Daniel Wedge

Daniel Wedge is a Principal Research Fellow at the University of Western Australia's School of Earth Sciences, affiliated with the Centre for Data-driven Geoscience. He holds a Ph.D. in computer science and has industry experience in image/video processing algorithms. His research focuses on applying machine learning and computer vision to geophysical/geological datasets for automated analysis.

Education: Ph.D. in Computer Science (details not specified).

Research interests include computer vision, machine learning, data visualization, and their application to mineral exploration, iron ore analysis, and geophysical survey techniques. He contributes to UN Sustainable Development Goals related to industry and resources.

Recent work highlights integration of neural networks with geophysical data (e.g., potential field analysis, FTIR spectroscopy), machine learning-enhanced magnetic grid resolution, and 3D geochemical modeling. His articles span 2021–2025, emphasizing interdisciplinary applications.

• Award: Vice Chancellor’s Award in Impact and Innovation (2015)

Key grants include projects on data fusion for drillhole analysis (2018–2021), geological interpretation tools (2014–2017), and reducing 3D geological uncertainty (2014–2018). He collaborates with industry partners like Technological Resources Pty Ltd and the Geological Survey of Western Australia.

His work is centered at the Centre for Data-driven Geoscience, advancing computational methods for geoscientific challenges.