Martin Ganahl
Senior Staff Scientist at SandboxAQ
Austria
Summary
🤩
Rockstar🎓
Top SchoolMartin Ganahl is a Senior Staff Scientist with nine years of experience at the intersection of computational physics, machine learning, and quantum technologies, currently advancing research and engineering at SandboxAQ. He holds a PhD in Condensed Matter and Materials Physics and has a strong track record from academia to moonshot R&D at X/Alphabet and Perimeter Institute, bringing deep expertise in tensor networks, high-dimensional optimization, and statistical modeling. Martin is an accomplished open-source contributor to the widely used TensorNetwork library, where he implemented core linear algebra routines and optimized tensor decomposition algorithms, reflecting a practical command of numerical methods and backend systems. Known for blending theoretical rigor with production-minded algorithm development, he often translates complex quantum and tensor techniques into efficient, reusable software. A subtle but valuable trait: he pairs scientific curiosity (quantum computing and tensor methods) with a playful creative streak visible in his GitHub signatures, like “silly avatars.”
9 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Condensed Matter and Materials Physics, Doctor of Philosophy - PhD Condensed Matter and Materials Physics at Technische Universität Graz
English, German, French
Github Skills (11)
mat10
jax10
matrix10
python10
algebra10
linear-algebra10
numpy10
algorithms9
back-end-development8
pytorch4
tensorflow4
Programming languages (4)
Github contributions (5)
google/TensorNetwork
Apr 2019 - Oct 2021
A library for easy and efficient manipulation of tensor networks.
Role in this project:
Back-end Developer & Algorithm Developer
Back-end Developer & Algorithm DeveloperContributions:3 releases, 77 reviews, 211 commits in 2 years 5 months
Contributions summary:Martin contributed significantly to the tensornetwork library by implementing and improving algorithms, with a focus on core linear algebra and tensor decomposition methods. Their work involved adding functionalities such as a general `eigsh_lanczos` and matrix inverse for backends. Further, the user refined and extended existing functionality, making modifications to existing decomposition algorithms such as QR and the SVD, and refactoring code for better efficiency and performance. The user's work demonstrates a clear understanding of matrix algebra and a good command of the library's internal functions.
manipulationpythonautomatic-differentiationdeep-learningneural-networks
mganahl/PyTeN
Feb 2019 - Jun 2020
Contributions:179 commits, 178 pushes, 4 branches in 1 year 4 months
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