FOPAM Conference Overview

FOPAM 2023, the conference on "Foundations of Process/product Analytics and Machine learning" organized by CACHE, will be held on July 30-August 3, 2023, at the University of California, Davis. Building on the successful FOPAM 2019, FOPAM aims to be the premier forum for researchers from industry and academia to discuss current status and future directions of data analytics and machine learning in the process industries. FOPAM 2023 is co-sponsored by AIChE's CAST Division and CoMSEF.

Conference Format

The format of the conference is 3-1/2 days of presentations and discussions, preceded by an optional 1-1/2-day workshop. The conference begins on Monday evening, July 31, with an opening keynote by Cenk Ündey, VP and Global Head of PTD Data & Digital at Roche/Genentech. In the next three days, morning and evening sessions will follow a format of two talks, followed by audience Q&A and serious technical discussions with the two speakers and an invited discussor. Audience research contributions will be by posters based on pre-submitted abstracts (due by April 28). Continental breakfast and lunch will be provided Tuesday-Friday. Two of the afternoons will be used for poster sessions, small-group discussions, and other unstructured activities. On the last evening, a banquet and brief rapporteur analyses will conclude the conference.

Schedule

Day	Morning	Afternoon	Evening
Sun, Jul 30, 2023	Arrival	Optional workshop by Leo H. Chiang, Dow; R. Bhushan Gopaluni, Univ British Columbia; and Ali Mesbah, Univ California Berkeley	Hospitality
Mon, Jul 31, 2023	Optional workshop (continued)	Optional workshop (concluded)	Welcoming Reception Conference introduction/overview Keynote speaker: Cenk Ündey, VP and Global Head of PTD Data & Digital, Roche / Genentech: "Unlocking Full Potential of Data and Digital: Enabling Speed, Efficiency and Culture in Pharmaceutical Technical Development" Followed by Hospitality
Tue, Aug 1, 2023	Emerging Methods in Machine Learning and Data Science Victor M. Zavala, Baldovin-DaPra Professor, University of Wisconsin Madison: "Topological Data Analysis: Concepts, Tools, and Applications" Ioannis Kevrekidis, Bloomberg Distinguished Professor, The Johns Hopkins University: "Physics-informed machine learning for equation-free and variable-free modeling"	Poster Session 1, small-group discussions and other unstructured activities	Industrial Data-Science Applications I Elif Ozkirimli, Head of Computational Science Products, Roche: "Natural language processing of biochemical languages - toward more generalizable protein-compound interaction prediction models" Detlef Hohl, Chief Scientist - Computation and Data Science, Shell: "What Artificial Intelligence really Is, and how it will change the chemical process industry" Followed by Hospitality
Wed, Aug 2, 2023	Machine Learning for Process and Product Chemistry Zachary Ulissi, Research Scientist, Fundamental AI Research Lab, Meta : "Large Open Datasets and Graph Neural Networks for Generalizable Models in Catalysis" Kristen Severson, Senior Researcher, Microsoft: "Data-driven design of concrete with amortized Gaussian processes and multi-objective optimization"	Poster Session 2, small-group discussions and other unstructured activities	Industrial Data-Science Applications II Salvador García Muñoz, Executive Director - Engineering, Eli Lilly: "Process analytics in pharma" Bea Braun, Sr. Research Scientist - Data Science and Hybrid Modeling, Dow: "Machine Learning in the Chemical Industry – Success Stories and What We Need to Move to Scale" Followed by Hospitality
Thu, Aug 3, 2023	Data Science for Processes and Control Rolf Findeisen, Prof. Dr.-Ing., Technische Universität Darmstadt: "Machine learning for cyber-physical systems" Xiaonan Wang, Associate Professor, Tsinghua University / National University of Singapore: "Smart systems engineering contributing to material and process development for a carbon-neutral future"	Past and Future of Process/Product Analytics & Machine Learning, including Education and Workforce Development Venkat Venkatasubramanian, Samuel Ruben-Peter G. Viele Professor of Engineering, Columbia University: "Combining Symbolic and Numeric AI: Challenges and Opportunities in Research and Education" John D. Hedengren, Professor, Brigham Young University: "Data-Driven Engineering Education with Hands-On Learning"	Conference Banquet with after-dinner rapporteurs

Conference Organization

Meeting Program Chairs

Leo H. Chiang (Dow Chemical)

Leo H. Chiang is a Technology Fellow at The Dow Chemical Company Technology responsible for Advanced Analytics and AI programs for global Operations. Leo has developed and implemented several data analytics techniques to solve complex manufacturing problems, resulting in 11 Dow Manufacturing Technology Center Awards. In 2016 he received the Dow R&D Excellence in Science Award in recognition of his scientific achievement in industrial research. Leo has a B.S. degree from University of Wisconsin at Madison and M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign, all in Chemical Engineering. Leo has contributed to over 40 externally refereed journal/proceedings papers and has given over 80 conference presentations and university lectures. Leo has co-authored two books published by Springer Verlag. His textbook Fault Detection and Diagnosis in Industrial Systems is available in English and Chinese and has received over 2,000 citations according to Google Scholar. Leo is active in American Institute of Chemical Engineers (AIChE), having served as 2014-2016 Computing and Systems Technology (CAST) director, 2016 CAST 10E programming chair, and 2017-2018 AIChE spring meeting program chair (MPC). Leo was instrumental in setting up the Big Data Analytics Topical Conference (2015 to 2017) and Industry 4.0 Topical Conference (2018-2019) at the AIChE spring meeting. He was recognized by the AIChE with the 2016 Herbert Epstein Award for his leadership on Big Data Analytics technical programming and 2016 Computing Practice Award for his world-class leadership in the development and application of methodologies in analytics for batch and continuous processes known as Big Data.

Phillip R. Westmoreland (North Carolina State University)

Phillip R. Westmoreland is a professor at North Carolina State University, Raleigh NC, in the Department of Chemical and Biomolecular Engineering. During November 2015 until July 2016, he was Leverhulme Trust Visiting Professor in the Department of Mechanical Engineering at Imperial College.He was a Research Engineer in the coal-conversion program at Oak Ridge National Laboratory from 1974-79, was on the faculty of the Chemical Engineering Department at the University of Massachusetts Amherst from 1986-2009, and served at the National Science Foundation in 2006-2009. He is also Honorary Professor at Nanjing University of Technology and served as Professeur invité at the Université de Lorraine. He served as 2013 President of AIChE, the American Institute of Chemical Engineers, and is a currently a trustee of the educational nonprofit CACHE Corporation, having served as its president in 2004-06. He is a past board member of the Combustion Institute (2002-2014), the Council for Chemical Research (2005-07), and AIChE (2009-11), and he was the founding Chair of AIChE's Computational Molecular Science and Engineering Forum.

Arrangements Chair

Ahmet Palazoglu (UC Davis)

Program Committee

Tom Badgwell (Collaborative Systems Integration)

Thomas A. Badgwell is Chief Technology Officer at Collaborative Systems Integration in Clinton, NJ. He received a BS degree from Rice University and MS and PhD degrees from the University of Texas at Austin, all in Chemical Engineering. Tom's career has focused on modeling, optimization, and control of chemical processes, with past positions at Setpoint, Fisher/Rosemount, Rice University, and Aspen Technology. He is a Fellow of the American Institute of Chemical Engineers (AIChE), where he recently served as a Director of the Computing and Systems Technology (CAST) Division. Tom received the Computing Practice Award from the AIChE CAST Division in 2013. He is an Associate Editor for the Journal of Process Control.

Richard Braatz (MIT)

Richard D. Braatz is the Edwin R. Gilliland Professor and Faculty Research Officer of Chemical Engineering at the Massachusetts Institute of Technology (MIT) where he does research in process data analytics and control theory and their application to advanced manufacturing systems. He has consulted or collaborated with more than 20 companies including IBM, United Technologies Corporation, Dow Chemical, Merck, and Novartis. His work has been recognized by the Donald P. Eckman Award, the Curtis W. McGraw Research Award, the IEEE Control Systems Society Transition to Practice Award, and the AIChE CAST Computing in Chemical Engineering Award, and he is Fellow of IEEE, IFAC, AAAS, and AIChE. His coauthored publications include the textbook "Fault Detection and Diagnosis in Industrial Systems" published by Springer Verlag in 2001. He is the President of the American Automatic Control Council and a member of the National Academy of Engineering.

Paulette Clancy (Johns Hopkins)

Selen Cremaschi (Auburn)

Selen Cremaschi is the B. Redd Associate Professor of Chemical Engineering at Auburn University. Her research interests are risk management, optimization, process synthesis, and planning under uncertainty. Her research group works at the intersection of operations research and chemical engineering, and develops systems analysis and decision support tools for complex systems, mainly focusing on healthcare and energy industry. Prior to joining Auburn University, she was a faculty member of the Russell School of Chemical Engineering at the University of Tulsa. She was the recipient of a Tulsa Tau Beta Pi Teaching Excellence award (2010), an NSF CAREER award (2011), and a Zelimir Schmidt Award for Outstanding Researcher (2013) among others. Her research work has been consistently supported by industrial collaborations in addition to federal agencies. She earned a Ph.D. from Purdue University and a M.S. and B.S. from Bogazici University (Turkey), all in chemical engineering.

Martha Grover (Georgia Tech)

Martha Grover is a Professor in the School of Chemical & Biomolecular Engineering at Georgia Tech. She earned her BS in Mechanical Engineering from the University of Illinois, Urbana-Champaign, and her MS and PhD in Mechanical Engineering from Caltech. She joined Georgia Tech as an Assistant Professor in 2002, and received an NSF CAREER award in 2004. In 2011 she received the Outstanding Young Researcher Award from the Computing and Systems Technology Division of AIChE. Her research program is dedicated to understanding, modeling, and engineering the self-assembly of atoms and small molecules to create larger scale structures and complex functionality. Her approach draws on process systems engineering, combining modeling and experiments in applications dominated by kinetics, including surface deposition, crystal growth, polymer reaction engineering, and colloidal assembly. She is a member of the NSF/NASA Center for Chemical Evolution, and the Georgia Tech Center for Organic Photonics and Electronics.

Jay Lee (KAIST)

Jean Tom (Bristol Myers Squibb)

Technical Sponsor

CACHE Corporation

CACHE (cache.org) is a 501(c)(3) not-for-profit that promotes the development and distribution of computer-related and/or technology-based educational aids for the chemical engineering profession.

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