Yucong Dai

Final-year PhD Candidate at Clemson University. Research interests in trustworthy AI, algorithmic fairness, and causality.

I am a final-year PhD candidate at Clemson University, advised by Prof. Yongkai Wu. My research focuses on trustworthy AI, with emphasis on algorithmic fairness, causality, and robust machine learning.

I develop methods that make learning systems more reliable under distribution shift, weak supervision, and real-world constraints such as model compression. My work has been published at venues including NeurIPS, ICLR, TMLR, and IJCNN.

Before joining Clemson, I received my undergraduate degree in Software Engineering from Wuhan University of Technology.

I am currently on the job market and actively seeking postdoctoral research positions in trustworthy AI, fairness, and causality. Please feel free to reach out if you are interested in collaboration or have opportunities to discuss.

News

Jun 15, 2026 Our paper FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization was accepted to TMLR.
Jun 10, 2026 Our paper Causally Fair Node Classification on Non-IID Graph Data was accepted to TMLR.
Jan 22, 2025 Our paper Towards Counterfactual Fairness through Auxiliary Variables was accepted to ICLR 2025.
Sep 20, 2024 Our paper SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning was accepted to NeurIPS 2024.

Selected Publications

  1. ICDMW
    FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
    Yucong Dai, Liang Zhang, Feng Luo, and 2 more authors
    In 2025 IEEE International Conference on Data Mining Workshops (ICDMW), 2025
  2. TMLR
    Causally Fair Node Classification on Non-IID Graph Data
    Yucong Dai, Liang Zhang, Yue Hu, and 2 more authors
    Transactions on Machine Learning Research, 2026
  3. TMLR
    FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization
    Yucong Dai, Jian Ji, Xing Ma, and 1 more author
    Transactions on Machine Learning Research, 2026
  4. ICLR
    Towards Counterfactual Fairness through Auxiliary Variables
    Boyu Tian, Zhe Wang, Shuo He, and 5 more authors
    In International Conference on Learning Representations, 2025
  5. NeurIPS
    SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
    Ying He, Zhe Wang, Zhe Shen, and 5 more authors
    In Advances in Neural Information Processing Systems, 2024