Anderson-Interface Chair and
Professor
Education
- Ph.D. Mechanical Engineering (2001), University of Michigan
- M.S. Mechanical Engineering (1998), Penn State University
- M.S. Precision Instruments (1996), Tsinghua University
- B.S. Precision Engineering (1993), University of Science & Technology of China
Expertise
- Data science
- Energy
- Manufacturing Applications
About
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. While at Texas A&M, he also served as Associate Department Head for Graduate Affairs of the Department of Industrial and Systems Engineering between 2012 and 2016 and Associate Director for Research Engagement of Texas A&M Institute of Data Science between 2020 and 2023. He received his B.S. in Precision Engineering from the University of Science and technology of China in 1993, a M.S. in Precision Engineering from Tsinghua University in 1996, a second M.S. in Mechanical Engineering from Penn State in 1998, and his Ph.D. in Mechanical Engineering from the University of Michigan in 2001.
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, among others,
- 2019 IISE Technical Innovation Award
- 2022 INFORMS Impact Prize
- 2024 IISE Energy Systems Division's Career Achievement Award
- 2024 ASME Blackall Machine Tool and Gage Award
- 2024 SME S. M. Wu Research Implementation Award
Dr. Ding served as the Program Chair for the 21th 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 IISE, INFORMS, and ASME.
Research
- Engineering Data Science
- Wind Energy
- Smart Manufacturing
Teaching
- Statistical Machine Learning
- Design of Experiments
- Statistical Process Control
- Forecasting for Renewables
Awards and Honors
- INFORMS Fellow, 2025
- S.M. Wu Research Implementation Award, Society of Manufacturing Engineers (SME), 2024
- Blackall Machine Tool and Gage Award, American Society of Mechanical Engineering (ASME), 2024
- Career Achievement Award, IISE Energy Systems Division, 2024
- Impact Prize, INFORMS, 2022
- The Engineering Genesis Award, Texas A&M Engineering, 2020
- The Association of Former Students (AFS) University Level Distinguished Achievement Award in Research, Texas A&M University, 2020
- Award for Technical Innovation in Industrial Engineering, Institute of Industrial & Systems Engineering (IISE), 2019
- Research Impact Award, Texas A&M Engineering, 2018
- Fellow, American Society of Mechanical Engineering (ASME), 2016
- Fellow, Institute of Industrial Engineers (IIE), 2015
- Best Application Paper Award, IIE Transactions on Quality and Reliability Engineering, 2014
- Charles H. Barclay, Jr.’45 Faculty Fellow, Texas A&M Engineering, 2013
- Best Conference Paper Award, from the Modeling and Simulation Track of the IIE Annual Conference, 2011
- Brockett Professorship Award, Texas A&M Engineering, 2009
- Best Paper Award, IIE Transactions on Quality and Reliability Engineering, 2006
- Montague - Center for Teaching Excellence Scholar, Texas A&M University, 2005.
- CAREER Award, National Science Foundation, 2004
- Best Paper Award, ASME Manufacturing Engineering Division, 2000
Representative Publications
1. Park, Huang and Ding (2010) “A computable plug-in estimator of minimum volume sets for novelty detection,” Operations Research, 58: 1469–1480.
2. Park, Huang, and Ding (2011) “Domain decomposition approach for fast Gaussian process regression of large spatial datasets,” Journal of Machine Learning Research, 12: 1697-1728.
3. Park, Huang, Ji, and Ding (2013) “Segmentation, inference and classification of partially overlapping nanoparticles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3): 669-681.
4. Pourhabib, Mallick and Ding (2015) “Absent data generating classifier for imbalanced class sizes,” Journal of Machine Learning Research, 16: 2695−2724.
5. Lee, Ding, Genton, and Xie (2015) "Power curve estimation with multivariate environmental factors for inland and offshore wind farms," Journal of the American Statistical Association, 110: 56-67.
6. Pourhabib, Huang, and Ding (2016) “Short-term wind speed forecast using measurements from multiple turbines in a wind farm,” Technometrics, 58(1): 138-147
7. Ezzat, Jun, and Ding (2019) “Spatio-temporal short-term forecast: A calibrated regime-switching method,” Annals of Applied Statistics, 13(3): 1484-1510.
8. Payne, Guha, Ding, and Mallick (2020) “A conditional density estimation partition model using logistic Gaussian processes,” Biometrika, 107(1): 173-190.
9. Ahmed, Hu, Acharya, and Ding (2021) “Unsupervised point anomaly detection using neighborhood structure assisted non-negative matrix factorization,” Journal of Machine Learning Research, 22(34): 1−32.
10. Ahmed, Galoppo, Hu, and Ding (2022), “Graph regularized autoencoder and its application in unsupervised anomaly detection,” IEEE Trans. on Pattern Analysis & Machine Intelligence, 44(8): 4110 – 4124
11. Prakash, Tuo, and Ding (2023) “The temporal overfitting problem with applications in wind power curve modeling,” Technometrics, 65(1): 70-82.
12. Tuo, He, Pourhabib, Ding, and Huang (2023) “A reproducing kernel Hilbert space approach to functional calibration of computer models,” Journal of the American Statistical Association, 118: 883-897.
13. Wang, Ding, and Shahrampour (2023) “Temporal adaptive kernel density estimator for real-time dynamic density estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 45: 13831 – 13843.
14. Jin, Bukkapatnam, Hayes, and Ding (2023) “Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes,” ASME Transactions, Journal of Manufacturing Science and Engineering, 145: 061007.
15. Latiffianti, Sheng, Rodgers, Sanderson, and Ding (2025) "An accumulation method for early fault warning and its application to wind turbine systems," Annals of Applied Statistics, 19(3): 2436-2456.