Bio

Hi, I’m Yueqing Liang (梁月清), a fourth-year Ph.D. student in the Department of Computer Science at Illinois Institute of Technology (IIT) since Spring 2022, advised by Prof. Kai Shu. Before joining IIT, I received my M.S. in Big Data in Business from the University of Sydney (USYD) in 2020, and my B.E. in Telecommunications Engineering with Management from Queen Mary University of London (QMUL) in 2018. I worked as an applied scientist intern at Microsoft Bing Ads Recommendation, Summer 2025.

My research focuses on large language models (LLMs), recommendation and personalization, and LLM agents. I am currently seeking research internship or full-time opportunity in the United States. I also welcome opportunities for collaboration, as well as invitations to give research talks or seminars.

Feel free to reach out to me at yueqingliang1 [at] gmail [dot] com or connect with me on WeChat (ID: Luna_lyq).

News

Publications

2025

• Benchmarking LLMs for Political Science: A United Nations Perspective.
  Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu.
  The 40th AAAI Conference on Artificial Intelligence, (AAAI 2026, Oral).
  [Paper] [Code]

• FABLE: Fairness Attack in Abusive Language Detection.
  Yueqing Liang, Lu Cheng, Ali Payani, Kai Shu.
  2025 IEEE International Conference on Big Data, (IEEE BigData 2025).
  [Paper]

• Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction.
  Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Philip S. Yu.
  2025 IEEE International Conference on Big Data, (IEEE BigData 2025, Best Paper Nomination).
  [Paper]

• SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting.
  Xiongxiao Xu, Yueqing Liang, Baixiang Huang, Zhiling Lan, Kai Shu.
  The 34th ACM International Conference on Information and Knowledge Management (CIKM 2025).
  [Paper]

• Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems.
  Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu.
  The Web Conference 2025 (WWW 2025).
  [Paper]

• Taxonomy-Guided Zero-Shot Recommendations with LLMs.
  Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu.
  The 31st International Conference on Computational Linguistics (COLING 2025).
  [Paper] [Poster] [Code]

• Piecing It All Together: Verifying Multi-Hop Multimodal Claims.
  Haoran Wang, Aman Rangapur, Xiongxiao Xu, Yueqing Liang, Haroon Gharwi, Carl Yang, Kai Shu.
  The 31st International Conference on Computational Linguistics (COLING 2025).
  [Paper] [Leaderboard] [Code]

2024

• Collaborative Contextualization: Bridging the Gap between Collaborative Filtering and Pre-trained Language Model.
  Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu.
  The 33th ACM International Conference on Information and Knowledge Management (CIKM 2024).
  [Paper]

• Investigating Gender Euphoria and Dysphoria on TikTok: Characterization and Comparison.
  SJ Dillon*, Yueqing Liang*(co-primary), H Russell Bernard, Kai Shu.
  International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2024).
  [Paper]

• Understanding the Concerns and Choices of the Public when Using Large Language Models for Healthcare.
  Yunpeng Xiao, Kyrie Zhixuan Zhou, Yueqing Liang, Kai Shu.
  arXiv preprint, Jan 2024. [Paper]

2023

• Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach.
  Yueqing Liang, Canyu Chen, Tian Tian, Kai Shu.
  Frontiers in Big Data 5, 129. Jan. 2023.
  [Paper]

2022

• Artificial Intelligence Algorithms for Treatment of Diabetes.
  Mudassir M. Rashid, Mohammad Reza Askari, Canyu Chen, Yueqing Liang, Kai Shu, Ali Cinar.
  Algorithms 2022, 15(9), 299.
  [Paper]

• When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes.
  Canyu Chen, Yueqing Liang, Xiongxiao Xu, Shangyu Xie, Yuan Hong, Kai Shu. Advances in Neural Information Processing Systems workshop on Trustworthy and Socially Responsible Machine Learning (TSRML@NeurIPS 2022) and workshop on Algorithmic Fairness through the Lens of Causality and Privacy (AFCP@NeurIPS 2022).
  [Paper]

2021

• Pre-training Graph Neural Network for Cross-Domain Recommendation.
  Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, Philip S. Yu.
  IEEE CogMI. 2021.
  [Paper]