Math @ Duke

Xiuyuan Cheng, Assistant Professor
As an applied analyst, I develop theoretical and computational techniques to solve problems in highdimensional statistics, signal processing and machine learning.  Contact Info:
Teaching (Fall 2019):
 MATH 561.01, NUMERICAL LINEAR ALGEBRA
Synopsis
 Gross Hall 104, TuTh 01:25 PM02:40 PM
 Education:
Ph.D.  Princeton University  2013 
 Recent Publications
(More Publications)
 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. 674688, Elsevier BV [doi]
 Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection,
Journal of Number Theory, vol. 185
(April, 2018),
pp. 4864, Elsevier BV [doi]
 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. 11851194 [abs]
 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. 41954204, PMLR
 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 1ยท7 million adults in a populationbased screening study (China PEACE Million Persons Project),
Lancet (London, England), vol. 390 no. 10112
(December, 2017),
pp. 25492558, Elsevier BV [doi]
 Recent Grant Support
 Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNAseq Data, Yale University, 2019/052023/01.
 Sloan Foundation Fellowship for Xiuyuan Cheng in Mathematics, Alfred P. Sloan Foundation, 2019/092021/09.
 CDS&E: Structureaware Representation Learning using Deep Networks, National Science Foundation, DMSNSF182082701, 2018/072021/06.
 Collaborative Research: Geometric Analysis and Computation of Generative Models, National Science Foundation, DMS1818945, 2018/072021/06.


dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
 
Mathematics Department
Duke University, Box 90320
Durham, NC 277080320

