Professor Andrew Ng started the Stanford ML Group in 2003, which has since expanded to the broader Stanford ML Group.

This page describes the research directed by Professor Ng.

Projects

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


METER-ML

Earth Observation Dataset for Methane Source Identification

Project Webpage

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

MedAgentBench

A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents

Project Webpage

People

Core

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Andrew Ng

Faculty

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Swati Dube Batra

Program Manager

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Yixing Jiang

PhD Student

PhD Alumni

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Adam Coates

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Anand Avati

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Andrew Maas

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Andrew Saxe

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Ashutosh Saxena

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Awni Hannun

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Brody Huval

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Hao Sheng

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Honglak Lee

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Jeremy Irvin

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Jiquan Ngiam

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Morgan Quigley

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Pieter Abbeel

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Quoc Le

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Pranav Rajpurkar

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Rajat Raina

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Richard Socher

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Rion Snow

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Sharon Zhou

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Ziang Xie

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Zico Kolter

Postdoc Alumni

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Alan Asbeck

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Ilya Sutskever

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Jeya Maria Jose

Bootcamps

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Bootcamp Current + Alumni

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Nomin-Erdene Bayarsaikhan

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Zikui Wang

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Yufei Zhao

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Abdulaziz Abdulrahman S Alharbi

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Aakriti Lakshmanan

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Alex Kwon

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Eish Maheshwari

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Melanie Zhang

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Riya Dulepet

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Emily Liu

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Santino Ramos

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Joyce Chen

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Ines Dormoy

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Jinyoung Kim

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Apoorva Dixit

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Mabel Jiang

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Ishan Sabane

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Li Tian

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Benjamin Yan

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Ethan Hellman

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Pratyush Muthukumar

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Senem Isik

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Pura Peetathawatchai

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Joanne Zhou

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Amol Singh

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Haijing Zhang

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Manuka Stratta

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Lucas Tao

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Ji Hun Wang

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Yuzu Ido

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Spencer Paul

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Quentin Hsu

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Maya Srikanth

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Eric Frankel

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James Zheng

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Daniella Hacco Grimberg

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Felipe Godoy

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Brian Hill

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Ayush Singla

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Beri Kohen Behar

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Lyna Kim

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Muhammad Ahmed Chaudhry

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Ha Tran

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Sahil Tadwalkar

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Aditya Gulati

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Alex Donovan

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Kathy Yu

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Finsam Samson

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Sameer Khanna

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Vivek Shankar

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Vrishab Krishna

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Xiaoli Yang

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Nicholas Lui

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Bryan Zhu

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Timothy Dai

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Suhas Chundi

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Yuntao Ma

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Langston Nashold

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Jimmy Le

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Jake Silberg

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Matt Kolodner

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Sarthak Kanodia

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Emily Ross

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David Dadey

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Gil Kornberg

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Raghav Samavedam

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Sergio Charles

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Collin Kwon

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Benjamin Liu

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Cecile Loge

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Daniel Michael

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Ekin Tiu

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Ellie Talius

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Niveditha Iyer

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Pujan Patel

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Raj Palleti

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Rehaan Ahmad

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Ryan Chi

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Tom Jin

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Lyron Co Ting Keh

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Jake Taylor

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Sonia Chu

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Mauricio Wulfovich

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Chris Rilling

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Andrew Yang

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Bryan Gopal

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Can Liu

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Emily Wen

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Gautham Raghupathi

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Mark Endo

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Nhi Truong Vu

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Pranav Sriram

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Ryan Han

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Soham Gadgil

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Yujie He

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Irena Gao

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Sam Masling

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Erfan Rostami

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Tatiana Wu

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Andrew Hwang

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Julie Fang

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JK Hunt

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Michelle Bao

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Eric Matsumoto

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David Liu

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Derrick Li

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Niranjan Balachandar

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Pratham Soni

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Richard Wang

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Stephanie Zhang

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Jared Isobe

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Eric Zeng

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Adriel Saporta

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Alex Gui

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Alex Ke

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Andy Kim

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Ishaan Malhi

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Kevin Tran

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Rayan Krishnan

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Siyu Shi

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William Ellsworth

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Andrew Ying

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Heejung Chung

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Avoy Datta

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Tai Vu

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Jenny Yang

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Tiger Sun

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Shawn Zhang

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Sasankh Munukutla

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Christopher Cross

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Akshay Smit

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Dahlia Radif

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Damir Vrabac

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Jiangshan Li

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Oishi Banerjee

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Saahil Jain

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Viswesh Krishna

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Zihan Wang

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Anirudh Joshi

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Cheuk To Tsui

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Ethan Chi

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Gordon Chi

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Hari Sowrirajan

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John Peruzzi

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Keyur Mithawala

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Michael Zhang

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Nick Phillips

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Phil Chen

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Sonja Johnson-Yu

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Eric Zelikman

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Cooper Raterink

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Neel Ramachandran

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Neethu Renjith

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Jiyao Yuan

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Ashwin Agrawal

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Christian Rose

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Emma Chen

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Jon Braatz

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Jose Giron

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Kaushik Ram Sadagopan

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Rui Aguiar

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Yancheng Li

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Fred Lu

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Andrew Kondrich

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Vincent Liu

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Jabs Aljubran

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Eva Zhang

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Will Deaderick

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Anuj Pareek

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Chris Wang

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Jingbo Yang

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Mark Sabini

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Minh Phu

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Nathan Dass

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Vinjai Vale

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Alex Wang

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Amirhossein Kiani

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Amit Schechter

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Andrew Kondrich

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Bora Uyumazturk

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Chloe O'Connell

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Jason Li

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Nishit Asnani

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Rebecca Gao

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Soumya Patro

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Bryan Casey

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Dan Beksha

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James Rathmell

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Zach Harned

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Behzad Haghgoo

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Ben Cohen-Wang

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Chris Chute

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Joe Lou

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Kelly Shen

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Meng Zhang

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Michael Ko

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Nidhi Manoj

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Philip Hwang

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Robin Cheong

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Silviana Ciurea Ilcus

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Yifan Yu

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Allison Park

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Andrew Huang

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Atli Kosson

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Chris Lin

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Erik Jones

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Henrik Marklund

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Jessica Wetstone

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Matthew Sun

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Michael Bereket

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Nicholas Bien

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Norah Borus

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Shubhang Desai

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Suvadip Paul

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Thao Nguyen

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Tanay Kothari

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Aarti Bagul

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Brandon Yang

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Daisy Ding

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Hershel Mehta

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Kaylie Zhu

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Tony Duan

Collaborating Faculty

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Curt Langlotz

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Nigam Shah

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Matt Lungren

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Jeanne Shen

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Sanjay Basu

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Bhavik Patel

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Kristen Yeom

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Leanne Williams

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Utkan Demirci

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Gozde Durmus

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Sidhartha Sinha

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Catherine Hogan

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Sebastian Fernandez-Pol

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Yaso Natkunam

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Mitchell Rosen

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Geoff Tison

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David Kim

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Andrew Beam

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Rob Jackson

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Ram Rajagopal

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Sara Knox

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Daniel Rodriguez

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Gavin McNicol

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Chris Field

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Jackelyn Hwang

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Peter Kitanidis

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Etienne Fluet-Chouinard

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Zutao Yang

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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. We can only work with Stanford students at this time.

Stanford PhD Students

  • Stanford PhD students interested in rotating with Professor Ng should email us at [email protected] 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 AIHC, AICC, or Medical AI bootcamp.
  • 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.

Contact us

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

[email protected]