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 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of a hybrid, vision-centric framework that integrates intuitive and deliberate visual processing methods. The goal is to create more robust visual intelligence capabilities that can adapt to complex, real-world environments beyond static image datasets. The $111,878 award, effective February 15, 2025 through January...
This $1,200,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to democratize access to large-scale visual AI models. The research will focus on developing novel learning approaches that reduce the data, computation, and expert knowledge required to create and deploy specialized computer vision applications. Key objectives include enhancing inference efficiency, enabling fast model specialization...
This $299,964 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enhance computer vision and shape recognition capabilities. The primary institution, The Research Foundation for the State University of New York doing business as Stony Brook University, will develop novel deep learning and graph-based frameworks to teach computers to better perceive and understand the shape configuration and...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $336,954 over three years starting on October 1, 2024 to Carnegie Mellon University. The project, titled "Collaborative Research: III: Medium: Retrieval-Enhanced Machine Learning Through an Information Retrieval Lens," focuses on developing novel architectures and optimization solutions that enable information access to...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research to develop robust machine learning methods for imaging applications. The $113,018 award to Michigan State University, with a project period from April 1, 2025 to March 31, 2030, aims to advance supervised and unsupervised learning approaches that can reconstruct high-quality images from limited or corrupted measurements. The...
This Project Grant award of $173,777, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program, supports research at the Rochester Institute of Technology (RIT) to develop new general-purpose neural networks for image processing. The goal is to create embedding models with self-consistency constraints derived from the principles of multicalibration, which will enable more trustworthy and robust visual representations. This...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant award, valued at $174,604, focuses on advancing the field of robotic visual perception. The key objectives are to develop novel frameworks for human-centered visual understanding and human-like visual learning, which are expected to enhance human-robot collaboration and benefit areas like disaster response, security, and healthcare. The project proposes to create a...
This $120,000 CAREER award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the Massachusetts Institute of Technology (MIT) to develop innovative computer vision approaches for global-scale biodiversity monitoring. The project aims to address key challenges in identifying rare, similar, and novel species categories in large-scale natural imagery datasets, adapt models for specialized tasks and new...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant award of $599,945 will fund the development of a 3D computer vision framework that integrates multiple sensing modalities, including RGB cameras, depth sensors, LiDAR, and event cameras. The research aims to enhance feature extraction, tracking, and large-scale scene reconstruction to improve perception accuracy and adaptability in unstructured environments. Key...