Di Wang's Homepage

Chinese: 王帝
Al Khawarizmi Building 1, Room 4341
Division of CEMSE
King Abdullah University of Science and Technology
Thuwal, Saudi Arabia, 23955-6900
Email : di.wang@kaust.edu.sa
Website: [KAUST Personal][Personal] [Laboratory]
Tel: +966 (012) 8080645

Short Bio

I am currently an Assistant Professor of Computer Science in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at the King Abdullah University of Science and Technology (KAUST), start from Spring 2021. I am also the PI of Privacy-Awareness, Responsibility and Trustworthy (PART) Lab .

Before that, I got my Ph.D degree in Computer Science at The State University of New York (SUNY) at Buffalo in 2020 under supervision of Dr. Jinhui Xu . Before my Ph.D study I took my Master degree in Mathematics at University of Western Ontario in 2015, and I received my Bachelor degree in Mathematics and Applied Mathematics at Shandong University in 2014.

My most recent resume (last updated in April, 2021) can be found here.

Dissertation: Some Fundamental Machine Learning Problems in the Differential Privacy Model.

Current Openings: I am always looking for Postdocs, PhD students, internship and visiting students (all are fully funded). If you are interested in working with me, please send me your CV and transcripts before applying. See PhD and Postdoc for details.


Research Interests

  • Private Data Analytics: Differential privacy, privacy-preserving machine learning/data mining and privacy attack in machine learning

  • Trustworthy Machine Learning: Robust statistics/estimation, interpretable machine learning, fairness in machine learning, adversarial machine learning

  • Statistical Learning Theory: high dimensional statistics, causal inference, statistical estimation, learning theory and quantum machine learning

  • Healthcare: Trustworthy issues in digital healthcare, biomedical imaging and bioinformatics


  • Teaching


    Current Group Memebers [Past Group Members]

    Postdocs

    PhD students

    Master Students

    Visiting Students/Reaserch Interns


    Selected Publications [Full List] [Google Scholar] [DBLP]

    1. Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data [Link] Abstract
      Di Wang*, Huanyu Zhang*, Marco Gaboardi and Jinhui Xu. (* equal contribution)
      The 32nd International Conference on Algorithmic Learning Theory (ALT 2021).

    2. On Sparse Linear Regression in the Local Differential Privacy Model. [Link] Abstract
      Di Wang and Jinhui Xu.
      IEEE Transactions on Information Theory, Volume 67, no. 2, Pages 1182-1200, Feb. 2021.

    3. Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. [Link] Abstract
      Di Wang, Marco Gaboardi, Adam Smith and Jinhui Xu.
      Journal of Machine Learning Research, Volume 21, 200 (2020), Pages 1-39.

    4. On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data [Link] Abstract
      Di Wang*, Hanshen Xiao*, Srini Devadas and Jinhui Xu (* equal contribution).
      The 37th International Conference on Machine Learning (ICML 2020).

    5. Facility Location Problem in Differential Privacy Model Revisited. [Link] Abstract
      [alphabetic order] Yunus Esencayi, Marco Gaboardi, Shi Li and Di Wang
      Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2019.

    6. Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. [Link] Abstract
      Di Wang, Changyou Chen and Jinhui Xu.
      The 36th International Conference on Machine Learning (ICML 2019).

    7. On Sparse Linear Regression in the Local Differential Privacy Model. [Link] Abstract
      Di Wang and Jinhui Xu.
      The 36th International Conference on Machine Learning (ICML 2019).
      Selected as Long Talk(Acceptance Rate: 140/3424= 4.1%) .

    8. Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. [Link] Abstract
      Di Wang, Marco Gaboardi and Jinhui Xu.
      Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018.

    9. Differentially Private Empirical Risk Minimization Revisited: Faster and More General. [Link] Abstract
      Di Wang, Minwei Ye and Jinhui Xu.
      Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017.

    Manuscripts/Working on Papers

    ( my Master/PhD/Intern/Visiting students)
    1. On Differentially Prrivate Stochastic Convex Optimization. Abstract
      Youming Tao*, Yulian Wu* and Di Wang.

    2. High Dimensional Sparse Estimation via One Bit Quantization. Abstract
      Junren Chen and Di Wang.

    3. Faster Rates of Differentially Private Stochastic Convex Optimization. Abstract
      Jinyan Su and Di Wang.

    4. Differentially Private Expectation Maximization Algorithm with Statistical Guarantees. Abstract
      Di Wang*, Jiahao Ding*, Zejun Xie, Miao Pan and Jinhui Xu (* equal contribution).

    5. Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. Abstract
      Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. (* equal contribution)

    6. High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. Abstract
      Lijie Hu*, Shuo Ni*, Hanshen Xiao and Di Wang. (* equal contribution)

    7. On Facility Location Problem in the Central and Local Differential Privacy Model. Abstract
      [alphabetic order] Yunus Esencayi, Marco Gaboardi, Shi Li and Di Wang

    8. Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness. Abstract
      Tianhang Zheng*, Di Wang*, Baochun Li and Jinhui Xu (* equal contribution).