About

For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. Artificial intelligence (AI) and machine learning (ML) hold tremendous promise in having an impact on the way scientific discovery is performed today at the fundamental level. However, to realize this promise, we need to identify priorities and outstanding open questions for the cutting edge of AI going forward. We are particularly interested in the following topics:

Attendance

We welcome people with diverse background and level of experience to attend our workshop. The attendance is not contingent upon paper submission. Consider to apply for a travel award.

Follow Us

Please follow us on Twitter and LinkedIn for the latest news, or join us on the Slack for active discussions.

Invited Talks (In alphabetical order)

Alán Aspuru-Guzik

Alán Aspuru-Guzik
University of Toronto
AI, Chemistry

Steven Brunton

Steven Brunton
UW
AI, Control and Simulation

Kyle Cranmer

Kyle Cranmer
UW-Madison
AI, Particle Physics

Su-in Lee

Su-in Lee
University of Washington
AI, Computational Biology

Azalia Mirhoseini

Azalia Mirhoseini
Google Brain
AI, Chip Design

Rick Stevens

Rick Stevens
University of Chicago
AI, Life Science

Fabian Theis

Fabian Theis
Technical University of Munich

Panel: Using AI to Accelerate Scientific Discovery

Todd Anderson

Todd Anderson
Department of Energy

Pierre Gentine

Pierre Gentine
Columbia, NSF

Thomas Kalil

Thomas Kalil
Schmidt Futures

Michael Littman

Michael Littman
Brown, NSF

David Spergel

David Spergel
Princeton, Flatiron Institute

Tentative Important Dates (Anywhere on Earth)

Submissions

Please submit your paper in Openreview. Our workshop is nonarchival, the accepted papers will be posted on our website.

Organizers and Contact

Organizers are in the alphabetical order. For any question, please contact ai4sciencecommunity@gmail.com.

Organizers

Max Welling

Max Welling
UvA, Microsoft Research

Jure Leskovec

Jure Leskovec
Stanford

Yuanqi Du

Yuanqi Du
Cornell
AI for Science

Chenru Duan

Chenru Duan
MSFT Quantum
AI for Chemistry

Wenhao Gao

Wenhao Gao
MIT
AI for Chemistry

Kexin Huang

Kexin Huang
Stanford
AI for Biology

Ziming Liu

Ziming Liu
MIT
AI for Physics

Rocío Mercado

Rocío Mercado
Chalmers

Miles Cranmer

Miles Cranmer
University of Cambridge

Shengchao Liu

Shengchao Liu
MILA
AI for Molecule Discovery

Lijing Wang

Lijing Wang
Lawrence Berkeley National Laboratory
AI for Earth Sciences

AI4Science Team