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Hao Wang

Hao Wang  王 皓

Associate Professor
School of Computer Science and Technology, University of Science and Technology of China (USTC)
Anhui Province Key Laboratory of Big Data Analysis and Application (BDAA)
Key Laboratory of Cognitive Intelligence(认知智能全国重点实验室)

Email: wanghao3 [AT] ustc.edu.cn
Office: B709, Xinzhi Building, USTC High-tech Campus, Hefei, Anhui, China, 230027.

Biography

Hao Wang is an Associate Professor in the School of Computer Science and Technology at USTC. He is affiliated with the Anhui Province Key Laboratory of Big Data Analysis and Application (BDAA) and the Key Laboratory of Cognitive Intelligence.

His work focuses on data science, artificial intelligence, large language models, recommender systems, and AI4Science. His research has been deployed in industrial systems (including Huawei Pangu foundation-model applications and Tencent industrial advertising), and the group open-source ecosystem has accumulated 2K+ GitHub stars.

Research Interests:

  • Data-centric AI: data selection, curation, and regeneration.
  • Recommender Systems: large recommendation models, sequential recommendation, and cross-domain recommendation.
  • Large Language Models: efficient inference, reasoning, and knowledge discovery.
  • AI for Science: machine learning methods for chemistry and biology.

He is actively recruiting self-motivated graduate and undergraduate students. Prospective students are welcome to contact him by email with a CV and research interests.

Experience

  • Apr 2025 - Present: Associate Professor, School of Computer Science and Technology, USTC.
  • Jan 2022 - Mar 2025: Associate Researcher, School of Computer Science and Technology, USTC.
  • Jan 2020 - Jan 2021: Visiting Student, Department of Computer Science, University of Illinois Urbana-Champaign (Advisor: Prof. Hanghang Tong).
  • Sep 2015 - Dec 2021: Ph.D. in Computer Science, USTC (Advisor: Prof. Enhong Chen).
  • Sep 2011 - Jul 2015: B.S. in Computer Science and Information Engineering, Hefei University of Technology.
News

  • Apr 2026: USTC-StarTeam maintains 38 public repositories and 48 followers on GitHub, with sustained activity in recommendation, LLM, and AI4Science projects.
  • Feb 2025: Served as Program Committee member for ICML 2025, SIGIR 2025, KDD 2025 (Research & ADS), ACL ARR, and NeurIPS 2025.
  • Jan 2025: One paper accepted by DASFAA 2025.
  • Jan 2025: Three papers and one tutorial accepted by WWW 2025.
  • Dec 2024: Released a survey, Scaling New Frontiers: Insights into Large Recommendation Models. See paper and project repository.
  • Oct 2024: One paper accepted by KDD 2025.
Publications   (Google Scholar Full List, Grouped by Year)   Google Scholar   GitHub

* indicates corresponding author. # indicates equal contribution.

Open-source Projects (USTC-StarTeam)

中文

课题组 GitHub: USTC-StarTeam。 截至 2026-04-14,共有 38 个公开仓库。

  • Awesome-Large-Recommendation-Models(119 stars):大推荐模型进展汇总。
    链接:https://github.com/USTC-StarTeam/Awesome-Large-Recommendation-Models
  • DR4SR(72 stars):KDD 2024 Best Student Paper 代码。
    链接:https://github.com/USTC-StarTeam/DR4SR
  • GE4Rec(36 stars):ICML 2025 CTR 生成式建模项目。
    链接:https://github.com/USTC-StarTeam/GE4Rec
  • ChemEval(32 stars):AI4Science/Chemistry 评测工具。
    链接:https://github.com/USTC-StarTeam/ChemEval

English

Group GitHub: USTC-StarTeam. As of April 14, 2026, the organization hosts 38 public repositories.

  • Awesome-Large-Recommendation-Models (119 stars): curated advances on large recommendation models.
    URL: https://github.com/USTC-StarTeam/Awesome-Large-Recommendation-Models
  • DR4SR (72 stars): KDD 2024 Best Student Paper codebase.
    URL: https://github.com/USTC-StarTeam/DR4SR
  • GE4Rec (36 stars): ICML 2025 project on generative CTR feature modeling.
    URL: https://github.com/USTC-StarTeam/GE4Rec
  • ChemEval (32 stars): chemistry/AI evaluation toolkit.
    URL: https://github.com/USTC-StarTeam/ChemEval
Services

中文

  • 编委/客座编辑:Entropy 专刊 Guest Editor。
  • 期刊审稿:IEEE TKDE、IEEE TSMC、ACM TOIS、ACM TIST、ACM TKDD、JCST、FCS。
  • 程序委员会:2025(ICML/NeurIPS/WWW/SIGIR/KDD);2024(CIKM/NeurIPS/AAAI/WWW/SDM/SIGIR/KDD)。

English

  • Editorial Roles: Entropy Special Issue Guest Editor.
  • Journal Reviewer: IEEE TKDE, IEEE TSMC, ACM TOIS, ACM TIST, ACM TKDD, JCST, and FCS.
  • Program Committee: 2025 (ICML/NeurIPS/WWW/SIGIR/KDD); 2024 (CIKM/NeurIPS/AAAI/WWW/SDM/SIGIR/KDD).
Honors and Awards

中文

  • 2025:王宽诚育才奖(校教育基金会奖)
  • 2025:HW 青年学者计划(HW)
  • 2024中国电子学会自然科学一等奖
  • 2024安徽省科技进步一等奖
  • 2024:省青年科技人才托举计划(安徽省科学技术协会)
  • 2024CCF-A类国际会议KDD2024 唯一最佳学生论文奖
  • 2024:腾讯精英人才计划入选学生导师
  • 2023:CCF-腾讯犀牛鸟基金入选学者
  • 2023:第九届中国智能技术与大数据会议优秀论文
  • 2023:美团最佳合作奖
  • 2022:中国科学技术大学“墨子杰出青年特资津贴”
  • 2019:国家公派联合培养博士研究生(CSC)

English

  • 2025: Wang Kuancheng Talent Award (USTC Education Foundation)
  • 2025: HW Young Scholar Program (HW)
  • 2024: Natural Science First Prize, Chinese Institute of Electronics
  • 2024: First Prize of Anhui Provincial Science and Technology Progress Award
  • 2024: Anhui Young S&T Talent Support Program (Anhui Association for Science and Technology)
  • 2024: The only Best Student Paper Award at KDD 2024 (a CCF-A international conference)
  • 2024: Selected student mentor of Tencent Elite Talent Program
  • 2023: CCF-Tencent Rhino-Bird Open Research Fund (selected scholar)
  • 2023: Excellent Paper Award, 9th China Conference on Intelligent Technology and Big Data
  • 2023: Meituan Best Collaboration Award
  • 2022: USTC Mozi Distinguished Young Talent Special Allowance
  • 2019: China Scholarship Council Joint Ph.D. Program (CSC)
Research Grants

中文

  • 2025-2028:国家自然科学基金面上项目。
  • 2025-2027:国家自然科学基金重点项目课题负责人。
  • 2023-2025:国家自然科学基金青年项目。
  • 2024-2027:安徽省科技创新攻坚重点项目课题负责人兼技术总师。

English

  • 2025-2028: NSFC General Program.
  • 2025-2027: NSFC Key Program (subtask PI).
  • 2023-2025: NSFC Young Scientists Fund.
  • 2024-2027: Anhui Provincial Key S&T Innovation Program (subtask lead and chief technical architect).
Teaching

中文

  • 深度学习:硕士选修课(2022,2024)。
  • 深度学习导论:本科核心课(2023,2024)。
  • 工程硕士专业英语(电子信息):硕士基础课(2023,2024)。

English

  • Deep Learning: postgraduate elective (2022, 2024).
  • Introduction to Deep Learning: undergraduate core course (2023, 2024).
  • Professional English for Engineering (Electronic Information): postgraduate foundational course (2023, 2024).
Recruitment

中文招生(重点)

  • 欢迎相关专业优秀、执行力强的本科生、硕士生、博士生加入课题组。
  • 欢迎低年级本科生提前参与大创、实习与科研训练。
  • 请发送简历与研究兴趣至:wanghao3 [AT] ustc.edu.cn

Recruitment (Highlighted)

  • We welcome excellent and self-driven undergraduate/master students.
  • Early-stage undergraduates are encouraged to join innovation projects and internships.
  • Please send your CV and research interests to: wanghao3 [AT] ustc.edu.cn