University of North Carolina at Chapel Hill

NSF Awards · FY 2026 · 2026-08

Life in streams, and other habitats, is predominantly supported by biomass that is made from the sun’s energy via photosynthesis at the base of the food web. According to the current paradigm, fish and other aquatic organisms consume biomass produced by photosynthesis locally in the water and supplemented by photosynthesis on land, which washes into waterways as dead and decaying organic matter. Emerging evidence suggests that photosynthesis is not, however, the only pathway which supports stream life. Alternative chemosynthetic pathways for generating biomass may also provide food sources for insects and fish, especially where light and oxygen are limited. Preliminary results show chemosynthetic biomass is produced from methane, sulfur, and ammonia oxidation, even where the key substrates were not present above detection limits in the water. Chemosynthetic biomass likely develops in dark, slow-moving waters, such as those between gravels and leaves, in backwater habitats, or in the subsurface waters. The importance of these alternative biomass pathways as food resources for stream animals is poorly understood, limiting stream science, models, and management to assumptions that food source production occurs only via light-dependent photosynthesis. This project will investigate how differences across stream landscapes support varying biomass production pathways. Then estimates of how these pathways contribute to the growth of insect consumers that are the base of food webs supporting fish will be made. Media, educational materials, and training programs which explain project results in the context of interconnected hydrology, biogeochemistry, and ecology will be developed and disseminated. This novel CAREER project will quantify chemosynthetic primary productivity and contributions to stream animal biomass across a range of gravel-bed stream and floodplain habitat types. By measuring rates of chemoautotrophy, methanotrophy, and photosynthetic primary production across varying biogeochemical conditions and hydrologic gradients, it will be possible to assess the relative contribution of chemosynthesis to biomass fixation. Stable isotope mixing models will be used to estimate contributions of each fixation pathway to invertebrate animal biomass. The results will be synthesized to compare variation in food resource usage across habitats in floodplain environments to assess the role of landscape heterogeneity. Classes will be developed for in-classroom and field teaching at UNC Chapel Hill and Flathead Lake Biological Station, in conjunction with mentorship of a graduate student and undergraduates. Two short public-access documentaries will explain how landscape structure can contribute to ecosystem services in rivers and correspond to in-person exhibits. The combined research and teaching objectives will enhance communication between research communities and local stakeholders. 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 2026 · 2026-08

Modern artificial intelligence (AI) systems can recognize simple actions in short video clips, yet they cannot understand the full complexity of events unfolding over time. They struggle to follow multi-step activities, explain why something happened, imagine what would happen under different circumstances, or use what they observe to guide physical actions. These limitations prevent AI from having a transformative impact in areas of national importance such as robotics, healthcare, and manufacturing, where understanding dynamic visual scenes is essential. This project will develop a new generation of AI systems that can watch videos of the real world, understand what is happening at multiple levels of detail, reason about causes and consequences, and translate that understanding into purposeful action. For example, a robot watching a person assemble furniture could learn the sequence of steps involved, reason about why a particular step failed, and adapt its own plan accordingly. The educational activities integrated into this project will develop new university courses, mentor undergraduate and high school students, and engage the broader public through outreach programs connecting AI to accessible topics such as sports and skill learning. All software, trained models, and datasets produced by this project will be released publicly to accelerate scientific progress and support the responsible development of AI. This project develops a unified framework for video perception, reasoning, and control, grounded in natural language supervision. The research is organized around three integrated thrusts. The first thrust develops structured, multi-level video representations by leveraging narrated instructional video. Actions are modeled as learnable transformations applied to object representations, enabling the system to recognize novel combinations of actions and objects not seen during training. A hierarchical extension captures complex activities at multiple temporal scales in hour-long videos by learning multi-level language descriptions conditioned on visual input. The second thrust builds reasoning capabilities on top of these representations. A hierarchical prediction model anticipates future events at multiple levels of abstraction, from fine-grained physical motions to high-level goals. A language-guided counterfactual simulation model answers targeted "what if" queries by modifying specific elements within the learned representations and predicting the resulting outcomes. Together, these modules support causal hypothesis testing by comparing predictions under actual and alternative conditions. The third thrust bridges perception and reasoning to physical action. A learning module uses both visual observations and natural language descriptions to discover abstract, hardware-independent action representations from human demonstration videos. Modular low-level control policies translate these action representations into specific robot motor commands, and a hierarchical planner integrates causal and counterfactual reasoning to select robust action sequences for tasks that unfold over extended time horizons. 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 2026 · 2026-07

Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods may require simplifying assumptions that are difficult to verify and may miss important relationships in the data. This project will develop a new way to understand such relationships by studying data at the level of the binary digits used by computers to represent information. Working at this basic level will help researchers build tools that are more reliable, interpretable, and broadly applicable across many types of data. The results will support advances in areas such as neuroscience, genetics, engineering, economics, and other fields where scientists need to distinguish meaningful patterns from noise. The project will advance the progress of science by improving the foundations of data analysis, strengthening reproducibility in scientific research, and providing research training for graduate, undergraduate, and high school students. This project will focus on developing the Binary Expansion Group Intersection Network (BEGIN) as a framework for statistical learning from data bits. The framework will construct graphical models directly from binary representations of data and will use ideas from binary expansion and abelian group theory to study conditional dependence, which describes how variables are related after accounting for other variables. The project will establish theory and methodology for testing and modeling conditional independence using data bits. This foundation will then be used to develop bit-based methods for causal inference and interpretable 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 2026 · 2026-07

Fluid-structure interaction occurs when moving fluids act on structures and the resulting structural motion or deformation changes the fluid flow. It plays a central role when blood flows through heart valves, organisms swim or fly, bio-inspired vehicles move through water or air, energy devices convert flow into power, aircraft and turbine components respond to aerodynamic loads, and medical devices interact with the body. Immersed boundary methods are mathematical and computational tools for simulating systems in which fluids and structures influence each other. These simulations can support scientific discovery, engineering design, and medical innovation, but current methods can sometimes fail to preserve volume, produce unrealistic fluid motion near pressurized surfaces, or give inaccurate estimates of local forces. This project will create more reliable simulation methods for fluid-structure interaction. By improving general-purpose tools that can impact cardiovascular modeling, medical device design, energy technology, aircraft and turbine analysis, and other engineered systems, the work will help advance national health, economic competitiveness, public welfare, and national defense. The project will also strengthen open-source software used by scientists and engineers, train students in computational mathematics and scientific computing, and support areas of Federal strategic interest, including biotechnology, advanced manufacturing, medical device design, energy technology, and artificial intelligence workflows that depend on high-quality simulation data for training, testing, and validation. This project will develop new mimetic immersed boundary (IB) methods for fluid-structure interaction. These methods use anisotropic regularized delta functions based on composite B-splines to preserve mathematical structures that are lost by conventional IB coupling methods based on isotropic regularized delta functions. The work has three connected goals. First, grid-adapted quadrature rules will be developed to align discrete integration with the polynomial structure of the composite B-spline coupling kernels, improving the accuracy of force spreading and velocity interpolation in both two- and three-dimensional settings. Second, stabilization strategies based on surface regularization and normal and tangential force decompositions will be developed and analyzed, with the goal of obtaining accurate, nonoscillatory pointwise interfacial forces. The effects of these stabilization strategies on energy conservation, volume conservation, and the suppression of spurious currents will also be quantified. Third, these tools will be integrated into an interfacial coupling strategy for volumetric fluid-structure interaction. The resulting methods will be implemented in the open-source IBAMR software library and assessed on a wide range of thin-interface and volumetric benchmark cases. 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 2026 · 2026-07

With widespread technological developments, it has become commonplace to collect high-dimensional time-series data, that is, intensive repeated measurement data on many variables and subjects simultaneously during daily life. This includes sensor-based physiological measurements (e.g., heart rate, skin conductance) and health and movement data (e.g., calorie tracking, Fitbit, GPS) across people, various macro-level indicators recorded over time for different economies, as well as data from many other noisy and complex systems evolving over time across subjects. Although technological advances including machine learning have decreased the burden associated with collecting such high-dimensional time-series data, these developments have also brought a newfound appreciation of the rich heterogeneity inherent to many biological, behavioral and other systems. For example, in neuroscience, extensive between-person heterogeneity is observed in the anatomical organization of brain regions and dynamic network activation profiles. The heterogeneity of firms, countries and other subjects has been well recognized and studied in economics and finance. As such, how best to model processes that exhibit meaningful heterogeneity across subjects is a critical open question in many disciplines, including precision medicine, computational psychiatry and economics, machine learning, and artificial intelligence. This project aims to develop the theoretical foundation, methodological approaches and computational tools needed to model time-dependent systems characterized by unknown heterogeneity. Broader impacts activities will also involve education and training of undergraduate and graduate students. The project advances a unified statistical and machine learning framework for the analysis of complex multivariate time series arising from multiple heterogeneous subjects. It brings together two directions in modern time series methodology: high-dimensional multivariate modeling and joint inference across subjects exhibiting potentially distinct dynamic behavior. A central challenge motivating the project is that heterogeneity may occur not only in the magnitude of model parameters, but also in the underlying structural form of the dynamics themselves, while the degree and nature of similarity across subjects are typically unknown a priori. The work focuses primarily on multi-VAR and multi-FAC frameworks for modeling high-dimensional time series across multiple subjects. Within these settings, the project seeks to establish rigorous theoretical guarantees, including consistency and related asymptotic properties for multi-VAR models, as well as conditions for recovering shared latent structures and dependence patterns in multi-FAC models. The project will further extend these frameworks to a range of applications relevant to empirical research, including: (a) time-varying parameter models, especially in intervention settings; (b) identification of subgroups and cluster-level dynamics when subjects share common dynamic features; and (c) non-Gaussian methods for count-valued and other discrete time series frequently encountered in behavioral research. In addition, the project will develop computational tools, optimization algorithms, and software packages to support the methodological and data-analytic components of the 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 2026 · 2026-06

This project aims to develop theory and methodology required for statistical inference on spatiotemporal rates of change or gradients, followed by extending their use to assess boundaries that track significant changes in spatiotemporal response. The current stage of spatial and temporal data science bears witness to the recording of massive spatiotemporally indexed data for the purpose of tracking changes in spatial and temporal variables. This project outlines the details of the methodology and software development for quantifying and understanding change within large and complex spatiotemporally referenced datasets. These are closely related to machine learning and artificial intelligence, and the developments are motivated by substantive questions arising in various fields where assessing regions of rapid change in space and time is crucial. The focus of applications is on biomedical and neuroimaging datasets, and the project provides research training opportunities for graduate students. Extending the statistical inference to larger domains, we leverage a low-rank projection-based approximation to exact Gaussian processes. The project will also develop classes of highly scalable Bayesian factor models and Graphical predictive processes for jointly modeling highly multivariate spatiotemporal data. The project will conduct rigorous investigations into statistical inference for rates of change associated with predictive processes and graphical predictive processes. The project aims to derive probability distributions that facilitate posterior inference within a Bayesian setting. This is followed by extending the inference to smooth surfaces within space-time that track rapid directional change. 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 2026 · 2026-06

This project aims to teach computers how to understand the physical properties of the world from images and videos. When humans look at a video, they intuitively understand how light reflects off surfaces and how objects move under force. However, artificial intelligence systems struggle to understand these physical rules. This project supports the development of new computer vision systems that can automatically figure out an object's three-dimensional shape, what it is made of, how it reacts to light, and how it moves, all from standard video recordings. Teaching computers this kind of physical reasoning will have major benefits for society. In healthcare, these tools can help robotic surgical systems safely navigate inside of the human body by understanding how tissues stretch and deform. In manufacturing, they can help robots handle delicate or flexible materials. The project also supports educational goals by creating new courses that teach physical understanding using artificial intelligence, providing research experiences for undergraduates, and attracting the participation of high school students and individuals from non-computing backgrounds to inspire the next generation of scientists and engineers. The technical goal of this project is to develop a generalizable machine perception framework that jointly infers three-dimensional object shape, parameters related to various physical properties of the object, and external physical entities from sparse visual inputs. This problem is technically challenging due to the ambiguity in estimating multiple spatial and physical properties from sparse-view videos. To resolve this ambiguity, the investigator will develop new techniques that combine physics-based modeling with strong statistical priors from generative models in an end-to-end framework. The investigator will achieve this through three main research activities. First, the project will develop generalizable inverse rendering models for static scenes by using controllable image or video diffusion models as priors to recover shape, reflectance, and lighting. Second, the project will tackle inverse simulation for dynamic scenes by combining feed-forward neural networks with a differentiable physics simulator to estimate initial physical states and material deformation parameters. Third, the project will unify these physical domains by building a controllable video generative model conditioned on both the physical parameters of lighting and deformation, enabling the joint estimation of object appearance and dynamics. The resulting algorithms will be validated on both synthetic and real-world datasets. Furthermore, the investigator will demonstrate the real-world impact of this foundational framework by applying it to healthcare challenges involving medical endoscopy videos. 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 2026 · 2026-06

Cells in our bodies migrate to repair wounds, fight infections, and build tissues. When these movements go awry, they contribute to diseases such as cancer metastasis, fibrosis, and chronic infection. To move efficiently, cells must push, pull, and squeeze their way through a complex three-dimensional environment filled with tiny pores and physical obstacles. This Faculty Early Career Development Program (CAREER) project seeks to understand how cells generate and coordinate the physical forces needed to navigate through tight spaces and to determine how these abilities vary across different environments and cell types. By revealing the mechanical rules that govern cellular movement, this research will inform future strategies to enhance tissue repair, limit cancer spread and guide the design of biomaterials and therapeutics. This research project will define the force-generation strategies that different types of cells use to migrate through confined 3D microenvironments with varying geometries and mechanical properties. To do so, we will develop the technologies to precisely pattern biomaterials at sub-cellular length scales and couple these approaches with advanced microscopy and machine learning to quantify the magnitude, direction, and timing of cell-generated forces. With these tools, we will experimentally test competing mechanistic models of propulsion during confined migration. Additionally, we will utilize molecular tension sensors to visualize how forces propagate inside cells and to map the biochemical signaling pathways that regulate movement through constrictions. These combined measurements will identify the mechanical design principles that allow cells to sense, respond to, and select propulsion modes in different tissue environments. The resulting insights will advance core knowledge in biomechanics and mechanobiology while providing broadly accessible tools for the research community to quantify force transmission and transduction during cell migration. 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 2026 · 2026-06

Modern biology has entered an era in which the central challenge is not the lack of data, but how to organize large and complex datasets into knowledge that scientists can understand and test. Artificial Intelligence has greatly improved the ability to find patterns in biological data, but many models still struggle to explain the biological mechanisms behind those patterns. This project addresses that challenge by developing new ways to use Artificial Intelligence to understand gene regulation, the process by which cells turn genes on and off in response to internal and external signals. The key idea is that protein structure can provide the missing biological context. Mutations that occur near one another in the folded shape of a protein may affect the same functional surface, interaction site, or regulatory region, leading to shared effects on gene activity. By using protein shape to organize genetic and molecular data, this project aims to make Artificial Intelligence models more interpretable, more biologically grounded, and more useful for discovery. The project advances current National Science Foundation priorities in Artificial Intelligence and biotechnology by developing explainable computational tools for biological discovery and using genome-editing experiments to test model predictions. The project also includes educational activities that expand access to Artificial Intelligence and biological data literacy training. Through interactive tutorials, workshops, and research experiences using real biological datasets, students will build skills in coding, data analysis, and interdisciplinary scientific discovery. This project will create a structure-informed framework for discovering how specific parts of proteins influence gene activity. The research will bring together genetic variation, gene expression, protein abundance, protein structure, and experimental perturbation data from large public datasets of cell line models. First, the team will map naturally occurring genetic changes onto three-dimensional protein structures and identify protein regions where changes are consistently linked to changes in gene activity. These regions will then be used to train interpretable Artificial Intelligence models that predict which proteins, and which parts of those proteins, are likely to control specific gene programs. Second, the team will test selected predictions using genome-editing experiments, beginning with the oxidative stress response, a protective system that helps cells respond to damaging conditions. Third, the team will examine how cells control the levels of key proteins that regulate genes, including systems that mark proteins for degradation. By connecting large-scale data analysis with targeted experiments, this work will provide a general strategy for turning complex biological datasets into mechanistic hypotheses that can be tested in the laboratory. The results will advance understanding of gene regulation and provide reusable computational and educational resources for the broader scientific community. 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 2026 · 2026-06

This project supports the NSF 2026 "Future of Nuclear Theory" Project Scoping Workshop. Theoretical nuclear physics is essential for interpreting experimental data and providing predictive insights that drive experimental nuclear physics programs at world-leading experimental facilities. Furthermore, emerging technologies like artificial intelligence (AI) and quantum information science (QIS) offer transformative potential for performing numerical simulations with unprecedented precision. In this broader context, this timely workshop provides a forum for practitioners to assess recent scientific progress and identify current challenges in nuclear theory. Participants will discuss strategies to overcome these obstacles by leveraging advancements in AI and QIS, ultimately mapping a strategic path forward for the success of nuclear physics and nuclear theory scientific programs in the U.S. 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 2026 · 2026-03

With support from the Chemical Structure and Dynamics (CSD) program in the Chemistry Section, Professor Andrew Moran at the University of North Carolina at Chapel Hill is exploring the use of two-dimensional organic-inorganic hybrid perovskite quantum wells as light sources with adaptable polarization states for quantum information technologies. Emerging quantum communication systems will require light sources whose properties can be precisely controlled, yet many existing approaches rely on external optical components to manipulate light after it is generated. Professor Moran and his students will investigate whether the internal physical behavior of perovskite quantum wells, particularly the way electronic spins evolve on very short timescales, can be used to control the properties of the emitted photons within the material itself. This approach could enable the development of new light sources suitable for secure communication and other quantum technologies. Their studies will advance understanding of how ultrafast material dynamics influence the quantum properties of light and will help clarify how perovskite materials could function as practical quantum light sources. The project will also contribute to education and outreach through training of students in interdisciplinary research and the development of public engagement tools that introduce quantum communication concepts to broader audiences. This project uses nonlinear optical spectroscopies to investigate how spin dynamics and many-body interactions in two-dimensional organic-inorganic hybrid perovskites shape the properties of emitted photons. Four-wave-mixing techniques are employed, in which the polarization ellipticity of the signal field serves as a sensitive probe of ultrafast spin relaxation processes within the exciton fine structure. By analyzing how these signal polarization states evolve as a function of time and experimental conditions, the research will establish quantitative links between microscopic material dynamics and the resulting photon properties. Particular attention will be given to biexciton resonances, which radiate photons with distinct polarization ellipses and have been shown to enhance the efficiency of polarization-encoded quantum communication. The project will also employ multidimensional spectroscopies to characterize correlations in energy level fluctuations and to distinguish homogeneous and inhomogeneous broadening mechanisms that influence photon coherence. These measurements will inform the design of quantum communication protocols in which four-wave-mixing signal photons are used to transmit ASCII messages via material-intrinsic encoding. Educational impacts will include the development of a simplified, hands-on version of the BB84 quantum key distribution protocol designed for use in high school and undergraduate learning environments. 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 2026 · 2026-01

This project investigates prehistoric farming practices to understand how wealth and inequality are generated under conditions of crisis. Intensive farming and the control of agricultural surplus have long been understood as key factors amplifying social inequality. In turn, during periods of ecological and social instability, it is assumed that farmers adopt more diverse and dispersed farming and herding strategies, resulting in limited wealth and inequality. Yet, this traditional account makes simplistic assumptions about the relationship between food production, the environment, and sociopolitical complexity. Can intensive farming persist despite conditions of drought, warfare, and political instability? Conversely, can the adoption of more diverse subsistence strategies actually lead to widening social inequality? Archaeology is well suited to investigate acute and long-term changes in farming, the environment, and human relationships. This project takes on immediate urgency in the present when traditional farmers and pastoralists in marginal environments around the world face increasing sociopolitical and ecological challenges. Through this project, the primary investigators train undergraduate and graduate students in transnational archaeological field methods and in cutting-edge analytical methods at their respective universities in the US, preparing the next generation for careers in science and cultural resource management. The research team analyzes direct proxies of agricultural and pastoral practices and compare them to measures of social inequality during a period of long-term drought and dramatic sociopolitical changes. The research takes place in a prehistoric highland valley, a region with significant deep time human occupation, vast agricultural infrastructure, and abundant natural resources. The researchers excavate a large settlement and associated agricultural terraces, analyze excavated materials and legacy collections from multiple sites, and conduct specialized chemical analysis of human, plant, animal, and soil remains to reconstruct food webs, field management systems, and patterns of human and herd mobility. They also analyze land use and settlement patterns using unmanned aerial vehicles, three-dimensional computer modeling, and Geographic Information Systems. The multi-methodological approach allows the researchers to investigate agropastoral strategies and sociopolitical organization across communities living in close proximity but with different ecological potentials and divergent sociopolitical histories. 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 2026 · 2026-01

The global climate is rapidly warming as a function of elevated green-house gas emissions. Earth’s climate has fluctuated between periods of warming and cooling across it’s ~4.5-billion-year history. One prominent period of past global warming that is similar to what we are observing today is the Paleocene-Eocene Thermal Maximum (PETM, ~56 million years ago) – where Earth’s climate warmed, and then cooled rapidly over a ~200,000 year interval. The PETM is an excellent archive of how the Earth-system can reduce excess carbon dioxide from the atmosphere through feedbacks with the landscape. The most prominent Earth-system way to remove atmospheric carbon dioxide is through carbon dioxide-enriched rain causing weathering and erosion of magnesium and calcium bearing rocks, which form carbonate minerals and can be stored in sedimentary rocks. However, our understanding of how efficient this process is to remove carbon dioxide is limited by how difficult it is to assess erosion across these specific paleoclimate excursions. This project applies thermochronology (radiometric dating of minerals which record timing of erosion) to a PETM archive in the Tremp-Graus basin in Spain in order to quantify shift in erosion across a prominent period of global warming. Results from this study will provide quantifiable estimates on how Earth’s landscapes react to global warming, and how these changes cycle carbon dioxide out of the atmosphere. This project will develop an interactive Earth-sciences outreach activity, designed for home-schooled students in the state of Oklahoma with a focus on Earth’s climate. Increased silicate weathering and erosion during periods of global warming is an important mechanism of CO2 drawdown, which is required for long-term climate stability. However, quantitative estimates on magnitude and duration of climate-enhanced erosion remain challenging. In the project, the researchers will test if lag-time thermochronology (apatite fission-track and U-Pb dating) is able to resolve shifts in erosion during abrupt paleo-global warming. The project will apply this proxy to the Tremp Graus Paleocene Eocene Thermal Maximum section in northern Spain in order to resolve erosion rates pre-, syn-, and post-PETM carbon isotope excursion. Lag-time thermochronology will be integrated with detrital zircon U-Pb to track provenance changes. From this novel and multi-method proxy approach the researchers will test different hypotheses relating to onset, duration, and cessation of climate-enhanced erosion, quantify magnitude of erosion, and parse out the relative contribution of both tectonic and surface processes. 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 2026 · 2026-01

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Jeffrey Johnson of the University of North Carolina at Chapel Hill is studying how to use metals like iron and copper that are readily available from the Earth’s crust for the laboratory preparation of chemical building blocks. This collaborative program will help define how we can productively control the environment around the metal in a way that maximizes the efficiency of the reactions being studied. The transformations that are targeted are valuable because they focus on reactions of feedstock aromatic chemicals that are often derived from petroleum refining or, with growing frequency, from sustainable sources. The expected reaction products will plug into a variety of useful applications, from materials science to chemical biology, that rely on functionalized organic compounds. Consistent and ongoing outreach efforts will enhance the impact of the project through connections with the broader community. This project is being conducted in collaboration with Prof. Benjamin Darses of the Université Grenoble Alpes in France and who is receiving separate financial support through the French National Research Agency (ANR). With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Jeffrey Johnson of the University of North Carolina at Chapel Hill is studying dearomatization reactions catalyzed by transition metal complexes derived from Earth-abundant metals such as copper, iron, and nickel. The collaborative project will advance our understanding of how the characteristics of metal-ligand complexes interface with different reagent combinations to promote challenging arene cyclopropanation reactions. The research will provide tools for the intramolecular and intermolecular dearomatization of heterocyclic and carbocyclic arenes, providing the chemical community with new chiral building blocks derived from feedstock aromatic starting materials. We will develop processes to compete with the current state of the art provided by rhodium complexes to address problems associated with the latter’s cost, scarcity, and environmental impact. By continuing and expanding existing relationships and outreach with the community, the impact of the project will be enhanced. 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 · 2025-10

Large river systems in mountain belts exert important controls on erosion, sediment flux to oceans, as well as the distribution and evolution, of species. These rivers are highly mobile systems and understanding the processes that modulate their development and evolution are fundamental questions in earth surface processes. This proposal will address the timing and factors that caused the growth of major longitudinal river systems in the northern Andes Mountains of Colombia by studying the modern and ancient sediment transported by these rivers. Data from this project will form the foundation for a new course at the University of Oklahoma. Exercises will be designed to introduce students to transferable skills including basic coding, plotting, and use of geospatial software. These exercises will be available online (at no charge) for others to use or modify in teaching or research. Additionally, this project will support and train undergraduate and graduate students and a postdoctoral scholar, and continue to enhance collaborations with international scientists. More specifically, this proposal will address growth of the Magdalena and Cauca Rivers in the northern Andes Mountains using combination of modern geomorphology, source characterization, and basin analysis. Geomorphic indices will be used to identify areas of modern drainage instability, and 10Be erosion rates will allow for assessment of along-strike variations of erosion rates over notable knick-points. Unique sediment sources in three sub-parallel mountain ranges (Western, Central, and Eastern Cordilleras) facilitate the use of provenance to track shifts in drainage networks through time. These distinctive sediment source regions will be characterized using provenance proxies on modern river sands. Provenance records will also be obtained in upper Miocene-Pliocene strata. Interbedded volcanic and volcaniclastic materials in the sedimentary intervals of interest will enable development of a robust chronostratigraphy. The refined chronostratigraphic framework and new provenance datasets will allow identification of upstream shifts in drainage networks through time. Integration of modern geomorphic proxies and provenance records will test the principal hypothesis that structural diversion led to the birth of longitudinal rivers in the northern Andes. A subset of the samples will be the basis for a series of teaching modules, which will be publicly available to the broader geoscience community as teaching 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 · 2025-10

This project investigates how avian influenza (bird flu) spreads and undergoes genetic changes and identifies the key factors driving these genetic changes and spread. It elicits the spatial and genetic patterns of avian influenza in birds, mammals, and humans, aiming to assess the pandemic potential of this virus, which has had a 50% mortality rate in people infected during the past 30 years. Understanding the risk of spillover to humans requires a comprehensive understanding of the influenza ecosystem, an interconnected network of factors involving humans, animals, and the environment. The findings are being organized into a database for public access to support translation of what is learned from the project to practice by informing and optimizing measures to mitigate both the economic impacts on the agricultural sector, a core sector of the bioeconomy, and the public health risks posed by emerging influenza variants. This study aims to understand the genetic evolution of avian influenza, particularly a highly pathogenic H5N1 virus lineage over time and identify the ecological factors that drive human infections and viral change. Central to the study is a systematic analysis and characterization of the spatiotemporal distributions of viral genotypes and their genetic divergence from precursor avian influenza viruses. It leverages advanced geospatial modeling, machine learning, and geospatial artificial intelligence (GeoAI) techniques to identify key viral traits, such as transmission potential and virulence, and to elucidate geographic ecosystem factors that influence the spread and evolution of the virus. The study generates a publicly available database that integrates information on more than 20,000 avian influenza viruses with associated human-animal-environment ecosystem variables. This database subserves translational support for research and private-sector preparedness. 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 · 2025-10

This project will study data management practices at user facilities with the goal of improving how research data is organized, stored, and shared. User facilities represent a major investment from NSF and each one has unique data formats and cyberinfrastructure. The project will target selected NSF Major and mid-scale facilities to examine current practices in order to create a roadmap aligned with FAIR principles that facilities can use for improvements in data management and to support open science and improve the national research infrastructure ecosystem. Current data management practices at selected user facilities will be assessed through surveys of the facility personnel and of the facilities' users. The assessment will include topics relevant to the FAIR principles, including data provenance, transfer, packaging, and storage, as well as how data is enriched and deposited for dissemination and citation. Best practices and data formats will be identified, and a roadmap will be created. The roadmap will be shared publicly for feedback through community-building workshops, and elements will be evaluated with hands-on pilot projects at the surveyed facilities. The findings are expected to be useful not only to the surveyed facilities, but to other user facilities and research facilities broadly for assessing and improving their data infrastructure. 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 · 2025-10

Modern information systems struggle to connect related data across organizations, making it difficult to solve complex national challenges that require insights from multiple sources. Knowledge graphs have emerged in many domains of science and technology as a powerful means of integrating, structuring, and mining information to extract new knowledge. Recognizing the importance of this paradigm, the National Science Foundation initiated the Prototype Open Knowledge Network program to build a prototype version of an integrated data and knowledge infrastructure called the Open Knowledge Network. As a federated but interconnected set of knowledge graphs, the Open Knowledge Network would support knowledge discovery across a wide array of application domains and would allow researchers and policymakers to see connections and patterns that are invisible when data remains isolated in separate databases. This project develops the essential foundation that enables different knowledge graphs to work together seamlessly, serving the national interest by supporting evidence-based policymaking, strengthening economic competitiveness through data-driven innovation, and accelerating scientific breakthroughs that address complex challenges. The Open Knowledge Network is envisioned as a national data infrastructure designed to empower users. The project will foster novel developments in three key areas: First, knowledge graph interoperability and the application of knowledge backgrounds to enable seamless data integration across domains. Second, the applicability of different federated query technologies under different circumstances to optimize data access and retrieval. Third, the use of natural language interfaces to interact with structured knowledge, making complex data accessible to broader audiences. The research activities include developing data and knowledge management systems, implementing federated query capabilities, and creating advanced artificial intelligence tools. The work is a key component in moving the Prototype Open Knowledge Network from its nascent state to a fully functioning public release. Through ongoing engagement with stakeholders to ensure responsiveness and transparency, the project will facilitate greater access to reliable knowledge for the public, empower researchers to discover new insights across disciplines, and accelerate the development of powerful models that help address complex challenges facing the nation while supporting economic growth and leadership in artificial intelligence. 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 · 2025-10

Sedimentary basins adjacent to convergent margin mountain belts host important economic resources and contain key records of past climatic and tectonic events. As such, basin archives are often used to reconstruct long-term variability in mountain building and climatic processes. New quantitative approaches provide direct links between signals of erosion in mountain belts and depositional processes preserved in basins, which can improve understanding of how to interpret these critical sedimentary archives. This project applies novel methodologies to the Alberta basin, next to the Canadian Rocky Mountains, which provides an excellent testing ground due to abundant subsurface datasets and constraints on the geometries of deformation within the mountain belt. This grant supports training of undergraduate and graduate students, collaborations between US and Canadian geoscientists, and development of training modules for the technical methods utilized in this proposal which will be made publicly available to other researchers. This project couples bedrock low-temperature thermochronology and thermokinematic modeling within the fold-thrust belt, with detrital thermochronology, subsidence curves, and provenance analysis in the foreland basin to assess linkages between fold-thrust belt shortening in the Canadian Rocky Mountains and depositional pulses in the Alberta foreland basin system. Low-temperature thermochronology (zircon fission-track and zircon (U-Th)/He), applied in an orogen-perpendicular sampling transect across major thrust sheets, will constrain the timing and pathways of rock cooling. In the foreland basin, stratigraphic sections and maximum depositional ages from detrital zircon geochronology will facilitate construction of sediment accumulation curves. Detrital zircons dated for U-Pb geochronology will also be dated via low temperature thermochronology, which will allow calculation of lag times. Ultimately, constraints from the fold-thrust belt and subsidence histories from the foreland basin will be combined in a flexurally validated, thermokinematic model, which will provide sequential, temporally constrained reconstructions of shortening magnitude, thrust belt geometry, and size and location of the orogenic load. These datasets will resolve: (1) the timing and magnitude of shortening, and whether it was a protracted or pulsed process, and (2) the relationships between thrust loading, unconformity development, and coarse clastic deposition. 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 · 2025-10

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Alexander Miller of the University of North Carolina at Chapel Hill is studying the ability of ions to control the outcomes of catalytic organic transformations of alkenes and alkynes. The research is inspired by Nature’s enzyme catalysts, which can respond to stimuli to change their structure and function. Synthetic cation-responsive catalysts will be prepared, enabling applications in switchable and tunable catalysis that could lead to more efficient and safer methods for preparing organic compounds. The project provides a platform for training graduate students in inorganic synthesis, thermodynamic and kinetic mechanistic analysis, and catalyst design and development. The project will impact the broader chemistry community through The Safety Net, a web resource designed to enhance communication about laboratory safety with synthetic chemists across the world. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Alexander Miller of the University of North Carolina at Chapel Hill is studying how organometallic catalysts with supramolecular receptor sites can enable cation-responsive transformations of alkenes. This project focuses on compact organometallic catalysts with cation receptor sites, particularly crown ethers, that can respond to external stimuli to alter the activity or selectivity of a reaction. The research approach involves synthesizing catalysts with structurally responsive cation receptor sites, developing catalytic reactions with switchable activity and selectivity that can be combined for tandem switchable catalysis, and performing mechanistic studies to elucidate the factors influencing cation-responsive catalysis. “Pincer-crown ether” ligands have been particularly successful in enabling cation-controlled catalysis, driving planned development of methods for tandem switchable catalysis. The broader research impacts of responsive catalysts include more sustainable synthetic schemes that combine several steps to minimize solvent use and separations, and access to molecules or materials that would be difficult to access with other methods and could be valuable in the fragrance, pharmaceutical, and commodity chemical industries. 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 · 2025-10

This Research Experiences for Undergraduates (REU) Site, Nanoscale Detectives, offers undergraduate students an immersive research experience investigating the fundamental structure, dynamics, and properties of hybrid perovskite materials — a class of materials with transformative potential in next-generation semiconductor technologies, including solar cells, photodetectors, and advanced computing. By engaging students from various academic disciplines in cutting-edge, hands-on research across the three academic institutions in making up North Carolina’s Research Triangle Area (NC State University, UNC Chapel Hill, and Duke), the program addresses the critical national need to cultivate a highly skilled STEM workforce equipped to tackle complex challenges in materials science, semiconductors and energy security. The REU Site promotes the progress of science through discovery-focused learning, while advancing national prosperity by preparing students for graduate education and careers in high-demand STEM fields. The program also recruits and trains students from community colleges as well as first-generation college students, in support of the nation’s commitment to expanding the STEM workforce in semiconductors and other strategic areas. The Nanoscale Detectives REU Site will host cohorts of twelve undergraduate students each summer for a ten-week research program at NC State University, UNC Chapel Hill, and Duke University. Participants will work closely with faculty and graduate student mentors on interdisciplinary projects that apply advanced synthesis, spectroscopy, microscopy, and computational modeling to elucidate the nanoscale structure and dynamic behavior of hybrid perovskite systems. The program includes a structured professional development curriculum, mentor training workshops, and outreach activities designed to enhance student skills in research communication, ethics, and career planning. Research outcomes will contribute new insights into the stability and performance of perovskite materials, with the potential to inform the design of more efficient and durable devices for renewable energy technologies. Program assessment will combine formative and summative evaluations, with anonymized data analyzed to inform continuous improvement and shared broadly with the undergraduate research community. Through these activities, the REU Site will advance fundamental understanding of hybrid perovskites and prepare the next generation of scientists and engineers to lead innovation in energy and materials science. This Site is supported in part by funds provided to the National Science Foundation by the Semiconductor Research Corporation. 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 · 2025-10

Non-technical Abstract: This project aims to revolutionize the discovery of new solid-state materials that can precisely control the mobility of ions and electrons, an essential step toward building the next generation of energy storage systems, neuromorphic computers, and smart sensors. By leveraging advanced artificial intelligence (AI), machine learning (ML), and automated synthesis tools, the team will develop a transformative approach to design solid-state ion conductors using multi-element doping, enabling materials tailored for next-generation energy and electronic systems. A central goal is to establish a new data-driven approach to achieve an optimal balance of ion and electron conductivities for targeted applications while ensuring material stability during operation, a task difficult to achieve using traditional trial-and-error techniques. The project will also provide hands-on research and training opportunities in AI-driven materials discovery, fostering collaboration among U.S. and Canadian universities, national laboratories, and industry partners. Technical Abstract: This research will develop and apply a closed-loop, data-driven framework to design and optimize multi-element co-doping strategies in alkali-ion conductors. By integrating AI/ML-accelerated property prediction, high-throughput computational modeling, autonomous synthesis, and in-situ characterization, this project will systematically investigate how co-doping influences ionic transport, electronic structure, and lattice stability across bulk phases, grain boundaries, and interfaces. A fast, iterative inner loop will enable the screening of thousands of dopant combinations, while a slower outer loop will focus on extracting mechanistic insights and ensuring scalability, feeding knowledge back into the predictive models. Target systems include sodium- and lithium-ion based oxides and halides, where varying the balance of ionic and electronic conduction is critical for applications ranging from batteries to neuromorphic computing. The project will generate foundational design rules for tuning transport properties through co-doping, creating new pathways for energy-efficient materials innovation. 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 · 2025-10

This project enhances international exploratory research by strategically deploying next-generation SmartNICs, specifically Data Processing Units (DPUs), at four existing international FABRIC sites, which builds upon FABRIC Across Borders (FAB). DPUs are an emerging technology that places programmable, high-performance processors directly on the network interface, which can accelerate data processing by orders of magnitude by offloading data-centric tasks like networking, storage, and security from traditional CPUs and GPUs. DPUs also reduce time-to-insight for data-intensive scientific experiments across various domains and offer strong workload isolation for researchers, enabling secure networking and encrypted storage experimentation. By integrating SmartNICs into the FABRIC ecosystem, this project will accelerate exploratory data-intensive scientific research across multiple fields, including high-energy physics, federated learning, and wireless networking between the USA and the international sites. Scientific discovery in fields such as high-energy physics, smart cities, and AI-driven applications increasingly depends on the rapid movement and analysis of vast amounts of data. Traditional networking approaches often limit the pace of discovery, as server CPUs become bottlenecks for processing high-volume data streams. SmartNICs—programmable network cards equipped with dedicated processors—can offload and accelerate tasks such as encryption, data filtering, storage management, and machine learning inference. By deploying SmartNICs on FABRIC's international sites, new classes of experiments for exploratory research could be pursued. The deployment of SmartNICs across FABRIC's international nodes will broaden access to advanced networking technologies for a global community of researchers, including students and early-career scientists. 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 · 2025-09

An award is made to the University of North Carolina at Chapel Hill, the University of Florida, and Smith College, to develop a large-scale network to study the abundance and seasonality of moths across Eastern USA. This project will combine counts of caterpillars (juvenile moths), generated by the citizen-science project Caterpillar Count!, with counts of adult moths that will be captured by automated, non-lethal traps. Moths and caterpillars are one of the most important insect groups because they eat plants such as crops and forest trees and also serve as food for wildlife. This work will generate resources necessary to share seasonal abundance data with other scientists and the general public. Completion of this project will contribute to an effective contemporary workforce by involving students and creating educational materials. This project will also contribute to elevating scientific literacy in the general public by recruiting citizen-scientists and by organizing moth observation events during National Moth Week. Moth Monitoring 2.0 will be the first large-scale monitoring network designed to integrate abundance and phenology data across both larval and adult life stages. This network will generate data through standardized sampling protocols that will be instrumental for understanding broad-scale abundance patterns of an ecologically important insect group across a large region. This project will result in the development of new hardware and software solutions for automated monitoring, machine-learning based identification, data submission, storage, and visualization, and will create a new repository for the sharing of biological material. The work will expand the existing network of Caterpillars Count! monitoring sites, and add new functionality for monitoring by the general public. All of these data will be freely available to researchers seeking to address questions about how disturbances, including land use and other global change drivers, are impacting insect declines and ecosystem health. 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 · 2025-09

Recent advances in data science, statistics, and machine learning have opened new possibilities in precision medicine, enabling clinicians to tailor treatments based on individual patient characteristics. This project focuses on developing a unified and efficient statistical framework to improve treatment decisions by leveraging rich demographic, socio-economic, and biomedical data. By advancing personalized decision-making, this research contributes to better health outcomes, more efficient healthcare delivery, and overall national well-being. The project also offers broad societal impact through its commitment to education, collaboration, and open science. The investigators will mentor graduate students and develop new coursework at the intersection of machine learning, statistics, and personalized medicine. In addition, all software tools developed will be released as open-source, supporting accessibility and reproducibility in scientific research. The interdisciplinary nature of the project encourages collaboration across statistics, medicine, and computer science, and prepares a next-generation workforce to tackle complex health data problems. This project aims to develop an efficient learning framework for estimating optimal individualized treatment rules (ITRs) across a broad range of personalized medicine settings. The proposed methodology is based on semiparametric modeling and is designed to address complex relationships among covariates, treatments, and outcomes. Key challenges addressed include handling multiple treatment options with cross-treatment structures, modeling a variety of outcome types, and accommodating multi-stage decision-making with time-varying, history-dependent effects. The framework also supports incorporation of domain knowledge for interpretability and practical implementation. From a statistical perspective, the proposed methods achieve double robustness (consistency under two separate model specifications) and statistical efficiency (minimal asymptotic variance), even under model misspecification and in high-dimensional or limited-data scenarios. These contributions advance the state of the art in both semiparametric theory and algorithmic design for ITR estimation. The resulting models are interpretable, scientifically meaningful, and directly applicable to real-world medical problems, including drug development and treatment recommendation. This work not only contributes to foundational statistical theory but also facilitates translational research in healthcare. 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.