Thomas Li

LecturerHeyang (Thomas) Li

Jack Erskine 720
Internal Phone: 93052

Research Interests

Thomas recently started as a full time 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, phylogenetic trees, classification trees, and deep learning applications.

Thomas completed his PhD research at The Australian National University, in the Department of Applied Mathematics, he worked on image processing algorithms for Micro 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, and two US patent publications for which he was one of the inventors.

Additionally, Thomas has a range of research network in Australia and New Zealand having briefly worked at Monash University, the Australian National University, CSIRO, and start-up company Illuminate Imaging for optimisation and image processing algorithms.

Recent Publications

  • Kingston A., Myers G., Latham S., Recur B., Li H. and Sheppard A. (2018) Space-filling X-ray source trajectories for efficient scanning in large-angle cone-beam computed tomography. IEEE Transactions on Computational Imaging http://dx.doi.org/10.1109/TCI.2018.2841202.
  • Kingston A., Latham S., Sheppard A., Myers G., Recur B. and Li H. (2017) Novel data processing in a tomographic imaging apparatus.Patent No. US20170059493A1, US.
  • Kingston A., Latham S., Sheppard A., Myers G., Recur B., Li H. and Varslot T. (2017) Novel acquisition and processing of data in a tomographic image apparatus.Patent No. US 20170052264.
  • Li H. (2017) Partially Coherent Lab Based X-ray Micro Computed Tomography. Canberra. The Australian National University.
  • Li H. and Candy R. (2017) Optimising Open-Pit Mining Using Multi-Grid Search. Hobart, Australia: 22nd International Congress on Modelling and Simulation, 3-8 Dec 2017. In https://www.mssanz.org.au/modsim2017/documents/MODSIM2017_book_abstracts.pdf.