Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
2374 The University of Texas at San Antonio (144262)
Principal Investigator: Markopoulos, Panagiotis
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 195,401
Exceeds $250,000 (Is it flagged?): No
Start and End Dates: 11/15/22 - 5/10/24
Restricted Research: YES
Academic Discipline: VPREDKE
Department, Center, School, or Institute: Matrix AI Sponsorships
Title of Contract, Award, or Gift: (YIP) Theory and Efficient Algorithms for Dynamic and Robust L1-norm Analysis of Tensor Data
Name of Granting or Contracting Agency/Entity:
Rochester Institute of Technology
CFDA: 0
Program Title: none
Note:
SAMs 1.1.1; The main objective of the research project is to develop theory and efficient algorithms for dynamic and robust data analysis, based on robust formulations, such as L1-norm projection/error. We will study stochastic/dynamic algorithms and investigate their theoretical underpinnings. The formal complexity and convergence of the proposed methods will be studied. Next, we will expand these methods for the analysis of tensor data (e.g., in the form of Tucker or CPD decomposition). Specific considerations will be made for adaptation to various changes of the processed tensor (e.g., new entries/slabs). Emphasis will be placed on the development of scalable algorithms that can be used in systems with limited computational resources. The developed algorithms will be prototyped/coded and rigorously tested in various applications, including image/video processing and compression of artificial neural networks. Results will be presented in PI/PM meetings and other peer-reviewed venues.
Discussion: No discussion notes