Bio

Hi, this is Yueqing Liang (梁月清). I am currently a third-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.

My research interests are in the broad areas of data mining and machine learning, including several related topics like fairness, NLP, LLMs, recommender systems, and transfer learning. I am currently seeking a research internship opportunity in the United States. I would also be glad to discuss potential collaborations or share my research through talks at relevant seminars. Feel free to reach out to me via email at yliang40 [at] hawk [dot] iit [dot] edu or connect with me on WeChat (ID: Luna_lyq).

News

       
[Feb 2024] Be invited to serve as a PC member for KDD 2024.
       
[Feb 2024] New preprint is online Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction.
       
[Feb 2024] Be invited to serve as a PC member for SMSociety 2024.
       
[Jan 2024] New preprint is online Understanding the Concerns and Choices of Public when Using Large Language Models for Healthcare.
       
[Jan 2024] Honored to be selected to participate 2024 CRA-WP Grad Cohort for Women. Looking forward to meeting you in April in Minneapolis!
       
[Dec 2023] Be invited to serve as a PC member for PAKDD 2024.
       
[Nov 2023] Be invited to serve as a PC member for The Web Conference 2024.
       
[Jul 2023] Be invited to serve as a PC member for AAAI 2024.
       
[Jul 2023] Be invited to serve as an external reviewer for ICDM 2023.
       
[Jul 2023] Be invited to serve as a PC member for ASONAM 2023.
       
[Jun 2023] Attended FAccT 2023 in Chicago.
       
[Jun 2023] Be invited to serve as an external reviewer for CIKM 2023.
       
[May 2023] Attended MMLS 2023 in Chicago.
       
[Apr 2023] Be invited to serve as an external reviewer for ECML PKDD 2023.
       
[Feb 2023] Be invited to serve as an external reviewer for SIGIR 2023.
       
[Jan 2023] Our paper Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach is accepted by Frontiers in Big Data.
       
[Aug 2022] Be invited to serve as an external reviewer for WSDM 2022.
       
[Jul 2022] Be invited to serve as a co-reviewer for ICDM 2022.
       
[Jul 2022] Be invited to serve as a subreviewer for SBP-BRiMS 2022.
       
[Jun 2022] Be invited to serve as a subreviewer for DisCoML 2022.
       
[Jun 2022] Be invited to serve as a subreviewer for CIKM 2022.
       
[Apr 2022] Be invited to serve as a subreviewer for TKDE.
       
[Mar 2022] Be invited to serve as a subreviewer for SIGIR 2022.
       
[Feb 2022] Be invited to serve as a subreviewer for TCSS.
       
[Feb 2022] Be invited to serve as an external reviewer for KDD 2022.
       
[Jan 2022] Be invited to serve as a subreviewer for SDM 2022.
       
[Jan 2022] Be invited to serve as a subreviewer for Knowledge-Based Systems.
       
[Dec 2021] Be invited to serve as a subreviewer for Information & Management.
      

Publications

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.
  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]

• Taxonomy-Guided Zero-Shot Recommendations with LLMs.
  Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu.
  arXiv preprint, Jun 2024.
  [Paper]

• SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting.
  Xiongxiao Xu, Yueqing Liang, Baixiang Huang, Zhiling Lan, Kai Shu.
  arXiv preprint, Apr 2024.
  [Paper]

• Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction.
  Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu.
  arXiv preprint, Feb 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

• Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models.
  Yueqing Liang, Lu Cheng, Ali Payani, Kai Shu.
  arXiv preprint, Nov 2023.
  [Paper]

• 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]

• On Fair Classification with Mostly 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]