Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.
Explores combinatorial optimization using simulated annealing to find ground states in frustrated systems and address challenges in satisfying all interactions simultaneously.
