Nick's dissertation examined the interplay between exploration and generalization in reinforcement learning, in particular the effects of structural assumptions and knowledge. To this end, his research integrates ideas in function approximation, hierarchical decomposition, and model-based learning. He has also worked at the IBM Watson Research Laboratory, applying ideas from reinforcement learning to challenging problems in the field of autonomic computing.