I'm a Ph.D. candidate in the Stanford Machine Learning Group advised by Andrew Ng and Stefano Ermon. I'm interested in the development and adaptation of multimodal foundation models, motivated by their potential to drive significant societal benefits including applications in climate change and in medicine. My current research is focused on advancing models for remote sensing and climate simulation data.
I founded the AI for Climate Change Bootcamp at Stanford which I currently lead. I'm also a core team member of Climate Change AI, where I co-organize the CCAI Summer School.
I received a double Bachelor's degree in Computer Science and Mathematics from UC Santa Barbara and a Master's degree in Computer Science from Stanford University. My Master's work included the development of medical AI technologies (AppendiXNet, CheXNeXt, MRNet, CheXNet) and datasets (CheXpert, MURA).
Most recent publications on Google Scholar.
‡, ‡‡ indicate equal contribution.
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon
Many-Shot In-Context Learning in Multimodal Foundation Models
Yixing Jiang‡, Jeremy Irvin‡, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H Chen, Andrew Y Ng
Deep learning for detecting and characterizing oil and gas well pads in satellite imagery
Neel Ramachandran‡, Jeremy Irvin‡, Mark Omara, Ritesh Gautam, Kelsey Meisenhelder, Erfan Rostami, Hao Sheng, Andrew Y Ng, Robert B Jackson
Nature Communications 2024
Automatic deforestation driver attribution using deep learning on satellite imagery
Neel Ramachandran‡, Jeremy Irvin‡, Hao Sheng, Sonja Johnson-Yu, Kyle Story, Rose Rustowicz, Andrew Y Ng, Kemen Austin
Global Environmental Change 2024
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
Hao Sheng‡, Jeremy Irvin‡, Sasankh Munukutla, Shawn Zhang, Christopher Cross, Kyle Story, Rose Rustowicz, Cooper Elsworth, Zutao Yang, Mark Omara, Ritesh Gautam, Robert B Jackson, Andrew Y. Ng
Spotlight at NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Jeremy Irvin‡, Hao Sheng‡, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin‡‡, Andrew Y Ng‡‡
NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning
AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining
Pranav Rajpurkar‡, Allison Park‡, Jeremy Irvin‡, Chris Chute, Michael Bereket, Domenico Mastrodicasa, Curtis P. Langlotz, Matthew P. Lungren, Andrew Y. Ng‡‡, Bhavik N. Patel‡‡
Nature Scientific Reports 2020
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin‡, Pranav Rajpurkar‡, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A Mong, Safwan S Halabi, Jesse K Sandberg, Ricky Jones, David B Larson, Curtis P Langlotz, Bhavik N Patel, Matthew P Lungren‡‡, Andrew Y Ng‡‡
Thirty-Third AAAI Conference on Artificial Intelligence 2019
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Pranav Rajpurkar‡, Jeremy Irvin‡, Robyn L Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis P Langlotz, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, Francis G Blankenberg, Jayne Seekins, Timothy J Amrhein, David A Mong, Safwan S Halabi, Evan J Zucker, Andrew Y Ng‡‡, Matthew P Lungren‡‡
PLoS medicine 2018
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar‡, Jeremy Irvin‡, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P Lungren‡‡, Andrew Y Ng‡‡
1st Conference on Medical Imaging with Deep Learning 2018
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar‡, Jeremy Irvin‡, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P Lungren‡‡, Andrew Y Ng‡‡
arXiv preprint 2017
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon
Many-Shot In-Context Learning in Multimodal Foundation Models
Yixing Jiang‡, Jeremy Irvin‡, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H Chen, Andrew Y Ng
Deep learning for detecting and characterizing oil and gas well pads in satellite imagery
Neel Ramachandran‡, Jeremy Irvin‡, Mark Omara, Ritesh Gautam, Kelsey Meisenhelder, Erfan Rostami, Hao Sheng, Andrew Y Ng, Robert B Jackson
Nature Communications 2024
Automatic deforestation driver attribution using deep learning on satellite imagery
Neel Ramachandran‡, Jeremy Irvin‡, Hao Sheng, Sonja Johnson-Yu, Kyle Story, Rose Rustowicz, Andrew Y Ng, Kemen Austin
Global Environmental Change 2024
Geo-bench: Toward foundation models for earth monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiaoxiang Zhu
NeurIPS 2023 Datasets and Benchmarks Track
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
Muhammad Ahmed Chaudhry‡, Lyna Kim‡, Jeremy Irvin‡, Yuzu Ido, Sonia Chu, Jared Thomas Isobe, Andrew Y Ng, Duncan Watson-Parris
USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
Jeremy Irvin‡, Lucas Tao‡, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, Andrew Y. Ng
An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
Maya Srikanth‡, Jeremy Irvin‡, Brian Wesley Hill, Felipe Godoy, Ishan Sabane, Andrew Y Ng
Weakly-semi-supervised object detection in remotely sensed imagery
Ji Hun Wang‡, Jeremy Irvin‡, Beri Kohen Behar, Ha Tran, Raghav Samavedam, Quentin Hsu, Andrew Y. Ng
Tackling Climate Change with Machine Learning at NeurIPS 2023
METER-ML: A Multi-sensor Earth Observation Benchmark for Automated Methane Source Mapping
Bryan Zhu‡, Nicholas Lui‡, Jeremy Irvin‡, Sahil Tadwalkar, Chenghao Wang, Zutao Ouyang, Frankie Y. Liu, Andrew Y. Ng, Robert B. Jackson
IJCAI-ECAI 2022 Workshop on Complex Data Challenges in Earth Observation
Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery
Meiqing Li‡, Hao Sheng‡, Jeremy Irvin‡, Heejung Chung, Andrew Ying, Tiger Sun, Andrew Y Ng, Daniel A Rodriguez
Environment and Planning B: Urban Analytics and City Science
CheXED: comparison of a deep learning model to a clinical decision support system for pneumonia in the emergency department
Jeremy A Irvin‡, Anuj Pareek‡, Jin Long, Pranav Rajpurkar, David Ken-Ming Eng, Nishith Khandwala, Peter J Haug, Al Jephson, Karen E Conner, Benjamin H Gordon, Fernando Rodriguez, Andrew Y Ng, Matthew P Lungren, Nathan C Dean
Journal of Thoracic Imaging
Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Jeremy Irvin‡, Sharon Zhou‡, Gavin McNicol‡, Fred Lu, Vincent Liu, Etienne Fluet-Chouinard, Zutao Ouyang, Sara Helen Knox, ..., Andrew Y. Ng, Rob Jackson
Agricultural and Forest Meteorology
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
Hao Sheng‡, Jeremy Irvin‡, Sasankh Munukutla, Shawn Zhang, Christopher Cross, Kyle Story, Rose Rustowicz, Cooper Elsworth, Zutao Yang, Mark Omara, Ritesh Gautam, Robert B Jackson, Andrew Y. Ng
Spotlight at NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Jeremy Irvin‡, Hao Sheng‡, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin‡‡, Andrew Y Ng‡‡
NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Eric Zelikman‡, Sharon Zhou‡, Jeremy Irvin‡, Cooper Raterink, Hao Sheng, Jack Kelly, Ram Rajagopal, Andrew Y Ng‡‡, David Gagne‡‡
NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning
Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression
Pranav Rajpurkar, Jingbo Yang, Nathan Dass, Vinjai Vale, Arielle S. Keller, Jeremy Irvin, Zachary Taylor, Sanjay Basu, Andrew Ng, Leanne M. Williams
JAMA Network Open 2020
Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
Jeremy Irvin, Andrew A Kondrich, Michael Ko, Pranav Rajpurkar, Behzad Haghgoo, Bruce E Landon, Robert L Phillips, Stephen Petterson, Andrew Y Ng, Sanjay Basu
BMC Public Health 2020
AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining
Pranav Rajpurkar‡, Allison Park‡, Jeremy Irvin‡, Chris Chute, Michael Bereket, Domenico Mastrodicasa, Curtis P. Langlotz, Matthew P. Lungren, Andrew Y. Ng‡‡, Bhavik N. Patel‡‡
Nature Scientific Reports 2020
DeepWind: Weakly Supervised Localization of Wind Turbines in Satellite Imagery
Sharon Zhou‡, Jeremy Irvin‡, Zhecheng Wang, Eva Zhang, Jabs Aljubran, Will Deadrick, Ram Rajagopal, Andrew Ng
NeurIPS Climate Change Workshop 2019
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Bhavik N Patel, Louis Rosenberg, Gregg Willcox, David Baltaxe, Mimi Lyons, Jeremy Irvin, Pranav Rajpurkar, Timothy Amrhein, Rajan Gupta, Safwan Halabi, Curtis Langlotz, Edward Lo, Joseph Mammarappallil, AJ Mariano, Geoffrey Riley, Jayne Seekins, Luyao Shen, Evan Zucker, Matthew Lungren
NPJ digital medicine 2019
Real-time electronic interpretation of digital chest images using artificial intelligence in emergency department patients suspected of pneumonia
Nathan Dean, Jeremy A Irvin, Pranav Rajpurkar, Al Jephson, Karen Conner, Mathew P Lungren
European Respiratory Journal 2019
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin‡, Pranav Rajpurkar‡, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A Mong, Safwan S Halabi, Jesse K Sandberg, Ricky Jones, David B Larson, Curtis P Langlotz, Bhavik N Patel, Matthew P Lungren‡‡, Andrew Y Ng‡‡
Thirty-Third AAAI Conference on Artificial Intelligence 2019
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet
Nicholas Bien‡, Pranav Rajpurkar‡, Robyn L Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, Safwan Halabi, Evan Zucker, Gary Fanton, Derek F Amanatullah, Christopher F Beaulieu, Geoffrey M Riley, Russell J Stewart, Francis G Blankenberg, David B Larson, Ricky H Jones, Curtis P Langlotz, Andrew Y Ng‡‡, Matthew P Lungren‡‡
PLoS medicine 2018
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Pranav Rajpurkar‡, Jeremy Irvin‡, Robyn L Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis P Langlotz, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, Francis G Blankenberg, Jayne Seekins, Timothy J Amrhein, David A Mong, Safwan S Halabi, Evan J Zucker, Andrew Y Ng‡‡, Matthew P Lungren‡‡
PLoS medicine 2018
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar‡, Jeremy Irvin‡, Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P Lungren‡‡, Andrew Y Ng‡‡
1st Conference on Medical Imaging with Deep Learning 2018
Radiology SWARM: novel crowdsourcing tool for CheXNet algorithm validation
Safwan Halabi, Matthew Lungren, Louis Rosenberg, David Baltaxe, Bhavik Patel, Jayne Seekins, Francis Blakenberg, David Mong, Timothy Amrhein, Pranav Raipurkar, David Larson, Jeremy Irvin, Robyn Ball, Curtis P Langlotz, Gregg Willcox
SiiM Conference on Machine Intelligence in Medical Imaging 2018
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar‡, Jeremy Irvin‡, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P Lungren‡‡, Andrew Y Ng‡‡
arXiv preprint 2017
Explicit Causal Connections between the Acquisition of Linguistic Tiers: Evidence from Dynamical Systems Modeling
Daniel Spokoyny, Jeremy Irvin, Fermin Moscoso del Prado Martin
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning 2016
Dynamical systems modeling of the child-mother dyad: Causality between child-directed language complexity and language development.
Jeremy Irvin, Daniel Spokoyny, Fermin Moscoso del Prado Martin
CogSci 2016
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