Steering Workflows with Artificial Intelligence
Logan Ward (Argonne National Lab)
April 16, 2025
11am-11.30 PST / 2pm-2.30 EST / 20:00-20:30 CEST
Computational workflows routinely execute tasks faster than a human scientist can understand and act on their outcomes, which means decisions about what tasks to run become outdated quickly. Artificial Intelligence (AI) algorithms have emerged as a route to adjust a workflow during operation, potentially increasing its effectiveness by learning which tasks may prove most informative. In this talk, we will discuss application patterns that integrate AI into scientific workflows and introduce software which simplify building such “AI steered applications.” The topics will include dissecting a materials design application built around a Generative AI model and middleware necessary to scale data-intensive AI workflows past thousands of GPU nodes.
About the Authors
Logan Ward
Computational Scientist
Logan Ward is a Computational Scientist in the Data Science and Learning Division of Argonne National Laboratory, which he joined in 2019 after a post-doc at the University of Chicago. Logan’s PhD dissertation was in Materials Science and Engineering and focused on the development of AI algorithms for materials, so most of his research focuses on the intersection between AI, HPC, and physical sciences.
