Eero Simoncelli
Professor of Neural Science, Mathematics, Data Science, and Psychology
Studies computational models of visual perception and image statistics, including perceptual image quality metrics and learned image denoising priors.
Eero Simoncelli's lab at NYU works on recognition, detection & segmentation, learning theory & optimization and robot perception & SLAM. 16 papers since 2023, 12 at top venues, mostly NeurIPS and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 75%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 4 papers2025 · T1 2
- 5 papers2024 · T1 4
- 6 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICLR, ICML
Research focuspaper titles that use each topic's words
- Learning theory & optimization9 papers
- image4 papers
- Recognition, detection & segmentation0 papers
- Robot perception & SLAM0 papers
- image quality0 papers
Paper list
From DBLP.
20261 paper
20254 papers
- ICLR (T1)Discriminating image representations with principal distortions (opens in a new tab)
- Trans. Mach. Learn. Res.Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models (opens in a new tab)
- NeurIPS (T1)Learning normalized image densities via dual score matching (opens in a new tab)
- PLoS Comput. Biol.Perceptual learning improves discrimination but does not reduce distortions in appearance (opens in a new tab)
20245 papers
- NeurIPS (T1)Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT (opens in a new tab)
- ICLR (T1)Generalization in diffusion models arises from geometry-adaptive harmonic representations (opens in a new tab)
- Trans. Mach. Learn. Res.Layerwise complexity-matched learning yields an improved model of cortical area V2 (opens in a new tab)
- NeurIPS (T1)Learning predictable and robust neural representations by straightening image sequences (opens in a new tab)
- NeurIPS (T1)Shaping the distribution of neural responses with interneurons in a recurrent circuit model (opens in a new tab)
20236 papers
- NeurIPS (T1)A polar prediction model for learning to represent visual transformations (opens in a new tab)
- ICML (T1)Adaptive Whitening in Neural Populations with Gain-modulating Interneurons (opens in a new tab)
- NeurIPS (T1)Adaptive whitening with fast gain modulation and slow synaptic plasticity (opens in a new tab)
- UniRepsComparing neural models using their perceptual discriminability predictions (opens in a new tab)
- NeurIPS (T1)Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations (opens in a new tab)
- ICLR (T1)Learning multi-scale local conditional probability models of images (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- The lab site and the paper record do not agree.
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- Papers
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- Alumni
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Tiers are venue classifications, not lab rankings.
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