Undergraduate Research & Scholarships

Zhengxu Yan

Adaptive Healthcare Planning with Reinforcement Learning

Healthcare facilities must adapt to growing demand with limited resources, but changes to space, staffing, and operations often have complex, interconnected effects. This project develops a new computational framework that uses reinforcement learning, a branch of artificial intelligence, to optimize how healthcare spaces, people, and activities are coordinated over time. Through simulation, we identify strategies that improve space use, patient experience, and operational efficiency at a cardiac catheterization lab. Our findings help reimagine healthcare buildings as dynamic, adaptable systems rather than static structures.

Message To Sponsor

I’m truly grateful for the opportunity to work on my research this summer. Thanks to your support, I was able to explore how healthcare spaces can better adapt to real-world challenges by combining simulation and artificial intelligence. This experience helped me grow both technically and personally, and has shaped how I think about designing technology for real-world impact. Thank you for believing in students like me and making experiences like this possible.