Hyeonggeun Yun

Australian National University, [email protected]

photo_geun.png

Canberra, ACT 2601

Bio: Hi all, this is Hyeonggeun Yun, but most of you who know me would know me as Geun, and I prefer to be called by this easier name.

I was born and lived my early childhood in South Korea, then moved to Australia where I have lived in different places, including Gold Coast, Townsville, and Canberra since 2014.

At the end of 2025, I completed Bachelor of Advanced Computing (Research and Development) (Honours) at the Australian National University (ANU) under the supervision of Prof. Hanna Suominen and Prof. Amanda Barnard AM. I am currently conducting research with Dr. Shahadat Uddin at the University of Sydney (USyd) and working as a teaching assistant at both ANU and USyd, with plans to begin a PhD at USyd in 2027.

In my free time, I enjoy playing boardgames, and try to stay active with some exercise.

Research area of interest: Explainable AI, algorithmic fairness at group and individual levels, graph machine learning, and health informatics. My research aims to develop transparent and equitable machine-learning methods.

news

Sep 22, 2026 Our Instance SHIELD paper has been accepted at International Journal of Medical Informatics!
Sep 15, 2026 Our IF-CRED paper has been accepted for an oral presentation at AJCAI 2026!
Dec 18, 2025 My Bachelor’s degree at ANU has now been conferred with First Class Honours and thesis mark of 90%.
Nov 13, 2025 I will be joining the University of Sydney from December to February through the Vacation Research Internship Program to work on “Fair Play in Machine Learning: Tackling Bias in Data.”
Oct 23, 2025 I have finally submitted my Honours thesis: SHIELD!
Sep 19, 2025 I am excited to announce the launch of my personal academic portfolio website! Here, I will mainly share updates on my research, projects, and publications.

selected publications

  1. Machine Learning
    Explainable AI
    Group Fairness
    Health Informatics
    Instance level analysis of equitable learning via dissimilar variable grouping on healthcare datasets
    Hyeonggeun Yun, Amanda Barnard*, and Hanna Suominen*
    International Journal of Medical Informatics, Sep 2026
    DOI Artefact
  2. Machine Learning
    Individual Fairness
    Reliable Individual Fairness Evaluation through Coverage, Metric Agreement, and Model Stability
    Hyeonggeun Yun and Shahadat* Uddin
    In Australasian Joint Conference on Artificial Intelligence, Sep 2026
    Artefact
  3. Machine Learning
    Explainable AI
    Group Fairness
    Health Informatics
    SHIELD: A SHapley and Information-theory based framework for Equitable Learning via Dissimilar feature grouping
    Hyeonggeun Yun, Hanna Suominen*, and Amanda Barnard*
    The Australian National University, Oct 2025
    DOI PDF Artefact Poster