Pavel Izmailov

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Iā€™m a PhD student in Computer Science at NYU, working with Andrew Gordon Wilson. In my research, I aim to build foundational understanding of models, training procedures, and their limitations. I use this understanding to develop practically impactful, interpretable, robust and broadly applicable methods and models. My interests include out of distribution generalization, probabilistic deep learning, representation learning, large models, and other topics. Our work on Bayesian model selection was recently recognized with an Outstanding Paper Award šŸ† at ICML 2022!

In years 2017ā€“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 at 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 worked with Lucas Beyer and Simon Kornblith at Google Brain.

I am on the academic job market!


Selected Papers