Project Grant 2515679
- The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Trustees of Columbia University in the City of New York to develop methods for efficient data removal and model interpretability in high-dimensional machine learning and artificial intelligence systems. The project aims to create computationally efficient algorithms that can approximate the output of a model trained without a given...
- This $174,999 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports a research project titled "Data Integration for Heterogeneous Data: A General Framework for Distribution Shift, Posterior Drift and Block Missing Data." The project aims to develop a new "Representation Retrieval (R2) Framework" to address key data integration challenges posed by heterogeneous datasets, including distribution...
- The National Science Foundation (NSF) awarded a $195,479 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The grant supports the development of new statistical machine learning approaches and theory, termed "minipatch learning", to analyze massive and complex datasets commonly found in domains like biomedicine, genomics, and neuroscience. The project aims to enable faster computation and...
- This three-year, $260,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will fund research towards designing optimal statistical learning procedures through precise medium-dimensional asymptotic analysis. The grantee, Columbia University, will develop a novel analytical framework to quantitatively characterize the performance of diverse learning algorithms and provide guidance on...
- The National Science Foundation (NSF) awarded a $163,000 Project Grant under the Mathematical and Physical Sciences grant program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The goal of this 3-year research project is to build mathematical foundations for understanding the behavior and limitations of modern machine learning systems, with a focus on how AI models represent and learn from data. The research aims to develop general principles for how features...
- 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 $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- Columbia University was awarded a $120,929 project grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund collaborative research on statistical inference methods for high-dimensional spatial-temporal process models from July 1, 2021 to June 30, 2024. As part of the Mathematical and Physical Sciences program's goal of advancing scientific knowledge and understanding of national...
- The National Science Foundation (NSF) awarded a $350,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Johns Hopkins University. The grant supports a research program to develop "Any-Dimensional Equivariant Learning" algorithms that can effectively handle input data of varying sizes, addressing a key limitation of modern artificial intelligence (AI) systems. The project aims to create parameterized families of solution mapping...
- This federal Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports collaborative research at Columbia University focused on developing incentive-driven algorithms and machine learning tools to improve the quality and representativeness of data used in high-stakes decision-making. The $575,000 award, with a project period from August 1, 2025 to July 31, 2028, aims to design data...
The National Science Foundation (NSF) awarded a $155,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to The Trustees of Columbia University in the City of New York. The grant, awarded on August 1, 2025 with a completion date of July 31, 2028, will develop powerful new methods for integrating diverse and evolving datasets - a critical challenge in modern science and technology. The research will create flexible and reliable tools to automatically integrate complex information, which will be applied to assess the safety of autonomous vehicles. The project will also develop open-source software and educational materials to benefit the broader scientific community. This research will establish dependable methodologies and theoretical foundations for data integration through three interconnected thrusts: transfer learning, temporal distribution shift adaptation, and latent structure learning across multiple data views.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $155.0k | 7/30/25 |