Excellence in Research: Collaborative Research: Strengthen the Foundation of Big Data Analytics via Interdisciplinary Research among HBCUs

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This project will be implemented by an interdisciplinary research team from two neighboring historical Black Colleges and Universities (HBCUs), Prairie View A&M University (PVAMU) and Texas Southern University (TSU), with the goals of strengthening the theoretical foundation of big data analytics and developing a novel deep learning software package based on this enhanced foundation. Challenges around big data impacts many areas and encourages exciting further investigation to understand the complex requirements of real-world applications. Specifically, this team aims to 1) improve the understanding and explainability of deep neural networks with the quantum theory of modern physics; 2) enhance the mathematical foundation of deep neural networks; 3) increase the computation efficiency of the deep learning training process with new algorithms that will scale; and 4) implement the deep learning research innovations into a new deep learning software package to deploy in cloud computing and High-Performance Computing platforms.This project will aid the research in a wide range of areas of applications, from academic studies to the oil and gas industry and the military by offering deep learning models and improved computational efficiency and scalability. Students will benefit from the opportunity to contribute to the forefront of science research and technology. The project will broaden participation by opening stimulating research opportunities to a diverse group of underrepresented minority students. The two campuses and the members of the interdisciplinary team complement each other; by exchanging and sharing expertise and students the collaboration therefore has a beneficial and synergistic effect on multiple levels.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.