Riley Simmons-Edler, PhD

Riley works on neuro-inspired reinforcement learning (RL) with an interest in bridging the generalization and transfer learning performance gap between computational neural network models and animals. In particular, he investigates modular RL methods, which learn to decompose task curricula into reusable subtask-specific modules. Before joining the Rajan lab, he was a PhD student at Princeton University, working with Sebastian Seung on sampling and exploration methods for reinforcement learning, and a research intern at Samsung AI Center NYC working on robotic RL. In his previous life, he worked on computational protein modeling as an undergrad at NYU. When not working, he enjoys building computers and cooking, but usually not at the same time.