Speaker
Description
Inverse problems are ubiquitous in hadron structure and tomography, where accurately characterizing uncertainties is crucial for unraveling new physics hiding within these uncertainties. In this new precision era of QCD, it is vital to create a translation between our physics and next generation AI/ML algorithms, using tools such as evidential deep learning and information-theoretic metrics to capture and separate contributions from aleatoric, epistemic, and distributional uncertainties. My research focuses on deploying evidence-based machine learning methods to decode parton distribution functions (PDFs) while exploring the vast parameter space of phenomenological and beyond-the-Standard-Model scenarios. Incorporating physics observables such as lattice QCD constraints and experimental measurements within these AI/ML paradigms refines the fidelity of PDF extractions and deepens our understanding of nonperturbative QCD. Ultimately, this integrated approach pushes the frontier of hadron structure discovery, aligning cutting-edge AI/ML progress with emerging opportunities at existing and future experimental physics facilities such as the EIC.
