Research

2026

1. Machine Learning

   Fairness

   Revisiting Preprocessing for Fair ML: Statistically Robust Evaluation and a Novel EMD‑Driven Optimisation Method

   Hyeonggeun Yun and Shahadat Uddin^*

   (Submitted to International Journal of Data Science and Analytics), Jun 2026

   Bib Artefact

   @misc{yun2026EMD,
     title = {Revisiting Preprocessing for Fair ML: Statistically Robust Evaluation and a Novel EMD‑Driven Optimisation Method},
     author = {Yun, Hyeonggeun and Uddin, Shahadat},
     journal = {(Submitted to International Journal of Data Science and Analytics)},
     year = {2026},
     month = jun,
     artefact = {https://github.com/geun-yun/preprocessing_comparative_study_for_fair_ml},
   }

2. Machine Learning

   Explainable AI

   Fairness

   Health Informatics

   Instance level analysis of equitable learning via dissimilar variable grouping on healthcare datasets

   Hyeonggeun Yun, Amanda Barnard^*, and Hanna Suominen^*

   (Submitted to International Journal of Medical Informatics), Jun 2026

   Bib Artefact

   @misc{yun2026SyntheticSHIELD,
     title = {Instance level analysis of equitable learning via dissimilar variable grouping on healthcare datasets},
     author = {Yun, Hyeonggeun and Barnard, Amanda and Suominen, Hanna},
     journal = {(Submitted to International Journal of Medical Informatics)},
     year = {2026},
     month = jun,
     artefact = {https://github.com/geun-yun/SHIELD},
   }

3. Machine Learning

   Explainable AI

   Fairness

   Health Informatics

   Equitable learning via dissimilar variable grouping for synthetic healthcare data

   Hyeonggeun Yun, Hanna Suominen^*, and Amanda Barnard^*

   (Submitted to Digital Health), Mar 2026

   Bib

   @misc{yun2026SyntheticSHIELE,
     title = {Equitable learning via dissimilar variable grouping for synthetic healthcare data},
     author = {Yun, Hyeonggeun and Suominen, Hanna and Barnard, Amanda},
     journal = {(Submitted to Digital Health)},
     year = {2026},
     month = mar,
   }

2025

1. Machine Learning

   Explainable AI

   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 Bib PDF Artefact Poster

   @mastersthesis{yun2025SHIELD,
     type = {Honours Thesis},
     title = {SHIELD: A SHapley and Information-theory based framework for Equitable Learning via Dissimilar feature grouping},
     author = {Yun, Hyeonggeun and Suominen, Hanna and Barnard, Amanda},
     school = {The Australian National University},
     numpages = {102},
     year = {2025},
     month = oct,
     doi = {10.25911/HR8T-9459},
     dimensions = {false},
     artefact = {https://github.com/geun-yun/SHIELD},
   }

2023

1. Logic

   Theorem Provers: One Size Fits All?

   Harrison Oates, Hyeonggeun Yun, and Nikhila Gurusinghe

   arXiv, Nov 2023

   DOI Bib PDF Artefact Poster

   

   @misc{yun2025TheoremProvers,
     title = {Theorem Provers: One Size Fits All?},
     author = {Oates, Harrison and Yun, Hyeonggeun and Gurusinghe, Nikhila},
     journal = {arXiv},
     numpages = {10},
     year = {2023},
     month = nov,
     doi = {10.48550/arXiv.2509.15015},
     url = {https://arxiv.org/abs/2509.15015},
     dimensions = {false},
     artefact = {https://github.com/HarrisonOates/COMP2560-Theorem-Prover-Code},
   }