The National Science Foundation (NSF) awarded a $237,438 Project Grant to Purdue University under the Social, Behavioral, and Economic Sciences grant program (CFDA 47.075) to advance statistical inference on dynamic systems. The 3-year project will leverage deep learning and statistical modeling to enhance the efficiency, accuracy, and interpretability of time-series analysis across various domains. The research will introduce a new neural inference framework for estimating and inferring dynamic...
Northeastern University received a $168,471 project grant award from the National Science Foundation Division of Information and Intelligent Systems. The award was made under the Computer and Information Science and Engineering program (CFDA 47.070) to support the "CRII: III: INFORMATIVE BAYESIAN LEARNING AND DATA GATHERING THROUGH EXPERT-ACQUIRED DATA" project from October 1, 2021 to June 30, 2022. The project aims to advance the development and use of research cyberinfrastructure...
The National Science Foundation (NSF) awarded a $100,000 Project Grant under its Integrative Activities program (CFDA 47.083) to Rutgers, The State University located in Piscataway, New Jersey. The three-year grant, awarded on August 1, 2023, will fund research to develop novel statistical inference tools and computationally efficient approaches for reinforcement learning in high-dimensional, non-identically distributed data settings. Key focus areas include statistical inference for...
This $375,000 federal Project Grant award, issued by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program, supports research that advances Bayesian inference methods for analyzing complex human data and computational models across the behavioral sciences. The research aims to: Generalize the scope of amortized Bayesian inference to support multiple model families, experimental designs, and real-time model/data adaptations; Develop novel...
This $271,343 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project to explore new methods for designing learning and inference systems that are robust to distributional uncertainty and data corruption. The project aims to advance research in areas such as statistical learning, optimization, control theory, network science,...
This $875,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is developing a probabilistic programming framework for modeling hybrid systems that combine continuous state evolution and discrete state changes. The project is applying this framework to domains such as epidemiology, medical devices, and autonomous systems, with the goal of enabling rigorous model-based decision-making. Key project...
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...
This $389,494 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is funding research at Northeastern University to advance the design and implementation of probabilistic programming languages (PPLs). The key objectives are to: Develop new high-level, ergonomic PPLs that can compile to low-level, tractable probabilistic models to enable scalable probabilistic inference. Create new compilation targets...
This $200,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) aims to develop novel feature selection techniques for supervised and unsupervised machine learning models. The research will focus on the "knockoff method" for identifying key predictive features while controlling false discoveries, incorporating microbiome data structures, handling missing values, and...
The National Science Foundation (NSF) awarded a 4-year, $252,007 Project Grant under the Engineering (CFDA 47.041) program to Northeastern University. The grant supports the development and testing of AI-based programming tools to assist social and natural scientists with computer programming tasks. The research team is developing large language models and associated tools to support programming languages commonly used in the sciences, such as MATLAB and R, in order to make programming easier...