Erin Acquesta
Erin C.S. Acquesta is a Mathematician and Principal Member of the Technical Staff with the Applied Information Sciences Center at Sandia National Laboratories. She received her MS and PhD from North Carolina State University in the field of applied mathematics. Her research areas of interest include scientific machine learning, uncertainty quantification, sensitivity analysis, and machine learning explainability; with an emphasis on providing credible, adaptive, and interpretable modeling capabilities for enhanced situational awareness in support of national security decision-making. Her primary domain area of expertise focuses on the mathematical properties of infectious disease models. As an area of professional service, she is a member of the ASME VVUQ standards committee, writing definitions for verification, validation, uncertainty quantification, and credibility for machine-learned models. She also volunteers her time for educational outreach as a mentor and head referee for the Albuquerque VEX VRC Robotics League and NM State VEX VRC Robotics Competitions.
Session
Scientific machine learning methods that balance mechanist equations with data-driven inference provide novel surrogates that decompose global from local behaviors inherent to high-fidelity stochastic models. This novel approach to surrogate modeling is pioneering advancements in hierarchal calibration of agent-based models that preserve foundational scientific knowledge.