Borja Balle (McGill University) is currently a postdoctoral fellow at McGill University. He obtained his PhD from the Universitat Politècnica de Catalunya (UPC) in July 2013, where he also obtained MS in Applied Mathematics in 2009 and BS in Telecommunication Engineering and in Mathematics in 2007. During his PhD Borja was a visitor student at NYU for several months, and before graduate school he spent a year as a software engineer at Intelligent Pharma. His research interests lie on the intersection between automata theory and machine learning, in particular on applications to natural language processing and grammatical inference. He also works on learning under algorithmic constraints, e.g. data streams.

Byron Boots (University of Washington) is a postdoctoral researcher in the Robotics and State Estimation Lab in the Computer Science and Engineering Department at the University of Washington. He received his B.A. in Computer Science and Philosophy from Bowdoin College in 2003, and his Ph.D. in Machine Learning from Carnegie Mellon University in 2012. Prior to becoming a graduate student, he was an Associate in Research in the Center for Cognitive Neuroscience at Duke University and an engineer at MobileRobots Inc. His research focuses on spectral and kernel methods for system identification, reinforcement learning, and control.

Yoni Halpern (New York University) is a Ph.D. candidate at New York University. He received his BASc in Engineering Science from University of Toronto in 2011. His research interests are in learning latent variable models for improving health care, focusing on the identifiability and statistical recoverability of large scale models to reason about latent clinical variables in the emergency department.

Daniel Hsu (Columbia University) is an Assistant Professor of Computer Science at Columbia University. Previously he was a postdoc at Microsoft Research New England, the Department of Statistics at Rutgers University, and the Department of Statistics at the University of Pennsylvania from 2010 to 2011. He received his Ph.D. in Computer Science in 2010 from UC San Diego and his B.S. in Computer Science and Engineering in 2004 from UC Berkeley. His research interests are in the algorithmic and statistical aspects of latent variable models, interactive learning, and privacy-preserving data analysis.

Percy Liang (Stanford University) is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research focuses on methods for learning richly-structured statistical models from limited supervision, most recently in the context of semantic parsing in natural language processing. He won a best student paper at the International Conference on Machine Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and is also a 2010 Siebel Scholar.

David Sontag (New York University) is Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. David’s recent work has focused on unsupervised learning of probabilistic models (e.g., for medical diagnosis) directly from clinical data found in electronic health records. Prior to joining Courant, he was a postdoctoral researcher for Microsoft Research New England, 2010-11. David's Ph.D thesis won the award for the best doctoral thesis in Computer Science at MIT in 2010. His research has received recognition including a Best Paper Award at the conference on Empirical Methods in Natural Language Processing in 2010, a Best Paper Award at the conference on Uncertainty in Artificial Intelligence in 2008, and an Outstanding Student Paper Award at the conference on Neural Information Processing Systems in 2007.