Sangam Lee

I'm a Ph.D student at Yonsei University, advised by Professor Dongha Lee.

My mission is to help people in the real-world 1) access accurate information without distortion, 2) do so more easily, and 3) better understand and make use of that information.

To achieve this, I have been conducting research about information retrieval.

Email / CV / Google Scholar / Linkedin / Github

Research

I'm interested in information retrieval and natural language processing. My research focuses on developing efficient retrieval systems and improving the quality of search results to help users find accurate information more effectively. I also explore the intersection of IR and NLP to enhance text understanding and information access.

	Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee arXiv, 2025 Paper / Code
	EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning Jaemin Jeon, Suwan Yu, Sangam Lee, Sang Cheol Kim, Hye-Yeong Jo, Inuk Jung, Kwangsoo Kim Bioinformatics, 2025 Paper / Code
	Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths Sangam Lee, Ryang Heo, SeongKu Kang, Susik Yoon, Jinyoung Yeo, Dongha Lee arXiv, 2024 Paper / Code
	Small language models are equation reasoners Bumjun Kim, Kunha Lee, Juyeon Kim, Sangam Lee arXiv, 2024 Paper / Code

Experience

	Ph.D. Student Data & Language Intelligence Lab, Yonsei University 2023-Present Advisor: Prof. Dongha Lee
	Research Intern Biomedical Informatics Lab, Seoul National University Hospital 2023
	Undergraduate Research Intern Service Management Lab, Seoul National University of Science and Technology 2021

Education

	Ph.D. Student in Artificial Intelligence Yonsei University 2024-Present Advisor: Professor Dongha Lee
	B.S. in Business Administration/Computer Engineering Seoul National University of Science and Technology 2017-2024

Feel free to steal this website's source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page.