Machine learning and artificial intelligence for nuclear physics, including how ML can be used to analyze data from experiments as well as study nuclear many-body problems.
Development of nuclear interactions and electroweak currents based on chiral effective field theory approaches and on their implementation in quantum Monte Carlo methods to study the structure and reactions of atomic nuclei and the equation of state of infinite nuclear matter.
Bayesian model calibration of nuclear forces derived within effective field theories. Uncertainty quantification for quantum Monte Carlo methods in nuclear structure and reactions.
