Xiyang Hu

H. John Heinz III College, Carnegie Mellon University

Remote Work From Home 5000 Forbes Ave, Hamburg Hall, Pittsburgh, PA 15213

Xiyang Hu (pronunciation: “SHEE-yung HOO”, 胡曦阳 in Chinese) is a Ph.D. candidate in Information Systems at Carnegie Mellon University. He got his M.Sc. in Statistical Science from Duke University, and B.Arch. in Architecture with a minor in Computer Science from Tsinghua University.

Feel free to contact me by Email (xiyanghu AT cmu DOT edu).

I am committed to promoting Diversity, Equity, and Inclusion in my work, teaching, and collaborations.

:link: CMU Official Profile Webpage

Research Interests

  • Machine/Deep Learning
  • Outlier Detection
  • Natural Language Processing
  • Social Impacts of AI
  • Information Systems

Professional Services

Program Committee and/or Reviewer: WITS(x2, 2022), NeurIPS (x3, 2022), ANDEA (2022), CSWIM (2022), CIST (2022), ICIS (x2, 2022), KDD (2022), AAAI (2021), CIST (×2, 2021), INFORMS (2021)

Honors & Awards

news

Jan 18, 2022 Excited to release the first comprehensive open-sourced graph outlier detection libraryPyGOD. :satisfied:

selected publications

  1. NeurIPS
    ADBench: Anomaly Detection Benchmark
    Hu, Xiyang, Han, Songqiao, Huang, Hailiang, Jiang, Mingqi, and Zhao, Yue
    Advances in Neural Information Processing Systems 2022
  2. NeurIPS
    Benchmarking Node Outlier Detection on Graphs
    Liu, Kay, Dou, Yingtong, Zhao, Yue, Ding, Xueying, Hu, Xiyang, Zhang, Ruitong, Ding, Kaize, Chen, Canyu, Peng, Hao, Shu, Kai, and others,
    Advances in Neural Information Processing Systems 2022
  3. ICIS
    Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit
    Hu, Xiyang, Huang, Yan, Li, Beibei, and Lu, Tian
    International Conference on Information Systems 2022
  4. preprint
    PyGOD: A Python Library for Graph Outlier Detection
    Liu, Kay, Dou, Yingtong, Zhao, Yue, Ding, Xueying, Hu, Xiyang, Zhang, Ruitong, Ding, Kaize, Chen, Canyu, Peng, Hao, Shu, Kai, and others,
    arXiv preprint arXiv:2204.12095 2022
  5. TKDE
    ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
    IEEE Transactions on Knowledge and Data Engineering 2022
  6. ICIS
    Uncovering the Source of Evaluation Bias in Micro-Lending
    Hu, Xiyang, Huang, Yan, Li, Beibei, and Lu, Tian
    International Conference on Information Systems 2021
  7. MLSys
    SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection
    Hu, Xiyang, Zhao, Yue, Cheng, Cheng, Wang, Cong, Wan, Changlin, Wang, Wen, Yang, Jianing, Bai, Haoping, LI, Zheng, Xiao, Cao, Wang, Yunlong, Qiao, Zhi, Sun, Jimeng, and Akoglu, Leman
    Conference on Machine Learning and Systems 2021
  8. KDD
    Uncovering the Source of Machine Bias [CIST 2021 Best Student Paper Nomination:trophy:]
    Hu, Xiyang, Huang, Yan, Li, Beibei, and Lu, Tian
    27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Machine Learning for Consumers and Markets Workshop 2021
  9. ICDM
    COPOD: Copula-Based Outlier Detection
    IEEE International Conference on Data Mining 2020
  10. NeurIPS
    Optimal Sparse Decision Trees [NeurIPS 2019 Spotlight:trophy: (Top 3%)]
    Hu, Xiyang, Rudin, Cynthia, and Seltzer, Margo
    Advances in Neural Information Processing Systems 2019