Emmanuel Abbe received his Ph.D. degree from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He previously obtained his M.S. degree from the Mathematics Department at the Ecole Polytechnique Federale de Lausanne. Before joining Princeton University, Emmanuel was a postdoctoral fellow in the School of Communication and Computer Sciences at the Ecole Polytechnique Federale de Lausanne. His research interests include information theory, learning theory, networks and discrete probability.
Anima Anandkumar is a faculty member at the EECS Department at University of California Irvine. She is a member of the center for pervasive communications and computing (CPCC). She also holds a joint appointment with the ICS Department at UCI. Anandkumar's research focus is in the high-dimensional learning of probabilistic graphical models and latent variable models. Broadly she is interested in machine learning, high-dimensional statistics, tensor methods, statistical physics, information theory and signal processing.
Nina Balcan is an Associate Professor in the School of Computer Science (MLD and CSD) at CMU. Her main research interests are in machine learning and theoretical computer science. She is a member of the machine learning group, computer science theory group, and the ACO program.
Sebastien Bubeck is a researcher in the theory group at Microsoft Research in Redmond, Washington. His research interests include: machine learning, combinatorial statistics, multi-armed bandits, online learning, stochastic optimization, and convex optimization.
Nicolo Cesa-Bianchi is a computer scientist and Professor of Computer Science at the Dipartimento di Scienze dell'Informazione of the University of Milan. He is a researcher in the field of machine learning. His research contributions focus on the following areas: Prediction with Expert Advice and Game-Theoretic Learning, Pattern Classification and Regression, Statistical Learning Theory, Boolean Learning and Learning with Queries, Bandit Problems.
Kamalika Chaudhuri is an Assistant Professor of Computer Science and Engineering at UC San Diego. Her research is on machine learning. Much of her work is on privacy-preserving machine learning and unsupervised learning, but she is broadly interested in a number of topics in learning theory, such as confidence-rated prediction, online learning, and active learning.
Ofer Dekel is a principal researcher in the Machine Learning Department at Microsoft Research in Redmond, Washington. His main research interests are machine learning, online prediction, algorithm engineering, statistical learning theory, and optimization. Recently, he's been focusing on large scale machine learning, online learning against adaptive adversaries, learning in multi-teacher environments, and learning predictors that are intelligible to humans.
Jian Ding is a faculty member at the Department of Statistics at the University of Chicago. His research interests are probability theory and statistical physics, with focus on random constraint satisfaction problems and random planar geometry.
Dean Foster is a faculty member at the University of Pennsylvania. His research interests include machine learning, natural language processing, and statistics.
Rong Ge received his Ph.D. from the Computer Science Department of Princeton University. His advisor was Sanjeev Arora. He was a post-doc at Microsoft Research, New England. Starting in August Ge will be an assistant professor at the Computer Science Department of Duke University. He is interested in Theoretical Computer Science and Machine Learning.
Nika Haghtalab is a second year Ph.D. student in the Computer Science Department at Carnegie Mellon University. She is interested in problems that lie in the intersection of Machine Learning and Game Theory. She is fortunate to be co-advised by Avrim Blum and Ariel Procaccia.
Sergiu Hart is a member of the Center for the Study of Rationality, Professor of Mathematics, and Professor of Economics, at the Hebrew University of Jerusalem. His main area of research of is game theory and economic theory, with additional contributions in mathematics, computer science, probability and statistics. Among his major contributions are studies of strategic foundations of cooperation; strategic use of information in long-term interactions ("repeated games"); adaptive and evolutionary dynamics, particularly with boundedly rational agents; perfect economic competition and its relations to models of fair distribution; and riskiness.
Sham Kakade will be joining the University of Washington as a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science. Currently, he is a Principal Research Scientist at Microsoft Research, New England. His research focus is in the area broadly construed as data science, focusing on large scale computational methods for statistics, machine learning, and signal processing. He has made contributions in various areas including statistics, optimization, probability theory, macine learning, algorithmic game theory and economics, and computational neuroscience.
Ravi Kannan is a Principal Researcher at Microsoft Research India, where he leads the algorithms research group. He is also the first adjunct faculty of Computer Science and Automation Department of Indian Institute of Science. His research interests include Algorithms, Theoretical Computer Science and Discrete Mathematics as well as Optimization. His work has mainly focused on efficient algorithms for problems of a mathematical (often geometric) flavor that arise in Computer Science.
Emilie Kaufmann is a postdoctoral researcher in the DYOGENE team, a joint research team between INRIA and ENS, that also belongs to the LINCS. she did her PhD at LTCI (Telecom ParisTech) in the 'Statistics and applications' team under the supervision of Olivier Cappe, Aurelien Garivier (Institut de Mathematiques de Toulouse) and Remi Munos (INRIA, Sequel team). Her main research interests lie in statistics and machine learning.
Tomer Koren is a PhD student at the IE&M Department of the Technion, under the supervision of Prof. Elad Hazan. His research interests lie at the interface of Machine Learning and Convex Optimization. Specifically, he is mostly interested in algorithmic aspects of online learning and sequential decision making under uncertainty, statistical learning theory, and convex optimization techniques.
Victoria Kostina is an Assistant Professor of Electrical Engineering at Caltech. Her research interests lie in information theory, theory of random processes, coding, and wireless communications. She is particularly interested in fundamental limits of delay-sensitive communications.
James Lee is an Associate Professor of Computer Science & Engineering at the University of Washington. His research interests include many aspects of algorithms, computational complexity and optimization. In particular he is interested in high-dimensional geometry, the geometry of discrete metric spaces, spectral graph theory, applied probability, and applications of geometry and analysis in theoretical computer science.
Yin Tat Lee is a PhD student in the Mathematics Department at the Massachusetts Institute of Technology advised by Professor Jonathan Kelner. His areas of research span convex optimization, linear programming, spectral graph theory and algorithmic graph theory. He is particularly interested in combining convex optimization and combinations techniques to design fast algorithms for fundamental cut/flow problems.
Lihong Li is a researcher in the Machine Learning and Intelligence Group, within the Machine Learning Department at Microsoft Research. His research focuses on machine learning in interactive problems, such as multi-armed bandits and reinforcement learning, where the objective is not merely to make accurate predictions, but to optimize a certain reward function by taking good actions.
Aleksander Madry is an Assistant Professor of Computer Science in the MIT EECS Department, a member of CSAIL and a part of the Theory of Computation group. He is mainly interested in algorithmic graph theory, i.e., design and analysis of very efficient (approximation) algorithms for fundamental graph problems. He also enjoys investigating topics in combinatorial optimization - especially the ones involving dealing with uncertainty. A frequent theme in his research is employing a mix of linear-algebraic methods (particularly, the tools of spectral graph theory) and continuous optimization techniques to purely combinatorial problems.
Laurent Massoulie is a researcher at Inria, Director of the Microsoft Research-Inria Joint Centre. His research focuses on modeling and algorithmic design for distributed systems such as content distribution networks, online social networks and peer-to-peer systems.
Ankur Moitra is an Assistant Professor of Applied Mathematics, Massachusetts Institute of Technology and a Principal Investigator, Computer Science and Artificial Intelligence Laboratory Research. He is a theoretical computer scientist, and a major goal in my work is to give algorithms with provable guarantees for various problems in machine learning.
Elchanan Mossel is a Professor of Statistics and Computer Science at University of Pennsylvania and U.C. Berkeley. He is working on novel algorithmic and statistical problems. Research Interests include: Combinatorial Statistics, Discrete Fourier Analysis and Influences, Randomized Algorithms, Computational Complexity, MCMC, Markov Random Fields, Social Choice, Game Theory, Evolution.
Lorenzo Orecchia is an Assistant Professor in the Computer Science Department at Boston University. His research focuses on the design of very efficient algorithms for combinatorial problems by leveraging techniques in convex optimization. He is particularly interested in the deployment of first-order methods both as an algorithmic design tool, e.g., for designing fast graph problems, and as a proof strategy, e.g., in the construction of interesting objects based on weak local guarantees.
Shyan Oveis Gharan is an Assistant Professor at the University of Washington Computer Science & Engineering department. He is interested in the design and analysis of algorithms.
Pablo Parrilo is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He is currently Associate Director of the Laboratory for Information and Decision Systems (LIDS), and is also affiliated with the Operations Research Center (ORC). His research interests include optimization methods for engineering applications, control and identification of uncertain complex systems, robustness analysis and synthesis, and the development and application of computational tools based on convex optimization and algorithmic algebra to practically relevant engineering problems.
Robin Pemantle is a Professor of Mathematics and Computer and Information Science at the University of Pennsylvania. His research interests include probability theory, where he studies random walks, urn schemes and reinforcement schemes, tree-indexed process, branching processes, any probability model involving trees, discrete potential theory, particle systems, percolation, mixing rates Markov chains, and pathwise properties of Brownian motion. He also studies combinatorics, including asymptotics of multivariable generating functions, optimization, enumerative combinatorics, and spanning trees of graphs.
Yuval Peres is a Principal Researcher in the Theory Group at Microsoft Research in Redmond, WA. He is known for his research in probability theory, ergodic theory, mathematical analysis, theoretical computer science, and in particular for topics such as fractals and Hausdorff measure, random walks, Brownian motion, percolation and Markov chain mixing times.
Sebastian Pokutta is an Assistant Professor at Georgia Tech. Pokutta's research concentrates on polyhedral combinatorics at the intersection of combinatorial optimization and theoretical computer science. A particular focus is on the theory of extended formulations, exploring the limits of computation in alternative models of complexity while leveraging techniques from communication complexity and information theory.
Sasha Rakhlin is an Associate Professor of Statistics at the University of Pennsylvania. His research interests include machine learning, online learning / sequential decisions, statistical learning theory, algorithms, optimization, game theory, empirical process theory, and applied probability.
Gireeja Ranade will be joining Microsoft Research, Redmond as a postdoc in August 2015. She recently finished her PhD in EECS at Berkeley working at the intersection of information theory and control. In Spring 2015 she was a Lecturer at Berkeley designing the new first year EECS course.
Ben Recht is an Associate Professor in EECS at UC Berkeley. His research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning. He explores the intersections of convex optimization, mathematical statistics, and randomized algorithms.
Ohad Shamir is a faculty member in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, Israel. His research interests are machine learning and its intersections with optimization, statistics and theoretical computer science. Much of his recent work focuses on learning with information constraints, such as distributed learning, online learning with partial information, memory-limited learning, and learning with partial data access.
Aaron Sidford is a PhD student in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology advised by Professor Jonathan Kelner. His research interests lie broadly in the theory of computation and the design and analysis of algorithms. He is particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
Mohit Singh is a researcher in the Theory Group at Microsoft Research, Redmond. His research is in combinatorial optimization, approximation algorithms and optimization under uncertainty.
Paul Valiant is an Assistant Professor Brown University. He is interested in how computational tools and perspectives can address fundamental problems in the wider world, including the other sciences.
Greg Valiant is an Assistant Professor in Stanford's Computer Science Department. His main research interests are in algorithms, learning, applied probability, and statistics; he is also interested in evolution and game theory, and have enjoyed working on problems in database theory.
Santosh Vempala is a Distinguished Professor of Computer Science at the Georgia Institute of Technology. His main work has been in the area of theoretical computer science, with particular activity in the fields of algorithms, randomized algorithms, computational geometry, and computational learning theory.
Mary Wootters is an NSF postdoctoral fellow in the CS department at Carnegie Mellon University, working with Venkat Guruswami. Her interests lie mostly in applied probability, with applications including randomized algorithms, signal processing, coding theory, matrix completion, and group testing. she dabbles in quantum information theory and complexity theory.
Lin Xiao is a Senior Researcher in the Machine Learning Groups at Microsoft Research, located in Redmond, WA. His current research interests include theory and algorithms for large-scale optimization, stochastic and online algorithms for machine learning, and parallel and distributed computing.
Alex Zhai is a graduate student in mathematics at Stanford University.
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Last updated: 20 August 2015