Alexander Ratner
Co-Founder And CEO at Snorkel AI
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexander Ratner is a machine learning systems builder and entrepreneur with 13 years of experience, currently co-founder and CEO of Snorkel AI and an Affiliate Assistant Professor at the University of Washington. He brings deep research roots from a Stanford CS PhD to practical ML infrastructure, having contributed core data-ingestion and UDF tooling to DeepDive and enhanced weak supervision workflows in the widely used Snorkel project. Based in Menlo Park, he blends hands-on backend engineering with product leadership to turn research ideas into production data pipelines and training-data systems. Early founder experience with SiftPage and a background in physics from Harvard underline a pattern of tackling hard, data-driven problems from first principles.
13 years of coding experience
2 years of employment as a software developer
A.B. Honors Physics, A.B. Honors Physics at Harvard UniversityThe Lawrenceville SchoolDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Stanford University
English
Github Skills (12)
data-analysis10
scikit-learn10
machine-learning10
postgresql10
trainings10
data-parsing10
python10
modeling10
scikit10
text-analysis9
data-engineering9
tensorflow9
Programming languages (6)
Github contributions (5)
snorkel-team/snorkel
Feb 2016 - Sep 2019
A system for quickly generating training data with weak supervision
Role in this project:
Data Scientist
Data ScientistContributions:6 releases, 878 commits, 195 PRs in 3 years 7 months
Contributions summary:Alexander appears to be focused on enhancing the Snorkel framework by working on candidate extraction, annotation, and model training for a machine learning pipeline. They modified example notebooks to adapt to changes in the Snorkel API, particularly for categorical variables and the text-based model. Their commits include efforts to ensure the stability of the learning tests and the overall structure of the package, which included updates for the framework and improved the documentation.
training-dataweak-supervisionmachine-learningailabeling
HazyResearch/deepdive
May 2015 - Feb 2016
DeepDive
Role in this project:Back-end Developer
Back-end DeveloperContributions:36 commits, 7 PRs, 27 pushes in 9 months
Contributions summary:Alexander primarily contributed to the development of utility tools within the `ddlib` library. Their work involved creating and integrating tools for parsing and printing data in the Postgres-style TSV (PGTSV) format, specifically designed for DeepDive's data processing workflows. They also implemented a UDF interface, enhancing the project's ability to process data through custom functions. These additions suggest a focus on data ingestion and transformation pipelines.
deep-learning
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