Pavel Izmailov
Assistant Professor
Studies reasoning and problem-solving in AI, reinforcement learning, interpretability and technical AI alignment of deep learning models.
Pavel Izmailov's lab at NYU works on trustworthy & safe ML, reinforcement learning and interpretability, evaluation & safety of LMs. 8 papers since 2023, 6 at top venues, mostly NeurIPS and CVPR. 1 alumnus, most often to Anthropic. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 75%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 1
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 3 · collecting
- 0 papers2025 · T1 0
- 0 papers2024 · T1 0
- 3 papers2023 · T1 3
T1 venuesOtherVenues: NeurIPS, CVPR, ECCV, ICLR, ICML
Research focuspaper titles that use each topic's words
- Reinforcement learning1 paper
- Trustworthy & safe ML0 papers
- Interpretability, evaluation & safety of LMs0 papers
- interpretability0 papers
- alignment0 papers
Paper list
From the lab homepage.
20265 papers
- Neural Information Processing Systems (NeurIPS), Evaluations and Datasets TrackDiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking (opens in a new tab)
- NeurIPS (T1)Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns (opens in a new tab)
- ECCV (T1)Lifting Embodied World Models for Planning and Control (opens in a new tab)
- Neural Information Processing Systems (NeurIPS), Position Paper TrackPosition: Semantic Uncertainty Measures Disagreement, Not Reliability (opens in a new tab)
- NeurIPS (T1)Understanding Reasoning from Pretraining to Post-Training (opens in a new tab)
Where alumni went1 listed, 100% to industry
Industry 1Academia 0
Most often to
- Anthropic
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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