Dr. Rivest is interested in the mathematical foundation of artificial and natural learning. He is particularly interested in how different systems in the brain collaborate to generate its amazing learning ability. For example, the cortex, the basal ganglia and the dopaminergic system, the limbic system and the hippocampus, and the cerebellum, all have sensibly different roles in learning. Beyond its potential application in neuropsychology, unleashing the brain's learning strategy might also help develop more general and powerful machine learning algorithms.

Animal and machine learning can both be studied under the reinforcement learning framework made of stimuli, actions and rewards. In particular, the brain receives a continuous stream of inputs in which the timing of the various events seems to strongly influence the animal learning and behaviours. How time intervals are learnt and influence learning remains unclear. Beyond timing, the brain's ability to naturally construct abstract representations remains unreplicated by today's best machine learning algorithms.

Starting from the animal behavioural and neurophysiological data, Dr. Rivest develops models that reproduce the animal's neural learning ability. In some cases, interesting solutions are also evaluated as potential machine learning algorithms. In particular, Dr. Rivest is currently applying discoveries in animals interval timing ability to the problem of time series prediction (or events prediction) and its potential use in reinforcement learning and robotics