Missouri University of Science and Technology

NSF Awards · FY 2026 · 2026-08

Many critical scientific and societal challenges involve interactions among groups of entities that cannot be adequately represented as simple pairwise relationships. For example, chemical reactions, biological signaling pathways, social group dynamics, and epidemic spread involve simultaneous multi-way interactions among more than two entities. Hypergraphs, a mathematical generalization of traditional graphs, provide a more accurate representation by allowing a single edge to connect any number of entities. Despite rapidly growing adoption of hypergraph-based models across biology, health sciences, social networks, and artificial intelligence, researchers lack the scalable, comprehensive, and user-friendly software needed to analyze hypergraphs effectively. This gap forces scientists to rely on simplified graph models that risk losing critical higher-order relationships in their data, potentially leading to incomplete scientific conclusions. This motivates the project - CHAI (Cyberinfrastructure for Hypergraph-based Analysis and Innovation), an open-source parallel software framework that addresses this critical gap. CHAI serves a broad scientific user base through a tiered interface accommodating users ranging from domain scientists who need ready-to-use analytical functions, to intermediate users who wish to tune algorithms, to advanced developers creating entirely new methods. For real-world validation, the CHAI team collaborates with researchers from social network analysis, bioinformatics, food web ecology, additive manufacturing, unmanned aerial vehicles, and cyber-physical systems. The CHAI project aims to develop three foundational technical innovations: (i) a unified data structure that efficiently supports both static and dynamic hypergraph representations on high performance computing platforms including GPUs, (ii) a compact motif-based representation that reduces memory requirements and accelerates hypergraph analysis, and (iii) an extensible parallel algorithm development framework for hypergraphs, enabling researchers to build and contribute new domain-specific algorithms. Together, these innovations enable CHAI to provide scalable and accurate hypergraph analysis. The developed software will be publicly distributed through GitHub, portable software containers, and a graphical drag-and-drop workflow interface that requires no specialized programming expertise, thereby maximizing accessibility across scientific disciplines. CHAI supports the training of graduate students and postdoctoral researchers in parallel algorithm design, high-performance computing, and scientific software development, contributing to building the next generation of computational scientists. Outreach activities extend research opportunities to a broad range of undergraduate and high school students. Community-building through workshops, tutorials, and conference mini-symposia will establish CHAI as the standard cyberinfrastructure platform for hypergraph analysis by enabling advances across a wide range of scientific domains that depend on accurate modeling of complex, multi-way interactions. 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

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Alfred University, Colorado School of Mines, and Missouri University of Science and Technology. An estimated 45 scholars pursuing undergraduate degrees in ceramic engineering and glass engineering will receive scholarships of up to $15,000 for up to five years. Scholars will receive faculty, peer, and industry mentoring and the project will build strong scholar cohorts through an intensive paid summer program, visits to industry partners, and participation in student professional society chapters. Additional activities for scholars include opportunities for undergraduate research, internships, and travel to conferences. The overall goal of this Track 3 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. There are a small number of accredited ceramic and glass engineering programs across the United States. However, professionals in these disciplines are important throughout the STEM workforce, including in the semiconductor, energy, and space sectors, as well as other key areas of national need. The project will be assessed by an experienced evaluator that will examine the project’s progress through surveys, interviews, focus groups, and a review of student artifacts. The data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

PFAS is a group of chemicals that are costly to remove from the environment. They do not break down naturally in soil and water because of the strong carbon-fluorine bonds. This project will take a new approach toward remediating PFAS by engineering special enzymes that can degrade these stable chemical bonds. If successful, the outcomes of the project could unlock exciting technologies for carbon-fluorine bond destruction, saving money and protecting human health and the environment. Long-term application of the enzymes could break down fluorinated chemicals in soil, drinking water, groundwater, biosolids produced from wastewater sludge, and industrial waste streams. Undergraduates will gain experience as part of the research team. Fluorinated organics, like perfluoroalkyl carboxylic acids (PFCAs), are common industrial additives which persist in the environment and have limited, expensive, and energy-intensive destruction technologies. So far, efforts to degrade PFCAs enzymatically have stalled, due to strong C-F bonds and a lack of evolutionary pressure to reward their destruction. As a result, PFCAs and other per- and polyfluoroalkyl substances (PFAS) are increasingly contaminating environmental media and require removal. Therefore, there is a critical need for new techniques to identify and enhance enzymatic defluorination mechanisms. This project adopts a new approach to discover and improve enzymatic defluorination, positing that proper evolutionary incentive and high-throughput screening techniques can create and discover biocatalysts that degrade perfluorinated carbon chains. The new approach uses a genetic circuit that links survival with defluorination to engineer defluorinases to degrade new substrates. Di- and tri-fluoroacetate will be targeted initially and later the substrate scope will be expanded to include PFCAs. The research will advance fundamental knowledge about enzymatic defluorination by uncovering beneficial mutations in catalytic proteins at the amino acid level, and, thereby, tunable mechanisms to enhance the desired reactions. If successful, the project will enable enzymatic defluorination of a perfluorinated carbon with mechanistic resolution for the first time. Putative defluorinating enzymes, identified in bulk defluorinating biological systems, will be expressed and characterized for activity towards multiple substrates. The successful completion of this project would establish new pathways for defluorination of perfluorinated carbon chains, opening the door for further enzymatic defluorination research and sustainable water treatment options. Finally, through a dedicated laboratory exchange, undergraduates from a primarily undergraduate institution (PUI) will engage in research training and mentoring to gain experience at a university with very high research activity (R1), and a graduate student will oversee student researchers at the PUI, gaining experience in undergraduate education and mentoring. 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

Nontechnical description: Chiral molecule sensing plays an important role in many areas such as pharmaceutical industry, biomedical diagnostics, and food analysis. It is challenging to accurately quantify the chirality of chiral medium due to the weak intrinsic circular dichroism signal of chiral molecules. Recently, chiral metasurfaces have been used to amplify the circular dichroism signal and improve the sensitivity in circular dichroism spectroscopy for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties. In this project, a new type of 2D chiral fingerprint metasensors with high sensitivity based on ultrathin 2D material chiral metasurfaces will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of various kinds of chiral molecules with high sensing performance. This research will benefit many biomedical and photonic applications in point-of-care healthcare, food analysis, environment monitoring, quantum spectroscopy, and quantum communication. This project also includes educational activities for training graduate students, recruiting students and promoting broad participation, and mentoring high school students through outreach activities. Technical description: Chiral metasurfaces have been utilized to enhance the circular dichroism signal and improve the detection sensitivity for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties, which are insufficient to cover the whole design space to achieve the optimal sensitivity in chiral molecule sensing. The goal of this project is to study a new type of 2D chiral fingerprint metasensors based on ultrathin 2D material chiral metasurfaces which will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of the mid-infrared vibrational fingerprints of chiral molecules with high sensitivity and selectivity. In this project, a new machine learning algorithmic methodology will be developed for rapid and precise inverse design of 2D chiral fingerprint metasensors with the maximized sensitivity. The underlying mechanism of high sensitivity in 2D chiral fingerprint metasensors will be revealed. Nanofabrication processes will be developed to fabricate the designed 2D material chiral metasurfaces. The 2D chiral fingerprint metasensors will be characterized for demonstrating the detection and identification of various kinds of chiral molecules with high sensitivity and selectivity. The project will advance the rapid design and integration of future 2D material-based photonic and optoelectronic metadevices. 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 Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) is a cosmology survey designed to map out one million galaxies in the distant Universe. In this project, a team of scientists from the Missouri University of Science and Technology will pioneer a method for mapping matter in the universe, called Line Intensity Mapping. The spectrum of light on each direction on the sky can reveal a map of undetected galaxies along the line of sight, which comes from special Lyman-alpha photons that were emitted by hydrogen gas in those galaxies and then were redshifted to a particular wavelength in the observed spectrum. To interpret the HETDEX data, the team will construct simulated data from theoretical simulations of the universe. As part of this project, the team will offer summer workshops for undergraduate students to teach them data science skills and for Missouri K-12 educators. The goal of this project is to detect and interpret the Lyman-alpha intensity mapping signal on large scales at 1.9<z<3.5 in HETDEX. Specifically, the team will measure the cross-correlation signal between Lyman-alpha emitters (LAE) and the Lyman-alpha intensity map in the HETDEX survey. To properly test the analysis pipeline and interpret the measurement, the team will make full use of empirical but realistic synthetic galaxy simulations as well as a hydrodynamical simulation, incorporating the physics of radiative transfer. The mock simulations will also be useful to understand how LAE galaxies formed and evolved and how Lyman-alpha photons propagate through the large-scale structure. Scientific products of this work will include the synthetic galaxy simulations as well as the analysis pipeline, which will be made available publicly for wider use in the community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Carbohydrate Memristor Empowered Environmentally Sustainable Processor-in-Memory Nontechnical description: Artificial intelligence (AI) systems have profound influence on societal wellbeing of humans and fueling significant economic growth. However, operation of conventional computing architecture in AI systems as well as manufacturing and disposal of conventional computing devices lead to significant energy consumption, depletion of nonrenewable natural resources, and ecological deterioration. Therefore, a serious concern of environmental sustainability to such increasingly pervasive computing systems has been raised, and improvements in system performance, energy efficiency, and ecological friendliness require new devices and systems. In this project, a new environmentally-sustainable processor-in-memory system is proposed to benefit the entire computing community including mobile and wearable computing, cloud computing and data center, electronic sensing and controlling, communication and networking. This project will also contribute to the development of high-quality workforce skilled in design, fabrication, testing, and modeling of memory devices and processor-in-memory computing systems for the growing needs in the US. The students and postdoctoral scholar participating in this project will receive unique training in engineering problem solving and technology development, and their research and educational experience will be enhanced by complementary expertise and close collaboration between the two research groups. Technical description: Processor-in-memory systems implemented with memristors have great potential to perform complex AI computations faster and on a smaller footprint. The goal of this project is to address the environmental sustainability challenge in computing by developing a novel brain-inspired processor-in-memory system empowered by memristors made from carbohydrate materials for energy-efficient operation, renewable material resource, sustainable device manufacturing, and ecologically-friendly disposal. The carbohydrate materials will be naturally extracted from plants, vegetable, and fruits with low cost and waste generation. Innovative fabrication techniques for carbohydrate-based memristor and processor-in-memory system will be developed with reduced water use and chemical waste, greenhouse gas emission, and manufacturing related energy consumption. The processor-in-memory system will be constructed by implementing carbohydrate memristors and reconfigurable peripheral circuits to achieve sustainable computing, enable performance improvement, and ensure high operation efficiency, longevity, and reliability. 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 broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to effectively address the safety of people with special needs. People with age-related or cognitive disabilities often find it hard to express themselves. They are not fully aware of their need for assistance when lost or wandering. Keeping track of people with special needs can be a daunting task for a caregiver and can lead to isolation, which significantly deteriorates their quality of life. While several assistive commercial devices are available to locate people with special needs, they have not yet been widely adopted due to various social and technical challenges. This PFI project develop a technology to address the challenges faced by existing solutions, including usability, privacy, deployability, and effectiveness. The proposed project will develop an intelligent, integrated system for people with special needs that includes multiple safety features, including preventive sentiment analysis, safe-zoning, and safe-tracking. It also enhances privacy protection, and reliability by leveraging the power of crowd sourcing in zoning and tracking. The project includes the development of the following components: i) an intelligent tag system, ii) software-based safety services, iii) beacon stuffing technologies, and iv) a privacy-aware tracking service. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project investigates smart farming technologies that use artificial intelligence (AI) to help farmers protect crops, use resources more efficiently, and reduce reliance on chemicals. Typically, farmers lose 20–40% of their harvest each year to pests, diseases, and water or nutrient shortages. By providing early warnings and real-time advice, the HARVEST project enables farmers to respond to these threats and avoid significant losses. By combining sensors, drones, mobile robots, and user-friendly web portals, the proposed approach guides farmers on how to apply water, fertilizer, and pesticides more precisely, thus increasing yields and reducing waste. An international collaboration across the United States, India, Japan, and Australia ensures the developed tools are tested in diverse environments and adapted to support small and medium-scale farms. This project accelerates global food security and fosters workforce development through student training in interdisciplinary AI and digital agriculture. The HARVEST project aims to develop an AI-driven system that addresses important challenges in agricultural production (specifically corn and rice), such as pest outbreaks, crop diseases, nutrient deficiencies, and water stress through three core innovations. First, an early-warning module merges rapid disease tests with image-based analysis to detect pests at their onset. Second, a Digital Twin platform creates a virtual model of each farm, enabling farmers to simulate management strategies without risking real-world impacts on their fields. Third, a multimodal generative AI framework learns from data collected in one region and adapts insights for use in other regions while preserving data privacy of each farm. The HARVEST system collects data through in-field sensors, robotic vehicles, and drones; processes this information using advanced machine learning algorithms; and delivers actionable recommendations via mobile applications. By validating the developed solutions across four countries, the project develops practical tools to enhance crop yields, improve farmer incomes, and strengthen agricultural economic prosperity. 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

Current computing systems are facing significant challenges including extremely high energy demand, tremendous consumption of nonrenewable materials, environmental and health issues by electronic waste, and lack of technologies to improve system performance by integration of complementary metal oxide semiconductor (CMOS) microchips with emerging device technologies (denoted X) – “CMOS+X”. This project will address these challenges by exploring an innovative technology to integrate CMOS periphery circuits with a novel memory technology - natural organic honey based resistive switching random access memory (honey-ReRAM) for a new brain-inspired neuromorphic computing system. The prototyped “CMOS + honey-ReRAM” computing system is promising to promote high performance, energy efficient, and sustainable in-memory computing capability for many high impact domains such as engineering, social science, national health, and defense. In addition, the proposed education activities offer unique training opportunities for underrepresented researchers including female, African American, and Native American students. This project targets to explore innovations in device fabrication and system integration technologies to optimize honey-ReRAM devices, establish the feasibility of 3D integration of CMOS circuits with honey-ReRAM arrays, and prototype a CMOS + honey-ReRAM enabled neuromorphic accelerator, an essential neural network hardware component for data processing in a vast range of devices in computing and artificial intelligence. The honey-ReRAM will have highly reproducible memory characteristics, thermal stability, long-term reliability, low-cost, as well as being sustainable. The honey ReRAM will also utilize an eco-friendly synthesis process and device manufacture. A novel three-dimensional (3D) architecture and fabrication technology with a formal design flow will be developed to ensure the compatibility of combining CMOS circuitry with honey-ReRAM arrays by a heterogeneous integration on the microchip. Furthermore, this research will provide a solution to the incompatibility problem in the integration between CMOS and X and effectively accelerate the development and application of CMOS+X technologies for system-level improvements in computing. 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

Scientific applications generate massive volumes of data through extensive simulations and physical experiments, requiring efficient transfer across globally distributed High-Performance-Computing clusters for further processing and collaborative research. These transfers support diverse scientific endeavors, including computational simulations and machine learning modeling. While high-speed networks manage this rapid data growth, current data transfer tools often fail to fully utilize these resources or focus excessively on network utilization using simplified architectures for quick convergence. These approaches frequently result in significant overhead on end systems and fairness issues. This project introduces a novel elastic design that maximizes unused resources without disrupting existing network and computing processes. It also adapts to the dynamic and evolving nature of computing clusters, where storage or network access patterns may shift unexpectedly. The project integrates advanced optimization algorithms that continually adapt to environmental variability, leveraging a comprehensive monitoring framework to capture real-time system dynamics and balance multiple objectives while tuning various transfer parameters. This project makes three key contributions: (i) it implements a robust monitoring framework to enhance system visibility, enabling global optimization and fair resource allocation; (ii) it uses the collected metrics to develop a simulator that replicates complex storage and networking dynamics, supporting offline and model-based training of generalized reinforcement learning agents; and (iii) it deploys pre-trained policies in production networks, continuously refining them to adapt to specific and evolving environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project includes theoretical investigations to challenge and support ongoing high-precision experiments with atomic hydrogen, the simplest chemical element, and with closely related hydrogen-like atoms, also called Rydberg atoms, which have one electron orbiting a closed-shell ion core. Because of their simplicity, hydrogen and its Rydberg cousins have long been used to make precise tests of QED (quantum electrodynamics), the most precise theory in all of physics. The spectrum of hydrogen can be calculated to better than one part in 100 trillions, and properties of its bound electron (such as its response to external magnetic fields) can be calculated to extreme accuracy. Reaching this level of accuracy is possible because quantum mechanics is stable under small perturbations, so it is possible to iteratively correct the theory. However, pushing such calculations to even greater accuracy, as required to advance recent experimental observations, poses numerous challenges, some of which are to be addressed in this project. This high-risk work will attempt to overcome the predictive limits of perturbative QED using novel and untried techniques. If successful, the efforts will be used to improve measurements of the Rydberg constant (a measure of the wavelengths of light emitted by the atom), which in turn, may help resolve the puzzle of the proton radius. The project will explore new pathways for high-precision spectroscopy and the determination of fundamental constants, both by advancing the fundamental theory and by working in close collaboration with experimental groups. New pathways for the two-photon excitation of bound Rydberg states through an unbound intermediate state will be explored. This will allow for very precise measurements of transition frequencies among Rydberg states of hydrogen, thereby conclusively addressing any remaining questions surrounding the so-called proton radius puzzle. Another aspect of the project concerns numerical calculations of quantum electrodynamic corrections to the spectrum of hydrogen-like systems, and to the g factor of bound electrons, which use ultra-precise numerical analysis to advance understanding of the bound states and to look for tiny deviations of theoretical predictions and experimental results, with concomitant discovery potential for New Physics. The project will also address the predictive limits of quantum field theory and the "diminishing returns" that are incurred when calculations of so-called Feynman diagrams become infeasible in view of computational limitations in higher orders of perturbation theory. Through an enhanced understanding of the nature of the perturbative expansion, pathways for overcoming the predictive limits of quantum field theory will be explored using a combination of path-integral methods, dispersion relations and a saddle-point expansion, which identify the asymptotic form of the perturbative expansions for very large orders of perturbation theory. 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

Lightweight cold-formed steel shear panels are widely used for the construction of nonresidential buildings across the United States due to their high strength-to-weight ratio and ease of assembly. However, these structural components are vulnerable to buckling during seismic events, which can lead to significant damage or collapse. While restoring the post-earthquake structural integrity of affected buildings is critical for community resilience; however, repairing buckled elements, particularly those with globally deformed cross-sections, remains a significant challenge. This grant for Engineering Research Initiation (ERI) will support research that explores a novel repair technique that combines thermal straightening and reinforcement with basalt fiber-reinforced polymers to restore the strength and stability of buckled cold-formed steel members. By addressing a critical gap in current repair methodologies, this work has the potential to improve post-earthquake recovery efforts, reduce economic losses, and enhance the sustainability of steel structures. The findings from this study will support the development of durable retrofitting strategies, ultimately advancing national welfare in disaster resilience and infrastructure sustainability. This award will contribute to NSF's statutory role in the National Earthquake Hazards Reduction Program (NEHRP). This project will develop a science-based analytical model to predict the cyclic performance of repaired cold-formed steel lipped channels under seismic loading. A key focus will be to model the bond-slip behavior at the interface between fibers and steel, which governs the effectiveness of repairs. The research will integrate full-scale experimental testing and numerical simulations to characterize the impact of thermal processes on surface bonding and develop a nonlinear hysteretic bond-slip model. The validated model will be used to generate seismic fragility functions for repaired structures, enabling the assessment of their resilience against multiple seismic events. The outcomes of this study will contribute to the broader field of earthquake engineering by providing a systematic approach to evaluate and enhance the performance of retrofitted cold-formed steel shear walls. Additionally, the research will yield practical insights for engineers and manufacturers, facilitating the adoption of more resilient and sustainable repair techniques for steel structures. Project data will be archived in the NHERI Data Depot (https://www.DesignSafe-ci.org). 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

Modern scientific enterprises generate enormous volumes of data, as many scientific fields have transitioned from being data poor to data rich recently. To make sense of this data, the scientific enterprise relies on new computational methods and computing infrastructure to realize new insights. In other words, the computational methods used for large scale data analysis are now tools of the scientific community as they are central to the knowledge generation process. This project aims to address a major limitation in large scale data analysis by designing and implementing novel methods that employ computational storage devices, which combine the storage inside of a computer with a configurable processor that resides on the storage device itself. This project will yield new insights into the efficient use of these computational storage devices and will demonstrate how they can be used to remedy the challenges faced in several scientific domains. The project will benefit society by training students in the use of cutting edge technologies that are of national importance and which have great potential to bolster the U.S. economy. Computational storage devices are an emerging technology for accelerating large scale data intensive computations because they combine storage with field programmable gate arrays. Relocating computational tasks from the host processor to a computational storage device eliminates data movement between the computational storage device and the central processor unit's main memory over the bandwidth constrained system interconnect, thereby reducing traffic, saving energy, and reducing latency. Using this paradigm can result in unprecedented acceleration of scientific applications that process large datasets, have data dependent performance characteristics, and have non uniform data access patterns. This project will conduct a design space exploration for selected computational tasks in order to identify the kernels and design parameters that provide the best acceleration for a system with multi core CPUs equipped with computational storage devices. The project will design and implement several indexing data structures having a wide range of properties that make them suitable across numerous application scenarios that are optimized for computational storage devices. The project will create and disseminate a user friendly programming framework that provides data parallel primitives for computational storage devices including map, reduce, filter and join, and collective communication functions including scatter, gather, and all to all communication. Furthermore, using our framework and the indexing data structures, we will design and implement range queries, similarity searches, and joins on points and polygon objects which are highly valuable for several scientific fields including astronomy, heliophysics, geoscience, and other geospatial domains. 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 Engineering Research Initiation (ERI) project support research that aims to enable rapid engineering design optimization involving complex design requirements. Engineering design is an approach for optimizing objective(s) (e.g., minimizing the drag of an airplane) while fulfilling design requirements (e.g., maintaining a lower limit of the lift generated by the airplane). Engineering design has achieved success in broad areas, including automobiles, aircraft, spacecraft, energy systems, and manufacturing equipment. Conventional design optimization, however, faces the following two challenges in practical applications. 1) Objective values and design requirements typically need to be computed hundreds or even thousands of times, which significantly slows down the optimization process. 2) Practical optimization typically involves complex design requirements, which make it difficult to find real optimal solutions. To overcome these challenges, this project will investigate developing an approach that ensures all design requirements are automatically fulfilled during the optimization process. If successful, this approach can reduce optimization complexity and increase optimization efficiency. This project seeks to develop a generative artificial intelligence approach for design requirement fulfillment, along with surrogate models to enable near real-time computation – ultimately supporting rapid engineering design. Generative artificial intelligence can learn from existing data (e.g., takeoff trajectories of electric drones) and generate similar data patterns (e.g., smooth takeoff trajectories without sudden drops). Surrogate models, also known as predictive models, are fast approximations of time-consuming mathematical models. In this project, generative artificial intelligence models will be guided by surrogate model predictions to assess whether design requirements are fulfilled by the generated designs. This feedback loop allows the generative artificial intelligence to generate designs that inherently fulfill all design requirements during optimization. Performance of the developed approach will be evaluated using an optimal takeoff trajectory design of an electric drone verified using NASA’s Dymos framework, as well as on two aerodynamic design benchmark problems developed by the AIAA Aerodynamic Design Optimization Discussion Group. In summary, this research project seeks to enable rapid engineering design under complex design requirements while maintaining high accuracy on the resulting optimal solutions. Moreover, this project aims to discover new knowledge about how design requirements can be effectively fulfilled using generative artificial intelligence and surrogate 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.

NSF Awards · FY 2025 · 2025-05

The objective of this Rapid Response Research (RAPID) project is to collect time-sensitive data on how housing infrastructure, risk perception, and warning communication influence people’s ability to take protective action during nighttime tornadoes. On 14-15 March 2025, a deadly outbreak of nocturnal tornadoes occurred across the Midwestern United States. When tornado disasters occur at night, they significantly increase fatality and injury rates, as they are difficult for the public to receive warnings and take protective action, as many are often asleep in homes that may lack adequate wind-resistant features or safe sheltering options. By integrating tools and methods from civil engineering and the social and behavioral sciences, the project aims to advance interdisciplinary knowledge about nighttime tornado sheltering vulnerability and decision-making processes. Using a convergent mixed-methods design and guided by the Protective Action Decision Model (PADM), the project conducts engineering assessments of wind damage across residential housing types and collects survey data on how households responded to the tornado outbreak. Project findings provide new understanding of converging social, behavioral, and built environment factors that facilitate or hinder effective protective action during nighttime tornado threats and will inform public warning systems, safe housing guidance, and emergency planning. Results are broadly shared with weather forecasters, emergency managers, and researchers as well as supporting student training and education across engineering and social science 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 · 2025-04

One supercell thunderstorm may produce a tornado, but another storm with similar vorticity and storm relative helicity (SRH) may not, which is a long-standing mystery in meteorology. This means that vorticity and SRH may not be sufficient to characterize the tornadogenesis process. To help NWS meteorologists to better assess which supercell thunderstorm has a high potential to produce a tornado, besides vorticity and SRH, this project will investigate one more parameter, which is the wind velocity spectra in supercell thunderstorm, when the tornado is forming and going through its life cycle. The characteristics of this parameter will lay the foundation to test the Research Hypothesis (RH) related to tornadogenesis of this project: only when the rear flank downdraft (RFD) facilitates the vortex tubes in the mesocyclone in experiencing inverse energy cascade, that is, only when the energy in the vortex tubes is transferred from smaller vortex eddies to larger vortex eddies, via the high pressure squeezing of RFD to the vortex tubes near the ground, a tornado will form. The Research Objective of this project is to test this RH by applying the coupled CM1 and high-resolution LES simulation approach to simulate the entire tornado life cycle to understand whether inverse energy cascade is a necessary (or indispensable) condition for forming a tornado, investigating the role of inverse energy cascade in tornadogenesis. If this RH is verified to be correct, this can be used to determine whether a certain supercell thunderstorm has a high potential to produce a tornado or not, improving the extreme weather forecasting of NOAA NWS. This project may reveal the long-standing mystery of why some supercell thunderstorms can produce a tornado, while others with similar conditions cannot, by examining whether the vortex tubes near ground are experiencing inverse energy cascade, instead of energy cascade. In this project, first, the coupled simulation approach will be applied to numerically simulate a variety of tornadoes with different intensities, flow structures and surface roughness, through their entire tornado life cycle, from genesis, to maturation, and to dissipation/death, to extract wind velocity time histories in the wind field. Then, considering the non-stationary wind characteristics of tornadoes, at each measurement location, the extracted velocity time histories will be strategically segmented to obtain the energy spectra. The wind spectra will be compared along time to determine whether an inverse energy cascade occurs during a tornadogenesis and whether an energy cascade occurs during a tornado dissipation/death. The obtained spectra will also provide data to develop the function of tornadic wind spectrum in terms of surface roughness, height, radial distance from tornado center, mean wind speed at a certain radial distance (related to tornado intensity), and flow structure (swirl ratio). This project will offer a new way to interpret the genesis of the tornado spawn in a supercell thunderstorm. It will bridge the knowledge gap on how energy is transferred between large eddies and small eddies at each stage of tornado life cycle. The developed wind spectra will help establish the standardization of tornado simulation, including simulation using a tornado simulator in the lab and simulation using CFD (numerical). This will lead to accurate tornadic wind loading, which in turn will inform tornado hazard mitigation to enhance community resilience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The change in terrain along a tornado’s path is a potential contributor to the strength of a tornado and the damage that it produces. However, the internal complexity of tornadoes and the difficulty of making direct observations of tornadic winds near the surface has limited the study of this phenomena. In this project, the research team will do post-storm analyses of multiple tornadoes that affected areas in Missouri and Arkansas in March 2025 to help to understand how local, small-scale fluctuations in terrain may have affected tornado intensities and the damage sustained by buildings and infrastructure. The broader societal impact of the work is the potential to provide stakeholders with data about vulnerable locations and real-time damage estimates. Byproducts of the study will also include the validation of insurance loss models and an assessment of what species of trees are more likely to survive extreme winds. A significant tornado outbreak occurred in the central and southeastern United States on March 14-15, 2025. Multiple EF-2 to EF-4 rated tornadoes affected populated areas of Missouri and Arkansas. This rapid-response award is for detailed aerial and ground surveys of damage to structures and trees, beyond what is typically surveyed by the National Weather Service and partners. The collected data will be used alongside weather radar data and a digital elevation model to investigate the influence of terrain on tornado intensity and behavior. Specifically, the research team plans six interrelated tasks: Task 1: Collect the post-damage data, including aerial videos by flying drones and photos taken on the ground; Task 2: Process satellite data to obtain the overall damage condition for the region to check whether the survey data is complete; Task 3: Process the structural damage data to estimate wind speed and tornado intensity (EF scale); Task 4: Process the tree damage data to identify the wind direction and estimate tornado intensity; Task 5: Process the radar data to obtain wind speed aloft and extract its relationship to tornado behavior; Task 6: Establish the correlation among storm intensity aloft, terrain and structural damage condition (EF scale) and develop a preliminary machine learning (ML) model that can predict tornado impact. 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 · 2025-04

This Partnerships for Innovation – Research Partnerships (PFI-RP) project has the potential to improve pest and disease management in agriculture, contributing to efforts to address increased food production. By integrating Artificial Intelligence (AI), Internet of Things (IoT), machine learning, and affordable communication networks, this project aims to reduce the reliance on manual labor and outdated practices in pest management. This innovation will also enhance scientific and technological understanding of agricultural pest management, providing valuable data and insights that can inform biotechnology. By collaborating with agricultural university extensions, the project will help create new job opportunities in fields such as agricultural data analysis. The project aims to develop an automated pest management system for rural farms by introducing a cyber-agricultural system framework that integrates multi-modal sensor data and enables real-time pest detection directly on the edge devices. The system optimizes data processing by employing dynamic data and model compression algorithms, reducing the need for extensive communication bandwidth and computational resources. The primary objective is to automate the pest monitoring process, reducing the manual labor and costs associated with traditional scouting methods. The project addresses critical gaps in current pest management practices, such as the reliance on inconsistent data collection and the risks of excessive chemical treatments. The approach offers a scalable solution that can be deployed to overcome the limitations of existing digital monitoring services, often hampered by high operational costs. The anticipated technical outcomes include developing a privacy-preserving, AI-driven system that predicts pest migration patterns and provides proactive, real-time recommendations to growers through an Integrated Pest Management-dedicated chatbot. The system is designed to be a cost-effective and user-friendly solution that enhances decision-making, reduces pesticide reliance, and ultimately improves crop yield. 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 · 2025-03

2521203 (Awuah-Offei). The US has a critical minerals and materials problem. The decay of American mining, processing, and refining capacity has led to supply chain disruption risks for many minerals and materials needed for national defense and economic development. The US currently imports $102B worth of processed mineral materials (mostly critical minerals) that ultimately support $3.62 trillion in economic activity. The US heavily relies on imports of 38 of the 50 critical minerals, including 26 that China effectively monopolizes. Addressing this challenge requires government, academia, and private industry to work collaboratively to establish the right policy framework, conduct research and development, build mines and processing facilities (including recycling facilities), train and develop a workforce, and other initiatives to ensure resilient supply of critical minerals to support the nation. Missouri University of Science & Technology (Missouri S&T) will convene a hybrid in-person/virtual workshop on August 6–7, 2025 at Missouri S&T in Rolla, MO. The theme for the workshop is “Empowering a Vibrant Workforce: Leveraging Critical Minerals Research to Drive Innovation and Growth.” The workshop will start with a keynote session on workforce development that sets the tone for the entire workshop. Following the keynote session will be a panel discussion on educational programs to prepare the workforce for the critical minerals sector. The workshop will then have three sessions on exploration and engineering, processing, and recycling, and policy. All these sections will have discussions on workforce, which will be the main theme for the entire 2025 workshop. All sessions will have facilitated breakout sessions that allow participants to collaboratively generate ideas to address the critical minerals and materials challenges. The combination of carefully selected keynotes to initiate the discussion and the facilitated breakout sessions promote vibrant interactions that that are anticipated to generate promising strategies. Supply of critical minerals and materials is recognized as a fundamental challenge by governments and industry. However, the nature of the problem requires cross-disciplinary discussions and theoretical framing to pose the right questions, which can lead to productive research directions. This challenge requires science-based policy in addition to fundamental and use-inspired research to overcome technical hurdles. This workshop will continue to bring together a wide-spectrum of stakeholders to discuss fundamental gaps and provide a roadmap to convergent research themes for researchers to address. The workshop will tackle issues related to workforce development and policies needed to increase participation in the critical minerals and materials field. This workshop addresses an issue of tremendous national and international importance. The geopolitical ramifications of critical minerals and materials supply chains cannot be overstated. 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 · 2025-03

This I-Corps project is based on the development of a sensor-equipped smart chair for early detection and monitoring of dementia. Currently, smart watch-based or video surveillance-based approaches cannot be used to diagnose or quantify the stage of dementia and can violate patient privacy while traditional diagnostic methods, such as brain imaging or cognitive assessments administered by experts, require trained personnel and medical equipment. This technology uses a non-invasive approach coupled with multi-modal data including physiological and cognitive markers associated with dementia. The solution is envisioned to be used in memory care facilities, veterans' homes, homes with elderly members, and hospitals, making it accessible to a wide range of users. This technology may reduce the financial burden and economic impact associated with dementia diagnosis, intervention, and care management. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a dementia detection system integrating physiological and cognitive markers through a non-invasive smart chair. This technology uses advanced machine learning algorithms to monitor early behavioral and cognitive changes, providing real-time analysis and improving detection accuracy. This technology may be used to track and analyze the progression and trends of dementia as well as identifying the disease stage. In addition, the solution integrates multi-dimensional data and significantly reduces false alarms. The technology may improve the limitations of traditional medical diagnostics through brain magnetic resonance imaging (MRI) and standard cognitive assessments as well as reduce costs, improve early-stage detection, and improve patient care and management. 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

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry Professor Pablo Sobrado of Missouri University of Science and Technology & Professor John Tanner of the University of Missouri Columbia are studying enzymes from plants that synthesize specialized molecules needed for defense against biotic and abiotic stresses and that endow plants with unique flavor and nutritional profiles. The conceptional organizing principle of the project is a focus on a group of enzymes known as flavin-dependent monooxygenases (FMOs), which leverage a derivative of vitamin B2, known as flavin, to catalyze a diverse array of chemical reactions. The project will study plant FMOs involved in the biosynthesis of the hormone auxin and sulfur-containing compounds that contribute to the unique taste of garlic. The project will explore a novel hypothesis concerning the role of molecular motion in the mechanism by which FMOs catalyze hydroxylation reactions. These activities seek to serve as a platform for teaching and training graduate, undergraduate, and high school students to enable them to develop critical thinking skills and synthesize knowledge in molecular biology, mechanistic enzymology, organic chemistry, computational biochemistry, and structural biology. This research project seeks to establish structure-function relationships for FMOs using a combination of biochemical and structural approaches. The chemical and kinetic mechanisms of FMOs will be quantitatively characterized using advanced transient-state kinetic methods. The three-dimensional atomic structures of FMOs will be determined using high-resolution X-ray crystallography. The combination of these approaches is synergistic and seek to generate new insight into how evolution solved difficult chemical problems using flavins as reaction centers. Ultimately, this research seeks to inspire the rational design of new catalysts and the engineering of biochemical pathways to produce high-value compounds, and lead to the development of crops with improved root development, more robust leaf morphogenesis and embryogenesis, and better response to abiotic stress. 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 2024 · 2024-11

With the support of the Chemical Structure, Dynamics, and Mechanisms-A (CSDM-A) Program in the Division of Chemistry, the research group of Professor Greg Tschumper at the University of Mississippi will carry out a computational chemistry study of subtle attractive interactions between molecules, specifically of non-covalent or non-bonding interactions. Although non-covalent interactions are generally much weaker, their collective contributions are central to a host of chemical processes in physics, chemistry and biology, from the structure of DNA to aerosols in the atmosphere and natural gas clathrate hydrates on the ocean floor. The team will use computational chemistry methods based on quantum mechanics (QM) to study both fundamental aspects of non-covalent interactions as well as practical applications for organic semiconducting devices. More broadly, the potential impact of this work includes accelerating the development of novel materials for flexible electronics, clean energy technologies, water purification, water harvesting and more. This research will contribute to the education and training of science students from underrepresented groups at the graduate and undergraduate levels, and will provide research experiences for community college students through a collaboration with Dr. Jeremy Carr of Central Alabama Community College (CACC). This research project in the Tschumper group will examine the use of halogen, chalcogen and pnictogen bonding to produce unique morphologies in nanoscale molecular assemblies for organic semiconducting devices as well as how these non-covalent interactions alter the opto-electronic properties of the components. This research team will also employ high-accuracy QM techniques to probe fundamental aspects of hydrogen bonding in clusters of water and small prototypes that provide important test systems for theoretically and experimentally studying cooperative effects associated with these non-covalent interactions. Emphasis is placed on the evaluation of spectroscopic signatures of these interactions to facilitate direct comparison to experimental measurements, and the ongoing development of the N-body:Many-body integrated QM:QM method facilitates the application of these demanding high-accuracy procedures to larger clusters with modest computational resources. 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 2024 · 2024-10

Federated Learning (FL) has emerged as a popular distributed machine learning paradigm in a wide range of sectors (e.g., healthcare, fintech, and autonomous driving) because of its potential of protecting people’s privacy - it does not require gathering all the data in one place for operation. Meanwhile, driven by the increasing ubiquity of mobile devices, FL applications are shifting from wall-plug powered artificial intelligence (AI) devices to battery-powered mobile AI systems (e.g., smartphones, tablets, wearables). Existing research largely ignores the role battery energy awareness plays in efficient FL training over mobile AI systems. This project addresses this challenge and innovates on developing an energy-efficient FL framework for mobile AI systems, making these systems more suitable for execution on everyday mobile devices without draining their batteries quickly. The project's broader significance and importance are its potential to advance mobile computing and AI technologies, ensuring both are energy-efficient and privacy-preserving. Furthermore, this project shares its research artifacts and results with the community and includes educational activities targeting under-represented groups in computing. This project investigates the efficiency, quality, and robustness of FL systems from an energy perspective, aiming to develop a comprehensive energy-efficient FL framework for mobile AI systems. The research is structured around three synergistic objectives. First, the project develops a universal energy estimation methodology applicable across a variety of devices engaged in FL training, incorporating Deep Neural Network (DNN) models with diverse architectures. Next, utilizing insights into energy consumption, the project explores strategies to enhance the energy efficiency of FL, particularly in high-speed communication scenarios such as autonomous driving and augmented/virtual reality. Additionally, the project integrates learning performance metrics, such as accuracy and latency, with energy parameters—including energy consumption and battery life—in the FL participant selection process. This integration aims to create a balanced and optimized learning environment. To support these goals, the project establishes a mobile AI testbed and energy measurement setup, equipped with real-world FL benchmarks and workloads. 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 2024 · 2024-10

Despite the growing success of Machine Learning (ML) systems in accomplishing complex tasks, their increasing use in making or aiding consequential decisions that affect people’s lives (e.g., university admission, healthcare, predictive policing) raises concerns about potential discriminatory practices. Unfair outcomes in ML systems result from historical biases in the data used to train them. A learning algorithm designed merely to minimize prediction error may inherit or even exacerbate such biases; particularly when observed attributes of individuals, critical for generating accurate decisions, are biased by their group identities (e.g., race or gender) due to existing social and cultural inequalities. Understanding and measuring these biases-- at the data level-- is a challenging yet crucial problem, leading to constructive insights and methodologies for debiasing the data and adapting the learning system to minimize discrimination, as well as raising the need for policy changes and infrastructural development. This project aims to establish a comprehensive framework for precisely quantifying the marginal impact of individuals’ attributes on accuracy and unfairness of decisions, using tools from information and game theories and causal inference, along with legal and social science definitions of fairness. This multi-disciplinary effort will provide guidelines and design insights for practitioners in the field of fair data-driven automated systems and inform the public debate on social consequences of artificial intelligence. The majority of previous work formulates the algorithmic fairness problem from the viewpoint of the learning algorithm by enforcing a statistical or counterfactual fairness constraint on the learner’s outcome and designing a learner that meets it. As the considered fairness problem originates from biased data, merely adding constraints to the prediction task might not provide a holistic view of its fundamental limitations. This project looks at the fairness problem through different lens, where instead of asking “for a given learner, how can we achieve fairness”?, it asks “for a given dataset, what are the inherent tradeoffs in the data, and based on these, what is the best learner we can design”?. In supervised learning models, the challenge in the proposed problem lies in the complex structures of correlation/causation among individuals’ attributes (covariates), their group identities (protected features), the target variable (label), and the prediction outcome (decision). In analyzing the dataset, the marginal impacts of covariates on the accuracy and discrimination of decisions are quantified from the data, via carefully designed measures accounting for the complex correlation/causation structures among variables and the inherent tension between accuracy and fairness objectives. Subsequently, methods to exploit the quantified impacts in guiding downstream ML systems to improve their achievable accuracy-fairness tradeoff will be investigated. Importantly, the proposed framework provides explainable solutions, where the inclusion of certain attributes in the learning system is explained by their importance for accurate as well as fair decisions. 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 2024 · 2024-09

Poromechanics plays an important role across various disciplines, including geosciences, medicine, and biophysics. A classical and widely used poromechanics model is the Biot’s model. Multiple-network poroelastic theory (MPET) has been introduced into poromechanics as a generalization of Biot’s theory. Over the past decade, MPET has found diverse applications in medicine and biomechanics, making it an active area of research. However, the computational complexity of multiple-network poromechanics models poses challenges, particularly due to the wide-ranging variations in physical parameters encountered in practical applications. Another computational challenge in poromechanics lies in preserving fundamental physical laws such as mass conservation and constitutive laws, as well as the conservation of angular momentum in numerical solutions. This project provides excellent opportunities for graduate and underrepresented undergraduate students to gain multidisciplinary training in physics, computational mathematics, and deep learning, and to develop cutting-edge numerical algorithms for applications in science and engineering. Furthermore, the research is conducted at Missouri University of Science and Technology, contributing positively to the rural Midwest region surrounding the institute. This project is devoted to a systematic study of physics-oriented numerical solutions for poromechanics, mainly focusing on MPET model and includes: i) developing physics-oriented parameter robust and more practical numerical discretizations strongly preserving the mass conservation in poromechanics and preserving the symmetry of the tress tensor exactly in those discretizations; ii) designing physics-oriented parameter robust and ecient iterative methods for linear system arising from those discretizations including preconditioning, multigrid methods and fixed-stress methods; iii) developing physics-oriented deep learning numerical methods in the cases the physical parameters are anisotropic, discontinuous, or even perform as randomly distributed function on a domain with complex geometry. 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.