Di Wang is an Assistant Professor of Computer Science and the Principal Investigator of the KAUST Privacy-Awareness, Responsibility and Trustworthy (PART) Lab. Prior to joining KAUST, he obtained his Ph.D. degree in computer science and engineering ('20) from the State University of New York (SUNY) at Buffalo, U.S.; his M.S. in mathematics ('15) from the University of Western Ontario, Canada; and his B.S. in mathematics and applied mathematics ('14) from Shandong University, China.

Research Interests

Professor Wang's research interests include machine learning (ML), security, theoretical computer science, and data mining.

Selected Publications

• Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". To appear in the 41st ACM Symposium on Principles of Database Systems (PODS 2022)
• Di Wang, Marco Gaboardi, Adam Smith, and Jinhui Xu. "Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy." Journal of machine learning research 21, no. 200 (2020).
• Di Wang, and Jinhui Xu. "On Sparse Linear Regression in the Local Differential Privacy Model." IEEE Transactions on Information Theory (2020).
• ​Di Wang, Hanshen Xiao, Srini Devadas, and Jinhui Xu. "On Differentially Private Stochastic Optimization with Heavy-tailed Data" In International Conference on Machine Learning. 2020. (ICML 2020)
• Yunus Esencayi, Marco Gaboardi, Shi Li, and Di Wang. "Facility Location Problem in Differential Privacy Model Revisited." Advances in neural information processing systems (2019). (NeurIPS 2019)
• Di Wang, Changyou Chen, and Jinhui Xu. "Differentially private empirical risk minimization with non-convex loss functions." In International Conference on Machine Learning, pp. 6526-6535. 2019. (ICML 2019)
• Di Wang, and Jinhui Xu. "On sparse linear regression in the local differential privacy model." In International Conference on Machine Learning, pp. 6628-6637. 2019. (ICML 2019)
• Di Wang, Marco Gaboardi, and Jinhui Xu. "Empirical risk minimization in non-interactive local differential privacy revisited." In Advances in Neural Information Processing Systems, pp. 965-974. 2018. (NeurIPS 2018)
• Di Wang, Minwei Ye, and Jinhui Xu. "Differentially private empirical risk minimization revisited: Faster and more general." In Advances in Neural Information Processing Systems, pp. 2722-2731. 2017. (NuerIPS 2017)

Education Profile

• Ph.D. degree in computer science and engineering from the State University of New York (SUNY), Buffalo, U.S. (2020).
• M.S. in mathematics from the University of Western Ontario, Canada (2015).
• B.S. in mathematics and applied mathematics from Shandong University, China (2014).