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Award Detail

Doing Business As Name:University of Texas at Arlington
  • Li Wang
  • (312) 722-0040
  • Ren-Cang Li
Award Date:07/11/2020
Estimated Total Award Amount: $ 290,135
Funds Obligated to Date: $ 290,135
  • FY 2020=$290,135
Start Date:09/01/2020
End Date:08/31/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.049
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Advanced Models and Algorithms for Large-Scale High-Dimensional Probabilistic Graph Structure Learning
Federal Award ID Number:2009689
DUNS ID:064234610
Parent DUNS ID:042000273
Program Officer:
  • Eun Heui Kim
  • (703) 292-2091

Awardee Location

Street:701 S Nedderman Dr, Box 19145
Awardee Cong. District:06

Primary Place of Performance

Organization Name:University of Texas at Arlington
Street:701 South Nedderman Drive
Cong. District:06

Abstract at Time of Award

With the rapid development of science and technology, vast amount of data has been and is being collected in nearly all fields of science and engineering for various practical purposes, such as medical data for advancing human knowledge in diseases and treatments to save lives, image data from the solar system and beyond for human's next frontier in space, and social network data for better understanding the society and economic developments. But to make these aims realities, data must be soundly analyzed to uncover what matters. While data analysis has been around for centuries, today's data is much bigger in amount and dimension and more complex, presenting notorious challenges to modern data analysis. Often real world data is noisy and conceals inherent hidden structures that can be concisely represented by graphs that use nodes for objects/events and edges for relations between nodes. Existing approaches rely on pre- and heuristically constructible graphs show their inability in handling nowadays complicated data. This project aims to change the status quo by developing novel mathematical models and efficient computational tools for scientists, engineers, and medical professionals who can use the models and tools to unearth the hidden structures to achieve scientific discoveries previously considered impossible. The principle investigators will integrate their research activities of this project with teaching and education, and will train undergraduate and graduate students in computational mathematics, data science, and interdisciplinary studies. The proposed research will result in advanced models and efficient algorithms for graph-based machine learning. Two major distinctions from existing graph-based learning methods are (1) new models have a built-in probabilistic component that can robustly deal with high noisy data, and (2) a dynamic graph structure learning component that can uncover hidden graph structures concealed in real world data and yet not obvious enough to be pre- or heuristically constructed. The models have much wider applicability than existing graph-based learning methods because graph structure is now a variable that will be optimized over so as to yield an optimal hidden graph structure for a given data set, and the algorithms not only are capable of producing robust embeddings with simultaneously learned hidden graph structures but also will be made practical for big data through landmark and low-rank matrix approximation strategies. The proposed research will have potentially high impacts scientifically in areas where analyzing high-dimensional datasets plays critically important roles, such as data visualization, discovering structural patterns in computational biology, brain networks and other areas. The project will open up a new research direction in statistical machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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