Pavel Izmailov

I’m a PhD student in Computer Science at NYU, working with Andrew Gordon Wilson. I am primarily interested in building a better understanding of deep neural networks and finding more efficient ways of training them. In particular my interests include loss surface analysis, Bayesian deep learning, out of distribution generalization and representation learning. I am also excited about generative models, uncertainty estimation, semi-supervised learning, Gaussian processes and other topics.

In years 2017–2019 I was a PhD student in Operations Research and Information Engineering at Cornell University, after which I received an MSc degree and transferred to NYU. I received a BSc in applied math and computer science from the faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University, where I was working in the Bayesian Methods Research Group under supervision of Dmitry Vetrov and Dmitry Kropotov.

In the summer of 2019 I completed a research internship at Amazon AWS in Palo Alto, working with Bernie Wang and Alex Smola. In the summer of 2020 I worked with Matt Hoffman at Google AI. Between June 2021 and February 2022 I worked with Alex Alemi and Ben Poole at Google as a research intern and a student researcher. In the summer of 2022 I am excited to return to Google to work with Lucas Beyer and Simon Kornblith.

You can contact me at and follow me on Twitter.


Recorded Talks


Workshop Papers