My work advances AI methods for modeling complex spatiotemporal dynamics to support trustworthy, data-driven decision-making, with primary applications in population health. Ongoing projects in my group include the development of physics-informed machine learning frameworks for spatiotemporal modeling and the design of differentiable simulators for agent-based models. We are also exploring conformal prediction techniques for time series and spatiotemporal forecasting, with the goal of providing reliable uncertainty estimates in dynamic environments.
Faculty
Alexander Rodríguez
Assistant Professor, Computer Science and Engineering
Contact
7346470674
[email protected]
Website
Research
Research Areas
AI; ML and Statistical InferenceBiology Applications and Engineering
Health Science
Mechanics and Dynamics
Simulations