Walter Dempsey: Learning While Treating and Inference after Optimization скачать в хорошем качестве

Walter Dempsey: Learning While Treating and Inference after Optimization

January 27, 2026 ASA Ann Arbor Chapter Meeting with Walter Dempsey speaking about Learning While Treating and Inference after Optimization: Challenges in Real-Time Health Optimization. Abstract: Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions in multiple domains of health sciences. In the treatment of individuals with chronic health conditions, a critical task is the design of sequential treatments to determine when and in which context to deliver treatments. We operationalize this task through the construction of decision rules that take as input the individual's current context and output a recommended treatment. There is increasing interest in personalization of these rules in real time as individuals experience the sequences of treatment. In this talk, we will discuss recent work on the design and deployment of online learning algorithms for use in personalizing mobile health interventions. We will then discuss how to perform post study inference from data collected using online learning algorithms. We will illustrate these methods with an analysis of an MRT for promoting physical activity in cardiac rehabilitation. Speaker: Walter Dempsey is an Associate Professor of Biostatistics and Associate Research Professor at the Institute for Social Research. His research focuses on statistical methods for digital and mobile health. His current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks. In the coming years, he will continue to design and apply novel statistical methodologies to make sense of complex longitudinal, survival, and relational datasets. This work will inform decision making in health by aiding in intervention evaluation and development. Prior to joining, he was a postdoctoral fellow in the Department of Statistics at Harvard University where he worked within the Statistical Reinforcement Learning Lab under the supervision of Susan Murphy. He received his PhD in Statistics at the University of Chicago under the supervision of Peter McCullagh.