Grants

NSF

Oaks dominate many ecosystems of California and the United States, making them crucial to biodiversity and economically valuable for timber products, such as lumber and furniture wood. Recent research has demonstrated that oaks are maladapted to current environmental conditions, being better adapted to cooler environments present 20,000 years ago. This unexpected maladaptation results in tree mortality, reduced growth, and increased susceptibility to pathogens. The project investigates the understudied concept of oak maladaptation by linking physiological traits, tree performance and fitness, and genomics. This project will investigate two widespread, ecologically important California tree oak species--Quercus lobata (valley oak) and Q. agrifolia (coast live oak)--which often grow together, but have different physiological responses to temperature and drought. Project goals are to understand how physiological traits determine tree growth, survival, and reproduction, whether these traits are genetically based, and how the underlying genetic gradients across the landscape can be used to predict which tree populations are most vulnerable and which are most resilient. Findings will both inform management strategies for oak restoration and conservation in areas where oaks have been removed by harvest or wildfire and also provide a case study for other forest tree species. The research will enhance the STEM workforce by educating students and postdoctoral scientists in cutting edge concepts and tools in integrative biology, ecology, evolution, and forestry. This project is an integrative study of the mechanistic and fitness response of trees to their environments using the understudied but ubiquitous phenomenon of maladaptation. Trees are particularly vulnerable to maladaptation due to their long generation time and long life span that can result in individuals being out of sync with current environments. Through these two contrasting California oaks, the project will first identify the physiological traits associated with response to high temperatures and drought through greenhouse and field experiments. Second, the studies will see whether traits contribute to the survival and growth across a tree’s life history of seedlings, saplings, and young adult trees, and determine how much key traits are genetically based. Third, a landscape genomic study will be conducted to identify geographic regions of maladaptation for each species based on genomic markers, and test whether these areas are the same as predicted to show maladaptation using empirical findings from physiological and fitness studies. This information can be used to identify seed sources for restoration and management of oak projects. This research will address the knowledge gap between selective processes and mechanisms affecting adaptation versus maladaptation. It will also demonstrate the innovative use of landscape genomics tools to detect maladaptation across a species range. By linking phenotypic mechanistic findings and relative fitness of trees with genomic information, this project will demonstrate how functional genomics can inform tree conservation. 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

Over one billion tons of nutrient-rich animal waste are produced annually in the United States, yet only a small portion is utilized, primarily as fertilizer. This project addresses a challenge of modern agriculture related to the sustainable management of animal manure, which, if not properly managed, can significantly impact the environment, public health, and the economy. The team seeks to implement a novel process to optimize nutrient recovery and produce engineered carbon materials from animal manure which not only repurposes animal manure but also enhances its value as an efficient fertilizer. The project aims to improve the efficiency of resource utilization and impact in the agricultural sector. If successful, this technology will substantially alleviate the environmental, health, and economic burden associated with nutrient release into water bodies. It may also enable local farmers and others to develop new products, thereby increasing revenues and reducing environmental pollution and waste disposal costs. The project will also provide unique training opportunities for graduate and undergraduate students. This project aims to develop a novel hydrothermal carbonization (HTC) technology that efficiently recovers nutrients from animal manures while also producing value-added engineered carbons. In this pH-swing HTC process, nitrogen and phosphorus will be recovered from solid animal manure to form liquid fertilizer by controlling the feedwater pH. This will also produce carbon-rich and low-impurity hydrochar, which will be further converted into engineered carbon for environmental applications. To achieve the goals of this project, the following three objectives will be pursued: 1) develop a novel pH-swing HTC technology to recover nutrients into a liquid phase from wet animal manures; 2) analyze the effects of different modification methods on the physicochemical properties as well as potential applications of the hydrochar; and 3) evaluate economic viability and environmental sustainability of nutrient recovery and engineered carbon synthesis. A range of laboratory experiments and model simulations will be conducted to understand the effects of pH-swing HTC processes and carbon-modification methods on the quality of liquid fertilizer and engineered carbon derived from animal manure. Technoeconomic analysis and life cycle assessment will be conducted to assess the environmental and economic benefits of nutrient recovery and engineered carbon synthesis. The success of the project will offer new insight into the chemical reactions and physical transformations occurring during the HTC of animal wastes. It will also be a significant step towards scaling-up and commercialization of the pH-swing HTC technology for a sustainable circular bioeconomy. 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

Over one billion tons of nutrient-rich animal waste are produced annually in the United States, yet only a small portion is utilized, primarily as fertilizer. This project addresses a challenge of modern agriculture related to the sustainable management of animal manure, which, if not properly managed, can significantly impact the environment, public health, and the economy. The team seeks to implement a novel process to optimize nutrient recovery and produce engineered carbon materials from animal manure which not only repurposes animal manure but also enhances its value as an efficient fertilizer. The project aims to improve the efficiency of resource utilization and impact in the agricultural sector. If successful, this technology will substantially alleviate the environmental, health, and economic burden associated with nutrient release into water bodies. It may also enable local farmers and others to develop new products, thereby increasing revenues and reducing environmental pollution and waste disposal costs. The project will also provide unique training opportunities for graduate and undergraduate students. This project aims to develop a novel hydrothermal carbonization (HTC) technology that efficiently recovers nutrients from animal manures while also producing value-added engineered carbons. In this pH-swing HTC process, nitrogen and phosphorus will be depleted from solid animal manure to liquid fertilizer by controlling the feedwater pH. This will also produce carbon-rich and low-impurity hydrochar, which will be further converted into engineered carbon for environmental applications. To achieve the goals of this project, the following three objectives will be pursued: 1) develop a novel pH-swing HTC technology to recover nutrients into liquid phase from wet animal manures; 2) analyze the effects of different modification methods on the physicochemical properties as well as potential applications of the hydrochar; and 3) evaluate economic viability and environmental sustainability of nutrient recovery and engineered carbon synthesis. A range of laboratory experiments and model simulations will be conducted to explore and understand the effects of pH-swing HTC processes and carbon-modification methods on the quality of liquid fertilizer and engineered carbon derived from animal manure. Technoeconomic analysis and life cycle assessment will be conducted to assess the environmental and economic benefits of nutrient recovery and engineered carbon synthesis. The success of the project will offer new insight into the chemical reactions and physical transformations occurring during the HTC of animal wastes. It will also be a significant step towards scaling-up and commercialization of the pH-swing HTC technology for sustainable circular bioeconomy. 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 program aims to create semiconductor amplifiers with outstanding noise performance in the microwave spectrum. Researchers from the California Institute of Technology (CalTech) and the University of Nevada-Reno (UNR) will accomplish this goal by leveraging a new nanofabrication method, atomic layer etching, which permits semiconductor device manufacturing with atomic-scale precision. If successful, these new amplifiers will be 30% more sensitive to radio emissions from space, meaning that less time will be needed for radio telescope measurements than is currently needed, potentially enabling new scientific discoveries. Based in part on this research, the team will develop a new college course at the UNR on nanofabrication, which will bring new technical training opportunities to students at UNR and in Northern Nevada. High electron mobility transistors (HEMTs) are used ubiquitously throughout radio astronomy observatories. Despite their impressive noise performance, their noise temperature has plateaued in recent years, in part due to challenges in scaling their dimensions even smaller. This project aims to overcome these limits by leveraging a new nanofabrication method, atomic layer etching, into the fabrication of low-noise HEMTs for the first time. The microwave noise performance has potential to improve by 30%, thus enabling significantly improved observational efficiency. These new HEMTs will be directly deployable to radio telescope systems as a drop-in replacement for the low-noise amplifier, meaning that an immediate improvement in sensitivity can be achieved without requiring any other upgrades. To maximize the broader impact of this project, the team will develop a new course on nanofabrication at the University of Nevada-Reno as well as incorporate graduate research activities into their new cleanroom facility. 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 program aims to create semiconductor amplifiers with outstanding noise performance in the microwave spectrum. Researchers from the California Institute of Technology (CalTech) and the University of Nevada-Reno (UNR) will accomplish this goal by leveraging a new nanofabrication method, atomic layer etching, which permits semiconductor device manufacturing with atomic-scale precision. If successful, these new amplifiers will be 30% more sensitive to radio emissions from space, meaning that less time will be needed for radio telescope measurements than is currently needed, potentially enabling new scientific discoveries. Based in part on this research, the team will develop a new college course at the UNR on nanofabrication, which will bring new technical training opportunities to students at UNR and in Northern Nevada. High electron mobility transistors (HEMTs) are used ubiquitously throughout radio astronomy observatories. Despite their impressive noise performance, their noise temperature has plateaued in recent years, in part due to challenges in scaling their dimensions even smaller. This project aims to overcome these limits by leveraging a new nanofabrication method, atomic layer etching, into the fabrication of low-noise HEMTs for the first time. The microwave noise performance has potential to improve by 30%, thus enabling significantly improved observational efficiency. These new HEMTs will be directly deployable to radio telescope systems as a drop-in replacement for the low-noise amplifier, meaning that an immediate improvement in sensitivity can be achieved without requiring any other upgrades. To maximize the broader impact of this project, the team will develop a new course on nanofabrication at the University of Nevada-Reno as well as incorporate graduate research activities into their new cleanroom facility. 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

Lakes can undergo rapid and sustained changes in water quality. These changes, often referred to as regime shifts, can substantially alter the lake environment, including food webs. Regime shifts often occur when conditions cross tipping points, but understanding the feedbacks and attributes that lead to regime shifts can be challenging. Increases in dissolved organic matter in lake water, termed lake browning, are occurring in many regions. Dissolved organic matter regulates water clarity, water temperature, ecosystem respiration, and many other lake attributes, suggesting potentially long-term and substantial impacts. Lake browning may stimulate lake dissolved oxygen losses, termed deoxygenation, that leads to further increases in dissolved organic matter and nutrients, ultimately resulting in sustained water quality impairment. This project seeks to understand the impacts of an anticipated lake regime shift driven by increases in dissolved organic matter and its impact on the lake overall. There may be tipping points associated with the low dissolved oxygen in lake bottom waters, driven by the increases in dissolved organic matter. Broader impacts include undergraduate student research experiences and community outreach activities. Using a sophisticated suite of sensors, manually-collected long-term data spanning multiple decades, experiments, and ecosystem models will help discern the consequences of lake browning and low dissolved oxygen availability in lakes. Studying multiple lakes that vary in dissolved organic matter, nutrients, and oxygen availability will provide the capacity to compare and assess feedbacks and tipping points resulting from rapid increases in dissolved organic matter that may push water quality conditions toward several potential future trajectories. This research will provide a foundation to understand the implications of widespread aquatic deoxygenation through research and data publications and training of early career scientists in the use of sophisticated sensors and other technologies. This project will extend a long-term data set of water quality variables sampled since the late 1980s of Lakes Lacawac and Giles in or near the Lacawac Sanctuary and Biological Field Station, located in northeastern Pennsylvania. The project will support a regional lake monitoring network in Pennsylvania and closely interface with GLEON, the Global Lake Ecological Observatory Network. 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

Lakes can undergo rapid and sustained changes in water quality. These changes, often referred to as regime shifts, can substantially alter the lake environment, including food webs. Regime shifts often occur when conditions cross tipping points, but understanding the feedbacks and attributes that lead to regime shifts can be challenging. Increases in dissolved organic matter in lake water, termed lake browning, are occurring in many regions. Dissolved organic matter regulates water clarity, water temperature, ecosystem respiration, and many other lake attributes, suggesting potentially long-term and substantial impacts. Lake browning may stimulate lake dissolved oxygen losses, termed deoxygenation, that leads to further increases in dissolved organic matter and nutrients, ultimately resulting in sustained water quality impairment. This project seeks to understand the impacts of an anticipated lake regime shift driven by increases in dissolved organic matter and its impact on the lake overall. There may be tipping points associated with the low dissolved oxygen in lake bottom waters, driven by the increases in dissolved organic matter. Broader impacts include undergraduate student research experiences and community outreach activities. Using a sophisticated suite of sensors, manually-collected long-term data spanning multiple decades, experiments, and ecosystem models will help discern the consequences of lake browning and low dissolved oxygen availability in lakes. Studying multiple lakes that vary in dissolved organic matter, nutrients, and oxygen availability will provide the capacity to compare and assess feedbacks and tipping points resulting from rapid increases in dissolved organic matter that may push water quality conditions toward several potential future trajectories. This research will provide a foundation to understand the implications of widespread aquatic deoxygenation through research and data publications and training of early career scientists in the use of sophisticated sensors and other technologies. This project will extend a long-term data set of water quality variables sampled since the late 1980s of Lakes Lacawac and Giles in or near the Lacawac Sanctuary and Biological Field Station, located in northeastern Pennsylvania. The project will support a regional lake monitoring network in Pennsylvania and closely interface with GLEON, the Global Lake Ecological Observatory Network. 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

After a period of record heat and rainfall in West Greenland, lakes which had been clear and blue rapidly turned brown and murky, a change that typically takes thousands of years. The causes and consequences of this dramatic change are largely unknown. This project offers a rare chance to understand how entire lake ecosystems respond to such rapid and severe disturbances, particularly in the sensitive Arctic biome which includes 40% of the world’s lakes. These dramatic changes directly impact ecosystem function of these lakes, which includes both the stability of the food webs as well as water security for local communities. This project also helps educate the public about changes to water quality in Arctic lakes and trains the next generation of scientists and teachers, developing curriculum for K-12 students in collaboration with current teachers and making this crucial knowledge widely accessible. This project also gives science teachers the opportunity to experience hands-on field work, sample collection, and data analysis. This project uses more than a decade of previously-collected data from ten Arctic lakes in West Greenland and continues to collect observations over the next 10 years to investigate the ecosystem response to extreme weather events. The research investigates how lake ecosystems are resilient against and recover from extreme weather events, creating a framework for understanding how Arctic lakes will change in the future. Studying these metrics across the whole lake ecosystem will gain a much deeper understanding of how these critical Arctic lakes respond to rapid environmental changes. The project objectives focus on (1) quantifying the ongoing effects of extreme weather events in multiple aspects of lake ecosystems, (2) assessing how a “stability framework” captures the variability in the response of ecosystem functions following these weather events, and (3) investigating how lake ecosystem processes affect the trajectory of ecosystem recovery from weather disturbances. This research will include ten years of additional data collection, experiments to test how specific lake processes respond to temperature, light, and nutrient conditions, and analysis of lake sediments to assess long-term changes in carbon nutrient cycling. 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

After a period of record heat and rainfall in West Greenland, lakes which had been clear and blue rapidly turned brown and murky, a change that typically takes thousands of years. The causes and consequences of this dramatic change are largely unknown. This project offers a rare chance to understand how entire lake ecosystems respond to such rapid and severe disturbances, particularly in the sensitive Arctic biome which includes 40% of the world’s lakes. These dramatic changes directly impact ecosystem function of these lakes, which includes both the stability of the food webs as well as water security for local communities. This project also helps educate the public about changes to water quality in Arctic lakes and trains the next generation of scientists and teachers, developing curriculum for K-12 students in collaboration with current teachers and making this crucial knowledge widely accessible. This project also gives science teachers the opportunity to experience hands-on field work, sample collection, and data analysis. This project uses more than a decade of previously-collected data from ten Arctic lakes in West Greenland and continues to collect observations over the next 10 years to investigate the ecosystem response to extreme weather events. The research investigates how lake ecosystems are resilient against and recover from extreme weather events, creating a framework for understanding how Arctic lakes will change in the future. Studying these metrics across the whole lake ecosystem will gain a much deeper understanding of how these critical Arctic lakes respond to rapid environmental changes. The project objectives focus on (1) quantifying the ongoing effects of extreme weather events in multiple aspects of lake ecosystems, (2) assessing how a “stability framework” captures the variability in the response of ecosystem functions following these weather events, and (3) investigating how lake ecosystem processes affect the trajectory of ecosystem recovery from weather disturbances. This research will include ten years of additional data collection, experiments to test how specific lake processes respond to temperature, light, and nutrient conditions, and analysis of lake sediments to assess long-term changes in carbon nutrient cycling. 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 research project develops new ways to model the US economy by using the assumption that economic decision makers learn by experience over time about underlying optimal decision rules. Individuals and organizations are faced with complex real world economic decisions and the best decision may not be immediately obvious. Many macroeconomic models assume that these decision makers have full information and always make decisions that best advance their interests. In contrast, this project models economic decision makers by using artificial intelligence in a model of learning through experience, and incorporates this new framework in classic economic cost-benefit tradeoffs. The new framework provides more realistic economic models that can better approximate the actual behavior of people and firms. These models can provide new insights into how U.S. government fiscal and monetary decisions can achieve desired economic outcomes. The project’s starting point is the observation that people and firms in real life typically learn in two ways about optimal behavior. The first one is reasoning: through introspective, abstract deliberations, economic units can better figure out their optimal course of action. The second one is accumulated experiences: the realized outcomes of past actions can update the perceived benefit of these past decisions. These two sources of information are conceptually distinct but limited, as experiences are observed only along the actual path taken by economic participants, while abstract thinking is a scarce cognitive resource. This research project develops a new interdisciplinary framework to learning through both reasoning and experience. There are three main components to the project. The first component develops the theoretical foundations of the framework and studies its deep fundamental properties, both in the short-run and the long-run. Learning can occur through cognition, which is costly, but beneficial in reducing decision makers' uncertainty about the best course of action. Decision makers trade off that benefit and cost of engaging cognitive resources, giving rise to constrained-optimal, or “resource-rational” choice of reasoning. These participants also update beliefs about optimal behavior based on the experienced flow utility each period. Critically, the effective precision of both reasoning and experiences in informing behavior is endogenous, as a function of the participant's beginning of period prior beliefs which evolve dynamically. This research advances the interest in bounded rationality within economics with novel methods that are rigorously grounded in established cognitive science findings. At the same time, the research also introduces several conceptual innovations to Reinforcement Learning literature, primarily by grounding the proposed learning theory in the constrained-optimal framework familiar to economists. In the second and third projects, the research team evaluates specific applications of the new bounded rationality theory in both household and firm settings. In the second project, the team studies the consumption-savings behavior in a rich model with participant heterogeneity, incomplete markets, consumption of durable and non-durable goods, and potentially investment in liquid and illiquid assets. In the last project, the team evaluates firm and investment dynamics, incorporating fixed costs of investment, endogenous entry and exit, and borrowing constraints. These two projects explore how the proposed learning friction could fundamentally alter economic literature's understanding of both demand and supply blocks, while providing novel and rich implications for decision making. 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 research project develops new ways to model the US economy by using the assumption that economic decision makers learn by experience over time about underlying optimal decision rules. Individuals and organizations are faced with complex real world economic decisions and the best decision may not be immediately obvious. Many macroeconomic models assume that these decision makers have full information and always make decisions that best advance their interests. In contrast, this project models economic decision makers by using artificial intelligence in a model of learning through experience, and incorporates this new framework in classic economic cost-benefit tradeoffs. The new framework provides more realistic economic models that can better approximate the actual behavior of people and firms. These models can provide new insights into how U.S. government fiscal and monetary decisions can achieve desired economic outcomes. The project’s starting point is the observation that people and firms in real life typically learn in two ways about optimal behavior. The first one is reasoning: through introspective, abstract deliberations, economic units can better figure out their optimal course of action. The second one is accumulated experiences: the realized outcomes of past actions can update the perceived benefit of these past decisions. These two sources of information are conceptually distinct but limited, as experiences are observed only along the actual path taken by economic participants, while abstract thinking is a scarce cognitive resource. This research project develops a new interdisciplinary framework to learning through both reasoning and experience. There are three main components to the project. The first component develops the theoretical foundations of the framework and studies its deep fundamental properties, both in the short-run and the long-run. Learning can occur through cognition, which is costly, but beneficial in reducing decision makers' uncertainty about the best course of action. Decision makers trade off that benefit and cost of engaging cognitive resources, giving rise to constrained-optimal, or “resource-rational” choice of reasoning. These participants also update beliefs about optimal behavior based on the experienced flow utility each period. Critically, the effective precision of both reasoning and experiences in informing behavior is endogenous, as a function of the participant's beginning of period prior beliefs which evolve dynamically. This research advances the interest in bounded rationality within economics with novel methods that are rigorously grounded in established cognitive science findings. At the same time, the research also introduces several conceptual innovations to Reinforcement Learning literature, primarily by grounding the proposed learning theory in the constrained-optimal framework familiar to economists. In the second and third projects, the research team evaluates specific applications of the new bounded rationality theory in both household and firm settings. In the second project, the team studies the consumption-savings behavior in a rich model with participant heterogeneity, incomplete markets, consumption of durable and non-durable goods, and potentially investment in liquid and illiquid assets. In the last project, the team evaluates firm and investment dynamics, incorporating fixed costs of investment, endogenous entry and exit, and borrowing constraints. These two projects explore how the proposed learning friction could fundamentally alter economic literature's understanding of both demand and supply blocks, while providing novel and rich implications for decision making. 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

Water that evaporates from ground surfaces cannot contribute to plant growth, streamflow or groundwater. Understanding the factors that control evaporation is therefore critical for predicting water availability and related natural resources. Plant litter—the leaves and twigs that accumulate on forest floors—plays a complex and poorly understood role. Some research shows plant litter acts like mulch in a garden bed, covering and shading soils to reduce evaporation. However, prior research also shows that plant litter can capture and store rainfall, causing much of it to evaporate before reaching soils. This study will quantify the relative influences of these competing effects: the mulching effect that reduces soil evaporation versus the interception effect that enhances evaporation. This research will combine observations in field sites across diverse United States (US) ecosystems, controlled laboratory and field experiments with collected samples, and development of a predictive simulation model. This integrated approach will advance understanding and capabilities to predict how litter properties, climate conditions, and ecosystem characteristics interact to influence evaporation across US landscapes. The project will train graduate students at University of Nevada Reno and Cleveland State University, develop new experiential learning modules, create openly available datasets, and support engagement with various audiences including land managers and other stakeholders. This project will pursue four key research tasks to advance understanding of water fluxes at the forest floor and their variation with weather, climate, and ecosystem traits. Litter-specific storage capacities and drainage parameters will be quantified using an in-lab precipitation generator and litter collected from 32 sites of the National Ecological Observatory Network (NEON). Soil and litter traits as well as surface evaporation rates will be measured in-situ at a subset of NEON sites. A replicated field experiment using soil plots with litter layers collected from NEON sites will test litter effects on soil water storages and fluxes. Evaporation modeling parameters will be developed from the collected laboratory and field data and used in a soil water-balance model to test how evaporation is affected by the interplay of meteorology and litter traits across a range of realistic climate scenarios. By using a systematic macro-scale approach and multiple prongs of investigation, this study will enhance mechanistic understanding of how litter enhances precipitation interception versus resisting soil evaporation, and reveal site-specific variations to develop generalizable conclusions that can ultimately support future applications. 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

Water that evaporates from ground surfaces cannot contribute to plant growth, streamflow or groundwater. Understanding the factors that control evaporation is therefore critical for predicting water availability and related natural resources. Plant litter—the leaves and twigs that accumulate on forest floors—plays a complex and poorly understood role. Some research shows plant litter acts like mulch in a garden bed, covering and shading soils to reduce evaporation. However, prior research also shows that plant litter can capture and store rainfall, causing much of it to evaporate before reaching soils. This study will quantify the relative influences of these competing effects: the mulching effect that reduces soil evaporation versus the interception effect that enhances evaporation. This research will combine observations in field sites across diverse United States (US) ecosystems, controlled laboratory and field experiments with collected samples, and development of a predictive simulation model. This integrated approach will advance understanding and capabilities to predict how litter properties, climate conditions, and ecosystem characteristics interact to influence evaporation across US landscapes. The project will train graduate students at University of Nevada Reno and Cleveland State University, develop new experiential learning modules, create openly available datasets, and support engagement with various audiences including land managers and other stakeholders. This project will pursue four key research tasks to advance understanding of water fluxes at the forest floor and their variation with weather, climate, and ecosystem traits. Litter-specific storage capacities and drainage parameters will be quantified using an in-lab precipitation generator and litter collected from 32 sites of the National Ecological Observatory Network (NEON). Soil and litter traits as well as surface evaporation rates will be measured in-situ at a subset of NEON sites. A replicated field experiment using soil plots with litter layers collected from NEON sites will test litter effects on soil water storages and fluxes. Evaporation modeling parameters will be developed from the collected laboratory and field data and used in a soil water-balance model to test how evaporation is affected by the interplay of meteorology and litter traits across a range of realistic climate scenarios. By using a systematic macro-scale approach and multiple prongs of investigation, this study will enhance mechanistic understanding of how litter enhances precipitation interception versus resisting soil evaporation, and reveal site-specific variations to develop generalizable conclusions that can ultimately support future applications. 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

Competition is fact of life: all organisms are constantly engaged in competition for mates and other resources, and they often do so aggressively. These competitive interactions can have important consequences not just for the individuals that engage in them but also for the evolution and persistence of populations and species. For example, scientists have shown that new species can evolve when territory holders direct more aggression towards competitors that resemble themselves compared to those that are dissimilar. However, we know very little about the physiological and neurobiological processes that control this kind of aggression that is biased toward individuals that look more similar to oneself. The proposed research will use a combination of social learning experiments, genetic analysis, neural activity mapping, and brain gene expression analyses to investigate this question using two highly social cichlid fish species that differ in body coloration. This highly integrative approach will advance our understanding of how social competition influences evolutionary change and biodiversity. The multidisciplinary project will build collaborative relationships among institutions in the US (two R2 and one R1), Switzerland, and Tanzania. It will also provide research and training opportunities for undergraduate and graduate students. This project will promote STEM engagement through the development of an interactive exhibit on cichlid social behavior at a local children’s discovery museum. Together, these efforts will answer fundamental questions in organismal biology while having a significant societal impact by preparing students to enter STEM fields. Male-male competition for mates and breeding territories is thought to play an important role in population differentiation and speciation. If males bias territorial aggression towards males that resemble their own phenotype, rare male phenotypes would be involved in fewer costly fights. The resulting negative frequency-dependent selection could facilitate the evolution of distinct phenotypes and stabilize the speciation process. However, the underlying proximate mechanisms remain mostly unknown. The proposed research will assess the cognitive, genetic, and neural basis of aggression biases in the cichlid species pair Pundamilia nyererei (males are red) and P. pundamilia (males are blue). These two species co-occur at different locations in the East African Lake Victoria with varying degrees of reproductive isolation, allowing us to study the mechanistic basis of aggression biases at different stages of speciation. The project has the following objectives: First, this research will estimate the number of genetic loci regulating aggression biases and body coloration and their degree of linkage using quantitative genetics and quantitative trait locus mapping. Second, this research will assess how social experience and social context shape aggression biases. Finally, the project will determine the neural mechanisms regulating aggression biases using a combination of neural activity mapping, pharmacological manipulation of dopamine signaling, and neural transcriptome analysis. This highly integrative approach will provide insights into how male-male competition contributes to speciation, agonistic character evolution, and species coexistence. 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

Competition is fact of life: all organisms are constantly engaged in competition for mates and other resources, and they often do so aggressively. These competitive interactions can have important consequences not just for the individuals that engage in them but also for the evolution and persistence of populations and species. For example, scientists have shown that new species can evolve when territory holders direct more aggression towards competitors that resemble themselves compared to those that are dissimilar. However, we know very little about the physiological and neurobiological processes that control this kind of aggression that is biased toward individuals that look more similar to oneself. The proposed research will use a combination of social learning experiments, genetic analysis, neural activity mapping, and brain gene expression analyses to investigate this question using two highly social cichlid fish species that differ in body coloration. This highly integrative approach will advance our understanding of how social competition influences evolutionary change and biodiversity. The multidisciplinary project will build collaborative relationships among institutions in the US (two R2 and one R1), Switzerland, and Tanzania. It will also provide research and training opportunities for undergraduate and graduate students. This project will promote STEM engagement through the development of an interactive exhibit on cichlid social behavior at a local children’s discovery museum. Together, these efforts will answer fundamental questions in organismal biology while having a significant societal impact by preparing students to enter STEM fields. Male-male competition for mates and breeding territories is thought to play an important role in population differentiation and speciation. If males bias territorial aggression towards males that resemble their own phenotype, rare male phenotypes would be involved in fewer costly fights. The resulting negative frequency-dependent selection could facilitate the evolution of distinct phenotypes and stabilize the speciation process. However, the underlying proximate mechanisms remain mostly unknown. The proposed research will assess the cognitive, genetic, and neural basis of aggression biases in the cichlid species pair Pundamilia nyererei (males are red) and P. pundamilia (males are blue). These two species co-occur at different locations in the East African Lake Victoria with varying degrees of reproductive isolation, allowing us to study the mechanistic basis of aggression biases at different stages of speciation. The project has the following objectives: First, this research will estimate the number of genetic loci regulating aggression biases and body coloration and their degree of linkage using quantitative genetics and quantitative trait locus mapping. Second, this research will assess how social experience and social context shape aggression biases. Finally, the project will determine the neural mechanisms regulating aggression biases using a combination of neural activity mapping, pharmacological manipulation of dopamine signaling, and neural transcriptome analysis. This highly integrative approach will provide insights into how male-male competition contributes to speciation, agonistic character evolution, and species coexistence. 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

Competition is fact of life: all organisms are constantly engaged in competition for mates and other resources, and they often do so aggressively. These competitive interactions can have important consequences not just for the individuals that engage in them but also for the evolution and persistence of populations and species. For example, scientists have shown that new species can evolve when territory holders direct more aggression towards competitors that resemble themselves compared to those that are dissimilar. However, we know very little about the physiological and neurobiological processes that control this kind of aggression that is biased toward individuals that look more similar to oneself. The proposed research will use a combination of social learning experiments, genetic analysis, neural activity mapping, and brain gene expression analyses to investigate this question using two highly social cichlid fish species that differ in body coloration. This highly integrative approach will advance our understanding of how social competition influences evolutionary change and biodiversity. The multidisciplinary project will build collaborative relationships among institutions in the US (two R2 and one R1), Switzerland, and Tanzania. It will also provide research and training opportunities for undergraduate and graduate students. This project will promote STEM engagement through the development of an interactive exhibit on cichlid social behavior at a local children’s discovery museum. Together, these efforts will answer fundamental questions in organismal biology while having a significant societal impact by preparing students to enter STEM fields. Male-male competition for mates and breeding territories is thought to play an important role in population differentiation and speciation. If males bias territorial aggression towards males that resemble their own phenotype, rare male phenotypes would be involved in fewer costly fights. The resulting negative frequency-dependent selection could facilitate the evolution of distinct phenotypes and stabilize the speciation process. However, the underlying proximate mechanisms remain mostly unknown. The proposed research will assess the cognitive, genetic, and neural basis of aggression biases in the cichlid species pair Pundamilia nyererei (males are red) and P. pundamilia (males are blue). These two species co-occur at different locations in the East African Lake Victoria with varying degrees of reproductive isolation, allowing us to study the mechanistic basis of aggression biases at different stages of speciation. The project has the following objectives: First, this research will estimate the number of genetic loci regulating aggression biases and body coloration and their degree of linkage using quantitative genetics and quantitative trait locus mapping. Second, this research will assess how social experience and social context shape aggression biases. Finally, the project will determine the neural mechanisms regulating aggression biases using a combination of neural activity mapping, pharmacological manipulation of dopamine signaling, and neural transcriptome analysis. This highly integrative approach will provide insights into how male-male competition contributes to speciation, agonistic character evolution, and species coexistence. 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

Tidal marshes are one of the most biologically productive ecosystems on earth, supporting critical ecosystem services including coastal storm defense, fisheries support, and unique biodiversity. Yet, tidal marshes are severely threatened by coastal flooding. Trapped tidal floodwaters break-up marsh meadows (“ponding”), and cause remnant marsh grass to become sparse and stunted. Healthy meadows are needed by marsh-breeding birds to prevent nest flooding, an increasing cause of nestling mortality leading to species declines as fast as 9% per year. To save marsh birds, practitioners have proposed a novel technique to drain trapped floodwaters by creating shallow channels (“runnels”). However, effective methods are still being developed, and whether this approach to restoration will work for tidal marsh birds is unknown. This project will examine marsh bird responses to ponding, measure ecosystem responses to runnels, and provide design metrics and restoration targets for runnels. In 2025, a National Audubon Society-led partnership (“Marshes for Tomorrow”) set an ambitious goal to restore thousands of acres of tidal marsh in Maryland by 2035. Using results from this project, Towson University, University of Maryland, and Audubon will take action toward this goal through evidence-based restoration. The project will produce technical protocols for practitioners, make public outreach presentations, and train undergraduate and graduate students in ecological science and conservation practice. The project will also engage with local communities to choose restoration sites, and share science-based management options with landowners to reduce flooding of their marshlands. This project investigates relationships between ecosystems, landscape-scale patterns of fragmentation (ponding), and consequences for tidal marsh bird persistence — integrating across spatial and temporal scales to link ecosystem function to bird population demographics. The study will focus on two tidal marsh endemics, the rapidly declining Saltmarsh Sparrow, and the Seaside Sparrow. Surveys and experiments will be conducted in twelve tidal marshes in the Maryland-portion of the Chesapeake Bay. This project includes three technical approaches: 1) large-scale habitat and breeding bird surveys using field studies and remote-sensing, 2) an ecosystem-scale restoration experiment monitored using field surveys and biogeochemical analyses, and 3) microhabitat studies using field-based nest observations and mesocosm experiments on plant and soil recovery. The project will test the hypothesis that tidal marsh bird densities and nesting success decline non-linearly as ponding increases (exploring the presence of tipping-points) and will derive habitat indices from remote-sensing data to direct site-selection and set target metrics for habitat recovery after restoration. Audubon will conduct hydrologic restoration at an expansive, moderately fragmented marsh that researchers will use as a whole-ecosystem experiment with a before-after-control-impact design. By assessing variation in water, soil, and plant responses to runnels the project will identify specific characteristics favorable for restoration. Spatial and temporal patterns in marsh bird responses to pond-fragmentation, ecosystem responses to restoration, and subsequent implications for habitat quality will inform basic science on resilience and recovery. 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

Tidal marshes are one of the most biologically productive ecosystems on earth, supporting critical ecosystem services including coastal storm defense, fisheries support, and unique biodiversity. Yet, tidal marshes are severely threatened by coastal flooding. Trapped tidal floodwaters break-up marsh meadows (“ponding”), and cause remnant marsh grass to become sparse and stunted. Healthy meadows are needed by marsh-breeding birds to prevent nest flooding, an increasing cause of nestling mortality leading to species declines as fast as 9% per year. To save marsh birds, practitioners have proposed a novel technique to drain trapped floodwaters by creating shallow channels (“runnels”). However, effective methods are still being developed, and whether this approach to restoration will work for tidal marsh birds is unknown. This project will examine marsh bird responses to ponding, measure ecosystem responses to runnels, and provide design metrics and restoration targets for runnels. In 2025, a National Audubon Society-led partnership (“Marshes for Tomorrow”) set an ambitious goal to restore thousands of acres of tidal marsh in Maryland by 2035. Using results from this project, Towson University, University of Maryland, and Audubon will take action toward this goal through evidence-based restoration. The project will produce technical protocols for practitioners, make public outreach presentations, and train undergraduate and graduate students in ecological science and conservation practice. The project will also engage with local communities to choose restoration sites, and share science-based management options with landowners to reduce flooding of their marshlands. This project investigates relationships between ecosystems, landscape-scale patterns of fragmentation (ponding), and consequences for tidal marsh bird persistence — integrating across spatial and temporal scales to link ecosystem function to bird population demographics. The study will focus on two tidal marsh endemics, the rapidly declining Saltmarsh Sparrow, and the Seaside Sparrow. Surveys and experiments will be conducted in twelve tidal marshes in the Maryland-portion of the Chesapeake Bay. This project includes three technical approaches: 1) large-scale habitat and breeding bird surveys using field studies and remote-sensing, 2) an ecosystem-scale restoration experiment monitored using field surveys and biogeochemical analyses, and 3) microhabitat studies using field-based nest observations and mesocosm experiments on plant and soil recovery. The project will test the hypothesis that tidal marsh bird densities and nesting success decline non-linearly as ponding increases (exploring the presence of tipping-points) and will derive habitat indices from remote-sensing data to direct site-selection and set target metrics for habitat recovery after restoration. Audubon will conduct hydrologic restoration at an expansive, moderately fragmented marsh that researchers will use as a whole-ecosystem experiment with a before-after-control-impact design. By assessing variation in water, soil, and plant responses to runnels the project will identify specific characteristics favorable for restoration. Spatial and temporal patterns in marsh bird responses to pond-fragmentation, ecosystem responses to restoration, and subsequent implications for habitat quality will inform basic science on resilience and recovery. 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 grant supports fundamental research in titanium alloys manufacturing and promotes the progress of science and engineering. Titanium alloys are promising structural materials due to their lightweight, high strength and toughness, high temperature and corrosion resistance, and biocompatibility and have many critical applications in transportation, such as airplane engine components, and healthcare, such as human implants. However, the manufacturing of titanium alloys requires the addition of expensive alloying elements and high processing temperatures, which leads to their high costs and significantly restricted commercial use. This project investigates the scientific mechanisms involved in deformation twinning and develops a prototype system for low-cost manufacturing of advanced lightweight titanium alloys. A combination of experimental, computation, and machine learning efforts is performed to search for new compositions of titanium alloys with low-cost alloying elements and activate novel deformation mechanisms in order to achieve their room-temperature manufacturing. The new knowledge generated by this project advances the titanium industry and promotes technologies to reduce carbon dioxide emissions and improve human health, thus promoting national prosperity and welfare. This research provides a platform to train the next generation of titanium experts and skilled workforce, especially those from underrepresented groups, in the manufacturing of advanced materials as well as high-performance computing. This project is jointly funded by Advanced Manufacturing (AM) program and the Established Program to Stimulate Competitive Research (EPSCoR). This project aims to advance cost-effective room-temperature manufacturing of titanium alloys by a novel alloy design and processing strategy. In this strategy, a large portion (greater than 50 volume percent) of the body-centered cubic beta phase is stabilized at room temperature using low-cost elements after casting and homogenization processes. Furthermore, room-temperature ductility and workability of these alloys in the subsequent cold deformation process are improved by activating sufficient highly-indexed deformation twinning modes in the beta phase utilizing coupled twinning-induced plasticity (TWIP) and transformation-induced plasticity (TRIP) mechanisms. Two specific approaches, involving integration of experiment, simulation and machine learning, are followed. The first approach is to identify and tune the coupling mechanisms between phase transformations and highly-indexed twinning in representative titanium alloys through advanced characterization, crystallography models and atomistic simulations. The second approach is to manipulate and investigate alloying effects on twinning and room-temperature workability of these alloys by iterative feedback between the machine learning models, informed by first-principles calculations, and high-throughput fabrication and mechanical testing experiments. These results guide the discovery of beta phase stabilized titanium alloys containing low-cost alloying elements and attain high room-temperature workability. Finally, large-scale samples of titanium alloys with optimized compositions are cold deformation processed by rolling and drawing into specific shapes and tested for mechanical behavior to verify their room-temperature workability. 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

Measurements of the spatial clustering of matter in the universe can reveal clues about the nature of the mysterious “dark energy” that is causing the universe’s expansion to accelerate. A new powerful way to measure this clustering at early epochs in the universe’s history is to use radio telescopes to detect the hydrogen gas that is ubiquitous in all galaxies. A team of scientists from Arizona State University, Massachusetts Institute of Technology, Yale University, and West Virginia University, is using a custom-built radio telescope, the Canadian Hydrogen Intensity Mapping Experiment (CHIME), to make one of the first measurements of dark energy from observations of radio waves emitted by hydrogen in the universe. This project will develop several new techniques to leverage the latest developments in signal processing, detector technology, and theoretical modeling. In parallel, this project will expand several outreach programs that teach high school and college-aged students about astronomy and scientific thinking. The goal of this project is to solve critical calibration and analysis challenges for CHIME that will reduce residual foreground contamination by an order of magnitude and enable the detection of the large-scale structure of the universe with the 21cm line, independent of other probes. CHIME’s recent measurements of cross-correlations between 21cm intensity maps and eBOSS galaxies up to redshift 1.4, and the Lyman-alpha forest up to redshift 2.3, have demonstrated CHIME’s potential for high-precision large-scale structure measurements. In this project, the team will develop new modeling frameworks and analysis pipelines for 21cm cross-correlations; generate improved beam models and integrate them into the analysis; implement new radio-frequency interference excision algorithms based on cyclostationary signal processing; and deploy innovative foreground filtering techniques that are robust to the dominant systematic errors in the data. These advances should lead to a CHIME-only auto-correlation detection of large-scale structure and ultimately the baryon acoustic oscillation signal. 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

Measurements of the spatial clustering of matter in the universe can reveal clues about the nature of the mysterious “dark energy” that is causing the universe’s expansion to accelerate. A new powerful way to measure this clustering at early epochs in the universe’s history is to use radio telescopes to detect the hydrogen gas that is ubiquitous in all galaxies. A team of scientists from Arizona State University, Massachusetts Institute of Technology, Yale University, and West Virginia University, is using a custom-built radio telescope, the Canadian Hydrogen Intensity Mapping Experiment (CHIME), to make one of the first measurements of dark energy from observations of radio waves emitted by hydrogen in the universe. This project will develop several new techniques to leverage the latest developments in signal processing, detector technology, and theoretical modeling. In parallel, this project will expand several outreach programs that teach high school and college-aged students about astronomy and scientific thinking. The goal of this project is to solve critical calibration and analysis challenges for CHIME that will reduce residual foreground contamination by an order of magnitude and enable the detection of the large-scale structure of the universe with the 21cm line, independent of other probes. CHIME’s recent measurements of cross-correlations between 21cm intensity maps and eBOSS galaxies up to redshift 1.4, and the Lyman-alpha forest up to redshift 2.3, have demonstrated CHIME’s potential for high-precision large-scale structure measurements. In this project, the team will develop new modeling frameworks and analysis pipelines for 21cm cross-correlations; generate improved beam models and integrate them into the analysis; implement new radio-frequency interference excision algorithms based on cyclostationary signal processing; and deploy innovative foreground filtering techniques that are robust to the dominant systematic errors in the data. These advances should lead to a CHIME-only auto-correlation detection of large-scale structure and ultimately the baryon acoustic oscillation signal. 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

Many naturally occurring microorganisms can produce soap-like substances, called biosurfactants, that alter how fluids and other substances in the liquids move through porous spaces such as soils and biological tissues. However, scientists still do not know how far and how fast these biosurfactants spread or how they redirect substances ranging from nutrients and pollutants to disease-causing bacteria. Yet, these microbial agents play important roles in the health of soils, plant roots, and even human lungs. This project tackles this knowledge gap by using laboratory models that mimic real soils to observe the microbes and biosurfactants in action and quantify their influence on the transport of fluids and dissolved chemicals. Outcomes from the project could support improved soil remediation strategies, more sustainable agricultural practices, and new methods to manage the spread of harmful bacteria. The project also promotes national goals in science and education by training undergraduate and graduate students, supporting interdisciplinary collaboration, and conducting outreach activities to help K-12 students appreciate fluid mechanics and microbiology and their relevance to daily life. This project integrates multiscale experiments and physics-based modeling to investigate how biosurfactants and the microbes that secrete them alter mass transport in unsaturated porous media. The goals are to: (1) quantify how time-dependent biosurfactant production alters transport behavior in two-dimensional porous systems; (2) characterize feedback loops between biosurfactant-induced flow, solute transport, and bacterial migration; and (3) measure and model biosurfactant-driven transport processes in three-dimensional porous media. Innovative visualization experiments paired with advanced multiscale models will reveal the intertwined dynamics of bacteria, biosurfactants, and transported substances. Model-data comparisons will guide the development of predictive tools that can be applied to environmental systems ranging from contaminated soils to biological tissues. The findings will provide a mechanistic framework for understanding biosurfactant-mediated mass transport, improving our ability to forecast chemical and microbial movement in complex, unsaturated porous media. 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

Many naturally occurring microorganisms can produce soap-like substances, called biosurfactants, that alter how fluids and other substances in the liquids move through porous spaces such as soils and biological tissues. However, scientists still do not know how far and how fast these biosurfactants spread or how they redirect substances ranging from nutrients and pollutants to disease-causing bacteria. Yet, these microbial agents play important roles in the health of soils, plant roots, and even human lungs. This project tackles this knowledge gap by using laboratory models that mimic real soils to observe the microbes and biosurfactants in action and quantify their influence on the transport of fluids and dissolved chemicals. Outcomes from the project could support improved soil remediation strategies, more sustainable agricultural practices, and new methods to manage the spread of harmful bacteria. The project also promotes national goals in science and education by training undergraduate and graduate students, supporting interdisciplinary collaboration, and conducting outreach activities to help K-12 students appreciate fluid mechanics and microbiology and their relevance to daily life. This project integrates multiscale experiments and physics-based modeling to investigate how biosurfactants and the microbes that secrete them alter mass transport in unsaturated porous media. The goals are to: (1) quantify how time-dependent biosurfactant production alters transport behavior in two-dimensional porous systems; (2) characterize feedback loops between biosurfactant-induced flow, solute transport, and bacterial migration; and (3) measure and model biosurfactant-driven transport processes in three-dimensional porous media. Innovative visualization experiments paired with advanced multiscale models will reveal the intertwined dynamics of bacteria, biosurfactants, and transported substances. Model-data comparisons will guide the development of predictive tools that can be applied to environmental systems ranging from contaminated soils to biological tissues. The findings will provide a mechanistic framework for understanding biosurfactant-mediated mass transport, improving our ability to forecast chemical and microbial movement in complex, unsaturated porous media. 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

Digital Twin technology aims to align the physical behavior of complex systems with online computational models, enabling real-time monitoring, prediction, and informed decision-making. Raw data gathered by sensors is often challenging to integrate into a model due to the sparsity of sensors. The sparse sensing problem is the central focus of this project. Along with developing theory and computational methods, the project focuses on applications to components of nuclear reactors. Open-source community software will be developed within the frameworks RAVEN, PySensors, and the Nuclear Data Research System. The project will involve traineeships, software carpentry, and open-source educational curricula. Curricula will be published using the University of Washington's Lightboard filming studio. The project aligns with the Presidential priorities in artificial intelligence and nuclear energy, and will enhance national leadership in these areas. Sparse sensors establish the critical bidirectional flow of information between virtual models and safety-critical decision-making in physical nuclear energy subsystems. These sensors are essential for estimating high-dimensional temperature fields, pressure gradients, and accident scenarios. However, in nuclear applications, sensor design, placement, and budgets are extremely constrained, making strategic design and budgeting of sensors crucial. This project develops fundamental theory, algorithms, and guarantees for sparse sensing optimization in nuclear subsystems. Control and information theory, statistical mechanics, and uncertainty quantification will be leveraged to develop robust, high-dimensional estimation methods with guaranteed performance. To achieve this, there are three major thrusts: 1) dynamical models and information theory of sparse sensing, 2) optimal regularization and uncertainty quantification using statistical mechanics, and 3) multi-objective decision-making in nuclear digital twins. Validation and verification of the methods and models will focus on high-dimensional estimation, anomaly detection and prediction within the transient water irradiation system at Idaho National Laboratory (INL). 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.