Scholars@Duke

• Background
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  Education, Training, & Certifications

  • Ph.D., Princeton University 2013
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  Duke Appointment History

  • Scholar In Residence of Mathematics, Mathematics, Trinity College of Arts & Sciences 2017
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  Academic Positions Outside Duke

  • Gibbs Assistant Professor, Yale University. 2015 - 2017
• Research
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  Selected Grants

  • Efficient Methods for Calibration, Clustering, Visualization and Imputation of Large scRNA-seq Data awarded by Yale University 2019 - 2023
  • Sloan Foundation Fellowship for Xiuyuan Cheng in Mathematics awarded by Alfred P. Sloan Foundation 2019 - 2021
  • CDS&E: Structure-aware Representation Learning using Deep Networks awarded by  National Science Foundation 2018 - 2021
  • CDS&E: Structure-aware Representation Learning using Deep Networks awarded by  National Science Foundation 2018 - 2021
  • Collaborative Research: Geometric Analysis and Computation of Generative Models awarded by  National Science Foundation 2018 - 2021
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Cheng, Xiuyuan, Manas Rachh, and Stefan Steinerberger. “On the diffusion geometry of graph Laplacians and applications.” Applied and Computational Harmonic Analysis 46, no. 3 (May 2019): 674–88. https://doi.org/10.1016/j.acha.2018.04.001.
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    • Cheng, Xiuyuan, Gal Mishne, and Stefan Steinerberger. “The geometry of nodal sets and outlier detection.” Journal of Number Theory 185 (April 2018): 48–64. https://doi.org/10.1016/j.jnt.2017.09.021.
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    • Lu, Jiapeng, Yuan Lu, Xiaochen Wang, Xinyue Li, George C. Linderman, Chaoqun Wu, Xiuyuan Cheng, 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 390, no. 10112 (December 2017): 2549–58. https://doi.org/10.1016/s0140-6736(17)32478-9.
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    • Pragier, Gabi, Ido Greenberg, Xiuyuan Cheng, and Yoel Shkolnisky. “A Graph Partitioning Approach to Simultaneous Angular Reconstitution.” Ieee Transactions on Computational Imaging 2, no. 3 (September 2016): 323–34. https://doi.org/10.1109/tci.2016.2557076.
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    • Zhang, Teng, Xiuyuan Cheng, and Amit Singer. “Marčenko–Pastur law for Tyler’s M-estimator.” Journal of Multivariate Analysis 149 (July 2016): 114–23. https://doi.org/10.1016/j.jmva.2016.03.010.
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    • Cheng, Xiuyuan, Xu Chen, and Stéphane Mallat. “Deep Haar scattering networks.” Information and Inference 5, no. 2 (June 2016): 105–33. https://doi.org/10.1093/imaiai/iaw007.
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    • Boumal, Nicolas, and Xiuyuan Cheng. “Concentration of the Kirchhoff index for Erdős–Rényi graphs.” Systems & Control Letters 74 (December 2014): 74–80. https://doi.org/10.1016/j.sysconle.2014.10.006.
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    • 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 02, no. 04 (October 2013): 1350010–1350010. https://doi.org/10.1142/S201032631350010X.
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    • E, Weinan, Xiang Zhou, and Xiuyuan Cheng. “Subcritical bifurcation in spatially extended systems.” Nonlinearity 25, no. 3 (March 1, 2012): 761–79. https://doi.org/10.1088/0951-7715/25/3/761.
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    • Lin, Ling, Xiuyuan Cheng, Weinan E, An-Chang Shi, and Pingwen Zhang. “A numerical method for the study of nucleation of ordered phases.” Journal of Computational Physics 229, no. 5 (March 2010): 1797–1809. https://doi.org/10.1016/j.jcp.2009.11.009.
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    • Cheng, Xiuyuan, Ling Lin, Weinan E, Pingwen Zhang, and An-Chang Shi. “Nucleation of Ordered Phases in Block Copolymers.” Physical Review Letters 104, no. 14 (n.d.). https://doi.org/10.1103/physrevlett.104.148301.
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  • Conference Papers

    • Qiu, Qiang, Xiuyuan Cheng, A Robert Calderbank, and Guillermo Sapiro. “DCFNet: Deep Neural Network with Decomposed Convolutional Filters..” In Icml, edited by Jennifer G. Dy and Andreas Krause, 80:4195–4204. PMLR, 2018.
    • Yan, B., P. Sarkar, and X. Cheng. “Provable estimation of the number of blocks in block models.” In International Conference on Artificial Intelligence and Statistics, Aistats 2018, 1185–94, 2018.
    • Cheng, X., U. Shaham, O. Dror, A. Jaffe, B. Nadler, J. Chang, and Y. Kluger. “A Deep Learning Approach to Unsupervised Ensemble Learning.” In Proceedings of the 33rd International Conference on Machine Learning, 48:30–39. PMLR, 2016.
    • Chen, Xu, Xiuyuan Cheng, and Stéphane Mallat. “Unsupervised Deep Haar Scattering on Graphs..” In Advances in Neural Information Processing Systems 27, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger, 1709–17, 2014.
• Teaching & Mentoring
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  Recent Courses

  • MATH 493: Research Independent Study 2019
  • MATH 532: Basic Analysis II 2019
  • MATH 561: Numerical Linear Algebra, Optimization and Monte Carlo Simulation 2019
  • MATH 631: Measure and Integration 2018
  • MATH 790-90: Minicourse in Advanced Topics 2018
  • MATH 690-40: Topics in Probability Theory 2017
  • STA 690-40: Topics in Probability Theory 2017
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  Teaching Activities

  • In Fall 2017, I am teaching the graduate-level course Math 690 Topics in Data Analysis and Computation. I am teaching Math 532 Basic Analysis II in Spring 2018.

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