[cs-talks] IVC Seminar - Thursday April 28

cs, Group cs at bu.edu
Wed Apr 27 11:08:42 EDT 2016

High-Dimensional Multi-Manifold Data Analysis via Parsimonious Modeling

April 28, 2-3pm

MCS 148

Prof. Ehsan Elhamifar

One of the most fundamental challenges facing scientists and engineers across different fields is the large amounts of high-dimensional data that need to be analyzed and understood. In this talk, I present robust, efficient and provably correct algorithms, based on sparse representation theory and convex optimization, for the analysis of high-dimensional datasets by exploiting their underlying low-dimensional structures. I talk about algorithms for the two fundamental problems of clustering and subset selection in unions of low-dimensional manifolds and discuss the robustness of the algorithms to data nuisances. I show that these tools effectively advance the state-of-the-art data analysis in real-world problems, such as segmentation of motions in videos, clustering of images of objects, video summarization and learning nonlinear dynamical models.

Ehsan Elhamifar is an Assistant Professor in the College of Computer and Information Science and is affiliated with the Department of Electrical and Computer Engineering at Northeastern University. Previously, he was a postdoctoral scholar in the Electrical Engineering and Computer Sciences Department at UC Berkeley. He obtained his PhD in Electrical and Computer Engineering from the Johns Hopkins University. Prof. Elhamifar’s research areas are machine learning, computer vision and optimization algorithms. He is broadly interested in developing efficient, robust and provable algorithms that can address challenges of complex and massive high-dimensional data and works on applications of these tools in computer vision.
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