Harlin Lee

Harlin Lee is an Assistant Professor at the University of North Carolina at Chapel Hill, affiliated with the School of Data Science and Society. His research spans multiple disciplines including Mathematics, Computer Science, and Computational Medicine. Prior to his current position, he was a postdoctoral researcher at UCLA Mathematics, advised by Andrea Bertozzi and Jacob G Foster. He holds a PhD in Electrical and Computer Engineering from Carnegie Mellon University (CMU), an MS in Machine Learning (CMU), and degrees from MIT (BS and MEng in EECS).

Harlin Lee's education includes:

• PhD in Electrical and Computer Engineering, Carnegie Mellon University, 2021
• MS in Machine Learning, Carnegie Mellon University, 2021
• MEng in Electrical Engineering and Computer Science, MIT, 2017
• BS in Electrical Engineering and Computer Science, MIT, 2016

His research interests focus on interdisciplinary applications of data science and machine learning, including:

• Graph-based methods in optimization and signal processing
• Healthcare data analysis, particularly pediatric sleep studies and medical imaging
• Statistical analysis and computational methods in social sciences
• Development of explainable AI systems and federated learning techniques

His recent publications highlight contributions to graph theory, healthcare analytics, and machine learning applications. Key areas include methodological advancements in graph trend filtering, applications in drug repurposing, and innovations in pediatric sleep signal processing. His work bridges theoretical foundations with practical implementations in interdisciplinary domains.

He has received several notable recognitions, including:

• Rising Stars in Data Science (2022) - University of Chicago
• Rising Stars in Computational and Data Sciences (2022) - UT Austin, Sandia National Labs, and Lawrence Livermore National Labs
• CMU ECE Outstanding Woman in Engineering (2021)
• UCLA Math Liggett Instructor Award (2023)
• CMU ECE Outstanding TA Award (2020)

In advising, he has mentored students and postdocs during his postdoctoral tenure. While currently not accepting new PhD students, he contributes to the UNC Data Science PhD program. His research has been supported through academic collaborations and institutional funding at UCLA and CMU.

He is part of the School of Data Science and Society at UNC, leading projects in computational medicine and machine learning applications. His work is conducted in collaboration with interdisciplinary teams across mathematics, computer science, and biomedical disciplines.