Hao Wang is currently a Postdoctoral Research Fellow at MBZUAI and CMU, mentored by Prof. Kun Zhang. Before that, he worked at Ant Group, Microsoft Research Asia, and Xiaohongshu (selected for the first RedStar program, ¥3,500/day), and received his Ph.D. degree at Zhejiang University in 2026. His research focuses on learning from imperfect supervision, addressing challenges such as selection bias, label noise, implicit feedback, incomplete observations, and label correlations, with applications to Time Series & World Model, Recommendation Systems, and Reward & Agents. He has published over 50 papers at top-tier conferences and journals including ICML, NeurIPS, ICLR, KDD, SIGIR, KDE, and TPAMI, with multiple oral/spotlight papers. He also serves as the area chair (AC) or program committee (PC) member for top-tier conferences including ICML, NeurIPS, ICLR, SIGKDD, WWW, AAAI, and the invited reviewer for prestigious journals such as TPAMI, TKDE, TNNLS, and Artificial Intelligence.

王浩是穆罕默德·本·扎耶德人工智能大学和卡内基梅隆大学的博士后研究员，合作导师为张坤教授。 在此之前，他曾在蚂蚁集团、微软亚洲研究院和小红书（首届RedStar项目，¥3,500/天）工作，并于2026年在浙江大学获得博士学位。 他的研究重点是基于不完美监督信号的机器学习，具体包括选择偏差、标签噪声、隐式反馈、不完整观测和标签伪相关等问题，应用于时间序列与世界模型、推荐系统和奖励与智能体。 他在顶级会议和期刊上发表了50余篇论文，包括ICML、NeurIPS、ICLR、KDD、SIGIR、KDE和TPAMI，其中多篇为口头报告/亮点论文。他担任ICML、NeurIPS、ICLR、SIGKDD、WWW、AAAI等顶级会议的领域主席或审稿人，以及TPAMI、TKDE、TNNLS和Artificial Intelligence等权威期刊的特邀审稿人。