This $250,000 project grant was awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, Los Angeles (UCLA). The project aims to develop generative artificial intelligence (AI) frameworks to aid scientific reasoning and accelerate sustainable development. Specifically, the grant will fund the creation of new generative AI architectures, objectives, and techniques to efficiently...
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...
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 $400,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework and tools for advancing data-centric artificial intelligence (AI) through generative approaches to feature space reconstruction. The project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search...
This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models. The goal is to overcome key challenges in scientific inverse design and accelerate scientific discovery, with a focus on advancing the frontiers of artificial...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to democratize the use of large visual learning models by reducing the computational, data, and expertise requirements needed to create and deploy such models. The $1.2M award to Georgia Tech Research Corporation will fund research on specialized learning approaches for fast model customization with limited data, efficient inference algorithms...
This $400,000 Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The project aims to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models, in order to increase the accountable and safe use of these advanced AI systems and mitigate potential harms. The key research thrusts involve: 1) new...
This $100,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the planning and development of a proposal to study the risks and benefits of generative AI-based systems for security and privacy. The University of Kansas Center for Research Inc., a non-profit subsidiary of the University of Kansas, will lead this planning effort. The goal is to create a fully realized proposal that addresses...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the University of California, San Diego (UCSD) to develop theoretical frameworks and computational methods for reconstructing complex 3D shapes using neural implicit representations. The key objectives are to enable the reconstruction of 3D shapes with intricate topologies, such as objects with holes, and to allow...
This Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop a hybrid, vision-centric framework that integrates intuitive and deliberate visual processing to create more robust visual intelligence. The $111,878 award to New York University (NYU) supports research focused on three primary directions: 1) advancing vision-centric parametric knowledge through techniques like visual...
This Project Grant award of $100,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of INFINIGEN, a free and open-source software program that uses procedural generation to create realistic-looking 3D scenes and annotated images for training AI systems, particularly in computer vision. The project aims to address the data challenge in 3D vision by generating an infinite number of synthetic images that can be used to teach computers to see 3D shapes from 2D images. The award will fund initial planning and community engagement efforts to prepare for the full development of INFINIGEN as large-scale infrastructure that can benefit multiple CISE disciplines, including computer vision, computer graphics, machine learning, and robotics. The project will also integrate research training, course development, and outreach activities to support education across K-12, undergraduate, and graduate levels.