- This event has passed.
Drs. Huang, Kovalsky, and Marzuola, UNC – Mathematical and Statistical Analysis of Compressible Data on Compressive Network
February 2 @ 3:30 pm - 4:30 pm
Title: Mathematical and Statistical Analysis of Compressible Data on Compressive Network
We present an overview of the FRG project on the “Mathematical and Statistical Analysis of Compressible Data on Compressive Networks”.
Compressible features of data include the low-rank, low-dimension, sparsity, and features from the classification/categorization/clustering process.
Discovering such compressible features is a major challenge in data analysis, which we will address using hierarchical decompositions derived from
spectral, statistical, and algebraic geometric analysis of data. We also study how to construct optimally defined compressive networks, specifically tailored
to the discovered compressible features, to enable an accurate and efficient extraction and manipulation of sparse representations in complex and
high dimensional systems in an inherently interpretable manner. Sample ongoing projects include the accurate and efficient representation of layer
potential using algebraic variety, spectral flow and fast computation of the eigensystems, frequency domain based statistical analysis, fast algorithms
for high dimensional truncated multivariate Gaussian expectations, and recursive tree algorithms for orthogonal matrix generation and matrix-vector
multiplications in rigid body Brownian dynamics simulations.