About

Dramatic developments in AI have led to its increasing adoption in science as a means to model complex phenomena, generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. The main goal of this series of workshop is to discover synergy across a variety of scientific fields, encourage interdisciplinary discussions, and enhance the flow of knowledge between AI and various scientific communities. Throughout history, bridging seemly different fields has brought overarching benefits, with notable examples: entropy in thermodynamics and information theory, neuroscience and AI, and algorithms inspired by discoveries in science (e.g. genetic algorithm, simulated annealing and diffusion-based generative models). In the current era, successes of AI methods in different fields of science have alluded to the general effectiveness of common themes: large simulated datasets, enforcing problem symmetries, and foundation model architectures. Our mission is to bring more scientists to attend ICML to share different perspectives on the use of AI, and to illuminate exciting research directions for AI researchers. We welcome submissions from all AI for Science areas, but we concentrate some of our talks and panels on scaling in AI for Science.

Social night

We are organizing a social event with AI Alliance at ICML 2024, Sign up here and join us in the party!

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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)

Sacha van Albada

Sacha van Albada
University of Cologne
AI, Neuroscience

Kevin Yang

Kevin Yang
Microsoft Research
AI, Biology

Megan Stanley

Megan Stanley
Microsoft Research AI4Science
AI, Climate

Max Jaderberg

Max Jaderberg
Isomorphic Labs
AI, Drug Discovery

Yian Yin

Yian Yin
Cornell University
AI, Science of Science

Gábor Csányi

Gábor Csányi
University of Cambridge
AI, Molecular Simulation, Materials

Lenka Zdeborova

Lenka Zdeborova
EPFL
AI, Statistical Physics

Panel: Pareto Frontier of Methodology, Scaling, Interpretability and Discovery

Max Welling (Moderator)

Max Welling (Moderator)
UvA
AI, Molecular Simulation

Yisong Yue

Yisong Yue
Caltech
AI, Autonomy, Knowledge, Discovery

Sam Rodriques

Sam Rodriques
Future House
AI Scientist

Jonas Köhler

Jonas Köhler
Microsoft Research
AI, Sampling, Comp. Chemistry

Gege Wen

Gege Wen
Imperial College London
AI, Earch Science

Quanying Liu

Quanying Liu
SUSTech
AI, Neuroscience

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.

Frequent Q&A

Organizers and Contact

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

Organizers

Peter Dayan

Peter Dayan
Max Planck Institute

Yuanqi Du

Yuanqi Du
Cornell
AI for Science

Ada Fang

Ada Fang
Harvard
AI + Medicine

Lixue Cheng

Lixue Cheng
MSR AI4Science
AI + Chemistry

Kevin Wenliang Li

Kevin Wenliang Li
Google DeepMind
AI + Neuroscience

Bowen Jing

Bowen Jing
MIT
AI + Biology

Di Luo

Di Luo
MIT
AI + Physics