Instructors
Meet our experienced team of instructors.
Instructors
Ankit Agrawal
Research Professor, Northwestern University
Ankit Agrawal is a research professor in the Department of Electrical and Computer Engineering at Northwestern University, USA. He specializes in interdisciplinary artificial intelligence (AI) and big data analytics via high performance data mining, based on a coherent integration of high-performance computing and data mining to develop customized AI solutions for big data problems with real-world impact. His research has contributed to large-scale data-driven discoveries in various scientific and engineering disciplines, such as materials science, healthcare, social media, and bioinformatics. He has co-authored 150+ peer-reviewed publications, co-developed and released 15+ software, delivered 50+ invited/keynote talks at major conferences, universities, and companies all over the world, been on program committees of 40+ conferences/workshops, and served as a PI/Co-PI on 15+ sponsored projects funded by various US federal agencies (e.g., NSF, DOE, AFOSR, NIST, DARPA, DLA) as well as industry (e.g., Toyota Motor Corporation Japan). He is one of the few computer scientists who are actively introducing AI and advanced data science techniques in the field of materials science and has successfully led several large-scale materials informatics projects. As an example, he is co-leading the AI group at the Center for Hierarchical Materials Design (CHiMaD), which is a $60 million NIST-sponsored center of excellence. He is also serving as the editor-in-chief of Computers, Materials & Continua.
David Blondheim, Jr.
Technical Advisor: Advanced Manufacturing Engineering and Analytics, Mercury Marine, a division of Brunswick Corporation
David Blondheim, Jr. graduated in 2004 with his B.S. in Mechanical Engineering from Michigan Technological University. He began his engineering career at a CNC machine job shop. While working full time, he became a Professional Engineer (PE), completed his MBA from UW-Oshkosh in 2008 and obtained his M.S. in Industrial Engineering from Purdue University in 2012. After nine years of progressive experience in engineering for machining, he entered the die cast industry with Mercury Marine in 2013. Blondheim serves as the engineering manager within the aluminum foundry and Technical Advisor leading IIoT/Connected Operations initiatives throughout Mercury’s different manufacturing plants. Blondheim is currently a Ph.D. candidate in Systems Engineering at Colorado State University working on his dissertation of improving die casting manufacturing system with machine learning.
Samantha Daly
Professor, University of California at Santa Barbara
Samantha Daly is a professor in the Department of Mechanical Engineering at the University of California at Santa Barbara (UCSB). She received her Ph.D. from Caltech in 2007 and subsequently joined the University of Michigan, where she was on the faculty until 2016 prior to her move to UCSB. Her research interests lie at the intersection of experimental mechanics, materials science, and machine learning. Currently, the group is engaged in the development of new methods for multi-scale material characterization and application of machine learning to understand the deformation and failure of metallic alloys and composites. Daly is a Fellow of The American Society of Mechanical Engineers (ASME) and a recipient of the NSF CAREER Award, ASME Eshelby Mechanics Award, Journal of Strain Analysis Young Investigator Award, Experimental Mechanics and IJSS Best Paper of the Year Awards, DOE Early Career Award, AFOSR-YIP Award, and ASME Orr Award. She currently serves on the Executive Board of the Society of Experimental Mechanics, and as an associate editor of Applied Mechanics Reviews, Experimental Mechanics, and Strain.
Sayan Ghosh
Lead Engineer, General Electric Research
Sayan Ghosh is lead engineer and works with probabilistic machine learning, design and optimization team at GE Research in New York, USA. He has over 10 years’ experience in probabilistic machine learning, digital twin, hybrid-physics modeling, uncertainty quantification and management, inverse modeling, reliability and risk analysis, multidisciplinary design, optimization, etc. At GE Research, he is leading multiple projects involving development and application of advanced probabilistic and machine-learning methods supporting various product lines, new product and technology integration, maintenance, services, and operations for GE business involving Aviation, Power, Additive, Renewables, etc. He has received his B.Tech. from IIT Kharagpur, M.S. from Iowa State University and Ph.D. from the Georgia Institute of Technology in Aerospace Engineer.
Vipul Gupta
Senior Materials Scientist, General Electric Research
Vipul Gupta is a senior materials scientist in the Materials Organization at the GE Research, Niskayuna, New York, USA. Dr. Gupta’s research focuses on understanding mechanical behavior of structural aluminum alloys, ceramic matrix composites, high-temperature nickel and cobalt-based superalloys subject to harsh environment and stress conditions. In recent years, he has led projects on metal additive manufacturing, including new additive alloy development, process parameter optimization, and high-throughput testing and characterization. He is also passionate about artificial intelligence (AI) and machine learning (M) for Materials; and has been working toward implementing AI/ML for alloy design, additive process optimization, material property predictions; and development of federated big data storage, visualization and analytics platform for additive manufacturing. He has co-authored more than 20 peer-reviewed publications and have delivered over 25 presentations at the international conferences, workshops, and university seminars.
Elizabeth Holm
Professor of Materials Science and Engineering; Director, Air Force Center of Excellence in Data Drive Discovery of Optimized Multifunctional Material Systems, Carnegie Mellon University
Elizabeth A. Holm is a Professor of Materials Science and Engineering and Director of the Air Force Center of Excellence in Data Drive Discovery of Optimized Multifunctional Material Systems at Carnegie Mellon University. Prior to joining CMU in 2012, she spent 20 years as a research scientist at Sandia National Laboratories. Her research areas include computational materials science at the atomic and mesoscale and AI and machine learning for materials science. 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. Holm has received several honors and awards, is a Fellow of ASM International and of TMS, 2013 President of TMS, an organizer of numerous international conferences, and has been a member of the National Materials Advisory Board. Holm has authored or co-authored over 175 publications.
Benji Maruyama
Principal Materials Research Engineer
Autonomous Research Lead
U.S. Air Force Research Laboratory Materials and Manufacturing Directorate
Benji Maruyama is a principal materials research engineer in the U.S. Air Force Research Laboratory Materials and Manufacturing Directorate and the autonomous materials lead and ACT3 (Autonomous Capabilities Team 3) liaison. His focus area is the synthesis and processing science of carbon nanotubes using ARES™ which is the first fully Autonomous Research (ARES) Robot for materials. Maruyama’s interests include the research process itself, for which he promotes Moore’s Law for the speed of research. He is also the point of contact for carbon materials for the AFRL Materials & Manufacturing Directorate. His materials interests include carbon nanomaterials, energy storage, flexible-hybrid materials and processes, field emission, carbon, polymer and metal matrix composites, imaging of complex 3D microstructures and AI/Machine Learning. He is currently involved in the study of the origins of chiral growth for carbon nanotubes, defect engineering for low dimensional materials, catalysis and autonomous experimentation.
Bryce Meredig
Chief Science Officer and Co-founder, Citrine Informatics
Bryce Meredig is cofounder and chief science officer of Citrine Informatics, a materials informatics platform company, where he leads the External Research Department (ERD). ERD conducts publishable research with collaborators in academia, government, and industry. Meredig's research interests include the development and validation of physics-aware machine learning methods specific to applications in materials science and chemistry; integration of physics-based simulations with machine learning; trust and interpretability of machine learning models of physical phenomena; and data infrastructure for materials science. Meredig received his Ph.D. from Northwestern University and BAS and MBA from Stanford University.
Kristofer Reyes
Assistant Professor, Department of Materials Design and Innovation, University at Buffalo
Kristofer G. Reyes is an assistant professor in the Department of Materials Design and Innovation, University at Buffalo. He applies machine learning and artificial intelligence methods to problems in materials science. He is particularly interested in making these methods relevant in the regime of sparse and noisy data, a regime in which much of materials science research is conducted. A major thrust of his work is autonomous science, which uses robot scientists to plan and execute experiments, and scientific machine learning to posit hypotheses. In this area, he develops methods for designing optimal experiments based on a limited set of information, algorithms for the characterization of rich and complex data resulting from experiments, and techniques for learning and leveraging physics-based and domain-expert knowledge. He received his Ph.D. in Applied Mathematics from the University of Michigan, where he modeled the synthesis of nanostructures grown by multiphase methods. His postdoctoral training was at the Department of Operations Research and Financial Engineering at Princeton University. There, he studied stochastic optimization and machine learning problems related to materials development and optimization.
Marius Stan is a retired senior scientist and leader of Intelligent Materials Design in the Applied Materials Division at Argonne National Laboratory. He is also a Senior Fellow at University of Chicago and Northwestern University. Marius and his group use AI and high-performance, multi-scale computer simulations to understand and predict physical and chemical properties of multi-component metals and ceramics. The applications include energy production (nuclear fuels and reactor materials), energy storage (batteries) and electronics. The group also uses AI to optimize complex processes for manufacturing applications such as 3-D printing and flame spray pyrolysis. Marius has extensively published in the scientific literature, holds several patents, and is currently writing a book on modeling and simulation.
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