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 (Spring 2020):

• 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. Cheng, X; Cloninger, A; Coifman, RR, Two-sample statistics based on anisotropic kernels, Information and Inference (December, 2019), Oxford University Press (OUP) [doi]  [abs]
2. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RotDCF: Decomposition of convolutional filters for rotation-equivariant deep networks (May, 2019)
3. 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]
4. 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]
5. 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]

Recent Grant Support

• Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNA-seq Data, Yale University, 2019/05-2023/01.      
• HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/10-2022/09.      
• 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.