Won's Page

Introduction

I enjoy doing research and often find myself thinking through new questions and ideas. With a background in nursing and some clinical experience, I have a strong interest in contributing to healthcare. That said, my curiosity isn’t limited to just one field. Right now, my work focuses on connecting the advances in computer science—particularly in natural language processing and large language models—with real-world healthcare needs. I’m especially interested in building tools that can support both clinicians and patients. If any of this resonates with you and you’re interested in collaborating, feel free to reach out by email. I’m always open to new ideas and conversations.

Academics

• (2023 September - current)
  Computer Science PhD student at Miner School of Computer & Information Sciences
  University of Massachusetts Lowell
• (2021 March - 2023 August)
  Master of Science - Department of Biomedical Systems Informatics
  Yonsei University
• (2013 March - 2017 Feburary)
  Bachelor of Science in Nursing - Wonju College of Medicine
  Yonsei University

Projects

• Smart Pharmacovigilance Platform - Nationwide project developing pharmacovigilance platform for hospitals (December '21)
• Clinical Data Synthesis with Cancer Dataset from General Hospitals in Korea (December '22)
• ChatBot NoteAid - Developing technologies that would aid patients to understand more about their electronic healthcare records to enhance health literacy (October '24)

Publications

• Sung & Jang et al (2023). Prognostic value of baseline and early treatment response treatment response of neutrophil-lymphocyte ratio, C-reactive protein, and lactate dehydrogenase in non-small cell lung cancer patients undergoing immunotherapy: Translational Lung Cancer Research
• Yang & Yao et al (2023).Performance of Multimodal GPT-4V on USMLE with Image: Potential for Imaging Diagnostic Support with Explanations : medRxiv
• Kim & Jang et al (2024).Synthetic Data improves Survival Prediction Models in Early-Onset Colorectal Cancer: Journal of Clinical Oncology
• Yao et al (2024). MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE Framework: Arxiv
• Kim et al (2024). PPFL: A personalized progressive federated learning method for leveraging different healthcare institution-specific features : iScience
• Jang et al (2025). Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation : Arxiv

Conference

• Kim et al (2023). Personalized Progressive Federated Learning with Leveraging Client-Specific Vertical Features : Computer Science & IT conference Proceedings

Honors and Awards

• Awarded Best Thesis of The Korean Society of Medical Informatics (KOSMI) Fall academic conference - November, 2022