Xun (Ryan) Huan is an Assistant Professor in the Department of Mechanical Engineering. His research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. His expertise focuses on bridging models and data: optimal experimental design, Bayesian methods for statistical inference, uncertainty propagation in high-dimensional settings, and methods that are robust to model misspecification. He seeks to develop efficient computational methods that integrate realistic models with big data, and combine uncertainty quantification with machine learning to enable robust prediction, design, and decision-making. He is interested in collaborative opportunities in various applications that can benefit from a better understanding of uncertainty and modeling. Current research activities include assessing uncertainty in deep neural networks, and developing sequential experimental design methods for improving autonomy.
Rohini Bala Chandran is an Assistant Professor in the Department of Mechanical Engineering. Her research focuses on developing computational models integrated with experimental analyses to probe the interplay of heat and mass transfer, fluid flow, and chemical reactions that play a central role in a host of thermal, thermochemical, and electrochemical energy systems. Her group leverages high-performance computing to understand coupled transport and kinetic phenomena, and to perform multi-parameter optimization to design and operate efficient devices for energy conversion and storage. Current research activities in her group focus on solar fuels from water and carbon dioxide, thermal energy storage at high temperatures, and wastewater treatment.