This $317,381 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to develop innovative methods for quantifying uncertainty in complex systems using machine learning (ML). The 5-year project aims to create a new framework integrating conformal prediction principles into ML model training to enable more reliable and trustworthy predictions, particularly in high-stakes applications like medical...
This Project Grant award, valued at $540,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project between researchers at Northeastern University to develop methods for designing "responsive uncertainty visualizations" that can effectively communicate the uncertainty in data to users with varying levels of expertise and decision-making needs. The...
This Project Grant award of $381,347.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to develop new methods for ensuring that deep learning models for image reconstruction remain reliable and accurate even when the data conditions shift. The central goals are to (i) quantify the extent of distribution shifts between training and test data, (ii) characterize the effect of shifts on...
This $148,654 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program aims to develop statistical tools to improve the reliability of artificial intelligence (AI) systems used in real-world applications like automated decision-making, financial forecasting, and neuroscience research. The research will focus on establishing mathematically rigorous methods for uncertainty quantification to build trustworthy AI, with applications in enhancing...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Engineering (CFDA 47.041) program to The Research Foundation for the State University of New York (RF-SUNY) at the University at Albany. The grant, titled "CCSS: Uncertainty-Aware Computational Imaging in the Wild: A Bayesian Deep Learning Approach in the Latent Space," aims to develop advanced Bayesian deep learning techniques for computational imaging systems that can effectively handle various...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) 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 advancing vision-centric parametric knowledge, incorporating human-like non-parametric mechanisms, and integrating these...
This $352,098 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new methods to ensure deep learning models for image reconstruction remain reliable and accurate even when the data conditions shift. The project, titled "COLLABORATIVE RESEARCH: CIF: MEDIUM: ROBUSTNESS TO DISTRIBUTION SHIFTS IN COMPUTATIONAL IMAGING - INFERENCE, SAMPLING, AND ADAPTATION", will introduce a unified mathematical...
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...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $169,938 to support research in self-supervised visual representation learning using mixed labeled and unlabeled data. Specifically, the University of California, Davis will study novel self-supervised learning algorithms that can learn rich visual features from unlabeled images and videos...
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...