Acacia Brunett’s research focuses on advanced reactor methods development, modeling and simulation, and technology transfer to industry. This includes advanced reactor safety analysis software development and qualification; passive system reliability and safety analysis for non-LWR designs; advanced reactor mechanistic source term methodology development and modeling and simulation; and dynamic probabilistic risk assessment (PRA) methods.
 
Acacia’s research interests include: broad deployment of artificial intelligence (AI)/machine learning (ML) methods in advanced reactor modeling and simulation for design and safety analysis; implementation of reactor technology-neutral neural networks and system reduction frameworks for expedited uncertainty quantification; and AI/ML-driven integration of low/higher-order simulation methods to address known modeling and simulation gaps in thermal hydraulic models. 

She holds a Ph.D. in Nuclear Engineering from The Ohio State University, an M.S. in Nuclear Engineering from The Ohio State University, and a B.S. in Engineering Physics from Juniata College.