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SUMMARY:Reinforcement Learning for Digital Health Interventions in the Dyadic Setting
DESCRIPTION:UW-ISyE looks forward to welcoming Susan Murphy Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute\, Harvard University. \n\n\n\n\n\n\n\nWe present our ongoing work on the development of an online reinforcement learning (RL) algorithm for dyadic digital intervention settings in which the task for the RL algorithm is to assist the target person with a difficult illness be adherent to behavioral activities.   To achieve this goal the RL algorithm will not only deliver digital interventions to the target person but also deliver interventions to assist the carepartner to manage caregiving burden and help the two individuals improve their relationship.  That is\, different RL components target different elements of the dyad.  The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad.  We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting.  This work is motivated by our development of the ADAPTS-HCT multi-agent RL algorithm\, designed to improve medication adherence by young adults who have undergone a blood and bone marrow transplant.  The RL algorithm will be deployed in summer 2026. \n\n\n\n\n\nBio: Susan A. Murphy is Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute\, Harvard University.  Her research focuses on improving sequential decision making via the development of online\, real-time reinforcement learning algorithms.  Her lab is involved in multiple deployments of these algorithms in digital health.  She is a member of the US National Academy of Sciences and of the US National Academy of Medicine.  In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making.  She is a Fellow of the College on Problems in Drug Dependence\, Past-President of Institute of Mathematical Statistics\, Past-President of the Bernoulli Society and a former editor of the Annals of Statistics.    
URL:https://engineering.wisc.edu/event/reinforcement-learning-for-digital-health-interventions-in-the-dyadic-setting/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
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