James Bockman

James Bockman

Higher Degree by Research Candidate

HDR Student

School of Computer and Mathematical Sciences

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.

  • Education
    Date Institution name Country Title
    University of Adelaide Australia Bachelor of Science (Physics & Chemistry)
    University of Adelaide Australia Master of Data Science

  • Journals
    Year Citation
    2023 Sun, L., Bockman, J., & Sun, C. (2023). A Framework for Leveraging Interimage Information in Stereo Images for Enhanced Semantic Segmentation in Autonomous Driving. IEEE Transactions on Instrumentation and Measurement, 72, 1-12.
    DOI Scopus5
    2023 Sai, N., Bockman, J. P., Chen, H., Watson-Haigh, N., Xu, B., Feng, X., . . . Gilliham, M. (2023). StomaAI: an efficient and user-friendly tool for measurement of stomatal pores and density using deep computer vision. New Phytologist, 238(2), 904-915.
    DOI Scopus7 WoS3 Europe PMC3
    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.
    DOI
    2022 Howe, M., Bockman, J., Orenstein, A., Podgorski, S., Bahrami, S., & Reid, I. (2022). The Edge of Disaster: A Battle Between Autonomous Racing and Safety. 1st ICML Workshop on Safe Learning for Autonomous Driving (SL4AD),
    2022    .

  • Conference Papers
    Year Citation
    2022 Sachdeva, R., Hammond, R., Bockman, J., Arthur, A., Smart, B., Craggs, D., . . . Reid, I. (2022). Autonomy and Perception for Space Mining. In 2022 International Conference on Robotics and Automation (ICRA) Vol. abs 1611 3673 (pp. 4087-4093). Philadelphia, PA, USA: IEEE.
    DOI Scopus3

  • Datasets
    Year Citation
    - Bockman, J. (n.d.). resnet18-smoothl1-lr1e-4-bs124 Video and logs.
    DOI

  • Preprint
    Year Citation
    2024 Bockman, J., Howe, M., Orenstein, A., & Dayoub, F. (2024). AARK: An Open Toolkit for Autonomous Racing Research.

Algorithm Design & Data Structures - SS 2019

Introduction to Programming (MATLAB & C) - S1/2 2019, S1 2020

Courseware Developer - Introduction to MATLAB

  • Position: HDR Student
  • Email: james.bockman@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor LG
  • Org Unit: Australian Institute for Machine Learning
Connect With Me
External Profiles