The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Virginia. The grant supports the development of new physics-guided graph network models to capture complex, non-stationary, and poorly observed water dynamics in freshwater ecosystems. Key innovations include new graph-based architectures, continual learning strategies, and model initialization methods that leverage...
This $126,270 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop new capabilities to monitor and understand forest carbon dynamics in the Earth system. The key products and services to be delivered include: Cross-platform and cross-region learning frameworks to enable fine-scale carbon dynamics monitoring at large geographic scales. High-fidelity fast approximations of theory-based carbon forecasting models using new meta-learning...
This $673,131 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) will develop a real-time, high-resolution monitoring framework to identify and manage urban environmental hazards in Pittsburgh, Pennsylvania. The project aims to leverage existing fiber-optic cable infrastructure to monitor water infrastructure and geological conditions, addressing challenges related to aging infrastructure, climate change, and equitable access to city services. Key...
This Project Grant award of $371,679, funded by the National Science Foundation's Geosciences Program (CFDA 47.050), supports research and education initiatives to quantify dynamic landscape connectivity and its impacts on water quality across the United States. The project, titled "CAREER: Dynamic Connectivity: A Research and Educational Frontier for Sustainable Environmental Management under Climate and Land Use Uncertainty," will leverage high-frequency aquatic sensors, deep...
The National Science Foundation awarded The Pennsylvania State University $315,000 under the Geosciences program (CFDA 47.050) to develop next-generation, differentiable global hydrologic models. The four-year project grant, awarded September 1, 2022 and concluding August 31, 2026, aims to improve understanding of global low flow dynamics under climate change. Specifically, Penn State will build upon machine learning techniques to create process-based hydrologic models that are learnable and...
This $300,000 federal Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of Maryland, College Park. The project aims to develop physics-guided generative artificial intelligence models to better understand and predict complex physical processes like pollution transport, virus spread, and wildfire evolution. By integrating physical equations with generative machine learning...
This $459,803 Project Grant awarded by the National Science Foundation (NSF) Biological Sciences program (CFDA 47.074) supports a collaborative research project titled "ULTRA-DATA: Developing Global Riverine Solute Regime and Synchrony Frameworks for Understanding Watershed-Scale Controls on River Biogeochemical Signals." The goal of this data-intensive research is to better understand and predict changes in river chemistry at the global scale. The investigators will analyze publicly...
This $300,000 federal Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop physics-guided generative artificial intelligence models for inverting chaotic advection-diffusion dynamics. The research aims to enable more accurate source identification from limited observations of complex physical processes like pollution transport, virus spread, and wildfire evolution, which are...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...
The National Science Foundation (NSF) has awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Old Dominion University Research Foundation (Odurf) to develop a new holistic and standardized graph learning framework for open-world and streaming network learning. Key objectives include characterizing complex and evolving graph data representations, identifying the emergence of new classes, and generalizing graph models across...