Jason Lines
Head Of Data & Insights
Norwich, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJason Lines is Head of Data & Insights and an Associate Professor in Computing Sciences at the University of East Anglia with seven years of professional experience bridging academic research and applied sports analytics. His research expertise is in time series data mining—particularly time series classification using transformations and ensemble methods—and he has implemented practical solutions in Java and Python. Jason is an active contributor to sktime, improving time series data ingestion and integrating elastic distance measures like DTW that underpin robust classification pipelines. He combines a PhD and starred first-class BSc with hands-on roles from The Alan Turing Institute to Ipswich Town FC, translating advanced algorithms into operational insights for sports teams. Notably, his work spans both foundational tool-building for the time series community and direct, performance-driven analytics in professional football.
7 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, (pass with no corrections), Doctor of Philosophy - PhD, Computer Science, (pass with no corrections) at University of East Anglia
Github Skills (14)
scikit10
data-preprocessing10
data-analysis10
pandas10
data-loading10
machine-learning10
time-series10
dataprep10
preprocessing10
python10
preprocess10
scikit-learn10
forecast9
forecasting9
Programming languages (3)
Github contributions (5)
sktime/sktime
Jan 2019 - Jan 2022
A unified framework for machine learning with time series
Role in this project:
Data Scientist
Data ScientistContributions:4 reviews, 83 commits, 27 PRs in 3 years
Contributions summary:Jason's commits focus on addressing issues with loading time series data and implementing new distance measures, indicating involvement in data science and machine learning aspects of the project. Their work includes modifications to the data loading utilities for handling different file formats (.ts, ARFF, .tsv), demonstrating efforts to improve data ingestion capabilities. Additionally, the user developed and integrated various elastic distance measures, like DTW, and its derivatives, which are core components for time series analysis and classification within the repository. This work supports the framework's functionality in time series analysis.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
jasonlines/jasonlines.github.io
Jul 2020 - Aug 2024
Contributions:9 pushes in 4 years 1 month
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