This $193,038 five-year Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support research at Colorado State University to develop flexible Bayesian record linkage models. The goal is to enable more accurate linking of multiple data sets without unique identifiers, which is crucial for leveraging diverse data sources to address complex problems. The research will focus on addressing key limitations in current record...
This $233,955 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support a research project at The Washington University in St. Louis to develop improved methods for probabilistic data integration and record linkage without the use of unique identifiers. The central objectives are to create computationally efficient and accurate techniques for merging large datasets from multiple sources, which is a critical...
This $256,157 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research to improve the effectiveness and efficiency of post-mortem execution analysis techniques for debugging software systems. The key innovations include embedding high-fidelity execution artifacts in low-fidelity formats, extrapolating low-fidelity artifacts into high-fidelity ones, and automatically navigating the...
This $363,931 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports a collaborative research project between multiple institutions to develop scalable clustering algorithms for processing large datasets. The goal is to create new clustering algorithms that can efficiently group billions of data points into meaningful clusters, addressing the limitations of current methods. The work will produce an...
This $118,760 CAREER award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research to advance the state-of-the-art in optimization theory and algorithms for modern data science challenges. The project aims to develop novel tools to analyze the complexity of widely-used optimization heuristics, and leverage problem structure to design faster, parallelizable algorithms that can leverage modern machine learning...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $250,000 to The Trustees of the University of Pennsylvania to develop advanced statistical methods for integrating and analyzing large-scale data from multiple sources, such as electronic health records and genomics data. The project aims to devise new data-driven algorithms with theoretical optimality guarantees for transfer learning, as well as adversarially...
This $174,118 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop new algorithms and software tools to enable robust causal inference from observational data, even when faced with model misspecification and uncertainty. The project seeks to build methods that allow data scientists to propose multiple causal models and combine effect estimates, as well as perform model selection that...
This $299,776 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an open digital research notebook ecosystem called "Knowledger". The goal is to create a suite of components for an open digital research notebook that can serve all research communities, putting control in the hands of the research community. This will promote data science and informatics as...
This $256,710 National Science Foundation award under the Computer and Information Science and Engineering (CFDA 47.070) program supports a collaborative research project led by the University of Texas at Austin. The project investigates full-stack implementation methodologies for developing expressive programming systems that bridge the gap between high-level specifications and high-performance implementations of complex reasoning tasks at scale. Key focus areas include extending declarative...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets. The research focuses on three main areas: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory and methods for...