Karthik Suresh

I am a member of the AI-Assisted Detector Design for the Electron-Ion Collider (AID²E) collaboration, where I serve as a key contributor to the development of a distributed software framework for optimizing detector designs. This framework leverages advanced optimization algorithms and high-performance computing to efficiently explore complex design spaces and accelerate the R&D cycle for next-generation particle detectors.

During my PhD with the GlueX experiment at Jefferson Lab, I worked at the intersection of data science, statistical modeling, and experimental physics. The analysis involved processing terabytes of complex detector data—high-dimensional, noisy, and incomplete. I developed robust detector calibration models to ensure accurate reconstruction of particle trajectories and energies, laying the groundwork for precise downstream analyses.

My primary contribution was in building advanced Bayesian and statistical inference pipelines for Partial Wave Analysis (PWA), a technique used to disentangle subtle quantum signals from overwhelming physical backgrounds. I designed custom high-performance data workflows and implemented scalable PWA algorithms to analyze the challenging neutral $b_1(1235)$ meson decay channel—one of the first such analyses performed in GlueX. My work not only produced new physics results but also introduced modular, reusable software tools now employed by the collaboration for other PWA and detector optimization tasks.