AI for Science: Let’s talk business

I will discuss how to transform disruptive AI approaches into production scale frameworks for AI-driven scientific discovery. I will use big data physics experiments as the driver for this discussion, and will touch on a number of different areas that are critical for the adoption and development of AI methodologies. I will show the type of science that may be realized when we pursue interdisciplinary approaches that combine in a nimble way advances in AI, innovative computing, scientific data infrastructure, scientific visualization, and modern computing environments. I will provide a vision to maximize the impact of AI for science programs to accelerate scientific discovery in science and engineering.

Eliu Huerta is the Lead for Translational AI at the Argonne National Laboratory, with faculty appointments in the Department of Computer Science at The University of Chicago and at the Department of Physics at the University of Illinois at Urbana-Champaign. He has broad research interests at the interface of artificial intelligence, theoretical astrophysics, mathematics, extreme-scale computing, and scientific visualization. Eliu enjoys assembling and working with interdisciplinary teams driven by ideas that disrupt the state-of-the-art to enable sustained innovation.