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DTSTART;VALUE=DATE:20261105T111500
DTEND;VALUE=DATE:20261105T120000
UID:20564@agenda.unifr.ch
DESCRIPTION:Talk as part of a colloquium in Data-Driven Materials discovery\n\nSummary: Modern AI agents for scientific discovery face two challenges: poor out-of-distribution (OOD) reliability and prohibitive computational and sample costs. We address these through a holistic approach spanning optimization algorithms, neural architectures, and specialized hardware. Algorithmically, we bridge uncertainty quantification and policy optimization to achieve state-of-the-art sample efficiency in protein search and therapeutic molecular design. Architecturally, we incorporate expressive state-space models to reduce computational complexity while improving OOD generalization in diverse state-tracking tasks. At the hardware level, we map these primitives onto emerging in-memory computing hardware, exploiting physical device properties for extreme energy efficiency. Together, these advances pave the way for efficient and reliable AI agents for autonomous scientific discovery.
SUMMARY:Toward Efficient and Reliable Agentic AI for Materials Discovery
CATEGORIES:Colloque / Congrès / Forum
LOCATION:PER 18\, Auditorium C 322\, Chemin des Verdiers 4\, 1700 Fribourg
URL;VALUE=URI:https://agenda.unifr.ch/e/fr/20564
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