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COPSS-NISS Leadership Webinar on March 28, 12-1pm ET: Leadership in Statistical Research

  • 1.  COPSS-NISS Leadership Webinar on March 28, 12-1pm ET: Leadership in Statistical Research

    Posted 03-26-2023 23:37

    Tuesday, March 28, 2023 | 12 - 1 pm ET / 9 - 10 am PT

    Join us for 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.

    Click here to Register on Zoom!


    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

    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 COPSS-NISS Leadership Webinar Series

    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.

    Lingzhou Xue
    Penn State University