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
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
Research Paper
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
Data Programming: Creating Large Training Sets, Quickly
A paradigm for labeling training datasets programmatically rather than by hand.
Research Paper
Data Programming: Creating Large Training Sets, Quickly
A paradigm for labeling training datasets programmatically rather than by hand.
Data Programming With DDLite: Putting Humans in a Different Part of the Loop
Introducing DDLite, an interactive development framework for data programming.
Research Paper
Data Programming With DDLite: Putting Humans in a Different Part of the Loop
Introducing DDLite, an interactive development framework for data programming.
