Welcome to Kaiyi Ji's Homepage

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About Me

I am an assistant professor at the Department of Computer Science and Engineering of the University at Buffalo, The State University of New York. I received my Ph.D. degree from the Electrical and Computer Engineering Department of The Ohio State University in December, 2021, advised by Prof. Yingbin Liang. I was a postdoctoral research fellow at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, in 2022, working with Prof. Lei Ying. I was a visiting student research collaborator at the department of Electrical Engineering, Princeton University. Previously I obtained my B.S. degree from University of Science and Technology of China in 2016.

Prospective students: I do not have PhD positions now. However, intern and visiting students are welcome! Please send me an email with your CV and Transcript or fill this form.

Recent News!

  • [Talk] 10/2024 I'll be giving a talk at the INFORMS Annual Meeting in Seattle from October 20-23. Love to connect if you are also attending.

  • [Software] 09/2024 Our FairGrad for multi-task/objective learning is now supported by open-source MTL Library LibMTL. Feel free to explore it and see if it can benefit your research!

  • [Job] 05/2024 Peiyao starts his internship at Amazon working on multi-objective optimization for recommendation.

  • [Award] 12/2023 Glad to receive CSE Junior Faculty Research Award from UB CSE. Thanks to the department and my students!

Research

My research focuses on developing foundational algorithms and theories in machine learning, optimization, deep learning, and wireless networking. My current interests include: (i) hierarchical machine learning, including bilevel optimization, meta-learning and hyperparameter optimization; (ii) multi-objective learning, including multi-objective optimization, multi-task learning, and model fusion; and (iii) knowledge transfer and unlearning, with emphasis on continual learning, fine-tuning, and machine unlearning. I am also exploring applications of these approaches in communication, robotics, and signal processing. Here are selected publications:

Continual Learning/Machine Unlearning
Multi-Objective/Task Learning
Bilevel Optimization: Theory and Applications
Distributed Learning over Networks