Page • TitleDescription/More Information Using Open Source and Proprietary models for Parallel Code Generation Siva Rajamanickam from Sandia National Laboratories gave this talk at the 2024 Chesapeake Large-Scale Analytics Conference. Sandia and Cerebras Team Demonstrate 1T Parameter Model Training in Decemberhttps://www.businesswire.com/news/home/20241210241089/en/Cerebras-Demonstrates-Trillion-Parameter-Model-Training-on-a-Single-CS-3-SystemArtificial Intelligence Safety Institute Red Teaming ExerciseA joint Sandia and Lawrence Livermore...
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ASC Co-Pilots for Code
Page • This project provides funding to evaluate emerging practical large-language model solutions for the ASC and the ND program. Our initial set of tasks/projects includes an evaluation of language models for software engineering and code modernization (particularly for HPC and scientific programming), using language models for knowledge capture and retrieval. Underpinning...
ASC Federated Models
Page • This project aims to develop large foundation models for generative AI capabilities tailored for ND applications across the three NNSA laboratories. By leveraging AI, the project seeks to significantly reduce the lengthy lifecycle times (over a decade) associated with ND modernization. The initiative will create a common framework for sharing...
ASC Industry Collaboration
Page • This project provides funding to evaluate emerging practical large-language model solutions. Our initial set of tasks/projects includes an evaluation of language models for software engineering and code modernization (particularly for HPC and scientific programming), using language models for knowledge capture and retrieval. Underpinning all of this work are efforts to...
Asmeret Naugle
Staff Page • R&D S&E, Computer Science. Biography Asmeret Naugle leads computational social science and human-AI teaming research at Sandia National Laboratories. She has interest and experience in: Human-AI teaming Trust and trustworthiness Dynamic simulation modeling, including system dynamics and agent based modeling Cognitive modeling Data science Asmeret is in the Applied Information...
BANYAN
Page • This project is exploring the development of new methods for building large multimodal generative artificial intelligence models and demonstrating neural methods on a mission-relevant exemplar. The models developed here will play a foundational role for Sandia developed multimodal models. BANYAN will demonstrate the approach by solving problems that were previously...
Cerebras
Page • Team members from Sandia's Advanced Memory Technology Program and BANYAN Institute collaborated with members of Cerebras onsite for a two-day training discussing HPC, ML, and AI. Sandia and Cerebras Team Demonstrate 1T Parameter Model Training in December: https://www.businesswire.com/news/home/20241210241089/en/Cerebras-Demonstrates-Trillion-Parameter-Model-Training-on-a-Single-CS-3-System
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Page • BANYAN-An Institute for Generative AI at Sandia BANYAN is a generative AI institute comprised of several projects funded by NNSA and Sandia LDRD. Uniting GenAI initiatives to leverage synergies across the lab by sharing datasets, models, software stacks, expertise, and industry collaborations. Research BANYAN Multimodal model training for ND, Stronglinks...
PARADIGM
Page • This project will investigate how trust in generative models such as Large Language Models (LLMs) and Large Multimodal Models (LMMs) can be enhanced by integrating with a symbolic reasoning engine that embodies SME knowledge. We will explore neurosymbolic methods, neural augmentation (specialization of models) and uncertainty quantification methods to provide...
Publications & Invited Talks
Page • Publications Publications will be added when available. Invited Talks Talk Location Presenter Scaling AI for Scientific Discovery: Faster Kernels, Efficient Models, Better PhysicsSNL: April17th, 2025Aparna ChandramowlishwaranUniversity of California, Irvine
Research
Page • Please use the left page navigation to further explore our different projects and research areas.
Team
Page • Meet Our Team