International Conference on Machine Learning

John Langford is a machine learning research scientist, a field which he says “is shifting from an academic discipline to an industrial tool”. He is the author of the weblog hunch.net and the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM’s Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor’s degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002. He was the program co-chair for the 2012 International Conference on Machine Learning.

Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning, computational aspects in economics and game theory, and algorithms. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She was a Program Committee Co-chair for COLT 2014, a board member of the International Machine Learning Society, and is currently a Program Committee Co-chair for ICML 2016.

Kilian Weinberger is an Associate Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul and his undergraduate degree in Mathematics and Computer Science from the University of Oxford. During his career he has won several best paper awards at ICML, CVPR, AISTATS and KDD (runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He is co-Program Chair for ICML 2016. Kilian Weinberger’s research focuses on Machine Learning and its applications. In particular, he focuses on learning under resource constraints, metric learning, machine learned web-search ranking, transfer- and multi-task learning as well as biomedical applications. Before joining Cornell University, he was an Associate Professor at Washington University in St. Louis and before that he worked as a research scientist at Yahoo! Research in Santa Clara.

Dr. Peder A. Olsen is a principal research staff member and head of the machine learning and data analytics area in the mathematics department at IBM’s Thomas J. Watson Research center in New York. Peder’s current main research areas are in machine learning and efficient, large-scale, non-smooth function optimization. Before that, he worked on speech recognition at IBM for more than 15 years. Peder obtained his mathematics Ph.D. degree in 1996 from the University of Michigan, Ann Arbor and his undergraduate degree in Industrial Mathematics from the Norwegian University of Science and Technology (NTNU), Trondheim Norway in 1991.

Marek Petrik is a Research Staff Member at the Solutions and Mathematical Sciences Department at IBM’s T. J. Watson Research Center. He received his Ph.D. in Computer Science from the University of Massachusetts, Amherst. His research focuses on machine learning and optimization with a special interest in robust and risk-averse optimization, stochastic sequential optimization problems, and reinforcement learning. He has worked on applications that include agricultural and environmental monitoring, supply chain optimization, revenue management, and online recommendations.

Alina Beygelzimer is a Senior Research Scientist at Yahoo Research in New York City, working on many aspects of scalable machine learning.

Bernhard Schölkopf’s scientific interests are in machine learning and inference. In particular, he studies kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years he also got interested in methods for finding causal structures that underly statistical dependences. He has worked on a number of different applications of machine learning, with a recent focus on photographt and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He received the J.K. Aggarwal Prize of the International Association for Pattern Recognition, the Max Planck Research Award, the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, and the Royal Society Milner Award.

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Departments of Statistics and Computer Science. In 2016 he moved to the Machine Learning Department at Carnegie Mellon University. Ruslan’s primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Connaught New Researcher Award, Google Faculty Award, and is a Senior Fellow of the Canadian Institute for Advanced Research.

John is an Assistant Professor in the Department of Statistics at Columbia University. His research covers a number of topics in machine learning, including dimensionality reduction, gaussian processes and kernel methods, approximate inference techniques, and more. He is also particularly interested in the application of machine learning to analyzing neural data. He received a BA from Dartmouth College (computer science), a MS and PhD from Stanford University (electrical engineering), and did a postdoc from University of Cambridge (machine learning).

Robert Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. In 2002, he became a Professor of Computer Science at Princeton University. He joined Microsoft Research in 2014. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a member of both the National Academy of Engineering and the National Academy of Sciences. His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.

David Sontag is an Assistant Professor at New York University’s Courant Institute of Mathematical Sciences and Center for Data Science. His research is on machine learning and approximate inference in graphical models, with an emphasis on applications to health care. Prior to joining NYU, he was a postdoctoral researcher at Microsoft Research New England. David’s research has received recognition including the Sprowls Award for the best doctoral thesis in Computer Science at MIT, best paper awards at EMNLP, UAI and NIPS, and the NSF CAREER award.

Dr. Jingrui He is an assistant professor in the School of Computing, Informatics and Decision Systems Engineering at Arizona State University. She received her PhD from Carnegie Mellon University. She joined ASU in 2014 and directs the Statistical Learning Lab (STAR Lab). Her research focuses on emerging behavior analysis, heterogeneous machine learning, active learning and semi-supervised learning, with applications in social media analysis and healthcare. She is the recipient of the NSF CAREER Award in 2016, the IBM Faculty Award in 2015 and 2014 respectively.

Jérémie Mary got his PhD from Paris XI and is Associate Professor with HDR at the university of Lille and member of the Inria team SequeL which works on sequential machine learning. He won (2011 and 2014) and organised (2012) three challenges about online recommendation within 3 of the major conferences in machine learning (ICML’11, ICML’12, RecSys’14) on problems and data provided by Yahoo!, Adobe and Twitter. He also has some advanced theoretical work about clustering in the field of clustering of ergodic processes. His current work is about the use of deep learning in the field of recommender systems.