About ACM A.M. Turing Award

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

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

New York, NY, April 10, 2024 – ACM, the Association for Computing Machinery, today named Avi Wigderson as recipient of the 2023 ACM A.M. Turing Award for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science.

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

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize with financial support provided by Google, Inc. The award is named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing.

What is Theoretical Computer Science?

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

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

Why is Randomness Important?

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

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

Wigderson’s Contributions

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

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

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

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

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

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

Mentoring

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

Robert Melancton Metcalfe is Emeritus Professor of Electrical and Computer Engineering (ECE) after 11 years at The University of Texas at Austin. He has recently become a Research Affiliate in Computational Engineering at his alma mater, the Massachusetts Institute of Technology (MIT) Computer Science & Artificial Intelligence Laboratory (CSAIL). Metcalfe graduated from MIT in 1969 with Bachelor degrees in Electrical Engineering and Industrial Management. He earned a Master’s degree in Applied Mathematics in 1970 and a PhD in Computer Science in 1973 from Harvard University.

Metcalfe’s honors include the National Medal of Technology, IEEE Medal of Honor, Marconi Prize, Japan Computer & Communications Prize, ACM Grace Murray Hopper Award, and IEEE Alexander Graham Bell Medal. He is a Fellow of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the National Inventors, Consumer Electronics, and Internet Halls of Fame.

Jack J. Dongarra has been a University Distinguished Professor at the University of Tennessee and a Distinguished Research Staff Member at the Oak Ridge National Laboratory since 1989. He has also served as a Turing Fellow at the University of Manchester (UK) since 2007. Dongarra earned a B.S. in Mathematics from Chicago State University, an M.S. in Computer Science from the Illinois Institute of Technology, and a Ph.D. in Applied Mathematics from the University of New Mexico.

Dongarra’s honors include the IEEE Computer Pioneer Award, the SIAM/ACM Prize in Computational Science and Engineering, and the ACM/IEEE Ken Kennedy Award. He is a Fellow of ACM, the Institute of Electrical and Electronics Engineers (IEEE), the Society of Industrial and Applied Mathematics (SIAM), the American Association for the Advancement of Science (AAAS), the International Supercomputing Conference (ISC), and the International Engineering and Technology Institute (IETI). He is a member of the National Academy of Engineering and a foreign member of the British Royal Society.

ACM named Jack J. Dongarra recipient of the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software to keep pace with exponential hardware improvements for over four decades. Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee. He also holds appointments with Oak Ridge National Laboratory and the University of Manchester.

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize, with financial support provided by Google, Inc. It is named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing.

Dongarra has led the world of high-performance computing through his contributions to efficient numerical algorithms for linear algebra operations, parallel computing programming mechanisms, and performance evaluation tools. For nearly forty years, Moore’s Law produced exponential growth in hardware performance. During that same time, while most software failed to keep pace with these hardware advances, high performance numerical software did—in large part due to Dongarra’s algorithms, optimization techniques, and production-quality software implementations.

These contributions laid a framework from which scientists and engineers made important discoveries and game-changing innovations in areas including big data analytics, healthcare, renewable energy, weather prediction, genomics, and economics, to name a few. Dongarra’s work also helped facilitate leapfrog advances in computer architecture and supported revolutions in computer graphics and deep learning.

Dongarra’s major contribution was in creating open-source software libraries and standards which employ linear algebra as an intermediate language that can be used by a wide variety of applications. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. Dongarra’s libraries also introduced many important innovations including autotuning, mixed precision arithmetic, and batch computations.

As a leading ambassador of high-performance computing, Dongarra led the field in persuading hardware vendors to optimize these methods, and software developers to target his open-source libraries in their work. Ultimately, these efforts resulted in linear algebra-based software libraries achieving nearly universal adoption for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers. These libraries were essential in the growth of the field—allowing progressively more powerful computers to solve computationally challenging problems.

“Today’s fastest supercomputers draw headlines in the media and excite public interest by performing mind-boggling feats of a quadrillion calculations in a second,” explains ACM President Gabriele Kotsis. “But beyond the understandable interest in new records being broken, high performance computing has been a major instrument of scientific discovery. HPC innovations have also spilled over into many different areas of computing and moved our entire field forward. Jack Dongarra played a central part in directing the successful trajectory of this field. His trailblazing work stretches back to 1979, and he remains one of the foremost and actively engaged leaders in the HPC community. His career certainly exemplifies the Turing Award’s recognition of ‘major contributions of lasting importance.’”

“Jack Dongarra's work has fundamentally changed and advanced scientific computing,” said Jeff Dean, Google Senior Fellow and SVP of Google Research and Google Health. “His deep and important work at the core of the world's most heavily used numerical libraries underlie every area of scientific computing, helping advance everything from drug discovery to weather forecasting, aerospace engineering and dozens more fields, and his deep focus on characterizing the performance of a wide range of computers has led to major advances in computer architectures that are well suited for numeric computations.”

Dongarra will be formally presented with the ACM A.M. Turing Award at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

SELECT TECHNICAL CONTRIBUTIONS

For over four decades, Dongarra has been the primary implementor or principal investigator for many libraries such as LINPACK, BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA, and SLATE. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. His software libraries are used, practically universally, for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers.

These libraries embody many deep technical innovations such as:

Autotuning: through his 2016 Supercomputing Conference Test of Time award-winning ATLAS project, Dongarra pioneered methods for automatically finding algorithmic parameters that produce linear algebra kernels of near-optimal efficiency, often outperforming vendor-supplied codes.

Mixed precision arithmetic: In his 2006 Supercomputing Conference paper, “Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy,” Dongarra pioneered harnessing multiple precisions of floating-point arithmetic to deliver accurate solutions more quickly. This work has become instrumental in machine learning applications, as showcased recently in the HPL-AI benchmark, which achieved unprecedented levels of performance on the world’s top supercomputers.

Batch computations: Dongarra pioneered the paradigm of dividing computations of large dense matrices, which are commonly used in simulations, modeling, and data analysis, into many computations of smaller tasks over blocks that can be calculated independently and concurrently. Based on his 2016 paper, “Performance, design, and autotuning of batched GEMM for GPUs,” Dongarra led the development of the Batched BLAS Standard for such computations, and they also appear in the software libraries MAGMA and SLATE.

Dongarra has collaborated internationally with many people on the efforts above, always in the role of the driving force for innovation by continually developing new techniques to maximize performance and portability while maintaining numerically reliable results using state of the art techniques. Other examples of his leadership include the Message Passing Interface (MPI) the de-facto standard for portable message-passing on parallel computing architectures, and the Performance API (PAPI), which provides an interface that allows collection and synthesis of performance from components of a heterogeneous system. The standards he helped create, such as MPI, the LINPACK Benchmark, and the Top500 list of supercomputers, underpin computational tasks ranging from weather prediction to climate change to analyzing data from large scale physics experiments.

ACM named Alfred Vaino Aho and Jeffrey David Ullman recipients of the 2020 ACM A.M. Turing Award for fundamental algorithms and theory underlying programming language implementation and for synthesizing these results and those of others in their highly influential books, which educated generations of computer scientists. Aho is the Lawrence Gussman Professor Emeritus of Computer Science at Columbia University. Ullman is the Stanford W. Ascherman Professor Emeritus of Computer Science at Stanford University.

Computer software powers almost every piece of technology with which we interact. Virtually every program running our world—from those on our phones or in our cars to programs running on giant server farms inside big web companies—is written by humans in a higher-level programming language and then compiled into lower-level code for execution. Much of the technology for doing this translation for modern programming languages owes its beginnings to Aho and Ullman.

Beginning with their collaboration at Bell Labs in 1967 and continuing for several decades, Aho and Ullman have shaped the foundations of programming language theory and implementation, as well as algorithm design and analysis. They made broad and fundamental contributions to the field of programming language compilers through their technical contributions and influential textbooks. Their early joint work in algorithm design and analysis techniques contributed crucial approaches to the theoretical core of computer science that emerged during this period.

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize, with financial support provided by Google, Inc. It is named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing.

“The practice of computer programming and the development of increasingly advanced software systems underpin almost all of the technological transformations we have experienced in society over the last five decades,” explains ACM President Gabriele Kotsis. “While countless researchers and practitioners have contributed to these technologies, the work of Aho and Ullman has been especially influential. They have helped us to understand the theoretical foundations of algorithms and to chart the course for research and practice in compilers and programming language design. Aho and Ullman have been thought leaders since the early 1970s, and their work has guided generations of programmers and researchers up to the present day.”

“Aho and Ullman established bedrock ideas about algorithms, formal languages, compilers and databases, which were instrumental in the development of today’s programming and software landscape,” added Jeff Dean, Google Senior Fellow and SVP, Google AI. “They have also illustrated how these various disciplines are closely interconnected. Aho and Ullman introduced key technical concepts, including specific algorithms, that have been essential. In terms of computer science education, their textbooks have been the gold standard for training students, researchers, and practitioners.”

A Longstanding Collaboration

Aho and Ullman both earned their PhD degrees at Princeton University before joining Bell Labs, where they worked together from 1967 to 1969. During their time at Bell Labs, their early efforts included developing efficient algorithms for analyzing and translating programming languages.

In 1969, Ullman began a career in academia, ultimately joining the faculty at Stanford University, while Aho remained at Bell Labs for 30 years before joining the faculty at Columbia University. Despite working at different institutions, Aho and Ullman continued their collaboration for several decades, during which they co-authored books and papers and introduced novel techniques for algorithms, programming languages, compilers and software systems.

Influential Textbooks

Aho and Ullman co-authored nine influential books (including first and subsequent editions). Two of their most widely celebrated books include:

The Design and Analysis of Computer Algorithms (1974)
Co-authored by Aho, Ullman, and John Hopcroft, this book is considered a classic in the field and was one of the most cited books in computer science research for more than a decade. It became the standard textbook for algorithms courses throughout the world when computer science was still an emerging field. In addition to incorporating their own research contributions to algorithms, The Design and Analysis of Computer Algorithms introduced the random access machine (RAM) as the basic model for analyzing the time and space complexity of computer algorithms using recurrence relations. The RAM model also codified disparate individual algorithms into general design methods. The RAM model and general algorithm design techniques introduced in this book now form an integral part of the standard computer science curriculum.

Principles of Compiler Design (1977)
Co-authored by Aho and Ullman, this definitive book on compiler technology integrated formal language theory and syntax-directed translation techniques into the compiler design process. Often called the “Dragon Book” because of its cover design, it lucidly lays out the phases in translating a high-level programming language to machine code, modularizing the entire enterprise of compiler construction. It includes algorithmic contributions that the authors made to efficient techniques for lexical analysis, syntax analysis techniques, and code generation. The current edition of this book, Compilers: Principles, Techniques and Tools (co-authored with Ravi Sethi and Monica Lam), was published in 2007 and remains the standard textbook on compiler design.

Biographical Background

Alfred Vaino Aho
Alfred Aho is the Lawrence Gussman Professor Emeritus at Columbia University. He joined the Department of Computer Science at Columbia in 1995. Prior to Columbia, Aho was Vice President of Computing Sciences Research at Bell Laboratories where he worked for more than 30 years. A graduate of the University of Toronto, Aho earned his Master’s and PhD degrees in Electrical Engineering/Computer Science from Princeton University.

Aho’s honors include the IEEE John von Neumann Medal and the NEC C&C Foundation C&C Prize. He is a member of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the Royal Society of Canada. He is a Fellow of ACM, IEEE, Bell Labs, and the American Association for the Advancement of Science.

Jeffrey David Ullman
Jeffrey Ullman is the Stanford W. Ascherman Professor Emeritus at Stanford University and CEO of Gradiance Corporation, an online learning platform for various computer science topics. He joined the faculty at Stanford in 1979. Prior to Stanford, he served on the faculty of Princeton University from 1969 to 1979, and was a member of the technical staff at Bell Labs from 1966 to 1969. A graduate of Columbia University, Ullman earned his PhD in Computer Science from Princeton University.

Ullman’s honors include receiving the IEEE John von Neumann Medal, the NEC C&C Foundation C&C Prize, the Donald E. Knuth Prize, and the ACM Karl V. Karlstrom Outstanding Educator Award. He is a member of the US National Academy of Engineering, the National Academy of Sciences, and the American Academy of Arts and Sciences, and is an ACM Fellow.

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

Beginning with their collaboration at Bell Labs in 1967 and continuing for several decades, Aho and Ullman have shaped the foundations of programming language theory and implementation, as well as algorithm design and analysis. They made broad and fundamental contributions to the field of programming language compilers through their technical contributions and influential textbooks. Their early joint work in algorithm design and analysis techniques contributed crucial approaches to the theoretical core of computer science that emerged during this period.

Aho and Ullman both earned their PhD degrees at Princeton University before joining Bell Labs, where they worked together from 1967 to 1969. During their time at Bell Labs, their early efforts included developing efficient algorithms for analyzing and translating programming languages.

In 1969, Ullman began a career in academia, ultimately joining the faculty at Stanford University, while Aho remained at Bell Labs for 30 years before joining the faculty at Columbia University. Despite working at different institutions, Aho and Ullman continued their collaboration for several decades, during which they co-authored books and papers and introduced novel techniques for algorithms, programming languages, compilers and software systems.

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

Aho's honors include the IEEE John von Neumann Medal and the NEC C&C Foundation C&C Prize. He is a member of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the Royal Society of Canada. He is a Fellow of ACM, IEEE, Bell Labs, and the American Association for the Advancement of Science.

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

Beginning with their collaboration at Bell Labs in 1967 and continuing for several decades, Aho and Ullman have shaped the foundations of programming language theory and implementation, as well as algorithm design and analysis. They made broad and fundamental contributions to the field of programming language compilers through their technical contributions and influential textbooks. Their early joint work in algorithm design and analysis techniques contributed crucial approaches to the theoretical core of computer science that emerged during this period.

Aho and Ullman both earned their PhD degrees at Princeton University before joining Bell Labs, where they worked together from 1967 to 1969. During their time at Bell Labs, their early efforts included developing efficient algorithms for analyzing and translating programming languages.

In 1969, Ullman began a career in academia, ultimately joining the faculty at Stanford University, while Aho remained at Bell Labs for 30 years before joining the faculty at Columbia University. Despite working at different institutions, Aho and Ullman continued their collaboration for several decades, during which they co-authored books and papers and introduced novel techniques for algorithms, programming languages, compilers and software systems.

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

Ullman’s honors include receiving the IEEE John von Neumann Medal, the NEC C&C Foundation C&C Prize, the Donald E. Knuth Prize, and the ACM Karl V. Karlstrom Outstanding Educator Award. He is a member of the US National Academy of Engineering, the National Academy of Sciences, and the American Academy of Arts and Sciences, and is an ACM Fellow.

Jeffrey David Ullman is the Stanford W. Ascherman Professor Emeritus at Stanford University and CEO of Gradiance Corporation, an online learning platform for various computer science topics. He joined the faculty at Stanford in 1979. Prior to Stanford, he served on the faculty of Princeton University from 1969 to 1979, and was a member of the technical staff at Bell Labs from 1966 to 1969. A graduate of Columbia University, Ullman earned his PhD in Computer Science from Princeton University.

Ullman’s honors include receiving the IEEE John von Neumann Medal, the NEC C&C Foundation C&C Prize, the Donald E. Knuth Prize, and the ACM Karl V. Karlstrom Outstanding Educator Award. He is a member of the US National Academy of Engineering, the National Academy of Sciences, and the American Academy of Arts and Sciences, and is an ACM Fellow.

Alfred Vaino Aho is the Lawrence Gussman Professor Emeritus at Columbia University. He joined the Department of Computer Science at Columbia in 1995. Prior to Columbia, Aho was Vice President of Computing Sciences Research at Bell Laboratories where he worked for more than 30 years. A graduate of the University of Toronto, Aho earned his Master’s and PhD degrees in Electrical Engineering/Computer Science from Princeton University.

Aho's honors include the IEEE John von Neumann Medal and the NEC C&C Foundation C&C Prize. He is a member of the US National Academy of Engineering, the American Academy of Arts and Sciences, and the Royal Society of Canada. He is a Fellow of ACM, IEEE, Bell Labs, and the American Association for the Advancement of Science.

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.

Stanford University Professor Patrick M. (Pat) Hanrahan was named co-recipient of the 2019 ACM A.M. Turing Award along with Edwin E. (Ed) Catmull 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.

One of Catmull’s first hires at Pixar was Pat Hanrahan. Hanrahan had received a PhD in BioPhysics from the University of Wisconsin-Madison in 1985 and had worked briefly at NYIT’s Computer Graphics Laboratory before joining Pixar.

Working with Catmull and other members of the Pixar team, Hanrahan was the lead architect of a new kind of graphics system, which allowed curved shapes to be rendered with realistic material properties and lighting. A key idea in this system, later named RenderMan, was shaders (used to shade CGI images). RenderMan’s functions separated the light reflection behavior from the geometric shapes, and computed the color, transparency, and texture at points on the shapes. The RenderMan system also incorporated the Z-buffering and subdivision surface innovations that Catmull had earlier contributed to the field.

During his time at Pixar, Hanrahan also developed techniques for volume rendering, which allows a CGI artist to render a 2-D projection of a 3-D data set, such as a puff of smoke. In one of his most cited papers, Hanrahan, with co-author Marc Levoy, introduced light field rendering, a method for giving the viewer the sense that they are flying through scenes by generating new views from arbitrary points without depth information or feature matching. Hanrahan went on to develop techniques for portraying skin and hair using subsurface scattering, and for rendering complex lighting effects—so-called global illumination or GI—using Monte Carlo ray tracing.

Hanrahan published his RenderMan research in a seminal 1990 paper that was presented at ACM SIGGRAPH. It would take five more years, however, for the computing hardware to develop to a point where the full-length 3-D computer animated movie Toy Story could be produced using Hanrahan’s RenderMan system.

After he left Pixar in 1989, Hanrahan held academic posts at Princeton and Stanford universities. Beginning in the 1990s, he and his students extended the RenderMan shading language to work in real time on powerful GPUs that began to enter into the marketplace. The programming languages for GPUs that Hanrahan and his students developed led to the development of commercial versions (including the OpenGL shading language) that revolutionized the writing of video games.

Edwin E. (Ed) Catmull is co-founder of Pixar Animation Studios and a former President of Pixar and Walt Disney Animation Studios. He earned Bachelor of Science degrees in Physics and Computer Science (1970) and a PhD in Computer Science (1974) from the University of Utah. During his career, Catmull was Vice President of the Computer Division of Lucasfilm Ltd., where he managed development in areas of computer graphics, video editing, video games and digital audio. He founded the Computer Graphics Lab at the New York Institute of Technology.

Catmull received the 1993 ACM SIGGRAPH Steven A. Coons Award for Outstanding Creative Contributions to Computer Graphics, and the 2006 IEEE John von Neumann Medal for fundamental contributions to computer graphics and a pioneering use of computer animation in motion pictures. He is a Fellow of ACM and of the Visual Effect Society. He is a member of the Academy of Motion Picture Arts & Sciences and of the National Academy of Engineering.

Patrick M. (Pat) Hanrahan is the CANON Professor of Computer Science and Electrical Engineering in the Computer Graphics Laboratory at Stanford University. He received a Bachelor of Science degree in Nuclear Engineering (1977) and a PhD in Biophysics (1985) from the University of Wisconsin-Madison. He held positions at the New York Institute of Technology and Digital Equipment Corporation in the 1980s before serving as a Senior Scientist at Pixar (1986-1989). He later served as an Associate Professor at Princeton University (1991-1994) and Professor at Stanford University (1994-present), where he has advised more than 40 PhD students. Hanrahan co-founded Tableau Software, a data analytics company that was acquired by Salesforce in August 2019.

Hanrahan’s many honors include the 2003 ACM SIGGRAPH Steven A. Coons Award for Outstanding Creative Contributions to Computer Graphics. He is a Fellow of ACM and of the American Academy of Arts & Sciences. He is a member of the National Academy of Engineering, in addition to induction into many other prestigious organizations.

Yoshua Bengio is a Professor at the University of Montreal, and the Scientific Director of both Mila (Quebec’s Artificial Intelligence Institute) and IVADO (the Institute for Data Valorization). He is Co-director (with Yann LeCun) of CIFAR's Learning in Machines and Brains program. Bengio received a Bachelor’s degree in electrical engineering, a Master’s degree in computer science and a Doctoral degree in computer science from McGill University.

Bengio’s honors include being named an Officer of the Order of Canada, Fellow of the Royal Society of Canada and the Marie-Victorin Prize. His work in founding and serving as Scientific Director of the Quebec Artificial Intelligence Institute (Mila) is also recognized as a major contribution to the field. Mila, an independent nonprofit organization, now counts 300 researchers and 35 faculty members among its ranks. It is the largest academic center for deep learning research in the world, and has helped put Montreal on the map as a vibrant AI ecosystem, with research labs from major companies as well as AI startups.

Geoffrey Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of The Vector Institute and a University Professor Emeritus at the University of Toronto. Hinton received a Bachelor’s degree in experimental psychology from Cambridge University and a Doctoral degree in artificial intelligence from the University of Edinburgh. He was the founding Director of the Neural Computation and Adaptive Perception (later Learning in Machines and Brains) program at CIFAR.

Hinton’s honors include Companion of the Order of Canada (Canada’s highest honor), Fellow of the Royal Society (UK), foreign member of the National Academy of Engineering (US), the International Joint Conference on Artificial Intelligence (IJCAI) Award for Research Excellence, the NSERC Herzberg Gold medal, and the IEEE James Clerk Maxwell Gold medal. He was also selected by Wired magazine for “The Wired 100—2016’s Most Influential People” and by Bloomberg for the 50 people who changed the landscape of global business in 2017.

Yann LeCun is Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and VP and Chief AI Scientist at Facebook. He received a Diplôme d'Ingénieur from the Ecole Superieure d'Ingénieur en Electrotechnique et Electronique (ESIEE), and a PhD in computer science from Université Pierre et Marie Curie.

His honors include being a member of the US National Academy of Engineering; Doctorates Honoris Causa, from IPN Mexico and École Polytechnique Fédérale de Lausanne (EPFL); the Pender Award, University of Pennsylvania; the Holst Medal, Technical University of Eindhoven & Philips Labs; the Nokia-Bell Labs Shannon Luminary Award; the IEEE PAMI Distinguished Researcher Award; and the IEEE Neural Network Pioneer Award. He was also selected by Wired magazine for “The Wired 100—2016’s Most Influential People” and its “25 Geniuses Who are Creating the Future of Business.” LeCun was the founding director of the NYU Center of Data Science, and is a Co-director (with Yoshua Bengio) of CIFAR's Learning in Machines and Brains program. LeCun is also a co-founder and former Member of the Board of the Partnership on AI, a group of companies and nonprofits studying the societal consequences of AI.

Whitfield Diffie , former Chief Security Officer of Sun Microsystems and Martin E. Hellman, Professor Emeritus of Electrical Engineering at Stanford University, are the recipients of the 2015 ACM A.M. Turing Award, for critical contributions to modern cryptography. The ability for two parties to communicate privately over a secure channel is fundamental for billions of people around the world. On a daily basis, individuals establish secure online connections with banks, e-commerce sites, email servers and the cloud. Diffie and Hellman’s groundbreaking 1976 paper, “New Directions in Cryptography,” introduced the ideas of public-key cryptography and digital signatures, which are the foundation for most regularly-used security protocols on the Internet today. The Diffie-Hellman Protocol protects daily Internet communications and trillions of dollars in financial transactions.