Project Grant 60NANB21D049

This Project Grant award of $174,965, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the development of an evidence-aided symbolic reasoning framework to enable trustworthy multi-modal, multi-agent machine learning (ML) systems. The key products and services to be delivered include: 1) Investigating evidence theory to develop trust metrics for assessing ML pipeline decisions and uncertainty; 2) Creating...

This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...

This three-year, $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of California, Davis to develop trustworthy machine learning systems through adversarial robust reinforcement learning. Specifically, the award supports investigating potential vulnerabilities in reinforcement learning models and algorithms, developing robust RL approaches that mitigate impacts from adversarial attacks, and...

This Project Grant award of $500,000.00 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to address the critical need for trustworthy Artificial Intelligence (AI) and Machine Learning (ML), particularly in reinforcement learning (RL) systems used in applications like healthcare, education, and commerce. The project seeks to advance trustworthy RL by addressing challenges of data privacy, robustness against corruption, and fairness across diverse user...

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program provides $464,781 to the Rochester Institute of Technology (RIT) to support an undergraduate Research Experiences for Undergraduates (REU) site focused on trustworthy artificial intelligence (AI). Over 3 years from January 2025 to December 2027, the award will engage 10 talented undergraduate students per year in summer research projects on topics such as AI...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $309,407 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop a framework for ensuring the safety and trustworthy deployment of generative artificial intelligence (AI) foundation models, particularly large language models. The project will pursue three key tasks: 1) Conduct in-depth analysis to identify root...

This Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $299,996 to Case Western Reserve University to develop a responsible AI framework that ensures ethical use, transparency, and fairness in finance and healthcare applications. The project focuses on three main components: 1) developing a decentralized data architecture, 2) creating responsible AI models trained on ethically sourced data, and 3)...

This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program will provide $334,794 to Michigan State University to investigate strategies for calibrating user trust in generative AI (GenAI) chatbots. The goal is to develop methods to mitigate unfounded cognitive heuristics that can lead users to overly trust GenAI systems. The research will focus on identifying the specific cognitive cues that drive user trust,...

This three-year, $300,000 project grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....

This Project Grant award for $180,000.00, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences federal grant program (CFDA 47.049), aims to advance the mathematical understanding of trustworthy artificial intelligence (AI) algorithms for threat detection. The primary objectives are to investigate few-shot learning techniques, which can build effective models from a very limited number of data samples, and to explore few-shot graph generation methods,...