privacy

Zihang Xiang

Ph.D. Student, Computer Science
Zihang Xiang is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Z. Xiang, M. M. Khan, U. Khalid and J. Xu, ”A DC link capacitors’ voltage balancing method in unidirectional cascaded H-bridge converter using multi-limb-core (MLC) transformers,” 2017 China International Electrical and Energy Conference (CIEEC),Beijing, 2017, pp. 606-611. U. Khalid, M. M. Khan, Z. Xiang and Y. Jianyang, ”Bidirectional modular Dual Active Bridge (DAB) converter

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. student under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST). Publications Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. "Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits." https://arxiv.org/abs/2106.02575. (Equal contributions) Education Profile M.S. in Statistics, East China Normal University, China B.S. in Mathematics and Applied Mathematics, East China Normal University, China

Lijie Hu

Ph.D. Student, Computer Science
Lijie Hu is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". https://arxiv.org/abs/2107.11136. To appear in the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022). Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan and Jinhui Xu. "Differentially Private Expectation

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.