Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2021

341  The University of Texas Rio Grande Valley  (84637)

Principal Investigator: Mohanty, Soumya

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 551,889

Exceeds $250,000 (Is it flagged?): Yes

Start and End Dates: 4/15/21 - 4/14/22

Restricted Research: YES

Academic Discipline: N/A

Department, Center, School, or Institute: Physics and Astronomy

Title of Contract, Award, or Gift: Finding unmolded signals in noisy big data: An integrated multi-disciplinary approach

Name of Granting or Contracting Agency/Entity: U.S. Department of Defense

Program Title: Basic, Applied, and Advanced Research in Science and Engineering
CFDA Linked: Basic, Applied, and Advanced Research in Science and Engineering


A collection of multidisciplinary research projects will bring together investigators from the departments of Physics and Astronomy (PHYA), Computer Science (CS), and Electrical and Computer Engineering (ECE) at UTRGV, a Hispanic Serving Institution, to address open challenges in the detection and estimation of rare signals embedded in “noisy big data”, i.e., multi-sensor, high noise, high velocity, and high-volume data. The acquisition of a Graphics Processing Unit (GPU) cluster will provide the computing firepower needed to run a suite of novel methods at scale on large data volumes. These methods will: (i) search for unmodeled chirp signals that are ubiquitous in nature and man-made systems; (ii) remove nonstationary high power interference from data; (iii) develop novel multiview learning methods to detect signals across a multi-sensor and (or) multi-messenger array; (iv) search for optimum neural architectures in artificial intelligence based signal detection. A discipline specific project will use the GPU cluster to address Multi-core/Many-core Cache Memory Simulation and Design, an area of computer architecture design that has a broad impact across all of high performance computing. The success of the multidisciplinary projects will be assessed on real world testbeds. Our primary target is data is from gravitational wave (GW) detectors. GW data is constituted of streaming time series from a sensor array that shares challenges of high noise, non-stationarity, clutter and interference, with many other areas. Thus, the findings from the research projects above will be applicable out of the box in many fields. At the same time, our search methods, all of which are novel in GW data analysis, could uncover new signals, leading to revolutionary scientific payoffs. SAMs 1.1.1 The research projects will engage integrated teams of PHYA, CS, and ECE undergraduate and graduate students. Through courses designed to use the GPU cluster and through participation in the above projects, students will acquire valuable skills in computing and data science while experiencing a multidisciplinary research environment. This will impart to students valuable teamwork and cross-discipline knowledge that is increasingly in demand for the 21st century job market. Students will engage in developing automated workflows for running large-scale data analysis pipelines, perform statistical analysis and interpretation of data, get the opportunity to become co-authors on journal publications, and build technical writing and oral/poster presentation skills. High school students will be attracted to STEM areas by leveraging a well-established summer research program at UTRGV. Graphical user interfaces, developed by graduate and undergraduate students above, will be used to engage high school students in real world data analysis in an exciting frontier science area that generates a lot of public excitement. Training in basic programming and statistical data analysis skills will be acquired as part of these activities. The GPU cluster will foster a strong multidisciplinary collaboration at UTRGV in areas of research that are of interest to the Department of Defense. It will significantly enhance research and research-related education as outlined above and open new funding opportunities.

Discussion: No discussion notes


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