This group develops mathematical and computational frameworks to understand complex, stochastic systems in biology, physics, and engineering. Their work spans numerical algorithms, Monte Carlo methods, and the analysis of nonlinear dynamics, with a focus on real-world phenomena from neuroscience to energy systems.
Themes:
- dynamical systems
- applied mathematics
- surrogate modeling
Members
- Michael Chertkov - Energy systems, data science / machine learning, statistical physics, control and optimization theory, fluid mechanics
- Kevin Lin - Stochastic nonlinear phenomena in biology and physics, especially problems from computational neuroscience and nonequilibrium statistical physics; Monte Carlo algorithms; numerical methods for stochastic differential equations; scientific computing.
- Samy Missoum - Surrogate modeling, uncertainty quantification, design optimization