May 15, 2018

COPSS Awards for Bin Yu and Susan Murphy

The Committee of Presidents of Statistical Societies (COPSS) presents awards annually to honor statisticians who have made outstanding contributions to the profession of statistics. Bin Yu has been selected as the 2018 recipient for the Elizabeth L. Scott Award, which is presented biennially (even-numbered years) to an individual, male or female, who has helped foster opportunities in statistics for women. Susan A. Murphy has been named as the 2018 R.A. Fisher Lecturer, an award with lectureship presented yearly to recognize a leading statistician who has contributed significantly to scientific investigations through the development and promotion of statistical methods. The award ceremony will take place on August 1, 2018, during the Joint Statistical Meetings (JSM2018) in Vancouver, Canada. More details, including Susan’s lecture abstract, can be found at ww2.amstat.org/meetings/jsm/2018/program.cfm.

Bin Yu is Chancellor’s Professor, Departments of Statistics and Electrical Engineering and Computer Sciences at the University of California at Berkeley. Growing up in China, Yu received her Bachelor’s degree in Mathematics from Peking University in 1984, and completed her Master’s (1987) and PhD (1990) in Statistics at UC Berkeley. Prior to becoming a faculty member at Berkeley in 1992, she was an assistant professor at the University of Wisconsin–Madison. She has held visiting professorships at several universities/institutes (incl. Yale, ETH, Bell Labs, Poincaré Institute, INRIA), and since 2005 has been a founding co-director of the Microsoft Joint Lab on Statistics and Information Technology at Peking University in China.

Yu’s research interests include statistical inference, machine learning, and information theory; currently she focuses on statistical machine learning methodologies and on theory and algorithms for solving high-dimensional data problems. Her collaborations are highly interdisciplinary and include scientists from genomics, neuroscience, precision medicine, and political science.

Yu has received numerous awards and honors. She is a member of the US National Academy of Sciences, became a Guggenheim Fellow in 2006, and is a Fellow of the ASA, IMS, AAAS, and IEEE; she was the IMS President in 2013–2014, and the IMS Rietz Lecturer in 2016.  Her impressive list of professional activities includes serving on many editorial boards, such as PNAS, Journal of Machine Learning Research, Technometrics, Annals of Statistics, and JASA.

The award to Bin Yu will be presented by Shirley Mills, Chair of the COPSS Elizabeth L. Scott Award Committee.

Susan A. Murphy is Professor of Statistics and Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences, and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University. Prior to joining Harvard in 2017, she was the H.E. Robbins Distinguished University Professor of Statistics, Professor of Psychiatry, and Research Professor at the Institute for Social Research at the University of Michigan. Growing up in southern Louisiana, Murphy received her BSc in Mathematics from the Louisiana State University in 1980, and her PhD in Statistics from UNC–Chapel Hill in 1989. She was a faculty member at Penn State (1989–97) before joining the University of Michigan.

Murphy’s research interests include experimental design and causal inference to inform sequential decision making. She developed the sequential, multiple assignment, randomized trial (SMART) design, which is used by scientists and clinical researchers to build better treatments for a broad range of health problems including ADHD, autism and depression. Her research lab focuses on methods for improving real-time sequential decision-making in mobile health, e.g. methods and algorithms that can be deployed on wearable devices, to deliver individually tailored treatments.

Among many honors, Murphy was inducted into the US National Academy of Sciences in 2016 and the National Academy of Medicine in 2014. She was awarded a MacArthur Fellowship in 2013, and is a Fellow of ASA, IMS and the College on Problems in Drug Dependence. She served as a co-editor of the Annals of Statistics (2007–09), was the 2015 IMS Wald Lecturer, and is the current President of the Bernoulli Society.

Susan A. Murphy will deliver the 2018 Fisher Lecture [abstract below] entitled “The Future: Stratified Micro-randomized Trials with Applications in Mobile Health” at 4pm on Wednesday, August 1, following the presentation of the Fisher award by Alicia Carriquiry, Chair of the COPSS Fisher Lecture Committee.

Susan Murphy, 2018 R.A. Fisher Lecture

The Future: Stratified Micro-randomized Trials with Applications in Mobile Health

Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been impacted by prior treatments. Furthermore the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided. Here we discuss our work on the design of a mobile health smoking cessation study in which the above two challenges arose. This work involves the use of multiple online data analysis algorithms. Online algorithms are used in the detection, for example, of physiological stress. Other algorithms are used to forecast at each vulnerable time, the remaining number of vulnerable times in the day. These algorithms are then inputs into a randomization algorithm that ensures that each user is randomized to each treatment an appropriate number of times per day. We develop the stratified micro-randomized trial which involves not only the randomization algorithm but a precise statement of the meaning of the treatment effects and the primary scientific hypotheses along with primary analyses and sample size calculations. Considerations of causal inference and potential causal bias incurred by inappropriate data analyses play a large role throughout.

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