Tony Bagnall
Professor Of Computer Science at University of Southampton
North Norfolk, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolTony Bagnall is a Professor of Computer Science and Turing Fellow based in the UK with a strong academic and open-source footprint in time series machine learning. He has led research and teaching at the University of East Anglia and now the University of Southampton, focusing on data mining, data science, and time series classification. As an active contributor to prominent toolkits such as aeon and sktime, he has driven refactors, implemented and tested advanced distance measures like DTW variants, and improved documentation and usability for practitioners. His work balances rigorous research with practical engineering—removing legacy classifiers, optimizing data loading, and adding classifier metadata to accelerate reproducible experiments. With a PhD in Computer Science and seven years of recorded industry-style contributions, he combines deep domain expertise with hands-on development that impacts both research and production toolchains.
7 years of coding experience
5 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at University of East Anglia
Github Skills (14)
data-analysis10
data-mining10
scikit10
classify10
algorithms10
machine-learning10
time-series10
python10
data-science10
classification10
numpy10
scikit-learn10
pandas8
documentation8
Programming languages (6)
Github contributions (5)
aeon-toolkit/aeon
Feb 2023 - Mar 2023
A toolkit for machine learning from time series
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 1233 reviews, 65 commits in 1 month
Contributions summary:Tony primarily contributed to the documentation and refactoring of code related to time series classification within the aeon-toolkit repository. They focused on improving the clarity of documentation through updates and correcting typos. The user also made substantial changes, including the removal of legacy classifiers, adjustments to data loading functions, and the introduction of new features such as adding tags to different classifiers.
data-miningdata-sciencemachine-learningscikit-learntime-series
sktime/sktime
Apr 2019 - Dec 2022
A unified framework for machine learning with time series
Role in this project:Data Scientist & Machine Learning Engineer
Data Scientist & Machine Learning EngineerContributions:391 reviews, 805 commits, 333 PRs in 3 years 9 months
Contributions summary:Tony refactored code related to time series distance measures, including Dynamic Time Warping (DTW), its derivatives, and Weighted DTW. They implemented and tested new distance functions, and also integrated these distance functions with a time series classifier. The focus was on improving efficiency and correctness within the context of a time series classification library.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
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