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).

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

  • ​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.
  • 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.
  • 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.
  • 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.
  • 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.