Project Grant H982300910005

The Carnegie Mellon University was awarded a $300,000 Project Grant from the National Science Foundation Division of Mathematical Sciences to support research focusing on long-term investing in high-dimensional open markets. The three-year award, made on August 1, 2022, will fund analysis of explicit, tractable models of open markets where the set of available assets can vary over time. In particular, the research will advance understanding of recently developed hybrid and rank Jacobi models,...

This three-year National Science Foundation project grant of $350,000 supports research at the University of California, Berkeley to develop novel nonparametric estimation methodologies under mixed derivative smoothness constraints. The investigator plans to expand methodology for nonparametric regression allowing restricted interaction orders, design faster algorithms, and prove theoretical accuracy results under more general assumptions. The possibility of near parametric rates under strong...

Project Grant Award Summary: Optimal Transport for Risk Management and Scenario Generation New York University received a $299,057 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) for the period September 1, 2025 through August 31, 2027. The OTRIMAGE (Optimal Transport for Risk Management and Scenario Generation) project delivers fundamental mathematical research and methodological...

The National Science Foundation (NSF) Division of Mathematical Sciences has awarded a $300,000 Project Grant to Cornell University to support research on stochastic models and their properties. The key objectives of this 3-year grant, which runs from July 15, 2023 to June 30, 2026, are to: Develop novel methodologies to characterize different types of orbits exhibited by Markov semigroups on Hilbert spaces, providing a comprehensive understanding of these structures. Utilize the classification...

This National Science Foundation Project Grant of $284,860 awarded on September 1, 2022 through August 31, 2025 under the Mathematical and Physical Sciences (CFDA 47.049) program will support research into partially observed systems in finance. Specifically, the awardee Illinois Institute of Technology will conduct two lines of research. The first involves investigating statistical inference methods for estimating unknown parameters in partially observed diffusion models, with a focus on...

The National Science Foundation Division of Mathematical Sciences awarded The Leland Stanford Junior University $174,629 on February 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop learning theory and mathematical frameworks for large-scale stochastic games in financial markets and economic systems. The project, running through January 31, 2029, focuses on mean-field games—mathematical models of systems with many interacting agents—and aims to construct new...

This Project Grant award from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $200,000.00 to Louisiana State University (LSU) to conduct research on learning methods for complex stochastic systems modeled by stochastic differential equations. The 3-year project aims to develop rigorous mathematical results to assess the accuracy of learning algorithms for these types of intricate systems, which have...

The National Science Foundation Division of Mathematical Sciences awarded a $110,000 Project Grant to the University of Florida Division of Sponsored Research under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The three-year award will support the development of novel modeling and Bayesian analysis methods for high-dimensional time series data. Specifically, the principal investigators will create a framework to represent multi-dimensional time series data as a...

This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of innovative statistical and mathematical methods for time series data analysis. The key objectives of this 2-year project are: a) Developing a variable selection method to identify significant exogenous covariates in autoregressive conditional heteroscedasticity (ARCH) models. b) Designing a novel nonparametric hypothesis test to...

This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $600,000 to the University of Michigan to develop a new analytical method for analyzing large-scale stochastic systems from July 2022 to June 2025. The method aims to obtain non-asymptotic performance bounds for stochastic networks and systems using Lyapunov drift analysis combined with ideas from Stein's method, dimensionality reduction, and reproducing kernel Hilbert...