朱兆伟，浙江工业大学网络空间安全研究院特聘研究员，专注“以数据为中心的可信 AI”。博士毕业于加州大学圣克鲁兹分校，研究方向聚焦于弱监督机器学习与数据质量提升，系统提出标签噪声检测—清洗—鲁棒训练全链路方案。共发表论文30余篇，其中 19 篇为 CCF/TH-CPL A 类；Google Scholar 引用 1500余次，h-index 15。原创工具 Docta 开源获3k+星标，曾获 IJCAI-WSRL 最佳论文奖。现任 ICML、NeurIPS、TPAMI 等会议、期刊的领域主席或审稿人，IJCAI 2023 数据质量主题竞赛与讲座发起人。

工作经历：

2024.09-至今 浙江工业大学滨江人工智能创新研究院，特聘研究员

2023.04-至今 杭州五维数据有限责任公司，联合创始人/CEO

教育经历：

2019.09-2023.08 加州大学圣克鲁兹分校，计算机科学，博士

2016.09-2019.06 上海科技大学，信息与通信系统，硕士

2012.09-2016.06 电子科技大学，通信工程，学士

个人主页：https://www.zzw.ai/

代表性工作：
 [ 1 ] Zhaowei Zhu, Yiwen Song, Yang Liu*. Clusterability as an Alternative to Anchor Points when Learning with Noisy Labels. 40th ICML, 2021.
 [ 2 ] Zhaowei Zhu, Zihao Dong, Yang Liu*. Detecting Corrupted Labels without Training a Model to Predict. 39th ICML, 2022.
 [ 3 ] Zhaowei Zhu, Jialu Wang, Yang Liu*. Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-quality Features. 39th ICML, 2022.
 [ 4 ] Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li, Yang Liu*. Weak Proxies Are Sufficient and Preferable for Fairness with Missing Sensitive Attributes. 40th ICML, 2023.
 [ 5 ] Zhaowei Zhu, Jialu Wang, Hao Cheng, Yang Liu*. Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models. 12th ICLR, 2024.
 [ 6 ] Jinlong Pang, Jiaheng Wei, Ankit P. Shah, Zhaowei Zhu*, et al. Improving Data Efficiency via Curating LLM-driven Rating Systems. 13th ICLR, 2025.
 [ 7 ] Jiaheng Wei*, Zhaowei Zhu*, Hao Cheng, et al. Learning with Noisy Labels Revisited: A Study Using Real-world Human Annotations. 10th ICLR, 2022.
 [ 8 ] Hao Cheng*, Zhaowei Zhu*, Xingyu Li, et al. Learning with Instance-dependent Label Noise: A Sample Sieve Approach. 9th ICLR, 2021.
 [ 9 ] Zhaowei Zhu, Tongliang Liu, Yang Liu*. A Second-order Approach to Learning with Instance-dependent Label Noise. CVPR, 2021 (Oral).
 [ 10 ] Hao Cheng*, Zhaowei Zhu*, Xing Sun, Yang Liu. Mitigating Memorization of Noisy Labels via Regularization between Representations. 11th ICLR, 2023.
 [ 11 ] Jiaheng Wei*, Zhaowei Zhu*, Tianyi Luo, et al. To Aggregate or Not? Learning with Separate Noisy Labels. ACM SIGKDD, 2025.
 [ 12 ] Yaxuan Wang, Hao Cheng, Jing Xiong, Zhaowei Zhu*, et al. Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels. ACM SIGKDD, 2025.
 [ 13 ] Zhaowei Zhu, Jiaheng Wei. Docta: A Plug-and-Play Toolkit for Label Noise Detection and Cleaning. GitHub, https://github.com/Docta-ai/docta.