Neel Rajeshbhai Vora is a research engineering associate in the Accelerator Technology & Applied Physics (ATAP) Division at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). Vora first joined Berkeley Lab as an intern in September 2023 while earning a Master of Science in Computer Science at the University of Texas at Arlington, where he specialized in machine learning (ML) and hardware systems. In September 2024, he assumed his current position in ATAP’s Berkeley Accelerator Controls and Instrumentation (BACI) Program, where he uses his background in ML, quantum control, and hardware design to tackle various instrumentation challenges.

What fueled your interest in particle accelerators and their applications?

I’ve always been drawn to large-scale scientific systems that require both precision and innovation. Particle accelerators—along with the control and instrumentation that support them—represent a fascinating intersection of physics, engineering, and computing. My interest comes from the opportunity to apply modern ML and hardware techniques within these environments, where even small improvements in reliability or performance can greatly impact scientific progress.

What attracted you to join the Berkeley Accelerator Controls & Instrumentation (BACI) Program?

The BACI Program’s focus on developing advanced control and instrumentation technologies closely matches my skills and interests. It offered the chance to work at the intersection of quantum information science, machine learning, and hardware, while directly supporting top-tier scientific research. The program’s collaborative and multidisciplinary nature was a key reason I chose to join, as it provided a platform to both contribute and learn from experts across various fields.

How have you found working at the Lab, and what research are you working on?

Working at the lab has been very rewarding. The collaborative environment encourages exploring new ideas and working on a variety of projects. Aside from my initial work in quantum information, I have teamed up with the fiber laser group, where I developed and implemented a timing synchronization solution, and with colleagues on magnet quench detection projects. Currently, my research focuses on quantum control optimization, specifically applying online learning and reinforcement learning for qubit calibration. This work aims to automate and accelerate calibration processes, making quantum experiments more reliable and scalable.

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