Speaker
Description
In this talk, I will discuss data-driven stochastic optimization strategies for the evolutionary race between a pathogenic cell population and a clinician. In this system, the clinician seeks to eliminate the adversarial cell population through optimally changing their environment and fitness, while conversely, the cells make optimal decisions to adapt and survive. I will present a stochastic differential equation (SDE) model of pathogens whose birth and death rates are influenced by drug dynamics. A clinician, in turn, controls the drug dynamics through a machine-learning-based optimization framework based on the probability distributions of pathogen population sizes. In addition to its applications in translational medicine, our work generalizes to businesses and institutions to optimize their adaptability and resilience to environmental stress. This is joint work with Tony Cicerone.
