Tom Horrocks


  • The University of Western Australia (M006), 35 Stirling Highway,

    6009 Perth


Calculated based on number of publications stored in Pure and citations from Scopus

Personal profile


Tom Horrocks graduated from UWA with a Bachelor of Engineering hons. (Software) and a Bachelor of Science (Physics, Applied Mathematics). He has worked as a professional software engineer for several years in industry and academia, both independently and in team settings. Tom’s doctoral studies (supported by the Robert and Maude Gledden Postgraduate Research Scholarship) investigated how machine learning algorithms can be modified and applied to solve common geoscientific problems, such as using t-SNE to visualise geochemistry in a compositionally coherent way, or using Gaussian Processes to spatially interpolate drill core properties in the presence multiple geophysical inversion models. Tom received multiple awards for his academic work, including the Nick Rock Memorial Prize (2018), the ASEG WA Branch Student Award (2015), the ASEG Best Student Poster (Minerals) (2015), and the UWA Vice Chancellor's Impact and Innovation Award (2015) as a part of the Geodata Algorithms Team.

Tom is currently a postdoctoral research fellow within the Geodata Algorithms Team at UWA, where he develops methods for predictive geological logging using FTIR spectra, geochemical assay data, rock chip photographs, and measurement while drilling logs. Tom continues to practice software engineering, and his research often culminates in externally deployed software that solves real-world problems through advanced statistical methods. Tom is currently funded by the Rio Tinto Data Fusion project.

Industry keywords

  • Mining and Resources

Research expertise keywords

  • Machine Learning
  • Geostatistics
  • Geoscientific data mining
  • Hyperspectral imaging and analysis


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