About the Speakers

Susan Murphy is Mallinckrodt Professor of Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute at Harvard University. Her research focuses on improving sequential, individualized decision-making in health, in particular, clinical trial design and data analysis to inform the development of just-in-time adaptive interventions in digital health. She developed the micro-randomized trial for use in constructing digital health interventions; this trial design is in use across a broad range of health-related areas. Her lab works on online learning algorithms for developing personalized digital health interventions. Dr. Murphy is a member of the National Academy of Sciences and of the National Academy of Medicine, both of the US National Academies. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision-making. She is a Past-President of IMS and of the Bernoulli Society and a former editor of the Annals of Statistics.

Trevor Hastie is the John A Overdeck Professor of Mathematical Sciences and Professor of Statistics and Biomedical Data Science at Stanford University. Hastie is known for his research in applied statistics, particularly in the fields of statistical modeling, bioinformatics, and machine learning. He has published six books and over 200 research articles in these areas. He invented principal curves and surfaces and generalized additive models. He has contributed toward the understanding of machine learning techniques through a statistical lens, in particular boosting, support vector machines, and random forests. Before joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for nine years, where he helped develop the statistical modeling environment popular in the R computing system. He has many popular packages in this environment, which are used by tens of thousands of researchers.  He received his B.Sc. in statistics from Rhodes University in 1976, M.Sc. from the University of Cape Town in 1979, and Ph.D. from Stanford in 1984. He is a fellow of the American statistical society, the Institute of Mathematical Statistics, and the Royal Statistical Society, and a member of the International Statistics Institute and the National Academy of Sciences.

About the Moderator

Lingzhou Xue is an Associate Professor in the Department of Statistics at The Pennsylvania State University. He received his B.S. degree in Statistics from Peking University in 2008 and Ph.D. degree in Statistics from the University of Minnesota in 2012. He was a long-term visitor at the Institute of Mathematics and its Applications from 2011-2012 and a postdoctoral research associate in the Department of Operations Research and Financial Engineering at Princeton University from 2012-2013. He is an Elected Member of the International Statistical Institute. His research interests include the statistical learning, high-dimensional nonparametric/semiparametric statistics, large-scale inference, graphical/network models, and convex/nonconvex statistical computation.