Thomas Li

LecturerHeyang (Thomas) Li

Jack Erskine 720
Internal Phone: 93052

Research Interests

Thomas is a Lecturer in Statistics and Data Science at University of Canterbury (UC), School of Mathematics and Statistics. His current research areas involve image processing and classification, 3D projective geometry, phylogenetic trees, classification trees, and deep learning applications. He currently leads the UC Spatial And Image Learning (SAIL) group, working on research in collaboration with NZ Transport Agency, Christchurch City Council, Christchurch Airport and Stats NZ. UC SAIL group is kindly support by the School, UC R&I and KiwiNet.

Thomas completed his PhD research at The Australian National University, in the Department of Applied Mathematics, he worked on image processing algorithms for Computed Tomography. The outcome of his PhD research improved the signal to noise properties. He has published papers in highly ranked international peer reviewed journals, presentations at international conferences, obtained over $400k of research grants, and two US patent publications for which he was one of the inventors.

Thomas has a wide research network in Australia and New Zealand, and are open to related research collaborations, and interests from prospective PhD students.

Recent Publications

  • Li HT., Todd Z., Bielski N. and Carroll F. (2021) 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Visual Computer
  • Li HT., Schaff F., Croton LCP., Morgan KS. and Kitchen MJ. (2020) Quantitative material decomposition using linear iterative near-field phase retrieval dual-energy X-ray imaging. Physics in Medicine and Biology 65(18)
  • Pavlov KM., Li HT., Paganin DM., Berujon S., Rougé-Labriet H. and Brun E. (2020) Single-shot x-ray speckle-based imaging of a single-material object. Physical Review Applied 13(5) 054023
  • Pavlov KM., Paganin DM., Li HT., Berujon S., Rougé-Labriet HN. and Brun E. (2020) X-ray multi-modal intrinsic-speckle-tracking. Journal of Optics (United Kingdom) 22(12)
  • Herbst L., Li H. and Steel M. (2019) Quantifying the accuracy of ancestral state prediction in a phylogenetic tree under maximum parsimony. Journal of Mathematical Biology 78(6): 1953-1979.