1st Annual Workshop on Data Sciences
(AWDS 2015)

Theme: High-Dimensional Data Analysis

91制片厂
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 91制片厂, College of Engineering on April 16-17, 2015.

This workshop will bring data science researchers from mathematics, engineering, and science听together to discuss the state-of-the-art topics in data sciences. The emphasis will be on听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

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Sponsored by NASA EPSCoR, NSF, and TN-SCORE听

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WORKSHOP HIGHLIGHTS

Keynote Speakers

(1) , Professor of Mathematics, Vanderbilt University
听 听 听 听 听 听Subspace Clustering and Its Applications

(2) , Associate Professor of Biomedical Engineering, Johns Hopkins University
听 听 听 听 听 听Algebraic, Sparse, and Low Rank Subspace Clustering


Mini Courses

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

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