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07/23/2025 02:00 PM
07/23/2025 03:00 PM
America/New_York
Tech Talk - AI-Accelerated Strategies and Solutions in Environmental Technology (AI-ASSET)
Florida International University (FIU) is conducting a series of D&D Tech Talks focusing on D&D topics relevant to the DOE EM Complex. On July 23, 2025, FIU featured a Tech Talk from Alejandro De La Noval, a MISSP Fellow at Savannah River National Laboratory (SRNL), titled “AI-Accelerated Strategies and Solutions in Environmental Technology (AI-ASSET)”. This presentation will focus on SRNL’s development AI-ASSET, an AI-powered toolkit designed to enhance expert decision-making and accelerate site closure by integrating multi-modal data into a knowledge graph, enabling advanced information retrieval, and automating data analytics and machine learning workflows. More information at https://www.dndkm.org/TechTalk/
https://www.dndkm.org/TechTalk/
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Florida International University (FIU) is conducting a series of D&D Tech Talks focusing on D&D topics relevant to the DOE EM Complex. On July 23, 2025, FIU will feature a Tech Talk from Alejandro De La Noval, a MSIPP Fellow at Savannah River National Laboratory (SRNL), titled “AI-Accelerated Strategies and Solutions in Environmental Technology (AI-ASSET)”. This presentation will focus on SRNL’s development AI-ASSET, an AI-powered toolkit designed to enhance expert decision-making and accelerate site closure by integrating multi-modal data into a knowledge graph, enabling advanced information retrieval, and automating data analytics and machine learning workflows.
Abstract
To accelerate the transfer of the ALTEMIS approach, Savannah River National Laboratory is developing an AI-enabled knowledge/data management and information retrieval/fusion toolkit – AI-ASSET. This system is envisioned to augment the ability of subject matter experts to capture relevant information, execute data analytics and AI/ML workflows, and accelerate the timeline to site closure. Here, we present the integration of multi-modal datasets to a knowledge graph, natural language processing techniques for retrieving relevant information and contextualizing findings from across the datasets, and demonstrate the automation of backend data analytics and machine learning workflows applied to groundwater datasets.
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Alejandro De La Noval
Savannah River National Laboratory MISSP Fellow
Alejandro De La Noval graduated with his Master's in Computer Science from FIU in 2024, where he applied AI/ML towards groundwater modeling and contaminant forecasting as a DOE fellow. Currently, Alejandro works at the Savannah River National Lab (SRNL), where he interned as part of the DOE fellows program, using natural language processing (NLP) and Large Language Model (LLM) orchestration to projects in projects that help automate knowledge transfer, groundwater analytics, and site long-term monitoring deployment.
His PhD thesis focused on developing novel deep learning methods for anomaly detection in sequential data. This work resulted in several publications in leading conferences and journals. In addition to developing AI models for cybersecurity, he also has a keen interest in exploring other interdisciplinary domains where AI can be applied to solve complex problems. This includes areas such as natural language processing, image and video analysis, and data privacy. By working at the intersection of AI and other fields, he aims to make meaningful contributions to both the AI and interdisciplinary communities. His article in SEDE conference was selected as the best paper. Currently, at ARC, he is involved in the DoD-TRMC research project on artificial intelligence (AI) for Cybersecurity.
Joining the meeting instructions
This event is being hosted using Microsoft Teams. It is required for every attendee to have this app installed on their desktop or mobile device. You can download this app from the following link or use the links of the sidebar for mobile devices.
https://www.microsoft.com/en-us/microsoft-365/microsoft-teams/download-app
This event is sponsored by The U.S. Department of Energy