Russell Barton

Background

About Russell Barton

Russell Barton is a Distinguished Professor at Penn State. He held courtesy visiting appointments at Durham University 2019-2022 and Ecole Centrale Paris Prior roles included Senior Associate Dean in the Smeal College of Business and a rotating assignment at NSF, managing funding for research in manufacturing services engineering. Other prior roles include Co-Director of the Master of Manufacturing Management degree program, and Associate Dean for Research and Ph.D./M.S. Programs. Dr. Barton has a courtesy appointment in Industrial and Manufacturing Engineering, and past roles included affiliation with design and quality laboratories in industrial and mechanical engineering. He had a key role in Learning Factory curriculum development; the program received the NAE Gordon Prize in 2006. He participated in an action group for graduate education in interdisciplinary design that was funded by NSF, with reports in ME Magazine and the ASME Transactions on Mechanical Design. He has published over 100 refereed papers and was PI or Co-PI for approximately $2 million in research and equipment funding, including 10 NSF grants. He has received seven awards for teaching and curriculum development. He served as committee chair for doctoral students in business and engineering at Penn State, and as committee member for doctoral students at Ecole Centrale Paris and ENSAM. He is a Fellow of IISE and INFORMS, and a Senior Member of an Advisory Board member for two INFORMS sections and was INFORMS Vice President for Sections and Societies. He was president of the 500-member INFORMS Simulation Society, and received the Society’s Distinguished Service Award in 2008, the Outstanding Simulation Publication Award in 2015, and Lifetime Professional Achievement Award in 2024. Dr. Barton holds a B.S.E.E. from Princeton and M.S./Ph.D. degrees in Operations Research from Cornell. He is a Certified Analytics Professional Expert (CAP). Specialties: Statistical process control, design of experiments, supply chain monitoring, discrete event simulation, engineering design, applied statistics