Virginia Polytechnic Institute and State University

NSF Awards · FY 2025 · 2025-10

Augmented reality (AR) glasses overlay virtual information on the real world and have the potential to provide anytime/anywhere access to relevant information for a myriad of tasks and situations. However, it is challenging to design effective user interfaces for AR glasses, because the best interface depends on multiple dynamic aspects of the context of use. In an industrial inspection scenario, AR glasses can display information that helps the inspector know what to do next, but the most effective placement of this information depends on the objects in the environment, and the most effective level of detail depends on the expertise of the inspector. This project investigates how AR glasses can use artificial intelligence to automatically adapt their interfaces based on contextual information that is sensed in real time. These “intelligent AR” systems will present the right information, at the right time, in the right way. Through a series of studies, both in the lab and in a real manufacturing environment, the project will develop a set of best practices for intelligent AR that will help designers understand when, how, and where to display augmented information based on the context. The work is aimed directly at facilitating economic and safety benefits by improving future inspection and maintenance work through the application of future intelligent AR technology. It is also expected that the results of this project in industrial inspection and maintenance will inform similar applications in other domains that share some of the characteristics of the manufacturing industry, such as dynamic nature, complexity, and the need for workers to have multiple skills. Such domains include construction, military, and healthcare. In addition, the project will provide opportunities for students at various levels to participate in cutting-edge AR research and will include outreach activities that generate excitement about human-centered computing research to young audiences. The technical work in this project makes progress towards the intelligent AR vision by designing, prototyping, and evaluating its use in a specific domain: equipment inspections in a complex manufacturing environment. The project leverages an industry partnership to provide real-world requirements and ecologically valid evaluations for intelligent AR prototypes. The prototypes will go beyond simple context-awareness, which is based on hand-crafted rules applied in specific scenarios. Instead, they leverage machine learning models to prescribe what information is needed, when to present it, and where it should be placed in scenarios never encountered before. A series of controlled experiments will provide focus on three critical research questions: what level of detail to present based on inspector expertise, when to present information based on inferred task progress, and where to present information based on an understanding of the surrounding environment. The results will identify how users prefer to work with intelligent AR, examining issues related to trust, preferences in intelligent automation, and to what degree users wish to control what, when and where information is presented. The project will conclude with a field test of a functioning intelligent AR prototype in a manufacturing environment in order to assess the impact of this approach on real-world inspection tasks. 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 growing use of artificial intelligence in healthcare and medical research has created a difficult challenge: researchers need to share their computer models to advance scientific discovery, but these models can reveal private information about the patients whose data was used to create them. Organizations are often reluctant to share their computer models because of privacy risks, even though withholding these models prevents broader societal benefits from medical research. This creates a barrier to scientific collaboration that could otherwise lead to better treatments, improved public health outcomes, and medical breakthroughs. This project addresses this challenge by developing methods that allow organizations to safely share models trained on sensitive patient data without compromising individual privacy. Proposed research will result in new techniques for auditing models, certifying their privacy guarantees, and providing actionable tools to fix any identified issues. This work serves the national interest by advancing medical research and scientific discovery, enhancing national health and prosperity through improved healthcare technologies, supporting American competitiveness in artificial intelligence innovation, and enabling secure collaboration while protecting personal privacy rights. This project develops an end-to-end framework for privacy-preserving sharing of machine learning models trained on sensitive data. Despite growing interest in sharing models rather than raw data, machine learning models remain vulnerable to privacy attacks, such as membership inference attacks, which can reveal whether an individual's data was used during training. The research activities include four technical components. First, the project will evaluate the privacy properties of shared models by subjecting them to existing and newly tailored privacy attacks, establishing a foundational understanding of their vulnerabilities. Second, the team will develop formal privacy guarantees using methods like differential privacy and establish privacy-utility tradeoffs, creating privacy certificates for machine learning models that may include legal and usage constraints. Third, the project will design explainable auditing tools and privacy patching mechanisms such as machine unlearning to help developers mitigate risks without compromising model utility. Fourth, the research will build user-friendly tools to deploy these methods, focusing on real-world applicability in healthcare and biomedical research. The project will introduce a novel privacy-risk scoring system, enabling developers and regulators to assess the privacy risks associated with a given model. Unlike existing point solutions, this comprehensive framework integrates auditing, certification, and remediation into a unified system. Results will be disseminated through tools, publications, and educational modules to support broad adoption and training. 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

Traditional machine learning often involves collecting data from multiple sources, which can raise significant privacy concerns. One approach has emerged as a promising solution to solve this challenge by enabling models to be trained across many different sources without directly sharing private data. This approach has become particularly valuable in sensitive sectors such as medical diagnostics, where individual data privacy is legally protected. Despite these advancements, existing systems for training models across multiple sources lack standardized assessment tools, posing challenges to research reproducibility, validation, and trust. Without proper testing tools, organizations cannot verify that their privacy protections work as intended, creating barriers to adoption in critical areas like healthcare, finance, and national security. This project addresses this challenge by developing comprehensive testing tools that ensure privacy-preserving artificial intelligence systems work reliably, serving the national interest by enabling secure collaboration on AI development while protecting individual privacy, supporting American competitiveness in artificial intelligence technologies, and strengthening data security across critical infrastructure. This project designs, develops, and sustains FLTest, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems, addressing gaps often overlooked by traditional tools. The research activities include developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration vulnerability detection systems, and building recommendation engines for optimization. The testbed's key innovation streamlines evaluations through automated orchestration assisted by a pitfall checker that detects configuration issues and vulnerabilities in privacy evaluations. FLTest empowers both novice and expert users with actionable insights tailored to real-world applications. The team will validate FLTest across multiple domains and datasets, develop standardized benchmarks for assessment, and create detailed reporting mechanisms for security analysis. By utilizing distinct datasets and offering a standardized solution, FLTest verifies model privacy and robustness across heterogeneous data distributions, supporting the development of reliable privacy-preserving federated learning systems. The project includes collaboration with three industry partners to ensure practical adoption and long-term sustainability. 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

Chemical Engineering and related programs such as Biochemical and Process Engineering are the only engineering fields that focus on molecules and their transformations. Chemical engineers have a vital role to play in the coming decades in fields including manufacturing, energy, food production, water purification, advanced materials, and many others. In 2022, chemical engineering was the 6th largest engineering major by degrees awarded in the US, and jobs for chemical engineers are predicted to grow in the next 10 years. To meet this growing need, we will need to recruit and educate more students in chemical engineering and related fields. This project will investigate how the unique experiences of non-traditional college students, such as transfer students, part-time students, older students, and veterans, impact their academic performance and progress toward bachelor's degrees in chemical engineering. This project will provide key insights that will be used to support the success of non-traditional students in these disciplines. Most engineering programs in US universities were designed for traditional students that enter a 4-year college immediately after high school and then progress sequentially through the coursework. Chemical engineering coursework in particular is highly sequential, and there are many potential challenges and barriers that make it difficult for students to successfully complete their degrees. This is true for traditional students and the challenges can be even larger for non-traditional students. This project will identify the challenges, barriers to success, and possible advantages experienced by non-traditional students in chemical engineering as well as the potential benefits they bring to the field. The results of the project will be used to improve the recruitment and retention of non-traditional students, expanding the number and quality of engineering graduates to support US industry and manufacturing. This project will create structured mentorship to support the PI in initiating an engineering education research program aimed at understanding how the differing experiences of students pursuing non-traditional educational paths (e.g. transfer, part-time, and older students) impact their academic performance and progress toward bachelor’s degrees in chemical engineering (CHE) and related fields. The research and mentoring activities are designed to help the PI develop the necessary expertise to conduct engineering education research. This collaborative study will result in an enhanced understanding of the experiences of students pursuing traditional and non-traditional pathways in CHE and related fields. The work will determine (1) how a student’s background and academic path affect their readiness to initiate a degree program in CHE; (2) how the experiences of non-traditional students in CHE programs differ from students following a traditional path; and (3) what specific challenges and advantages non-traditional students encounter when navigating the curricula of chemical engineering and related programs. The project involves a combination of quantitative and qualitative assessments (surveys and semi-structured interviews) administered over an 18-month period starting with student entry into CHE programs at Virginia Tech, the University of Virginia, and Virginia Commonwealth University. The assessment instruments will be developed and interpreted in the context of a conceptual model that incorporates student background in the form of “transfer” capital, the early and late college environments, and outcomes such as student retention and academic success. The proposed research will provide key insight into the experiences of non-traditional CHE students that can be used to guide curricular changes and student success initiatives. The results of the research will be shared with collaborators at partner universities, and will also be disseminated broadly via journal publications, conference papers, and conference presentations, and we anticipate that the results will also provide insights that can be used by similarly sized engineering programs such as mining, materials science, and biomedical engineering. 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) is increasingly used to combat cyberthreats. ML enables tools known as security classifiers to identify potential cyberthreats, e.g., to detect malicious software ("malware") or a network intrusion. Such classifiers are typically developed by collecting data on threats (e.g., malware samples) and benign entities (e.g., legitimate software), then building an ML model that learns patterns in the gathered training data that suggest the presence of threats. The model is then used in real systems to help identify new undetected threats. However, for many security problems, good training data is hard to find. Threats may be relatively rare, or not shared by people and companies that experience them. This leads to unbalanced datasets that contain mostly benign cases, which ML models often struggle with. Threats also change over time, as malicious software is constantly evolving, and models may quickly go out of date. This project will develop ways to address these data challenges by developing methods for Generative Artificial Intelligence (GenAI) tools to create synthetic but useful data for network and application security tasks. Through this, the project will advance knowledge of both GenAI systems and more practical, effective defenses against cyberthreats. The project team will also create novel educational resources on AI and security topics and provide educational opportunities for pre-college teachers and students and research opportunities for undergraduate students. The project's goal is to boost and maintain the performance of a security task by addressing training data challenges. The work is structured around three research thrusts. The first thrust focuses on conducting an in-depth study to evaluate the effectiveness of existing GenAI schemes in addressing data challenges in ML-based network and application security tasks, highlighting cases where they fall short and where there are opportunities for improvement. The second thrust is to develop a novel GenAI framework called Aura, which will be purpose-built for the security domain to generate high-quality synthetic data, even when training data are limited, biased, or have noisy labels. The third thrust will extend Aura to support security operations after deployment by designing novel techniques to mitigate concept drift and by enabling continual learning against evolving security threats. Aura will also provide novel model interpretation schemes to attribute predictions to synthetic data in the training set. Beyond the contributions to the specific problem of generating useful synthetic data, the project will also provide a case study of the larger goal of leveraging AI-based techniques to support security and privacy, an area of high interest to the research 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 2025 · 2025-09

Technologically advanced and sustainable deconstruction is crucial for meeting the nation’s growing demand for demolition services and a skilled workforce to mitigate the impact of extreme weather events, including wildfires, floods, and hurricanes, which can pose safety and time barriers that prevent deconstruction professionals from engaging in efficient and sustainable demolition practices. This need is also combined with the imperative to renew the aging urban infrastructure, ensuring resilient, resource-efficient rebuilding that minimizes environmental impact and maximizes community safety. To address this need, this project aims to serve the national interest in several ways: (1) advance the knowledge and innovation by transforming demolition practices to be more technologically advanced and sustainable thereby mitigating the impact of natural hazards, reducing safety risks, minimizing material disposal costs, alleviating supply chain disruptions, and minimizing demolition environmental impact; (2) collaborate with professionals in the sustainable demolition construction practice to understand their role-specific competencies and how they perceive being part of an innovative profession focusing on environmental impact and efficiency and adding value to the general engineering profession, and (3) support the development of skilled deconstruction engineers fluent in digital literacy and able to implement technologies for construction and demolition waste management, and conducting safe, efficient, sustainable, and economic demolition operations. This work aligns with research on the professional formation of engineers, which seeks to better understand how engineering education programs develop future engineers. This proposed study aims to understand the competencies required of the future workforce to engage in technology and data-driven sustainable demolition practices and how such understanding could shape the professional identities of professionals in demolition-related fields. A mixed-method research study will be conducted to answer research questions that address (1) competencies required for the future workforce to engage in technology and data-driven sustainable demolition practices in construction and (2) the impact of acquiring these competencies on shaping the professional identities among sustainable demolition professionals and what specific identities are most likely to emerge. A nationwide survey of demolition contractors who actively implement or are transitioning to sustainable practices will be conducted to identify the skills required for technologically advanced and sustainable demolition practices, and to determine the value and demand for these skills in the construction industry. In the second phase of the project, the influence of acquiring the identified competencies on the formation and evolution of professional identities among practitioners in construction-related fields will be examined. Industry practitioners with expertise in sustainable demolition and construction practices that utilize technology will be interviewed to understand the types of identities they have developed through acquiring these competencies, how they developed their professional identities, and how those identities have evolved. Results will be cross-validated through focus group discussions with both practitioners and researchers who specialize in studying professional identity development and its formation in technical fields such as the construction industry. Currently, there is limited research exploring the specific competencies required for implementing advanced technologies and data-driven analytics in sustainable demolition practices. Additionally, no existing study has explored how acquiring these competencies can alter the perception of being part of an innovative profession that focuses on enhancing safety, productivity, and environmental performance in the demolition industry. We aim to leverage our research to bridge the knowledge gap, where developed competency models and pedagogical guidelines will be adaptable across construction-related engineering programs, thereby broadening participation in STEM, accommodating diverse learning needs, and leaving a lasting impact on both education and practice. 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 digital twin (DT) paradigm presents a wide array of opportunities for modeling complex systems in biomedical sciences in a realistic manner, allowing researchers and healthcare professionals to explore various “what-if” scenarios. In dental sciences, DTs can serve as virtual replicas of a patient's periodontal tissues and structures, enabling clinicians to address a variety of tasks such as simulating periodontal conditions, forecasting treatment outcomes, and personalizing dental care plans. However, achieving this vision is impossible without building confidence in making DTs in healthcare trustworthy which requires the development of novel mathematical and statistical foundations behind such fundamental questions as verification, validation, and uncertainty quantification (VVUQ) of dental DTs, robustness of dental DTs to uncertainties, and cohesive integration of multi-modal health-related data at disparate scales. This project aims to develop novel mathematical and statistical methodology to establish a foundation of the artificial intelligence (AI)-driven framework for constructing reliable and personalized DTs for periodontal health. By integrating principles from statistical learning, topological data analysis, and generative AI, specifically, probabilistic diffusion models on graphs, the project opens a pathway to build ensembles of individualized dental DTs, termed “periodontal digital siblings.” These DTs will capture patient variability and uncertainty, offering a more precise representation of individual health profiles. This inherently interdisciplinary effort bridges mathematics, statistics, machine learning, dental science, and healthcare, and promotes widely adoption of DTs in dentistry, with an ultimate goal to transform the prevention and treatment of periodontal disease through personalized, data-driven care. Additionally, this project offers a broad range of unique opportunities for interdisciplinary research training at the nexus of mathematical sciences, AI, and dental medicine, equipping the next generation of researchers with critical skills, and fostering cross-domain innovation and translational science. This project is co-funded by the Statistics Program in the Division of Mathematical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Metal ions are essential for the proper structures and functions of many proteins. The efficient incorporation of correct metals into respective client proteins is critical for life; however, high levels of free metals are toxic to cells. Thus, many metal-dependent proteins require helper proteins – termed metallochaperones – that sequester and deliver specific metals to the correct client proteins. This project is focused on metallochaperones involved in nickel delivery pathways in methanogenic archaea (methanogens), which are microorganisms that produce methane as a by-product of their unique energy metabolism know as methanogenesis. Methanogens are uniquely dependent on nickel as a micronutrient since they employ several different nickel-dependent enzymes. However, the putative nickel metallochaperones remain poorly defined. Thus, this project will elucidate the proteins involved in trafficking and delivering nickel to each of the different nickel-dependent enzymes in these organisms. The knowledge gained from this project could support efforts to engineer methanogenic metabolism to produce biofuels and commodity chemicals from low-cost precursors. In addition to providing training and professional development opportunities to undergraduate and graduate students, the project will engage middle- and high-school students in hands-on microbiology activities to highlight the key role of microbes in diverse natural environments and in biotechnology. Methanogens have several different nickel-dependent enzymes that play key roles at the interface of energy metabolism and carbon assimilation. However, minimal knowledge exists regarding nickel homeostasis – including nickel uptake, nickel storage/export, and nickel-dependent enzyme assembly – in these organisms. Thus, this project will employ in vivo crosslinking, quantitative proteomics, targeted gene deletions, and in vitro biochemical experiments to define the nickel delivery networks and elucidate the functions and physiological significance of a series of nickel metallochaperones that are responsible for delivering nickel to each of the respective nickel-dependent enzymes. Model methanogens with highly developed genetic tools are emerging as biotechnologically useful hosts to produce valuable molecules from low-cost precursors. Thus, the knowledge gained in this project could be employed to modulate the activities of nickel-dependent enzymes in methanogens to direct metabolism toward the production of desired molecules. This project is supported by the Systems and Synthetic Biology Cluster in the Molecular and Cellular Biosciences Division of the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Accurate and timely channel state information (CSI) is essential for the performance of next-generation wireless systems, particularly those operating in high-frequency bands with large-scale antenna arrays. These systems, including future 6G networks, rely on spatially resolved CSI to support tasks such as beamforming, mobility management, and interference mitigation. However, acquiring reliable CSI in practical environments remains challenging due to high measurement cost, environmental complexity, and real-time constraints. This project develops a modeling framework that integrates limited radio measurements with spatial priors derived from environmental sensing. Specifically, the project investigates how geometric and visual information can be used to infer signal behavior in environments with constrained sensing capability. Rather than introducing a new channel abstraction, the project focuses on applying radiance-inspired modeling to characterize local electromagnetic behavior as a function of position and direction. The resulting models aim to support compact, data-efficient CSI reconstruction for structured scenarios such as indoor or urban deployments. Broader impacts include the integration of project outcomes into advanced wireless curriculum and engagement of students through interdisciplinary research. Project data, code, and validation tools will be open-sourced to support reproducibility and research dissemination. The proposed research explores a data fusion framework for reconstructing spatially varying signal behavior using sparse CSI measurements and environmental priors. The approach involves applying array signal processing techniques to derive location-specific channel measurements and aligning them with 3D environmental layouts obtained through vision-based reconstruction. The resulting signal model is localized and designed to approximate the directional energy distribution of wireless signals in space. The framework supports efficient channel prediction under constrained deployment settings and is applicable to emerging mmWave and sub-THz systems. The project also includes experimental validations across different frequency bands. Emphasis is placed on practical challenges such as limited sensor coverage, partial line-of-sight, and real-time inference under sparse signal measurements. The research outcomes are expected to inform the development of practical CSI estimation tools for deployment in complex high-frequency wireless 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 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, C. Adrian Figg of Virginia Tech will develop methods to precisely place chemical groups within polymer chains. The importance of polymer sequences on resulting material properties is largely unknown due to the limited number of ways to precisely place functional groups within polymers. However, learning the structure–function relationships of polymers is critical to designing better materials and integrating polymers into underexplored research areas (for example, biomedical science, drug delivery, responsive materials). This research will use light activated catalysts to control the exact number of chemical groups at multiple sites within polymers. To achieve this goal, the polymer composition and reaction rates will be measured to identify how to incorporate single sites of functionality within polymers. An educational component will develop a mail-order science kit for high schoolers to learn photochemistry and discover polymer antimicrobials. The broader impacts of this work include developing the next generation of STEM professionals by training students to use precision polymer chemistry to tackle global issues such as developing compatibilizers for recycling, high-performance plastics, and materials for biomedical science, antimicrobials, and therapeutics. The Figg lab aims to use photocatalysts to controllably place single vinyl ether units within vinyl polymers to achieve multisite-defined copolymers. Photoinduced electron/energy transfer chemistry will be used in combination with reversible addition-fragmentation chain-transfer (RAFT) polymerization to controllably synthesize polymers. The slow polymerization kinetics of vinyl ethers will be used to place exactly one monomer at defined positions via a single unit monomer insertion reaction. The effect of polymer identity (for example, polyacrylate vs. polymethacrylate vs. polystyrenic) on vinyl ether addition will be studied through kinetic measurements. Multiple sites of insertion will be developed where the maximum number of sites will be determined according to reaction conversions. This research will enhance our fundamental understanding of the kinetics of RAFT polymerization and the influence this has on controlling sequence in multisite-defined RAFT copolymers. These advances will enable the synthesis of multisite-defined vinyl copolymers, where the identity, number, sequence, and relative of position of single vinyl ethers are all controllable, approaching the complexity of the sequence-defined nature of biological polymers, such as proteins and oligonucleotides. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project develops a method, based on cryptographic protocols, that can be used in emerging radio spectrum markets so Federal agencies can acquire spectrum quickly when needed without disclosing information that may compromise their missions. Radio spectrum is a vital resource for wireless communication and sensing. Market approaches such as the Pay-As-You-Go (PAYG) model and the Spectrum Bux currency have been proposed to more efficiently use the congested radio spectrum. Since Federal agencies are a significant fraction of the spectrum ecosystem, Federal agency use of these emerging market mechanisms is necessary for the market mechanisms to provide the desired overall benefits for the nation. However, Federal agency missions sometimes require accessing spectrum without delay; most market designs require pre-registration and other slow steps. Agency missions sometimes require protecting information about sensitive operations; most market designs disclose the identity of the buyer to the seller so the seller can enforce payment terms. The cryptographic methods developed in this project overcome these constraints and thereby help emerging radio spectrum markets succeed. The architecture supports policy adaptation and microeconomic experiments to inform future spectrum policy and market design decisions. The project also helps educate the next-generation spectrum workforce. The core of the project is development of novel protocols based on cryptographic credentials and zero-knowledge proof (ZKP) technologies. The research effort has three primary thrusts. Thrust one creates a ZKP-capable spectrum credential system to enable efficient attribute-based authentication for spectrum access. This allows users with valid spectrum credentials to request access without prior registration or disclosure of sensitive identifiers. Thrust two develops a secure and auditable Spectrum Bux payment system in the PAYG model. The payment system enables asynchronous settlement that guarantees the band manager receives payment after a user successfully obtains a spectrum access assignment, without disclosing private details about the user. Thrust three develops a simulator for studying the interactions between markets using the new credential and payment systems, user and agency pricing strategies, service-level agreements and other contract types, and Federal spectrum policy choices such as rules for protecting spectrum incumbents. The simulator is used for microeconomic experiments under diverse scenarios, ultimately identifying optimal policy and pricing strategies for specific spectrum-sharing contexts. The outcomes of this research will be made publicly available online, including publications, tutorials, and open-source 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 2025 · 2025-09

The ability to preserve fragile quantum information for extended periods is a foundational requirement for practical quantum computing. This project develops a new class of ultra-high-performance quantum memories by systematically exploiting symmetries, underlying patterns that govern how physical systems behave, to enhance the protection of quantum data. In doing so, it supports the progress of science by advancing core knowledge at the intersection of quantum information theory, physics, and machine learning. The results of this research may accelerate the realization of large-scale quantum computers, enabling breakthroughs across areas such as materials design, secure communication, and artificial intelligence. The project also contributes to the national interest by strengthening U.S. leadership in quantum technologies and preparing a diverse future workforce through interdisciplinary training of students and postdocs. Technically, the project integrates symmetry principles into all aspects of quantum memory design and decoding through three coordinated thrusts. The first develops capacity-achieving low-density parity-check (LDPC) codes for biased noise using emergent symmetries generated via Clifford transformations, enabling more efficient and accurate decoding. The second investigates a novel route to self-correcting quantum memories by constructing stabilizer codes in three dimensions that display symmetric properties between different types of quantum errors, offering potential for finite-temperature stability without needing higher-dimensional architectures. The third thrust enhances machine learning-based quantum decoders by embedding code symmetries into neural architectures and developing a recursive feature machine-based framework to systematically refine learned representations. Together, these thrusts aim to define a new paradigm for fault-tolerant quantum memory that bridges theoretical elegance with experimental relevance. 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 strength of solder joints can be reduced by pores caused by bubbles trapped during the soldering process. Small bubbles are especially likely to be trapped because their buoyancy is relatively weak, especially in reduced-gravity environments; hence, they do not quickly rise to the surface. This research project will explore acoustic waves as a means of quickly expelling small bubbles from molten solder. Acoustic methods have been used successfully in other situations, e.g. to remove bubbles from cell culture media. This project will transplant those methods to molten solder. Experiments in microgravity will allow acoustically driven bubble motion to be isolated from the effects of buoyancy-driven motion. These data will be used to validate simulations of bubble motion and improve future predictions of the same. The results look to speed up soldering operations and reduce the heat energy needed to keep the solder molten. Additional benefits will come from training next-generation aerospace and mechanical engineers. Experiments in space research provide excellent outreach opportunities targeting high-school students. This project aims to develop an acoustics-assisted soldering technique and identify the optimal acoustic parameters (e.g., power, frequency, and activation duration) for effective bubble removal. An acoustic transducer will be used to generate waves within molten solder, actively displacing bubbles. This looks to significantly improve the mechanical strength and thermal/electrical conductivity of soldered joints. Experiments in microgravity aboard the International Space Station (ISS) will eliminate the effects of buoyancy and natural convection. These results will be compared against ground-based experiments, thus decoupling the effects of buoyancy vs. acoustically-driven bubble dynamics. All experiments will be guided by thermal-acoustofluidics simulations, and experimental results will, in turn, be used to validate simulations. This project seeks to expand the application of acoustic manipulation of matter from traditional gels and colloidal materials to molten metals. The knowledge gained from this project intends to benefit manufacturing industries such as automotive, aerospace, and semiconductors, where defects in soldering processes have impeded device performance and jeopardized the longevity of mechanical structures. Beyond benefits on Earth, the microgravity experiments look to also help soldering operations in space, which are important for the emerging industry of space-based manufacturing. Beyond technological benefits, the project will also train students in advanced manufacturing methods and conduct outreach from pre-college to graduate levels. Experiments on the ISS provide an attractive vehicle to communicate the excitement of research, especially to younger 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

Scientific knowledge is growing at an unprecedented rate, with millions of research articles published annually. However, accessing and organizing this vast amount of information remains a major challenge, slowing the pace of discovery and innovation. This project aims to develop automated technologies that allow computers to extract high-quality, trustworthy scientific information from massive text collections without requiring extensive human annotation. By enabling easier access to accurate scientific knowledge, this work will accelerate breakthroughs in fields such as biomedicine, chemistry, and engineering. It will also foster transparency and trust in science and inspire future generations of researchers through education and outreach efforts focused on diversity and inclusion. This project proposes a new paradigm for scientific information extraction that eliminates the need for costly expert annotations while achieving expert-level accuracy. The first thrust focuses on co-optimizing fine-grained typing of diverse scientific entities and relationships within large textual contexts by integrating multiple weak supervision signals, enabling scalable and domain-specific extraction. The second thrust introduces retrieval-augmented techniques that leverage multimodal domain knowledge, such as scientific knowledge bases and related data, to enhance understanding beyond the text. The third thrust develops methods for joint label and explanation generation to build trustworthiness in automated extraction without human labeling. These innovations are implemented using scalable algorithms based on optimal transport theory and Token Turing Machine architectures, with planned deployment of a beta extraction system on PubMed for real-world evaluation. Comprehensive evaluation and ethical safeguards ensure the methods are practical, reliable, and widely applicable across scientific disciplines. 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

Heavy-tailed probability distributions are routine across many scientific disciplines, from astronomy to ecology and finance and network modeling. Such distributions are often utilized in statistical modeling to incorporate non-linearity, robustness to large observations, and sparsity in high-dimensional data. The overarching goal of this project is to build new scalable statistical methods that incorporate heavy-tailed prior distributions in three disparate application areas: independent component estimation that recovers independent, latent sources from their observed mixtures, astronomical distance estimation from parallax measurements, and statistical modeling of compositional data. The research will result in powerful Bayesian tools with rigorous theoretical justification. This project will also narrow the critical gap between methodological advances in statistics and the tools used by the scientific community and promote increased usage and transparency of state-of-the-art Bayesian tools. The research findings will be incorporated into various educational activities to engage K-12 students. The project will provide research opportunities and training for graduate students and will enhance undergraduate and graduate curricula, accompanied by a monograph. This project develops Bayesian methodologies to address three significant statistical challenges: (1) unifying feature extraction techniques via novel latent space representations in independent component analysis, (2) improving astronomical distance estimation by incorporating measurement errors and non-linear relationships in parallax data, and (3) constructing prior distributions tailored for high-dimensional simplex-valued data that can adapt to arbitrary sparsity and dependence patterns. By leveraging heavy-tailed priors within hierarchical models, this work provides a new framework for controlling higher-order moments in blind source separation and as mixing densities for normal scale mixtures for handling non-linearity, robustness, and sparsity. The methods to be developed will be rigorously tested in applications spanning astronomy, blind source separation, community detection, and ecological modeling of species diversity and affinity, demonstrating their broad utility. The results will be disseminated through peer-reviewed publications in statistics, machine learning, and other scientific journals, and software implementations will be openly accessible as R packages that benefit the wider quantitative science 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 2025 · 2025-09

Flocculation, a dynamic process that binds fine muddy sediments with organic material in saltwater to form larger porous aggregates, is a fundamental process in estuarine and coastal zones that controls particle settling velocity and the vertical distribution of sediment; hence, it plays an important role in sediment deposition/erosion patterns, light attenuation in the water column, nutrient and carbon cycling, and water quality. To advance the general understanding and predictive capability of coupled flocculation dynamics and sediment transport, this project will integrate field, laboratory, and modeling approaches to address the knowledge gaps in 1) understanding the control of floc size and settling velocity in the estuarine boundary layer and their relationship to bottom shear stress and suspension and deposition; 2) evidence-based model coefficients for a flocculation model that reflects natural mud properties; 3) the relationship between floc size and settling velocity, especially for high organic content environments and muds with varying amounts of silt; 4) computationally efficient yet reliable coupling of flocculation dynamics in coastal models. This study has the potential to transform our ability to understand and include flocculation dynamics in coastal modeling under different levels of primary productivity due to seasonal and spring-neap variability. As such, it will impact broader research communities in biogeochemistry, carbon cycling, ecosystems and water quality. Field and laboratory data will inform the development of flocculation models to be effectively coupled with the existing open-source coastal models COAWST and OpenFOAM, already widely used by researchers from different disciplines. The project will support two PhD students for their research and three undergraduate students for their research experience in field experiments and sensors. The project also utilizes two international collaborations on FLOCMOD model development and quantifying transparent exopolymer particles (TEP) which the flocculation aggregates are made of. The investigators leading the project and the graduate students involved will participate in outreach efforts organized in their respective institutions. All the codes, numerical models are open-source, and all field and laboratory data will be made publicly available. This collaborative study will integrate four focused field campaigns (spring/fall during neap and spring tide), uniquely designed laboratory experiments, and numerical and data-driven modeling to address the knowledge gaps with the objectives to 1) quantify the importance of flocculation and its seasonal variabilities on sediment transport in the estuarine boundary layer via field observations that integrate several in-situ techniques to concurrently measure profiles of floc size, settling velocity, sediment concentration, turbulence, as well as characterization of organic content and concentration such as chlorophyll-a and TEP; 2) carry out extensive laboratory experiments to characterize floc size distributions and settling velocities over a large range of environmental conditions with an emphasis on varying organic and silt content in natural muds and measuring the transient response of the flocs to inform flocculation models solving the size-class population balance equations (PBE) and other reduce-complexity models; 3) provide an enhanced size-class PBE flocculation model for settling velocity coupled with existing hydrodynamic and sediment transport models COAWST and OpenFOAM to simulate cohesive sediment transport in the estuarine boundary layer, including validation with field observations; 4) implement machine learning methods to tackle upscaling challenges in flocculation, including the development of evidence-based model coefficients for the PBE flocculation model and surrogate models for solving PBEs. Novel aspects of this work include the concurrent deployment of unique instrumentation (e.g., PICS, LISSTS, and FlocARAZI) to provide unprecedented details of in-situ data to reveal the interplay of turbulent shear, resuspension/deposition, and floc properties in the water column; the design of new laboratory experiment focusing on the transient response of flocs beyond equilibrium state to provide the largest dataset of floc sizes under different conditions produced to date; and rigorous specification of flocculation model coefficients informed by lab data and a data-driven approach for tackling the upscaling challenge of including flocculation effects in coupled sediment transport and hydrodynamic modeling. 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

While the octopus is known for the remarkably dexterous grasping and manipulation abilities of its eight arms, the mechanisms by which they so effectively control their slender, flexible arms are not well understood. This award supports research with the aim to identify the strategies that an octopus uses to coordinate and control its soft arms. Using ultrasound imaging, the 3D motions of the arms will be measured and quantified. The measurements will be used to develop a model of the dynamics and kinematics of the arm movement and control, with the aim of developing a soft robotic arm with capabilities similar to the octopus. This research has the potential to provide new knowledge about complex arm motion, and develop the tools to translate this knowledge to controls for soft robotic arms. This project will also engage high school students through public outreach and participation in STEM camps. It will leverage the natural curiosity and enthusiasm evoked by the octopus to engage students and introduce them to bioinspired engineering at the intersection of biology and robotics. Overall, this project will advance our understanding and modeling of how the octopus is able to control its arms and translate that knowledge into improved control of soft robotic arms, which have numerous industrial, healthcare, defense and agriculture applications. This project will advance understanding of the dynamics of soft, fibrous structures. Through a novel application of ultrasound imaging, this project will provide a detailed 3D dataset of octopus arm kinematics, an area where data has previously been sparse due to the inherent challenges of 3D tracking the dynamics of a continuously deformable octopus arm. The 3D kinematic data will be analyzed through curvature- and topology-based analysis to identify principles of soft arm control and offer new perspectives on how these soft structures achieve their precise and coordinated movements. The insights gained will significantly advance understanding of how biological octopuses coordinate their arms during complex behaviors (a capability unparalleled in the animal kingdom) and provide the foundation for new modes of soft robotic control that replicate the flexibility, dexterity, and adaptability of biological octopus arms. At its core, this work will expand fundamental knowledge of how soft, compliant structures can be controlled and manipulated in both biological and 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 2025 · 2025-08

Engineering programs across the United States are committed to developing an industry workforce capable of maintaining and enhancing the nation’s economic success and technological leadership. Today’s graduating engineers must be fully prepared to enter the workplace and contribute to both the economic success of their employers and the national priorities of the country from Day 1. Despite this commitment from colleges and universities, employers continue to report critical gaps in how well new engineers are prepared for work. To develop effective professional engineers, programs need to do a better job of connecting what happens in the classroom to what happens on the job. One potential challenge to this work is the lack of industry experience among the faculty who teach engineering. Without practical work experience, faculty may not fully understand what engineers do and may struggle to connect classroom teaching to industry needs and practices in ways that most effectively prepare new graduates for the standards, norms, and practices of engineering work. This project will help address that problem by first determining what engineering faculty across the country do know about engineering work, and then understanding if and how they use that knowledge in their teaching. We will use our findings to create workshops, handouts, and other resources to help faculty better connect their teaching to the needs of employers and new graduates making the transition from school to work. We will work with teacher development programs, professional organizations, and others to help ensure that the people who teach the nation’s engineers know what their students will do after graduation and know how to connect their teaching to the real world. To address this gap, we propose a three-phase study that integrates Mental Models Theory (MMT) with the Theory of Planned Behavior (TPB) to address four research questions: 1) What experiences have engineering faculty members had with engineering work? 2) What are engineering faculty members’ mental models of engineering work? 3) In what ways do those models interact with faculty decisions about teaching? 4) How do the results vary by factors such as institution type and discipline? The study will begin with a national survey, followed by focus groups and then individual interviews, to better understand how faculty beliefs about engineering work impact courses and curricula. First, we will conduct the first large-scale national survey of engineering faculty member’s work experience in more than 30 years to generate reliable data on how much and what kinds of experiences the country’s engineering educators have with current industry practices. Second, we will conduct focus groups at a targeted set of universities, using data from each program’s own graduates, to test our protocols and develop a preliminary model of faculty mental models of engineering work and the impact of those models on teaching. Third, we will conduct semi-structured interviews with a purposive sample survey of respondents to elicit mental models and connections between teaching and post-graduation work in more detail across a wider population. These interviews will help us fully understand how engineering teachers connect what they do in the classroom to what they think their students will do after graduation. The outcomes will enable us to identify specific gaps, problems, or challenges, including gaps in what faculty know about current engineering industry practices and challenges they experience in trying to connect their teaching to those practices. The results will then be used to develop interventions, including faculty development workshops, handouts, and web resources, that can address the gaps identified through the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Super Dual Auroral Radar Network, or SuperDARN, is an international collaborative experiment for observations of plasma motions in Earth’s upper atmosphere. By observing ionospheric plasma motions, a multitude of geophysical processes are being studied. These processes range from fundamental plasma instabilities to the global-scale plasma response to changes in the solar-terrestrial environment. Each of these areas of study contributes to developing an understanding of the coupling of energy from the Sun into Earth’s upper atmosphere and its effects on humanity and technological systems. This project will support operations and maintenance of the U.S. SuperDARN radars in the northern hemisphere by the consortium of Penn State University, Virginia Tech, Dartmouth College, and the Johns Hopkins University Applied Physics Laboratory. The collaboration operates twelve radars that cover a vast region from Alaska to Iceland at high latitudes, and Oregon to Virginia at middle latitudes. In addition to operation and maintenance activities, the project will support a program of research that exploits new capabilities that have been developed over the last several years. This includes providing improved fidelity in measurements (plasma convection mapping and imaging), extending the area over which measurements are obtained (bistatic observations), and providing new types of measurements (sounding). SuperDARN has a long-standing commitment to including graduate students in all aspects of the program. The SuperDARN observations are also important for space weather applications since HF radio propagation is sensitive to perturbations in the bottomside ionosphere, e.g., solar flares. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. The IPON project seeks to investigate complex climate-health emergencies, which are crises caused by co-occurring and compounding medical, social, economic, and environmental risks that overwhelm health systems. Extreme events are among the major emerging drivers of these emergencies, intersecting with complex regional and global challenges such as biodiversity loss, land degradation, food and nutrition insecurity, and emerging diseases. Local communities face unique health challenges exacerbated by rapid environmental change, compounded by social determinants of health such as limited access to healthcare. The project will document, understand, and monitor the factors affecting the creation, evolution, and impact of complex climate-health emergencies among local communities in Uganda, Sri Lanka, India, Peru, Bolivia, and Argentina. Through the development and use of Observatories, the project will leverage real-time community-driven monitoring to document and analyze how interacting climatic and non-climatic stresses affect health systems and lived experiences. This approach deepens understanding of complex climate-health emergencies, establishes scalable solutions and offers innovative strategies for adaptation and mitigation applicable across various climate-sensitive regions. By integrating knowledge systems and co-developing pilot interventions, this project will generate transformative insights into how extreme climatic events mediate complex climate-health emergencies through food systems, inform resilience-building strategies, and establish an international knowledge-sharing network to guide health risk management policies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will plan for a wireless network testbed with integrated Artificial Intelligence (AI) capabilities to enable developing and testing AI-driven solutions for emerging industries critical to national prosperity, including agriculture, transportation, unmanned aerial systems, and smart cities. The testbed will enable modeling and design of a range of applications such as AI-powered drones for delivering supplies to citizens, improving spotty coverage in locations like farms, enhancing transportation safety, and securing supply chains. The project will also provide hands-on interdisciplinary training to prepare a talented workforce for careers at the intersection of AI and wireless technologies. Through open access, collaborative workshops, and robust governance, the testbed will support the transition to next-generation AI-enabled wireless systems. The proposed testbed will facilitate development of specialized AI foundation models trained on domain-specific knowledge, benchmark datasets, communication protocol settings, and user activity. These models will leverage real-time wireless data to optimize network configurations, resource allocation, and autonomous agent behaviors. The testbed infrastructure will feature a large-scale indoor radio grid and an outdoor corridor spanning farm, urban, and drone environments, all accessible remotely via secure cloud-native platforms. The planning project will organize scenario-based workshops with academic and industry stakeholders to define use cases, access policies, and governance structures, ensuring long-term sustainability and responsiveness to community needs. Expected outcomes include a scalable, AI-Ready testbed supporting end-to-end experimentation, new methods for adaptive and resilient wireless networks, and a foundation for future innovations in distributed AI applications and next-generation wireless research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will address the critical need of educating and graduating more engineers to meet state and national workforce needs. Retaining undergraduate engineering students can be challenging. One factor that may help with the retention of engineering students is increased psychological safety on engineering teams. Psychological safety is the shared belief that a team is safe for interpersonal risk-taking without fear of repercussions. Psychological safety can improve teamwork and may be linked with sense of belonging and expectations of success for engineering students, which are two key constructs linked with the retention in engineering. This project will advance our knowledge on how first-year students’ psychological safety changes in engineering teams over a two-semester course sequence. We will also investigate how psychological safety relates to their feelings of belonging and belief in their ability to succeed in engineering. By documenting trends in psychological safety over time and its connection to belonging and expectations of success, this project will suggest key times during students’ first year where psychological safety may drop, thus identifying ideal times for educational interventions to improve psychological safety. It will also help us to better understand links between psychological safety, belonging, and expectations of success over time, leading to improved first-year educational experiences for engineering undergraduates. This work aligns with the Research Initiation in Engineering Formation program in understanding the formation and evolution of psychological safety, belonging, and expectations of success in undergraduates over time and in expanding the engineering education research network through the mentoring and development of the principal investigator. This project will conduct a longitudinal cohort study of first-year engineering students at the University of Arkansas. The study will use a convergent mixed methods design to address three research questions: (1) How does psychological safety fluctuate throughout a two-semester introduction to engineering course? (2) How does psychological safety relate to first-year students’ sense of belonging and expectations of success over time? And (3) Are there differential effects of psychological safety on sense of belonging and expectations of success in engineering? Data will be collected through a survey using validated measures for psychological safety, sense of belonging, and expectations of success, along with open-ended responses to provide depth on student experiences in first-year courses. Students will respond to the survey four times per semester for a total of eight times during the project. Quantitative data will be analyzed using multilevel modeling, which accommodates the nested data structure where time is nested within students within teams. This will allow us to capture changes in psychological safety over time, to evaluate the relationship between psychological safety, sense of belonging, and expectations of success, and to evaluate whether there are differential effects of psychological safety on sense of belonging and expectations of success. Qualitative data will be analyzed using thematic analysis to explore potential reasons for changes in psychological safety over time and examine why psychological safety, belonging, and expectations of success may be related. The intellectual merit of this work lies in identifying trends in psychological safety over time in engineering education, which may allow us to identify key points in time to incorporate interventions to increase psychological safety. The work will also clarify possible relationships between psychological safety, sense of belonging, and expectations of success in engineering. Broader impacts of this work include improving first-year experiences for engineering undergraduates, improving retention of students at the University of Arkansas, where retention rates are below the national average, and developing the STEM workforce by retaining and educating more engineers broadly. This work will be shared through publications, conference presentations, and workshops for faculty involved in first-year engineering programs. 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

Research in quantum information sciences (QIS) across a spectrum of forefronts, including education, is important for the advancement and national security of the United States. With the necessary rise of QIS, an important challenge in undergraduate STEM education is understanding how university students make sense of the foundational interconnectedness of mathematics in quantum, and how professors can best facilitate both deep physical and mathematical understanding in quantum instruction to ensure that all students succeed in their degree programs and have access to the quantum workforce. The purpose of the Student Understanding of Linear Algebra across Quantum Information Science project is to conduct fundamental basic research regarding student understanding at the intersection of mathematics and quantum information science across disciplines. This project has the potential to benefit society by increasing what is known about students' interdisciplinary understanding of mathematics and quantum information science, generating insight into student cognition that will be relevant to subject matter, courses, and disciplines across STEM beyond those directly investigated in the project. Results have the potential to inform best practices in QIS instruction, which in turn, supports the growth of the quantum workforce--a workforce that is increasingly critical to international competitiveness in information technology during the coming century. The project research questions are: How do students understand various linear algebra concepts that are central to quantum information science concepts across disciplines? How do students reason about and symbolize linear algebra concepts in coordination with quantum information science concepts across disciplines? These questions are pursued through qualitative analyses of interviews conducted with and written work collected from students enrolled in four QIS-focused courses that are part of an interdisciplinary Quantum Information Science and Engineering minor. These four courses include a lower-division and an upper-division physics course, a chemistry course, and a computer science course. Each of the four courses relies on a similar throughline of core linear algebra concepts, such as linear combination, matrix equations and linear operators, unitary and Hermitian matrices, orthonormality, basis and change of basis, inner product, and tensor product, and eigentheory, as well as Dirac notation and matrix notation. Data analyses will aim to characterize students' meanings for these linear algebra concepts, as well as how students leverage linear algebra in their understanding of quantum topics such as qubits and entanglement. Thus, answering the project research questions will produce explanatory, research-based knowledge about how humans make sense of core mathematical concepts and their interconnectedness with QIS beyond disciplinary lines, in contexts that underly STEM in the modern world. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Using simulations of biological and natural processes, this project will generate real-time forecasts of water quality in three reservoirs in Appalachia. Droughts, wildfires, and other hazards alter normal ecosystem processes, which can harm drinking water quality. Water utilities are increasingly concerned about the effects that fires and floods have on water supply reservoirs. Appalachia is experiencing increasing hazards as well as aging infrastructure. Researchers will create the first integrated, real-time system that forecasts future water quality. With this information, water managers may act preemptively to address anticipated changes. This will help decrease costs and improve drinking water safety. Researchers and water managers will work together to produce the forecasting system, which will ensure forecasts are integrated into decision-making. This project will improve STEM education by developing teaching modules on forecasting and reservoir-catchment dynamics for high school and community college students. The improved forecasting that will result from this research project will help reduce the effects of hazards on water quality. Drinking water quality is threatened globally by changing Earth system hazards. To build resilience in drinking water supplies, water utilities and communities are seeking new predictive tools for guiding catchment and reservoir management decisions. Most water quality research has focused on the influence of hazards on bodies of water separately from their catchments. This project will create a coupled catchment-reservoir forecasting system that will predict future water quality one day to six months prior to treatment. This system will couple terrestrial and freshwater models to fully represent the direct and indirect environmental processes and hazards controlling reservoir water quality. Including the feedback between terrestrial and freshwater ecosystems can help to mitigate increasing risks to drinking water supplies. Researchers and water managers will work together to develop forecast tools, risk models, and data visualizations. This will ensure their broad usability for guiding decision-making. This project will develop teaching materials to improve education for K-12 and community college students. The coupled catchment-reservoir forecasting system developed by this work will be made available globally as a model for drinking water systems. This project is jointly funded by the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences and the Office of Advanced Cyberinfrastructure through the National Discovery Cloud for Climate initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project helps young students learn about robots and artificial intelligence (AI) ethics through a fun and creative afterschool program. While new technologies make our lives easier and more comfortable, they also bring up new problems. e.g., "How can people protect their privacy when using AI?", or "What happens if people depend too much on robots and AI?" Schools have begun teaching about robots, AI, and coding, but it is often done by sitting in a classroom and listening. Ethical topics may not always be discussed in such a setting. This project tries to let students learn robot and AI ethics by acting, moving their bodies, and using arts like music, drawing, and dance. This will help them better understand what is right or wrong when working with robots and AI in their everyday lives. The team hopes students can learn how robots and AI work and start thinking about how these tools affect people and the world around them. To make this happen, the team of researchers will first talk with students and teachers to find out what works and what is challenging. Second, they will design a hands-on curriculum and build tools for learning. Finally, they will run and test programs in schools and museums. The project will result in lesson plans, learning tools, data from the research, and useful tips for teachers and researchers. This research will also support anyone interested in teaching children about robot and AI ethics using creative methods. As demand for robots and AI literacy is rapidly increasing, schools introduce more education programs about robots and AI. However, formal education settings for this topic are still unfamiliar and intimidating. In this project, the research team will design and implement an embodied, informal STEAM (STEM + arts) education program. There will be a focus on robots and AI ethics for young learners (4th-5th graders) so that they can experience how to live with these technologies. This interactive learning program will consist of diverse modules (e.g., acting, dance, music and sound, and drawing). There are three primary research thrusts to the project. First, the team will conduct literature reviews, focus groups with stakeholders. They will identify experiences and challenges for learning AI and robotic ethics using surveys and interviews. Second, the project team will develop a creative afterschool program using participatory design workshops. The research team will also create technologies and tools to support the AI and robotic learning objectives. The final thrust will implement and evaluate the creative afterschool program in schools and museums. The proposed creative afterschool program will draw upon interdisciplinary expertise and experiences in psychology, computer science, engineering education, interactive arts, and human-robot interaction. This research contributes to the creation of a modularized curriculum, accessible interfaces, and tools for children. The outcomes will produce data and knowledge about children’s understanding and perception of robots and AI ethics. This project will produce guidelines for future iterations of this type of creative afterschool 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.