Project Details
Program
Computer Science
Field of Study
Causal Inference, Fairness, Transfer Learning, Deep Learning
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
Project Description
The exact topic depends on the student's interest, student's background, and previous research experience. Generally speaking, there are mainly three topics of this project:
1. Designing Fair Machine Learning Algorithms. In this project, students will focus on how to make the current machine learning algorithms be fair. They will also explore the fairness issue of the current machine learning algorithms, especially for healthcare data.
2. Causality as a tool for de-biasing current deep learning algorithms. Students will use the idea of causality to different deep learning tasks to de-bias the datasets or algorithms in order to improve the accuracy and trustworthiness.
3. Causality as a tool for invariant learning. This project mainly focuses on transfer learning, students will use causality to design transfer learning algorithms.
About the Researcher
Di Wang
Assistant Professor, Computer Science
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
During the project, students will have opportunity to learn about some topics in trustworthy machine learning, especially fair learning, transfer learning and causal learning. They will learn and implement the SOTA methods. Hopefully, they may produce some publication after the intern.
Recommended Student Background
Computer Science
Applied Mathematics
Statistics
Deep Learning