I’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 🏆 at ICML 2022!
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 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!
Feature Learning and Distribution Shift
Stanford, Chelsea Finn’s group; December, 2022
Understanding Knowledge Distillation
MIT, Tommi Jaakkola’s group; November, 2022
Feature Learning and Spurious Correlations
University of Washington, Ludwig Schmidt’s group; November, 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Google Research, Shannon’s Bandwagon meeting; July, 2022
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Google Research, Sample Efficient Learning meeting; July, 2022
What Are Bayesian Neural Network Posteriors Really Like?
AABI Invited Talk [video] and
Max Plank Institute MIS and UCLA joint Seminar: Math Machine Learning [video] and
Teams at Google Brain and Perception and
Oxford Applied and Theoretical Machine Learning Group and
Bayesgroup seminar and
International Conference on Machine Learning (ICML);
Does Knowledge Distillation Really Work?
Teams at Google Brain and Perception; 2021
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
University of Freiburg, Frank Hutter's group; 2021
How Do We Build Neural Networks We Can Trust?
Broad Institute of MIT and Harvard MIA Seminar; Invited Talk 2019
Scalable Bayesian inference in low-dimensional subspaces
Bayesgroup seminar; 2019
Subspace Inference for Bayesian Deep Learning
Harvard, Finale Doshi-Velez group; 2019
Averaging Weights Leads to Wider Optima and Better Generalization
UAI Oral presentation, 2018
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Artificial Intelligence and Statistics (AISTATS) Oral presentation; 2018