Guojing Li

Guojing Li 李国静

PhD Student, School of Data Science, City University of Hong Kong
Joint PhD Student, School of Labor and Human Resources, Renmin University of China
Affiliated with Applied Machine Learning Lab (AML), City University of Hong Kong

I am a PhD student in Data Science at the City University of Hong Kong, supervised by Professor Xiangyu Zhao, and a joint PhD student in Human Resource Management at Renmin University of China, supervised by Professor Wenxia Zhou. My current work sits at the intersection of natural language processing, large language models, text-based assessment, and user-facing intelligent applications. More specifically, I am interested in how NLP and AI-supported methods can be used to build interpretable assessment, feedback, and support tools for real-world applications.

Research Summary

My recent research focuses on natural language processing, large language models, text mining, AI-supported assessment, and intelligent support systems. I am particularly interested in text-based assessment, structured feedback, competency extraction, and recommendation-oriented applications that integrate NLP, psychometrics, and domain knowledge.

Research Interests

  • Natural Language Processing (NLP), Large Language Models (LLMs), and user-facing intelligent applications.
  • Text mining and information extraction from recruitment texts, open-ended responses, and other real-world text corpora.
  • AI-supported assessment, structured feedback, and multi-source evaluation.
  • Job recommendation, competency extraction, and intelligent support for career-development contexts.

Education

  • PhD in Data Science, City University of Hong Kong, 2024–present.
  • PhD in Human Resource Management, Renmin University of China, 2020–present.
  • M.S. in Applied Psychology, City University of Macau, 2013–2016.
  • B.S. in Applied Mathematics, Beijing Normal University, Zhuhai, 2009–2013.

Selected Publications and Research Outputs

Published / Accepted
  • Fu, Z., Wu, X., Li, G., et al. (2025). Model Merging for Knowledge Editing. ACL 2025 Industry Track (Oral).
  • Fu, Z., Wu, X., Li, G., et al. (2026). Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning. ACL 2026 Findings.
  • Li, J., Qian, X., Zhang, Y., et al. (2026). Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization. ICML 2026 Spotlight.
  • Han, Z., Cheng, Y., Ren, Z., Wang, D., & Li, G. (2024). Meta-analysis of Work Connectivity Behavior and Work–Life Conflict: From the Perspective of the Work–Family Resource Model. Advances in Psychological Science.
Selected Recent Work
  • Fu, Z., Li, G., Wang, Y., et al. (2026). Sliding Window Attention Training for Efficient Large Language Models. COLM 2026 submission.
  • Fu, Z., Wu, X., Li, G., et al. (2026). Collaborative Question Answering with Cross-Sequence Attention. ICML 2026 submission.
  • Fu, Z., Gao, J., Li, G., et al. (2026). Feeling of Knowing in Large Language Models. NeurIPS 2026 submission.
  • Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Advertisements.
  • LLM-based Job Recommendation and Competency Extraction from Multi-Domain Recruitment Texts.

Research and Professional Experience

  • Research Consultant & Data Analyst Engineer, BoYi Data (a subsidiary of eRS Information Technology Limited), 2018.07–2021.07.
    Worked on text classification, sentiment analysis, topic modeling (LDA), named entity recognition, and other text-mining projects, including application-oriented NLP model development and deployment.
  • Teaching / Research Assistant, School of Labor and Human Resources, Renmin University of China, 2020.09–present.
    Contributed to projects on vocational skill improvement, organizational evaluation, and career-success research, including participation in a National Social Science Fund project on social capital, human capital, and career success in the AI era. Responsible for data analysis, reporting, and research support in collaborative projects.
  • Visiting Scholar, Scott College of Business, Indiana State University, 2022.09–2024.04.

Technical Skills and Languages

  • Programming / Analytics: Python, R, Java, SPSS, SAS, MATLAB, Amos.
  • NLP / ML Experience: text mining, information extraction, sentiment analysis, topic modeling, named entity recognition, machine learning, and large language model applications.
  • Assessment / Application Experience: AI-supported assessment, multi-source evaluation, structured feedback, and user-facing intelligent applications.
  • Languages: Mandarin Chinese (native), Cantonese (native), English (IELTS 6.5).

Selected Awards

  • Excellent Paper Award, Algorithmic Management and Human–Machine Integration Forum.
  • China Scholarship Council (CSC) Scholarship.
  • C-Class Scholarship for PhD Students, Renmin University of China.