Maja Matarić, Professor, University of Southern California, receives the ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics for pioneering socially assistive robotics (SAR) for improving wellness and quality of life for users with special needs.

Over the past two decades, Matarić has been the leading figure in the field of socially assistive robotics. These robots are designed to gain insights into the drivers of human behavior related to overcoming challenges. The goal of this field is to provide people with personalized assistance to enhance their abilities in areas such as convalescence, rehabilitation, training, and education. Socially assistive robotics is an interdisciplinary field which emphasizes co-design and user participation throughout the development process. Her research is aimed at major challenges, including post-stroke rehabilitation, cognitive and social skills training for children with autism spectrum disorders, cognitive and physical exercises for Alzheimer’s patients, study support for students with ADHD, and personalized therapy interventions for students with anxiety and/or depression.

Manish Parashar, Professor, University of Utah, receives the ACM Distinguished Service Award for service and leadership in furthering the transformative impact of computer and computational science on science and engineering.

Parashar’s record of service includes leadership at the National Science Foundation (NSF), where he developed NSF’s strategic vision for a national cyberinfrastructure, as well as at the White House’s Office of Science and Technology Policy (OSTP), where he developed the Future Advancement Computing Ecosystem Strategic Plan (FACE). For ACM, Parashar served two terms as editor-in-chief of ACM Transactions on Autonomous and Adaptive Systems (ACM TAAS), and has led steering, organizing and programming committees for numerous ACM conferences.

Dan Garcia, Teaching Professor, UC Berkeley, and Brian Harvey, Teaching Professor Emeritus, UC Berkeley, receive the Karl V. Karlstrom Outstanding Educator Award for their advocacy of and advances in education to bring the beauty and joy of computing to all students, especially those from historically underrepresented communities.

Together Garcia and Harvey have been instrumental in expanding computer science education, most notably through the development of the Beauty and Joy of Computing (BJC) curriculum, which began as a national pilot for the CSforALL movement. A key part of this effort was Snap!, a blocks-based programming language on which Harvey collaborates with principal developer Jens Mönig. Subsequently Garcia and Harvey and BJC co-PI Tiffany Barnes went on to expand BJC’s reach by training over 1,000 teachers, offering the curriculum in Spanish, and developing a middle school version, BJC Sparks. Importantly, the BJC Course at Berkeley is the only EECS course to exceed 50% female enrollment and once exceeded 70%.

Ilias Diakonikolas, a Professor at the University of Wisconsin, Madison, is the recipient of the 2024 ACM Grace Murray Hopper Award. He is cited for making contributions to the field of algorithmic robust statistics by introducing new techniques to robustly estimate high-dimensional distributions along with a surprising variety of algorithmic applications.

The primary focus of Diakonikolas' work is the mathematical foundations of data analysis, machine learning and algorithmic statistics. He is most well-known for his work on robust statistical algorithms for high-dimensional data. “Robust algorithms” are algorithms that perform well even when the data significantly deviates. The new paradigms that Diakonikolas developed changed the way we think about what is possible for efficient algorithms that process high-dimensional data—overcoming problems that have stymied researchers since the 1960s.

In 2016, Diakonikolas and collaborators gave the first efficient algorithms for learning the parameters of a high-dimensional distribution. These breakthrough algorithms are robust to arbitrary corruption in a constant fraction of data, with the constant being independent of the dimension. Subsequent work by Diakonikolas showed these new methods are not only of theoretical interest but can be made practical. In turn, these new methods can also be used to tackle more complex robust high-dimensional statistical problems. In mixtures of distributions, for example, there are cases where Diakonikolas’ algorithms are more efficient than even the prior state-of-the-art non-robust methods. Among other applications, robust algorithms are critical in order to develop reliable machine learning systems.

In addition to his breakthrough research contributions, Diakonikolas has also played a leading role in establishing a vibrant community around this work. He has organized workshops, given tutorials, and co-authored (with Daniel Kane) Algorithmic High-Dimensional Robust Statistics, a new textbook which has become an essential resource for this emerging field.

Judith Gal-Ezer, Professor Emerita, Open University of Israel, receives the Outstanding Contribution to ACM Award in recognition of her sustained contributions to computer science education policy and research and, more broadly, to the ACM Europe Council.

Gal-Ezer has been an internationally recognized leader in computing education. For her accomplishments, she has received the ACM Karl V. Karlstrom Outstanding Educator Award as well as the ACM SIGCSE Award for Outstanding Contribution to Computer Science Education. Gal-Ezer has been very active in the ACM Europe Council and its sub-committees. She represents ACM Europe in the Informatics for All (I4All) coalition—a collaboration between ACM Europe, Informatics Europe, CEPIS and IFIP. This ambitious initiative was created to promote informatics education in primary and secondary schools across Europe. The sustained advocacy of I4All has been instrumental in the European Commission’s decision to prioritize informatics education at all stages of the curriculum.

William Gropp, University of Illinois; Pavan Balaji, Meta; Rajeev Thakur, Yanfei Guo, Kenneth Raffenetti, and Hui Zhou (all of Argonne National Laboratory), receive the ACM Software System Award for MPICH , which has powered 30 years of progress in computational science and engineering by providing scalable, robust, and portable communication software for parallel computers.

The development of the MPICH software system began in 1992 as proof-of-concept for the emerging Message Passing Interface (MPI) standard. The project not only demonstrated the feasibility of MPI but also played a pivotal role in shaping the standard itself—guiding it toward a design that was both straightforward to use and practical to implement. The name "MPICH" reflects a combination of "MPI" with "Chameleon," a predecessor system on which the original implementation was based.

By making MPI broadly accessible and effective in practice, MPICH established MPI as a universal standard for parallel computing. It enabled researchers and developers to write portable parallel programs that could seamlessly move across teams, institutions, and platforms—unlocking unprecedented collaboration and accelerating progress in science and engineering worldwide.

ACM has named Cordelia Schmid, Research Director at Inria, the French National Institute for Research in Digital Science and Technology, as the 2025-2026 ACM Athena Lecturer. Schmid is recognized for outstanding contributions to computer vision in image retrieval, object recognition, and video understanding. Her work has helped computers understand, perceive, and interact with the visual world.

Initiated in 2006, the ACM Athena Lecturer Award celebrates women researchers who have made fundamental contributions to computer science. The award includes a $25,000 honorarium provided by Two Sigma.

Computer Vision
Computer vision has captivated the public and enables innovative technologies in many fields today, including robotics, automated vehicles, and medicine. By employing special algorithms and massive amounts of data, this technology allows computers to recognize and interpret objects in photographs, videos, and the physical environment.

For humans, understanding visual information is a simple exercise, but for computers it’s a very difficult task. Schmid made foundational contributions to this field beginning in the 1990’s. Her early work with semi-local image descriptors helped classify textures and recognize patterns. Following on these insights, she also explored how these characteristics could be combined with spatial and geometric details.

Schmid’s later innovations helped computers recognize complex objects in a scene even when clutter or other visual objects are present. In turn, these accomplishments enabled computer vision technologies to process more complex and realistic settings.

Video Analysis
Within the broader field of computer vision, the goal of video analysis is focused specifically on recognizing actions and events in real-world video footage. Schmid’s significant contributions in video analysis include designing methods and training computers to identify such actions and events. From still images to sequences, her work has played a large role in the advancements of modern image detection that technologies from digital video cameras to industrial robotics use today.

Leadership
In keeping with the Athena Lecturer Award’s goal of honoring both technical and service contributions, Schmid is recognized for building an active research community. For example, the research groups she founded lead the world and she has contributed extensively to the field, editing its major journals and chairing its most important conferences. Schmid’s skills in mentorship and supervision are also renowned among her peers.

Cordelia Schmid is a Research Director at Inria, the French National Institute for Research in Digital Science and Technology, and a Director at Google. She also serves as a Director of the ELLIS program on Machine Learning and Computer Vision and as a member of the Board of Directors for the Computer Vision Foundation.

Schmid earned a PhD in Computer Science from the Institute National Polytechnique de Grenoble (France) and an MS in Computer Science from the University of Karlsruhe (Germany). Among her many honors, Schmid is the only three-time recipient of the prestigious Longuet-Higgins Prize and a recipient of the Koenderink Prize, both awarded for fundamental contributions in computer vision. She has also received the European Inventor Award. She is a Fellow of IEEE and a Member of the German National Academy of Sciences, Leopoldina.

Peter Stone, Professor, University of Texas at Austin and Chief Scientist, Sony AI, receives the ACM - AAAI Allen Newell Award for significant contributions to the theory and practice of artificial intelligence (AI), especially in reinforcement learning, multiagent systems, transfer learning, and intelligent robotics .

As a leading figure in AI research, Stone has fundamentally advanced how autonomous agents learn, plan, and collaborate. His groundbreaking work on reinforcement learning algorithms has enabled robots to acquire skills through experience. At the same time, his innovations in multiagent coordination have transformed how teams of agents operate collectively toward shared goals.

Hugo Krawczyk, Senior Principal Scientist, Amazon, receives the ACM Paris Kanellakis Theory and Practice Award for pioneering and lasting contributions to the theoretical foundations of cryptographically secure communications, and to the protocols that form the security foundations of the Internet.

Krawczyk’s most high-profile contribution is his work on the SIGMA authenticated key-exchange protocol. SIGMA has become a cornerstone of the most widely used communication protocols on the Internet. It is now implemented in billions of devices and web browsers, making it a fundamental component of online security. This widespread adoption underscores the importance and impact of Krawczyk’s work in the field of cryptography.

ACM announced that Jason Cong, the holder of the Volgenau Chair for Engineering Excellence at the UCLA Samueli School of Engineering, is the recipient of the 2024 ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award . Cong is recognized for fundamental contributions to the design and automation of field-programmable systems and customizable computing.

The ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award recognizes individuals or groups who have made surprising, disruptive, or leapfrog contributions to computing ideas or technologies. The award is accompanied by a $100,000 prize with financial support provided by Microsoft.

During his career in both academia and industry, Cong developed an extraordinary array of tools to automate integrated circuit design, mostly focused on tools for Field-Programmable Gate Arrays (FPGAs). FPGAs are special integrated circuits that can be programmed after they have been manufactured. The ability of FPGAs to change their functionality after manufacturing has made them part of the standard hardware in many applications including data centers, telecommunications, aerospace, defense, and automotive engineering.

While FPGAs are programmable, creating the configuration files for them is a complex task and difficult for users to complete. Cong has spent much of his career building tools to address this problem. For example, his work has made it possible to use software programming languages such as C or C++ to program an FPGA, significantly broadening their accessibility and usability. In addition to working out the basic algorithms, Cong and his students have created commercial t ools that embed these algorithms to power the FPGA design tools in use today.

In the late 1990s, Cong worked on how to map logic onto the look-up tables that are the building blocks of FPGAs. This was a difficult problem which was solved using heuristics. Cong and his students made a major theoretical breakthrough when they showed this problem could be solved exactly in polynomial time. This insight led to the creation of Aplus Design Automation to commercialize this technology, and now it is used in all FPGA synthesis tools.

While Cong’s early work enabled designers to use a hardware description language such as Verilog to design FPGAs, it was still difficult for software application engineers to program these circuits. In the 2000s, Cong’s group started working on high-level synthesis techniques to enable FPGAs to be programmed from C/C++ descriptions. This successful effort led to AutoESL, a spin-off from his lab at UCLA, which was acquired by Xilinx Inc. in 2011 (now part of Advanced Micro Devices). AutoPilot, a commercial product based on the AutoESL technology, is the basis of AMD/Xilinx’s high-level synthesis tools today.

Having created these tools, Cong applied them to the field of customizable domain-specific computing, where he and his team designed a wide range of domain-specific hardware accelerators with FPGAs, including deep learning, medical image processing, genomic sequencing, data compression, satisfiability solving, and many other computationally intensive tasks. An important benefit of these customized computing solutions is that they have shown drastically improved energy efficiency over conventional CPU-based computing approaches.

The ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award recognizes individuals or groups who have made surprising, disruptive, or leapfrog contributions to computing ideas or technologies. Recipients of the award are expected to give the ACM Breakthrough Lecture at a major ACM conference. The award is accompanied by a $100,000 cash prize, with financial support provided by Microsoft.

Jason Cong is the holder of the Volgenau Chair for Engineering Excellence at the UCLA Samueli School of Engineering. Cong’s research interests include design automation of VLSI circuits and systems, customizable computing, quantum computing, and highly scalable algorithms. He has published over 500 research papers, led over 100 research projects, and received several patents.

A graduate of Peking University, Cong earned MS and PhD degrees in Computer Science from the University of Illinois at Urbana-Champaign. His honors include the Phil Kaufman Award, the IEEE Robert N. Noyce Medal, and the ACM/IEEE A. Richard Newton Technical Impact Award in Electronic Design Automation, among many others. He is a Fellow of ACM and IEEE and is a member of the American Academy of Arts and Sciences and the US National Academy of Engineering.

ACM named Rachid Guerraoui the recipient of the inaugural ACM Luiz André Barroso Award for theoretical and applied contributions to distributed computing and impactful work on promoting computer science in Africa. Guerraoui is a Professor in the School of Computer and Communications Sciences at EPFL, where he is also Director of the Distributed Computing Laboratory.

The ACM Luiz André Barroso Award was established to recognize researchers from historically underrepresented communities who have made fundamental contributions to computer science. The award is named after Luiz André Barroso, a Brazilian computer engineer who pioneered the design of the modern data center. Barroso, who grew up in a diverse community, was a strong supporter of equal opportunity for everyone.

Technical Contributions
Rachid Guerraoui has made groundbreaking contributions that have shaped the landscape of distributed computing. Distributed computing is the process of making multiple computers in different locations work together to solve a common problem. A common thread that runs through Guerraoui’s work is providing principled theoretical and practical foundations for distributed computing mechanisms such as transactional memory, agreement protocols, and asynchronous information dissemination algorithms.

His work on e-Transaction and opacity concepts has provided new insights into managing transactions in concurrent environments. By establishing a theory of Transaction Contention Management and developing STMBench7, he has provided tools and frameworks for understanding and optimizing transaction performance.

Guerraoui proposed effective solutions to distributed agreement problems, which he applied to the realm of cryptocurrency. He demonstrated how to build scalable asynchronous abstractions that can support secure and decentralized digital currencies. Furthermore, his work on asynchronous dissemination protocols has paved the way for fully decentralized publish-subscribe systems, enabling efficient and reliable communication in distributed environments. In recent years, Guerraoui has ventured into the exciting field of Byzantine Machine Learning, which seeks to implement large-scale machine learning algorithms in the presence of machine failures. This work has opened new avenues for research at the intersection of machine learning and distributed computing.

Computing in Africa
Beyond his technical contributions, Guerraoui has also been a passionate advocate for computing education in Africa. Born in Morocco, he has been committed to fostering academic excellence on the African continent. By co-initiating the EPFL’s Excellence in Africa program, for example, Guerraoui has promoted the development of junior faculty and graduate students, providing them with opportunities to excel in their research and careers. The program has benefited researchers from Rwanda, Ivory Coast, Ghana, Cameroon, South Africa, Tanzania, Tunisia, and Morocco. His involvement in the creation of the UM6P College of Computing in Morocco has further expanded access to high-quality computer science education in the region.

Additionally, Guerraoui has played a key role in fostering collaboration and exchange among African computer science researchers through the Netys conference, which he also co-initiated and is held in Morocco. Netys is an exchange forum for African computing researchers who cannot easily travel to the US and Europe because of visas and financial issues.

Rachid Guerraoui is a Professor in the School of Computer and Communications Sciences at EPFL (the Swiss Federal Institute of Technology in Lausanne), where he is Director of the Distributed Computing Laboratory. He is also head of the Advisory Board of the UM6P (University Mohammed 6 Polytechnic) College of Computing in Morocco. Guerraoui received an MSc in Computer Science from Sorbonne University, and a PhD in Computer Science from Orsay University.

Guerraoui’s many honors include the Dahl-Nygaard Award (2024), a 10-Year Most Influential Paper Award, and best paper awards from several conferences. Guerraoui was named an ACM Fellow in 2012 for contributions to the theory and practice of reliable distributed computing and Professor of College de France in 2018.

ACM named Torsten Hoefler, a Professor at ETH Zurich, the recipient of the 2024 ACM Prize in Computing for fundamental contributions to high-performance computing and the ongoing AI revolution. Hoefler developed many of the core capabilities of modern supercomputers and defined key aspects of the algorithms for distributing AI models on them.

The ACM Prize in Computing recognizes early-to-mid-career computer scientists whose research contributions have fundamental impact and broad implications. The award carries a prize of $250,000, from an endowment provided by Infosys Ltd, a global leader in next-generation digital services and consulting.

Overview
High-performance computing (HPC) plays a critical role in AI applications, which require a great deal of computing power. The work of Hoefler and his colleagues to scale network design and programming in supercomputers has revolutionized the capabilities of these large systems. For example, AI algorithms can now be processed on hundreds of thousands of nodes (computers or servers).

Hoefler’s advances in interconnection networks, programming, and parallel algorithms broke new ground in facilitating the use of large-scale massively parallel clusters. His numerous innovations across the whole supercomputer stack—including key contributions such as MPI-3 nonblocking collective operations, foundational parallelism strategies for AI models, and high-performance networking systems—have pushed the boundaries of parallel systems design and translated into dramatic improvements in supercomputer performance and scalability. Many of those innovations are incorporated into the largest and most powerful machines today.

Key Contributions

Message Passing Interface 3
Hoefler played a major role in the evolution of the Message Passing Interface (MPI), an informal industry standard for exchanging messages between numerous individual nodes throughout an HPC network. A messaging standard allows synchronization of the activities of each individual computer, sharing data between nodes, and direction and control of the entire parallel network. The MPI-3 standard, in which Hoefler played a leading role, was adopted in 2012 and made possible many of the critical advances in HPC for simulations and AI applications over the past several years.

For MPI-3, Hoefler chaired both the “Process Topologies” and “Collective Operations” working groups. His nonblocking collective operations such as Allreduce, Allgather, Bcast, and their respective blocking versions are included in various collective communication libraries—even beyond MPI-3. These operations power the core of distributed deep learning today.

3D Parallelism
Hoefler was among the first to develop and discover the now well-known notion of “3D parallelism,” which drives infrastructure design for the whole AI industry. Subsequently, he and his collaborators continued to develop many innovative techniques for efficient pipelining, sparse communication, model sparsity, and quantization. This work has enabled a cumulative 10-1000x acceleration of AI workloads in modern computers.

Routing Protocols and Network Topologies
The low-level network routing protocols and network topologies that Hoefler and his colleagues developed for networks such as Myrinet and InfiniBand power thousands of AI and HPC supercomputers. These contributions form central pieces of modern high-performance AI systems that are used to train large-language models such as ChatGPT.

 Torsten Hoefler is a Professor of Computer Science at ETH Zurich (the Swiss Federal Institute of Technology), where he serves as Director of the Scalable Parallel Computing Laboratory. He is also the Chief Architect for AI and Machine Learning at the Swiss National Supercomputing Centre (CSCS). Hoefler received a Diplom Informatik (Master of Computer Science) from Chemnitz University of Technology and a PhD in Computer Science from Indiana University.

Hoefler’s honors include the Max Planck-Humboldt Medal, an award for outstanding mid-career scientists; the IEEE CS Sidney Fernbach Award, which recognizes outstanding contributions in the application of high-performance computers; and the ACM Gordon Bell Prize, which recognizes outstanding achievement in high-performance computing. He is a member of the European Academy of Sciences (Academia Europaea), a Fellow of IEEE, and a Fellow of ACM.

ACM has named Andrew G. Barto and Richard S. Sutton as the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning—one of the most important approaches for creating intelligent systems.

Barto is Professor Emeritus of Information and Computer Sciences at the University of Massachusetts, Amherst. Sutton is a Professor of Computer Science at the University of Alberta, a Research Scientist at Keen Technologies, and a Fellow at Amii (Alberta Machine Intelligence Institute).

The ACM A.M. Turing Award, often referred to as the “Nobel Prize in 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.

What is Reinforcement Learning?

The field of artificial intelligence (AI) is generally concerned with constructing agents—that is, entities that perceive and act. More intelligent agents are those that choose better courses of action. Therefore, the notion that some courses of action are better than others is central to AI. Reward—a term borrowed from psychology and neuroscience—denotes a signal provided to an agent related to the quality of its behavior. Reinforcement learning (RL) is the process of learning to behave more successfully given this signal.

The idea of learning from reward has been familiar to animal trainers for thousands of years. Later, Alan Turing’s 1950 paper “Computing Machinery and Intelligence,” addressed the question “Can machines think?” and proposed an approach to machine learning based on rewards and punishments.

While Turing reported having conducted some initial experiments with this approach and Arthur Samuel developed a checker-playing program in the late 1950s that learned from self-play, little further progress occurred in this vein of AI in the following decades. In the early 1980s, motivated by observations from psychology, Barto and his PhD student Sutton began to formulate reinforcement learning as a general problem framework.

They drew on the mathematical foundation provided by Markov decision processes (MDPs), wherein an agent makes decisions in a stochastic (randomly determined) environment, receiving a reward signal after each transition and aiming to maximize its long-term cumulative reward. Whereas standard MDP theory assumes that everything about the MDP is known to the agent, the RL framework allows for the environment and the rewards to be unknown. The minimal information requirements of RL, combined with the generality of the MDP framework, allows RL algorithms to be applied to a vast range of problems, as explained further below.

Barto and Sutton, jointly and with others, developed many of the basic algorithmic approaches for RL. These include their foremost contribution, temporal difference learning, which made an important advance in solving reward prediction problems, as well as policy-gradient methods and the use of neural networks as a tool to represent learned functions. They also proposed agent designs that combined learning and planning, demonstrating the value of acquiring knowledge of the environment as a basis for planning.

Perhaps equally influential was their textbook, Reinforcement Learning: An Introduction (1998), which is still the standard reference in the field and has been cited over 75,000 times. It allowed thousands of researchers to understand and contribute to this emerging field and continues to inspire much significant research activity in computer science today.

Although Barto and Sutton’s algorithms were developed decades ago, major advances in the practical applications of RL came about in the past fifteen years by merging RL with deep learning algorithms (pioneered by 2018 Turing Awardees Bengio, Hinton, and LeCun). This led to the technique of deep reinforcement learning.

The most prominent example of RL was the victory by the AlphaGo computer program over the best human Go players in 2016 and 2017. Another major achievement recently has been the development of the chatbot ChatGPT. ChatGPT is a large language model (LLM) trained in two phases, the second of which employs a technique called reinforcement learning from human feedback (RLHF), to capture human expectations.

RL has achieved success in many other areas as well. A high-profile research example is robot motor skill learning in the in-hand robotic manipulation and solution of a physical (Rubik’s Cube), which showed it possible to do all the reinforcement learning in simulation yet ultimately be successful in the significantly different real world.

Other areas include network congestion control, chip design, internet advertising, optimization, global supply chain optimization, improving the behavior and reasoning capabilities of chatbots, and even improving algorithms for one of the oldest problems in computer science, matrix multiplication.

Finally, a technology that was partly inspired by neuroscience has returned the favor. Recent research, including work by Barto, has shown that specific RL algorithms developed in AI provide the best explanations for a wide range of findings concerning the dopamine system in the human brain.

Biographical BackGround

Andrew Barto is Professor Emeritus, Department of Information and Computer Sciences, University of Massachusetts, Amherst. He began his career at UMass Amherst as a postdoctoral Research Associate in 1977, and has subsequently held various positions including Associate Professor, Professor, and Department Chair. Barto received a BS degree in Mathematics (with distinction) from the University of Michigan, where he also earned his MS and PhD degrees in Computer and Communication Sciences.

Barto’s honors include the UMass Neurosciences Lifetime Achievement Award, the IJCAI Award for Research Excellence, and the IEEE Neural Network Society Pioneer Award. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of the American Association for the Advancement of Science (AAAS).

Richard Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company based in Dallas, Texas) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). Sutton was a Distinguished Research Scientist at Deep Mind from 2017 to 2023. Prior to joining the University of Alberta, he served as a Principal Technical Staff Member in the Artificial Intelligence Department at the AT&T Shannon Laboratory in Florham Park, New Jersey, from 1998 to 2002. Sutton’s collaborations with Andrew Barto began in 1978 at the University of Massachusetts at Amherst, where Barto was Sutton’s PhD and postdoctoral advisor. Sutton received his BA in Psychology from Stanford University and earned his MS and PhD degrees in Computer and Information Science from the University of Massachusetts at Amherst.

Sutton’s honors include receiving the IJCAI Research Excellence Award, a Lifetime Achievement Award from the Canadian Artificial Intelligence Association, and an Outstanding Achievement in Research Award from the University of Massachusetts at Amherst. Sutton is a Fellow of the Royal Society of London, a Fellow of the Association for the Advancement of Artificial Intelligence, and a Fellow of the Royal Society of Canada.

ACM has named 56 Distinguished Members for their impact in the field. All of the 2024 inductees are registered members of the prestigious scholarly society and were selected by their peers for significant technical achievements as well as volunteer service to their professional community.

“Each year we look forward to selecting a new class of ACM Distinguished Members from among our worldwide association of 110,000 colleagues,” explained ACM President Yannis Ioannidis. “ACM’s motto is ‘advancing computing as a science and profession.’ To fulfill our mission, we rely completely on our volunteers—from organizing technical conferences to editing research journals and managing professional development activities. In turn, all these efforts lay a foundation that supports computing professionals throughout their careers. The Distinguished Members Program not only celebrates innovation but also underscores the value of being part of a vibrant technical community.”

The 2024 ACM Distinguished Members work at leading universities, corporations, and research institutions in Australia, Bangladesh, Canada, China, Cyprus, Denmark, Germany, Ireland, India, Switzerland, Turkey, the United Kingdom, and the United States. This year’s class of Distinguished Members made contributions in autonomous vehicles, artificial intelligence, cybersecurity, mobile networks, software, and numerous other areas.

The ACM Distinguished Member program recognizes up to 10 percent of the worldwide ACM membership based on professional experience and significant achievements in computing beyond the norm. To be nominated, a candidate must have at least 15 years of professional experience in the field and 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. Also, a Distinguished Member is expected to have served as a mentor and role model to younger professionals.

Read the ACM news release.

ACM, the Association for Computing Machinery, has named 55 Fellows for transformative contributions to computing science and technology. All the 2024 inductees are longstanding ACM Members whose accomplishments were selected by their peers for making possible the computing technologies we use every day.

"Computing technology has had a tremendous impact in shaping how we live and work today,” said ACM President Yannis Ioannidis. “The ACM Fellows program honors the creativity and hard work of ACM members whose specific accomplishments drive innovation and make broader advances possible. In announcing a new class of Fellows each year, we celebrate the impact some of our community’s pioneers make, and highlight the many technical areas of computing in which they work.”

In keeping with ACM’s global reach, the 2024 Fellows represent universities, corporations, and research centers in Australia, Canada, Chile, China, Denmark, Germany, India, Israel, Italy, the Netherlands, Singapore, the United Kingdom, and the United States. The contributions of the 2024 Fellows run the gamut of the computing field―including computer graphics, cybersecurity, human-computer interaction, data management, machine learning, artificial intelligence, algorithms, visualization, and many more.

ACM will formally recognize the 2024 Fellows at its annual Awards Banquet on June 14, 2025, in San Francisco, California. Additional information about the 2024 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows website.

Read the news release.

The ACM India 2024 Doctoral Dissertation Award goes to Priyanka Golia for her dissertation titled "Functional Synthesis via Formal Methods and Machine Learning." Priyanka’s dissertation combines ideas from many areas—constraint sampling, machine learning, and formal methods, and based on this, she built a solver, Manthan, for synthesis of Boolean functions. Manthan is available as open-source and is currently the state-of-the-art tool for Boolean functional synthesis. Priyanka’s dissertation work was done at IIT Kanpur under the supervision of Prof. Subhajit Roy, IIT Kanpur and Prof. Kuldeep Meel of University of Toronto.

At its core, Manthan uses a data-driven approach to solve the problem. It works in four stages: (1) data generation, (2) learning an ML model, (3) translating ML model to Boolean functions, and (4) formal verification and repair. While her initial work on Manthan led to strong publications, Priyanka did not stop there. She improved Manthan via many strong algorithmic and engineering innovations: a pre-pass to identify uniquely determined Skolem functions, learning a multi-class decision tree instead of a binary classifier, and using lexicographical MaxSAT. After this, Priyanka further lifted Manthan to learning Boolean functions with explicit dependencies, addressing the well-known problem of learning Henkin functions for DQBF instances. After her stint with developing tools for functional synthesis, Priyanka explored applications for such functions, and the possibility of using similar approaches for real-world problems. Priyanka therefore receives the DDA award for her contributions to functional synthesis, building an open-source tool based on her ideas, and exploring the application of these ideas to real-world applications.

The Honorable Mention for 2024 goes to Abhijeet Awasthi for his dissertation titled "Learning Language Processing Models With Limited Labeled Data." Abhijeet’s thesis makes pioneering contributions to data-efficient NLP, including methods for collecting efficient supervision, synthetic data generation, and specialized architectures for learning with limited labeled data. These techniques have applications in question-answering over databases, grammatical-error-correction, text-to-AP! calling, and speech-recognition. Abhijeet’s dissertation work was done at IIT Bombay under the supervision of Prof. Sunita Sarawagi.

Abhijeet has made extremely valuable contributions to grammar error correction and has influenced the model that was adopted in the well-known tool, Grammarly, for error correction. His framework, PIE, obtains almost SOTA accuracy in GEC for much less computational resources, thereby addressing practical issues for real-world applications. PIE provided inference-time speed-ups of up to 1,400% relative to the then state-of-the-art Grammatical Error Correction Models from Google while also offering a slightly higher accuracy. Abhijeet has released high-quality packages that have gotten hundreds of stars on GitHub, and models that have been included in third party libraries. Almost every paper is accompanied by a public release of code and datasets. For example, the work on high-level supervision has been incorporated in three standard libraries and benchmarks: Astra (Microsoft), WRENCH (University of Washington), and Spear (IIT Bombay).

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation in Computer Science and related disciplines from a degree-awarding institution based in India for each academic year, running from July 1 of one year to June 30 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award, given to nomination(s), if any, that missed the award by a narrow margin, is accompanied by a prize of ₹1,00,000, that is shared among the recipient(s). The winning dissertation(s) will be published in the ACM Digital Library. Tata Consultancy Services Limited (TCS) is the founding sponsor of these awards. Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

Please join us in congratulating Priyanka Golia and Abhijeet Awasthi for their significant achievements.

ACM named an eight-member team drawn from Australian and American institutions as the winner of the 2024 ACM Gordon Bell Prize for the project, “Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials.”

The members of the team are Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C.Y. Yu, Calum Snowdon, Elise Palethorpe (all of Australian National University); Jakub Kurzak (Advanced Micro Devices, Inc.); Dmytro Bykov (Oakridge National Laboratory); and Giuseppe M.J. Barca (University of Melbourne).

Molecular dynamics is a computer simulation method that has been developed to better understand the movements of atoms and molecules within a system. Among the different approaches taken are Ab Intio (or first principles) calculations, whereby scientists use what is known of the fundamental laws of nature to develop the algorithms they run on computers.

Accurate simulations of the properties of molecules and atoms (as well as how they interact) can lead to a wide range of societal benefits, including developing therapeutic drugs, producing biofuels, recycling plastics, and engineering medical biomaterials.

Although the use of computers for performing molecular dynamics simulations goes back several decades, many traditional approaches have been limited by the accuracy of the force fields (computational models computer scientists develop to describe the forces between atoms and molecules). These limits have undermined the accuracy of the resulting simulations.

While other approaches such as quantum mechanical methods have delivered the desired accuracy, they were not able to scale on powerful supercomputers to model the thousands of atoms within a biosystem.

To address this challenge, the Gordon Bell Prize-winning team developed a new technique combining methods called molecular fragmentation and MP2 perturbation theory.

Using their algorithmic innovations on a powerful exascale computer, the team was able to achieve a record-breaking performance of simulating more than one million electrons for a computational chemistry application, and to scale their algorithm to an EFlop/s (processing a quintillion calculations per second). The Gordon Bell Prize-winning team’s resulting simulation is 1,000 times larger in system-size than the existing state-of-the-art, and was processed 1,000 times faster than any previous model.

The Gordon Bell Prize-winning team performed several Ab Initio Molecular Dynamics (AIMD) time steps on a molecular cluster with over two million electrons utilizing 9,400 nodes on the Frontier exascale supercomputer, significantly larger than any previous AIMD or static energy and/or gradient calculation at a comparable level of accuracy. These calculations achieve 1006.7 PFLOP/s providing a throughput efficiency of 59% of attainable FP64 peak on 99.9% of the machine. In addition, the team demonstrated low time step latency of 3.4 s/timestep on a protein fragment with 1,496 atoms and over 5,500 electrons attaining a simulation throughput of 25,000 time steps per day on 1,024 nodes of Perlmutter.

In their paper, the 2024 ACM Gordon Bell Prize-winning team claims, “This leap forward is not merely incremental; it redefines the boundaries of what is computationally feasible in molecular dynamics, setting a new benchmark for accuracy and efficiency in large-scale simulations. The enhanced scalability and accuracy of our simulation techniques empowers the scientific community to tackle longstanding challenges in both chemistry and biology.”

The Frontier supercomputer, located at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, is the world’s first and fastest exascale supercomputer. It can perform a quintillion (a billion billion) operations per second. When Frontier came online in 2022, it was 2.5 times faster than the world’s second most powerful supercomputer. As of November 2024, Frontier is ranked as the world’s second most powerful supercomputer. The Perlmutter supercomputer is housed at the (US) National Energy Research Scientific Computing Center (NERSC). It is used primarily in applications including climate analysis, quantum information science, clean energy technologies, as well as semiconductors and microelectronics. As of November 2024, it is ranked as the world’s 19^th most powerful supercomputer.

ACM today presented a 12-member team with the ACM Gordon Bell Prize for Climate Modelling for their project “Boosting Earth System Model Outputs and Saving PetaBytes in Their Storage Using Exascale Climate Emulators.” The award recognizes innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Sameh Abdulah, Marc G. Genton, David E. Keyes, Zubair Khalid, Hatem Ltaief, Yan Song, Greorgiy L. Stenchikov and Ying Sun (all of King Abdullah University of Science and Technology, Saudi Arabia); Allison H. Baker (NSF National Center for Atmospheric Research, USA); George Bosilca, (NVIDIA, USA); Qinglei Cao (St. Louis University, USA); and Stefano Castruccio (University of Notre Dame, USA).

Scientists have warned that global warming, caused by the human use of fossil fuels, is reaching a crisis point. Experts assert that the prevalence of more intense storms, hurricanes, droughts, wildfires, as well as a loss of biodiversity, are signs that the crisis is worsening rapidly. They warn that, if not urgently addressed, global warming poses an existential threat to life on Earth.

Using computational tools to better understand the rate and impacts of climate change is considered a valuable tool in developing strategies to address the problem. While climate modelling has been a scientific practice since the 1950’s, recently introduced exascale supercomputers (which can process a quintillion calculations each second) offer the opportunity to understand climate change at a far more advanced level than ever before. With the use of exascale computers, computer scientists and climate scientists have developed extremely high-resolution Earth System Models (ESM’s).

ESM’s offer great promise in understanding the Earth’s climate but they are computationally expensive—i.e., they require a great deal of computation time and energy, and they require a tremendous amount of storage for the massive quantity of data they generate.

To address this problem, the prize-winning team presented the design and implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. In computing, emulators allow for a dynamic interplay between different computers—with one computer system (called the host) behaving like another computer system (the guest). The growing use of emulators in climate modeling has become more common as emulators can combine and enhance efficient algorithms to handle large datasets, as well as the distribution of computations across multiple processors.

Climate emulators have come to play a pivotal role in alleviating the computational burden and storage requirements associated with climate modeling and simulations. Because the resolution of a climate model is impacted by the trade-off between the computational costs and the representation of the climate system, improving both computational and data storage challenges in a high-performance computer allows for more advanced climate modeling capabilities.

The ACM Gordon Bell Prize for Climate Modelling winning team contends their emulator could save several petabytes of computing storage space. By way of comparison, one petabyte is equal to the storage capacity of approximately 170 top-end servers.

The team’s ultra-high resolution model of the earth’s climate included 54,486,360 spatial locations around the globe, as well as 318 billion hourly and 31 billion daily observations.

The team achieved its results using high performance computing methods called Spherical Harmonic Transform (SHT) and Cholesky factorization. In the introduction to their paper, the Prize-winning team wrote: “We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in climate data. This provides tunable spatio-temporal resolution and significantly improves the fidelity and granularity of climate emulation, achieving an ultra-high spatial resolution of 0.034 ^◦ (∼3.5 km) in Space.”

The team ran mixed-precision computations on a PaRSEC dynamic runtime system, running on 9,025 nodes on Frontier, 1,936 nodes on Alps, 1,024 nodes on Leonardo, and 3,072 nodes on Summit, with the hybrid Flop/s rates 0.976 EFlop/s, 0.739 EFlop/s, 0.243 EFlop/s, and 0.375 EFlop/s, respectively.

The team concludes that their exascale climate emulator holds significant potential for the climate community, advancing climate research and policy making. They also maintain that their work holds significant potential in advancing the development of machine learning (ML) and AI-driven methods for forecasting or prediction applications in climate science.

New York, NY, September 18, 2024 – ACM, the Association for Computing Machinery, and IEEE Computer Society have named David A. Padua, Donald Biggar Willett Professor Emeritus in Engineering at the University of Illinois Urbana-Champaign, as the recipient of the 2024 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes groundbreaking achievements in parallel and high performance computing (HPC). Padua is cited for innovative and usable contributions to the theory and practice of parallel compilation and tools, as well as service to the computing community.

Parallel computing is a technique which involves taking a large computational task and breaking it up into smaller tasks so that it can be handled simultaneously on multiple processors. The overall goal is to reduce the amount of time required to process a task. While parallelization was initially employed as a tool in high performance computing, today it is used in almost every computing device including smartphones.

Padua has made fundamental contributions that have helped make parallel computation universally useful. He has worked at the levels of fundamental algorithms for parallelism, general tools for parallel programming (compilers and debuggers), and domain-specific languages, applications, and tools, as well as autotuning methods and compiler quality evaluation. His specific contributions include:

• Synchronization of multiple threads during computation is necessary for high performance computers to produce correct results. Two key techniques introduced by Padua and his students to enable the correct synchronization of multiple threads during parallel computing include speculative parallelism and array privatization.
• Padua and his students introduced much of the basic research that has made general parallel programming tools common today. He was directly involved in developing race detection for debugging parallel programs, as well as array tiling to improve parallelism and cache performance.
• In HPC, domain-specific parallelism has been especially effective in various special purpose applications. Padua’s contributions in this area include the development of parallel Matlab compilation, and contributions to compilation aspects of signal processing including the SPIRAL project.
• Padua and his students have made important contributions to the evaluation of compilers, including the vectorizing compiler evaluation techniques which have been used by compiler teams at Intel and other companies.

In addition to his technical contributions, Padua is recognized for outstanding service to the field. He integrated the ideas of parallel computing as editor of the Encyclopedia of Parallel Computing (Springer), a four-volume publication that is widely respected in the field. Padua has also served as editor of several esteemed parallel computing journals, chaired and/or participated in program committees for over 70 conferences, and supervised 37 PhD dissertations. His former students represent a new generation of talented individuals who have gone on to make significant contributions to both academia and industry.

The Ken Kennedy Award will be formally presented to Padua in November at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC24).

Biographical Background
David A. Padua is the Donald Biggar Willett Professor Emeritus of Engineering at the University of Illinois Urbana-Champaign. Padua received his Bachelor of Science degree in Computer Science from the Universidad Central de Venezuela, and a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

Padua is a Fellow of ACM, IEEE, and the American Association for the Advancement of Science (AAAS). His honors also include receiving the Harry H. Goode Memorial Award from the IEEE Computer Society, and an honorary PhD from the University of Vallodolid, Spain.

New York, NY, August 14, 2024 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that Ke Fan of the University of Illinois at Chicago and Daniel Nichols of the University of Maryland are the recipients of the 2024 ACM-IEEE CS George Michael Memorial HPC Fellowships. The George Michael Memorial Fellowship honors exceptional PhD students throughout the world whose research focus is high-performance computing (HPC) applications, networking, storage, or large-scale data analytics.

Fan is recognized for her research in three key areas of high-performance computing: optimizing the performance of MPI collectives, enhancing the performance of irregular parallel I/O operations, and improving the scalability of performance introspection frameworks. Nichols is recognized for advancements in machine-learning based performance modeling and the advancement of large language models for HPC and scientific codes.

Ke Fan

Fan’s research focuses on improving the performance of data movement associated with collective communication and parallel file I/O operations on large-scale supercomputers. Her most recent focus has been on two main areas: (1) Optimizing inter-process data movement, particularly in the context of all-to-all collectives, where all processes engage in data exchange. In this area, she has developed a new class of parameterized hierarchal algorithms that substantially improve the performance of both uniform and non-uniform all-to-all collectives. (2) Optimizing parallel I/O, targeting applications that generate unbalanced, irregular I/O workloads. Fan has specifically developed spatially aware data aggregation techniques that enhance load balancing and improve overall parallel I/O performance.

In addition to these two areas, she has made significant progress in improving the scalability of performance introspection frameworks, which help developers understand data movement capabilities in HPC systems. With these new insights, developers can identify bottlenecks and optimize performance at scale.

Daniel Nichols

Nichols’ research is broadly centered around the intersection of machine learning (ML) and high-performance computing. His most recent focus has been on two main areas: (1) developing novel ML-based performance models to make use of all available performance data when making predictions about code runtime properties, and (2) adapting state of the art large language model (LLM) techniques to HPC applications. By utilizing recent advances in representation learning and further advancing them to handle the unique challenges of performance modeling, Nichols’ research seeks to develop models that make use of all available data when predicting performance. This research has the potential to significantly improve both the quality and applicability of performance models.

By adapting LLM’s to HPC applications, Nichols’ work has improved their performance on HPC development tasks. He has created scientific and parallel code capable LLMs and methods for improving the quality of current models for HPC. This is part of his goal towards creating specialized LLMs to solve software complexities and allow scientists to focus on their domain research and less on the intricacies of HPC development.

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.

ACM and IEEE Computer Society named Wen-mei W. Hwu, a Senior Distinguished Research Scientist at NVIDIA and Professor Emeritus at the University of Illinois, Urbana-Champaign, the recipient of the ACM-IEEE CS Eckert-Mauchly Award. Hwu is recognized for pioneering and foundational contributions to the design and adoption of multiple generations of processor architectures. His fundamental and pioneering contributions have had a broad impact on three generations of processor architectures: superscalar, VLIW, and throughput-oriented manycore processors (GPUs).

Hwu was one of the original architects of the High-Performance Substrate (HPS) model that pioneered superscalar microarchitecture, introducing the concepts of dynamic scheduling, branch prediction, speculative execution, a post-decode cache, and in-order retirement. He co-authored the two original 1985 HPS papers, “Critical Issues Regarding HPS, a High Performance Microarchitecture” and “HPS, A New Microarchitecture: Rationale and Introduction,” both of which received the inaugural MICRO Test-of-Time Award in 2014.

By 1987, the rapid increase in hardware execution resources created pressing needs for instruction-level parallelizing compilers. Hwu addressed the problem by constructing a revolutionary compiler infrastructure in his paper, “IMPACT: An Architectural Framework for Multiple-Instruction Issue,” which demonstrated compilers can generate code with far more parallelism than most researchers thought possible. This paper also pioneered architecture support for control speculation and received the 2006 ISCA Most Influential Paper Award.

For his work on architecture support for ILP compilers, he received ACM SIGARCH’s first Maurice Wilkes award in 1998. He published foundational papers on superblock and hyperblock structures. The superblock is a pervasive compiler technique, adopted by major vendor compilers and the GNU C Compiler. In academia, the hyperblock work influenced many projects, most notably the TRIPS project at the University of Texas. In 1999, Hwu received the ACM Grace M. Hopper Award “for the design and implementation of the IMPACT compiler.”

Since 2006, Hwu has focused on designing and deploying throughput-oriented heterogeneous parallel computing architectures. His team pioneered the programmer optimization principles in their PPoPP 2008 paper and the Pareto-optimal pruning of search space for auto-tuning in their CGO 2008 paper for GPUs. The CGO 2008 paper won the 2018 CGO Test-of-Time Award  These works not only enabled wide adoption of CUDA-enabled GPUs but also helped the NVIDIA architecture team to improve the programmability of several generations of GPUs. The four editions of the textbook by Hwu and David Kirk (former Chief Scientist of NVIDIA), Programming Massively Parallel Processors, have sold more than 25,000 copies and the book has been translated into five languages.

Hwu’s contributions to education also include three offerings of the Coursera course on Heterogeneous Parallel Programming that were attended by more than 20,000 students, with 5,000 completing all exams and quizzes to receive a certificate. Hwu and Kirk are widely credited for their contributions in making the GPU the computing device of choice for the HPC/ML communities. Hwu’s architecture and compiler techniques have impacted billions of processors.

Hwu will be formally recognized with the Eckert-Mauchly Award during an awards luncheon on Tuesday, July 2, at the International Symposium on Computer Architecture (ISCA 2024 ).

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications,” which demonstrated wireless and batteryless sensor nodes using novel materials and radio backscatter.

Arora’s research envisions creating sustainable computational materials that operate by harvesting energy from the environment and, at the end of their life cycle, can be responsibly composted or recycled. Her research process involves working at the intersection of materials, methods of fabrication, low-power systems, and HCI . She actively looks to apply her work to application domains such as smart homes, health, climate change, and wildlife monitoring.

Arora’s dissertation makes truly groundbreaking contributions to the fields of Ubiquitous Computing and Human-Computer Interaction. Today’s Internet of Things (IoT) devices are bulky, require battery maintenance, and involve costly installation. In contrast, Arora shows how the computational capabilities of sensing, communication, and display can be diffused into materials and everyday objects. She builds interactive stickers that are inexpensive, and easy to deploy and sustainably operate by harvesting energy from body heat or indoor light. She demonstrates this idea over a series of projects. Her first effort, SATURN, is a thin, flexible multi-layer material that is a self-sustaining audio sensor. Specifically, it uses the vibration itself to power the ability to capture and encode the vibration sensor. SATURN was extended to ZEUSSS to use passive RF backscatter for wireless transmission on the vibration signal. She followed this up with the MARS platform that produces an extremely low-power (less than a microwatt) resonance circuit that varies its frequency based on user interaction with interfaces that create inductive or capacitive loads on the circuit. Coupling this circuit with FM passive backscatter and ambient power harvesting allows user interfaces such as touch-sensitive buttons, sliders, and vibration sensors to communicate at a distance. The result of these three projects is a flat user interface in a post-it note form factor that can be deployed in the environment simply by sticking it to a flat surface. The flat user interface and mobile design allows for applications such as light switches or audio volume sliders that can simply be pasted where they are needed without worrying about wiring the infrastructure or maintaining batteries.

The final project, VENUS, adds output in the form of low-power display technologies to provide immediate feedback on the surface of the computational material, opening a wide variety of user-facing interaction scenarios. Her work also showed that it is possible to power these circuits through the transfer of body heat when a user touches the button, which can also be used to protect privacy.

Arora is an Assistant Professor in the Electrical and Computer Engineering and (by courtesy) Computer Science Department, as well as the Allen K. and Johnnie Cordell Breed Jr. Professor of Design at Northwestern University. Her research involves rethinking the computing stack from a sustainability-first approach for its entire life-cycle: manufacturing, operation, and disposal. Arora received a PhD in Computer Science and an MS In Human-Computer Interaction from the Georgia Institute of Technology.

Honorable Mentions

Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Farina’s dissertation, “Game-Theoretic Decision Making in Imperfect-Information Games” was recognized for laying modern learning foundations for decision-making in imperfect-information sequential games, resolving long-standing questions, and demonstrating state-of-the-art theoretical and practical performance.

Farina is an Assistant Professor in the Electrical Engineering and Computer Science Department (EECS) at the Massachusetts Institute of Technology. His research interests include artificial intelligence, machine learning, optimization, and game theory. He received a PhD in Computer Science from Carnegie Mellon University.

Kuszmaul’s dissertation, “Randomized Data Structures: New Perspectives and Hidden Surprises,” is recognized for contributions to the field of randomized data structures that overturn conventional wisdom and widely believed conjecture.

Kuszmaul’s research focuses on algorithms, data structures, and probability. He received a PhD in Computer Science from the Massachusetts Institute of Technology and is presently doing Post Doctoral work at Harvard University. In August, he will be starting as an assistant professor in the Computer Science Department at Carnegie Mellon University.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Arora’s research envisions creating sustainable computational materials that operate by harvesting energy from the environment and, at the end of their life cycle, can be responsibly composted or recycled. Her research process involves working at the intersection of materials, methods of fabrication, low-power systems, and HCI . She actively looks to apply her work to application domains such as smart homes, health, climate change, and wildlife monitoring.

Arora is an Assistant Professor in the Electrical and Computer Engineering and (by courtesy) Computer Science Department, as well as the Allen K. and Johnnie Cordell Breed Jr. Professor of Design at Northwestern University. Her research involves rethinking the computing stack from a sustainability-first approach for its entire life-cycle: manufacturing, operation, and disposal. Arora received a PhD in Computer Science and an MS In Human-Computer Interaction from the Georgia Institute of Technology.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Kuszmaul’s dissertation, “Randomized Data Structures: New Perspectives and Hidden Surprises,” is recognized for contributions to the field of randomized data structures that overturn conventional wisdom and widely believed conjecture.

Kuszmaul’s research focuses on algorithms, data structures, and probability. He received a PhD in Computer Science from the Massachusetts Institute of Technology and is presently doing Post Doctoral work at Harvard University. In August, he will be starting as an assistant professor in the Computer Science Department at Carnegie Mellon University.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Farina’s dissertation, “Game-Theoretic Decision Making in Imperfect-Information Games” was recognized for laying modern learning foundations for decision-making in imperfect-information sequential games, resolving long-standing questions, and demonstrating state-of-the-art theoretical and practical performance.

Farina is an Assistant Professor in the Electrical Engineering and Computer Science Department (EECS) at the Massachusetts Institute of Technology. His research interests include artificial intelligence, machine learning, optimization, and game theory. He received a PhD in Computer Science from Carnegie Mellon University.

Margaret Martonosi is the Hugh Trumbull Adams ’35 Professor of Computer Science at Princeton University. She also recently served a four-year term as the National Science Foundation (NSF) Assistant Director leading the Directorate for Computer and Information Science and Engineering (CISE).

Martonosi earned MS and PhD degrees in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Cornell University.

Her many honors include the ACM-IEEE-CS Eckert-Mauchly Award, the Undergraduate Research Mentoring Award by the National Center for Women & Information Technology, Princeton's Graduate Mentoring Award, as well as numerous Test-of-Time and Best Paper Awards. Martonosi is a Fellow of ACM and IEEE.  She is a member of the National Academy of Engineering.

Maja J. Matarić is the Chan Soon-Shiong Chair and Distinguished Professor of Computer Science at the University of Southern California, where she is the founding director of the USC Robotics and Autonomous Systems Center. She is also a Principal Scientist at Google DeepMind.

A graduate of the University of Kansas, Matarić earned an SM in Computer Science and a PhD in Computer Science and Artificial Intelligence from MIT. Her many publications cover a wide range of topics, including distributed robotics, robot learning, human-robot interaction, and socially assistive robotics, and are highly cited (h-index 105; over 45600 citations) .

Matarić is a member of the American Academy of Arts and Sciences (AMACAD), and Fellow of the American Association for the Advancement of Science (AAAS), the Association for the Advancement of Artificial Intelligence (AAAI), IEEE and ACM, and recipient of the US Presidential Award for Excellence in Science, Math, and Engineering Mentoring.

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

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

Each Cutler-Bell Prize winner receives a $10,000 cash prize. The prize amount is sent to the financial aid office of the institution the student will be attending next year and is then put toward each student’s tuition or disbursed. This year’s Cutler-Bell Prize recipients will be formally recognized at the Computer Science Teachers Association’s 2024 Annual Conference, July 16-19, in Las Vegas.

The winning projects illustrate the diverse applications the next generation of computer scientists is developing.

Shobhit Agarwal, Reedy High School, Frisco, Texas

Every three years, Shobhit Agarwal visits his grandparents in Jhansi, India, a town with the poorest healthcare infrastructure in northern India—where 85% of the population does not attend yearly checkups. After seeing a mental decline in his grandfather, Agarwal immersed himself in his grandparents’ hometown, interviewing local citizens and doctors. He was driven to create a low-cost system that recognizes a variety of diseases while simultaneously forecasting the progress of the disease. This system is OmniDoc, a framework that accurately predicts diagnoses, prognoses, and treatments given a patient profile. This system is currently deployed in Jhansi in two ways. First, hospital volunteers at the Naja Hospital conducted door-to-door visits to collect patient information. This data is inputted into Agarwal’s framework, and if the algorithm detects a disease, a patient books a free appointment with a local physician; the model data is subsequently forwarded to the doctor. Five hundred homes were visited, and nearly 40% of those patients were sent to clinics based on algorithm-identified risk. Hospital statistics show that 95% of the patients who arrived in the clinic due to the campaign had the algorithm’s diagnosis subsequently confirmed by a physician, indicating that the model accurately identified patient condition with minimal data. This success led to the deployment of OmniDoc at the Jhansi Orthopedic Hospital for their 15-person radiology department.

Franziska Borneff, Hidden Valley High School, Cave Spring, Virginia

During her junior year, Franziska Borneff became interested in monitoring the flow rates of Arctic rivers. Using her newly developed coding skills, she began to plot and trend analysis for climate research. Her own research aligned with the news articles about climate change, and this influenced her to continue to study these data trends. The Arctic rivers are important in the process of detecting climate change, as they directly influence ecosystems and the livelihoods of humans. Borneff researched the relationship between atmosphere and waterways, concluding that air temperature, river discharge, and sea ice concentration are the most significant data points. Her research creates a method to track critical dates related to the spring thaw of Arctic rivers and assess their impact on local populations. She collected daily temperature, river discharge, and sea ice cover data from publicly available sources for the six major Arctic rivers, finding the thaw date is earlier than ever. Borneff then connected with the students of the Yupik Eskimo Village in Marshall, Alaska, to see how the earlier thaw impacted the local community. Through this meeting, she read stories about how the familiar rhythms of nature have been disrupted and the uncertain future of the Yupik people as a result of these shifts. Borneff hopes her research will spark studies in sensitive Arctic regions and enlighten politicians to initiate government action that is informed by scientific understanding. The collaboration between quantitative data documenting warming and human narratives testifying to the accuracy of the data is essential.

Daniel Mathew, Poolesville High School, Poolesville, Maryland

Daniel Mathew’s project, MiniMesh, danced between invention and improvement. Starting out on his bedside desk, Mathew dumped out scrap electronic parts and assembled a device named PreVis to track the location of a single point on a human for movement analysis with a LiDAR mounted on a controllable servo. PreVis went everywhere, from discussing him in Congress to presenting him at research conferences. PreVis was Mathew’s first step toward human-computer interaction. PreVis then evolved into MiniProse, a free mobile application. Mathew studied the mathematics of current pose estimation solutions and then developed the core algorithm for MiniProse. The final algorithm, MiniMesh, scrapped the original two prototypes and was built from the ground up. Mathew developed a new framework. Mathematically proving optimization identities and writing machine learning algorithms, MiniMesh could reconstruct the entire human mesh with thousands of points. Mathew took this algorithm to companies for skin cancer research and military organizations for augmented-reality surgery. By predicting the location of thousands of points on the human body on portable devices, all in real-time, MiniMesh has applications in several fields. In the original biomechanical use case, MiniMesh could be used for at-home gait analysis by tracking joint locations and improving sports techniques by analyzing the metabolic cost of human movements. For assistive surgery, MiniMesh accurately and efficiently maps the human topology to aid surgeons. For animation, MiniMesh replicates expensive VFX, enabling amateur animators at no cost.

Kosha Upadhyay, Bellevue High School, Bellevue, Washington

Kosha Upadhyay watched her neighbor of eight years struggle with dementia. Following his passing, she wanted to understand the struggle of people with dementia and began volunteering at a dementia care center. Upadhyay knew she couldn’t give these patients back what they lost but wanted to preserve what they had left. This inspired her to work on creating a better therapy for those suffering from dementia. This therapy, MemSpark, is an automated end-to-end system that creates a novel brain-training therapy using virtual reality and tracks dementia progression through artificial intelligence. Upadhyay studied brain morphology and neurodegeneration to create a therapy that affected dementia at its root cause. Since decline can affect any part of the brain, she designed a novel set of games that exercised all parts of the brain. Eight serious games were designed to exercise all cognitive functions by considering a set of promotive factors (immersion, confidence, focus) and preventive factors (anxiety, frustration, self-pity). Each serious game produced a set of two features—accuracy and time which were then inputted into an AI model for profiling. The data generated by the Virtual Reality (VR) system was pre-processed and passed into an AI model. After studying multiple AI models, Upadhyay chose a multi-layer perceptron neural network due to its suitability for the mode of data, along with its high accuracy and regression adaptability. The neural network produced cognitive profiling scores across three categories: recall, reasoning, and executive function. The profiles were aligned to the ADAS-Cog test–an industry standard test for evaluating dementia. Upadhyay tested her therapy across 14 people split into experimental and control groups. Her solution was able to slow down dementia progression by 65%—which surpasses current mainstream therapies and presents the potential to enhance the way we approach dementia care.

Amanda Randles is the Alfred Winborne and Victoria Stover Mordecai Associate Professor of Biomedical Sciences at Duke University. A graduate of Duke University with a BA in Physics and Computer Science, Randles received a PhD degree in Applied Physics and an SM degree in Computer Science from Harvard University.

Randles’s honors include the ACM SIGHPC Emerging Woman Leader in Technical Computing Award, the NIH Pioneer Award, the NSF CAREER Award, the ACM Grace Murray Hopper Award, and the Alexandra Jane Noble Epiphany Award. Randles is a National Academy of Inventors Fellow, an ACM Distinguished Member, an IEEE Senior Member, and was named as an MIT TR35 Visionary.

ACM has named Avi Wigderson as recipient of the 2023 ACM A.M. Turing Award for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science.

Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. He has been a leading figure in areas including computational complexity theory, algorithms and optimization, randomness and cryptography, parallel and distributed computation, combinatorics, and graph theory, as well as connections between theoretical computer science and mathematics and science.

What is Theoretical Computer Science?

Theoretical computer science is concerned with the mathematical underpinnings of the field. It poses questions such as “Is this problem solvable through computation?” or “If this problem is solvable through computation, how much time and other resources will be required?”

Theoretical computer science also explores the design of efficient algorithms. Every computing technology that touches our lives is made possible by algorithms. Understanding the principles that make for powerful and efficient algorithms deepens our understanding not only of computer science, but also the laws of nature. While theoretical computer science is known as a field that presents exciting intellectual challenges and is often not directly concerned with improving the practical applications of computing, research breakthroughs in this discipline have led to advances in almost every area of the field—from cryptography and computational biology to network design, machine learning, and quantum computing.

Why is Randomness Important?

Fundamentally, computers are deterministic systems; the set of instructions of an algorithm applied to any given input uniquely determines its computation and, in particular, its output. In other words, the deterministic algorithm is following a predictable pattern. Randomness, by contrast, lacks a well-defined pattern, or predictability in events or outcomes. Because the world we live in seems full of random events (weather systems, biological and quantum phenomena, etc.), computer scientists have enriched algorithms by allowing them to make random choices in the course of their computation, in the hope of improving their efficiency. And indeed, many problems for which no efficient deterministic algorithm was known have been solved efficiently by probabilistic algorithms, albeit with some small probability of error (that can be efficiently reduced). But is randomness essential, or can it be removed? And what is the quality of randomness needed for the success of probabilistic algorithms?

These, and many other fundamental questions lie at the heart of understanding randomness and pseudorandomness in computation. An improved understanding of the dynamics of randomness in computation can lead us to develop better algorithms as well as deepen our understanding of the nature of computation itself.

Wigderson’s Contributions

A leader in theoretical computer science research for four decades, Wigderson has made foundational contributions to the understanding of the role of randomness and pseudorandomness in computation.

Computer scientists have discovered a remarkable connection between randomness and computational difficulty (i.e., identifying natural problems that have no efficient algorithms). Working with colleagues, Wigderson authored a highly influential series of works on trading hardness for randomness. They proved that, under standard and widely believed computational assumptions, every probabilistic polynomial time algorithm can be efficiently derandomized (namely, made fully deterministic). In other words, randomness is not necessary for efficient computation. This sequence of works revolutionized our understanding of the role of randomness in computation, and the way we think about randomness. This series of influential papers include the following three:

• “Hardness vs. Randomness” (co-authored with Noam Nisan)
  Among other findings, this paper introduced a new type of pseudorandom generator, and proved that efficient deterministic simulation of randomized algorithms is possible under much weaker assumptions than previously known.
• “BPP Has Subexponential Time Simulations Unless EXPTIME has Publishable Proofs” (co-authored with László Babai, Lance Fortnow, and Noam Nisan)
  This paper used `hardness amplification’ to demonstrate that bounded-error probabilistic polynomial time (BPP) can be simulated in subexponential time for infinitely many input lengths under weaker assumptions.
• “P = BPP if E Requires Exponential Circuits: Derandomizing the XOR Lemma” (co-authored with Russell Impagliazzo)
  This paper introduces a stronger pseudo-random generator with essentially optimal hardness vs randomness trade-offs.

Importantly, the impact of these three papers by Wigderson goes far beyond the areas of randomness and derandomization. Ideas from these papers were subsequently used in many areas of theoretical computer science and led to impactful papers by several leading figures in the field.

Still working within the broad area of randomness in computation, in papers with Omer Reingold, Salil Vadhan, and Michael Capalbo, Wigderson gave the first efficient combinatorial constructions of expander graphs, which are sparse graphs that have strong connectivity properties. They have many important applications in both mathematics and theoretical computer science.

Outside of his work in randomness, Wigderson has been an intellectual leader in several other areas of theoretical computer science, including multi-prover interactive proofs, cryptography, and circuit complexity.

Mentoring

In addition to his groundbreaking technical contributions, Wigderson is recognized as an esteemed mentor and colleague who has advised countless young researchers. His vast knowledge and unrivaled technical proficiency—coupled with his friendliness, enthusiasm, and generosity—have attracted many of the best young minds to pursue careers in theoretical computer science.

Biographical Background

Avi Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. He has been a leading figure in areas including computational complexity theory, algorithms and optimization, randomness and cryptography, parallel and distributed computation, combinatorics, and graph theory, as well as connections between theoretical computer science and mathematics and science.

Wigderson’s honors include the Abel Prize, the IMU Abacus Medal (previously known as the Nevanlinna Prize), the Donald E. Knuth Prize, the Edsger W. Dijkstra Prize in Distributed Computing, and the Gödel Prize. He is an ACM Fellow and a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences.

ACM, the Association for Computing Machinery, has named 68 Fellows for transformative contributions to computing science and technology. All the 2023 inductees are longstanding ACM Members who were selected by their peers for groundbreaking innovations that have improved how we live, work, and play.

“The announcement each year that a new class of ACM Fellows has been selected is met with great excitement,” said ACM President Yannis Ioannidis. “ACM is proud to include nearly 110,000 computing professionals in our ranks and ACM Fellows represent just 1% of our entire global membership. This year’s inductees include the inventor of the World Wide Web,the “godfathers of AI, and other colleagues whose contributions have all been important building blocks in forming the digital society that shapes our modern world.

In keeping with ACM’s global reach, the 2023 Fellows represent universities, corporations, and research centers in Canada, China, Germany, India, Israel, Norway, Singapore, the United Kingdom, and the United States. The contributions of the 2023 Fellows run the gamut of the computing field―including algorithm design, computer graphics, cybersecurity, energy-efficient computing, mobile computing, software analytics, and web search, to name a few.

Additional information about the 2023 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows website.

Read the news release.

ACM has named 52 Distinguished Members for significant contributions. All the 2023 inductees are longstanding ACM Members and were selected by their peers for work that has advanced computing, fostered innovation across various fields, and improved computer science education.

“The ACM Distinguished Members program recognizes both career achievement as well as participation in ACM,” said ACM President Yannis Ioannidis. “Many of these new 52 Distinguished Members have been selected for important technical achievements, while others have been chosen because of their service and/or work in computer science education, which lays the foundation for the future of our field. With the Distinguished Member designation, ACM also highlights how individual computing professionals maintain the health and growth of a global scientific society through membership and active engagement with their colleagues.” v vThe 2023 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Belgium, Canada, China, Denmark, Finland, France, Germany, India, Israel, Italy, Switzerland, the United Kingdom, and the United States. This year’s class of Distinguished Members made advancements in areas including AI and economics, principles of data management, software development, human-computer interaction, developing technology for people with disabilities, mobile and wireless sensing systems, and 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.

The ACM India 2023 Doctoral Dissertation Award goes to Pranjal Dutta for his dissertation titled “A Tale of Hardness, De-randomization and De-bordering in Complexity Theory.” Pranjal’s dissertation makes breakthrough contributions in the study of hardness, approximation and derandomization of algebraic circuits, and develops new mathematical tools, laying the foundation for future developments in algebraic complexity theory. Pranjal’s dissertation work was done at Chennai Mathematical Institute under the supervision of Prof. Nitin Saxena.

Pranjal Dutta’s dissertation makes multiple contributions to algebraic complexity theory. In an early result, he shows that the existence of a polynomial with a slightly largish special representation would resolve the long-standing VP vs VNP problem, and also provide non-trivial algorithms for polynomial identity testing (PIT) – effectively attacking two flagship problems in algebraic complexity theory. In the context of PIT, his dissertation provides a nearly polynomial time algorithm for derandomization of a special class of depth 4 algebraic circuits, where the top fanin is bounded and the bottom gates compute low degree polynomials. This is among the strongest results in PIT known so far. Pranjal also provides a deep understanding of the power of closure of algebraic circuits -- an important problem in the Geometric Complexity Theory approach to the P vs NP problem. Specifically, he shows that the closure (or border) of (bounded fanin) depth 3 circuits can be captured by algebraic branching programs, making it one of the first such "de-bordering" results in this area. Additionally, his work also establishes an efficient algorithm for PIT for this class, and shows a surprising exponential-gap hierarchy-theorem for depth-3 constant-top-fanin circuits.

The Honorable Mention for 2023 goes to Jogendra Nath Kundu for his dissertation titled “Self-supervised Domain Adaptation Framework for Computer Vision Tasks.” Jogendra’s dissertation makes significant and timely contributions to unsupervised domain adaptation and self-supervised learning techniques for structured prediction based vision tasks, advancing the practical deployment of intelligent machines in real-world scenarios. His dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Prof. Venkatesh Babu Radhakrishnan.

Jogendra Nath Kundu’s doctoral dissertation makes three important contributions. First, he considers image-like dense-prediction tasks, where he addresses shortcomings in existing classification-based domain adaptation algorithms. The content-preserving mechanisms introduced by him via cyclic consistency objectives have been shown to yield state-of-the-art performance. Next, he introduces source-side procurement stage learning, a novel approach to source-free adaptation, which significantly enhances adaptability in scenarios with restricted data-sharing. Finally, Jogendra’s thesis applies domain adaptation concepts to the complex task of 3D human pose estimation. He shows how this can be achieved by incorporation of effective prior-enforcing mechanisms alongside development of novel self-supervised techniques leveraging inter-entity relations. The insights in Jogendra’s dissertation have the potential to revolutionize the deployment of intelligent vision systems across diverse industries, from healthcare to virtual and augmented reality.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation in Computer Science and related disciplines from a degree-awarding institution based in India for each academic year, running from July 1 of one year to June 30 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award, given to nomination(s), if any, that missed the award by a narrow margin, is accompanied by a prize of ₹1,00,000, that is shared among the recipient(s). The winning dissertation(s) will be published in the ACM Digital Library. Tata Consultancy Services Limited (TCS)  is the founding sponsor of these awards. Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

ACM, the Association for Computing Machinery, named an eight-member team drawn from American and Indian institutions as the winner of the 2023 ACM Gordon Bell Prizefor the project, “Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys.”

The members of the team are: Sambit Das (University of Michigan), Bikash Kanungo (University of Michigan), Vishal Subramanian, (University of Michigan), Gourab Panigrahi (Indian Institute of Science, Bangalore), Phani Motamarri (Indian Institute of Science, Bangalore), David Rogers (Oakridge National Laboratory), Paul M. Zimmerman (University of Michigan), and Vikram Gavini (University of Michigan).

Molecular dynamics is a process by which computer simulations are used to better understand the movements of atoms and molecules within a system. Ab initio (Latin for “from the beginning”) is a branch of molecular dynamics that has been shown to be an especially effective technique when applied to important problems in physics and chemistry—including efforts to better understand microscopic mechanisms, gain new insights in materials science, and prove out experimental data.

Despite the successes of ab initio approaches in a wide range of computer simulations, the team notes that efforts to employ quantum mechanical ab initio methods to predict materials’ properties has not been able to achieve quantum accuracy and scale on the powerful supercomputers needed to perform these simulations. In their abstract to their Gordon Bell Prize-winning project the authors write, “ Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum Many-Body (QMB) methods realize quantum accuracy but fail to scale.”

To address this challenge, the Gordon Bell Prize-winning team developed a framework that combines the accuracy provided by QMB methods with the efficiency of Density-Functional Theory (DFT) to access larger length scales at quantum accuracy—a goal that existing approaches have not been able to achieve.

The 2023 ACM Gordon Bell Prize-winning team writes, “We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.”

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 (SC23), which was held in Denver, Colorado.

ACM, the Association for Computing Machinery, presented a nineteen-member team with the inaugural  ACM Gordon Bell Prize for Climate Modelling for their project, “The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System.” The new award aims to recognize innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Mark A. Taylor, Luca Bertagna, Conrad Clevenger, James G. Foucar, Oksana Guba, Benjamin R. Hillman, Andrew G. Salinger (all of Sandia National Laboratories); Peter M. Caldwell, Aaron S. Donahue, Noel Keen, Christopher R. Terai, Renata B. McCoy, David C. Bader (all of Lawrence Livermore National Laboratory); Jayesh Krishna, Danqing Wu (both of Argonne National Laboratory); Matthew R. Norman, Sarat Sreepathi (both of Oakridge National Laboratory); James B. White III (Hewlett Packard Enterprise); and L. Ruby Leung (Pacific Northwest National Laboratory).

To develop the most effective carbon emission reduction policies, governments are working with scientists to better understand the relationship between carbon emissions, the earth’s atmosphere, and climate change. Because of the mind-boggling number of variables in understanding climate phenomena (e.g., temperature, humidity, precipitation), scientists have increasingly used powerful supercomputers to process all these variables in order to develop high resolution simulations.

Climate scientists are especially interested in understanding convective clouds (clouds that are formed by the process of warmer air rising above a less dense atmosphere). Deep convective clouds (which can be many kilometers thick) are particularly important to simulate correctly because they drive the tropical overturning circulation and modulate energy transfer over much of the planet.

A class of algorithmic models known as global cloud-resolving models (GCRMs) have been used to attempt to simulate deep convective clouds and have been accurate in certain instances such as providing simulations of short time periods or limited physical areas. But the Prize-winning team notes that the drawbacks of GCRM’s include the fact that running these algorithms on existing supercomputers has been slow and computationally expensive (e.g., the algorithms require too many steps).

The team proves that by using just-introduced exascale supercomputers along with a new algorithmic model they have introduced, the longstanding challenge of developing efficient and accurate simulations of deep convective clouds can be accomplished. The prize-winning team introduces the new algorithmic model, “Simple Cloud Resolving E3SM Atmosphere Model (SCREAM).“

The ACM Gordon Bell Prize for Climate Modelling aims to recognize innovative parallel computing contributions toward solving the global climate crisis. Climate scientists and software engineers are evaluated for the award based on the performance and innovation in their computational methods.

New York, NY, October 26, 2023 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that James Gregory Pauloski of the University of Chicago and Rohan Basu Roy of Northeastern University are the recipients of the 2023 ACM-IEEE CS George Michael Memorial HPC Fellowships. Hua Huang of the Georgia Institute of Technology received an Honorable Mention. Pauloski is recognized for developing systems for optimal HPC resource usage from scalable optimization methods for deep learning training to data fabrics for sophisticated applications spanning heterogeneous resources. Roy is recognized for enhancing the productivity of computational scientists and environmental sustainability of HPC with novel methods and tools exploiting cloud computing and on-premise HPC resources. Huang is recognized for contributions to high performance parallel matrix algorithms and implementations and their application to quantum chemistry calculations.

J. Gregory Pauloski

Pauloski’s aim is to build tools that are approachable and easily used by HPC novices and experts alike.

His research approaches HPC from two aspects: efficient large scale machine learning (ML) training, and data fabrics that support distributed and federated scientific applications. The rapid increase in demand for AI tools (e.g., ChatGPT, LaMDA, etc.) has promoted scalable deep learning to a core challenge for HPC. Pauloski has made advancements in system software and algorithms to efficiently use novel hardware systems for AI applications. He has also worked on the development of federated applications which span heterogeneous systems composed of specialized accelerators, edge devices, cloud compute, and HPC. Pauloski’s work on data fabrics enables autonomous actors to communicate efficiently and reliably, independent of location.

In addition to his technical contributions, Pauloski’s colleagues have cited his work as a role model and mentor for younger students.

Rohan Basu Roy

Roy designs new tools and methods for enhancing HPC programmer productivity and making large-scale computing systems more cost-effective and environmentally sustainable.

The key challenges computational scientists face include the time required to performance-tune their code and the time required to efficiently provision and utilize computing resources. To address these challenges, Roy has designed HPC performance auto-tuner tools to significantly improve the program productivity. Roy’s research contributions include the first demonstration of significant productivity and performance advantages of the serverless computing model for complex scientific workflows, including quick elasticity in the cloud (eliminating the long queue wait time), ease of use, and opportunistic co-location for better resource utilization.

Opportunistic co-location of workloads in the cloud reduces the carbon footprint of on-premise HPC cluster/supercomputer -- Roy continues to aim toward improving the environmental sustainability of HPC systems as their carbon footprint is increasing rapidly.

Hua Huang

Huang has focused on developing new algorithms and implementations for high-performance matrix computations. His research problems have mostly come from the area of quantum chemistry and electronic structure calculations. Huang’s innovations have been integrated into widely used codes such as Psi4, NWChem, and SPARC, as well as a proxy application, GTFock, from Georgia Tech.

His many contributions include developing a high-performance, multi-purpose, rank-structured matrix library for multiple scientific computing tasks, designing innovative parallel algorithms for large-scale matrix operations, etc. He also introduced new optimization strategies for constructing the Fock and density matrices in quantum chemistry calculations.

About the ACM IEEE CS George Michael Memorial Fellowship

The ACM-IEEE CS George Michael Memorial HPC Fellowshipis 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.

New York, NY, October 4, 2023 – ACM, the Association for Computing Machinery, and IEEE Computer Society have named Keshav Pingali, the W.A. ”Tex” Moncrief Chair of Grid and Distributed Computing at the University of Texas at Austin, as the recipient of the 2023 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes groundbreaking achievements in parallel and high-performance computing. Pingali is cited for contributions to high-performance parallel computing for irregular algorithms such as graph algorithms. He is also cited for leadership on the Galois Project, which provides a unifying framework for parallelizing both irregular and regular algorithms.

Pingali has made deep and wide-ranging contributions to many areas of parallel computing including programming languages, compilers, and runtime systems for multicore, manycore and distributed computers. These include program transformation algorithms for cache optimization, representations for program restructuring, and symbolic analysis techniques for complex numerical algorithms. These contributions have been incorporated into most open-source and commercial compilers.

Pingali’s most recent research has focused on foundational parallel programming abstractions and implementations for irregular algorithms, which use complex data structures like sparse matrices and graphs. Traditional techniques for exploiting parallelism in regular dense matrix algorithms fail when applied to irregular algorithms. The Ken Kennedy award recognizes Pingali’s “operator formulation of algorithms,” which is a programming and execution model that is remarkably simple yet powerful enough to capture patterns of parallelism in both regular and irregular algorithms. The Galois system implements this model, and it is used in diverse areas including real-time intrusion detection in computer networks, parallel tools for asynchronous circuit design, and machine learning on graphs for drug discovery. In addition, Pingali is being recognized for his distinguished mentoring of computer science leaders and students during his career.

The award will be formally presented to Pingali in November at The International Conference for High-Performance Computing, Networking, Storage and Analysis (SC23).

Biographical Background
Keshav Pingali is the William “Tex” Moncrief Chair of Grid and Distributed Computing at the University of Texas at Austin. Before moving to UT Austin, Pingali was the India Chair of Computing in the Department of Computer Science at Cornell University. Pingali received the Bachelor of Technology (BTech) degree from the Indian Institute of Technology, Kanpur, where he was awarded the President’s Gold Medal; the Master of Science (S.M.) and Electrical Engineering (E.E.) degrees from the Massachusetts Institute of Technology; and a Doctor of Science (ScD) degree from the Massachusetts Institute of Technology.

He is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE), a Foreign Member of the Academia Europaea, and a Distinguished Alumnus of the Indian Institute of Technology, Kanpur. In March 2023, he was awarded the IEEE Charles Babbage award. In 1998, the College of Arts & Sciences at Cornell University awarded him the Stephen & Margery Russell Distinguished Teaching for "superlative performance in the classroom.” He has served on the NSF CISE Advisory Committee (2009-2012), and he was co-Editor-in-Chief of the ACM Transactions on Programming Languages and Systems (2007-2010).

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

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

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

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

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

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

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

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

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

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

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

Honorable Mentions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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