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Di Wang

PHD candidate
Department of Computer Science and Engineering
State University of New York at Buffalo

Email : dwang45 "at" buffalo.edu, shao3wangdi "at" berkeley.edu
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Short Bio

I am a fourth year PHD student in the Department of Computer Science and Engineering at The State University of New York (SUNY) at Buffalo under supervision of Dr. Jinhui Xu . Before that I got my Master degree in Mathematics at University of Western Ontario in 2015, and I got my Bachelor degree in Mathematics and Applied Mathematics at Shandong University in 2014.



Research Interests

  • Generally speaking, I am interested in Private Data Analysis, Machine Learning and Algorithmic Fairness.
  • Specifically, my research contains differential privacy, private machine learning, privacy-preserving data mining and crowdsourcing, staistical privacy, adversarial machine learning, large scale optimization, fairness in machine learning.
  • Main type of problems I am working on are (Locally) Differentially Private Empirical Risk Minimization , Differentially Private High Dimensional Statistics and Private Crowdscourcing.



  • News


    Resume


    Professional Experience


    Manuscripts

    1. Estimating High Dimensional Robust Mixture Model via Trimmed Expectation-Maximization Algorithm. Abstract
      Di Wang*, Xiangyu Guo* and Jinhui Xu . (* equal contributions)
    2. Lower Bound of Sparse Covariance Matrix Estimation in the Local Differential Privacy Abstract
      Di Wang and Jinhui Xu .
    3. Differentially Private Empirical Risk Minimization with Non-convex Loss Functions Abstract
      Di Wang, Changyou Chen and Jinhui Xu .
    4. Locally Differentially Private Principal Component Analysis Abstract
      Di Wang and Jinhui Xu .
    5. On the Sparse Linear Regression Under Local Differential Privacy Abstract
      Di Wang and Jinhui Xu .
    6. Privacy-aware Synthesizing for Crowdsourced Data Abstract
      Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu , Aidong Zhang.

    Journal Papers

    1. Differentially Private High Dimensional Sparse Covariance Matrix Estimation Abstract
      Di Wang and Jinhui Xu.
      Submitted to Theoretical Computer Science.
    2. On the Emprical Risk Minimization In the Non-interactive Local Differential Privacy Model Abstract
      Di Wang, Marco Gaboardi, Adam Smith and Jinhui Xu .
      Submitted to Journal of Machine Learning Research (JMLR).
    3. Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning Abstract
      Di Wang and Jinhui Xu .
      Submitted to ACM Transactions on Intelligent Systems and Technology (TIST).
    4. Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization Abstract
      Di Wang and Jinhui Xu .
      Submitted to ACM Transactions on Privacy and Security (TOPS).

    Conference Papers

    1. Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding Abstract
      Di Wang, Jinhui Xu and Yang He.
      53rd Annual Conference on Information Sciences and Systems (CISS 2019).
    2. Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations Abstract
      Di Wang, Adam Smith and Jinhui Xu .
      The 30th International Conference on Algorithmic Learning Theory (ALT 2019).
    3. High Dimensional Sparse Linear Regression under Local Differential Privacy: Power and Limitations Abstract
      Di Wang, Adam Smith and Jinhui Xu .
      NIPS 2018 Workshop on Privacy Preserving Machine Learning.
    4. Differentially Private Empirical Risk Minimization with Smooth Non-convex Loss Functions: A Non-stationary View. Abstract
      Di Wang and Jinhui Xu .
      Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019). Selected as Oral Presentation.
    5. Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. Abstract
      Di Wang, Marco Gaboardi and Jinhui Xu .
      Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2018.
    6. Differentially Private Sparse Inverse Covariance Estimation. Abstract
      Di Wang, Mengdi Huai and Jinhui Xu .
      2018 6th IEEE Global Conference on Signal and Information Processing (2018 GlobalSip). Selected as Oral Presentation.
    7. Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. Abstract
      Di Wang and Jinhui Xu .
      Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018). Selected as Oral Presentation.
    8. Differentially Private Empirical Risk Minimization Revisited: Faster and More General. Abstract
      Di Wang, Minwei Ye and Jinhui Xu .
      Conference on Neural Information Processing Systems (NIPS/NeurIPS), 2017.

    Teaching


    Professional Activities


    Awards