The following individuals have been involved in stringently curating programming and organizing AIM 2024.
Adam Kopper (Lead Organizer)
Mercury Marine
Adam Kopper has been with Mercury Marine, in Fond du Lac, Wisconsin, for 16 years and is a technical specialist in the Manufacturing Foundry Operations group. His work in the foundry focuses on casting program management, aluminum casting evaluation, new technology assessments, alloy development, and continuous improvement. Prior to Mercury, Kopper worked in the automotive casting supply base at CMI International and INTERMET over a span of eight years.
Kopper has a Bachelor of Science degree from the University of Michigan and a Master of Science degree from Worcester Polytechnic Institute (WPI); both in Materials Science and Engineering. He is currently a Ph.D. candidate in the Department of Materials Science and Engineering at WPI researching the application of machine learning to manufacturing process data in high-pressure die-casting.
Kopper was honored with the 2020 William Frishmuth Foundry Person of the Year Award by the AFS Aluminum and Light Metals Division. His casting development research has been published by SAE, NADCA, AFS, and TMS.
Adrian Sabau (Programming Chair)
Oak Ridge National Laboratory
Adrian Sabau received an engineer diploma in mechanical and materials processing from the University of Craiova, Romania and a Ph.D. degree in mechanical engineering from Southern Methodist University in 1996. In 1999, Sabau joined Oak Ridge National Laboratory as a research staff member of the Materials Science and Technology Division, where he worked as a senior research staff member until 2008. Since 2018, Sabau has served as a computational materials scientist in the Computational Sciences & Engineering Division. Sabau seeks to advance the materials processing, metal casting, photonic processing, and materials for energy applications through the development of computational and experimental methodologies for the property measurement, process simulations, and materials behavior in response to conditions experienced in service. Sabau is an ASME fellow, an ASM fellow, and the recipient of three R&D 100 awards in process sciences. He co-edited 6 books on materials processing. He was granted 9 patents, 2 software copyrights, and published 170 technical papers.
Rémi Dingreville
Sandia National Laboratories
Rémi Dingreville is a distinguished member of the technical staff at Sandia National Laboratories and staff scientist at the Center for Integrated Nanotechnologies (CINT) a DOE Office of Science user facility. His current research is at the intersection of computational materials and data sciences to understand and characterize process-structure-properties for materials reliability across scales. He leads a few research programs at Sandia focused on the discovery of resilient materials and manufacturing processes via AI-guided approaches. Dingreville holds a Ph.D. in mechanical engineering from the Georgia Institute of Technology.
Roger H. French
Case Western Reserve University
Roger H. French is the Kyocera Professor in the Case School of Engineering, Case Western Reserve University (CWRU), Cleveland, Ohio. His primary appointment is in Materials Science and Engineering, with secondary appointments in Computer and Data Sciences, Macromolecular Science, Biomedical Engineering, and Physics. He is the director of the DOE-NNSA Center of Excellence for Materials Data Science for Stockpile Stewardship. He is co-PI of the Center for Advancing and Distributed Fertilizer Production (CASFER), an NSF sponsored IUCRC. He is the faculty director of the CWRU Applied Data Science program which offers graduate courses and graduate certificates and an undergraduate minor university wide. He is the director of the SDLE Research Center, an Ohio Third Frontier center focused on advancing materials data science, big data analytics, and reliability of long-lived technologies.
His group of more than 45 students and associates develops Hadoop2/Hbase/Spark/Ozone-based distributed computing environments for Petabyte-scale data science and analytics of complex systems. This allows multi-factor real world performance to be integrated with lab-based experimental datasets to, for example, identify degradation mechanisms and pathways active over a technology’s lifetime. These approaches use network modeling, structural equations, and graph and deep neural network modeling for spatiotemporal systems such as PV power plants and buildings.
Prior to joining Case Western Reserve University in 2010, French was a research fellow in Central Research and Development at DuPont Co. (starting in 1985) and an adjunct professor of Materials Science at the University of Pennsylvania (from 1994). He received his B. S. from Cornell University and his Ph.D. from Massachusetts Institute of Technology, both in materials science.
He has published 164 journal articles, 149 proceedings papers, 1 book, 21 book chapters, 12 software packages, 6 open datasets, 165 invited talks, and 302 conference presentations, and has 29 issued U. S. patents.
Thilo Muth
BAM Federal Institute for Materials Research and Testing
Thilo Muth Thilo Muth works as group leader of the eScience Section in the Department of Quality Infrastructure at the Federal Institute for Materials Research and Testing (BAM) in Berlin, Germany. One of his primary current interests focuses on setting up research data infrastructures and data science-driven applications for materials scientists and engineers. The main objective of the horizontal eScience group at BAM is to develop computational workflows and software tools that are robust and reusable for the scientific community.
BAM is a center of excellence for “Safety in Technology and Chemistry” that integrates research, assessment and consultation in chemistry, materials sciences, and engineering. BAM develops cutting-edge key technologies and undertakes interdisciplinary research within its five focus areas Analytical Sciences, Materials, Energy, Environment, and Infrastructure.
Elsa Olivetti
Massachusetts Institute of Technology
Elsa Olivetti is the Esther and Harold E. Edgerton Career Development Professor in the Department of Materials Science and Engineering (DMSE) at the Massachusetts Institute of Technology. Her research focuses on improving the environmental and economic sustainability of materials in the context of rapid-expanding global demand. Olivetti received her B.S. in engineering science from the University of Virginia and her Ph.D. in materials science engineering from MIT.
Taylor Sparks
University of Utah
Taylor Sparks joined the Materials Science and Engineering Department at the University of Utah as an Assistant Professor in 2013. He completed his MS in materials at the University of California, Santa Barbara and his PhD in applied physics at Harvard University. He is currently the Director of the Materials Characterization Laboratory at the University of Utah and teaches classes on ceramics, materials science, characterization, and technology commercialization. His current research centers on the discovery, synthesis, characterization, and properties of new materials for energy applications.
Pawan Tripathi
Case Western Reserve University
Pawan Tripathi is currently a research assistant professor in the Department of Materials Science and Engineering at Case Western Reserve University. He holds a dual degree Ph.D. in computational materials science from the Indian Institute of Technology, Kanpur, India, and National Yangming Chiao Tung University, Taiwan, focusing on a theoretical study of the role of interfaces and surfaces on phase transformation and deformation mechanisms in metals, alloys, and nanomaterials using atomistic simulations. He is currently leading projects related to materials data science at the DOE-NNSA-funded Center of Excellence for Materials Data Science for Stockpile Stewardship. His expertise lies in interface structural simulations and data analysis developing automated analysis pipelines for large multimodal datasets from diverse experiments. He is currently leading projects on data FAIRification, deep learning, image processing, semantic segmentation, and statistical modeling. These approaches are applied to Advanced Manufacturing (LPBF); High Energy X-ray Diffraction (HEXRD) characterization at the synchrotron facilities at APS (Argonne National Laboratory), CHESS (Cornell), SRIS-Tohoku (Japan); and X-ray Computed Tomographic (XCT), in collaboration with Lawrence Livermore, Los Alamos, and Sandia National Laboratories respectively.
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