
Elchanan Mossel became a professor of mathematics at MIT in 2016. He was previously a professor of statistics at the University of Pennsylvania. Elchanan was awarded a Ph.D. in mathematics from Hebrew University of Jerusalem. Since then, he was appointed as a postdoc in the Theory Group at Microsoft Research Redmond, an Alfred P. Sloan Research Fellow, a Miller Research Fellow at University of California, Berkeley, a professor of mathematics, applied mathematics and computer science at the Weizmann Institute of Science and a professor of statistics and computer science at University of California, Berkeley.
He studies fundamental problems in probability and analysis, computational complexity and algorithms, as well as applications to machine learning, Markov chain Monte Carlo methods, social choice and networks, and computational biology.
Aaron Roth is the class of 1940 Bicentennial Term associate professor of Computer and Information Sciences at the University of Pennsylvania, and codirector of the Networked and Social Systems Engineering (NETS) program. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, and research awards from Amazon, Google, and Yahoo. His research focuses on data privacy, algorithmic fairness, game theory, and machine learning. Together with Cynthia Dwork, he is the author of "The Algorithmic Foundations of Differential Privacy". Together with Michael Kearns, he is the author of “The Ethical Algorithm”, forthcoming in 2019 from Oxford University Press.
Sergei Vassilvitskii is a Research Scientist at Google New York. Previously he was a Research Scientist at Yahoo! Research and an Adjunct Assistant Professor at Columbia University. He completed my PhD at Stanford Universty under the supervision of Rajeev Motwani. Prior to that he was an undergraduate at Cornell University.
Jennifer G. Dy is a Professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, where she first joined the faculty in 2002. She received her M.S. and Ph.D. in 1997 and 2001 respectively from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, and her B.S. degree from the Department of Electrical Engineering, University of the Philippines, in 1993. Her research spans both fundamental research in machine learning and their application to biomedical imaging, health, science and engineering, with research contributions in clustering, multiple alternative clustering, dimensionality reduction, feature selection and sparse methods, learning from uncertain experts, active learning, and Bayesian nonparametric models. She received an NSF Career award in 2004. She has served or is serving as Secretary for the International Machine Learning Society, associate editor/editorial board member for the Journal of Machine Learning Research, Machine Learning journal, IEEE Transactions on Pattern Analysis and Machine Intelligence, organizing and or technical program committee member for premier conferences in machine learning and data mining (ICML, ACM SIGKDD, AAAI, IJCAI, UAI, AISTATS, SIAM SDM), and program cochair for SIAM SDM 2013 and ICML 2018.
Jakub Łącki is a research scientist working on graphmining and largescale optimization teams. He received his PhD from Univeristy of Warsaw in 2015, advised by Piotr Sankowski. Before joining Google he was a postdoctoral researcher at Sapienza University of Rome, working with Stefano Leonardi.
Haipeng Luo is an assistant professor at the CS department of the University of Southern California. He obtained his PhD from Princeton University in 2016 and spent a year at Microsoft Research, NYC as a postdoc researcher afterwards. His research interest is in theoretical and applied machine learning, with a focus on online learning, bandit algorithms, and interactive machine learning. He received several awards over the years, including three best paper and best student paper awards at ICML, NeurIPS, and COLT respectively.
Varun Kanade is an Associate Professor in Computer Science at the University of Oxford, and a Turing Fellow at the Alan Turing Institute in London. He was awarded a Ph.D. from Harvard University in 2012. Subsequently, he was a postdoctoral fellow supported by the FSMP at ENS, Paris and by the Simons Foundation at UC Berkeley. His research interest lie in theoretical aspects of machine learning and has recently been working on various problems related to clustering, randomized graph algorithms and random graphs, and supervised learning.
Michael Mahoney is in the Department of Statistics at the University of California, Berkeley. His research focuses on algorithmic and statistical aspects of modern largescale data analysis and largescale machine learning. Specific topics include randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, 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. He served 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 runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets; and he spent fall 2013 at UC Berkeley coorganizing the Simons Foundation's program on the Theoretical Foundations of Big Data Analysis.
Andrew McGregor is an Associate Professor at the University of Massachusetts, Amherst. He received a B.A. degree and the Certificate of Advance Study in Mathematics from the University of Cambridge and a Ph.D. from the University of Pennsylvania. He also spent a couple of years as a postdoc at UC San Diego and Microsoft Research SVC. He is interested in many areas of theoretical computer science and specializes in data stream algorithms, linear sketching, and communication complexity. He received the NSF Career Award in 2010.
Krzysztof Onak is a computer scientist who works at the IBM T.J. Watson Research Center near Yorktown Heights, NY. He is interested in computation with limited resources, including sublineartime algorithms, streaming algorithms, and algorithms for modern parallel systems. Krzysztof received his Master's degree from the University of Warsaw and his PhD from the Massachusetts Institute of Technology. Before joining IBM, he was a Simons Postdoctoral Fellow at Carnegie Mellon University.
Ludwig Schmidt is a postdoctoral researcher at UC Berkeley working with Moritz Hardt, Ben Recht, and Martin Wainwright. Ludwig’s research interests revolve around the foundations of machine learning, often with the goal of making machine learning more reliable. Before Berkeley, Ludwig completed his PhD at MIT under the supervision of Piotr Indyk. Ludwig received a Google PhD fellowship, a Microsoft Simons fellowship, a best paper award at the International Conference on Machine Learning (ICML), and the Sprowls dissertation award from MIT.
Aravindan Vijayaraghavan is an Assistant Professor of Computer Science at Northwestern University. After obtaining his PhD in Computer Science from Princeton University in 2012, he was a Simons Postdoctoral Fellow at Carnegie Mellon University. He also spent a year as a postdoc at the Courant Institute, with the Simons Collaboration on Algorithms & Geometry. His research interests are in designing efficient algorithms for problems in Combinatorial Optimization and Machine Learning, and in using paradigms that go Beyond WorstCase Analysis to obtain good algorithmic guarantees.
Joshua Wang is a Research Scientist at Google. Prior to that, he did a PhD at Stanford University, advised by Tim Roughgarden. His current research interests include submodular functions, graph algorithms, and algorithmic game theory. Joshua has won a SPAA 2016 Best Paper Award as well as an ESA 2014 Best Student Paper Award. His work has also been recognized with oral presentations at NeurIPS 2017 and 2018.
David Woodruff is an Associate Professor at Carnegie Mellon University in the School of Computer Science. Prior to that he spent ten years at the IBM Almaden Research Center, which he joined in 2007 after completing his Ph.D. at MIT in theoretical computer science. His research interests include communication complexity, data stream algorithms, machine learning, numerical linear algebra, sketching, and sparse recovery. He is the author of the book "Sketching as a Tool for Numerical Linear Algebra". He is a recipient of the 2014 Presburger Award and Best Paper Awards at STOC 2013 and PODS 2010. At IBM he was a member of the Academy of Technology and a Master Inventor.
Vahab Mirrokni is a Principal Research Scientist, heading the algorithms research group at Google Research, New York. He received his PhD from MIT in 2005 and his B.Sc. from Sharif University of Technology in 1999. He joined Google Research in New York in 2008, after spending a couple of years at Microsoft Research, MIT and Amazon.com. He is the cowinner of a SODA'05 best student paper award and ACM EC'08 best paper award. His research areas include algorithms, algorithmic game theory, combinatorial optimization, and social networks analysis. At Google, he is mainly working on algorithmic and economic problems related to search and online advertising. Recently he is working on online ad allocation problems, distributed algorithms for largescale graph mining, and mechanism design for advertising exchanges.
Grigory Yaroslavtsev is an assistant professor of Computer Science at Indiana University and the founding director of the Center for Algorthms and Machine Learning at IU. Prior to that he was a postdoctoral fellow at the Warren Center for Network and Data Sciences at the University of Pennsylvania. He was previously a Postdoctoral Fellow in Mathematics at Brown University, ICERM. He received his Ph.D. in Theoretical Computer Science in 2014 from Pennsylvania State University and an M.Sc. in Applied Mathematics and Physics from the Academic University of the Russian Academy of Sciences in 2010. Grigory works on efficient algorithms for sparsification, summarization and testing properties of large data, including approximation, parallel and online algorithms, learning theory and property testing, communication and information complexity and private data release.