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 2018-2019 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. This year’s Cutler-Bell Prize recipients will be formally recognized at the Computer Science Teachers Association’s annual conference, July 7-10, 2019 in Phoenix, Arizona.

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.

ACM and IEEE Computer Society named Mark D. Hill, a professor at a professor at the University of Wisconsin—Madison, the recipient of the 2019 Eckert-Mauchly Award. Hill was cited for contributions to the design and evaluation of memory systems and parallel computers. Widely regarded as the leading memory systems researcher in the world today, Hill made seminal contributions to the fields of cache memories, memory consistency models, transactional memory, and simulation. Hill’s work with over 160 co-authors, which has received more than 20,000 citations, has been guided by the tenet that researchers should develop designs and models. The Eckert-Mauchly Award is considered the computer architecture community’s most prestigious award.

In the 1980s Hill developed the “3C” model of cache misses. A “cache miss” is an instance when data requested for processing by software or hardware is not found in the computer’s cache. Cache misses can cause delays as the program or application must then access the data elsewhere. Hill’s 3C model classified these misses into “compulsory misses,” “capacity misses,” and “conflict misses.” The model was influential, as it led to important innovations such as victim caches and stream buffers, and is now a standard concept in computer architecture textbooks.

Many regard Hill’s work in in memory consistency models as his most significant contribution. With his student Sarita Adve, he developed SC for DRF: a consistency model using sequential consistency (SC), where data races can be avoided (data race free, or DRF). Hill’s SC for DRF model has had significant impact for computer architects, especially as multiprocessors became ubiquitous and architects had to reason about which memory consistency model to use in their architectures and implementations. Years after Hill developed SC for DRF, it became the basis of Java and C++ memory models and, more recently, is being used with graphics processing units (GPUs) to understand memory consistency with heterogeneous processors.

Hill’s third major contribution is his work in transactional memory, a technique to minimize blocking due to critical sections. With David Wood he developed the LogTM transactional memory system, one of the first and widely-cited approaches to transactional memory. For the first time, this system enabled transactions to overrun their buffer and cache capacities, making transactions significantly easier for programmers to implement.

Hill (with David Wood and others) also made significant contributions to the evaluation of parallel computers. The Wisconsin Wind Tunnel project, for instance, pioneered fast parallel simulation running on parallel machines. Other important tools Hill has produced to evaluate memory systems and parallel computers include his Dinero cache simulator, as well as the GEMS full system simulator and gem5, which have been cited over 3,000 times by researchers and practitioners. BadgerTrap, one of his most recent tools, studies virtual memory behavior. Hill has also had significant influence on virtual memory implementation. For example, he proposed the idea of “page reservation,” which is now used in Linux.

Hill will be formally recognized with the award at the ACM/IEEE International Symposium on Computer Architecture (ISCA) to be held June 22-26 in Phoenix, Arizona.

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.

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.

ACM named Constantinos Daskalakis and Michael J. Freedman recipients of the ACM Grace Murray Hopper Award.

Daskalakis, a professor at the Massachusetts Institute of Technology, is recognized for his seminal contributions to the theory of computation and economics, particularly the complexity of Nash Equilibrium.

Strategic interaction greatly complicates behavior in socioeconomic environments, from traditional markets and offline social networks to modern technological systems such as online advertising platforms, kidney exchanges, cryptocurrencies, sharing economy applications, and online social networks. To analyze behavior in such strategic environments, economists have long relied on concepts of equilibrium. Daskalakis’s work, with Goldberg and Papadimitriou, has challenged equilibrium theory by showing that Nash equilibrium is computationally intractable and thus unattainable, in general. His work has influenced an ongoing reshaping of the study of strategic behavior, showing that computation must play an essential role in the foundations of game theory and economics. Daskalakis’s more recent work has resolved long-standing open problems in multi-dimensional mechanism design, and advanced several other fields, including machine learning, probability theory and statistics.

Freedman, a professor at Princeton University, is cited for the design and deployment of self-organizing geo-distributed systems.

By introducing new algorithms and protocols, Freedman has shown how to build scalable, performant, and autonomous distributed systems for modern heterogeneous deployments and realistic workloads. Some of Freedman’s most popular systems include CoralCDN, a content distribution infrastructure that has been deployed at hundreds of network sites worldwide and been used by millions of clients to share images, videos and other content; the JetStream system, which employs an innovative approach to data streaming analytics; and TimescaleDB, an open source time series database that provides complex queries at scale on both historical and fresh data. Additionally, in more fundamental research, Freedman and colleagues have demonstrated that theoretically deep cloud systems need not be slow or scale poorly.

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.

Pavel Pevzner, a professor at the University of California San Diego, receives the ACM Paris Kanellakis Theory and Practice Award for pioneering contributions to the theory, design and implementation of algorithms for string reconstruction and to their applications in the assembly of genomes.

Pevzner’s research interests span the field of computational biology, and his work has been guided by tailoring algorithmic ideas to biological problems. The life sciences have been transformed by the ability to rapidly sequence and assemble genomes for organisms from existing and extant species and use these assembled genomes to answer fundamental and applied questions in biology, medicine and other sciences. Pevzner has made fundamental contributions to the theoretical study of string algorithms and to their application to scalable reconstruction of genomes and other biological sequences such as antibodies and antibiotics. Pevzner’s algorithms underlie almost all sequence assemblers used today and were used to reconstruct the vast majority of genomic sequences available in databases.

The ACM Paris Kanellakis Theory and Practice Award honors specific theoretical accomplishments that have had a significant and demonstrable effect on the practice of computing. This award is accompanied by a prize of $10,000 and is endowed by contributions from the Kanellakis family, with additional financial support provided by ACM's Special Interest Groups on Algorithms and Computation Theory (SIGACT), Design Automation (SIGDA), Management of Data (SIGMOD), and Programming Languages (SIGPLAN), the ACM SIG Projects Fund, and individual contributions.

Henry Kautz was honored for contributions to artificial intelligence and computational social science, including fundamental results on the complexity of inference, planning and media analytics for public health.

Beginning with his doctoral dissertation, Kautz, now a professor at the University of Rochester, has studied how computers can infer the goals and plans of people by studying their behavior. He has made a range of fundamental contributions to theory and practice in knowledge representation and reasoning, planning and plan recognition and computational social science. Kautz was one of the pioneers in analyzing the computational complexity of knowledge representation formalisms. He was also a co-developer of the first randomized local search algorithms for Boolean satisfiability testing, which have found practical application in planning, graphical models, and software verification.

In the area of pervasive computing and social media analytics, his trailblazing projects have included a system to help cognitively disabled people find their way by inferring the transportation destinations of selected groups of people; a project that uncovered the central role of air travel in the spread of diseases by analyzing social media data; and an initiative to improve the efficiency of restaurant health inspections by combining social media reports of food poisoning with location data.

The ACM - AAAI Allen Newell Award is presented to an individual selected for career contributions that have breadth within computer science, or that bridge computer science and other disciplines. The Newell award is accompanied by a prize of $10,000, provided by ACM and the Association for the Advancement of Artificial Intelligence (AAAI), and by individual contributions.

ACM named Gerald C. Combs recipient of the ACM Software System Award for for creating the Wireshark network protocol analyzer, an essential tool for nearly anyone who designs, deploys, analyzes and troubleshoots the wide range of network protocols that tie the internet together, and for continued leadership of the international Wireshark developer community.

Combs started Wireshark as an open source project in 1997 under the name Ethereal. The software quickly became the most commonly used system for visually analyzing network protocol traffic. Before the advent of Ethereal and Wireshark, protocol analyzers were expensive, dedicated pieces of hardware that were only available to large institutions. The creation of an open source network protocol analyzer democratized access to network protocol analysis. It also enabled people to learn about network protocols, as they were able to visualize the traffic on their own networks. In addition, Wireshark has also had significant influence on the areas of network engineering and cybersecurity. Engineers who work alongside security experts in financial institutions and other high-profile businesses make extensive use of Wireshark in their ongoing fight against cybercrime.

Combs, who serves as Director of Open Source Projects at Riverbed Technology, has continued to work on the Wireshark code. He spent 20 years guiding the open source community that has developed around the software and leading SharkFest, an annual educational conference focused on sharing knowledge, experience and best practices among the Wireshark developer and user communities.

The ACM Software System Award is presented to an institution or individual(s) recognized for developing a software system that has had a lasting influence, reflected in contributions to concepts, in commercial acceptance, or both. The Software System Award carries a prize of $35,000. Financial support for the Software System Award is provided by IBM.

Robert Sedgewick was named recipient of the Karl V. Karlstrom Outstanding Educator Award for developing classic textbooks and online materials for the study of algorithms, analytic combinatorics, and introductory computer science that have educated generations of students worldwide. Sedgewick is best known for his series of Algorithms textbooks, which have been bestsellers for four decades (12 books in four editions covering five programming languages). The books develop a scientific approach to the study of algorithms, based on experiments with real code to validate hypotheses about performance based on mathematical analysis.

His recent book (with Kevin Wayne), Computer Science: An Interdisciplinary Approach, is a comprehensive introduction to the field and was named by ACM Computing Reviews as “Best of Computing Notable Book” for 2017. His book Analytic Combinatorics (with Philippe Flajolet) is an advanced graduate text that has been recognized with the 2019 Leroy P. Steele Prize for Mathematical Exposition.

More recently, Sedgewick has been extremely active as a pioneer and innovator in online education. He has co-developed extensive online content associated with his books that attract millions of visitors annually. Sedgewick has also recorded over 100 hours of online lectures on programming, computer science, and algorithms that reach hundreds of thousands of people around the world. The Sedgewick-Wayne Algorithms online course has been listed as one of the top 10 Massive Open Online Courses (MOOCs) of all time.

The Karl V. Karlstrom Outstanding Educator Award is presented annually to an outstanding educator who is appointed to a recognized educational baccalaureate institution.  The recipient is recognized for advancing new teaching methodologies; effecting new curriculum development or expansion in Computer Science and Engineering; or making a significant contribution to the educational mission of ACM. Those with 10 years or less teaching experience are given special consideration. A prize of $10,000 is supplied by Pearson Education.

Victor Bahl was named recipient of the ACM Distinguished Service Award for significant and lasting service to the broad community of mobile and wireless networking, and for building strong linkages between academia, industry, and government agencies. His efforts have led to the creation of a prolific global community with a strong foundation that has created leaders and fostered and supported tens of thousands of researchers and engineers worldwide working in these areas. Bahl, a Distinguished Scientist at Microsoft Research, was a co-founder and the driving force behind ACM SIGMobile, ACM’s Special Interest Group dedicated to all things mobile. He created MobiSys, the International Conference on Mobile Systems, Applications and Services, and for nearly two decades has steered MobiCom, the International Conference on Mobile Computing and Networking. Under Bahl’s leadership, both of these conferences have grown to become highly respected international events and publication venues. Also in the vein of publishing and disseminating the best in mobile technology research, Bahl founded GetMobile, a quarterly scientific publication related to wireless communications and mobility, and served as its first editor.

He has been especially active in advancing the field by strengthening ties and fostering better understanding among academics, practitioners, and government officials. For example, he has positively influenced spectrum policies of the US Federal Communications Commission and that of several European, South American and Asian countries, through technology inventions, demonstrations, and technical evangelism involving academia-industry-government collaboration on opportunities around dynamic spectrum sharing, and he has initiated key efforts in mobile computing and wireless networking within the US National Science Foundation.

In the beginning of the last decade, when experimental wireless research was hampered by the lack of realistic hardware and software tools (especially in academia), Bahl led, through Microsoft Research, the creation and free distribution of the Mesh Academic Research Kit, a significant enabler for research in Wi-Fi systems, which has been adopted by more than 1,200 academic institutions worldwide. This toolkit allowed academic researchers to experiment with mesh technologies. He also drove the creation created of the complementary Microsoft Digital Inclusion program that disbursed more than $1.2 million in funding to academic institutions that applied wireless mesh and related technologies to bridge the digital divide in communities around the world. In the early days of cloud computing, Bahl created a research and training program, with tools and services, on cloud-powered mobile computing. Over 60 large universities offered senior and graduate-level courses based on this program.

The ACM Distinguished Service Award is presented on the basis of value and degree of services to the computing community. The contribution should not be limited to service to the Association, but should include activities in other computer organizations and should emphasize contributions to the computing community at large.

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.

Chris Stephenson was named recipient of the Outstanding Contribution to ACM Award for advancing CS education by architecting and nurturing the Computer Science Teachers Association to incorporate more than 22,000 K-12 CS educators and partners into the ACM community.

Central to Stephenson’s vision has been the idea that advancing computing as a professional field requires K-12 students to be introduced to computer science by educators with the tools, training, knowledge, and confidence both to teach the subject matter and to inspire students with their passion. She also has been guided by the ideas that K-12 teachers will be more effective in the classroom if actively engaged with other members of the professional computing community, and that K-12 computer science education is more effective when it is informed by academic research and industry expertise.

To realize her vision, Stephenson founded the Computer Science Teachers Association (CSTA) in 2004, with the support of ACM, and thereafter became its founding Executive Director. During her 10 years leading CSTA, she grew the organization to include 20,000 members around the world and 60 regional chapters.

Her scholarly research contributions were disseminated in several influential reports including: Bringing Computational Thinking to K-12: What Is Involved and What Is the Role of the Computer Science Education Community?; the inaugural CSTA K-12 Computer Science Standards; Running on Empty: The Failure to Teach K–12 Computer Science in the Digital Age; and Bugs in the System: Computer Science Teacher Certification in the U.S. These reports have led to projects, initiatives, and policy changes that have deeply and positively impacted K-12 education and educators globally.

Now Head of Computer Science Education Strategy at Google, Stephenson has continued her work with ACM education initiatives. She is currently a member of the ACM Education Board, where she has been actively engaged in developing curricular materials to meet the needs of computer science educators and students, both in the US and abroad. She also recently co-chaired the Board’s retention in undergraduate computer science task force.

The Outstanding Contribution to ACM Award recognizes outstanding service contributions to the Association. Candidates are selected based on the value and degree of service overall, and may be given to up to three individuals each year.

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.

ACM named Elisa Bertino of Purdue University the 2019-2020 ACM Athena Lecturer for pioneering and impactful contributions to data management, security, and privacy, along with outstanding contributions to broadening participation in computing via professional leadership and mentoring. Bertino is recognized as one of the top data management and data security experts in the world, and has made contributions to data security and privacy in many different contexts, including context-based access control; digital identity management; data integrity; Internet of Things and sensor network security; secure and privacy-preserving provenance; privacy-preserving analytics; protection from insider threats; and cloud security. Through these efforts, she provided formal foundations and implementations of mechanisms that have become commonplace in industrial products. Bertino is also an outstanding educator and mentor who has been especially active in encouraging young women to pursue careers in computing.

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 is invited to present a lecture at an ACM event. Bertino chose to give her Athena Lecture at the ACM Conference on Data Application, Security and Privacy (CODASPY 2019) in Dallas, Texas.

“There are few issues more important to the computing field, and the broader society, than cybersecurity,” said ACM President Cherri M. Pancake. “However, modern cybersecurity approaches need to take into account the way we live now. Elisa Bertino has made fundamental contributions that allow people access to systems based on their roles, the time of day, as well as their locations. These contributions are especially significant because of the mobile revolution and Internet of Things—as well as the fact that systems can be attacked from anywhere in the world. Beyond her extensive research contributions, Bertino has had a lasting impact on the field through her mentorship of younger colleagues.”

Security Access Control Based on Time and Location

In the computer security field, role-based access control (RBAC) allows only authorized users to access a system. Bertino was a trailblazer in extending RBAC controls to take contextual information into account, including time and space considerations. Her 2001 paper T-RBAC: A temporal role-based access control model, co-authored with Piero Andrea Bonatti and Elena Ferrari, outlined how access to a system could be made available at certain times and unavailable at others. The paper has been cited more than 1,000 times and transformed the design of security systems developed by industry.

In 2007 Bertino and co-authors made another significant contribution to role-based access control in the paper GEO-RBAC: A Spatially Aware RBAC, in which the access depends on user location. Bertino’s GEO-RBAC model was introduced before mobile computing became ubiquitous, and has become an essential component of most security systems.

Bertino has made several other contributions to access control models and enforcement mechanisms, including original contributions to privacy-aware access control, attribute-based access control, encryption-based access control for data on the cloud and tools for policy analysis.

Security of Cellular Networks

Bertino’s pioneering work on the security of cellular networks is exemplified in her recent paper, LTEInspector: A Systematic Approach for Adversarial Testing of 4G, which introduced a model-based testing approach to investigate the security and privacy of the 4G LTE protocol. Bertino and colleagues uncovered 10 new, as well as nine prior, attacks. Security experts see the LTEInpsector approach as an important tool in securing 4G as well as 5G networks. For this recent work, Bertino was named to the GSMA Mobile Security Research Hall of Fame.

Professional Leadership and Mentoring

In the spirit of the Athena Award, Bertino has been a strong advocate and mentor for women. For example, 16 of the 35 PhD students she has mentored are women, and five of the PhD students currently working in her lab are women. To address the ongoing gender imbalance in the cybersecurity field, Bertino recently co-founded (with Danfeng Yao) the Workshop for Women in Cybersecurity (CyberW).

Biographical Background

Elisa Bertino is the Samuel Conte Professor of Computer Science at Purdue University, where she also heads the Cyber Space Security Lab. She held positions in industry, including the IBM Almaden Research Center, and academia, most notably at the University of Milan, before joining Purdue University in 2004. Bertino received her Dr degree in Computer Science from the University of Pisa.

Bertino is a Fellow of ACM, IEEE and AAAS, and has received several awards and honors, including the IEEE Computer Society Technical Achievement Award, the Tsutomu Kanai Award, and the ACM SIGSAC Outstanding Contributions Award.

The ACM Athena Lecturer Award is named after Athena, the Greek goddess of wisdom. With her knowledge and sense of purpose, Athena epitomizes the strength, determination, and intelligence of the “Athena Lecturers.” 

Bertino will formally receive the Athena Lecturer 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.