Zihang Xiang

I am a 4th-year Ph.D. student at King Abdullah University of Science and Technology (KAUST), advised by Di Wang. I also work closely with Tianhao Wang at University of Virginia. I received my Bachelor's degree in Electrical Engineering at Shanghai Jiao Tong University (SJTU) IEEE Pilot Class . I also received my Master's degree from SJTU and I worked on power electronics (specialized in hardware design and control theory) during that time.

My current research is on data privacy (specifically, differential privacy). I am interested in making differential privacy usable via principled approaches in various applications including federated learning and learning on graphs.

Email  /  Google Scholar

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Publications (= means co-first author )

Revisiting Differentially Private Hyper-parameter Tuning
Zihang Xiang, Tianhao Wang, Chenglong Wang, Di Wang,
Preprint
Preserving Node-level Privacy in Graph Neural Networks
Zihang Xiang, Tianhao Wang, Di Wang,
45th IEEE Symposium on Security and Privacy (IEEE S&P 2024, Oakland)
CSAW'24 Applied Research Competition Finalist
[PDF] [GitHub]
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction
Hanshen Xiao=, Zihang Xiang=, Di Wang, Srini Devadas
44th IEEE Symposium on Security and Privacy (IEEE S&P 2023, Oakland)
[PDF] [GitHub]
PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data
Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Chencheng Xu, Di Wang, Xin Gao
Science Advances, VOLUME 10, ISSUE 5, 2 FEB 2024
[PDF]
Practical Differentially Private and Byzantine-resilient Federated Learning
Zihang Xiang, Tianhao Wang, Wanyu Lin, Di Wang
International Conference on Management of Data (SIGMOD), 2023
[PDF] [GitHub]
Privacy-preserving Sparse Generalized Eigenvalue Problem
Lijie Hu=, Zihang Xiang=, Jiabin Liu, Di Wang
The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
[PDF]
Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem
Lijie Hu=, Zihang Xiang=, Jiabin Liu, Di Wang
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
[PDF]


Teaching

TA of CS229: Machine Learning, KAUST
TA of BioE394E: Deep Learning for Bioengineering, KAUST
TA of CS325: Private Data Analysis


Service

(Sub)-Reviewer: AISTATS'2025, CCS'(2024,2023), NDSS'2023, PETS'2023, ICML'2023, IEEE-TNNLS