Weizhong Zhang

Tenure-track Professor School of Data Science Fudan University Contact weizhongzhang@fudan.edu.cn

About Me

I am now a Tenure-track Professor at School of Data Science, Fudan University. I was a Postdoctoral Researcher and Research Assistant Professor in The Hong Kong University of Science and Technology, working with Prof. Tong Zhang. Before that I was also a Research Scientist in Tencent AI Lab. I received my Ph.D. on Computer Science at Zhejiang University under my supervisors Prof. Xiaofei He and Prof. Deng Cai. I was a Joint PhD student in The University of Michigan, Ann Arbor under the supervision of Prof. Jieping Ye and Prof. Jie Wang. I have broad interests in machine learning, deep learning algorithms, federated learning etc. My current research focuses on developing algorithms to improve the training/inference efficiency and generalization ability of deep neural networks and their applications in other areas (e.g., computer vision). I have published more than 40 papers in peer-reviewed top-tier journal papers (JMLR, IEEE TPAMI, IEEE TKDE, etc.) and conference papers (ICML, NeurIPS, CVPR, etc.).

Our team recruits several self-motivated students (Ph.Ds., masters, undergraduate students, interns, gap year students) to work on model compression, federated learning, efficient training/finetuning/inference algorithms for LLM and diffusion models etc. If you are interested in my group, please contact me as soon as possible.

Research Interests

• Machine Learning

• Model Compression, Efficient Training/Inference Algorithms

• Federated Learning

• Out of Domain Generalization

• Machine Learning Methods in other areas (e.g., computer vision)

Selected Publications (*equal contribution，#corresponding author)

1. Zhiwei Li*, Yiqiu Li*, Binbin Lin, Zhongming Jin, Weizhong Zhang#. Low Precision Local Training is Enough for Federated Learning. In proceedings of the 38th Annual Conference on Neural Information Processing Systems, 2024. (NeurIPS 2024)

2. Weizhong Zhang#, Zhiwei Zhang, Renjie Pi, Zhongming Jin, Yuan Gao, Jieping Ye, Kaini Chen. Efficient Denoising Diffusion via Probabilistic Masking. In Forty-first International Conference on Machine Learning, 2024. (ICML 2024)

3. Yanlu Cai, Weizhong Zhang#, Yuan Wu, Cheng Jin#. PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2124-2133. 2024. (CVPR 2024)

4. Yibin Wang*, Weizhong Zhang*, Jianwei Zheng, Cheng Jin. High Fidelity Person-centric Subject-to-Image Synthesis. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7675-7684. 2024. (CVPR 2024)

5. Yanlu Cai, Weizhong Zhang#, Yuan Wu, Cheng Jin#. FusionFormer: A Concise Unified Feature Fusion Transformer for 3D Pose Estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 2, pp. 900-908. 2024. (AAAI 2024)

6. Weizhong Zhang*#, Renjie Pi*, Yueqi Xie*, Jiahui Gao, Xiaoyu Wang, Sunghun Kim, Qifeng Chen. DynaFed: Tackling Client Data Heterogeneity with Global Dynamics. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12177-12186, 2023. (CVPR 2023)

7. Weizhong Zhang*, Xiao Zhou*, Renjie Pi*, Yong Lin, Tong Zhang. Probabilistic Bilevel Coreset Selection. In International Conference on Machine Learning, pp. 27287-27302, 2022. (ICML 2022)

8. Xupeng Shi, Pengfei Zheng, A. Adam Ding, Yuan Gao, Weizhong Zhang#. Finding Dynamics Preserving Adversarial Winning Tickets. In International Conference on Artificial Intelligence and Statistics, pp. 510-528, 2022. (AISTATS 2022)

9. Weizhong Zhang*, Xiao Zhou*, Hang Xu, Tong Zhang, Effective Sparsification of Neural Networks with Global Sparsity Constraint. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3599-3608, 2021. (CVPR 2021)

10. Weizhong Zhang*, Xiao Zhou*, Zonghao Chen, Shizhe Diao, Tong Zhang, Efficient Neural Network Training via Forward and Backward Propagation Sparsification. In proceedings of the 32nd Annual Conference on Neural Information Processing Systems 34(2021). (Neurips 2021)

11. Weizhong Zhang*, Yihong Gu*, Cong Fang, Jason Lee, Tong Zhang, How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. In proceedings of the 32nd Annual Conference on Neural Information Processing Systems,33 (2020): 3797-3807. (NeurIPS 2020)

12. Weizhong Zhang*, Bin Hong*, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang, Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction. Journal of Machine Learning Research, 20 (2019): 121-1. (JMLR 2019)

13. Weizhong Zhang, Tingjin Luo, Shuang Qiu, Jieping Ye, Dengcai, Xiaofei He, Jie Wang, Identifying Genetic Risk Factors for Alzheimer's Disease via Shared Tree-guided Feature Learning across Multiple Tasks. IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, 2018. (TKDE 2018)

14. Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang, Safe Element Screening for Submodular Function Minimization. In Proceedings of the 35th International Conference on Machine Learning, pp. 5786-5795, 2018. (ICML 2018)

15. Weizhong Zhang, Lijun Zhang, Zhongming Jin, Rong Jin, Deng Cai, Xuelong Li, Ronghua Liang, Xiaofei He: Sparse Learning with Stochastic Composite Optimization. IEEE Transduction on Pattern Analysis and Machine Intelligence, 39(6):1223-1236, 2017. (TPAMI 2017)

16. Weizhong Zhang*, Bin Hong*, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang, Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction. In Proceedings of the 34th International Conference on Machine Learning, pp. 4016-4025, 2017. (ICML 2017)

17. Weizhong Zhang, Lijun Zhang, Yao Hu, Rong Jin, Deng Cai, Xiaofei He: Sparse Learning for Stochastic Composite Optimization. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 893-899, 2014. (AAAI 2014）

Projects

• 国家(教育部)人才计划，2024/01-2026/12, 90万元，主持

• 国家自然科学基金面上项目，大规模稀疏深度神经网络学习理论和优化算法研究，2025/01-2028/12，51万元，主持

• 上海集成电路技术与产业促进中心，云平台资源智能调度和配置技术研发，2024/09-2025/12, 100万元，主持

• 其他纵向项目, 主持

Teaching

• Fall 2025 Computation Theory (DATA130031)

• Fall 2025 Principles of Computer Systems (AIE210006)

• Spring 2024 Multivariate Statistics (MATH 620156)

• Fall 2024 Computation Theory (DATA130031)

• Spring 2023 Multivariate Statistics (MATH 620156)

• Summer 2022 Applied Statistics (MATH 2411)

• Fall 2021 Calculus and Linear Algebra (MATH 1003)

• Fall 2020 Calculus and Linear Algebra (MATH 1003)

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