Christopher Mitcheltree
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolChristopher Mitcheltree is a PhD researcher at Queen Mary University of London specializing in gradient-based optimization and AI for music, with 12 years of software engineering experience. He combines rigorous academic training (Waterloo BEng, Tokyo Tech MSc) with hands-on backend development, notably contributing robust feature-transforms and testing to the well-known aerosolve ML package. Based in London, he builds developer-facing tools like the Neutone SDK and explores modulation techniques hinted at in his GitHub bio. His work bridges research and production: implementing deep-copy and tokenizer transforms that improve ML data pipelines while maintaining real-world robustness. Colleagues value his ability to turn complex algorithmic ideas into practical, testable software components.
12 years of coding experience
Master's Degree, Computer Science, Master's Degree, Computer Science at Tokyo Institute of TechnologyDoctor of Philosophy - PhD, Artificial Intelligence and Music, Doctor of Philosophy - PhD, Artificial Intelligence and Music at Queen Mary University of LondonBachelor's Degree, Computer Engineering, Bachelor's Degree, Computer Engineering at University of Waterloo
Japanese, Spanish, French, German, English
Github Skills (9)
javas10
machine-learning10
java10
algorithms9
testing9
data-structure9
algorithm9
data-structures9
nlp7
Programming languages (4)
Github contributions (5)
airbnb/aerosolve
Jan 2016 - May 2016
A machine learning package built for humans.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:69 commits, 25 PRs, 52 pushes in 3 months
Contributions summary:Christopher primarily focused on enhancing the `aerosolve` machine learning package by implementing and improving core functionalities. They added features to copy data features between example contexts, and implemented deep copy operations for float and dense features, which involved modifying the Transformer class. Moreover, the user introduced a string tokenizer transform and various helper transforms to improve the overall processing pipeline within the project. They also improved the software's robustness by implementing various testing procedures to go with the newly created functions.
for-humanspythonmachine-learningdata-science
christhetree/scrapl-ddsp
Nov 2023 - Feb 2025
Scattering with Random Paths as Loss for DDSP
Contributions:2 PRs, 106 pushes, 1 branch in 1 year 3 months
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