Xiuyuan Cheng, Assistant Professor

As an applied analyst, I develop theoretical and computational techniques to solve problems in high-dimensional statistics, signal processing and machine learning.

Contact Info:

Office Location:	293 Physics
Office Phone:	(919) 600-2825
Email Address:
Web Page:	https://services.math.duke.edu/~xiuyuanc/

Teaching (Fall 2018):

• MATH 631.01, REAL ANALYSIS Synopsis

  Gross Hall 318, MWF 03:20 PM-04:10 PM

• MATH 790-90.06, MINICOURSE IN ADVANCED TOPICS Synopsis

  Gross Hall 318, MW 04:40 PM-05:55 PM

• MATH 532.01, BASIC ANALYSIS II Synopsis

  Physics 205, MW 03:05 PM-04:20 PM

Education:

Ph.D.

Princeton University

2013

Recent Publications   (More Publications)

1. Lu, J; Lu, Y; Wang, X; Li, X; Linderman, GC; Wu, C; Cheng, X; Mu, L; Zhang, H; Liu, J; Su, M; Zhao, H; Spatz, ES; Spertus, JA; Masoudi, FA; Krumholz, HM; Jiang, L, Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project), Lancet (London, England), vol. 390 no. 10112 (December, 2017), pp. 2549-2558 [doi]
2. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory (October, 2017) [doi]
3. Pragier, G; Greenberg, I; Cheng, X; Shkolnisky, Y, A Graph Partitioning Approach to Simultaneous Angular Reconstitution, vol. 2 no. 3 (September, 2016), pp. 323-334 [doi]
4. Zhang, T; Cheng, X; Singer, A, Marčenko–Pastur law for Tyler’s M-estimator, Journal of Multivariate Analysis, vol. 149 (July, 2016), pp. 114-123 [doi]
5. Cheng, X; Shaham, U; Dror, O; Jaffe, A; Nadler, B; Chang, J; Kluger, Y, A Deep Learning Approach to Unsupervised Ensemble Learning, Proceedings of The 33rd International Conference on Machine Learning, vol. 48 (June, 2016), pp. 30-39, PMLR

Recent Grant Support

• CDS&E: Structure-aware Representation Learning using Deep Networks, National Science Foundation, DMS-NSF-1820827-01, 2018/07-2021/06.      
• Collaborative Research: Geometric Analysis and Computation of Generative Models, National Science Foundation, DMS-1818945, 2018/07-2021/06.