个人简介
江伟森，华南理工大学未来技术学院教授，博士生导师，国家级人才青年项目入选者。研究聚焦于大模型、深度学习与AI4Science，在 NeurIPS、ICML、ICLR、TPAMI 等顶会顶刊发表论文二十余篇，并担任 ICLR、NeurIPS 等顶级 AI 会议的领域主席（Area Chair）。提出的大模型知识增强技术（ICLR 2024 & ACL 2024，引用 1400+）被 DeepSeek、Qwen 等知名机构应用验证。他于香港科技大学获得博士学位，此前曾在腾讯和美团担任推荐和排序系统的研究员；长期与腾讯、美团等互联网企业保持科研合作，可推荐学生前往上述企业实习。
每年招收博士生 2 名，硕士生 2–4 名，长期招收博士后与科研助理，欢迎对大模型、深度学习及 AI4Science 感兴趣的同学联系；本校大三、大四同学如对科研感兴趣，也欢迎联系。
联系邮箱：wsjiang@scut.edu.cn
来信请按“[Research Position] 姓名-学校-研究兴趣”的格式拟定邮件主题，例如：“[Research Position] 李明-华南理工大学-大模型后训练”。
教育背景
2020 - 2024，香港科技大学，博士，计算机科学
2012 - 2015，中国科学院大学，硕士，运筹学与控制论
2008 - 2012，华南理工大学，学士，自动化
工作经历
2026 至今，华南理工大学 未来技术学院，教授
2024 至 2026，香港中文大学 计算机科学与工程系，副研究员
2017 至 2019，腾讯，高级算法工程师
2015 至 2017，美团，算法工程师
标志性成果
说明：“*”表示共同第一作者，“†”表示通讯作者
近五年代表性论文
1. Weisen Jiang†, Shuhao Chen, Sinno Jialin Pan, MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification, ICML 2026.
2. Weisen Jiang†, Sinno Jialin Pan, MetaDefense: Defending Fine-tuning based Jailbreak Attack Before and During Generation, NeurIPS 2025.
3. Longhui Yu*, Weisen Jiang*, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu, MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models, ICLR Spotlight 2024.
4. Weisen Jiang, Yu Zhang, James T. Kwok, Effective Structured Prompting by Meta-Learning and Representative Verbalizer, ICML 2023.
5. Weisen Jiang, James T. Kwok, Yu Zhang, Subspace Learning for Effective Meta-Learning, ICML 2022.
其他相关工作
1. Shuhao Chen, Weisen Jiang, Yifei Gong, Sen Luo, Cheng Zhuo, Zhenguo Li, James T. Kwok, Yu Zhang, SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance–Diversity Data Selection, ICML 2026.
2. Baijiong Lin, Weisen Jiang, Pengguang Chen, Feiyang Ye, Yu Zhang, Ying-Cong Chen, Shu Liu, Ivor W. Tsang, James T. Kwok, Dual-Balancing for Multi-Task Learning, Neural Networks, 2026.
3. Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, Ying-Cong Chen, MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders, IEEE TPAMI, 2025.
4. Baijiong Lin, Weisen Jiang, Yuancheng Xu, Hao Chen, Ying-Cong Chen, PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model, ICML 2025.
5. Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok, Forward-Backward Reasoning in Large Language Models for Mathematical Verification, Findings of ACL 2024.
6. Shuhao Chen*, Weisen Jiang*, Baijiong Lin, James T. Kwok, Yu Zhang, RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models, NeurIPS 2024.
7. Tao Li*, Weisen Jiang*, Fanghui Liu, Xiaolin Huang, James T. Kwok, Learning Scalable Model Soup on A Single GPU: An Efficient Subspace Training Strategy, ECCV 2024.
8. Yanbin Wei, Shuai Fu, Weisen Jiang†, Zhiyuan Zhang, Zhen Zeng, Qi Wu, James T. Kwok, Yu Zhang, GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning, NeurIPS 2024.
9. Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok, An Adaptive Policy to Employ Sharpness-Aware Minimization, ICLR 2023.
10. Weisen Jiang, James T. Kwok, Yu Zhang, Effective Meta-Regularization by Kernelized Proximal Regularization, NeurIPS 2021.
