Elizabeth A. Holm
Prior to joining CMU in 2012, Professor Holm spent 20 years as a computational materials scientist at Sandia National Laboratories, working on simulations to improve processes for lighting manufacture, microcircuit aging and reliability, and the processing and welding of advanced materials. Prof. Holm obtained her B.S.E in Materials Science and Engineering from the University of Michigan, S.M in Ceramics from MIT, and dual Ph.D. in Materials Science and Engineering and Scientific Computing from the University of Michigan. Active in professional societies, Prof. Holm has received several honors and awards, is a Fellow of ASM International and TMS, 2013 President of The Minerals, Metals, and Materials Society, an organizer of several international conferences, and has been a member of the National Materials Advisory Board. Prof. Holm has authored or co-authored over 140 publications.
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.
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.