Higher Degree by Research Candidate
School of Computer Science
Faculty of Sciences, Engineering and Technology
As the use of autonomous systems in solving complex problems increases, so too does the demand for decision-making models capable of adapting in response to dynamic environments in real time. Suitable inference techniques in computer vision to achieve most decision enabling tasks exist, and the visual domain provides a rich source of data that can be used to inform agents. Time, however, is a critical constraint: existing inference techniques typically require a large number of computational resources and cannot produce decisions rapidly enough to enable agents to keep pace with human perception. The primary focus of my research is to address the requirement for real-time analysis of visual input enabling decision making in autonomous systems in addition to ways lower inference time can be exploited in the creation of new data modelling techniques.
Date Institution name Country Title University of Adelaide Australia Bachelor of Science (Physics & Chemistry) University of Adelaide Australia Master of Data Science
Year Citation 2022 Sachdeva, R., Hammond, R., Bockman, J., Arthur, A., Smart, B., Craggs, D., . . . Reid, I. (2022). Autonomy and Perception for Space Mining. Proceedings - IEEE International Conference on Robotics and Automation, 4087-4093.
2022 Sai, N., Bockman, J. P., Chen, H., Watson-Haigh, N., Xu, B., Feng, X., . . . Gilliham, M. (2022). SAI: Fast and automated quantification of stomatal parameters on microscope images.
Howe, M., Bockman, J., Orenstein, A., Podgorski, S., Bahrami, S., & Reid, I. (n.d.). The Edge of Disaster: A Battle Between Autonomous Racing and Safety. 1st ICML Workshop on Safe Learning for Autonomous Driving (SL4AD),
Algorithm Design & Data Structures - SS 2019
Introduction to Programming (MATLAB & C) - S1/2 2019, S1 2020
Courseware Developer - Introduction to MATLAB
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