Louisiana State University

NSF Awards · FY 2026 · 2026-07

Artificial intelligence (AI), large-scale data, connected devices, cloud and high-performance computing systems, that together form the nation's cyberinfrastructure, are central to national competitiveness, yet most computing students encounter them only in advanced elective courses. This project infuses aspects of AI, Big Data, and Parallel and Distributed Computing concepts and practices into three foundational computing courses. The first two form the usual introductory programming sequence, and third is the computer systems course that is often taken shortly after them. Thus, all computing majors, not only those pursuing upper-level elective courses, will develop critical skills for understanding and contributing to the modern computational ecosystem. The project addresses a persistent barrier to such curriculum modernization: many instructors need focused preparation, classroom-tested examples, and adaptable teaching materials before they can confidently introduce these topics in early courses. To overcome this bottleneck, the project develops courses and materials to train about 200 current and future instructors through three intensive in-person summer workshops, an additional six hybrid tutorials at major conferences, and complementary online workshops. Summer trainees adapt and implement the course exemplars at their own institutions and contribute evaluation data, classroom-tested refinement and local adaptation, enabling broader adoption. With the potential to impact about 250,000 students over 5-10 years, the project serves NSF's mission by strengthening computing education, expanding access to AI and advanced cyberinfrastructure skills, and building the nation's long-term technological and research workforce capacity. The project advances knowledge in computing education by producing rigorously classroom-tested exemplars infused with Artificial Intelligence (AI), Big Data (BD), and Parallel & Distributed Computing (PDC) for Computer Science 1 (CS1), Computer Science 2 (CS2), and Computer Systems courses. Implementation across 60 diverse institutions will generate evidence-based models that can be widely adopted, thereby transforming early computing education at scale. The project investigates how AI-enabled learning tools and pedagogy can modernize core curricula by enabling students to construct, explore, and reason about modern computing systems earlier and in more depth than was previously possible. Evaluation data from trainees' implementations - including student learning, retention, and institutional adoptability - will contribute to generalizable knowledge on the design and scaling of Cyberinfrastructure-centric curriculum innovations. The project incorporates aspects of AI, BD, and PDC concepts and practices into the foundational computing courses ensuring that all computing majors, not only those pursuing upper-level electives, develop critical Cyberinfrastructure-ready skills. The project's three core course exemplars will be nationally adoptable, with trainees providing local adaptations to build a community-driven ecosystem of shared materials. Two key innovations in this project are: (i) harnessing AI both for pedagogy and for enabling course modernization: AI tools will make it possible for introductory students to develop, experiment with, and understand software, data, and other artifacts that were previously too complex to explore meaningfully at scale; and (ii) explicit integration of how BD and PDC power AI, giving computing students insight into the Cyberinfrastructure ecosystems underlying AI-driven discovery and innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Antarctica holds vast, hidden reservoirs of salty groundwater beneath its ice and frozen soils; an extensive network that may influence earth system variability, ocean ecosystems, and ice sheet stability. This project will directly measure groundwater discharge and potential associated gas seeps along the Antarctic coast, revealing how these subsurface waters transport nutrients, trace metals, microorganisms, and atmospheric-reactive gases to the Southern Ocean. Understanding these exchanges is vital because they can shape marine productivity, influence carbon cycling, and control the release or storage of gases. The project strengthens U.S. and New Zealand scientific collaboration in alignment with the “Antarctica InSync” initiative, supporting coordinated, sustainable research in one of the world’s most logistically challenging environments. Insights from this work will help improve predictions of how Antarctica both responds to and influences global environmental variability. This collaborative RAPID project investigates how Antarctic groundwater drives ecosystem connectivity across the McMurdo Sound coastal zone, focusing on the Cape Evans and New Harbor regions of the Ross Sea. The team will identify groundwater discharge using in situ gamma radiation sensors, deploy seepage meters and OsmoSamplers for fluid and gas flux measurements, and collect water and sediment samples for detailed geochemical and microbial analyses. These data, combined with land-based geophysical, SCUBA, and ROV surveys by New Zealand partners will quantify groundwater pathways, flux rates, and biogeochemical properties. The project tests the hypothesis that Antarctic groundwater significantly affects coastal geochemistry, microbial diversity, and glacial flow, influencing the sensitivity of Antarctic coastal margins to earth system dynamics. The findings will provide foundational data for future multinational monitoring, modeling, and management of Antarctic critical zones. 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 · 2026-05

Online scams increasingly rely on sustained, real-time conversations in which attackers manipulate victims through multiple communication channels. These interactive scams cause billions of dollars in losses each year. However, effective detection systems are difficult to build because little data exists about how these conversations unfold. The interactions typically occur privately between attackers and victims, making them difficult for researchers to analyze. One of the project’s novelties is the systematic study of knowledge generated by the global scambaiting community, individuals who deliberately engage scammers in conversations to waste their time. Another novelty is combining insights from this community with controlled research methods to advance understanding of how scammers persuade victims during interactive attacks. The project's broader significance and importance are advancing society’s ability to detect and disrupt online scams while improving public awareness of how interactive social engineering attacks operate. The project also engages the wider community through educational workshops that raise awareness of scam tactics and strengthen resilience against online fraud. This project develops a research framework for measuring and defending against interactive social engineering attacks on the internet. The research analyzes practices of the scambaiting ecosystem to study tools and techniques used by scambaiters during real-time engagements to keep conversations with scammers active. Building on these insights, the project develops controlled systems that engage scam perpetrators and collect datasets of conversational interactions while incorporating ethical safeguards. These datasets support detection methods that leverage conversational patterns, behavioral signals from computer activity, and voice characteristics to identify scam attempts. In addition, the research examines how detection signals should be presented through user interfaces to minimize false alarms while maintaining usability. The expected outcomes include new measurement methodologies, improved scam detection techniques, publicly disseminated research findings, and educational resources that strengthen community resilience against online fraud. 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 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at Louisiana State University. This work is conducted in collaboration with experts at the University of California, Los Angeles. Through the fellowship, the PI will address the critical challenge of detecting and preventing hidden system failures in advanced vehicle technologies before they lead to roadway accidents. Using computer science and engineering approaches, the research will develop new methods to help vehicles respond safely to complex and unpredictable driving situations. The project will strengthen vehicle safety and reliability, potentially reducing crashes and property damage affecting millions of drivers. It will also expand Louisiana's technical workforce by training students in artificial intelligence, transportation safety, and cyber-physical systems. This project will develop a multi-phase safety assurance framework for advanced driver assistance systems that integrates design-time validation with runtime monitoring. The research will advance the state of the art by combining formal verification techniques with machine learning to improve the transparency and verifiability of autonomous driving systems. The methodology will employ program analysis and natural language processing to identify critical fault-related variables and guide targeted fault injection through reinforcement learning. At runtime, domain-informed predictive models will detect emerging hazards and enable early mitigation of unsafe behaviors. The fellowship will strengthen research infrastructure at Louisiana State University by advancing faculty expertise in cyber-physical systems and providing specialized student training in artificial intelligence safety. The project will also establish a sustained research collaboration with UCLA and release open-source software and datasets to advance reproducibility and support workforce development. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows (ERF). The ERF program supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

The rapid advancement of quantum computing poses a major threat to digital security. The risk is urgent because many software systems rely on cryptographic methods that could become insecure in the presence of large-scale quantum computers, including those that protect banking, medical records, and critical infrastructure. However, upgrading to quantum-safe cryptography is not as simple as replacing one encryption package with another. Security mechanisms are often spread across many parts of a system, intertwined with how data moves, how identities are verified, and how outside components connect, so piecemeal changes can break compatibility or introduce new weaknesses. This research develops practical, trustworthy ways to understand quantum-era exposure and carry out safe upgrades in an auditable manner. The project's novelties are a quantitative Quantum-Readiness Index that summarizes system-level quantum risk, a dependency-aware view that connects vulnerable cryptography to the surrounding software behaviors that rely on it, and an evidence-driven pathway that turns measured risk into actionable upgrade steps. The project achieves these goals by developing a methodology that turns scattered cryptographic usage into prioritized risk rankings and reliable upgrades. First, it defines a Quantum-Readiness Index that evaluates software components by integrating mission context and threat levels, producing priorities for where quantum-era risk exists. Next, it develops an analysis of cryptographic usage through a hybrid of static and dynamic techniques organized within a novel Cryptographic-Dependence Graph, improving coverage and precision across code, configurations, and third-party dependencies. Then, it develops migration-oriented cryptographic analysis that identifies coupled operations and derives dependency-aware upgrade sequencing, reducing the chance of incompatible intermediate states during transition. Finally, it creates a plan-aware migration pipeline that uses large language models to generate post-quantum cryptography patches, which are then checked against tests and cryptographic constraints. Together, the work promotes safer and more resilient digital systems, protecting critical infrastructure, medical records, and other essential services while strengthening trust in the security of modern software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This award supports participation at the 2026 Southern Regional Number Theory Conference (SRNTC), to be held March 7 and 8, 2026 on the Baton Rouge campus of Louisiana State University. Number theory is an active research area in mathematics encompassing a broad spectrum of topics and tools. It has many connections to other research fronts, and has applications in physics and information technology. The SRNTC series serves as a flagship annual meeting for the number theory research community of the US Gulf Coast region. The 2026 conference brings together researchers from the region, while also attracting other world experts. Participants will disseminate and discuss fundamental new research results in various branches of number theory. Broader impacts of the conference include the formation of networks that promote interaction between experts and graduate students, and opportunities for early-career researchers to see a wide array of problems and techniques, and to present their own research through contributed talks. During the conference, senior and junior researchers will share recent developments in the field, with a special eye toward fostering communication and community among number theorists in the US Gulf Coast region. The plenary speakers are Veronica Fantini, Bernhard Heim, Matilde Lalín, Yingkun Li, Cristian D. Popescu and Charlotte Ure. Their expertise spans algebraic geometry, arithmetic geometry, Iwasawa theory, the study of L-functions, and the theory of automorphic forms. More information can be found on the conference website: https://www.math.lsu.edu/~srntc/nt2026/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Benthic nepheloid layers (BNLs) are persistent layers of enhanced particle concentrations near the seafloor. Intense BNLs of a few hundred meters thick have been observed globally and may significantly influence the cycling and overall budget of sediment-sourced trace elements and isotopes (TEIs). BNLs can act as elemental sources or sinks, potentially enhancing or suppressing elemental fluxes across the sediment-water interface. However, sampling of BNLs has been limited and very few chemical measurements have been made. In conjunction with a previously-funded research expedition in the Labrador Sea, this project will conduct high-resolution sampling of particle composition and isotopes in BNL particles and surface sediments. The overall goal of the project is a deeper understanding of the role of BNLs in regulating deep-ocean chemistry. Beyond the scientific contributions, the project will train graduate and undergraduate students, engage the public in collaboration with a science media specialist, incorporate scientific and outreach content into undergraduate teaching curricula, and foster international collaboration. The overarching goal of the proposed research is to generate process-level understanding of how BNLs change the net flux of TEIs into the overlying water column. To achieve this, the team aims to address three key questions: (1) What is the benthic flux of TEIs from sediments in the Labrador Sea? (2) How do BNLs modify net benthic fluxes of TEIs? (3) What characteristics of the BNL are the most important controls on TEI concentrations? The investigators hypothesize that BNL regions will exhibit a higher benthic flux of TEIs at the sediment-water interface, as determined by radium-thorium disequilibrium, but that the net benthic flux of particle-reactive TEIs will be lower in BNLs with more manganese oxides due to their greater scavenging capacity. This project will evaluate different scavenging intensities by estimating partition coefficients using particle composition data and investigate the particulate Mn mineral phases responsible for scavenging using synchrotron techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

A recent deep-sea volcanic eruption on the East Pacific Rise provides a rare, time-sensitive opportunity to investigate how deep-ocean ecosystems recover from catastrophic disturbance. Because this site has been studied for decades, researchers can compare current events to data from before the eruption and from past eruptions, offering insight into how ocean life responds to sudden natural change. Through this project, the scientific team is investigating how microorganisms and animals return to the area, how chemical conditions influence their survival, and whether early colonizers influence the long-term development of the communities. By examining a range of organisms from microbes to drifting larvae and newly-settled animals, the investigators are building a detailed understanding of how deep-sea ecosystems recover and how carbon and energy move through these systems after a disturbance. The project is supporting the training of graduate students and early-career researchers and is leveraging collaboration across multiple institutions. More broadly, understanding how seafloor communities recover is increasingly important as society considers the benefits and risks of deep-sea mining. On April 29, 2025, an eruption started at the 9°50'N vent field on the East Pacific Rise (EPR), presenting a unique chance to understand factors governing biological production in the ocean and to observe how vent ecosystems recover. Scientists have been monitoring this site for decades and these sustained data sets provide essential context for interpreting the influence of this most recent eruptive perturbation on the ecosystem. Through this project, the investigators are seeking to understand the biogeochemical and ecological processes that govern ecosystem recovery across microbial and animal communities. They are using integrative techniques to examine the eruption-related impacts on microbial activity and community structure, including measurements of microbial primary productivity, exoenzyme activity, and associated in situ fluid chemistry. When combined with a characterization of the microbial community’s diversity (amplicon-based), gene expression (metatranscriptomics), and protein production (proteomics), the investigators are identifying active microorganisms, quantifying their contribution to deep sea carbon cycling, and exploring their potential as settlement cues for pioneer animal colonists. The microbial studies are co-located with sampling of animal colonists on the seafloor to identify the pioneers and document their association with microbial consortia. The investigators are identifying and quantifying larvae in the plankton to compare pre-and post-eruption availability. The range of data collected is laying the foundation for understanding the drivers of post-eruption succession and for testing a biophysical model currently in development to explore the influence of mesoscale eddies on inter-segment vent larval dispersal. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at Louisiana State University (LSU). This work is conducted in collaboration with Dr. Frédéric Mentink-Vigier at the National High Magnetic Field Laboratory (NHMFL). Through the fellowship, the PI will investigate how uropathogenic Escherichia coli bacteria, which cause urinary tract infections, form resilient biofilms that resist antibiotics. The project integrates chemistry, microbiology, and biophysics to study how biofilm components like cellulose and curli proteins interact to form protective barriers. The research will use advanced nuclear magnetic resonance (NMR) techniques to reveal the biofilm structures in their native state, which may inform new strategies for treating persistent infections. Other benefits of the award include expanding Louisiana’s capacity in biomolecular NMR, training a STEM workforce in cutting-edge methods, and fostering collaboration with national research facilities. This project will investigate the atomic-level and supramolecular architecture and dynamics of extracellular polymeric substance (EPS) in Escherichia coli biofilms, with a focus on cellulose and curli amyloid fibers. The intellectual contribution includes the development of new NMR and dynamic nuclear polarization (DNP) methods for probing intact biofilms and advancing understanding of how EPS interact to form protective matrices in their native state. The PI will employ multidimensional solid-state NMR spectroscopy, selective isotope labeling, and DNP to suppress bacterial cellular signals, enabling selective detection of EPS components in situ. The project will enhance research infrastructure by supporting development in biomolecular NMR and strengthening LSU’s capacity for cutting-edge magnetic resonance techniques. Activities will integrate with broader institutional goals by expanding methodology for biofilm research, fostering student mentorship, and promoting workforce development in structural biology. Collaboration with NHMFL will provide LSU researchers with access to state-of-the-art instrumentation and catalyze future partnerships. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

Males and females often respond differently to the same environmental challenge. Such differences can hurt chances for population survival by reducing the numbers of one sex but they may enhance population resilience by favoring different genetic variants in each sex, elevating overall genetic variation and the population's capacity to adapt. Ocean waters are becoming more acidic, making it harder for corals to build the tropical reefs that harbor enormous diversity and sustain island economies. This research will test whether the response of a coral to ocean acidification varies between sexes in ways that enhance the coral's ability to cope with acidification. The work will center on a coral species living in the Gulf of California, where a natural gradient in ocean acidity allows comparisons of populations under different levels of stress. Growth rates for this coral in acidic regions can be half those found elsewhere and are especially low for females. The proposed research will compare patterns of how genes are turned off and on between sexes and between regions with different acidity, identify genes tied to growth rates in each sex, and use artificial intelligence to distinguish the skeletons of male and female colonies. Graduate students from the US and Mexico will work together on the project and a new undergraduate teaching lab will take part in the artificial intelligence work. Results will facilitate study of sex-specific traits and suggest whether corals with separate sexes are better prepared to respond to a changing ocean than their hermaphroditic kin. The central hypothesis of the work postulates that the genomic response to ocean acidification in the coral, Porites panamensis, varies between sexes and between populations, with sex-specific genetic adaptations enhancing the coral's ability to cope with acidification, thereby influencing the sex ratio and population viability over time. The first objective is to identify candidate sexual conflict genes that are up-regulated under acidic conditions and show sex-biased expression. The second objective will scan male and female genomes for sex-specific divergent outliers and test whether levels of variability close to candidate sexual conflict loci are enhanced relative to other regions of the genome. The third objective aims to identify sex-specific transcripts and genome regions, allowing for non-histological measurement of the sex ratio. Machine learning will also be used to distinguish male and female colonies using skeletal images. Together, the results will help determine whether sex-specific differences in coping with acidification, in an organism with little obvious sexual dimorphism, contribute to adaptive genetic variation. 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

Mobile applications (apps), though useful, can create privacy risks for their users by leaking sensitive information from SMS text messages, location data, contacts, and photos, etc. to the app developers or to data brokers. This is a critical problem since both the number of mobile applications and people's use of them have grown greatly over the last decade. This expansion of choices and uses makes it hard for people to find apps that meet both their functional and privacy needs. Recommender systems, which provide personalized suggestions based on user ratings, usage, and other data, are often used in other domains to help people decide between many choices. However, applying existing recommendation methods to mobile apps can expose users to options that may appear useful but pose substantial privacy risks. This project introduces a new genre of recommender systems that selects a small set of candidate apps that might work for people based on their functional needs; analyzes the way those apps access, transform, and share data; and combines those rankings with people's privacy expectations to suggest apps that best meet individual people's needs. Further, the system will be designed to clearly communicate the privacy risks involved with suggested apps. Together, the work will promote safer, more trustworthy mobile experiences while advancing people's understanding of privacy online. The project will achieve this goal by grounding conflicting information about app behavior, such as discrepancies between app metadata and findings from static software analysis, through simulated user interactions with apps. First, new static analysis techniques will be developed by introducing novel program slicing methods for mobile apps that emphasize user-interpretable actions, incorporate permission awareness, and account for critical code in life cycle methods, event callbacks, and inter-component communication. Next, a multi-step deep reinforcement learning framework will be designed to simulate user interactions with apps under different configurations, enabling estimation of true app behavior in realistic settings. Finally, an interactive conversational recommendation system will be created to integrate privacy considerations through targeted interventions on app aspects derived from users' historical interactions and to refine recommendations based on insights from grounded app behaviors. This approach will enhance user safety while maintaining satisfaction by effectively balancing privacy and functionality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Non-technical description: The goal of this project is to build and steer swarms of micron-scale magnetic colloidal particles that come together and move cooperatively through complex environments, much like schools of fish, flocks of birds, or swarms of insects. These swarms are activated by a time-varying magnetic source (for example, an electromagnet or a moving permanent magnet) which functions as an external remote controller. The magnetic controller can direct swarms to propel through fluids, maneuver over surfaces and around obstacles, detect and respond to changes in their surroundings, and carry passive cargo. This project aims to advance the field of magnetic swarms by integrating large computer simulations, theoretical modeling, and experimental approaches within a cohesive framework. Mastering life-like swarm behavior could enable miniature ARMS robots that deliver medicine inside the body, inspect subsurface pipelines, or remove contaminants from water supplies. By opening new frontiers in materials science and programmable matter, this project advances the nation’s health, prosperity, and security while strengthening technological leadership. This project will also provide K-12, undergraduate, and graduate students with interdisciplinary training in computational and experimental techniques for materials science, physics, and engineering to develop our domestic workforce, improve public scientific literacy, and stimulate engagement with science and technology. Technical description: While magnetic swarms capable of dynamic reorganization have been demonstrated, a systematic approach to designing swarms with increasingly sophisticated functions in porous environments and unbounded 3D fluids remains a challenge. Large-scale simulations will capture the coupled magnetic, hydrodynamic, and contact interactions that drive collective motion across multiple length and time scales. Analytical theory will translate these data into design rules, while inverse-design algorithms will search efficiently for particle shapes, magnetic moments, and field protocols that enable adaptive aggregation to move through complex structures. Lithographically fabricated and chemically synthesized particles will test these predictions; high-speed imaging, particle tracking, and force mapping experiments will measure swarm structure, flow fields, and cargo transport efficiency. By combining computational analysis with experimental methods, swarm functionalities for advanced applications, such as adaptive organization, precise navigation, and targeted cargo transport in complex environments will be expanded. These advancements will create a foundation for future applications of colloidal swarms in sensing and delivery, turning theoretical insights into practical outcomes. More broadly, the proposed methods to accelerate swarm design will benefit other active material systems where flows of energy, matter, and information animate material structures to enable life-like capabilities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project seeks to develop a new generation of quantum sensors that are not only highly sensitive but also scalable and robust under real-world conditions. Traditional quantum sensing technologies, while powerful, often rely on delicate quantum states that are easily disrupted by noise and loss, limiting their practical use outside controlled laboratory settings. This research takes a fundamentally different approach by designing quantum sensors that extract useful quantum features from classical light sources and operate effectively even in noisy or lossy environments. At the heart of this work is a novel sensing platform based on plasmonic nanostructures—metallic surfaces that can tightly confine light and support complex light–matter interactions. These structures will be paired with quantum protocols that enable the extraction of multiparticle quantum systems, even when the light fields originate from classical or partially coherent sources. The resulting sensors are expected to achieve sensitivity beyond the shot-noise limit, and resolve spatial features smaller than the wavelength of light. This makes them ideal for applications such as gas sensing and the detection of fragile biological samples, where strong illumination could cause damage. Beyond the scientific contributions, the project includes a strong educational component. Undergraduate and graduate students will receive hands-on training in quantum optics, nanofabrication, and data science. The PI’s lab will also engage a wider audience in the implications of quantum sensing and imaging. Demonstrations of quantum sensing techniques will be integrated into classroom instruction, fostering broader interest and understanding of quantum science. Technical Summary This project will establish a robust and scalable framework for quantum plasmonic sensing by developing multiparticle systems extracted from classical plasmonic waves. The central innovation lies in overcoming a longstanding limitation in quantum plasmonics: the intrinsic optical losses that typically degrade quantum coherence and sensitivity. Instead of avoiding these losses, the project introduces methods to distill and exploit quantum correlations from classical or thermal plasmonic fields using photon-number-resolving detection. This opens a new path toward implementing quantum sensing schemes in noisy, lossy, and realistic environments. The theoretical framework supporting the project includes modeling the density matrix of partially coherent plasmonic systems in the Fock basis and using projective measurements to isolate specific quantum subsystems. This approach will be combined with finite-difference time-domain simulations to design and optimize the nanostructures responsible for inducing and manipulating quantum statistical behavior in the sensing field. By addressing both fundamental and practical challenges in quantum plasmonics, this project will contribute to the development of scalable quantum technologies capable of operating in noisy, realistic environments. The outcome will be a robust, multiparticle quantum sensing platform that integrates classical sources, advanced detection, and engineered plasmonic interactions to push the boundaries of what is achievable in quantum-enhanced plasmonic sensing. 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 tracheostomy procedure is frequently performed in children to bypass an obstructed airway due to blockage in the upper throat or for chronic lung conditions related to prematurity. Tracheostomy-dependent children living at home require continuous monitoring, and over 25% of them experience serious complications. The nationwide shortage of home care nurses makes continuous observation of these children impractical, hence the critical need for home-based monitoring systems. Such monitoring systems usually require training on images of tracheostomy-dependent infants. However, an important challenge is the scarcity of videos at any single healthcare provider, leading to the demand to share such videos between different healthcare providers. Nevertheless, healthcare privacy laws make such sharing dependent on the consent of privacy-aware caretakers. This project establishes a framework for sharing such sensitive data in a privacy-constrained manner, supported by formal privacy guarantees that can be explained and demonstrated to caretakers and healthcare providers. The project’s novelties stem from a comprehensive suite of privacy mechanisms for moving images, where the private information pertains to the identities of humans. The project's broader significance and importance are that the developed framework is readily applicable to videos of human subjects across all age groups, thus holding potential for use in any fields requiring people video supervision of , such as daycare centers, nursing homes, hospices, or prisons. This project brings together a set of advanced technologies—face identification and pixelation in video, adversarial generative privacy mechanisms, video component disentanglement, and AI-driven text-to-video generation—not as isolated tools, but as interdependent components within a unified system tailored to a real-world application governed by stringent safety, ethical, and regulatory requirements. The novelty lies in the integration of these technologies into a coherent framework that addresses complex, high-stakes challenges. By moving beyond controlled or idealized settings, the project enables practical evaluation and refinement of methods that have thus far seen limited deployment outside the lab. Due to the extremely limited access to real data via cooperation agreements, the first research focus is on generating a synthetic video dataset to train privacy mechanisms, identity disentanglement/recombination systems, and various other classifiers. The second focus is on evaluating multiple independent privacy designs by comparing their detection accuracy and privacy guarantees. The third focus is on establishing formal privacy guarantees, often lacking in some designs, by extending current theoretical notions of differential privacy and integrating them with empirical validation through specialized classifiers and feedback from medical personnel and patients' caretakers. 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 project aims to serve the national need of preparing highly qualified middle school STEM teachers to provide engaging, research-based instruction in artificial intelligence (AI) and cybersecurity. Many rural and high-need schools struggle to recruit and retain teachers with strong technological expertise. This project responds to that need by offering practicing teachers authentic research experiences in AI and cybersecurity, paired with professional learning to help translate these experiences into classroom instruction. Teachers will participate in summer research at a university lab, develop lessons that connect students to real-world technology applications, and receive mentorship during the academic year. The project has potential to increase teacher confidence, improve instructional quality, and inspire students to consider future careers in computing fields. These outcomes are significant to the general public because they expand access to high-quality computer science education, preparing students with critical thinking and problem-solving skills that are essential for the nation’s workforce. This project at Louisiana State University includes partnerships with East Baton Rouge Parish high-need middle schools and the university’s Applied Cybersecurity Lab. Project goals include preparing thirty practicing middle school STEM teachers over three years through six-week summer research experiences focused on AI and cybersecurity. Teachers will develop and implement inquiry-based lessons using the Understanding by Design framework, emphasizing critical thinking and authentic problem-solving. Research questions examine how participation in research experiences influences teacher self-efficacy, instructional practices, and retention in high-need schools, as well as the impact on student engagement and learning outcomes. A mixed-methods evaluation will include surveys, interviews, classroom observations, and analysis of teacher-developed materials. Dissemination will occur through research poster presentations, national conference sessions, and practitioner-oriented publications, contributing to the knowledge base on how research experiences influence teacher practice and student learning in K–12 computer science education. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K–12 STEM teachers and experienced, exemplary K–12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K–12 STEM teachers in high-need school districts. 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 I-Corps project is based on the development of materials capable of autonomous adaptation, self-healing, and reliable performance in extreme environments. Industries such as energy, aerospace, and civil engineering face billions of dollars in losses each year due to recurring material failures and labor-intensive repairs. Current materials often cannot perform in extreme environments or recover from damage. This technology introduces a new class of smart materials that respond to damage and temperature changes without manual intervention, enabling longer service life and more efficient deployment. In addition, these materials are ultraviolet (UV) curable, customizable, and compatible with additive manufacturing, making them ideal for both in-field and factory use. The use of these materials may reduce operational downtime and replacement frequency by offering adaptive, more durable alternatives. They may be used in a wide range of industries including oil and gas, aerospace, and infrastructure that needs in-situ repair. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of high-temperature, 4D printable shape memory polymer inks and UV-curable self-healing composite prepregs. These materials are designed for smart manufacturing and in-situ structural repair applications where high thermal resistance, damage recovery, and programmability are required. The core technology is based on acrylate monomer systems with tunable glass transition temperatures, enabling photopolymerization-based 4D printing with enhanced dimensional control. The self-healing prepregs use reversible polymer network architectures activated by thermal stimuli. Unlike traditional composites, this solution integrates shape memory and damage recovery, reducing the need for external repair and increasing lifecycle performance. Research has shown recovery efficiencies above 90% and thermal stability exceeding 200°C. The combination of reprogrammability, high printing resolution, and mechanical resilience is an advance over conventional thermosets or thermoplastics. The goal is to provide a solution to unmet needs in the aerospace, energy, and infrastructure industries while identifying barriers to commercial deployment such as processing scale, qualification standards, and field applicability. 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 establish partnerships among traditional public schools and public charter schools in South Louisiana in both urban and rural school districts (Baton Rouge, New Iberia, etc.) and university partners (Louisiana State University, the New Jersey Institute of Technology, and the University of Chicago) to investigate the integration of computer science (CS) into core curricular topics in order to lay foundational support for a newly passed high school CS graduation requirement. The project team, composed of experienced K-8 practitioners and researchers, will design, develop, and pilot prototype instructional materials for integrating computer science into core elementary (grades 3-5) content areas. These instructional materials will be designed to primarily support South Louisiana students. The project has the potential to foster genuinely collaborative and responsive approaches teaching strategies and to produce high-quality curricular materials that teachers find easy to implement long after the project ends. The project starts its work in the domain of elementary school classrooms by adapting a previously developed, research-based approach to solving the problem of finding time for CS, a non-core subject, in the elementary school day. By focusing on elementary students, Time4CSforLouisiana has the potential to impact students' STEM interest and CS identity formation. Time4CSforLouisiana is a small Research-Practice Partnership (RPP) between public and public charter elementary school districts in South Louisiana, Louisiana State University, the New Jersey Institute of Technology, and the University of Chicago that investigates options to increase students' STEM and computer science (CS) identity through participatory design approaches. The project goals are to assemble a strong and well-integrated RPP team that builds a new partnership between educators from 4 school networks; design, develop, and pilot prototype instructional materials for integrating computer science into core elementary content areas; and generate new knowledge about participatory design of curriculum, focused on issues central to the participants' urban and rural communities. Time4CSforLouisiana will leverage work from a previous CSforAll project (Time4CSforAll, NSF #2031424) that designed a model for curriculum developers to authentically embed Universal Design for Learning and responsive pedagogy into elementary integrated Science+CS content, according to the needs and preferences of a partner school district in Florida. The project will result in example integrated curriculum materials being co-developed with 6 elementary school teachers and piloted with students in 6-12 South Louisiana classrooms, over an 18-month period. The RPP will explore how efforts to integrate CS into core elementary school curriculum can lay foundational support for nascent high school CS graduation requirements. Further, the project will explore the potential of community-inclusive participatory design approaches to develop STEM and CS curriculum and instruction that prioritizes the preferences and needs of children and teachers and their local contexts. The project will result in a protocol for authentically and respectfully including community members in Research-Practice Partnerships focused on bringing CS to their communities. This project is funded through the Computer Science for All: Research and RPPs program. 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 I-Corps project investigates the commercial potential of high-performance wood (HPW) for sustainable civil infrastructure development. The construction industry increasingly demands sustainable and cost-effective building materials. HPW has been developed to address this need by providing strength and stiffness comparable to traditional construction materials. As the market for new materials continues to grow, construction companies are seeking alternatives that effectively balance high mechanical properties and cost-effectiveness with environmental sustainability. While current renewable options such as untreated timber may be insufficient for heavy-load applications, HPW has shown superior mechanical properties. HPW may meet the increasing need for various applications, including deep foundations, housing, and structural supports. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of high-performance wood (HPW), which is produced through a process of partial delignification followed by hot-pressing, resulting in a material that offers excellent strength and stiffness, but with a more sustainable production process as compared to steel and concrete. Traditional delignification methods face challenges when applied to larger wood samples due to limited chemical diffusion. To overcome this, a pressurized delignification technique was developed, ensuring deeper and more uniform chemical penetration throughout wood. This innovation enables the production of HPW at larger scales, suitable for demanding civil infrastructure applications. This technology offers users a sustainable, high-strength alternative to steel and concrete, with superior mechanical performance and reduced environmental impact. The pressurized delignification process enables scalable, uniform treatment of large wood elements, making it a candidate for building resilient civil infrastructures. 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

Computational thinking is central to many science, technology, engineering, and mathematics (STEM) careers of the future, yet many young children do not have opportunities to develop the computational thinking (CT) skills that lay the foundation for future STEM trajectories leading to these careers. This project will address this opportunity gap through developing and researching a story-based approach to fostering computational thinking among young children. As part of this approach, in the context of stories, children will use discovery-based tinkering activities to develop solutions to problems using computational thinking. While children will initially use this "tinker-telling" approach in the context of libraries and museums, the project will provide professional learning materials and opportunities to informal educators in these settings on how they can, in turn, support caregivers in extending the tinker-telling approach to computational thinking using stories and everyday materials in their homes. The project team, informal educators, and caregivers will iteratively co-design, test, and refine a guidebook, which will highlight how to teach computational thinking through playful story-based tinkering in museums, libraries, and homes. Research will explore whether and how the professional learning experiences, and the use of the guidebook, increase children's computational thinking skills and dispositions, as well as promote caregivers' sense of confidence in supporting computational thinking among their young children. The guidebook will be disseminated widely through professional associations of libraries, museums, and other informal learning institutions, with a focus on rural regions across the United States. Ultimately, this project is intended to result in improved CT skills and dispositions by resulting in an empirically tested guidebook on how to provide effective CT learning experiences for young children within and across informal settings. Mixed-method design-based implementation research will be used to iteratively develop, evaluate, and refine developmentally appropriate methods for encouraging CT skills and dispositions for children from ages four to eight. These methods include play-based approaches to supporting computational thinking, via stories and tinkering, in the context of libraries and museums. Informal educators in these settings will further learn how to support caregivers in extending the CT learning experiences in their homes. Mixed methods research will investigate whether and how the professional learning experiences, in conjunction with the guidebook, supported the informal educators' capacity to use tinker-telling to build computational thinking among young children. Research will further explore whether and how the guidebook supported caregivers' confidence and capacity in supporting their children's development of CT dispositions and skills. Finally, this research will examine the extent to which the tinker-telling approach promoted the development of CT dispositions and skills among children. The empirical research resulting from this project will be published to audiences of STEM educational researchers and informal educators. Given that millions of families visit museums or libraries each year, this project has the potential to expand the number of people prepared to enter CT-based careers, by resulting in empirically based approaches on how library and museum educators can provide experiences and materials to families that enable them to build their children's computational thinking at home. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences. 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 surge of artificial intelligence (AI) and data science, increasing data and parameters of machine learning models come with high-order structures, also known as tensors. Tensor decomposition (TD) is commonly used to compress the data and analyze the underlying information. This project aims to develop both theoretically justified and practically efficient TD algorithms, as well as algorithms for low-rank tensor approximations and tensor completions. Such tools have wide applications in data science, statistics, engineering, and industry, including multi-view learning, convolutional neural networks (CNNs), and large language models (LLMs). The project also includes training of undergraduate and graduate students studying in scientific computing and data science. This project studies a range of challenging research tasks centered on the computation and analysis of tensor decompositions. A major goal is to overcome limitations of existing algebraic-based and optimization-based methods, which are either computationally expensive or theoretically insufficient. The tensor decomposition algorithms utilized in this project are based on generating polynomials to reformulate and understand the non-symmetric TD. The developed TD algorithms will have the following advantages: computationally efficient in terms of speed and memory, easy to implement in linear algebra friendly software, have theoretical guarantees, can be used to detect certain tensor ranks, and support higher tensor ranks. For generic tensors satisfying certain rank bounds, the approach in this project is to construct the TD by solving linear equations. When the tensor rank is higher, the problem is reformulated as a nonlinear optimization with linear constraints and can be solved using modern optimization methods. The generating polynomial-based framework can also be utilized to reformulate and solve low-rank tensor approximation and tensor completion problems, which are widely used in data science applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Chemical elements, such as Carbon, Oxygen, and Nitrogen, are made within stars. The first stars were made of pristine Hydrogen and Helium only, and they were the first cosmic factories to synthesize advanced chemical elements such as Carbon. The first generation of stars also ended their lives with powerful explosions, spreading complex chemical elements in their nearby environment. This program will carry out advanced numerical simulations to study the explosive end of the first generation of stars to better understand the origin of chemical elements. This project includes a robust outreach initiative designed to engage students in the State of Louisiana through innovative educational opportunities. This program will offer hands-on research experiences to high school and undergraduate students at the LIGO Gravitational Wave Observatory and the University of Crete Institute of Astrophysics, in Greece. This project aims to investigate the transient phenomena of the high-redshift universe, focusing on Pulsational Pair-Instability Supernovae (PPISNe) and massive stellar mergers occurring in low- and zero-metallicity environments. By using cutting-edge hydrodynamic simulations and radiation transport models, the study will explore how these explosive events influence the chemical enrichment of the primordial gas and contribute to the formation of early cosmic structures, such as the first stars, galaxies, and black holes. The project will investigate how the rapid rotation and episodic mass ejections of PPISNe, along with the merger-induced mixing and mass loss in stellar mergers, contribute to cosmic reionization and the creation of the first heavy elements. This program will support the training of a postdoctoral fellow, a graduate student, and several undergraduate 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 · 2025-09

The mathematics of this research project is in the area of topology, which studies spaces up to continuous deformation. Here, spaces are considered the same if one can be transformed into the other without cutting or gluing. One is often interested in computing algebraic invariants which can distinguish spaces up to this equivalence. One such invariant is homology, which lets one study the shape of spaces using linear algebra. In low dimensions, homology measures very concrete aspects of a space; for instance it detects the number of loops in a graph or the number of holes in a surface. In high dimensions, it measures more complicated features, and is generally more difficult to compute. The PI will study several long-standing conjectures on the topology of aspherical manifolds, as well as recent breakthroughs connecting homology growth to various aspects of fibering. This project will also promote graduate and undergraduate education through the writing of a textbook on L^2-homology and the development of new Vertically Integrated Research courses at Louisiana State University. The primary goal of the research program is to study the growth of homology (with various coefficients) in a residual tower of finite regular covers of an aspherical manifold. One of the motivating conjectures is that for a large class of closed, aspherical manifolds, sublinear homology growth in all degrees and all field coefficients implies that the manifold has a finite cover which fibers over the circle. This is a key step towards developing a high-dimensional analogue of Agol's Virtual Fibering Theorem ​in dimension 3. In one direction, the PI plans to construct closed, aspherical Gromov hyperbolic manifolds of any odd dimension which have nontrivial F_p-homology growth. The PI will also study fibering (and various group theoretic analogues) for Gromov-Thurston manifolds and other classical examples. In a related direction, the PI will continue work on Singer conjecture's on vanishing of L^2-homology groups for certain locally CAT(0) cubulated manifolds. In the final part of the project, the PI will investigate Atiyah's conjecture on integrality of L^2-Betti numbers for some classes of Artin groups. 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

Vibrations are modeled by the Laplace operator on partial differential equations (PDE). Laplacian eigenfunctions are the fundamental modes of vibrations. In music, eigenfunctions are oscillations of a guitar string or vibrations of a drum's membrane. In mathematics, eigenfunctions are the higher dimensional analogs of the familiar trigonometric functions for the circle. Just as in music, one expects different shapes or concentrations to become more apparent as the frequency becomes larger. Thus, in mathematics, it is essential to understand how these changes of shapes or concentrations depend on the high frequency and how the amplitude of vibrations propagates from one region to other regions. These lead to the quantitative studies of solutions of PDE. This project provides training opportunities for undergraduate and graduate students, as well as outreach activities aimed at K-12 students and the general public. The research objectives of the project focus on the sizes of the level sets such as zero-level sets for different PDE models, quantitative propagation of smallness, and their applications to other subjects. The Principal Investigator (PI) studies the sizes of the level sets of eigenfunctions for second order elliptic equations, higher order elliptic equations, and periodic elliptic homogenization. The studies of eigenfunctions advance the progress of PDE in general. The PI also studies the quantitative Cauchy uniqueness and propagation of smallness for elliptic equations with singular weights on measurable sets of positive measure. These problems are essential in the study of Laplacian eigenfunctions and PDE. The applications of quantitative unique continuation to null controllability for heat equations and energy density in spectral theory are also investigated. The progress on the quantitative studies of eigenfunctions and other PDE models will also benefit other related subjects in mathematics, such as control theory and spectral theory. 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

Louisiana State University (LSU) renews its thriving CyberCorps® Scholarship-for-Service (SFS) program that supports undergraduate and graduate students in computer science. LSU’s Department of Computer Science has experienced record-breaking growth since introducing of the CyberCorps® cybersecurity program in 2017. LSU is designated by the National Security Agency as a Center of Academic Excellence in Cyber Operations (CAE-CO) and offers SFS awardees a tightly integrated combination of applied cybersecurity experiences in the classroom and impactful research in state-of-the-art labs, addressing important problems in memory forensics, reverse engineering, malware analysis, Artificial Intelligence, network security, and more. LSU has established relationships with many federal and state partners to expose our students early to federal and state service. These include Cybersecurity and Infrastructure Security Agency (CISA), Idaho National Labs, Department of Homeland Security, the United States Secret Service, United States Army Cyber Command, the Cyber Crime Unit of the Louisiana State Police, and the Louisiana National Guard. All LSU SFS awardees participate in a federal or state cybersecurity-related internship program and perform focused cybersecurity research under the guidance of an LSU faculty member. Students also have the opportunity to study for and obtain cybersecurity certifications, attend cybersecurity-themed conferences, and become members of the LSU Cybersecurity Club, which offers cybersecurity activities such as Capture-the-flag (CTF) competitions and bootcamps focused on deep learning. The LSU SFS program also has significant benefits for society as a whole, as it substantially increases the number of highly trained cybersecurity professionals, a critical issue in defending our nation and its infrastructure. The scholarships help the LSU continue to recruit and retain excellent undergraduate and graduate cybersecurity students and provide resources for these exceptional students to pursue cybersecurity career. 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 project is jointly funded by the CyberCorps® Scholarship for Service (SFS) 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 · 2025-08

Coastal regions are vulnerable to flooding from rivers and rising seas, increasing storm strength, and destruction of ecologically-fragile areas. River deltas are especially impacted by the balance between increasing water levels from sea-level rise and tides, and land surface elevation changes. Bangladesh’s Ganges-Brahmaputra Delta (GBD), the world’s largest delta, is a particularly excellent place to investigate this problem. The land is sinking (subsiding), worsening the impact of sea-level rise, but the rivers supply ample sediment to elevate the land. However, there is a mismatch in the distribution of sediment and land subsidence; some areas are maintained by sediments, while others are at serious risk of land loss. This project will combine local, on-the-ground measurements of elevation change with broad satellite observations, and develop a comprehensive numerical model of elevation change. The numerical model will enable synthesis of all measurements and incorporate shallow processes that are missing from most models. Results will contribute to Bangladesh’s coastal planning through established collaboration with government agencies, academic institutions, and non-governmental organizations. This project will support 2 postdocs and 3 graduate students in the U.S. as well as build capacity for students and faculty in Bangladesh. U.S. undergraduate students will participate in the proposed research through internship programs and a capstone course that includes a Spring Break field trip to Bangladesh. The model will have great applicability for use in coastal areas prone to flood risk, especially lowland deltas worldwide including the Mississippi Delta. Unraveling the intersecting processes that contribute to vertical land-surface dynamics is critical for forecasting sustainability of lowland deltas into the future. This project will employ multidisciplinary research that integrates an existing delta-wide network of sediment cores and geospatial instruments with broad-scale, multi-sensor satellite remote-sensing analyses, producing novel high-resolution maps of decadal surface-elevation change, topography, and land-use across the coastal zone. A state-of-the-art poroelastic model will be developed, validated, and applied to coastal Bangladesh. The team hypothesizes that at any given site on the delta, surface-elevation change reflects the vertical integration of sedimentation, near-surface soil consolidation, subsurface compaction of Holocene sediment, and deep tectonic/isostatic response of the lithosphere. Across the delta, surface-elevation change reflects how modern land use restricts surface sedimentation and accelerates consolidation, and how ancient river dynamics constructed the alluvial architecture of compacting Holocene sediments. These hypotheses will be tested with a process-based, holistic understanding of vertical land-surface dynamics, and will guide coastal hazard mitigation and sustainability efforts on the GBD and other deltas that face similar environmental and anthropogenic stressors (e.g., Mississippi and Sacramento-San Joaquin river deltas). 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.