Andrew Fitzgibbon
Technical Fellow at Graphcore
Cambridge, England, United Kingdom
Summary
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Senior🎓
Top SchoolAndrew Fitzgibbon is a Technical Fellow at Graphcore with over three decades of research and engineering experience translating computer vision and machine learning ideas into production systems. He led algorithms for the Emmy-winning 3D tracker boujou, helped introduce large-scale synthetic training data for Kinect, and co-developed the core math behind HoloLens 2 real-time hand tracking. His work spans computer vision, graphics, ML, neuroscience and programming languages, earning numerous awards and ten best-paper prizes, plus the Royal Academy of Engineering Silver Medal and the BCS Roger Needham award. A prolific academic with highly cited publications and a PhD in AI from Edinburgh, he also contributes to open-source ML tooling—recently improving PyTree operations in the widely used XLA compiler. Based in Cambridge, his background blends deep theory with practical system-building, and he even once ferried passengers as a water taxi driver, hinting at an early taste for hands-on problem solving.
13 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Artificial Intelligence, Doctor of Philosophy (Ph.D.), Artificial Intelligence at The University of EdinburghBachelor’s Degree, Mathematics and Computer Science, I, Bachelor’s Degree, Mathematics and Computer Science, I at University College CorkHeriot-Watt University Edinburgh CampusHigh School, High School at Colaiste Chriost Ri
English, French, Irish
Github Skills (3)
machine-learning10
python10
unit-testing9
Programming languages (17)
Github contributions (5)
openxla/xla
May 2022 - Jun 2022
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:
ML Engineer
ML EngineerContributions:8 commits in 24 days
Contributions summary:Andrew primarily contributed to the `xla` repository by modifying and enhancing Python code related to PyTree operations. Their work involved adding new functionalities, such as passing node data to tree walkers, refactoring code for improved readability and maintainability, and improving unit tests. The changes demonstrate an understanding of tree structures and their applications within a machine-learning context.
compilercommunity-drivenmachine-learningmodular
awf/awf-matrix-factorization
Jul 2013 - Sep 2015
Contributions:23 commits, 1 push in 2 years 2 months
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