Pennsylvania State Univ University Park

NSF Awards · FY 2025 · 2025-09

This award provides funding for participants in “Semi-annual Workshop in Dynamical Systems and Related Topics at Penn State,” to be held on the Penn State University Park campus November 13-16, 2025. This represents the Penn State half of the Semi-annual Workshop in Dynamical Systems and Related Topics, cosponsored for the last 35 years by the dynamics groups of Penn State and University of Maryland; the Maryland half is held each spring. The workshop is devoted to recent developments in dynamical systems and many applications to related fields. The goals of this conference are to promote the communication of mathematical results, to facilitate interaction and progress in dynamical systems and related fields, to nurture the sense of community and common mission in these fields, and to contribute to the training of graduate students and recent Ph.D. recipients and to their integration into the dynamics community. This conference series began as a meeting place for mathematicians based primarily in the northeastern United States working in dynamical systems and related topics, and during the past twenty years has grown to attract outstanding mathematicians from around the world. In recent years the conference has featured several special sessions focused on the latest achievements and current research trends in dynamics and its applications. The themes for the 2025 edition of the workshop include classification of measures for homogeneous and non-homogeneous systems, ergodic and mixing properties of parabolic flows and sharp bounds on their deviation of ergodic averages, new developments for random systems, recent trends in the theory of Hamiltonian dynamics, statistical properties of non-uniformly hyperbolic systems and their thermodynamic formalism. More information can be found on the conference website at https://science.psu.edu/math/research/dynsys/workshop. 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

The BRIDGE-TECH (Building Relationships in Innovation and Development for Growth in Emerging Technologies) project brings together a cohort of five primarily undergraduate institutions (PUIs) committed to strengthening regional innovation ecosystems by removing barriers to research, workforce development, and technology commercialization. These institutions each serve regions with untapped economic and technological potential but face common challenges such as limited research infrastructure, fragmented partnerships, and underdeveloped support for technology transfer. Through a coordinated, cohort-based approach, the project will empower faculty and communities by developing sustainable industry partnerships, supporting entrepreneurial programming, and expanding access to emerging technologies. Ultimately, BRIDGE-TECH seeks to build ecosystems where innovation thrives and contributes to long-term economic benefits. Technically, BRIDGE-TECH proposes a scalable model to increase innovation capacity at PUIs through strategic capacity-building and shared institutional development. The project focuses on five interrelated goals: (1) increase industry-sponsored research, (2) enhance innovation culture and technology transfer, (3) develop design thinking and innovation accelerator, (4) drive and expand innovation in rapid manufacturing, and (5) create synergistic opportunities for academic research. Each institution will implement customized, locally grounded activities, such as creating innovation centers, forming regional councils, conducting ecosystem assessments, and launching faculty fellows programs, while leveraging cohort-wide training workshops and shared resources. The anticipated outcomes include increased research funding, expanded partnerships with industry entities, improved IP and commercialization processes, and the training of over 40 faculty members. The collaboration ultimately aims to elevate the role of PUIs in national innovation networks while supporting sustainable economic 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 · 2025-09

Bacteria commonly swim through complex biological fluids like mucus, playing a crucial role in health and disease, from infections in the lungs to microbial imbalances in the gut. Understanding how bacteria move through biological fluids is the first step toward developing new ways to cure and prevent such infections. Many mathematical tools describing how microorganisms swim through fluids like water were developed in the 1950s-1970s. These foundational theories continue to be used today. However, mucus is a far more complex and challenging environment than water. It is composed of macromolecular proteins (mucins) that confer it viscoelastic properties, simultaneously flowing like a fluid, yet capable of recoiling like elastic solids. Mathematical tools for studying bacterial locomotion through such complex biological fluids are lacking. This research will combine mathematics, computer simulations, and laboratory experiments to create a more comprehensive picture of this process. It will first investigate the fluid mechanics of propulsion through complex fluids using a single bacterial flagellum. This will be followed by a study of how multiple flagella bundle together, a standard feature of many bacteria like E. coli. Finally, the collective behavior of large groups of bacteria in fluids like mucus will be investigated. Knowledge so gained will be instructive in the design of new medicines, the prevention of dangerous infections of mucosal surfaces, and in the management of stubborn biofilms. The research focuses on bacterial flagellar propulsion in mucus, and in a better-controlled anisotropic, viscoelastic fluid: a lyotropic liquid crystal (LC). Using mathematical modeling and analysis, numerical simulations, and experiments, this project will address three interconnected problems. First, a novel slender body theory will be derived from first principles, alongside controlled experiments, to quantify the forces, flow fields, and resulting dynamics of individual bacterial flagella within a nematic LC environment. Theories will be tested against full numerical simulations of Ericksen-Leslie and Beris-Edwards model LC fluids. The first aim will be extended to encompass the coordinated behavior of multiple flagella forming helical bundles, a key aspect of bacterial locomotion. Finally, the emergent behavior and dynamics of many bacteria interacting within LCs will be modeled and analyzed, bridging the gap between individual flagellar mechanics and population-level phenomena. The expected outcomes include significant advances in our understanding of general fluid-structure interactions in complex biological media. The mathematical machinery developed will be applicable to a wide range of nearby problems in biology and engineering and will illuminate new mechanical aspects of evolutionary biology. 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

This award permits the principal investigators to study how resources are managed, especially regarding marine and estuarine resources like fisheries and shellfisheries. Much of the contemporary work on issues of resource management has limited temporal scope. That is, most contemporary work does not consider the time depth and long-term implications of resource management strategies that archaeological studies can offer. Archaeological cases provide long-term perspectives on the mechanisms by which management is successfully or unsuccessfully implemented. Archaeology provides a retrospective view on what “does and does not work” regarding how societies mediate potential overharvesting of key resources under such pressures like population growth and/or shifting ecological conditions. Overharvesting, resource availability, and environmental changes are key challenges in the face of rapidly expanding populations. While this certainly includes productive agricultural land, forest resources, and pasturelands, such challenges may be most rapidly facing societies living along our coasts, where over 40% of the world’s population lives. The interdisciplinary approach, methodologies, and analyses employed create unique opportunities to train students in tackling modern-day societal challenges across disciplinary boundaries to develop real-world solutions. This study advances NSF investments in understanding human adoption of biotechnology innovations through its implementation of biotechnology methods of isotope analysis. The research goal is to understand the specific ways the management of estuarine and marine resources may have changed over time as large towns grew and conditions fluctuated in the past. The research is designed to investigate the specific decisions, rules, and institutions of governance leveraged by rapidly growing communities that promoted long-term large-scale resource base (fisheries) that are highly sensitive and prone to overharvesting. Combining archaeological, ecological, and geochemical approaches, the researchers reconstruct fisheries and shellfisheries management practices by communities who resided along the southeastern Atlantic coast for thousands of years. In examining a case of known success in achieving a sustainably extractive system, this interdisciplinary work examines the variability in (1) how communities implement differential strategies in determining access or use-rights to resources and (2) how they adaptively deploy strategic practices that ensure resource extraction under critical internal and external pressures. The research team generates new insight from historical/archaeological datasets that can be translated for analysts. These are valuable and informative datasets that are rarely leveraged to their full potential to contribute to solving contemporary challenges facing societies today. 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

This project develops a new framework for digital twin modeling of Alzheimer’s disease (AD), combining clinical data, biomedical research, and advanced computational methods to support personalized medicine. A digital twin is a computational replica of an individual’s health state, enabling the prediction of disease progression and the evaluation of treatment options tailored to the patient. The project contributes to national efforts in healthcare innovation by addressing the urgent need for a better understanding, prediction, and treatment of Alzheimer’s disease, which affects millions of Americans. This work also advances the broader field of personalized medicine by demonstrating how digital twin tools, powered by large language models, machine learning, and causal inference, can accelerate discovery and improve health outcomes. In addition, the project supports interdisciplinary collaboration across artificial intelligence, mathematics, and medicine, while offering new training opportunities for students in data science, modeling, and biomedical research. This project builds a unified modeling framework for population-based and personalized digital twins of AD. The approach uses large language models (LLMs) to extract causal networks of AD biomarkers from scientific literature and combines this with clinical data to generate personalized predictions. Conformal prediction techniques are applied to quantify uncertainty in model outputs, and optimization under limited data is achieved by integrating gradient-based learning with LLM-guided parameter search. The digital twin models simulate disease trajectories and support digital clinical trials. Treatment planning is formulated as a Markov Decision Process and solved using deep reinforcement learning to identify optimal, individualized therapeutic strategies. The framework integrates causal modeling, machine learning, generative AI, and decision theory, advancing both the science of Alzheimer’s disease and the computational tools for biomedical digital twin development. While centered on AD, the methods are generalizable and contribute broadly to AI-enabled modeling under data constraints in biomedical research. This award by the Division of Mathematical Sciences in the Mathematical and Physical Sciences Directorate is jointly supported by the Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate. 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

Training methods in manufacturing have not kept pace with advances in how manufacturing processes are designed. Instead of teaching from a static handbook of unchanging material properties, novice scientists and engineers can now use AI-infused data science to understand and predict the complex range of properties and performance of materials as they are processed. In addition, traditional training does not include environmental, economic, and social sustainability considerations – from sourcing through processing to recycling, reuse, or disposal as part of materials selection or design although these aspects are increasingly demanded by end users. This skills gap necessitates a new educational paradigm in which data science is natively integrated into sustainable materials and process design, enabling consideration of the full life cycle of materials while accelerating their conceptualization and discovery. This National Science Foundation Research Traineeship (NRT) award to the Pennsylvania State University will equip the next generation of engineers, physical scientists, and social scientists with the tools required to effect transformative change in sustainable materials processing. The project (Sus-Mat for short) anticipates training 50 Ph.D. students, including 23 funded trainees, from Materials Science and Engineering, Chemical Engineering, Civil and Environmental Engineering, Computer Science and Engineering, Architecture, and Public Policy. This NRT will merge essential but commonly siloed fields of sustainability, data science, advanced materials processing, and public policy to create a holistic, data-driven materials and process design ecosystem. Trainees will learn to harness flexible data science tools including artificial intelligence (AI) integration, enabling them to understand how emergent processing approaches impact material properties and sustainability metrics and then employ those relationships to design sustainable materials and processes. The project will integrate the Sus-Mat themes of sustainability, data science, advanced materials processing, and public policy in pursuit of three core interdisciplinary research themes: (1) active learning for advanced materials processing optimization; (2) generative AI-based models for materials design; and (3) materials sustainability assessment framework. The interdisciplinary research will be enabled by the traineeship ecosystem consisting of: new core courses for foundational training, micro-credentials bolstered by experiential training, internships to facilitate knowledge translation, cohort-building activities to aid retention and community building, convergent research facilitated by co-advising, capstone experiences for broader outreach, and professional development that trains policy-savvy leaders in sustainable materials and process design. Sus-Mat’s combination of research projects using a range of materials, data availability, and processing technologies and skills training informed by public policy will modernize STEM workforce training in emergent materials processing technologies and accelerate the adoption of innovative, sustainable methodologies in high-tech domestic manufacturing with locally sourced materials. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high-priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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

This project will investigate the role of gut microorganisms in controlling human health by focusing on microorganism interactions with the gut sugar polymer mucin. Mucin is a component of gut mucus and is very important for maintaining the integrity of the gut barrier between microorganisms and the human host. The human body needs beneficial microorganisms to help digest food and to develop the immune system, but it must keep both beneficial and pathogenic, or disease-causing, bacteria out of the bloodstream. Previous research indicates that beneficial and pathogenic bacteria secrete products to change the oxidation/reduction state of mucin. Therefore, this work will use miniaturized models of synthetic gut bacterial communities to explore gut modifications by beneficial and pathogenic bacteria in the presence of a collection of bacteria from healthy mice. The approach includes an electrochemical mucin-on-chip model and elimination of pathogens using engineered, beneficial bacteria. This work will enhance the understanding of how human health is dependent on a healthy gut microbial community and identify methods to correct gut communities that are causing disease. This project aims to investigate the uncharacterized role of invasive, pathogenic Escherichia coli (E. coli) strains in the distribution of mucin, a layer of heavily glycosylated proteins essential for maintaining the integrity of the gut barrier, and its involvement in inflammatory bowel diseases (IBD). Recent research indicates that alterations in mucin glycosylation may provide a means to differentiate between Crohn's disease and other forms of IBD. Preliminary results show that pathogenic E. coli reduces mucin while the microbiome acts as an oxidative agent. Building on these results and methodologies, a robust mucin-on-chip model will be used to investigate the role of this bacterium in the gut, both with and without an intact microbiome. Three complimentary aims will be completed to achieve mechanistic insights into mucin-bacteria interactions. Aim 1 will utilize the mucin-on-chip system as an engineered model for biofilm-mucin interactions and their outcomes. Interactions between E. coli and mucin will be identified using a specialized chip system to analyze the redox state of mucin and biofilm characteristics of various E. coli strains, compared with several probiotic strains. Aim 2 will Investigate sequence-based RNA silencing of bacterial antitoxins in mucin-associated biofilms and its impact on the fitness of the mucin and the microbial counterpart. Sequence-based interventions utilizing toxin-antitoxin (TA) systems to control the viability of E. coli before, during and after the colonization of mucin and accurately probe the response of the tissue will be developed. In Aim 3, predictor models for microbiome intervention on mucin will be engineered. To analyze the contribution of E. coli in disturbing mucin homeostasis within a complicated microbiome, the most efficient sequence-based therapies developed above in a three‐dimensional (3D) tissue model will be used. If successful, this project will provide methods for engineering microbial biofilms to treat gastrointestinal (GI) disorders. Educational activities include outreach to middle and high school science teachers to implement age-appropriate teaching materials about the microbiology of the GI tract, and the creation of an undergraduate unit operations laboratory to demonstrate a multi-species GI tract biofilm. 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

High-dimensional time series data arise in many fields such as economics, epidemiology, neuroscience, and social science, where large numbers of measurements are collected over time. These data often exhibit complex patterns, including shifts in behavior and extreme values that violate classical statistical assumptions. This project addresses fundamental challenges in analyzing such time series, especially when they are not stationary and prone to abrupt structural changes. The research in this project aims to develop new methods that are robust to extreme events and better suited to the realities of modern data. By improving the ability to detect and interpret changes in large, evolving systems, this project may be used to support scientific discovery across disciplines. It also provides training opportunities for graduate students, helping build a more data-literate workforce. The project advances the frontiers of science and supports the development of innovative statistical tools that can enhance decision-making in dynamic environments. The research conducted within the scope of this project develops a new tail-robust statistical framework for the analysis of high-dimensional nonstationary time series. The project focuses on two interrelated goals: (1) to construct robust estimators of autocovariance structures that remain accurate in the presence of outliers and large deviations, and (2) to develop efficient procedures to detect and quantify structural changes over time. The investigators plan to address methodological challenges associated with high dimensionality, nonstationarity, and heavy-tailed distributions by integrating techniques from robust statistics, random matrix theory, and change-point analysis. The methods are expected to accommodate piecewise stationary processes with unknown structure changes and offer valid inference in settings where the traditional approaches fail. This work aims to yield powerful data analytic tools for complex time-dependent data and to open new directions in time series modeling, particularly in settings where classical assumptions break down. 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

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Jon Camden at the University of Notre Dame and Lasse Jensen at Pennsylvania State University are combining sophisticated experimental and computational approaches to disentangle the relative contributions of electromagnetic and chemical enhancements in surface-enhanced spectroscopy. While it has been known for half a century that chemical effects play an important role in surface enhanced spectroscopies, a comprehensive understanding of these contributions remains elusive. Furthermore, employing current theoretical methods to predict the magnitude of chemical enhancements can be in error by several orders of magnitude, limiting their utility. Therefore, Professor Camden, Jensen, and their students will employ a non-traditional approach to understanding the chemical enhancement mechanism by combining experimental measurements using nonlinear spectroscopy with newly developed theoretical methods for calculating the nonlinear response properties. Their discoveries could advance the use of surface-enhanced spectroscopy by enabling high-quality predictions of the chemical enhancements and the rational design of molecular systems that maximize the spectroscopic response of molecules at surfaces. This work will additionally support a STEM teacher residency program and tools for visualizing molecular vibrations for the undergraduate chemistry curriculum, which will enable the proposed research to foster the next generation of STEM students. Specifically, this proposal addresses three outstanding fundamental scientific questions and challenges related to the chemical mechanism of surface enhanced spectroscopy. First, a comparison of surface-enhanced Raman scattering (SERS) and surface-enhanced hyper-Raman scattering (SEHRS) spectra of non-resonant probe molecules will be undertaken to address how static chemical effects can modify the overall enhancement factors. Second, a wavelength-scanned measurement of SEHRS spectra for resonant probe molecules will address how resonant chemical effects can modify the overall enhancement factors. Third, the first experimental measurements and theoretical calculations of non-degenerate SEHRS will be made to establish a benchmark and further characterize resonance effects in the enhancement mechanism. The experimental measurements will be complimented by the development of new computational approaches to model and interpret SEHRS. The combined experimental and theoretical studies will provide detailed insights into the chemical mechanism and serve as a comprehensive benchmark of the theoretical models. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Schaak of Penn State University will establish new ways of making nanoparticles that contain five or more metallic elements. Nanoparticles with such complex compositions are anticipated to have unique properties compared to those containing a smaller number of metals. These properties could be useful for applications that include catalysis and electronics. In this research project, Prof. Schaak and his research group aim to identity and understand how various combinations of chemical reagents interact and react to form compositionally complex nanoparticles. Using the knowledge gained from these studies, they will learn how to control nanoparticle composition, structure, shape, and size, which are features that can influence properties. Schaak and his group will also develop a series of nanochemistry-focused tutorials on compositionally complex nanoparticles and introduce automated synthesis and AI-driven exploration of chemical complexity into undergraduate classes. The complex compositions of high entropy materials, which incorporate five or more principal elements, can give rise to synergistic chemical interactions that modify electronic structure and reactivity. Nanoparticles of these materials are especially promising for catalysis, given their high surface areas, but synthesizing them can be challenging because of the different reactivities and reaction rates of the various reagents. Schaak and his group will dynamically modulate reagent delivery protocols to balance reactivities and to gain mechanistic insights into how high entropy nanoparticles form. They will also investigate the role of chemical synergy in high entropy nanoparticle formation and leverage chemical reactivity to synthesize new high entropy nanoparticles with anticipated synergistic properties. This knowledge is expected to enable simultaneous synthetic control over composition, structure, shape, and size for a broad range of high entropy nanoparticle systems. 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

Human bipedal walking underlies numerous aspects of our ecology and behavior. Knowledge of foot anatomy and function is essential because the foot interfaces directly with the ground during walking or running, and it is important to understand how variation in foot anatomy may impact biomechanics. This research advances current understandings of hominin bipedalism by integrating anatomic and kinematic data and developing AI-driven 3D computational foot models for apes and humans. The study provides funding and training for students and includes outreach activities directed to the public. Results from this study aid in the development of new translational methods for investigating anatomical variation associated with foot pathology (e.g., flat feet) and inform about the effects of footwear on foot muscle use and soft tissue disorders (e.g., plantar fasciitis). This study applies an integrative experimental modeling-simulation approach. The study conducts direct comparisons regarding the intrinsic foot biomechanics of human and ape, informing and comparing their musculoskeletal design. The study: (1) conducts loading experiments on human and ape feet; (2) builds musculoskeletal models of human and ape feet; (3) integrates experimental data with these models to calculate intrinsic foot dynamics, and; (4) performs dynamic simulations to link intrinsic foot musculoskeletal structure to walking kinematics, kinetics, muscle activation, and metabolic cost. Advanced AI-supported computational modeling techniques are applied to test long-held ideas regarding the form-function relationships in hominin feet. The study informs interpretations of the hominin fossil record, yields soft tissue and mechanical property data, and generates detailed musculoskeletal models that inform translational 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 · 2025-08

This project belongs to the field of dynamical systems, which is the mathematical study of processes that evolve over time according to fixed rules. These processes often exhibit complicated and chaotic behavior yet display underlying patterns that can be described in terms of stationary measures, that is, certain probabilistic objects that remain stable as the underlying dynamics evolve. The focus here is on stationary measures that arise in nonlinear systems, with particular attention to their geometric and analytic structure. The goal is to understand when such measures are absolutely continuous, when their Fourier transform decays, and how these properties relate to the dynamics that generate them. The work will bring together researchers based in the United States and Israel and will involve the training of graduate students and postdoctoral fellows. A particular emphasis will be placed on maintaining strong collaborative ties between research groups working in dynamics, geometry, and analysis. The main technical goal of this project is to study the regularity and dimension of stationary measures arising from nonlinear actions, such as self-conformal systems and random matrix products. When the maps involved are real analytic and satisfy appropriate separation properties, one expects to be able to compute their dimension in simple terms such as entropy and Lyapunov exponents, and to determine whether they are absolutely continuous. The project aims to establish these properties by studying the behavior of the system under repeated iteration and by using tools that reveal how randomness and geometry interact at different scales. The project will develop via methods from hyperbolic dynamics, harmonic analysis, homogeneous dynamics, spectral theory of transfer operators, and additive combinatorics. These include the use of appropriate disintegrations of measures, spectral gaps for transfer operators under appropriate assumptions, and comparisons between different criteria for separation. The broader aim is to clarify how non-linearity results in stationary measures enjoying rich multiscale structures, which in turn governs their analytic and geometric properties, and to use this understanding to characterize rigidity and regularity phenomena in these systems. 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

Cirrus and other high-altitude clouds are made of ice crystals that form at extremely cold temperatures. The crystals form and grow from water vapor, but the rate of growth and the different shapes that are formed are difficult to observe in nature. This award will allow researchers to control ice crystal growth in a laboratory setting, resulting in new knowledge about the role of changes in ice crystals due to environmental changes in temperature and humidity and impurities in the crystals. The primary societal impact of this research is through new data that can be used to improve numerical weather modeling. Additionally, there are training activities for students, new educational materials, and public outreach components of the project. This award will address questions that have arisen from prior research on ice crystal growth at temperatures colder than -20°C. The research team will use several laboratory chambers to make time-series measurements of growing crystals. A diffusion chamber and one vertical flow chamber will be used to grow isolated ice particles under precisely controlled conditions. The chambers will be used to determine the growth rates, and poorly known surface parameters, of crystals grown at temperatures between -20 to -70°C at atmospherically relevant supersaturations. The measured growth time-series will be used to critique and improve theories of ice growth, and improved theories will be used in cloud model parameterizations to explore the atmospheric consequences of laboratory-determined quantities. In particular, the research team will focus on questions related to: 1) The influence of solutions and heterogeneous nuclei on the growth of crystals, 2) Substrate crystal growth in a constant environment, and 3) Substrate crystal growth in a changing environment. 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

The Super Dual Auroral Radar Network, or SuperDARN, is an international collaborative experiment for observations of plasma motions in Earth’s upper atmosphere. By observing ionospheric plasma motions, a multitude of geophysical processes are being studied. These processes range from fundamental plasma instabilities to the global-scale plasma response to changes in the solar-terrestrial environment. Each of these areas of study contributes to developing an understanding of the coupling of energy from the Sun into Earth’s upper atmosphere and its effects on humanity and technological systems. This project will support operations and maintenance of the U.S. SuperDARN radars in the northern hemisphere by the consortium of Penn State University, Virginia Tech, Dartmouth College, and the Johns Hopkins University Applied Physics Laboratory. The collaboration operates twelve radars that cover a vast region from Alaska to Iceland at high latitudes, and Oregon to Virginia at middle latitudes. In addition to operation and maintenance activities, the project will support a program of research that exploits new capabilities that have been developed over the last several years. This includes providing improved fidelity in measurements (plasma convection mapping and imaging), extending the area over which measurements are obtained (bistatic observations), and providing new types of measurements (sounding). SuperDARN has a long-standing commitment to including graduate students in all aspects of the program. The SuperDARN observations are also important for space weather applications since HF radio propagation is sensitive to perturbations in the bottomside ionosphere, e.g., solar flares. 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 award provides support to U.S. researchers participating in a project competitively selected by a 55- country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. BEDMAC seeks to develop a multidisciplinary approach to understand ways a well-designed and operated built environment can mitigate the risk of malaria transmission. Using comparative case studies from the participating regions, the project team will derive research insights and address transdisciplinary methodological gaps at the intersection of the built environment and malaria’s impacts on human health. Specifically, the project will develop a novel transdisciplinary methodology drawing from convergent approaches in global comparative research to enhance our understanding of common drivers of malaria and context-specific processes that can help deliver a malaria-smart building sector. 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

Mauna Loa Volcano in Hawai’i is one of the largest active volcanoes on Earth. The volcano showed clear signs of unrest from 2014, leading up to its November 2022 eruption. The long duration of activity gives scientists a chance to study the causes of earthquakes and ground movement in a complex volcano that has multiple magma chambers and rift zones. Researchers will use satellite data (InSAR) to detect ground changes linked to volcanic unrest. This research will use studies of earthquake patterns and other seismic data to help scientists understand the causes of volcano deformation. In this work, scientists will learn about the warning signs before major eruptions on Mauna Loa’s flanks that can endanger many people. Researchers will also mentor young scientists and produce a course activity about volcano monitoring. Since 2014, Mauna Loa Volcano has been closely monitored using dense Interferometric Synthetic Aperture Radar (InSAR) geodetic and seismic datasets, leading up to its November 2022 eruption. Researchers will use this rich dataset to study ground deformation patterns and identify short-term deformation events. They will develop and apply an advanced deep learning method—based on a convolutional neural network. This approach will help separate real volcanic deformation signals from noise caused by the atmosphere in the InSAR data. The method will account for random and layered atmospheric effects and will be tested against GNSS ground-based data to ensure accuracy. Next, the team will link the deformation signals to magma movement beneath the surface, which eventually caused the eruption. This will be done using a complementary seismic analysis of earthquake locations, migration patterns, and fault-plane solutions. Special attention will be given to the interactions between shallow magma reservoirs and fault systems beneath the summit caldera. A Coulomb stress change analysis will help test the hypothesis that earthquake activity near the volcano is influenced by how the shallow magma system is inflating or deflating. The project will provide research opportunities and support for a postdoctoral scholar at Penn State, as well as an undergraduate honor student, who will contribute to InSAR data processing and the creation of a new catalog of volcano-tectonic earthquakes. This work will be done in collaboration with the USGS Hawaiian Volcano Observatory (HVO), including on-site training and seismic analysis guidance. Findings from this project will help HVO improve its volcano monitoring and eruption forecasting capabilities. More broadly, the results have important implications for understanding and forecasting eruptions at basaltic volcanoes with complex rift systems around the world. One of the project’s key outcomes will be the creation of a new deep learning training dataset and denoising approach, with the goal of improving the accuracy of InSAR measurements worldwide. These tools, including algorithm codes, will be made publicly available through Penn State’s ScholarSphere repository. Additionally, a new online learning activity will be developed for GEOSC 30: Volcanoes!, a general education course at Penn State. The activity will focus on analyzing volcanic unrest signals, especially GNSS data before, during, and after the 2022 Mauna Loa eruption. It will also include hazard assessment through lava flow mapping using satellite imagery. 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

The lowest level of the atmosphere, known as the boundary layer, is critical to understanding and predicting the weather. During the daytime when the sun heats the Earth’s surface, the resulting rising motions mix the atmosphere, and the boundary layer deepens and becomes what is known as the convective boundary layer (CBL). Information about the depth of the CBL and the entrainment zone (EZ) at the top of the CBL can provide significant insight into atmospheric processes and how they are handled in numerical models. This project will use a newly developed technique to investigate the CBL and EZ using the US national weather radar system. The resulting data will inform and influence weather model development and the project includes a significant educational component. This award will provide funding to expand the creation of radar-derived CBL and EZ data to many more sites and to analyze that data to address unknowns related to how wind shear and atmospheric stability impact entrainment in the CBL. CBL depth and EZ depth will be derived at sites across the US from data produced by the WSR-88D radar network. These radars can measure turbulent mixing where air parcels of different densities meet. By using dual-polarization differential reflectivity measurements and the quasi-vertical profile (QVP) technique, the researchers can produce a time series of the height of the top of the CBL and the depth of the EZ. This data will then be compared to profiles of the atmosphere, primarily from weather balloons, to verify the results and analyze the data to find the best atmospheric predictors of CBL and EZ properties. This data can then be compared to numerical models to inform the development of boundary layer parameterization schemes. 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

The Pennsylvania State University’s College of Information Sciences and Technology proposes a cybersecurity education and training initiative that serves a critical national need, preparing the next generation of skilled cybersecurity professionals to protect the United States against digital threats. As cyberattacks grow more frequent and complex, both public and private sectors face an urgent shortage of qualified cybersecurity personnel. This project will recruit and support a diverse cohort of undergraduate and graduate students to receive specialized training in cybersecurity, with a particular emphasis on artificial intelligence (AI)-infused skills. Through scholarships, mentoring, and hands-on learning, the program equips students with cutting-edge technical knowledge and professional development opportunities to ensure they are ready for careers in government service. By placing 100% of its graduates into cybersecurity roles, the initiative directly contributes to national security, supports economic stability, and promotes the progress of science and technology. It also aligns with NSF’s mission by advancing education, increasing participation in the cyber workforce, and strengthening the nation's capacity to respond to emerging cyber threats. The College of Information Sciences and Technology at The Pennsylvania State University, designated as a National Center of Academic Excellence in Cyber Defense (CAE-CD) since 2003, proposes a focused cybersecurity education program aimed at recruiting, training, and placing at least nine students (seven undergraduates and two graduate students) into cybersecurity roles with federal, state, local or tribal governments. This project emphasizes the integration of AI into the cybersecurity curriculum, thereby advancing a key area of strategic importance in both national defense and cybersecurity research. The program includes full scholarships of two years, structured mentoring, and career development activities to ensure successful placement of all participants into qualifying government positions upon graduation. The curriculum will incorporate hands-on learning, interdisciplinary research opportunities, and targeted skill development in cyber-relevant fields. Participating students will also take at least four AI courses to obtain AI-infused cybersecurity skills. This project is supported by the CyberCorps® Scholarship for Service (SFS) program in the Division of Graduate Education, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal government organization for the same duration as their scholarship support. 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

DNA in chromosomes of human and other eukaryotic cells is packed into open and active euchromatin or repressed and condensed heterochromatin. Packing into these alternative chromatin states governs DNA accessibility and expression of the genetic code without altering the sequence, akin to obtaining information from open but not closed pages of a book. Resolving the structures and mechanisms underpinning euchromatin and heterochromatin formation is crucial for understanding fundamental biological processes underlying gene expression, cell differentiation, chromosomal stability, and genetic abnormalities. This project will investigate 3-dimensional (3D) structures and mechanisms that direct native chromosome folding into euchromatin and heterochromatin and thus find new ways of regulating chromosome accessibility and gene expression without altering DNA sequences. The research will be integrated with postdoctoral training and graduate and undergraduate education and provide new opportunities to inspire student enthusiasm for scientific discoveries and careers in STEM. The goal of this project is to reveal the 3D structural motifs and molecular mechanism by which the natural nucleosome spacing distribution directs the differential chromatin folding underlying genomic euchromatin and heterochromatin states and associated genetic functions. A recent breakthrough in understanding native chromatin folding was achieved by applying cryo-electron tomography (Cryo-ET) that challenged established models of regular chromatin higher-order folding. Three specific aims will employ Cryo-ET to accomplish the overall goal: Aim 1 is to determine 3D chromatin structures and associated linker DNA length distributions in isolated human eu- and heterochromatin fractions; Aim 2 is to determine nucleosome array folding paths and compaction associated with eu- and heterochromatin compartments in mouse cells cryo-vitrified in situ; Aim 3 is to generate reconstituted nucleosome arrays reproducing native nucleosome spacing diversity and the eu/heterochromatin structural dichotomy for Cryo-ET studies. This work is expected to uncover fundamental mechanism(s) governing higher-order folding of eukaryotic chromatin, and provide new native-like chromatin models, imaging tools, and datasets, with which specific structural motifs and nucleosome interactions mediating native chromatin accessibility for transcription and other DNA-dependent functions can be readily visualized in 3D and reproduced in chromatin computational models. 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 how plants interact with microbes and insects is critical for conserving ecosystems and promoting sustainable agriculture. These interactions form complex networks that shape the stability of ecological communities and support valuable ecosystem services, such as biological pest control. This research investigates how plant chemistry and leaf-associated microbes influence the behavior and population dynamics of herbivores and their predators in both natural and agricultural settings. The results will inform strategies to reduce herbivore damage, protect biodiversity, and strengthen biological control, an ecosystem service valued at $14 billion annually in the U.S. These findings can inform efforts to enhance ecosystem resilience and sustainability across both natural and agricultural landscapes. Additionally, this project will provide students with opportunities to develop critical scientific skills, gain hands-on experience, and explore careers in ecological research and applied science, supporting the scientific training of a workforce equipped to address pressing societal challenges. Plant–arthropod systems comprise over 90% of Earth’s macroscopic biomass, highlighting the critical need to understand how plant defenses shape ecological communities. As major drivers of herbivore population dynamics, plant defenses also play a pivotal role in regulating the strength of interactions between herbivores and their natural enemies. However, their broader effects on multi-level ecological interactions remain underexplored. Additionally, plant-associated microbial communities and their metabolic products (microbiomes) can significantly impact plant-arthropod interactions and community dynamics. This research employs a cross-disciplinary approach to determine the interactive effects of plant chemistry and microbiome on biodiversity, food web structure, and ecosystem stability. Specifically, it will (i) investigate how plant defenses and microbial communities influence trophic cascades and the structure of plant-microbe-arthropod networks, ii) characterize how plant defenses shape microbiome assembly, and (ii) experimentally dissect the contributions of plant chemistry and microbiota to carnivorous arthropod preference, behavior, and performance. The project also integrates a strong educational component by providing hands-on, field-based research opportunities for high school, undergraduate, and graduate students. 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

Large Language Models (LLMs) such as ChatGPT, Llama, Claude, and Gemini and their empowered applications (such as retrieval-augmented generation systems and autonomous agents) have been widely integrated into advanced cyberinfrastructure (CI) systems to enhance data management, collaboration, and scientific discovery by assisting with tasks such as large-scale text analysis, automated data classification, knowledge extraction, and domain-specific question answering. However, many research studies have shown that LLMs and their applications are vulnerable to various attacks, such as jailbreak, prompt injection, knowledge corruption, data poisoning, and privacy attacks. These attacks pose significant concerns for integrating LLMs into CI systems, as well as broad applications in security- and privacy-critical domains such as healthcare, finance, and law. Despite various research studies that have identified the cybersecurity risks associated with LLMs, there remains a huge training gap among many stakeholders. This gap stems from two factors: emphasis on utility and efficiency over security, and lack of expertise in LLM security. This training gap is particularly concerning as CI systems increasingly rely on LLMs for critical decision-making, code generation, and sensitive data analysis, which potentially exposes them to sophisticated cyber threats, especially for security-critical CI systems. This project aims to bridge this training gap. This project will develop a CyberTraining program to train undergraduate and graduate students across the nation to identify, analyze, and mitigate different attack vectors targeting LLM-empowered advanced CI systems. The program is centered on eight core training modules, which serve as its foundational framework. Based on these modules, a series of sustainable training activities are developed to prepare, nurture, and grow the workforce for supporting the development of LLM-empowered advanced CI systems. The training activities include 1) Hands-on exercises through an interactive learning platform that helps students gain practical experience in LLM security; 2) A two week onsite summer bootcamp designed to foster deeper engagement with faculty and industry mentors during the training modules while promoting professional development; and (3) Degree and curriculum development that selectively incorporates training modules into courses related to AI and Cybersecurity. The training modules and materials developed in this project will also be made publicly available for broad adoption. 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 envisions a prosperous and secure Arctic region focusing on Alaska that can build, maintain, and operate resilient and sustainable coastal and interior civil infrastructure and can adapt to the dynamic marine and terrestrial environmental changes. This vision will be achieved by engaging with Alaskan communities, industry, and local-to-federal government entities, thereby building a pipeline for workforce development of future scientists, engineers, and skilled workers with expertise in Arctic environments. The team will collaborate with the North Slope Borough and the communities in Seward Peninsula to co-develop and implement the solutions to emerging challenges, notably coastal and riverine erosion in the Arctic coastal communities, infrastructure failures induced by permafrost degradation, and flooding. The resilience solutions and technologies, from ideation to implementation, will be co-developed through close collaborations with partners of Indigenous communities, industry, local to federal government, and six academic institutions. The impacts include improved well-being and resilience of individuals and communities in the U.S. Arctic, increased economic competitiveness of the U.S., improved national security, and increased public scientific literacy and public engagement with science and technology. The project will generate new understanding of how the Earth system (including the northern and northwestern Alaska region, permafrost, and coast-land interface) changes, and its interactions with the built and sociocultural systems, thus building the foundational knowledge base to develop solutions to emerging problems. At the end of Phase-1, the project will (1) identify and specify the solutions needed to address the U.S. Arctic challenges from permafrost degradation, erosion, and flooding, (2) identify data gaps and devise approaches to collect new data for the technology development, (3) define specific requirements for the technologies and solutions, and (4) identify application sites for the technologies and solutions and collaborating partners. Project costs and feasibility in translation of research to solutions will be demonstrated by conducting techno-economic analysis on enabling technologies and system-level solutions. 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

Daily movements – from catching a ball to staying balanced while opening a heavy door – showcase the brain's remarkable ability to control movement and at the same time maintain stability. While these actions appear simple, they involve sophisticated interactions among different sensory systems, including vision, proprioception (muscle sense), and balance. This research project aims to understand how the brain coordinates these sensory systems to enable goal-oriented movement and stability. Using innovative virtual reality and robotic technology, the research team studies how humans control their movements and balance when interacting with moving objects. The team will develop computer models simulating how different sensory systems work together during these interactions. This knowledge is crucial for improving human-robot interactions in environments that require physical collaboration. The project includes educational outreach through summer camps teaching middle school students about the brain and movement, emphasizing how neurological conditions affect balance and movement coordination. Camps for high school students include exploration of educational and career opportunities at the intersection of neuroscience, movement science, and robotics. While the individual sensory processing pathways are well characterized, the mechanisms by which the brain integrates multiple sensory signals to produce complex actions, such as intercepting a moving ball, remain poorly understood. The project aims to elucidate how the nervous system processes visual motion signals to modulate anticipatory postural adjustments and compensatory postural adjustments during interactions with moving objects. The research tests two theoretical frameworks: the feedback error learning model, which proposes that skill acquisition occurs through iterative updating of internal models, and the hierarchical sensory predictive control model, which posits that internal models update intersensory mappings (between vision, proprioception, and vestibular sensation) to regulate motor responses. The experimental paradigm employs a novel virtual reality and robotic system that allows precise control of object motion and contact forces. Participants interact with moving virtual objects while researchers measure smooth pursuit eye movements, muscle activation patterns, and limb dynamics. The experimental design systematically manipulates visual tracking conditions and object motion parameters to investigate how different sensory inputs contribute to motor learning. Data analysis combines traditional motor control measures with computational modeling approaches. This research advances our fundamental understanding of how the brain controls movement while generating insights relevant to human-robot interaction and rehabilitation medicine. 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

The goal of this project is to create simulated walking data that can be used together with real walking data to train machine learning tools. The simulated data this project is creating is called synthetic data. Once this data is created, machine learning tools could be used to identify patterns in how a person walks to predict how likely someone is to fall. This information could be used to help people get an appropriate intervention to prevent falls. Modern machine learning methods require a huge amount of data in order for them to work, so synthetic data can help make more powerful machine learning tools because collecting enough real walking data is too expensive and time consuming. This is one of the first projects to try to create synthetic walking data. Synthesizing gait data is challenging due to the complexity of the physical system, the sequential nature of the data, and the high variation in gait patterns across individuals and in different physical scenarios. Many of the existing synthetic data generation methods used in other fields do not guarantee that the generated data is physically possible, as existing methods do not allow for the physical constraints of realistic gait. They also generally assume independence of the observed data being synthesized instead of recognizing the sequential, time series nature of gait data. To address this gap, this study will develop and evaluate multiple data generation techniques including various methods which will use a central pattern generator (CPG)-based model in conjunction with a linear or nonlinear higher-order controller to ensure physical feasibility, as well as methods that use nonlinear data manipulation or nonparametric models custom built for gait time series. To create the models used to synthesize gait data, existing experimental data that has a wide range of step types to ensure that the proposed methods can capture the full richness of human gait will be used. The end result will be a suite of methods for synthesizing gait data, and a formal evaluation and comparison of these methods. Synthesizing realistic gait data has the potential to be revolutionary as it will enable the use of data-hungry modern nonparametric methods, which could lead to quantum leaps in our ability to predict fall risk. 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

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.