- Assistant Professor of Mathematics
As an applied analyst, I develop theoretical and computational techniques to solve problems in high-dimensional statistics, signal processing and machine learning.
NSF-BSF: Group Invariant Graph Laplacians: Theory and Computations awarded by National Science Foundation (Principal Investigator). 2020 to 2024
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
Collaborative Research: Geometric Analysis and Computation of Generative Models 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
Cheng, Xiuyuan, et al. “Two-sample statistics based on anisotropic kernels.” Information and Inference: A Journal of the Ima, Oxford University Press (OUP), Dec. 2019. Manual, doi:10.1093/imaiai/iaz018. Full Text
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
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
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
Cheng, X., et al. “RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks.” 7th International Conference on Learning Representations, Iclr 2019, 2019.
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.
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 »