Casey Peat
Machine Learning Engineer at Halter
Christchurch, Canterbury, New Zealand
Summary
👤
Senior🎓
Top SchoolCasey Peat is a PhD-trained machine learning engineer with eight years of experience building and deploying deep learning systems, specialising in computer vision, 3D reconstruction, and low-level CUDA/C++ optimisation. They have driven state-of-the-art research in NeRFs and stereo vision—publishing four papers and delivering sub-millimetre reconstruction and registration accuracy improvements over prior work. Comfortable from research to production, Casey has extended PyTorch with custom kernels, deployed real-time models on robotic platforms, and built a surgical-grade stereo tracking system that achieved sub-degree pose accuracy. They combine strong theory (teaching attention and backprop from scratch) with hands-on engineering for real-world robotics and agricultural applications, and have a knack for turning complex differentiable rendering ideas into efficient, deployable code. Based in Christchurch, Australia, Casey brings both academic rigor and practical ingenuity to ML projects, often bridging gaps between partial 3D data and robust, field-ready systems.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of Canterbury
Github Skills (8)
rendering6
mlp5
pytorch4
gpu4
cpp3
deep-learning3
cuda3
neural-network3
Programming languages (2)
Github contributions (5)
caseypeat/NeRF
Feb 2022 - Dec 2022
Contributions:102 commits, 95 pushes, 9 branches in 9 months
uc-vision/NeRF-Reconstruction
Jan 2023 - Aug 2023
Contributions:2 PRs, 111 pushes, 10 branches in 7 months
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