Susan Murphy, Professor of Statistics at Harvard University
Abstract: Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in a Digital Health. However after an reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
Bio: Susan Murphy is Professor of Statistics at Harvard University, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University, and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her lab works on clinical trial designs and online learning algorithms in sequential decision making, in particular in the area of digital health. She developed the micro-randomized trial for use in constructing mobile health interventions which is in use across a broad range of health related areas. She is a 2013 MacArthur Fellow, a member of the National Academy of Sciences and the National Academy of Medicine, both of the US National Academies. She is a Past-President of IMS and of the Bernoulli Society and a former editor of the Annals of Statistics. She is a prior recipient of the RA Fisher Award from COPSS and was awarded the Guy Medal in Silver from the RSS.
