ISE Graduate Seminar
Data-driven medication policies with wearable movement trackers for Parkinson’s patients
PhD candidate, UT
Friday, April 23, 2021
Abstract: Baucum’s research focuses on improving the application of reinforcement learning to medical decision-making problems. He specializes in solving treatment-planning problems through the use of environment models, which are models that can simulate realistic patient symptom trajectories. These models are learned from real-world patient data and, when properly constructed and validated, can be paired with reinforcement learning algorithms to identify optimal treatment policies. Some of my past work focuses on designing new methods for building these environment models, while other work applies them to current healthcare challenges. He works under Dr. Anahita Khojandi and is scheduled to graduate in summer 2021.
Friday, April 23 at 3:30pm to 4:20pmVirtual Event