Researched machine learning methods for particle physics with Professor Benjamin Nachman, focused on unfolding: statistically recovering the true distributions of physical quantities from noisy, high-dimensional, detector-distorted collider data.

Responsibilities

• Developed generative and adversarial models to reconstruct latent distributions of jet observables from high-dimensional, noisy collider data
• Invented machine learning algorithms for signal recovery and inference under uncertainty, including Moment Unfolding, Reweighting Adversarial Networks, and Neural Posterior Unfolding
• Implemented deconvolution code enabling statistical estimation in distorted measurement spaces, reducing run-time cost by ~100x while maintaining precision
• Collaborated with the CMS experiment on data analysis at the Large Hadron Collider
• Mentored undergraduate researchers on machine learning projects
• Published in Physical Review X, Physical Review D, the European Physical Journal C, the Journal of Instrumentation, the Annals of Applied Statistics, and NeurIPS, and presented at CERN, KIAS, NeurIPS, and APS meetings

Technologies & Skills

• Python, PyTorch, TensorFlow, and JAX
• Generative models, normalizing flows, and adversarial networks
• Statistical inference, deconvolution, and uncertainty quantification
• High-performance computing on NERSC systems