diff --git a/_data/speakers.yml b/_data/speakers.yml index 036485e935..ffbe918d5a 100644 --- a/_data/speakers.yml +++ b/_data/speakers.yml @@ -6,6 +6,18 @@ image: argonne.png country: us link: https://www.anl.gov/profile/stephen-hudson - talk_num: 1 + talk_num: 0 photo: hudson.jpg bio: "Stephen Hudson is a Principal Software Engineer at Argonne National Laboratory, working on workflow systems for high-performance computing." + +- name: Logan Ward + role: Computational Scientist + institution: + name: Argonne National Laboratory + link: https://www.anl.gov + image: argonne.png + country: us + link: https://www.anl.gov/profile/logan-ward + talk_num: 1 + photo: ward.jpg + bio: "Logan Ward is a Computational Scientist at 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." diff --git a/_talks/talk1.html b/_talks/2025_04_16.html similarity index 54% rename from _talks/talk1.html rename to _talks/2025_04_16.html index 9d7911fbe5..e547faf2e9 100644 --- a/_talks/talk1.html +++ b/_talks/2025_04_16.html @@ -1,8 +1,8 @@ --- layout: page -title: Placeholder talk -authors: Stephen Hudson (Argonne National Lab) -event_date: March 12, 2025 (Tentative) +title: Steering Workflows with Artificial Intelligence +authors: Logan Ward (Argonne National Lab) +event_date: April 16, 2025 times: 11am-11.30 PST / 2pm-2.30 EST / 20:00-20:30 CEST talk_number: 1 @@ -15,21 +15,28 @@
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- TBC. The first talk will soon be announced. -
-- Topics (EXAMPLE): + 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a + human scientist can understand and act on their outcomes, which + means decisions about what tasks to run become outdated quickly.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.
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@@ -54,4 +62,4 @@