Dr. Feng’s recent research focuses on machine learning theory and algorithm design, reinforcement learning in stochastic control, conditional McKean–Vlasov SDEs, data-driven modeling, and the signature method. He is also interested in stochastic analysis, rough path theory, and sub-Riemannian geometry.

Dr. Dulebenet’s research group builds different types of AI-based algorithms (e.g., heuristics, meta-heuristics, hyper-heuristics, hybridized methods, swarm intelligence) for challenging decision problems.

Dr. Chipilski is a computational scientist focused on data assimilation for numerical weather prediction. His current research leverages generative AI to develop novel algorithms that improve the accuracy and efficiency of integrating observational data. Broadly interested in atmospheric dynamics, his work is interdisciplinary, spanning algorithmic theory and Earth system modeling.