We develop mathematical and computational models for disease prediction, prevention, and control analysis. Our research is in areas of systems simulation and network modeling, simulation-based parametric and control optimization, and stochastic processes. The objective is to develop the math necessary to capture the interactions between multiple interrelated diseases and interventions that influence the social determinants of health (SDOH), such as poverty, food and housing insecurity, access to healthcare, which are the common risk factors for multiple diseases. Our work is funded by the National Institutes of Health, the National Science Foundation, and the World Health Organization. Our work is in collaboration with the US Centers for Disease Control and Prevention, the World Health Organization, the International Agency for Research on Cancer, and multiple academic institutes.



Simulation, networks, machine learning, reinforcement learning, simulation-based optimization, dynamic programming, stochastic processes