As an expert in molecular imaging of single cell signaling in cancer, I develop integrated systems of molecular, cellular, optical, and custom image processing tools to extract rich data sets for biochemical and behavioral functions in living cells over minutes to days. Data sets composed of thousands to millions of cells enable us to develop predictive models of cellular function through a variety of computational approaches, including ODE, ABM, and IRL modeling.
Accomplishments and Awards
- 2021 Propelling Original Data Science (PODS) Grant Award: Discovering Causes of Cancer Recurrence Through Inverse Reinforcement Learning