Photo of Marco Cusumano-Towner

Marco Cusumano-Towner

Computer scientist working on programming languages and other infrastructure for probabilistic inference, cognitively inspired AI, and human-computer interaction.

About Me

I recently completed my PhD in EECS at MIT, advised by Vikash Mansinghka and Josh Tenenbaum. During my PhD I created the Gen probabilistic programming system. Before MIT, I was an early member of a molecular diagnostics startup, a Master’s student at Stanford University, and an undergraduate student in computer science at UC Berkeley, where I worked with Pieter Abbeel on probabilistic robotics. My graduate school research has been funded by the NSF graduate research fellowship and the NDSEG graduate fellowship program, among other sources.

Contact Me

The best way to contact me is via email at imarcoam at gmail dot com.

PhD Thesis

Gen: A High-Level Programming Platform for Probabilistic Inference (PDF), PhD thesis, Massachusetts Institute of Technology, 2020.


August 2020
I submitted the final version of my PhD thesis!

July 2020
I have new a paper on arXiv describing how Gen automates the low-level implementation of a flexible class of inference algorithms called involutive MCMC.

May 2020
I succesfully defended my thesis!

December 2019

October 2019

October 2019

October 2019

September 2019
I gave a talk on probabilistic programming at the Strange Loop conference in St. Louis.

July 2019

June 2019
I presented our paper on Gen at PLDI (short video abstract here).

April 2019
Reviewed for UAI conference.

February 2019
I completed my EECS Research Qualifying Exam

January 2019
I co-taught an MIT IAP class on probabilistic programming in Gen.

January 2019
Feras Saad presented our paper on Bayesian program synthesis at POPL.

October 2018
Gave a talk on Gen at the first PROBPROG conference (video).

September 2018
First release of Gen.