Title: Data-driven large eddy simulation of atmospheric turbulence Abstract: Over the last few years, machine learning has been critical in science and engineering and emerged as a data-driven turbulence model. However, machine learning depends on training data from previous experiments on turbulent flows. Typically, training data capture only a fraction of the active scales of […]
Title: Scattering Resonances Through Subwavelength Holes and Their Applications in Imaging and Sensing Abstract: The so-called extraordinary optical transmission (EOT) through metallic nanoholes has triggered extensive research in modern plasmonics […]
Title: Using Mutual Information of Hypergraph Compressions for Clustering Abstract: Hypergraphs are often used to represent higher order observed relationships between subjects of study. In particular, the vertices of a […]
Title:Geometric Scattering on Measure Spaces Abstract: Geometric Deep Learning is an emerging field of research that aims to extend the success of convolutional neural networks (CNNs) to data with non-Euclidean […]
Title: Identification of the order of semilinear subdiffusion with memory Abstract: See attached abstract
Title:Inferring birth and death rates from population size time series data Abstract: Models of population dynamics are usually formulated and analyzed with net growth rates. However, separately identifying birth and […]
Title: A common pathway to cancer: oncogenic mutations abolish p53 oscillations. Abstract: The tumor suppressor p53 oscillates in response to DNA double-strand breaks, a behavior that has been suggested to be […]
Title: PLSS: A Projected Linear Systems Solver (joint work with Michael Saunders) Abstract: Iteratively solving linear systems has proven to be useful for many large applications. Projection methods use sketching […]
Title: Modeling size distributions and collisions in cloud microphysics Abstract: Feedbacks between a warming atmosphere, emission of aerosols, and clouds and precipitation are one of the most difficult aspects for […]
Title: Towards Understanding the Success of First Order Methods in Training Mildly Overparameterized Networks Abstract: For most problems of interest the loss landscape of a neural network is non-convex and […]
Title: Understanding SARS-CoV-2 viral rebounds with and without treatments. Abstract: In most instances, the characteristics of SARS-CoV-2 mirror the patterns of an acute infection, with viral load rapidly peaking around […]
Title: The Hartman-Watson distribution: numerical evaluation and applications in mathematical finance Abstract: The Hartman-Watson distribution appears in several problems of applied probability and financial mathematics. Most notably, it determines the […]
