Employment Experience

  • 2018.12-2019.12, Visiting Associate Professor
    Department of Industrial & Systems Engineering, University of Washington, Seattle.
  • 2016.01-2016.02, Research Associate
    Department of Mathematics, the City University of Hong Kong
  • 2015.04-2016.04, Visiting Assistant Professor
    Department of Business Statistics and Econometrics, Guanghua School of Management, Peking University

Education

  • 2013-2014, Ph.D., Applied Math, XJTU, China
  • 2010-2012, Visiting Ph.D. Student, Statistics, UC Berkeley, U.S.
  • 2009-2010, Ph.D. Candidate, Applied Math, XJTU, China
  • 2008-2009, M.S., Applied Math, XJTU, China
  • 2003-2007, B.S., Applied Math, XJTU, China

Research Interests

  • Statistics & Machine Learning
  • Business Data Science

Course

  • Statistical Machine Learning for Business Analytics, Spring 2017
  • Data Analytics with R for Social Science, Summer School at XJTU 2018

    • Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Lecture 6

  • Topics in Machine Learning, Spring 2020
  • Optimization Theory and Algorithm I, Spring 2021
  • Optimization Theory and Algorithm II, Fall 2021

    • Group Members

    • Ying Wu (吴莹)
    • Hao Di (狄浩)
    • Shuai Liu (刘帅)
    • Junbo Hao (郝俊波)
    • Mengmeng Wu (武梦梦, Co-Advisor with Prof. Wei Huang)
    • Jun Shang (尚俊)
    • Zhenyu Liu (刘真瑀)
    • Zhiyi Zeng (曾智亿)
    • Zhihong Liu (刘志宏)
    • Zihang Zhang (张子航)
    • Jialu Du (杜佳璐)
    • Yunxuan Jiang (蒋云轩)

    Alumni

    • Jingzhou Shen (申旌周, AI Engineer at Vivo 2018)
    • Rui Zhang (张蕊, Data Scientist at Finupgroup 2018)
    • Yinghui Huang (黄颖晖, PhD Student at HKUST 2018)
    • Chunyan Li (李春艳, China Guangzhou Bank 2020)
    • Muzhi Yang (杨穆之, AI Engineer at JD.com 2021)
    • Feiran Zhang (张斐然, Master Student at USC 2021)
    • Zhikai Yang (杨智凯, ByteDance 2022)
    • Yi Yang (杨逸, Assistant Professor at Xi’an Jiaotong-Liverpool University 2022)
    • Xi Zheng (郑曦, PhD Student at UW 2023)
    • Jialiang Tian (田家亮, AI Engineer at BYD 2023)
    • Tangzhi Yuan (袁堂植, Software Engineer at Huawei 2024)
    • Tong He (何通, SGIT AI Lab 2024)

    Selected Publication (Google Scholar)

      Statistics & Machine Learning

    • Shao-Bo Lin, Xiangyu Chang, and Xingping Sun. Kernel Interpolation of High-Dimensional Scattered Data. SIAM Journal on Numerical Analysis. 2024.
    • Xiao Guo, Yixuan Qiu, Hai Zhang and Xiangyu Chang. Randomized spectral co-clustering for large-scale directed networks. Journal of Machine Learning Research. 2023.
    • Mengmeng Wu, Ruoxi Jia, Changle Lin, Wei Huang and Xiangyu Chang. Variance Reduced Shapley Value Estimation for Trustworthy Data Valuation. Computers & Operations Research. 2023
    • Haishan Ye, Dachao Lin, Xiangyu Chang and Zhihua Zhang. Towards Explicit Superlinear Convergence Rates for SR1. Mathematical Programming. 2022.
    • Hai Zhang, Xiao Guo and Xiangyu Chang. Randomized Spectral Clustering in Large-Scale Stochastic Block Models. Journal of Computational and Graphical Statistics. 2022.
    • Yi Yang, Shuai Huang, Wei Huang and Xiangyu Chang. Privacy-preserving cost-sensitive learning. IEEE Transactions on Neural Networks and Learning Systems. 2020.
    • Xuening Zhu, Xiangyu Chang, Runze Li and Hansheng Wang. Portal Nodes Screening for Large Scale Social Networks. Journal of Econometrics. 2019
    • Xiangyu Chang, Yan Zhong, Yao Wang and Shaobo Lin. Unified Low-Rank Matrix Estimation via Penalized Matrix Least Squares Approximation. IEEE Transactions on Neural Networks and Learning Systems, 2018.
    • Xiangyu Chang, Danyang Huang and Hansheng Wang. A Popularity Scaled Latent Space Model for Network Structure Formulation. Statistica Sinica, 2018.
    • Xiangyu Chang, Shao-Bo Lin and Ding-Xuan Zhou. Distributed Semi-supervised Learning with Kernel Ridge Regression. Journal of Machine Learning Research, 18(46): 1-22, 2017.
    • Xiangyu Chang, Yu Wang, Rongjian Li and Zongben Xu. Sparse K-means with l0/l∞ Penalty for High-dimensional Data Clustering. Statistica Sinica, 2017.
    • Xiangyu Chang, Shao-Bo Lin, and Yao Wang. Divide and Conquer Local Average Regression. Electronic Journal of Statistics, 11(1): 1326-1350, 2017.
    • Xiangyu Chang, Qingnan Wang, Yuewen Liu, and Yu Wang. Sparse Regularization in Fuzzy c-Means for High-Dimensional Data Clustering. IEEE Transactions on Cybernetics, 2016.
    • Yu Wang, Jinshan Zeng, Zhimin Peng, Xiangyu Chang and Zongben Xu. Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing. IEEE Trans on Signal Processing, 63(11), 2957-2971, 2015.(Best Paper Award, ICCM 2018)
    • Peter Bickel, David Choi, Xiangyu Chang and Hai Zhang. Asymptotic Normality of Maximum Likelihood and Its Variational Approximation for Stochastic Blockmodels. The Annals of Statistics, 41(4):1922-1943, 2013.
    • Zongben Xu, Xiangyu Chang, Fengmin Xu and Hai Zhang. L1/2 regularization: A Thresholding Representation Theory and a Fast Solver. IEEE Transactions on Neural Networks and Learning Systems, 23(7):1013-1027, 2012.

    • Business Data Science

    • Yi Yang, Ying Wu, Mei Li, Xiangyu Chang and Yong Tan. Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making. Production and Operations Management. 2024.
    • Xiangyu Chang, Yinghui Huang, Mei Li, Xin Bo, and Subdoha Kumar. Efficient Detection of Environmental Violators: A Big Data Approach. Production and Operations Management. 2021.
    • Ying Wu, Shuai Huang and Xiangyu Chang. Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study. BMC Medical Informatics and Decision Making. 2021.
    • Ling Tang, Jiabao Qu, Zhifu Mi, Xin Bo, Xiangyu Chang, Laura Diaz Anadon, Shouyang Wang, Xiaoda Xue, Shibei Li, Xin Wang, Xiaohong Zhao. Substantial emission reductions from Chinese power plants after the introduction of ultra-low emissions standards. Nature Energy, 4(11), 929-938, 2019.
    • Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang. Detect depression from communication: how computer vision, signal processing, and sentiment analysis join forces. IISE Transactions on Healthcare Systems Engineering. 2018.
    • Xiangyu Chang, Jingzhou Shen, Xiaoling Lu, and Shuai Huang. Statistical Patterns of Human Mobility in Emerging Bicycle Sharing Systems. PLoS one 13(3), e0193795, 2018.

    • Peer Review Conference

    • Hao Di, Hiashan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor Tsang. Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. ICML (Spotlight Paper), 2024.
    • Hao Di, Hiashan Ye, Xiangyu Chang, Guang Dai, Ivor Tsang. Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. ICML, 2024.
    • Zhihong Liu, Hoang Anh Just, Xiangyu Chang, Xi Chen, and Ruoxi Jia. 2D-Shapley: A Framework for Fragmented Data Valuation. ICML, 2023.
    • Xiang Li, Jiadong Liang, Xiangyu Chang, and Zhihua Zhang. Statistical Estimation and Online Inference via Local SGD. COLT, 2022.
    • Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, and Shuai Huang. Predicting depression severity by multi-modal feature engineering and fusion (short paper). AAAI 2018.