Pavel Izmailov
Contact: pi390@nyu.edu, Twitter
I am a Researcher at Anthropic. I am primarily interested in reinforcement learning, reasoning, AI for science and AI alignment.
Starting in Fall 2025, I will be joining NYU as an Assistant Professor in the Tandon CSE department, and Courant CS department by courtesy. I am also a member of the NYU CILVR Group.
Previously, I worked on reasoning and superintelligent AI alignment at OpenAI.
I am hiring PhD students to work with me at NYU starting Fall 2025. Please apply to the PhD program in the CSE department (deadline on December 1) or the CS department (deadline on December 12) and mention my name in your application. You are welcome to email me at pavel.recruiting@gmail.com with your CV and short description of your research interests. Admissions happen through a centralized committee.
Due to a high volume of applications, I will be unable to respond to all emails! Please do not be discouraged if you do not hear back from me.
My research interests are broadly in understanding how deep neural networks work. I am excited about a broad array of topics in core machine learning, including:
- • Problem-solving and reasoning in AI
- • Reinforcement learning, planning and search
- • Interpretability of deep learning models
- • AI for scientific discovery and math
- • Generalization and robustness of AI models
- • Technical AI alignment
- • Probabilistic deep learning, uncertainty estimation and Bayesian methods
Recent Highlights
I contributed to the recent OpenAI o1 models, a new state-of-the-art in LLM reasoning. Our work on weak-to-strong generalization was covered by a WIRED, MIT Technology Review and others. Our work on Bayesian model selection was recognized with an Outstanding Paper Award 🏆 at ICML 2022!
Links
- [Home, Bio, Publications, Talks, CV, GitHub, Google Scholar, Semantic Scholar]
Talks
-
A Bayesian Odyssey in Uncertainty: from Theoretical Foundations to Real-World Applications
ECCV Tutorial; September, 2024
[video] -
Weak-to-strong generalization
NYU AI Safety Reading Group; May, 2024 -
Symposium on the Impact of Generative AI in the Physical Sciences (Panelist)
IAIFI, MIT; March, 2024 -
Weak-to-strong generalization
Columbia Human-Guided Machine Learning Seminar; February, 2024 -
Weak-to-strong generalization
OpenAI Forum; January, 2024 -
Neural network loss surfaces and Bayesian neural nets
Caltech, guest lecture; May, 2023 -
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);
2021 -
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
[video] -
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
[video] -
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Artificial Intelligence and Statistics (AISTATS) Oral presentation; 2018