About

I am now pursuing my Ph.D. career in Artificial Intelligence at the College of Computer Science and Technology, Zhejiang University (ZJU), China, supervised by Prof. Chao Wu and I am now a visiting researcher at the University of Cambridge under the supervision of Prof. Nicholas Lane from Oct. 2024. I am honored to work with Prof. Tao Lin at Westlake University/EPFL.

During my Ph.D., I also earned a minor degree in Technology-based (BEST) MBA from the School of Management, ZJU. Before Ph.D., I received my bachelor’s degree in Engineering from ZJU; meanwhile, I obtained an honorary undergraduate degree from Chu Kochen Honors College of ZJU and a minor degree in Public Affairs (social governance).

My main research interests lie in i) optimization, generalization, and personalization of deep learning models, especially under distributed/federated/collaborative setups, which are generally empowered by deep learning phenomena and mechanistic interpretability; ii) trustworthy model manipulation for foundation models: understanding and improving foundation models (e.g., large language model, vision transformer, and diffusion transformer) from the model parameter perspective, i.e., model fusion, editing, pruning, stitching, growth, unlearning, and generation.

I am on the job market now and will be graduated in the 2025 summer. I am open to both academic and industrial positions! Please contact me if you have matched positions.

Contact

Email: zexi.li[at]zju.edu.cn / tomleeze[at]gmail.com

Wechat and Phone: (+86) 18868104540

Selected Publications

• [NeurIPS 2024] WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
  Peng Wang*, Zexi Li*, Ningyu Zhang#, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen#
• [Preprint] Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization
  Zexi Li*, Lingzhi Gao*, and Chao Wu#
• [ICML 2023] Revisiting Weighted Aggregation in Federated Learning with Neural Networks
  Zexi Li, Tao Lin#, Xinyi Shang, and Chao Wu#
• [Patterns, Cell Press] Can We Share Models If Sharing Data Is Not an Option?
  Zexi Li, Feng Mao#, and Chao Wu#
• [ICCV 2023] No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
  Zexi Li, Xinyi Shang, Rui He, Tao Lin#, and Chao Wu#
• [ACM SIGIR 2023] Edge-cloud Collaborative Learning with Federated and Centralized Features
  Zexi Li*, Qunwei Li*, Yi Zhou, Wenliang Zhong#, Guannan Zhang, and Chao Wu#
• [IEEE Transactions on Big Data] Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
  Zexi Li, Jiaxun Lu, Shuang Luo, Didi Zhu, Yunfeng Shao, Yinchuan Li, Zhimeng Zhang, Yongheng Wang#, and Chao Wu#

Recent News

• [2024.09] I am happy to share our WISE, a model editor for large language models’ lifelong model editing, is accepted to NeurIPS 2024!
• [2024.05] Two papers are accepted by KDD 2024, and one paper is accepted by ICML 2024.
• [2023.11] I am presented with Guoqiang Scholarship of Zhejiang University (Top 0.5%) and First Prize of Chunxiao Scholarship of BEST-MBA (Top 3%) by Zhejiang University.
• [2023.07] Two papers are accepted by International Conference on Computer Vision (ICCV 2023)! One is about federated learning and the other is about domain adaptation. Many thanks to my coauthors!
• [2023.06] We will have an oral presentation of our paper FedCNI at ICME 2023 on July 11th.
• [2023.04] Our paper “Revisiting Weighted Aggregation in Federated Learning with Neural Networks” is accepted by The Fortieth International Conference on Machine Learning (ICML 2023)! Many thanks to my coauthors!
• [2023.04] Our paper “Edge-cloud Collaborative Learning with Federated and Centralized Features” is accepted by The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)! Thanks a lot to Ant Group’s coauthors!
• [2023.03] Our paper “Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients” is accepted by IEEE International Conference on Multimedia and Expo 2023 (ICME 2023)! Congratulations to me and Chenrui!
• [2023.03] I have joined the BEST-MBA (Business, Engineering, Science, and Technology based MBA for on-campus postgradutes) program of School of Management, Zhejiang University. <!–* [2022.11] Our paper “Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching”[arxiv] is online now.
• [2022.11] Our paper “Can we share models if sharing data is not an option?” is online now.
• [2022.11] Our paper “Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching” has been accepted by IEEE Transactions on Big Data! This paper had been through a dark time in the last year, and the acceptance means a lot to me. Congratulations to myself!
• [2022.10] I have passed the Mid-term Assessment of Doctoral Program.
• [2022.10] I am presented with Outstanding Postgraduate Student Award on 2021-2022 by Zhejiang University.
• [2022.09] Our new perspective paper “Can we share models if sharing data is not an option?” is accepted by Patterns, Cell Press. Patterns is a child-journal about data science of Cell Press, and it will have a first strong impact factor in 2023.–>

Academic Service

• Invited Reviewers: TKDE, IJCV, AISTATS 2024, CVPR 2024, ICML 2024, NeurIPS 2024, ICLR 2024.