EungGu Yun

Education

Korea Advanced Institute of Science and Technology (KAIST)

Graduate School of AI

M.S. in Artificial Intelligence

2021. 03 - 2023. 02

Daejeon, Korea

  • Supervisor: Prof. Juho Lee
  • Lab: Statistical Inference and Machine Learning Lab (SIML)
  • Thesis: Traversing Between Modes in Function Space for Fast Ensembling
  • Research interests: Loss landscape, Neural processes
  • GPA: 4.08 / 4.3
SungKyunKwan University (SKKU)

Department of Computer Science and Engineering

B.S. in Computer Science and Engineering

2017. 03 - 2020. 08

Seoul, Korea

  • Total GPA: 4.33 / 4.5
  • Major GPA: 4.47 / 4.5

Experience

Saige Research

Research Team

Machine Learning Researcher

2023. 03 - Now

Seoul, Korea

  • Research on Image Anomaly Detection for industrial inspection.
Artificial Intelligence Institute of Seoul National University (AIIS)

Deep Representation Learning Research Group (DRL)

Research Intern

2020. 07 - 2020. 09

Seoul, Korea

  • Supervisor: Prof. Wonjong Rhee
  • Research on model interpretability and activation on-off patterns.
  • Reproduce CNN visualization methods, including Grad-CAM, (C)LRP, etc.
Electronics and Telecommunications Research Institute (ETRI)

Artificial Intelligence Research Laboratory

Research Intern

2020. 01 - 2020. 02

Daejeon, Korea

  • Supervisor: Yoo-mi Park
  • Test and debug ETRI Deep Learning HPC Platform Dashboard.
  • Implement AlexNet and ResNet models with DL-MDL to serve as example deep learning models.

Awards and Honors

The National Scholarship for Science and Engineering

Korea Student Aid Foundation (KOSAF)

2019 Spring - 2020 Spring

  • Supports undergraduates with strong academic performance in science and engineering.
SungKyun Software Scholarship

SungKyunKwan University (SKKU)

2017 Spring - 2018 Fall

  • Supports students with an outstanding GPA.
Dean's List Award

College of Computing, SungKyunKwan University (SKKU)

2017 Spring - 2019 Fall

  • In recognition of high scholastic achievement. (6 times)

Publications

Conference
Probabilistic Imputation for Time-series Classification with Missing Data

2023

SeungHyun Kim*, Hyunsu Kim*, EungGu Yun*, Hwangrae Lee, Jaehun Lee, Juho Lee

ICML

  • *: Equal contribution
Traversing Between Modes in Function Space for Fast Ensembling

2023

EungGu Yun*, Hyungi Lee*, Giung Nam*, Juho Lee

ICML

  • *: Equal contribution
Martingale Posterior Neural Processes

2023

Hyungi Lee, EungGu Yun, Giung Nam, Edwin Fong, Juho Lee

ICLR (Spotlight)

    Scale Mixtures of Neural Network Gaussian Processes

    2022

    Hyungi Lee, EungGu Yun, Hongseok Yang, Juho Lee

    ICLR

      Journal
      Recent advances of radiative transfer emulator in WRF model

      2022

      Hwan-Jin Song, Soonyoung Roh, Park Sa Kim, Juho Lee, Giung Nam, EungGu Yun, Jongmin Yoon

      KOMES

        Benefits of stochastic weight averaging in developing neural network
        radiation scheme for numerical weather prediction

        2022

        Hwan-Jin Song, Soonyoung Roh, Juho Lee, Giung Nam, EungGu Yun, Jongmin Yoon, Park Sa Kim

        JAMES

          Projects

          Bayesian inference for time-series data with missing values

          Samsung Research

          2022. 08 - 2023. 02

          • Developing a Bayesian deep learning method that can quantify uncertainty within missing values.
          • Propose multivariate time-series classification model using a regularization method called ObsDropout.
          • Validate proposed method on PhysioNet 2012, MIMIC-III, and UCI human activity datasets.
          Developing artificial intelligence based emulator for physics processes in numerical models

          National Institute of Meteorological Sciences (NIMS)

          2021. 05 - 2022. 07

          • Research on the developing alternative techniques of physical processes in the numerical weather prediction (NWP) model based on AI to reduce computational costs and to improve the accuracy of NWP.