Project Grant FA95502410150

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...

This $1,200,000 federal Project Grant award from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) will support a convergent research effort to study the dynamics of ambiguity, uncertainty, and confusion (AU&C) in STEM education. The project will develop methods for collecting and analyzing longitudinal, multimodal data from students to understand how individuals and groups productively engage with AU&C during STEM problem-solving and learning....

This 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), provides $1,093,364 to develop tools and algorithms to help identify and avoid the use of spurious information in artificial intelligence (AI) models. Specifically, the awardee, New York University, will pursue two technical thrusts. The first focuses on improving the interpretability of AI models to help...

This $763,741 project grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to better align how human and artificial intelligence (AI) models process language. The researchers at New York University will explore techniques to modify AI architectures, such as adopting semantic training objectives and leveraging causal intervention methods, to bring them more in line with how humans derive meaning from sentences and handle...

This $150,000 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the development of Algorithm-Informed Neural Networks (AINNs) - a new approach that integrates well-established algorithmic principles into the design of neural networks. The goal is to enhance the explainability, reliability, and efficiency of AI systems, making them more transparent and reducing their dependency on large...

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 at the Rochester Institute of Technology (RIT) to develop new uncertainty-aware visual representation-learning models. The goal is to create general-purpose neural networks for image processing that can effectively represent and communicate their own uncertainty, leading to more robust and reliable computer vision...

This Project Grant award, with a total funding amount of $599,997, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The award, effective October 1, 2024 through September 30, 2027, aims to develop advanced techniques and tools to improve the management and resolution of ambiguity within collaborative visual analytics. The key objectives are to: 1) Identify sources of ambiguity in collaborative...

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 $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...

The National Science Foundation (NSF) awarded a $270,000 EAGER (Early-Concept Grants for Exploratory Research) grant to the Massachusetts Institute of Technology (MIT) to develop an Artificial Intelligence (AI) or Large Language Model (LLM)-powered conversational tutoring system for Quantum Information Science and Engineering (QISE). The project, titled "TRUSTWORTHY AND ETHICAL AI TUTORS WITH FIRST-PRINCIPLES/AXIOMATIC REASONING", aims to create an interpretable AI tutor, called...