George Box once said "All models are wrong, but some are useful." We the Engineers should be particularly concerned with the usefulness of our models.

Dr. Yu Ding is the Anderson-Interface Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech in 2023, he was the Mike and Sugar Barnes Professor of Industrial and Systems Engineering at Texas A&M University.  

Dr. Ding's research is in the area of engineering data science, aiming to fill the critical void between pure data-driven methods developed in computer science and statistics and physical knowledge and engineering principles in model development and decision-making. His engineering data science methods contribute primarily to two engineering applications: wind energy and smart manufacturing.  Dr. Ding is the author of the CRC Press book, Data Science for Wind Energy, and a co-author of the Springer Nature book, Data Science for Nano Image Analysis.  His research work is recognized by the 2018 Texas A&M Engineering Research Impact Award, 2019 IISE Technical Innovation Award, 2020 AFS Texas A&M University-Level Distinguished Achievement Award in Research, 2022 INFORMS Impact Prize, 2024 IISE Energy Systems Division's Career Achievement Award, 2024 ASME Blackall Machine Tool and Gage Award, and 2024 SME S. M. Wu Research Implementation Award.  

Dr. Ding served as the Program Chair for the 21st IEEE Conference on Automation Science and Engineering (CASE 2025) and the 14th Editor-in-Chief of IISE Transactions (2021-2024). He is currently serving as the Editor-in-Chief for INFORMS Journal on Data Science and Senior Vice President for International Operations of IISE.  Dr. Ding is a Fellow of ASME, IISE, and INFORMS.