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Email

eunjung.holden@unimelb.edu.au

Credentials


Position
Professor of Digital Innovation
School of Computing and Information Systems
ORCID

0000-0002-8752-1639

Prof Eun-Jung Holden

Professor of Digital Innovation
School of Computing and Information Systems

86 Scholarly works
1 Projects

HIGHLIGHTS

  • 2026

    Journal article

    Deep-learning-based hybrid model with iterative lithology constraints for the enhanced prediction of missing well-logs
    DOI: 10.1016/j.cageo.2026.106106
  • 2026

    Book Chapter

    Paired Hierarchical VAEs for Image-to-Image Translation and Cross Reconstruction
    DOI: 10.1007/978-981-95-4972-6_12
  • 2025

    Journal article

    Implicit neural representation for potential field geophysics
    DOI: 10.1038/s41598-024-83979-z
  • 2025

    Research contracts (non-grants)

    ChatGeo: Forging Trust in Spatial Semantic Document and Knowledge Retrieval
  • 2025

    Journal article

    Haromonic Curvature and Bedding Uncertainty Across Scales
    DOI: 10.5194/egusphere-egu24-7785
  • 2024

    Journal article

    Automated geological logging from FTIR reflectance spectra utilising geochemical and historical logging constraints
    DOI: 10.1016/j.oregeorev.2024.106004
  • 2023

    Conference Proceedings

    Workshop on Document Intelligence Understanding
    DOI: 10.1145/3583780.3615312
Eun-Jung Holden

Latest Honours,
Awards and Fellowships


2022
Women in AI Awards Australia New Zealand (AI in Mining)
2019
Women in Technology, WA (WiTWA) Tech [20+] Awards
2015
The UWA Vice Chancellor’s Award in Impact and Innovation

RECENT SCHOLARLY WORKS

  • 2023

    Journal article

    Anomaly detection in Fourier transform infrared spectroscopy of geological specimens using variational autoencoders
    DOI: 10.1016/j.oregeorev.2023.105478
  • 2022

    Journal article

    Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane
    DOI: 10.1016/j.oregeorev.2022.105119
  • 2021

    Journal article

    A comparative study of neural network methods for first break detection using seismic refraction data over a detrital iron ore deposit
    DOI: 10.1016/j.oregeorev.2021.104201
  • 2021

    Journal article

    Understanding ore-forming conditions using machine reading of text
    DOI: 10.1016/j.oregeorev.2021.104200

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