Aidong "Adam" Ding is an associate professor of statistics in the Department of Mathematics. He develops and applies statistical methodologies to solve problems in various scientific fields.

Ding’s research centers around prediction and confidence intervals, artificial neural networks, high-dimensional empirical linear prediction, and biostatistics. Within clinical trials and the fields of cybersecurity, engineering, and finance, he applies artificial intelligence to modern statistical analysis. His deep learning models have led to collaborative applications in cybersecurity and secure and privacy-preserving data analysis.

Ding has published more than 70 refereed articles on topics ranging from statistical and machine learning methodologies to statistical theory. Those studies have appeared in various journals, including the Journal of American Statistical Association, Journal of Machine Learning Research, Biometrics, Journal of the Royal Statistical Society, and the International Association for Cryptologic Research Transactions on Cryptographic Hardware and Embedded Systems, Biostatistics, Statistics in Medicine.

Over the years, he has served as visiting faculty member at Harvard University, the University of Rochester, and the University of Florida. Ding received his doctoral degree from Cornell University.