John Arevalo
Principal Machine Learning Scientist at Genentech
South San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJohn Arevalo is a Principal Machine Learning Scientist at Genentech with 11 years of experience focused on deploying predictive ML systems in healthcare. A prolific researcher, he has authored papers across deep learning, medical imaging, computer vision, NLP, and multimodal learning, including a notable ICLR 2017 contribution from his time at the University of Houston. He bridges academia and industry, translating cutting-edge research into scalable production systems in the Bay Area and previously at the Broad Institute. An active open-source contributor, he has maintained and extended Theano-based libraries like blocks and pylearn2, emphasizing usability, compatibility, and robust data handling for ML workflows. Based in South San Francisco, he holds a PhD in Computer Science from Universidad Nacional de Colombia and has built a career across academia, biotech, and AI startup ecosystems.
12 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Universidad Nacional de ColombiaBachelor of Engineering - BE, Computer Science, Telematics Engineer, Bachelor of Engineering - BE, Computer Science, Telematics Engineer at Universidad Distrital Francisco José de Caldas
English, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (18)
python10
machine-learning10
data-preprocessing10
tensorflow210
tensorflow10
theano10
testing9
machinelearningmodel9
machine-learning-models9
numpy9
web-frameworks9
machine-learning-model9
neural-networks9
web-framework9
deep-learning8
Programming languages (9)
Github contributions (5)
mila-iqia/blocks
Jun 2015 - Feb 2017
A Theano framework for building and training neural networks
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 6 PRs, 4 comments in 1 year 8 months
Contributions summary:John's commits primarily focus on improving the functionality and documentation within the `blocks` framework. They addressed syntax errors in documentation, expanded the types supported by lookup indexing, and refined hyperparameters within the algorithms. The user updated deprecated parameters in the convolution and pooling modules. These changes suggest a focus on maintaining and improving the usability and correctness of the core machine-learning library.
pytorchdeep-learningtheanoneural-networksmachine-learning
lisa-lab/pylearn2
Mar 2014 - Jul 2014
Warning: This project does not have any current developer. See bellow.
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 1 PR, 1 comment in 3 months
Contributions summary:John primarily contributed to the pylearn2 project by addressing compatibility issues, specifically adding backward-compatibility with Python 2.6 format syntax. The user modified several files related to data loading, preprocessing, and model definitions, including `svhn.py` and `svhn_preprocessing.py`, demonstrating a focus on data handling and preparation within the context of machine learning. Furthermore, the user made changes to the testing framework, enabling and correcting tests for the MLP model.
javascripttypescript
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