Xiuyuan Cheng

Xiuyuan Cheng
  • Assistant Professor of Mathematics
External address: 120 Science Drive, 293 Physics Building, Durham, NC 27708
Internal office address: 120 Science Drive, P.O. Box 90320, Durham, NC 27708

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

Education & Training
  • Ph.D., Princeton University 2013

Selected Grants

Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNA-seq Data awarded by Yale University (Principal Investigator). 2019 to 2023

HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022

Sloan Foundation Fellowship for Xiuyuan Cheng in Mathematics awarded by Alfred P. Sloan Foundation (Principal Investigator). 2019 to 2021

CDS&E: Structure-aware Representation Learning using Deep Networks awarded by National Science Foundation (Principal Investigator). 2018 to 2021

CDS&E: Structure-aware Representation Learning using Deep Networks awarded by National Science Foundation (Principal Investigator). 2018 to 2021

Collaborative Research: Geometric Analysis and Computation of Generative Models awarded by National Science Foundation (Principal Investigator). 2018 to 2021

Cheng, Xiuyuan, et al. “On the diffusion geometry of graph Laplacians and applications.” Applied and Computational Harmonic Analysis, vol. 46, no. 3, Elsevier BV, May 2019, pp. 674–88. Crossref, doi:10.1016/j.acha.2018.04.001. Full Text

Cheng, Xiuyuan, et al. “The geometry of nodal sets and outlier detection.” Journal of Number Theory, vol. 185, Elsevier BV, Apr. 2018, pp. 48–64. Crossref, doi:10.1016/j.jnt.2017.09.021. Full Text

Lu, Jiapeng, et al. “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).” The Lancet, vol. 390, no. 10112, Elsevier BV, Dec. 2017, pp. 2549–58. Crossref, doi:10.1016/s0140-6736(17)32478-9. Full Text

Pragier, Gabi, et al. “A Graph Partitioning Approach to Simultaneous Angular Reconstitution.” Ieee Transactions on Computational Imaging, vol. 2, no. 3, Institute of Electrical and Electronics Engineers (IEEE), Sept. 2016, pp. 323–34. Crossref, doi:10.1109/tci.2016.2557076. Full Text

Zhang, Teng, et al. “Marčenko–Pastur law for Tyler’s M-estimator.” Journal of Multivariate Analysis, vol. 149, Elsevier BV, July 2016, pp. 114–23. Crossref, doi:10.1016/j.jmva.2016.03.010. Full Text

Cheng, Xiuyuan, et al. “Deep Haar scattering networks.” Information and Inference, vol. 5, no. 2, Oxford University Press (OUP), June 2016, pp. 105–33. Crossref, doi:10.1093/imaiai/iaw007. Full Text

Boumal, Nicolas, and Xiuyuan Cheng. “Concentration of the Kirchhoff index for Erdős–Rényi graphs.” Systems & Control Letters, vol. 74, Elsevier BV, Dec. 2014, pp. 74–80. Crossref, doi:10.1016/j.sysconle.2014.10.006. Full Text

CHENG, X. I. U. Y. U. A. N., and A. M. I. T. SINGER. “The Spectrum of Random Inner-product Kernel Matrices.” Random Matrices: Theory and Applications, vol. 02, no. 04, Oct. 2013, pp. 1350010–1350010. Manual, doi:10.1142/S201032631350010X. Full Text

E, Weinan, et al. “Subcritical bifurcation in spatially extended systems.” Nonlinearity, vol. 25, no. 3, IOP Publishing, Mar. 2012, pp. 761–79. Crossref, doi:10.1088/0951-7715/25/3/761. Full Text

Lin, Ling, et al. “A numerical method for the study of nucleation of ordered phases.” Journal of Computational Physics, vol. 229, no. 5, Elsevier BV, Mar. 2010, pp. 1797–809. Crossref, doi:10.1016/j.jcp.2009.11.009. Full Text

Pages

Yan, B., et al. “Provable estimation of the number of blocks in block models.” International Conference on Artificial Intelligence and Statistics, Aistats 2018, 2018, pp. 1185–94.

Qiu, Qiang, et al. “DCFNet: Deep Neural Network with Decomposed Convolutional Filters..” Icml, edited by Jennifer G. Dy and Andreas Krause, vol. 80, PMLR, 2018, pp. 4195–204.

Cheng, X., et al. “A Deep Learning Approach to Unsupervised Ensemble Learning.” Proceedings of the 33rd International Conference on Machine Learning, vol. 48, PMLR, 2016, pp. 30–39.

Chen, Xu, et al. “Unsupervised Deep Haar Scattering on Graphs..” Advances in Neural Information Processing Systems 27, edited by Zoubin Ghahramani et al., 2014, pp. 1709–17.

Xiuyuan Cheng

The Alfred P. Sloan Foundation congratulates the winners of the 2019 Sloan Research Fellowships. These 126 early-career scholars represent the most promising scientific researchers working today. Their achievements and potential place them among the... read more »