Dr. Shanbhag works at the interface of soft matter and numerical algorithms. Over the past couple of years, he has started to apply machine learning methods to suitable problems in the area.

Dr. Sanghyun Lee is an associate professor of mathematics at FSU. His research develops high-fidelity numerical methods and scientific machine learning for multiphysics and multiscale PDEs, focusing on subsurface applications like poroelasticity and fracture. He has published widely on finite element methods and is a co-PI on a DOE project for subsurface energy systems.

Dr. Gubanov is an assistant professor and a lead PI on a multi-year NSF ($550K) grant and a Casey DeSantis Cancer Innovation Fund ($1.2M) consortium grant with Moffitt Cancer Center to develop new AI for better cancer practices. He earned his PhD from the University of Washington and completed a postdoc at MIT CSAIL. His honors include the Amazon AI AWS Research Award, the Communications of the ACM (CACM) Award, the ACM SIGMOD Research Highlights Award, and the IEEE ICDE Best Paper Award.

Dr. Dexter is an assistant professor of scientific computing at FSU. His research focuses on computational mathematics, scientific machine learning, and uncertainty quantification, applied to areas like energy systems and deep learning. He has published at premier venues like NeurIPS and ICML and is passionate about advancing interdisciplinary research.