University of Utah

NSF Awards · FY 2025 · 2025-03

This project aims to develop a detailed 3D model of the Los Angeles (LA) Basin, critical for accurately estimating earthquake ground motion and assessing seismic hazard for this densely populated area. The LA metropolitan area sits above a deep sedimentary basin that significantly affects the level of ground shaking from local and regional earthquakes. The basin has a complex tectonic history of extension and compression and is also crosscut by numerous faults. This has resulted in a complicated subsurface structure that leads to significant variation in site amplification of seismic waves across the basin, as evidenced by the recorded ground shaking following the 2019 Ridgecrest earthquake. In the summer of 2022, a temporary 300-node geophone array was deployed across the entire LA basin, providing a seismic data set with uniform and dense coverage for the first time. In this project, the researchers will analyze these data and combine them with other available seismic and gravity observations in the region to construct a detailed 3D basin model. The model will help explain the level of amplification in different areas of the basin as well as its lateral variations The resulting model may be used to model ground motion for realistic earthquake scenarios, which is vital for evaluating infrastructure preparedness. In addition, this project will explore the connection between the resulting seismic velocity model with mapped geological features, and the tectonic evolution of the LA Basin. Through the research, the project will support graduate and postdoctoral education, and the scientific findings will contribute to seismic hazard assessment efforts in southern California. The density of the 300 sensor LA nodal array deployed in 2022 will enable the use of novel passive seismic imaging methods. By extracting Rayleigh and Love surface waves from multi-component ambient noise correlations, it will be possible to measure their velocity dispersion and Rayleigh wave ellipticity in the region. Techniques based on particle motion and apparent slowness will be developed to isolate different modes of surface waves. Both isotropic as well as radially and azimuthally anisotropic basin structures will be investigated using surface wave measurements. By using receiver function and autocorrelation methods, in addition to surface wave properties, it will be possible to determine crustal discontinuities, including major intra-basin sedimentary interfaces, the bottom of the basin, and the shape of the Moho beneath it. The use of gravity data will guide the identification of converted and reflected phases and determine the tectonic extension that the basin has experienced, enabling evaluation of the extent of thermal subsidence that has occurred within it. The dataset that will be analyzed is unique in an academic setting for its density, regularity, and completeness of coverage, allowing for the exploration of new methodologies. These include mapping shallow seismicity to identify possible unknown faults, using reflected surface waves to map the properties of faults within the basin as well as discover new ones, and determining aspects of the stress field through anisotropy. The 3D basin model constructed in this project is expected to be more accurate than the current community velocity models (CVMs), enabling more reliable ground motion predictions for various earthquake rupture scenarios. The new model will be validated through simulations of recent earthquakes. 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 · 2025-02

Glass is an important material for numerous applications due to its unique properties: it is an excellent electrical insulator, optically transparent, chemically inert, and has a smooth surface that resists contamination. Innovations in glass materials and manufacturing have consistently transformed technologies, from thin, flexible glass revolutionizing interactive displays to optical fibers reshaping telecommunications. Recent advances in printable glass promise to expand its utility in microfluidics, sensors, and the creation of hollow molds for shaping other materials. Printable glass manufacturing, a bottom-up process, overcomes the limitations of traditional top-down fabrication, potentially offering unprecedented precision and efficiency. This research project addresses the challenge of developing scalable manufacturing processes for printable glass, integrating laser processing to enable transformative applications, including sensors, nanosatellite optics, and microstructural engineering. The collaboration brings together leading researchers from the United States, Ireland, and Northern Ireland, fostering international partnerships and providing participating student researchers with exceptional collaborative research experiences. This work will develop a scalable, laser-driven manufacturing process for printable glass, leveraging the interplay of additive and subtractive manufacturing technologies. The approach combines advanced beam shaping and femtosecond lasers. This research is grounded on multi-physics based computational modeling and design, which will enable a fundamental understanding of the complex dynamics in the process. The process will be applied to fabricate a planar resonant sensor, potentially extending into other applications such as micromechanical sensors and nanoscale optics. Compared to traditional methods, the researched approach promises up to eighty percent reduction in energy consumption for glass manufacturing. Furthermore, potential contributions of this project include advancements in scalable glass manufacturing, integration of glass structures with next-generation devices, and transformative impacts on mechanical sensing and advanced optics fabrication. 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 · 2025-02

This award provides student travel support for the 2025 International Workshop on Verification of Scientific Software (VSS) to be held as a satellite workshop of ETAPS 2025 (International Joint Conferences on Theory and Practice of Software), on Sunday, May 4, 2025, at McMaster University, Hamilton, Ontario, Canada. The VSS workshop provides an important and valuable educational opportunity for students to study foundational topics that lie at the intersection of scientific computing and verification. The significance and importance of the workshop arises because scientific software plays an increasingly important role in scientific and engineering disciplines: Weather prediction, drug discovery, the design of buildings, vehicles, and aircraft, simulations of astrophysical phenomena, and prediction of seismic activity are some of the many applications. In these contexts, verification itself must overcome new challenges posed by resilience, floating-point computations, and massive parallelism. The significance and importance of the workshop include disseminating novel ideas for implementing and reasoning about reliable scientific software; building international community and cooperation in foundational research areas; and enhancing education of US students, including underrepresented groups, by exposure to and interaction with leading-edge research and researchers. By supporting US-based students, the school will thus train the next generation of researchers and practitioners in both industry and academia. 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 · 2025-02

Many people with amputated arms stop using their prosthetic devices because they lack a sense of touch, making everyday tasks difficult and less fulfilling. This project aims to develop artificial touch that feels natural and is easy to use for people with bionic arms. This natural feeling will be created through pulsed electrical stimulation that mimics the natural nerve signals in the skin. By using low-cost electrodes on the skin surface, this technology can be more easily commercialized and integrated with prosthetic arms, unlike technologies that require surgical implantation. Additionally, this project supports a collaboration between a primarily undergraduate institution and a major research university, allowing undergraduate students to engage in high-impact research to enhance their education and career opportunities while at the same time exposing graduate students to potential careers at primarily undergraduate institutions. Noninvasive neurostimulation to restore touch often feels electrical, unnatural and unpleasant. This proposal develops novel biomimetic electrocutaneous (EC) stimulation algorithms to enhance the naturalness of sensations and improve functional performance and reduce cognitive load during closed-loop sensorimotor tasks (e.g., fragile object manipulation). To create natural feeling sensory percepts, several obstacles unique to noninvasive EC stimuli need to be overcome. Because pulse frequencies above 50 Hz feel more natural but higher pulse frequencies are less able to convey a change in stimulus strength, frequency modulation alone reduces the range of perceived intensities. However, variations in electrical conductivity and build-up of electric charge at the electrode-skin interface can create unpleasant and painful sensations. Therefore, this research explores novel multi-modal biomimetic stimulation paradigms designed to avoid activation of pain or itch nerve fibers while also providing a useful range of stimulation intensity. Individuals with transradial amputation will be recruited at both a primarily undergraduate institution and an R1 research university to test the biomimetic EC stimulation algorithms while controlling bionic arms with electromyographic signals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The goal of this project is to empower Artificial Intelligence (AI) researchers to more easily search, discover, and use AI-ready data sets. This will potentially streamline and democratize AI research. The project will investigate and develop data discovery services using innovative techniques that are themselves based on AI methods and can extract data set information from scientific papers. The resulting discovery services will be integrated into the National Data Platform Pilot (NSF award #2333609), providing scientists and students with an end-to-end research environment that connects them to national computing and storage resources. The project will conduct outreach and training efforts that will engage both scientists and students, particularly those at minority-serving institutions, who will help evaluate the technology. This project advances data search and discovery capabilities by using AI techniques to automatically extract and store data citation information, which must frequently be inferred, from research publications. This capability will help scientists and students, particularly those new to AI research, to identify AI-ready data sets that are relevant to their research from related publications. This removes startup impediments to creating new AI pipelines. Integrating these search and discovery services into the National Data Platform Pilot will enable users to more seamlessly conduct AI research on national-scale research resources that can scale beyond their personal computing and storage. The project uses AI-ready datasets from the National Artificial Intelligence Research Resource (NAIRR) to demonstrate and evaluate the effectiveness of the service. It also develops a generalized approach to support the integration of additional AI-ready NAIRR datasets and open corpora. The project democratizes the discovery and use of data in support of AI and other research through outreach and community engagement activities, including integration with hands-on workshops and hackathons within the National Data Platform Pilot. It also supports evaluating and reporting on the use and value of data by automatically producing usage statistics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The National Science Foundation (NSF) Computational Mathematics (CompMath) Principal Investigators (PI) Meeting, "NSF Computational Mathematics Meeting 2025" will be held on May 8 - 9, 2025, at the University of Utah, in Salt Lake City, Utah. The meeting will bring together program officers of the NSF Division of Mathematical Sciences (DMS) CompMath program, program officers from related programs, and researchers working in computational mathematics and related fields. Participation is open to all including those already funded by the NSF CompMath program, as well as participants seeking funds from the NSF, such as early career faculty, postdoctoral fellows, and graduate students. The major goals of the meeting are to provide a forum for the NSF CompMath-sponsored researchers to showcase their projects regarding intellectual merit and broader impacts, to raise awareness of the breadth of the program's topics and their impacts, and to allow the computational mathematics community to assess the programs in their entirety. In addition, the meeting is intended to facilitate the exchange of ideas and spur collaboration on the development of crucial insights into future directions of computational mathematics, to broaden the expertise of the community by introducing junior researchers to the NSF CompMath program, and to help communicate to the public the scope of the impacts of the computational mathematics field. The meeting is open to all interested in computational mathematics. The NSF DMS CompMath program supports fundamental mathematical, applied, and interdisciplinary research in diverse areas where computation plays a central and crucial role. Algorithms and numerical simulations have long become necessary and unavoidable for the description, analysis, and predictions of real-world phenomena. However, the proliferation of computation continues at an accelerated pace, aided by advances in available computational power. The unprecedented growth in the scope of applications of computational mathematics highlights the need for continuing progress in the development of revolutionary algorithms to address, for example, a broad range of complex multiscale and multiphysics problems to maintain the pace of scientific, engineering, technological, and societal discoveries. This unique meeting and forum for the computational mathematics discipline will provide invaluable overviews of the broad spectrum of research topics within the field and showcase many achievements of the projects funded by the NSF CompMath program. The meeting will help to further strengthen the computational mathematics community by creating a supportive and engaging atmosphere for new interactions and collaborations among participants. In addition, the meeting will provide an important platform for exposing junior investigators, from graduate students to postdoctoral researchers and early-career faculty, to all the exciting directions of modern computational mathematics, as well as will give them an opportunity to learn more about the NSF DMS CompMath program and various funding options to support the research and educational activities in the area. The focus areas of the program for the NSF CompMath meeting 2025 range from more classical areas to novel emergent directions. Topics include the design of numerical algorithms for the solution of mathematical models based on differential equations, the development of algorithms for inverse problems, numerical analysis, scientific computing, optimization, mathematical aspects of data science and artificial intelligence, mathematical and computational aspects of the development of digital twins, quantum computing, and applications of these numerical analysis and computational tools for the solutions of pressing scientific, engineering, and societal problems. The meeting website: https://sites.google.com/gcloud.utah.edu/nsfcompmath-meeting-2025/home 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

Neuromuscular disorders affect millions of individuals worldwide, yet efficient tools to accelerate diagnosis and assess therapeutic interventions are currently lacking. Existing methods for evaluating muscle health face significant limitations, including clinical impracticality due to cumbersome procedures, reliance on highly trained personnel, high costs, and safety concerns stemming from associated pain or the use of ionizing radiation. The emergence of electrical impedance myography (EIM) offers a promising avenue for assessing muscle health. EIM is sensitive to changes in muscle structure and composition brought about by a variety of neuromuscular disorders as well as by disuse, producing unique disease signatures that will vary with muscle status. Thus, EIM analysis can provide a method to rapidly, quantitatively, and reliably diagnose and monitor neuromuscular diseases at the bedside, act as a tool to help tailor care for individual patients and streamline and improve clinical drug trials. This CAREER project integrates research with educational outreach by offering students hands-on experience in innovative translational research. This is interlaced with a long-term educational objective of mentoring new generations of students by providing them with experiences in cutting-edge research, developing and implementing activity-based style courses to motivate students’ self-learning in the classroom, encouraging students to choose a science, technology, engineering or math (STEM) degree by participating in research, and assisting undergraduate students in their own translational research efforts. This CAREER project will establish the scientific foundations of future generation EIM tools and enhance diagnostic accuracy by integrating artificial intelligence algorithms with simulation and analytical methods to extract quantitative muscle insights that are currently inaccessible. The tools developed and data collected are expected to lead to a deeper understanding of the role played by muscle electrical properties in EIM, understanding that is needed for the development of new and more accurate EIM tools for evaluating neuromuscular disorders (NMD). Research objectives include (1) developing a physics-informed analytical and simulation framework to model the entangled multicellular architecture underlying tissues and automate the extraction of relevant physical and biological information from EIM data, (2) assessing EIM biological variability in silico, and (3) evaluating the robustness of models generating EIM data. In silico simulations and ex vivo measurements will provide proof of principle to optimally determine the minimal yet sufficiently biophysical relevant mechanisms needed to build robust virtual EIM predictions for healthy and prototypical diseased conditions necessary to interpret EIM outcomes in patients. 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

There is an ongoing need for tailored, porous materials for a variety of applications including biomedical materials, energy materials, semiconductors, and high-strength low-weight composites, to name a few. One promising advanced manufacturing technique to produce these tailored, porous materials is freeze casting, which is also known as ice templating. Upon freezing, the growing ice crystals template a low-viscosity colloidal slurry. In particular, the reliance of freeze casting on the solidification of low-viscosity, often water-based, colloidal slurries allow for applied energized fields to easily impact the process. This allows for the potential for the application of energized fields to apply user-defined microstructures and associated physical properties on the resultant porous materials generated by the process. This award supports fundamental research to investigate currently used energized field sources, specifically electrical, magnetic, and ultrasound, in combination with freeze casting. In particular, as these energized fields each will interact with the colloidal slurry based on different physics, both their direct impacts and interactions will be characterized. Once completed, this knowledge base will provide a foundation for the use of energized fields to create tailored materials both in freeze casting and within other advanced manufacturing processes that employ colloidal slurries, such as tape casting, injection molding, and deposition modeling. This award will also provide education and research opportunities for undergraduate student researchers. This grant will support basic research into understanding the operating space of an advanced manufacturing process that includes all current forms of external energized fields, such as electrical, magnetic, and ultrasound, applied simultaneously to the freeze-casting process, which is referred to as Mixed-Energized Field (MEF) Freeze Casting. The application of these energized fields and their interactions will be explored experimentally through a factorial study and characterized by microstructural and physical property measurements. In addition, a constitutive theory of the MEF Freeze Casting process will be developed based on the physics of each component of the process that allows for prediction of the final structure and properties of the tailored, porous materials fabricated with the MEF Freeze Casting process. This new constitutive theory will be used to identify the energized-field parameters that will produce the most favorable properties in the final tailored, porous materials. 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 · 2024-12

Power grids are fundamental to modern civilization. However, power grids not only distribute electricity, but they also generate power line emissions (PLEs) at the fundamental frequency of 50/60 Hz as well as power line harmonic radiation (PLHR) at harmonic frequencies. These emissions propagate into and through the ionosphere where they can be detected by satellites. As power grids have continued to expand and increase in complexity over time, PLEs / PLHR have become widely recognized as a significant source of artificial electromagnetic pollution in the near-Earth environment. Physical measurements of PLEs and PLHR have been collected over many years, but conflicting conclusions have been made by different groups as the data has been analyzed. Further, there is a lack of agreement on the propagation mechanism of low-latitude (where most power grids are located) whistler mode waves (the propagation mode of PLEs / PLHR in the ionosphere). The research plans to undertake a systematic study to advance our understanding of how PLEs and PLHR couple to and propagate through the complex ionosphere to put these emissions on a firmer physical basis when they are measured either intentionally or unintentionally by satellites. Improving the quality of these studies is vital for developing more robust and advanced communication, remote sensing, ground-based backup navigation etc. All these systems are critical for our nation’s success. To help understand the behavior and characteristics of these power line emissions and higher frequency harmonics, the work aims at generating Maxwell’s equations models of how this noise propagates through the ionosphere, which is a magnetized plasma. Three specific objectives that are planned to be achieved by employing the robust, grid-based finite-difference time-domain (FDTD) method are: 1. Analyze the coupling of PLEs / PLHR from the atmosphere to the inhomogeneous ionosphere, especially for varying magnetic field directions and ducting conditions. 2. Determine the propagation mechanism for low-latitudes whistlers. 3. Characterize the impact of complex ionospheric inhomogeneities on PLEs / PLHR at varying altitudes and latitudes, such as equatorial plasma bubbles, ducts, and polar cap patches. The research will assist a wide variety of science missions to better anticipate and isolate PLEs and PLHR in their measured data. Numerous studies depend on electromagnetic measurements in the frequency range of PLEs and PLHR, including studies of lightning, sprites, the Van Allen radiation belts, and the effect of solar and cosmic activity on the ionosphere. 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 · 2024-12

Distributed intelligence with large amounts of local measurement data hinges on the design of devices that are capable of sensing, processing, and exchanging local information. Reliable decision-making in a collective manner is, however, a challenging task in resource-constrained scenarios, where devices are limited by communication costs and/or processing power. This project aims to develop a new framework for designing efficient decentralized algorithms such that the average proportion of wrong decisions in the network is bounded by a prescribed target threshold. This line of research has implications for a broad range of real-world applications, including environmental monitoring using battery-powered mobile sensors, coordination of unmanned aerial vehicles for target tracking, and multimedia wireless sensor networks in surveillance. The project will also provide mentoring and training of future algorithm designers. This project investigates the structural properties of optimal decision rules under the false discovery rate (FDR) control, which provides guidance for new co-design of summary statistics and aggregation mechanisms, thereby enabling efficient decentralized processing in resource-constrained environments. The research program will explore three main thrusts: (i) develop communication-efficient algorithms (measured in bits) for multi-hop networks with provable FDR control; (ii) characterize computation-efficient approximations of the optimal decision rule in both the finite-sample and asymptotic regimes; and (iii) develop distributed feature selection with FDR control when all the features are shared among devices, focusing on privacy, robustness, and computation efficiency. 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 · 2024-12

Data is central to the scientific process and a key enabler of innovations and discoveries across all disciplines, especially given the increasing use of computational techniques, including artificial intelligence and machine learning (AI/ML). This project provides a pilot data archiving and disaster management service as part of a more holistic data cyberinfrastructure plan to serve the various institutions in the State of Utah. This cyberinfrastructure serves the data needs of academic institutions across the State of Utah while also integrating with national cyberinfrastructure resources in a manner that does not impact the local performance, local capacity, and local security requirements. Science areas supported include material manufacturing, genetics, biology, large language models, cardiac disease, image learning, human health, visual sciences, astronomy, climate, asthma and air quality, evolution, preserving scientific reproducibility, biomedicine, and others. The data cyberinfrastructure will support the entire data lifecycle including data management, sharing, and broad and equitable data access to ensure that data is Findable, Accessible, Interoperable and Reusable (FAIR). The project also addresses the growing data services and support needs of multiple academic institutions across the state through live instructor online training courses, development and dissemination of specific training material, in-person and online one-on-one sessions for faculty and students, events highlighting integration with national resources, and outreach activities providing university students with hands-on experiences. The project deploys data cyberinfrastructure comprising a Ceph-based S3 object storage system at the University of Utah and an integrated offsite disaster recovery and archive storage infrastructure at the Tonaquint Data Center in St. George, Utah. This regional system federates with national cyberinfrastructure including the Open Science Data Federation (OSDF) Pelican Platform, and the National Data Platform via the direct peering of the Utah Education and Telehealth Network (UETN) with Internet2. 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 support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Long Luo and his group at Wayne State University are working to develop a powerful new approach for the identification and quantitative analysis of an important class of chemical compounds. Specifically, they are targeting molecules known as surfactants, which are widely used as emulsifiers, detergents, fabric softeners, and wetting agents in many household cleaners and industrial products and processes. Naturally occurring surfactants such as glycolipids, lipopeptides, lipoproteins, fatty acids, neutral lipids, and phospholipids are also essential to the functioning of biological systems. Current methods for surfactant analysis do not detect specific products (selectivity) and low concentrations (sensitivity). Professor Luo and his group are developing new electrochemical approaches to address these challenges. They are also working on applying the tools for on-site detection of polyfluoroalkyl substances (PFAS), a group of emerging surfactants that are contaminants of concern in drinking water. The integrated educational aims of this work seek to increase STEM awareness, interest, and career preparedness across a range of ages, educational levels, and socioeconomic and cultural backgrounds by performing science shows at public locations; developing STEM exposure programs for middle and high school students; and establishing and running a student chapter of the Electrochemical Society at Wayne State University. Notably, the team will reach out to the public about the relevance of science in everyday life by organizing and performing a science show at the Great Lakes Crossing Outlets Mall and Detroit Zoo, the two largest family attractions in Michigan. Limited selectivity and sensitivity have been a long-standing problem in surfactant analysis. To address these issues, the Luo group is using the gas bubble-surfactant interaction to establish a selective and sensitive detection method coupled with rapid and efficient preconcentration. Specifically, the unique amphiphilic characteristics of surfactants affect electrochemical gas bubble nucleation, which in turn impacts an electrochemical signal used for the quantification of surfactants. An approach mimicking sea-spray aerosol enrichment is used to preconcentrate the surfactants. These features are being combined in a portable device to address the unmet need for on-site detection of PFAS in drinking water, addressing concerns arising from U.S. Environmental Protection Agency identification of PFAS quantitation as a national priority. Broader impacts of the educational plan include increasing public interest in science and technology, promoting middle and high school students’ engagement in analytical chemistry activities, and boosting undergraduate and graduate students’ interest in electrochemistry. 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

Laser technology is one of the most transformative inventions of the modern era, which has become an indispensable tool for scientific research and technological innovation - revolutionizing the semiconductor industry, telecommunications, healthcare, and defense. However, current laser design and manufacturing approaches remain stagnant, stymieing further breakthroughs. Developing novel integrated systems of laser architectures, components, and techniques leveraging digital twins (DT) is imperative to expand frontiers in intensity, wavelength regime, and high average power. This project will fill this gap using state-of-the-art predictive and generative artificial intelligence (AI) coupled with physical principles and high-fidelity, close-loop, rapid feedback between digital models and physical systems. Graduate students and postdoctoral researchers will also be integrated within the research team as part of the training of the next generation of scientists required to advance the field. This project will develop theoretical foundations for AI-assisted DTs to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering (HPLSE) to enable efficient design, failure and performance prediction, operational optimization, and emerging lasing conditions. Laser technologies are extremely complex to model because they rely on a cascaded set of mode-locked laser dynamics and a manifold of architectures and configurations of chirped pulse amplification, and nonlinear optical stages, such as parametric amplification. Their architectural complexity and multi-dimensional data far exceed current modeling and analysis tools. The project will address these challenges by (1) extracting reduced representation of scientific data from experiments or high-fidelity HPLSE simulation, (2) building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification, and (3) developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems. The project will be AI-focused, multi-disciplinary, and involve a diverse workforce of future scientists and engineers. The project will also include an education thrust to integrate the research results into interdisciplinary education. The project will bolster AI foundations and its application curricula at both UCLA and the University of Utah. More critically, it will forge a robust collaboration among mathematics, data science, and laser 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 2024 · 2024-10

Cloud technologies underpin many of the apps and computing services that we use every day, including social networking, online commerce, email, video conferencing, and many more. To be able to do research that drives forward the fundamental architecture of the cloud, scientists need an environment in which they can experiment with the primary building blocks of the cloud: compute power, storage, and networks. CloudLab provides an environment where researchers can build their own clouds, observing and changing how they operate all the way to the bottom layers of software. CloudLab is a large facility, consisting, as of the start of this award, of more than 1,800 servers hosted at the University of Utah, Clemson University, and the University of Wisconsin-Madison. These servers include cutting-edge compute (such as several different processor architectures, graphics processing units, and field programmable gate arrays), networking (OpenFlow, user-programmable switching), and storage (hard drives, solid state drives) technologies. This phase IV award will support the expansion of this facility by several hundred servers to meet high demand. It will also add new technologies, such as Internet of Things devices, programmable network cards, and non-volatile random access memory, which will in turn support research on data-intensive computing and computing at the edge of the network. CloudLab is available to researchers who work on the fundamentals of cloud computing. These users do research in areas such as networking, security, and databases; in turn, this work has broad impact, as these are the fundamental technologies upon which we build things such as smart cities, telehealth, online education, and other socially important computer services. Because it is offered at no cost for research and education to academic users, it acts as a level playing field on which institutions large and small, and those with many resources and those with few, can conduct work on an equal footing. CloudLab can be found online at https://cloudlab.us/, which is the primary interface through which research users interact with the facility, making it accessible from anywhere. This award will support continued operation of the facility, including the aforementioned hardware expansion, development of new features, and user support. 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

This project contributes 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 the University of Utah. The University of Utah is the flagship, doctoral-granting, research-intensive institution leading the Responsible AI initiative in the state of Utah. Over its six-year duration, this project seeks to fund two-year scholarships for 31 unique full-time students who are pursuing graduate degrees at the Master and Doctoral levels in NSF-supported STEM fields of Cognitive/Neuroscience, Geography, Sociology, Anthropology, Economics, and Human Developmental Sciences, and Statistics. The project aims to build an interdisciplinary talent pool of graduate students seeking careers with data science and analytics skills to meet workforce needs in the United States. Using a cohort-based model and proseminar format, the project intends to adapt mentorship strategies to promote success, develop workshops to democratize data access and analysis, and engage in industry partnerships to promote the professional formation of graduate students. A significant outcome of this project is the contribution of new knowledge to gaps in our understanding of the academic, social, and institutional factors that promote the success of diverse low-income graduate students in data science programs. The broader impacts of this project include the dual goals of increasing access to advanced degree opportunities for low-income graduate students and serving the diverse needs of the regional and national workforce. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The three specific aims of the program include: 1) preparing students with hands-on skills in data science, statistics, visualization, and data management, 2) using an asset-based approach to support the holistic success of low-income graduate students, with targeted outreach to, and support for, student veterans and 3) providing graduate students with career placement guidance, internships, and job preparation. We expect to find that holistic mentorship supports graduate student resilience and addresses emotional regulation throughout their graduate trajectories in response to stressors. The project seeks to also lead to new knowledge about workforce development for data science career placement among graduate students including student veterans. This project intends to leverage a Federal Research Data Center to empower students to identify, use, and link large datasets to address the grand challenges of today and the future. Scholars should benefit from interdisciplinary "methods labs" that seek to advance innovation in research question development and from emergent applications for artificial intelligence (AI) in big data sets. A professional evaluator brings the expertise needed to provide formative feedback iteratively so adjustments can be made to ensure timely progression toward the project’s aims. The project team leaders intend to disseminate findings via traditional (conference and journal) and electronic (podcasts and social media) modes of communication, including a scholars web portal. Consistent with other narrative approaches that feature student success at the University of Utah, the project team aims to produce short documentaries for dissemination to be featured via social media and on the Scholars website. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented 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 2024 · 2024-10

The future of science-enabled discoveries critically relies on the speed of high-performance simulations conducted at large scales and high resolutions. Unfortunately, lacking such performance and scale, current approaches cannot keep up with the backlog of problems in areas of paramount societal consequence, such as climate science and the spread of pandemics. A principal reason for these shortfalls is the rising cost of moving huge amounts of simulation data between supercomputer memories and processors – a cost that increasingly dwarfs the time spent in actual computations. Thus, developing techniques to reduce the volume of data exchanged without sacrificing accuracy is key to future progress in computation-enabled research. Such data reduction is even more important in the emerging area of Scientific Machine Learning (SciML), where simulations are assisted by artificial intelligence (AI) based surrogate models, an area where the data exchange needs are often much higher. The investigators’ expertise in scientific machine learning, data compression, compilers, and program correctness will be central in our collaboration to help SciOPT achieve its goal of fast and reliable AI-assisted scientific simulations. The impact of this project will be to establish new technologies that reduce data volume without sacrificing accuracy in both high-performance computing and the emerging area of SciML. These technologies, in turn, translate directly into societal benefits such as improved healthcare and safer environments. The project will increase participation in this area by offering undergraduate research opportunities to students. This research project, entitled SciOPT, will principally rely on data compression to reduce the amount of data moved: simulation data will be compressed before transmission and decoded upon reception before applying computations. The investigators will also pursue the potentially even more impactful approach of compressing the data and applying computations directly on the compressed data. SciOPT will evaluate both of these approaches in the context of challenging SciML applications that are currently bottlenecked by data exchanges. To ensure higher degrees of automation and productivity, SciOPT will develop efficient compiler-based methods to manage compressed data layout and locality. Moreover, it will automatically generate high-speed compression algorithms that are tailored to the data. To ensure the veracity of the computational results produced by these compressed-data simulations, SciOPT will include rigorous correctness-checking methods at multiple stages to guard the overall simulation workflows. 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

Data visualizations---from simple charts and graphs to complex models and dashboards---are used by millions to make sense of and communicate data. However, designing effective data visualizations requires a unique combination of skills, including statistics, graphic design, and expertise within a target domain such as medicine, public health, or engineering. This project aims to enable computers to serve as more reliable and robust assistants that provide guidance and feedback to help analysts make effective visualization design decisions. A central goal is to develop ``provably effective'' visualization analyses that can be tested against current “best practices”, theoretical models, and experimental data. This approach enables us to automate visualization design decisions that already have strong support within the scientific literature. By integrating these automated features into visualization tools, we can help thousands of analysts quickly navigate millions of data-driven decisions in their daily work. By uniting existing scientific theories under a single framework, we can also help researchers implement their findings within new and existing data visualization tools as well as rigorously test these tools to ensure they behave as intended. To reach this goal, the project targets three interleaved technical challenges, bridging reasoning about user goals and knowledge, formal visualization specifications, and actual visual output. The first track models a user's prior knowledge and goals as knowledge graphs to reason about visualization strategies. Given a task context, a formal space of visualization specifications can be searched to identify those that accord with both the task and perceptual design guidelines. The second track concerns specification-level visualization reasoning by incorporating richer notions of task and data developed in the first track, as well as by creating design knowledge bases via novel methods for identifying gaps and learning both design constraints and their weights. However, reasoning only about specifications stops short of the visual output that people see. In response, the third track develops an operational semantics of visualization to analyze and validate the effects of specification changes on graphical output. This approach enables deduction of new chart-level design constraints and systems-level optimizations. The project combines the results of these tracks into an integrated system for visualization design reasoning, which in turn will be applied to support scalable visualization and multi-view dashboard designs. Resulting tools and ideas will be disseminated through open-source software, tutorials, and visualization course curricula. 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

Recent high-profile software-borne security breaches show that scientific research institutions are particularly targeted for their proximity to national security interests such as nuclear energy. Unfortunately, scientific software security is concerningly overlooked: despite having many exploitable security vulnerabilities and growing calls for more stringent secure development practices, the scientific community currently lacks the suitable tools to thoroughly vet their software. As much of the software world has embraced the vulnerability-finding strategy known as “fuzzing”, this project aims to transition recent advancements in cybersecurity, software engineering, and computer systems to enable thorough, systematic fuzzing of today’s complex scientific software. The outcomes of this proposal will enhance the overall security of scientific software—reducing the likelihood of future software-borne security breaches against the users, communities, and institutions that use it. Existing fuzzing tools generally target small, single-language code with well-known input specifications, and thus fail to support the often multi-language, large, and esoteric nature of scientific software. Accordingly, this work aims to tackle these asymmetries by introducing (1) performant instrumentation with cross-language support; (2) fully-automated synthesis of thorough fuzzing harnesses; and (3) automated mining of formal input specifications. Beyond their release to the broader scientific software community, the tools and techniques resulting from this project are projected to be deployed on large-scale cyberinfrastructure through UVA’s ACCORD initiative as well as collaborating National Lab partners. 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

Research funded by this award aims to enhance current understanding of the compaction of granular soils, a critical construction process for many civil infrastructure systems. Compaction is the most common method of soil improvement for these soils. Yet, traditional compaction often relies heavily on engineering experience and post-construction quality control, leading to under or over-compaction problems in the field. This research project will provide new insights into the effects of granular soil properties and compaction equipment characteristics on compaction efficiency, which may lead to more efficient construction practices and reduced carbon footprints of civil infrastructure systems. This project is a collaborative effort between two Penn State campuses, Altoona, primarily an undergraduate institution, and University Park, a research institution. It will provide substantive research experiences to undergraduate students from Altoona, exposing them to contemporary knowledge such as sensing technology and data transmission. These experiences will enrich the engineering curricula at both campuses. In particular, the improved curriculum will benefit the Rail Transportation Engineering program at Penn State – Altoona, the nation’s first and only four-year bachelor’s degree program in railroad transportation. The central hypothesis of this research is that particle kinematics can be used as a proxy of soil compaction, rather than surface settlement, to study the state of compaction in granular soils. This hypothesis will be tested through an integrated experimental and numerical investigation. The project will involve laboratory compaction tests, which will be instrumented with geophones, accelerometers, a linear variable differential transducer, and a load cell; these instruments will record the dynamic response of soil in the compaction zone and the reaction force to the compactor due to soil-compactor interaction. In particular, wireless sensing devices, SmartRocks, will be embedded at various locations in the compaction zone to record the evolution of particle kinematics (e.g., acceleration and rotation) during compaction. The compaction test results will be used to calibrate and validate a computing model based on the idea of fusing SmartRock measurements and discrete element simulations to increase the accuracy of the simulations. The validated computing model will be used to extend the insights gained from the laboratory tests to field conditions that resemble the compaction of a moving vibratory roller compactor. This research will, for the first time, yield insights into the effect of granular soil properties, equipment characteristics, and operating frequency on the particle kinematic behavior (e.g., rotation, acceleration, and contact stress) in different zones during compaction. 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

Social integration (interconnection to others and location within the social network) has been associated to health, survival, and reproductive success in several social species, including humans and non-human primates. Despite its importance, the pathways through which non-human primates develop social integration are poorly understood. This study addresses this issue through the longitudinal study of a social non-human primate species that has significant commonalities with humans (e.g., fission-fusion dynamics and cooperative infant care). The study follows individuals from birth to adulthood, analyzes allomaternal (non-mother) care, and assesses how social bonds form, and how individuals become integrated into social networks. The longitudinal nature of the study allows for the examination of social integration changes, as well as its impacts, across the life span. The study develops educational materials for the public and K-12 students. The study also trains students from diverse geographic regions. To investigate how early rearing environments and maternal social networks impact the origins and development of social integration the study focuses on a non-human primate species with high variability in rearing strategies. Given that individuals from this species are born in litters the study compares sibling, that shared the same rearing environment, as well as non-siblings. The selected species has a short developmental period that allows for the longitudinal study of social integration across the lifespan. Researchers collect data on rearing strategies (single vs. communal nesting, allomaternal care presence/absence as well as quantity), social integration (through social network analysis), and rearing choices. Ultimately, the study evaluates the impact of allomaternal care and cooperative breeding on the evolution of social integration. This research is supported by the Biological Anthropology and Animal Behavior 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 2024 · 2024-09

Civil and construction engineering students may encounter various construction components, including structural elements, materials, equipment, and operations, in their everyday lives, such as when walking in an urban environment. These unstructured observations can offer great learning opportunities; however, without expert support, it is unlikely that students will effectively learn from such observations on their own. If educators were physically available during students' everyday activities, they could direct students’ attention to the main construction components and explain their observations in real-time. However, since this is not feasible in the real world, this project aims to design, develop, and test a transformative learning system that uses Artificial Intelligence (AI) as an on-demand educator. The envisioned AI-enhanced learning system relies on a digital platform in the form of a mobile application. When students face a construction project, they can look at the project through their smartphones using the mobile app, which will help students learn from their observations by 1) directing their attention to the main construction components they encounter in their everyday life or formal site visits, 2) explaining the observations, 3) linking the observations to students’ formal engineering education materials available on web-based learning management systems, and 4) generating automated reports about students’ observations and performance for instructors to help them adjust the course activities accordingly. To promote equity and accessibility in education, the mobile app will be designed to operate on the most basic and affordable smartphones and will use color palettes compatible with the needs of users with color vision deficiency (CVD), along with subtitles and audio narrations. The envisioned AI-enhanced learning system will be designed based on the Activity Learning Theory, which asserts that the human mind is an integral part of environmental interactions and positions activity—whether sensory, mental, or physical—as a precursor to learning. The AI-enhanced platform will be designed based on human-centered principles and will operate using a novel hybrid image-audio processing system that can efficiently and effectively recognize and classify various construction components. In addition to integrating imagery and audio data through this novel hybrid approach, the project will introduce two major technological innovations in audio processing and sound recognition. First, the hybrid use of collected audio and imagery data will improve the overall performance of the system by capturing a more comprehensive range of construction components and operations. Second, by using innovative audio processing and signal source separation algorithms, the need for multiple microphones will be eliminated, enabling the entire system to be encapsulated in a single device (i.e., a student’s smartphone) with the ability to sense and analyze audio signals from distances of up to 100 feet. Throughout this project, the proposed AI-enhanced teaching and learning approach will be implemented in multiple undergraduate construction engineering courses to empirically evaluate its effectiveness on students’ learning processes and outcomes, as well as the perceptions of both students and educators regarding this innovation as a formal pedagogical method. Although the AI-enhanced learning platform will be developed in the context of construction engineering, the proposed learning method and the intellectual merit of this project can be transferred to other disciplines. This project will also assess the broader applicability of the proposed innovation. This project is funded by the “Research on Innovative Technologies for Enhanced Learning (RITEL)” program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

The many microbial species that colonize plants are in competition with one another on the plant. To win this battle, pathogenic plant microbes employ diverse weapons capable of killing neighboring microbes that belong to other strains without harming microbes of the same strain. The goal of this project is to determine how a potent and common microbe-killing weapon is targeted to some bacteria and not others. If the mechanism of targeting is understood at a basic molecular level, it may become possible to engineer novel antibiotics that kill only certain pathogens without harming “good” bacteria. The project has significant potential to benefit society. Foremost is the security of food to feed people. The mechanisms discovered in this research will apply to food crops and the pathogens that destroy those crops. Discoveries about the evolution of pathogens and how they can be coerced into fighting each other will help agricultural scientists protect food crops of the future. Additionally, the molecules central to this plant-bacteria system are relevant to animals and humans. Understanding how plant pathogens identify and kill each other has the potential for precise control of human pathogens, even those that are rapidly developing resistance to traditional antibiotics. The proposed work will enroll trainees with the NSF-funded STEM Ambassador Program. Each trainee will identify scientifically underserved community groups, design and execute outreach activities, and measure the effectiveness to increase understanding and appreciation of science among nonscientists. In this project a team of scientists at two sites, in the US and the UK, will work together to unravel mechanisms that Pseudomonas pathogens of plants use to target and kill one another. Bacteria that invade and cause disease in plants make use of molecular killing machines that were derived originally from the tail apparatus of bacteriophages - the viruses of bacteria - and are therefore called tailocins. Tailocins exhibit high specificity in their killing - the tailocins made by a plant pathogen do not attack plant cells or structures, instead kill a subset of other competing bacteria, and somehow avoid killing bacteria of the same strain. Intellectual Merit: The hypothesis to be tested by this project is that killing specificity depends on a particular molecular receptor built into the lipopolysaccharide (LPS) component of the outer membrane. Differences in the carbohydrate composition of the LPS between bacterial strains and species render some strains susceptible to specific tailocins while others are resistant. In this project the LPS differences between bacterial strains that confer resistance and susceptibility to tailocins will be elucidated through a combination of synergistic approaches including plant infection studies, biochemistry, and bioinformatic analysis. In total, the project will discover mechanisms - evolutionary and structural - that suppress a common pathogen in plants and in so doing will address fundamental questions of how to prevent the spread of single strains of bacteria, and how to maintain microbial diversity. 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

This new REU Site: Intelligent Systems for Healthcare, Imaging, Communications and Infrastructure is hosted by the University of Utah. Intelligent systems perceive and respond to the world around them. They can be used to better utilize scarce resources, and provide increased reliability, comfort, and convenience This REU Site connects ten students each summer with mentors and research projects relating to intelligent systems featuring wearable and implantable healthcare, communications and infrastructure, and vision and imaging. REU participants will be able to engage in opportunities to test and improve implantable sensors related to traumatic brain injury; develop computer codes to help prosthetic limbs on amputees move as intended based on the person’s thoughts; increase agricultural yields on smart farms; enhance measurement data analysis capabilities to improve our understanding of how the atmosphere changes over time; and develop better algorithms to help vehicles see and navigate through challenging environments. Through project activities, undergraduates will better understand how this research benefits society by improving the intelligence capabilities of systems in the areas of healthcare, communications, infrastructure, vision, and imaging. Machine learning will increasingly be integrated into intelligent systems and/or be involved in the development of those systems. REU participants will engage in hands-on experiences with machine learning. Students will receive professional development sessions that will enable them to explore STEM career development opportunities, prepare for graduate studies, and practice public speaking skills with feedback. One unique feature of the project will have REU participants pursue the idea of entrepreneurship. They will be housed at Lassonde Studios, located at the Lassonde Institute (which has helped hundreds of students launch startup companies). Faculty mentors will provide insights and advice to students for pursuing a variety of STEM careers. Lastly, participants will attend workshops organized by the Office of Undergraduate Research (OUR), attend a Grad School Mini Expo, and present their research at the culminating Research Symposium. 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

Part 1 Under what conditions do states surveil and censor their citizens? How are the two tactics related to each other and other forms of repression and control? To what extent have states concealed their use of these tactics? States have increasingly wielded surveillance and censorship, both digital and physical, as tools of political influence at home and abroad. Yet, there exist scant theoretical and empirical advances to help understand these phenomena. Consequently, scholars know little of how and when states employ these levers and how their use has evolved with technological advancements. To address this critical knowledge gap, the investigators produce, analyze, and disseminate a novel dataset, the Global Surveillance and Censorship Scores (GSCS) database. The project utilizes mixed methods to collate quantitative and qualitative historical and contemporary data, including a diverse set of existing human rights reports. The resulting dataset allows academics, practitioners, and policymakers to advance the study of human rights and repression. The project has key implications for American national security and policy, ranging from finance and healthcare to human and drug trafficking, which have been affected by surveillance and censorship practices. Part 2 Surveillance and censorship are key levers of power to control information. States have increasingly wielded them, digitally and physically, to compete for political influence at home and abroad. Yet, scant theoretical and empirical advances exist to help understand the phenomena. Consequently, we know little of how and when actors employ these levers, and how their use as repressive techniques evolve with technological advancements in the 21st century. The investigators use mixed-methods to collect quantitative and qualitative historical and contemporary data to develop the Global Surveillance and Censorship Scores (GSCS) database. Information is extracted on surveillance and censorship from a diverse set of existing human rights reports and other documents. Given the clandestine nature of surveillance and censorship, a latent variable models developed to address missing information and to assess the sensitivity of the model estimates to understand the extent to which states conceal the use of such tools. This Bayesian latent variable model is able to predict cases of missing information and aggregate information into country-year estimates. To further address bias in the reports, the investigators also conduct case studies using expert information, interviews, and archival research to validate the data. The project will allow researchers to generate new theories and empirical evidence to advance the study of human rights, censorship, and surveillance. The data and analytic deliverables will serve as a public good for the community of academics, practitioners, and policymakers. Surveillance and censorship increasingly shape geopolitics by controlling and manipulating information; understanding the evolution of censorship and surveillance can thus contribute to the development of sound foreign and security policies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professor Jessica Kramer of the University of Utah is studying the development of cellular glycocalyx models to understand processes that regulate cell growth and survival. The glycocalyx is a protein and sugar coating on the surface of cells. In locations such as the eyes, lungs, gastrointestinal and reproductive tract, the glycocalyx is rich in mucin proteins, which are also found in mucus. The mucin glycocalyx has complex biological functions and diverse roles in health and disease but has not been systematically studied at the chemical level. Through the course of this project, the PI seeks to create chemically tunable models of the glycocalyx and apply them to study cellular pathways essential for life. This pursuit allows graduate and undergraduate students to acquire specialized training in sugar and protein chemistry, as well as cell biology. This project is also integrated into an outreach program adaptable for K-12 students to learn about the building blocks of life. Mucin glycoproteins are crucial for life but challenging to study due to their inherent chemical heterogeneity. These rigid proteins span the cell membrane and perform complex biological functions on both the extracellular and cytosolic sides of the bilayer. During this project, the PI will employ chemoenzymatic techniques to synthesize a series of chemically and mechanically tunable mucin glycodomains. These synthetic mucins will be used to engineer the glycocalyx of live cells by attaching the synthetic glycodomains to their surfaces through a combination of genetic engineering and chemical conjugation. This approach seeks to enable a systematic investigation of mucin glycocalyx-mediated cellular signaling functions both intra- and extracellularly. The PI will utilize their glycocalyx model to explore how glycan-mediated clustering initiates biochemical signaling and the role of mucins in regulating cell extrusion events that maintain epithelial homeostasis. The ultimate objective of this research is to develop tools for studying the glycocalyx and to contribute to the understanding of its fundamental biochemical and biophysical roles. 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.