Hunmin (Troy) Lee

University of Minnesota Twin Cities, Minneapolis, Minnesota, USA

lee03915@umn.edu Google Scholar LinkedIn CV (PDF) Resume (PDF)

I am a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Minnesota Twin Cities, advised by Prof. Catherine Qi Zhao in the VIP Lab.

I am currently collaborating with Fasikl Incorporated on AI-powered neural decoding using neural computing interface and with Google DeepMind on federated learning for distributed networks.

Before joining the Ph.D. program, I received an M.S. in Computer Science from Georgia State University (2022), where I was advised by Prof. Donghyun Kim and Prof. Yingshu Li, and a B.E. in Computer Engineering from Chonnam National University, South Korea (2020). From Jun. 2020 to Aug. 2020, I worked in the Korea Electric Power Corporation.

My Ph.D. program is generously supported by the Doctoral Dissertation Fellowship from the University of Minnesota.

News

May 2026 Joined Fasikl Incorporated as a Senior Research Engineer Intern.

May 2026 Paper accepted in IEEE Transactions on Neural Systems and Rehabilitation Engineering (IF: 5.2).

May 2026 Honored to receive the Doctoral Dissertation Fellowship ($26,000) from the University of Minnesota!

Mar. 2026 Paper published in Scientific Reports (IF: 3.9).

Jan. 2026 Paper published in IEEE Internet of Things Journal (IF: 9.9).

Nov. 2025 Paper published in IEEE Internet of Things Journal (IF: 9.9).

Jan. 2026 Received the IOP Outstanding Reviewer Award for the Journal of Neural Engineering, IOP Publishing.

May 2025 Joined Fasikl Incorporated as a Research Engineer Intern.

Apr. 2025 Project with Google DeepMind — "Efficient Learning for Distributed Network via Federated Learning"

Jul. 2025 Two papers published in IEEE Transactions on Biomedical Engineering (IF: 4.5) and IEEE Transactions on Network Science and Engineering (IF: 7.9).

Dec. 2024 Paper published in IEEE Transactions on Neural Systems and Rehabilitation Engineering (IF: 5.2).

Jul. 2024 Two papers accepted at IEEE EMBC 2024 - (Oral and poster).

Jul. 2024 Paper published in IEEE Internet of Things Journal (IF: 9.9).

May 2024 Paper published in Journal of Neural Engineering (IF: 3.8).

Apr. 2024 Paper published in IEEE Sensors Journal (IF: 4.5).

Apr. 2023 Paper published in IEEE Access (IF: 3.6).

Aug. 2022 Paper published in IEEE/ACM Transactions on Computational Biology and Bioinformatics (IF: 3.93).

Feb. 2022 I am happy to accept the Ph.D. offer from University of Minnesota - Twin Cities!

Statement

My research focuses on building machine learning systems that generalize across distributed, heterogeneous environments — with applications in biomedical signal processing, neural decoding, human-centered AI, and IoT networks. I design federated learning frameworks and neural network architectures that enable efficient, generalizable, and personalized AI for scalable real-world deployment. At the core of this work is a belief that AI should be deeply aligned with human needs: systems that adapt to individual users, operate reliably in clinical settings, and ultimately improve people's quality of life. This human-centered philosophy is reflected in my publications across leading journal venues, and I see my Ph.D. as one chapter in a longer journey — defined by a commitment to bridging principled machine learning research and meaningful real-world impact.