Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program.
4:30pm – 5:15pm: Presentation
5:15pm – 5:30pm: Q&A
5:30pm – 6:00pm: Reception
Abstract: While whether Artificial Intelligence (AI) yet provides capabilities exceeding that of humans is debatable, it clearly provides capabilities outside of computational approaches. Different, of course, does not mean better; only that growth is possible by embracing it. In this talk, we will discuss how to combine AI and conventional computational approaches to solve modern challenges in engineering. Examples will include how generative AI can increase the effectiveness of scientific workflows and how supervised learning can avoid unnecessary computations when modeling dynamic systems. We will use each example to explore the systems design and algorithmic challenges necessary to make such approaches a tool available to all scientists.
Logan Ward, Assistant Computational Scientist at Argonne National Laboratory. Logan is an Assistant Computational Scientist in the Data Science and Learning Division. He currently works as part of the Exascale Computing Project and JCESR on developing machine learning tools for designing new materials on leadership-class supercomputing systems. Logan’s research interests focus on lowering the barriers for other scientists to use AI in their own research through creating new software and methods suited for science.
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
Campus North Parking
5505 S Ellis Ave