Russell Standish
Principal at High Performance Coders
Sydney, New South Wales, Australia
Summary
👤
Senior🎓
Top SchoolRussell Standish is a Principal computational scientist and software engineer based in Sydney with over four decades of technical experience and 12 years in senior professional roles focusing on high-performance C++ and Fortran systems. He leads High Performance Coders, delivering HPC, parallelisation and vectorisation expertise across scientific computing, Monte Carlo finance, agent-based modelling and neural network applications. Russell combines deep academic training (PhD in theoretical physics) with hands-on engineering—optimising code for supercomputers, building Beowulf clusters, and contributing bug fixes and stabilisation work to notable open-source projects like OpenNN and the Aravis vision library. He is equally comfortable in low-level performance tuning (OpenMP/MPI, Fortran90) and applying ML to network metadata, and brings a knack for spotting subtle assumptions in legacy code that unlock measurable reliability and speed gains.
12 years of coding experience
21 years of employment as a software developer
Australian National UniversityBSc, Physics, Mathematics, BSc, Physics, Mathematics at The University of Western AustraliaTAE, TAE at Hale
French, German
Stackoverflow
41reputation
868reached
2answers
0questions
Github Skills (22)
gobject10
c-language10
c1110
glib10
c1710
camera10
neural-network10
cprogramming-language10
gstreamer9
udp9
computer-engineering9
embedded8
sys8
networking8
debug8
Programming languages (9)
Github contributions (5)
AravisProject/aravis
Aug 2019 - Oct 2020
A vision library for genicam based cameras
Role in this project:
Back-end Developer & Embedded Systems Engineer
Back-end Developer & Embedded Systems EngineerContributions:1 review, 9 commits, 14 PRs in 1 year 2 months
Contributions summary:Russell primarily contributes to the `arvgvfakecamera` component of the project, focusing on emulating a GenICam-based camera. Their work includes implementing features like handling discovery packets, adding an accessor method for the camera class, and listening for discovery packets on the subnet broadcast address. They also added support for the RGB8 pixel format and incorporated image offset parameters within buffer objects. Finally, they marked the global discovery socket as reusable, which is vital for multiple fake camera instances to co-exist on the same network interface.
cameravideomesongigegobject-introspection
Artelnics/opennn
Feb 2017 - Jan 2019
OpenNN - Open Neural Networks Library
Role in this project:Back-end Developer
Back-end DeveloperContributions:18 commits, 18 PRs, 10 comments in 1 year 10 months
Contributions summary:Russell primarily focused on bug fixes and refactoring related to the neural network assignment operator within the OpenNN library. They corrected an invalid assumption regarding member pointers and addressed issues related to the removal and subsequent reversion of the `conio.h` header. Additionally, they merged master branch changes into a bugfix branch for ongoing neural network assignment issues. These contributions demonstrate a focus on improving the core functionality and stability of the neural network library.
deep-learningpytorchneural-networkneural-networks
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