ACM today announced that Allen Liu is the recipient of the ACM Doctoral Dissertation Award for his dissertation “Learning Theoretic Foundations for Understanding Quantum Systems,” toward a PhD earned at the Massachusetts Institute of Technology. 

Liu’s thesis reshapes our understanding of quantum systems through perspectives in learning theory. His dissertation resolved two major questions that have been considered central to understanding how to harness exotic quantum phenomena and use computing to simulate physics. Firstly, given many copies of a quantum state at thermal equilibrium, can we measure that quantum state and estimate its local interactions? Conversely, given a description of the local interactions of the quantum state and the temperature, can we prepare the state that describes what it looks like at equilibrium?

Liu developed groundbreaking algorithms that not only resolved these problems but proved a new physical law in the process. The quantum computing community is still trying to unravel the far-reaching implications of his work.

Honorable Mentions for the ACM Doctoral Dissertation Award go to Gal Arnon, for his dissertation “New Advancements in Interactive Oracle Proofs: Theory, Practice, and Limitations,” toward a PhD earned at the Weizmann Institute of Science; and Rachit Nigam for his dissertation “Modular Abstractions for Efficient Hardware Design,” toward a PhD earned at Cornell University.

Kevin Leyton-Brown, Professor, University of British Columbia, receives the ACM - AAAI Allen Newell Award for fundamental contributions to artificial intelligence and machine learning, focusing on applications to multiagent systems, heuristic algorithms, social impact, and market design.

Leyton-Brown has made numerous significant contributions to artificial intelligence, specifically in the areas of computational economics and game theory and automated configuration/design of algorithms using machine learning. He is internationally recognized as a leader in these areas, and as a scientist who tackles interesting, impactful problems in a creative manner. Complementing his strong theoretical skills is an empirical approach to identifying and solving difficult and important problems.

In the area of multi-agent systems, Leyton-Brown has made numerous contributions which have proven to have a sustained impact on the field. He is perhaps best known for his work on combinatorial auctions—auctions in which bidders can express their valuations for collections of goods rather than single goods—leading to much greater economic efficiency.

Erdal Arikan, Professor, Bilkent University, receives the ACM Paris Kanellakis Theory and Practice Award for the discovery of channel polarization and the construction of polar codes—the first explicit, capacity-achieving codes with efficient encoding and decoding adopted in global wireless standards.

In his foundational 2009 paper, Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Binary-Input Memoryless Channels, Arıkan solved a fundamental open problem in information theory that had persisted since Claude Shannon’s 1948 work: the construction of an explicit coding scheme that achieves channel capacity with low computational complexity. Arıkan introduced a method that transforms independent noisy channels into synthesized channels that are either essentially noise-free or completely noisy. This polarization effect allowed for a deterministic code construction that reaches the theoretical limits of data transmission using highly efficient encoding and decoding processes.

The transition of polar codes from a profound theoretical milestone to a cornerstone of modern communication infrastructure represents an extraordinary achievement in the field of computing. By bridging the gap between asymptotic theory and practical performance, Arıkan’s discovery moved from academic publication to a global industrial standard within a single decade.

Ben Mildenhall, co-founder, World Labs, and Pratul Srinivasan, Research Scientist, Google DeepMind, are the recipients of the ACM Grace Murray Hopper Award for contributions to radiance field representations, 3D scene capture and rendering, and pioneering neural implicit representations and 3D generative AI.

Mildenhall and Srinivasan’s research fundamentally transformed computer graphics and vision by introducing Neural Radiance Fields (NeRF), a breakthrough that replaced decades of reliance on explicit geometric representations with differentiable neural scene representations. This paradigm shift not only achieved unprecedented realism in rendering novel views, but also established the broader framework of neural fields, now widely adopted across scientific domains including medical imaging, astronomy, and computational physics.

Their contributions underpin widely deployed systems in major products including immersive mapping, 3D commerce, and large-scale scene visualization, and have been adopted across leading technology companies. By bridging neural rendering and generative modeling, they have driven the emergence of 3D-aware AI systems and opened a rich frontier that will shape research and applications for years to come.

Claudia Maria Bauzer Medeiros, Professor, University of Campinas, is recognized for long-standing and significant contributions to the Brazilian and Latin American computing communities as well as to ACM. She has been a pioneer in the spirit of data-centered interdisciplinary and multidisciplinary research. Working with various organizations, she has addressed challenges posed by large, real-world applications, ranging from chemistry, biology, and biodiversity to urban planning, agro-environmental sciences, and social sciences. She is the first woman to have been elected president of the Brazilian Computer Society and has served as an ambassador of the ACM Committee for Women in Computing (ACM-W).

Stephen Ibaraki, Futurist Investor, is cited for long-standing and considerable contributions to ACM and to the global professional computing community. His numerous volunteer efforts have included establishing the very successful TechTalks series and leading the ACM ByteCast and Professional Development Committees. A co-founder of the United Nations’ “AI for Good” initiative, Ibaraki has helped create one of the world's largest AI innovation programs, showcasing AI solutions across the 17 UN Sustainable Development Goals.

Scott Delman, Director of Publications, Association for Computing Machinery, is honored for significant contributions to the realization of the ACM Open publication model. During Delman’s tenure as ACM’s Director of Publications, he has led a first-of-its-kind transition of the ACM Digital Library from a paywalled content repository to a comprehensive Open Access offering. His participation in the conception and design of the ACM Open model and its tiered structure of institutional subscriptions for publishing, as well as his relentless efforts, convinced more than 3000 institutions to sign on to ACM’s model.

Ed Felten, Professor, Princeton University, receives the ACM Policy Award for contributions to technology policy, particularly on electronic voting, copyright, consumer protection, and artificial intelligence.

Felten’s security analysis of electronic voting systems fundamentally shifted the national understanding of technological risks in democracy. Furthermore, his research into digital rights management exposed the "chilling effects" that the US Digital Millennium Copyright Act can have on legitimate scientific inquiry.

At Princeton, Felten founded the Center for Information Technology Policy. In public service, he brought technical credibility directly into government, serving as the first Chief Technologist of the US Federal Trade Commission and later as Deputy US Chief Technology Officer in the White House. Taken as a whole, his work has aided policymakers directly, influenced major national debates, and inspired a generation of computer scientists to pursue research with immediate policy relevance.

Wayne Graves, Director of the Digital Library, Association for Computing Machinery, is cited for significant contributions to development of the ACM Digital Library. As the Director of the platform, he has led the evolution of the ACM Digital Library from an early repository into a modern platform offering a broad set of value-added services and serving as a critical resource for the computing community. Working closely with a dedicated team of staff and volunteers, he has guided its continued development. This work reflects a sustained commitment to furthering ACM’s mission.

Jodi Tims, Program Manager, Center for Inclusive Computing, Northeastern University, receives the Outstanding Contribution to ACM Award for increasing the worldwide participation of women in ACM and helping to reinforce ACM’s commitment to computing education.

Tims was instrumental in expanding the reach of the ACM Committee for Women in Computing (ACM-W) internationally. She led the Celebrations of Women in Computing project, which supports regional conferences of women in technology across industry, academia, and government. Under her leadership as Vice-Chair and Chair of ACM-W, both the Celebrations and student and professional chapters grew significantly, particularly in Europe and India.

Tims has also been actively involved with ACM’s Education Board. She was the impetus behind a 10-year study that gathered enrollment, degree, and salary data directly from undergraduate programs at non-doctoral universities. Her dedication to these efforts elevated the visibility of non-doctoral institutions within ACM and has fostered greater equity across the computing education landscape.

Yasmin B. Kafai, Professor of Learning Sciences at the University of Pennsylvania, and Mitchel J. Resnick, Professor of Learning Research at the Massachusetts Institute of Technology, receive the Karl V. Karlstrom Outstanding Educator Award for long-lasting and collective contributions to creative computing, including Scratch and electronic textiles, developing new ways for young people to create and collaborate within learning communities. 

Scratch has become the world’s largest coding community for young people, with more than 150 million registered users who have created over 1 billion projects. Electronic textiles have been used to teach circuitry and coding in new ways, helping to broaden participation and ideas about computing.

Kafai and Resnick have demonstrated that providing young people with access to digital tools is not enough. Their research has highlighted how project-based, interest-driven, collaborative approaches to computing education enable young people to bring their imagination to life, learn from one another by sharing and remixing creations, and make deeper connections to powerful ideas.

Monika Henzinger of the Institute of Science and Technology Austria (ISTA) has been named the 2026-2027 ACM Athena Lecturer. Henzinger is recognized for outstanding contributions to the fields of dynamic graph algorithms and web algorithms, and for dedicated mentoring and service to these communities.

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

Dynamic Graph Algorithms and Web Algorithms

Monika Henzinger’s research focuses on the design and analysis of efficient algorithms for processing large, dynamic data. Her work spans fundamental areas of computer science, including graph algorithms, data structures, information retrieval, and web search technologies, and many of her contributions have made their way into standard textbooks. She developed the first linear-time algorithms for a variety of algorithmic problems such as computing shortest paths in planar graphs.

She has made significant contributions to dynamic algorithms, which maintain solutions efficiently as data changes, particularly in network and graph settings; establishing, for example, the first poly-logarithmic upper and lower bounds in the time per operation for the fundamental problem of graph connectivity.

A major theme of her research is handling massive, real-world datasets such as web graphs and social networks. She contributed to early developments in web search and link analysis, helping shape modern search engine technology. For her contributions, she was awarded the SIGIR Test of Time Award in 2017 and she is the co-inventor of over 80 patents in that field. More recently, her work has expanded to privacy-preserving data analysis, developing algorithms that ensure strong protection of individual information through differential privacy. Her research also addresses algorithmic challenges in distributed systems, network optimization, and approximation algorithms. Ultimately, her work bridges theory and practice, advancing fundamental algorithmic theory while applying it to large-scale, real-world problems.

Leadership Within the Field
In addition to her technical contributions, Monika Henzinger is a prominent leader in the research community. She has laid the foundations for several research fields such as data streams, web search algorithms, and the empirical evaluation of dynamic graph algorithms, co-initiated major conferences such as the ACM Conference on Web Search and Data Mining, and helped shape the trajectory of major technology companies. She serves in editorial capacities for leading journals and has chaired numerous conferences and award committees. Her mentorship is widely recognized; her research group members are considered worldwide leaders in dynamic and web algorithms.

Monika Henzinger is a Professor and Vice-President of Technology Transfer at the Institute of Science and Technology Austria (ISTA) and was a Visiting Scientist at the Simons Institute at UC Berkeley and at Stanford University. Monika has held prominent academic positions at institutions including Cornell University and École Polytechnique Fédérale de Lausanne (EPFL). In addition to her academic career, Henzinger has played a significant role in industry, notably as the first Director of Research at Google, where she contributed to the development of large-scale web search technologies, and as a member of Research Staff at Digital Equipment Corporation’s Systems Research Center.

She holds a PhD from Princeton University and an Honorary Doctorate from the Technical University of Dortmund. An ACM and EATCS Fellow, she is also a member of the Academia Europaea, the German National Academy of Sciences Leopoldina, and the Austrian Academy of Sciences. Her honors include two ERC Advanced Grants, the Carus Medal of the Leopoldina, the Wittgenstein Award of the Austrian Science Fund, the European Young Investigator Award, and the NSF Career Award.

ACM named Nicki Washington the recipient of the 2025 ACM Frances E. Allen Award for Outstanding Mentoring. Washington is recognized for exceptional commitment to diversifying the computing community at all education levels, demonstrating creativity and breadth in her approaches.

The ACM Frances E. Allen Award is presented biennially to an individual who has exemplified excellence and/or innovation in mentoring, with particular attention to recognition of individuals who have shown outstanding leadership in promoting diversity, equity, and inclusion in computing. The award is accompanied by a prize of $25,000 to the awardee and an additional $10,000 cash contribution to an approved charity of the awardee’s choice. Financial support is provided by Microsoft Research.

Washington became a role model to students from groups that are historically underrepresented in computing early on. Upon joining Howard University, a Historically Black College and University (HBCU), she became the first Black woman to serve on the computing faculty. She immediately saw a need to connect classroom learning to real-world applications. This led her to launch Howard’s Google-in-Residence program. The original idea was to have Black Google software engineers serve as guest lecturers in introductory computer science (CS) courses. However, after discussions with Google leadership, the program was expanded to hire a Google staffer as a full-time instructor in the department for one semester. Undergraduates benefited from the new arrangement by receiving additional career and technical training.

While the Google-in-Residence program was piloted at Howard, it extended to other HBCUs in subsequent years and ultimately led to the development of the Google Tech Exchange program. Under this new framework, students from HBCUs and Hispanic-Serving Institutions (HSIs) spent one semester at Google working on applied problems and fostering connections among fellow students as well as Google staff members. Launched in 2012, the Google-in-Residence program continues to this day.

In her current role at Duke University, Washington’s impact on the greater CS community is evident by her leading the Alliance for Interdisciplinary Innovation in Computing Education (AiiCE). AiiCE aims to broaden participation in K-16 computing through interventions that target people, policies, and practices. Through this work, AiiCE targets three areas for broadening participation: students, educators, and scholarship.

Building upon in-person mentoring opportunities, Washington has also leveraged online technologies to create an impactful national network. She created and co-directs the Cultural Competence in Computing (3C) Fellows program, a two-year, cohort-based, virtual professional development program for computing educators that examines postsecondary computing policies and practices from a humanities and social sciences lens to understand how they impact student and faculty recruitment, retention, and success. Computing faculty, post-doctoral researchers, and graduate students are encouraged to reflect on their own roles within the educational landscape, learn practices for making computing more inclusive, and develop and lead sustainable projects (e.g., courses, modules, and other activities) that further embed these topics into their home departments.  To date, over 200 3C Fellows across the U.S., Canada, Nigeria, and Austria completed the program, developing over 120 identity-inclusive projects that have impacted over 13,000 undergraduates, 600 graduate students, 500 faculty, and 70 staff. In addition to these activities, Washington has informally mentored countless students and early-career faculty across her twenty-year career in the academy.  

Washington will be formally presented with the ACM Frances E. Allen Award for Outstanding Mentoring at the annual ACM Awards Banquet, which will be held this year on Saturday, June 13 at the Palace Hotel in San Francisco.

Nicki Washington is the Cue Family Professor of the Practice of Computer Science and Professor of the Practice of Gender, Sexuality, & Feminist Studies at Duke University, with a secondary appointment in African & African-American Studies. She is the author of the book Unapologetically Dope: Lessons for Black Women and Girls on Surviving and Thriving in the Tech Field.

A graduate of Johnson C. Smith University, Washington earned MS and PhD degrees in Computer Science from North Carolina State University. Among her honors, Washington is a Distinguished Member of ACM, and she received the ACM Karl V. Karlstrom Outstanding Educator Award (with Shaundra Daily).

ACM named Ricardo Baeza-Yates as the recipient of the 2025 ACM Luiz André Barroso Award, recognizing his pioneering contributions to algorithms and information retrieval as well as his leadership in fostering a vibrant transnational research community across Latin America.

The ACM Luiz André Barroso Award recognizes 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
Baeza-Yates is widely regarded as one of the world’s foremost researchers in information retrieval, celebrated especially for pioneering innovative data structures that have shaped the field. His work has produced influential algorithms for string searching and fuzzy matching, including the well-known Shift-Or algorithm. Over the course of a prolific career, Baeza-Yates has authored more than 500 highly cited papers and co-written several books. His textbook, Modern Information Retrieval (co-authored with Berthier Ribeiro-Neto), remains the most widely used and authoritative reference in the discipline.

Building the Latin American Computing Community 
Baeza-Yates has played a pivotal role in strengthening the Latin American computing community. At the University of Chile, he was the founding director of the Center for Web Research which became a hub for attracting top talent and supporting young researchers. The efforts of Baeza-Yates and others gradually led to a vibrant technology sector in Chile—reflected in today’s moniker of “Chilecon Valley.”

Later, as President of the Centro Latinoamericano de Estudios en Informática (CLEI), he led the association of computing science departments in Latin America. He also was one of the coordinators of the Ibero-American cooperation program in science and technology for development (CYTED). He also co-founded two of Latin America’s most influential research conferences—String Processing and Information Retrieval Symposium (SPIRE), focused on string processing (now an international meeting), and the Alberto Mendelzon Workshop (AMW), dedicated to databases and web research.

During this decade, he has focused on responsible AI, being one of the leading authors of ACM’s Principles for Responsible Algorithmic Systems published in October 2022. He is currently a member of several technology policy committees including ACM, IEEE, OECD, and WEF.

A respected mentor, Baeza-ates has advised 34 PhD students, many from Latin America, with 50% of them female, 53% from Latin America, and 68% from developing countries. Young people have also been excited and inspired by the South American Programming Contest which he helped to bootstrap. Launched in 1996, the Contest held its 30th competition in 2025.

Ricardo Baeza-Yates, a native of Chile, is currently the Search Chief Scientist at You.com, holding part-time professor appointments at KTH Royal Institute of Technology (Sweden), Universitat Pompeu Fabra (Spain) and Universidad de Chile. As a practitioner, Baeza-Yates served as VP of Research for Yahoo Labs, secured 14 patents, and co-founded several startups in Chile and Spain, the latest one Theodora AI, devoted to mitigating technological bias.

Baeza-Yates is an engineering graduate of the Universidad de Chile, where he also earned Master’s Degrees in Computer Science and Electrical Engineering. He earned a PhD in Computer Science from the University of Waterloo. Among his honors, Baeza-Yates received the CLEI Distinction for Contributions to Computing in Latin America in 2009, the Spanish “Ángela Ruiz Robles” Award for research excellence and entrepreneurship in applied computing in 2018, the 2024 Chilean National Prize for Applied Sciences and Technology, and the first Merit Award from the Chilean Computing Science Society in 2025. He is a member of Academia Europaea, and a Fellow of ACM and IEEE.

ACM named Matei Zaharia as the recipient of the ACM Prize in Computing for his visionary development of distributed data systems and computing infrastructure, which has enabled large-scale machine learning, analytics, and AI at global scale.

The ACM Prize in Computing recognizes early-to-mid-career computer scientists whose work has had broad and lasting impact. The award carries a $250,000 prize, with financial support provided by an endowment from Infosys Ltd, a global leader in next-generation digital services and consulting.

Zaharia’s work addressed a central challenge in computing: how to work with and analyze rapidly growing volumes of data efficiently, and at a scale previously accessible only to the largest technology companies. Early distributed data systems were limited in speed and poorly suited to emerging workloads such as machine learning and interactive analysis. Through a sequence of open-source systems, each targeting a distinct bottleneck, Zaharia changed what any organization could do with massive datasets.

As a doctoral student at UC Berkeley, Zaharia started Apache Spark, a new approach to distributed computing that reliably leverages memory to accelerate computations. This design made Spark dramatically faster than existing frameworks for the kinds of iterative computations essential to machine learning, while its unified architecture allowed batch processing, streaming, graph computation, and interactive queries to run within a single system. Spark quickly moved from research into widespread use and is now the de facto standard for large-scale data analytics, deployed across tens of thousands of organizations and integrated into major cloud platforms. Zaharia’s doctoral dissertation on Spark received the ACM Doctoral Dissertation Award in 2014.

With the shift to the cloud, Zaharia turned to a different problem: the lack of reliability and consistency in sprawling cloud data lakes – or the massive, centralized, and often unmanaged repositories storing vast amounts of raw data. He co-developed Delta Lake to bring transactional guarantees and principled data management to cloud object stores, making data pipelines more dependable and enabling a new class of architecture – the data lakehouse – that combines the flexibility of data lakes with the reliability of traditional data warehouses. Delta Lake is now widely adopted across industries, handling exabytes of data daily.

The growing use of machine learning introduced additional complexity. Zaharia started MLflow, another open-source platform to address fragmentation in machine learning and AI workflows, where teams struggled to track experiments, reproduce results, and deploy models consistently. MLflow provided a structured framework for managing the machine learning lifecycle – from experiment tracking and model versioning to deployment across diverse tools and environments – and has become a leading platform for operationalizing AI at scale. Together, these systems reshaped how data is leveraged in practice.

By building tools that any organization could freely use and extend, Zaharia ensured that the benefits of scalable computing became accessible to researchers, nonprofits, and enterprises across every industry. As investment in artificial intelligence accelerates, the infrastructure he built remains key to how data is processed, managed, and used to train and deploy AI applications and agents.

Today, Zaharia is focusing research on AI development, specifically how to build and scale reliable agents. He is a co-author on recent open source research, including DSPy and GEPA, which focus on auto-optimizing prompts and models to improve agent quality for specific tasks.

Matei Zaharia is an Associate Professor of EECS at the University of California, Berkeley, and a Cofounder and CTO of Databricks. He started the Apache Spark open-source project during his PhD at UC Berkeley in 2009, and has worked broadly on other widely used data and AI software, including Delta Lake, MLflow, Dolly and ColBERT. He currently works on a variety of research projects in cloud computing, database management, AI and information retrieval. Zaharia’s honors include the 2014 ACM Doctoral Dissertation Award, an NSF CAREER Award, the SIGOPS Mark Weiser Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).

ACM has named Charles H. Bennett and Gilles Brassard as the recipients of the 2025 ACM A.M. Turing Award for their essential role in establishing the foundations of quantum information science and transforming secure communication and computing.

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.

Bennett and Brassard are widely recognized as founders of quantum information science, a field at the intersection of physics and computer science that treats quantum mechanical phenomena not merely as properties of matter, but as resources for processing and transmitting information.

In 1984, inspired by the insights of their late collaborator Stephen Wiesner, Bennett and Brassard introduced the first practical protocol for quantum cryptography, now known as BB84. The paper, “Quantum Cryptography: Public Key Distribution and Coin Tossing,” demonstrated that two parties could establish a secret encryption key with security guaranteed by the laws of physics, even against adversaries with unlimited computational power and technological sophistication such as a quantum computer.

In 1949, mathematician and computer scientist Claude Shannon proved that perfect secrecy in communications is only possible between parties who share ahead of time a secret key that is at least as long as the message itself. Public-key cryptography later provided a powerful workaround by relying on mathematical problems which were believed to be hard to solve—assumptions embedded in modern digital infrastructure but shown by Peter Shor as early as 1994 to become insecure when a full-size quantum computer is available. In sharp contrast, BB84 achieves information-theoretic security without computational assumptions, instead relying on a fundamental property of quantum information: it cannot be copied or measured without disturbance. Any attempt at eavesdropping leaves detectable traces before any information can be compromised.

As research advances toward large-scale quantum computers, governments and industry are reassessing the long-term resilience of widely deployed public-key cryptographic systems. Quantum cryptography, alongside emerging, hopefully quantum-resistant classical approaches for which no proofs of security are known, represents one pathway toward securing digital communications in the decades ahead. Variants of BB84 have already been implemented in operational quantum communication networks around the world, using both landlines via fiber and free space communication through satellites.

Beyond cryptography, Bennett and Brassard’s work reshaped the theoretical foundations of computing. In 1993 and with other collaborators, they introduced quantum teleportation, demonstrating how an arbitrary quantum state could be transmitted between distant parties using quantum entanglement—the surprisingly correlated behavior of particles too far apart to influence one another—and classical communication. This discovery showed that entanglement, once viewed primarily as a philosophical curiosity, could serve as a practical resource. Experimental verification of related phenomena was recognized by the 2022 Nobel Prize in Physics.

Their subsequent work on entanglement distillation in 1996 demonstrated how imperfect entanglement could be strengthened into high-quality entanglement, a critical step toward scalable quantum communication. These ideas underpin ongoing efforts to build quantum networks and ultimately a quantum internet capable of transmitting quantum information across global distances.

Over four decades, Bennett and Brassard’s collaboration bridged two previously distinct disciplines: physics and computer science. By incorporating quantum principles into computational models, their work has influenced cryptography, algorithm design, computational complexity, learning theory, interactive proofs, and mathematical physics. Their research helped catalyze a generation of physicists and computer scientists to work across disciplinary boundaries.

Their recognition comes on the heels of the United Nations’ designation of 2025 as the International Year of Quantum Science and Technology, reflecting the growing global investment in quantum computing, communication, and sensing. Many of today’s ambitious efforts to build large-scale quantum systems trace their conceptual foundations to the theoretical breakthroughs pioneered by Bennett and Brassard.

Looking ahead, the next chapter of quantum information science includes the pursuit of fault-tolerant quantum computers, new quantum algorithms, and long-distance quantum communication enabled by satellites and quantum repeaters. Teleportation, entanglement swapping, and distillation—once abstract theoretical ideas—are now central components of practical quantum engineering.

Biographical Background

Charles H. Bennett is an American physicist whose research has shaped the foundations of quantum information science, quantum cryptography, and quantum teleportation, and who has played a central role in establishing quantum information science as a rigorous scientific discipline. After earning his Bachelor’s degree from Brandeis University and his PhD from Harvard University, Bennett joined IBM Research in 1973 (and still works there today), where he has spent his career exploring connections between physics (especially thermodynamics and quantum mechanics) and computer science (cryptography, computability, computational complexity, and information theory), to advance the theoretical and practical understanding of computation and quantum mechanics. He is a recipient of several prominent awards including the Wolf Prize in Physics, the Micius Quantum Prize, the BBVA Foundation Frontiers of Knowledge Award in Basic Sciences, and the Breakthrough Prize in Fundamental Physics. He is also a Member of the US National Academy of Sciences and a Foreign Member of the Royal Society.

Gilles Brassard is a Canadian computer scientist widely recognized as the first in the world to have delved into the uncharted territory of quantum information science. He earned his Bachelor’s and Master’s degrees from the Université de Montréal, and his PhD in theoretical computer science from Cornell University in 1979 under the direction of 1986 Turing Award laureate John E. Hopcroft. He joined the faculty of the Université de Montréal shortly thereafter and was Canada Research Chair in Quantum Information Science from 2001 to 2021. An Officer of the Order of Canada and of the Ordre national du Québec, Brassard has received numerous honors including the Wolf Prize in Physics, the Micius Quantum Prize, the BBVA Foundation Frontiers of Knowledge Award in Basic Sciences, and the Breakthrough Prize in Fundamental Physics. He is a Fellow of the Royal Society and an International Member of the U.S. National Academy of Sciences.

ACM has named 71 new Fellows. ACM Fellows are registered members of the society and were selected by their peers for achieving remarkable results through their technical innovations and/or service to the field. This year’s honorees hail from 14 countries and were chosen from among ACM’s global membership of more than 100,000 computing professionals.

“These men and women represent the top 1% of professionals in our association,” explained ACM President Yannis Ioannidis. “I personally enjoy reviewing the list of achievements of the new Fellows because it offers a snapshot of what’s happening in our field at the moment. This year, for example, we are honoring members working in well-established disciplines such as computer architecture and software engineering, alongside innovators in emerging disciplines like swarm intelligence or scene recognition. As we congratulate the new Fellows for their accomplishments, we hope that their work will also serve as an inspiration to the next generation. We especially encourage those who are not yet ACM members to join us and become part of a vibrant global community.”

The 2025 ACM Fellows work at leading universities, corporations, and research institutions in Australia, Belgium, Canada, China, France, Germany, Israel, Italy, Japan, Saudi Arabia, Singapore, Switzerland, the United Kingdom, and the United States. This year’s Fellows are cited for contributions in a wide range of computing research areas including AI for healthcare, computer graphics, data management, electronic mail, human-computer interaction, mobile computing, networked systems, robotics, security, sustainability, and numerous other areas.

ACM Fellows serve as ambassadors for the organization and are often called upon to offer their expertise to the media, public officials, and industry leaders. The ACM Fellows will be formally recognized during an awards banquet on June 13 in San Francisco.

Read the news release.

ACM has named 61 new Distinguished Members. All the 2025 inductees are registered members and were selected by their peers for significant technical achievements and/or volunteer service.  

“We see the annual announcement of a new class of Distinguished Members as one of our most important events and an excellent way to cap off the year,” said ACM President Yannis Ioannidis. “It’s our opportunity to recognize computing innovators whose efforts and creativity underpin the technologies that shape our world. And for those new Distinguished Members who are being recognized for their service, we’re underscoring that our field depends on colleagues who volunteer their time to serve the greater good. From conference organizers to editorial board members of ACM journals and computer science curriculum developers, these peers support the community that benefits us all. In many ways, becoming an ACM Distinguished Member is only a milestone towards greater achievements. We’re excited to see how these leaders will continue to shape our increasingly digital society.”

The 2025 ACM Distinguished Members work at leading universities, corporations, and research institutions in Australia, Belgium, Canada, Chile, China, Germany, India, Korea, Mexico, New Zealand, Portugal, Singapore, Switzerland, the United Kingdom, and the United States. This year’s Distinguished Members are cited for contributions in a wide range of computing research areas including AI for healthcare, computing education, data management, human-computer interaction, mobile computing, networked systems, security, software, sustainability, and numerous other areas.

The ACM Distinguished Member program recognizes up to 10 percent of the worldwide ACM membership for their technical achievements and/or service to the field.

Read the ACM news release.

ACM presented a 26-member team with the ACM Gordon Bell Prize for Climate Modelling in recognition of their project “Computing the Full Earth System at 1 km Resolution.” The award honors innovative contributions to parallel computing toward solving the global climate crisis.

Climate change is responsible for more extreme hurricanes, more destructive wildfires, severe droughts, and increased human disease, among other harmful outcomes. Experts warn that if carbon emissions are not significantly reduced within a few decades, the damage to the Earth’s ecosystem will be irreversible.

Among the most effective tools scientists have developed to understand climate change are digital simulations of the Earth. These simulations are produced by developing specific algorithms to run on the world’s most powerful supercomputers. But simulating how human activity influences the climate has been an extraordinarily difficult challenge. A mind-boggling number of variables need to be taken into consideration—such as the cycles of water, energy, and carbon, how those factors relate to each other, and how diverse physical, biological, and chemical processes interact over space and time. For these reasons, previous state-of-the-art simulations have not been able to achieve what is referred to as a “Full Earth System” simulation.

The Gordon Bell Climate Prize-winning team reached a landmark this year by being the first team ever to develop a Full Earth Simulation at 1 km (extremely high) Resolution. In their introduction, they explain, “We present the first-ever global simulation of the full Earth system at 1.25 km grid spacing, achieving highest time compression with an unseen number of degrees of freedom. Our model captures the flow of energy, water, and carbon through key components of the Earth system: atmosphere, ocean, and land. To achieve this landmark simulation, the team harnessed the power of 8192 GPUs on Alps and 4096 GPUs on JUPITER, two of the world’s largest GH200 superchip installations.”

The groundbreaking innovations the team employed to make the Full Earth Simulation possible include (1) exploiting functional parallelism by efficiently mapping components to specialized heterogeneous systems and (2) simplifying the implementation and optimization of an important component by separating its implementation in Fortran from the optimization details of the target architecture.

In the conclusion to their paper they write, “This has enormous and enduring potential to provide full global Earth system information on local scales about the implications of future warming for both people and eco-systems, information that otherwise would not exist.”

Team Members 

The members of the team are: Daniel Klocke, René Redler, Reiner Schnur, Helmuth Haak, Luis Kornblueh, Cathy Hohenegger, Nils Brüggemann, and Bjorn Stevens (all of the Max Planck Institute for Meteorology); Claudia Frauen, Jan Frederik Engels, and Hendryk Bockelmann (all of Deutsches Klimarechenzentrum); Dmitry Alexeev (NVIDIA),; Fatemeh Chegini (University of Hamburg), Manoel Römmer, Lars Hoffmann, Sabine Griessbach, Mathis Bode, and Andreas Herten (all of Forschungszentrum Jülich), Jonathan Coles, Miguel Gila, and William Sawyer (all of the Swiss National Supercomputing Centre); as well as Alexandru Calotoiu, Torsten Hoefler, Yakup Budanaz, Pratyai Mazumder, Marcin Copik, and Benjamin Weber (all of ETH Zurich).

The ACM Gordon Bell Prize for Climate Modelling was presented at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC25), held in St. Louis, Missouri.

ACM named an eight-member team drawn from US institutions as the winner of the 2025 ACM Gordon Bell Prize for their project,  “Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone.” 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.

Existing state-of-the art high-performance computing simulations for early tsunami warning are developed primarily through models which process seismic data. The drawbacks of these approaches include: 1) They do not allow for enough warning time, as destructive tsunami waves can arrive onshore in under ten minutes, and 2) They fail to capture the complexities of earthquake ruptures which cause the Tsunamis.

The Gordon Bell Prize-winning team created a far more predictive early warning framework by developing a full-physics Bayesian inversion framework—popularly called “digital twin.” A digital twin is a virtual simulation of a physical process (or object) that uses real-time data from sensors to match its physical counterpart. The digital twin developed by this year’s winning team enables real-time, data-driven tsunami forecasting with dynamic adaptivity to complex source behavior.

With this approach, they achieved the fastest time-to-solution of a partial differential equation (PDE)-based Bayesian inverse problem with 1 billion parameters in 0.2 seconds, a ten-billion-fold speedup over the existing state-of-the-art. This is the largest-to-date unstructured mesh finite element (FE) simulation with 55.5 trillion degrees of freedom (DOF) on 43,520 GPUs, with 92% weak and 79% strong parallel efficiencies in scaling over a 128× increase of GPUs on the full-scale El Capitan system—the world’s largest supercomputer.

The team simulated a Tsunami in an area in the Pacific Ocean called the Cascadia Subduction Zone, which stretches 1000 km from northern California to British Columbia. This area has been eerily quiet for over 300 years—but is considered overdue for a magnitude 8.0–9.0 megathrust earthquake.

The members of the ACM Gordon Bell Prize-Winning team are Stefan Henneking, Sreeram Venkat, Milinda Fernando, and Omar Ghattas (all of The University of Texas at Austin); Veselin Dobrev, John Camier, Tzanio Kolev (all of Lawrence Livermore National Laboratory); and Alice-Agnes Gabriel (University of California San Diego).

Honorable Mention

This year an Honorable Mention for the ACM Gordon Bell Prize was given to a 10-member team from ETH Zurich for their project “Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance.” Team members include Nicolas Vetsch, Alexandros Nikolaos Ziogas, Alexander Maeder, Vincent Maillou, Anders Winka, Jiang Cao, Grzegorz Kwasniewski, Leonard Deutschle (also affiliated with NVIDIA), Torsten Hoefler, and Mathieu Luisier.

The ACM Gordon Bell Prize was presented during the International Conference for High-Performance Computing, Networking, Storage and Analysis (SC25) in St. Louis, Missouri.

New York, NY, October 15, 2025 – ACM, the Association for Computing Machinery and IEEE Computer Society have named Saman Amarasinghe of the Massachusetts Institute of Technology as the recipient of the 2025 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes groundbreaking achievements in parallel and high-performance computing (HPC). Amarasinghe is cited for fundamental contributions pioneering high-performance domain-specific languages, exceptional mentorship, and service advancing the global computing community.

Modern hardware presents a complex model of computation that promises high performance, but only if the compiler can successfully bridge the challenging gap between software and hardware to fully exploit the hardware resources. Amarasinghe has produced language designs and sophisticated compiler algorithms that successfully bridge this gap.

His specific technical contributions include:

• SLP: The Superword-Level Parallelism (SLP) compiler optimization was developed by Amarasinghe in collaboration with his PhD student Sam Larsen. It automatically generates code optimized for modern short vector units such as AVX, Neon, and AltiVec. Today, SLP is included of all modern compilers such as LLVM and GCC.
• Halide: This image processing language was developed in collaboration with Amarasinghe's then-PhD student (now Associate Professor) Jonathan Ragan-Kelly and Amar Bose Professor of Computing Fredo Durand, both in MIT’s Department of Electrical Engineering and Computer Science (EECS). By separating the program into an algorithmic language and a scheduling language, Halide enables programmers to create portable algorithms and rapidly iterate over different schedules to obtain the highest performance on each architecture. Halide is included in many popular applications, including YouTube backend, Android Camera pipeline, and Adobe Photoshop.
• TACO: The Tensor Algebra Compiler (TACO) was developed in collaboration with PhD student Frederick Kjolstad (now a Stanford professor). TACO is the first compiler to generate efficient code for arbitrary tensor expressions over multiple tensor formats. This is important because the best tensor formats vary across computations and data sizes.
• StreamIt: The stream processing language (Streamlt) was developed in collaboration with Amarasinghe's PhD student Bill Thies. It was one of the early domain-specific languages to directly support emerging multicore processors.

In addition to his technical mentorship, Amarasinghe has trained young people to start businesses, especially in emerging economies. For 15 years he has served as Faculty Director of MIT’s Global Startup Labs (GSL). GSL has conducted hundreds of summer programs to guide university students through the process of launching information technology-based startups. These entrepreneurship programs have seeded a vibrant ecosystem of startups in Africa, South America, and South Asia.

Saman Amarasinghe is the Thomas and Gerd Perkins Professor in the Department of EECS at the Massachusetts Institute of Technology. He leads the Commit compiler research group in MIT’s Computer Science & Artificial Intelligence Laboratory (CSAIL), which focuses on programming languages and compilers that maximize application performance on modern computing platforms.

Born in Sri Lanka and schooled at Royal College, Colombo, Amarasinghe is a graduate of Cornell University and earned his Master’s and PhD degrees from Stanford University. He was elected as an ACM Fellow in 2019.

Ana Veroneze Solórzano of Northeastern University and Yafan Huang of The University of Iowa are the recipients of the 2025 ACM-IEEE CS George Michael Memorial HPC Fellowships. Aristotle Martin of Duke University receives an honorable mention this year. 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.

Solórzano is recognized for broadening the societal impact of HPC using privacy-preserving and incentive-driven mechanisms. Huang is recognized for advancing exascale high performance computing by creating ultra-fast lossy compression algorithms and versatile program-agnostic fault tolerance.

Ana Veroneze Solórzano
Solórzano’s research reflects her interest in advancing the design and operation of HPC systems. She has explored a broad range of technologies and developed solutions that enhance system capabilities. Her work highlights include an incentive-based power-control strategy on the Fugaku supercomputer—the first in-production deployment of such a strategy on one of the fastest supercomputers in the world. Fugaku’s incentive program, Fugaku Points, provides users with knobs to apply power-control functions and improve the overall power efficiency of the system, contributing to HPC sustainability. The insights from this project have opened new research directions in incentive-driven resource management and user engagement for energy efficiency.

She is one of the leading researchers to bring differential privacy (DP) to the HPC community (i.e., protecting personally identifiable information when sharing traces/logs across multiple parties). She designed and implemented the first tool to apply differential privacy to HPC system traces. Her tool supports both aggregated and raw data and facilitates configuring the amount of noise added or exploring different privacy budgets for adoption by HPC system administrators. Solórzano has demonstrated the effectiveness of her tool by evaluating it on several real-world HPC traces.

Yafan Huang
Huang’s primary research focus lies in robust data compression and fault tolerance software support for HPC systems—two areas of critical importance for HPC and large-scale scientific computing. One of the major aspects of Huang’s research is providing robust software support for soft error detection in HPC systems. With the growing scale of supercomputers, soft errors, also known as transient hardware faults, in memory and computation pose a serious threat to reliability. Huang has developed novel techniques that go beyond traditional error detection approaches by considering complex fault patterns. His work also integrates compiler-level code transformations and program analysis to ensure high detection effectiveness without significantly impacting performance.

In addition to his work in fault tolerance, Huang has made groundbreaking contributions to data reduction for scientific computing. He developed cuSZp, an ultra-fast GPU lossy compression framework that significantly outperforms existing state-of-the-art solutions. Unlike traditional compression techniques, which often struggle to balance speed, compression ratios, and data quality, cuSZp achieves all three by algorithm innovations and system optimizations, making it a transformative tool for scientific applications that require real-time in-situ data processing and memory footprint reduction.

Honorable Mention

Aristotle Martin’s work involves developing a heterogeneous, performance-portable multiscale modeling framework leveraging exascale systems for large-scale adhesive transport simulations of circulating tumor cells.

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 André Seznec as the recipient of the ACM-IEEE CS Eckert-Mauchly Award. Seznec, a Fellow Research Director at INRIA/IRISA and a Fellow at SiFive, is recognized for his extensive impact on computing, most notably pioneering contributions to branch prediction and cache memories. 

Seznec’s inventions can be found in billions of CPUs worldwide. These include the TAGE branch predictor and skewed-associative cache. In fact, Seznec’s work has served as a gold standard of branch prediction for the last 15 years, with most current structures in industrial designs rooted in his trailblazing contributions.

His early contributions were in vector architectures, particularly the memory system. Since the early 1990s, his main research activity has been focused on the architecture of microprocessors, including caches, pipelines, branch predictors, speculative execution, multithreading, and multicores. His research has influenced the design of many high-end industrial microprocessors, particularly the caches and the branch predictors.

Over the course of his career, Seznec has published more than 100 papers with many of his most notable contributions published solely under his name, demonstrating the profound impact he has had in solving some of the most challenging problems in computer architecture.

In addition, Seznec has been a dedicated supporter of industry education, collaboration, and advancement. He served multiple years on the organizing committee of the IEEE/ACM International Symposium on Computer Architecture (ISCA) acting as general chair in 2010 and program committee chair in 2016. He has also supported the future of computing as an educator, having graduated 28 PhD students from 1991 to 2020.

ACM today announced that Ashish Sharma is the recipient of the ACM Doctoral Dissertation Award for his dissertation “Human-AI Collaboration to Support Mental Health and Well Being,” toward a PhD earned at the University of Washington. Sharma is a Senior Applied Scientist at the Microsoft Office of Applied Research.

Sharma developed fundamental advances in natural language processing to positively impact the mental health of many people. His approach involves constructing new machine learning models and algorithms that demonstrate psychological and societal understanding.

Importantly, Sharma’s work addresses the longstanding challenge that access to mental health services can be expensive. His most recent AI-supported mental health tool, for example, has been publicly deployed and used by a group of more than 160,000 people, the majority of whom are low-income. More than fifty percent of the people using these tools report a household income of less than $40,000 per year.

In developing these machine learning models and algorithms, Sharma incorporates principle-based ethical frameworks. Several mental health and technology organizations have adopted these tools, including Mental Health America and TalkLife.

Honorable Mentions for the ACM Doctoral Dissertation Award go to Alexander (Zander) Kelley for his dissertation “Explicit Pseudorandom Distributions for Restricted Models of Computation” toward a PhD earned at the University of Illinois Urbana-Champaign; and Sewon Min for her dissertation “Rethinking Data Use in Large Language Models” toward a PhD earned at the University of Washington.

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.

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.