Important Dates

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.

Thomas A. Badgwell is an Advanced Research Associate in the Data Analytics & Optimization Section, Corporate Strategic Research, at the ExxonMobil Research & Engineering Company 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 and serves as a Trustee of the Computer Aids in Chemical Engineering (CACHE) Corporation.

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.

Michael Baldea is Associate Professor and Frank A. Liddell, Jr. Centennial Fellow in the McKetta Department of Chemical Engineering at The University of Texas at Austin. He received his Diploma and MSc degrees from "Babes-Bolyai" University of Cluj-Napoca, Romania and his PhD from the University of Minnesota, all in Chemical Engineering. His research focuses on the optimal design and control of integrated and intensified process and energy systems, as well as the integration of production management and control decisions. He has authored one book and more than 100 peer-reviewed publications in these areas, and is co-inventor on several patents.

Leo H. Chiang is Associate Technology Director at The Dow Chemical Company, leading Chemometrics and Big Data Analytics implementations for Manufacturing. 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.

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.

Bhushan Gopaluni is a professor in the Department of Chemical and Biological Engineering and an Associate Dean for Education and Professional Development in the Faculty of Applied Science at the University of British Columbia. He is also an associate faculty in the Institute of Applied Mathematics, the Institute for Computing, Information and Cognitive Systems, Pulp and Paper Center and the Clean Energy Research Center. He was the Elizabeth and Leslie Gould Teaching Professor from 2014 to 2017. He is currently an associate editor for Journal of Process Control, The Journal of Franklin Institute. He received a Ph.D. from the University of Alberta in 2003 and a Bachelor of Technology from the Indian Institute of Technology, Madras in 1997 both in the field of chemical engineering. From 2003 to 2005 he worked as an engineering consultant at Matrikon Inc. (now Honeywell Process Solutions) during which he designed and commissioned multivariable controllers in British Columbia's pulp and paper industry, and implemented numerous controller performance monitoring projects in the Oil & Gas and other chemical industries. He is the recipient of several awards that include Province of Alberta Graduate Fellowship, Captain Thomas Farell Graduate Memorial Scholarship from the University of Alberta and the prestigious Killam Teaching Prize and the Dean's service medal from the University of British Columbia.

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.

Johannes Hachmann is an Assistant Professor of Chemical Engineering at the University at Buffalo (UB), a Core Faculty Member of the UB Computational and Data-Enabled Science and Engineering graduate program, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern data science (i.e., the use of database technology, machine learning, and informatics) to advance a data-driven discovery and rational design paradigm in the chemical and materials disciplines. One of the centerpieces of the group's efforts is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. This work was recognized with a 2018 NSF CAREER Award.

Heather J. Kulik is an Assistant Professor in Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from Cooper Union in 2004 and her Ph.D. from Materials Science and Engineering at MIT in 2009. She completed postdocs at Lawrence Livermore (2010) and Stanford (2010-2013), prior to joining MIT as a faculty member in November 2013. Her work has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface (2012-2017), Office of Naval Research Young Investigator Award (2018), DARPA Young Faculty Award (2018), AAAS Marion Milligan Mason Award (2019-2020), NSF CAREER Award (2019), the Industrial & Engineering Chemistry Research "Class of Influential Researchers", the ACS COMP Division OpenEye Award for Outstanding Junior Faculty in Computational Chemistry, and the Journal of Physical Chemistry B Lectureship (ACS PHYS Division award), among others.

Jay H. Lee obtained his B.S. degree in Chemical Engineering from the University of Washington, Seattle, in 1986, and his Ph.D. degree in Chemical Engineering from California Institute of Technology, Pasadena, in 1991. From 1991 to 1998, he was with the Department of Chemical Engineering at Auburn University, AL, as an Assistant Professor and an Associate Professor. From 1998-2000, he was with School of Chemical Engineering at Purdue University, West Lafayette, and then with the School of Chemical Engineering at Georgia Institute of Technology, Atlanta from 2000-2010. Since 2010, he is with the Chemical and Biomolecular Engineering Department at Korea Advanced Institute of Science and Technology (KAIST), where he was the department head from 2010-2015. He is currently a Professor, Associate Vice President of International Office, and Director of Saud Aramco-KAIST CO2 Management Center at KAIST. He has held visiting appointments at E. I. Du Pont de Numours, Wilmington, in 1994 and at Seoul National University, Seoul, Korea, in 1997. He was a recipient of the National Science Foundation's Young Investigator Award in 1993 and was elected as an IEEE Fellow and an IFAC (International Federation of Automatic Control) Fellow in 2011 and AIChE Fellow in 2013. He was also the recipient of the 2013 Computing in Chemical Engineering Award given by the AIChE's CAST Division and the 2016 Roger Sargent Lecturer at Imperial College, UK. He is currently an Editor of Computers and Chemical Engineering and also the chair of IFAC Coordinating Committee on Process and Power Systems. He published over 180 manuscripts in SCI journals with more than 13000 Google Scholar citations. His research interests are in the areas of system identification, state estimation, robust control, model predictive control, and reinforcement learning with applications to energy systems, biorefinery, and CO2 capture/conversion systems.

Jonathan Moore received a B.S. in engineering science from Lipscomb University in 1994 and a Ph.D. from the University of Tennessee, Knoxville in 1999. He is currently a Research Scientist at Dow Chemical where he has worked for about twenty years in materials and polymer science (experimental and modeling), molecular modeling and simulation, and machine learning. Applications that he has worked on include cellulose ether structure-property relationships and new product development (food and construction materials applications), adhesives, data-driven modeling of high-throughput data (coatings applications), metal coatings for food and beverage applications, and next-generation insulation materials (building applications).

Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is currently Director of the Center for Machine and Process Intelligence and Fluor Professor at the Viterbi School of Engineering of the University of Southern California. Dr. Qin is a Fellow of AIChE, Fellow of IEEE, and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published in Control Engineering Practice. He has served as a Senior Editor of Journal of Process Control, Editor of Control Engineering Practice, a Member of the Editorial Board for Journal of Chemometrics, and Associate Editor for several journals. He has published over 140 papers in SCI journals and book chapters. He delivered over 40 invited plenary or keynote speeches and over 100 invited technical seminars worldwide. He has over 12,000 Web of Science citations with an associated h-index of 51 and over 30,000 Google Scholar citations. Dr. Qin's research interests include process data analytics, machine learning, process monitoring and fault diagnosis, model predictive control, system identification, building energy optimization, smart manufacturing and control, and control performance monitoring.

Nick Sahinidis is the John E. Swearingen Professor and Director of the Center for Advanced Process Decision-making at Carnegie Mellon University. He joined Carnegie Mellon in 2007 after a sixteen-year long career at the University of Illinois at Urbana, where he taught in Industrial Engineering and Chemical Engineering. His research has included the development of theory, algorithms, and the BARON software for global optimization of mixed-integer nonlinear programs. Professor Sahinidis' research activities have been recognized by the INFORMS Computing Society Prize in 2004, the Beale-Orchard-Hays Prize from the Mathematical Programming Society in 2006, the Computing in Chemical Engineering Award in 2010, the Constantin Carathéodory Prize in 2015, and the National Award and Gold Medal from the Hellenic Operational Research Society in 2016. Professor Sahinidis is a fellow of INFORMS and AIChE. He is the Editor-in-Chief of Optimization and Engineering.

Fengqi You is the Roxanne E. and Michael J. Zak Professor at Cornell University, and is affiliated with the Graduate Fields of Chemical Engineering, Electrical and Computer Engineering, Operations Research and Information Engineering, Systems Engineering, Mechanical Engineering, Civil and Environmental Engineering, and Applied Mathematics. He also serves as Chair of Cornell Systems Engineering PhD Studies and Associate Director of Cornell Energy Systems Institute. He was on the faculty of Northwestern University from 2011 to 2016, and worked at Argonne National Laboratory as an Argonne Scholar from 2009 to 2011. He has published more than 120 peer-reviewed journal articles, and has an h-index of 50. Some of his research results have been editorially highlighted in Science and Nature, featured on journal covers (e.g. Energy & Environmental Science, ACS Sustainable Chemistry & Engineering, and Industrial & Engineering Chemistry Research), and covered by major media outlets (e.g. The New York Times, BBC, BusinessWeek, and National Geographic). His recent awards include American Institute of Chemical Engineers (AIChE) W. David Smith, Jr. Publication Award (2011), Northwestern-Argonne Early Career Investigator Award (2013), National Science Foundation CAREER Award (2016), AIChE Environmental Division Early Career Award (2017), AIChE Sustainable Engineering Research Excellence Award (2017), Computing and Systems Technology (CAST) Outstanding Young Researcher Award from AIChE (2018), Cornell Engineering Research Excellence Award (2018), and ACS Sustainable Chemistry & Engineering Lectureship Award (2018), as well as a number of best paper awards. He is currently an Editor of Computers & Chemical Engineering, a Consulting Editor of AIChE Journal, and an editorial board member of several leading journals (e.g. ACS Sustainable Chemistry & Engineering and Industrial & Engineering Chemistry Research). His research focuses on novel computational models, optimization algorithms, statistical machine learning methods, and multi-scale systems analysis tools for smart manufacturing, digital agriculture, data analytics, energy systems, and sustainability. At Cornell, he has developed and regularly teaches two courses: "ChemE 6880/SysEn 5880: Industrial Big Data Analytics & Machine Learning" and "ChemE 6888/SysEn 5888: Deep Learning". For more information about his research group: www.peese.org

Victor M. Zavala is the Baldovin-DaPra Associate Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison. Before joining UW-Madison, he was a computational mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He holds a B.Sc. degree from Universidad Iberoamericana and a Ph.D. degree from Carnegie Mellon University, both in chemical engineering. He is on the editorial boards of the Journal of Process Control, Mathematical Programming Computation and IEEE Transactions on Control Systems and Technology. His research interests are in the areas of mathematical modeling of energy and agricultural systems, high-performance computing, optimization under uncertainty, and model predictive control.