Projects

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


MURA

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

Project Webpage

Palliative Care

Using Electronic Health Record Data to direct palliative care resources.

Project Webpage

CheXNet

Radiologist-level pneumonia 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.

Project Webpage

People

Current Members

Andrew Ng

Professor

Swati Dube Batra

Program Manager

Anand Avati

PhD Student

Awni Hannun

PhD Student

Hao Sheng

PhD Student

Pranav Rajpurkar

PhD Student

Ziang Xie

PhD Student

Aarti Bagul

MS Student

Allison Park

MS Student

Atli Kosson

MS Student

Chris Lin

MS Student

Daisy Ding

MS Student

Dillon Laird

MS Student

Guillaume Genthial

MS Student

Henrik Marklund

MS Student

Hershel Mehta

MS Student

Jeremy Irvin

MS Student

Jessica Wetstone

MS Student

Joseph Lee

MS Student

Suvadip Paul

MS Student

Tony Duan

MS Student

Andrew Huang

BS Student

Anirudh Jain

BS Student

Brandon Yang

BS Student

Erik Jones

BS Student

Kaylie Zhu

BS Student

Manan Shah

BS Student

Mason Swofford

BS Student

Matthew Sun

BS Student

Michael Bereket

BS Student

Nathan Dalal

BS Student

Nicholas Bien

BS Student

Norah Borus

BS Student

Prathik Naidu

BS Student

Shubhang Desai

BS Student

Stanley Xie

BS Student

Tanay Kothari

BS Student

Thao Nguyen

BS Student

Will Hang

BS Student

Programs

Volunteer 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 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 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. We expect that volunteers will commit 25 hours a week as a minimum. Due to a high number of applicants we may be unable to respond to individual emails.

Stanford Students

  • 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

  • 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 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