The research focuses on the mathematical foundations and applications of machine learning and data-driven optimization and control, especially on robustness (distributional robustness, adversarial robustness, generalization, causal intervention, robust optimization), and interfacing dynamical systems and learning.
Flexible Research Platform
- Modeling, Analysis, and Scaling Limits for Bulk-Interface Processes
- Data-driven Optimization and Control
- Quantitative Analysis of Stochastic and Rough Systems
- Numerical Methods for Innovative Semiconductor Devices
- Probabilistic Methods for Dynamic Communication Networks
- Former Groups