Scholars

Ding, Yu

Professor

Positions:

Faculty Affiliate, Energy Institute, Texas A&M Engineering Experiment Station (TEES), Division of Research
Professor, Industrial and Systems Engineering, College of Engineering

UN Sustainable Development Goals:

Single Page Bio

Profile Summary

yuding@tamu.edu

1 979 458 2343

Co-author Network

Map of Science

Co-investigator Network

Websites

Advanced Metrology Lab

UN Sustainable Development Goals:

overview

Dr. Yu Ding is the Mike and Sugar Barnes Professor of Industrial & Systems Engineering, Professor of Electrical & Computer Engineering, Associate Director for Research Engagement of Texas A&M Institute of Data Science (TAMIDS), and a Faculty Affiliate with Texas A&M Energy Institute and TEES Institute of Manufacturing Systems. Dr. Ding serves on the Executive Committee of Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). Dr. Ding's research interest is in data and quality science. Dr. Ding is the Editor-in-Chief of IISE Transactions (formerly IIE Transactions) for the term of 2021-2024. He previously served as an Editor for IEEE Transactions on Automation Science and Engineering and a Senior Editor for INFORMS Journal on Data Science. He also served as a Department Editor for IISE Transactions from 2005 to 2020, as an Associate Editor for IEEE Transactions on Automation Science and Engineering from 2006 to 2009, and a special issue Guest Co-Editor for Technometrics from 2013 to 2015.

education and training

Ph.D. in Mechanical Engineering, University of Michigan-Ann Arbor - (Ann Arbor, Michigan, United States) 2001
M.S. in Mechanical Engineering, Pennsylvania State University - (State College, Pennsylvania, United States) 1998
M.S. in Precision Instrument, Tsinghua University - (Beijing, Beijing, China) 1996
B.S. in Precision Engineering, University of Science and Technology of China - (Hefei, China) 1993

selected publications

Academic Articles81

Shin, Y. E., Zhou, L., & Ding, Y. u. (2022). Joint estimation of monotone curves via functional principal component analysis. Computational Statistics & Data Analysis. 166, 107343-107343.
Prakash, A., Tuo, R., & Ding, Y. u. (2022). Gaussian Process-Aided Function Comparison Using Noisy Scattered Data. Technometrics. 64(1), 92-102.

SDG 7
Tuo, R., He, S., Pourhabib, A., Ding, Y. u., & Huang, J. Z. (2021). A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models. Journal of the American Statistical Association. 1-15.
Ding, Y. u., Kumar, N., Prakash, A., Kio, A. E., Liu, X., Liu, L., & Li, Q. (2021). A case study of space-time performance comparison of wind turbines on a wind farm. Renewable Energy. 171, 735-746.

SDG 7
Ezzat, A. A., Liu, S., Hochbaum, D. S., & Ding, Y. u. (2021). A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-Type Spatial Pattern Recognition in Wafer Bin Maps. IEEE Transactions on Semiconductor Manufacturing. 34(2), 194-206.

Conference Papers18

Ahmed, I., Galoppo, T., & Ding, Y. u. (2019). O-LoMST: An Online Anomaly Detection Approach And Its Application In A Hydropower Generation Plant. 2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE). 762-767.
Vijayaraghavan, V., Kianfar, K., Ding, Y. u., & Parsaei, H. (2018). A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). 00, 836-842.
Ahmed, I., Dagnino, A., Bongiovi, A., & Ding, Y. u. (2018). Outlier Detection for Hydropower Generation Plant. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). 00, 193-198.

SDG 7
Vijayaraghavan, V., Kianfar, K., Ding, Y. u., & Parsaei, H. (2017). An L1-minimization based algorithm to measure the redundancy of state estimators in large sensor systems. 2017 13th IEEE Conference on Automation Science and Engineering (CASE), 2017 13th IEEE Conference on Automation Science and Engineering (CASE 2017). 2017-August, 424-428.
Sy, E., Jacobs, S. A., Dagnino, A., & Yu Ding. (2016). Graph-based clustering for detecting frequent patterns in event log data. 2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016 IEEE International Conference on Automation Science and Engineering (CASE). 2016-November, 972-977.