Yifei Shen
Welcome to my homepage!
I’m a first-year Computer Science PhD student at the University of Florida, advised by Prof. Zhe Jiang.
Before that, I received my master’s degree from the University of Washington and my bachelor’s degree from Xi’an Jiaotong-Liverpool University, with a dual degree from the University of Liverpool. Feel free to reach out if you’re interested in my research.
News
- 07/2026: Our work Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA was accepted to ACL 2026 Main.
- 03/2026: I will join the University of Florida CISE department as a PhD student in Fall 2026, advised by Prof. Zhe Jiang.
- 02/2026: Our work Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL was selected for an oral presentation at EACL 2026 Main.
- 01/2026: Our work Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL was accepted to EACL 2026 Main.
- 05/2025: Our work SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification was accepted to ACL 2025 Main.
- 05/2025: Our team won 2nd Place at the ENGINE 2025 Showcase for Efficient Inference of Large Language Models on a Single GPU, sponsored by Lenovo Research.
Research
I’m interested in multimodal foundation models, LLM agents, and AI for science. Most of my work builds and evaluates models that reason over scientific and clinical evidence. Some papers are highlighted.
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
ACL 2026 Main
ExpRAG retrieves similar patient experiences from electronic health records to improve discharge-related clinical question answering.
Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL
EACL 2026 Main (Oral)
ClinSQL evaluates models on clinically grounded text-to-SQL tasks that require temporal reasoning and patient-similarity cohort construction.
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
ACL 2025 Main
SciVer benchmarks foundation models on 3,000 expert-annotated claims grounded in scientific text, charts, and tables.