author
Co-Founder
,
Snorkel AI
Professor @ Stanford University
I’m a professor in the Stanford AI Lab (SAIL), the center for research on foundation models (CRFM), and the Machine Learning Group (bio). Our lab works on the foundations of the next generation of AI systems.
- On the AI side, I am fascinated by how we can learn from increasingly weak forms of supervision, the basis of new architectures, the role of data, and by the mathematical foundations of such techniques.
- On the systems side, I am broadly interested in how machine learning is changing how we build software and hardware. I’m particularly excited when we can blend AI and systems, e.g,. Snorkel, Overton (YouTube), or Together.
Our work is inspired by the observation that data is central to these systems, and so data management principles (re-imagined) play a starring role in our work. This sounds like Silicon Valley nonsense, but oddly enough, these ideas get used due to amazing students and collaborations with Google ads, YouTube, Apple, and more.
While we’re very proud of our research ideas and their impact, the lab’s real goal is to help students become professors, entrepreneurs, and researchers. To that end, over a dozen members of our group have started their own professorships. With students and collaborators, I’ve been fortunate enough to cofound a number of companies and a venture firm. For transparency, I try to list companies I advise or invest in here and our research sponsors here. My students run the ML Sys Podcast.
The latest from Chris
Training Classifiers with Natural Language Explanations
Introducing BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision.
Research Paper
Training Classifiers with Natural Language Explanations
Introducing BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision.
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Research Paper
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Presenting Snorkel MeTal, an end-to-end system for multi-task learning.
Fonduer: Knowledge Base Construction From Richly Formatted Data
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
Research Paper
Fonduer: Knowledge Base Construction From Richly Formatted Data
Introducing Fonduer, a machine-learning-based KBC system for richly formatted data.
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Research Paper
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Research Paper
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Learning the Structure of Generative Models Without Labeled Data
Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.
Research Paper
Learning the Structure of Generative Models Without Labeled Data
Proposing a structure estimation method that is 100x faster than a maximum likelihood approach for training data.
Inferring Generative Model Structure With Static Analysis
Presenting Coral, a paradigm that infers generative model structure, significantly reducing the amount of data required to learn structure.
Research Paper
Inferring Generative Model Structure With Static Analysis
Presenting Coral, a paradigm that infers generative model structure, significantly reducing the amount of data required to learn structure.
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
Research Paper
Swellshark: A Generative Model for Biomedical Named Entity Recognition Without Labeled Data
Introducing SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly.
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Introducing Socratic learning, a paradigm that uses feedback from a discriminative model to automatically identify latent data subsets in training data.
Research Paper
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Introducing Socratic learning, a paradigm that uses feedback from a discriminative model to automatically identify latent data subsets in training data.
