Research

RESEARCH INTERESTS

Data-driven Stochastic Modeling and Prediction for Earth and Space Sciences, Theory-Informed Machine Learning, Advanced Spectral Methods and Time Series Analysis,Nonlinear Dynamical Systems, Data Assimilation. 

Curriculum Vitae

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• ORCID

Presentation at Kavli Institute for Theoretical Phsyics Program on "Machine Learning and the Physics of Climate"

REAL-TIME CLIMATE PREDICTION

  1. ARCTIC SEA ICE

  2. ENSO

 

PROJECTS 

  1. NSF: Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations, Lead PI, 2024-2026

  2. NSF: EAGER Machine Learning and Data Assimilation for Discovery of Generalized Fokker-Planck Equation for Radiation Belt Modeling, PI, 2022 - 2024

  3. NSFGEO-NERC: Multiscale Stochastic Modeling and Analysis of the Ocean Circulation, Lead PI, 2017 - 2020

  4. NSF: Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate, Lead PI, 2013 - 2017

  5. ONR-MURI: Extended-Range Environmental Prediction Using Low-Dimensional Stochastic-Dynamic Models: A Data-driven Approach, co-PI, 2012 - 2017

  6. UC: UCLA-LANL RADIATION BELTS REANALYSIS PROJECT, co-I, 2009 - 2012

  7. NSF: EAGER Direct Assimilation of Low-altitude Magnetic Perturbations in a Global Magnetosphere Model, co-PI, 2013 - 2016

  8. NSF: CLIMATE SENSITIVITY, STOCHASTIC MODELS AND GCM-EASM OPTIMIZATION, co-PI,  2011 - 2014

  9. NSF: GAP FILLING OF SOLAR WIND DATA BY SINGULAR SPECTRUM ANALYSIS, PI, 2011-2013

SOFTWARE

  1. • SINGULAR SPETCRUM ANALYSIS AND SSA-MTM TOOLKIT 

  2.  DATA-ADAPTIVE DECOMPOSITION AND STOCHASTIC MODELING