Jeremy Irvin

PhD Student, Stanford Machine Learning Group

jirvin16 [AT] stanford.edu

Bio

I'm a Ph.D. student in the Stanford Machine Learning Group advised by Andrew Ng. I'm interested in developing machine learning tools for climate change and medicine. My current research is focused on developing machine learning approaches for mapping energy and transportation infrastructure, predicting methane emissions from natural wetlands, identifying the drivers of deforestation, and forecasting solar energy. I'm also a core team member of Climate Change AI.

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).

Publications

Most recent publications on Google Scholar.
, ‡‡ indicate equal contribution.

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

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

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

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

FLUXNET Methane Synthesis: Modeling Wetland Methane Fluxes from Sites to the Globe

Gavin McNicol, Etienne Fluet-chouinard, Zhen Zhang, Jeremy Irvin, Sharon Zhou, Fred Lu, Andrew Kondrich, Vincent Liu, Andrew Ng, Sara Knox, Benjamin Poulter, Robert B Jackson

AGU Fall Meeting 2019

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

Projects

OGNet
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
ForestNet
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Solar Forecasting
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
CheXpedition
Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
CheXpert
A Large Chest X-Ray Dataset And Competition
CheXNeXt
Deep learning for chest radiograph diagnosis
MRNet
A Knee MRI Dataset And Competition
MURA
Bone X-Ray Deep Learning Competition
CheXNet
Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

Teaching

In the News

AI for Climate Change
AI for Medicine

Vitæ

Full CV in PDF.

Acknowledgement

This website uses the website design and template by Martin Saveski.