Mike is interested in the development of data science driven analysis and methodology with application to digital Earth and planetary science challenges. Specifically, his research focuses on the quantitative exploration of diverse and sparse geoscientific datasets to characterize and reconstruct Earth’s long-term tectonic and geodynamic evolution, and in the development of new machine learning methods for efficient automated processing and analysis of geospatial and planetary remote sensing data.
tectonics, plate reconstruction, paleomagnetism, geodynamics, machine learning, remote sensing
Ph.D., Tectonics/Data Science, The University of Sydney, Australia
B.S. (Hons), Tectonics/Paleomagnetics, The University of Sydney, Australia
B.S., Geophysics/Geology, Macquarie University, Australia