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Join us for our another insightful webinar on the topic of leadership in the field of statistics and data science. The focus of this webinar is on leadership in statistical research. Hear from two of the leading statisticians strategizing about the future of statistical research.
Professor Susan Murphy is Mallinckrodt Professor of Statistics and of Computer Science at Harvard University. Professor Trevor Hastie is John A. Overdeck Professor of Mathematical Sciences at Stanford University. Our two distinguished panelists will share their leadership journeys and insights in a discussion moderated by Dr. Lingzhou Xue of Penn State University.
Susan Murphy, Harvard University
Mallinckrodt Professor of Statistics and of Computer Science,
Radcliffe Alumnae Professor at the Radcliffe Institute
Trevor Hastie, Stanford University
John A. Overdeck Professor of Mathematical Sciences
Professor of Statistics
Professor of Biomedical Data Science
Lingzhou Xue (Penn State University)
Associate Professor of Statistics
COPSS Leadership Academy, 2022
The COPSS-NISS Leadership Webinar Series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) Emerging Leaders in Statistics and the National Institute of Statistical Sciences (NISS). The purpose of the webinar series is to promote leadership skills for members of the statistical societies at any stage in their careers. The series features conversations with leaders throughout the discipline, including leaders from major academic and government institutions, and companies. Invited speakers share their leadership stories and answer questions about their experiences. Each webinar is moderated by a member of the COPSS Emerging Leaders in Statistics program.
Natalie Dean (Chair), Emory University, COPSS Leadership Academy
Claire Bowen, Urban Institute, COPSS Leadership Academy
Amita Manatunga, Emory University, COPSS
Jim Rosenberger, NISS
David S. Matteson, NISS, Cornell University
Mary Ryan, Yale University
Lingzhou Xue, Penn State, COPSS Leadership Academy, NISS
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.