Professor Holm uses the tools of computational materials science to study a variety of materials systems and phenomena. Her research areas include the theory and modeling of microstructural evolution in complex polycrystals, the physical and mechanical response of microstructures, mechanical properties of carbon nanotube networks, atomic-scale properties of internal interfaces, machine vision for automated microstructural classification, and machine learning to predict rare events. Computational techniques applied to these problems range from the atomic scale (molecular dynamics) through the mesoscale (Monte Carlo, phase field, cellular automata) to the continuum scale (finite element). A particular focus is identifying useful concepts from data science, including machine learning, machine vision, evolutionary computing, and network analysis, and developing them to answer materials science questions.
In 1992, I was a member of the inaugural class of PhD recipients from the MICDE PhD in Scientific Computing program. I might even be the first? At that time, I was an IBM Predoctoral Fellow in Scientific Computing.
Computer vision extracts and analyzes individual particles from an image of a powder used in additive manufacturing (a.k.a. 3D printing) of metal alloys.