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Robust/Differentially Private Machine Learning

Project

Project Details

Program
Applied Mathematics and Computer Science
Field of Study
Machine Learning, Data Privacy, High Dimensional Statistics
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
https://www.kaust.edu.sa/en/study/faculty/di-wang

Project Description

The topic is flexible and depends on student's background, mathematical knowledge, previous research experience. Generally, this project mainly focuses on how to design robust (especially robust against to outliers or heavy-tailed distributions) or private (or forgettable) algorithms for some foundamental problems in machine learning, deep learning or statistics. Students will provide theoretical guarantees via using mathematical tools from probability, learning theory, optimization and high dimensional statistics. Also, student will analyze utility-privacy tradeoff or robustness-utility tradeoff.

About the Researcher

Di Wang
Assistant Professor, Computer Science
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • 2020 Ph.D State University of New York at Buffalo
  • 2015 M.S. Western University, 2014 B.S. Shandong University

Research Interests

Professor Wang's interests are differential privacy, privacy-preserving machine learning, privacy-preserving data mining, privacy attack in machine learning, trustworthy machine learning, statistical learning theory. He is also interested in trustworthy issues in digital healthcare, biomedical imaging and bioinformatics.a€‹

Selected Publications

  • Di Wang, Hanshen Xiao, Srini Devadas, and Jinhui Xu. ""On Differentially Private Stochatsic 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.

Desired Project Deliverables

Students will learn some fundamental techniques and results in learning theory, high dimensional statistics, optimization and differential privacy. They will also implement machine learning or statistics algorithms via using Matlab or Python. Hopefully they could have publications after the project.

Recommended Student Background

Optimization
Differential Privacy
High Dimensional Statistics
Learning Theory
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3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year