James Keal

Work

Machine Learning Engineer at Harrison.ai: November 2022 - Current
Embedded Software Engineer at Micro-X: April 2021 - November 2022
- Wrote firmware for medical imaging devices. Drove product design using radiation transport simulations. Provided dev-ops for a remote team in the US. Contributed to CT image reconstruction algorithm design.
Intern at Defence Science and Technology, Adelaide: March 2018 - August 2018
- Defence applications of artificial intelligence. Developed software under Joint and Operations Analysis Division.
Software Engineer at Quantum Code: 2015 - 2016
- Developer of online services. Worked with a small team to create web-app and database solutions for small businesses and local government.
Self-employed Web Developer: 2013 - 2015
- Created websites on a contractual basis.

PhD in Medical Physics, University of Adelaide: 2017
- Thesis on the application of machine learning to dosimetry in radiotherapy. Developed novel deep learning architectures and data processing pipelines. Coursework in radiobiology and medical imaging. Founded the Adelaide Artificial Intelligence Club.
Honours in Medical Physics, University of Adelaide: 2016. Awarded First Class.
- Awarded first class. Double major in theoretical physics and computer science. Courses taken in software engineering and electronics.
BSc. in High Performance Computational Physics: 2015.

G. Tuckwell, J. Keal, C. Gupta, S. Ferguson, J. Kowlessar, G. Vincent: “A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.”, Sensors, 2022.
M. Douglass, J. Keal: “DeepWL: Robust EPID based Winston-Lutz Analysis using Deep Learning and Synthetic Image Generation”, Physica Medica, 2021.
J. Keal, A. Santos, S. Penfold, M. Douglass: “Radiation dose calculation in 3D heterogeneous media using artificial neural networks”, Medical Physics, 2020.
J. Kowlessar, J. Keal, D. Wesley, I. Moffat: “Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia”, Pattern Recognition Letters, 2020.
“Simplex noise as training data for learned 3D dose calculation”, 6th Loma Linda Workshop on Particle Imaging and Radiation Treatment Planning, 2020.
“3D modelling of EBRT radiation dose in heterogeneous media using artificial neural networks”, Engineering and Physical Sciences in Medicine, 2017.
“Keynote: The art of becoming a researcher in science”, Australasian Conference of Undergraduate Research, 2017.
“3D modelling of radiation dose in inhomogeneous media using artificial neural networks”, Australasian Conference of Undergraduate Research, 2016.
“Machine learning for external beam radiotherapy”, Beacon Conference for Undergraduate Research, 2016. Winner of Best Abstract.