Jared Dunnmon

I am currently in a public service role supporting the Department of Defense; 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    


  • [June 2023] Paper applying structured state space models and graph neural networks to multivariate biosignals awarded Best Paper at ACM CHIL 2023.
  • [December 2022] xView3 challenge paper to be featured at Neurips 2022. Thanks to all who participated!
  • [January 2022] Excited to discuss two recent lines of work -- self-supervision for EEG signals and cross-modal embeddings for model debugging -- at ICLR 2022!
  • [March 2021] Study combining weak supervision, multi-task learning, and published in Nature Communications.
  • [November 2020] Work using learned representations to identify poorly performing subclasses and improve model performance on them published in NeurIPS.
  • [May 2020] Paper on cross-modal data programming for medical imaging published in Cell Patterns.
  • [April 2020] Exciting collaboration with neurologists on weak supervision for EEG monitoring reported in NPJ Digital Medicine.
  • [February 2020] Recent work using weak supervision for science and medicine featured in MLSys 2020 keynote; check out our blog post for more!
  • [February 2020] Research describing hidden stratification as a problem in medical imaging to be featured as an oral spotlight in ACM CHIL 2020.


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.

Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared Dunnmon, Liangqiong Qu, Khaled Saab, Tina Baykaner, Christopher Lee-Messer, Daniel Rubin
ACM Conference on Health, Inference, and Learning, 2023. Best Paper (Models & Methods Track).
Predicting 30-day All-cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks
Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik Patel, Imon Banerjee
IEEE Journal of Biomedical and Health Informatics, 2023.
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Fernando Paolo*, Tsu-tin Tim Lin*, Ritwik Gupta*, Bryce Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, Jared Dunnmon
Neurips, 2022. Datasets and Benchmarks Track.
Domino: Discovery Systematic Errors with Cross-modal Embeddings
Sabri Eyuboglu*, Maya Varma*, Khaled Saab*, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré
ICLR, 2022 (Oral Spotlight).
Self-supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Siyi Tang, Jared Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, Christopher Lee-Messer
ICLR, 2022.
Independent Assessment of a Deep Learning System for Lymph Node Metastasis Detection on the Augmented Reality Microscope
David Jin, Joseph Rosenthal, Elaine Thompson, Jared Dunnmon, Arash Mohtashamian, Daniel Ward, Ryan Austin, Hasan Tetteh, Niels Olson
Journal of Pathology Informatics, 2022.
Multi-task Weak Supervision Enables Anatomically-resolved Abnormality Detection in Whole-body FDG-PET/CT
Sabri Eyuboglu*, Geoffrey Angus*, Bhavik Patel, Anuj Pareek, Guido Davidzon, Jin Long, Jared Dunnmon**, and Matthew Lungren**
Nature Communications, 2021.
Responsible AI Guidelines in Practice: Lessons Learned from the DIU AI Portfolio
Jared Dunnmon, Bryce Goodman, Peter Kirechu, Carol Smith, Alexandrea Van Deusen
Defense Innovation Unit, 2021.
Impact of Upstream Medical Image Processing on the Downstream Performance of a Head CT Triage Neural Network
Sarah Hooper*, Jared Dunnmon*, Matthew Lungren, Domenico Mastrodicasa, Daniel Rubin, Christopher Ré, Adam Wang, Bhavik Patel
Radiology: Artificial Intelligence, 2021.
Data Valuation for Medical Imaging Using Shapley Value and Application to Large-scale Chest X-ray Dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared Dunnmon, James Zou, Daniel Rubin
Scientific Reports, 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.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré
Neurips, 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.
Weak Supervision as an Efficient Approach for Automated Seizure detection in Electroencephalography
Jared Dunnmon*, Khaled Saab*, Christopher Ré, Daniel Rubin, and Christopher Lee-Messer
NPJ Digital Medicine, 2020.
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner*, Jared Dunnmon*, Gustavo Carneiro, and Christopher Ré
ACM Conference on Health, Inference, and Learning, 2020 (Oral Spotlight).
Comparison of Segmentation-free and Segmentation-dependent Computer-aided Diagnosis of Breast Masses on a Public Mammography Dataset
Rebecca Sawyer-Lee*, Jared Dunnmon*, Ann He, Siyi Tang, Christopher Ré, and Daniel Rubin
Journal of Biomedical Informatics, 2020.
Doubly Weak Supervision of Deep Learning Models for Head CT
Khaled Saab*, Jared Dunnmon*, Roger Goldman, Hersh Sagreiya, Alexander Ratner, Christopher Ré, and Daniel Rubin
MICCAI, 2019 (Oral Spotlight).
Weakly Supervised Classification of Rare Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences
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.
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré
Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.
Assessment of Convolutional Neural Networks for Automated Triage of Chest Radiographs
Jared Dunnmon, Darvin Yi, Curtis Langlotz, Christopher Ré, Daniel Rubin, and Matthew Lungren
Radiology, 2018.
[Supplement, Editorial]
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Alexander Ratner, Braden Hancock, Jared Dunnmon, Shreyash Pandey, and Christopher Ré
Proceedings of the Second ACM DEEM Workshop, 2018.
Transformational Adversarial Networks for Data Augmentation
Alexander Ratner, Henry Ehrenberg, Zeshhan Hussain, Jared Dunnmon, and Christopher Ré
Advances in Neural Information Processing Systems, 2017.
An Investigation of Internal Flame Structure in Porous Media Combustion via X-ray Computed Tomography
Jared Dunnmon, Sadaf Sobhani, Meng Wu, Rebecca Fahrig, and Matthias Ihme
Proceedings of the Combustion Institute, 36(3):4399-4408, 2017.
Investigation of Lean Combustion Stability and Pressure Drop in Porous Media Burners
Sadaf Sobhani, Bret Haley, David Bartz, Jared Dunnmon, Jonathan Sullivan, and Matthias Ihme
Proceedings of Turbo Expo, 2017.
Characterization of Scalar Mixing in Dense Gaseous Jets Using X-Ray Computed Tomography
Jared Dunnmon, Sadaf Sobhani, Tae Wook Kim, Anthony Kovscek, and Matthias Ihme
Experiments in Fluids, 56(10):193, 2015.
2012 2011
Power Extraction from Aeroelastic Limit Cycle Oscillations
Jared Dunnmon, Samuel Stanton, Brian Mann, and Earl Dowell
Journal of Fluids and Structures, 27(8):1182-1198, 2011.
Other Collaborations

( * Equal Contributors)