428 Shaw Ln., East Lansing, Michigan, 48824 | (+1) 734-263-4022 | xuhan1@msu.edu | https://cse.msu.edu/ xuhan1/
Fourth year Ph.D student of Computer Science and Engineering at Michigan State University.
Research interests include adversarial attacks, deep learning model robustness and safety.
PhD, Computer Science and Engineering, Michigan State University, Advisor: Dr. Jiliang Tang, 2018 to Present
M.S., Applied Statistics, University of Michigan, Ann Arbor, 2016 to 2018
B.S., Mathematics, Nankai University, China, 2012 to 2016
Google Scholar: https://scholar.google.com/citations?user=mX2rL3IAAAAJ&hl=en
Conference and Journal Publications
Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang. To be robust or to be fair: Towards fairness in adversarial
training. In the Proceedings of International Conference on Machine Learning. PMLR, 2021.
Han Xu, Yaxin Li, Xiaorui Liu, Jiliang Tang, Yet Meta Learning Can Adapt Fast, it Can Also Break Easily. In the Proceed-
ings of the 2021 SIAM International Conference on Data Mining
Yaxin Li, Wei Jin, Han Xu, Jiliang Tang, DeepRobust:a Platform for Adversarial Attacks and Defenses Proceedings of
the AAAI Conference on Artificial Intelligence. Vol. 35. No. 18. 2021.
Wenqi Fan, Yao Ma, Han Xu, Xiaorui Liu, Jianping Wang, Qing Li, Jiliang Tang, Deep Adversarial Canonical Correlation
Analysis, In the Proceedings of the 2020 SIAM International Conference on Data Mining
Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain, Adversarial Attacks and Defenses in
Images, Graphs and Text: A Review, International Journal of Automation and Computing. 17, 151–178 (2020).
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Jiliang Tang, Adversarial Attacks and Defenses on Graphs: A Review and Empirical
Study, Published in ACM SIGKDD Explorations Newsletter, Volume 22, Issue 2, 2020.
Conference Tutorials
Han Xu, Yaxin Li, Xiaorui Liu, Wentao Wang, Jiliang Tang, KDD 21’Adversarial Robustness in Deep Learning: From
Practices to Theories Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data
Mining, August 2021, Pages 4086–4087
Han Xu , Yaxin Li, Wei Jin, Jiliang Tang, KDD 20’ Adversarial Attacks and Defenses: Frontiers, Advances and Practice,
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Preprints and Submissions
Han Xu, Wentao Wang, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, Jiliang Tang Imbalanced Adversarial Training
with Reweighting , Preprint: arXiv: 2107.13639, 2021.
Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang, Towards the Mem-
orization Effect of Neural Networks in Adversarial Training , Preprint: arXiv: 2016.04794, 2021.
Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang , Jianping Wang, Charu
Aggarwal, Jointly Attacking Graph Neural Network and its Explanations , Preprint: arXiv: 2018.03388, 2021.
Yaxin Li, Han Xu, Xiaorui Liu, Jiliang Tang, Towards Malicious Meta Learning via Installing Adversarial Backdoors
Han Xu, Xiaorui Liu, Jiliang Tang. Defense Against Spatially Transformed Adversarial Examples: An Adversarial Train-
ing Approach
Software Development
DeepRobust: A Pytorch Library for Adversarial Attacks and Defenses. Github: github.com/DSE-MSU/DeepRobust.
A popular and user friendly platform for adversarial learning practitioners including more than 20 attack & defenses
algorithms, on image and graph domain. AAAI-21 Demonstrations Program.
Algorithm Engineer (Virtual) at Artificial Intelligence Lab at TAL Education Group, June 2020 - August 2020, Beijing,
China. Working on ML/NLP/Speech problems and their applications in education. Focused on leveraging human anno-
tation bias to enhance language proficiency assessment tools’ performance.
Algorithm Engineer at Data Science Lab at JD.com, June 2018 - August 2018, Beijing, China. Entry-level working on
adversarial attacks and defenses.
Research Analyst at Washtenaw Community College, June, 2017 - April, 2018, Ann Arbor, Michigan. Working on database
management and students constitutional data analysis.
KDD CUP 2020: Regular Machine Learning Competition Track: Adversarial Attacks and Defense on Academic Graph,
Receive Top 10 Winner Awards.
Spring 2020 - Graduate Teaching Assistant for Discrete Structures Computational Science. Michigan State University.
Fall 2019 - Graduate Teaching Assistant for Discrete Structures Computational Science. Michigan State University.
Michigan State University Engineering Distinguished Fellowship, 2018-2019
KDD-2020 Student Registration Award
SDM-2019 Student Travel Award
KDD CUP 2020: Graph Attacks and Defenses, top 10 winner.
Serve as PC Members: AAAI-2022, AAAI-2021, CIKM-2021, CIKM 2020, WSDM 2021, AI4EDU 2020
Serve as sub-reviewers: ICLR-2020, NeurIPS-2020, KDD-2020, WWW-2020, TKDD
Serve as volunteer: KDD-2020, KDD-2021
Serve as Conference Session Chair: KDD-2021