Projects

We work on developing AI solutions for a variety of high-impact problems


ForestNet

Deforestation driver classification using satellite imagery.

Project Webpage

Solar Forecasting

Calibrated probabilistic solar irradiance forecasting.

Project Webpage

OGNet

Oil and gas infrastructure mapping in aerial imagery.

Project Webpage

CheXphoto

Chest X-Ray Transformation Dataset And Competition

Project Webpage

CheXpedition

Generalizability of top chest X-ray models on real world challenges.

Project Webpage

NGBoost

Probabilistic Prediction with Gradient Boosting

Project Webpage

CheXpert

A Large Chest X-Ray Dataset And Competition

Project Webpage

ECG Arrhythmia

Cardiologist-level arrythmia detection from ECG signals.

Project Webpage

MRNet

Diagnosis of abnormalities from Knee MRs

Dataset Webpage

PPG Arrhythmia

Arrythmia detection from ambulatory free-living PPG signals.

Project Webpage

CheXNeXt

Chest radiograph diagnosis of multiple pathologies

Project Webpage

MURA

Introducing a large dataset for abnormality detection from bone x-rays.

Project Webpage

Countdown Regression

New approach to probabilistic time to event predictions.

Project Webpage

CheXNet

Radiologist-level pneumonia detection from chest X-rays.

Project Webpage

Palliative Care

Using Electronic Health Record Data to direct palliative care resources.

Project Webpage

Education

Designing natural language models to detect writing errors and provide feedback.

Project Webpage

People

Core

Andrew Ng

Faculty

Swati Dube Batra

Program Manager

Anand Avati

PhD Student

Hao Sheng

PhD Student

Jeremy Irvin

PhD Student

Sharon Zhou

PhD Student

PhD Alumni

Adam Coates

Alan Asbeck

Andrew Maas

Andrew Saxe

Ashutosh Saxena

Awni Hannun

Brody Huval

Honglak Lee

Ilya Sutskever

Jiquan Ngiam

Morgan Quigley

Pieter Abbeel

Quoc Le

Pranav Rajpurkar

Rajat Raina

Richard Socher

Rion Snow

Sameep Tandon

Tao Wang

Ziang Xie

Zico Kolter

Bootcamps


Bootcamp Current + Alumni

Quentin Hsu

Maya Srikanth

Eric Frankel

James Zheng

Daniella Hacco Grimberg

Felipe Godoy

Brian Hill

Ayush Singla

Beri Kohen Behar

Lyna Kim

Muhammad Ahmed Chaudhry

Ha Tran

Yuanjun Li

Sahil Tadwalkar

Aditya Gulati

Alex Donovan

Kathy Yu

Finsam Samson

Sameer Khanna

Vivek Shankar

Vrishab Krishna

Xiaoli Yang

Nicholas Lui

Bryan Zhu

Timothy Dai

Suhas Chundi

Yuntao Ma

Langston Nashold

Jimmy Le

Jake Silberg

Matt Kolodner

Sarthak Kanodia

Emily Ross

David Dadey

Gil Kornberg

Raghav Samavedam

Sergio Charles

Collin Kwon

Benjamin Liu

Cecile Loge

Daniel Michael

Ekin Tiu

Ellie Talius

Niveditha Iyer

Pujan Patel

Raj Palleti

Rehaan Ahmad

Ryan Chi

Tom Jin

Lyron Co Ting Keh

Jake Taylor

Sonia Chu

Mauricio Wulfovich

Chris Rilling

Andrew Yang

Bryan Gopal

Can Liu

Emily Wen

Gautham Raghupathi

Mark Endo

Nhi Truong Vu

Pranav Sriram

Ryan Han

Soham Gadgil

Yujie He

Irena Gao

Sam Masling

Erfan Rostami

Tatiana Wu

Andrew Hwang

Julie Fang

JK Hunt

Michelle Bao

Eric Matsumoto

David Liu

Derrick Li

Niranjan Balachandar

Pratham Soni

Richard Wang

Stephanie Zhang

Jared Isobe

Eric Zeng

Adriel Saporta

Alex Gui

Alex Ke

Andy Kim

Ishaan Malhi

Kevin Tran

Rayan Krishnan

Siyu Shi

William Ellsworth

Andrew Ying

Heejung Chung

Avoy Datta

Tai Vu

Jenny Yang

Tiger Sun

Shawn Zhang

Sasankh Munukutla

Christopher Cross

Akshay Smit

Dahlia Radif

Damir Vrabac

Jiangshan Li

Oishi Banerjee

Saahil Jain

Viswesh Krishna

Zihan Wang

Anirudh Joshi

Cheuk To Tsui

Ethan Chi

Gordon Chi

Hari Sowrirajan

John Peruzzi

Keyur Mithawala

Michael Zhang

Nick Phillips

Phil Chen

Sonja Johnson-Yu

Eric Zelikman

Cooper Raterink

Neel Ramachandran

Neethu Renjith

Jiyao Yuan

Ashwin Agrawal

Christian Rose

Emma Chen

Jon Braatz

Jose Giron

Kaushik Ram Sadagopan

Rui Aguiar

Yancheng Li

Fred Lu

Andrew Kondrich

Vincent Liu

Jabs Aljubran

Eva Zhang

Will Deaderick

Anuj Pareek

Chris Wang

Jingbo Yang

Mark Sabini

Minh Phu

Nathan Dass

Vinjai Vale

Alex Wang

Amirhossein Kiani

Amit Schechter

Andrew Kondrich

Bora Uyumazturk

Chloe O'Connell

Jason Li

Nishit Asnani

Rebecca Gao

Soumya Patro

Bryan Casey

Dan Beksha

James Rathmell

Zach Harned

Behzad Haghgoo

Ben Cohen-Wang

Chris Chute

Joe Lou

Kelly Shen

Meng Zhang

Michael Ko

Nidhi Manoj

Philip Hwang

Robin Cheong

Silviana Ciurea Ilcus

Yifan Yu

Allison Park

Andrew Huang

Atli Kosson

Chris Lin

Erik Jones

Henrik Marklund

Jessica Wetstone

Matthew Sun

Michael Bereket

Nicholas Bien

Norah Borus

Shubhang Desai

Suvadip Paul

Thao Nguyen

Tanay Kothari

Aarti Bagul

Brandon Yang

Daisy Ding

Hershel Mehta

Kaylie Zhu

Tony Duan

Collaborating Faculty

Nigam Shah

Matt Lungren

Curt Langlotz

Jeanne Shen

Sanjay Basu

Bhavik Patel

Kristen Yeom

Leanne Williams

Utkan Demirci

Gozde Durmus

Sidhartha Sinha

Catherine Hogan

Sebastian Fernandez-Pol

Yaso Natkunam

Mitchell Rosen

Geoff Tison

David Kim

Andrew Beam

Rob Jackson

Ram Rajagopal

Sara Knox

Daniel Rodriguez

Gavin McNicol

Chris Field

Jackelyn Hwang

Peter Kitanidis

Etienne Fluet-Chouinard

Zutao Yang

Duncan Watson-Parris

Work with us

By working with our group, you will:

  • Work on important problems in areas such as healthcare and climate change, using AI.
  • Build and deploy machine learning / deep learning algorithms and applications.

Values

Here are some values that we would like to see in you:

  • Hard work: We expect you to have a strong work ethic. Many of us work evenings and weekends because we love our work and are passionate about the AI mission. We also value velocity, and like people that get things done quickly.
  • Flexibility: You should be willing to dive into different facets of a project. For example, besides developing machine learning algorithms, you may also need to work on data acquisition, conduct user interviews, or do frontend engineering. This may also require going outside your comfort zone, and learning to do new tasks in which you’re not an expert.
  • Learning: You should have a strong growth mindset, and want to learn continuously. This can involve reading books, taking coursework, talking to experts, or re-implementing research papers. We will also prioritize your learning and help point you in the right direction; but you need to put in the work to take advantage of this.
  • Teamwork: We work together in small teams. You are expected to support and collaborate with others; in turn you will also receive support from your teammates.

Prerequisites

You should have a strong ML background, or a strong software engineering background.

  • ML/AI background: You have a solid background in probability and linear algebra, and have done well in AI/ML coursework. For example, Stanford students should have taken CS229 before applying. Previous ML/AI research experience would be a plus but is not required.
  • Software engineering background: We also encourage engineers without much AI background who are interested in developing ML applications to apply. Applicants should have made significant contributions to software projects in the past, for example through developing software systems at a company or through significant open source contributions.

Applying

Please see below for how to apply to work with our group. Due to a high number of applicants we may be unable to respond to individual emails.

Stanford PhD Students

  • Stanford PhD students interested in rotating with Professor Ng should email us at ml-apply@cs.stanford.edu using their Stanford email with the subject line “FirstName LastName PhD Rotation”.

Other Stanford Students

  • We encourage other Stanford students who want to work with us to apply to either the AICC or Medical AIbootcamp.
  • Outside of coursework, we expect this to be your primary academic activity.
  • As it takes time to familiarize oneself with a research project and to make significant contributions, we expect that students will be involved for at least two quarters, with a strong preference for those who can potentially stay involved for the full school year.

Volunteers

  • Please email us at ml-apply@cs.stanford.edu with your resume (and your transcript if you're a student) and two paragraphs on why you’d like to get involved.
  • You must be authorized to work in the United States and able to work on the Stanford University campus. We are not able to sponsor visas nor take on volunteers that want to work remotely.
  • Volunteers must be available for at least 12 weeks of research, with a strong preference for volunteers who can potentially stay involved for longer. We expect that volunteers will commit 25 hours a week as a minimum.

We believe that having a diverse and inclusive team will help us to advance AI, for the betterment of human life. We value different viewpoints. All backgrounds, ideas, and perspectives are welcome.

Contact us

If you're looking to partner or work with us, contact us at

ml-apply@cs.stanford.edu