Laure Zanna
Joseph and Herbert Keller Professor in Applied Math & Professor of Mathematics and Data Science
Combines theory, simulation and machine learning to study ocean and climate dynamics, including ML parameterizations of ocean turbulence.
Laure Zanna's lab at NYU works on physics, climate & scientific computing, physics-based simulation & animation and data management & big data. 44 papers since 2023, 2 at top venues, mostly NeurIPS and PNAS. 4 PhD students. 21 alumni, most often to AI Researcher Startup and Meta. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 5%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 21
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 0 · collecting
- 15 papers2025 · T1 1
- 10 papers2024 · T1 1
- 12 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, PNAS
Research focuspaper titles that use each topic's words
- ocean20 papers
- Physics, climate & scientific computing12 papers
- climate11 papers
- Data management & big data9 papers
- Physics-based simulation & animation1 paper
Paper list
From the lab homepage.
20267 papers
- Science AdvancesAdvancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning (opens in a new tab)
- GRLCalibration of a neural network ocean closure for improved mean state and variability (opens in a new tab)
- GRLData-Driven Probabilistic Air-Sea Flux Parameterization (opens in a new tab)
- GRLFloeNet: A mass-conserving global sea ice emulator that generalizes across climates (opens in a new tab)
- GRLMachine learned equations for vertical mixing coefficients in the ocean surface boundary layer (opens in a new tab)
- JAMESParameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model (opens in a new tab)
- GRLSamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators (opens in a new tab)
202515 papers
- JAMESA Data-Driven Approach for Parameterizing Ocean Submesoscale Buoyancy Fluxes (opens in a new tab)
- Journal of ClimateA fluctuation-dissipation theorem perspective on radiative responses to temperature perturbations (opens in a new tab)
- Journal of Physical OceanographyA landscape of mesoscale eddy vertical structure: the influence of bathymetric slope and roughness on kinetic energy (opens in a new tab)
- Journal of Physical OceanographyA theory for how the depth of meltwater injection impacts regional sea level evolution (opens in a new tab)
- Machine Learning: Science and TechnologyAccelerating scientific discovery with the common task framework (opens in a new tab)
- JAMESAddressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines (opens in a new tab)
- Machine Learning: Science and TechnologyData-driven multiscale modeling for correcting dynamical systems (opens in a new tab)
- GRLGeneralizable neural-network parameterization of mesoscale eddies in idealized and global ocean models (opens in a new tab)
- GRLLearning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders (opens in a new tab)
- Nature GeosciencesObservational constraints imply limited future Atlantic meridional overturning circulation weakening (opens in a new tab)
- GRLSamudra: An AI Global Ocean Emulator for Climate (opens in a new tab)
- JAMESThe GFDL-CM4X climate model hierarchy, Part I: model description and thermal properties (opens in a new tab)
202410 papers
- JAMESA Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization (opens in a new tab)
- Published as a workshop paper at ”Tackling Climate Change with Machine Learning”, ICLRBuilding Ocean Climate Emulators (opens in a new tab)
- NeurIPS (T1)ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulations (opens in a new tab)
- Physical Review EData-driven dimensionality reduction and causal inference for spatiotemporal climate fields (opens in a new tab)
- Bulletin of the American Meteorological SocietyEarth System Reanalysis in Support of Climate Model Improvements (opens in a new tab)
- ESDEstimating freshwater flux amplification with ocean tracers via linear response theory (opens in a new tab)
- JOSELearning Machine Learning with Lorenz-96 (opens in a new tab)
- Geophysical Research LettersMachine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations (opens in a new tab)
- Nature Climate ChangePushing the frontiers in climate modelling and analysis with machine learning (opens in a new tab)
- JAMESVertical Structure and Energetic Constraints for a Backscatter Parameterization of Ocean Mesoscale Eddies (opens in a new tab)
202312 papers
- Climate DynamicsA new conceptual model of global ocean heat uptake (opens in a new tab)
- Geophysical Research LettersBackground Pycnocline depth constrains Future Ocean Heat Uptake Efficiency (opens in a new tab)
- JAMES, 15, e2022MS003258Benchmarking of machine learning ocean subgrid parameterizations in an idealized model (opens in a new tab)
- ICLR WorkshopData-driven multiscale modeling of subgrid parameterizations in climate models (opens in a new tab)
- JAMESDeep learning of systematic sea ice model errors from data assimilation increments (opens in a new tab)
- Physics ReportsDiscovering causal relations and equations from data (opens in a new tab)
- Env. Data SciExploring the non-stationarity of coastal sea level probability distributions (opens in a new tab)
- JAMESGenerative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model (opens in a new tab)
- JAMESImplementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model (opens in a new tab)
- JAMESParameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks (opens in a new tab)
- ICML WorkshopReliable coarse-grained turbulent simulations through combined offline learning and neural emulation (opens in a new tab)
- Geophysical Research LettersRemote Versus Local Impacts of Energy Backscatter on the North Atlantic SST Biases in a Global Ocean Model (opens in a new tab)
Where alumni went21 listed, 35% to industry
Industry 7Academia 13
Most often to
- AI Researcher Startup
- Meta
Also to
- Caltech
- MIT
- Princeton University
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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