Mike Gartrell

I'm a lead AI research scientist at Sigma Nova, working on generative AI topics for scientific foundation models. My research interests include Bayesian deep learning (BDL), diffusion models, and probablistic modeling. I'm particularly interested in using scalable stochastic gradient Markov chain Monte Carlo (MCMC) approaches from BDL to enable uncertainity quantification, improve data efficiency, and reduce hallunications in scientific foundation models.

Previously I worked as a senior researcher at the Criteo AI Lab in Paris, France, where I pursued research on generative AI, recommender systems, and a class of models for subset selection known as determinantal point processes (DPPs). Prior to Criteo I worked as a post-doc researcher at Microsoft, where I focused on recommender systems and DPPs. I received my PhD in 2014 from the University of Colorado Boulder, where I worked on recommender systems and was supervised by Richard Han.