Eric Michaud

Eric is a fourth-year undergraduate at UC Berkeley studying mathematics. He is broadly interested in understanding why deep learning works so well, and in investigating the general principles that give rise to intelligence in physical systems.

As a CHAI intern, Eric is working with Adam Gleave to develop interpretability techniques for reward functions. He hopes that this work will help to detect when learned reward functions fail to reflect human preferences, without having to train a policy and without knowing a ground-truth reward.

Before working at CHAI, Eric interned with the Berkeley SETI Research Center and the Lawrence Livermore National Laboratory. You can learn more about him at his personal website.