Project Grant 2515923
- 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 $275,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support collaborative research to develop new statistical approaches for comparing and aligning networks. The research will focus on investigating optimal transport-based distances for Markov embeddings of networks, developing new methods for network alignment and comparison, and establishing theoretical results about these approaches. The...
- This $180,000 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of novel community detection tools and frameworks for analyzing weighted network data, with a focus on applications in bioinformatics and biological science. The primary goals are to identify highly correlated gene modules by leveraging covariance or correlation matrix representations, and to provide a systematic, computationally...
- 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...
- The National Science Foundation (NSF) awarded a $146,738 Project Grant under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to The Washington University for the collaborative research project "Statistical Inference for Multivariate and Functional Time Series via Sample Splitting." The project aims to develop novel nonparametric inference procedures that can accommodate high dimensionality and diverse data-generating processes for analyzing multivariate and...
- This $155,000 project grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) will develop new simulation-based inference (SBI) methods. These innovations aim to empower scientists to make better use of complex models across diverse domains such as genetics, ecology, biology, economics, and psychology, supporting more scalable, efficient, and reliable decision-making. The project will address two core challenges...
- The University of Pittsburgh was awarded a $150,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop new statistical inference tools and theories for analyzing data with network dependency. Specifically, the award will support modeling and inference research for data exhibiting complex interpersonal dependency characterized by networks, with a focus on developing...
- This $400,000 National Science Foundation Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of statistical methods and machine learning techniques for analyzing complex structured and count data. Over a three-year period ending in August 2025, the University of Washington will advance the state of knowledge in big structured and count data analysis through two tracks of research. The first track will focus on revising and...
- This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences program (CFDA 47.049) will fund research to develop scalable subsampling algorithms for statistical inference on large-scale networks. The $150,000 grant, awarded on August 15, 2024, will be used to investigate theoretical properties of these subsampling methods and apply them to network data from social and natural sciences, including the study of mindfulness-based therapies for disorders...
- This $229,461 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the development and analysis of novel self-supervised probabilistic graph structure learning models. The goal is to uncover latent representations hidden within large datasets, which can provide valuable insights across diverse applications like cancer research and environmental analysis. The research will involve creating advanced mathematical models,...
This Project Grant award for $139,848 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research aimed at developing broadly applicable statistical tools for drawing inferences from relational data, such as that collected from biological networks. The three key research aims are: 1) Developing uncertainty quantification methods for array prediction problems with complex missingness patterns, 2) Creating an assumption-lean inference framework for network-linked regression data, and 3) Developing variants of the permutation test for two-sample problems involving network data. This research will utilize a jointly exchangeable array framework to ensure the methods are widely applicable across scientific domains. The award will also provide research training opportunities for graduate students. The project will be carried out by the Washington University in University City, Missouri over a 3-year period from September 2025 to August 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $139.8k | 8/14/25 |