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 and Speaker’s 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.