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Pavel Izmailov

Reinforcement Learning AI Alignment Interpretability Of Deep Learning Models AI For Scientific Discovery Probabilistic Deep Learning
New York University Department of Computer Science

About

I am an Assistant Professor in the NYU Tandon CSE department, and Courant CS department by courtesy. I am also a member of the NYU CILVR Group. I am also a Researcher at Anthropic. I am primarily interested in reinforcement learning, reasoning, AI for science and AI alignment. Previously, I worked on reasoning and superintelligent AI alignment at OpenAI. 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

Research Performance Summary

42
Total Papers
12436
Total Citations
26
H-Index
2016-2026
Active Research Span

First Recorded Paper

Gaussian Processes for Machine Learning

Year: 2016

Citations: 0

Venue: N/A

Latest Recorded Paper

Reliable and Responsible Foundation Models: A Comprehensive Survey

Year: 2026

Citations: 0

Venue: arXiv preprint arXiv:2602.08145

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
2
2025
4
2024
4
2023
3
2022
7
2021
3
2020
6
2019
4
2018
6
2017
2
2016
1

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 14
Journal 2
Book 1

Top Coauthors

Izmailov Wilson

Research Impact by Period

2025-2026

Period Stats
Papers 6
Citations 3
Avg. Citations / Paper 0.5
H-Index 1

2020-2024

Period Stats
Papers 23
Citations 6831
Avg. Citations / Paper 297
H-Index 17

2015-2019

Period Stats
Papers 13
Citations 5602
Avg. Citations / Paper 430.9
H-Index 10

Contact and Professional Links

Contact Information

pi390@nyu.edu

Detected Research Keywords

Reliable Responsible Foundation Responsible Foundation Models Inference Bayesian Learning Tensor Train Decomposition Bayesian Model Averaging