Peter Macgregor
Lecturer In Computer Science at University of St Andrews
Greater Edinburgh Area United Kingdom
Summary
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Senior🎓
Top SchoolPeter Macgregor is a Lecturer in Computer Science at the University of St Andrews who blends rigorous theoretical research in algorithms for data science and machine learning with practical systems experience from industry. He completed a PhD and postdoctoral research at the University of Edinburgh, spent a semester at the Simons Institute, and was a visiting researcher at UC Berkeley, reflecting a strong collaborative academic network. Before returning to academia he spent several years as a software engineer at Metaswitch, where he developed and debugged large-scale telecommunications systems, giving him hands-on expertise in production-grade software. His work emphasizes designing theoretically sound algorithms that remain effective in real-world settings, and he has practical experience with deep learning tools from an internship at MathWorks. Based in the Greater Edinburgh area, he brings over a decade of combined research and engineering experience, with a background in AI and first-class undergraduate training. An understated strength is his ability to move between low-level systems engineering and high-level algorithmic theory, making him effective at translating research into deployable solutions.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Edinburgh
Stackoverflow
143reputation
7kreached
1answer
1question
Github Skills (28)
slack-api10
spectra9
arpack9
slack9
cli9
python-bindings8
eigenvalue8
linear-algebra8
cli-app7
nlp6
cpp6
driverless6
autodoc6
natural-language-processing6
nltk6
Programming languages (4)
Github contributions (5)
pmacg/py-sgtl
Nov 2021 - Oct 2022
A library of spectral graph theory methods in python.
Contributions:9 releases, 2 reviews, 116 commits in 11 months
graph-theorypython
pmacg/algpy
Aug 2024 - Sep 2025
Framework for performing experiments with algorithms in Python.
Contributions:6 releases, 3 PRs, 30 pushes in 1 year 1 month
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