Xingye Qiao
Professor; Chair, Data Science TAE
Department of Mathematics and Statistics
Background
Xingye Qiao, currently a professor in the Department of Mathematics and Statistics at Binghamton University, is a distinguished researcher and educator in statistics, machine learning and causal inference. Before joining the department in 2010, he earned a BS from Fudan University in 2005 and both an MS and a PhD from the University of North Carolina at Chapel Hill in 2007 and 2010, respectively.
Qiao's research is dedicated to developing predictive and inferential tools for tackling complex data challenges, such as imbalanced classes, high-dimensional data, transfer learning and inference from observational studies. He is recognized for creating efficient, theoretically sound algorithms with built-in confidence guarantees and for his commitment to enhancing the trustworthiness of statistical methods in critical domains such as healthcare.
Beyond his technical contributions, Qiao is a fervent advocate for data science ethics, AI fairness and promoting an inclusive approach to data science. His interdisciplinary collaborations and outreach activities underscore his commitment to advancing his field and ensuring its ethical application in society.
Publications
- Wang, Z. and Qiao, X. (2023), “Set-valued Classification with Out-of-distribution Detection for Many Classes”, Journal of Machine Learning Research, 24(375):1–39.
- Wang, W. and Qiao, X. (2023), “Set-Valued Support Vector Machine with Bounded Error Rates,” Journal of the American Statistical Association, 118, 544, 2847–2859.
- Meng, H. and Qiao, X. (2022), “Augmented Direct Learning for Conditional Average Treatment Effect Estimation with Double Robustness,” Electronic Journal of Statistics, 16(1), pp. 3523–3560.
- Ren, Z., Jung, S. and Qiao, X. (2022), “Covariance-engaged Classification of Sets via Linear Programming,” Statistica Sinica, 32, pp. 1515–1540.
- Duan, J., Qiao, X., and Cheng, G. (2020), “Statistical Guarantees of Distributed Nearest Neighbor Classification,” In Advances in Neural Information Processing Systems, 33, pp. 229–240.
Education
- PhD, University of North Carolina at Chapel Hill
- MS, University of North Carolina at Chapel Hill
- BS, Fudan University
Research Interests
- Statistics
- Machine Learning
- Causal Inference
- High-dimensional Inference
- Precision Medicine
Teaching Interests
- Statistical Learning & Data Mining
- Theory of Machine Learning
- Causal Inference
- Principles of Data Science
- Mathematical Statistics
Awards
- 2019 The Binghamton Council/Foundation Award. Citation: for having served the campus with outstanding dedication and provided exemplary leadership to the University.
