Research

Our research includes novel stochastic optimization algorithms for exploring high-dimensional, non-linear variable spaces and modeling the complex, spatio-temporal dynamics of physical and biological systems, as well as the analysis of multi-basin energy/fitness landscapes. We continue to make contributions in both the foundations and applications of ML and deep learning in diverse disciplines, from the life sciences to engineering. Highlights of our recent work include optimization for deep learning, deep generative (latent variable) models in generative AI for modeling small and large molecules. Lately, we are advancing large language models for bio & health informatics.

Highlight: Computational Structural Biology Research