University of Arkansas

NSF Awards · FY 2026 · 2026-09

The United States is home to nearly 4,000 species of native bees, which are important for the ecosystem. Unfortunately, declines in many economically important species have been documented in past years. Recent studies found regenerating forests that are managed for timber in the United States can be refuges for wild and native pollinators, including rare and economically important species of bees. However, despite this knowledge, there remains a lack of sustainable management practices for conservation of wild bees in managed forests. Moreover, monitoring bee pollinators in forests is currently very difficult and unfortunately destructive in nature, as it requires lethal trapping of individuals, which are then identified in a laboratory. Lethal trapping methods can have negative impacts on pollinator populations and are labor-intensive and inefficient. Furthermore, pollinators may be shifting their activity based on changes in average temperature, and we currently do not have effective ways to track these changes. The primary goal of this project is to develop, test, and implement non-lethal methods for monitoring pollinators in forests using acoustics and camera-based artificial intelligence (AI). Native bee species in the Unites States contribute as much as 3.5 billion dollars annually to agricultural pollination, but bees are on the decline. This project will develop AI technology that can be deployed in a field setting to automatically identify pollinator species in real time, thereby tracking patterns of activity. By combining different cutting-edge AI techniques, the system will learn and adapt over time, making it more accurate and user-friendly. The goal is to create easy-to-use software that can help track pollinators in the wild, giving scientists and conservationists valuable insights into how structural changes affect these important species. The new technology will enable assessment of the status of pollinators across forests in the southeastern and northeastern United States in real-time, tracking of changes in pollinator activity, and determination of how changes in the forest landscape may affect pollinator abundance and diversity. This information will then be integrated into a harvest scheduling program that forest companies can use to help them in conservation planning, which is part of sustainable forest certification programs. This research will inform conservation strategies, helping protect pollinators and the ecosystems that depend on them. 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 · 2026-09

This Faculty Early Career Development Program (CAREER) award supports research to apply novel biomechanics and engineering methods to determine how mechanical forces and cellular energy constraints work together to guide the motion of cells. Cells often move as groups rather than as single cells. This process is important in tissue formation, wound repair, and disease progression, such as cancer invasion and tissue fibrosis. Yet it remains difficult to determine how cells coordinate movement in three-dimensional environments that more closely resemble living tissues. In these settings, cells must both transmit forces to one another and use energy to sustain motion. Current methods do not allow these processes to be studied well within realistic tissue environments. The knowledge and tools developed through this research project will provide a foundation for future studies of tissue growth, repair, and diseases such as cancer. The project will also contribute to broader national interests in biotechnology by advancing methods to study coordinated cell behavior in realistic tissue environments and help advance the national health. In addition, it will connect research with education through hands-on learning, outreach activities, and broad sharing of experimental and analytical tools for students and researchers. In this way, the project will help establish a sustained research and training effort in biomechanics and mechanobiology. This CAREER award supports research that focuses on understanding how intercellular forces and cellular energy constraints regulate collective migration in three-dimensional environments. The research will use a recently developed method for mapping intercellular stresses in three-dimensional cell collectives, together with bioenergetic measurements, engineered tissue models, and deep learning-based image analysis, to determine how force patterns and energy constraints shape coordinated movements. The objectives are to determine how intercellular forces, especially compressive stresses, direct collective migration; to define the active mechanical and energetic roles of follower cells; and to determine how energy gradients and physical confinement guide directional flow and spatial organization during collective migration. The research will be closely integrated with education and outreach, using the same toolset and experimental framework to support research training, hands-on course activities, outreach programs, and practical workshops for researchers. The project will advance fundamental work in biomechanics and mechanobiology by establishing a force-energy framework for collective migration in tissues, while making the new enabling tools more broadly accessible. 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

This award supports student travel and registration expenses for the 32nd International Conference on DNA Computing and Molecular Programming (DNA32), which will take place August 3-7, 2026, in Fayetteville, AR. This support will provide students, the next generation of researchers, with the opportunity to present their work and interact with students from other institutions and senior researchers in the field. This conference emphasizes topics that bridge computation, biology, and nanotechnology and attracts top researchers in the fields of computer science, mathematics, chemistry, molecular biology, and nanotechnology. The scope of topics for invited and contributed talks include folding, design, evolution, and interactions of RNA molecules; computation using DNA-based chemical reaction networks; foundations of neural computing and artificial life, the design and construction of DNA and RNA nanostructures; demonstrations of biomolecular switches and circuits that process chemical information in vitro and in cells; molecular motors and molecular robots; studies of fault-tolerance and error correction in molecular self-assembly and molecular computation; synthetic biology and molecular evolution; DNA data storage; and software tools for analysis, simulation, and design of molecular structures and circuits. These topics have applications spanning engineering, physics, chemistry, biology, medicine, and education. Conference organizers will use this award to support the travel of up to 20 students to travel to DNA 32. Students who plan to present their work at the conference, especially students from institutions that would otherwise be unable to afford conference attendance, and trainees in molecular computing research who might not otherwise have the opportunity, will be prioritized for awards. Requests for student applications will be circulated in regular conference announcements, and a committee of conference organizers will make selections from the received applications. The awards will support registration, travel, and accommodations for students. The availability of this support will foster broader conference attendance and help advance science and develop new technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This CAREER project will investigate one of biology’s most fundamental questions: how entirely new genes with novel biological functions arise and help organisms adapt to challenging environments. The project uses fish antifreeze proteins, molecules that allow some fishes to survive in icy seawater, as a window into how evolution repeatedly solves the same problem through different genetic routes. By revealing how genomes generate new functions, the work will advance basic knowledge in evolutionary biology and genomics, with broader relevance to biotechnology and data-intensive biology. The project also serves the national interest by strengthening STEM education and workforce development. It will bring hands-on evolution outreach to K–12 students and families in Arkansas through annual museum events, integrate project-generated multi-omic datasets into classroom research experiences for more than 100 college students, and provide mentoring and research training for students and postdoctoral scholars. Together, these activities will expand public scientific literacy, broaden participation in genomics, and prepare future scientists to analyze complex biological data. The research will use type I antifreeze proteins (AFPI), which occur in four distantly related fish lineages and appear to have evolved independently despite their similar protein sequences and functions. The project will combine comparative genomics, transcriptomics, epigenomics, and phylogenetic analyses to determine how these genes originated, how they acquired regulatory elements and became integrated into gene networks, and how they were maintained, diversified, or lost under different environmental conditions. Specifically, it will analyze strategically selected AFPI-bearing species and close AFPI-lacking relatives to identify precursor sequences and mechanisms of gene birth; map gene expression, open chromatin, histone-marked regulatory regions, and promoter-enhancer interactions to reconstruct regulatory integration; and use broader taxonomic sampling to test how natural selection shaped gene family expansion/contraction, retention, pseudogenization, and loss. The expected outcome is a general framework for understanding new gene emergence, regulatory innovation, and functional evolution across lineages, extending beyond antifreeze proteins to other adaptive traits. 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

Understanding the human brain is a vital area of research with broad scientific and societal importance. Revealing the underlying mechanisms of brain function has the potential to advance medical diagnosis and treatment planning for neurological and psychiatric disorders, e.g., Parkinson's disease, obsessive-compulsive disorder, and many others. In addition, a deeper understanding of brain processes and how the brain represents information can inspire the development of the next generation of Artificial Intelligence (AI) systems and significantly benefit a wide range of scientific and engineering fields. At the same time, quantum computing offers new ways to represent and process complex information, but its potential for understanding the human brain remains largely unexplored. This project aims to develop new quantum machine intelligence approaches for modeling human brain activity using functional magnetic resonance imaging data. The outcomes of this project could advance scientific understanding of neuroscience and quantum machine learning and inspire more efficient AI systems. The project will also support student training, interdisciplinary education, public workshops, webinars, open-source software, and collaborations that broaden access to research at the intersection of quantum computing, neuroscience, and artificial intelligence. Despite the urgent need to accurately model human brain activity, research on quantum machine intelligence has been limited, particularly for analyzing large-scale functional magnetic resonance imaging data to advance vision-brain understanding. Existing machine learning approaches often struggle to represent complex, high-dimensional, and brain-wide neural dynamics, while current quantum machine learning methods remain underdeveloped for human brain understanding. To address these limitations, this project will develop a new framework for vision-brain understanding by integrating quantum theory with machine learning models for functional magnetic resonance imaging. First, the project will introduce new quantum-inspired neural networks that leverage superposition and entanglement to model complex relationships between visual stimuli and brain activity. Second, a new quantum feature encoding will be developed to represent large-scale brain imaging signals in a quantum feature space, improving the ability to capture high-dimensional neural patterns with higher fidelity and better task performance. Third, the project will propose a novel hierarchical quantum circuit gate model that operates on quantum machines for scalable vision-brain modeling. All algorithms developed will be released as open-source software to support accessibility and reproducibility. This project is expected to advance human brain understanding, quantum machine learning, and brain-inspired vision systems by enabling richer, more scalable modeling of complex brain-wide dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

NON-TECHNICAL PART: The Research Experiences for Undergraduates (REU) site at the University of Arkansas, Physics Department provides exciting research opportunities on experimental and theoretical projects in biophysics, nanomaterials, condensed matter, photonics, lasers, and quantum optics. The participants, selected, but not exclusively, from undergraduate institutions in the Ozark region and surrounding states with limited or no infrastructure to support undergraduate research will acquire skills and practices essential to scientific problem solving by working in a collaborative research environment through mentoring by experienced faculty and their research group. They will participate in professional development activities that include visit to high-tech manufacturing and STEM startup companies, weekly-seminars, workshops on laboratory-safety, machining, electronics, library-database searches, career options, oral and written communication, scientific ethics course, and educational activities with other REUs. REU participants will leave having acquired scientific and technical skills for advanced scientific research and softer skills to be a productive and thoughtful members of the nation’s scientific and technical workforce. TECHNICAL SUMMARY REU students will engage on carefully selected research projects from topics of current interest such as fabrication/characterization of novel nanoscale two-dimensional quantum materials; robotic- and AI-assisted atomically thin quantum devices; symmetries and topology in the electronic structure of quantum materials; solid-state nanopore fabrication and detection of single protein, RNA, DNA molecules; structured light beams; quantum correlations in quantum optics; super-resolution fluorescence and phase-contrast microscopy for studying biological systems; and statistical and nonlinear physics of extremophiles and the brain. Through engagement on their research projects, the participants will receive technical training in using the state-of-the art instruments, and computational and analytical research techniques. They will contribute by building a device for data acquisition, writing software for data analysis, calibrating an instrument, carrying out a proof-of-principle experiment or an experiment verifying a theoretical prediction. They will participate in workshops, group seminars, reviews of research, planning, and discussions, and through this interaction develop a professional network of faculty and peers. Working on their projects, they will experience the creative and collaborative process involved in scientific research, starting from the formulation of a problem to its solution, through ups and downs to the eventual thrill of a discovery. At the end of the summer, students will present their research at a REU research symposium and submit a final report in the format of a Physical Review paper. Many will present the results of their research at national/international scientific research conferences and publish papers based on their work in scientific research journals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This NSF CAREER project aims to overcome critical barriers limiting the deployment of wide bandgap (WBG) semiconductors in medium to high voltage power conversion that support the modern electric grid. Rapid growth in electricity demand, driven by artificial intelligence infrastructure and transportation electrification, requires more efficient and compact power conversion technologies. However, existing high voltage (>10 kV) WBG modules remain immature and offer limited current capability, restricting practical adoption. This project introduces a transformative Aggregated WBG Multi-Chip (AWMC) module paradigm that combines mature, lower-voltage chips in series and parallel to achieve both high voltage and high current in a scalable, cost-effective manner. By integrating advanced control, innovative circuitry, and new packaging architectures, the project ensures reliable operation under extreme electrical and thermal stress. The intellectual merit of the project includes: 1) a systematic framework for simultaneous voltage, current, and thermal balancing in multi-chip high voltage modules; 2) an active gate driving scheme with integrated sensing and real-time balancing control; and 3) a scalable packaging architecture featuring symmetric gate paths, integrated control circuitry, and reduced parasitics and partial discharge risk. The broader impacts of the project include accelerating the transition from legacy silicon-based systems to efficient WBG infrastructure, supporting grid modernization and domestic manufacturing, and preparing a highly skilled workforce through interdisciplinary education, open-access design tools, and outreach to K–12 and university students. To advance WBG adoption in high voltage power conversion, the project develops AWMC modules formed by series–parallel interconnection of mature WBG chips to achieve high current (>100 A) capability, scalable to high voltage (>10 kV) operation. Realizing this vision requires addressing three fundamental challenges: achieving simultaneous voltage, current, and thermal balancing (SVCTB); overcoming the incompatibility of conventional silicon-based packaging with multi-chip high-voltage WBG modules; and establishing modeling and design methodologies for large-scale AWMC modules. The research program is organized around three synergistic thrusts: 1) Develop fundamental understanding of SVCTB mechanisms in multi-chip interconnections; 2) Design and validate real-time SVCTB control strategies using active gate driving and integrated sensing; and 3) Establish a reliable, scalable packaging architecture and systematic design methodology for high voltage, high current WBG modules. Educational objectives include establishing a multidisciplinary training platform spanning semiconductor devices, packaging, and power electronics systems; developing an open-access high-voltage module packaging design tool; and building a STEM pathway from pre-college to graduate education to cultivate the next generation of power electronics 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 · 2026-04

This Faculty Early Career Development Program (CAREER) award supports fundamental research to enable high-resolution additive manufacturing of metal and other inorganic structures and components for miniaturized electronic and robotic devices. Additively manufactured components and devices made of pure inorganic materials at the micro- and nano-scale offer the potential for significant performance advantages over similar components and devices made of polymers and nanocomposites. However, current commercially available laser-based additive manufacturing systems have limited capability to fabricate structures smaller than one hundred micrometers using pure inorganic materials. This research aims to reveal the fundamental interactions between short-wavelength lasers and inorganic nanoparticles, with the aim of overcoming these limitations. The resulting knowledge will establish a pathway toward affordable and scalable high-resolution manufacturing technology for high-performance functional devices, strengthening economic competitiveness and enabling applications in sensing, robotics, energy, communication, and national security. These advances will contribute to the health, prosperity, and welfare of the American people. In addition to research, this award will support educational programs at the intersections of materials, manufacturing, and devices, preparing the future workforce with cross-disciplinary capabilities in micro- and nano-manufacturing and functional electronic and robotic systems. Outreach efforts through various initiatives and programs will engage local K-12 students, fostering awareness and sparking interest in engineering careers. Additionally, partnerships with industry will help ensure long-term technological viability and facilitate translation to manufacturing and high-technology sectors in the United States. Affordable and scalable high-resolution patterning of inorganic materials offers substantial opportunities for next-generation technologies. However, the fundamental interactions between light and inorganic nanomaterials which could be harnessed for high-resolution manufacturing of functional devices remain unclear. The goal of this CAREER project is to understand the photothermal and photochemical transformation of materials during the photo-excitation of nanoparticles induced by short-wavelength lasers. This project will fill the knowledge gaps of how plasmonic effects enhance localized heating, how short-wavelength illumination reduces the energy budget for sintering, and how nonlinear optical effects enable feature sizes beyond the diffraction limit. Molecular dynamics simulations and numerical modeling, coupled with advanced material characterization techniques, will elucidate the processing physics and provide predictive tools for micro- and nano-scale fabrication. Furthermore, this project will investigate the processing-structure-property relationships for functional devices and related structure physics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

This award funds the scientific analysis of materials recovered from the site of Broken Mammoth in central Alaska. This site has figured heavily in debates about the peopling of the Americas and yielded a vast collection of artifacts and butchered animal bones--far more diverse and numerous than from other early Beringian sites. These materials hold clues to understanding human adaptations during the transition from the last Ice Age to the modern environment. Through analysis of the site collection, the research team will explore changes in foraging ecology and seasonal land use strategies, reconstruct subsistence economies, and investigate changes in site activities through time. Broader impacts include making site materials and primary data available to the scientific community, support for several students and a post-doctoral researcher, educational opportunities for Indigenous youth, and a cultural heritage summit with regional Tribal leadership. Investigations at Broken Mammoth (1989–2010) in central Alaska revealed a deeply buried site with multiple occupations dating from 13,310 to 2250 years ago, including two rare residential camps. The site has been highly influential in developing broad spectrum foraging models and has yielded the largest faunal assemblage in Beringia by species richness and count of identified specimens. However, no detailed artifact, faunal, or spatial analyses have been conducted. The project team will curate and analyze this large collection in order to reconstruct human resource and risk management strategies from initial human settlement to the recent past. The team will employ zooarchaeological, biomolecular (proteomic and genetic species identifications), and lithic technological analyses. Objectives include reconstructing site subsistence economies through time (including diet breadth and seasonal land use strategies); investigating raw material use and procurement; and identifying site structure and activity patterns through plant macrofossil, spatial and hearth biochemical analyses. This integrated analytical strategy will enable the research team to document changes in human behavior through time and link human adaptations to regional environmental dynamics. 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 10th Annual Meeting of the Society for Industrial and Applied Mathematics Central States Section (SIAM-CSS) will be held at the University of Arkansas, Fayetteville, on October 11–12, 2025. By convening applied and computational mathematicians from Arkansas, Colorado, Iowa, Kansas, Mississippi, Missouri, Nebraska, Oklahoma, and beyond, the meeting promotes the progress of science and strengthens regional and national research capacity. Participants will share cutting-edge advances, receive professional mentoring, and form collaborations that translate mathematical innovation into technological, biomedical, and economic benefits. T​he SIAM-CSS conference will feature plenary lectures by distinguished scientists and a series of minisymposia spanning numerical analysis, partial differential equations, optimization, scientific computing, data-driven modeling, and interdisciplinary applications such as engineering and life sciences, to name just a few. Interactive poster sessions will showcase emerging methods such as structure-preserving algorithms, high-performance computing strategies, and machine-learning–enhanced simulation. By disseminating new theories, algorithms, and open-source software, the meeting will accelerate discovery and identify research directions of strategic importance to the computational and applied mathematics community. The conference website is https://siam.uark.edu/. 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 civic-university partnership develops a low-cost approach to monitor Alaska's Qanirtuuq river, located on the Yukon-Kuskokwim (Y-K) Delta. Rural villages are among the 75% of Alaskan communities with fewer than 1,000 people who rely primarily on subsistence, including the iconic pacific salmon. Like much of the Arctic, the Y-K Delta has grappled with unpredictable salmon escapements, and river dynamics threaten infrastructure and shipping routes. The Qanirtuuq is part of a braided river system, which has a tendency migrate laterally and to avulse (i.e., change course abruptly to a different path across the floodplain). Over the past decade residents near the river have documented signs that an avulsion may be imminent, thus threatening their source of water, fish, and transport and will force the town to relocate at great financial cost. Determining when and whether to move the village is therefore the community’s top priority. This project involves researchers from the University of Arkansas and Nalaquq, a geospatial company based in Quinhagak, to study riverine ecosystem stability and to develop efficient, technology-driven solutions to monitor changes. The project draws from fluvial geomorphology, remote sensing, salmon ecology, stable isotope analysis, and local knowledge. The pilot project establishes a geospatial database that incorporates data integral for environmental and ecological modeling with local observations and develops maps and community-based workflows that can assess the risk of migration and avulsion, as well as population stability in the five species of salmon. It also delivers geospatial training so community leadership can: 1) monitor river movement to determine whether the village will need to resettle in a new location, 2) identify areas susceptible to waterway erosion impacting infrastructure and navigation, 3) and monitor the ecological health of salmon populations. This multidisciplinary approach fills a gap between community priorities and scientific research, and it means that rural communities that are impacted by environmental change will move towards independent, efficient, and locally led decision making for interventions. Thus, this collaborative effort delivers a model that other rural Alaskan communities can adapt, replicate, and deploy. 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

Tree ring reconstruction of paleoclimate in the tropics is challenging because tropical trees rarely have reliable annual banding. Consequently, the climate history of tropical areas like the Amazon of South America is poorly known, including how recent flood and drought extremes are typical of natural climate fluctuations, or if they are attributable to global climate warming and deforestation. Cedrela odorata has been demonstrated to grow annual rings that can be used to reconstruct wet-season precipitation in the eastern Amazon. This project will use C. odorata and historical records to create a 250 to 300 year long record of rainfall extremes in the eastern Amazon, constrain the elevations of high and low river levels in the 19th century, and use climate and water cycle models to determine what are the physical mechanisms that govern the occurrence of floods and droughts in the region, including the impact of deforestation. The project will also investigate the paleoclimate potential of another species, Denezia excelsa, develop an outreach and education website on the forests and climate of the Amazon, and lead workshops on tree ring dating methods. The amplitude of difference between the seasonal high and low flows of the Amazon appear to be increasing, however in the absence of a long-term record of high- and low-stands through time, it is not clear whether these recent observations are within the range of natural fluctuations, or if anthropogenic climate change or deforestation play roles. This project will use new tree-ring records of hydroclimate and historical records from the Brazilian Digital library to reconstruct Amazon River extremes through time. The physical mechanisms behind drought and flood extremes in the Amazon, including Pacific and Atlantic sea surface temperature forcing and the potential role of a fully forested vs partially deforested watershed will be explored with the Community Earth System Model Version 2.1 (CESM2.1). Climate model output will be input into a hydrologic model of the Amazon River system for the spatial reconstruction of 19th century high and low flow extremes identified with tree-ring and historical information and to test the impact of forest cover loss on river levels in the Amazon. 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

Insects and other arthropods make up almost 85% of all known animals on the planet, and most existing species are still unknown to science. For centuries, researchers have collected arthropod samples from habitats worldwide, many of which contain thousands of specimens. The time and expertise required to sort through these samples and make identifications are prohibitive. Consequently, hundreds of millions of specimens, representing vast amounts of undiscovered biodiversity, are unstudied and stored away in museums and research collections. There are few scientists capable of working on the backlog of specimens, and therefore, samples continue to sit in storage. This project will develop artificial intelligence-based tools to automate the imaging and identification of arthropods in samples from terrestrial habitats. Researchers will simply pour a raw sample into an imaging system, allowing the computer to image and identify the specimens, thereby greatly minimizing the time and expertise required to process samples. Successful development of this tool will have profound impacts on both ecology and biodiversity sciences, as it will allow researchers to extract more data from samples than previously possible and will unlock tremendous amounts of biodiversity data from the immense backlog of samples. Since many of these samples are decades or centuries old and come from habitats that have degraded or destroyed, identification of specimens will provide data on how biodiversity is changing through time and provide critical information about species that have gone extinct. The advanced AI algorithms developed in this project can be further extended in healthcare, robotics, manufacturing, or new material discovery applications, and used to accelerate use and development of other AI systems. The project also will provide training opportunities in entomology or computer science to pre-college, undergraduate, and graduate students. This project will facilitate the incorporation of all arthropods into studies examining soil samples, providing an efficient, user-friendly, and low-cost method to unlock the tremendous amounts of dark biodiversity data in research collections. The AI identification system will be able to identify soil arthropods from across the US and will continually learn and expand its recognizable diversity as new images and identifications are added. Additionally, the proposed AI methods go beyond applications in arthropod identification. Our developed technologies will automate image segmentation and provide a novel approach to learning with limited annotated data and fairness awareness. The developed continual learning approach provides an efficient way to adaptively update the model with new data that helps the model improve over time. Therefore, the outcomes of this project are expected to fundamentally advance the fields of biodiversity science and ecology and promote the development of broadly applicable computer vision and machine learning technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Epidemic viruses use surface proteins to bind to and invade host cells, which is a critical first step in triggering infection. Understanding how these proteins interact is essential for developing strategies to prevent disease spread and to reveal key biological mechanisms. However, frequent mutations in proteins generate new variants that complicate experimental testing and delay timely responses. This project aims to develop computational mathematical tools to better understand protein structures and interactions, even across a wide range of mutations. The approach combines mathematical modeling, machine learning, and biology to analyze the complex shapes of proteins. These tools will help researchers make faster and more accurate predictions about the infectivity of a given virus strain, potentially enabling quicker public health responses. The project also supports education development by training students in the interdisciplinary studies of mathematics and biology. Overall, this research aims to strengthen society’s ability to anticipate and respond to emerging viral threats by applying efficient and scalable mathematical approaches to pressing biological challenges. Broader impacts include interdisciplinary training and the development of publicly available opensource software to support the biomedical and mathematical sciences communities. This project develops mathematical and computational frameworks for predicting protein-protein binding free energies upon mutations using low-dimensional representations of high-dimensional biomolecular data. Protein-protein interactions play a fundamental role in many biological processes and are particularly critical for mediating viral entry into host cells. However, due to the vast mutation space and the structural complexity of biomolecules, experimental evaluation is limited. The research leverages techniques from algebraic topology, differential geometry, and manifold learning to construct mathematical models that capture both the spatial conformation and physicochemical properties of viral proteins. These models are integrated with deep learning architectures to predict binding affinities efficiently across mutational variants. The novelty lies in the mathematical treatment of biomolecular functions as continuous entities embedded in high-dimensional space, enabling low-dimensional, information-rich manifold representations suitable for learning tasks. The research will develop computational tools that generalize across virus mutations, providing new insight into viral infectivity and epidemiology. 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

Artificial intelligence (AI) technologies are revolutionizing fields such as healthcare, robotics, agriculture, public safety, and many more. However, the increasing reliance on AI also brings concerns about trust, reliability, and real-world conditions that pose significant risks to the long-term value of AI. This project tackles these challenges by developing a human-inspired AI framework that processes data in ways similar to human perception - drawing information from multiple sensory inputs, providing transparent and explainable decisions, performing reliably even with noisy or missing inputs, and operating efficiently with reduced energy use. These innovations will help ensure that AI technologies are safe, and accountable, aligning with national priorities for secure, ethical, and responsible AI development. It will offer hands-on experiences to K–12 students, mentoring to undergraduate and graduate students, and developing new college courses on trustworthy machine learning, thereby cultivating a next generation of scientific leaders. Despite significant advances in AI, current systems remain limited by key challenges: they are often task-specific, brittle to real-world disruptions, computationally intensive, constrained to single modalities, and lack interpretability. To address these limitations, this project develops a unified multimodal analytics framework centered on three core research goals: interpretability, robustness, and efficiency. First, it introduces a trustworthy spatiotemporal perception model that leverages dynamic semantic graph composition to represent scene entities, their attributes, and interactions across multiple levels of granularity, thereby enhancing transparency and interpretability in decision-making. Second, it proposes a robust multimodal learning architecture that fuses diverse sensory inputs, including visual modalities (RGB, depth, infrared, motion) and non-visual modalities (audio, text), through a collaborative expert-agent architecture combining Sparse Multimodal-aware Experts (SMaE) and a Unified Multi-Agent (UMA) system. This design is specifically engineered to maintain performance under real-world conditions involving noisy or missing data. Third, to reduce the computational and energy demands of AI deployment, the project presents a spectrum-preserving energy minimization approach for token merging, inspired by spectral graph theory, which compresses models while preserving critical information. The effectiveness of these innovations will be demonstrated through applications in unified multimodal understanding tasks such as robotic perception, video analytics (including recognition, captioning, and retrieval), and animal behavior analysis, using a wide range of benchmark datasets and real-world deployment scenarios. 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 Navier-Stokes (NS) equations are fundamental mathematical models with a wide range of applications in computational math and various engineering fields. The major objective of this project is to explore high-order accurate structure-preserving methods for various NS equations, including compressible, incompressible, and compressible flow with incompressible limit. The outcomes have potential applications in aeronautics and astronautics, petroleum industries for enhancing oil recovery, and possibly extended to benefit computational materials science. In addition, this project will provide valuable research opportunities for graduate and/or undergraduate students in computational mathematics. Designing high-order methods for NS equations that preserve fundamental principles such as conservation, bounds, and energy law, while ensuring efficiency for large-scale simulations, presents significant challenges. The current state of high-order accurate structure-preserving numerical methods for NS equations is still far from being practically satisfactory. The PI will explore high-order structure-preserving algorithms for various NS equations, emphasizing efficiency and robustness in simulating real-world problems. For compressible NS, a novel approach combining large-scale non-smooth optimization with discontinuous Galerkin methods will be applied to construct invariant-domain-preserving schemes. This methodology is extensible to other methods, such as finite volume and finite difference by incorporating constraints on cell averages and point values. For incompressible NS, the PI will explore both theoretical analysis and algorithm design on splitting methods. The outcomes are crucial for designing reliable simulators and can be extended to other fields, such as phase-field equations. High-order asymptotic-preserving methods for compressible flow and incompressible limit will be explored. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to develop a new class of paint-like bioadhesive materials that can be directly applied to injured or damaged soft tissues. These materials are inspired by the natural adhesion mechanisms of mussels and are designed to adhere to wet biological surfaces, conform to complex shapes, and promote tissue healing. A unique aspect of this work is the use of biological matrices derived from decellularized tissues that are rich in biological factors that support cell growth and tissue regeneration. The project will compare different formulations based on naturally occurring adhesive molecules combined with biopolymers to create a flexible, easy-to-apply platform for regenerative therapies. The technology has broad potential to impact multiple areas of medicine, such as wound healing and organ regeneration. The project will also provide interdisciplinary training to Arkansas K-12, undergraduate, and graduate students in biomaterials, tissue engineering, and biofabrication, thereby advancing education and workforce development in biomedical engineering. This project will advance our understanding of design criteria for paintable formulations of decellularized tissue extracellular matrix (dECM) cocktails. Current dECM formulations and the biologics derived from these materials are limited in their therapeutic potential for tissue repair and regeneration due to challenges with post-operative adhesions and insufficient mechanical strength. Our proposed paintable dECM formulations have the unique capability to overcome these limitations by offering bio-adhesive properties to wet surfaces enabling minimally invasive placement onto delicate or irregular tissues where traditional scaffolds/patches are impractical. By leveraging catecholamine chemistries with naturally derived polymer backbones via both enzymatic and chemical crosslinking approaches, this work will generate formulations with strong adhesion, rapid polymerization, and in vivo biocompatibility. Further, the use of decellularized tissues from the nervous system including ECM-rich sciatic nerve and proteoglycan-rich spinal cord as starting materials harbors vast untapped potential for widespread use in regenerative medicine. The paint formulations will be optimized, and structure-function relationships will be assessed in vitro and in vivo through subcutaneous implants. The fundamental underpinnings that will be learned through this work will enable a new class of biomaterials that will transform applications in tissue engineering and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

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

NSF Awards · FY 2025 · 2025-08

The increasing promotion of tobacco-related content on social media significantly contributes to the rising prevalence of tobacco use, particularly among youth. Despite this, limited research has evaluated the critical connection. Understanding the relationship between tobacco use and abuse and social media promotion is vital for advancing community health and well-being. This project aims to develop novel artificial intelligence (AI)-based approaches, including multi-modality, privacy-preserved machine learning, and human-in-the-loop methodologies, to automatically detect tobacco promotion and ascertain the association between tobacco on social media use and the onset of youth tobacco use and abuse. The project outcomes will encompass system accuracy and portable AI-based software for the early detection of tobacco promotion threads on social media platforms, thereby protecting youth across the United States from harmful exposure. Despite the urgent need to identify the connection between tobacco promotion and use and abuse, limited research has explored advanced AI techniques for analyzing tobacco-related content on social media. Utilizing AI for this purpose raises significant concerns regarding fairness and trustworthiness, as developed algorithms must avoid amplifying existing biases to align with the broader goals of responsible AI in health applications. To address these limitations, this project will introduce new theories and technical approaches for extensive multimodal learning on social media platforms. Initially, a comprehensive data collection pipeline for tobacco-related content will be established to gather a large-scale multimodal dataset. Subsequently, the project will develop multimodal AI models using adaptive vision-language transformers and debiased learning to ensure equitable analysis across diverse content and demographics. Moreover, a novel privacy-preserving federated learning system, integrated with differential privacy and secure multi-party computation, will be introduced to protect data privacy. Lastly, the research will propose a new fairness continual learning system that incorporates human-in-the-loop integration to ensure ethical alignment while adapting to evolving data. All algorithms developed will be deployed as cloud services and released as open source to advance AI research in public health. This project will pave the way for new theoretical and practical approaches, bridging biomedical and behavioral insights to analyze the impact of social media on youth tobacco use and guide targeted interventions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This goal of this project is to turn two greenhouse gases — carbon dioxide (CO₂) and methane (CH₄) — into methanol, which is a useful chemical and a cleaner fuel. The process uses a plasma, which is a special state of matter made from superheated particles, with clean energy such as solar power. Instead of capturing and storing CO₂, this project takes it from the air near places where methane is released and turns it into methanol. This approach could reduce costs, make use of the U.S.'s natural gas supplies, and improve national security. It could also boost the economy by creating new markets for these gases. The project will train college students in science and technology and include hands-on activities to interest younger students in science and engineering. While conventional thermal dry methane reforming (DMR) transforms CO2 and CH4 into CO and H2, the project team has demonstrated that the use of perovskites and a non-thermal plasma route promotes the direct production of methanol. Successful completion of this project will advance the understanding of the plasma-catalyst interactions at the nanoscale responsible for the oxygenate yield. Another intellectual contribution will be the development of plasma-activated/plasma-enhancing perovskites and dual-function metal-perovskites for CO2 capture and conversion, which can be applied across a wide range of approaches. Additionally, a comparative study of the thermal vs. plasma route will provide insights into the isolated effects of the fields and plasma-charged particles. The objectives for this project are to fundamentally understand: (1) the interactions between plasma-originated species and perovskites under different plasma scenarios and their implications for reactivity, (2) the mechanistic impact of plasma properties and composition and individual species during CO2 conversion, (3) the material response (charging, polarization, electric field) to plasma and surface properties of perovskites that enable the CO2 catalytic conversion to methanol, (4) the link between surface properties and plasma conditions and the boundaries between thermal and plasma effects, and (5) the link between material composition and CO2 binding and reactivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This EArly-concept Grants for Exploratory Research (EAGER) award supports fundamental research to enable semiconductor additive manufacturing of single-crystal silicon at micro/nano-scale. Single-crystal silicon is essential to emerging technologies, including artificial intelligence, optical communication and computing, sensing, and energy, which are also critical to national security. Current mainstream semiconductor manufacturing methods rely on a subtractive approach, which is time-consuming, inefficient, and generates substantial material and energy waste when converting bulk silicon ingots into nanostructures. Additive manufacturing (or 3D printing) offers a promising alternative to directly form single-crystal silicon structures and devices with greater speed and efficiency. However, crystallization is a stochastic process, and precise control of single-crystal formation and nanoscale structural patterning remains a significant challenge in additive manufacturing. This research project aims to uncover the fundamental science behind these challenges and establish a scientific foundation for additive manufacturing of single-crystal silicon. Upon completion, the resulting knowledge and technology could revolutionize the semiconductor industry and significantly enhance US economic competitiveness. In addition, this award will support the development of new course modules to help students understand the melting and solidification behavior of nanomaterials, while also providing interdisciplinary research opportunities for both graduate and undergraduate students. These efforts will train the next-generation workforce for advanced semiconductor manufacturing and generate long-term economic benefits. The research seeks to develop a laser transfer printing method to realize single-crystal silicon additive manufacturing. Molecular dynamics (MD) simulations will be used to explore the evolution of silicon films and the transformation, nucleation, and crystallization of silicon nanodroplets during printing. The effects of nanoscale confinement and surface atoms on crystallization behavior will also be studied to guide experimental efforts. Complementary experiments will systematically examine how film morphology (e.g., thickness) and laser parameters (e.g., power, pulse width, repetition rate) affect crystallization dynamics and the resulting crystal structure. By integrating simulation and experimental insights, this project seeks to establish a foundational framework for additive nanomanufacturing of silicon, including nanoscale phase transformations and crystal growth. The research will also look to establish process–nanostructure–property relationships critical for future device design. 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

Cells in the human body contain many small compartments with distinct chemical environments. Many biomolecules are localized within the specific compartments that enable them to carry out biochemical reactions that maintain cellular functions. It is now understood that compartmentalization has an important role in human health, including cell signaling and disease. This project will focus on how the shape of a biomolecule may affect its compartmentalization. Experiments will reveal the impact of biomolecule shape – spherical compared with rod-like, for example - on the formation, stability, and composition of these compartments. Results will help identify the fundamentals underlying this crucial biological process that can be helpful in designing bio-based devices, such as sensors that detect DNA and proteins. The insights gained from the project will be used to create experimental modules on “Soft Materials in Everyday Life” for hands-on training of high school students. This project will address the fundamental question of “how liquid-liquid separation of particles is affected by particle shape”? Experiments will study a liquid-liquid crystal phase separation when the participating polymers/particles have a rod-like shape. Orientation-dependent interactions of the rod-like particles will be studied within the framework of associative and depletion-mediated processes. Experiments will elucidate the phenomenology behind electrostatics-driven liquid-liquid crystal phase separation leading to the formation of liquid crystalline coacervates. Additional experiments will focus on liquid crystal ordering at the surface of a coacervate droplet. Liquid crystal ordering will be harnessed for developing sensors that report the presence of proteins. Experiments will also uncover fundamentals of depletion-mediated liquid-liquid crystal phase separation in suspensions of binary rod-rod particles. The experiments will map the phase behavior of water-based liquid crystals in the presence of DNA having different base pair lengths. Additionally, the phase diagrams of the liquid crystals will be engineered such that live DNA amplification can be reported when the concentration of the depletant exceeds a critical threshold. 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

Next generation transportation and logistics systems include those that are autonomous, electrified, interconnected, and shared. These systems are transforming traditional supply chains. The Arkansas Smart Transportation Research Incubator through Data Engineering and Science (AR-STRIDES) project aims to increase statewide research capacity. AR-STRIDES also aims to advance economic competitiveness in Arkansas through statewide network building, use-inspired research, and synergistic workforce development related to data-driven supply chain technologies and data analytics solutions for next generation transportation and logistics. Recent growth in data analytics, artificial intelligence, and computing expertise in Arkansas will be leveraged to further develop the next generation transportation and logistics ecosystem across Arkansas. Project outcomes have potential to provide significant and sustainable traction for the Arkansas’ innovation economy in accordance with the Arkansas Science & Technology Plan. Project partners include the University of Arkansas, Arkansas State University, Southern Arkansas University, University of Arkansas at Pine Bluff, and North Arkansas College. AR-STRIDES will address the needs of the transportation and logistics industry in Arkansas by strengthening Arkansas' research competitiveness and capabilities in computing and data analytics. Employed research methods will include access control models, artificial intelligence, big data technologies, cryptographic techniques, data replication and backup strategies, machine learning, real-time data processing, scalable storage architectures, security audits, and statistical models. The Arkansas High Performance Computing Center will provide expertise, hardware, storage, support services, and training to enable computationally- and data-intensive research. The scientific vision has potential to break down silos across Arkansas institutions of higher education and industry by creating meaningful connections between data science and analytics researchers and transportation and logistics stakeholders. Expected jurisdiction-wide impacts will result in Arkansas being positioned as a national leader in its defined domain: a statewide network delivering modern data-centric solutions to next-generation transportation and logistics challenges. Use-inspired perspectives and societal impacts will be integrated across research and workforce development activities leading to expanded employer access to skilled and retained talent in Arkansas. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Incubators for STEM Excellence (E-RISE). E-RISE supports the development of sustainable research infrastructure and capacity in EPSCoR jurisdictions through collaborative, hypothesis-driven, or problem-driven research and workforce development to improve competitiveness in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The rapid rise of large-scale multimodal models (LMMs) has promoted their advancement in numerous fields, including social media analysis and healthcare. However, while LMMs have shown outstanding performance, they can produce misleading outputs because these models do not know what they do not know, raising concerns about their reliability. The inaccurate, irrelevant, or unintelligible output produced by LMMs is often called hallucinations. Addressing the hallucination problem in LMMs is an important topic in the next five years since it will improve their trustworthiness and impact in many other fields. This project will develop novel hallucination mitigation approaches to improve the trustworthiness and applications of LMMs in healthcare, including sample use cases of tobacco advertisement prevention and autism behavior prediction. Outcomes from the research will impact the field by providing a foundational and practical study needed for future research. It will also train students to conduct and use research to improve community health and mental health outcomes. There are three primary factors leading to hallucinations in LMMs. First, it is widely understood that biased data distribution causes significant challenges in data-driven responsible AI approaches. However, biased distribution influence on predictions is also a leading cause of hallucinations in LMMs. Second, the misalignment between input modalities could result in the LMMs overconfidently relying on a particular input modality and ignoring others. As a result, the LMMs could hallucinate the predictions based on the dominant modality. Third, training LMMs often requires large-scale training data. The limited data could result in hallucinations in LMMs due to the lack of knowledge and diverse information. The overarching goal of this project is to develop robust and trustable LMMs to mitigate hallucination in large language models. First, to address the imbalanced data, new learning approaches will be introduced to mitigate the hallucinations caused by imbalanced data. Second, novel shuffling learning approaches will be developed to address the problem of misalignment across input modalities. Third, to overcome the problem of limited data, new adaptive learning approaches will be developed to improve the performance and trustworthiness of the LMMs. The LMMs in this project are then deployed into practical healthcare applications. The research effort in this project will pave the way to develop new theoretical and practical approaches to distribution modeling, contrastive learning, and shuffling learning to address hallucinations in LMMs. 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 I-Corps project focuses on the translation of two-dimensional materials for use in a range of advanced electronics applications including next-generation computing, quantum computing, low-noise electronics, and other electronic devices. By isolating flakes of these two-dimensional materials that are only one to three atoms thick, these materials gain exciting properties that are not found in their bulk forms. The difficulty of fabricating such atomically thin layers has thus far prevented commercial applications, but the new robotic technologies enable the fabrication at these tiny scales. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of autonomous robotic systems to generate two-dimensional materials quickly and consistently, stack these materials into heterostructures, and fabricate these heterostructures into electronic devices using the existing silicon paradigm. This technology shortens the time for two-dimensional heterostructure and device fabrication, enabling rapid experimentation and development of commercial applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.