author
Co-Founder & CEO
,
Snorkel AI
Faculty, University of Washington
Alex Ratner is the co-founder and CEO at Snorkel AI, and an affiliate assistant professor of computer science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in computer science advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project. His research focused on data-centric AI, applying data management and statistical learning techniques to AI data development and curation.
The latest from Alex
A Survey on Programmatic Weak Supervision
This paper presents a comprehensive survey of recent advances in Programmatic Weak Supervision (PWS), and discusses related approaches to tackle limited labeled data scenarios.
Research Paper
A Survey on Programmatic Weak Supervision
This paper presents a comprehensive survey of recent advances in Programmatic Weak Supervision (PWS), and discusses related approaches to tackle limited labeled data scenarios.
Blog
Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI
Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the AI space by storm, going viral—even beyond technical practitioners—thanks to incredible capabilities around text generation, image synthesis, and more. However, enterprises face fundamental barriers to using these foundation models on real,…
Creating Training Sets via Weak Indirect Supervision
This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.
Research Paper
Creating Training Sets via Weak Indirect Supervision
This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.
Blog
Snorkel AI welcomes industry leaders to the team
Blog
Snorkel AI Raises $85m Series C at $1b Valuation for Data-Centric AI
We started the Snorkel project at the Stanford AI lab in 2015 around two core hypotheses:
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. We called for both long and short papers and received 26 submissions, all of which were double-blindly reviewed by a pool of 29 reviewers. In total, 15 papers were accepted. All the accepted contributions are listed...
Research Paper
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction…
WRENCH: A Comprehensive Benchmark for Weak Supervision
This paper introduces a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
Research Paper
WRENCH: A Comprehensive Benchmark for Weak Supervision
This paper introduces a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
Blog
Introducing Application Studio and Announcing Our $35m Series B Funding
Over the past year, we’ve worked hard to deliver Snorkel Flow, the first AI platform to provide all the power of machine learning without the pains of hand-labeling. Snorkel Flow lets you label data programmatically, train models flexibly, improve performance iteratively, and deploy AI applications quickly. We are incredibly proud of the value that our customers, including two of the…
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
This paper proposes cross-modal data programming (XMDP) for machine learning (ML) in medicine.
Research Paper
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
This paper proposes cross-modal data programming (XMDP) for machine learning (ML) in medicine.
