Hi there! I am a 3rd year PhD student in the Department of Physics at MIT. Previously, I was a math major at UC Berkeley. Here is my GitHub and a CV. I am also on Twitter @ericjmichaud_.

My Research

My current research focuses on improving our scientific/theoretical understanding of deep learning -- understanding what deep neural networks do internally and why they work so well. This is part of a broader interest in the nature of intelligent systems, which previously led me to work with SETI astronomers, with Stuart Russell's AI alignment group (CHAI), and with Erik Hoel on a project related to integrated information theory. I am now supervised by Max Tegmark and supported by the NSF Graduate Research Fellowship Program.


Here is my Google Scholar page

Selected Talks

Pre-PhD Life

In my undergrad years, I was fortunate to work with some really lovely people on a variety of projects.

In the summer of 2020, I interned with Stuart Russell's AI safety group, the Center for Human-Compatible AI. Mentored by Adam Gleave, I worked on a paper exploring the use of interpretability techniques on learned reward functions. We presented the paper at the Deep RL Workshop at NeurIPS 2020.

In 2020, I also worked with the neuroscientist Erik Hoel. Our paper measuring effective information and integrated information in deep neural networks was published in the journal Entropy. The code is available here.

Previously, I worked with the Berkeley SETI Research Center (the Breakthrough Listen Initiative), and wrote a paper on the idea of doing radio-frequency SETI searches from the far side of the Moon. More info on the project, with some more links, can be found here. This work was the subject of a lovely article on supercluster.com.