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Elizabeth Holm, University of Michigan, USA
Elizabeth Holm, University of Michigan, USA
Professor Elizabeth Holm has over 25 years of expertise as a computational materials scientist. 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, and network analysis, and developing them to answer materials science questions. Prior to joining the University of Michigan, Elizabeth held positions at Sandia National Laboratories and Carnegie Mellon University. She has received several honors and awards, is a Fellow of ASM International, 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. Elizabeth has been inducted as a 2019 Fellow of The Minerals, Metals & Materials Society for pioneering achievements and leadership in computational materials science and Integrated Computational Materials Engineering.
Invited Talk: Quality over Quantity: Data Frugal Computer Vision for Microstructural Science
Luther McDonald, University of Utah, USA
Luther McDonald, University of Utah, USA
Luther McDonald is an associate professor in the Department of Civil and Environmental Engineering and the Nuclear Engineering Program at the University of Utah (UU). He joined the UU in January 2014 and has led the development of a radiochemistry laboratory, mentoring over forty students, and managing research projects from NEUP, DTRA, NNSA, and DHS, including the Department of Homeland Security’s Nuclear Forensics Undergraduate Summer School in 2016 – 2017. Before joining the UU, Luther performed a postdoctoral fellowship at Pacific Northwest National Laboratory in National Technical Nuclear Forensics, worked as a visiting scientist at the Commissariat à l’énergie atomique in Saclay, France. He served as the elected Secretary of the American Chemical Society’s Division of Nuclear Chemistry and Technology from 2013 – 2016 and was named one of Forbes 30 under 30 in Science in 2017.
Invited Talk: Morphological Signatures of the Nuclear Fuel Cycle
Joshua Stuckner, NASA Glenn Research Center, USA
Joshua Stuckner, NASA Glenn Research Center, USA
Joshua Stuckner is a Materials Informatics Scientist at NASA Glenn Research Center. His research interests and expertise include applying computer vision and deep learning to materials design and discovery, automatic microscopy analysis, Transmission Electron Microscopy (TEM), in-Situ TEM, and surrogate modeling. Joshua has been working on the team that has recently released pretrained MicroNet models for deep-learning-based microscopy segmentation and analysis with less training data. There were over 10,000 downloads of these models over the last two years.
Invited Talk: Enhancing Microscopy Image Analysis with In-Domain Pre-trained Encoders
Jianwei Yang, Microsoft Research, Redmond, USA
Jianwei Yang, Microsoft Research, Redmond, USA
Jianwei Yang is a principal researcher in Deep Learning Group at Microsoft Research, Redmond. His research interests span in computer vision, multi-modality, and machine learning. Currently, Jianwei is focusing on building next-generation vision and multi-modal foundation models. The team he has been working with is the first few in this line of research, and have produced a series of works, like UniCL, RegionCLIP, GLIP, KLITE, and the foundation model Florence, generalist decoder X-Decoder, and SEEM, a promptable and interactive model for segmenting everything everywhere all at once in an image.
Invited Talk: Promptable Vision Foundation in the Wild: From Head to Tail
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