Joan Bruna
Professor of Computer Science and Data Science
Studies the mathematical foundations of deep learning, including high-dimensional statistics, neural network optimization and geometric deep learning.
Joan Bruna's lab at NYU works on learning theory & optimization, graph learning and geometry processing & modeling. 19 papers since 2023, 17 at top venues, mostly NeurIPS and ICLR. 3 PhD students. 25 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 89%
- PhD students
- 3
- MS students
- 0
- alumni listed
- 25
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 8 papers2025 · T1 8
- 4 papers2024 · T1 4
- 7 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, COLT
Research focuspaper titles that use each topic's words
- Graph learning4 papers
- Learning theory & optimization3 papers
- Geometry processing & modeling1 paper
- mathematical foundations0 papers
- network optimization0 papers
Paper list
From the lab homepage.
20258 papers
- NeurIPS (T1)Axial Neural Networks for Dimension-Free Foundation Models
- ICLR (T1)Benefits of Rank in Attention Layers (opens in a new tab)
- NeurIPS (T1)Compositional Reasoning with Transformers, Rnns, and Chain of Thought (opens in a new tab)
- NeurIPS (T1)Emergence of Linear Truth Encodings in Language Models
- ICLR (T1)How Truncating Weights Improves Reasoning in Language Models (opens in a new tab)
- COLT (T1)Propagation of Chaos in One-Hidden-Layer Neural Networks beyond Logarithmic Time (opens in a new tab)
- NeurIPS (T1)The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models (opens in a new tab)
- ICML (T1)Thermalizer: Stable Autoregressive neural emulation of spatio-temporal chaos (opens in a new tab)
20244 papers
- COLT (T1)Computational-Statistical Gaps in Gaussian Single-Index Models (opens in a new tab)
- NeurIPS (T1)Provable Posterior Sampling with Denoising Oracles through Tilted Transport (opens in a new tab)
- NeurIPS (T1)Stochastic Optimal Control Matching (opens in a new tab)
- ICLR (T1)Symmetric Single-Index Learning (opens in a new tab)
20237 papers
- NeurIPS (T1)A neural Collapse perspective on feature evolution in graph neural networks (opens in a new tab)
- ICML (T1)Conditionally strongly log-concave generative models (opens in a new tab)
- ICLR 23 Climate and AI workshopData-driven multiscale modeling of subgrid parametrizations in climate models (opens in a new tab)
- NeurIPS (T1)Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation (opens in a new tab)
- ICML (T1)On Gradient Descent Convergence beyond the Edge of Stability (opens in a new tab)
- NeurIPS (T1)On Single-Index Models beyond Gaussian Data (opens in a new tab)
- ICML 23 SynS ML WorkshopReliable coarse-grained turbulent simulations through combined offline learning and neural emulation (opens in a new tab)
Where alumni went25 listed
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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