Kyle Kastner
Senior Research Scientist at Google DeepMind
United States
Summary
👤
Senior🎓
Top SchoolKyle Kastner is a senior research scientist with 14 years of experience at the intersection of speech, audio, and machine learning, currently advancing speech and audio research at Google DeepMind. He holds a PhD from Université de Montréal and a long track record of research roles and internships at Google Brain, Facebook AI Research, IBM, and Mila, blending probabilistic models and neural approaches for content generation and structured prediction. An active open-source contributor, he has contributed to scikit-learn by improving classifiers, evaluation examples, and tests—demonstrating a commitment to reproducible ML tooling. Comfortable moving between research and engineering, he combines deep theoretical grounding with practical software development, and brings a longtime personal interest in music-informed computing to his professional work.
14 years of coding experience
15 years of employment as a software developer
High School, High School at Brady High SchoolContinuing Education Various, Continuing Education Various at CourseraElectrical Engineering Communications and Networks, Electrical Engineering Communications and Networks at Texas State UniversityUniversity of Texas at San AntonioDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Université de Montréal
Github Skills (8)
scikit-learn10
machine-learning10
python10
scikit10
data-analysis9
statistics8
numpy8
unit-test8
Programming languages (3)
Github contributions (5)
scikit-learn/scikit-learn
Jun 2013 - Sep 2014
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:46 commits, 205 comments, 4 issues in 1 year 2 months
Contributions summary:Kyle primarily contributed to the scikit-learn repository by implementing and improving machine learning examples and functionalities. They added decision function support to the `OneVsRestClassifier` and included related tests. Additionally, they revised and expanded the precision-recall and ROC examples, incorporating improvements to documentation and utilizing the `train_test_split` function. Further contributions included test refinements and code cleanup within the `OneVsRestClassifier` implementation.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
kastnerkyle/net
Dec 2014 - Feb 2015
Contributions:37 commits, 21 pushes in 2 months
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