Project Grant N000142612116

This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program, with a total funding amount of $999,967.00, supports the development of techniques that integrate imperfect artificial intelligence (AI) models with human-in-the-loop feedback and rigorous statistical estimation. The goal is to enable high-throughput, high-precision scientific measurements with quantified uncertainty from large-scale datasets in applications spanning...

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 $1.7 million project grant from the National Science Foundation's STEM Education program (CFDA 47.076) will fund the development of a novel AI-powered student support system to improve metacognitive calibration skills at the University of Illinois from August 2022 to July 2025. The project aims to research methods for enhancing students' ability to accurately estimate their own level of knowledge using short, personalized training exercises delivered through a decentralized AI framework....

This Project Grant award of $900,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is focused on improving human-AI decision-making partnerships. The key research activities include: (1) developing Bayesian inference frameworks to evaluate the evolving abilities of humans and AI agents, (2) creating adaptive optimization algorithms to manage decision policies under uncertainty, and (3) exploring human-centered aspects like...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research by the Rochester Institute of Technology (RIT) to develop new neural network architectures for image processing that can represent and quantify their own uncertainty. The $173,777 award, effective from September 1, 2025 to August 31, 2027, aims to create general-purpose neural networks that can effectively communicate what...

This $299,549 EAGER project award from the National Science Foundation's Social, Behavioral, and Economic Sciences program (CFDA 47.075) is developing hybrid (human-AI) technologies for forecasting complex sociotechnical outcomes. The project's main objectives are to advance machine learning and AI to support the integration of human inputs through hybrid prediction markets, wherein humans and AI-enabled traders buy and sell assets representing future outcomes. This will enable the forecasting...

This two-year, $198,375 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems will support the development of scalable and robust computational methods to advance artificial intelligence through innovations in deep learning with big imbalanced data. Funded under the Computer and Information Science and Engineering program, the grant aims to address data imbalance challenges that occur when training data for AI models does not adequately represent...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, titled "CAREER: EFFICIENT AND TRUSTWORTHY MACHINE LEARNING VIA POST-PROCESSING," provides $474,838 to the University of Illinois to conduct research on improving the accuracy, robustness, and interpretability of machine learning (ML) models. The project will focus on three key technical thrusts: (1) developing a unified framework for understanding and...

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 from the National Science Foundation (NSF) under CFDA 47.070 - Computer and Information Science and Engineering is for $395,927 over the period of Sep 1, 2024 to Aug 31, 2027. The award aims to develop theoretical and algorithmic foundations for building a safe and robust human-AI ecosystem, where machine learning (ML) and artificial intelligence (AI) techniques are used in applications involving humans, such as recommendation systems, lending, and healthcare. The key...