Yu Ding

Mike and Sugar Barnes Professor, Industrial & Systems Engineering
Associate Director for Research Engagement - Texas A&M Institute of Data Science
Affiliated Faculty, Electrical & Computer Engineering
Affiliated Faculty, Multidisciplinary Engineering

Educational Background

- Data and Quality Science
- Data Science for Wind Energy
- Data Science for Material/Manufacturing

Impact Prize, Institute for Operations Research and the Management Sciences (INFORMS), 2022
The Association of Former Students (AFS) University-Level Distinguished Achievement Award in Research, Texas A&M University, 2020
Technical Innovation Award, IISE, 2019
Research Impact Award, Texas A&M Engineering, 2018
Montague – Center for Teaching Excellence Scholar, Texas A&M University, 2005
CAREER Award, National Science Foundation, 2004

Park, C., Huang, J. Z., and Ding, Y., 2010, “A computable plug-in estimator of minimum volume sets for novelty detection,” Operations Research, Vol. 58 (5), pp. 1469–1480
Lee, G.; Byon, E., Ntaimo, L, and Ding, Y., 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” Annals of Applied Statistics, Vol. 7, pp. 2034-2061
Park, C., Huang, J.Z., Ji, J., and Ding, Y., 2013, “Segmentation, inference and classification of partially overlapping nanoparticles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 (3), pp. 669-681
Pourhabib, A; Mallick, B.K., Ding, Y., 2015, “Absent data generating classifier for imbalanced class sizes,” Journal of Machine Learning Research, Vol. 16, pp. 2695-2724
Lee, G.; Ding, Y., Genton, M. G., and Xie, L., 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” Journal of the American Statistical Association, Vol. 110(509), pp. 56-67
Hwangbo, H.; A. L. Johnson, and Y. Ding, 2018, “Spline model for wake effect analysis: characteristics of single wake and its impacts on wind turbine power generation.” IISE Transactions, Vol. 50 (2), pp. 112-125