chair, or general chair of many conferences for topics in computer architecture, machine learning, and logic.

In parallel with his volunteer work, Altman has had a prolific industrial research career spanning computer architecture, compilers, and parallel computing. He worked on IBM Research’s Dynamically Architected Instruction Set from Yorktown (DAISY) project, which solved many difficult problems in dynamic binary instruction translation and had significant research impact and influence. He has twice received an IBM Outstanding Innovation Achievement Award. Altman has more recently led efforts to do early detection of cyber-attacks using continuous learning in multiple dimensions and has explored the generation of synthetic financial and medical training data for use in machine learning models.

Mark Allen Weiss was named recipient of the Karl V. Karlstrom Outstanding Educator Award for advancing the art and science of computer science (CS) education through his textbooks, research, and curriculum design, which have affected thousands of instructors and students worldwide.

Weiss’s most significant contributions to the evolution of the data structures and programming curriculum have been through his textbooks, which have been used in numerous countries and published in multiple editions over three decades (from the 1990s to the 2010s). He was one of the first authors to include advanced topics such as splay trees and amortized analysis with detailed implementations that matched the theoretical results outlined in the programming textbooks. His books were also groundbreaking in that they included in-depth presentations of C++ features and syntax, predating the Standard Template Library (STL) with vector and string classes.

Weiss has also led and contributed to important education projects. Beginning in the late 1990s, he was part of the Advanced Placement (AP) CS Development Committee that designed the AP curriculum and wrote the AP exams taken by US high school students. He chaired the committee for four years during the early 2000s, when the exam design was changing from C++ to Java, and the underlying curriculum was putting greater emphasis on object-oriented design and abstraction principles. He is also co-leading a project to help the US National Science Foundation set priorities for CS education research.

Notably, he has also been a champion for increasing diversity in the computing field, especially through partnership programs with other universities in the state of Florida. These programs include pooling courses to improve access to relevant subject matter, providing support for especially challenging courses early in the computing curriculum, and increasing financial support for high-ability students with economic needs. As Associate Dean of Undergraduate Education at Florida International University, Weiss’s leadership in these programs has significantly increased the four and six-year graduation rates at his institution.

Among his many honors, Weiss has received the ACM SIGCSE Award for Outstanding Contributions to Computer Science Education, the IEEE-CS Taylor Booth Education Award, and the IEEE Sayle Education Achievement Award.

ACM named Éva Tardos, a Professor at Cornell University, as the 2022-2023 ACM Athena Lecturer. Tardos is recognized for fundamental research contributions to combinatorial optimization, approximation algorithms, and algorithmic game theory, and for her dedicated mentoring and service to these communities. Initiated in 2006, the ACM Athena Lecturer Award celebrates women researchers who have made fundamental contributions to computer science.

Tardos is one of the most influential leaders in the field of theoretical computer science. Her impact spans deriving deep theoretical results, shaping new research areas, and influencing a broad range of applications. Her key contributions in combinatorial optimization include the first strongly polynomial-time algorithm for the minimum-cost flow problem (for which she received the Fulkerson Prize) and a general framework for fast approximation of packing and covering linear programs.

She developed fundamental approximation algorithms, developing new algorithmic techniques for the use of linear programming and rounding in network design problems. The applications of her work include solving problems in facility location, network routing, and the spread of influence in social networks. Tardos also played a key role in founding the field of algorithmic game theory by developing algorithms in the presence of self-interested agents that are governed by incentives and economic constraints.

Her pioneering work using game-theoretic ideas to quantify the performance gaps between centrally managed network traffic and the flow of traffic directed by self-interested agents (selfish routing) was recognized with the Gödel Prize. She subsequently developed new approaches to analyzing dynamic games and new algorithms for mechanism design including composable ones.

Tardos is an outstanding educator, mentor, and leader in her scientific community. She has received awards for excellence in teaching and leadership for her work supporting women in computer science, and several of her former students are now prominent figures in the field. She co-authored one of the leading textbooks used in undergraduate computer science, Algorithm Design, and co-edited Algorithmic Game Theory, a significant book at the intersection of economics and computation.

“Each year ACM honors a preeminent woman computer scientist as the Athena Lecturer,” said ACM President Gabriele Kotsis. “Athena Lecturers are recognized for both enduring technical contributions, as well as their community service and mentoring. Éva Tardos has played a central role in shaping the field of algorithms over three decades, and she has been one of the foremost authorities in the emerging field of algorithmic game theory. Her work, her generosity to younger colleagues, and her service to the wider field have been outstanding. We look forward to presenting her with this award and we know she will continue to make contributions for years to come.”

Tardos will be formally presented with the ACM Athena Lecturer Award at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco. The ACM Athena Lecturer Award carries a cash prize of $25,000, with financial support provided by Two Sigma.

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.

ACM named Carla E. Brodley the recipient of the inaugural ACM Frances E. Allen Award for Outstanding Mentoring. Brodley is recognized for significant personal mentorship and leadership in creating systemic programs that have increased diversity in computer science by creating mentoring opportunities for thousands at Northeastern and other universities across the United States.

An internationally recognized leader in the fields of machine learning, data mining, and artificial intelligence, Brodley has shown a deep commitment to mentoring and increasing diversity in computer science throughout her academic career. She has worked to develop and disseminate data-driven mentoring practices to make computer science more diverse, inclusive, and equitable in a sustainable and systemic way.

Brodley’s efforts have been remarkably successful. For example, while Dean of Northeastern University’s Khoury College of Computer Science from 2014 – 2021 enrollments grew from 2,125 students to 6,169. The total enrollment of undergraduate women grew from 19% to 32% (the national average is 21.5 %) and the enrollment of female MS students increased from 33% to 42% (the national average is 25.7 %). The representation of students from races and ethnicities historically marginalized in tech increased from 10% to 19%. These increases are attributed to several academic innovations Brodley championed including developing 30 interdisciplinary undergraduate computing majors, as well as the “Align MS in CS” which is a master’s in computing for individuals with undergraduate degrees in any field. In addition to student enrollment, Brodley also diversified the faculty at Khoury. Of the 46 new tenure track hires made during her tenure as Dean, 13 (28%) were women (double the national average) and 8 (17%) were from races and ethnicities that are underrepresented in technology (the national average of faculty from these groups is 1%).

“Computing is so essential to the way we live now and will live in the future,” said ACM President Gabriele Kotsis. “At ACM, we believe it is a matter of utmost importance to ensure that all people, regardless of their gender or racial background, learn about the possibilities of pursuing a career in computer science and feel welcome in our field. Carla E. Brodley not only put effective strategies into practice at Northeastern University, but she has developed a program to help dozens of computer science departments around the US effectively diagnose their diversity challenges and systemically address them. Thousands of students have benefited from her work, and we encourage everyone who is interested in broadening participation in academia or the private sector to learn from her example.”

Brodley has also led important initiatives to enhance the participation of women of all races and ethnicities in computer science departments across the United States. At Northeastern University, she founded the Center for Inclusive Computing (CIC) to focus on systemic changes that faculty and administrators can implement at their own universities to increase the representation of women in undergraduate computing. The CIC provides funding and mentorship to colleges and universities to support this objective.

With Brodley’s leadership, the CIC has provided over $13 million in Implementation and Diagnostic grants to 56 schools. Diagnostic grantees receive two years of funding to cover the cost of collecting detailed intersectional data related to enrollment, persistence, retention, and graduation. Implementation grantees receive 2-4 years of funding to make systemic sustainable change. The CIC does not just provide the money, it guides grantees through a multi-stage process that includes mentoring faculty and administrators in how to diagnose their own institutional barriers and how to implement the appropriate evidence-based practices to ensure that all students can discover, enjoy and persist in computing.

Over the years, she has also done significant work with the Computing Research Association’s Committee on Widening Participation (CRA-WP), serving as a Board Member and Co-Chair.

“Microsoft is proud to provide financial support for the inaugural Frances E. Allen Award for Outstanding Mentoring,” said Fatima Kardar, Vice President and COO at Microsoft. “An award recognizing great mentors is an excellent and long overdue idea. All of us can point to someone in our professional life who inspired us, helped us at a pivotal moment, or took time away from their own work to offer us advice or guidance. A spirit of cooperation has defined our field from the beginning—and we should encourage and celebrate it. We also congratulate Carla Brodley for her work in enhancing diversity, especially the participation of women in the field. At Microsoft we strive to create a deeply inclusive environment. It is the right thing to do, and we know that different perspectives on our teams lead to better products and services for our customers.”

Brodley will be formally presented with the ACM Frances E. Allen Award for Outstanding Mentoring at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

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.

ACM named Pieter Abbeel the recipient of the 2021 ACM Prize in Computing for contributions to robot learning, including learning from demonstrations and deep reinforcement learning for robotic control. Abbeel pioneered teaching robots to learn from human demonstrations (“apprenticeship learning”) and through their own trial and error (“reinforcement learning”), which have formed the foundation for the next generation of robotics. Abbeel is a Professor at the University of California, Berkeley and the Co-Founder, President and Chief Scientist at Covariant, an AI robotics company.

Early in his career, Abbeel developed new apprenticeship learning techniques to significantly improve robotic manipulation. As the field matured, researchers were able to program robots to perceive and manipulate rigid objects such as wooden blocks or spoons. However, programming robots to manipulate deformable objects, such as cloth, proved difficult because the way soft materials move when touched is unpredictable. Abbeel introduced new methods to enhance robot visual perception, physics-based tracking, control, and learning from demonstration. By combining these new methods, Abbeel developed a robot that was able to fold clothes such as towels and shirts ─ an improvement over existing technology that was considered an important milestone at the time.

Abbeel’s contributions also include developing robots that can perform surgical suturing, detect objects, and plan their trajectories in uncertain situations. More recently, he has pioneered “few-shot imitation learning,” where a robot is able to learn to perform a task from just one demonstration after having been pre-trained with a large set of demonstrations on related tasks.

Another especially promising area where Abbeel has made important contributions is in deep reinforcement learning for robotics. Reinforcement learning is an area of machine learning where an agent (e.g., a computer program) seeks to progress towards a reward (e.g., winning a game). While early reinforcement learning programs were effective, they could only perform simple tasks. The innovation of combining reinforcement learning with deep neural networks ushered in the new field of deep reinforcement learning, which can solve far more complex problems than computer programs developed with reinforcement learning alone.

Abbeel’s key breakthrough contribution in this area was developing a deep reinforcement learning method called Trust Region Policy Optimization. This method stabilizes the reinforcement learning process, enabling robots to learn a range of simulated control skills. By sharing his results, posting video tutorials, and releasing open-source code from his lab, Abbeel helped build a community of researchers that has since pushed deep learning for robotics even further ─ with robots performing ever more complicated tasks.

Abbeel has also made several other pioneering contributions including: generalized advantage estimation, which enabled the first 3D robot locomotion learning; soft-actor critic, which is one of the most popular deep reinforcement learning algorithms to-date; domain randomization, which showcases how learning across appropriately randomized simulators can generalize surprisingly well to the real world; and hindsight experience replay, which has been instrumental for deep reinforcement learning in sparse-reward/goal-oriented environments.

“Teaching robots to learn could spur major advances across many industries ─ from surgery and manufacturing to shipping and automated driving,” said ACM President Gabriele Kotsis. “Pieter Abbeel is a recognized leader among a new generation of researchers who are harnessing the latest machine learning techniques to revolutionize this field. Abbeel has made leapfrog research contributions, while also generously sharing his knowledge to build a community of colleagues working to take robots to an exciting new level of ability. His work exemplifies the intent of the ACM Prize in Computing to recognize outstanding work with ‘depth, impact, and broad implications.’”

“Infosys is proud of our longstanding collaboration with ACM, and we are honored to recognize Pieter Abbeel for the 2021 ACM Prize in Computing,” said Salil Parekh, Chief Executive Officer, Infosys. “The robotics field is poised for even greater advances, as innovative new ways are emerging to combine robotics with AI, and we believe researchers like Abbeel will be instrumental in creating the next great advances in this field.”

Abbeel will be formally presented with the ACM Prize in Computing at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

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.

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.

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 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.

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.

Ayanna Howard is the Dean of the College of Engineering at The Ohio State University. She is the first woman to lead the College of Engineering at Ohio State. Prior to joining Ohio State, Howard was the Linda J. and Mark C. Smith Professor and Chair of the School of Interactive Computing in the College of Computing at the Georgia Institute of Technology, where she founded and was Director of the Human-Automation Systems Lab (HumAnS). She is also founder and President of the Board of Directors of Zyrobotics, a company that develops mobile therapy and educational products for children with special needs.

A graduate of Brown University, Howard earned her MSEE and PhD in Electrical Engineering from the University of Southern California. She also earned an MBA from Claremont University’s Drucker School of Management.

Howard has authored 250 publications in refereed journals and conferences, including serving as co-editor/co-author of more than a dozen books and/or book chapters. She has also been awarded four patents and has given over 140 invited talks and/or keynotes. She is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the Institute of Electrical and Electronics Engineers (IEEE). Among her many honors, Howard received the Computer Research Association’s A. Nico Habermann Award and the Richard A. Tapia Achievement Award.

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).

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.”

Scott Aaronson is the David J. Bruton Jr. Centennial Professor of Computer Science at the University of Texas at Austin. His primary area of research is theoretical computer science, and his research interests center around the capabilities and limits of quantum computers, and computational complexity theory more generally.

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

A graduate of Cornell University, Aaronson earned a PhD in Computer Science from the University of California, Berkeley. His honors include the Tomassoni-Chisesi Prize in Physics (2018), a Simons Investigator Award (2017), and the Alan T. Waterman Award of the National Science Foundation (2012).

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.

Jeffrey David 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.

Stanford University professor and CEO of Gradiance Corporation Jeffrey David Ullman was named co-recipient of the 2020 ACM A.M. Turing Award along with Alfred Vaino Aho for fundamental algorithms and theory underlying programming language implementation and for synthesizing these results and those of others in their highly influential books.

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.

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.

Together Aho and Ullman co-authored nine influential books, the most popular being (with John Hopcroft) The Design and Analysis of Computer Algorithms (1974) and their definitive 1977 book on compiler technology, Principles of Compiler Design (nicknamed the "Dragon Book").

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.

Alfred Vaino 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.

Columbia University Professor Alfred Vaino Aho was named co-recipient of the 2020 ACM A.M. Turing Award along with Jeffrey David Ullman for fundamental algorithms and theory underlying programming language implementation and for synthesizing these results and those of others in their highly influential books.

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.

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.

Together Aho and Ullman co-authored nine influential books, the most popular being (with John Hopcroft) The Design and Analysis of Computer Algorithms (1974) and their definitive 1977 book on compiler technology, Principles of Compiler Design (nicknamed the "Dragon Book").

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.

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.

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