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Research Fellow & Founding Team
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Snorkel AI
Vincent Sunn Chen is a Research Fellow on the founding team at Snorkel AI. His work centers on systems for high quality AI evaluation & data development with experts in the loop. He currently leads the Open Benchmarks Grants, a $3M commitment to funding benchmarks and infrastructure for frontier agents. Prior to Snorkel, Vincent was a researcher at the Stanford AI Lab, where he studied the foundations of data-centric AI systems.
The latest from Vincent
Blog
How data slices transform enterprise LLM evaluation
Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.
Blog
How to tackle advanced classification challenges using Snorkel Flow
When done right, advanced classification applications cultivate business value and automation, unlock new business lines, and reduce costs.
Blog
Design Principles for Iteratively Building AI Applications
Enabling iterative development workflows with Snorkel Flow’s Application Studio. Consider this scenario— we’re AI engineers, and we’re building a social media monitoring application to track the sentiment of Fortune 500 company mentions in the news.
Slice-Based Learning: A Programming Model for Residual Learning
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning models can achieve quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on these critical subsets---we define these as slices, the key abstraction in our approach. To address slice-level performance, practitioners often train separate "expert" models on slice subsets or use multi-task hard parameter sharing. We propose Slice-based Learning, a new programming model in which the...
Research Paper
Slice-Based Learning: A Programming Model for Residual Learning
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and “question” sentences might be important to a dialogue agent’s language understanding for product purposes. While machine learning models can achieve quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on these critical subsets—we…
Scene Graph Prediction With Limited Labels
As deep learning models are applied to increasingly diverse problems, a key bottleneck is gathering enough high-quality training labels tailored to each task. Users therefore turn to weak supervision, relying on imperfect sources of labels like pattern matching and user-defined heuristics. Unfortunately, users have to design these sources for each task. This process can be time consuming and expensive: domain experts often perform repetitive steps like guessing optimal numerical thresholds and developing informative text patterns. To address these challenges, we present Snuba, a system to automatically generate heuristics using a small labeled dataset to assign training labels to a large,...
Research Paper
Scene Graph Prediction With Limited Labels
As deep learning models are applied to increasingly diverse problems, a key bottleneck is gathering enough high-quality training labels tailored to each task. Users therefore turn to weak supervision, relying on imperfect sources of labels like pattern matching and user-defined heuristics. Unfortunately, users have to design these sources for each task. This process can be time consuming and expensive: domain…
