Juseung Lee

Hi! I am a Ph.D. student at University at Buffalo, SUNY Computer Science & Engineering, advised by Dr. Xi Lu. My research explores how human-AI collaboration and data-driven tracking tools can support personal informatics and midlife health, empowering non-experts to understand and manage complex, often stigmatized health data.

I completed my B.S. and M.S. in Computer Science at Illinois Institute of Technology where I was advised by Dr. Boris Glavic as part of IIT DBGroup.

news

Sep 11, 2025	✨ Submitted one paper to CHI 2026!
Jun 09, 2024	✈️ Attending SIGMOD to present our paper at the HILDA workshop 📍 Santiago, Chile!
May 06, 2024	📄 Our paper on streamlining integration of longitudinal survey instruments is accepted to HILDA 2024!
Aug 28, 2023	🚀 Started my Ph.D. program at University at Buffalo, SUNY!
Dec 17, 2022	👩🏻‍🎓💐 Graduated with my M.S. from Illinois Institute of Technology!
Sep 03, 2022	🇦🇺🦘 Presenting our demo at VLDB 2022 in Sydney, Australia!

selected publications

HILDA

Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments

Pratik Pokharel^*, Juseung Lee^*, Oliver Kennedy, Marianthi Markatou, Andrew Talal, Jeff Good, and Raktim Mukhopadhyay

In Proceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics, Santiago, AA, Chile, 2024

Abs DOI PDF

We explore data management for longitudinal study survey instruments: (i) Survey instrument evolution presents a unique data integration challenge; and (ii) Longitudinal study data frequently requires repeated, task-specific integration efforts. We present DDM (Drag, Drop, Merge), a user interface for documenting relationships among attributes of source schemas into a form that can streamline subsequent efforts to generate task-specific datasets. DDM employs a "human-in-the-loop" approach, allowing users to validate and refine semantic mappings. Through a simulation of user interactions with DDM, we demonstrate its viability as a way to reduce cognitive overhead for longitudinal study data curators.