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Hi there 👋

I'm a M.S candidate in department of Biomedical Systems Informatics, Yonsei University College of Medicine.

My research interest focuses on developing models and algorithms for tackling callenges in applying data science such as AI to real-world healthcare domain.

Specifically, I mainly focus on the following methods:

  • Federated learning
  • Interpretable machine learning
  • Continual learning and Transfer learning
  • Semi-supervised / self-supervised learning

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Tae Hyun's Projects

bayes-nn icon bayes-nn

Lecture notes on Bayesian deep learning

d2l-en icon d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.

drl4recsys icon drl4recsys

Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems

federated icon federated

A collection of Google research projects related to Federated Learning and Federated Analytics.

jpor_codes icon jpor_codes

Codes for the book "Julia Programming for Operations Research"

python-causality-handbook icon python-causality-handbook

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

ucrarchivefixes icon ucrarchivefixes

2018 UCR Time-Series Archive: Backward Compatibility, Missing Values, and Varying Lengths

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