师资队伍

汪子乔，同济大学计算机科学与技术学院长聘副教授、特聘研究员，博士毕业于加拿大渥太华大学。入选国家级海外高层次青年人才、上海市白玉兰海外高层次青年人才，主持和参与多项国家级及省部级项目。研究方向为机器学习基础理论、大模型学习理论与算法以及信息论。近几年主要成果发表在人工智能与机器学习等相关领域国际顶级会议，涵盖NeurIPS、ICML、ICLR等，博士论文被提名2025年加拿大人工智能协会最佳博士论文奖，以及提名2025年渥太华大学总督学术奖章和Pierre Laberge论文奖。曾担任 ICLR、NeurIPS等会议领域主席（Area Chair），2025年中国具身智能大会注册主席，以及2024 年 IEEE 北美信息论暑期学校（NASIT）联合程序主席。

个人主页：https://ziqiaowanggeothe.github.io/

招生信息：https://tongji.teacher.360eol.com/teacherBasic/preview?teacherId=36636

近年代表性论文如下：

• Weak-to-Strong Generalization via Bregman Bias–Variance Decomposition

  Gengze Xu, Wei Yao,Ziqiao Wang†, and Yong Liu†

  ICML 2026

• Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach

  Haiyun He, Yepeng Liu,Ziqiao Wang, Yongyi Mao, and Yuheng Bu

  NeurIPS 2025

• Generalization in Federated Learning: A Conditional Mutual Information Framework

  Ziqiao Wang, Cheng Long, and Yongyi Mao

  ICML 2025

• Generalization Bounds via Conditional f-Information

  Ziqiao Wangand Yongyi Mao

  NeurIPS 2024

• On f-Divergence Principled Domain Adaptation: An Improved Framework

  Ziqiao Wangand Yongyi Mao

  NeurIPS 2024

• Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds

  Ziqiao Wangand Yongyi Mao

  NeurIPS 2023

• Tighter Information-Theoretic Generalization Bounds from Supersamples

  Ziqiao Wangand Yongyi Mao

  ICML 2023(Oral)

• Information-Theoretic Analysis of Unsupervised Domain Adaptation

  Ziqiao Wangand Yongyi Mao

  ICLR 2023

• Over-Training with Mixup May Hurt Generalization

  Zixuan Liu*,Ziqiao Wang*, Hongyu Guo, and Yongyi Mao

  ICLR 2023

• On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications

  Ziqiao Wangand Yongyi Mao

  ICLR 2022