Nathan Gavenski
PhD Candidate
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolNathan Gavenski is a PhD candidate at King's College London with a decade of experience spanning machine learning research, software development, and quality engineering. He teaches deep learning for NLP at PUCRS and brings practical product-focused front-end experience from roles at ADP and large-scale testing and automation expertise from IBM. Comfortable across languages and tools—from AngularJS and Selenium to Python and Ruby—he blends rigorous academic research in safe and trusted AI with hands-on implementation and UX-minded feature work. As a former scrum master and educator, he emphasizes human-centered processes and reproducible engineering practices. His international background and language studies underpin a collaborative, cross-cultural approach to research and teaching. Notably, he has transitioned from digital content production to advanced ML education, reflecting both technical breadth and a knack for communicating complex ideas.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at King's College LondonMaster's degree, Machine Learning, Master's degree, Machine Learning at Pontifícia Universidade Católica do Rio Grande do Sul
German, English, Spanish, Portuguese
Github Skills (48)
reinforcement-learning9
robotics9
pytorch9
stable-baselines9
optimization9
pybullet9
hyperparameter-tuning9
deep-reinforcement-learning9
hyperparameter-optimization8
openai8
imitation-learning6
wordcloud6
wikidata5
benchmarking5
openai-gym5
Programming languages (6)
Github contributions (5)
NathanGavenski/NathanGavenski.github.io
Aug 2020 - Jun 2026
My personal Github Page
Contributions:1 release, 2 PRs, 111 pushes in 5 years 11 months
NathanGavenski/Tensorboard-Wrapper
Jul 2020 - Aug 2022
A Wrapper class for the Tensorflow's Tensorboard.
Contributions:2 releases, 20 commits, 8 PRs in 2 years 1 month
tensorboardtensorflow
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