Please join us in welcoming Yu Ding, the Mike and Sugar Barnes Professor at Texas A&M University.
Wind energy is one of the fastest-growing clean energy sources. Despite the significant growth in the past two decades, wind energy missed some intermediate goals set forth earlier. One critical element needed for accelerating wind energy growth is to significantly reduce its operational cost and further boost its market competitiveness. In his book, Data Science for Wind Energy, the speaker demonstrated how statistical and machine learning methods can help address research needs in wind energy applications. The speaker will discuss some challenges encountered in wind applications and present use cases in which statistical/machine learning models and solutions make sensible impacts.
Yu Ding is the Mike and Sugar Barnes Professor of Industrial and Systems Engineering at Texas A&M University and Associate Director for Research Engagement of Texas A&M Institute of Data Science. Ding received his Ph.D. degree from the University of Michigan in 2001. His research interest is in data and quality science, with impact targeting two broad sets of applications: wind energy and manufacturing. Ding authored the CRC Press book, Data Science in Wind Energy, in 2019 and co-authored the Springer book, Data Science for Nano Image Analysis, in 2021. Ding is a recipient of the 2018 Texas A&M Engineering Research Impact Award, 2019 IISE Technical Innovation Award, 2020 Texas A&M’s University-Level Distinguished Achievement Award, and 2022 INFORMS Impact Prize. Ding is a Fellow of IISE and ASME and is serving as the Editor-in-Chief of IISE Transactions for the term of 2021-2024.
