Applying mathematical models, computation and simulation, and statistical machine learning to solve engineering problems.
Using molecular models, computer simulations, and electronic structure calculations to discover new materials, elucidate relationships between structure and properties, and explain phenomena in materials science and chemical engineering. Applications include nanoporous materials for storing, separating, and sensing gases, catalysts for renewable energy applications and material degradation.
Leveraging recent advances in statistical machine learning (regression, classification, dimensionality reduction, clustering) to make predictions, identify patterns/structure in data, and learn relationships.
Mathematical modeling of fluid flow, heat transfer, mass transfer, and chemical reaction to design and optimize processes for various applications, including cryopreservation of cells, tissues and organs, etc.