DISEASE MODELING LAB
PI: Chaitra Gopalappa, Mechanical and Industrial Engineering, UMass Amherst GoogleScholar | LinkedIn | GITHUB
Our fundamental research is in areas of systems simulation and network modeling, artificial intelligence and machine learning, simulation-based parametric and control optimization, and stochastic processes. Recent work includes:
- Large language models, machine learning, and stochatsic models to understand the interdependencies between diseases and social determinants of health (SDoH) (e.g., poverty, food and housing insecurity, health access, etc.)
- Integrated simulation and deep learning for addressing modeling gaps in joint modeling synergistic diseases.
- Deep reinforcement learning for identifying optimal resource allocation strategies in dynamic health and other social systems.
- Multi-agent reinforcement learning for epidemic control under jurisditioncal heterogeneity and cross-jurisdictional interactions.
- Network modeling and machine learning for early detection of disease outbreaks and rapid response.
We develop mathematical and computational decision analytic tools for evaluation of national and global public health strategies for cancers, HIV, and STIs, in collaboration with the U.S. Centers for Disease Control and Prevention, the World Health Organization, the International Agency for Research on Cancer, and academic institutes.
Funding
Our work is funded by the National Institutes of Health, the National Science Foundation, Centers for Disease control and Prevention, and the World Health Organization.
Collaborators
Our work is in collaboration with academic and non-academic researchers and institutes in multiple countries.
RESEARCH AREA: Simulation, networks, machine learning, reinforcement learning, simulation-based optimization, dynamic programming, stochastic processes