The Trustees of the University of Pennsylvania received a $143,659 Project Grant award from the National Science Foundation Division of Mathematical Sciences. The grant was awarded on July 1, 2022 under the Mathematical and Physical Sciences program (CFDA 47.049) to support the research project "COLLABORATIVE RESEARCH: FINE-GRAINED STATISTICAL INFERENCE IN HIGH DIMENSION: ACTIONABLE INFORMATION, BIAS REDUCTION, AND OPTIMALITY." The University will conduct collaborative research through...
This $250,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) supports collaborative research to develop advanced statistical tools for efficient integrative analysis of electronic health records and genomics data. The key goals are to: 1) devise data-driven algorithms with theoretical optimality guarantees for transfer learning in areas like high-dimensional...
The Trustees of the University of Pennsylvania received a $392,992 Project Grant from the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research and education activities aimed at developing machine learning techniques that break through the fairness-accuracy tradeoff paradigm. Specifically, the university researchers will draw on ideas from learning theory and...
This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
This $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
This $299,999 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds research into distance-based statistical methods for analyzing complex, high-dimensional data. The Washington University is the primary awardee, with work conducted from Oct. 2021 through Jun. 2024. The University of Pennsylvania serves as a subawardee, contributing to study design, implementation, analysis and manuscripts through the work of Dr. Bhaswar B....
This $1.6 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Pennsylvania from October 2022 through September 2026. The research focuses on developing theoretical tools to build an understanding of why deep neural networks (DNNs) work and when they can fail. Investigators will seek to identify common themes in how artificial and biological systems like the human brain learn. They will...
This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $701,674 to Princeton University from July 2022 through June 2026 to advance knowledge in artificial intelligence research, automatic driving and control, e-commerce, molecular mechanisms, biological processes, genetic associations, brain functions, and economic and financial risks. Specifically, the university will conduct research at the interface of statistical...
The Trustees of the University of Pennsylvania received a $275,000 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to conduct research on geometrization approaches toward understanding deep learning. Specifically, the three-year award funds research projects analyzing symmetries in trained deep neural networks, examining dynamics of deep learning training, and investigating how deep learning separates data across neural network...
This $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
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 through July 2023.
The research aims to develop modern statistical theory characterizing the performance of classical algorithms in high dimensions, suggest proper corrections to statistical procedures for sample-starved regimes, and design computationally efficient algorithms that can attain fundamental statistical limits, or identify potential computational barriers. The transformative potential lies in establishing a foundational data analytics theory through novel combinations of disciplines offering scalable statistical inference and learning algorithms. Findings will directly impact applications like machine learning, DNA sequencing, disease analysis, and natural language processing. The program also provides cross-university training opportunities for students, with a commitment to underrepresented and women students in STEM fields.