Jonathan Kim

Jonathan Kim

Principal Software Engineer

Applied Technologies

Efficient software & hardware architectures in Data Vision

My work as Principal Software Engineer within the Applied Technologies division is mainly in machine learning, computer vision  and software engineering using embedded systems. 

Throughout my career, I have had the privilege of collaborating with kiwi companies, from startups to well-established organisations for whom I design robust and efficient hardware/software architectures that meet the unique needs of each project. It is rewarding to work alongside incredibly brilliant and intelligent colleagues within Callaghan Innovation, fostering a stimulating environment for continuous learning and knowledge sharing. We all know that our work is ultimately contributing to the growth and progress of New Zealand Inc.

Machine learning has come a long way since it was first named by Arthur Samuel in 1959 when he created a computer algorithm to play checkers (draughts) against humans.

I have been involved in several notable projects, such as building a dynamic 3D scanner for log identification, performing golf-swing analysis using interconnected Machine Learning models via a REST-API, and developing a Large Language Model for Iwi Data Discovery.

Areas of expertise
  • Machine Learning
  • Computer Vision
  • Software Engineering
  • Embedded Systems
  • IT/Networking
  • Cloud Architecture
  • PhD, MEng, BEng, University of Auckland
  • AWS Solutions Architect, AWS
  • Microsoft Certified Systems Engineer, Microsoft
  • Microsoft Certified Database Administrator, Microsoft
Memberships and awards
  • Member of IEEE Computational Intelligence & Systems Council
  • Member of AI Researchers Association New Zealand
  • Member of Machine Learning Group in University of Auckland
Selected publications
  • "Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes," J. J. Y. Kim, M. Urschler, P. J. Riddle and J. S. Wicker, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 4352-4358, doi: 10.1109/IROS47612.2022.9981542.
  • "SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words," J. J. Y. Kim, M. Urschler, P. J. Riddle and J. S. Wicker, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 5425-5425, doi: 10.1109/IROS51168.2021.9636622.
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