1st Annual Workshop on Data Sciences (AWDS 2015)

Theme: High-Dimensional Data Analysis

Tennessee State University
Nashville, Tennessee

April 16-17, 2015

(updated 4/15/2015) Important information about parking and the location of the workshop. click Here

Registration is Closed (
for more information, please contact Dr. Sekmen at asekmen@tnstate.edu)

The workshop will take place in Tennessee State University, College of Engineering on April 16-17, 2015.

This workshop will bring data science researchers from mathematics, engineering, and science together to discuss the state-of-the-art topics in data sciences. The emphasis will be on subspace clustering and high-dimensional data analysis. Additionally, two sessions will be included to provide mathematical background (with fundamentals from real-analysis and advanced linear algebra) for graduate students and faculty who might be interested in data sciences.

Nashville Skyline

 

Sponsored by NASA EPSCoR, NSF, and TN-SCORE 

        Nasa EPSCoR                     NSF                      TN-Score

       

 


 

WORKSHOP HIGHLIGHTS

Keynote Speakers

(1) Dr. Akram Aldroubi, Professor of Mathematics, Vanderbilt University
           Subspace Clustering and Its Applications

(2) Dr. René Vidal, Associate Professor of Biomedical Engineering, Johns Hopkins University
           Algebraic, Sparse, and Low Rank Subspace Clustering


Mini Courses

Real Analysis Fundamentals
         - Various spaces with examples:
                  * Normed Vector Space,
                  * Inner Product Space,
                  * Metric Space,
                  * Topological Space,
                  * Banach Space, and
                  * Hilbert Space.
         - Subspaces and their properties
                  * Subspace angles and distances
         - Projections
         - Introduction to manifolds
         - Infinite dimensional spaces

Linear Algebra for Data Clustering
         - Various matrix norms
         - Singular Value Decomposition (SVD) and its geometric meaning
                  * Closest rank-k approximation
         - Principle Component Analysis
         - Spectral Clustering
         - Subspace segmentation problem
                    * Special case: Motion segmentation problem