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
Snuba: Automating Weak Supervision to Label Training Data
Presenting Snuba, a system to automatically generate heuristics using a small labeled dataset to assign training labels to a large, unlabeled dataset in the weak supervision setting.
Research Paper
Snuba: Automating Weak Supervision to Label Training Data
Presenting Snuba, a system to automatically generate heuristics using a small labeled dataset to assign training labels to a large, unlabeled dataset in the weak supervision setting.
Slice-Based Learning: A Programming Model for Residual Learning
Proposing Slice-based Learning, a new programming model in which the slicing function (SF), a programmer abstraction, is used to specify additional model capacity for each slice.
Research Paper
Slice-Based Learning: A Programming Model for Residual Learning
Proposing Slice-based Learning, a new programming model in which the slicing function (SF), a programmer abstraction, is used to specify additional model capacity for each slice.
Scene Graph Prediction With Limited Labels
This paper introduces a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples.
Research Paper
Scene Graph Prediction With Limited Labels
This paper introduces a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples.
Osprey: Weak Supervision of Imbalanced Extraction Problems Without Code
Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.
Research Paper
Osprey: Weak Supervision of Imbalanced Extraction Problems Without Code
Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.
Multi-Resolution Weak Supervision for Sequential Data
Proposing Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data.
Research Paper
Multi-Resolution Weak Supervision for Sequential Data
Proposing Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data.
Medical Device Surveillance With Electronic Health Records
Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.
Research Paper
Medical Device Surveillance With Electronic Health Records
Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.
Learning Dependency Structures for Weak Supervision Models
This work focuses on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real world tasks.
Research Paper
Learning Dependency Structures for Weak Supervision Models
This work focuses on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real world tasks.
Interactive Programmatic Labeling for Weak Supervision
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
Research Paper
Interactive Programmatic Labeling for Weak Supervision
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
A Machine-Compiled Database of Genome-Wide Association Studies
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
Research Paper
A Machine-Compiled Database of Genome-Wide Association Studies
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
