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Dr. Recktenwald is a lecturer in Mechanical Engineering at Michigan State University where he teaches courses in in mechanics and mathematical methods. He completed his degree in Theoretical and Applied Mechanics at Cornell University in stability and parametric excitation. His active areas of research are dynamic stability, online assessment, and instructional pedagogy.

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Allison Godwin, Ph.D. is an Assistant Professor of Engineering Education at Purdue University. Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students’ identity development. She is the recipient of a 2014 American Society for Engineering Education (ASEE) Educational Research and Methods Division Apprentice Faculty Grant. She has also been recognized for the synergy of research and teaching as an invited participant of the 2016 National Academy of Engineering Frontiers of Engineering Education Symposium and 2016 New Faculty Fellow for the Frontiers in Engineering Education Annual Conference. She also was an NSF Graduate Research Fellow for her work on female empowerment in engineering which won the National Association for Research in Science Teaching 2015 Outstanding Doctoral Research Award.

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Anant Sahai got his BS degree in EECS from Berkeley in '94 and then got his MS and PhD degrees from MIT in '96 and '01. Before joining Berkeley as a faculty member in '02, he spent a year at the wireless startup Enuvis working on ultra-sensitive software-radio algorithms for GPS. His current research interests are in the foundations of information theory for control, low-latency wireless communication protocols to support the high performance Active IoT applications of the future, and wireless spectrum sharing (where his interests span machine learning, system architectures, game theory, law, and policy). In 2015-2016, he co-taught and scaled-up a radically new "introduction to EECS" course that is required for Berkeley EECS Freshmen and introduces them to circuits and systems concepts while teaching them linear algebra, all while doing interesting labs and application-oriented problems. In 2013 and 2014, he was also instrumental in helping scale up the sophomore discrete math and probability course to 500+ students. In 2017-2018, he is bringing this philosophy to the flagship machine learning course for EECS.

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Matthew West is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. Prior to joining Illinois he was on the faculties of the Department of Aeronautics and Astronautics at Stanford University and the Department of Mathematics at the University of California, Davis. Prof. West holds a Ph.D. in Control and Dynamical Systems from the California Institute of Technology and a B.Sc. in Pure and Applied Mathematics from the University of Western Australia. His research is in the field of scientific computing and numerical analysis, where he works on computational algorithms for simulating complex stochastic systems such as atmospheric aerosols and feedback control. Prof. West is the recipient of the NSF CAREER award and is a University of Illinois Distinguished Teacher-Scholar and College of Engineering Education Innovation Fellow.

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