Mike Stannett
Senior Lecturer at School of Computer Science, University of Sheffield
Sheffield, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolMike Stannett is a Senior Lecturer at the University of Sheffield who applies deep theoretical training to the formal verification of physical theories using Isabelle/HOL. Trained as a pure mathematician (PhD in topology from Sheffield and BA from Oxford), he bridges logic, relativity and formal methods and has organised numerous international workshops in these areas. His interdisciplinary background includes a postgraduate diploma in social science and earlier work in macroeconomic forecasting, bringing empirical perspective to formal research. Mike also contributes to open-source ML tooling—extending Gaussian process kernels in the SheffieldML/GPy library to handle histogram/binned data—demonstrating a practical bent alongside theory. A member of the London Mathematical Society and the Association for Symbolic Logic, he is based in Sheffield and brings over a decade of research and teaching experience.
11 years of coding experience
BA, Mathematics, BA, Mathematics at University of OxfordPhD, General Topology, Analytic Topology, Stone-Cech Compactification, PhD, General Topology, Analytic Topology, Stone-Cech Compactification at The University of Sheffield
English, French, German, Hungarian
Github Skills (9)
gpy10
gaussian-process10
mathematical10
python10
modeling10
numpy10
kernel-methods10
gaussian-processes10
machine-learning9
Programming languages (9)
Github contributions (5)
SheffieldML/GPy
Jun 2016 - May 2019
Gaussian processes framework in python
Role in this project:
Data Scientist
Data ScientistContributions:54 commits, 5 PRs, 44 pushes in 2 years 11 months
Contributions summary:Mike primarily contributed to the development of integral kernels for the GPy framework, a Gaussian processes library in Python. Their work involved implementing new kernel types, specifically for handling histogram or binned data, as well as updating and testing existing kernel functionalities. The changes included significant code modifications to classes like `Integral_Limits` and `Multidimensional_Integral_Limits` for improved performance and features. This indicates a focus on expanding the capabilities of the library for specific data types.
gaussiangaussian-processespython
lionfish0/GPAdversarialBound
Jan 2018 - Feb 2022
Contributions:48 pushes, 1 branch in 4 years 1 month
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