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Ben Blaiszik, Research Scientist, University of Chicago and the Data Science and Learning Division, Argonne National Laboratory
July 11, 2022 (All day) to July 17, 2022 (All day)

AT&T Hotel and Conference Center - University of Texas, Austin

Keynote - Software and Services to Fully Realize the Impact of AI, Machine Learning, Automation, and Community Engagement in Science

Date: Wednesday, July 13

Time: 9:15 - 10 a.m. 

Location: Zlotnik Ballroom

The past six years have seen exciting developments across science and engineering as new machine learning (ML) and other artificial (AI) methods are applied, adapted, or created for specific applications. While certainly no panacea, their successes in accelerating complex simulations, predicting folded protein shapes, understanding new materials, detecting rare physics events, predicting molecular properties, and designing molecular therapeutics, for example, have already proved transformative. Concurrently, newly upgraded experimental user facilities, nascent autonomous laboratories, and Exascale computational platforms are on the way, heralding a vast new spectrum of capabilities. Connecting the advances in ML/AI with these new capabilities and making them simple for users to access is a grand challenge for the next decade of research that promises to unlock a new era in scientific discovery and understanding. In this talk, we describe key Python software services, and tools that our group is developing to: 1) act as a platform to facilitate creation of user applications and automations; 2) reduce friction to move, access, publish, and discover high-value datasets generated in a variety of research domains; 3) promote incorporation of both physical (e.g. robotics) and software automation into research workflows to dramatically increase efficiency and reach towards autonomous laboratories; 4) leverage AI/ML coupled with Leadership scale computing to steer large campaigns of simulations to probe chemical and material design spaces; and 5) connect the community in new ways to tap into latent talent to accelerate scientific research. Coauthors: Ben Blaiszik, Aristana Scourtas, KJ Schmidt, Ethan Schondorf, Ben Galewsky, Ryan Chard, Raf Vescovi, Logan Ward, Kyle Chard, Nick Saint, Mark Hereld, Paul Voyles, Dane Morgan, Nicholas Schwartz, Mike Papka, Ian Foster