Projects

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


Palliative Care

Using Electronic Health Record Data to direct palliative care resources.

Project Webpage

CheXNet

Radiologist-level pneuomonia detection from chest X-rays.

Project Webpage

Arrhythmia

Cardiologist-level arrythmia detection from ECG signals.

Project Webpage

Education

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

People

Current Members

Andrew Ng

Professor

Swati Dube Batra

Program Manager

Anand Avati

PhD student

Awni Hannun

PhD student

Pranav Rajpurkar

PhD student

Ziang Xie

PhD student

Hao Sheng

PhD student

Dillon Laird

Masters student

Jeremy Irvin

Masters student

Daisy Ding

Masters student

Hershel Mehta

Masters student

Aarti Bagul

Masters student

Tony Duan

Masters student

Guillaume Genthial

Masters student

Brandon Yang

Undergraduate student

Kaylie Zhu

Undergraduate student

Manan Shah

Undergraduate student

Prathik Naidu

Undergraduate student

Stanley Xie

Undergraduate student

Mason Swofford

Undergraduate student

Will Hang

Undergraduate student

Past Members

  • Adam Coates

  • Alan Asbeck

  • Andrew Maas

  • Andrew Saxe

  • Ashutosh Saxena

  • Brody Huval

  • Honglak Lee

  • Ilya Sutskever

  • Jiquan Ngiam

  • Morgan Quigley

  • Pieter Abbeel

  • Quoc Le

  • Rajat Raina

  • Richard Socher

  • Rion Snow

  • Sameep Tandon

  • Tao Wang

  • Zico Kolter

Volunteering with us

By working with our group, you will:

  • Work on important problems in areas such as healthcare and education, 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 either a strong ML/AI 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 CS221 and/or 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

Stanford Students

  • We expect that students will commit 20 hours a week as a minimum. Students who complete successful research projects typically commit significantly more time than this.
  • 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.

Non-Stanford student Volunteers

  • Please email us at ml-apply@cs.stanford.edu with your resume, unofficial transcript if you are a current student or recent graduate, and two paragraphs on why you’d like to get involved. Due to a high number of applicants we may be unable to respond to individual emails.
  • We expect that volunteers will be able to work on campus a minimum of 40 hours/week, though significantly more time than this is likely required to complete a successful project. This should be your primary work commitment. We may ask that you complete a MOOC or other coursework before joining the group. Volunteers must be available for at least 12 weeks of research, with a strong preference for volunteers who can potentially stay involved for longer.
  • You must be currently living in the United States (or already be authorized and willing to move to 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.

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 volunteer with us, contact us at

ml-apply@cs.stanford.edu