Emily L. Kang

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Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is(More)
Calculating the statistical linear response of turbulent dynamical systems to the change in external forcing is a problem of wide contemporary interest. Here the authors apply linear regression models with memory, AR(p) models, to approximate this statistical linear response by directly fitting the autocorrelations of the underlying turbulent dynamical(More)
This paper presents a fast reduced filtering strategy for assimilating multiscale systems in the presence of observations of only the macroscopic (or large-scale) variables. This reduced filtering strategy introduces model errors in estimating the prior forecast statistics through the Heterogeneous Multiscale Methods (HMM) based reduced climate model as an(More)
Fundamental barriers in practical filtering of nonlinear spatio-temporal chaotic systems are model errors attributed to the stiffness in resolving multiscale features. Recently, reduced stochastic filters based on linear stochastic models have been introduced to overcome such stiffness; one of them is the Mean Stochastic Model (MSM) based on a diagonal(More)
Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most important disease of cucurbits globally, and the pathogen is disseminated aerially over a large spatial scale. Spatio-temporal spread of the disease was characterized during phase I (low and sporadic disease outbreaks) and II (rapid increase in disease outbreaks) of the(More)
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