IRIS-HEP Fellow: Eric Kim



Fellowship dates: Jul – Sep, 2026

Home Institution: University of California, Berkeley


Project: AI-Aided Kalman Filtering for Scalable Track Finding

This project investigates how neural network models can be integrated into Kalman-filter-based tracking workflows for high-energy physics experiments. The primary goal is to improve the computational scaling of track finding by guiding candidate-hit compatibility scoring, hit-association ranking, branch pruning, and candidate termination within a CKF-style workflow, while preserving reconstruction quality.



More information: My project proposal

Mentors:

  • Louis-Guillaume Gagnon (Lawrence Berkeley National Laboratory)

  • Rocky Bala Garg (Stanford)

Presentations and Publications
Current Status


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