This $240,000 Project Grant awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of computationally efficient algorithms to approximate the impact of removing data subsets from high-dimensional machine learning models. The research aims to advance scientific understanding of artificial intelligence, improve the robustness of decision-making systems, and contribute to the development of privacy-preserving...
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...
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 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,...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with CFDA number 47.049, will fund research to address the challenge of reliable and interpretable reinforcement learning (RL) systems in complex, data-limited environments. The $154,999 award, effective August 1, 2025 through July 31, 2028, aims to develop theoretical foundations and methods for robust inference and decision-making in RL, including tools for contextual bandits...
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 $599,411 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a "Trustworthy Toolbox for Double-Correct Predictive Modeling in Sciences." The project aims to create advanced artificial intelligence (AI) and machine learning (ML) models that can make accurate predictions while also providing transparent, scientifically-grounded rationales for their outputs. This...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
This $462,500 Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070), supports the development of advanced causal inference methods for data-driven decision making. Key products and services to be delivered include: Automated and robust causal AI systems that integrate machine learning and causal inference techniques to enable more decision-makers to leverage causal analysis. The project will...