Project Grant FA95502410105

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program aims to advance the efficiency of machine learning model inference through a compression-aware computing framework. The $171,387 project, awarded to the Stevens Institute of Technology, will develop machine learning models capable of self-awareness in response to lossy compression techniques like sparsification and quantization. This will enable the recovery and...

This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $240,000 to The Trustees of Columbia University in the City of New York to advance research in unsupervised learning and nonlinear dimension reduction. The project aims to develop new statistical frameworks that leverage empirical Bayes methods and variational inference to enable principled, scalable inference for large-scale scientific datasets, as well as...

This National Science Foundation (NSF) Project Grant award of $384,214 to Northeastern University will develop statistical inference methods that leverage advanced techniques like reinforcement learning, Bayesian statistics, and machine learning. The project aims to 1) incorporate expert knowledge into the modeling process without requiring expert oversight, and 2) systematize data collection for accurate inference of complex systems and processes. The proposed approaches will be applied in...

This Project Grant award of $598,744.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at Rensselaer Polytechnic Institute (RPI) to develop a hybrid AI model that integrates deep learning with probabilistic graphical models. The goal is to create a framework that can effectively incorporate prior knowledge into deep learning models to improve their data efficiency, generalization, and interpretability across...

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 $287,594 to support collaborative research addressing challenges in learning and inference from large-dimensional data. The awardee, The Trustees of the University of Pennsylvania doing business as the Clinical Practices of the University of Pennsylvania, will conduct the research from January 2022...

This $245,190 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with a performance period from July 2025 to June 2028, aims to advance the frontiers of nonparametric Bayesian methodology, theory, and applications. The research program has two overarching objectives: 1) Developing a novel Bayesian inferential framework for generative models using modern machine learning tools to enable statistically principled inference in complex...

This Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $375,000 in funding to Rensselaer Polytechnic Institute (RPI) for a project titled "Amortized Bayesian Inference for Computational Models of Cognition and Behavior." The project aims to advance Bayesian inference methods to enable efficient analysis of complex and large-scale human data using computational models, which are widely used across...

This National Science Foundation Project Grant of $250,000 supports research at the University of California, San Diego to develop automated techniques for lemma synthesis in interactive theorem provers. The goal is to reduce the manual proof effort required when using interactive theorem provers to prove correctness and security properties of software. The project will explore multiple formulations of reducing the lemma synthesis problem to data-driven program synthesis, where the objective...

This four-year, $750,000 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems will support the development of foundational principles for modular probabilistic programming and inference. Funded under the Computer and Information Science and Engineering program, the grant aims to advance scalable and efficient machine learning model design through the creation of a new probabilistic programming language called MODPPL. Key deliverables include the...

This Project Grant award of $125,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project conducted by the University of California, Berkeley. The project aims to develop new methods for causal learning and inference from complex data using modern machine learning techniques. Specifically, the research focuses on three key goals: (1) integrating flexible machine learning models with statistical...