PNNL

Recent advancements in artificial intelligence (AI) methods are transforming how organizations access, analyze, and act on environmental information to support remediation decision-making. As AI-enabled tools become part of everyday research and operational workflows, understanding how these systems function and use data is essential for using them effectively and evaluating their outputs critically. Data preparation is the necessary, but often overlooked, process that bridges raw information and defensible AI results. Given ambiguous, unvalidated, or context-stripped inputs, AI can produce incomplete, misleading, or incorrect results. Without a clear understanding of how these results were produced, a user may place undeserved confidence or reliability in these results.

In this “Data Readiness for Artificial Intelligence Applications in Environmental Remediation” seminar, a panel of data scientists will provide an overview of AI, machine learning (ML), and large language model (LLM) methods; discuss data needs with insight on what environmental data readiness means for use with AI/ML methods; and talk about approaches for extracting, cleaning, and preparing data for environmental applications of AI.

Presenters

• Anurag Acharya, PhD, Data Scientist, Pacific Northwest National Laboratory
• Nick Sayresmith, PhD, Data Scientist, ddms, Inc.
• Theresa Pham, Data Science Post Masters Research Associate, Pacific Northwest National Laboratory

Learn more about the speakers and their presentations on the RemPlex Seminars web page.