Speaker
Description
The BNL Hadron Injector Complex serves as an active testbed for applying AI/ML techniques to accelerator operations, including beam tuning and optimization, predictive modeling, anomaly detection, virtual diagnostics, and operator decision support. Here we provide an overview of ongoing AI/ML efforts at BNL and the practical experience gained from integrating machine learning with accelerator control systems and operational workflows. We will discuss challenges and lessons learned in data access, model deployment, real-time control interfaces, and operational reliability, as well as our involvement in the ongoing NARAD project, which is developing community standards for accelerator data access and control system information models. These experiences help identify the infrastructure, software, and interoperability requirements needed to enable robust AI/ML deployment at future accelerator facilities and dedicated AI/ML test stands.