Xiuyuan Cheng, Associate 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
Email Address:
Web Page:	https://services.math.duke.edu/~xiuyuanc/

Education:

Ph.D.

Princeton University

2013

Recent Publications   (More Publications)

1. Repasky, M; Cheng, X; Xie, Y, Neural Stein Critics with Staged L²-Regularization, IEEE Transactions on Information Theory, vol. 69 no. 11 (November, 2023), pp. 7246-7275 [doi]  [abs]
2. Landa, B; Cheng, X, Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling, SIAM Journal on Mathematics of Data Science, vol. 5 no. 3 (September, 2023), pp. 589-614, Society for Industrial & Applied Mathematics (SIAM) [doi]
3. Lee, J; Xie, Y; Cheng, X, Training Neural Networks for Sequential Change-Point Detection, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June (January, 2023), ISBN 9781728163277 [doi]  [abs]
4. Cheng, X; Wu, N, Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation, Applied and Computational Harmonic Analysis, vol. 61 (November, 2022), pp. 132-190 [doi]  [abs]
5. Tan, Y; Zhang, Y; Cheng, X; Zhou, X-H, Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions., Scientific reports, vol. 12 no. 1 (October, 2022), pp. 16630 [doi]  [abs]

Recent Grant Support

• CAREER: Learning of graph diffusion and transport from high dimensional data with low-dimensional structures, National Science Foundation, 2023/09-2028/08.      
• RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/07-2026/06.      
• Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency, Georgia Tech Research Corporation, 2022/01-2025/12.      
• Collaborative Reseach: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable NETworks (THEORINET), National Science Foundation, 2020/09-2025/08.      
• Collaborative Reseach: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable NETworks (THEORINET), Simons Foundation, 2020/09-2025/08.      
• NSF-BSF: Group Invariant Graph Laplacians: Theory and Computations, National Science Foundation, 2020/07-2025/06.      
• HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/10-2023/09.      
• Sloan Foundation Fellowship for Xiuyuan Cheng in Mathematics, Alfred P. Sloan Foundation, 2019/09-2023/09.      
• Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNA-seq Data, Yale University, 2019/05-2023/01.      
• CDS&E: Structure-aware Representation Learning using Deep Networks, National Science Foundation, DMS-NSF-1820827-01, 2018/07-2022/06.      
• Collaborative Research: Geometric Analysis and Computation of Generative Models, National Science Foundation, DMS-1818945, 2018/07-2022/06.