Title: Data science and applications in dynamic topic modeling Abstract: The shockwaves of the big data boom have thrown into sharp relief the critical need for domain-driven, large-scale data analytic […]
Title: What is the best shape? Geometric problems arising in aggregation models Abstract: How do pair interactions shape the large-scale behaviour of a cloud of particles (animals, social agents ...) […]
Title: Computational Approaches to Optimization Problems in Inhomogeneous Rods and Plates Abstract: In this talk, we will show the experiments of the vibration of plates to generate Chladni's figures and discuss computational approaches to optimization problems of inhomogeneous rods and plates. We consider both optimization of eigenvalues and localization of eigenfunctions. These problems are motivated by physical problems […]
Title: Understanding scientific fields with network analysis and topic modeling Abstract: As scientific disciplines get larger, it becomes impossible for an individual researcher to be familiar with the entire body of […]
Title: Approximating Quasi-Stationary Distributions with Interacting Reinforced Markov Chains Abstract: An important question in ecology is what conditions must be met for a population of interacting species to coexist. In […]
Title: Kolmogorov, Onsager and a Dyadic Model for Turbulence Abstract: We will briefly review Kolmogorov’s ( 41) theory of homogeneous turbulence and Onsager’s ( 49 ) conjecture that in 3-dimensional […]
Title: Mathematical modeling of polymerization processes in physiology Abstract: Polymerization, or aggregation, is essential for many physiological systems. For example, the emergence of a fibrin polymer mesh during the formation of […]
Title: Randomized Sums of Graph Spectra Abstract: The adjacency matrix of an Erdős-Rényi-Gilbert graph is a random symmetric matrix whose entries are Bernoulli random variables. These entries, modulo the constraints […]
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. […]
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 geometric structure. Despitebeing in its relative infancy, this field has already found great success in many applications such as recommender systems, computer graphics, and traffic […]
