Interests
- M²LInES
- Momentum closures in ocean
- Large-eddy simulation
Latest
- Design and implementation of a data-driven parameterization for mesoscale thickness fluxes
- Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines
- Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
- Data-Driven Probabilistic Air-Sea Flux Parameterization
- Learning Machine Learning with Lorenz-96
- An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
- Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields
- Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
- Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
- Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
- Causal inference in spatiotemporal climate fields through linear response theory
- Benchmarking of machine learning ocean subgrid parameterizations in an idealized model