Dr. Bottou's primary research interest is in Machine Learning. His contributions to this field address theory, algorithms and large scale applications. Dr. Bottou's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology. He has published over 60 papers. He is serving or has served on the boards of the Journal of Machine Learning Research, IEEE Transactions on Pattern Analysis and Machine and Pattern Recognition Letters. He also serves on the scientific advisory board of Kxen Inc. He co-chaired the International Conference on Machine Learning (ICML) in 2009. In 2007 he was awarded the New York Academy of Sciences Blavatnik Award for Young Scientists.

William S. Cleveland is a Professor of Statistics and Courtesy Professor of Computer Science at Purdue University. Previous to this he was a Distinguished Member of Technical Staff in the Statistics Research Department at Bell Labs, Murray Hill; for 12 of his years at Bell Labs he was a Department Head. His areas of Research have included data visualization, computer networking, machine learning, data mining, time series, statistical modeling, visual perception, environmental science, and seasonal adjustment. He has developed many new statistical models and methods, including visualization methods, that are widely used in engineering, science, medicine, and business. Cleveland has twice won the Wilcoxon Prize and once won the Youden prize from the statistics journal Technometrics. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association of the Advancement of Science, and is an elected member of the International Statistical Institute. In 1996 he was chosen Statistician of the Year by the Chicago Chapter of the American Statistical Association. In 2002 he was selected as a Highly Cited Researcher by the American Society for Information Science and Technology in the newly formed mathematics category.

Jiawei Han, Professor, Department of Computer Science, University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, database systems, data mining from spatiotemporal data, multimedia data, stream and RFID data, social network data, and biological data, with over 350 journal and conference publications. He has chaired or served in over 100 program committees of international conferences and workshops, including PC co-chair of 2005 (IEEE) International Conference on Data Mining (ICDM), Americas Coordinator of 2006 International Conference on Very Large Data Bases (VLDB), and senior PC member for 2008 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. He is also serving as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received 2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society Technical Achievement Award. His book “Data Mining: Concepts and Techniques” (2nd ed., Morgan Kaufmann, 2006) has been popularly used as a textbook worldwide. (Adapted from http://ebiquity.umbc.edu/person/html/Jiawei/Han/)

Marina Meilă's work is in Machine Learning by probabilistic methods and reasoning in uncertainty. She creates algorithms that make these tasks efficient for large high-dimensional data sets. In the past Prof. Meilă has worked on a diverse set of topics including: approximate inference and structure finding in belief networks, fast algorithms for learning graphical models in high dimensions, optimal triangulation of Bayes nets, transfer of learning, reinforcement learning, mixtures of experts. She received her Ph.D. from MIT in 1999 for her dissertation on “Learning with mixtures of trees”.

Dale Schuurmans' research interests are in Machine Learning, Artificial Intelligence, Statistics, and Optimization. He has made a number of original contributions to all these areas and has co-authored over 100 papers and supervised over 20 Ph.D. students. He has served on the editorial boards of IEEE PAMI, Artificial Intelligence (AIJ), Journal of Machine Learning Research (JMLR), Machine Learning (MLJ), and Journal of Artificial Intelligence Research (JAIR). He was the program co-chair of NIPS 2008 and the general chair of NIPS 2009 besides serving on the program committees of numerous conferences. He has received outstanding paper awards a number of prestigious conferences.

Prof. Singh's main research interest is in the old-fashioned goal of Artificial Intelligence (AI), that of building autonomous agents that can learn to be broadly competent in complex, dynamic, and uncertain environments. The field of reinforcement learning (RL) has focused on this goal and his deepest contributions are in RL. He is a full professor at the University of Michigan at Ann Arbor since 2008. Prior to that he was an Associate Professor (2002–2008) at the same university. In the past, Prof. Singh held positions as a Chief Scientist at Syntek Capital, Principal Member Technical Staff in the Artificial Intelligence department at AT&T Labs, Assistant Professor in the Department of Computer Science at the University of Colorado, Boulder, Senior Research Scientist in the Adaptive Systems Group at Harlequin Inc., and Postdoctoral Fellow with Prof. Michael Jordan in the Brain and Cognitive Science Department at Massachusetts Institute of Technology.

Prof. Smola's research interest are in nonparametric methods for estimation, in particular kernel methods and exponential families. With Prof. Scholkopf he is the author of 'Learning with Kernels', widely considered as the authoritative book on the kernel methods. Prof. Smola has co-authored over 150 papers in various conferences and journals, edited 4 books, and served on the program committees of numerous conferences. He is a member of the editorial boards of Journal of Machine Learning Research (JMLR), Statistics and Computing, and IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE PAMI). Prior to joining Yahoo! research in 2008 he was the program leader of the of the Statistical Machine Learning program at NICTA, a research lab funded by the Australian government. He is an associate editor of the Machine Learning Journal.

S V N Vishwanathan is an associate Professor in the Departments of Statistics and Computer Science at Purdue University. Prior to coming to Purdue in fall 2008 Vishwanathan was a principal researcher in the Statistical Machine Learning program of NICTA with an adjunct appointment at the College of Engineering and Computer Science, Australian National University. Vishwanathan received his ME and Ph.D. from the Indian Institute of Science in 2000 and 2003 respectively. Vishwanathan's research interests are in the broad area of machine learning with emphasis on optimization, kernel methods, and structured prediction.

Manfred K. Warmuth received the undergraduate degree in computer science from Friedrich Alexander Universitat, Germany, in 1978. He received the M.S. and Ph.D. degrees in computer science from the University of Colorado at Boulder in 1980 and 1981, respectively. Since then he has spent most of his time at the University of California in Santa Cruz. His current research interests are Machine Learning, online learning, statistical decision theory, and game theory. He has co-authored over 140 papers in top conferences and journals and has served as a program committee member of COLT, NIPS, and ICML the top three conferences in Machine Learning. Dr. Warmuth received a Fulbright fellowship.

Dr. Karsten Borgwardt studied Computer Science and Biology at LMU Munich, the University of Oxford and NICTA Canberra. After his PhD at LMU Munich under the supervision of Prof. Dr. Hans-Peter Kriegel and Prof. Dr. Alex Smola, he spent a year as a postdoc with Prof. Dr. Zoubin Ghahramani at the University of Cambridge, before moving to the Max Planck Institutes in Tübingen, where he now heads a group of 10 members as a W2 research group leader ( ~ associate professor). His awards include the Heinz-Schwärtzel-Award for Foundations of Computer Science in 2007, and the NIPS 2009 Outstanding Student Paper Award as supervisor and co-author.

Chris Clifton is an Associate Professor of Computer Science and (by courtesy) Statistics at Purdue University, and director of the Indiana Center for Database Systems. His primary research is on technology ensuring privacy in the analysis and management of data. He also works on challenges posed by novel uses of data mining technology, including data mining of text and data mining techniques applied to interoperation of heterogeneous information sources. He earned his Bachelor's and Master's degrees from the Massachusetts Institute of Technology in 1986, and his Ph.D. from Princeton University in 1991. Prior to joining Purdue, Dr. Clifton was a principal scientist in the Information Technology Division at the MITRE Corporation. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University.

François Fleuret got the PhD degree in Mathematics from the University of Paris VI in 2000 and the habilitation degree in Applied Mathematics from the University of Paris XIII in 2006. He holds a Senior Researcher position at the Idiap Research Institute in Switzerland. His research is at the interface between statistical learning and algorithmic, with a strong bias toward applications in computer vision. He is the author/co-author of 40 reviewed journal and conference papers, and is or was expert for the Research Council of the Academy of Finland, the Austrian Research Fund, and the French National Research Agency. He is the coordinator of the MASH European project on the design of very large families of image feature extractors, and is the site manager for the PASCAL2 Network of Excellence at the Idiap Research Institute.

Alexander Gray received Bachelor's degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University, and worked in the Machine Learning Systems Group of NASA's Jet Propulsion Laboratory for 6 years. He currently directs the FASTlab (Fundamental Algorithmic and Statistical Tools Laboratory) at Georgia Tech, consisting of ~20 people including 12 PhD students, which works on the problem of how to perform machine learning/data mining/statistics on massive datasets, and related problems in scientific computing and applied mathematics. Employing a multi-disciplinary array of technical ideas (from discrete algorithms and data structures, computational geometry, computational physics, Monte Carlo methods, convex optimization, linear algebra, distributed computing), the lab has developed the current fastest algorithms for several fundamental statistical methods, and also develops new statistical machine learning methods for difficult aspects of real-world data, such as in astrophysics and biology. This work has enabled high-profile scientific results which have been featured in Science and Nature, and has received a National Science Foundation CAREER award, three best paper awards, and three best paper award nominations. He has given tutorials and invited talks on efficient algorithms for machine learning at venues including ICML, NIPS, SIAM Data Mining, and is a member of the National Academies Committee on the Analysis of Massive Data. He is a frequent invited speaker in the emerging area of astrostatistics/astroinformatics.

Sergey Kirshner is an Assistant Professor in the Department of Statistics at Purdue University. He received his PhD in Information and Computer Science from the University of California, Irvine in 2005. Prior to joining the Purdue University in 2008, he was a Postdoctoral Fellow at the Alberta Ingenuity Centre for Machine Learning at the University of Alberta. His primary research interests are in unsupervised learning, and modeling of high-dimensional data, in particular with applications to atmospheric sciences.

David D. Lewis, Ph.D. (www.DavidDLewis.com) is a Chicago-based consulting computer scientist working in the areas of information retrieval, data mining, natural language processing, and the evaluation of complex information systems. He formerly held research positions at AT&T Labs, Bell Labs, and the University of Chicago. He has published more than 75 scientific papers and 8 patents, and was elected a Fellow of the American Association for the Advancement of Science in 2006.

Jennifer Neville is an assistant professor at Purdue University with a joint appointment in the Departments of Computer Science and Statistics. She received her PhD from the University of Massachusetts Amherst in 2006. She received a DARPA IPTO Young Investigator Award in 2003 and was selected as a member of the DARPA Computer Science Study Group in 2007. In 2008, she was chosen by IEEE as one of “AI's 10 to watch.” Her research focuses on developing data mining and machine learning techniques for relational domains, including citation analysis, fraud detection, and social network analysis.

Karthik Ramani is a professor in the School of Mechanical Engineering at Purdue University. He earned his B.Tech from the Indian Institute of Technology, Madras, in 1985, an MS from The Ohio State University, in 1987, and a Ph.D. from Stanford University, in 1991, all in Mechanical Engineering. He has worked as a summer intern in Delco Products and as a summer faculty intern in Dow Plastics. He has been awarded the Dupont Young Faculty Award, the National Science Foundation (NSF) Research Initiation Award, the NSF CAREER Award, the Ralph Teetor Educational Award from the Society of Automotive Engineers, the Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers, and the Ruth and Joel Spira Award for Outstanding Contributions to the Mechanical Engineering Curriculum. In 2002, he was recognized by Purdue University through a University Faculty Scholars Award. In 2005, he won the Discovery in Mechanical Engineering Award for his work in shape search. In 2006, he won the innovation of the year award (finalist) from the State of Indiana. He has also developed many successful new courses. He serves on the editorial board of the Elsevier Journal of Computer-Aided Design and the ASME Journal of Mechanical Design. His interests are in digital and computational geometry, high-dimensional mathematics, shape search, and computer support for early design. The NSF-Computers and Information Science in Engineering, NSF-Partnership for Innovation, NSF-Innovations in Engineering Education, National Institute of Health (NIH), General Electric, and Siemens/Parametric Technology Corporation/Boeing are supporting his current work. He is also currently serving on the NSF Advisory Committee for Industrial Innovation and Partnerships. In 2006 and 2007, Professor Ramani won the Most Cited Journal Paper award from Computer-Aided Design, the Research Excellence award in the College of Engineering at Purdue University, and the Thomas French Award for outstanding educator from The Ohio State University. In 2009, he won the Outstanding Commercialization award from Purdue University and the ASME Best Paper Award at the International Design Engineering Technical Conference. He has published his recent work in the Computer Vision and Pattern Recognition conference in 2011 which forms the basis for the MLSS talk presented.

Christopher Raphael heads the Music Informatics program in the School of Informatics and Computing at Indiana University, as well as holding adjunct appointments in the Jacobs School of Music, Cognitive Science, and Statistics. After receiving his PhD in Applied Mathematics from Brown University in 1991, he worked on a wide range of problems in both industry and academia including Arabic character recognition, magnetic resonance spectroscopy and mine detection before coming to focus on music. His musical research includes accompaniment systems, computer generated musical analysis, musical signal processing, and modeling of musical interpretation.

Dr. Si is with Computer Science Department and Statistics Department (by courtesy) at Purdue University. Dr. Si's research interests include information retrieval, applied machine learning techniques, and text mining techniques for different applications, which result in more than 80 publications. He is an associate editor of ACM Transactions on Information System and an editorial board member of Information Processing and Management. Dr. Si has been served as area chairs for conferences such as SIGIR, WWW and CIKM. He got NSF Career Award in 2008. Dr. Si got his Ph.D. degree from Carnegie Mellon University in 2006.