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Research Initiative
AI + Science

Overview

Artificial intelligence (AI) and machine learning (ML) will fundamentally change the nature and pace of scientific discovery. Widespread adoption of AI in the sciences has the potential to integrate scientific inquiry with modes of hypothesis generation, data analysis, simulation, and testing that will transform our capacity to address scientific problems that currently appear intractable.

People and Partners

Pritzker AI+Science Joint Initiative with CalTech

A conference that brings together an elite and diverse cohort of leading researchers in core AI and domain sciences to lead conversations and drive partnerships that will shape future inquiry, industry investment, and entrepreneurial opportunities.

The Eric and Wendy Schmidt AI in Science Fellowship, a Program of Schmidt Sciences

Training scholars who seek to advance and accelerate the adoption of artificial intelligence (AI) in the natural sciences

Schmidt Faculty Fellows

A program that supports faculty at Nigerian universities interested in applying AI methodology to address significant research questions in the natural sciences and engineering.

Summer School

The goal of this yearly program is to introduce a new generation of diverse interdisciplinary graduate students and researchers to the emerging field of AI+Science.

The US National Science Foundation and Simons Foundation-funded SkAI Institute

The US National Science Foundation and Simons Foundation-funded SkAI Institute is synergistically accelerating astro-AI research while also educating and expanding the astro-AI workforce.

NITMB

The NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB) integrates the disciplines of mathematics and biology to transform the practice of biological research and inspire new mathematical discoveries.

Center for Living Systems

A National Science Foundation Physics Frontier Center

Related Insights, News, and Past Events

DSI NewsJun 30, 2025

Under the Hood: The Mathematics of AI

CEO of Together.ai and Neubauer Associate Professor of Computer Science and Data Science Ce Zhang was one of the DSI faculty who presented highlights from the year at this year's Research Day.
DSI NewsMay 22, 2025

2025 Research Day Highlights Growth with Global Impact

From left to right, Hackathon organizer and mentor Kyle Crocker, members of the winning Reinforcement Learning team, graduate students Qianyi He (MSDS ’26) and Zhuyiheng Chu (MCAM ’26), along with Hackathon Organizer Stephen Ihle.
DSI NewsMay 19, 2025

2025 AI+Science Hackathon Challenges Students to Take on Cutting-Edge Scientific Problems with AI

Campus NewsSep 18, 2024

NSF and Simons Foundation launch $20 million National AI Research Institute in Astronomy

Fig: Formation of HIV capsid simulated by a coarse-grained model.
From: https://www.google.com/url?q=https://www.science.org/doi/epdf/10.1126/sciadv.add7434&sa=D&source=docs&ust=1715014663521469&usg=AOvVaw3fATTBC_yAvxPv6WPYI17_
BlogMay 22, 2024

AI as a Great Teacher for Molecular Dynamic Modeling

DSI NewsMay 22, 2024

Eric and Wendy Schmidt AI in Science Postdoctoral Fellows host first-ever hackathon

DSI NewsMay 17, 2024

AI+Science Conference receives funding to expand across the next three years

AI methods and models have enabled a huge leap in our understanding of how images are processed in the brain.
We used to describe visual neurons as “edge detectors” and “face detectors”. Using deep neural networks, we have
discovered that images like these (which really can’t be described with words) are richer models of single neurons in
our visual system. I liken these AI-enabled descriptions of neural function, perhaps ironically, to a whole new kind of
vocabulary that neuroscientists can now use to explain the visual system. (Images from various papers including a,b, c, d, e , f)
BlogMay 16, 2024

Expanding Our Vocabulary of Vision Using AI

Caption: (left) We want to find networks which can easily switch between two different motions. (right)
Alternating the design process for each motion results in networks which can easily switch between the two motions.
BlogMay 09, 2024

Teaching materials to adapt

Fig: The neural network will be trained on a dataset generated by validated quantum mechanics methods on small molecules. The model will then be used to predict the x-ray damage effect in more complex systems.
BlogMay 02, 2024

From Protein Structures to Clean Energy Materials to Cancer Therapies: Using AI to Understand and Exploit X-ray Damage Effects

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