Francis Bach is currently a researcher at INRIA, working in the Willow project, which is part of the Computer Science Laboratory at Ecole Normale Superieure. He completed his Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris. He is interested in statistical machine learning, and especially in graphical models, sparse methods, kernel-based learning, vision and signal processing.
Mladen Kolar is currently a Ph.D. student in Machine Learning Department at Carnegie Mellon University, working with Professor Eric Xing. His research is focused on high-dimensional statistics with applications to computational biology, in particular, learning the structure of time-varying regulatory networks and structured high-dimensional multi-task regression problems. For his work on complex networks he has been awarded the Facebook Fellowship.
Han Liu is an assistant professor in Biostatistics and Computer Science at Johns Hopkins University. He completed his joint Ph.D. in Machine Learning and Statistics at Carnegie Mellon University, working with John Lafferty and Larry Wasserman. His research lies at the boundary of Modern Statistics and Computer Science. Especially he is interested in large-scale nonparametric methods, which directly conduct inference in infinite dimensional spaces and are more flexible to capture the subtleties in modern scientific applications. His long-term research goal is to develop a new generation of more powerful and principled statistical theories and machine learning algorithms to explore, understand, and predict large-scale, complex datasets.
Guillaume Obozinski is a researcher in the Sierra INRIA project-team, in the Computer Science department of the Ecole Normale Supérieure, Paris, France. He earned his PhD in 2009 from the Statistics department of the University of California at Berkeley, under the supervision of Michael Jordan, and was a postdoctoral researcher in the Willow INRIA team until 2010. His research interests include machine learning, statistics, optimization, sparsity and their applications to computer vision, text processing and computational biology.
Eric P. Xing
Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. He received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley, working with Michael Jordan, Richard Karp, and Stuart Russell. At CMU, he directs the SAILING Lab whose research spans a broad spectrum of topics ranging from theoretical foundations to real-world applications in machine learning, statistics, and computational biology, including, 1) theories and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social network, computer vision, and natural language processing problems.