Project Grant N000142612099

This $600,000 Project Grant awarded by the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to the University of Illinois aims to advance video understanding capabilities by developing new techniques for analyzing extended video streams. The project will create a framework centered on token selection and context-aware representation to enable intelligent systems to comprehend complex, time-varying visual information from a broad spectrum of video...

This four-year, $1.264 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of VOCAL, an open-source system for video organization and interactive compositional analytics. VOCAL will consist of domain-agnostic tools to support end-to-end video analytics, including interactively organizing large-scale video data, expressing and executing complex queries, and querying multi-view camera deployments. The...

This Cooperative Agreement award from the Defense Advanced Research Projects Agency (DARPA) under the Research and Technology Development federal grant program (CFDA 12.910) provides $250,000 in funding to the University of Illinois to develop an "agentic framework" for revolutionizing multimodal video understanding. The proposed research aims to create a system capable of processing and analyzing extremely long video contexts with unprecedented depth and efficiency, enabling...

This Project Grant from the National Science Foundation's $524,474 Computer and Information Science and Engineering program will support the development of artificial intelligence systems capable of understanding general real-world tasks and providing step-by-step visual and language guidance to solve complex problems. Over a three-year period from June 2022 to May 2025, researchers at the University of Minnesota will create a new dataset annotating diverse everyday tasks and solutions,...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $171,387.00 to The Trustees of the Stevens Institute of Technology to advance the efficiency of machine learning model inference through the development of a compression-aware computing framework. The key objectives are to: 1) enable machine learning models to self-detect and adapt to lossy compression techniques like sparsification and...

This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant award of $274,623 to Ai Acuity, a for-profit organization, aims to develop improved image compression technology that targets machine learning-based detection algorithms. The key objective is to reduce the size of Earth observation imagery, thereby lowering the costs associated with transmission, storage, and processing. This technology will also have applications in high-efficiency...

The University of California, Santa Barbara will use a $301,709 project grant from the National Science Foundation's Computer and Information Science and Engineering program to develop an artificial intelligence framework for transforming online introductory STEM video lectures. The framework will extract gestures and voice from existing instructor-led videos and reconfigure them into agent-based presentations delivered by a diverse set of emotionally and socially sensitive animated on-screen...

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 research to develop a systematic framework for visualizing, understanding, and rewriting the learned computations of multimodal generative AI models. The key objectives are to: 1) create new methodologies to visualize the internal mechanisms and hierarchical structures of pre-trained multimodal generative models, 2) explore model...

This $841,392 Project Grant awarded by the National Science Foundation's STEM Education program (CFDA 47.076) will develop and evaluate a scalable, explainable, teacher-in-the-loop adaptive learning system built on large language models. The project aims to address challenges in traditional adaptive learning systems by leveraging generative AI to automatically generate high-quality learning content and recommendations, while ensuring educators remain central to the instructional process. The...

The National Science Foundation (NSF) awarded a $175,000 Computer and Information Science and Engineering (CISE) Program grant to The Trustees of the Stevens Institute of Technology to develop a compressor-assisted collective communication framework for large-scale deep learning on GPU-based systems. The two-year project aims to address challenges with the communication overhead of training massive deep learning models by investigating efficient lossy compression techniques for gradient data and...