Thomas Badgwell

Thomas Badgwell is a Professor of Practice in the Department of Chemical Engineering at The University of Texas at Austin, affiliated with the Cockrell School of Engineering. He holds a Ph.D., M.S., and B.S. in Chemical Engineering from UT Austin (1992) and Rice University (1982). His research focuses on modeling, optimization, and control of chemical processes, with notable contributions to model predictive control (MPC) and integration with machine learning. He teaches courses including CHE 348 (Numerical Methods), CHE 354 (Transport Processes), and CHE 360 (Process Dynamics and Control).

Awards & Honors:

• 2024: Babatunde A. Ogunnaike Control Practice Award (AACC)
• 2024: Distinguished Industrial Lecturer (IEEE Control Systems Society)
• 2022: Control Global Process Automation Hall of Fame
• 2013: Computing Practice Award (CAST Division)
• 2011: Fellow of AIChE

His research bridges theoretical advancements and industrial applications, emphasizing MPC's role in process automation. Recent work explores machine learning integration, reinforcement learning for control systems, and digital manufacturing platforms. He has advised on advanced control systems for industries like oil refining and catalytic processes.