Project Grant 2311103
- This $150,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of new clustering techniques and software packages at San Jose State University. The awardee will create a family of versatile mixture models to analyze mixed-type data with asymmetry, outliers, and missing values. Novel statistical approaches and latent class models will allow the techniques to handle high-dimensional, continuous, discrete,...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
- This three-year, $359,998 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of statistical and computational tools for analyzing high-dimensional heterogeneous data. Specifically, the awardee, Columbia University, will create new methodologies for clustering and identifying latent structures in complex data involving multiple attributes and relationships. The research has three parts. The first will develop...
- The National Science Foundation awarded a $150,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Illinois for the period of August 15, 2022 through July 31, 2025. The grant funds research to develop and study optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the principal investigators will first develop and analyze subdata selection for clusterwise linear...
- This $100,000 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research at Purdue University on advanced probabilistic models and methods for cutting-edge machine learning techniques. The project aims to bring mathematical rigor to complex systems in image processing, reinforcement learning, and generative AI, leveraging recent breakthroughs in stochastic analysis to develop new feature extraction, decision-making, and...
- This $363,931 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports a collaborative research project between multiple institutions to develop scalable clustering algorithms for processing large datasets. The goal is to create new clustering algorithms that can efficiently group billions of data points into meaningful clusters, addressing the limitations of current methods. The work will produce an...
- This $240,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support fundamental research on unsupervised learning and nonlinear dimension reduction. The project aims to develop new statistical frameworks and cutting-edge techniques for analyzing complex, high-dimensional scientific datasets, ranging from single-cell RNA sequencing to astronomy data. Key deliverables include: (1) new empirical Bayes methods for...
- This Project Grant award of $160,000.00 from the National Science Foundation (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) grant program, will support a "Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning" project. The research aims to develop new statistical methods and frameworks for reliable uncertainty quantification in high-dimensional structure learning problems...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at Oregon State University (OSU) to develop flexible and scalable cluster analysis methods for longitudinal microbiome data. The goal is to identify functional groups of microbes that may serve as biomarkers of host or ecosystem health and potential therapeutic targets. Specifically, the project aims to: 1) innovate functional cluster...
- The National Science Foundation Division of Mathematical Sciences awarded Purdue University $182,271 under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to support research on bulk-interface coupled response in novel materials from September 1, 2022 to August 31, 2025. Specifically, the award will fund the development of new mathematical models to analyze migration patterns of interfacial layers and microfluidic flows in composites containing multi-phase bulks and...
This National Science Foundation (NSF) grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $160,000 to Purdue University from July 1, 2023 to June 30, 2026. The project will develop new statistical methods for clustering and classifying multivariate data using copula-based density mixture models. This work aims to improve the reliability of clustering solutions for applications in areas like drug development and gene interaction analysis. The project will also engage undergraduate and graduate students, with a focus on increasing participation from underrepresented groups in statistical sciences. The proposed algorithms are expected to have broad applicability for clustering biological entities, text documents, and other high-dimensional multivariate data.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $160.0k | 6/16/23 |