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Haoyang Li

李昊阳

Undergrad Student
Hong Kong Baptist University
LI_Haoyang@life.hkbu.edu.hk


About Me

I am an undergraduate student at Hong Kong Baptist University (HKBU), pursuing a major in Sociology and a minor in Computer Science. My studies have provided rigorous training in sociological theory and research methods, complemented by extensive coursework in computer science with a focus on Artificial Intelligence (AI).

Driven by a strong conviction in the transformative potential of interdisciplinary research, I am passionate about exploring the convergence of social science and AI in the era of big data and intelligent technologies. I am particularly interested in how these sociological theories-guided LLM agents can enable novel insights into social simulations and complex societal dynamics.

Priviously, I serve as a Research Intern at Tsinghua University’s Future Intelligence Lab under the supervision of Prof. Fengli Xu, where I focus on LLM-driven agents. I am also currently supervised by Prof. Hongru Du at Department of Systems and Information Engineering, University of Virginia and collaborating with him to focus on the research of theory-guided LLM agent-based modeling and exploring its applications in domains including population mobility and public health. Moreover, I am also actively working as a Research Assistant to explore LLM Agents that simulate urban policy planner and human decision-making behavior under the supervision of Prof. Zhanzhan Zhao, Joint Assistant Professor, School of School of Artificial Intelligence & Division of Computational Social Science, The Chinese University of Hong Kong, Shenzhen.

For more details, please see my CV (last updated 29 Sep, 2025).

I am actively seeking M.Phil./Ph.D. opportunities (Fall 2026 intake) to further develop my research at the intersection of computational social science and LLM Agent.

Research Interests

News

Publication

  1. Qingbin Zeng, Ruotong Zhao, Jinzhu Mao, Haoyang Li, Fengli Xu, Yong Li
    We proposed CrimeMind, a novel LLM-driven Agent-Based Modeling (ABM) framework for urban crime simulation. Integrating criminology and sociology theories into agent design, it improves crime hotspot prediction and spatial distribution accuracy by 24% versus traditional ABM and deep learning baselines.

Selected Project

  1. Haoyang Li
    Quantitatively investigated stratifying effects of cultural capital on higher education access inequality, leveraging nationally representative CFPS 2020 data through ordinal logistic regression modeling.

Awards & Honors