XIE, Liyan （to join in Summer 2021）
Ph.D. in Industrial Engineering (Statistics major), Georgia Institute of Technology, 2021
B.S in Statistics, University of Science and Technology of China, 2016
Professor Liyan Xie will join the Chinese University of Hong Kong, Shenzhen as an Assistant Professor in Summer 2021. Professor Xie completed Bachelor’s degree at University of Science and Technology of China with Statistics major in 2016. She will receive her Ph.D. degree in Industrial Engineering (with Statistics major) from Georgia Institute of Technology in 2021. Professor Xie has been invited to be reviewers of several top conferences, such as AAAI, ICML, Neurips, AISTATS, ICLR.
Professor Xie’s research interests include theoretical and methodological foundations of data science inspired by important applications (sensor networks and health care), with a particular interest in sequential change detection and robust hypothesis test through the lens of robust optimization.
Journal Articles (published or submitted)
- Liyan Xie, George V. Moustakides, and Yao Xie. “Sequential Subspace Changepoint Detection.” Sequential Analysis, vol. 39, no. 3, pp. 307-335, 2020. (Finalist of INFORMS QSR Best Student Paper Award 2019.)
- Yang Cao, Liyan Xie, Yao Xie, and Huan Xu. “Sequential Change-Point Detection via Online Convex Optimization.” Entropy, vol. 20, no. 2, pp. 108, 2018.
- Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, and Yao Xie. “Convex Parameter Recovery for Interacting Marked Processes.” IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 3, pp. 799-813, 2020.
- Liyan Xie and Yao Xie. “Sequential Change Detection by Optimal Weighted l2 Divergence.” arXiv:2010.11285. Submitted.
- Liyan Xie, Shaofeng Zou, Yao Xie, and Venugopal V. Veeravalli. “Sequential Change Detection: Classical Results and New Directions.” Survey paper. To appear in 2021, IEEE Journal on Selected Areas in Information Theory.
Preprints and Working Paper
- Xi He, Liyan Xie, Yao Xie, and Pinar Keskinocak. “Graph Based Variable Selection For Survival Analysis.” Working paper.
- Liyan Xie, Rui Gao, and Yao Xie. “Data-driven Robust Hypothesis Testing with Wasserstein Uncertainty Sets.” Working paper. (Runner up for INFORMS Computing Society Student Paper Prize 2019.)
- Shixiang Zhu, Alexander Bukharin, Liyan Xie, Mauricio Santillana, Shihao Yang, and Yao Xie. “High-Resolution Spatio-Temporal Model for County-level COVID-19 Activity in the US.” arXiv:2009.07356, 2020. Under revision.
- Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, and Yao Xie. “Distributionally Robust k-Nearest Neighbors for Few-Shot Learning.” arXiv:2006.04004, 2020.