Project Grant 2209974
- 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...
- 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...
- This $364,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports a collaborative research project to develop scalable, high-performance graph-based clustering algorithms for large datasets. The project aims to create a publicly available toolkit that enables efficient, accurate clustering of datasets with billions of entities. Key focus areas include new algorithms for graph construction and...
- 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 $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...
- 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...
- The National Science Foundation awarded a $275,000 Project Grant to the University of Southern California under the Mathematical and Physical Sciences program (CFDA 47.049) from July 1, 2023 to June 30, 2026. The grant funds the development of new exploratory data analysis and inference methods for complex data that lack fundamental vector space properties. The university will create a practical toolkit of theoretically sound, user-friendly tools to enable common data analysis tasks like...
- This $149,961 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support research into optimal subdata selection methods using mixture-of-experts models to account for heterogeneity in large datasets. Specifically, the awardee, George Mason University, will develop and study subdata selection frameworks and methods based on clusterwise linear regression and logistic-normal mixture models. Information-based optimal subdata...
- The National Science Foundation awarded a $116,000 Project Grant to the University of Massachusetts Dartmouth under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The two-year award will support research from September 2021 to August 2023 to develop computationally efficient fuzzy clustering algorithms for distributed big data applications. As the parent organization of the awardee, the University of Massachusetts will oversee delivery of research...
- This $152,997 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Southern California (USC) on computer-intensive statistical inference methods for high-dimensional and massive datasets. The project aims to develop efficient, scalable, and statistically robust inferential procedures for two classical problems - change point detection/identification and computationally-aware statistical...
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, categorical, and mixed data types. Undergraduate and graduate students will directly participate in the research and be trained in data analysis. The results will enrich course content and improve the flexibility and robustness of model-based clustering methods for skewed clusters, outliers, and missing data. The funding period is from July 15, 2022 to June 30, 2025.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 7/15/22 |