Portrait of the author

My name is Xiaojun Mao, and I am currently a tenure-track associate professor at the School of Mathematical Sciences, Shanghai Jiao Tong University. My current research interests include distributed statistical inference, matrix completion and recommender systems, and high-dimensional statistical inference. Before joining SJTU, I held a faculty position at Fudan University.

Academic Position

Shanghai Jiao Tong University

Tenure-track Associate Professor in School of Mathematical Sciences • Shanghai, China • 2022 - Present

Fudan University

Assistant Professor in School of Data Science • Shanghai, China • 2018 - 2021

Research

Research Interests

  • Distributed Statistical Inference
  • Matrix Completion and Recommender Systems
  • High-dimensional Statistical Inference

Openings (Drop me an email if you are interested)

  • We are currently looking for highly-motivated undergraduates, graduates, postdocs, and visiting research students.

Publications

Selected Refereed Journals

  1. Liu, W., Mao, X., Zhang, X., and Zhang, X. (alphabetical) (2024) "Robust Personalized Federated Learning with Sparse Penalization". Journal of the American Statistical Association, To appear.

  2. Liu, W., Tu, J., Mao, X., and Chen, X. (2024) "Majority Vote for Distributed Differentially Private Sign Selection". The Annals of Statistics, 52(4), 1671-1690.

  3. Tu, J., Liu, W., and Mao, X. (2024) "Distributed Estimation on Semi-Supervised Generalized Linear Model". Journal of Machine Learning Research, 25(76), 1-41.

  4. Liu, W., Mao, X., Zhang, X., and Zhang, X. (alphabetical) (2024) "Efficient Sparse Least Absolute Deviation Regression with Differential Privacy". IEEE Transactions on Information Forensics and Security, 19, 2328-2339.

  5. Tu, J., Liu, W., Mao, X., and Xu, M. (2024) "Distributed Semi-Supervised Sparse Statistical Inference". IEEE Transactions on Information Theory, 70(6), 4197-4217.

  6. Mao, X., Wang, H., Wang, Z., and Yang, S. (alphabetical) (2024) "Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness". Journal of Computational and Graphical Statistics, To appear.

  7. Liu, W., Mao, X., and Zhang, X. (alphabetical) (2022) "Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach". IEEE Transactions on Signal Processing, 70, 797-809.

  8. 陈松蹊，毛晓军，王聪。(2022) "大数据情境下的数据完备化：挑战与对策"。 《管理世界》，第38卷，第1期，第196-207页。

  9. Mao, X., Wong, R. K. W., and Chen, S. X. (2021) "Matrix Completion under Low-Rank Missing Mechanism". Statistica Sinica, 31(4), 2005–2030.

  10. Tu, J., Liu, W., Mao, X., and Chen, X. (2021) "Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference". Journal of Machine Learning Research, 22(84), 1–67.

  11. Chen, X., Liu, W., Mao, X., and Yang, Z. (alphabetical) (2020) "Distributed High-Dimensional Regression under a Quantile Loss Function". Journal of Machine Learning Research, 21(182), 1–43.

  12. Mao, X., Chen, S. X., and Wong, R. K. W. (2019) "Matrix Completion with Covariate Information". Journal of the American Statistical Association, 114(525), 198-210.

    Selected Refereed Proceedings

    1. Xia, H., Liu, W., and Mao, X.. (2024) "ST$_k$: A Scalable Module for Solving Top-k Problems". NeurIPS, To appear.

    2. Zhang, Y., Xu, M., Mao, X., and Wang, J. (2022) "Uncertainty Modeling in Generative Compressed Sensing". ICML, 26655-26668.

    3. Wang, J., Wong, R. K. W., Mao, X., and Chan, K. C. G. (2021) "Matrix Completion with Model-free Weighting". ICML, 10927-10936.

    4. Liu, W., Mao, X., and Wong, R. K. W. (alphabetical) (2020) "Median Matrix Completion: from Embarrassment to Optimality". ICML, 6294-6304.

      5. Selected Preprints

      1. Liu, W., and Mao, X. (alphabetical) (2024+) "Fast Decentralized Median Value Estimation". Submitted.

      2. Li, Z., Wei, Z., Huang, Z., Mao, X., and Wang, J. (2024+) "One-bit Matrix Completion with Differential Privacy". Submitted.

      3. Mao, X., Wang, H., and Wang, Z. (alphabetical) (2024+) "Group-Sparse Inductive Matrix Completion through Transfer Learning". Submitted.

      4. Liu, X., Liu, W., Mao, X., and Zhang, X. (2024+) "Decentralized Reduced Rank Regression for Response Partition". Submitted.

      Software

      1. Li, Y-S., and Mao, X.. (2019) "PowerfulMaxEigenpair". The Comprehensive R Archive Network (CRAN).   Source Code

      2. Chen, M-F., and Mao, X.. (2017) "GlobalMaxEigenpair".   Source Code

      3. Chen, M-F., and Mao, X.. (2017) "EfficientMaxEigenpair". The Comprehensive R Archive Network (CRAN).   Source Code

Teaching

Shanghai Jiao Tong University, China

Course Instructor

STAT6105: Big Data Analysis 2024 Fall

MATH3708: Big Data Analysis 2023 Fall

MA385: Big Data Analysis 2022 Fall

STAT6001H: Basic Mathematical Statistics 2022-2024 Spring

Fudan University, China

Course Instructor

DATA 130044: Multivariate Statistical Analysis 2020-2021 Spring

DATA 630007: Text Mining and Analytic 2019 Spring

DATA 630011 : Big Data Analytics 2018-2021 Fall

DATA 130004h : Adavanced Regression Analysis 2021 Fall

Iowa State University, USA

Course Instructor

Stat 105XW: Introduction to Statistics for Engineers 2017 Fall

Teaching Assistant

Stat 546: Nonparametric Statistics 2015 Fall

Stat 543: Theory of Probability and Statistics II 2015 Spring

Stat 542: Theory of Probability and Statistics I 2014 Fall

Stat 231: Engineering Probability 2014 Spring

Stat 305: Engineering Statistics 2013 Fall

Education

Iowa State University

PhD in Statistics • Ames, IA, USA • 2013 - 2018

Iowa State University

Master in Statistics • Ames, IA, USA • 2013 - 2015

Shanghai Jiao Tong University

Bachelor in Mathematics with minor in Physics• Shanghai, China • 2009 - 2013

Contact



Address:
No. 800 Dongchuan Road, Shanghai 200240

© Copyright 2024 Xiaojun Mao