Zhenmei Shi

Ph.D.

Computer Sciences
University of Wisconsin-Madison, US

Contact:
zhmeishi [at] cs [dot] wisc [dot] edu
Office 5388, Department of Computer Sciences, UW-Madison

Google Scholar | Github | LinkedIn | CV


About Me

I am a Ph.D. candidate in Computer Science at the University of Wisconsin-Madison advised by Yingyu Liang. I obtained my B.S. degree in Computer Science and Pure Mathematics Advanced, from the Hong Kong University of Science and Technology in 2019.

I will be an AI Research Scientist Intern in summer 2024 at Salesforce, Palo Alto, working with Yu Bai, Shafiq Joty and Huan Wang. Currently, I am a Research Scientist Intern at Adobe, Seattle, working with Zhao Song and Jiuxiang Gu. Previously, I had two internships at Google YouTube Ads Machine Learning team and Google Pixel Camera team, Mountain View; one internship at Megvii (Face++) Foundation Model team, Beijing; and two internships at Tencent YouTu team, Shenzhen.

My research interest mainly focuses on Understanding the learning and adaptation of Foundation Models, including Large Language Models, Vision Language Models, Diffusion Models, Shallow Networks, and so on. Feel free to contact me for collaboration.

Publications

* denotes equal contribution or alphabetical ordering
Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
Jiuxiang Gu*, Yingyu Liang*, Heshan Liu*, Zhenmei Shi*, Zhao Song*, Junze Yin*

arXiv, 2024
[ arXiv ]
Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond
Jiuxiang Gu*, Chenyang Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song*

arXiv, 2024
[ arXiv ]
Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang

ICML 2024
[ Openreview ]
[ Workshop ] [ Workshop Poster ]
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Zhuoyan Xu*, Zhenmei Shi*, Yingyu Liang

ICLR 2024 Workshop
[ OpenReview ] [ Code ] [ Slides ] [ Poster ]
Fourier Circuits in Neural Networks: Unlocking the Potential of Large Language Models in Mathematical Reasoning and Modular Arithmetic
Jiuxiang Gu*, Chenyang Li*, Yingyu Liang*, Zhenmei Shi*, Zhao Song*, Tianyi Zhou*

ICLR 2024 Workshop
[ OpenReview ] [ arXiv ] [ Poster ]
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, Yingyu Liang

ICLR 2024
[ OpenReview ] [ arXiv ] [ Code ] [ Slides ] [ Poster ] [ Video ]
[ Workshop ] [ Workshop Poster ] [ Workshop Slides ]
Domain Generalization via Nuclear Norm Regularization
Zhenmei Shi*, Yifei Ming*, Ying Fan*, Frederic Sala, Yingyu Liang

CPAL 2024   Oral
[ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ]
[ Workshop ] [ Workshop Poster ]
Provable Guarantees for Neural Networks via Gradient Feature Learning
Zhenmei Shi*, Junyi Wei*, Yingyu Liang

NeurIPS 2023
[ OpenReview ] [ arXiv ] [ Video ] [ Slides ] [ Poster ]
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Yiyou Sun, Zhenmei Shi, Yixuan Li

NeurIPS 2023   Spotlight
[ OpenReview ] [ arXiv ] [ Video ] [ Code ] [ Slides ]
When and How Does Known Class Help Discover Unknown Ones? Provable Understandings Through Spectral Analysis
Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li

ICML 2023
[ OpenReview ] [ arXiv ] [ Video ] [ Code ]
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi*, Jiefeng Chen*, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha

ICLR 2023   Spotlight (Accept Rate: 7.95%)
[ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ] [ Video ]
[ Workshop ] [ Workshop Poster ]
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Zhenmei Shi*, Junyi Wei*, Yingyu Liang

ICLR 2022
[ OpenReview ] [ arXiv ] [ Poster ] [ Code ] [ Slides ] [ Video ]
Attentive Walk-Aggregating Graph Neural Networks
Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang

TMLR 2022
[ OpenReview ] [ arXiv ] [ Code ]
Deep Online Fused Video Stabilization
Zhenmei Shi, Fuhao Shi, Wei-Sheng Lai, Chia-Kai Liang, Yingyu Liang

WACV 2022
[ Paper ] [ arXiv ] [ Poster ] [ Project ] [ Code ] [ Dataset ]
Structured Feature Learning for End-to-End Continuous Sign Language Recognition
Zhaoyang Yang*, Zhenmei Shi*, Xiaoyong Shen, Yu-Wing Tai

arXiv, 2019
[ arXiv ] [ News ]
Dual Augmented Memory Network for Unsupervised Video Object Tracking
Zhenmei Shi*, Haoyang Fang*, Yu-Wing Tai, Chi Keung Tang

arXiv, 2019
[ arXiv ] [ Project ]

Technical Projects

Velocity Vector Preserving Trajectory Simplification
Guanzhi Wang*, Zhenmei Shi*, Cheng Long, Ya Gao, Raymond Wong
Technical Report, 2018
[ Paper ] [ Code ]
Deep Colorization, 2018.
[ News ]

Research & Work Experience

Research Assistant
University of Wisconsin-Madison
2019 - Now | Yingyu Liang
AI Research Scientist Intern
Salesforce
Summer 2024 | Yu Bai, Shafiq Joty and Huan Wang.
Research Scientist Intern
Adobe
Spring 2024 | Zhao Song and Jiuxiang Gu
Software Engineering Intern
Google in Mountain View, CA
Summer 2021 | Myra Nam
Summer 2020 | Fuhao Shi
Research Intern
Megvii (Face++) in Beijing
Summer 2019 | Xiangyu Zhang
Research Intern
Tencent YouTu in Shenzhen
Winter 2019 | Zhaoyang Yang and Yu-Wing Tai
Winter 2018 | Xin Tao and Yu-Wing Tai
Research Assistant
Hong Kong University of Science and Technology
2018 - 2019 | Chi Keung Tang
2017 - 2018 | Raymond Wong
Summer 2016 | Ji-Shan Hu
Research Intern
Oak Ridge National Laboratory in the USA
Summer 2017 | Cheng Liu and Kwai L. Wong

Academic Services

Conference Reviewer at ICLR 2022-2024, NeurIPS 2022-2024, ICML 2022 and 2024, ICCV 2021-2023, CVPR 2021-2022, ECCV 2020-2022, WACV 2022
Journal Reviewer at JVCI, IEEE Transactions on Information Theory

Teaching

Teaching Assistant of CS220 (Data Programming I) at UW-Madison (Spring 2020)
Teaching Assistant of CS301 (Intro to Data Programming) at UW-Madison (Fall 2019)
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Last updated: April 11, 2024