AI & Science Summer School 2023

Modern artificial intelligence and machine learning will fundamentally change scientific discovery. We are just beginning to understand the possibilities presented by an era of extraordinarily powerful computers coupled with advanced instruments capable of collecting enormous volumes of high-resolution experimental data. Off-the-shelf machine learning tools cannot fully extract the knowledge contained in these datasets, let alone generate new theories and propose future experiments.
The AI + Science Summer School will be held from July 17th – 21st, jointly hosted by the Data Science Institute (DSI), the Institute for Mathematical and Statistical Innovation (IMSI) at the University of Chicago, and Schmidt Futures via our Eric and Wendy Schmidt AI in Science Fellowship program at the University of Chicago.
This year’s speakers will focus on applications of AI and Machine learning in core areas of domain science – materials and chemistry, physics, climate change and biology. The goal of the program is to introduce a new generation of diverse interdisciplinary graduate students and researchers to the emerging field of AI + Science. We also hope this program can build community and spur new research directions focused on AI-enabled scientific discovery across the physical and biological sciences.
AGENDA with Video Recordings and Speaker Bios (click on each day and speaker):
Images from the Event:

The organizing committee for the AI + Science Summer School includes Peter Lu, Niksa Praljak, Jordan Shivers, Yihang Wang, Simona Ahmed, Yuxin Chen, Aaron Dinner, Ian Foster, Eric Jonas, Yuehaw Khoo, Risi Kondor, David Miller, Brian Nord, Surinarayanan Vaikuntanathan, and Rebecca Willett.
Information & Images from the 2022 AI & Science Summer School
Speakers
Chibueze Amanchukwu
Dane Morgan
Grant Rotskoff
Haruko Wainwright
Krithika Manohar
Kohitij Kar
Anoushka Joglekar
Aishik Ghosh
Simon Kornblith
Katie Malone
DSI Summer Programs 2025: Research Symposium
UnCommon Core | AI and the Future of Work with Career Advancement
Esteban Real (Google DeepMind): Automatically Discovering Learning Algorithms with Hardware Constraints