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Tom Horrocks graduated from the University of Western Australia with a Bachelor of Engineering honors in Software and a Bachelor of Science in Physics and Applied Mathematics. He has worked as a professional software engineer for years in both industry and academia, in both independent and team settings. His doctoral studies, supported by the Robert Maude Gledden Postgraduate Research Scholarship, focused on machine learning algorithms modified to address common geoscientific problems, utilizing t-SNE for visualizing geochemistry in a compositionally coherent manner and Gaussian Processes for spatially interpolating drill core properties. Tom has received several prestigious awards for his academic work, including the Nick Rock Memorial Prize in 2018 and the UWA Vice Chancellor's Impact Innovation Award in 2015 as part of the Geodata Algorithms Team. He is currently a postdoctoral research fellow within the same team, where he develops predictive geological logging methods using FTIR spectra, geochemical assay data, and rock chip photographs. His research is supported by the Rio Tinto Data Fusion project, and he continues to practice software engineering, focusing on applying advanced statistical methods to solve real-world problems.
University of Western Australia • Perth, Australia
Conducts research focused on predictive geological logging and geoscientific data analysis incorporating advanced machine learning techniques.
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