Utah State University

NSF Awards · FY 2026 · 2026-09

Artificial intelligence (AI) tools are rapidly transforming how undergraduate students learn, solve problems, and make decisions in educational and professional settings. On one hand, AI systems are increasingly able to generate solutions and explanations, and to offer students new opportunities to enhance their learning through reflection, exploration of their ideas, and a deeper understanding of technical content. On the other hand, students' use of these tools may also weaken their independent problem-solving abilities and decrease their accountability for decisions they have made. Such decreased accountability is a threat to the values of the engineering profession. This project will explore the ways that AI use in engineering education is changing the professional formation of engineers considering engineering students' abilities to regulate their own learning, exercise sound judgment, and take responsibility for complex decisions. Researchers will explore how undergraduate engineering students learn to balance human judgment with AI assistance over time, exploring how students develop trust in AI tools, determine when to rely on or challenge AI-generated outputs, and maintain responsibility for decisions in AI-supported problem-solving contexts. The project will generate evidence-based guidelines and policy recommendations to support the responsible integration of AI tools into engineering education to strengthen students’ metacognitive awareness, ethical reasoning, and problem-solving autonomy. These outcomes will help educators, curriculum developers, and policymakers prepare engineers who can work effectively and responsibly with intelligent technologies. By modeling how students develop judgment, accountability, and cognitive flexibility in AI-augmented learning environments, the project will help prepare a technologically skilled and ethically grounded engineering workforce capable of addressing complex societal challenges, supporting the national interest and NSF's mission. This project examines engineering students’ use of AI learning tools over their second year through their fourth academic years, as they transition from foundational coursework to complex capstone design experiences. Researchers will study: (1) how AI use supports or hinders students’ metacognitive regulation (including planning, monitoring, and reflection) during both routine and ill-structured engineering tasks; (2) how students’ perceptions of trust, control, and risk change over time and influence their engagement with AI tools; and (3) how increasing the complexity of tasks shapes students’ attribution of responsibility and agency between themselves and AI systems. The project will use a two-stage sequential mixed-methods longitudinal design. In Stage 1, about 100–150 undergraduate engineering students will complete the revised Physics Metacognition Inventory. During their second, third, and fourth academic years, they will then complete open-ended surveys on self-regulated learning across different types of engineering problems. In Stage 2, researchers will interview an intentional and small subsample of students during these academic years, and adding senior year, to understand how students' perceptions, decision-making processes, and regulatory behaviors have changed over time. Researchers will analyze data from both stages to develop a longitudinal model of student–AI interaction. This model will explain how students' metacognitive regulation, trust calibration, and responsibility attribution develop over time and as they engage with AI tools. The project will share its results through open-access publications, workshops, and instructional resources that translate research insights into practical strategies for engineering instructors to integrate AI responsibly into the education of undergraduate engineers. 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 · 2026-08

Life on Earth has been challenged repeatedly by periods of catastrophic change that shift the structure and function of communities and ecosystems. The consequences of environmental upheaval have traditionally been studied by paleontologists reconstructing the appearance and disappearance of species in the fossil record. That approach has revealed much about extinction as a process, but has left questions unanswered about the properties of species that lead to persistence. Contemporary changes in the abundance of wild plants and animals provide biologists with the opportunity to track and study natural populations responding to environmental fluctuations that include extremes of weather and drought. This project builds on one of North America's longest-running observational studies of insect populations by continuing data collection at six sites in the Sierra Nevada Mountains of Northern California and Nevada. Encompassing more than 500 species of butterflies and moths, researchers are investigating habitat use by adult butterflies and by caterpillars to better understand direct and indirect effects of temperature and precipitation on insect populations. Results from this work will advance the use of artificial intelligence (AI) in forecasting insect populations, and will continue to inform our understanding of the health and stability of pollinators and other insects that are crucial for national health and prosperity. Project participants interact with the public through talks, field days, and a novel forecasting website, as well as with local school groups and teachers, supporting science education in urban and rural communities. The coming years of this project represent the completion of a decadal plan to advance and expand upon fifty years of research in a dynamic system that has played an important role in our understanding of insects in the Anthropocene. Ongoing work with this long-term dataset suggests that the impacts of environmental extremes, including drought, are in some cases as important as the effects of habitat loss and degradation through pesticide accumulation and other processes; additional discoveries include organismal traits that mediate abiotic effects in ways that are population-specific and predictable. In addition to observations of adult butterflies that have been recorded for decades, other lines of information being gathered include phenology of plant communities and fine-scale environmental data on microsites associated with caterpillar occurrence. Heterogeneous lines of information are being integrated into a statistical modeling framework that will take advantage of neural networks and other approaches in artificial intelligence (AI) to forecast insect populations, with real-time, publicly available model validation. Outcomes from this project will include interdisciplinary tools for prediction with heterogeneous data sources, as well as advances on ecological theories of animals interacting with topographic complexity while responding to novel environmental conditions. 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 · 2026-05

PROJECT SUMMARY/ABSTRACT Neurological disorders are the leading source of disability globally, and Parkinson's disease (PD) is the fastest growing neurological disorder in the world. The gold standard of care for treating dysarthria are behavioral interventions, or “speaker strategies,” that reduce or compensate for the underlying speech deficits to improve listener understandability. The speech of people PD (hypokinetic dysarthria) typically sounds mumbled and rapid, which inform the two primary targets for intervention: clear speech and slow speech. Another speaker strategy involves using simple language. While not directly tied to the underlying speech deficit, it supports the listeners' ability to recognize words, thus compensating for the degraded acoustics. While people with hypokinetic dysarthria demonstrate the ability to use these speaker strategies in the clinic and in controlled research contexts, strategy use does not generalize to their everyday conversations. Here, we proposal to exploit the natural process of entrainment, in which people adapt their speech and language behaviors to align with the same behaviors of their communication partner. This entrainment of behavior has been demonstrated in many different aspects of speech and language, including articulatory precision, speech rate, and word choice. It is also predictive of broader measures of communication success. The purpose of this study is to explore the extent to which, and under what circumstances (clear speech, slow speech, simple language), patients with hypokinetic dysarthria entrain to, or align to the patterns of, communication partners (i.e., confederates) who are intentionally using those strategies. Grounded in the interactive alignment model, we hypothesize automatic entrainment to the speech and language behavior produced by a confederate would facilitate improved communication of the participant with PD, specifically making it easier and more natural for them to use clear speech, slow speech, or simple language in the conversation. We also expect benefits to extend beyond greater use of the strategy, to greater communicative efficiency and conversational satisfaction. This proposal sets the stage for an innovative and much needed extension of dysarthria management to include the communication partner in intervention. Toward that end, 30 participants with mild to moderate hypokinetic dysarthria associated with PD will be instructed to use a designated speaker strategy. They will then engage in a controlled conversational task (Diapix task), which involves collaborative problem-solving, with two different confederates – one who will also use the selected strategy (manipulation), the other confederate will not (control), for a total of 6 conditions. We will assess the impact of strategy use by the confederate (relative to no strategy use) on the participant with PD with regards to quantitative speech and language changes, communicative effectiveness, and conversational satisfaction. This work will tap into the potential of entrainment to facilitate the carryover of speaker strategies to conversation, and thus improve real world communication for people with PD.

NSF Awards · FY 2026 · 2026-04

When silicate minerals in rocks are exposed to air and water, a “chemical weathering” reaction occurs that consumes carbon dioxide from the atmosphere. This process acts as a feedback on Earth’s climate – past, present, and future. Microscopic fossils preserved in deep-sea sediment record ancient warming events and their recoveries. This project will analyze fossils from two distinct events in Earth history to understand how weathering rates respond to warm climates. The results will help predict the role of silicate weathering in regulating future climate. Education and Outreach activities will engage students from K-12 through the PhD level through novel classroom and field experiences at Utah State University. An interactive museum exhibit at the Loveland Living Planet Aquarium will introduce visitors to plankton and deep-sea fossils. Three intersecting approaches will apply the Germanium/Silicon ratio of siliceous microfossils to constrain ancient silicate weathering rates. Approach 1 will develop and calibrate this proxy in modern siliceous sediments, focusing on radiolarians, which are common in Early Cenozoic marine sediment but whose Ge/Si ratios are understudied. Approach 2 will generate new down-core records from legacy International Ocean Discovery Program cores spanning two key climate events: the Paleocene-Eocene Thermal Maximum (PETM, 56 million years ago) and the Middle Eocene Climatic Optimum (MECO, 40 million years ago). These events provide a contrast: the PETM is easily reconciled with a strong silicate weathering feedback driving its recovery, while several aspects of the MECO apparently contradict a dynamic silicate weathering response. Pilot data already document clear and opposing Ge/Si trends over the two events, indicating contrasting behavior of silicate weathering and demonstrating the utility of Ge/Si to resolve ancient weathering changes. Complementary siliceous microfossil-hosted datasets (isotopes of oxygen, silicon, and boron) will be measured on PETM and MECO samples shared with collaborators, which will assist in more fully understanding these events, and demonstrate the potential of siliceous sediments as powerful records for reconstructing paleoceanography and paleoclimate. Approach 3 will develop and apply a numerical model of the coupled carbon and Si cycles equipped with Ge/Si tracers to interrogate downcore records, quantify changes in silicate weathering implied by Ge/Si shifts across the PETM and MECO, and test hypothesized mechanisms for climate and carbon cycle change during these events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Solar Energetic Particle (SEP) events are bursts of high-energy particles from the Sun that can disrupt satellites, navigation systems, and human spaceflight. Predicting these events remains challenging because they are rare and driven by complex solar activity. This project will apply machine-learning methods to space-based observations spanning two solar cycles to improve identification of patterns that precede SEP events. By strengthening space weather forecasting, the research will enhance protection of systems that support national security, economic activity, and space exploration. Findings will also be incorporated into data science coursework to train students in advanced analytical approaches for space weather applications. The project will develop a comprehensive multimodal dataset by integrating data from space-based observatories, including the Solar Dynamics Observatory (SDO), Solar and Heliospheric Observatory (SOHO), Wind, Geostationary Operational Environmental Satellites (GOES), and Advanced Composition Explorer (ACE) (Thrust 1). This dataset will consolidate magnetograms, extreme ultraviolet images, radio observations, in situ particle flux measurements, and solar wind data. The structured dataset will facilitate detailed investigations into the solar conditions that precede SEP events and improve the identification of precursor conditions and event timing. Building on this dataset, the project will develop an advanced ensemble model that combines global and local learning strategies for SEP prediction (Thrust 2). The global model will learn from the comprehensive dataset gathered in Thrust 1, capturing complex interactions and complementarities among various data sources. In parallel, local models will analyze specific data modalities to extract unique patterns, providing more granular insights into SEP events. This dual strategy ensures that the ensemble framework leverages both the strength of integrated data and the specialized knowledge offered by individual observational sources. Finally, the project will apply unsupervised learning techniques to analyze SEP events post-occurrence to better understand their acceleration mechanisms and propagation characteristics (Thrust 3). Clustering algorithms will group SEP events based on charge states, decay times, and interplanetary conditions, helping to distinguish between different acceleration mechanisms (e.g., solar flares vs. coronal mass ejections) and identify patterns in SEP transport through the heliosphere. The final operational model will be deployed at the Community Coordinated Modeling Center (CCMC), supported by NSF and NASA, and made publicly available through open-access repositories. 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

Solar Energetic Particle (SEP) events are powerful bursts of radiation from the Sun that can endanger astronauts, damage satellite electronics, disrupt high-frequency radio communication, and increase risks for high-altitude aviation. While these events have significant societal and technological implications, they remain difficult to predict due to their rarity and complex origins. This project addresses a key limitation in space weather forecasting: the lack of high-quality, diverse training data for artificial intelligence (AI) models. By generating realistic synthetic data representing SEP conditions across multiple observational sources, the project will enable the development of more accurate and generalizable forecasting models. These contributions will directly support national efforts to safeguard space operations and infrastructure. The project will proceed in three integrated research thrusts. First, it will construct a multimodal dataset of SEP events by aligning multivariate time series data from multiple satellite missions that record x-ray and proton flux, solar wind properties, and photospheric magnetic fields. Second, to overcome data scarcity, the project will develop a generative modeling framework that synthesizes realistic SEP data across modalities, ensuring both temporal and cross-modal consistency. This approach is designed to preserve physical interdependencies between data types while expanding the pool of training samples. Third, the team will develop a hybrid learning framework that combines unimodal and multimodal representations for SEP prediction and applies unsupervised clustering to group events by energy level and precursor characteristics. The resulting tools and datasets will be shared with the scientific community via open-source repositories. Educational impacts include interdisciplinary training of graduate students in computer science and physics, curriculum integration in data science courses, outreach to K–12 students through STEM events, and public dissemination of research through community science programs and national workshops. 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 award will support research in the field of algebra and representation theory with connections to theoretical physics. Representation theory is the branch of mathematics concerned with the study of symmetry using techniques from linear algebra, such as vectors and matrices. Classical examples of symmetry include rotations of a circle and reflections of a square. Modern research in mathematics naturally encounters more abstract notions of symmetry and requires the development of more advanced techniques for their study. The PI will identify and study concrete instances of these abstract symmetries and apply the results to advance the mathematical understanding of quantum field theory in dimensions two and three. The research will also develop new connections between algebra, representation theory, and low dimensional topology. This project integrates education, research, and career training opportunities for high school, undergraduate, and graduate students. Examples include creating a series of videos which will improve the algebra education of Utah's high school mathematics teachers-in-training, and organizing the graduate student focused Moab Topology Conference. In more detail, the PI will engage in three related research projects. Each project is motivated by and has direct implications for the long-standing conjecture that Rozansky-Witten theory, a three dimensional supersymmetric quantum field theory, defines a non-semisimple topological field theory. In the first project the PI will further develop and extend the theory of relative modular categories and their associated non-semisimple topological field theories to find new connections with homological blocks of 3-manifolds. In the second project the PI will develop the representation theory of affine L-infinity algebras and unrolled quantum groups and use the results to construct abelian approximations of Rozansky-Witten theory. In the third project the PI will extend his work on twisted equivariant matrix factorizations to construct non-semisimple, possibly unoriented, topological field theories for Landau-Ginzburg theory and explore their interpretation as Rozansky-Witten defects. The physical origins of the representation theories considered suggest many non-trivial interrelations. 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 Chemical Measurement and Imaging Program in the Division of Chemistry, Dr. Yi Rao and his students of Utah State University will develop a new nonlinear optical spectroscopy to directly probe molecular behaviors on surfaces of single droplet. Droplets play important roles in chemical reactions, biological activity, environmental pollution, and physical processes. Currently, single-droplet instrumentation is limited to overall characteristics, neglecting unique surface phenomena. With the development of the single-droplet surface spectrometer, these shortcomings will be addressed by directly studying the surface chemistry of small-volume single droplets. A broader impact of this work is to increase our understandings of surface reactivity of droplet chemistry. The work will integrate their findings to enhance teaching at the undergraduate and graduate levels and allow students to gain valuable experience in cutting-edge laser technology. Dr. Rao and his students also plan to improve science communication and host outreach events to educate and inspire the next generations of STEM students, professionals, and educators. Surface-specific single particle spectroscopy will be investigated in two scenarios: 1) Buildup and analysis of second-harmonic, electronic sum-frequency, and vibrational sum-frequency scattering spectroscopies of single droplet surfaces; 2) Investigation of adsorption of molecules onto droplet surfaces from the surrounding gas as well as the effect of physical and chemical settings on the adsorption processes. The team will advance nonlinear spectroscopy, align experiments and theory, and provide a means to precisely analyze surface chemistry at droplets in-situ. 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 Paleocene-Eocene Thermal Maximum (PETM, 56 million years ago) is an important paleoclimate event used to understand how Earth’s climate system responded to rapid increases in atmospheric methane and carbon dioxide. One approach to study this interval is chemical analysis of microfossils known as foraminifera preserved in deep-sea sediment. Foraminifera grow calcium carbonate shells that record the environmental conditions of the organism’s habitat. However, PETM foraminiferal records suffer from two well-known limitations: first, ocean acidification at the PETM onset can lead to the dissolution of the carbonate shells and, second, vertical sediment mixing (bioturbation) can intermingle shells from different time periods together. Until recently, vertical mixing was a significant drawback because analyses required multiple shells to have sufficient accuracy. This project will remedy those issues by constructing records of individual foraminiferal geochemistry and morphology across the PETM at International Ocean Discovery Program (IODP) Site U1580 located on the Agulhas Plateau in the Southern Ocean. Site U1580 features abundant microfossils that were not dissolved. Cutting-edge analytical techniques will permit measurements on individual foraminifera to disentangle signals affected by bioturbation. Results will produce new estimates of surface and deep-water warming and carbon cycle dynamics across the PETM onset. The proposed work will support a team of three early career researchers plus graduate students and postdoctoral scholars. The project integrates educational outreach; investigators will create an open educational resource on the PETM and its relevance to contemporary climate. The presence of well-preserved foraminifera throughout the PETM onset at IODP Site U1580 offers a unique opportunity to reconstruct the magnitude, pace, and dynamics of climate change during the earliest phases of the PETM. However, pilot data demonstrate extensive vertical mixing of individual foraminifers across the event (as observed at other sites). The investigators will disentangle vertical sediment mixing by applying a series of measurements performed on individual shells. Shells will first be imaged by Scanning Electron Microscopy (SEM) and Computed Tomography (MicroCT) to characterize preservation and document morphology. Shells will then be analyzed for Mg/Ca (a paleotemperature proxy) via Laser Ablation Inductively Coupled Plasma Mass Spectrometry, then finally analyzed for their stable carbon and oxygen isotopic composition using a new CryoFocusing technique adapted for small carbonate samples. The resulting individual foraminifera carbon isotope data will distinguish pre-PETM from PETM individuals, allowing quantification of shell morphology, Mg/Ca-based temperature, hydrologic change, and the carbon isotope shift across the PETM onset. Any observed structure in the geochemical data will provide insight into the pace of change or lead-lag relationships between aspects of the carbon cycle and climate. 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 2025 · 2025-08

PROJECT SUMMARY Kyasanur Forest disease virus (KFDV), an emerging tick-borne pathogen, is endemic in India and causes life- threatening KFD, characterized by hemorrhagic fever with neurological manifestations. In recent years, KFDV has spread beyond the endemic region, posing a growing threat to global public health. In the US, the virus is an NIAID category C priority pathogen and an HHS select agent because it poses potential dangers to public health and national security; however, neither an adequate vaccine nor a specific antiviral drug is available against the pathogen. Within the family Flaviviridae, KFDV belongs to the genus Flavivirus, which also includes Japanese encephalitis virus (JEV) and Zika virus (ZIKV). Recently, we developed a novel vaccine platform based on a full- length infectious cDNA clone of the live-attenuated JEV vaccine SA14-14-2 approved for human use. We then used our SA14-14-2-based vaccine platform to generate a recombinant chimeric rJEV/ZIKVprME virus as a live- attenuated vaccine candidate for ZIKV, by replacing the two viral envelope protein prM and E genes of JEV SA14- 14-2 with the corresponding genes of ZIKV P6-740. Our preclinical data showed that rJEV/ZIKVprME is fully attenuated and induces robust long-term protective immunity following a single immunization in mice, indicating that our SA14-14-2-based chimeric vaccine development strategy is applicable to other flaviviruses. The current proposal seeks to capitalize on our SA14-14-2-based chimeric vaccine development strategy, combined with our understanding of the structure and function of two neutralizing antibody-mediated protective immunity-inducing flavivirus envelope proteins (prM and E), to create and refine prM-E gene-replaced rJEV/KFDVprME viruses that are genetically stabilized, fully attenuated, strongly immunogenic, and highly efficacious against KFDV infection. Toward this goal, we will pursue the following specific aims: In Aim 1, we will examine the degree of attenuation and genetic stability of rJEV/KFDVprME variants, each adapted to growth in a cell line certified for human vaccine production, by determining their potential for lack of pathogenicity and transplacental transmissibility and by deep sequencing the replicating chimeric viruses in BALB/c mice, respectively. In Aim 2, we will assess the balance of attenuation and immunogenicity of the rJEV/KFDVprME variants by comparing their degree of attenuation to the levels of KFDV prM/E-specific humoral and cellular immune responses elicited in BALB/c mice after infection with each of the rJEV/KFDVprME variants. In Aim 3, we will analyze the impact of humoral immunity on the short- and long-term protective efficacy of rJEV/KFDVprME variants by performing (i) challenge experiments with KFDV in BALB/c mice at various time points after a single immunization with each of the two most promising rJEV/KFDVprME variants and (ii) passive transfer of IgG purified from the immune sera to naïve BALB/c mice prior to a challenge with KFDV. Collectively, our efforts will establish an optimal balance of attenuation, genetic stability, immunogenicity, and protective efficacy in one or more live-attenuated chimeric vaccine candidates for KFDV and elucidate the molecular basis for attenuation and the immunologic correlates of protection.

NSF Awards · FY 2025 · 2025-08

Removing excess heat from electronic devices is essential for energy efficiency, overall performance, reliability, and lifespan. Next-generation electronic devices, including communication systems, electrical vehicles, high-performance computation, and military technologies, will need to operate at much higher frequencies and power levels. Meeting this demand will require new semiconductors materials with capabilities beyond those of silicon and models that can accurately describe the heat flow behavior within complex microelectronics. This project will develop unique measurement tools by harnessing ultraviolet laser pulses to study heat flow in these future semiconductor materials. The results will provide critical information about heat behavior at extremely small size and timescales, which will advance models of heat flow for the semiconductor industry. In addition, the project will encourage undergraduate and graduate students to work closely with industry professionals, which will help educate the future workforce in microelectronics. This project will demonstrate and advance a nondestructive, noncontact ultrafast, deep-ultraviolet transient grating spectroscopy technique to probe phonon-dominated thermal transport in wide- and ultrawide-bandgap thin films and substrates as a function transport length-scale. By coupling the extensive previous work in visible-based transient grating spectroscopy with the high-photon energy and short-wavelength of ultrafast deep-ultraviolet laser pulses, this project will harness these new tools to observe phonon flow over effective transport scales in the sub-100 nm regime with sub-ps temporal precision. Specifically, these results will fill much-needed gaps in the literature on the thermal transport properties of gallium oxides, boron nitride, crystalline diamond, and other materials. More importantly, this project will investigate the exotic behaviors of nanoscale hot spots in these materials induced by highly-nonequilibrium phonon distributions, such as thermal viscous and memory effects that were recently observed in crystalline silicon and germanium. By collaborating closely with state-of-the-art theory, these measurements will provide more insight on the fundamental picture of heat flow in semiconductors and validate new models, such as the mesoscopic hydrodynamic approach, with the capability to predict behavior in complex and multi-scales devices. 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

Lie theory is a uniform framework for contextualizing symmetry in algebra and geometry. It is thereby central to mathematically rigorous realizations of symmetry reduction: the notion that the symmetries of a mathematical object should give rise to a quotient of that object. Successful realizations of this philosophy include geometric invariant theory in algebraic geometry, and Hamiltonian reduction in symplectic geometry. In this project the PI will harness the theory of centralizers, generalized Hamiltonian reduction, and Morita abelianization, with a view toward creating new avenues of research on algebraic symmetry reduction and adjacent subjects. The project will also provide research training opportunities for students. In more detail, this project has three broad objectives. The first is to develop a theory of universal abelianized centralizers for sheets in semisimple Lie algebras, in part based on generalized Hamiltonian reduction; one specific goal is to find new instances of mirror symmetry and computable examples of Coulomb branches. The second objective, the PI will use a principle of Morita abelianization to construct new topological quantum field theories adjacent to the long-standing Moore-Tachikawa conjecture; one impetus is to generalize and ultimately prove the Moore-Tachikawa conjecture. Finally, the third objective is to develop new, Lie-theoretic versions of de Concini-Procesi wonderful models. 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 · 2025-08

ABSTRACT Autistic and neurotypical individuals often struggle to engage in successful conversations with one another. Yet there are currently no valid methods for quantifying conversational outcomes for autistic individuals and their conversation partners. Accordingly, it is extremely difficult for clinicians to identify difficulties and establish strategies to increase conversational success. This is particularly detrimental during adolescence, a period of life when conversation plays a markedly pivotal role in establishing friendships and emotional closeness with others. Speech entrainment provides a valid and quantifiable measure of conversational outcomes. Speech entrainment is the tendency for interlocutors to modify their speech behaviors to align with the behaviors of their conversational partner. Key here is the extensive body of theoretical and empirical research documenting a robust relationship between speech entrainment and real-world metrics of conversational success. Speech entrainment has been linked to conversational efficiency and quality, better rapport, and stronger interpersonal relationships. The majority of this research has focused on adults. However, in our previous NIH-funded research, we created and validated a robust methodology for measuring entrainment in neurotypical adolescents and demonstrated its ability to predict tangible metrics of conversational success. Nevertheless, an understanding of entrainment in autistic adolescents is lacking. In our previous work, we demonstrated differences in the entrainment patterns of autistic and neurotypical adults in highly controlled laboratory settings. Pilot data collected for this project from 28 dyads in naturalistic peer-based conversations suggests similar differences in adolescence as well. However, beyond this data, research in this area is virtually nonexistent. Thus, addressing this gap represents the requisite first step towards our ultimate goal—to develop a valid and objective measure of conversational success that can be used to optimize treatment outcomes for autistic adolescents. Here, we propose an investigation focused on gaining a comprehensive understanding of entrainment in autistic adolescents and their communication partners and determining the relationship between entrainment and real-world metrics of conversational success in these individuals.

NSF Awards · FY 2025 · 2025-08

Collaborative researchers from the University of Colorado at Boulder, University Corporation for Atmospheric Research, and Utah State University will scale and expand their prior NSF-funded research, STEM Career Connections, across three rural school districts. The project will increase rural middle and high school students' skills in technology and computing explicitly required for workforce-related technology and computing careers. Participating school districts are located in tourism-oriented, rural communities with large income disparities and a high percentage of economically-disadvantaged families. Students have limited exposure to information and communication technologies, internet access and bandwidth. Many communities have inadequate funding for technology infrastructure and have challenges in adapting technology to local community needs. Such challenges profoundly limit opportunities for students to develop relevant skills for lucrative technology and computationally-intensive STEM jobs and careers within their communities. Moreover, teacher training gaps exist in digital literacy, along with access to technology-enabled teaching resources. The project will provide courses for middle and high school students that will greatly expand students' proficiency in technology design, computer programming, and deploying sensor systems using core curricula aligned with national standards for computer and network technologies, sensor technologies, and big data. To address the shortage of STEM and technical career teaching and learning opportunities, the project will develop a community infrastructure of technology partnerships that will support students' access to STEM career pathways. Community partnerships, workplace apprenticeships, and mentoring by educators, community stakeholders, and local businesses, will broaden participation of students in STEM-related jobs and careers in technology and computing. The STEM Career Connections model employs research-based strategies for working with rural mountain communities. The instructional design will involve direct engagement of experts in curriculum co-design who will collaborate with researchers, teachers, and district leaders on curriculum development, development of mentorship materials, and STEM disciplinary support. The design-based research approach will employ quantitative and qualitative research methods that will address critical research questions to understand project impacts, including (1) how and to what extent the education activities implemented in collaboration with local partners are shown to be effective in supporting students' development of computing and STEM disciplinary knowledge and skills; (2) how and to what extent students will be able to apply their acquired disciplinary skills and understandings to scientific problem-solving; (3) to what extent will the project result in students' aspiration to pursue technology and computing jobs and careers; (4) how and in what ways does the partnership co-design process contribute to the success of the learning model within rural contexts, and inform a model of common characteristics for technology and computing education relevant and beneficial to other rural communities. Data sources will include surveys, observations of classrooms and partnership meetings, interviews with youth, teachers, and community members, student assessments, and analyses of student-created artifacts. Data analyses will examine students' evolving interest, awareness, and disciplinary knowledge. Analyses will inform practices for rural technology education; build knowledge on the common characteristics of rural mountain communities; and inform how local and regional partnerships can coordinate mutually beneficial interests to play an essential role in communities with limited access to technology and STEM-related technology jobs. Project evaluation will be conducted by Utah State University. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

Plastic pollution is a global threat to human and environmental health. During everyday use and weathering, plastics breakdown and release highly toxic plastic additives. There is an urgent need for safe plastic additives. In this project, flavones, a class of natural chemicals found in plants and food, will be explored as sustainable plastic additives. Flavones are excellent candidates for light stabilizer additives and can kickstart plastic breakdown. This project will address the end-of-life management of plastics. Research outcomes will create: 1) safe alternatives to the toxic plastic additives that are currently in use; and 2) solutions to enhance the breakdown of biodegradable plastics to minimize plastic pollution. The project will provide educational opportunity to train the next generation of environmental engineers. The overall goal of this CAREER project is to advance flavones as sustainable plastic additives to enable the tuning of biodegradable plastic stability and persistence. A major focus is to understand how flavone chemical structure impacts fundamental photophysical and photochemical properties. The project will first evaluate flavones as light stabilizers in biodegradable plastics. Flavone functional groups that control light absorber properties will be identified by exploring the fundamental photophysical pathways of flavones with spectroscopy and reactive chemical probes. Key stabilization performance metrics will be evaluated with an array of materials characterization, chemical analysis, and aquatic toxicity testing to understand flavones’ 1) ability to stabilize biodegradable plastics; 2) photostability within biodegradable plastics; and 3) ecological effects. Finally, the project will evaluate flavones as pro-oxidants to increase biodegradable plastic deterioration. Overall, this CAREER project will advance flavones as sustainable plastic additives, opening the door to a new class of non-toxic additives derived from renewable resources. More broadly, it will advance light-triggered, pro-oxidant additives as an avenue to enhance biodegradable plastic deterioration in slow degrading environments such as surface waters. This project will enhance student training in sustainable chemistry with a focus on circularity, the end-of-life management of materials, and toxicity reduction and hazard prevention through 1) enhancing environmental engineering coursework; 2) connecting students to industry perspectives on sustainability; and 3) introducing sustainable chemistry to high school STEM students through outreach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project supports seven PIs, one postdoctoral fellow, five graduate students, and two undergraduate students from the five U.S. universities to study how the availability of marine nutrients such as nitrate and phosphate may have fueled the expansion of eukaryotes (organisms with nuclei in their cells), transformed their ecological roles, and eventually revolutionized the marine ecosystem during the Tonian Period (1000–720 million years ago). This research will help scientists to better understand the ecological resilience of the marine ecosystem in the present and future. The project takes advantage of unique and complementary geologic records from two continents, leverages available collections and resources, and brings together an array of research expertise. It offers opportunities for the training of a globally engaged STEM workforce, as well as public outreach activities engaging national (geo)parks. This project will test the hypothesis that increasing nutrient availability in Tonian oceans drove the diversification and ecological rise of eukaryotes, which in turn transformed the scope of biodiversity from a prokaryote-dominated world to one teeming with eukaryotes. The researchers will systematically collect and integrate paleontological, geochemical, sedimentological, and stratigraphic data from early Tonian strata in North China and late Tonian strata in the Grand Canyon of Arizona. The data will be integrated with global compilations and an Earth system model to reconstruct nutrient availability, eukaryote taxonomic and functional biodiversity, and marine geochemical cycles to test the hypothesis stated above. The intellectual merit of the project lies in its potential to illuminate the complex feedbacks among nutrient availability, functional biodiversity, and biodiversity dynamics in a major transition in Earth history. The broader impacts of the project will catalyze multidisciplinary research, create synergies between the National Park System and research institutions, foster informal geoscience education, and prepare the next-generation of STEM workforce. This project is funded by the BIO/DEB Biodiversity of a Changing Planet (BoCP) Program and the GEO/EAR Life and Environments through Time (LET) 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 · 2025-07

The project aims to serve the national need of building capacity for a potential Noyce Track 4 research project focusing on how middle and high school general and special education teachers collaborate to identify components of effective secondary mathematics education to support teaching and learning. Middle and high schools often have difficulty hiring and retaining special education teachers and mathematics teachers. When teachers experience a lack of support, they are more likely to leave their positions. This negatively impacts students. Students with special needs, who are often in general or regular education environments for mathematics instruction, are especially impacted because they may have a lower likelihood of meeting graduation requirements or later accessing college courses in STEM fields. This project aims to understand how school districts in Utah conceptualize effective mathematics instruction. The project also aims to refine the process through which pairs of general and special education teachers of mathematics at the same school collaborate and participate in the project related research activities. This project at Utah State University includes partnerships with specialists in and teachers of secondary mathematics instruction in both general and special education at the Utah State Board of Education and Cache County School District. Project goals include (a) strengthening collaboration between faculty in the Mathematics and Statistics Department and those in the Special Education and Rehabilitation Counseling Department; (b) developing a broad understanding of mathematics effectiveness across school districts in Utah to establish a measure of teacher effectiveness; and (c) piloting recruitment strategies to include a general and a special education mathematics teacher from the same school in research. The project will achieve these goals by implementing two research projects. The first project consists of a state-wide survey to get an overview of types of mathematics instruction, views on effective mathematics teaching, and the use of collaborative teaching models between secondary general and special education mathematics teachers. The second project includes a mixed-methods social network analysis focused on personal and professional support systems, collaboration, and intent-to-stay of pairs of secondary mathematics teachers in general and special education settings within the same school. By studying the effectiveness and retention of secondary general and special education mathematics teachers, this project has the potential to improve the quality of life of students. Through being taught by effective and experienced teachers, these students may increase their completion of STEM graduation requirements and college preparation courses. As a result, more students may have the opportunity to pursue STEM careers and enter the STEM workforce. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Termites are highly abundant and impactful social insects found across more than half of the Earth’s land surface, mainly in tropical and subtropical regions. They play a major role in decomposing up to 60% of plant litter and fallen wood in these areas. During this process, termites rely on a partnership with microorganisms in their guts to help break down organic matter. In the process, some termites can also produce substantial amounts of methane that can be released into the atmosphere. There is still much uncertainty about how much methane termites release worldwide due to a lack of understanding of the processes that determine termite methane production and emission. The goals of this project are to understand the differences in methane production rate across termite species and how that relates to the microorganisms in their guts, and to estimate total methane emissions from termites across large areas using field measurements and remote sensing data. The fieldwork focuses on termites in the tropical savannas and forests of Odzala-Kokoua National Park, home to an exceptionally wide variety of termite species. The results from this project could greatly improve understanding of how much methane termites produce and how it happens, from individual species to whole colonies and larger areas, helping to fill important gaps in knowledge of termites' role in contributing to atmospheric methane. The project will train early-career scientists and engage K-12 students and the public through field, education, and museum programs in the United States and beyond, enhancing public understanding of insect ecology and their impact on the environment. Researchers will conduct field surveys to assess termite populations across tropical savannas and forests in Odzala-Kokoua National Park. An incubation method will measure methane production rates across termite species, considering variations in castes, nesting behaviors, and feeding groups, followed by DNA isolation and sequencing to characterize the methanogen community in termite symbiotic systems. A mass balance approach will determine the oxidation rates of termite-produced methane across different colonies, while laboratory incubations and metagenomic analyses will assess methanotrophic activities and communities within the colonies. Field measurements of colony-level methane emissions will be integrated with high-resolution, drone-based, LiDAR remote sensing to develop algorithms for upscaling termite methane emissions to landscape scales. The data collected will be used to address: 1) How do methane production rates vary across termite species, and what are the underlying mechanisms for these variations? 2) What are the oxidation rates of termite-produced methane within colonies and surrounding soils, and what are the mechanisms driving these processes? 3) How much methane is emitted by termites across tropical savanna and forest landscapes? This project has the potential to transform knowledge of the rates and underlying processes of termite methane production and emissions, from individual species to colonies to landscapes, filling crucial knowledge gaps and advancing understanding of this overlooked yet important contribution of termites to global atmospheric methane cycling. 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 · 2025-06

The objective of this Grants for Rapid Response Research (RAPID) project is to gather perishable data that will inform research and practice on vehicle abandonment during fast-moving wildfire evacuations. Evacuation by vehicle is common, however evacuees sometimes abandon their vehicles if traffic congestion makes safely escaping difficult. And potential obstruction to firefighting efforts could be significant. The absence of high resolution, granular data on this topic prevents emergency managers from predicting and planning for when and where evacuees are most likely to abandon vehicles. This research aims to improve evacuation safety and elevate awareness for vehicle abandonment risk. This project collects ephemeral data from the 2025 Palisades and Eaton Fires on evacuee vehicle abandonment, creating a strong basis for incorporating this phenomenon into evacuation behavior modeling and risk management strategies. It investigates (1) why and where evacuees abandon their vehicles, (2) the obstacles these abandoned vehicles pose to emergency responders, and (3) how agencies coordinate to move these vehicles during fast-moving wildfire evacuations. Data is collected through semi-structured interviews with evacuees and agency personnel, including firefighters, law enforcement, and emergency management, and analyzed through qualitative analysis to distill key findings. Combined with agency dispatch records and routes, emergency notification and agency call logs, and traffic congestion data, this project gains valuable insights on evacuee behavior and mitigation of fast-moving wildfires and other disasters. 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 · 2025-06

This project investigates social-ecological relationships within Pacific Northwest high-elevation ecological zones, emphasizing long-term cultural burning practiced by indigenous communities. The primary objectives of the project are to conduct archaeological surveys of subalpine ecosystems; interpret processes of high-elevation subsistence during the early Holocene and subsequent changes in burning practices; and investigate how subsistence land management influenced historical fire regimes and long-term ecosystem stability/change. This study employs an interdisciplinary approach that interweaves archaeology, paleoecology, participatory mapping, and computational modeling to evaluate how human-environment interactions shape high-elevation landscapes. This research develops partnerships with federal land managing agencies and indigenous partners in the co-interpretation of research results. Historically, fire has been used as a part of a set of tools to manage ecosystems but relatively little is known about the influence of historical practices of burning in high-elevation ecosystems. This project evaluates the spatial distribution and chronology of high elevation land-use, documents long-term trends in climate and human influence on fire and vegetation, and records material evidence for cultural burning practices to rejuvenate or maintain important plant species. This research develops a novel paleo-fire reconstruction and introduces new methods of soil charcoal analysis to make more substantial connections between past human land-use, cultural fire, and vegetation change. Datasets resulting from this project are evaluated using a computational “virtual laboratory” to examine dynamics among climate and cultural burning that may not be interpretable from the archaeological and fire history data alone. Results from this project advance the understanding of social-environmental change and stability in high-elevation ecosystems to enhance cooperative management strategies (e.g., co-stewardship, co-management), maintain cultural ecosystem services, reduce potential future wildfire severity, and buffer against ecosystem loss in these unique landscapes. 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 2025 · 2025-06

Abstract In March 1993 a National Institutes of Health Consensus Development Conference recommended that all newborns be screened for permanent hearing loss before being discharged from the birth hospital. Since that time, universal newborn hearing screening programs have been implemented throughout the United States with all 59 states and jurisdictions now receiving federal funding to assist with the operation and improvement of an Early Hearing Detection and Intervention (EHDI) program. The goal of these EHDI programs is to ensure that all newborns are screened for permanent hearing loss before one month of age, those not passing the screen receive a diagnostic audiological evaluation before three months of age, and those identified with permanent hearing loss are referred to an early intervention program before six months of age. These benchmarks are important because undetected hearing loss leads to significant delays in language, social and emotional development, and academic achievement for children. An important contributor to helping EHDI programs meet the EHDI benchmarks has been the annual National EHDI Conference, which has been held since 2001 and is now attended by more than 1,000 people each year. For almost 40 years, the National Center for Hearing Assessment and Management (NCHAM) at Utah State University has assisted EHDI stakeholders in expanding and improving EHDI programs, including the organization and management of the National EHDI Conference. The EHDI Act of 2022 (PL 117-241) reauthorized funding for EHDI programs through FY2027 and is administered by the U.S. Department of Health and Human Services. The law directs the Health Resources and Services Administration (HRSA), the Centers for Disease Control and Prevention (CDC), and the National Institutes of Health (NIH) to continue coordinating and advancing “a national program for the early identification and diagnosis of [deaf or hard of hearing (DHH)] newborns and infants”. The National EHDI Conference provides information, tools, and collaboration opportunities for those implementing the law, including state and federal EHDI program staff, researchers, providers of services, students, families, advocates, and others who help improve outcomes for children who are DHH. The National EHDI Conference seeks to address these matters through poster and podium presentations, plenary sessions, instructional sessions, networking, ancillary meetings, and other innovative methods while making the conference fully accessible for the attendees who are DHH by providing American Sign Language interpreting and real-time captioning services for all plenary, podium, and instructional sessions.

NIH Research Projects · FY 2026 · 2025-05

Summary: Recent advances in genomic methods have made it possible to characterize the genes and even mutations contributing to adaptation in a variety of organisms. However, most of our knowledge of genetics falls short of pinpointing causal variants in sets of point mutations, and we have mounting evidence that chromosomal rearrangements, that is structural variants (SVs), such as deletions, duplications, inversions and translocations, play a major role in trait variation and adaptive evolution. The research projects outlined in this proposal will investigate the contribution of SVs to trait variation and adaptation in three insect systems: Timema stick insects, Callosobruchus maculatus seed beetles, and Lycaeides butterflies. The overarching questions to be addressed are: How common are different types of SVs? How do they affect trait variation? And what processes maintain SVs in natural populations? Answering these questions will immediately advance our understanding of the material basis of adaptive evolution and has longer-term implications for understanding trait variation in diverse organisms, including humans. Previous work in my lab implicated SVs in (i) the evolution of cryptic coloration and host use in Timema stick insects, (ii) rapid adaptation to a low-quality host plant, lentil, in C. maculatus seed beetles, and (iii) hybrid fitness in Lycaeides butterflies. Proposed work in each system will combine multiple chromosome-level phased genome assemblies, population genomic data, and computational modeling to build on past results and elucidate the general importance and evolutionary history of SVs in these systems. In Timema, work will focus on the evolutionary history of the diverse SVs associated with color and color pattern in different populations and species, and on the phenotypic and adaptive relevance of genome-wide SVs. Proposed work in C. maculatus will shed additional light on the reuse of SVs by determining whether the same SVs confer adaptation to the same and different hosts in geographically distinct populations, and whether these same SVs affect fitness in hybrid beetle populations. In Lycaeides, planned projects will elucidate processes driving contemporary evolution of SVs and connect these SVs to trait variation and fitness in current and ancient hybrids. Overall, this research program will provide a comprehensive assessment of the importance of SVs for trait variation and adaptation across diverse systems that span different time scales (several generations to millions of years) and contexts (cryptic coloration, host use, contemporary evolution and hybrid fitness). By identifying the mechanisms responsible for the maintenance of SV polymorphisms within populations and species, this work has broader implications for understanding the genetic basis of adaptive evolu- tion. Additionally, insights gained from this research could inform our understanding of the genetics and evolution of human health, given the relevance of SVs in human genomics and disease-related traits.

NSF Awards · FY 2025 · 2025-04

After several decades of research on the neuroscience of social bonding, most discoveries have come from studies of monogamous rodents and to a lesser extent in monogamous primates. Monogamous pair bonding is relatively rare in rodents and primates, but all wild canid species studied to date exhibit monogamy. This renders canids particularly suitable for the study of social bond maintenance and loss, especially coyotes (Canis latrans), which exhibit exclusive mating and stable pair bonds across multiple breeding seasons. Coyotes are highly intelligent mammals that have expanded rapidly across North America and have successfully infiltrated nearly all available ecological niches. Coyotes are often viewed as pests and are frequently hunted, trapped, and killed, often leaving their pair-mate (and any offspring) abandoned. Advancing our understanding of the biological drivers of pair bonding and the impact of pair-mate loss—and translating that knowledge to the public—can increase compassion for their capacity to form life-long pair bonds, which can ultimately lead to increased support for and use of non-lethal management tools in coyotes. This work can also provide a better understanding of pair bond development and behavior overall. This project will investigate the behavioral, hormonal, and neural basis for coyote pair bonding and will provide ample training opportunities for undergraduate researchers. The educational goals will improve the institution’s undergraduate neuroscience curriculum, will provide mentored summer research opportunities for college students, and will engage with federal researchers specializing in predator management as well as local stakeholders such as ranchers to establish reciprocal partnerships to improve community education and inform future research priorities. This project will establish a foundation for the neurobiological basis for canid pair bonding by studying a captive research population of coyotes. This work will be the first systematic investigation of coyote pair-bonding behavior and the coyote brain. A modified version of the three-arena partner preference test will be used to describe coyote pair-bond behavior and determine the influence of individual sex, season, and pair-bond duration on partner preference and selective aggression in male-female pairs. Coyote brain tissue will be used to characterize the receptor distributions for the neurotransmitters involved in pair-bond formation (oxytocin receptors, vasopressin receptors, and mu-opioid receptor) and pair-bond maintenance (corticotropin releasing factor receptors, kappa-opioid receptors) and determine the effect of pair-bond loss on these receptor densities over time. In targeted brain regions informed by receptor binding results, multiplex fluorescence in situ hybridization will be used to visualize and quantify oxytocin receptor and vasopressin receptor gene expression and to co-localize these mRNA transcripts with cell-type specific markers to determine the cellular identity of oxytocin- and vasopressin-sensitive neurons. These tissues will also be used to generate an open-source coyote brain atlas. Finally, to determine whether central oxytocin signaling is necessary for coyote partner preference, coyotes will be administered a selective oxytocin receptor antagonist prior to partner preference testing. This research will contribute to a more complete understanding of the shared versus unique brain mechanisms underlying complex social behavior across species who display pair bonding behaviors and represents a unique opportunity to investigate the neurobiology of pair bond loss. 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 · 2025-01

The University of Chicago, University of Illinois, and Utah University seek to transform online computer science theory education by developing exercises delivered by online tools to provide better learning experiences for a broad set of students. Theory courses teach critical skills that help software engineers write efficient code, allowing them to optimize for saving energy, speed, reliability, etc. Undergraduate computer science instruction is utilizing an increasing amount of online learning in a variety of ways, including online homework activities only, online lectures with access to in-person office hours, and fully online courses. While introductory coding instruction has made great strides in developing exercise types amenable to online instruction, theory education lags behind. Algorithms and discrete math courses have long depended on hand-graded, large start-to-finish homework exercises, hindering the quality of its online instruction. If successfully developed and integrated into computer science instruction, such innovative solutions will increase student success in obtaining computer science degrees, especially students who are less confident in their abilities. This Computing in Undergraduate Education Transformation project will improve career outcomes for computing students and build a stronger computing workforce. This project will explore the design and use of online homework problem types for theory instruction integrating several attributes: instant, automated feedback, isolated skills, adaptive complexity, and culturally responsive contexts. First, the project team will explore isolated skill exercises, inspired by Parsons problems in coding and recent Proof Blocks in proof-building. These problem types will focus on individual skills rather than the entire problem-solving process. The purpose is to improve both student confidence and skills. Second, the project will explore adaptively scaffolded sequences, responding to student mistakes through gradually easier problems that scaffold their learning of an isolated skill. The purpose is to improve both student confidence and skills. After developing a robust set of base problems, a Large Language Model (LLM) will be used to produce equivalent variations of those base problems, all with different contexts. The purpose of these problems is to improve student engagement and skills. When analyzing challenges and strategies, TheoryABCs will address two focal populations: online students and students who are struggling academically in undergraduate algorithms courses. All activities will recruit from all students taking theory courses and will study how the interventions work on all populations. Through the three phases of the project—development, piloting, and evaluation—the project team will employ several techniques: during development, they will utilize think-alouds and focus group member checks; during pilot sessions, they will collect detailed automated data on student behavior, surveys on student reactions, and student submissions for student performance; and during evaluation, they will run a quasi-experimental comparison study between classrooms using the new exercise types and classrooms that do not. These strategies will improve career outcomes for computing students and build a stronger computing workforce. 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 · 2024-12

PROJECT SUMMARY / ABSTRACT Diagnostic tests for hearing loss miss many underlying sensory and neural pathologies that contribute to hearing problems. Treatments such as hearing aids and cochlear implants are prescribed without knowledge of the underlying site-of-lesion or remaining function, resulting in large variability in benefit and satisfaction. Furthermore, undetected and unaccounted for pathologies may be a cause of the mixed success in current clinical trials of pharmaceutical treatments for hearing loss. Early-latency auditory evoked potentials (AEPs) are an objective measure of auditory physiology that could identify and differentiate sensory and neural pathologies. Despite the clinical availability of early-AEPs, there are barriers to their widespread use that we argue largely result from reliance on visual inspection of features in the evoked response waveform to identify pathology. Reliable analysis using visual inspection requires significant training and more time than is customary for a typical hearing evaluation appointment. Reliable interpretation requires experience that is too burdensome for a non- specialist. A recently developed technique overcomes these barriers by automatically analyzing the response waveform and extracting more features than can be seen by the human eye. Though the preliminary version of the technique's analysis is automatic, it requires some manual adjustment beforehand to work on each new dataset. Furthermore, there is minimal existing research on the benefit of the additional extracted features analyzed by the technique in their ability to identify certain auditory pathologies. Therefore, the proposed study has two aims. The first aim automizes the adjustment process of the technique such that it works immediately on early-AEPs from different species, stimuli, and recording parameters. The second aim tests whether the features analyzed by the technique can better identify several sensory and neural pathologies in animals than the traditional method of visual inspection. To achieve these aims, existing AEP responses from human and animal research labs will be collected. The technique will be trained to work automatically on healthy human and animal waveforms, similar to training artificial intelligence, with an expected outcome being a user-friendly technique that requires minimal input (e.g., species and stimulus rate) to automatically analyze responses. For the second aim, several animal models of different sensorineural diseases will be used to compare the technique to visual inspection and other automated approaches in their ability to identify diseased ears from healthy controls. The proposed study is the next step on the path to creating a no-cost user-friendly tool for automated analysis and assistance in interpreting features to identify disease. This effort is needed to work toward a subsequent R01 translating animal morphological biomarkers to human early-AEPs. The long-term goal of this line of research is to identify pathologies underlying sensorineural hearing loss so that new treatments can be developed that target specific pathologic mechanisms in the ear.