Improving Health-Care: Challenges and Opportunities for Reinforcement Learning
Joelle Pineau, Associate Professor of Computer Science McGill University
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.
Joelle Pineau is the Co-director of the Reasoning and Learning Lab in the School of Computer Science. Prof. Pineau?s research focuses on developing new models and algorithms that allow computers to learn to make good decisions in complex real-world domains, even in circumstances where there is incomplete or incorrect information. She also works on applying these algorithms to complex problems in robotics and health care. Prof. Pineau is a Senior Fellow of the Canadian Institute for Advanced Research and a member of the Center for Intelligent Machines at McGill.
REGISTRATION ENCOURAGED VIA https://pineau-at-dsi.eventbrite.com/
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: http://datascienceseminar.nyc/