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Michael Kosorok Receives George W. Snedecor Award

1 May 2023 286 views No Comment
Sebastien Haneuse and Eric Laber

    Michael Kosorok

    Michael R. Kosorok, W. R. Kenan Jr. Distinguished Professor of Biostatistics and professor of statistics and operations research, has been named the 2023 recipient of the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. The award was given in recognition of Kosorok’s foundational, creative, and original contributions to mathematical statistics; methodological developments in empirical processes and machine learning; advancement of precision health; and mentoring of students, postdocs, and junior faculty.

    Kosorok has authored more than 200 peer-reviewed manuscripts appearing in top-tier journals and conference proceedings. In 2008, he published his monograph, “Introduction to Empirical Processes and Semiparametric Inference,” which quickly became the canonical introduction to the area. Shortly thereafter, he focused his research on artificial intelligence and precision medicine. He was among the first to provide rigorous theoretical results for machine learning methods for estimation of optimal treatment regimes, including some of the earliest applications of Q-learning and direct search estimation. His paper on outcome-weighted learning (cited nearly 750 times) began lines of research on direct-search estimation and revealed connections between optimal treatment regimes and classification.

    Kosorok is awarded …
    For foundational, creative, and original contributions to mathematical statistics; for methodological developments in empirical processes and machine learning; for advancement of precision health; and for mentoring of students, postdocs, and junior faculty.

    In the Biometrics paper titled “Estimating Individualized Treatment Regimes from Crossover Designs,” Kosorok and his co-authors developed a novel direct-search estimator of the optimal regime under a 2×2 crossover design. Such crossover designs are common in pilot testing, rare diseases, and other settings in which recruiting a large pool of participants is difficult. Nevertheless, prior to this publication, there were no direct search estimators for estimating an optimal treatment regime under such a design. The proposed method accounts for carryover effects, uses a convex relaxation for computational efficiency, and is Fisher consistent.

    This publication is an illustration of Kosorok’s research modus operandi. He identifies an important practical problem, develops a novel methodological approach, and then provides a rigorous and complete description of the method’s operating characteristics.

    In addition to his prolific publication record, Kosorok has shaped the field through his service and mentoring. He served as head of the biostatistics department at The University of North Carolina from 2006–2020 and chair of the COPSS Presidents’ Award committee. He is currently president-elect of the Institute of Mathematical Statistics and has mentored more than 50 PhD students, many of whom now hold prominent positions at academic institutions or in industry.

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