Applied Math Seminar
Applied Math Seminar – Evie A. Malaia, Communicative Disorders, University of Alabama
302 Gordon Palmer HallTitle: Mathematical models in cognitive neuroscience: advances and opportunities Abstract: High prevalence of neurodegenerative (Parkinson’s, Alzheimer’s) and neurodevelopmental (Autism spectrum disorders, ADHD) disorders in modern population increased the demand for precision therapeutic interventions. However, the current understanding of how those diseases develop and affect brain processing over time is incomplete, and testing of in-vivo interventions
Applied Math Seminar – Di Liu, Michigan State University
302 Gordon Palmer HallTitle: Multiscale Modeling and Computation of Optically Manipulated Nano Devices Abstract: We present a multiscale modeling and computational scheme for optical-mechanical responses of nanostructures. The multi-physical nature of the problem is a result of the interaction between the electromagnetic (EM) field, the molecular motion, and the electronic excitation. To balance accuracy and complexity, we adopt the semi-classical
Applied Math Seminar – Shibin Dai, University of Alabama
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Mean field models for thin film droplet coarsening Abstract: A thin liquid film coating a solid substrate is unstable and the late stage morphology is essentially quasiequilibrium droplets connected by an ultra thin film. Droplets exchange mass and coarsening occurs — the total number of droplets N(t) decreases while the average size of droplets
Applied Math Seminar – Brendan Ames, University of Alabama
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Exact clustering by semidefinite programming under the heterogeneous planted cluster model. Abstract: Clustering, or the sorting of data into groups of similar items, is a fundamental task in machine learning and statistical analysis. Until recently, most computational methods for clustering relied on heuristics with no theoretical guarantee ensuring that clusters present in the data
Applied Math Seminar – Yuhui Chen, University of Alabama
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Bayesian Nonparametric Models and Its Applications Abstract: Polya tree priors are random probability measures that are easily centered at standard parametric families, such as the normal. As such, they provide a convenient avenue toward creating a parametric/nonparametric model for data. Briefly, we center a Polya tree at an initial parametric guess on data; then
Applied Math Seminar – Shibin Dai, University of Alabama
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Degenerate Diffusion in Phase Separations Abstract: Phase separations are widely observed phenomena in materials science. One model of phase separation is the Cahn-Hilliard equation with a smooth double-well potential, and with phase-dependent diffusion mobilities. The latter is a feature of many materials systems and makes the analysis and accurate numerical simulations challenging. In this
Applied Math Seminar – Steven Wise, University of Tennessee
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Convergence Analyses of some Nonlinear Multi-Level Algorithms for Non-Quadratic Convex Optimization Problems via Space Decomposition and Subspace Correction Abstract: Nonlinear multi-level methods, such as the full approximation storage (FAS) multigrid scheme, are widely used solvers for nonlinear problems. In this presentation, a new framework to analyze FAS-type methods for convex optimization problems is developed.
Applied Math Seminar – Rongjie Lai, Rensselaer Polytechnic Institute
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Understanding Manifold-structured Data via Geometric Modeling and Learning Abstract: Analyzing and inferring the underlying global intrinsic structures of data from its local information are critical in many fields. In practice, coherent structures of data allow us to model data as low dimensional manifolds, represented as point clouds, in a possible high dimensional space. Different
Applied Math Seminar – Trang Dinh, University of Alabama
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Understanding Tensor and Tensor Decompositions Abstract: Tensors are multidimensional arrays that can play a key role in the representation of big data. Decompositions of higher-order tensors have applications in biochemistry, signal processing, data mining, neuroscience, and elsewhere. The talk will present commonly used tensor operations and different types of tensor decomposition. Specifically, it will
Applied Math Seminar – Dengfeng Sun, Purdue University
346 Gordon Palmer Hall 505 Hackberry Lane, Tuscaloosa, AL, United StatesTitle: Improving the Convergence Rate of the Distributed Gradient Descent Method Abstract: This talk presents our recent work on the accelerated Distributed Gradient Descent (DGD) method for distributed optimization problems. We observed that the inexact convergence of the DGD algorithm can be caused by the inaccuracy in the consensus procedure in a distributed optimization setting.