Pennsylvania State Univ University Park

NSF Awards · FY 2025 · 2025-08

Non-technical Abstract: The uneven surface of a sandbox shows that soft solids like sand, soil, or skin cream hold memories of their history. Their microscopic structure also holds memories, making their properties hard to control. In this project, researchers will deform a solid made of tightly packed oil drops, and a two-dimensional solid made of beads, while monitoring how the drops or beads rearrange. These tests will reveal where and how memory is stored. Results will help predict and control the strength of materials like soil, grain, and foam. These kinds of materials are vital to infrastructure, and industries from agriculture and food to personal care products. The research will also identify ways to design and use advanced materials that adapt to their environments. The project will train graduate and undergraduate students. A K-12 teacher will participate in this research and will develop a curriculum on materials and research methods. Teacher recruiting will include rural school districts in Central Pennsylvania. Technical Abstract: Many kinds of matter are challenging to describe because they do not relax to equilibrium; their properties depend on the past. This is true of amorphous solids, including glass, sand, soil, and mayonnaise, which share common challenges for predicting and controlling mechanical behavior. The disordered arrangement of particles in these materials is metastable, and it changes each time it is deformed. Prior work has shown a simple way that 2D samples encode and recall the amplitudes of oscillatory shear, but this picture omits some of amorphous solids’ most distinctive and challenging features. In this project, experiments will study a 2D solid made of repulsive colloids at an oil-water interface, and a 3D concentrated emulsion. While varying the amplitude and frequency of shear, tests will record the locations of particle rearrangements via particle tracking in 2D, and light scattering in 3D, measuring the number, size, hysteresis, and dynamics of individual plastic events. Major questions include how mechanical preparation alters the response to shear, what information is recoverable and what is erased, and how memory and plasticity differ from 2D to 3D. Some experiments will target unusual memories made possible when one rearrangement inhibits another, providing data about interactions that reflect the glassy character of these systems. The result will be a new experimental picture of these materials’ mechanical properties, collective dynamics, and history dependence from the microscopic to bulk scale; evidence that certain kinds of memory are generic signatures of glassiness; and design motifs for metamaterials. The project will also yield methods for studying memory in glassy matter and tracking relaxations in bulk emulsions, and improvements to widely used particle tracking software. 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 Awards · FY 2025 · 2025-08

Cells age just as organisms do, but the mechanisms by which cell aging occurs are unknown. Metabolic imbalances and the inability to respond effectively to stress are key markers of cellular aging. At the heart of these processes is a vital molecule called NAD+, which plays a central role in cellular metabolism, repair of DNA, the ability of genes to direct protein formation, and chemical modification of those proteins. As cells age, NAD+ levels decline, which impairs cell ability to respond to stress. However, it remains unclear whether aging directly causes the decline in NAD+ levels, or whether disruptions in NAD+ metabolism accelerate aging. This project aims to uncover the biochemical mechanisms behind cellular aging by studying how NAD+ metabolism is maintained, regulated, and where it breaks down as cells age. There is still much to learn about metabolic dysfunction and aging, and this research seeks to fill those gaps by revealing how cellular stress influences the aging process. The project goal is to create a comprehensive database and metabolic map of NAD+ biology throughout the lifespan. The results could have implications for understanding the basis of aging and of many common diseases that affect aging animals, from worms to people. In addition to advancing scientific knowledge, this project will involve undergraduates in meaningful research, providing early, structured opportunities to train the next generation of scientists. It will also include an annual "Science in Action" workshop in rural Mississippi, designed to inspire curiosity and provide scientific exposure to students in that state. Nicotinamide adenine dinucleotide (NAD+) is a central metabolite that orchestrates cellular responses to environmental stimuli and plays a fundamental role in metabolism across all living organisms. Despite its essential and evolutionarily conserved role, significant gaps remain in our understanding of how NAD+ regulates homeostasis and participates in cellular stress responses, particularly over an organism’s lifespan. While much research focuses on NAD+ metabolism in health and disease, this proposal aims to explore the compensatory mechanisms that maintain NAD+ homeostasis and how these mechanisms fluctuate during cellular stress and aging. The central hypothesis posits that prolonged cellular stress leads to a decline in NAD+ levels, disrupting organismal homeostasis and driving physiological aging. To address this, the project will: (1) Determine how altering NAD+ metabolism affects redox balance; (2) Examine how cytoplasmic and mitochondrial [NAD+]/[NADH] ratios shift with stress and aging, and; (3) Create a research framework that enables undergraduates to explore the effects of NAD+ manipulation on behavior, phenotype, and lifespan. This research will integrate genetic manipulation with multi-omics profiling to establish a comprehensive baseline for understanding NAD+ dynamics. Quantitative isotope tracing coupled with metabolic flux analysis and spatially resolved redox imaging in C. elegans will reveal how NAD+ metabolism is maintained or disrupted. By addressing these objectives, the project seeks to elucidate how cells achieve and lose metabolic homeostasis, offering insights into physiological regulation over the lifespan. These findings will deepen our understanding of NAD+ biology and its broader implications for aging and stress resilience. This research is funded by the Cellular Dynamics and Function Program of the Division of Molecular and Cellular Biosciences. 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 Awards · FY 2025 · 2025-08

This project investigates the quantum nature of spacetime geometry, driving advances in quantum gravity and quantum information science. The cross-fertilization of these two fields is at the roots of a series of foundational results in theoretical physics, including the area law for the vacuum entanglement entropy, the Page curve for the typical entropy of random states in black hole evaporation, and the Eigenstate Thermalization Hypothesis for the thermalization of subsystems in isolated quantum systems. The disruptive aspect of this project is the extension of these methods to primordial cosmology. The project helps create human resources in the US through the training of a graduate student and a postdoctoral scholar in the area of gravitational physics, with expertise in quantum information science. The project is articulated into two parts, in which new quantum-information methods tailored to quantum gravitational systems are developed, and these methods, together with standard tools from information theory and condensed-matter physics, are applied to quantum gravitational systems. The two parts focus on: (i) non-perturbative methods in Loop Quantum Gravity, and (ii) perturbative methods at the transition from quantum geometry to quantum fields. The long-term objective is to combine the results of the two lines of work to investigate models of quantum gravity where a semiclassical spacetime with small quantum fluctuations and long-range correlations arises from the non-perturbative theory, and robust predictions for the primordial state of the universe and the late stages of black hole evaporation can be extracted. 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 Awards · FY 2025 · 2025-08

Pennsylvania State University will organize and host the Third Penn State SETI Symposium, from August 18-21, 2025. This international conference will bring together a broad range of expertise to the problem of the Search for Extraterrestrial Intelligence (SETI), host a gathering of early career SETI researchers, and feature breakout sessions on specialized topics such as SETI post-detection protocols. The symposium will include theory, practice, and social aspects of the field, including radio, laser, exoplanetary, and solar system SETI. The symposium will cater heavily towards junior scientist involvement. The symposium will include contributions from anthropologists, historians, and others outside the physical sciences. SETI is a broad and interdisciplinary endeavor. Thinking about how technological life will be discovered requires consideration of more than just physics or astrophysics. Discovery might be via radio waves, or optical communication, or via atmospheric pollutants of exoplanets, via a probe in the solar system, or from the detection of the waste heat of industry in space around another star. Exploring this requires input from exoplanetary astrophysics, stellar astrophysics, planetary science, radio astronomy, electrical engineering, high energy astrophysics, and more. Technology also implies and underlying social structure, which means we must also consider how alien species might communicate, evolve, spread, and innovate. This breadth and interdisciplinarity cannot be well represented at any typical scientific meeting; it requires a deliberate and holistic approach over multiple days, and participation by some who may not be SETI experts or even astronomers. 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 Awards · FY 2025 · 2025-08

Understanding animal movement is imperative for the conservation and management of highly threatened species. However, frameworks that leverage existing data sets are currently lacking and needed to improve our knowledge of how animals move across the landscape, and to understand what factors most impact these movements. For example, there is an increasing need to better understand the effect of weather patterns and habitat conditions on annual movements of migratory species. This knowledge is often limited because it requires an understanding of patterns of movement and linkages of populations across large spatial scales for a large number of individuals. Currently, there is ever-increasing data available for wildlife populations, such as long-term, fine-scale GPS-tracking data of individual animals, as well as large-scale observation networks that capture the distribution of wildlife populations. Fine-scale information is usually obtained by fitting individual animals with GPS-units that allow for near continuous location-tracking throughout the year. Citizen science networks include observations collected by members of the public on species that they detect when engaging in various activities (e.g., hiking, birding), which can be used to map the distribution of the species through time. This work is focused on the development of statistical models that will allow, for the first time, the integration of individual movement and species distribution data to learn about large-scale species-level movement behavior. The methods and tools developed will advance society's ability to learn about difficult to observe processes that shape the distribution of migratory populations in space and time. Despite extensive work on methods for integrating multiple data sources, previous attempts to integrate tracking data with citizen-science data fail to leverage the statistical advances made in each individual field and fail to formally integrate the two data-sources in a robust and comprehensive framework. Tracking data provides information about individual movements but represents a small subset of the population. Distribution data provides comprehensive information about species distribution, but represents aggregate behavior of individuals that differ in their migratory routes and behavioral response to the landscape. The integrated framework proposed in this research will formally integrate these two data sources using flexible, interpretable parametric statistical models, which will allow researchers to obtain previously unavailable insights into the link between individual variation, subpopulations, and drivers of movement, while accounting for uncertainty and the spatio-temporal dynamics of species distributions. In addition, this research will develop models for the full-annual cycle of an individual, as opposed to just the migratory route, allowing for additional inference regarding migratory connectivity and seasonal behavior. Further, this work will apply the novel statistical models to understand the migratory behavior of species of both conservation and hunting concern, which will help managers understand the spatio-temporal risks to which different proportions of the population are exposed. This information can help prioritize when and where to take management actions. 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 Awards · FY 2025 · 2025-08

This Smart and Connected Communities (SCC) Civic Innovation Challenge (CIVIC) project is focused on increasing resilience of North Slope Alaskan Arctic coastal communities threatened by increased coastal erosion, flooding, and ground subsidence as a result a rapidly warming Arctic, changes that are threatening property and infrastructure and impacting food security and community well being. The project creates high spatial-resolution, reliable, hazard risk-assessment, forecasting, decision-support tools that can be used for disaster management and community planning. It is a collaboration between the Pennsylvania State University; University of Alaska Fairbanks; Ukpeaġvik Iñupiat Corporation Science; the Alaskan North Slope Borough government; and the Alaskan coastal towns of Utqiaġvik and Wainwright. The work uses ground- and drone-based data collection and site monitoring of permafrost changes as well as satellite remote sensing data to generate GIS- and web-based maps for quantitative risk assessment and hazard forecasting. This yields hazard maps that are easily accessible and understandable by community and civic parties. It also creates data collection and analysis protocols that allow local governmental and tribal officials to communicate coastal, ground subsidence, decomposing permafrost, and flooding hazards to community residents which can inform and accelerate development of mitigations and new strategies for future scenarios involving infrastructure planning, resource allocation, repair of critical coastal sections, and community relocation. Broader impacts include approaches and tools that can be adopted by other coastal regions in Alaska and across the lower US 48 states. Impacts also include workforce development through participatory community data collection and analysis efforts in obtaining and processing drone images. This will help local communities to update the maps and understand how to use project deliverables to allow project results and data collection/analysis efforts to continue beyond the end of the one-year CIVIC award. This project and the process and procedures it develops fills a critical need of coastal communities in Alaska and elsewhere. Project design will involve significant community and civic government input into project requirements and result in a sustainable data collection/analysis framework that will sustain project results into the future. The work involves establishing ground- and drone-based monitoring of permafrost changes and combining that information with enhanced remote sensing and image processing. These data will be used, in addition to other inputs, to create three types of high-resolution GIS- and web-based maps that have the capability of projecting future changes in the landscape that could impact zones of habitation and infrastructure installation. Products will include permafrost maps that show near-surface ground temperature (0 to 10 meters depth), including active and talik layer thickness; Arctic coastal hazard index maps that incorporate coastal erosion, flooding, and land subsidence due to permafrost thawing; and infrastructure (i.e., buildings, roads, pipelines, etc.) performance maps that take into account ground subsidence/settlement and load bearing capacity of foundations. Web-based tools will be easy to understand by community and civic partners and can be displayed on computers or smart phones. These allow users to choose various “what-if” climate and environmental scenarios to inform decision-making for coastal developments as well as explore possible impacts of erosion and other ground instabilities that are increasing in number due to decomposition of permafrost and other environmental changes. Data collection and input is designed to be straightforward for easy maintenance; and maps have high spatial resolution and fine temporal scale that should allow year-by-year projections of coastal risks in for up to, at least, the next 20 years. 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 Awards · FY 2025 · 2025-07

Part 1. Non-technical summary Plastic mixed conductors, which are plastic materials capable of conducting ions and electrons, are poised to advance healthcare, energy storage and personal electronics. But, much remains unknown about the structure of these materials, in particular how constituent molecules pack and arrange themselves. This project will advance electron imaging approaches to reveal molecular organization of these materials. The work will leverage recent advances in electron microscope instrumentation to push the limits of what can be visualized. Visualization of frozen molecules will reveal how organization in solution affects molecular packing in solid films. Furthermore, the project will advance the three-dimensional characterization of plastic mixed conductors to reveal new aspects on how to tune conductivity. The project aims to study multiple materials using multiple approaches, which increases the likelihood of success and generation of knowledge. Advancing imaging of plastics could also advance a variety of other fields, including water purification, adhesives, and electronic materials. Educational and outreach efforts aim to support graduate student success and support retention of undergraduate students through pre-first-year summer programs and research experiences. Part 2. Technical summary Recent instrumentation advances in electron microscopy are transforming biology and material science. This project aims to leverage these recent developments to push the limits of polymer microscopy. Overall, this project will image mixed ionic-electronic polymer conductors at high resolution, to reveal chain packing and how morphology emerges after film casting. The project will use cryo-EM to image solution aggregates of polymers based on poly(ethylene dioxythiophene) and polystyrene sulfonate, e.g., PEDOT:PSS, and how these aggregates are perturbed with various additives, such as salts. The development of automated image acquisition enables collection of many high-resolution images, such that large data set analyses can reveal conductive pathways and correlations between crystalline domains. The project will also image swollen films of ladder-type mixed conductors such as poly(benzimid azobenzophenanthroline) (BBL) by vitrifying hydrated films, and reveal how ion uptake occurs. The work further aims to demonstrate how the 3D structure of mixed conductors can affect out-of-plane conductivity, and how structure is affected by swelling and salt uptake using electron tomography. Pushing the limits of resolution and techniques based on electron microscopy has the potential to advance many polymer-related fields, including charged polymers, membranes, actuators, gels, adhesives and electronic materials. This research is integrated with the proposed educational objectives. A partnership with the College of Engineering will advance professional development for graduate students and disseminate the excitement of polymer microscopy. 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 Awards · FY 2025 · 2025-07

Large language models (LLMs) have revolutionized various fields, including education, healthcare, media, and national security, enabling powerful AI-driven applications to enhance productivity and decision-making. However, their widespread deployment has raised significant safety concerns about potential misuse, as they can be manipulated to generate harmful or misleading content. As AI safety threats and countermeasures continue to evolve in a dynamic arms race, a critical gap remains: the lack of a standardized evaluation framework to systematically assess the true safety risks of LLMs. This project aims to close this gap by developing an open, community-driven evaluation infrastructure that engages researchers, practitioners, and policymakers. By advancing AI safety research, fostering public awareness, and strengthening workforce training in responsible AI practices, this initiative will support national interests in trustworthy AI, ultimately ensuring that LLMs benefit society in a safer manner. Building on the research and outreach expertise of the project team, this project focuses on four primary objectives to advance community efforts in LLM safety evaluation: (i) conducting structured surveys and interviews with experts across computing disciplines to identify critical safety concerns, assess existing evaluation gaps, and gather insights on infrastructure requirements; (ii) organizing a workshop to facilitate discussions and advancements on evaluation strategies, and refine the design for a shared evaluation infrastructure; (iii) developing a prototype evaluation infrastructure based on gathered insights and iterative feedback, featuring a comprehensive suite that includes a data library, an LLM model zoo, an attack/defense repository, and an evaluator hub, ensuring both usability and scalability; (iv) hosting an LLM safety challenge to engage the community in testing and improving the evaluation infrastructure. Collectively, these efforts will establish a foundational framework that evolves with the community needs, fostering a deeper understanding, comprehensive evaluation, and continuous enhancement of LLM safety. The outcomes of this project not only advance LLM research but also drive progress in the broader community of machine learning. 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 Awards · FY 2025 · 2025-07

Network interference is a fundamental driving force behind phenomena such as intervention spillover, behavioral contagion, and information diffusion in interconnected systems. In this project, the investigator aims to develop statistical methods to understand and quantify whether, and to what extent, an individual’s behavior or status is influenced by others through network interactions. This project focuses on two central challenges in estimating network interference from observational data: individual heterogeneity and network confounding. To overcome the challenges, the investigator will develop an expressive and interpretable statistical model that adaptively learn how the effect of interference depends on individual-level contextual and network information. This model is expected to provide insights and an analytics tool towards the mechanisms of network interference. In addition, the investigator will design novel estimators to measure the causal effects of network interference, leveraging advanced machine learning techniques to address complex confounding among connected individuals and to make efficient use of limited experimental data. The methodologies developed in this project will advance the fields of causal inference and graph-based machine learning. The tools developed will have broad applicability to network data in social science, public health, political science, economics, and business, and will support new theoretical developments in areas such as social influence, disease transmission, marketing, cultural evolution, and collective behavior. The project will provide research opportunities for graduate students. In this project, the investigator aims to (1) design a data-driven framework for estimating heterogeneous spillover effects using graph neural networks; (2) develop intervention effect estimation method that integrates active learning to address sample size limitation which is common in real applications; and (3) construct a directed graphical model to identify latent propagation patterns in heterogeneous network cascades. The methodological foundation consists of two main innovations: an attention-based neural network model for robustly estimating individual exposure mapping, and a network mixture model for recovering diffusion structures at the population level from cascade data. To further improve estimation efficiency, the project introduces novel data augmentation strategies that leverage contextual information and network structure, enhancing the causal estimation accuracy of the intervention effect, even in data-scarce settings. The main advantage of the new methods is the decomposition of target estimands into two components: a global network-based interference structure and local individual heterogeneity. The latter is approximated using advanced graph machine learning techniques, enabling the model to strike a balance between expressiveness and interpretability. Overall, this research will provide theoretical and computational tools for studying network interference, leading to the development of open-source software tailored for practical applications across various disciplines. 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 Awards · FY 2025 · 2025-07

Building materials and building envelopes – the barrier separating a building’s interior from the environment – can be compromised by exposure to short, frequent, and intense cycles of freeze and thaw, dry and wet, and hot and cold. Impaired materials in an envelope can reduce a building’s operational energy performance and diminish its thermal insulation, structural integrity and moisture control capabilities. This project will use a combination of experiments and modeling to examine how and to what extent the degraded performance and integrity of building envelopes influence maintenance, repair needs, and heating and cooling demands of affected buildings. The research will bridge knowledge gaps associated with extreme weather-induced material degradation and sustainability of buildings. It will also inform utility providers to more accurately forecast energy demands and optimize power infrastructure for efficient supply-demand balance. By identifying the impact of extreme weather on building envelope performance and its subsequent effects on utility expenses, occupant comfort, and overall well-being of people, this project will help residential and commercial communities to remain sustainable and resilient during extreme temperature and humidity conditions. This project will examine the impacts of heat-moisture swings on envelope performance and life cycle emissions, which helps narrow the performance gap between simulated and measured energy use of buildings and enhances the fidelity of physics-based energy simulations to reflect extreme weather conditions. The study will 1) investigate extreme weather’s influence on the degradation of thermal, optical, and moisture control properties of building envelope via both lab tests and simulations; 2) examine the life cycle emissions impacts caused by deteriorated envelope performance over 55-years of building service life in current and future weather conditions; and 3) develop a novel methodology to integrate the space-time degradation of envelope performance into life cycle energy and carbon modeling. The research results offer architects, engineers, builders, general contractors, facility managers, material manufacturers, and policymakers a critical insight for informed decision-making in extreme weather strategies. The proposed in situ experiential and immersive virtual reality-based learning activities will equip all students with foundational knowledge essential for STEM education and careers to reinforce the United States building construction industry. The research training and mentoring activities for all students will yield long-term workforce benefits, thereby bolstering economic prosperity and technological leadership in the nation. 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 Awards · FY 2025 · 2025-07

Einstein manifolds and minimal submanifolds, which describe the vacuum spacetimes in Einstein's theory of general relativity and soap films, respectively, are objects of fundamental interest in mathematics and physics. This project will use methods from conformal geometry, where one is allowed to change lengths but not angles, to construct and classify geometric invariants which can control the possible behaviors of Einstein manifolds and their minimal submanifolds. Particular emphasis will be placed on conformally compact Einstein manifolds and their minimal submanifolds, which are fundamental in string theory. This project includes research problems designed for undergraduate and graduate students that will help quickly integrate them into high-level mathematics research. This project is split into three subprojects. The first subproject will develop the PI's recently developed notion of straightening into a classification of global conformal invariants of Einstein manifolds and of their minimal submanifolds. The second subproject uses these invariants to prove gap theorems that isolate spaceforms and their totally geodesic submanifolds via higher-order analogues of the L^2-norm of the Weyl tensor and the Willmore energy of a surface. It will also use these invariants to construct invariants whose positivity obstructs the existence of an Einstein metric on a given smooth manifold and develop tools to compute those invariants. The third subproject will develop new analytic tools for studying Q-curvature, including a strong maximum principle for higher-order GJMS operators and an Obata-type classification of conformally Einstein metrics with constant Q-curvature. 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 Awards · FY 2025 · 2025-07

This project's goal is to build better methods for assessing privacy risks in machine learning (ML) models trained using data in table-based formats. ML models trained on tabular data (e.g., patient records, loan application records) are commonly used in privacy-sensitive domains such as health or finance. This makes them valuable targets for attackers who want to steal private data. One critical threat to privacy in ML models is model inversion attacks, in which adversaries strategically query the model to infer attributes of the data used to build it. Model inversion attacks have been well-studied in image datasets, but are much less understood in table-based datasets. Further, attribute inference risks are often studied as a global property of the model; however, because training data may be unbalanced in terms of what it captures about the world, specific groups or individuals may be at much higher risk of attribute inference than others. Finally, models in sensitive domains are often trained using a technique called "federated learning", where multiple participants who each have some private data (but not enough to train a model) can jointly train a model without having to share the sensitive data directly. Federated learning has the potential to protect privacy, but it also poses new risks if some of the participants are adversaries. To address these questions, the project team will develop methods for auditing attribute inference risks and disparities in both centralized and federated learning ML models, along with defenses aimed at mitigating these risks. Together, the work will increase the privacy of people whose data is used in machine learning models, allowing them to be used more safely in important applications. The technical aims of the project will be accomplished through three interconnected thrusts. First, the team will develop a framework to systematically audit attribute inference vulnerabilities by introducing an adaptable adversary model, designing novel attack algorithms, and developing an automated ML privacy auditing tool for comparative analysis across a wide spectrum of adversaries. Second, the team will develop the first-ever mathematical formalization to characterize disparity in attribute inference risks, along with novel attack techniques that exploit attribute inference disparity and target more vulnerable groups and nested groups by analyzing the ML model behaviors. Third, the project team will develop robust defenses for different phases of the ML pipeline, including data pre-processing, training, and inference, to mitigate attribute inference attacks and disparity in both centralized and federated settings. The novel defenses will balance privacy and utility by focusing on high-risk records and will also ensure privacy and utility fairness by designing novel selective and adaptive differential privacy-based and subspace learning-based solutions. The team will also leverage the research to create security competitions aimed at model inversion and public dashboards of privacy vulnerabilities in machine learning models in order to increase the impact of the project. 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 Awards · FY 2025 · 2025-07

The workshop participants will discuss the current status and future research directions of two broad areas of research: Security, privacy, and trust in System/Platform/Infrastructure; AI/ML enabled security and privacy. It offers a valuable and fruitful opportunity for researchers involved in these programs to learn about each other’s research directions. The workshop will provide an opportunity for US and Japanese researchers in cyber security areas to exchange research ideas and explore teaming up opportunities. The workshop proposal seeks support to cover the travel expenses for invited participants from U.S. institutions to attend the proposed workshop in 2025. By enabling in-person exchanges among leading researchers, the workshop aims to foster impactful collaborations in cybersecurity research. 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 Awards · FY 2025 · 2025-07

This Faculty Early Career Development (CAREER) award will fund research that intends to develop an interdisciplinary framework to improve the resilience of unreinforced masonry buildings in tornado-prone areas of the United States. Unreinforced masonry structures, prevalent in rural town centers and inner-city neighborhoods, play an important role in preserving cultural identity and contributing to social and economic resilience; however, their vulnerability to tornadoes is not well understood or modeled. Through integrating engineering, social science, and preservation perspectives, this research aims to advance the science behind vulnerability and recovery models for unreinforced masonry buildings. The project intends to enhance community resilience, preserve cultural heritage, and support sustainable development in historically underserved areas. A key component of this project is its educational program, which will cultivate a new generation of professionals equipped with interdisciplinary skills to address disaster resilience and recovery challenges in communities dense with older, unreinforced masonry structures. The educational program will include new undergraduate and graduate coursework, hands-on research opportunities for students, workforce development programs, and community co-learning experiences. By bridging the gap between academic research and practical application, these educational activities will prepare students, professionals, and community members to tackle real-world challenges in disaster preparedness and recovery. This award will contribute to NSF's role in the National Windstorm Impact Reduction Program (NWIRP). This research intends to develop a framework for understanding and modeling the complex dynamics of older unreinforced masonry buildings during tornado events and recovery. By identifying and operationalizing key structural, social, and preservation features to create more accurate predictive models for damage and recovery, this project intends to advance both pre-disaster mitigation strategies and post-disaster recovery efforts. The research methodology will include (1) collecting and analyzing quantitative and qualitative data from tornado-affected communities using remote sensing techniques, in-situ surveys, and socio-economic assessments; (2) performing feature importance analysis through machine learning algorithms such as random forests and gradient boosting; and (3) developing updated fragility curves using Bayesian statistical methods and Monte Carlo simulations. The project outcomes will include refined damage and recovery models for unreinforced masonry structures, practitioner guides, and community tip sheets, thus translating research findings into actionable resources for disaster preparedness and recovery planning. This approach intends to provide communities, policymakers, and practitioners with valuable tools to enhance the recovery and resilience of unreinforced masonry structures in tornado-prone areas. By providing the first interdisciplinary framework for assessing and enhancing the resilience of unreinforced masonry structures in tornado-prone areas, this research will advance the field of disaster resilience, transforming how communities approach disaster preparedness, mitigation, and recovery while preserving irreplaceable cultural heritage. Project data will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). 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 Awards · FY 2025 · 2025-07

Turbulent air and water flows often carry not only momentum but also heat and other substances such as moisture, pollutants, or nutrients. For many decades, scientists believed that momentum and heat are transported within these flows in similar ways. This assumption works well for flow over smooth surfaces, but it breaks down when the surface is rough, such as forest canopies or textured surfaces. This project will investigate why and how this breakdown occurs, focusing on the fundamental differences in how heat and momentum are transferred to and from rough surfaces. The research team will study the detailed behavior of turbulent flow and heat transfer near complex roughness patterns using experiments in water and glycerin tunnels and high-fidelity computer simulations. Understanding this behavior can assist in improving predictions in weather modeling, pollutant dispersion, heat exchanger design, and even blood flow in roughened vessels. The project will also provide hands-on research experience to graduate students, support STEM education, and launch a public podcast series to communicate the science and its relevance to society. The technical objective of this project is to explain the physical differences between momentum and scalar (e.g., thermal) transport in rough-wall turbulent flows. The research will propose and test a new hypothesis: momentum exchange is governed by unsheltered windward surface areas, while scalar transfer depends on unsheltered planar surfaces. Experiments will be performed in two complementary tunnel facilities, one optimized for velocity measurements and the other for thermal fields, with matched flow and roughness conditions. Direct Numerical Simulations will provide fully resolved flow-field data to validate the experimental setup and guide measurement location. The combined data will be used to inform and train predictive “volumetric” models for heat and scalar transport in rough-walled environments referred to as Distributed Element Roughness Models. These models will combine physics-based reasoning with data-driven techniques to produce robust, generalizable predictions that can be adopted in science and engineering practice over an array of 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 Awards · FY 2025 · 2025-07

This project addresses a critical challenge in high-performance computing (HPC): the growing inefficiency and energy demands of moving massive volumes of data between storage and compute units - a bottleneck that hampers scientific progress and national innovation. As the U.S. advances toward exascale computing and increasingly data-centric scientific discovery, the ability to analyze and process large amounts of data efficiently is paramount in nationally significant domains such as energy systems, artificial intelligence (AI), astrophysics, and precision health. This project focuses on Computational Storage Devices (CSDs), a transformative technology that embeds processing power directly into storage hardware, dramatically reducing data movement and enabling faster, more energy-efficient computation. However, programming and optimizing CSDs remains highly complex, limiting their impact. This project develops a scalable, automated software infrastructure (including compilers, runtime systems, and programming interfaces) that makes it practical to deploy and benefit from CSDs across diverse and critical HPC workloads of national importance. By unlocking the potential of near-storage computing, this project directly contributes to national priorities such as AI, energy systems, and advanced manufacturing. Additionally, the project produces open-source tools, CSD-focused benchmarks, and educational resources, contributing to the U.S. leadership in next-generation computing while preparing a workforce equipped to tackle the grand challenges of tomorrow. As modern HPC applications become increasingly data-driven - integrating traditional simulations with machine learning and data analytics - the overhead of moving data between storage and compute units has emerged as a major performance and energy bottleneck. CSDs promise to mitigate this issue by embedding compute engines within storage devices (e.g., solid state devices), allowing select computations to be executed near the data. However, current CSD programming models are low-level, hardware-specific, and lack standardized abstractions, making them difficult to adopt at scale. This project addresses these limitations through five integrated research thrusts: (1) characterizing diverse HPC workloads across real and simulated CSD configurations to identify offload opportunities and performance tradeoffs; (2) exposing CSDs to the software stack via rich abstractions and interfaces that communicate device heterogeneity, memory capacity, and compute capabilities; (3) developing compiler-directed optimizations for code and data placement, loop parallelization, and CSD-to-CSD data migration; (4) designing a runtime system that dynamically manages code fragments offloaded to CSDs, handles multi-application scheduling, and coordinates compute/data mapping with the compiler; and (5) building a simulation platform for futuristic CSD architectures and releasing a benchmark suite tailored to CSD-aware optimizations. Together, these efforts will lower the programming barrier to CSD adoption, enable intelligent orchestration across heterogeneous storage-side compute resources, and advance the state of the art in near-storage computing. Scientifically, this project aims to establish foundational tools, abstractions, and methodologies that accelerate the integration of CSDs into production HPC systems, drive innovation in compiler-runtime co-design for heterogeneous storage environments and enable new levels of energy-efficient performance in data-intensive scientific workloads. 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 Awards · FY 2025 · 2025-07

Modern crop production is sustained by the application of phosphorus fertilizers. The ultimate source of this phosphorus is non-renewable deposits of high-grade phosphate rock. The logistic costs of extracting and transporting a bulky commodity, along with unpredictable fluctuations of the global market, have seen fertilizer prices rise over recent years, with unanticipated spikes placing strain on already tight farm budgets. And yet, although great effort and resources are invested to deliver phosphorus to farmer’s fields, only a small percentage of what is applied is acquired by the plants. Much of the phosphorus is washed out to end up in water courses and, ultimately, the ocean, contributing to the well-documented environmental problem of algal blooms. In wild ecosystems, most plant species acquire phosphorus from the soil at high efficiency through association with symbiotic soil fungi. Staple crop species retain the capacity to form such fungal associations, but the system is far from optimized for agricultural conditions. This project will build on the latest molecular and genetic understanding of the mechanisms regulating cereal interactions with beneficial soil fungi. Specifically, the project will employ natural plant genetic variants to boost the level of interaction with soil fungi in corn and rice and evaluate the impact on plant performance under both standard and low phosphorus field conditions. Additional molecular studies will characterize the nature of the fungal communities in cultivated fields and assess the capacity of novel plant varieties to better work with soil fungi towards more efficient uptake and use of phosphorus fertilizers. This project develops new biotechnology critical to the future security of key crops, e.g., rice and corn. This project aims to evaluate and enhance arbuscular mycorrhizal symbiosis in rice and maize for greater nutrient efficiency. Although modern cereals retain the molecular machinery necessary for formation of mycorrhizal associations, the symbiosis, which dates back 450 million years, is not optimized to the high-input agroecosystems developed over the last century of agricultural intensification. Specifically, crops, as all plants, will reject the fungus under high-nutrient conditions. Selection and breeding in such environments have generated varieties that are not best placed to take full advantage of arbuscular mycorrhizal symbiosis. In this project, plant mutants with modulated sensing of internal phosphorus status will be evaluated for their capacity to sustain arbuscular mycorrhizal symbiosis under high phosphorus conditions. Material will be characterized in the field, with novel sequencing-based approaches used to characterize soil mycorrhizal and broader microbial communities in parallel with plant molecular, physiological and agronomic evaluation. In the context of a tri-national collaboration with partners in the U.K. and Germany, this work will contribute to fundamental understanding of crop nutrient signaling and mutualistic microbial associations in parallel with field-testing high-colonization host plants as a basis for breeding towards great nutrient efficiency through enhanced arbuscular mycorrhizal symbiosis. This award was funded as part of a lead agency opportunity between NSF, UKRI-BBSRC (UK Research and Innovation - Biotechnology and Biological Sciences Research Council; Lead) and DFG (Deutsche Forschungsgemeinschaft/German Research Foundation) where NSF funds the U.S. investigator, UKRI-BBSRC funds the U.K. partner and DFG funds the German partner. 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 Awards · FY 2025 · 2025-07

This grant provides funding to support student participation in the 2025 Modeling, Estimation, and Control Conference (MECC 2025), which will be held 5-8 October 2025, in Pittsburgh, Pennsylvania. MECC is a premier conference held annually and aims to serve the scientific and engineering communities in the cross-disciplinary areas of modeling, estimation, and control of dynamical systems. It will provide a platform for the dissemination and discussion of the state-of-the-art in dynamics and controls research and create opportunities for networking with colleagues in relevant fields. The conference features contributor sessions, invited sessions, workshops, special sessions, plenary talks, keynote speeches, student and young professional programs, industry programs, and conference awards ceremonies. Funding will empower undergraduate students with high academic potential in their early stages of research development by providing them opportunities to attend the conference and have direct conversation with researchers at the forefront of dynamical systems and control. Such early engagement is expected to help build a pipeline for future rising stars in dynamical systems and control research. The grant will also support educational activities for high school students by introducing them to the field of dynamical systems, controls, and robotics. Ultimately, this grant will help significantly expand the horizon and impact of MECC 2025 to both scientific communities and society at large. Student participation support focuses on reaching out to a much-broadened student body, enabling high school, undergraduate, and early-stage graduate students with high academic potential to observe first-hand outcomes from state-of-the-art research and to develop professional networks with researchers at the forefront of their fields. Multiple events and activities will be developed as part of this grant, including: i) Outreach activities in systems, controls, and robotics offered to high school students, through collaboration with industries partners in control systems and robotics; ii) Professional networking lunches and panel sessions, where participants will be coached on building professional networks and identifying pathways for pursuing either academic or industry careers; and iii) Special poster sessions where students can present their research interests and receive personalized advice and feedback, helping them to shape their research path and enhance their technical and professional development. Participant support is expected to enhance students’ scientific, technical, and professional development. Broadening access will have a huge impact on the future development of dynamics and control community and building a positive culture for future control conferences in reaching out to a broader audience and in better connecting education, research, and technology development. 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 Awards · FY 2025 · 2025-07

Spinal cord injuries affect millions of individuals worldwide, causing paralysis and dramatically reducing their quality of life. These injuries lead to significant long-term healthcare costs due to the challenges in restoring motor function. Electrical stimulation of the spinal cord has shown promise in restoring motor function. However, current approaches often rely on rigid electrodes that cause additional tissue damage and require invasive spinal cord surgery, limiting their effectiveness and increasing infection risks. To overcome these limitations, this project aims to develop novel soft electrodes that mimic the properties of soft tissue. These electrodes will be designed to be minimally invasive, enabling precise activation of spinal cord neurons without the need for conventional spinal cord surgery. Educational components of the project include establishment of a Neural Engineering Club for college students and development of hands-on activities and outreach programs for K-12 students that are designed to inspire enthusiasm for science, technology, engineering, and mathematics (STEM) among students from diverse backgrounds. These efforts aim to broaden societal impact and foster the next generation of innovators in neural engineering, ultimately advancing research in this interdisciplinary field. The research goal of this CAREER award is to develop injectable, stretchable hydrogel-based electrodes that offer superior biocompatibility, flexibility, and functional integration with spinal tissue. These electrodes will be designed to be injected through ultra-fine needles, enabling direct and stable motor neuron stimulation and sensory neuron recording. This project will include three major research objectives: (1) Developing stretchable, biocompatible hydrogel materials with outstanding electrical performance for neural interfaces, focusing on understanding the interplay between biocompatibility, mechanical properties, electrical performance, and long-term stability.(2) Creating minimally invasive, surgery-free hydrogel intraspinal electrodes that ensure long-term stability within the challenging environment of the spinal cord, reducing the risks and discomfort associated with traditional surgical methods. (3) Pioneering in vivo spinal cord motor neuron stimulation and sensory neuron recording that will demonstrate the therapeutic potential of hydrogel-based electrodes for direct stimulation of motor neurons and integration of sensory information. 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 Awards · FY 2025 · 2025-07

Rotary systems, such as spinning blades on helicopters, drones, energy systems, and even artificial heart pumps, play a critical role from mechanical and aerospace engineering to biomedical applications and energy systems. However, testing these spinning devices to ensure they operate efficiently and safely is expensive, especially at full-size. This project tackles that problem by enabling engineers to use small-scale models in lower-cost wind tunnels while still capturing the full-scale behavior of the flow. This advancement will be transferable to a broad range of rotary systems for long-term cost reduction of wind tunnel testing. In addition to addressing a critical knowledge gap in fluid mechanics, the project will help educate the future workforce through a modernized curriculum, hands-on research experiences, and outreach activities. The proposed research seeks to validate the hypothesis that rotor thrust and induced power coefficient, rather than total power coefficient, provide sufficient conditions for replicating vortex wake turbulence and stability across scales. The research integrates advanced computational approaches, including inverse blade design, Large Eddy Simulation (LES) of rotary system wakes using actuator lines, and blade-resolved hybrid Unsteady Reynolds-Averaged Navier-Stokes (URANS)/LES methods, with extensive experimental campaigns. Testing will be conducted in three distinct wind tunnel facilities: a compressed-air tunnel for studying Reynolds-dependent roughness and rotational effects, a boundary-layer wind tunnel for exploring sheared turbulent inflow and platform motion effects, and an aerodynamic wind tunnel for analyzing blade loads and wake stability. This research will generate a publicly accessible database, advancing the fundamental understanding of rotary system wakes and providing critical insights for applications ranging from rotorcraft and propellers to distributed propulsion systems and energy technologies. 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 Awards · FY 2025 · 2025-07

This grant provides participant support for attendees of the 24th American Conference on Crystal Growth and Epitaxy (ACCGE-23) to be held in Stevenson, Washington, 13-18 July 2025. The conference focuses on new research and development activities in bulk single crystal and epitaxial thin film growth. Students and faculty present their latest research results on crystal growth and epitaxy through oral and poster sessions. The conference impacts the advanced manufacturing and broader engineering communities. This award benefits the nation through the education of a skilled and diverse manufacturing workforce, which is better prepared to provide transformative solutions to the challenges in their chosen fields. The conference is an opportunity for participants to showcase their scientific accomplishments, interact with peers and colleagues in academia, government labs and industry and extend their network within the broad materials processing and advanced manufacturing communities. The conference plays an important role in supporting and sustaining the important field of crystal growth and epitaxy, which is an important component of semiconductor and microelectronics research. This conference is timely because of renewed interest in the revitalization of microelectronics and semiconductor manufacturing in the US. This participant support is expected to benefit the students' professional, scientific, and technical development. Attendance at the conference gives the students and faculty a broader view of crystal growth and thin film epitaxy, its fundamentals, advanced manufacturing, advanced characterization, and applications. Symposium topics include fundamentals of materials synthesis, crystal structure analysis, crystal growth techniques and methods, high throughput manufacturing, process modeling, monitoring and control, and specialized topics, such as 2D nanomaterials, ceramics, ferroelectrics, and microfluidic processing. Attendees will learn about state-of-the-art research in their technical areas via access to several technical and professional development talks by leading domestic and international speakers. Students and young faculty enhance their communication skills through oral and poster presentations and in-depth discussions of their work with peers in their technical areas. This interactive experience significantly broadens student education, increases their enthusiasm for their research topic, acquaints them with expectations for scientific careers, and exposes them to new approaches for innovative research. 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 Awards · FY 2025 · 2025-07

The collisions between neutron stars and between neutron stars and black holes are among the most energetic phenomena in the Universe. These events can be studied using gravitational-wave observatories, such as NSF's Laser Interferometer Gravitational-Wave Observatory (LIGO), and ground-based and space-based observatories, such as NSF's Vera Rubin Observatory and NASA's James Webb Space Telescope. Through these cosmic collisions it is possible to study, among others, the nature of matter at supernuclear densities, testing quantum chromo dynamics in a regime that cannot be probed on Earth, the nuclear physics involved in the formation of rare-earth elements, gold, and alkali metals, and the outcome of the evolution of massive stars. This project aims to develop the theoretical framework necessary to interpret upcoming observations and advance research in these areas. In particular, the team will construct a large template of synthetic neutron star merger observations, leveraging new national computing resources to perform sophisticated general-relativistic simulations. These data will then be used to develop statistical and artificial intelligence models that can be used to interpret real observations. This project will train one US graduate student and three US undergraduate students at the interface between high-performance computing, computational fluid dynamics, and machine learning, thus strengthening the US STEM workforce. This project will also develop new simulations and data analysis techniques and produce open-source code for the benefit of the broader STEM community. This work will be performed in collaboration with researchers at the Friedrich-Schiller-Universität, funded by the German science agency DFG, and will include a research exchange program with Germany, thereby strengthening bilateral ties with Germany. The project will assemble a comprehensive, publicly accessible database of double neutron-star and black-hole neutron-star merger simulations and leverage it for multimessenger astronomy. The database will contain about 1,000 simulations, varying in the binary masses and spins, as well as in the equation of state used to describe neutron-star matter. For each simulation, the team will release the multipole-decomposed gravitational-wave signal, mass ejecta, and remnant properties, including 3D snapshots that could be used as initial conditions for longer-term postmerger simulations. The team will use the data to inform and calibrate 1) merger and post-merger gravitational-wave waveform models; 2) understand the critical physical dependencies of the post-merger evolution of binary neutron-star and black-hole neutron-star mergers; 3) data-driven models of the ejecta; and of 4) the expected thermal and non-thermal counterparts to mergers. These data and models will be the theoretical foundation for the interpretation of future merger observations. The database will also support independent theoretical and observational investigations by other teams. 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 Awards · FY 2025 · 2025-07

This project aims to serve the national interest by improving curricula in computer science education. Computing professionals need to understand the possibilities and limitations of computation in order to design efficient algorithms for problems that can be solved in practice, or to avoid large investments in attempts to implement solutions for problems which have been proven to require unreasonable amounts of time or other resources. Modeling computation is an important building block for this understanding, however, students often struggle with abstract modeling and visualization. A prior Level 1 Engaged Student Learning project resulted in a prototype tool which provides immediate feedback on the computational models designed by students. This Level 2 Engaged Student Learning project aims to add features to the tool, improve its usability and adaptability, and investigate its impact on student problem-solving at a larger scale, in different educational settings. The existing Automated Feedback for Computing Theory (AFCT) prototype tool was built on the widely used Java Formal Languages and Automata Package (JFLAP) visualization tool that aids students in learning the basic concepts of formal languages and automata theory. The enhanced tool developed in this project will initially be deployed and outcomes assessed in theoretical computer science courses at the five collaborating institutions. It will be made available under an opensource license to enable others to use and modify the software to suit their needs. The research questions are focused on understanding the impacts of the tool on students' behavior, performance, and learning of computing theory; whether students from different types of institutions are impacted in significantly different ways; and the effects of various types of feedback on students' learning. The tool's added functionality, improved usability, and availability as opensource software will encourage its adoption at other institutions and increase its educational benefits. The project, including the upgraded feedback tool and the associated research study, will provide new insights into pedagogical approaches for improving student learning and will help students to be better prepared to develop high-quality software. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 Awards · FY 2025 · 2025-07

Understanding how plant cells synthesize cellulose not only lays the scientific foundation for using genetic tools to modify plant cell walls for advances in sustainable energy but also provides insights into fundamental questions regarding how plant cells control expansion and shape. This project aims to utilize a combination of quantitative live-cell imaging, quantitative proteomics, and functional genetics to investigate how plant cells couple exocytosis and endocytosis to regulate the abundance of cellulose synthase at the plasma membrane—a process that remains largely unexplored. These findings will enhance knowledge of both cell wall biosynthesis and the mechanisms underlying plant cell growth. Furthermore, the project will create research opportunities for undergraduate students. By tailoring research projects to various undergraduate research programs, this initiative will attract talented students and prepare them for graduate education and academic careers. Additionally, the project will include a hands-on research workshop for local high school students and their teachers at Pennsylvania State University, aiming to inspire interest in the biological sciences. The ability to produce renewable energy is crucial for both the economy and the environment, with cellulosic biomass expected to be a key source for biofuel production. Cellulose microfibrils are synthesized at the plasma membrane by a protein complex known as the cellulose synthase complex (CSC), which converts sugar molecules into energy-rich crystalline cellulose, the most abundant biopolymer on Earth. Since cellulose synthesis occurs exclusively at the plasma membrane, understanding how CSCs are trafficked to the cell surface is essential. The abundance of CSC at the plasma membrane is tightly regulated. Cellulose synthase proteins can remain stable for over 48 hours in vivo, and their regulation relies on exocytosis, endocytosis, and recycling, rather than protein turnover. Unlike mammalian and yeast systems, where exocytosis and endocytosis are well-studied, cellulose synthase represents a plant-specific cargo protein, making it a unique subject of investigation. The principal investigator’s pioneering work includes developing advanced in vivo imaging techniques and genetic tools tailored for analyzing cellulose synthase trafficking. This proposal employs interdisciplinary approaches—quantitative proteomics, live-cell imaging, genome editing, and machine learning—to investigate how various trafficking components coordinate with exocytosis and endocytosis machinery to maintain steady CSC levels at the plasma membrane in a microtubule-dependent manner. 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 Awards · FY 2025 · 2025-07

This I-Corps project focuses on investigating the commercial potential of an advanced wastewater treatment system that integrates ion exchange materials with biological processes to effectively remove pollutants from wastewater. Traditional wastewater treatment methods often face challenges, such as high energy consumption and insufficient removal of contaminants, which can adversely affect environmental quality and public health. The hybrid approach developed in this project addresses these issues by enhancing treatment efficiency and significantly reducing energy requirements. Water pollution remains a pressing national issue, with billions of gallons of wastewater generated daily, affecting ecosystems, drinking water sources, and public health across communities nationwide. By providing a more efficient and cost-effective method to manage wastewater, this project helps protect the environment and conserve resources. Economic benefits include reduced operational costs for municipal treatment plants, promoting economic growth, and enhancing community welfare through improved water quality. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an innovative wastewater treatment process combining ion exchange technology with biological nitrification and anaerobic ammonium oxidation. Unlike conventional methods, this hybrid system operates efficiently at lower temperatures and low nitrogen loadings and under varying pollutant loads, significantly reducing energy usage and operating costs. Critical scientific advancements include ion exchange resins capable of nitrogen removal and enhanced microbial processes enabling nitrogen removal at low nitrogen loadings in mainstream wastewater. Potential adopters benefit from improved treatment performance, reduced chemical and energy costs, decreased environmental impacts, and enhanced system stability. 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.