Project Grant 2240160

Award Date 8/15/23
Completion Date 7/31/28
Dollars Obligated $600K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
La Jolla, CA 92093, USA
Similar Awards
The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $600,000 Project Grant to the University of California, San Diego (UCSD) under the Computer and Information Science and Engineering (CFDA 47.070) program. The 3-year grant, effective July 1, 2023, supports research to develop dynamic neural network architectures that can efficiently enable multimodal perception, including vision, audio, and language processing. The research aims to address...
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...
The National Science Foundation awarded a $500,000 Project Grant to the University of California, San Diego under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support the "PANOPTIC 3D PARSING IN THE WILD" research project. The three-year award, which runs from October 1, 2021 through September 30, 2024, will fund the development of technologies to enable panoptic 3D scene parsing using computer vision techniques for unconstrained images in...
This four-year, $400,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop new techniques for 3D scene understanding. Specifically, the Stanford University researchers will explore implicit neural representations to model scene structure and details from raw images and videos. They will integrate findings into course development and partner with organizations to teach artificial intelligence, computer vision, and...
The University of California, San Diego will use a $500,000 project grant from the National Science Foundation to support research titled "SMALL: PHYSICALLY-BASED LEARNING FOR SHAPE, LIGHTING AND MATERIAL IN COMPLEX INDOOR SCENES" from October 1, 2021 through September 30, 2024. The grant is funded through the NSF's Computer and Information Science and Engineering program (CFDA #47.070), which supports investigator-initiated research and education in computing, communications, and...
This $600,000 project grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, will support research to develop a new Bayesian diffusion model framework for advanced visual perception and cognition systems. The University of California, San Diego (UCSD) will serve as the primary awardee, with the goal of revisiting the analysis-by-synthesis methodology by integrating generative priors into the learning and inference...
The University of California, San Diego (UCSD) was awarded a $455,058 project grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The 4-year project, which started on July 1, 2024, focuses on developing techniques for a mathematical understanding of deep learning and its application to a variety of neural network models and data sets. The key objectives are to: 1) compare different networks and understand...
This $348,956 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research at Carnegie Mellon University (CMU) to develop computational systems with an "actionable understanding" of the 3D world. The project aims to bridge the gap between 2D visual perception and 3D reasoning about actions and their effects. Specifically, the research will focus on: a) learning 2D affordances and...
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 $250,000 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant, awarded to the University of California, Irvine on June 1, 2025, aims to transform the way artificial intelligence (AI) transfers learned knowledge from simulated environments to real-world applications. The project will develop a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata and knowledge graphs to...

The National Science Foundation (NSF) has awarded a $600,000 five-year Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, San Diego (UCSD) to conduct research on "Interaction-Oriented 3D Representation Learning on Point Cloud". The research aims to develop novel deep learning frameworks that can learn 3D representations from point cloud data and leverage these representations to optimally plan and execute actions in physically embodied AI systems. This research is expected to advance the understanding of 3D perception, cognition, and interaction, with applications in areas such as smart manufacturing, robotics, autonomous driving, and augmented reality. The project will explore learning algorithms and 3D neural network architectures to seamlessly integrate the perception-cognition-interaction cycle, while also addressing new challenges in 3D vision. To support this effort, UCSD will collect interaction data in virtual and real-world settings.

Generated 5/14/24, 1:31 AM