Project Grant 2427948
Award Date
9/1/24
Completion Date
8/31/27
Dollars Obligated
$200K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Pittsburgh, PA 15213, USA
Similar Awards
This $299,998 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research at Carnegie Mellon University to develop new big data algorithms that are robust to adversarial input. The key focus areas include: 1) adversarial robustness in black-box and white-box streaming settings, and 2) adaptive data analysis with bounded space. The research team will also explore emerging attack...
This Project Grant award for $180,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), aims to advance the mathematical understanding of trustworthy artificial intelligence (AI) algorithms for threat detection. The primary objectives are to investigate few-shot learning techniques, which can build effective models from a very limited number of data samples, and to explore few-shot graph generation methods,...
This federal Project Grant award, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), will support research on optimization techniques and geometrically constrained neural networks for threat detection. The $250,000 award to Colorado State University (CSU) will run from September 1, 2024, to August 31, 2027. The research will focus on adapting and tuning mathematical tools, including geometry, topology, optimization, and machine learning,...
This $207,737 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to develop a new class of machine learning models called "Programmatic Foundation Models" that can efficiently analyze large-scale satellite, aerial, and ground imagery. The goal is to create interpretable, robust AI models that can understand global and local phenomena from images, providing insights...
This $180,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences supports the development of a multimodal transformer-based model for time-series prediction and spatiotemporal analysis. The project aims to create algorithms for forecasting time-series, predicting spatial dynamics, and detecting anomalies, which can be applied to data analysis and high-consequence decision-making. The research will address how to utilize contextual information with...
The National Science Foundation (NSF) awarded a Project Grant of $599,649 to Carnegie Mellon University (CMU) under the Computer and Information Science and Engineering (CFDA 47.070) program. The goal of this 3-year project, which commenced on April 15, 2024, is to develop perception systems that can infer the 3D structure of generic objects or scenes from 2D images, even with partial observations. Key technical efforts will include formulating mechanisms for learning novel 3D generative...
The federal Project Grant award of $257,623.00 was provided by the Office of Naval Research under the Basic and Applied Scientific Research program (CFDA 12.300) to Carnegie Mellon University (CMU) on September 1, 2024. The grant supports fundamental research to explore theoretical and algorithmic questions related to foundation models in machine learning. This includes collaborative research with a sub-awardee, the University of California, Berkeley, to contribute to all aspects of the...
This $330,000 Project Grant award was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The project will develop new AI-based techniques to detect anomalous behavior of individuals and groups based on their GPS location data. The investigators will create a temporal graph neural network (GNN) to characterize normal and abnormal location behavior patterns, and a dynamic graph anomaly detection (DGAD) approach to...
Carnegie Mellon University was awarded a three-year, $750,205 Project Grant from the National Science Foundation to conduct research titled "HEMISPHERIC AND TOPOGRAPHIC NEURAL ORGANIZATION OF HIGH-LEVEL VISUAL REPRESENTATIONS." The grant is part of NSF's Social, Behavioral and Economic Sciences program (CFDA 47.075), which aims to support basic research and education in social, behavioral and economic sciences. Under this award, Carnegie Mellon University researchers will investigate...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, awarded to Carnegie Mellon University, aims to provide a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative models. The $400,000 award will focus on three key research thrusts: 1) developing new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2)...