Teaching Process Data Analytics and Machine Learning at MIT

作者

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https://doi.org/10.18260/2-1-370.660-130947

摘要

This article describes experiences with teaching process data analytics and machine learning, including in (1) a joint undergraduate/graduate course for students in chemical and mechanical engineering and engineering management and (2) an undergraduate chemical engineering concentration in process data analytics. The article also describes challenges in teaching data science to chemical engineers, and strategies for overcoming those challenges.

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Moo Sun Hong is a postdoctoral researcher in the group of Prof. Richard D. Braatz at the Massachusetts Institute of Technology (MIT). He received an M.S. in Chemical Engineering Practice and Ph.D. from MIT. His research is in advanced biopharmaceutical manufacturing systems. Honors include the AIChE PD2M Award for Excellence in Integrated QbD Practice, the AIChE Separations Division Graduate Student Research Award, and the Jefferson W. Tester Award from the MIT Chemical Engineering Practice School.

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Weike Sun received a B.S. from Tsinghua University and a Ph.D. from MIT. She is an expert in process data analytics who is the primary author of multiple open-source software packages including ALVEN for algebraic learning via elastic net for static and dynamic model identification and SPA for smart process analytics which automatically selects and applied process data analytics and machine learning methods based on domain knowledge, the specific data characteristics, and nested cross-validation procedures.

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Brian W. Anthony is Director of the MIT Master of Engineering in Manufacturing Program and Co-Director of the Medical Electronic Device Realization Center. He received a B.S. from Carnegie Mellon and M.S. and Ph.D. from MIT. He has over 25 years of commercial, research, and teaching experience in product realization and advanced manufacturing. He has over 20 patents and received an Emmy from the Academy of Television Arts and Sciences for sports broadcast technical innovation.

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Richard D. Braatz is the Edwin R. Gilliland Professor at the Massachusetts Institute of Technology (MIT) where he does research in process data analytics, design, and control of advanced manufacturing systems. He received MS and PhD from the California Institute of Technology and was the Millennium Chair and Professor at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT. Honors include the Donald P. Eckman Award from the American Automatic Control Council, the Curtis W. McGraw Research Award from the Engineering Research Council, the IEEE Control Systems Society Transition to Practice Award, and the AIChE CAST Computing in Chemical Engineering Award. He is a Fellow of the AIChE, IEEE, IFAC, and AAAS, and a member of the U.S. National Academy of Engineering.

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已出版

2022-10-25

期

卷 56 编号 4 (2022): Fall 2022

栏目

Manuscripts