University Of Nebraska Lincoln

NSF Awards · FY 2026 · 2026-10

Underwater wireless communication and networking are critical for monitoring aquatic environments, improving maritime safety, supporting offshore exploration, and enhancing national security. However, research progress in this field has been slow compared with terrestrial wireless systems because conducting underwater wireless experiments is challenging. Natural underwater environments are uncontrollable, and indoor pools and tanks are static and small, which significantly limits research reproducibility, innovation, and public accessibility. To bridge this gap, this project implements a remotely accessible underwater communication and networking platform in a water tunnel that enables experimentation, dataset collection, and artificial intelligence model building under a range of controlled, reconfigurable, reproducible conditions. The testbed, datasets, and developed software enable new wireless communication technology development without the high cost and complications of natural underwater deployments. By sharing advanced experimental tools, datasets, and software with the research community, this project advances scientific discovery and strengthens national leadership in next-generation underwater communication and networking systems. In addition, this project integrates research with education and actively trains students in communication, networking, sensing, and artificial intelligence to support workforce development and address critical national needs. This project designs and deploys a hybrid underwater acoustic, magnetic, and visible light networking system that integrates a reconfigurable water-tunnel testbed, physics-informed multi-modal deep generative channel models, and a scalable digital twin for dynamic underwater networking simulation and optimal control. First, the remotely accessible, reconfigurable testbed instrument enables the collection of acoustic, magnetic, and visible light communication channel data under dynamic water flow and blockage conditions. Second, the collected datasets are used to train physics-informed deep generative channel models that extend beyond the physical testbed to enable large-scale, measurement-driven simulations. Last, the physical testbed and channel models are integrated to develop a networking digital twin, which allows researchers to evaluate multi-modal scheduling strategies, resource allocation schemes, and networking protocols under realistic dynamic underwater conditions. All software, datasets, models, and documentation will be publicly released through open repositories and public websites. By linking physical experimentation with scalable digital simulation, this project will provide sustainable cyberinfrastructure that accelerates data-driven and artificial intelligence-enabled innovation in underwater wireless communication and networking. 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 · 2026-10

The United States needs engineers who are prepared to meet the demands of rapidly evolving industries. With rapid technological advancement, the rise of artificial intelligence, aging infrastructure, and increasing pressures on supply chains and national security, the engineering workforce must be adaptable and prepared for complex challenges. Engineering education research (EER) plays a critical role in ensuring that engineers are not only technically competent, but also skilled in leadership, communication, ethical reasoning, and lifelong learning. Through theory-driven research on how engineers learn, collaborate, and develop as professionals, EER generates evidence-based findings that inform curriculum design, classroom instruction, and pathways to professional preparedness. Despite its importance, EER faces challenges in recruiting future scholars due to limited undergraduate pathways, a small number of degree programs, and low awareness of the field among students and faculty. This project supports a Research Experiences for Undergraduates (REU) Site at the University of Nebraska–Lincoln that addresses these challenges by providing immersive research experiences that connect engineering education research to real-world engineering practice and U.S. workforce development. By engaging students in projects such as developing evidence-based safety training in construction contexts, the program highlights the societal relevance of EER and its potential to improve professional practice and save lives. The program prioritizes access by recruiting students from institutions with limited research opportunities and aims to broaden participation in STEM, strengthen pathways into graduate education, and contribute to a more prepared and adaptable engineering workforce, thereby advancing national health, prosperity, and welfare. This REU Site builds on a successful program at the University of Nebraska–Lincoln that has engaged over 50 undergraduate researchers, including 21 NSF-supported participants, resulting in multiple conference papers and research presentations. The proposed program advances a unique embedded model of engineering education research, in which EER faculty are housed within traditional engineering departments, enabling interdisciplinary collaboration and authentic integration of educational research within disciplinary engineering contexts. The overall aim is to prepare undergraduate STEM students for independent research while facilitating a greater understanding of engineering education research and its connections to U.S. workforce development. Participants will engage in rigorous research aligned with established principles, including posing significant empirical questions, using appropriate quantitative and qualitative methods, developing coherent chains of reasoning, and disseminating findings for professional scrutiny. The program also emphasizes ethical and just research practices and engages students with multiple forms of evidence and research purposes. Through mentored research, cohort-based professional development, and structured reflection, participants will develop transferable research skills in design, data analysis, and scholarly communication. The program will recruit a diverse cohort, with at least 50% from institutions with limited research opportunities, and will evaluate outcomes related to research skill development, understanding of EER, and interest in graduate study. This project will strengthen pathways into engineering education research, expand participation in the field, and advance scholarship at the intersection of engineering education and 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 2026 · 2026-07

The open radio access networks (open RAN) initiative, e.g., O-RAN, shows sweeping momentum in shaping, revolutionizing, and defining next-generation 6G mobile networks. As the key focus of 6G, network autonomy is much anticipated to autonomously manage, operate, and optimize real-world networks, particularly leveraging Artificial Intelligence / Machine Learning (AI/ML) techniques. Existing domain-agnostic AI/ML techniques fail to safely adapt and generalize under unforeseeable, evolving network dynamics, which has become one of the foremost stumbling blocks for the telecommunication industry to fully embrace AI-assisted network automation toward next-generation networks. The research goal of this CAREER project is to achieve trustworthy network autonomy by optimizing for safety, generalizability, and interpretability. To this end, domain-informed AI/ML innovations will be developed to address generic online network control problems in ever-evolving real-world mobile networks. The education goal is to develop next-generation STEM workforce, cultivate and retain talents locally, and engage and inspire K-12 students. The integrated research and education will contribute to bridging the digital divide in Nebraska, rural America, and beyond. The research program of this CAREER project will derive a novel systematic framework of trustworthy AI-native network autonomy in open RAN. First, new online task-oriented digital network twin (DNT) frameworks will be designed to derive DNTs with all the attributes of fidelity, synchronicity, and tractability. Second, new safe deep reinforcement learning (DRL) frameworks will be designed to achieve verifiable safety for online resource allocation (e.g., dApps/xApps) in real-world networks. Third, new explanation-guided Bayesian optimization frameworks will be designed to achieve interpretable safety for online network configuration (e.g., rApps) under time-evolving dynamics. Moreover, the education program will develop a new campus-wide wireless educational platform based on Husker-Net, a private cellular edge network, to serve multiple courses and educate hundreds of students; establish a new graduate connect program to promote graduate student success and retain talent in Nebraska; and launch a new virtual Hour-of-Code event to engage, inspire, and educate K-12 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 · 2026-06

This request is for partial funding for the 40th Annual Gibbs Conference on Biothermodynamics, which will be held at the Touch of Nature Education Center in Carbondale, IL September 26th-29th, 2026. This long-standing conference has a history of hosting both established and junior investigators who address questions in the energetics of the biochemical processes that are essential to support life. An understanding of how biological molecules function with great specificity and perform the challenging chemical reactions necessary to support life is essential for continued progress in biotechnology, medicine and agriculture. This gathering fosters collaborations among the attendees, facilitating rapid progress through team efforts. The Gibbs conference welcomes investigators who address questions in the energetics of the chemical processes that confer biological function. The conference attendees come from an array of computational, biochemical and structural fields and are broadly interested in thermodynamics. The conference design encourages formal and informal discussions that will promote new collaborations and contribute to the professional development of young investigators and trainees. Successful outcomes of the conference will include new collaborations that drive the field forward. 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

The 2026 IEEE Vehicular Technology Conference (VTC) will be held in Boston, Massachusetts, on September 6–9, 2026. It will bring together leading researchers and practitioners from academia and industry to present and discuss the latest advances in wireless, mobile, and vehicular technologies. VTC 2026 will cover a wide range of crucial research topics, including mobile and wireless networking, vehicular communications, edge intelligence, AI/ML for communications, resilient connectivity under mobility and blockage, spectrum sharing, security, and integrated terrestrial–aerial–space networking. As the flagship conference series of the IEEE Vehicular Technology Society (VTS), VTC provides a unique opportunity for graduate students to engage with cutting-edge research, interact with senior researchers, and become part of an active international research community. Participation in VTC is a critical component of graduate training. It enables students to present their work, receive valuable feedback, and build professional networks. This project provides travel support for approximately 10 U.S.-based graduate students to attend VTC 2026 and its associated workshops. Funded students will actively participate in the conference by attending plenary talks, tutorials, technical/application sessions, and an industry track. Students will have the opportunity to showcase their research, receive feedback from leading experts, and gain exposure to cutting-edge developments in wireless, mobile, and vehicular networks. These activities will not only enhance students’ technical knowledge and professional skills but also accelerate the translation of research into real-world wireless and networking systems. By supporting student participation, this project helps cultivate a skilled workforce capable of advancing wireless and networking technologies that underpin critical societal services, such as healthcare, energy systems, transportation, emergency response, education, and national security. 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

Viruses cause disease in humans, plants, animals, and even microbes in natural ecosystems. Biologists are only beginning to understand how, through microbial infection and mortality, viruses play a vital role in regulating microbial activities, like the movement of key nutrients in the environment. A keen understanding of nutrient cycling is particularly important in lakes, which are a vital freshwater resource for all Americans. Recent research indicates that viruses may have a bigger and more complex role in nature than previously understood, including serving as a food source for larger microorganisms. The process of consuming viruses as food is called “virovory.” In this project, researchers will examine the importance of viruses as a food source for microbes in lakes, and whether their consumption helps to move nitrogen and phosphorus through aquatic food webs. This project will combine experimental and mathematical approaches to assess the impacts of virovory on nutrient cycling in lakes to determine the degree to which viral nutrients move up through food webs, and to develop a general framework for how these processes function across a variety of lake types. This work will provide potentially transformational insight into the structure and function of nutrient cycles in America’s lakes, with strong potential for applications in biotechnology, and enabling more effective management and helping to improve our understanding of how viruses help maintain important freshwater resources. This study aims to combine manipulative experiments with observational data collection and theoretical modelling to assess the biological impacts of virovorous interactions on both the viral communities of lakes and the cycling of nitrogen and phosphorus within them. Researchers will deploy isotopic tracers to quantify the movement of nitrogen (15N) and phosphorus (32P) from isotopically labelled viral material directly into microzooplankton virovores (ciliate protozoa) and indirectly into their mesozooplankton predators (copepods). This will provide quantitative measurements of the rate of nutrient flux from the viral fraction into higher trophic levels. These measurements will be compared with microcosm trials measuring the rates of virus removal by virovores, the changes in viral abundance and community composition (using metagenomic approaches), and the growth response of higher trophic levels. The experiments will employ both lab strains and extant communities collected from local lakes and reservoirs, providing a window into the impacts of virovory at both the community and ecosystem scales. Data will be synthesized into a mathematical model to assess in situ rates of virovory in lakes spanning a trophic gradient, providing a tool to determine the impacts of virovory on a variety of natural systems of societal interest. The viral metagenomic data generated in this project will comply with data standards set by the National Center for Biotechnology Information (NCBI) and may facilitate discovery of novel viruses and genetic segments with potential biotechnological applications. Undergraduate and graduate students will be trained in the collection and analysis of metagenomic data, providing a significant and directed focus on education and workforce development for the future of biotechnology. 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

This REU Site award to the University of Nebraska-Lincoln, located in Lincoln, NE, will support the training of 10 students for 10 weeks during the summers of 2026-2028. This REU Site in the field of Plant Pathology will provide students with laboratory and field research experiences investigating how pathogens cause diseases in plants and the strategies that plants use to fight those pathogens. Understanding plant diseases is essential to developing targeted strategies to control pathogens and mitigate their economic impact on farmers and to food security worldwide. Students in this REU Site will also participate in campus-wide professional development workshops that will build transferable career and soft skills to support their future career goals. At the end of the program students will present the results of their work at the REU Campus-wide Research Symposium. Assessment of this program will be done through pre- and post-assessment surveys. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with NSF priorities in Biotechnology. Plant diseases have a detrimental effect on crop productivity and associated food supply for humans and animals, and environmental changes can dramatically enhance plant disease outcomes. In addition, plant pathogens can also become threats to national security when deployed as biological weapons. Students participating in this REU Site will conduct research on fundamental aspects of plant diseases in the context of abiotic and biotic stress. Research projects include investigating the evolution and geographic distribution of plant pathogens, dissecting mechanisms of pathogenesis by different groups of plant pathogens and unraveling the molecular and cellular processes operating that plants deploy to recognize and respond to pathogens and other types of stress. Students will be mentored by faculty, postdoctoral researchers and graduate students affiliated with the Department of Plant Pathology. In addition to the campus-wide professional development activities, students will also participate in cohort-specific activities to increase their awareness of Plant Pathology as a discipline that advances fundamental research while providing solutions to agricultural productivity and food security. 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

Cold temperatures and unexpected frost events cause billions of dollars in agricultural losses each year and limit where crops can be grown. Plants survive freezing conditions by protecting their cellular membranes, which are highly sensitive to temperature and dehydration. This project seeks to understand a natural strategy plants use to survive freezing: the production of unusual membrane lipids in chloroplasts during cold stress. These lipids appear only during freezing and disappear when temperatures recover, suggesting they provide a temporary but critical protective function. By uncovering how these lipids stabilize membranes, this work will advance fundamental knowledge of how living systems adapt to environmental stress. The results have broad societal relevance, including informing the development of cold-tolerant crops that would improve food security, reduce economic losses, and expand agricultural production into new regions. In addition, the findings may inform technologies in medicine, such as improved preservation of cells and tissues, and in materials science, where freezing processes are widely used. The project also supports interdisciplinary training of students and engages the public through educational outreach on plant resilience and climate adaptation. This project will determine how tri- and tetra-galactolipids (TGDG and TeGDG), which accumulate in chloroplast membranes during freezing, contribute to membrane stability at low temperatures. The central hypothesis is that the unique combination of large sugar headgroups and highly unsaturated fatty acid chains in these lipids enables membranes to maintain hydration, fluidity, and resistance to damaging phase transitions. To test this, the research integrates molecular dynamics simulations, biophysical experiments, and plant genetic approaches. First, the project will quantify how TGDG/TeGDG headgroups alter membrane hydration, lipid packing, and fusion propensity using simulations, Langmuir monolayers, X-ray scattering, and vesicle assays. Second, it will determine how fatty acid unsaturation modulates membrane fluidity, permeability, and phase behavior by comparing natural and modified lipid variants. Third, the project will investigate how these lipids function in living plants by engineering Arabidopsis with altered lipid compositions and by identifying genetic suppressors that restore freezing tolerance. Together, these approaches will produce an integrated mechanistic model linking lipid structure to membrane behavior and plant survival under freezing conditions, providing a foundation for future efforts to engineer stress-resilient biological membranes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant professor and training for a graduate student at the University of Nebraska-Lincoln. This work is conducted in collaboration with Iowa State University. Through the fellowship, the PI will develop an artificial intelligence platform that helps plant scientists analyze and query emerging single-cell protein data through standardized use cases. While many studies measure gene activity, proteins carry out most biological functions, and new technologies allow researchers to measure proteins in plant cells. However, these data are difficult to interpret and often inaccessible without advanced training. This project will create user-friendly tools that integrate protein and gene data and allow researchers to ask biological questions using natural language. The work combines computer science, plant biology, and data science, and will strengthen research capacity, workforce training, and digital agriculture innovation in Nebraska. This project will establish a prototype artificial intelligence test bed for integrating plant single-cell proteomics and transcriptomics into a unified analytical framework. The intellectual contribution lies in developing plant-specific data standards and scalable computational pipelines that address the sparsity and heterogeneity of single-cell protein measurements. The PI will implement cross-modal embedding methods, machine learning-based imputation, protein-protein interaction network integration within a web-accessible environment. A chatbot-guided interface will be developed to support structured query translation into validated analytical workflows, improving interpretability and usability for domain scientists. The fellowship will expand research infrastructure at the University of Nebraska-Lincoln by enhancing expertise in proteomics-driven artificial intelligence, strengthening collaborations with Iowa State University, and creating training opportunities for graduate students in computational plant biology. The project will integrate research, workforce development, and cross-institutional collaboration to advance artificial intelligence applications in plant systems science. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows (ERF). The ERF program supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The Heartland Mathematics Conference (HMC) is an annual graduate student mathematics conference to be held at Kansas State University (KSU), University of Nebraska-Lincoln (UNL), and University of Kansas (KU) in 2026, 2027, and 2028. The conferences are organized by graduate students at the three participating universities, under the guidance of Professors Dave Auckly (KSU), Alex Zupan (UNL), and Jeremy Martin (KU). The HMC provides a unique and valuable opportunity for graduate students to experience the benefits of presenting and taking part in a research conference and helps train organizing students in project administration. HMC is a continuation of the Kansas Mathematics Graduate Student Conference, which started in Fall 2021 and has run for four years as a joint project between graduate students at KU and KSU. With mostly graduate students and undergraduates in attendance, the conference provides a low-pressure environment in which graduate students can present their research and improve upon their presentation abilities. The conference introduces participants to current trends in multiple fields of research and provides an environment for interdisciplinary collaboration, with the hope of creating strong research groups moving forward. The networking possibilities and the experience gained from giving a research talk enable attendees to mature as mathematicians and as future faculty members or professionals outside of academia. The conference website is: https://sites.google.com/view/heartlandmathconference 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.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Metabolic dysfunction-associated steatotic liver disease affects approximately 30% of U.S. adults with rising prevalence. It can progress to metabolic dysfunction-associated steatohepatitis (MASH). Although steatosis, inflammation, and coagulation contribute to the development of fibrosis in MASH, the molecular mechanisms linking them are elusive, hindering the development of therapeutic approaches to treat MASH. Matrin-3 is a DNA- and RNA-binding protein involved in different biological processes by regulating RNA processing, RNA stability, and chromatin remodeling. Matrin-3 genetic variations are associated with amyotrophic lateral sclerosis and cardiometabolic traits in humans. However, the role of matrin-3 and its downstream molecules in regulating signaling pathways and gene expression remains largely unknown in MASH. We found that matrin-3 liver- specific knockout (LKO) mice display increased hepatic steatosis compared with matrin-3 floxed mice. Integrated analysis of our transcriptomic and genomic data suggests that constitutive androstane receptor (CAR) is a main transcription factor that mediates transcriptome changes and hepatic steatosis in the liver of matrin-3 LKO mice. Importantly, we found that matrin-3 LKO mice display increased infiltration of inflammatory cells and hepatic fibrosis in a mouse model of MASH. To reveal mechanistic insights, we performed single-nucleus Multiome analysis and inferred intercellular communications. The study revealed that hepatocytes and hepatic stellate cells (HSC) have the strongest intercellular communication in the livers of LKO mice. The protease-activated receptors (PARs) mediate the topmost incoming signaling pattern in HSCs of LKO livers. Among the four PARs, PAR1, PAR3, and PAR4, except PAR2, are thrombin receptors. We identified F2, a gene that encodes prothrombin, as a new target gene of CAR through a combination of genomic and biochemical approaches. We found that CAR signaling is required to repress F2 expression through an unknown mechanism in healthy livers. In support of the potential role of thrombin in hepatic fibrosis, we found that: 1) F2 expression is induced in the hepatocyte cluster revealed by single-nucleus RNA-seq in the livers of LKO mice and patients with MASH; 2) PAR1 is the dominantly expressed thrombin receptor in the HSC cluster; and 3) thrombin activates mouse primary HSCs. Together, these preliminary data lead to our hypothesis that the matrin-3-CAR axis constrains hepatic inflammation and fibrosis in part by repressing the paracrine effects of hepatocytes on hepatic stellate cells through thrombin and its receptor PAR1 in diet-induced MASH. Three specific aims are to: 1) identify the molecular mechanisms by which matrin-3 regulates CAR signaling, 2) determine the protective role of the matrin- 3-CAR axis in hepatic inflammation and fibrosis in a preclinical model of MASH, and 3) reveal mechanistic insights into the matrin-3-CAR axis-regulated hepatic stellate cells activation and liver fibrosis. Completion of the project will identify the matrin-3-CAR-thrombin axis as a new connection linking steatosis, inflammation, and coagulation that contributes to liver fibrosis and will provide novel therapeutic targets with translational impact.

NIH Research Projects · FY 2026 · 2026-04

PROJECT ABSTRACT The development of a universal influenza vaccine that would provide long-lasting protection against divergent strains has been a high-priority for many years. Some strategies have shown promise and very real improvements in the overall breadth of protection. For example, consensus vaccines have shown improvement over wildtype immunogens. Another interesting strategy focuses on driving stalk-directed immunity against influenza, especially since the HA2 stalk domain is indisputably much more conserved than the HA1 head domain. Approaches such as stabilized HA2 stalk immunogens, stalk-ferritin fusion viral particles, or chimeric stalk domains fused to irrelevant avian HA1 head domains were used to drive stalk-directed immunity. Additionally, the strategy of sequential immunization with variable irrelevant HA1 head domains, specifically amplified anti-stalk immunity and increased the breadth of immunity against divergent influenza virus strains. These stalk immunogens have been delivered using recombinant proteins, viral vectors, and, more recently, mRNA-lipid nanoparticles (LNPs). Ironically, most of these strategies employ a single monovalent immunogen, which is counterintuitive to the multivalent trivalent and quadrivalent vaccine strategies that have shown significant improvement in vaccine design. A novel Epigraph strategy that uses a computationally-optimized trivalent immunogen has shown broad cross-protective immunity. Experiments in mice, ferrets, and swine have shown this Epigraph vaccine strategy to be superior to wildtype immunogens and commercial vaccines. We propose to combine the strengths of five of these approaches to create a more universal vaccine immunogen than when applied individually. This application describes an influenza vaccine that combines the immunodominant HA1 heads of Epigraph (1) genes with the conserved HA2 stalk of consensus (2) immunogens to create novel multivalent chimeric (3) optimized (ECChO) immunogens. We will use a sequential prime- boost/boost immunization strategy (4) that has shown optimal and maximized immunity to amplify stalk-directed immunity. Finally, we will deliver the multivalent vaccine using recombinant Adenoviral viral vectors (rAd) and/or mRNA-LNP delivery systems (5). This vaccine approach will be investigated for efficacy against H2N2, H5N1, H7N9, and H9N2 influenza viruses as all of these avian influenza viruses are actively detected in the U.S. and there is an ongoing endemic of avian H5 infections in cattle, poultry and humans. In addition, H7N9 and H9N2 human infections result in severe respiratory illness and death in approximately 40% of hospitalized cases. All of these avian influenza viruses have significant disastrous pandemic potential.

NSF Awards · FY 2026 · 2026-01

The rapid evolution of engineering – driven by AI, aging infrastructure, and national security needs – demands engineering graduates that are prepared for the shifting landscape of today’s industry. Despite this critical need, there is no clear consensus on what is means to be “prepared” for modern engineering work. While national reports dating back to 1918 argue that graduates lack essential skills, recent studies suggest many graduates feel prepared for work. Despite these contradictions, no study has yet explored workplace preparedness from the perspectives of multiple stakeholders, including industry, faculty, and new engineers themselves. As a result, the question persists – are engineering graduates prepared to tackle the challenges of today’s shifting engineering landscape? If the United States is to remain competitive on a global scale and effectively serve its people, the answer to this question must be a resounding “yes.” Reaching this point must begin with a clearer understanding of what preparedness means for today’s engineers. To meet this need, the purpose of this project is to explore how “preparedness” is defined by key stakeholders – industry practitioners, faculty, and new engineers – by comparing their perspectives and uncovering nuances in their conceptualizations. Ultimately, these insights will support the development of more targeted and effective strategies to equip all engineering graduates with the skills, knowledge, and mindsets necessary to thrive in an evolving professional landscape. This project will strengthen the U.S. workforce by enhancing the readiness of engineering graduates. This project is well-aligned with the NSF Research in the Formation of Engineers program in that it focuses on transitions between education levels and addresses lifelong learning by the engineering workforce. Informed by Role Theory and recent conceptualizations of preparedness, this project will answer the following research questions: What constitutes a “prepared” new engineer, from the perspective of industry, faculty, and new engineers? How do stakeholders’ roles influence their perspectives of preparedness? How do new engineers’ perceptions of their preparedness change over the first two years of work? To answer these questions, the research team will employ a three-phase multiple-methods study focused on the civil and mechanical engineering disciplines comprising a longitudinal qualitative investigation of new engineers’ perceptions to capture how their perceptions evolve over the first two years of work, a cross-sectional qualitative investigation of industry practitioners and faculty perceptions through interviews to capture how their perspectives vary from students and each other, and a nationwide survey of all stakeholders to broaden and generalize findings from the first two phases. Findings from this research will address a critical gap in the literature by advancing theory by bringing coherence to what is meant by “preparedness” for work. The outcomes of this project will provide a clear understanding of how preparedness is conceptualized across different stakeholder groups, highlighting areas of alignment and divergence. By identifying gaps between educational and industry expectations, this study will inform curriculum development, enhance engineering education practices, and bridge the disconnect between academia and industry. Insights will be translated through broader impacts activities to students, faculty, and engineering industry professionals through the development of student-facing, faculty-facing, and industry-facing resources that define the kinds of workplace readiness that employer’s look for and that new engineers say they have. 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

Spatial skills are usually considered to be important for success in STEM (Science, Technology, Engineering, and Math). However, the way these skills are currently taught does not always connect with real-world engineering work. Currently, many spatial skills training materials use simple blocks or cubes which are much easier to work with than the complex objects that engineers will encounter on the job. A typical spatial problem might ask a student to rotate a cube shape in their mind. But a civil engineer might need to envision a complex building or a three-dimensional landscape. Research shows that students learn these spatial skills better when the training is connected directly to the specific type of engineering they are studying. However, we currently lack an understanding of the spatial problems that today’s engineers encounter. This project will study how spatial skills are used in two areas of engineering: civil and mechanical engineering. The findings will help us understand what kind of spatial skills are important to each area of engineering. The outcomes of the research will have a direct impact on recruiting and retaining students in engineering who have a range of spatial skills, advancing our understanding of the professional formation of engineers. Rather than continuing to promote the idea that general spatial skills are important for engineering degree attainment, the proposed work utilizes a discipline-specific approach to classify the ways in which engineers represent and communicate spatial information in different work contexts. To address this knowledge gap, we will focus on two disciplines, civil and mechanical engineering, and will 1) use ethnographic methods to identify spatial problems embedded in practice, 2) verify the ethnographic findings through interviews of practicing engineers nationwide, and 3) use data from 1) and 2) to identify which spatial skills are important in contemporary engineering practice in civil and mechanical engineering. This project will yield rich descriptions of the spatial problems that engineers encounter in practice and identify the resulting spatial skills needed to solve those spatial problems. The research approach is informed by prior research that indicates that real-world spatial skills are discipline-specific and answers the call for a deeper understanding of discipline-specific spatial problems and skills. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Nebraska-Lincoln. This work is conducted in collaboration with Hongjie Dong at Brown University. Through the fellowship, the principal investigator (PI) will explore a fundamental question about the mathematical equations used to model real-world phenomena, such as fluid motion. The project lies in the STEM disciplines of applied mathematics and mathematical physics and investigates the existence and uniqueness of solutions to these equations starting from any given initial data. These questions are crucial because reliable predictions depend on the uniqueness of solutions. The graduate student will gain exposure to cutting-edge research and benefit from networking with students and faculty at Brown. This project will enable the PI and a graduate student to conduct mathematically rigorous analysis on prominent partial differential equations in fluid mechanics and mathematical physics at Brown University. The PI will prove the uniqueness of weak solutions of the Cahn-Hilliard equation with minimal regularity assumptions, providing a rare example where convex integration techniques cannot be used to demonstrate non-uniqueness. Additionally, the PI will develop a global solution theory for the surface quasi-geostrophic equation with stochastic forcing, contributing to the broader understanding of singular stochastic partial differential equations. The PI will also receive mentoring in research collaboration, student and postdoctoral supervision, and academic service such as journal editorship. As part of this collaboration, the PI’s fellowship advisor will visit the University of Nebraska-Lincoln to deliver a colloquium and a career development session, extending the project’s benefits to a broader student audience. This project will foster long-term collaboration and student training between Nebraska and Brown University. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Emerging extended reality technologies, including augmented reality, mixed reality, and virtual reality, demand high-speed low-latency communication to support interactive immersive applications such as three-dimensional (3D) telemedicine and video conferencing. However, achieving both high speed and low latency can lead to a high bit error rate, which in turn reduces communication reliability. Thus, it is essential to develop a new error detection and correction coding solution that can tolerate communication errors in such scenarios. In communication systems, bit errors exhibit varying levels of semantic significance. Errors with low significance can be tolerated, while those with high significance must be corrected. This project will explore topological information from source data for error detection and correction in communication systems, which can ensure data fidelity at the topology level rather than the bit level, aiming to detect the significance of errors in the received data and correct them by recovering the original topological information. This approach offers new insights into communication errors and can potentially enable the development of novel codes for next-generation communication systems. In addition, this project will provide research opportunities for both graduate and undergraduate students and will leverage outreach activities at the University of Nebraska-Lincoln to share research outcomes with K-12 students and teachers using specially developed educational modules. In this project, topological data analysis, particularly persistent homology and persistence diagrams, will be used to extract and encode topological information from source data. The topological information captures high-level data relations, which can be more compact and semantically meaningful than bit-level relations. This project will study the feasibility and evaluate the performance of topological error detection and correction using point cloud data as a primary data modality. A foundational relation between bit errors and topological errors will be established, aiming to guide the development of topological error detection algorithms using uncompressed persistence diagrams to detect and evaluate the significance of errors. In addition, topological error correction algorithms will be designed by optimizing persistent homology functions to correct significant errors in the received data. 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 East Coast Operator Algebra Symposium (ECOAS) is an annual research conference centered around the theory of operator algebras and their applications. The first meeting was at Vanderbilt University in the Fall of 2003, and since then meetings have occurred annually. This award will partially support participants for this year's event, the 21st meeting of ECOAS, held at University of Nebraska-Lincoln, tentatively scheduled for October 10-11, 2025. The conference will provide a venue for researchers to learn about developments at the forefront of operator algebras, share their work with the broader community, and network with both early career researchers and well-established members of the community. This event focuses on C*-algebras, von Neumann algebras and a wide variety of applications, including to quantum physics, representation theory, and dynamical systems. Thirty to sixty participants are expected. The plenary speakers will review recent advances, enabling participants to keep abreast of recent developments in a vast and rapidly expanding subject. More information is available at the conference website: https://math.unl.edu/east-coast-operator-algebras-symposium-2025/ 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 Great Plains region faces significant challenges in accessing advanced computing resources required for data-intensive research, particularly at smaller institutions with limited infrastructure. The growing use of AI and Machine Learning in both research and education has created a growing need for both high capacity and high performance storage. The Great Plains Scalable Tiered Object Repository (GP-STOR) addresses these challenges by deploying a distributed storage infrastructure accessible to institutions throughout the entire Great Plains region and physically located at the University of Nebraska-Lincoln, University of Missouri, and University of South Dakota. The augmented storage and computational capabilities directly address NSF’s mission by expanding access to essential cyberinfrastructure resources, fostering greater collaboration, improving scientific reproducibility, and accelerating data-driven research that could significantly impact fields such as public health, agriculture, and energy. GP-STOR consists of three identical storage clusters strategically distributed across different geographic locations contributing a combined 14PB of high capacity storage and 384TB of high-speed, high-IOPS storage. Each site features 11 high-capacity CEPH nodes and one NVMe node. These clusters are connected to high-speed 100 Gigabit per second (Gbps) Science DMZs to resources in the National Research Platform (NRP), through the Great Plains Network and national optical research networks. Resource access is streamlined via the NRP, which provides robust block device interfaces for performance-intensive tasks and a user-friendly web interface. The infrastructure is designed to support numerous research areas including space-borne computer vision, dark matter detection experiments, proteomics research, and explainable artificial intelligence applications in biomedical data analysis. 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

Lightweight formal methods offer a way to increase the use of rigorous techniques for finding more bugs during everyday software development. Runtime verification (RV) is such a method; it monitors program executions against behavioral safety properties that are specified in logic. In many open-source projects, RV has helped find hundreds of confirmed bugs that were missed during software testing; but RV is often too slow. While several optimizations have been proposed for speeding up RV, no extant framework implements all of them. The project’s novelties are a framework for implementing RV optimizations and re-implementations of existing optimizations in that framework. The project’s impacts are faster RV during software testing and a platform for investigating future optimizations. The project will (i) develop a framework for making optimizations pluggable into implementations of popular but often slow-for-testing RV tools; (ii) re-engineer two existing RV tools to accommodate that framework; and (iii) re-implement two existing optimizations within that framework. The investigators plan to evaluate the proposed framework by (i) comparing their framework-enhanced tools with state-of-the-art tools that do not implement said optimizations, and by (ii) assessing the ease with which a third optimization can be plugged into the framework. The investigators will use their existing RV tools and their benchmarks of formal specifications and actively develop open-source projects for this evaluation. 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

Cyberization is the foundation of automated and intelligent vehicles, requiring the deployment of ever-increasing onboard sensing, communication, and computing services. However, vehicle cyberization also creates new problems, including the potential for software bugs, security vulnerabilities, and erroneous sensor readings, which can degrade vehicle reliability and disrupt the automotive industry. For optimal performance, vehicle diagnostics must consider the interactions across the physical and cyber domains, the reliance on vulnerable in-vehicle networks, and the inability to address unknown anomalies. This award will support fundamental research to address the cyber anomalies of vehicles using automotive batteries. The approach will leverage topics from different disciplines, including vehicular systems, battery management, data analysis, and graph theory. The results will augment current vehicles’ diagnostic ability and thus benefit all parties in the automotive ecosystem, from automakers to car owners. As such, the results from this research will benefit the U.S. economy and society. In addition, this muti-disciplinary research will help broaden student participation in engineering and computing, especially from underrepresented groups. The battery-enabled diagnostic system can overcome all the above-stated limitations of existing solutions, with the advantages of being trustworthy, universally applicable to all vehicles, and reliable throughout the vehicle life. However, some scientific barriers are yet to be overcome to realize the full application potential. To this end, this research will model the dependency between vehicle operation and battery power using a cyber-physical approach, abstract the vehicle based on these dependencies using a 2-layer graph model, and use the graph to guide the diagnostics of vehicle anomalies with four progressive steps: a) detect anomalies using the battery as a root-of-trust, b) verify the detected anomalies to reduce false alarm, c) identify the faulty vehicle module via graph decomposition, and d) mitigate anomalies to reduce their negative impacts on vehicle operation via information recovery. 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

Charts, graphs, and other visual representations of statistical data are often used to help people communicate and make decisions. However, when creating these graphics, many designers use rules of thumb, guidelines, and individual opinions that are often not well-grounded in experimental evidence. Further, studies that do assess the design of graphics tend to focus on a relatively narrow set of uses and quality measures. These risks can lead designers to design graphics that harm rather than help communication and decision-making. This project's goal is to develop a more evidence-based and holistic approach to studying the quality of graphics and creating guidelines for designing effective graphics. This will include developing a wider range of measures and use cases to consider when studying how people use graphics, then conducting experiments using these measures and developing guidelines for effective design based on the results. These guidelines and experimental methods will then be made available to scientists who create graphics when talking about their work with the public. The research team will also integrate the work with math and statistics education courses, as well as outreach activities with middle and high school math teachers, in order to provide additional data for the research itself while enhancing those educational activities. Together, the project will improve visual communication of data by developing design guidelines supported by empirical studies that evaluate graphics across a range of real-world tasks. The research focus of the project is to develop and validate an integrated approach to the study of data visualizations that examines multiple levels of user engagement with each chart, using simultaneous talk-aloud, direct annotation, and interactive tools that support graphical decision-making and record participant strategies for engaging with the graphics. Once an optimal combination of measurement methods has been selected that balances cognitive load with holistic assessment, the research team will develop a statistical framework and research infrastructure to collect multi-modal data for the analysis of visualization experiments. Combining multiple data streams will enable both the PI and other researchers to assess design decisions with respect to their impact on different types of user engagement. Leveraging these measurement and analysis methods, the project will empirically assess common chart design guidelines to determine how design decisions impact a range of different interactions with data visualizations, including estimation, inference, prediction, and application. The empirical assessment of chart design decisions will provide experiential learning opportunities for undergraduate students in introductory statistics and numerical literacy courses, reinforcing the material taught in class and increasing student awareness of the importance of data visualization for effective communication. The research team will also improve quantitative literacy by developing continuing education modules to equip secondary school science and mathematics educators with hands-on activities that integrate statistics and data collection with civics, health, and art to connect with and excite students about math and 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 2025 · 2025-10

The building sector is responsible for 28% of the U.S. primary energy use and 35% of the carbon emissions. Yet most buildings continue to rely on outdated control systems that are difficult to customize and fail to adapt to the changing occupant needs or environmental conditions. This project supports the development of intelligent building technologies that reduce energy use while maintaining comfort, helping address the national goals helping reduce operational costs and improve the economic efficiency of building operations at scale. The research introduces a new control framework that uses building knowledge and available building information to enable energy-efficient, autonomous operation with minimal human intervention. Compared to the conventional systems that require significant engineering effort to scale across different buildings, the proposed approach offers a novel flexible alternative. The project also includes education and outreach efforts that engage K-12 teachers, and undergraduate students through hands-on learning, curriculum development, and research experiences in smart building technologies and cyber-physical systems. The research focuses on developing a causal reinforcement learning (RL) framework that integrates structured knowledge about building systems and occupant behavior to accelerate learning and improve control performance. The core innovation is the use of structural causal models (SCMs) that describe cause-effect relationships within the building environment. These models, built from building topology and expert knowledge, guide RL agents to learn more efficiently and safely from limited sensor data. The project includes three technical components: (1) scalable methods for constructing SCMs through topology reduction; (2) causal RL algorithms that use these models to optimize energy and comfort; and (3) adaptive strategies that adjust control policies based on the building characteristics and occupant preferences, implemented on low-cost sensing stations. The framework will be validated in both simulation and a real-world office testbed. Expected outcomes include reductions in energy consumption, improved occupant experience, and a generalizable framework for intelligent control in data-constrained, heterogeneous 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

Non-technical description: Twisted oxide heterostructures are artificial materials made by stacking two or more complex oxide thin layers on top of each other with a twist angle — meaning one layer is rotated relative to the other. This twist creates a moiré pattern at the interface — a repeating interference pattern that changes the local atomic arrangement and the electronic environment. This can dramatically alter the material’s properties and create novel functionalities useful for applications. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to design, create, and understand novel electronic, magnetic, and structural phases emerging in free-standing oxide membranes assembled into twisted heterostructures. The research combines advanced characterization techniques with theoretical modeling and data analytics to accelerate the discovery and development of new materials with engineered properties. The project leverages an iterative feedback loop between theory, synthesis, and characterization and involves the U.S.-Israel collaboration supported by Binational Science Foundation (BSF). Educational and outreach activities within this project are targeted at advancing workforce development through interdisciplinary training of graduate students and postdoctoral researchers in integrated experimental and theoretical approaches to materials research. Technical description: This DMREF project aims to explore fundamental phenomena emerging in oxide moiré heterostructures, including structured two-dimensional polarization-vortex crystals, topological spin textures at twisted oxide interfaces, oxide flat-band systems at large twist angles, coupled quantum dot arrays, and dynamically strained interfacial electron and hole gases. The project introduces moiré periodicity and modulated intra-moiré-cell atomic registry as new design parameters in thin-film oxides. The strong interlayer coupling in oxide systems generates a strong periodic potential, enabling robust quantum states and new physical phenomena through the interplay between intrinsic oxide properties and moiré-engineered periodicity. The research combines state-of-the-art theoretical modeling approaches, advanced synthesis techniques for creating oxide membranes, and unique characterization methods, particularly Quantum Twisting Microscopy, which enables momentum-resolved spectroscopy with nanoscale spatial resolution. The education/outreach component of this project includes DMREF Workshops providing training experience for students and postdoctoral researchers, collaboration with secondary school teachers from Puerto Rico and Wisconsin to develop teaching modules incorporating DMREF principles, and integration of the undergraduate research with a First Experiences in Quantum program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by supporting departmental change teams in institutions of higher education who are working to improve their undergraduate mathematics programs. The goal of this IUSE Level 1, Track 1 Institutional and Community Transformation grant, Sustaining Undergraduate STEM Transformation And Improvement Networks through Change Agent Turnover (SUSTAIN-CAT), is to better understand how STEM educational change networks sustain themselves and their efforts when people leave or shift roles (i.e., change agent turnover; CAT). It is well-documented that U.S. mathematics achievement needs to be improved for most students. Improvement efforts depend on a core group of change agents to provide leadership and momentum for a change project. Peripheral individuals are also involved (department chairs, deans, other partners), whose positional power can positively or negatively impact project progress. One constant is that across a multi-year project, the people involved change. Thus, the primary research question guiding this project is: How do institutional and community transformation projects sustain their critical change efforts through personnel turnover, while also honoring and incorporating the strengths and interests of incoming change team members? A significant contribution of SUSTAIN-CAT is the focus on planning effectively for personnel turnover; such plans are rare among existing change efforts. SUSTAIN-CAT has three main goals: (1) Leverage existing data and expert feedback to identify processes, structures, relationships, and states that support the sustainability of change efforts beyond CAT. Analysis of secondary data from five existing, NSF-funded change projects (collectively representing 118 STEM education change teams) will inform and augment current change theories in STEM education, specifically by providing insight into the phenomenon of CAT. Further, experienced change agents will contribute to theory-building about strategies to sustain or expand change efforts through CAT, by participating in a modified Delphi process. Collectively, these analyses will result in the development of detailed vignettes of CAT. (2) Develop, pilot, and refine an interactive toolkit to support change agent teams in proactively planning for sustainability beyond CAT. The vignettes will be included in an interactive toolkit and accompanying workshops focused on supporting change agent teams through CAT. Change agent teams will pilot this toolkit and engage in a series of workshops to support their use of the toolkit. Results from this pilot stage will support revisions to the toolkit, which will then be shared broadly. (3) Understand how interactions with the toolkit influence processes, structures, relationships, and states of teams experiencing CAT. Collection and analysis of data from change agent teams piloting the toolkit will inform the development of theory about CAT. Findings related to toolkit implementation will be shared with both researcher and practitioner audiences through presentations at relevant conferences and manuscripts (both practical and theory-extending). Although change efforts are local, context-dependent, and individualized for particular goals, teams, and situations, all change teams inevitably encounter CAT; failing to accommodate CAT typically results in change efforts fizzling out. Change teams that proactively plan for accommodating CAT are much better positioned to weather the changes and even use the CAT to accelerate or broaden their change efforts. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 separation of small molecules by high-performance liquid chromatography (HPLC or LC) and identification of the molecules by mass spectrometry (MS) has aided organic, analytical, and natural products chemists for 40 years (LC-MS). This technique has improved greatly over that time so that a nonexpert can get useful information about a compound with much less effort. The purchase of a low-resolution LC-MS impacts the training of a future generation of chemists, many of whom are the first in their families to attend college. This LC-MS would be incorporated into the undergraduate and graduate curriculum, used by undergraduates participating in the Research Experiences for Undergraduates (REU – CHE2447813) and the Undergraduate Creative Activities and Research Experiences (UCARE) program. Beyond the undergraduate mission, this LC-MS will aid in training graduate students, who will use it independently for their research. Many will use LC-MS in their first job after leaving UNL. This LC-MS will drive the research of routine users and increase the knowledge of instrumentation for our undergraduates, graduates and postdoctoral fellows. This will be done through a combination of lectures on LC and MS, as given by experts at the University of Nebraska-Lincoln in these fields, and a workshop or practicum in which students are given practical experience in working with these methods on the new LC-MS system. 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.