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:	120 Science Drive, 293 Physics Building, Durham, NC 27708
Office Phone:	(919) 600-2825
Email Address:
Web Page:	https://services.math.duke.edu/~xiuyuanc/

Teaching (Fall 2019):

• MATH 561.01, NUMERICAL LINEAR ALGEBRA Synopsis

  Bio Sci 155, TuTh 01:25 PM-02:40 PM

Education:

Ph.D.

Princeton University

2013

Recent Publications   (More Publications)

1. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis, vol. 46 no. 3 (May, 2019), pp. 674-688, Elsevier BV [doi]
2. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory, vol. 185 (April, 2018), pp. 48-64, Elsevier BV [doi]
3. Yan, B; Sarkar, P; Cheng, X, Provable estimation of the number of blocks in block models, International Conference on Artificial Intelligence and Statistics, Aistats 2018 (January, 2018), pp. 1185-1194  [abs]
4. Qiu, Q; Cheng, X; Calderbank, AR; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters., edited by Dy, JG; Krause, A, Icml, vol. 80 (2018), pp. 4195-4204, PMLR
5. 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, Elsevier BV [doi]

Recent Grant Support

• Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNA-seq Data, Yale University, 2019/05-2023/01.      
• Sloan Foundation Fellowship for Xiuyuan Cheng in Mathematics, Alfred P. Sloan Foundation, 2019/09-2021/09.      
• 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.