ACM and IEEE Computer Society named Luiz André Barroso, Vice President of Engineering at Google, recipient of the 2020 Eckert-Mauchly Award for pioneering the design of warehouse-scale computing and driving it from concept to industry. Today’s datacenters contain hundreds of thousands of servers and millions of disk drives, and make possible the most prevalent applications used by the public today, including cloud computing, powerful search engines, and internet services.

Barroso is widely recognized as the foremost architect of the design of these new ultra-scale datacenters. The cornerstone of his architectural vision was to think of a system holistically, weaving together the individual compute, storage, and networking components into an overall design across large-scale distributed systems.

Barroso has been a thought leader in the field, writing seminal papers and books which reconsidered every aspect of data center and system design. At the same time, he has also guided industry efforts in this area. He was the lead architect of Google’s first custom-built data centers and has been the primary technical leader steering the development of Google’s computing infrastructure for much of the last two decades. This work has been replicated by other large companies. Virtually all the hardware architectures that power today’s internet services and cloud computing systems feature elements introduced by Barroso and his team at Google.

Warehouse-scale computing
Barroso proposed the idea that a datacenter should be designed as a single, massive warehouse-scale computer, popularizing the phrase “the datacenter is the computer.” The workloads of these computers are internet services that run on thousands of CPUs across high-bandwidth networks and require specialized storage systems. Barroso’s designs paired inexpensive hardware with powerful distributed systems software to dramatically change system design. When Barroso’s designs were introduced in the mid-2000s, they garnered a new term: “hyperscale datacenters.” Those designs were attractive not only because they could manage the mushrooming workloads from internet services and cloud computing, but because they also reduced hardware and operating costs. By 2022, the hyperscale datacenter market is expected to grow to more than $80 billion annually.

Hyperscale system architecture
Barroso and his colleagues at Google were the first to abandon traditional server products and work directly with commodity component manufacturers to build low-end servers that were specifically optimized for the efficiency and scalability needs of internet services. In his well-cited IEEE Micro paper, “Web Search for a Planet,” he and his co-authors Jeffrey Dean and Urs Hölzle described the hardware requirements for emerging web services, arguing for designs that used modular hardware coupled with simple robust software. This approach helped dramatically drive down complexity to make systems less expensive to buy and build, easier to maintain, and more adaptable to rapidly-changing workloads.

Energy efficiency
In one of his most influential papers, which has been cited more than 2,300 times, “The Case for Energy-Proportional Computing,” Barroso (with co-author Urs Hölzle) called for a new approach to achieving energy efficiency, where the energy used would be roughly proportional to the utilization of the systems in question. The paper’s key ideas resulted in significant energy efficiencies when computers were operating below peak capacity. It has been documented that standard servers circa 2006 used 70% peak power even when nearly idle, whereas since 2012, after Barroso’s ideas on energy proportionality had been implemented throughout the industry, a standard server consumed only a small fraction of its peak power at idle.

Other key contributions by Barroso include co-leading the Piranha chip project at DEC Western Research. Piranha was ​one of the first multicore processor architectures proposing multiple “wimpy” cores, and many of Piranha’s designs have since been adopted in today’s commercial processors. Barroso’s book, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, (co-authored with Urs Hölzle and Parthasarathy Ranganathan) is widely accepted as the authoritative textbook in the field.

Barroso will be formally recognized with the ACM-IEEE CS Eckert-Mauchly Award during the ACM/IEEE International Symposium on Computer Architecture (ISCA), which is being held virtually May 29 – June 3, 2020.

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

ACM has named Noga Alon of Princeton University and Tel Aviv University; Phillip Gibbons of Carnegie Mellon University; Yossi Matias of Google and Tel Aviv University; and Mario Szegedy of Rutgers University recipients of the ACM Paris Kanellakis Theory and Practice Award for seminal work on the foundations of streaming algorithms and their application to large-scale data analytics.

Alon, Gibbons, Matias and Szegedy pioneered a framework for algorithmic treatment of streaming massive datasets. Today, their sketching and streaming algorithms remain the core approach for streaming big data and constitute an entire subarea of the field of algorithms. Additionally, the concepts of sketches and synopses that they introduced are now routinely used in a variety of data analysis tasks in databases, network monitoring, usage analytics in internet products, natural language processing and machine learning.

In their seminal paper, “The Space Complexity of Approximating the Frequency Moments,” Alon, Matias and Szegedy laid the foundations of the analysis of data streams using limited memory. Follow-up papers, including “Tracking Join and Self-join Sizes in Limited Storage,” by Alon, Gibbons, Matias, and Szegedy, and “New Sampling-Based Summary Statistics for Improving Approximate Query Answers,” by Gibbons and Matias, expanded on the idea of data synopses and were instrumental in the development of the burgeoning fields of streaming and sketching algorithms. This work has been applied to query planning and processing in databases and the design of small synopses to monitor vast quantities of data generated in networks.

ACM has named Lydia E. Kavraki of Rice University and Daphne Koller of Stanford University and Insitro recipients of the ACM - AAAI Allen Newell Award.

Lydia Kavraki is recognized for pioneering contributions to robotic motion planning, including the invention of randomized motion planning algorithms and probabilistic roadmaps, with applications to bioinformatics and biomedicine.

Kavraki conducted foundational work on physical algorithms and developed efficient high-dimensional search frameworks that impacted robotics (motion planning, hybrid systems, formal methods in robotics, assembly planning, and micro- and flexible manipulation), as well as computational structural biology, translational bioinformatics, and biomedical informatics.

Kavraki has authored more than 240 peer-reviewed publications and is a co-author of the widely used robotics textbook, Principles of Robot Motion. Her seminal paper, “Probabilistic Roadmaps for Path Planning in High Dimensional Configuration Spaces,” (with Svestka, Latombe and Overmars) was the first to establish a probabilistic approach to developing roadmaps for high-dimensional spaces, which has become one of the key techniques for motion planning for complex physical systems.

Kavraki’s contributions go beyond robotics to address problems underlying the functional annotation of proteins, the understanding of metabolic networks, and the investigation of molecular conformations and protein flexibility. She has contributed to problems that involve reasoning about the three-dimensional structure of biomolecules and their ability to interact with other biomolecules primarily for drug design and, more recently, for personalized cancer immunotherapy.

Daphne Koller is recognized for seminal contributions to machine learning and probabilistic models, the application of these techniques to biology and human health, and for contributions to democratizing education.

Koller was a leader in the development and use of graphical models, including learning the model structure as well as its parameters, and pioneered the unification of statistical learning and relational modelling languages. She also developed foundational methods for inference and learning in temporal models. Her textbook (with Nir Friedman), Probabilistic Graphical Models, is the definitive text in this area.

As an early leader in bringing machine learning methods to the life sciences, she developed Module Networks, wherein she and her colleagues harnessed modularity in gene regulatory programs to build an effective model of gene activity. She has developed groundbreaking applications of machine learning to pathology, work that not only demonstrated the ability of machine learning to outperform human pathologists, but also was one of the first to highlight the importance of the stromal tissue in cancer prognosis (now well-recognized).

Koller is also the co-founder and former co-CEO of Coursera, a platform offering free education from top universities to people worldwide. Coursera, now in its eighth year, has touched the lives of over 50 million learners in every country in the world. Koller is currently the founder and CEO of Insitro, a biotech startup that works to discover better medicines through the integration of machine learning and biology at scale.

ACM named Paul Mockapetris recipient of the ACM Software System Award for development of the Domain Name System (DNS), which provides the worldwide distributed directory service that is an essential component of the functionality of the global internet.

When the internet was first deployed in the early 1980s, the online community relied on a centrally-managed directory that matched human-friendly host names to numerical IP addresses of computers on the network. As the internet began to grow more rapidly, maintaining a single centralized host directory became slow and unwieldy, necessitating a new scalable architecture. To address this need, in 1983, Mockapetris designed and built the Domain Name System (DNS), creating the associated query protocol, a server implementation, and initial root servers. Taken together, these components provided the first stable operational DNS system.

When Mockapetris initially designed the DNS, the number of name lookups to establish the associated IP address were in the few thousands per day. Today, while still employing the core components that Mockapetris introduced 37 years ago, the DNS manages 350 million separately-managed domains, and responds to several tens of billions of queries each day.

DNS serves as a foundation for dozens of other applications, including email and web addresses. The Universal Resource Locator (URL) and Universal Resource Identifier (URI)—core components the World Wide Web—rely on domain names as introduced in the DNS system. While many of the new features of the DNS have been added by others, the ability of Mockapetris’s original design to incorporate these updates is a testament to his work.

Michael Ley was named recipient of the ACM Distinguished Service Award for creating, developing, and curating DBLP, an extraordinarily useful and influential online bibliographic resource that has changed the way computer scientists work.

Until the early 1990s, finding relevant literature and compiling the bibliographic references for a paper or a dissertation was a manual and tedious effort for students and authors. Ley, of the University of Trier and Schloss Dagstuhl − Leibniz Center for Informatics, created DBLP in 1993 to cover proceedings and journals from the fields of database systems and logic programming (from which the acronym “DBLP” arose). The author pages provided links to co-authors and corresponding table-of-contents entries, forming a browsable person-publication network. After positive feedback from the database community, Ley added data from further computer science disciplines.

Today, DBLP lists more than 5 million publications and is used to search for bibliographic entries (its original intent), as well as to evaluate persons or institutions, and to support program committee chairs, editors, and reviewers. Strengths of DBLP include the quality of its data, which results in a very low rate of errors, as well as the unique identification of authors. DBLP has changed the way computer scientists use bibliographic data and has become an invaluable asset for virtually every researcher in the field.

During the 1990s and 2000s, DBLP was largely a one-man endeavor. In the last decade, Ley organized a DBLP team at Schloss Dagstuhl − Leibniz Center for Informatics. Through DBLP, Ley has made the enormous body of published computer science research more accessible and useful to the entire community.

Mordechai (Moti) Ben-Ari was named recipient of the Karl V. Karlstrom Outstanding Educator Award for his pioneering textbooks, software tools and research on learning concurrent programming, program visualization, logic, and programming languages, spanning four decades and aimed at both novices and advanced students in several subfields of computing.

Ben-Ari, a professor at the Weizmann Institute of Science in Israel, has authored 15 well-known and widely-used textbooks on topics including concurrent and distributed programming, programming languages, model checking, and mathematical logic. Many of these books are the definitive textbooks in their respective areas, and several have been translated into many languages.

In addition to his textbooks, Ben-Ari has developed several open source software tools for teaching computer science. The tools he developed and co-developed for teaching various subject areas include: for distributed and concurrent programming (DAJ, Jbaci); for model checking (JSpin and EriGone); for program visualization (the Jeliot animation tool); and for SAT solving (LearnSAT).

Ben-Ari has also been a leader in the area of computer science education theory, having written seminal research papers on constructivism and situated learning. Demonstrating his broad range of interests, he recently co-authored (with Francesco Mondada) Elements of Robotics, an open source textbook for high school students. Ben-Ari’s work has helped to educate and inspire generations of students in computer science.

Arati Dixit was named recipient of the Outstanding Contribution to ACM Award for contributing to the growth and diversity of ACM programs in India, especially ACM-W India.

Dixit is currently a Senior Scientist at Applied Research Associates, Inc. in Raleigh, North Carolina, as well as a Teaching Associate Professor in the ECE department at North Carolina State University. Dixit has been an active member of ACM-W India, an initiative that focuses on the empowerment of women, for many years. In 2013, she was involved in launching the first ACM-W Celebration of Women in Computing event in Pune. ACM-W Celebrations are events that are designed to build a sense of community among women in computing and can include anything from a technical session, to a graduate panel, to a career fair. When she became Chair of ACM-W India in 2017, Dixit expanded the number of ACM-W celebrations to eight diverse regions of the country in both rural and metropolitan settings. She also championed the creation of an annual ACM-W India hackathon.

In 2017, when the broader ACM India Council initiated a program of summer schools across the country to encourage undergraduate students to take up graduate studies, Dixit proposed the idea of having one of the schools dedicated exclusively to women. Dixit organized the first school in Pune in 2017, and an additional summer school was added in Bengaluru in 2018. These women-only summer schools were a success and the model has been repeated. The number of ACM-W chapters across India also grew during Dixit’s tenure. When she stepped down as Chair at the end of 2019, there were 35 active ACM-W student chapters and three ACM-W professional chapters in the country.

Dixit’s other prominent public contribution to ACM was her work as the founding Vice Chair of the ACM India Special Interest Group on Computer Science Education (iSIGCSE), in which she made tireless efforts to promote ACM curriculum implementation across India. As an ACM India Eminent Speaker, she has delivered more than 50 talks on diverse topics. She has been especially active with her local ACM professional chapter in Pune, having served as Chair, Vice Chair, and Secretary/Treasurer.

ACM has named Sarit Kraus of Bar-Ilan University the 2020-2021 ACM Athena Lecturer for foundational contributions to artificial intelligence, notably to multi-agent systems, human-agent interaction, autonomous agents and nonmonotonic reasoning, and exemplary service and leadership in these fields. Her contributions span theoretical foundations, experimental evaluation, and practical applications. Multi-agent systems are regarded as vital to the increasingly complex challenges within artificial intelligence and have broad applications in a number of areas.

Kraus is recognized as one of the world’s leading researchers in multi-agent systems, in which a distributed group of agents (computers, robots, and/or humans) interact and work collaboratively to solve problems. Beyond her work in multi-agent systems, Kraus has made significant contributions to knowledge representation (another area of artificial intelligence research) by incorporating nonmonotonic reasoning, and to randomized policies for security applications by combining methods from game theory, machine learning and optimization. Kraus is also recognized for her service to the field as an outstanding educator and mentor, as well as for her conference, editorial, and leadership roles.

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

“Each year, it is ACM’s honor to put a spotlight on the instrumental role that women play in the computing field by selecting an Athena Lecturer,” said ACM President Cherri M. Pancake. “The ability of multi-agent systems to effectively work together is at the core of AI research and will be the lynchpin of many of the technologies that will shape the future. With seminal work in AI stretching back to the early 1990s, it is fair to say that Sarit Kraus has introduced new ways of thinking in multi-agent systems research, while also shepherding research ideas into practical applications. Her colleagues also cite her generosity and sensitivity in mentoring the next generation of researchers, which aligns perfectly with our mission in bestowing this particular award.”

Multi-agent Systems

In a multi-agent system (MAS), a distributed network of agents (which could include computers or humans) work together to solve a problem that is beyond the capacity of a single agent. For example, if two autonomous vehicles were approaching an intersection, the software systems (agents) in each vehicle would communicate with each other via sensors, and perhaps a central computer coordinating traffic in the neighborhood in order to ensure the vehicles would not collide. Multi-agent systems are increasingly used in a wide range of areas, from Internet of Things (IoT) applications like smart cities, supply-chain management, smart electric grids, robotics, and online trading to many mobile computing services.

The focus of Kraus’s research in multi-agent systems has been to develop intelligent agents that can interact proficiently with each other and with people, in both cooperative and conflicting scenarios. As with other areas of AI, this work requires an understanding of human behavior and decision-making in order to develop models for how the agents will make decisions. Toward this goal, Kraus developed innovative methods combining machine learning techniques for human modeling, formal decision-making and game theory approaches to enable agents to interact well with people. In a series of seminal papers, Kraus developed formal models of agent interaction that have been used in several practical applications including the ARMOR project, which combines game theory and optimization methods to improve robotics security at venues such as the Los Angeles International Airport; the Sheba Project, which deploys machine-learning techniques to facilitated training and rehabilitation (including speech therapy) at hospitals in Israel; developing a virtual suspect, integrating psychological models and machine learning, to enhance law enforcement training; recommendation systems for smart cars; and developing an automated mediator for use in studies on the influence of different mediation types on intra- and inter-country negotiations.

Kraus’s work in multi-agent systems also includes the shared plans framework for collaborative planning and acting, models of coalition formation, automated negotiation, and culture-sensitive agents. For example, her 1996 paper, co-authored with Barbara Grosz, “Collaborative plans for complex group action,” provided a framework for investigating fundamental questions about how agents collaborate and has been especially influential in the development of MAS research, having been cited nearly 1,400 times.

Automated Negotiation

Kraus is recognized as a world leader in a subfield of multi-agent systems called automated negotiation, in which the goal is to build computers that can reach agreements with other computers, negotiate on behalf of humans, or perhaps do a better job than human negotiators. Automated negotiation systems are designed to operate without any human intervention. Automated negotiation especially comes into play in economic domains and has garnered increasing interest with the rise of e-commerce applications. As with her broader work within multi-agent systems, Kraus designed models and protocols for automated negotiation algorithms that she introduced in dozens of seminal papers stretching back to the early 1990s. Kraus is a co-author (with Michael Wooldridge and Shaheen Fatima) of the book Principles of Automated Negotiation, a state-of-the-art treatment of the subject.

Nonmonotonic Reasoning
In designing artificially intelligent systems, researchers seek to simulate the logic humans use to make assumptions about the world, even in the face of incomplete or new information. In the traditional example, if a human is told that an animal is a bird, the human will logically assume that it can fly. However, when the human is informed that the bird is a penguin, which cannot fly, the human must adjust his/her logic to allow for potential exceptions to the general rule “all birds fly.” In artificial intelligence, nonmonotonic reasoning refers to the ability of a system to take back conclusions when initial assumptions were incorrect and/or new information is given. Nonmonotonic systems are also designed to reach new/alternative conclusions in these instances. Kraus was the lead author (with Daniel Lehmann and Menachem Magidor) of the landmark 1990 paper “Non-monotonic Reasoning, Preferential Models and Cumulative Logics,” which has been recognized as a highly useful attempt to provide theoretical foundations for nonmonotonic logic that could be used in AI systems. Kraus’s subsequent publications in nonmonotonic logic have shaped the development of this important subfield of AI and opened up new lines of research.

Service to the Field and Mentoring
Over the years, Kraus has served the research community by taking on many volunteer roles. She has been a General, Program or Workshop Chair for several leading AI conferences including IJCAI, ICMAS, AAMAS, and ECAI. Currently, she is serving on the editorial boards of the Journal of Artificial Intelligence Research, Journal of Autonomous Agents and Multi-agent Systems, and Annals of Maths & AI. As an educator and mentor, she has supervised 62 Master’s students and 34 PhD students. In the spirit of the ACM Athena Lecturer Award, Kraus is also a recognized leader in efforts to increase the participation of women in science.

Biographical Background
Sarit Kraus is a Professor of Computer Science at Bar-Ilan University in Ramat Gan, Israel, where her research is focused on intelligent agents and multi-agent systems. She received her Bachelor’s, Master’s and PhD degrees from The Hebrew University of Jerusalem.

Kraus has written six books, 122 journal articles, and 176 conference papers, and has received nine patents. Twelve of her publications have won best paper awards and two have won the IFAAMAS Influential Paper Award. She is a Fellow of ACM, the European Association for Artificial Intelligence (EurAI), and the Association for the Advancement of Artificial Intelligence (AAAI). Her honors include receiving the IJCAI Computers and Thought Award in 1995, the ACM SIGART Autonomous Agents Research Award in 2007, and the prestigious Israel EMET Prize in 2010.

ACM named Maria Florina “Nina” Balcan of Carnegie Mellon University the recipient of the 2019 ACM Grace Murray Hopper Award for foundational and breakthrough contributions to minimally-supervised learning. Balcan’s influential and pioneering work in machine learning has solved longstanding open problems, enabled entire lines of research crucial for modern AI systems, and has set the agenda for the field for years to come.

The ACM Grace Murray Hopper Award is given to the outstanding young computer professional of the year, selected on the basis of a single recent major technical or service contribution. This award is accompanied by a prize of $35,000. The candidate must have been 35 years of age or less at the time the qualifying contribution was made. Financial support for this award is provided by Microsoft.

“Nina Balcan wonderfully meets the criteria for the ACM Grace Murray Hopper Award, as many of her groundbreaking contributions occurred long before she turned 35,” said ACM President Cherri M. Pancake. “Although she is still in the early stages of her career, she has already established herself as the world leader in the theory of how AI systems can learn with limited supervision. More broadly, her work has realigned the foundations of machine learning, and consequently ushered in many new applications that have brought about leapfrog advances in this exciting area of artificial intelligence.”

Select Technical Contributions

Semi-supervised Learning
Semi-supervised learning is an approach to machine learning in which algorithms use large amounts of easily available unlabeled data to augment small amounts of labeled data to improve predictive accuracy. When semi-supervised learning was first explored, early research suggested some promising results. However, prior to Balcan’s work, there were no general principles for designing and providing formal guarantees for algorithms that leverage both labeled and unlabeled data. By introducing the first general theoretical framework, Balcan showed how to achieve provable guarantees on the performance of such techniques with concrete implications for many different types of semi-supervised learning methods. Her foundational principles for learning from limited supervision were instrumental in advancing this important tool in machine learning and supporting the subsequent work of many other researchers in this area.

Active Learning/Noise Tolerant Learning
Balcan also made significant contributions in the related area of active learning. In active learning, the algorithm processes large volumes of data and intelligently chooses the datapoints to be labeled. Balcan established performance guarantees for active learning that hold even in challenging cases when “noise” is present in the data. These guarantees hold under arbitrary forms of noise, that is, anything that distorts or corrupts the data. This can include anything from a blurry photo, a unit of data that is improperly labeled, meaningless information, or data that the algorithm cannot interpret. Building on this work, Balcan and her collaborators also developed algorithms that can learn more efficiently under more specialized forms of “label noise.” Examples of label noise might include a researcher not being given all of the health symptoms when annotating data to make predictions about a disease, or the data being encoded incorrectly. Her work in active learning in the presence of noise was regarded as a breakthrough in the field.

Clustering
Clustering is an unsupervised learning technique in which an algorithm groups datapoints with similar properties. One goal of clustering is to find meaningful structure in data. An early challenge in the field, however, was to establish a theoretical foundation for what constituted a “meaningful structure” in a dataset. In her early work, Balcan proposed a theoretical foundation for understanding the general kinds of structures that can be detected by clustering, as well as characterizing the functionality of specific clustering algorithms. As she developed her theoretical framework further, she also devised novel clustering algorithms that were derived from these theoretical foundations, and showed applications of these algorithms to computational biology and web search.

ACM named David Silver the recipient of the 2019 ACM Prize in Computing for breakthrough advances in computer game-playing. Silver is a Professor at University College London and a Principal Research Scientist at DeepMind, a Google-owned artificial intelligence company based in the United Kingdom. Silver is recognized as a central figure in the growing and impactful area of deep reinforcement learning.

Silver’s most highly publicized achievement was leading the team that developed AlphaGo, a computer program that defeated the world champion of the game Go, a popular abstract board game. Silver developed the AlphaGo algorithm by deftly combining ideas from deep-learning, reinforcement-learning, traditional tree-search and large-scale computing. AlphaGo is recognized as a milestone in artificial intelligence (AI) research and was ranked by New Scientist magazine as one of the top 10 discoveries of the last decade.

AlphaGo was initialized by training on expert human games followed by reinforcement learning to improve its performance. Subsequently, Silver sought even more principled methods for achieving greater performance and generality. He developed the AlphaZero algorithm that learned entirely by playing games against itself, starting without any human data or prior knowledge except the game rules. AlphaZero achieved superhuman performance in the games of chess, Shogi, and Go, demonstrating unprecedented generality of the game-playing methods.

Computer Game-Playing and AI
Teaching computer programs to play games, against humans or other computers, has been a central practice in AI research since the 1950s. Game playing, which requires an agent to make a series of decisions toward an objective—winning—is seen as a useful facsimile of human thought processes. Game-playing also affords researchers results that are easily quantifiable—that is, did the computer follow the rules, score points, and/or win the game?

At the dawn of the field, researchers developed programs to compete with humans at checkers, and over the decades, increasingly sophisticated chess programs were introduced. A watershed moment occurred in 1997, when ACM sponsored a tournament in which IBM’s DeepBlue became the first computer to defeat a world chess champion, Gary Kasparov. At the same time, the objective of the researchers was not simply to develop programs to win games, but to use game-playing as a touchstone to develop machines with capacities that simulated human intelligence.

“Few other researchers have generated as much excitement in the AI field as David Silver,” said ACM President Cherri M. Pancake. “Human vs. machine contests have long been a yardstick for AI. Millions of people around the world watched as AlphaGo defeated the Go world champion, Lee Sedol, on television in March 2016. But that was just the beginning of Silver’s impact. His insights into deep reinforcement learning are already being applied in areas such as improving the efficiency of the UK’s power grid, reducing power consumption at Google’s data centers, and planning the trajectories of space probes for the European Space Agency.”

“Infosys congratulates David Silver for his accomplishments in making foundational contributions to deep reinforcement learning and thus rapidly accelerating the state of the art in artificial intelligence,” said Pravin Rao, COO of Infosys. “When computers can defeat world champions at complex board games, it captures the public imagination and attracts young researchers to areas like machine learning. Importantly, the frameworks that Silver and his colleagues have developed will inform all areas of AI, as well as practical applications in business and industry for many years to come. Infosys is proud to provide financial support for the ACM Prize in Computing and to join with ACM in recognizing outstanding young computing professionals.”

Silver is credited with being one of the foremost proponents of a new machine learning tool called deep reinforcement learning, in which the algorithm learns by trial-and-error in an interactive environment. The algorithm continually adjusts its actions based on the information it accumulates while it is running. In deep reinforcement learning, artificial neural networks—computation models which use different layers of mathematical processing—are effectively combined with the reinforcement learning strategies to evaluate the trial-and-error results. Instead of having to perform calculations of every possible outcome, the algorithm makes predictions leading to a more efficient execution of a given task.

Learning Atari from Scratch
At the Neural Information Processing Systems Conference (NeurIPS) in 2013, Silver and his colleagues at DeepMind presented a program that could play 50 Atari games to human-level ability. The program learned to play the games based solely on observing the pixels and scores while playing. Earlier reinforcement learning approaches had not achieved anything close to this level of ability.

Silver and his colleagues published their method of combining reinforcement learning with artificial neural networks in a seminal 2015 paper, “Human Level Control Through Deep Reinforcement Learning,” which was published in Nature. The paper has been cited nearly 10,000 times and has had an immense impact on the field. Subsequently, Silver and his colleagues continued to refine these deep reinforcement learning algorithms with novel techniques, and these algorithms remain among the most widely-used tools in machine learning.

AlphaGo
The game of Go was invented in China 2,500 years ago and has remained popular, especially in Asia. Go is regarded as far more complex than chess, as there are vastly more potential moves a player can make, as well as many more ways a game can play out. Silver first began exploring the possibility of developing a computer program that could master Go when he was a PhD student at the University of Alberta, and it remained a continuing research interest.

Silver’s key insight in developing AlphaGo was to combine deep neural networks with an algorithm used in computer game-playing called Monte Carlo Tree Search. One strength of Monte Carlo Tree Search is that, while pursuing the perceived best strategy in a game, the algorithm is also continually investigating other alternatives. AlphaGo’s defeat of world Go champion Lee Sedol in March 2016 was hailed as a milestone moment in AI. Silver and his colleagues published the foundational technology underpinning AlphaGo in the paper “Mastering the Game of Go with Deep Neural Networks and Tree Search” that was published in Nature in 2016.

AlphaGo Zero, AlphaZero and AlphaStar
Silver and his team at DeepMind have continued to develop new algorithms that have significantly advanced the state of the art in computer game-playing and achieved results many in the field thought were not yet possible for AI systems. In developing the AlphaGo Zero algorithm, Silver and his collaborators demonstrated that it is possible for a program to master Go without any access to human expert games. The algorithm learns entirely by playing itself without any human data or prior knowledge, except the rules of the game and, in a further iteration, without even knowing the rules.

Later, the DeepMind team’s AlphaZero also achieved superhuman performance in chess, Shogi, and Go. In chess, AlphaZero easily defeated world computer chess champion Stockfish, a high-performance program designed by grandmasters and chess programming experts. Just last year, the DeepMind team, led by Silver, developed AlphaStar, which mastered the multiple-player video game StarCraft II, which had been regarded as a stunningly hard challenge for AI learning systems.

The DeepMind team continues to advance these technologies and find applications for them. Among other initiatives, Google is exploring how to use deep reinforcement learning approaches to manage robotic machinery at factories.

Computer scientist and former president of Pixar and Disney Animation Studios Edwin E. (Ed) Catmull was named co-recipient of the 2019 ACM A.M. Turing Award along with Patrick M. (Pat) Hanrahan for fundamental contributions to 3-D computer graphics, and the revolutionary impact of these techniques on computer-generated imagery (CGI) in filmmaking and other applications.

Ed Catmull and Pat Hanrahan have fundamentally influenced the field of computer graphics through conceptual innovation and contributions to both software and hardware. Their work has had a revolutionary impact on filmmaking, leading to a new genre of entirely computer-animated feature films beginning 25 years ago with Toy Story and continuing to the present day.

Catmull and Hanrahan made pioneering technical contributions which remain integral to how today’s CGI imagery is developed. Additionally, their insights into programming graphics processing units (GPUs) have had implications beyond computer graphics, impacting diverse areas including data center management and artificial intelligence.

In his PhD thesis while at the University of Utah, Catmull introduced the groundbreaking techniques for displaying curved patches instead of polygons, out of which arose two new techniques: Z-buffering (also described by Wolfgang Strasser at the time), which manages image depth coordinates in computer graphics, and texture mapping, in which a 2-D surface texture is wrapped around a three-dimensional object. While at Utah, Catmull also created a new method of representing a smooth surface via the specification of a coarser polygon mesh. After graduating, he collaborated with Jim Clark, who would later found Silicon Graphics and Netscape, on the Catmull-Clark Subdivision Surface, which is now the preeminent surface patch used in animation and special effects in movies. Catmull’s techniques have played an important role in developing photo-real graphics, and eliminating “jaggies,” the rough edges around shapes that were a hallmark of primitive computer graphics.

After the University of Utah, Catmull founded the New York Institute of Technology (NYIT) Computer Graphics Lab, one of the earliest dedicated computer graphics labs in the US. Even at that time, Catmull dreamed of making a computer-animated movie. He came a step closer to his goal in 1979, when George Lucas hired Catmull, who in turn hired many who made the advances that pushed graphics toward photorealistic images. At LucasFilm, Catmull and colleagues continued to develop innovations in 3-D computer graphic animation, in an industry that was still dominated by traditional 2-D techniques. In 1986, Steve Jobs bought LucasFilm’s Computer Animation Division and renamed it Pixar, with Catmull as its President.

Under Catmull’s leadership, Pixar would make a succession of successful films using RenderMan. Pixar also licensed RenderMan to other film companies. The software has been used in 44 of the last 47 films nominated for an Academy Award in the Visual Effects category, including Avatar, Titanic, Beauty and the Beast, The Lord of the Rings trilogy, and the Star Wars prequels, among others. RenderMan remains the standard workflow for CGI visual effects.

Catmull remained at Pixar, which later became a subsidiary of Disney Animation Studios, for over 30 years. Under his leadership, dozens of researchers at these labs invented and published foundational technologies (including image compositing, motion blur, cloth simulation, etc.) that contributed to computer animated films and computer graphics more broadly. Both Hanrahan and Catmull have received awards from ACM SIGGRAPH, as well as the Academy of Motion Picture Arts & Sciences for their technical contributions.

The winners of the 2019-2020 Cutler-Bell Prize in High School Computing were announced by ACM and the Computer Science Teachers Association (CSTA). Four high school students were selected from among a pool of graduating high school seniors throughout the US. Eligible students applied for the award by submitting a project/artifact that engages modern technology and computer science. A panel of judges selected the recipients based on the ingenuity, complexity, relevancy and originality of their projects.

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

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

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

Kevin Meng, Plano West Senior High School, Plano, Texas
Two years ago, Kevin Meng’s grandmother suffered from a slip-and-fall injury that resulted in skull fracture. This accident, which was suffered out of the view of cameras, got Meng thinking: what if we could see through walls? In his project, Meng uses VisionRF, a deep neural network model that accepts raw radio frequency signals and outputs continuous video of 15-point human skeletons behind obstruction. Because radio camera data on its own is harder to analyze, analysis through Raspberry Pi-based programming supports mobile, real-time inference. This results in accurate and complete predictions of the human skeletons. The implications of this project are broad and can be used to support military operations, monitor the health of patients non-invasively and aid first responders in search and rescue missions.

Lillian Kay Petersen, Los Alamos High School, Los Alamos, New Mexico
Lillian Kay Petersen’s younger, adopted siblings faced food insecurity in their previous homes. Inspired by their experiences and the news of crop failures in Ethiopia, she became determined to help aid organizations in increasing food security in developing countries. To accomplish this, Petersen developed a tool to inform cost-effective nutrition interventions in sub-Saharan Africa, inclusive of predicting grain harvests, predicting acute malnutrition prevalence and optimizing the supply logistics of specialized nutritious foods. The tools can be adjusted to include-real time data, enabling aid organizations to adjust distributions accordingly. As the result of her work, Petersen was invited to speak at eleven aid and research organizations, including USAID, the USDA and the International Food Policy Research Institute. She was also an invited speaker at multiple conferences, including the 2018 and 2019 CGIAR Big Data in Agriculture Conventions in Kenya and India.

Axel S. Toro Vega, Dr. Carlos González High School, Aguada, Puerto Rico
While identifying topics for his research project, Axel Toro Vega read that more than 36 million people in the world are visually impaired and more than 217 million have some type of severe visual impairment. As a result, he decided to focus his research on developing a device to assist the visually impaired in having a healthier, safer, and more enjoyable lifestyle. Toro Vega created an initial prototype consisting of an ultrasonic sensor mounted onto a pair of glasses. He continued to test different sensor arrangements and tweaked the software for a simple and efficient user experience. After gathering additional feedback after a presentation at the Intel International Science and Engineering Fair, Toro Vega took his prototype further by integrating artificial intelligence. This project made Toro Vega realize the great accomplishments that can be reached through computer science and the core meaning of CS for Good.

Zeyu Zhao, Montgomery Blair High School, Silver Spring, Maryland
Inspired by his grandfather who is facing chronic kidney disease, Zeyu Zhao began researching the kidney exchange system in the U.S. and was shocked to learn that 3,000 kidneys are wasted each year and 13 people die daily, in part, due to failed matches. Zhao wanted to use computer science—specifically machine learning—to improve the current kidney exchange system. He created a data-driven approach to solving the kidney matching problem through the designation of a Graph Neural Network to guide a Monte Carlo Tree Search. Zhao identified baselines for his project and tested his algorithms against this baseline, thus improving the current kidney exchange by developing a data-driven approach to finding matches. The research from Zhao’s project could be extended to other applications, such as operations research.

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

“ACM has been a leader in integrating computer science into the K-12 curriculum for several decades and our participation in the annual Cutler-Bell Prize is an extension of our commitment in this area,” said ACM President Cherri M. Pancake. “It is always intriguing to learn about the Cutler-Bell Prize-winning projects, which reflect the students' creativity and ingenuity as well as what they have learned in the classroom. These projects embody what we call "computational thinking"—a unique way of approaching problem-solving inspired by the computing revolution. We are grateful for Gordon Bell and David Cutler's financial support of the prize, and we congratulate the students and their teachers for developing these inspiring projects.”

“This year’s winning projects are outstanding examples of the power of a high quality, K-12 computer science education," said Jake Baskin, Executive Director of CSTA. "These students' creativity and commitment to using their knowledge and skills to improve the world are inspiring and I cannot wait to see what they do next. CSTA is proud to honor their work and thanks Gordon Bell and David Cutler for their continued support of the award."

ACM has named 58 members ACM Fellows for wide-ranging and fundamental contributions in areas including artificial intelligence, cloud computing, combating cybercrime, quantum computing and wireless networking. The accomplishments of the 2019 ACM Fellows underpin the technologies that define the digital age and greatly impact our professional and personal lives. ACM Fellows comprise an elite group that represents less than 1% of the Association’s global membership.

"Computing technology has had a tremendous impact in shaping how we live and work today,” said ACM President Cherri M. Pancake in announcing the 2019 ACM Fellows. “All of the technologies that directly or indirectly influence us are the result of countless hours of collaborative and/or individual work, as well as creative inspiration and, at times, informed risk-taking. Each year, we look forward to welcoming some of the most outstanding individuals as Fellows. The ACM Fellows program is a cornerstone of our overall recognition effort. In highlighting the accomplishments of the ACM Fellows, we hope to give credit where it is due, while also educating the public about the extraordinary array of areas in which computing professionals work."

Underscoring ACM’s global reach, the 2019 Fellows hail from universities, companies and research centers in Australia, Canada, China, Egypt, France, Germany, Israel, Italy, Switzerland, and the United States.

The contributions of the 2019 Fellows run the gamut of the many sub-disciplines of the computing field―including artificial intelligence, cloud computing, computer graphics, computational biology, data science, security and privacy, software engineering, quantum computing, and web science, to name a few.

Additional information about the 2019 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows site.

News release

ACM named a six-member team from the Swiss Federal Institute of Technology (ETH) Zurich recipients of the 2019 ACM Gordon Bell Prize for their project, “A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations.”

The ETH Zurich team introduced DaCe OMEN, a new framework for simulating the transport of electrical signals through nanoscale materials (such as the silicon atoms used in transistors). To better understand the thermal properties of transistors, the team simulated how electricity would be transported through a two-dimensional slice of transistor consisting of 10,0000 atoms. The ETH Zurich researchers simulated the 10,000-atom system 14 times faster than an earlier framework that was used for a 1,000-atom system. The DaCe OMEN code they developed for the simulation has been run on two top-6 hybrid supercomputers, reaching a sustained performance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) in double precision, and 90.89 Pflop/s in mixed precision.

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented by ACM President Cherri M. Pancake and Arndt Bode, Chair of the 2019 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) in Denver, Colorado.

Today’s commercial microchips contain 100,000,000 transistors in the span of a single millimeter, and managing heat generation and dissipation is one of the central problems in computer architecture. As the transistors on each microchip have become smaller and more densely packed, the amount of heat they generate has steadily increased. The cooling systems needed to keep supercomputers and data centers from overheating have become increasingly expensive. They estimate that cooling can consume up to 40% of the total electricity needed for data centers, amounting to cumulative costs of many billions of dollars per year.

Today’s supercomputers, which can perform up to 200 quadrillion calculations per second, allow scientists in many disciplines to gain new insights by processing a staggering number of variables. The ETH Zurich team used their simulation to develop a map of where heat is produced in a single transistor, how it is generated and how it is evacuated. It is hoped that a deeper understanding of these thermal characteristics could inform the development of new semiconductors with optimal heat-evacuating properties.

In recent years, the OMEN framework has been a popular quantum transport simulator for modeling nanoscale materials, but has experienced scaling bottlenecks. The ETH Zurich Team wrote a variation of OMEN that is Data Centric (DaCe OMEN). “We show that the key to eliminating the scaling bottleneck is in formulating a communication-avoiding algorithm,” the team writes in their paper. The ETH Zurich team’s solver yields data movement characteristics that can be used for performance and communication modeling, communication avoidance, and dataflow transformations. They go on to note that the speedup made by the DaCe OMEN framework is two orders of magnitude faster per atom than the original OMEN code.

The ETH Zurich team also built a graphical interface for the DaCe OMEN framework that includes a visualization of dataflow in lieu of a simple textual description. Anyone running the code can use the image representation to interact with the data directly. The team believes this new innovation could be applied to numerous scientific disciplines beyond nanoelectronics.

Winning team members include Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider and Torsten Hoefler, from ETH Zurich’s Scalable Parallel Computing Laboratory, as well as Guillermo Indalecio Fernández and Mathieu Luisier from ETH Zurich’s Integrated Systems Laboratory.

ACM has named 62 Distinguished Members for outstanding contributions to the field. All 2019 inductees are longstanding ACM members and were selected by their peers for a range of accomplishments that have contributed to technologies that underpin how we live, work and play.

"Each year it is our honor to select a new class of Distinguished Members,” explains ACM President Cherri M. Pancake. “In everything we do, our overarching goal is to build a community wherein computing professionals can grow professionally and, in turn, contribute to the field and the broader society. We are delighted to recognize these individuals for their contributions to computing, and we hope that the careers of the 2019 ACM Distinguished Members will continue to prosper through their participation with ACM."

The 2019 ACM Distinguished Members work at leading universities, corporations and research institutions around the world, and hail from Canada, China, Germany, Ireland, Qatar, Singapore, Taiwan, the United Kingdom and the United States. These innovators have made contributions in a wide range of technical areas including artificial intelligence, human-computer interaction, computer engineering, computer science education, cybersecurity, graphics, and networking.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience as well as significant achievements in the computing field. To be nominated, a candidate must have at least 15 years of professional experience in the computing field, 5 years of continuous professional ACM membership, and have achieved a significant level of accomplishment, or made a significant impact in the field of computing, computer science and/or information technology. In addition, it is expected that a Distinguished Member serves as a mentor and role model, guiding technical career development and contributing to the field beyond the norm.

The Association for Computing Machinery (ACM) and IEEE Computer Society IEEE-CS) named Geoffrey C. Fox of Indiana University Bloomington as the recipient of the 2019 ACM-IEEE CS Ken Kennedy Award. Fox was cited for foundational contributions to parallel computing methodology, algorithms and software, and data analysis, and their interfaces with broad classes of applications. The award will be presented at SC19: The International Conference for High Performance Computing, Networking, Storage and Analysis, November 17-22, in Denver, Colorado.

Through a long and distinguished career, Fox has made several important technical contributions to high performance computing. Fox identified the principles behind the use of decomposition and efficient message passing in early MIMD (multiple instruction, multiple data) hypercubes, which pioneered application development on parallel machines. In several well-received papers, Fox demonstrated the synergies between Message Passing Interface (MPI) and MapReduce. In one paper, for instance, he introduced the programming model and architecture of Twister, an enhanced map-reduce runtime that supports iterative MapReduce computations efficiently. His more recent Twister 2 system systematically provides HPC performance with functionalities similar to Apache Spark, Flink, Storm, and Heron. His recent work at the interface of HPC and data-intensive computing has resulted in the SPIDAL (Scalable Parallel and Interoperable Data-intensive Application Library) project. SPIDAL supports a very diverse collection of data-intensive applications on high performance computing platforms.

ACM and the IEEE Computer Society co-sponsor the Kennedy Award, which was established in 2009 to recognize substantial contributions to programmability and productivity in computing and significant community service or mentoring contributions. It was named for the late Ken Kennedy, founder of Rice University’s computer science program and a world expert on high performance computing. The Kennedy Award carries a $5,000 honorarium endowed by the SC Conference Steering Committee.

Milinda Shayamal Fernando of the University of Utah and Staci A. Smith of the University of Arizona are the recipients of the 2019 ACM-IEEE CS George Michael Memorial HPC Fellowships. Fernando is recognized for his work on high performance algorithms for applications in relativity, geosciences and computational fluid dynamics (CFD). Smith is recognized for her work developing a novel dynamic rerouting algorithm on fat-tree interconnects. The Fellowships are jointly presented by ACM and the IEEE Computer Society.

Milinda Fernando
New discoveries in science and engineering are partially driven by simulations on high performance computers―especially when physical experiments would be unfeasible or impossible. Fernando’s research is focused on developing algorithms and computational codes that enable the effective use of modern supercomputers by scientists working in many disciplines

His key objectives include: making making computer simulations on high performance computers easy to use (by using symbolic interfaces and autonomous code generation); portable (so they can be run across different computer architectures); high-performing (because they make efficient use of computing resources); and scalable (so that they can solve larger problems on next next-generation machines).

Fernando’s work has enabled improved applications in areas of computational relativity and gravitational wave (GW) astronomy. In the universe, when two supermassive black holes merge, they bring along corresponding clouds of stars, gas and dark matter. Modeling these events requires powerful computational tools that consider all the physical effects of such a merger. While recent algorithms and codes to develop simulations of black hole mergers have been developed, they were limited because they could only handle simulations when the masses of the two black holes were comparable. Fernando developed algorithms and code for mergers of black holes, or neutron stars, of vastly different mass ratios. These computational simulations help scientists understand the early universe as well as what is going on at the heart of galaxies.

Staci Smith
A general problem in high performance computing occurs when multiple distinct jobs running on supercomputers send messages at the same time, and these messages interfere with each other. This inter-job interference can significantly degrade a computer’s performance.

Smith’s first research paper in this area, “Mitigating Inter-Job Interference Using Adaptive Flow-Aware Routing,” received a Best Student Paper nomination at SC18, the premiere supercomputing conference. Her paper had two goals: to explore the causes of network interference between jobs (in order to model that interference); and to develop a mitigation strategy to alleviate the interference.

As a result of this work, Smith recently developed a new routing algorithm for fat-tree interconnects called Adaptive Flow-Aware Routing (AFAR), which improves execution time up to 46% when compared to other default routing algorithms. As part of her ongoing PhD research, she continues to develop algorithms to improve the performance and efficiency of HPC workloads.

About the ACM-IEEE CS George Michael Memorial HPC Fellowship
The ACM-IEEE CS George Michael Memorial HPC Fellowship is endowed in memory of George Michael, one of the founding fathers of the SC Conference series. The fellowship honors exceptional PhD students throughout the world whose research focus is on high performance computing applications, networking, storage or large-scale data analytics using the most powerful computers that are currently available. The Fellowship includes a $5,000 honorarium and travel expenses to attend SC19 in Denver, Colorado, November 17-22, 2019, where the Fellowships will be formally presented.

ACM President Cherri Pancake honored Vinton G. Cerf with the 2019 ACM Presidential Award. The award was presented to Cerf at ACM's annual Awards Banquet on June 15 in San Francisco.

His citation reads:
In addition to his well-publicized technical contributions, for which he won the Turing Award, Vint Cerf crafted a unique vision of what ACM could be and achieve as an organization. He has served as a member of ACM Council three times, and was elected ACM President in 2012. After completing his term as Past President, he became the Awards Co-Chair. That much is in the public record. But his singular contribution to ACM remains largely unknown: Vint was the principal driver in establishing the ACM Fellows Program in 1993. The Fellows program, of course, recognizes the top 1% of ACM members from around the world for their outstanding accomplishments and service to the computing community. The luster of becoming a Fellow has not diminished with time. Indeed, with the newer Distinguished Member grade, and with the eminence of each year's Fellows class (13 of whom have gone on to win the Turing Award), the program has only grown in stature. As for impact, Fellows constitute some of ACM's best ambassadors and serve as models for younger members. While the Fellows program is now an established part of the ACM "landscape," this was not always the case—and likely wouldn't be had Vint not championed the concept. This 2019 ACM Presidential Award recognizes his extraordinary record of service to ACM.

Chelsea Finn of the University of California, Berkeley is the recipient of the 2018 ACM Doctoral Dissertation Award for her dissertation, “Learning to Learn with Gradients.” In her thesis, Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small datasets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.

Deep learning has transformed the artificial intelligence field and has led to significant advances in areas including speech recognition, computer vision and robotics. However, deep learning methods require large datasets, which aren’t readily available in areas such as medical imaging and robotics.

Meta-learning is a recent innovation that holds promise to allow machines to learn with smaller datasets. Meta-learning algorithms “learn to learn” by using past data to learn how to adapt quickly to new tasks. However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures. In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures. Finn’s MAML methods have had tremendous impact on the field and have been widely adopted in reinforcement learning, computer vision and other fields of machine learning.

At a young age, Finn has become one of the most recognized experts in the field of robotic learning. She has developed some of the most effective methods to teach robots skills to control and manipulate objects. In one instance highlighted in her dissertation, she used her MAML methods to teach a robot reaching and placing skills, using raw camera pixels from just a single human demonstration.

Finn is a Research Scientist at Google Brain and a postdoctoral researcher at the Berkeley AI Research Lab (BAIR). In the fall of 2019, she will start a full-time appointment as an Assistant Professor at Stanford University. Finn received her PhD in Electrical Engineering and Computer Science from the University of California, Berkeley and a BS in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Honorable Mentions for the 2018 ACM Doctoral Dissertation Award go to Ryan Beckett and Tengyu Ma, who both received PhD degrees in Computer Science from Princeton University.

Ryan Beckett developed new, general and efficient algorithms for creating and validating network control plane configurations in his dissertation, “Network Control Plane Synthesis and Verification.” Computer networks connect key components of the world’s critical infrastructure. When such networks are misconfigured, several systems people rely on are interrupted—airplanes are grounded, banks go offline, etc. Beckett’s dissertation describes new principles, algorithms and tools for substantially improving the reliability of modern networks. In the first half of his thesis, Beckett shows that it is unnecessary to simulate the distributed algorithms that traditional routers implement—a process that is simply too costly—and that instead, one can directly verify the stable states to which such algorithms will eventually converge. In the second half of his thesis, he shows how to generate correct configurations from surprisingly compact high-level specifications.

Beckett is a researcher in the mobility and networking group at Microsoft Research. He received his PhD and MA in Computer Science from Princeton University, and both a BS in Computer Science and a BA in Mathematics from the University of Virginia.

Tengyu Ma’s dissertation, "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding,” develops novel theory to support new trends in machine learning. He introduces significant advances in proving convergence of nonconvex optimization algorithms in machine learning, and outlines properties of machine learning models trained via such methods. In the first part of his thesis, Ma studies a range of problems, such as matrix completion, sparse coding, simplified neural networks, and learning linear dynamical systems, and formalizes clear and natural conditions under which one can design provable correct and efficient optimization algorithms. In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.

Ma is an Assistant Professor of Computer Science and Statistics at Stanford University. He received a PhD in Computer Science from Princeton University and a BS in Computer Science from Tsinghua University.

Meenakshi Balakrishnan was named recipient of the Eugene L. Lawler Award for research, development, and deployment of cost-effective embedded-system and software solutions addressing mobility and education challenges of the visually impaired in the developing world.

Balakrishnan, a professor at the Indian Institute of Technology, Delhi, has dedicated more than a decade to addressing the challenges of the visually impaired by developing low-cost, computing technology-based solutions. Each of his devices has been developed by the meticulous integration of hardware, software, and firmware. His applications have not only improved the quality of life for countless people, but also have made their day-to-day lives dramatically safer. These technologies are especially valuable in the developing world, where there are fewer resources for the visually impaired.

Perhaps his best-known technology is the SmartCane project, which allows the visually impaired to detect items above their knees within a distance of 3 meters. Balakrishnan equipped the probing cane with ultrasonic ranging, wherein the cane conveys the distance of obstacles using vibrations. Balakrishnan has also worked tirelessly to bring the SmartCane to market at an affordable cost. Working with for-profit, nonprofit, and government organizations, he introduced the SmartCane at 5% of the cost of a comparable product in the West. Within India he has made over 70,000 devices available through government initiatives and 45 partner agencies. SmartCane has also won numerous awards, including the Best Paper Award at the International Conference on Mobility and Transport for Elderly and Disabled Citizens (TRANSED) 2010.

Additional technologies Balakrishnan and his lab have developed include the OnBoard bus identification and homing system, which helps the visually impaired identify bus routes and locate the entry door, and The Refreshable Braille, which allows the visually impaired to read digital text line-by-line through a tactile interface.

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

ACM named Mendel Rosenblum of Stanford University the recipient of the inaugural ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award. Rosenblum is recognized for reinventing the virtual machine for the modern era and thereby revolutionizing datacenters and enabling modern cloud computing. In the late 1990s, Rosenblum and his students at Stanford University brought virtual machines back to life by using them to solve challenging technical problems in building system software for scalable multiprocessors. In 1998, Rosenblum and colleagues founded VMware. VMware popularized the use of virtual machines as a means of supporting many disparate software environments to share processor resources within a datacenter. This approach ultimately led to the development of modern cloud computing services such as Amazon Web Services, Microsoft Azure, and Google Cloud.

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.

“The new paradigm of cloud computing, in which computing services are delivered over the internet, has been one of the most important developments in the computing industry over the past 20 years,” said ACM President Cherri M. Pancake. “Cloud computing has vastly improved the efficiency of systems, reduced costs, and been essential to the operations of businesses at all levels. However, cloud computing, as we know it today, would not be possible without Rosenblum’s reinvention of virtual machines. His leadership, both through his early research at Stanford and his founding of VMware, has been indispensable to the rise of datacenters and the preeminence of the cloud.”

As the name suggests, virtual machines are systems comprised of software, hardware, or a combination of the two, that enable one computer to behave like another. IBM and others developed the idea of virtualization in the 1960s to enable timesharing. However, as new methods of timesharing were developed and the price of hardware dropped, virtual machines fell out of favor. By the late 1980s, virtualization was considered an irrelevant and obsolete idea.

In the late 1990s, Rosenblum and his students at Stanford University revisited the idea of virtual machines to develop system software for FLASH, an experimental large-scale multiprocessor. They recognized that existing operating systems could not support large numbers of processors, and modifying one to work efficiently on FLASH would have been very difficult. Instead, they decided to use virtual machines to run multiple operating system instances on FLASH, each with only a few virtual processors.

The success of his work on FLASH prompted Rosenblum to found the company VMware in 1998 with Diane Greene, Edouard Bugnion, Scott Devine, and Ellen Wang. VMware popularized the use of virtual machines as a means of allowing any disparate software environments to share processor resources within a datacenter. Today, every commercial cloud environment, including major providers such as Amazon Web Services, Microsoft Azure, and Google Cloud, is based on virtualization concepts developed by Rosenblum and his colleagues.

“We’re excited to see the contributions of Mendel Rosenblum recognized with the inaugural ACM Charles P. Thacker Breakthrough Award,” said Eric Horvitz, Technical Fellow and Director of Microsoft Research. “The award was envisioned to honor the intellect and vision of Chuck Thacker, who was known for upending conventional thinking and introducing breakthrough innovations that changed the trajectory of computing. Mendel Rosenblum is a fabulous choice to receive the inaugural Thacker Award. Rosenblum sought to address a daunting new challenge by reimagining virtualization, an approach that many had bypassed. Virtual machines are essential to the way cloud computing functions, and it is hard to overstate the importance of cloud computing for the computing field as well as for industry more generally.”

Biographical Background

Mendel Rosenblum is the DRC Professor in the School of Engineering and Professor of Electrical Engineering at Stanford University. In 1998, he co-founded VMware, a private company that developed many of the core technologies that underpin cloud computing today. As a subsidiary of Dell Technologies, VMware remains a leader in cloud computing and platform virtualization software and services, employing more than 21,000 people.

A graduate of the University of Virginia, Rosenblum earned his Master’s and doctoral degrees in Computer Science from the University of California, Berkeley. Rosenblum is a Fellow of ACM, and his numerous honors include receiving the ACM Software System Award for VMware Workstation 1.0; the ACM/SIGOPS Mark Weiser Award for innovation in operating system research; the IEEE Reynolds B. Johnson Information Storage Award (with John Ousterhout); and the ACM Doctoral Dissertation Award for his dissertation “The Design and Implementation of a Log-Structured File System.” Rosenblum is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

Rosenblum will formally receive the award at ACM’s annual Awards Banquet on June 15, 2019 in San Francisco.

Jack Dongarra of the University Tennessee was awarded the 2019 SIAM/ACM Prize in Computer Science and Engineering on February 28 at the SIAM Conference on Computational Science and Engineering (CSE19) in Spokane, Washington.

Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee.

The prize honors Dongarra for his key role in the development of software and software standards, software repositories, performance and benchmarking software, and in community efforts to prepare for the challenges of exascale computing, especially in adapting linear algebra infrastructure to emerging architectures.

He is a Fellow of the AAAS, ACM, IEEE, and SIAM, and a member of the National Academy of Engineering. He also received the 2013 ACM/IEEE Ken Kennedy Award.

For more information read the SIAM news release.