Area of research
Statistical and Nonlinear Physics · Global and Planetary Change
Research interest
Research interests include Model Reduction and Neural Networks, Climate variability and models, Meteorological Phenomena and Simulations, and Oceanographic and Atmospheric Processes.
Embedding classical dynamics in a quantum computer
Kernel-based prediction of non-Markovian time series
Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques
Koopman spectra in reproducing kernel Hilbert spaces
Extended-range statistical ENSO prediction through operator-theoretic techniques for nonlinear dynamics
Galerkin approximation of dynamical quantities using trajectory data
Data-driven Koopman operator approach for computational neuroscience
Extraction and prediction of coherent patterns in incompressible flows through space–time Koopman analysis
Koopman analysis of the long-term evolution in a turbulent convection cell
The Seasonality and Interannual Variability of Arctic Sea Ice Reemergence
Analog forecasting with dynamics-adapted kernels
Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability
Nonparametric forecasting of low-dimensional dynamical systems
Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*
Sea‐ice reemergence in a model hierarchy
Predicting the cloud patterns of the Madden‐Julian Oscillation through a low‐order nonlinear stochastic model
Reemergence Mechanisms for North Pacific Sea Ice Revealed through Nonlinear Laplacian Spectral Analysis*
Symmetric and Antisymmetric Convection Signals in the Madden–Julian Oscillation. Part I: Basic Modes in Infrared Brightness Temperature
The symmetries of image formation by scattering I Theoretical framework
The symmetries of image formation by scattering II Applications
Nonlinear Laplacian spectral analysis: capturing intermittent and low‐frequency spatiotemporal patterns in high‐dimensional data
Comparing low‐frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis
Information theory, model error, and predictive skill of stochastic models for complex nonlinear systems
FRG: Collaborative Research: Non-Smooth Geometry, Spectral Theory, and Data: Learning and Representing Projections of Complex Systems
FRG: Collaborative Research: Non-Smooth Geometry, Spectral Theory, and Data: Learning and Representing Projections of Complex Systems
EAGER: Data-driven Koopman Operator Techniques for Chaotic and Non-Autonomous Dynamical Systems
Novel Kernel Methods for Data Analysis in Dynamical Systems: Applications to Dimension Reduction and Prediction in Atmospheric and Oceanic Dynamics