Dominic Jack
AI Lead at Smart Linework
Greater Brisbane Area Australia
Summary
👤
Senior🎓
Top SchoolDominic Jack is an AI Lead with 15 years of technical experience and a 2020 PhD in deep learning for 3D computer vision, now applying research-grade ML to real-world OCR and NLP products for the Australian powerline industry. He has blended academic and industry experience—postdoc work on deep graph networks for cybersecurity and an internship at Microsoft Research focused on reinforcement learning for compiler optimization—bridging theory and applied systems. A hands-on contributor to major open-source projects (Keras, TensorFlow, TensorFlow Graphics and Tensor2Tensor), he has fixed tricky numerical and reproducibility bugs and improved testing and optimizer infrastructure. Dominic’s background in mesh processing and voxel algorithms (trimesh) and his work on moving-MNIST datasets reveal a practical strength in geometric and sequence data pipelines as well as reproducible ML engineering. Based in Greater Brisbane, he’s currently turning research insights into accessible tools for document accessibility and developer-focused ML components.
15 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at QUT (Queensland University of Technology)Villanova CollegeBachelor of Applied Science (B.A.Sc.), Honours, Mathematics, Bachelor of Applied Science (B.A.Sc.), Honours, Mathematics at Queensland University of Technology
Github Skills (32)
geometry10
python10
image-processing10
testing10
video-processing10
machine-learning10
numpy10
keras10
deep-learning10
tensorflow10
neural-network10
computer-vision10
jax10
graph-neural-network10
data-set9
Programming languages (6)
Github contributions (5)
tensorflow/graphics
May 2020 - May 2020
TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 20 PRs, 61 comments in 1 day
Contributions summary:Dominic focused on enhancing the training and testing capabilities within the TensorFlow Graphics project, particularly for the PointNet project. Their contributions involved adding mock data for testing, adjusting the training process, and modifying test configurations. They integrated TensorFlow Datasets (TFDS) for data loading and testing, demonstrating practical application of ML model testing methodologies. They refactored the test setup, and fixed command line argument handling.
differentiablecomputer-graphicslayerstensorflowgraphics
tensorflow/datasets
Jan 2019 - May 2019
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Role in this project:ML Engineer
ML EngineerContributions:16 commits, 13 PRs, 54 comments in 3 months
Contributions summary:Dominic contributed to the `tensorflow/datasets` repository by adding test data and mapping functions for moving MNIST video datasets. They implemented functions for creating and merging moving image sequences, demonstrating expertise in image processing and sequence generation. Furthermore, the user made adjustments to the video documentation and corrected errors, which is helpful to improve the quality of the repository.
datanumpydeep-learningdatasetmachine-learning
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