Seungyeon Lee

Short Biography

I’m a 4th year Ph.D. student at the Department of Computer Science and Engineering, The Ohio State University (OSU). I’m fortunately working with Prof. Ping Zhang in AIMed (Artificial Intelligence in Medicine) Lab. My research lies at the intersection of deep learning and causal inference, with applications in healthcare and biomedicine. I am particularly interested in treatment effect estimation using real-world patient data to support evidence-based and personalized healthcare decisions.

Publications

• A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data
  Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’25
  [Paper]

• The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective
  Thai-Hoang Pham, Jiayuan Chen, Seungyeon Lee, Yuanlong Wang, Sayoko Moroi, Xueru Zhang, Ping Zhang
  39th Conference on Neural Information Processing Systems, NeurIPS ’25
  [Paper]

• Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging
  Seungyeon Lee, Thai-Hoang Pham, Zhao Cheng, Ping Zhang
  ACM Transactions on Computing for Healthcare, 2025
  [Paper]

• The recent trend of twin epidemic in the United States: a 10-year longitudinal cohort study of co-prescriptions of opioids and stimulants
  Seungyeon Lee, Wenyu Song, David W. Bates, Richard D. Urman, Ping Zhang
  The Lancet Regional Health - Americas, 2025
  [Paper]

• SubgroupTE: Advancing treatment effect estimation with subgroup identification
  Seungyeon Lee, Ruoqi Liu, Wenyu Song, Lang Li, Ping Zhang
  ACM Transactions on Intelligent Systems and Technology (TIST), 2025
  [Paper]

• Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder
  Seungyeon Lee, Ruoqi Liu, Wenyu Song, Ping Zhang
  IEEE International Conference on Data Mining, ICDM 2023
  [Paper]

• Stable clinical risk prediction against distribution shift in electronic health records
  Seungyeon Lee, Changchang Yin, Ping Zhang
  Patterns, 2023
  [Paper]

• DREAM: Domain Invariant and Contrastive Representation for Sleep Dynamics
  Seungyeon Lee, Thai-Hoang Pham, Ping Zhang
  IEEE International Conference on Data Mining, ICDM 2022
  [Paper]

• Deep Learning based Recommender System using Cross Convolutional Filters
  Seungyeon Lee, Dohyun Kim
  Information Science, 2022
  [Paper]

• Analysis of Tabular Data based on Graph Neural Network using Supervised Contrastive Loss
  Seungyeon Lee, Minyoung Park, Younggeun Ahn, Gyeong Bok Jung, Dohyun Kim
  Neurocomputing, 2024
  [Paper]

• Additive Ensemble Neural Networks
  Minyoung Park*, Seungyeon Lee*, Sangheum Hwang, Dohyun Kim (*equal contributions)
  IEEE Access, 2020
  [Paper]

Services

• Conference reviewer: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD); Confer- ence on Neural Information Processing Systems (NeurIPS); IEEE International Conference on Data Mining (ICDM).
• Conference sub-reviewer: International Conference on Machine Learning (ICML); International Conference on Learning Representations (ICLR).

Powered by Jekyll and Minimal Light theme.