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.

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

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.

Speakers (alphabetical order)

Alán Aspuru-Guzik

Alán Aspuru-Guzik
University of Toronto
AI, Chemistry

Peter Battaglia

Peter Battaglia
DeepMind
AI, Physical Simulation

Steven Brunton

Steven Brunton
UW
AI, Control and Simulation

Fei Chen

Fei Chen
Broad Institute of MIT and Harvard
AI, Biology

Azalia Mirhoseini

Azalia Mirhoseini
Google Brain
AI, Chip Design

Rick Stevens

Rick Stevens
University of Chicago
AI, Life Science

Larry Zitnick

Larry Zitnick
Meta AI
AI, Chemistry

Organizing Committee (alphabetical order)

Daisy Yi Ding

Daisy Yi Ding
Stanford
AI for Multiomics

Yuanqi Du

Yuanqi Du
Cornell
AI for Science

Tianfan Fu

Tianfan Fu
Gatech
AI for drug design and development

Wenhao Gao

Wenhao Gao
MIT
AI for Chemistry

Kexin Huang

Kexin Huang
Stanford
AI for Biology

Hanchen Wang

Hanchen Wang
Stanford/Genentech
AI for Medical Science

Lijing Wang

Lijing Wang
Stanford
AI for Earth Sciences

Advisory Committee

Jure Leskovec

Jure Leskovec
Stanford

Rocío Mercado

Rocío Mercado
Chalmers