Joohyung Lee
Contact: joohyunglee [dot] research [at] gmail [dot] com
Machine Learning Researcher
at AITRICS
I am a machine learning researcher at AITRICS, a healthcare AI startup in South Korea, advised by Prof. Eunho Yang and collaborating closely with Prof. Juho Lee.
My research focuses on inductive bias and symmetry-aware learning: how to design learning systems that generalize reliably by encoding principled regularities (e.g., geometry, acquisition processes, and temporal structure). I have worked on geometric priors [1], acquisition-informed priors [2, 3], and temporal/observation-process priors for multimodal EHR trajectories [4, 5].
Previously, I worked as a researcher at the Korea Electronics Technology Institute (KETI) and in the RCV Lab at KAIST with Prof. In So Kweon. I also collaborated with clinicians and researchers at the National Cancer Center.
I am currently investigating invariance-equivariance interference. During my PhD, I aim to move beyond hand-specified groups and geometry: toward learned symmetries and semantic/physical plausibility constraints (Research Statement).
News
| Mar 02, 2026 | Two papers, “Status-Aware Self-Supervised Forecasting for Irregular Clinical Time Series” and “Structure-Aware Set Transformers: Temporal and Variable-type Attention Biases for Asynchronous Clinical Time Series,” have been accepted to the ICLR 2026 Time Series in the Age of Large Models Workshop. |
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| Jan 26, 2026 | One paper Soft Equivariance Regularization for Invariant Self-Supervised Learning was accepted by ICLR 2026. |