Aiden Nibali
Senior Machine Learning Engineer at Harrison.ai
Greater Melbourne Area Australia
Summary
👤
Senior🎓
Top SchoolAiden Nibali is a Senior Machine Learning Engineer with 16 years of experience blending academic research and production ML across Australia’s Greater Melbourne Area. He holds a PhD in Artificial Intelligence and has taught and published in areas including deep learning for digital histopathology and 3D human pose estimation, then transitioned to clinical and commercial AI roles at Franklin.ai and now Harrison.ai. Aiden contributes to open-source tooling—work on Kornia’s differentiable spatial-to-numerical operations and maintenance of a PyTorch Docker image demonstrate both algorithmic rigor and deployment-minded engineering. He excels at turning research-grade models into reliable, tested components and automating reproducible builds and CI for GPU workloads. Colleagues know him for logical problem-solving, a penchant for performance-focused code (JIT compilation experience), and a preference for roles that continually expand technical breadth.
16 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at La Trobe University
Stackoverflow
87reputation
6kreached
3answers
2questions
Github Skills (30)
pytorch10
github-ci10
docker10
docker-image10
machine-learning10
dockers10
deeplearning-ai10
deep-learning10
cuda10
computer-vision10
githubaction-workflow10
python9
conda9
automation9
automations9
Programming languages (17)
Github contributions (5)
anibali/docker-pytorch
May 2017 - Jan 2023
A Docker image for PyTorch
Role in this project:
DevOps Engineer
DevOps EngineerContributions:1 review, 44 commits, 12 PRs in 5 years 8 months
Contributions summary:Aiden focused on automating and streamlining the process of creating and deploying Docker images for PyTorch. They added support for various CUDA versions, upgraded the base images, and fixed build issues. Their contributions included creating and maintaining Dockerfiles, configuring the build process, and setting up GitHub Actions workflows to automate image builds and pushes. The user overhauled the project to make managing image versions easier.
cudapytorchdocker-imagedocker
kornia/kornia
Jun 2019 - Oct 2020
🐍 Geometric Computer Vision Library for Spatial AI
Role in this project:ML Engineer
ML EngineerContributions:10 commits, 6 PRs, 44 comments in 1 year 4 months
Contributions summary:Aiden primarily contributed to the implementation and testing of differentiable spatial to numerical (DSNT) operations within the Kornia library. This involved writing new functions for spatial softmax and soft-argmax, as well as render_gaussian_2d functionality. The user also added tests for these DSNT operations, updated existing tests, and integrated JIT compilation to enhance performance.
pytorchdifferentiablepythonvisiondeep-learning
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