University of Alabama in Huntsville

NSF Awards · FY 2026 · 2026-08

Metastasis is the spread of tumor cells through blood or the lymph system to form new tumors in otherwise healthy organs. Circulating tumor cells (CTCs) in the bloodstream often form clusters of CTCs. Some evidence suggests that when CTCs form clusters, they become more resistant to the body’s immune responses. However, other evidence suggests that cluster formation does not significantly affect metastasis. This CAREER project will use computer modeling and artificial intelligence (AI) to examine CTC clusters in the circulatory system. Simulations will describe how CTC clusters move through the bloodstream, how they respond to challenges from the immune system, and how they differ in different types of cancer. AI will be used to analyze large data sets, find patterns, and predict the behavior of CTC clusters. The models will be integrated into user-friendly, open-source software. The project will develop interactive K-12 workshops that teach students about the structure of cells and, for advanced students, how to model them. Research findings will be integrated into courses at the University of Alabama in Huntsville. The project outcomes may lead to better ways to diagnose and treat cancer, especially by targeting CTC clusters. This CAREER project will provide insight into outstanding questions on CTC clusters from their entry to the circulatory system to their exit at a secondary location. The project will use a three-component computational modeling framework built on a comprehensive and continuously updated set of experimental data. The first component of the framework will be a new model of CTC cluster dynamics that combines mechanistic modeling of the clusters with environmental cues, interactions with other cells, intercellular communication, and the physiological state of clustered CTCs. The second component will result in a detailed kinetic model of CTC cluster metabolism that integrates multiple experimental datasets through advanced statistical methods, machine learning, and information theory. The third component will be a combination of the first two, leading to a new platform capable of executing realistic “what-if” scenarios. The framework will be used to examine CTC cluster formation and stability, understand how CTC clusters react to challenges encountered in the circulatory system, and compare the characteristics of CTC clusters from different primary cancers during that stage of their migration. Advanced machine learning techniques will be used to identify unknown parameters and missing components in kinetic models of cellular metabolism. Together, these efforts will direct experimental research, support the development of new therapeutic and diagnostic strategies, and pave the way toward computational predictions of metastasis and its targets from the state of primary cancer cells. 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

Understanding how metals deform under varying temperatures and loading conditions is essential to advancing the engineering of materials used in critical infrastructure, transportation, and energy systems. One important phenomenon influencing the reliability of structural materials is dynamic strain aging, which arises from interactions between moving crystal defects and diffusing atoms, often leading to unstable deformation and reduced ductility. Despite extensive prior research, the mechanisms governing dynamic strain aging remain incompletely understood, particularly across materials with different crystal structures. This Engineering Research Initiation (ERI) project seeks to establish a fundamental understanding of how crystal structure affects dynamic strain aging and the resulting mechanical response. The research will advance the engineering design of materials with improved reliability and performance in demanding environments. In addition to advancing fundamental knowledge, the project will contribute to national priorities by enabling safer and more efficient engineering systems. It will also support the education and training of undergraduate and graduate students in mechanics of materials, integrating computational modeling with experimental analysis, and will include outreach activities in science and engineering. The objective of this project is to develop a mechanism-based constitutive modeling framework to describe deformation instabilities associated with dynamic strain aging in metallic systems with different crystal structures. The research will combine experimental data analysis, atomistic simulations, and continuum modeling to quantify key physical parameters, including solute diffusion rates, dislocation–solute interaction energies, and temperature-dependent dislocation mobility. The framework will explicitly account for differences in dislocation behavior across crystal structures, such as edge-dominated motion in face-centered cubic systems and temperature-dependent screw dislocation motion in body-centered cubic systems. Model predictions will be calibrated and validated against mechanical testing data across a range of temperatures and strain rates, including measurements of strain rate sensitivity and deformation instabilities. By establishing a unified and predictive modeling framework, this work will provide new insights into the role of crystal structure in dynamic strain aging and support the engineering of more reliable metallic materials for advanced applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Understanding how populations differ - and how new species arise - is a central challenge in biology. While genetic and environmental influences on evolution are well studied, a key missing link is how animal behavior connects these processes. Behavior shapes how organisms move, interact, and respond to their environment, yet it is rarely studied in ways that link individual actions to large-scale patterns such as population connectivity and diversification. This project addresses that gap by examining how differences in behavior among individuals and populations contribute to variation in movement, how environmental conditions help maintain biological diversity within populations, and how interactions among individuals influence behavioral traits and their evolution. Because behavioral variation is widespread across organisms, the findings will be broadly relevant to processes such as species invasions, disease spread, and responses to environmental change. This project also contributes to workforce development by providing hands-on research opportunities for undergraduate and graduate students, creating new inquiry-based learning experiences in university courses, and developing adaptable teaching materials for K–12 educators. Outreach efforts will engage broader audiences through community partnerships and accessible science communication and provide critical information to managers and citizens world-wide about a species that is invasive in many parts of the world. This project investigates how behavioral variation contributes to ecological and evolutionary processes by integrating behavioral measurements, environmental data, and population genomic analyses across natural populations. The central hypothesis is that environmental variation shapes behavioral traits and movement tendencies, that these behaviors influence patterns of genetic connectivity, and that both environmental and social contexts contribute to the maintenance and evolution of phenotypic variation. The research is organized around three objectives. First, it will quantify variation and co-variation in multiple behavioral traits within and among populations and test how these traits predict movement tendencies and genetic connectivity. Second, it will evaluate how environmental factors contribute to the maintenance of discrete phenotypic variation in natural populations, providing insight into mechanisms that preserve genetic diversity under selection. Third, it will examine how social interactions among individuals influence the expression of behavioral traits and their evolutionary dynamics, including effects on trait correlations and population-level patterns. To address these objectives, the project will combine standardized behavioral assays, controlled measurements of movement, and field-based characterization of environmental conditions across multiple populations. Environmental data will include both traditional field measurements and emerging approaches such as environmental DNA (eDNA) to characterize ecological communities and variation in biotic interactions. Population genomic data will be generated to estimate genetic structure, gene flow, and demographic history. By integrating behavioral ecology, environmental variation, and genomics within a unified framework, this project will provide generalizable insight into how behavior contributes to the generation, maintenance, and evolution of biological diversity. 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 · 2026-04

Renewable fuels for power generation and transportation can broaden the U.S. energy portfolio. However, renewable fuels produce a relatively low amount of energy on a volumetric basis compared with traditional fossil fuels. Combustion is typically carried out using a subsonic process called deflagration. An alternative is detonation, which is a supersonic process. The combustion efficiency of renewable fuels can be increased using detonation, especially with additives that enhance the process. This CAREER project will conduct experiments to determine whether ammonia-based additives enhance detonation of renewable fuels. The research team will use x-ray diagnostics to examine details of fuel atomization and subsequent detonation. They will study how detonation is influenced by adding ammonia borane to the fuel to increase overall efficiency. Results of the project will help establish how renewable fuels can contribute to U.S. energy self-sufficiency. The team will collaborate with the U.S. Space and Rocket Center in Huntsville, AL to provide STEM education to rural K-12 communities and the public. The team will also integrate interactive demonstrations into the University of Alabama in Huntsville laboratory curriculum. This CAREER project will uncover underlying physics that are key to controlling multiphase detonation using additive-enriched renewable fuels to affect secondary atomization and vaporization. Interactions of individual droplets and droplet ensembles with a traveling detonation will be investigated for renewable fuels (e.g., ethanol and methanol) with varying concentrations of soluble ammonia borane. The study will evaluate whether secondary atomization and vaporization can be altered through the addition of ammonia borane to renewable fuels to enhance detonation performance. Specific contributions include (1) quantifying rate-limiting timescales associated with multiphase detonations for both pure and additive-enriched renewable fuels to establish high-strength detonation control techniques; (2) characterizing droplet-detonation interactions to determine breakup topologies and the energy-release field; and (3) exploring droplet ensemble-detonation interactions to uncover the global impact of tailored secondary atomization and vaporization on the detonation propagation and structure. Droplet breakup processes will be captured using high-speed x-ray imaging, while the detonation energy release field provided using high-speed chemiluminescence; simultaneous high-speed Schlieren will be used for visualization of the detonation wave structure. Ultimately, the physical insight of the governing physics for multiphase detonations garnered from this research will enable the development of potential detonation control approaches using renewable fuels. 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 · 2026-02

Warm, rain-bearing clouds play a vital role in the global water and energy balances that help regulate Earth's weather system. Predicting the effects of cumulus clouds (low level clouds with a puffy cotton-like appearance) on weather requires precise quantification of water droplet sizes and their growth rates. Current cloud models are limited by an inability to predict droplet growth rates and measured droplet size distributions (DSDs). This award will combine an accurate numerical model of cloud dynamics with theoretical models for predicting the growth of water droplets into rain drops in tropical maritime clouds. The project will train undergraduate and graduate students in interdisciplinary research. Outreach activities to high school and undergraduate students will be conducted. This award is uniquely positioned to advance cloud physics research on Earth and other planets. Elucidating the physics of droplet growth in the 15–40 micrometer “size-gap” range is essential for accurately predicting the DSDs of droplets and the formation of rain-size drops in warm clouds. Drops grow by processes spanning a wide range of scales from the turbulence-induced fluctuations in cloud thermodynamic properties to the continuum and non-continuum hydrodynamic interactions between droplets. Capturing this physics requires the combination of state-of-the-art large-eddy simulations (LESs) and precise theoretical models for the droplet-scale hydrodynamics including continuum breakdown on close approach. An integrated LES-theory cloud model will be developed to achieve transformative insights into the interactions between large-scale processes such as turbulent fluctuations in supersaturation and temperature and small-scale processes including non-continuum hydrodynamic, van der Waals and electrostatic forces. Varying macroscopic conditions such as the cloud height, temperature and pressure at the cloud base, cloud-boundary fluxes of heat and water vapor, and nuclei concentration while monitoring the resulting changes in turbulent dissipation rate, supersaturation fluctuations, and drop collision rates will reveal the origins of the DSD evolution. 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

Unmanned Aerial Vehicles (UAVs) are becoming increasingly vital for applications such as disaster response, environmental monitoring, infrastructure inspection, and cybersecurity. These airborne platforms can collect diverse types of data in real time, offering valuable input for training high-performance machine learning models. However, conventional machine learning techniques often rely on centralized training paradigms that require transmitting all raw data from UAVs to centralized servers: an approach that is often impractical due to privacy concerns, limited bandwidth, and latency constraints. To address these challenges, this project introduces a novel distributed learning framework based on federated learning, which enables UAVs to collaboratively train models while keeping their raw data local. In particular, the project’s novelties are centered on enabling multi-modal federated learning across UAV networks, where each UAV may observe a distinct combination of data modalities/types (e.g., imagery, environmental readings, or network traffic). This data modality imbalance introduces new challenges in learning coordination, model convergence, and system-level optimization. To this end, the proposed research develops a unified approach that addresses modality imbalance, adversarial threats, and system-level heterogeneity in computation, communication, and storage. The project's broader significance and importance will thus lie in advancing the resilience and reliability of distributed AI systems in airborne platforms, particularly in time-sensitive, resource-constrained, and adversarial environments. Also, beyond its technical impact, the project supports national workforce development by integrating its findings into university curricula, hosting public workshops and seminars, and providing hands-on research opportunities for undergraduate and graduate students in UAV-based sensing and communication, multi-modal federated learning, and cyber-physical system security. This project pioneers the paradigm of secure multi-modal federated learning over UAV networks. First, it establishes mechanisms to mitigate modality-level heterogeneity in multi-modal federated learning, where UAVs possess different combinations of data types, by tuning local learning rates, optimizing modality scheduling, and designing convergence-aware local model gradient adjustment techniques. Along this direction, the project introduces adaptive resource allocation strategies that account for the storage, computational, and communication disparities across UAVs, including energy-aware multi-modal learning schedules, data migration protocols for non-private data, and fine-grained modality-aware batch size selection. Second, the project introduces modality-aware differential privacy and robust model aggregation schemes to defend UAV-enabled multi-modal federated learning against privacy leakage and model poisoning, incorporating wavelet-based noise calibration and modality-aware attack detection scores. Third, the project proposes jamming-resilient UAV-enabled multi-modal federated learning approaches through channel hopping and trajectory design strategies that respond to active jamming and passive eavesdropping threats by leveraging UAV mobility and spectral awareness. These three research thrusts collectively form the pillars of Secure, Heterogeneous, Intelligent, Efficient, and Learning-driven Design (SHIELD) framework for UAV-based federated learning. The research is validated through simulations, a real-world UAV testbed, and collaboration with national labs. This project will enable safe and efficient deployment of distributed multi-modal intelligence over UAV systems, enhancing national capabilities in security, disaster response, and autonomous sensing and surveillance. 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

The University of Alabama in Huntsville will organize and host a conference entitled “the 4th Time Domain and Multi-Messenger Astrophysics (TDAMM) Workshop,” October 27-30, 2025. The workshop, the fourth in a series, will focus on developing community observing plans to enable the rapid and coordinated follow-up of explosive transients with NSF affiliated ground-based observatories, along with NASA's space-based assets. The workshop will bring together scientists from diverse fields of astrophysics, including electromagnetic observations, gravitational waves, neutrinos, and cosmic rays. The final product of the workshop will be a publicly available report with findings for the National Science Foundation. The conference will specifically target early career scientists. The key goals of the conference are to foster multidisciplinary studies. Individuals who work in this area are among the most useful for society, both within and external to academia. Thus, this meeting and the specific deliverables will help to train a globally competitive STEM workforce. The conference will be organized with these guiding questions in mind: 1) How does the community enable and coordinate rapid follow-up of explosive transients by space- based and ground-based observatories? 2) How can we leverage existing and planned facilities to perform key measurements that answer open questions of TDAMM science. Participants will collaborate to help define actionable strategies to ensure NSF and NASA observatories can maximize scientific return by coordinating observations and reducing redundancy during the discovery of rare and compelling transient and multi- messenger events. The workshop aims to foster consensus on these community observing plans, which will be considered for adoption by NSF affiliated facilities. The discussions will also emphasize the integration of current observational networks, the prioritization of follow-up targets, and the efficient dissemination of data and analysis products to the broader astrophysical community. The meeting will help ensure the greatest scientific return of major NSF facilities including the Vera Rubin Observatory, LIGO, IceCube, their upgrades, and their facilities across the electromagnetic spectrum including the ngVLA. 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

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals, and certain legacy PFAS may pose health and environmental risks due to the strong chemical bonds. This project addresses the challenge of PFAS removal and conversion by integrating expertise in materials science, separations, reaction engineering, electrochemistry, process systems, multiscale modeling, artificial intelligence, and social science. Spanning Delaware, Alabama, and South Carolina, the project aims to build regional research capacity and infrastructure to support PFAS mitigation within a circular economy framework. Led by the University of Delaware, in collaboration with Delaware State University, University of Alabama at Huntsville, Alabama A&M University, University of South Carolina, Clemson University, and Benedict College, the project has the potential to revolutionize defluorination technologies across water, air, and soil, impacting medical, agricultural, and industrial sectors. Education and outreach efforts will train skilled educators, scientists, and engineers to tackle PFAS challenges and advance national health, prosperity, and economic growth. The project will employ a multi-scale research framework, integrating experiments and modeling, to create innovative knowledge and robust technologies for PFAS separation and conversion, aiming for near-zero fluoro-pollution. It will address critical knowledge gaps in PFAS concentration and defluorination within a circular economy context, while tackling engineering challenges, such as complex water matrices, pilot-scale testing, and environmental and cost analyses. The major research goals include: (i) advancing PFAS adsorption and electrochemical separation across diverse water sources; (ii) uncovering mechanisms for selective electrochemical and plasma-assisted PFAS reduction; and (iii) designing energy-efficient, modular systems that couple up-concentration with reduction processes. The project will strengthen STEM capacity and research infrastructure across three EPSCoR jurisdictions by building PFAS expertise, launching sustainable STEM education and training programs across seven partner institutions, and fostering long-term collaboration with national labs, industry, and communities to cultivate a diverse new generation of innovators and educators. This project is supported by the EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations (FEC), which supports interjurisdictional teams of EPSCoR investigators to perform research in topics that align with NSF priorities, with the goals of driving discovery and building sustainable STEM capacity. 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 addresses the needs of scientific communities studying the Earth, planets, stars, and their cosmic environments for a robust and extensible modeling framework specifically adapted to the surface of a sphere. The aim is to develop a very general scientific software framework called GeoMesh-HAMMER that is suitable for running on supercomputers. GeoMesh-HAMMER will be used to study the dynamics of different space plasma environments, including the magnetospheres around planets, stellar winds, and planetary nebulae. The novelty of this approach is in leveraging recent advances in modeling technologies for rarefied plasmas that are not in a state of equilibrium, but whose thermal properties are affected by magnetic fields and the presence of different gas species. In addition, the framework aims to implement state of the art techniques to concentrate the computational resources on the regions of greatest interest, such as a shock wave in front of a magnetosphere, or a burst of plasma from the sun known as a coronal mass ejection, with a much sparser allocation of computing resources for the surrounding space. This approach dramatically improves the efficiency of computer models, resulting in increased productivity and reduced power usage. GeoMesh-HAMMER will be built using an open source development strategy in collaboration with space science and astrophysics communities to maximize the impact on as many fields of study as possible. The adaptive multi-level framework is not limited to solar or space physics, but could also find applications in modeling weather, ocean currents, geodynamics, and digital mapping, all of which play immensely important roles in modern society. Computational astrophysics and computational space physics are fields of study that have seen an increasing amount of shared interest in recent years. Both fields rely on simulating novel flow physics that was not accessible until recently. Both fields have a great need to accurately simulate problems on meshes that are optimized for spherical geometry, are adaptive, and are free of coordinate singularities. Finally, both fields are seeing the need for going beyond the magnetohydrodynamic approximation. The commonality in the needs for the space physics and astrophysics communities argues for a common framework between the two communities. This project is based on the realization that computational physics informs the development of innovative cyberinfrastructure (CI), which in turn supports the best physics-driven needs of the two communities. This project addresses community building around the proposed CI with the goal of realizing a decades long goal of both communities to model rarefied multi-component magnetized plasmas not in the state of thermodynamic equilibrium. To achieve the best advantage of these advances, the solution methods for these equations have to be embedded in a CI that supports spherical meshes with adaptive mesh refinement (AMR) and are free from singularities. The proposed framework will incorporate state of the art technologies such as fluctuation-form update to handle non-conservative terms and physical constraint preservation so that high Mach number flows and strong magnetic fields can be simulated. The CI, called GeoMesh-HAMMER, will be a powerful new tool for both communities to carry out simulations of planetary and stellar environments on spherical geodesic-based meshes. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics in the Directorate for Mathematical and Physical Sciences. 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 supports a collaborative research effort by the Space Science Institute, the University of Alabama in Huntsville, and Ben-Gurion University in Israel. The main goal of the project is to understand fundamental properties of shock waves in astrophysical plasmas. Shocks are strong disturbances that may occur when objects move through a medium with very large speed. Plasma shocks are very important phenomena because they can efficiently convert flow energy into heat and acceleration of individual particles to high energies. For example, shocks produced by astrophysical explosions are thought to operate as giant particle accelerators, producing some of the most energetic particles in the Universe. Similarly, a shock standing in front of the Earth slows down the solar wind, a stream of plasma coming from the Sun. As a result, this shock plays an important role in controlling space weather, understanding which is essential for protecting critical communication and navigation satellites, as well as power grids here on Earth. This project will use a combination of computer simulations, analytical theory, and spacecraft observations to uncover a relation between properties of the shocks and the heating and acceleration of plasma behind the shock. The project outcomes will inform a wide variety of investigations, including studies of space weather. Concurrently with advancing the fundamental science of plasma shocks, the award supports several educational and outreach efforts for university and K-12 students. Collisionless shocks are some of the most ubiquitous strongly nonlinear phenomena in space and astrophysical plasmas. They rapidly convert energy of the upstream flow into heating, acceleration of particles to high energies, and magnetic field generation. Relaxation and self-organization processes behind the shock have a strong impact on the overall energy partition and may determine the coupling of the shock to the host system. This study will advance the understanding of how the time-dependent shock rippling, accompanied by plasma instabilities and turbulence, provides necessary relaxation of the plasma to a self-organized state with a high energy particle population. This will be accomplished using a combination of theoretical and numerical kinetic modeling combined with observational data analysis, with the study of energetic particle acceleration in collisionless astrophysical shocks contributing to the goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics" meta-program. The project outcomes are expected to impact understanding of many geospace and astrophysical systems, including studies of the Earth’s magnetosheath and its dynamical magnetopause, shock waves associated with coronal mass ejections, and of their geomagnetic effects. Through training of students, postdocs, and early-career scientists, the project will promote education in computational science and plasma physics. 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 aims to improve coastal community resilience to hurricane wind-related hazards and potential compounding hazards. When a heatwave hits a region reeling from hurricane damage to its electrical grid, societal effects become even more extreme because air conditioners and other electric devices cannot function. To help prepare communities for these combined risks, the team will collaborate with community partners in the Gulf Coast region of Texas and Louisiana to develop methods that precisely depict wind gusts over land during hurricane landfalls. The research will inform hurricane forecasting and also help risk communication protocols and inspire educational initiatives that create safer and better prepared coastal communities. To help coastal communities become better prepared for the combined risks of hurricane winds and their cascading hazards, this project will leverage Doppler radar wind retrievals and near-surface wind measurements to understand the low-level wind profiles of landfalling hurricanes. Researchers will use novel approaches combining high-resolution computer simulations, e.g., large-eddy simulations coupled with artificial intelligence, to better understand and predict strong, gusty winds near the ground, especially in coastal and urban areas. Qualitative and geospatial social science methods will be employed to better understand how communities and decision makers respond to wind-related hazards and the challenges they pose. This project involves partnerships with local organizations to create practical tools and risk communication products that will help people better understand and prepare for compound hurricane wind-related risks. This project is jointly funded by the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences, and the Office of Advanced Cyberinfrastructure through the National Discovery Cloud for Climate initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EPSCoR awards support a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at the University of Alabama in Huntsville (UAH) will support fifteen EPSCoR Graduate Fellows with funding from the National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP). This support is aligned with goals and programs within NSFs Directorate for Computer and Information Science and Engineering (CISE), the Directorate for Engineering (ENG), the Directorate for Geosciences (GEO), and the Directorate for Mathematical and Physical Sciences (MPS). It seeks to increase the number of doctorates awarded in six signature UAH STEM fields. These fields are Astronomy & Astrophysics, Helio physics & Low-Temperature Plasmas, Atmospheric Sciences, the Mechanics of Materials & Structures for Aerospace Systems, Engineering Design & Systems Engineering, and Computing & AI. The expertise and natural networking provided by the UAH/Huntsville research environment will help Fellows transition successfully into the workforce, regardless of whether this transition leads to academia, the government, or the private sector which are in unique abundance in the Huntsville area. UAH is directly involved with maintaining critical partnerships between academia, government, and industry, supporting the research and education infrastructure, and supporting national security and the defense industry, which all directly benefit society. Further increasing STEM-Ph.D. production with full-time domestic students at UAH thus strengthens these partnerships and helps contribute to the Nation’s interests, including security, while also helping to maintain the economic competitiveness of North Alabama and the Tennessee Valley technology corridor. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The interaction between turbulence and shocks is ubiquitous in heliophysics. Shocks are created in space plasmas when a supersonic flow interacts with an obstacle such as a coronal mass ejection (CME) or a planet. For example, interplanetary shocks are often driven by interplanetary CMEs propagating through the solar wind. The downstream region between the shock and the ejecta is commonly known as the sheath. Additionally, pairs of forward-reverse shocks can form at stream interaction regions (SIRs) due to the compressive interaction of fast and slow solar wind streams. These interplanetary shocks will interact with turbulent solar wind fluctuations. Another example is the Earth’s bow shock, which is created by the solar wind slowing down upon reaching the Earth’s magnetosphere. Strong turbulence is generated downstream of the bow shock, known as the magnetosheath. For the Earth’s bow shock, the upstream region is also affected by backstreaming particles, leading to a foreshock region where the fluctuation level is enhanced compared to the quiet solar wind. In both cases of interplanetary shocks and planetary bow shocks, the shocks interact with turbulent fluctuations in the solar wind. The complete problem of the interaction between low-frequency MHD wave modes and different shock geometries has not been systematically investigated. This project is to investigate this interaction; specifically, how upstream incident waves are transmitted and reflected upon interaction. The following objectives will be achieved: (1) To extend the present theoretical model of MHD wave-shock interaction to a more general case. Quantifying the differences caused by the shock geometry, shock strength, field and plasma parameters, and upstream incident wave mode. (2) To understand the relationship between the MHD waves observed upstream and downstream of heliospheric shocks through spacecraft data analysis guided by the theoretical framework of MHD wave-shock interaction. (3) To characterize the properties of low-frequency turbulence upon crossing the shocks, including cross helicity, compressibility, and anisotropy, based on the properties of the transmitted waves. 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 · 2025-04

Ice nucleating particles are small airborne particles that enable ice crystals to form in clouds. These particles often consist of desert dust, marine aerosols, or biological aerosols. In this project, the research team will equip a set of drones with advanced measurement capabilities and launch them during a research voyage in the eastern Atlantic Ocean to study the prevalence of ice nucleating particles and their role in precipitation, biogeochemical cycles, and ocean fertilization. The resulting data will help researchers to better understand and forecast clouds and precipitation. The project also includes early-career scientists and students, which ensures the development of the next generation of scientists. In this project, the NSF-funded research team will join a scientific cruise from the German Research Vessel Meteor in the eastern Atlantic Ocean in Summer 2025. The researchers will deploy a set of three drones and other instrumentation on the ship to study the prevalence of ice nucleating particles with a special focus on biological particles, known as bioaerosols. Measurements will be targeted around convective precipitation regions, including cold pools from rain-cooled air. The research team will address hypotheses about the origin of the ice nucleating particles, the impact of convective storms on particle concentrations and vertical distributions, and potential feedbacks to convective properties. This project is jointly funded by the Physical and Dynamic Meteorology Program and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2025-03

This Faculty Early Career Development (CAREER) award supports fundamental research focused on how engineers formulate, analyze, and validate engineering problem spaces by creating a new theoretical foundation that integrates rigorous logic-based methods with real-world engineering practice. Modern society depends on large-scale, complex engineering systems like communication satellites, aircraft, rockets, and critical infrastructure. Developing these systems requires engineers to correctly understand and represent the "problem space", that is, the fundamental questions about what the system should do and how it should interact with its environment. If these early problem formulations are incomplete or inconsistent, costly design errors can arise down the road, affecting reliability, safety, and mission success. By ensuring that needs, requirements, and system functions are represented in a mathematically sound, complete, and consistent manner, this research intends to reduce costly rework, enhance system safety, and improve performance. This effort intends to enable national leadership in cutting-edge engineering, promote scientific progress, and advance societal welfare by ensuring that future large-scale systems are designed with mathematical rigor from the earliest stages. The educational component of the project is focused on transforming the way students learn about and approach engineering problem formulation, equipping the next generation of engineers with advanced formal reasoning skills. The intellectual merit of this CAREER project is on developing theoretical foundations for representing and reasoning about engineering problem spaces through the integration of Modal Preference Logic, Systems Theory, and Set Theory. The research establishes formal definitions, axioms, and theorems for analyzing the consistency, completeness, and validity of problem space elements and their transformations. Automated reasoning tools will be developed to support practical application. The methodology will be validated through formal mathematical proofs and real-world case studies of space systems at National Aeronautics and Space Administration (NASA) Marshal Space Flight Center’s Advanced Concepts Office. Scientific contributions include a unified logical framework for representing diverse problem space elements, formal criteria for assessing problem formulation quality, and theoretical foundations for verification and validation integrated directly into the problem formulation process. The framework will be implemented through an ontology that enables automated reasoning about complex system specifications while maintaining mathematical rigor. The educational component of this project integrates these formal methods into the engineering curriculum through advanced undergraduate and graduate courses, ensuring that students gain hands-on experience with rigorous logic, problem formulation, and automated reasoning. Partnerships with the Alabama Space Grant Consortium will expose high-school and university students to cutting-edge methods and tools, broadening participation in engineering fields and preparing a workforce that can confidently meet the complex engineering challenges of tomorrow. By linking cutting-edge research with classroom teaching, and educational outreach, the project provides a comprehensive solution that supports both the scientific community and the broader national interest in reliable, efficient, and innovative engineering 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 · 2025-03

This award funds planning activities for a proposed new Industry University Cooperative Research Center (IUCRC), the Center for Smart Manufacturing using AI-based Revolutionary Technologies (SMART). Advancements in machine learning (ML) and artificial intelligence (AI) have profoundly impacted numerous fields. However, the manufacturing sector has faced challenges in integrating AI at the same pace. The SMART center will leverage data collected through sensors and cameras during the manufacturing process to create and integrate AI technologies that transform the manufacturing sector, delivering enhanced productivity, product quality, factory sustainability, and workforce safety. The center aims to enhance the global strength and competitiveness of the U.S. manufacturing industry, which plays a critical role in economic stability and growth while sustaining technological leadership in key sectors including automotive, aerospace, electronics, and pharmaceuticals. The SMART center is also dedicated to workforce development through specialized training programs that will equip workers with the skillset essential for 21st-century industries. Through a collaborative approach involving universities, industry leaders, and government agencies, the SMART center aligns with national priorities to bolster economic resilience and advance technological innovation. The mission of the SMART center is to foster collaborations among stakeholders in advanced manufacturing to conduct and disseminate applied, pre-competitive research on AI-driven technologies, methodologies, and tools that enable the transformation of the manufacturing sector. The SMART center’s research focuses on four thrust areas: manufacturing productivity, product quality, factory sustainability, and workforce safety. By leveraging advanced AI and machine learning (ML) technologies, including deep learning, reinforcement learning, and large language models, these efforts aim to: (i) optimize various aspects of manufacturing processes, (ii) improve product quality through advanced defect detection systems utilizing analytics and deep neural networks, (iii) enhance resource sustainability by improving energy efficiency and reducing waste, and (iv) boost workplace safety through real-time monitoring and predictive analysis of potential hazards. The UAH site will specifically focus on the integration of ML with AI in the manufacturing sector and leverage Digital Twins (DT) to simulate the manufacturing environment. Camera-based imaging and sensors at UAH will be used to benchmark advancement of the ML/AL algorithms for seamless integration into manufacturing systems and ensure their reliability and efficiency. DT will replicate systems in industry to simulate equipment conditions in real-time, predict maintenance needs, and preemptively address potential failure. 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 · 2025-02

This project will establish a collaborative partnership among three southern EPSCoR jurisdictions—Alabama, Mississippi, and Louisiana—focused on advancing education, scientific research, and workforce development in data science and related fields. The initiative is led by the University of Alabama in Huntsville, with key participation from Alabama A&M University, one of Alabama’s largest HBCUs, as well as Mississippi State University and Louisiana State University. The partnership will implement a range of impactful programs, including workshops prioritizing early-career professionals, data science summer camps targeting high school students, and development of college-level educational and research activities. As data science-related careers continue to grow nationwide, this collaboration aims to position the partnering jurisdictions to compete effectively in the global market. The project focuses on advancing data science and machine learning techniques to address systematic errors and their impact on data analyses within the physical sciences. A key activity of the project includes hosting a workshop focused on the emerging multidisciplinary field of data science. Through the workshop, the project aims to address systematic, or epistemic, errors in statistical data analysis. A central feature of the workshop is the engagement of scientists and students from diverse disciplines, including the physical sciences, statistics, machine learning, and computer science. The initiative will also foster the development of educational and research programs for both grade-level and college students, encompassing undergraduate and graduate certificate and degree offerings. These programs aim to prepare participants for careers in this rapidly growing field. The ultimate objective of this project is to establish a permanent multidisciplinary data science infrastructure in the South, supporting long-term growth and innovation. This project is supported by the EPSCoR Workshops and Outreach investment strategy. EPSCoR funds workshops, conferences and other community-based activities to explore opportunities in emerging areas of science and engineering, and to share best practices in strategic planning, broadening participation, communication, cyberinfrastructure, evaluation and other areas of importance to EPSCoR jurisdictions. 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 · 2025-02

Nonlinear optical (NLO) materials, characterized by their noncentrosymmetric (NCS) lattice structures, are essential for generating new light wavelengths through interactions with intense laser beams. These materials are vital in various fields, including telecommunications, spectroscopy, and biomedical imaging. Although many potential NLO materials have been discovered, key NLO characteristics such as coefficients, refractive indices, and laser damage thresholds often remain unexplored due to the challenges in obtaining high-quality large single crystals and the lack of advanced measurement facilities. This project aims to bridge this gap by facilitating the growth of high-quality large single crystals of NCS compounds and characterizing their properties. This research will enhance the understanding of the structure-property relationship in NCS crystals and may lead to the development of new NLO materials. Additionally, the project will engage students at various educational levels, graduate, undergraduate, and high school students, with an emphasis on underrepresented groups, including women, African Americans, and first-generation college students. The findings will be incorporated into current chemistry courses and showcased at local science exhibitions to maximize their broader impact. This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Associate Professor and training for a graduate student at the University of Alabama in Huntsville. This work will be conducted in collaboration with researchers at the University of Houston. The team will grow large single crystals of NCS compounds and analyze their NLO properties. The PI and a graduate student will learn to grow centimeter-sized single crystals using the top-seeded solution growth technique, which involves investigating sample congruence, optimizing flux composition, and selecting seed orientations. They will also analyze the NLO properties of the prepared large NCS crystals, including NLO coefficients, second-harmonic generation (SHG) intensity, phasematchability, refractive indices, transparency, and laser damage thresholds. Additionally, small crystals of NCS compounds will be synthesized using traditional hydrothermal and solid-state methods and structurally characterized using single crystal and powder X-ray diffraction at UAH. This initiative will not only expand the range of NLO materials but also deepen our understanding of the relationship between NCS crystal structures and properties. Moreover, the methodologies and knowledge gained from this fellowship can be applied to the growth and characterization of large crystals of other inorganic functional materials, such as photoluminescent and scintillation materials. This effort has the potential to significantly enrich and expand the research capabilities of the PI’s lab and the home institution, as well as benefit researchers across Alabama and the United States. 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 2024 · 2024-12

The Division of Atmospheric and Geospace Sciences (AGS) operates the Community Instruments and Facilities (CIF) program to enhance the scientific community’s access to instrumentation that is otherwise too costly or complicated to operate for most institutions. This award is for COWNET, which is a set of mobile radars and instruments that are used to study a variety of societally impactful topics, from tornadoes to snowfall. The COWNET instruments will also be used to teach younger generations through outreach events and hands-on training. COWNET is comprised of two C-band radars (COW and Mini-COW), two X-band radars (Rapid Scan DOW and CROW-X), and a network of 4 vehicles with instruments to measure temperature, humidity, pressure, and wind. The COWNET facility is available for studies of severe and high impact weather, convective initiation, storm transitions and upscale growth, winter storms, orographic precipitation, precipitation microphysics, hydrological processes, tornado and hurricane structure, weather modification validation/refutation, fire weather, land use impacts, and more. COWNET is also available for outreach and/or educational purposes. 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 2024 · 2024-11

How strong are the winds in tornadoes? How do they cause damage? What causes some tornadoes to be intense and others weaker? How can we build better to reduce the risks from tornadoes? Observations in and near tornadoes are needed to better answer these questions. But, observations very near the ground, where people live, and inside tornadoes are very rare since they are difficult to predict and even harder to observe. Some, rare, observations show that tornado winds can exceed 300 mph, and that the most intense winds are very near the ground, where they are especially hard to measure. In order to mitigate the hazards posed by tornadoes, it is critical to better understand their basic structure and intensity. The Boundary-layer Evolution and Structure of Tornadoes (BEST) project will address some of these questions, deploying mobile radars, Tornado Pods, and SwarmSonde balloons in and near tornadoes. The goal is to resolve tornado structure, evolution, the intensity of winds, and the temperatures and humidities near tornadoes that likely affect how intense they are. BEST will also examine decades of mobile radar and other data collected in and near tornadoes to better understand how strong, large, and potentially damaging they are. BEST is a multi-focus study of tornado structure and evolution. BEST plans a field phase during which unprecedentedly-fine scale kinematic and thermodynamic data will be collected by proximately-deployed DOW radars, Tornado Pods, and SwarmSonde lagrangian drifter balloons. Dual-DOW baselines of 3-6 km will allow, for the first time, integrated mapping of tornado vector wind structures combined with detailed thermodynamic mapping provided with the densely-deployed Tornado Pods and SwarmSondes. BEST will compare these new data with a unique database of DOW wind measurements over 200 unique tornadoes. Approximately 20 of these include dual-Doppler vector wind resolving data. One includes data from an extremely fortuitous 3 km - baseline dual-DOW deployment near a very large multi-vortex tornado, with dual-Doppler data uniquely resolving the structure and evolution of sub-tornado vortices. A critical aspect of BEST is the combination of single-case study analysis of rare or very fortuitously observed events, with broader statistical analysis of several to over 200 events. 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 2024 · 2024-10

The Division of Atmospheric and Geospace Sciences (AGS) operates the Community Instruments and Facilities (CIF) program to enhance the scientific community’s access to instrumentation that is otherwise too costly or complicated to operate for most institutions. This award is for two Doppler on Wheels mobile radars and an array of surface weather stations, known as DOWNET. Mobile Doppler radars are used to study a variety of societally-impactful topics, from tornadoes to snowfall. The DOWNET instruments will also be used to teach younger generations through outreach events and hands-on training. The DOWNET facility consists of two mobile, dual-polarization, dual-frequency, Doppler X-band radars and an array of surface- and pole-based deployable weather stations. The DOWNET facility is available for studies of severe and high impact weather, convective initiation, storm transitions and upscale growth, winter storms, orographic precipitation, precipitation microphysics, hydrological processes, tornado and hurricane structure, weather modification validation/refutation, fire weather, land use impacts, and more. DOWNET can be used in mobile or fixed mode. DOWNET is also available for outreach and/or educational purposes. 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 2024 · 2024-10

The Internet of Things (IoT) has revolutionized daily life, yet securing IoT devices remains a significant challenge due to limited resources on IoT devices and the lack of visibility into their internal operations once deployed. Unlike conventional side-channel vulnerabilities, the electromagnetic (EM) side-channel emission leaks information over wireless channel, offering a unique medium to investigate IoT security without accessing or instrumenting the device. Unfortunately, current research on the EM side-channel attacks of IoT devices has been severely limited by the lack of a practical sensing system that can extract weak EM signals emitted by computer processors or memories, separate those EM signals of co-located devices, and robustly unravel their semantics. To tackle these challenges, this project will design, implement, and evaluate a practical EM sensing system named EMRadar to enable research on EM side-channel attacks and defenses in real environments. The results of this project will deepen our understanding of EM side-channel attacks and inform emission security standards. Practical EM side-channel defenses enabled by this project will transform various security-critical IoT applications, including detecting program deviations in medical devices that are highly resource-constrained, such as the implantable cardiac devices and the smart insulin pumps. The project is dedicated to fostering diversity and inclusion in STEM by blending research with education and outreach activities. The PI will actively involve local high school students by providing hands-on activities and presentations elucidating fundamental concepts of IoT technologies and cybersecurity. To bridge the gap in STEM participation, the project will engage students from underrepresented minority groups in Detroit and Pontiac as well as various Michigan-based organizations for girls. This endeavor is supported by collaborations with the Oakland University's STEM summer camps and field trips, as well as the Michigan Aspirations in Computing Committee. Through these initiatives, the project will inspire a broad spectrum of students to pursue futures in engineering and science. This project will design, implement, and evaluate the EMRadar, a practical EM sensing system that enables research on EM side-channel attacks and defenses in realistic environments. The proposed research will build on a three-layer architecture model. (1) In the sensing layer, EMRadar will incorporate innovative signal processing techniques to extract weak EM signals that are deeply buried in noise and contaminated by interference. To achieve this, the EMRadar will exploit the unique characteristics of EM emissions from a processor or memory and IoT software behavior, enabling novel methods that can adapt and optimize signal processing techniques used in classic Radar systems for highly sensitive EM sensing. (2) In the representation layer, this project will explore universal representation and classification of EM signals to unravel EM side-channel emissions of processors and memories. Despite extensive studies on EM side-channel emissions, accurate explanation of the semantics of EM side-channel signals remains highly challenging due to significant variation of EM signal pattern and the EM interference produced by workloads running on the same device. This project will leverage the recent advances in time series analysis and machine learning to revisit the representation of EM signals, and conduct a systematic measurement study to understand the robustness of universal representation across IoT devices with different architectures. (3) In the application layer, the EMRadar will be evaluated in real-world setups and applications. The PI will collaborate with domain experts to explore applications of the system developed in this project, such as supporting efficient IoT-based applications in healthcare, automobile industry, and intelligent buildings. 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 2024 · 2024-09

Atomically thin materials, such as transition metal dichalcogenides, exhibit unique optical and electronic properties, making them promising candidates for the development of optical devices and quantum information processing applications. These applications have gained momentum due to the ability to utilize light to generate specific quantum states in such materials and their compatibility with graphene, forming heterostructures for efficient electron capture and transport. While such potential applications paint an exciting picture for quantum-related technologies, several major hurdles impede their progress. These include the ultrafast decay of quantum properties and the challenge of accessing quantum information without distorting the systems. The objective of this project is to address these challenges by developing a transformative approach wherein, instead of attempting to tame the very fast processes that hinder applications of such systems for quantum processing, they will be leveraged to access the quantum states and to encode and encrypt information. This project establishes a novel platform that allows the use of well-established electronic systems, such as field-effect transistors, for gaining access to quantum states via electronic currents, and application of systems consisting of atomically thin semiconductor combined with metallic structures with nanoscale sizes for quantum encryption of optical information and processing. This platform can also enable the development of quantum nanosensors capable of detecting individual biological molecules and even their structural features and promote the application of quantum mechanical processes for the transport of energy and information. Additionally, this project will enhance the research capacities of the University of Alabama in Huntsville and Kansas University, leading to the development of new curricula, research, educational, and outreach programs in the states of Alabama and Kansas. This project develops a transformative approach by harnessing the decay of plasmons to gain electronic access to quantum states and processes within systems composed of transition metal dichalcogenides monolayers and plasmonic nanoantennas. In such systems the non-radiative decay of plasmons into hot electrons will be exploited to convert spin-valley quantum information into currents in field-effect transistors, thus establishing an electronic quantum readout. Therefore, the temporal variations in hot electron generation will bear the signatures of coherent spin-valley processes and quantum information. The proposed field-effect transistor devices will feature atomically-designed super-heterostructures comprising transition metal dichalcogenides monolayers, semiconductor oxide layers with sub-2-nm thicknesses, and Au nanoantennas. These heterostructures will support atomically-tunable ultra-thin high-mobility Schottky barriers capable of efficiently capturing hot electrons and transporting them without scattering. Within these field-effect transistors, quantum information will be encoded into the hot electron current by modulating their generation rates over time through spin valley exciton-plasmon coupling. An in-vacuo Atomic Layer Deposition system integrated with in-situ characterization tools will be developed for fabrication of defect-free sub-2-nm oxide/Au Schottky junctions with ultra-high mobility and atomistically controlled dimensions. These Schottky junctions will efficiently capture hot electrons and transport them without scattering, while being ultrathin allows efficient exciton-plasmon coupling. To encode quantum information into the current, we will utilize spin valley exciton-plasmon coupling to create a set of quantum states in the time domain. Quantum information will then be decoded by analyzing the dynamics of the output circuit current and mapping the evolution of the dynamic states in Bloch space. The outcomes of this project have the potential for multidisciplinary impact, spanning from quantum sensors to optical coherent transistors and coherent energy transfer. Additionally, this research presents a unique opportunity for graduate and undergraduate students to engage in cutting-edge scientific exploration. The associated outreach program aims to provide high school students in North Alabama and Kansas with hands-on experiences in nanoscience and quantum information science through experimental and tutorial sessions. 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 2024 · 2024-09

An award is made to the University of Alabama in Huntsville (UAH) to acquire an advanced confocal microscope system, which will enable innovative and multidisciplinary research by a broad group of investigators from UAH and neighboring institutions. The microscope will be integrated into three on-campus classes and serve as a pivotal tool for numerous student research projects, providing experiential learning opportunities with cutting-edge equipment. Faculty and students from two neighboring historically black universities will have access to the microscope, enhancing research and education opportunities while fostering inter-institutional partnerships. UAH is located minutes from Cummings Research Park, the second-largest research park in the United States. The instrument will bolster local industry by promoting collaborations between UAH and biotechnology companies, attracting students to the biotechnology field, and enhancing their training. Additionally, the confocal microscope will be integrated into community outreach initiatives to enhance scientific literacy and encourage interest in science, technology, engineering and math (STEM) disciplines. These initiatives will include annual public workshops on fluorescence microscopy and K-12 educational events. The high speed, high resolution, and live-cell imaging capabilities of the microscope will enable investigators to address key questions about the molecular processes governing a variety of phenomena, including 1) growth and maintenance of dopaminergic neurons throughout neurodevelopment, 2) formation and changes in biofilm structures, 3) regulation of lipid production, storage, and metabolism in mammalian cells, 4) adaptations of bacteria to environmental stress, 5) modulation of protease enzyme activities by natural products, 6.) regulation of cartilage structure, and 7.) synthesis of artificial lymphoid organs. Findings from these studies will be disseminated through peer-reviewed manuscripts and scientific conferences, with inclusion of undergraduate and graduate student coauthors. 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 2024 · 2024-07

Intelligent cyber-physical systems (CPS) represent a symbiotic integration of physical systems, sensors, actuators, and learning-based intelligent controllers through communication networks. These systems are increasingly prevalent in diverse applications, including smart grids, robotic swarms, and autonomous vehicles. While learning-based controllers are used to upgrade the capabilities of CPS, providing numerous benefits, the introduction of a learning component adds an additional layer of security challenges, which adversaries can exploit via cyber attacks. This project strives to uncover the characteristics and effects of information patterns that can deceive an intelligent decision-making agent or a learning-based controller, manipulating it into taking biased and unsafe actions. These findings should enable trustworthy secure-by-design solutions for developing real-time learning-based controllers suitable for safety-critical CPS. The research outcomes have direct applicability in remote sensing, smart infrastructure, and robotics, reinforcing the overall safety and reliability of these crucial CPS. The project aligns with efforts to promote inclusivity in computing, workforce development, and education. Example initiatives include annual summer camps for K-12 students on learning systems and their security in robotics, and engagement with undergraduate and graduate students to prepare them for secure-CPS research and workforce development. The primary goals of the collaborative project are to develop a) a real-time reward manipulation scheme for learning-based controllers, b) multi-level attack schemes on reward signals in a distributed control architecture for CPS, and c) data-enabled strategies for their detection. The scientific merit of the project is to gain insight into the information patterns that can stealthily manipulate learning-based controllers in uncertain CPS to increase control costs and threaten their stability. The reward manipulation, from an attacker’s perspective, may be formulated as a dynamic-constrained optimization problem. An online approximate solution will be developed to determine the optimal perturbation that can be added to the reward signal by an adversary. The optimization problem will be extended to address multi-level attacks using multiplayer Nash games. From a defender’s perspective, attack detection and isolation methods using time-series analysis and perturbation theory will be developed. This research will equip learning-based control schemes with built-in resiliency from their design phase. The success of this research will advance control-theoretic and learning tools, fostering advances that ensure secure and trustworthy autonomy, precise control, and safe operations. This project is jointly funded by the Secure and Trustworthy Cyberspace (SaTC) program and the Established Program to Stimulate Competitive Research (EPSCoR). 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.