This three-year, $260,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will fund research towards designing optimal statistical learning procedures through precise medium-dimensional asymptotic analysis. The grantee, Columbia University, will develop a novel analytical framework to quantitatively characterize the performance of diverse learning algorithms and provide guidance on...
This federal Project Grant award for $240,000.00 was provided by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The award is intended to support research by the Trustees of Columbia University in the City of New York to develop a comprehensive understanding of when simpler approximations can be effectively used to model complex statistical models, and to provide rigorous guarantees for...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will enable the development of new statistical machine learning approaches and theory termed "minipatch learning." The goal is to enable faster computation and improved statistical efficiency for uncovering insights from massive, complex datasets commonly found in fields like biomedicine, genomics, and neuroscience. The award of $195,479 to The Trustees of...
The Columbia University was awarded a $100,000 Project Grant from the National Science Foundation Division of Computer and Network Systems on October 1, 2021 to be completed by September 30, 2022. The grant supports research under the Computer and Information Science and Engineering program (CFDA #47.070), which aims to advance computing and information sciences through investigator-initiated research and cyberinfrastructure development. Specifically, the Columbia University will conduct...
This Project Grant award for $117,910 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop new statistical estimation methods and algorithms that can efficiently process complex, high-dimensional datasets. The research will focus on three key areas: (1) providing rigorous theoretical guarantees for the performance of high-dimensional statistical estimation techniques, (2) establishing computational limits and efficiencies for modern...
The Trustees of Columbia University in New York City received a $499,756 Project Grant award from the National Science Foundation Division of Information and Intelligent Systems. The grant is part of the NSF's Computer and Information Science and Engineering program (CFDA #47.070) to support investigator-initiated research and education across computing, communications, and information science and engineering fields. Specifically, the October 1, 2021 award will fund research into "New...
This Project Grant award for $241,682 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a consolidated privacy protection framework for machine learning systems. The project will comprehensively address the trade-offs between computational privacy and critical properties of machine learning models, including utility, fairness, and distributed learning. The research aims to enhance the...
The National Science Foundation (NSF) awarded a 3-year, $250,015 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The primary goal of this collaborative research project is to develop advanced statistical and machine learning techniques to forecast and analyze high-dimensional extreme events, such as extreme climate conditions and social phenomena. Key research thrusts include: 1) Learning the...
This Project Grant award of $236,099 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop robust algorithms for machine learning and optimization under dynamic and uncertain data conditions. The research aims to address limitations of existing approaches by leveraging the structure of fundamental supervised learning tasks like regression and classification. The research will advance techniques in...
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...