AI accelerated through cutting edge science

Our research team partners with world-leading organizations to develop new advancements in data-centric AI that powers production systems across a wide range of organizations and government agencies.

Browse blog posts, patents, and 100+ peer reviewed academic papers published on data-centric AI and foundation models.

Research Paper

Foundation Models Can Robustify Themselves, For Free

Learn More about Foundation Models Can Robustify Themselves, For Free

Research Paper

Skill-It! A Data-Driven Skills Framework for Understanding and Training Language Models

Learn More about Skill-It! A Data-Driven Skills Framework for Understanding and Training Language Models

Research Paper

On the Opportunities and Risks of Foundation Models

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Research Paper

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Learn More about PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Research Paper

Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening

Learn More about Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening

Research Paper

Shrinking the Generation-Verification Gap Weak With Verifiers

Learn More about Shrinking the Generation-Verification Gap Weak With Verifiers

Research Paper

Systems and Methods for Programmatic Labeling of Training Data for Machine Learning Models via Clustering and Language Model Prompting

Learn More about Systems and Methods for Programmatic Labeling of Training Data for Machine Learning Models via Clustering and Language Model Prompting

Research Paper

Scalable Approach to Medical Wearable Post-Market Surveillance

Learn More about Scalable Approach to Medical Wearable Post-Market Surveillance

Research Paper

Zero-Shot Robustification of Zero-Shot Models with Foundation Models

Learn More about Zero-Shot Robustification of Zero-Shot Models with Foundation Models

Research Paper

The Llama 3 Herd of Models

Learn More about The Llama 3 Herd of Models

Research Paper

The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators

Learn More about The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators

Research Paper

Red Teaming Large Language Models in Medicine: Real-World Insights on Model Behavior

Learn More about Red Teaming Large Language Models in Medicine: Real-World Insights on Model Behavior

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Featured Benchmarks

Exclusive to Snorkel, these benchmarks are meticulously designed and validated by subject matter experts to probe frontier AI models on demanding, specialized tasks.

Deep research roots

Born out of the Stanford AI lab in 2019 and in collaboration with leading research institutions, Snorkel-affiliated researchers have published more than 170 peer-reviewed research papers on weak supervision, AI data development techniques, foundation models, and more — with special recognition at events such as NeurlPS, ICML, and ICLR. Our researchers are closely affiliated with academic institutions including Stanford University, University of Washington, Brown University, and the University of Wisconsin-Madison