Arizona State University

NSF Awards · FY 2026 · 2026-08

Multi-metal additive manufacturing enables the creation of multi-metal architectures that exceed the intrinsic property limits of single alloys by spatially combining complementary materials within a single component. Such capability is critical for next-generation aerospace systems, energy technologies, biomedical devices, and national defense applications. Despite its promise, the widespread use of multi-metal additive manufacturing is limited by cracking at dissimilar-metal interfaces, which undermines reliability and discourages industrial adoption. This Faculty Early Career Development Program (CAREER) project addresses this fundamental challenge by developing a science-based understanding of how and why interfacial cracks form during processing and how they can be avoided. By enabling defect-free multi-metal components, the project supports U.S. competitiveness in advanced manufacturing while contributing to workforce development. Integrated research, education, and outreach activities engage K–12 students, undergraduates, and graduate researchers through hands-on design-to-manufacture challenges and a new forensic learning framework that emphasizes evidence-based reasoning, creativity, and critical thinking. The research objective is to develop a rigorously validated multi-physics framework that predicts process-induced interfacial cracking in multi-metal additive manufacturing. The project integrates multicomponent heat and mass transport, grain-scale crystal plasticity, and coupled fracture mechanics to capture the interactions among residual stress evolution, liquid-metal embrittlement with Kirkendall porosity, and brittle intermetallic layer formation. Two representative alloy systems, Cu-10Sn/904L stainless steel and Ti-6Al-4V/AlSi10Mg, are studied to isolate distinct cracking mechanisms and validate the framework across different metallurgical regimes. Model predictions are validated using in-operando synchrotron X-ray imaging and diffraction and high-resolution electron backscatter diffraction, with an industrial testbed for fabricating a next-generation multi-metal rocket engine. The resulting theory and computational tools are expected to provide quantitative guidance for selecting process parameters and material combinations that suppress crack initiation by maintaining the local crack-driving force below the evolving interfacial fracture resistance. The outcomes are expected to extend beyond additive manufacturing to inform dissimilar-metal joining technologies and the design of robust, multifunctional engineered 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 2026 · 2026-07

Quantum computing has the potential to transform scientific discovery and technological innovation in areas such as artificial intelligence, molecular simulation, biotechnology, and secure information processing. Variational quantum algorithms are among the most promising approaches for near-term quantum computing, but existing optimization methods used to train these algorithms often become unreliable when quantum measurements are highly noisy or when problems become very large. These limitations present major difficulties to realizing the practical benefits of quantum computing technologies. This research addresses these challenges by developing mathematically rigorous and scalable optimization frameworks that remain effective under severe noise and computational uncertainty. The project advances the mathematical and computational foundations needed for trustworthy quantum algorithm training and evaluation, while also enabling systematic assessment of when quantum computing can provide advantages over classical methods. By strengthening core capabilities in quantum computing, the research supports national priorities in scientific innovation. The project also contributes to workforce development through interdisciplinary training opportunities for undergraduate and graduate students in computational mathematics, optimization, and quantum computing. Research outcomes, including open-source software, benchmark test problems, and educational materials, will be broadly disseminated to accelerate adoption by the scientific community and support the emerging quantum technology workforce in the United States. This project studies large-scale unconstrained and constrained quantum-specific optimization problems arising in variational quantum algorithms. The research develops advanced noise-aware, derivative-free, and factorization-free optimization algorithms that rely solely on noisy objective and constraint evaluations, avoid costly matrix factorizations, and accommodate realistic quantum hardware conditions, including stochastic and potentially unbounded measurement noise. Rigorous convergence, complexity, and robustness analyses establish theoretical guarantees for algorithmic performance, scalability, and resource efficiency. The research also integrates modern linear-system solution techniques to improve scalability while maintaining convergence behavior comparable to leading factorization-based methods. The resulting frameworks will be validated across a broad range of quantum computing applications, including quantum approximate optimization algorithms, variational quantum linear solvers, and quantum neural networks, with potential downstream applications in artificial intelligence, molecular design, and computational biotechnology. The resulting methodologies are also broadly applicable to large-scale stochastic optimization problems arising in science, engineering, and advanced computational technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Nontechnical Description Remarkable advances in communications, computing, and sensing have been achieved by precise control of interactions between light and matter through photonic metamaterials. These are engineered optical materials that manipulate light through nanoscale structures. However, these materials are reaching their fundamental limits. Future technologies will need access to the molecular and quantum interactions to control optical behavior. This CAREER project establishes a fundamentally different approach. The PI will create a new class of photonic metamaterials in which optical behavior is determined by molecular arrangements and underlying quantum interactions. The research takes advantage of the properties of layered halide perovskites. These materials are composed of atomically thin layers and have tunable electronic and optical properties. These hybrid materials will be used as a platform for embedding tailored molecular building blocks that intrinsically and directly shape optical responses. The project advances fundamental materials research while opening pathways for emerging quantum and sensing technologies. The project integrates research with education to embed materials and photonics concepts in the curriculum. Students engage in concept-linked coursework to pair content-based subject matter with skills development and enhance learning. Thereby, this project strengthens pathways from fundamental discoveries to STEM careers in materials science and photonics. Technical Description Recent breakthroughs in nanophotonics and metamaterials have enabled powerful control of light-matter interactions. However, existing approaches remain fundamentally limited by structural elements defined at the nanoscale through lithography or nanoparticle assemblies. These constraints restrict access to deeper length scales where quantum wavefunctions governed by chemical identity, spatial confinement, and intermolecular interactions can overlap and directly reshape optical properties. The scientific problem addressed in this project is how to advance metamaterials design principles into the sub-nanometer regime to understand, encode, and tailor light-matter interactions at the molecular level. The overarching goal is to establish molecular metamaterials whose optical parameters are intrinsically defined by molecular framework and quantum interactions rather than by traditional nanopatterning. The research uses layered halide perovskites as a model platform, leveraging their sub-nanometer interlayer gaps and molecular intercalation versatility to construct structured molecular meta-layers. The project tests the hypothesis that molecular motifs embedded within these gaps can be systematically encoded to transform fundamental optical parameters beyond those achievable in conventional perovskites and nanostructured metamaterials. The research scope is organized into three integrated thrusts: establishing quantum-informed molecular meta-layers through controlled molecular intercalation; tuning optical permittivity and permeability via molecular building blocks that modulate dielectric and quantum confinement; and elucidating intermolecular symmetry-driven optical anisotropy and spin-selective effects using symmetry-controlled molecular motifs. The approach integrates molecular synthesis, structural and spectroscopic characterization, and optical modeling to connect molecular-scale interactions with emergent optical behavior. These efforts establish molecular-level meta-photonic design rules, laying the foundation for next generation of molecular tailored, quantum-informed photonic technologies. As part of this project, an open-access, curated database of optical constants for layered halide perovskites will be developed to support data-driven materials design and reuse by the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Bees and other pollinators are essential to healthy ecosystems and food security, yet they are declining at alarming rates. A 2025 study found that over 22% of North American pollinator species now face an elevated risk of extinction, and United States beekeepers lost over 60% of their honeybee colonies in the past year alone, representing more than $600 million in economic losses, jeopardizing the sustainability of an industry critical to food production. Because pollinating insects contribute over $15 billion annually to North American agriculture, their continued decline poses a serious threat to food security, farm economies, and biodiversity. This project addresses pollinator declines by developing new mathematical and artificial-intelligence-based tools to predict how interacting threats (such as disease, pesticides, habitat loss, and other environmental pressures) combine to harm pollinator communities and to identify effective strategies for protecting them. Rather than studying these threats in isolation, the research links what happens inside a single colony to broader patterns across farming landscapes, combining mathematical rigor with artificial intelligence to address one of the most pressing ecological and agricultural challenges of our time. The project's tools and findings will be made freely available to researchers, growers, beekeepers, and other stakeholders. It will also train undergraduate and graduate students through integrated cross-disciplinary mentorship at the intersection of mathematics, biology, artificial intelligence, and sustainability, strengthening both scientific capacity and food system resilience. This project pioneers new mathematical theory and artificial intelligence-enhanced modeling frameworks that link colony-level mechanisms with ecosystem-scale processes, yielding predictive, adaptive tools for sustainable pollination management through an interdisciplinary collaboration between mathematicians and pollinator biologists. The research follows a coherent progression across three aims. The first develops mechanistic models of honeybee colony resilience and collapse, incorporating Allee effects to capture tipping-point behavior under coupled temperature-toxicity, pesticide, parasite, and disease stressors, grounded in long-term laboratory and field observations. The second extends these models to multi-species plant–pollinator–pathogen–pest–agrochemical networks using stochastic reaction-diffusion-taxis partial differential equations that account for spatial fragmentation, nonlinear feedback, and environmental variability. The third designs adaptive management strategies using Filippov optimal control for piecewise-smooth ecological systems, Approximate Bayesian Computation for parameter inference, and reinforcement learning and hybrid dynamical systems-AI methods for AI-enabled decision support. These approaches are calibrated and validated using 40-hive field experiments and 190-colony longitudinal datasets. The project advances foundational mathematics by developing persistence theory (in invariant domains) for nonautonomous delay systems with Allee effects, coexistence criteria for stochastic spatial systems, and optimal control under ecological uncertainty. Its artificial intelligence and machine-learning components—embedded across parameter learning, uncertainty quantification, and adaptive control—provides data-informed tools for evidence-based pollination management at colony and agroecosystem scales. 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

Today's confidential computing hardware provides the fundamental building blocks for data privacy in the cloud. However, current solutions built on this technology fail to deliver the level of security or the performance needed, while still demanding prohibitive resources. This project identifies the root cause as the inappropriate application of software abstractions originally designed for traditional computing environments to confidential computing contexts. Its goal is to evolve these abstractions to support elastic confidential computing and translate research outcomes into practical, widely accessible learning opportunities that position confidential computing as a first-order software design principle rather than an afterthought. The project's novelty lies in identifying the key primitives missing from confidential computing for elastic cloud settings and designing secure and automated mechanisms to realize them. Beyond advancing a technology capable of transforming data privacy and accelerating growth in the public cloud domain, the project's broader impact and significance also stem from coordinated translational efforts with the confidential computing industry. This project advances confidential computing through four innovations. First, it develops a compiler-driven analysis and validation framework to automate the adoption of trustworthy isolation primitives within Confidential Virtual Machines (VMs). Second, it introduces a multi-process Library operating system design that enables compatibility with elastic container workloads and essential features. Third, it creates a secure GPU sharing abstraction that compartmentalizes critical user- and kernel-level components to guarantee confidentiality and integrity. Fourth, it proposes a collaborative page-swapping architecture that enables Confidential VMs and hypervisors to efficiently leverage remote, disaggregated memory. Results are widely disseminated through research forums and direct industry collaborations. The integrated education plan broadens participation in computer science and confidential computing by expanding initiatives, open-source tools, and accessible online platforms to reach learners nationwide. In summary, the project integrates research, education, and translationanal aspects to advance trustworthy data protection in the cloud. 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

Evolution shapes how organisms respond to stress, influencing challenges that directly affect society, including the rise of drug-resistant pathogens, the stability of agricultural systems, and the resilience of ecosystems under environmental change. However, most studies of evolution focus on extreme conditions, overlooking the subtle environmental differences that organisms routinely experience in nature. This limits our ability to predict how evolution unfolds in realistic settings. This project addresses this gap by examining how small changes in environmental conditions alter which genetic changes are favored during adaptation. By revealing how even slight environmental differences can redirect evolutionary outcomes, the project will improve our ability to anticipate and manage biological responses to stress. In addition to advancing fundamental knowledge, the project integrates research with education through a course-based undergraduate research experience (CURE), providing hands-on training in experimental evolution to the next generation of scientists. The project will also engage the public through outreach activities and openly share all data and tools, contributing to workforce development, scientific literacy, and the broader scientific community. This project uses massively parallel experimental evolution in the model eukaryote Saccharomyces cerevisiae to quantify how adaptive mutations respond to finely resolved environmental gradients. The research will evolve thousands of uniquely barcoded yeast lineages across a series of subtly different temperatures, enabling high-resolution measurement of fitness effects across environmental conditions. Aim 1 will characterize how the genetic basis of adaptation and the distribution of fitness effects (DFE) change across temperature gradients by tracking barcode frequency dynamics and sequencing adaptive lineages. Aim 2 will re-measure the fitness of adaptive mutants across all environments to quantify genotype-by-environment (GxE) interactions and identify patterns in how fitness varies across conditions. Aim 3 will determine the phenotypic basis of these patterns using high-throughput single-cell RNA sequencing to link transcriptomic states to fitness outcomes. Together, these approaches will generate a large, publicly accessible dataset that enables rigorous tests of evolutionary theory, including predictions from Fisher’s geometric model, and supports the development of more accurate models of adaptation. By integrating high-throughput experimentation, single-cell genomics, and data-driven modeling, this project advances the goal of making evolutionary biology a more predictive science. 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

Artificial intelligence (AI) is accelerating demand for faster computing and data communication, but meeting this demand with electronics alone is becoming increasingly expensive and power-hungry. As AI workloads grow, achieving higher performance often requires disproportionately more energy and hardware cost. Integrated photonics uses light on a chip to move and process information at light speed and low energy, creating a promising path to more efficient hybrid electronic-photonic systems. However, designing and manufacturing photonic chips remains slow and costly, in part because the supporting design software, models, and workflows are not yet mature or widely accessible. This project will deliver an open-source, end-to-end workflow that enables scalable, rapid, and high-quality design and simulation of electronic-photonic chips, while improving manufacturability and reducing design iterations. The resulting productivity gains and lower barriers to entry will broaden access to this technology and accelerate its adoption in computing, communication, and sensing systems. The project will also expand education and workforce development by integrating hands-on modules into undergraduate and graduate courses, offering online materials and courses, and growing a public seminar series that connects students with researchers and industry. The technical goal of this project is to establish a full-stack electronic-photonic design automation framework for large-scale electronic-photonic integrated chips for AI computing, optical interconnects, and sensing. The research will develop (1) fast, physics-guided simulation and co-simulation methods, including a learned electromagnetic solver for photonic device modeling and a circuit-level co-simulator for mixed electronic-photonic systems; (2) fabrication-robust inverse design methods that model process variations to improve yield and reduce costly redesign cycles; and (3) scalable physical design automation that synthesizes large-scale photonic circuit layout under manufacturing rules and performance constraints. These capabilities will be integrated into a system-level modeling and design-space exploration engine that connects device behavior and layout effects to end-to-end system metrics such as speed, energy, area, and robustness. The expected outcome is an open, reproducible ecosystem with benchmarks and reference flows that accelerate design iteration and enable closed-loop co-design of next-generation hybrid electronic-photonic AI 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 2026 · 2026-05

NON-TECHNICAL SUMMARY: Many promising new medicines fail to reach patients because they do not dissolve well in water, making it hard for the body to absorb them. This poor solubility affects over 90% of drugs in development, limiting treatment options, increasing costs, and often forcing researchers to abandon otherwise effective compounds. Polymeric micelles are tiny, self-assembled carriers made from long-chain molecules that offer a powerful way to encapsulate and deliver these hydrophobic drugs safely and efficiently. However, current designs often achieve only low drug loading, requiring excessive carrier material that limits treatment effectiveness and clinical outcomes. This CAREER project tackles this challenge using artificial intelligence (AI) and machine learning to decode the fundamental molecular interactions between polymers and drugs. By systematically evaluating key interaction forces such as π-π stacking and hydrogen bonding, and integrating advanced quantum computational tools with precisely curated experimentation, the research establishes predictive principles for drug loading. These physics-based interaction energies and structures train machine learning models to predict drug loading and recommend improved carrier designs, linking quantum-level insight to data-driven prediction. This shifts drug carrier design from a tedious trial-and-error paradigm into rational, AI-guided engineering of high-loading carriers tailored to specific drugs, paving the way for a new generation of nanomedicine and more effective, affordable treatments. Broader impacts of this work include training students in AI-enabled materials and drug delivery, developing new undergraduate laboratory modules, and an elective course that integrates computation with formulation science, providing research experiences for K-12 and graduate students, and creating an educational game, "Code-a-Cure," that introduces learners to nanomedicine design and modern AI-driven discovery. TECHNICAL SUMMARY: Over 90% of active pharmaceutical ingredients in development exhibit poor aqueous solubility, severely limiting bioavailability and creating a major bottleneck in drug formulation. Polymeric micelles can encapsulate hydrophobic drugs through non-covalent interactions, yet drug loading is low for most compounds because current design approaches lack a predictive, mechanistic understanding of specific polymer-drug forces. This CAREER project establishes molecular principles that govern polymer-drug affinity and converts them into physics-informed machine learning tools for rational, high-loading carrier design. The central hypothesis is that systematic quantification of π-π interaction energetics and valency, hydrogen bonding strengths, and their interplay in mixed-functionality copolymers yields predictive descriptors of loading capacity and efficiency. Building upon this understanding, research is organized into three synergistic thrusts: (1) mapping π-π interactions by systematically varying electronics and pendant density to capture non-linear valency effects; (2) evaluating hydrogen bond donor/acceptor strengths and valency; and (3) investigating synergistic or antagonistic effects in mixed functionality copolymers. Experimental characterization of loading is integrated with density functional theory (DFT) calculations into supervised machine learning models with uncertainty estimation and an active-learning loop that prioritizes the most informative polymer-drug experiments. A generative forward design search strategy further recommends polymer structures and compositions predicted to improve loading. This proposed work aims to broaden participation while highlighting the importance of biomaterials, nanomedicine, and polymer science through integrated educational initiatives such as the development of a "Code-a-Cure" educational game, new undergraduate laboratory modules, an elective course incorporating AI and drug delivery concepts, and outreach providing hands-on experiences for K-12 and 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 2026 · 2026-05

Digital technologies are ubiquitous at institutions of higher education. Their use generates enormous amounts of data that could improve teaching and learning processes. However, the accidental leakage or intentional misuse of such data can create severe privacy and safety risks for students and others at these institutions. Thus, the goal of this project is to develop a systematic method for data governance that incorporates formal risk assessments and proactive mitigation techniques. The project seeks to leverage institutional data to advance science without harming data subjects. This project aims to improve student privacy protections and education research pertaining to data security. The project focuses on three activities. First, it formalizes the privacy risk modeling process at institutions of higher education and creates a threat ontology. Second, it develops two classes of novel mitigation techniques to privacy attacks: one based on censoring internal representations of data by machine learning models, and another based on causality-aware, differentially private synthetic data generation. Third, following a participatory design approach, it develops a web-based, visual, and interactive privacy risk assessment tool that builds on the threat ontology and mitigation techniques. In addition, the project team is testing the usability of the threat modeling tool and deploying it at three universities along with conducting training sessions with data stewards and privacy officers. 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

Inflammation of the brain plays a major role in diseases such as stroke and dementia. Delivering medicines to the brain is difficult because often they do not reach damaged brain tissues. This CAREER project will produce biomimetic nanoparticles that have molecules specifically targeting brain blood vessels. The project will determine how the nanoparticles adhere to and cross inflamed brain blood vessels in disease-mimicking models. The project will integrate nanotechnology, bioengineering, vascular biology, and neuroscience to find how engineered biomimetic nanoparticles interact with disease microenvironments. Results will suggest new strategies for precision nanomedicine. The project will support undergraduate research opportunities. It will also develop biomaterials learning modules and podcasts to communicate bioengineering advances to a broad audience. Cerebrovascular inflammation drives and amplifies neurological disorders. However, an activated brain vascular endothelium and a compromised blood-brain barrier present opportunities for targeted therapeutic delivery. This CAREER research project will leverage multivalent interactions between surface-modified biomimetic nanoparticles and inflammatory vascular receptors to achieve precise delivery to cerebrovascular lesion sites. The project will build on a monocyte membrane-cloaked nanoparticle platform that exploits natural monocyte adhesion mechanisms to target inflamed endothelial linings. These monocyte-mimetic nanoparticles will be engineered to surface-display a brain-vasculature-targeting peptide that binds to a receptor highly expressed at the blood-brain barrier. The research objectives are to: 1) Engineer surface ligand display on monocyte-mimetic nanoparticles and determine binding mechanisms across distinct endothelial beds; 2) Model the targeting, adhesion, and transport of surface-modified monocyte-mimetic nanoparticles using a three-dimensional inflamed brain microvascular network-on-a-chip; and 3) Evaluate targeting specificity, delivery efficacy, and blood interaction profiles of surface-modified monocyte-mimetic nanoparticles in a mouse model of cerebrovascular disease. Findings from this project will establish design principles governing how surface-engineered biomimetic nanocarriers interact with inflamed cerebrovascular interfaces, advancing fundamental understanding of neurovascular inflammation-dependent nanoparticle transport in complex biological environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Legionnaires’ disease is a severe respiratory illness caused by the bacterium Legionella pneumophila and closely related species. It is frequently associated with cooling towers, which are large mechanical systems used to remove heat from buildings and industrial facilities. Cooling towers can emit fine mists and aerosols that may contain bacteria. Once released, these aerosols can travel through the air and may expose nearby populations. However, it is not fully known how environmental conditions and system design influence infection risks. This project will combine field data, laboratory experiments, and modeling to identify and quantify the biological and environmental parameters that determine risks of disease transmission. Results from the project will help improve the management of cooling tower water quality, treatment, and operating conditions to reduce health risks. In addition, the project will provide training opportunities for students and early-career researchers in environmental engineering and public health, helping to strengthen the science and engineering workforce needed to address environmental health challenges. This project will develop a mechanistic and predictive framework to understand how environmental and operational conditions influence the persistence, aerosolization, transport, and risk of Legionella pneumophila bacteria from cooling tower systems. The research will integrate laboratory experiments, field measurements, and modeling to quantify interactions among Legionella, free-living amoeba hosts, and cooling tower water chemistry under realistic operating conditions. Controlled experiments will evaluate how factors such as nutrient levels, disinfectant residuals, pH, and desiccation influence pathogen–host dynamics in both pure cultures and pilot-scale cooling tower reactors designed to replicate operational environments. Field campaigns at full-scale cooling towers will measure aerosol generation rates and droplet size distributions using inert tracer techniques and microbial sampling to characterize pathogen-containing drift emissions. These data will inform the development of spatially resolved aerosol dispersion and risk models that incorporate pathogen persistence, environmental decay, and historical surveillance data from outbreak investigations. The resulting models will be used to hindcast known outbreaks and to generate a field-ready sampling triage framework that prioritizes high-risk downwind locations during outbreak investigations. By quantitatively linking cooling tower operational conditions with pathogen-host dynamics and aerosol transport, the project will advance fundamental understanding of opportunistic pathogen persistence in engineered water systems and improve predictive tools for microbial risk assessment and public health response. 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 IEEE Information Theory Workshop, is a well-established conference of the IEEE Information Theory Society, having been held annually for over two decades. The 2026 conference will be focused on the areas of information theory in machine learning, quantum information and coding, and coding for 6G. The conference offers a unique opportunity to its participants for sharing of scientific discoveries related to information and learning theory, quantum information theory, machine learning and statistics, coding theory, and their applications, as well as interactions between information theory and broader areas such as artificial intelligence, biology, finance, and signal processing. This award will enable participation of roughly 30 students enrolled in US institutions. Such participation will have a positive impact on both information and coding theory research in US institutions and workforce development. It will contribute to the preparation of the next cadre of engineering professionals who can advance and ensure integration of communication, data science, machine learning, and quantum information and coding, and network information and coding. Award recipients will be better positioned to contribute to academic, industrial, and governmental research efforts across sectors such as information theory, coding theory, machine learning, quantum information and coding theory, cloud computing, and data privacy. 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

Researchers use a variety of software tools and datasets to study and simulate traffic in cities. These tools are vital for city planning and proactively managing traffic. The OpenLibSignal project aims to develop an open-source ecosystem of users and content developers that can improve connectivity among three popular open-source traffic management tools and datasets - CityFlow, LibSignal, and RL-Signal. Creating an integrated ecosystem around these tools will allow for more reliable and reproducible analysis of traffic patterns among different sites, which will improve the quality of urban systems research and assist policymakers. The ecosystem will also serve to improve the security and long-term sustainability of these resources. OpenLibSignal is a community-driven, open-source ecosystem for reproducible, low-cost, and scalable research in artificial intelligence (AI)-enabled urban simulation. Despite the rapid growth of open-source platforms for traffic management and urban simulation, current systems remain fragmented and difficult to sustain. Existing tools are often tied to a single simulator, lack standardized evaluation workflows, and provide only ad hoc support for community contributions, making reproducibility and comparability across sites inconsistent. Governance and sustainability mechanisms are also underdeveloped, leaving projects dependent on small core teams and vulnerable to single points of failure. To address these challenges, this project unifies three existing open-source artifacts — CityFlow (simulator), LibSignal (benchmarking algorithms), and RL-Signal (datasets/pipelines) — into a single, comparable workflow that supports traffic signal control, multi-modal mobility planning, and sustainability-focused policy evaluation. Phase I (1) establishes a distributed development workflow with rigorous testing and reproducibility checks; (2) designs and pilots a governance model with clear roles, processes, and release policies; and (3) grows a developer–user–educator community through targeted onboarding, tutorials, and events. By lowering barriers for municipalities, startups, and academic groups to prototype and evaluate urban system strategies before field deployment, OpenLibSignal advances reproducible AI research and contributes to national goals in transportation efficiency, environmental stewardship, and urban resilience. 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

Public health and bioeconomics are two examples of fields that can benefit from the funded work. By developing a framework to study higher order interactions, i.e., simultaneous interactions, the funded work will provide novel tools to analyze complex systems. The COVID-19 pandemic was challenging to control because people could catch the disease from accumulating many short exposures to multiple infected people, i.e., from higher order interactions, which are rarely considered in epidemiological models. Similarly, the efficient transfer of goods was another casualty of the pandemic due to supply-chain disruptions. Higher order interactions, in which goods are exchanged simultaneously, can substantially expedite the transfer of goods and increase the robustness and resilience of supply-chains to disruptions. The general framework that will be developed in this grant will use a tractable biological system to develop mathematical tools to study the causes and consequences of higher order interactions. The mathematical models and tools developed will be general, to allow application to other systems, such as communication, disease transmission, and social learning. The work will be published in general journals with a wide interdisciplinary readership and the analysis code will be made publicly available. Both PIs have a strong track record of recruiting and facilitating the success of students from groups that are unrepresented in the sciences and this commitment to mentoring a diverse population of trainees in interdisciplinary work will continue. To further disseminate the work to the general public, podcast episodes will be produced and distributed widely. Collective outcomes, such as the social behavior of animals, emerge from interactions among system components. While substantial work has been devoted to examining the intricate network of interactions among animals, these interactions are described and analyzed as dyadic events. However, multiple individuals can interact simultaneously. For example, an alarm call is broadcast to multiple individuals at once rather than through multiple one-on-one interactions. Despite the important conceptual and functional differences between dyadic and higher order interactions, there are only few methodological approaches that emphasize the higher order nature of social interactions. The proposed work will examine the causes and consequences of higher order interactions, and the feedback between them, by adapting and implementing existing mathematical tools from algebraic topology, simplicial sets, in novel ways. Specifically, the aims include to determine the conditions under which higher order interactions emerge; to examine the consequences of higher order interactions; and to investigate feedback between causes and consequences of higher order interactions to uncover potential evolutionary pathways for their emergence. Social insects are an especially powerful system for examining the questions in the proposal because of the profound fitness consequences of interactions among individuals for the group. Therefore, the proposed work will use foraging and food transmission of Argentine ants (Linepithema humile) as a model system to examine the internal and external causes and consequences of higher order interactions. Project outcomes will enable innovative approaches to fundamental and generalizable questions which are currently beyond our reach. 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

Engineering shapes nearly every aspect of modern life, from artificial intelligence to smartphones and online retail. Yet many graduate engineering programs prioritize technical training, offering limited preparation in ethics, leadership, or effective collaboration across disciplines. As a result, engineers may be underprepared to address real-world societal challenges. This National Science Foundation Innovations in Graduate Education (IGE) award to Arizona State University supports a bold redesign of graduate engineering education that reflects the complexity of modern engineering practice and prepares students to lead ethically and effectively in service of industry and society. The project addresses both workforce development and institutional capacity-building needs in graduate education. The Track 1 IGE award supports an initiative that integrates industry-engaged mentorship, ethical development, and industry experiences as core components of STEM professional training. It expands graduate pathways through multiple entry points, structured exit options, and stackable credentials that are designed to meet the needs of today’s learners. Students will engage in stakeholder-informed projects while developing core competencies such as decision-making, civic leadership, and interdisciplinary problem-solving. The project is designed to provide graduate students in STEM opportunities to develop skills, knowledge, and core competencies needed in STEM careers. The initiative includes a redesign of Arizona State’s existing Master of Science in Engineering program and the launch of a graduate certificate in Principled Engineering Leadership, open to all engineering graduate students. The program integrates interdisciplinary coursework, multi-level mentoring networks, short-term industry residencies, and stakeholder-engaged capstone projects. Students will document their training through digital portfolios. Faculty and industry partners will collaborate to strengthen mentoring practices through structured training and peer exchange. Mentorship quality, student learning, and long-term career impacts will be evaluated, and findings will guide continuous improvement and support scalable models. By aligning technical expertise with ethical and civic learning, the project will prepare the next generation of engineers to navigate complexity and advance the nation's STEM enterprise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The 11th Thermal and Fluids Engineering Conference (TFEC), organized by the American Society of Thermal and Fluids Engineers, will take place March 9 - 12, 2026 on the campus of Arizona State University in Tempe, Arizona. TFEC brings together thermal and fluids researchers from academia, industry, and national laboratories to advance the field and educate the next generation of researchers. NSF support will provide partial travel and registration costs for US-based researchers who would not otherwise be able to participate. Students and post-doctoral researchers selected through a competitive process will receive travel grants to help offset expenses. This support ensures the most deserving and promising junior investigators can participate, facilitating vital mentorship and networking. Ultimately, this sustains and enhances the vibrancy of the US thermal and fluids engineering enterprise. The thermal and fluids engineering research and education community addresses critical national challenges, including industrial process heating, building efficiency, data center cooling for AI, electronics thermal management, and aerospace propulsion. NSF funding will support the research community in discussions and critiques of current work while enabling junior investigators, including students and post-doctoral researchers, to receive feedback on their findings and engage with leaders in the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The primary quantum process behind many phenomena in our daily lives is the light-driven motion of electrons. Important examples of this include photosynthesis, vision, vitamin D synthesis from sunlight, DNA damage by UV light, and more. As a first step towards developing a better understanding of such phenomena, it is important investigate and harness the electronic motions inside atoms and simple molecules with light pulses. However, the electrons are extremely fast, and can move on the timescale of an attosecond – a billionth of a billionth of a second! To capture the images of what transpires in the quantum realm at such dizzying speeds, one needs to use a sophisticated camera alongside an extremely fast flash or strobe light. The PI’s team employs advanced technologies such as a charged particle velocity imaging detector, which serves the purpose of a camera film, and ultrafast laser pulses that play the role of a strobe light. The proposed research project will investigate how electronic charge gets distributed after excitation by light, and how the changes in atomic positions within a molecule impact this process. Graduate and undergraduate students working on this this project will develop an important scientific skillset and will be empowered to generate new ideas and devise applications of their research. This impact will multiply as they move to the next stage of their careers in universities, national labs, and tech companies, thus fostering scientific innovation and productivity in the society. Attosecond extreme ultraviolet and soft-x-ray spectroscopy techniques form a very powerful toolkit for fundamental, real-time investigations of electron dynamics. In the proposed work, the PI and graduate students will employ these approaches for time-resolved study of coherent electronic wavepacket motion. Specifically, they will investigate XUV induced Rydberg, ionic, and many-electron excitations in atoms and molecules. To quantify the vibronic couplings that mix electronic states due to nuclear motion, the research team will conduct pump-probe measurements near conical intersections in small molecules. They will also aim to perform elementally specific transient absorption studies to monitor the coherent evolution of charge in photoionized molecules. Results obtained here will guide the development of theoretical methods that can accurately model the light-matter interaction, and the coupled and correlated evolution of electron and nuclei. The spatio-temporal mapping of charge dynamics in complex systems will serve to establish attosecond science as a versatile spectroscopy technique. These objectives will be achieved while training the graduate and undergraduate students in the frontier fields of attosecond science, laser technologies, and optical and x-ray spectroscopies. The PI will also place an emphasis on the participation of students from minority and underrepresented groups. Annual outreach events will be used to engage and educate the community about the importance and impact of physics research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

This project seeks to train and develop a skilled technical workforce that keeps pace with modernization of manufacturing technologies and meets industry demand for advanced manufacturing proficiencies. The emphasis on developing advanced manufacturing skills among veterans, their families, and active-duty military personnel transitioning to the civilian workforce will contribute to the nation's prosperity, the state of Arizona's economic security, and the financial security of participants. Participants’ inherent knowledge and experience in how the military works complement advanced technology skills, increasing their potential to secure national defense through future employment by the Department of Defense and defense contractors who supply products and equipment to the military. Building blocks that progressively develop skills needed to earn industry-recognized credentials via hands-on experiential learning within an ecosystem of social support will strengthen those who lack confidence in successfully pursuing a related STEM career. More broadly, the proposed effort will determine the components and impactfulness of community-based cross-sector partnerships that intentionally serve participants by providing timely and essential educational, professional, and social supports that collectively address barriers to improve persistence and success. It will also translate findings to address broader workforce development gaps and recommend adaptations to other groups, thus contributing to the goal of making America skilled again. Participants will engage in hands-on training with ASU's industrial robotics and manufacturing equipment and instructors. Upon completing the labs and modules, they will earn a digitally verified Micro-Badge —a stackable credential aligned with industry needs and developed with input from partners such as Aerospec and JagCo. These courses are designed to support skills-based education (SE), foster career readiness in advanced technology, grow personal career ownership, and adaptability. While actual job placements are beyond the scope of the program, participants will be eligible for internships or employment opportunities through partner networks, such as AZ Coalition for Military Families, ASU’s Office for Veteran & Military Academic Engagement, AZNext, Intersources, and TechStaff. Long-term outcomes will be documented through follow-up reports and a qualitative rubric. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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 · 2025-12

This project will improve our ability to predict and manage agricultural pest outbreaks by linking the study of how insects use nutrients with how nitrogen changes in the environment. Locusts are grasshoppers that can transform into large swarms, causing massive agricultural losses and threatening food security around the world. Poor land practices, like overgrazing, decrease the amount of nitrogen in plants, which unexpectedly encourages locusts to form destructive swarms. By using computer modeling, this project will connect how locusts choose and use foods, how nitrogen cycles through their environments, and how these factors interact across large areas over time. These insights are important not just for science, but for society, because understanding when, where, and why locust swarms occur can help farmers, local communities, and organizations better prepare for and respond to these unpredictable, damaging events. The project will also train university students and build partnerships with groups on the front lines of protecting crops, ensuring the research has a direct, positive impact on food security and sustainable land management. This Mid-Career Advancement (MCA) offers a unique training opportunity for a mid-career faculty member, expanding her capacity to connect her research on how organisms use nutrients to large landscape-scale models. The PI has led interdisciplinary efforts exploring links between land use, livelihoods, agricultural markets, and locust outbreaks, advancing both fundamental science and its practical use in agriculture. Contrary to the common assumption of a positive relationship between nitrogen concentration and herbivore performance, her team’s work uncovered that nitrogen-depleted environments promote swarms by providing locusts with low-protein, high-carbohydrate plants that fuel their high activity levels. However, connecting locust nutrition to nitrogen cycling at landscape scales remains a major challenge. This MCA will address this gap through three objectives, all supported by two expert partners: 1) Research training for the PI in modeling and handling large datasets, 2) Development of mechanistic niche models that integrate nutritional ecophysiology to improve predictions of the development of gregarious phenotypes and locust swarms, 3) Use of agent-based models to investigate how ecosystem nitrogen cycling influences the development and movement of swarms across landscapes. The results will be translated through collaborations with plant protection organizations responsible for monitoring and managing locusts, ensuring a tangible, real-world impact. In addition, this MCA training will have lasting impacts on the PI’s future teaching, mentoring, and collaborations throughout her expected 25+ years as a professor at the largest public university in the US. 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

Multimodal sensing refers to the integration of multiple sensor types to collect various environmental data. By leveraging the strengths of different modalities, it enables more accurate and comprehensive understanding of complex scenarios. As a result, multimodal sensing is integral to mission-critical Internet-of-Things (IoT) systems such as smart cities, intelligent transportation, and defense applications. Robust cybersecurity is essential to their reliable operation, ensuring core objectives like encryption, authentication, and access control. However, while IoT systems increasingly rely on multimodal sensing, cybersecurity approaches that exploit this capability remain largely unexplored. This project addresses this gap by developing multimodal sensing-based security mechanisms to enhance the protection of mission-critical IoT systems. It also lays the foundation for broader applications of multimodal sensing in security. In parallel, the research advances scientific knowledge at the intersection of wireless networking, mobile sensing, and AI. Educational materials will be made publicly available, and the research will be integrated into curriculum development, undergraduate research, and increased participation in computing. This project pursues a challenging research agenda, termed MSS, focused on developing, prototyping, and evaluating innovative Multimodal Sensing-Based Security (MSS) mechanisms for mission-critical IoT systems with inherently heterogeneous sensing capabilities. The research is organized into three integrated thrusts. Thrust 1 develops MSS-Key, a novel framework for establishing ad hoc secure keys between IoT devices by leveraging indirectly correlated multimodal sensing data shaped by unforgeable physical-domain randomness. Thrust 2 focuses on MSS-Val, a framework designed to protect multimodal sensing-native IoT systems against manipulated sensor inputs. Thrust 3 introduces MSS-Aug, a framework that streamlines and automates the integration of new sensing modalities into existing MSS systems via autonomous labeling and cross-domain generative learning. To support and evaluate the proposed techniques, the project will also develop a dedicated MSS testbed equipped with a wide range of sensor modalities. This testbed not only enables rigorous experimental validation but also serves as a hands-on educational platform, engaging students and promoting broader public awareness of cybersecurity challenges in IoT 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 · 2025-10

Firefighters are faced with myriad stressors from hazardous work conditions and exposure to extreme heat that place their health at significant risks. The intense heat, smoke, shift work, long working hours, and stressful work put firefighters at substantial risk for heat-related injuries, long-term chronic complications, and mental health challenges. The health risks are further compounded in the Phoenix metropolitan area, which has one of the highest heat indexes in the nation. Therefore, there is a need to develop technologies to objectively assess the impacts of extreme heat and harsh working conditions on firefighters’ health and to provide actionable information to mitigate the health risks. This project develops, HeatMind, an AI-powered sensor-based platform that provides firefighters and community organizations with the tools to objectively monitor heat-related health and provide intervention strategies to minimize risks. The project brings together researchers with expertise in AI, pervasive computing, social and behavioral science, user-centered design, community engagement, heat resilience, and hydration science to collaborate with community partners including firefighters, fire and forestry departments, and nonprofit organizations. The project aims to improve physical and mental health of firefighters, reduce healthcare costs, improve performance, and enhance job satisfaction and efficiency. The developed technologies can be further refined for use in other communities, such as construction workers, miners, and agricultural workers, who experience prolonged heat exposure. This interdisciplinary project will design a scalable and adaptable infrastructure for continuous and objective heat-related health monitoring and proactive decision making by developing new methods for community engagement, passive monitoring of key aspects of heat-related health, real-time risk mitigation, and sustaining engagement in digital platforms. Specifically, the project will (1) establish a structured community engagement approach, called Design Studios for Health, where each design studio session will focus on collaborative discussions, prototype testing, and structured feedback collection from firefighters and community partners; (2) design deep learning algorithms that use multimodal wearable sensor data to continuously assess firefighters’ health; (3) develop AI methods that identify mitigation strategies to minimize firefighters’ health risks by generating counterfactual explanations that reason about the machine learning predictions and provide counterfactual feedback to avert impending high-risk events; (4) develop new techniques to ensure robust inference of the AI algorithms so that the HeatMind platform can be reliably deployed in uncontrolled settings and across different environments; and (5) implement a community-facing testbed that integrates sensors, data, and algorithms in a unified framework for data collection, visualization, inference, and intervention delivery. 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

Machine learning (ML) and artificial intelligence (AI) technologies are being adopted for a wide range of applications. This has spurred interest in the use of ML and AI for chip design. Chip design is currently heavily automated using electronic design automation (EDA) software. Recent work has shown that AI and ML methods can further increase the level of automation as well as improve the quality of existing EDA tools. However, ML methods pose their own dangers and risks. They have been shown to be easily tricked by small changes in their inputs. They can also be easily "backdoored" by modifying only a tiny fraction of their training data. While these risks have been extensively studied in other domains, their impact has not been extensively examined in AI/ML based EDA and chip design tools. This project's novelties are (1) the first comprehensive look at the impact of input and training data perturbations and attacks on the quality, performance and security of AI/ML based EDA tools; and (2) the first thorough investigation into mechanisms to defend against such attacks. The project's broader significance and importance is that enables the trustworthy adoption of AI/ML methods in the chip design industry, resulting in greatly enhanced productivity and chip design quality, while ensuring trustworthiness. The project pursues these aims in three research thrusts. Thrust 1 focuses on discovering meaningful and contextual perturbations of inputs to the different steps in the EDA flow, starting from design specification to logic synthesis, test-point insertion and physical design. To this end, the project investigates a new "EDA vs. EDA" threat model, where tools from competing vendors in the same seek to degrade each other's performance by injecting targeted functionality-preserving transformations in the inputs of a downstream tool. Thrust 2 evaluates the impact of training data poisoning and backdooring attacks on ML-based EDA tools spanning both pre- and post-silicon use cases. This Thrust demonstrates how carefully inserted stealthy triggers like netlist and layout patterns, comments in RTL code or temporal sequences of instructions can result in undesirable outcomes. Thrust 3 builds robust ML-based EDA tools that can withstand attacks demonstrated in the prior two thrusts. This Thrust explores techniques for meaningfully infusing ML for EDA with constraints from the relevant EDA domains during model training. Overall, the three Thrusts synergistically work together to create the foundations for next-generation trustworthy AI/ML-native EDA, while also training hardware design students with a fundamental understanding of security and trust concerns in AI/ML. 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

What can allow a few isolated cases of an infectious disease to blossom into an outbreak and further expand into a true pandemic? This is the driving question for the Center for Analysis and Prediction of Pandemic Expansion (APPEX). Biomedical and physical, ecological, socio-behavioral, economic, built and natural environmental, and information access factors are all likely to contribute to these perfect storm scenarios. In isolation, the contribution of each aspect may seem minor, or even overlooked, only leading to serious impacts when acting in synergy. This vastly complicates how to study, understand, and prepare to address pandemic risks. The APPEX Center is predicated on the idea that the greatest barriers to multidisciplinary insights in pandemic science exist when disciplinary researchers fail to appreciate, or even be aware of, the value of other fields in addressing complex research questions. The APPEX Center focuses on enabling multidisciplinary collaborations specifically focused on combinatorial risk scenarios that need simultaneous consideration by multiple domains and disciplines. In this way, APPEX provides for the development of a rigorous hierarchy of evidence for pandemic risk, leading to improved methodologies for scenario-to-scenario comparison, and creates and meets audacious challenges in multidisciplinary hypothesis generation, model/tool building, and information infrastructure. The APPEX Center assembles a core team of researchers and practitioners spanning many areas of expertise to foster participation from the entire science community. Bringing together and materially supporting diverse teams of experts and decision makers in pandemic science, APPEX seeks to tackle questions about pandemic expansion that can only be answered at the interface among disciplines and domains. Operationally, APPEX research groups employ a previously piloted Guided Self-Organizing Teaming Process (GSOTP) in which targeted research questions are inspired by proposals from individuals, but tackled by a multidisciplinary team that coalesces around the idea and collaboratively refines it into a clear, compelling challenge, motivating the engagement of all team members and their domains. APPEX goes beyond existing research on disciplinarily targeted factors affecting pandemic risks and instead provides an enabling framework for synergy, complementing domain-driven research efforts. As such, APPEX ensures that the vision of pandemic science is proactive, focusing on framing how to meet complex challenges, improving both our ability to respond to existing disease threats and to be flexible, nimble, and adaptable to the next emerging pathogen we cannot yet anticipate to increase health security regionally, nationally, and globally. 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 Noyce Track 3 project aims to serve the national need of preparing highly-qualified STEM teachers and developing effective STEM teacher leaders through a three-year professional development program that emphasizes artificial intelligence (AI) curriculum. Additionally, this project will support two cohorts of 15 each, for a total of 30 Master Teacher Fellows (MTFs) in all STEM fields by providing stipends for professional development experiences. The proposed project components will enable high-achieving practicing teachers to effectively use AI with their students by illustrating how machine learning algorithms learn from data to make predictions, thereby making the technology more accessible and relevant to students’ everyday lives. This project at Arizona State University includes partnerships with the Arizona K12 Center, Tolleson Union High School District and Phoenix Union High School District. Project goals include: 1) MTFs will develop skills in engineering habits of mind, including design thinking, critical and creative thinking, collaboration, problem-solving under constraints, and iterative testing within the context of foundational AI concepts, including virtual assistants, recommendation systems, machine learning, and deep learning; 2) MTFs will explore machine learning principles through exploration of object detection, sound recognition, pose classification, and data categorization models and illustrate real-world applications of machine learning to make AI concepts relevant and accessible; 3) MTFs will engage diverse stakeholders (e.g., students, parents, educators, and industry professionals) in conversations about AI literacy and build community awareness of AI careers and educational opportunities through targeted outreach initiatives. Thirty new teacher leaders in all STEM fields will be produced over a five-year period in the state of Arizona. This project will be iteratively evaluated. Evaluation of the project will be guided by the following evaluation questions: (a) After participating in project activities and supports, what improvements are observed in teachers' motivation/attitudes and self-efficacy? (b) What are the MTFs' perceived affordances and constraints of using AI in secondary classrooms? The results of this project will be disseminated to help enhance the field. This Track 3: Master Teaching Fellowships 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 · 2025-10

Cloud computing has enabled individuals and organizations to delegate complex computations to external servers, which helps enhance scalability and reduce the need for local computational resources. Despite its significant advantages, cloud-based computation raises serious concerns about data privacy and security, especially when handling sensitive information. Homomorphic encryption (HE), which enables computations directly on encrypted data, provides a powerful approach to addressing these concerns. However, current HE schemes face major limitations in multi-user environments, such as limited support for dynamic participation, vulnerability to malicious users or servers, and high computational and communication overhead. This project will develop a novel threshold homomorphic encryption scheme that avoids the need for trusted setups, maintains constant cipher-text size regardless of the number of users, and supports asynchronous operations. The research will also include the development of protocols that ensure security in the presence of malicious clients and servers, support variable threshold access levels among users, and incorporate hardware-based security mechanisms to further enhance performance. These innovations will be applied to real-world scenarios such as federated learning and heavy hitter detection, with resulting tools implemented in a publicly available software library. The project will contribute to both the theory and practice of secure multiparty computation, offering broader impact across privacy-preserving data analysis, cybersecurity, distributed systems, finance, and healthcare. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.