Meeting Resources

TMS Machine Learning for Materials Science 2018

September 25–26, 2018
Embassy Suites by Hilton Pittsburgh Downtown • Pittsburgh, Pennsylvania, USA

Instructors

Each of the course instructors will share their expertise in this small group setting to allow for a more customized learning experience. Meet the leading professionals in machine learning who will teach this course:

Bryce Meredig, Lead Organizer
Co-founder and Chief Science Officer, Citrine Informatics

Bryce Meredig is the Chief Science Officer and co-founder of Citrine Informatics. His research interest is the application of machine learning to materials science. He earned his Ph.D. in materials science from Northwestern University, where he focused on materials informatics, and his B.A.S. and M.B.A. at Stanford University, where he is also on the faculty of the Department of Materials Science and Engineering. He is the author of more than 20 peer-reviewed publications and regularly gives invited talks at materials conferences including MRS, TMS, and MS&T, as well as plenaries and keynotes at workshops focused on data-driven materials research. He was an Arjay Miller Scholar and Terman Fellow at Stanford and a Presidential Fellow and NDSEG Fellow at Northwestern.

Erin Antono
Data Scientist, Citrine Informatics

Erin Antono joined as the first member of the Data Science Group at Citrine Informatics. She has worked with Citrine’s customers and partners to derive data-driven insights in problems ranging from advanced coatings to polymer processing to ionic conductors. She received her M.S. and B.A. in materials science and engineering from Stanford University, where she studied energy materials.

Brian DeCost
National Research Council Postdoctoral Fellow, National Institute of Standards and Technology (NIST)

Brian DeCost is a National Research Council Postdoctoral Fellow working in the Materials for Sustainable Energy and Development group at the National Institute of Standards and Technology (NIST). He earned a Ph.D. in materials science and engineering at Carnegie Mellon University. DeCost's research focuses on developing and applying machine learning methods and automation tools to address fundamental and applied problems in microstructure science and alloy design. https://bdecost.github.io/about

John C. Mauro
Professor of Materials Science and Engineering, Pennsylvania State University

John C. Mauro earned a B.S. in glass engineering science, B.A. in computer science, and Ph.D. in glass science, all from Alfred University. He joined Corning Incorporated in 1999 and served in multiple roles there, including senior research manager of the Glass Research Department.

Mauro joined the faculty at Pennsylvania State University in 2017 and is currently a world-recognized expert in fundamental and applied glass science, statistical mechanics, computational and condensed matter physics, thermodynamics, and the topology of disordered networks. He is the inventor or co-inventor of several new glass compositions for Corning, including Corning Gorilla® Glass products. Mauro is a pioneer in the use of physics-based and machine learning models for the design of new glassy materials. He is the inventor of new models for supercooled liquid and glass viscosity, glass structure and topology, relaxation behavior, and thermal and mechanical properties. Mauro also holds an adjunct faculty appointment at Wuhan University of Technology and is a guest chair professor at Qilu University of Technology in China. He is the author of more than 200 peer-reviewed publications and has given more than 200 presentations at international conferences and seminars. His publications have been cited more than 5,500 times, with an h-index of 38. Mauro has 34 granted U.S. patents and an additional 35 patents pending. He is editor of the Journal of the American Ceramic Society and associate editor of the International Journal of Applied Glass Science. He also serves as an editorial board member for the Journal of Non-Crystalline Solids.

Jason Hattrick-Simpers
Materials Research Engineer, National Institute of Standards and Technology (NIST)

Jason Hattrick-Simpers is a Materials Research Engineer in the Materials and Manufacturing for Sustainable Development group at the National Institute of Standards and Technology (NIST). He obtained his Ph.D. in materials science and engineering at the University of Maryland and afterwards was a National Research Council Post-Doctoral Fellow at NIST. Prior to re-joining NIST, he was an assistant professor of chemical engineering at the University of South Carolina in the SmartStateTM Center for the Strategic Approaches to the Generation of Electricity.

Josh Tappan
Community Manager, Citrine Informatics

Josh Tappan manages community programs and academic collaborations at Citrine Informatics, where he promotes materials informatics education and open scientific data initiatives and helps the academic community discover how machine learning can enhance their materials research efforts. Tappan earned his B.Sc. in electrical engineering from Northeastern University and M.Ed. from Harvard University. Prior to joining Citrine in 2017, Tappan was an electrical engineer, middle school physical science teacher, and head of community engagement at an education technology company.

Christopher Wolverton
Jerome B. Cohen Professor of Materials Science and Engineering, Northwestern University

Christopher Wolverton is the Jerome B. Cohen Professor of Materials Science and Engineering at Northwestern University. Before joining the faculty, he worked at the Research and Innovation Center at Ford Motor Company, where he was group leader for the Hydrogen Storage and Nanoscale Modeling Group. He received his B.S. in physics from the University of Texas at Austin, his Ph.D. in physics from the University of California, Berkeley, and performed postdoctoral work at the National Renewable Energy Laboratory (NREL). His research interests include computational studies of a variety of energy-efficient and environmentally friendly materials via first-principles atomistic calculations, high-throughput and machine learning tools to accelerate materials discovery, and “multiscale” methodologies for linking atomistic and microstructural scales. He is a Fellow of the American Physical Society.

For More Information

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