Reinforcement learning offers a powerful paradigm for automatically discovering and optimizing sequential treatments for chronic and life-threatening diseases. In particular, we will focus on how data collected in multi-stage sequential trials can be used to automatically generate treatment strategies that are tailored to patient characteristics and time-dependent outcomes. We will also examine promising methods to improve the efficiency of clinical trials through adaptation. Examples will be drawn from several ongoing research projects on developing new treatment strategies for epilepsy, mental illness, diabetes, and cancer.
This event is part of the NYC Data Science Seminar Series, organized by MSR NYC, Facebook, NYU Center for Data Science, Columbia University, and Cornell Tech, with the Jacobs Technion-Cornell Institute.
More information can be found here.