Scheinker joins editorial board of accelerator science journal

Brings expertise in generative AI and adaptive control for dynamic systems

Scheinker Feature — Alexander Scheinker leads the adaptive machine learning team at Los Alamos, which develops tools for complex time-varying systems, such as the injector of an electron accelerator shown here. Credit to: Los Alamos National Laboratory

Alexander Scheinker was named an associate editor of the American Physical Society journal Physical Review Accelerators and Beams (PRAB). He will lead sections covering artificial intelligence, machine learning, computers, algorithms, beam control, diagnostics and feedback.

About Scheinker:

Leads the adaptive machine learning team in the Instrumentation and Controls group for Accelerator Operations and Technology at Los Alamos National Laboratory.
Develops safe and robust generative AI tools for automatic tuning and control of complex time-varying systems, such as for use in accelerators worldwide, including for the Los Alamos Neutron Science Center (LANSCE) accelerator, and for 3D dynamic imaging.
Published new research on creating a novel adaptive generative diffusion model for noninvasive beam diagnostics at LANSCE.
Earned a doctorate in dynamic systems and control theory from the University of California, San Diego.

LA-UR-25-31133