Project Grant 2527533
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) aims to advance weak form scientific machine learning (WSCIML) methods and apply them to modeling the dynamics of insect pests and their viral pathogens in forest ecosystems. The $240,000 award to the University of Colorado will fund collaborative research to develop interpretable machine learning algorithms that can generate accurate predictive models directly...
- The University of Chicago received a $328,466 Project Grant award from the National Science Foundation Division of Environmental Biology. The grant is part of the NSF's Biological Sciences program (CFDA 47.074) to promote progress in the biological sciences and strengthen the nation's scientific enterprise. Under the two-year award completing in June 2023, the University will conduct research titled "Understanding How Climate Change Will Alter the Ability of Pathogens to Control Gypsy...
- The National Science Foundation awarded a $252,937 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to the University of Chicago. The purpose of this 3-year grant, which runs from September 1, 2023 to August 31, 2026, is to enhance statistical methods for analyzing temporally observed, multi-sample data in fields such as environmental science, epidemiology, and economics. The research team will develop innovative approaches to estimate and infer trends in data...
- This $180,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports research on the development of a broad, model-independent framework for studying complex invasion fronts, which play a key role in the self-organized development of coherent structures in scientific fields like epidemiology, developmental biology, and materials science. The project aims to make theoretical advances and develop computational tools to...
- This $364,196 Project Grant award from the National Science Foundation (NSF) Biological Sciences (CFDA 47.074) program will fund a collaborative research project led by Lake Forest College. The project aims to study the impacts of periodical cicada "pulses" on the foraging behaviors and population dynamics of ants in forest ecosystems. The research team will document how the sudden abundance of cicada prey affects the varied ecosystem services provided by ants, which are relevant for...
- This $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research to accurately quantify recurring seasonal effects in ecological models. The $247,495 award to Texas Tech University System will develop mathematical approaches to represent the complex interactions between short-term and seasonal behaviors in ecosystems, with a focus on assessing the identifiability of model parameters and their ability to match real-world...
- The National Science Foundation (NSF) awarded a $429,998 project grant under the Biological Sciences (CFDA 47.074) Federal Grant Program to the University of North Carolina at Chapel Hill, the University of Florida, and Smith College. This collaborative research project, titled "Moth Monitoring 2.0: Developing an Integrated Monitoring Framework Across Life Stages to Understand Insect Declines," aims to develop a large-scale network to study the abundance and seasonality of moths across...
- This $245,190 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to advance the integration of modern machine learning tools, such as deep learning and Bayesian additive regression trees, into statistical modeling frameworks. The research program has two key objectives: Developing a novel Bayesian inferential framework for "generative models" - statistical models where data is viewed as stochastic outputs of...
- This Project Grant award, valued at $100,000.00 and provided by the National Science Foundation (NSF) Biological Sciences (CFDA 47.074) Federal Grant Program, supports a collaborative research project titled "ACED: Planet-Scale AI for Accelerating Environmental Science - Invasive Species and Beyond." The research aims to develop a novel AI framework that combines multiple data sources, including satellite imagery and other visual data, to automatically discover interpretable scientific...
The National Science Foundation awarded a $160,000 project grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Chicago. The grant, awarded on September 1, 2025, supports collaborative research to develop interpretable machine learning algorithms that can discover mathematical models directly from sparse and noisy ecological data. The goal is to create accurate models to predict how and when insect-killing viruses will protect forests from defoliating insect pests like the Douglas-fir tussock moth and spongy moth. The project aims to advance "weak form scientific machine learning" theory and methodology to enhance the usefulness of host-pathogen dynamic models for guiding the microbial control of forest pests. The project will foster interdisciplinary research experiences for graduate and undergraduate students, as well as outreach and training activities for high school and university communities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $160.0k | 8/14/25 |