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.
- Li H., Todd Z. and Bielski N. (2023) Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods. Sensors 23(7) http://dx.doi.org/10.3390/s23073381.
- Alloo SJ., Paganin DM., Morgan KS., Kitchen MJ., Stevenson AW., Mayo SC., Li HT., Kennedy BM., Maksimenko A. and Bowden JC. (2022) Dark-field tomography of an attenuating object using intrinsic x-ray speckle tracking. Journal of Medical Imaging 9(3) http://dx.doi.org/10.1117/1.JMI.9.3.031502.
- Li HT., Todd Z., Bielski N. and Carroll F. (2022) 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Visual Computer 38(5): 1759-1774. http://dx.doi.org/10.1007/s00371-021-02103-8.
- Li SZ., French MG., Pavlov KM. and Li HT. (2022) Shallow U-Net Deep Learning Approach for Phase Retrieval in Propagation-Based Phase-Contrast Imaging. In.
- Maliszewski KA., Vetrova V., Li H., Kolenderski P. and Kolenderska SM. (2022) Dispersion-contrast imaging using machine learning. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11948 http://dx.doi.org/10.1117/12.2612671.