Juhyeon Lee

I am a Ph.D. student in Computer Science at the University of Massachusetts Amherst. I joined the AHHA Lab, advised by Prof. Sunghoon Ivan Lee.

My research interests:

Health Informatics:
- Analyzing sensor data (wearable or video) from patients' impaired movements to estimate disease severity or motor performance objectively using machine learning
- Designing patient‑centered, personalized health information systems
Machine learning: Deep learning, Reinforcement learning

I worked as a research engineer at Robotics Lab., CTO division, LG Electronics for about five years (from July 2014 to March 2019). I received the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) and the B.S. degree in Electronics Engineering from Ewha Womans University.

My CV [pdf]

My LinkedIn profile [link]

My Google scholar [link]

My E-mail: juhyeonlee at cs dot umass dot edu

News

I worked as a Machine Learning Engineer Intern at Apple Inc. during Summer 2023.
Our paper "Estimation of ataxia severity in children with ataxia-telangiectasia using ankle-worn sensors" is accepted at Journal of Neurology.

Journal Publications

[J4] Juhyeon Lee, Brandon Oubre, Jean-Francois Daneault, Sunghoon Ivan Lee*, and Anoopum S. Gupta*, "Estimation of Ataxia Severity in Children with Ataxia-telangiectasia using Ankle-worn Sensors", Journal of Neurology, pp.1-5, June 2023 [*Co-corresponding authors] [html]

[J3] Hee-Tae Jung, Yoojung Kim, Juhyeon Lee, Sunghoon Ivan Lee*, and Eun Kyoung Choe*, "Envisioning the Use of In-Situ Arm Movement Data in Stroke Rehabilitation: Stroke Survivors’ and Occupational Therapists’ Perspectives", PLOS ONE, 17(10), e0274142, Oct 2022 [*Co-corresponding authors][html]

[J2] Juhyeon Lee, Brandon Oubre, Jean-Francois Daneault, Christopher D. Stephen, Jeremy D. Schmahmann, Anoopum S. Gupta*, and Sunghoon Ivan Lee*, "Analysis of Gait Sub-Movements to Estimate Ataxia Severity using Ankle Inertial Data", IEEE Transactions on Biomedical Engineering (IEEE TBME), vol. 69, no. 7, pp. 2314-2323, July 2022 [*Co-corresponding authors]. [html][poster]

[J1] Minhae Kwon, Juhyeon Lee, Hyunggon Park, "Intelligent IoT Connectivity: Deep Reinforcement Learning Approach", IEEE Sensors Journal, vol. 20, no. 5, pp. 2782-2791, March 2020. [link]

Conference / Workshop Publications

[C3] Juhyeon Lee, Hee-Tae Jung, Sunghoon Ivan Lee, "Estimating the Quality of Reaching Movements in Stroke Survivors", IEEE Intl. Conf. on Biomedical and Health Informatics (IEEE BHI), July 2021. [pdf][poster]

[W2] Minhae Kwon*, Juhyeon Lee*, Hyunggon Park, "Learning To Activate Relay Nodes: Deep Reinforcement Learning Approach", Neural Information Processing Systems (NeurIPS) Deep Reinforcement Learning Workshop, 2018. (*equal contribution) [arxiv]

[W1] Minhae Kwon*, Juhyeon Lee*, Hyunggon Park, "Self-activating Relay Nodes for Emergent Communications", Neural Information Processing Systems (NeurIPS) Emergent Communication Workshop, 2018. (*equal contribution) [arxiv]

[C2] Juhyeon Lee, Jae Hyun Lim, Hyungwon Choi, Dae-Shik Kim, "Multiple Kernel Learning with Hierarchical Feature Representations", In Proceedings of International Conference on Neural Information Processing (ICONIP), 2013. [pdf]

[C1] Jun-Cheol Park, Jae Hyeon Yoo, Juhyeon Lee, Dae-Shik Kim, "Apparent Volitional Behavior Selection Based on Memory Predictions", In Proceedings of International Conference on Neural Information Processing (ICONIP), 2012. [pdf]