Confirmed Speakers

Scott Aaronson

Scott Aaronson is David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Before coming to UT Austin, he spent nine years as a professor in Electrical Engineering and Computer Science at MIT. Aaronson's research in theoretical computer science has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation’s Alan T. Waterman Award, the United States PECASE Award, and the Tomassoni-Chisesi Prize in Physics.

Srinivasan Arunachalam

Srinivasan Arunachalam is a Research Staff Member in IBM Research. Before joining IBM, Srinivasan was a postdoctoral researcher at MIT, hosted by Aram Harrow. Srinivasan received his PhD from Centrum Wiskunde & Informatica and QuSoft, Netherlands, supervised by Ronald de Wolf, after his M.Math degree from University of Waterloo and Institute of Quantum computing, Canada, supervised by Michele Mosca. His research interests include quantum algorithms, quantum complexity theory and Analysis of Boolean functions.

Yuliy Baryshnikov

Yuliy Baryshnikov graduated as an applied mathematician from MIIT in Moscow, then worked till 1990 at the Institute for Control Sciences.

He spent the next decade in Europe, first as Alexander von Humboldt research fellow, then as a Habilitandedstipendiat of the DFG, and, finally, as a professor at the Math department of UVSQ in France.

In 2001 Yuliy joined Bell Labs (then at Lucent Technologies), first as a Member of technical staff, later as a department head. He moved to the University of Illinois in 2011, where he is a Professor in Mathematics and Electrical and Computing Engineering.

His areas of interest include probability, dynamical systems, singularities, analytic combinatorics, social choice, and, lately, applied topology.

Charles H. Bennett

Charles H. Bennett is a physicist and information theorist at IBM's Research Division, best known for his work on the physics of information processing, including the thermodynamics of computation, the Maxwell's demon problem, quantum cryptography, quantum computing, quantum teleportation and quantum channel capacity. Lately he has become interested in the application of quantum information and computational complexity theory to problems in cosmology. He is a Fellow of the American Physical Society, and a member of the National Academy of Sciences. In 2020 he delivered the (virtual) Shannon Lecture.

Isaac Chuang

Prof. Chuang is a pioneer in the field of quantum information science. His experimental realization of two, three, five, and seven quantum bit quantum computers using nuclear spins in molecules provided the first laboratory demonstrations of many important quantum algorithms, including Shor's quantum factoring algorithm. The error correction, algorithmic cooling, and entanglement manipulation techniques he developed provide new ways to obtain complete quantum control over light and matter, and lay a foundation for possible large-scale quantum information processing systems.
Prof. Chuang came to MIT in 2000 from IBM, where he was a research staff member. He received his doctorate in Electrical Engineering from Stanford University, where he was a Hertz Foundation Fellow. Prof. Chuang also holds two bachelors and one masters degrees in Physics and Electrical Engineering from MIT, and was a post-doctoral fellow at Los Alamos National Laboratory and the University of California at Berkeley. He is the author, together with Michael Nielsen, of the textbook Quantum Computation and Quantum Information.

Petros Drineas

Petros Drineas is a Professor at the Computer Science Department of Purdue University. He earned a PhD in Computer Science from Yale University in 2003 and a BS in Computer Engineering and Informatics from the University of Patras, Greece, in 1997. From 2003 until 2016, Prof. Drineas was an Assistant (until 2009) and then an Associate Professor at Rensselaer Polytechnic Institute. His research interests lie in the design and analysis of randomized algorithms for linear algebraic problems, as well as their applications to the analysis of modern, massive datasets, with a particular emphasis on the analysis of population genetics data. Prof. Drineas is the recipient of an Outstanding Early Research Award from Rensselaer Polytechnic Institute as well as an NSF CAREER award. He was a Visiting Professor at the US Sandia National Laboratories during the fall of 2005, a Visiting Fellow at the Institute for Pure and Applied Mathematics at the University of California, Los Angeles in the fall of 2007, a long-term visitor at the Simons Institute for the Theory of Computing at the University of California Berkeley in the fall of 2013, and has also worked for industrial labs (e.g., Yahoo Labs and Microsoft Research). From October 2010 to December 2011, he served the US National Science Foundation as a Program Director in the Information and Intelligent Systems (IIS) Division and the Computing and Communication Foundations (CCF) Division. Prof. Drineas has published over 130 papers (cited approx. 9,000 times) in Theoretical Computer Science, Applied Mathematics, and Population Genetics venues, including the Proceedings of the National Academy of Sciences, PLOS Genetics, Genome Research, the Journal of Medical Genetics, PLoS One, the Annals of Human Genetics, etc.Prof. Drineas has presented keynote talks and tutorials in major conferences (e.g., SIAM ALA, KDD, VLDB, SDM, etc.) and over 90 invited colloquia and seminar presentations in the US and Europe. He received two fellowships from the European Molecular Biology Organization for his work in population genetics and his research has been featured in various popular press articles, including SIAM News, LiveScience, ScienceDaily, Scitizen, the National Geographic, Yahoo! News, etc. Prof. Drineas has co-organized the widely attended Workshops on Algorithms for Modern Massive Datasets held bi-annually from 2006 to 2016 and is an editor of the SIAM Journal on Matrix Analysis and Applications (SIMAX), the SIAM Journal on Scientific Computing (SISC), the Applied and Computational Harmonic Analysis (ACHA) journal, and PLoS One.

Nicolas Gisin

Prof. Nicolas Gisin was born in Geneva, Switzerland, in 1952. His interests cover a wide range of topics, from the foundations of quantum physics and philosophy, to applications in quantum communications. He has authored a popular book on Quantum Chance and Non-locality and is a co-founder of the company IDQ.

https://www.unige.ch/gap/qic/gisin/

Aram Harrow

Aram Wettroth Harrow is an Associate Professor of Physics in the Massachusetts Institute of Technology's Center for Theoretical Physics.Harrow works in quantum information science and quantum computing. Together with Avinatan Hassidim and Seth Lloyd, he designed a quantum algorithm for linear systems of equations, which in some cases exhibits an exponential advantage over the best classical algorithms. The algorithm has wide application in quantum machine learning.He is a steering committee member of Quantum Information Processing (QIP), the largest annual conference in the field of quantum computing. Harrow is a co-administrator of SciRate, a free and open access scientific collaboration network. He also co-runs a blog, The Quantum Pontiff. His collaborators include Peter Shor and Charles H. Bennett.

Alexander Holevo

Alexander S. Holevo graduated from the Moscow Institute of Physics and Technology in 1966, diploma in ``Applied mathematics and computer science''. From 1969 on he works at the Steklov Mathematical Institute, Moscow. At present he is Head of Department of Probability and Mathematical Statistics at this Institute. Candidate of phys-math sciences — 1969, Doctor of phys-math sciences — 1975, Professor — 1986. Elected Full Member of the Russian Academy of Sciences in 2019. Awards: A. A. Markov Prize, Russian Academy of Sciences for the series of works on the noncommutative probability theory — 1997; Awards of the Russian Academy of Sciences for the best research achievements in 1992, 1995, 2008, 2015; International ``Quantum Communication Award'' — 1996; A. von Humboldt Research Award — 1999; IEEE IT Claude E. Shannon Award -- 2016.

Gil Kalai

Gil Kalai was born in Tel Aviv and studied at the Hebrew University of Jerusalem. Kalai is the Henry and Manya Noskwith Professor of Mathematics at the Hebrew University of Jerusalem, and a Professor of Computer Science at IDC, Herzliya. He has a long term visiting position at Yale University.
Kalai's main research areas are Combinatorics, Convexity and their applications. Kalai is known for his work on face numbers and diameter of polytopes, Helly-type theorems, and for finding subexponential versions of the simplex algorithm. An influential 1988 paper by Kahn, Kalai and Linial on Boolean functions gave an early application of Fourier analysis in theoretical computer science, and subsequently Kalai has applied Fourier methods to the study of random graphs, percolation, noise, social choice, and other areas. In 1993, Kalai and Kahn found a geometric object in 1325 dimensions that disproved the famous Borsuk Conjecture of 1933. Since 2005 he has studied noisy quantum computation. Kalai's writes a blog "Combinatorics and More" http://gilkalai.wordpress.com/
Kalai is the recipient of the 1992 Polya Prize, the 1993 Erdos Prize, the 1994 Fulkerson Prize, and the 2012 Rothschild Prize. He is a member of the Center for the Study of Rationality as well as the Center for Quantum Information Science at the Hebrew University and the Center for the Foundation of Cryptography at IDC. He is a member of the Israeli, European and Hungarian Academies of Science and was a plenary speaker in the 2016 European Congress of Mathematics and the 2018 International Congress of Mathematicians.

Michael Mahoney

Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council's Committee on the Analysis of Massive Data, he co-organized the Simons Institute's fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute's 2016 PCMI Summer Session on The Mathematics of Data, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He is currently the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

Ilya Nemenman

After graduating from Princeton, Nemenman trained at the Kavli Insitutute for Theoretical Physics at UCSB and at the Columbia University Medical Center. He was a permanent staff member at the Los Alamos National Laboratory before joining Emory University Departments of Physics and Biology, where he is now a Winship Distinguished Research Professor and a Director of the Theory and Modeling of Living Systems Initiative. He is a Fellow of the American Physical Society (APS), Member of the Aspen Center for Physics, and a James S. McDonnell Foundation Complex Systems Scholar Awardee. He works on understanding how physical limitations on information processing shape biological organization, from the structure of neural networks responsible for learning in animals, to design of signaling pathways in microorganisms. His work involves paper-and-pencil theory, numerical simulations, and analysis of high-throughput experimental datasets.

Christos Papadimitriou

Christos Harilaos Papadimitriou is the Donovan Family Professor of Computer Science at Columbia University. Before joining Columbia in 2017, he was a professor at UC Berkeley for the previous 22 years, and before that he taught at Harvard, MIT, NTU Athens, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to optimization, databases, control, AI, robotics, economics and game theory, the Internet, evolution, and the brain. He holds a PhD from Princeton (1976), and eight honorary doctorates. He is a member of the National Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering, and he has received the Knuth prize, the Go"del prize, the von Neumann medal, as well as the 2018 Harvey Prize by Technion. In 2015 the president of the Hellenic republic named him commander of the order of the Phoenix. He has also written three novels: “Turing ,” “Logicomix” and his latest “Independence.”

Sandu Popescu

Sandu Popescu has been Professor of Physics at the University of Bristol since 1999. He studied with Yakir Aharonov, followed by postdoctoral research positions with François Englert, and then with Abner Shimony and Bahaa Saleh. From 1996 to 1999 he was Reader at the Isaac Newton Institute, University of Cambridge. Popescu's main body of work is in the foundations of quantum mechanics and quantum information, where he was one of the pioneers of the field, and more recently in the foundations of statistical mechanics and quantum thermodynamics. He received the Dirac Medal in 2016 and is a Fellow of Royal Society. For more information, visit www.sandupopescu.com.

Chris Sims

Chris Sims received both his MS and PhD degrees in Cognitive Science from Rensselaer Polytechnic Institute. He served as Assistant Professor in the Psychology Department at Drexel University from 2015–17 before joining the faculty of Cognitive Science at Rensselaer Polytechnic Institute. His research is centered on visual memory and perceptual expertise, sensori-motor control and motor learning, and learning and decision-making under uncertainty.

Susanne Still

Susanne Still is Professor of Information and Computer Sciences (ICS) at the University of Hawai`i at Mānoa. She leads the Machine Learning and Physics of Information Lab, and developed the ICS machine learning curriculum. A physicist by training, she also serves as contributing graduate faculty in the Department of Physics and Astronomy. She is a member of the Foundational Questions Institute, from which she has received several research grants. She works on the physics of information processing, in particular the thermodynamic analysis of learning, the stochastic thermodynamics of strongly coupled systems, and the thermodynamics of information engines. She contributed information theoretically motivated approaches to dynamical and interactive learning, and generalizations to quantum information processing. She worked on the foundations of information theory, and applied her machine learning expertise to mathematical finance and robotics. During her Ph.D. at ETH, Zürich, she worked on neuromorphic hardware with the late Misha Mahowald, and before joining the University of Hawai`i, she was a postdoctoral researcher in William Bialek's Theoretical Biophysics Group at Princeton University.

Madhu Sudan

Madhu Sudan is a Gordon McKay Professor in the John A. Paulson School of Engineering and Applied Sciences at Harvard University, where he has been since 2015. Madhu Sudan got his Bachelors degree from IIT Delhi in 1987 and his Ph.D. from U.C. Berkeley in 1992. Between 1992 and 2015, Madhu Sudan worked at IBM Research (Research Staff Member 1992-1997), at MIT (Associate Professor 1997-2000, Professor 2000-2011, Fujitsu Chair Professor 2003-2011, CSAIL Associate Director 2007-2009, Adjunct Professor 2011-2015), and at Microsoft Research (Principal Researcher, 2009-2015). Madhu Sudan is a recipient of the Nevanlinna Prize awarded by the International Mathematical Union for outstanding contributions to mathematics of computer and information science, and the Infosys Foundation Prize in Mathematical Sciences. Madhu Sudan is a fellow of the Association for Computing Machinery, the Institute of Electrical and Electronics Engineers and the American Mathematical Society. He is a member of the American Academy of Arts and Sciences and the National Academy of Sciences.Madhu Sudan's research interests revolve around mathematical studies of communication and computation. Specifically his research focusses on concepts of reliability and mechanisms that are, or can be, used by computers to interact reliably with each other. His research draws on tools from computational complexity, which studies efficiency of computation, and many areas of mathematics including algebra and probability theory. He is best known for his works on probabilistic checking of proofs, and on the design of list-decoding algorithms for error-correcting codes. His current research interests include property testing which is the study of sublinear time algorithms to estimate properties of massive data, and communication amid uncertainty, a mathematical study of the role of context in communication.

Wojciech Szpankowski

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University where he teaches and conducts research in analysis of algorithms, information theory, analytic combinatorics, random structures, and stability problems of distributed systems. He held several Visiting Professor/Scholar positions, including McGill University, INRIA, Stanford, Hewlett-Packard Labs, Universite de Versailles, University of Canterbury, New Zealand, Ecole Polytechnique, France, the Newton Institute, Cambridge, UK, ETH, Zurich, Hawaii University, and Gdansk University of Technology, Poland. He is a Fellow of IEEE, and the Erskine Fellow. In 2010 he received the Humboldt Research Award and in 2015 the Inaugural Arden L. Bement Jr. Award. In 2020 he was the recipient of the Flajolet Lecture Prize. He published two books: "Average Case Analysis of Algorithms on Sequences", John Wiley & Sons, 2001, and "Analytic Pattern Matching: From DNA to Twitter", Cambridge, 2015. In 2008 he launched the interdisciplinary Institute for Science of Information, and in 2010 he became the Director of the NSF Science and Technology Center for Science of Information.

Naftali Tishby

Dr. Naftali Tishby is a professor of computer science and a founding member of the interdisciplinary center for neural computation (ICNC) at the Hebrew university of Jerusalem, Israel. He received his PhD in theoretical physics from the Hebrew university in 1985, and has been a researcher at MIT, Bell Labs, NECI, UPenn, UCSB, and the IAS at Princeton. His current work is focused on the interface between computer science, statistical physics, and computational biophysics. He introduced methods from statistical mechanics and information theory into computational learning theory and machine learning, in particular in the role of phase transitions in learning and cognitive science. He has been working on the information theoretic foundations of biological and cognitive processes and developed theoretical frameworks for extracting optimal relevant representations. He is best known for introducing the Information Bottleneck method and its applications to Deep Learning.

David Tse

David Tse is the Thomas Kailath and Guanghan Xu Professor in the School of Engineering at Stanford University. He is a member of the U.S. National Academy of Engineering. He received the 2017 Claude E. Shannon Award and the 2019 IEEE Richard W. Hamming Medal. He is the inventor of the proportional-fair scheduling algorithm, used in all modern-day cellular systems serving 3 billion subscribers around the world. His research interests are in information theory, blockchains and machine learning.

Yuhai Tu

Yuhai Tu graduated from the Special Class of the Gifted Young at University of Science and Technology of China in 1987. He came to the US under the CUSPEA program and received his PhD in physics from University of California, San Diego in 1991. He was a Division Prize Fellow at Caltech from 1991-1994. He joined IBM Watson Research Center as a Research Staff Member in 1994 and served as head of the theory group during 2003-2015. He is a member of APS and was elected APS Fellow in 2004. He served as the APS Division of Biophysics (DBIO) Chair in 2017. Yuhai Tu has broad research interests, which include nonequilibrium statistical physics, surface physics, bioinformatics, biophysics, and recently machine learning. Trained in statistical physics and nonlinear dynamics, he has applied his analytical and computational skills in a diverse set of areas and accomplished many highly original work, such as the Toner-Tu equation in flocking theory, the growth dynamics of the Si-aSiO2 interface, the Genes@Work pattern discovery algorithm for RNA microarray analysis, the standard model of bacterial chemotaxis, and the energy-speed-accuracy tradeoff in sensory adaptation, biochemical oscillation and synchronization. In many cases, his pioneering work has opened up new directions of research, e.g., collective behaviors in active matter systems, pattern discovery in large scale biological data, and nonequilibrium thermodynamics of biochemical networks.

Leslie Valiant

Leslie Valiant was educated at King's College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His work has ranged over several areas of theoretical computer science, particularly complexity theory, learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence and is the author of two books, Circuits of the Mind, and Probably Approximately Correct. He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).

David Wolpert

David Wolpert is a professor at the Santa Fe Institute, external professor at the Complexity Science Hub in Vienna, and adjunct professor at ASU. He is the author of three books (and co-editor of several more), over 200 papers, has three patents, is an associate editor at over half a dozen journals, has received numerous awards, and is a fellow of the IEEE.He has over 26,000 citations, with most of his papers inthermodynamics of computation, foundations of physics, dynamics of social organizations, machine learning, game theory, distributed optimization, and molecular biology. In particular his machine learning technique of stacking was instrumental in both winning entries for the Netflix competition, and his papers on the no free lunch theorems have over 7,000 citations. (Details at http://davidwolpert.weebly.com).Most of his current research involves two topics:1) Combining recent revolutionary breakthroughs in nonequilibrium statistical physics with computer science theory to lay the foundations of a modern theory of the thermodynamics of computation. 2) Modeling social organization (command and communication networks within social groups) using information theory. Before his current position he was the Ulam scholar at the Center for Nonlinear Studies, and before that he was at NASA Ames Research Center and a consulting professor at Stanford University, where he formed the Collective Intelligence group. He has worked at IBM and a data mining startup, and is external faculty at numerous international institutions.His degrees in Physics are from Princeton and the University of California.

Mary Wootters

Mary Wootters is an assistant professor of Computer Science and Electrical Engineering at Stanford University. She received a PhD in mathematics from the University of Michigan in 2014, and a BA in math and computer science from Swarthmore College in 2008; she was an NSF postdoctoral fellow at Carnegie Mellon University from 2014 to 2016. She works in theoretical computer science, applied math, and information theory; her research interests include error correcting codes and randomized algorithms for dealing with high dimensional data. She is the recipient of an NSF CAREER award and was named a Sloan Research Fellow in 2019.

Lydia Zakynthinou

Lydia Zakynthinou is a PhD student in the Computer Science program at Northeastern University’s Khoury College of Computer Sciences, advised by Professors Jonathan Ullman and Huy Lê Nguyễn. Before joining Northeastern, Lydia earned a diploma in Electrical and Computer Engineering from the National Technical University of Athens and a Master of Science from the University of Athens. Her research interests lie in the areas of learning theory and differential privacy.

Doron Zeilberger

Doron Zeilberger is the Board of Governors Professor of Mathematics at Rutgers University. He is widely known for the development of “WZ” (Wilf-Zeilberger) Theory and Zeilberger’s algorithm which are used extensively in modern computer algebra software. Zeilberger was the first to prove the elusive result in combinatorial theory known as the alternating sign matrix conjecture. Among his honors are: the American Mathematical Society Steele Prize for seminal contributions to research (co-recipient with Herb Wilf); the Institute of Combinatorics and Its Applications Euler Medal for “Outstanding Contributions to Combinatorics”; the Laura H. Carnell Professorship at Temple University; and the MAA Lester R. Ford award for a paper in The American Mathematical Monthly.

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