The National Science Foundation (NSF) awarded a $350,796 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to South Dakota State University (SDSU) to develop statistical methods for detecting and characterizing latent subpopulations within large, complex datasets. The research aims to create flexible, stable, and trustworthy models for "few-shot" or "one-shot" learning problems, where there are only a few examples in each data category. The...
This $199,280 federal Project Grant award from the National Science Foundation's (NSF) Integrative Activities program (CFDA 47.083) will fund a workshop for artificial intelligence (AI)-powered materials discovery at the University of South Dakota (USD). The goal is to unite researchers from nine EPSCoR jurisdictions with experts in AI, engineering, materials science, physical science, and data science to create a world-class, data-driven materials research platform. The workshop will leverage...
This $126,025 five-year Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance machine learning techniques through the development of new mathematical tools for analyzing and visualizing complex, high-dimensional data. The key objectives are to create robust manifold learning algorithms that can handle noisy data, preserve local and global geometric details, and effectively cluster collections of manifolds. The...
This $329,432 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to enhance the research and training capacity in scientific machine learning (SciML) for undergraduate students at Texas A&M University-San Antonio (A&M-SA), a Hispanic-Serving and primarily undergraduate institution. The project has two key research thrusts: 1) Developing ML-enhanced iterative coupling approaches to model complex...
This $300,000 EAGER award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports a project by South Dakota State University (SDSU) to analyze collaboration patterns and socioeconomic impacts of science and technology advancements. The key initiatives of this project include: 1) using machine learning to forecast emerging science and technology areas, 2) evaluating the regional societal and economic impacts of leading research...
The National Science Foundation awarded a $360,723 Project Grant to the South Dakota School of Mines and Technology under the Mathematical and Physical Sciences program (CFDA 47.049) for an REU Site titled "Back to the Future." The grant will support research experiences for undergraduate students from March 1, 2022 to February 28, 2025. As the awardee, South Dakota School of Mines and Technology will administer research opportunities and training for undergraduates to increase their...
This $798,445 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support the acquisition of a field emission scanning transmission electron microscope (FE-STEM) at the South Dakota School of Mines and Technology (SDSM&T). The instrument will enable advanced materials research and characterization across multiple engineering and science fields, including energy conversion and storage, biomaterials, 2D materials, and materials for harsh...
This National Science Foundation (NSF) project grant, awarded under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support collaborative research to develop real-time topological data analysis capabilities for high-rate, nonlinear, and nonstationary dynamic systems. The $266,000 project, awarded on May 15, 2023 to the University of South Carolina, aims to integrate topological data analysis with machine learning to improve predictive modeling and...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $122,648 in funding to Iowa State University over a 3-year period from May 1, 2024 to April 30, 2027. The project aims to deliver mathematical innovations that will improve the reliability and time resolution of machine learning algorithms for national security applications, such as rapid detection and classification of potential threats. Key objectives...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
This $124,943 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research by South Dakota State University (SDSU) on topological machine learning and its applications to cryogenic electron microscopy (cryo-EM). The key objectives are to:
Develop new mathematical and computational techniques for understanding the symmetries and geometric structures represented by neural networks, with the goal of improving their performance and rigorous applications.
Apply these topological machine learning insights to address the inverse problem in heterogeneous cryo-EM imaging, where particle images of an unknown sample must be matched to an underlying manifold representation.
Collaborate with Jefferson High School in Sioux Falls, South Dakota to engage underrepresented students in exploring advanced mathematics beyond the classroom curriculum.
The award period runs from September 1, 2024 to August 31, 2026. This project aims to advance fundamental knowledge at the intersection of machine learning, mathematics, and biology, while also promoting STEM education and diversity.