Jacob Montiel
Applied Scientist at Amazon
United States
Summary
🤩
Rockstar🎓
Top SchoolJacob Montiel is an Applied Scientist at AWS Security with a decade of experience bridging academic research and production ML systems. He holds a PhD in Computer Science from Université Paris-Saclay and has held postdoctoral and research positions across Télécom Paris, The University of Waikato, and the Artificial Intelligence Institute, specializing in machine learning for evolving and streaming data. Before transitioning fully to ML research, he led embedded software development and prognostics projects at GE Aviation, giving him rare expertise in safety-critical, airborne systems and industrial IoT. He contributes to open-source ML tooling—most notably improving Hoeffding Tree implementations in scikit-multiflow—bringing algorithmic rigor and performance optimizations to streaming classifiers. Based in the United States, Jacob combines deep theoretical grounding with hands-on engineering to operationalize adaptive models in high-stakes environments.
10 years of coding experience
10 years of employment as a software developer
Master's Degree, Computer Science, Master's Degree, Computer Science at Tokyo Institute of TechnologyBachelor's Degree, Electrical and Electronics Engineering, Bachelor's Degree, Electrical and Electronics Engineering at Instituto Tecnológico de PueblaDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Université Paris-SaclayComputer Science, Computer Science at McGill University
English, Spanish, japansese, French
Github Skills (7)
data-stream10
scikit-learn10
machine-learning10
ensembles10
python10
data-streams10
scikit10
Programming languages (5)
Github contributions (5)
scikit-multiflow/scikit-multiflow
May 2017 - Nov 2020
A machine learning package for streaming data in Python. The other ancestor of River.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:13 releases, 766 commits, 118 PRs in 3 years 7 months
Contributions summary:Jacob contributed to the development and implementation of core machine learning methods by introducing and improving the implementations related to the Hoeffding Tree classifier. The user was involved in improving the overall performance of the algorithms and in refactoring existing components. Additionally, they contributed to a more robust, and stable structure of the estimators.
moapythonstreaming-datariverancestor
taiao/taiao-docs
Dec 2020 - Jan 2022
Contributions:12 reviews, 91 commits, 26 PRs in 1 year 1 month
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