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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, robust estimation, large scale/ high dimension optimization, adversarial machine learning, 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. On Differentially Private Empirical Risk Minimization with Heavy-tailed Data. Abstract
      Di Wang and Jinhui Xu .
    2. Learning Halfspaces in Non-interative Local Differential Privacy Model with Public Unlabeled Data. Abstract
      Di Wang*, Huanyu Zhang* and Jinhui Xu . (* equal contributions)
    3. Inferring Ground Truth From Crowdcourcing Data Under Local Differential Privacy. Abstract
      Di Wang*, Mengdi Huai*, Chenglin Miao, Jinhui Xu , Aidong Zhang(* equal contribution)
    4. Pairwise Learning with Differential Privacy Guarantee. Abstract
      Mengdi Huai*, Di Wang*, Chenglin Miao, Jinhui Xu , Aidong Zhang(* equal contribution)
    5. Differentially Private Facility Location Problem Revisited. Abstract
      Yunus Esencayi, Marco Gaboardi, Shi Li and Di Wang (alphabetic order)
    6. Estimating GLM in Non-interative Local Differential Privacy Model with Public Unlabeled Data. Abstract
      Di Wang*, Huanyu Zhang*, Marco Gaboardi and Jinhui Xu . (* equal contributions)
    7. Robust Estimation of High Dimensional Mixture Model via Trimmed EM Algorithm. Abstract
      Di Wang*, Xiangyu Guo*, Shi Li and Jinhui Xu . (* equal contributions)

    Journal Papers

    1. Differentially Private High Dimensional Sparse Covariance Matrix Estimation Abstract
      Di Wang and Jinhui Xu.
      Submitted.
    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.
    3. Faster Large Scale Constrained Linear Regression via Two-Step Preconditioning Abstract
      Di Wang and Jinhui Xu .
      Submitted.
    4. Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization Abstract
      Di Wang and Jinhui Xu .
      Submitted.

    Conference Papers

    1. Tight Lower Bound of Sparse Covariance Matrix Estimation in the Local Differential Privacy Abstract
      Di Wang and Jinhui Xu .
      28th International Joint Conference on Artificial Intelligence (IJCAI 2019).
    2. Locally Differentially Private Principal Component Analysis Abstract
      Di Wang and Jinhui Xu .
      28th International Joint Conference on Artificial Intelligence (IJCAI 2019).
    3. Privacy-aware Synthesizing for Crowdsourced Data Abstract
      Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu , Aidong Zhang.
      28th International Joint Conference on Artificial Intelligence (IJCAI 2019).
    4. Differentially Private Empirical Risk Minimization with Non-convex Loss Functions Abstract
      Di Wang, Changyou Chen and Jinhui Xu .
      36th International Conference on Machine Learning (ICML 2019).
    5. On the Sparse Linear Regression Under Local Differential Privacy Abstract
      Di Wang and Jinhui Xu .
      36th International Conference on Machine Learning (ICML 2019). Selected as Long Talk.
    6. 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).
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

    Workshop Papers

    1. 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.

    Teaching


    Professional Activities


    Awards