Jiahan Zhang, PhD

Dr. Zhang received his PhD in physics from Syracuse University in Syracuse, New York. He then completed a medical physics residency at Duke University in Durham, North Carolina.

Through his research, Dr. Zhang proposes a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. The hyperplane model allows physicians and planners to explore the best clinically achievable plans with different organs at risk sparing goals before inverse planning. In addition, in order to systematically address complex tradeoffs and achieve consistent high plan quality efficiently, he has formulated the interactions between the planner and the TPS into a finite-horizon reinforcement-learning (RL) model. After extensive training, the RL planning bot gains planning experience and completely automates the planning process. The robustness of the planning bot has been validated in clinical environments for pancreas stereotactic body radiotherapy (SBRT) treatment planning. He is working to include knowledge-based modeling in the agent decision-making process, and to develop a set of clinical plan scoring metric based on patient toxicity outcome.

Dr. Zhang is a recipient of several awards including:

• AAPM Annual Conference Best in Physics Award, 2020
• AAPM Expanding Horizon Travel Grant, 2020
• AAPM Annual Conference Science Council Award, 2019