Huw Campbell
Principal Consultant at Simple Machines Pty Ltd
Greater Sydney Area Australia
Summary
🤩
Rockstar🎓
Top SchoolHuw Campbell is a Principal Consultant and machine learning specialist based in Greater Sydney with 12 years' experience building petabyte-capable data pipelines, probabilistic experiments, and causal inference models. He combines deep theoretical training (PhD in Physics) with hands-on engineering—optimising model training, feature engineering at massive scale, and orchestration of terabyte-scale production workflows for diverse clients. His background spans research science roles at Ambiata/NICTA and practical software development for scientific instruments, reflecting a rare blend of academic rigour and production-focused pragmatism. An active Haskell contributor, Huw has implemented core features in deep-learning and testing libraries (including dropout layers and property-testing improvements), showing a taste for robust, type-safe tooling beyond standard ML stacks. He is known for translating complex probabilistic and causal questions into reliable, deployable systems that measurably improve customer targeting and business outcomes.
11 years of coding experience
5 years of employment as a software developer
B.Sc.(Hons I), Physics, B.Sc.(Hons I), Physics at University of New South Wales
Github Skills (12)
property-based-testing10
machine-learning10
option-parser10
functional-programming10
deep-learning10
parse10
haskell10
testing10
quickcheck9
deep-neural-networks9
neural-network9
convolutional-neural-networks9
Programming languages (20)
Github contributions (5)
HuwCampbell/grenade
Jun 2016 - Sep 2018
Deep Learning in Haskell
Role in this project:
Backend Developer
Backend DeveloperContributions:1 release, 147 commits, 54 PRs in 2 years 3 months
Contributions summary:Huw primarily implemented features related to the Dropout layer in the Haskell-based deep learning framework. Their work involved creating the Dropout layer, integrating it into the network structure, and ensuring its correct behavior during both training and testing phases. Furthermore, the user updated the project's cabal file and imported necessary modules to incorporate the new layer.
deep-learninghaskellmachine-learninggenerative-adversarial-networksdeep-neural-networks
pcapriotti/optparse-applicative
Feb 2015 - Nov 2022
Applicative option parser
Role in this project:Backend Developer
Backend DeveloperContributions:17 releases, 14 reviews, 302 commits in 7 years 9 months
Contributions summary:Huw significantly contributed to the `optparse-applicative` library by implementing several key features, including the ability to combine Unix-style flags and handling missing arguments. Their work involved modifying the core parsing logic within the `Options/Applicative` directory and adding new test cases to ensure the correctness of the implemented features. Furthermore, the user addressed a critical issue by improving the display of missing arguments in error messages.
haskellapplicativeparsecoptionabstract-syntax-tree
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