Jared Dunnmon (original) (raw)
 |
Jared Dunnmon This website describes my ongoing academic work. I was previously a postdoctoral researcher in the Department of Computer Science at Stanford advised by Chris Ré, as well as a Visiting Scholar at the Stanford Department of Biomedical Data Science. My research focuses on developing weakly supervised machine learning systems to support applications in areas such as medicine, energy and environment, and intelligence analysis wherein the costs of failure are high and labeled data is scarce. By modeling noisy, programmatic sources of supervision applied to unlabeled data, directly integrating human domain knowledge, and leveraging related tasks, my work aims to enable reliable application of state-of-the-art machine learning models to these problems with the speed, scale, and performance levels required for practical deployment. Email LinkedIn Google Scholar GitHub |
|---|
Projects
 |
Snorkel is a system for rapidly creating, modeling, and managing training data by leveraging a variety of weak supervision sources in a principled manner. Today's state-of-the-art machine learning models require massive labeled training sets, which usually do not exist for real-world applications. Particularly complex problems are often composed of multiple tasks, and may have many different types of weak supervision that provide labels for one or more of these tasks. In Snorkel MeTaL, we use a new modeling approach to denoise this massively multi-task weak supervision before training an auto-compiled multi-task neural network. |
|---|
 |
xView3 was a public machine learning prize competition run in 2021 that aimed to build open-source models using open-source Synthetic Aperture Radar (SAR) data that could accurately detect and characterize fishing vessels in support of the international fight against Illegal, Unreported, and Unregulated (IUU) fishing. Partners included the Defense Innovation Unit, Global Fishing Watch, National Oceanographic and Atmospheric Administration, United States Coast Guard, and the National Maritime Intelligence-Integration Office. All relevant information -- including an updated leaderboard and model deployments -- can be found on the project website. |
|---|
Publications
2025 2024 2023 2022 2021
Observational Supervision for Medical Image Classification Using Gaze Data
Khaled Saab, Sarah Hooper, Nimit Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared Dunnmon, Hongyang Zhang, Daniel Rubin, Christopher Ré
MICCAI, 2021.
2020
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Jared Dunnmon*, Alexander Ratner*, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, and Christopher Ré
Patterns, 2020.
2019
Jason Fries, Paroma Varma, Vincent Chen, Ke Xiao, Heliodoro Tejada, Saha Priyanka, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, Scott Delp, Euan Ashley, Christopher Ré, and James Priest
Nature Communications, 2019.
2018 2017 2015 2012 2011 Other Collaborations
( * Equal Contributors)