Adam Hall
Director at Xanadu Cybersecurity and Intelligent Systems
City of Edinburgh, Scotland, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAdam Hall is a Director and privacy-first ML practitioner with seven years of experience building and directing teams at the intersection of security, identity and machine learning. He leads Xanadu Cybersecurity and Intelligent Systems, advising on identity, privacy and autonomy while drawing on leadership roles at OpenMined and research positions at MIT and Array Insights. Adam has hands-on research credentials in split learning and contributed a notable SplitNN tutorial to OpenMined’s PySyft project, demonstrating practical distributed deep learning on MNIST. His background blends a PhD-level focus in machine learning and cybersecurity with product-facing incubation of privacy-preserving technologies across differential privacy, federated learning and cryptography. Based in Edinburgh, he pairs academic rigor with consultancy pragmatism and a track record of growing open-source communities and teams. An outdoors instructor earlier in his career, he brings an unexpected practical leadership style forged outside the lab.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BSc) Computer Science and Philosophy, Bachelor of Science (BSc) Computer Science and Philosophy at University of StirlingDoctor of Philosophy - PhD Machine Learning and Cybersecurity, Doctor of Philosophy - PhD Machine Learning and Cybersecurity at Edinburgh Napier University
English, Bahasa Melayu
Github Skills (9)
pytorch10
deeplearning-ai10
deep-learning10
python10
federated-learning10
technical-writing10
mnist9
secure-computation7
cryptography5
Programming languages (3)
Github contributions (5)
OpenMined/PySyft
Dec 2019 - Feb 2021
Perform data science on data that remains in someone else's server
Role in this project:
Data Scientist
Data ScientistContributions:27 commits, 7 PRs, 2 pushes in 1 year 2 months
Contributions summary:Adam contributed significantly to a split neural network tutorial. Their work involved creating and refining the tutorial, including code implementation and explanatory text, focusing on the architecture and advantages of SplitNNs. They also addressed model shape parameters and formatting, indicating a hands-on approach to implementing and presenting the concepts. The contributions were primarily centered on demonstrating distributed deep learning techniques within the PySyft framework, specifically focusing on MNIST dataset.
pytorchcryptographyacquiringpythonscience
H4LL/GridExperiment
Feb 2020 - Mar 2020
Contributions:50 commits, 46 pushes, 5 branches in 26 days
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