4 papers
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8
We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ milli…
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Aristeidis Tsaris, Isaac Lyngaas, John Lagregren +6
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images fr…
Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure
Du Wu, Enzhi Zhang, Isaac Lyngaas +14
Effective road infrastructure management is crucial for modern society. Traditional manual inspection techniques remain constrained by cost, efficiency, and scalability, while came…
Scale-up Unlearnable Examples Learning with High-Performance Computing
Yanfan Zhu, Issac Lyngaas, Murali Gopalakrishnan Meena +8
Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when…