Sam Banning
Software Engineer at OpenText
Canada
Summary
🤩
Rockstar🎓
Top SchoolSam Banning is a software engineer with 8 years of experience building production-grade systems across startups and large tech firms, currently contributing at OpenText in Canada. He has a strong foundation in mathematics and computer science from the University of Toronto and a track record at Microsoft and quantum-computing company Xanadu. Sam combines practical engineering with machine learning research experience from Borealis AI and contributed to the widely used PennyLane library by improving differentiable unitary calculations and multi-framework autodiff support. His background spans full-stack and mobile work, cofounding a startup (FamilyVerse) and building Android/scientific tools early in his career. Comfortable shipping both research-oriented and enterprise software, he brings a mix of rigorous algorithmic thinking and hands-on implementation to complex problems.
8 years of coding experience
6 years of employment as a software developer
Honors Bachelor of Science Mathematics Major and Computer Science Major, Honors Bachelor of Science Mathematics Major and Computer Science Major at University of Toronto
Stackoverflow
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Github Skills (13)
quantum-computing10
pytorch10
automatic-differentiation10
q-learning10
tensorflow10
jax10
python10
autograd10
pytest9
numpy9
testing9
machine-learning9
optimization8
Programming languages (6)
Github contributions (5)
PennyLaneAI/pennylane
Oct 2021 - Dec 2022
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Role in this project:
ML Engineer
ML EngineerContributions:62 reviews, 395 commits, 47 PRs in 1 year 1 month
Contributions summary:Sam focused on enhancing the PennyLane library by implementing and testing differentiable calculations for unitary matrices. They added support for autodiff frameworks like JAX, PyTorch, TensorFlow, and Autograd. Furthermore, they implemented batching for the `BasicEntanglerLayers` template and fixed a Hamiltonian casting bug. Finally, the user addressed an issue with device gradients and implemented a custom return object, demonstrating a focus on improving the library's differentiability and usability for quantum machine learning applications.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
puzzleshark/forrest-spotify-currator
Sep 2021 - Dec 2022
Make sure your music is in tune.
Contributions:2 PRs, 53 pushes, 2 branches in 1 year 2 months
musicmusic-playertune
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