Research Interests
Nuclear theory for BSM searches
My research focuses on precision searches for physics beyond the Standard Model using low-energy probes that are sensitive to high-scale dynamics. By combining first-principles nuclear theory with high-precision experimental observables—such as electromagnetic moments, beta decays, and symmetry-violating processes—I constrain possible extensions to the Standard Model in a model-independent effective field theory framework. A central goal of my work is to rigorously quantify theoretical uncertainties and propagate them consistently from fundamental interactions to measurable observables. This enables robust interpretations of experimental results and strengthens the discovery potential of precision nuclear and atomic experiments in the search for new fundamental interactions.
AI for nuclear physics
I develop physics-constrained machine learning methods to accelerate and enhance theoretical predictions in nuclear physics. My work integrates statistical inference, uncertainty quantification, and modern ML architectures with domain knowledge encoded through physical symmetries and effective theories. By building global emulators that map fundamental nuclear interactions to experimental observables, I enable fast, uncertainty-aware predictions across wide regions of parameter space. These tools support experimental design, parameter estimation, and the systematic exploration of Beyond-the-Standard-Model scenarios—while maintaining interpretability and physical consistency.