Hao Wang is currently an Associate Researcher at School of Computer Science and School of Computer Science and Technology, University of Science and Technology of China, also a member of Anhui Province Key Laboratory of Big Data Analysis and Application (BDAA) led by Prof. Enhong Chen and Key Laboratory of Cognitive Intelligence. My research interests include Machine Learning, Data Mining, and Deep Learning, expecially focus on following topics: Representation Learning, Graph Mining, Sequential Modeling, Large Language Model & In-context Learning, and also other relevant applications in data mining recently.

Main Research:
1. User Modeling & Recommendation System
2. Large Lauguage Model & Agent
3. AI for Sciences (Specifically fouce on AI for Chemistry, Collaborating with the Vice President Prof. Yao Fu )

I am always looking for self-motivated students to join us, feel free to contact me.

• Jan. 2022 ~ Present,  Associate Researcher,  School of Computer Science and Technology,  University of Science and Technology of China (USTC)
• Sep. 2015 ~ Dec. 2021,  Ph.D,  School of Computer Science and Technology,  University of Science and Technology of China (USTC),  Advisor: Prof. Enhong Chen
• Jan. 2020 ~ Jan. 2021,  Visiting Student,  Department of Computer Science,  University of Illinois Urbana-Champaign (UIUC),  Advisor: Prof. Hanghang Tong
• Sep. 2011 ~ Jul. 2015,  B.S.,  School of Computer Science and Information Engineering,  HeFei University of Technology (HFUT)

• [Call for papers]: I'm serving as the special issue guest editor of Entropy Journal (SCI:Q2) on "Information Network Mining and Applications". Deadline for manuscript submissions is extended to Oct.31, 2023. Detailed information can be referred to this website [Link].
• [LLM for Recommendation Survey]: We released a survey for Large Language Models with Recommendation, which presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), and identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. If you are interested in it, please refer to our Survey Paper and Github Project or directly contact us to help continuously improve this paper.
• [User Modeling Tutorial]: User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. We introduce our tutorial on “User Behavior Modeling with Deep Learning for Recommendation: Recent Advances” at RecSys'2023, aiming to offer an in-depth exploration of this evolving research topic. [Offical Website][Slides]
• [05.2024] One paper was accepted by KDD'2024. Congratulations to Mingjia and all co-authors!
• [05.2024] I will server as the PC members of CIKM'24 and NeurlPS'24.
• [04.2024] One paper was accepted by DASFAA'2024. Congratulations to Luankang and all co-authors!
• [01.2024] One paper was accepted by WWW'2024. Congratulations to Yongqiang and all co-authors!