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Official registration for the 2018 Workshop on Machine Learning in Materials Science. Hosted at the University of Utah 

April 6, 2018

8 am to 6 pm

Peterson Heritage Center (PHC 1A)

co-sponsored by University of Houston, Department of Chemistry and University of Utah, Department of Materials Science and Engineering

                                                     

Speaker Bios                                


Elsa Olivetti, Ph.D. 
Atlantic Richfield Assistant Professor Department of Materials Science & Engineering
Massachusetts Institute of Technology
Cambridge, MA 02139
E-mail: elsao@mit.edu

Biographical Sketch
Elsa Olivetti is the Atlantic Richfield Assistant Professor of Energy Studies in the Department of Materials Science and Engineering. Her research focuses on improving the environmental and economic sustainability of materials using methods informed by materials economics, machine learning, and techno-economic analysis. She has received the NSF Career award for her experimental research focused on beneficial use of industrial waste materials. Dr. Olivetti received her B.S. degree in Engineering Science from the University of Virginia. Her Ph.D. in Materials Science and Engineering from MIT was focused on development of cathode materials for lithium ion batteries.

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Gus Hart, Ph.D. 
Associate Dean Department of Physics and Astronomy
Brigham Young University
Provo, UT 84602

Gus Hart is a professor in Department of Physics and Astronomy at Brigham Young University. His research foci include high-throughput computational materials science, developing algorithms for alloy modeling, thermodynamic simulations, lattice-configuration enumeration, and using compressive sensing for building physical models. He is also co-developer of the UNCLE code for cluster expansion modeling. 

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Ankit Anrawal, Ph.D. 
Research Associate Professor Department of Electrical Engineering and Computer Science
Northwestern University
Evanston, Illinois 60208-3118

Ankit Agrawal is a Research Associate Professor in the Department of Electrical Engineering and Computer Science at Northwestern University. He got his PhD in Computer Science from Iowa State University in 2009, and B.Tech in Computer Science and Engineering from Indian Institute of Technology (IIT) Roorkee, India in 2006. His research on high performance big data mining aims at a coherent integration of high performance computing and data mining to develop customized solutions for big data problems with an emphasis on interdisciplinary impact. His research has contributed to large-scale data-guided discovery in various science/engineering disciplines, such as materials science, healthcare, bioinformatics, and social media, with several interdisciplinary collaborations with domain scientists in respective fields. He has co-authored 100+ peer-reviewed journal and conference publications, including those at top-tier computer science venues such as KDD, ICDM, CIKM, SDM, ICDE, Supercomputing, HiPC, and interdisciplinary ones like Nature Scientific Reports, APL Materials, npj Computational Materials, JAMIA, PRB, and TCBB. He has also developed and released several open-source software, been on program committees of major research conferences, and has served as a PI/Co-PI on funded research grants from NSF, DOE, AFOSR, NIST, DARPA, Toyota Motor Corporation, and Northwestern Data Science Initiative.

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Bryce Meredig, Ph.D., MBA
Cofounder of Citrine Informatics
San Francisco Bay Area

Dr. Meredig's 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 BAS and MBA at Stanford University, where he is also on the faculty of the Department of Materials Science and Engineering. He is the author of over 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. Dr. Meredig was an Arjay Miller Scholar and Terman Fellow at Stanford and a Presidential Fellow and NDSEG Fellow at Northwestern.

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Kaai Kauwe, B.S. 
Graduate Research Assistant, Department of Materials Science and Engineering
University of Utah
Salt Lake City, UT 84112

Kaai Kauwe received a B.S. in Chemical Engineering from Brigham Young University. He is currently a graduate student working under the guidance of Taylor Sparks in the Department of Materials Science and Engineering at the University of Utah. His research focus is the data driven discovery of thermoelectrics using machine learning.  He won the American Institute of Chemical Engineers (Utah Chapter) Outstanding Senior award during his undergraduate studies and is the recipient of the Gerald Stringfellow Fellowship. 

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Aria Mansouri, B.S. 
Graduate Research Assistant, Department of Chemistry
University of Houston
Houston, TX 77004

Aria Mansouri received a B.S. in Materials Engineering & Metallurgy from Amirkabir University of Technology in 2014. He is currently a graduate student in the group of Jakoah Brgoch at the University of Houston. He focuses his research on earth-abundant superhard materials. 

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Ross Whitaker, Ph.D.
Director, School of Computing; Professor, School of Computing
University of Utah
Salt Lake City, UT 84112

Ross Whitaker graduated Summa Cum Laude with B.S. degree in Electrical Engineering and Computer Science from Princeton University in 1986. From 1986 to 1988 he worked for the Boston Consulting Group, entering the University of North Carolina at Chapel Hill in 1989. At UNC he received the Alumni Scholarship Award, and completed his Ph.D. in Computer Science in 1994. From 1994-1996 he worked at the European Computer-Industry Research Centre in Munich Germany as a research scientist in the User Interaction and Visualization Group. From 1996-2000 he was an Assistant Professor in the Department of Electrical Engineering at the University of Tennessee and received an NSF Career Award. Since 2000 he has been at the University of Utah where he is the Director of the School of Computing and a faculty member of the Scientific Computing and Imaging Institute. He is an IEEE Fellow. He teaches discrete math, scientific visualization, and image processing. He has leads graduate-level research group in image analysis, geometry processing, and scientific computing, with a variety of projects supported by both federal agencies and industrial contracts.

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