Grants

Cade Museum Foundation Inc

Cade Museum Foundation Inc is an active grantmaking foundation based in Gainesville, FL. Focus: arts. Contact the foundation directly for current funding opportunities.

Putnam

Cady Brook Bridge & Five Mile River Bridge

Cocina Cultura LLC

Cafe' de olla Packaging

Disabilities Aging Ind. Living

Cahedral Square Corporation - SASH (Support and Services at Home)

Cahuenga Housing Foundation

Cahuenga Housing Foundation is an active grantmaking foundation based in Chatsworth, CA. Focus: community development. Contact the foundation directly for current funding opportunities.

NSF

The rapid expansion of artificial intelligence (AI) infrastructure across the United States presents challenges for water resource management. AI infrastructure requires a large water supply for cooling. This project will develop three new AI models for water resources. The models will identify where water resources exist that can reliably support the growth of AI infrastructure. First, a large-scale AI model will provide insights for water availability to choose sites. Second, digital twins will be created to reveal hazards that may disrupt local water supply. Finally, an AI method will help predict impacts of wastewater and thermal pollution. The broader impacts include practical guidance on water management and sustainability. The project’s primary scientific goal is to develop a multi-scale hydrologic modeling framework that integrates physics-informed AI and hierarchical digital twin technologies to inform water management for AI infrastructure development. The project consists of three main technical components for analyzing water resource sustainability and identifying optimal sites for AI infrastructure. First, an AI-driven hydrologic model will analyze geospatial data across the contiguous United States to identify regions with adequate water resources. Second, digital twins will be created for selected sites, enabling scenario analysis to understand the potential impacts of natural hazards and water availability fluctuations. Third, a fractional-calculus-based modeling framework will be developed to assess environmental outputs associated with data centers. The outcomes include novel methods for large-scale water resource analysis, new AI algorithms tailored specifically to geoscience applications, and practical guidance on environmental management strategies. The research team includes experts in hydrology, AI, and applied mathematics, supported by collaborations with national labs and water management agencies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Extreme weather events—such as heat waves, cold snaps, wildfires, and heavy rainfall—pose increasing risks to society. These events are often driven by shifts in powerful atmospheric jet streams, which typically flow from west to east across the midlatitudes but can sometimes meander dramatically north or south. While recent advances in AI have shown promise in improving weather forecasts, many AI models still struggle with long-term stability, limiting their effectiveness in predicting extreme events. This project seeks to address that challenge by combining physics-based atmospheric science with state-of-the-art machine learning techniques. The goal is to enhance our ability to forecast high-impact weather events, ultimately supporting more effective planning and response in sectors such as energy, transportation, water management, and public health. Additionally, the project will contribute to education and workforce development. Graduate students will receive advanced training in both physics-based and data-driven approaches to atmospheric modeling, while several undergraduate students will gain hand-on experience in applying statistical analysis and AI techniques to weather data. The models and tools developed through this research will be made publicly available, fostering collaboration across the broader scientific community. The proposed research will advance understanding of atmospheric circulation and extreme weather by integrating a hierarchy of stochastic models with large-scale atmospheric dynamics. This hierarchy will include both deep learning-based generative models and traditional linear and nonlinear inverse models. The latter will serve as rigorous benchmarks to evaluate the long-term stability and reliability of the AI-based approaches, particularly in capturing the full probability distribution of midlatitude weather systems. Using these stochastic models, the project will generate large ensembles of simulated weather events, with a focus on extreme events and their subseasonal precursors. The research will also investigate the influence of stratospheric and tropical processes on midlatitude circulation, as well as the dynamic coupling between atmospheric flow and water vapor, using both linear and nonlinear frameworks. By bridging machine learning and atmospheric dynamics, this work strives to improve the performance of AI-based forecasting systems and contribute to a more comprehensive understanding of large-scale atmospheric variability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project establishes an artificial intelligence (AI)-driven framework to accurately detect sinkhole precursors based on multiple Earth monitoring data sources. Sinkholes are a pervasive and destructive geohazard, threatening critical infrastructure and public safety. Yet accurately detecting sinkhole precursors is challenging due to the subtle and varying nature of these signals. While recent advances in remote sensing and AI have improved our ability to monitor ground movement, existing methods often rely on general-purpose AI algorithms that overlook the specific complexities of sinkhole development. This project introduces novel AI algorithms specifically designed to meet these unique challenges. In particular, the proposed AI-driven framework is capable of integrating different data types, automatically identifying and grouping environmental conditions and adapting detection criteria accordingly. The findings from this research will improve the early detection of sinkhole precursors, supporting public safety and hazard mitigation. The findings will also help city planners make better decisions about zoning, site stability, and infrastructure resilience. Additionally, the project will develop educational materials and outreach programs by organizing a geoscience AI challenge for students and creating new AI-driven geoscience curricula for undergraduate and graduate courses. This research aims to establish advanced multimodal AI methodologies that fuse large heterogeneous geoscience data streams using a shared latent representation and a nonparametric Bayesian clustering algorithm. The primary objectives include: (i) establishing a structured data processing pipeline for collecting, organizing, and aligning multimodal geoscience datasets; (ii) creating a multimodal dual-variational autoencoder architecture for accurate detection and localization of sinkhole precursor signals; (iii) developing an adaptive nonparametric Bayesian clustering algorithm to support interpretable analysis; and (iv) validating the proposed framework using real-world datasets from known sinkhole-prone regions. The contributions of this research span both geoscience and AI. In geoscience, it will advance our understanding of the predictive mechanisms of sinkhole formation. In AI, it will introduce novel algorithms capable of handling multimodal and incomplete data, performing adaptive anomaly detection, and enhancing model interpretability in complex settings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Rocks are the primary recorder of more than 99% of Earth’s History. Whether a rock chronicles processes deep in Earth’s interior, or on the wave agitated shores of a barrier island, rocks contain the clues that help geoscientists reconstruct ancient environments and piece together the history of life on Earth. Such study involves collecting hundreds or thousands of rock samples, photographing their cross-sections, sometimes reconstructing three-dimensional volumes, and ultimately creating a detailed catalogue of the contents of each rock, which can consist of hundreds or thousands of different objects and textures. For each embedded element, a geologist must identify its type (for example, fossil or crystal or bedform) and measure its shape, size, and abundance. Such cataloguing is traditionally done by hand and is very labor intensive, limiting scientific progress. This project seeks to develop new AI technologies that can automatically perform this cataloguing under the guidance of geoscientists, accelerating discovery and improving reproducibility. This research also is integrated with undergraduate and graduate education, and K12 outreach activities. This project aims to develop new AI capabilities to enable reliable automated analysis of images of rock cross-sections. The key idea is to address the bottleneck of training data, by creating a large amount of labeled synthetic data of rock cross-sections through computer graphics and physics-based simulation. In particular, the project employs a technique called “procedural generation", creating 3D objects and their simulations based on mathematical rules that allow precise control and unlimited variation. The synthetic data then are used to pretrain foundation models that can be further fine-tuned with a small number of real images to perform various visual analysis tasks on rock images. As a case study of advancing geoscience with the new AI capabilities, this project uses an extensive field collection from South Australia to test the hypothesis that the rise of metazoan reefs played a role in the coevolution of animals and their environments during the Cambrian radiation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Wetlands provide vital ecosystem services despite covering only a small area of the Earth’s land surface. Earth System Models are increasingly used to inform decisions on land use and water quality conservation. However, wetlands are poorly represented because of their small size and complex interactions with surrounding rivers and landscapes. This project develops novel artificial intelligence (AI) tools that simulate wetland processes in Earth System Models. Three AI tools will be developed to simulate different aspects of wetlands for integration in Earth System Models. A transformer-based AI model will model nutrient and pollutant transport. A graph neural network will simulate wetland flow connectivity. A contrastive-learning model will predict wetland distribution from remote sensing data. The project will also support interdisciplinary education and training in geoscience and AI. It will provide new learning opportunities for students and advance public understanding of wetlands. Wetlands play a vital role in maintaining ecosystem health by storing organic material, cycling nutrients, and protecting water quality. Despite covering a small area of the Earth’s land surface, they are a critical buffer for waterborne pollutants. However, because of their small size and complex interactions with surrounding rivers and landscapes, wetlands are poorly represented in large-scale Earth System Models used to predict atmospheric circulation and manage water resources. This project will create a unified, physics-guided AI framework to improve models of wetland hydrology and biogeochemical modeling across sub-grid to watershed scales. The project will develop three AI models to represent wetlands within Earth System Models. First, a two-level transformer-based model will integrate mass balance and biochemical kinetics to predict wetland inundation and nutrient transport. Second, hydrology-aware graph neural network will use flow-conditioned attention and memory mechanisms to model wetland connectivity. Third, a contrastive learning-based model will predict wetlands in unmonitored regions using remote sensing data. All components will be integrated into a feedback system to enhance prediction accuracy, scalability, and generalizability. The models will be tested using data from the Western Lake Erie Basin and applied in other U.S. wetland regions. By embedding physical constraints into advanced AI architectures, this work bridges a key gap in Earth system modeling and enables robust, interpretable predictions of wetland function under broad environmental conditions. The project will also support interdisciplinary education and training at the intersection of environmental science and AI. Societal benefits include new learning opportunities for students and advancing public understanding of wetlands. All models and data will be openly shared to ensure accessibility and reuse. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Advancing our ability to predict weather and air quality is essential for protecting public health, supporting emergency response, and developing effective adaptation strategies. This project will bring together artificial intelligence (AI) and geoscience to create a new generation of forecasting tools that are faster, more accurate, and more responsive than current numerical model-based systems. By leveraging large volumes of observations from satellites, ground monitors, and physical models, the outcome from this research will enable real-time forecasting of air pollution and weather patterns across wide regions with high resolution. The tools to be developed in this study will help inform policymakers to respond to extreme weather and pollution events, improving public safety and risk management planning. Research outputs and educational materials will be available through online platforms. This project will support a graduate student involved in this work. Technically, this study aims to develop a machine learning-based surrogate for chemical transport models (CTMs), which are widely used in atmospheric research but often too computationally expensive for real-time forecasting. The proposed AI model will be trained on geoscientific big data, including meteorological inputs, satellite remote sensing, and ground-based pollutant measurements, and will learn to replicate the spatial-temporal behavior of CTMs. Unlike most existing AI models, this framework explicitly incorporates meteorology-chemistry interactions, such as the feedback between air pollutants and weather conditions. This allows the model to provide more realistic and scientifically based forecasts. The approach enhances predictive accuracy, expands spatial coverage, and reduces computational costs, making it suitable for integration into operational forecasting systems. The research outcomes include better air quality management, more accurate weather forecasting, supporting disaster preparedness, and promoting international collaboration on climate and environmental issues. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The Arctic’s permafrost — persistently frozen ground — holds an enormous amount of carbon, roughly twice the amount currently in the Earth’s atmosphere. As permafrost thaws, the associated release of dissolved carbon into the ocean causes acidification that threatens marine life, disrupts ecosystems, and puts coastal communities and food security at risk. This project brings together geoscience and artificial intelligence (AI) experts to develop new tools that will help us better understand and predict the impacts of thawing permafrost and the associated release of carbon into oceans. Through a combination of data-driven and physics-driven approaches, model development will overcome limitations of data sparsity that have hindered previous attempts to quantify and predict sources and rates of permafrost-driven carbon release. This project also offers hands-on research opportunities for graduate and undergraduate students and develops a new course on using machine learning for subsurface flow modeling. All software and training datasets will be made publicly available alongside documentation that will foster broad reuse for energy and environmental research and education. This project aims to develop efficient surrogate models to accurately capture the multi-physics processes within the thawing permafrost and to quantify permafrost carbon dynamics and associated uncertainties across the Arctic shelf. The research team will develop novel progressive neural operators (PNOs) that leverage multi-level, reduced-physics training datasets while addressing challenges such as catastrophic forgetting in machine learning. The developed PNO models will be integrated with a wide range of compiled datasets to predict seabed methane fluxes across the Arctic shelf and quantify the associated uncertainties. This project will also generate time-dependent, three-dimensional distributions of permafrost dynamics (e.g., temperature, ice content, pore water pressure, and salinity) and carbon transformations (e.g., organic carbon decomposition rates, methane concentration, and relict organic carbon content) beneath the sediment-water interface. These results will significantly enhance our understanding of the Arctic permafrost and provide new insights into how permafrost carbon affects ocean chemistry and the global carbon cycle. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Solar Energetic Particle (SEP) events are powerful bursts of radiation from the Sun that can endanger astronauts, damage satellite electronics, disrupt high-frequency radio communication, and increase risks for high-altitude aviation. While these events have significant societal and technological implications, they remain difficult to predict due to their rarity and complex origins. This project addresses a key limitation in space weather forecasting: the lack of high-quality, diverse training data for artificial intelligence (AI) models. By generating realistic synthetic data representing SEP conditions across multiple observational sources, the project will enable the development of more accurate and generalizable forecasting models. These contributions will directly support national efforts to safeguard space operations and infrastructure. The project will proceed in three integrated research thrusts. First, it will construct a multimodal dataset of SEP events by aligning multivariate time series data from multiple satellite missions that record x-ray and proton flux, solar wind properties, and photospheric magnetic fields. Second, to overcome data scarcity, the project will develop a generative modeling framework that synthesizes realistic SEP data across modalities, ensuring both temporal and cross-modal consistency. This approach is designed to preserve physical interdependencies between data types while expanding the pool of training samples. Third, the team will develop a hybrid learning framework that combines unimodal and multimodal representations for SEP prediction and applies unsupervised clustering to group events by energy level and precursor characteristics. The resulting tools and datasets will be shared with the scientific community via open-source repositories. Educational impacts include interdisciplinary training of graduate students in computer science and physics, curriculum integration in data science courses, outreach to K–12 students through STEM events, and public dissemination of research through community science programs and national workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Actors' Shakespeare Project

Caitlin Corbett Dance Company 2007

Caitlin Corbett Dance Company

Caitlin Corbett Dance Company 2009

Cal Anderson Park Alliance

Activate Cal Anderson Park through a series of events for the community that also engage the community and help enhance feelings of safety and comfort in the park.

Department of Forestry and Fire Protection

Eligible business development projects include facilities, operations, and professional services that support the restoration of healthy, resilient forests. Eligible workforce development projects include universities, colleges, government and community organizations, and businesses that aim to increase workforce capacity in the fields of logging, fuels treatment, manufacturing, or other support services that bolster the development of a resilient forest sector workforce. Research proposals that have the potential to facilitate an immediate benefit to California’s forest-sector businesses and/or workforce will also be considered. Check out the Wood Products website and subscribe for updates.     

Department of Forestry and Fire Protection

This Fiscal Year 2025-26 solicitation will include one grant type: • Forest Health  

Cal Poly Pomona Philanthropic Foundation

Cal Poly Pomona Philanthropic Foundation is an active grantmaking foundation based in Pomona, CA. Focus: philanthropy. Contact the foundation directly for current funding opportunities.

Cal State Fullerton Philanthropic Foundation

Cal State Fullerton Philanthropic Foundation is an active grantmaking foundation based in Fullerton, CA. Focus: philanthropy. Contact the foundation directly for current funding opportunities.

Forests, Parks & Recreation

Calais Rural Roadside Ash Removal

NCI - National Cancer Institute

Abstract Immune checkpoint blockade has become a cornerstone treatment for many solid tumors. However, not all patients respond effectively to this therapy. To enhance its efficacy, researchers are exploring the combination of radiation and immunotherapy, a strategy known as radio-immunotherapy. Despite its promise, radio- immunotherapy has not consistently demonstrated clinical benefit, in part due to a lack of activated dendritic cells (DCs) within tumors. Among these immune cells, a specific subset known as cDC1s plays a crucial role in antigen cross-presentation to T cells in the tumor-draining lymph nodes (TDLNs) to initiate anti-tumor immune responses. Emerging studies suggest that cDC1s in the TDLNs are also critical targets for immune checkpoint inhibitors. Tumors resistant to immunotherapy or radio-immunotherapy often exhibit low cDC1 levels, and their migration to TDLNs can be impeded by the immunosuppressive tumor microenvironment. This project aims to develop a calcium nanoparticle-based combinatory treatment to enhance radio- immunotherapy by improving both tumor infiltration and TDLN migration of cDC1s. We hypothesize that surface- engineered calcium nanoparticles, termed αCaNPs, can selectively deliver calcium, a key second messenger for immune cell migration, into circulating cDC1s, thereby enhancing their tumor infiltration in response to chemotactic signals. In addition, we anticipate that αCaNPs, by elevating cytosolic calcium levels, will promote cDC1 maturation inside tumors and afferent migration to TDLNs. Under immune checkpoint blockade with PD- L1 antibody, we expect that cDC1s will prime T cells in the TDLNs, leading to clonal expansion and robust anti- tumor immunity. To evaluate the efficacy of this approach, we will use murine models of head and neck squamous cell carcinoma (HNSCC). If successful, our technology could be extended to other cancers currently under intensive clinical investigation for radio-immunotherapy.

NIA - National Institute on Aging

Introduction: Calcium pyrophosphate deposition disease (CPPD), or “pseudogout,” is an idiopathic metabolic arthropathy characterized by calcium pyrophosphate dihydrate (CPP) crystal deposition in and around joints, particularly in articular cartilage and fibrocartilage. CPPD afflicts 1.3-in-1000 adults overall, with incidence increasing dramatically with age, at over 21% of people by 60, and 50% of people over 80 years old presenting with this painful, disfiguring, and debilitating disease. While under 5% of CPPD cases are associated with the ANKH genetic locus, the mechanisms of the disease origin in most cases remains idiopathic. CPPD affects 30 million people worldwide, yet is highly understudied, with a dearth of research reports on disease models, systems biology approaches, and potential therapeutic interventions for this age-related disease. Significance: This research will test a high-risk, high-reward approach to understanding the dynamics of CPPD crystal arthropathy development in aging, addressing a major public health problem with rationally designed animal models. This research endeavors to elucidate quantifiable, dose-related molecular mechanisms of CPPD development to systemically improve heath for this understudied yet common joint arthropathy. Innovation: Few models of CPPD have been reported in the literature, with no model yet established to study CPPD disease origin, progression, or remedy. One possible CPPD disease model mechanism is elevated lactic acid levels, which has been widely associated with CPPD in the literature, although not yet directly and mechanistically tested as a possible disease-promoting substance. By controllably inducing CPPD in vivo, advanced metabolomic, phenomics and molecular studies can be performed to understand better the disease's progression and present molecular pathways for treatment and diagnostics, which are currently lacking. This proposal will explore mechanistic models of CPPD to drive future medicinal advances. Hypothesis: Calcium pyrophosphate dihydrate crystals form in vivo via elevated lactic acid and arthritic joint bone erosion-related mechanisms. Aim 1: To determine the in vivo CPP crystal prevalence, metabolomics, and phenomics of rabbits with degenerated knees administered lactic acid locally in the knee synovial capsule. Aim 2: To assess the in vivo CPP crystal prevalence, metabolomics, and phenomics of rabbits with degenerated knees and elevated systemic metabolic acidosis. Aim 3: To investigate the role of lactate dehydrogenase (LDH) in CPPD pathogenesis in degenerated rabbit knees. Expected Outcomes CPP crystals will be induced in vivo via a lactic acid and eroded-bone-related mechanisms. This research will putatively present a causal disease formation mechanism for CPPD, presenting the potential for future novel therapeutic interventions, diagnostics, and underlying disease mechanisms.

NIGMS - National Institute of General Medical Sciences

ABSTRACT Calcium homeostasis is essential for cellular signaling and mitochondrial metabolic regulation, which are critical for meeting the metabolic demands of various cell types. Calcium modulates cellular functions by binding to calcium-sensing domains on numerous proteins. Many calcium sensors have multiple calcium binding domains with varying affinities, allowing for diverse and graded signaling. Calcium concentrations vary across cellular compartments, and the localization of calcium sensors facilitates subcellular and organelle-specific signaling. Mitochondrial calcium flux is necessary for cell viability, especially in cells with high energy needs. Dysregulated mitochondrial calcium leads to mitochondrial dysfunction, including ATP depletion, reactive oxygen species production, loss of mitochondrial membrane potential, mitochondrial permeability transition pore opening, and apoptogen release. These disruptions cause metabolic stress, triggering cell death and contributing to pathological conditions. Mitochondrial-induced cell death plays a key role in degenerative diseases and other cell death-related disorders. Understanding the molecular mechanisms behind mitochondrial stress responses may identify new therapeutic targets for conditions linked to mitochondrial dysfunction. Recent studies have shown that mitochondrial calcium uptake protein 1 (MICU1), located in the intermembrane space, regulates the cristae structure through the mitochondrial contact site and cristae organizing system (MICOS) independently of its established role in controlling mitochondrial calcium uniporter (mtCU) channel opening. This finding suggests that calcium sensors may have additional molecular functions beyond mitochondrial calcium uptake. MICU proteins, including MICU1, MICU2, and MICU3, regulate mtCU activity by controlling its open probability in response to calcium. This regulation depends on calcium binding to EF-hand domains, with mtCU remaining closed until cytosolic calcium levels reach a threshold. Knockout models for MICU proteins show abnormalities in mitochondrial ultrastructure not observed in mtCU-specific knockout models, suggesting that MICUs regulate other mitochondrial processes. The current research hypothesis is that MICU proteins influence mitochondrial processes beyond mitochondrial calcium uptake. Over the next five year, our research program will focus on three key questions: 1) do MICU proteins universally affect MICOS and mitochondrial ultrastructure? 2) what is the cell type-specific regulatory role of MICU proteins in mitochondrial ultrastructure? 3) do MICU proteins collaborate in regulating calcium sensing at mtCU and MICOS? By employing genetic, molecular, imaging, and protein biochemistry approaches in cellular and mouse models, this investigation aims to clarify the molecular mechanisms involved in mitochondrial calcium sensing and its effects on cellular physiology. This research addresses critical gaps in understanding mitochondrial physiology, focusing on the interplay between mitochondrial calcium sensing, mitochondrial calcium flux, and mitochondrial ultrastructure. The outcomes may reveal new strategies for treating diseases related to mitochondrial dysfunction.