RESEARCH FOCUS
Michael’s research is focused on using AI/ML tools, specifically generative modeling techniques, to efficiently explore the space of material structures and properties. Current projects include creating new algorithms to generate highly accurate polycrystalline microstructures, solving inverse materials problems with Bayesian methods, active learning, and learning in data spaces with incomplete observations.
