In Seop Lee
Affiliations: | 2001 | University of Illinois, Urbana-Champaign, Urbana-Champaign, IL |
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"In Lee"Parents
Sign in to add mentor| W. Kenneth Jenkins | grad student | 2001 | UIUC | |
| (Generalized data-aided equalizers for digital communication channels.) | ||||
Children
Sign in to add trainee| Sijun Zhu | grad student | 2005 | UIUC (Neurotree) |
| Christopher T. Zugates | grad student | 2007 | UIUC (Neurotree) |
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Publications
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| Choi JY, Kim H, Kim JK, et al. (2023) Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Medical & Biological Engineering & Computing |
| Kim BR, Yoo TK, Kim HK, et al. (2022) Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine. The Epma Journal. 13: 367-382 |
| Kamiya K, Ryu IH, Yoo TK, et al. (2021) Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics: Phakic lens vault prediction using machine learning. American Journal of Ophthalmology |
| Yoo TK, Ryu IH, Kim JK, et al. (2020) Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Computer Methods and Programs in Biomedicine. 197: 105761 |
| Yoo TK, Ryu IH, Choi H, et al. (2020) Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level. Translational Vision Science & Technology. 9: 8 |
| Yoo TK, Oh E, Kim HK, et al. (2020) Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study. Plos One. 15: e0231322 |
| Yoo TK, Ryu IH, Lee G, et al. (2019) Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. Npj Digital Medicine. 2: 59 |