Dr. Rijeesh Keloth

As a Research Scientist at Virginia Tech, my work centers on building and operating precision detectors to probe physics beyond the Standard Model. I study neutrinos—the universe’s most elusive messengers—through careful measurements of oscillation parameters and targeted searches for sterile-neutrino signatures that could reveal hidden sectors of nature. While the Standard Model is extraordinarily successful, it doesn’t fully account for dark matter, matter–antimatter asymmetry, or the complete landscape of neutrino properties; my doctoral research rigorously tested alternative frameworks aimed at closing these gaps. Previously with the SoLid reactor experiment in Belgium, I led machine-learning–based particle identification to enhance heavy neutral lepton (HNL) sensitivity, and I served as Data Quality & Operations convener and later Data Manager, overseeing detector performance and the integrity of large, real-time data streams. Earlier, in NOvA at Fermilab, I contributed to short-baseline sterile-oscillation searches and analysis/operations—experience that grounded my approach to robust systematics and large-scale production workflows. Today, in DarkSide-20k, an ultra-sensitive liquid-argon TPC dark-matter search, I focus on the design, development, and optimization of the wire-grid system—a critical element for precise charge collection and electric-field uniformity. This role tightly couples hardware design, simulation, and qualification testing to deliver reliable performance in a high-stakes underground environment. I also collaborate with ProtoDUNE at CERN, further strengthening my expertise across detector design, calibration, and large-scale instrumentation.

Here's all the stuff I do.

"Our life is what our thoughts make it" - Marcus Aurelius

Experimental Neutrino Physics and Dark Matter Search

The next few years are pivotal with a range of experiments being conceived to improve ... Read More

Teaching

I have been fortunate enough to be a teaching fellow in a number of courses at ... Read More

Hardware–Software Integration and Intelligent Automation

Bridging instrumentation and computation through automation, AI, and embedded design. Combines experience in C++, Python, and data-driven frameworks with microcontroller and FPGA-based hardware development. Develops intelligent QA devices, automated calibration systems, and adaptive monitoring frameworks for large-scale physics experiments. Focuses on creating unified systems that connect detector hardware, control electronics, and AI-based analysis for precision and reliability.

Awards and Committees

Best Ph.D. thesis award 2019- Dept. of Physics, ... Read More

Leadership and Outreach

Author of two books about ‘Light’ and ‘Basic electronics’ for high school students in ...Read More

New Perspectives 2018 Talk at Fermilab

Talk delivered in `New Perspectives 2018` at Fermilab. ...Watch Video