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Noru-Kang/README.md

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TaeYoung Kang

Applied Statistics Major · Software Double Major · Chung-Ang University

I am interested in Foundation Models, Representation Learning, and Robust Adaptation under Distribution Shift.

My research experience spans ECG, EEG, PSG, audio, and speech, with a focus on how pretrained models adapt across changes in source, site, subject, session, and data conditions.

Research Interests
Foundation Models · Domain Generalization · Domain Adaptation ·
Parameter-Efficient Adaptation · Representation Steering · Cross-Domain Learning

Blog Email


🔬 Selected Research

PhysioNet Challenge 2026

PSG Foundation Model · Representation Steering · Domain Shift

  • Built a downstream prediction pipeline using a pretrained PSG Foundation Model
  • Applied SAE-based Representation Steering to investigate site-associated latent features
  • Evaluated representation-level intervention under recording-site domain shift

EEG-AAD

EEG Foundation Model · Selective Unfreezing

  • Applied a pretrained EEG Foundation Model for auditory attention decoding
  • Implemented a 128 → 200 Hz TemporalResampler
  • Compared 10% / 30% / 50% Selective Unfreezing
  • Investigated audio-only → audio-visual condition shift

PhysioNet Challenge 2025

ECG · Cross-Source Generalization

  • Developed a 1D ResNet-BiGRU model with physiologically motivated ECG features
  • Designed and implemented RBBB/LAFB-related handcrafted features
  • Investigated robustness under heterogeneous source distributions
  • Extended the work to a journal paper as a co-first author

📄 Publications

H. Im*, N. Lee*, T. Kang*, T. Kim, D. Kim, D. Lee, S. Oh, W. Gong, I. Y. Kwak
“Auxiliary-conditioned Cross-Attention with Physiologically Interpretable Features for Chagas Disease Detection from 12-lead ECGs.”
Physiological Measurement, 2026.
* Equal contribution

H. Im, N. Lee, T. Kang, T. Kim, D. Kim, D. Lee, S. Oh, W. Gong, I. Y. Kwak
“ResNet-BiGRU with Conditioned Query-Based Cross-Attention and Weighted Loss for Automated Chagas Disease Detection from 12-Lead ECG.”
Computing in Cardiology (CinC 2025), 2025.


📚 Latest Blog Posts


🛠️ Stack

Python PyTorch scikit-learn R Linux Git

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