University of Minnesota-Twin Cities

NSF Awards · FY 2025 · 2025-10

The wireless research community continues to face major challenges in conducting rigorous, repeatable experiments to evaluate next-generation wireless networks and Internet of Things (IoT) systems. Existing testbeds are limited in availability and often fixed to specific environments, making it difficult for researchers to test how their innovations perform under different conditions. This project addresses these challenges by creating UnionLabs, a new cloud-based federation of wireless testbeds that aims to democratize access to experimental research resources. By enabling seamless remote access to testbeds distributed across multiple U.S. institutions, UnionLabs promotes wider participation in wireless research. It also helps accelerate research in areas such as Artificial Intelligence and Machine Learning (AI/ML) for autonomous systems, mobile edge computing, and spectrum sharing. Through integration into university courses, hands-on training opportunities, and public workshops, UnionLabs supports both workforce development and broader engagement. The project establishes an innovative two-tier infrastructure that combines a centralized public cloud platform with edge computing resources located at individual testbed sites. A federation plane hosted on Amazon Web Services (AWS) enables seamless integration and remote access to geographically distributed experimental facilities through a unified web-based interface. To validate and demonstrate the scalability of this platform, UnionLabs will federate testbeds across four institutions with complementary strengths including University at Buffalo (5G and UAV systems), University of Florida and University of Utah (IoT technologies), and Northeastern University (programmable 5G and beyond systems). The platform’s standardized federation APIs will support easy onboarding of additional grassroots testbeds over time, laying the foundation for a dynamic and sustainable national research infrastructure. 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

Traditional machine learning often involves collecting data from multiple sources, which can raise significant privacy concerns. One approach has emerged as a promising solution to solve this challenge by enabling models to be trained across many different sources without directly sharing private data. This approach has become particularly valuable in sensitive sectors such as medical diagnostics, where individual data privacy is legally protected. Despite these advancements, existing systems for training models across multiple sources lack standardized assessment tools, posing challenges to research reproducibility, validation, and trust. Without proper testing tools, organizations cannot verify that their privacy protections work as intended, creating barriers to adoption in critical areas like healthcare, finance, and national security. This project addresses this challenge by developing comprehensive testing tools that ensure privacy-preserving artificial intelligence systems work reliably, serving the national interest by enabling secure collaboration on AI development while protecting individual privacy, supporting American competitiveness in artificial intelligence technologies, and strengthening data security across critical infrastructure. This project designs, develops, and sustains FLTest, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems, addressing gaps often overlooked by traditional tools. The research activities include developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration vulnerability detection systems, and building recommendation engines for optimization. The testbed's key innovation streamlines evaluations through automated orchestration assisted by a pitfall checker that detects configuration issues and vulnerabilities in privacy evaluations. FLTest empowers both novice and expert users with actionable insights tailored to real-world applications. The team will validate FLTest across multiple domains and datasets, develop standardized benchmarks for assessment, and create detailed reporting mechanisms for security analysis. By utilizing distinct datasets and offering a standardized solution, FLTest verifies model privacy and robustness across heterogeneous data distributions, supporting the development of reliable privacy-preserving federated learning systems. The project includes collaboration with three industry partners to ensure practical adoption and long-term sustainability. 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

Twenty-first century research is enabled by the availability of vast amounts of data, collected across a wide range of temporal and spatial scales, often in real time. Entire fleets of satellites, drones and other devices monitor the Earth at ever-increasing resolution, adding to the enormous corpus of maps and metadata that enable sciences from geology to biodiversity. While citizen science - or crowdsourcing science - has been successfully leveraged over the past several decades to close the analysis gap arising from large amounts of data, the sheer scale and complexity of these new data sets presents new challenges. This project addresses these challenges through novel Citizen Science Cyberinfrastructure (CSCI) where new tools and techniques, including teaming humans with Artificial Intelligence (AI), are being developed to enable researchers to efficiently extract the best results from large and complex data sets. This effort incorporates mapping, machine learning, and data sharing innovations in biodiversity, geoscience, and astronomy research and expands the capacity of research communities across a wide range of disciplines to use citizen science as a suitable, open and sustainable research methodology. This project leverages NSF-supported cyberinfrastructure, including the Zooniverse citizen science platform with its nearly three million volunteers, to provide a novel cyberinfrastructure by: (1) integrating mapping infrastructure into Zooniverse to accelerate accurate processing and curation of often complex data sets for geoscience and biodiversity projects; (2) providing an "incubator" hub for researchers and developers to design and deploy innovative citizen science projects with a fast production turn-around particularly for AI training; and (3) providing cyber-pathways to existing cyberinfrastructure, creating documentation for Zooniverse projects to responsibly deposit their data into appropriate repositories, and facilitating workshops for Zooniverse project teams to develop best practices for data and model sharing. To ensure wide dissemination of the new CSCI, a strong Community of Practice is engaged and supported throughout the project effort. The project is led by the University of Minnesota in collaboration with the Adler Planetarium and the University of Oxford, as core Zooniverse institutions and astrophysics expertise, and joined by the University of Florida and the Florida Museum of Natural History with expertise in biodiversity, museum specimen collections, and data repositories, as well as Northern Arizona University with expertise in Earth Sciences, mapping cyberinfrastructure, and the study of the Antarctica’s Dry Valleys. As the largest citizen science platform, and with key tools for human-in-the-loop data analysis tasks, the Zooniverse is uniquely positioned to develop skills and best practices to lower the barrier for research teams to responsibly share data and models. This is carried out through fact-finding and demonstration with several key groups who are exemplars in the data and model sharing ecosystem, including GitHub, the National Science Data Fabric (NSDF), the Global Biodiversity Information Facility (GBIF), and Open Science Chain. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Converting hydrogen and carbon-containing feedstocks, such as CO and CO2, to synthetic fuels would augment US energy security and independence. Specifically, CO and CO2 hydrogenation are critical components in the industrial production of methanol, one of the most important platform chemicals in the chemical industry. Improving our understanding of these reactions will improve the competitiveness of the US chemical industry while potentially lowering energy and capital costs. These reactions rely on catalytic conversion processes that occur at low temperatures and high pressures. The high pressure drives the CO and CO2 molecules onto the metal catalyst surface, resulting in crowded surfaces, where molecular interactions among bound intermediates play a key role in changing the reaction dynamics and activating strong chemical bonds. This project will examine the mechanistic role of densely covered surfaces in mediating the various CO and CO2 hydrogenation reactions. The effort will focus on two benchmark systems: methanol synthesis on Cu-based catalysts and methanation on Ni-based catalysts. These systems will be probed with complementary kinetic, spectroscopic, isotopic, and computational studies examining the role of these extended catalytic microenvironments. Educational videos discussing scientific principles and methods in catalysis research broadcast over social media channels accessible to researchers of all backgrounds will provide training and broaden awareness of science and engineering principles involved in the production of fuels. This research is based on the premise that higher entropic demands of bimolecular reactions among bound surface intermediates on densely crowded surfaces are compensated by lower energy barriers for catalytic reactions on such surfaces. The research efforts are motivated by previous work, which showed the high pressure and low temperature conditions of methanol synthesis over Cu-based catalysts result in Cu surfaces highly covered with H-adatoms. Such high H-coverages provide novel routes for CO2 activation via molecular intermediates that are only stable on surfaces with high H-adatom coverage. The project will involve the systematic, intentional generation of high H-atom coverage microenvironments. Microenvironments will be controlled by changing pressures, temperatures, gas composition, titrants, and surface composition. Reversibility formalisms, spectroscopic methods, and density functional theory (DFT) calculations will be used to develop a kinetic framework to understand the reaction environments. The collaborative computational and experimental nature of this project will provide unique opportunities for interdisciplinary education and training of undergraduate and graduate 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 · 2025-09

The Geospace Environment Modeling (GEM) Workshops provide a uniquely valuable platform for community-driven collaborative research that complements traditional scientific conferences. The structure of GEM permits the identification of shared goals, the formulation of a path to address these goals, and the opportunity to make sustained and measurable progress towards them. This project will offer travel funds and mentoring programs to support scientists in the earliest stages of their career to successfully attend GEM, helping to ensure access for participants from all institutions, and the opportunity for participants to form invaluable connections to the community, develop the skills to succeed as a scientist, and form collaborations and connections with researchers from academia, national labs, and government agencies. Furthermore, GEM science contributes to the understanding and prediction of extreme space weather events, which is crucial for safeguarding humans and hardware in space, aircraft and passengers operating on polar routes, communication networks, and power infrastructure. By supporting scientific advances in space weather, the GEM Workshop serves the public interest and contributes to national resilience, economic security, and technological competitiveness. This project supports organizing the annual GEM workshop for a three-year term from 2026 through 2028. There are 5 Research Areas that make up the GEM Workshop, each focusing on particular aspects of the geospace environment: 1) Solar Wind - Magnetosphere Interaction (SWMI); 2) Magnetotail and Plasma Sheet (MPS); 3) Inner MAGnetosphere (IMAG); 4) Magnetosphere – Ionosphere Coupling (MIC); and 5) Global System Modeling (GSM). By collaboratively addressing the complex and coupled solar wind-magnetosphere-ionosphere-ground coupled system, the GEM Workshop supports NSF's goals for addressing grand scientific challenges through integrative approaches. The GEM Workshop plays a vital role in cultivating an open and collaborative scientific community and expanding opportunities for participation in science and engineering for all Americans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Science Museum of Minnesota (SMM), in partnership with the University of Minnesota (UMN) and Bakken Museum, is conducting a Practitioner-Driven Synthesis of Museum Family Learning Conversations (FLC) Research. The project team, which includes researchers, librarians, museum educators, and experience designers, aims to bridge research and practitioner knowledges to produce bidirectional insights for the future of museum design and museum-based learning research. The research involves gathering, scoping, and synthesizing 25 years of research and evaluation on Family Learning Conversations conducted in museum settings, and formally incorporating practitioner-generated knowledge to spark the next generation of design and research on family learning in museums. This project is structured differently than a traditional synthesis in order to achieve the broadest impacts. Museum practitioners drive the synthesis, contributing their own practice-based knowledge, questions, and critical commentary to each part of the synthesis work. For practitioners, literature syntheses that identify how their existing ideas relate to research findings provide ecologically grounded and accessible insights for design. For researchers, understanding how practitioners identify learning or valuable interactions distinct from what has been valued in FLC research historically provides fertile ground for innovation in research methods and research questions. By bringing researchers with expertise in informal learning, video-based discourse analysis, and evidence synthesis into collaboration with museum educators, designers, and evaluators from institutions of varying size and emphases, the project is synthesizing research knowledge and practice knowledge that is fundamentally practitioner-driven, making knowledge in informal learning research more accessible to practitioners, and generating new questions and insights for further practical and research development. This work contributes directly to advancing knowledge through synthesis of 25 years of museum Family Learning Conversations research in direct response to museum practitioners, while attending to how shifting values in museum education have shaped prior research and require new directions for future work. Using both rigorous literature synthesis processes and ongoing collaborative partnership with a cohort of museum educators, designers, and evaluators, the project is reactivating this literature and research approach, providing practical guidance for research design based in intertwined research and practitioner knowledges and charting a new vision forward for museum family learning research. The work involves first describing the existing knowledge of research and practitioners regarding design for FLC and then generating a focused mixed methods meta-synthesis on a practitioner-prioritized line-of-research. The project team is also engaging in a critical reflection on research methods used in FLC research and identifying underlying values shaping current knowledge and new avenues of investigation to further knowledge generation. Through targeted dissemination to museum practitioner and museum family learning researchers, the project aims to create bi-directional learning and to make the wealth of FLC research more accessible and interpretable through a rigorous synthesis that centers the values and priorities of museum practitioners at every stage of the work. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The 2026 Talbot Workshop will bring together senior researchers with around thirty early career mathematicians — graduate students and postdocs — for a week in an intensive and informal setting. Talbot has been a tradition for more than twenty years; each workshop since 2004 has focused on a topic that, at the time, brought participants to the forefront of topology research. The primary purpose of the workshop is research training and professional development: the goal is for junior researchers to teach each other about a contemporary research topic under the guidance of two more senior experts in the topic. Participants will give talks on a predetermined focused research area and develop a trusting and collaborative relationship with each other and the mentors. Workshop attendees will include mathematicians from a variety of institutions and backgrounds to promote the formation of new collaborative and pedagogical ties, all while exposing students and postdocs to an area of vibrant contemporary mathematics research. The focus on a single research topic, the collective nature — mentors and participants sharing meals, housing, and activities for a week — and graduate student organization make the Talbot Workshops unique among mathematics events. The workshop syllabus and notes from the talks will be posted on the workshop’s website: https://sites.google.com/view/talbotworkshop/current-talbot. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The accumulation of plastics in the environment is an urgent and critical problem. Plastics break down into micro- and nanoplastics, which readily infiltrate environmental media and biological tissues. They are found globally in natural environments and in many parts of the human body. However, they can be difficult to detect in real-world samples. This project will focus on a new mechanism for assessing how nanoplastics are released from bulk plastics. During and after their useful lifetime, plastics often slide over surfaces and collide with surfaces. For example, plastic litter slides over land surfaces driven by wind or stormwater. Plastics in moving sediments or agricultural soil collide repeatedly with hard surfaces. The project will use an experimental technique called atomic force microscopy to visualize on the nanoscale the deformation, abrasion and release of nanoplastics under conditions that simulate environmental circumstances. Factors that can influence nanoparticle release will be studied, including abrasion conditions, plastic properties, and pre-exposure of the plastic to sunlight. The research will be integrated with educational activities to broaden participation in STEM among K6-12 students. The team will partner with high schools in the Twin Cities school districts to develop novel research curricula that investigate weathering of plastic goods that general micro- and nanoparticles. The project will leverage the Industrial Partnership for Research in Interfacial and Materials Engineering platform to organize workshops for industrial experts, academics, and Minnesota policymakers to share knowledge, identify collaborations, and discuss knowledge gaps toward risk assessment and sustainable design of plastics Microplastics and nanoplastics are ubiquitous and have significant impacts on human health and ecosystem functions. Micro- and nanoplastics are primarily derived from the breakdown of plastics in natural and engineered systems. To inform risk and mitigation, it is critical to identify sources, release mechanisms, and rates of nanoplastic release from bulk plastic degradation during natural weathering. This project focuses on a less explored mechanism - surface abrasive wear at the nanoscale. Abrasive wear results from the penetration of a harder particle and/or protruding surface (i.e., asperity) into the softer plastic surface, causing plastic deformation and potentially debris removal in sliding contact. The central hypothesis is that plastics sliding over a surface with nanoroughness creates nanoscale wear and the release of nanometer-size debris. The project leverages the novel single-asperity nanoscratch method and other advanced atomic force microscopy modes to visualize the mechanism of nanoplastic generation and release from abrasive wear and to identify key physicochemical properties of nanoplastics that result in health impacts. Results will reveal how and why different abrasion conditions, plastic properties, and pre-exposure to sunlight shape the nanoplastic release profile, which can enable risk assessment and screening tools for the design of sustainable polymers. Engagement and networking with industry will accelerate the discovery of possible solutions to mitigate nanoplastic pollution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project supports research at the intersection of geometry, topology, and algebra as well as educational activities focused on local mathematical communities. The PI will investigate the relationship between the algebraic properties of groups and the geometric and topological properties of the spaces on which the groups act on. The educational activities of the project include a workshop for students and faculty in the upper Midwest region, a research seminar series bringing together geometers and topologists from nearby institutions, and supporting undergraduates from underrepresented groups through the Mathematics Project at Minnesota. The first research goal of the project is to study virtual properties of fundamental groups of hyperbolic manifolds, that is, properties of their finite-index subgroups since these correspond to finite-sheeted covers of the manifolds. The PI will consider the case of hyperbolic 3-manifolds as well as hyperbolic manifolds in higher dimensions with an emphasis on those constructed via arithmetic methods. The second research goal of the project is to explore the virtual properties of certain classes of groups which generalize and extend fundamental groups of hyperbolic manifolds. The PI will consider questions on the subgroup structure of these groups and their geometric actions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With funding from the Chemical Synthesis Program of the Division of Chemistry, Dr. Hoye and his research group at University of Minnesota will i) capitalize on underdeveloped or ii) discover entirely new reactions in organic chemistry. Alkynes are a class of compounds containing a carbon-carbon triple bond. They have inherently high potential energy, which means they can undergo chemical reactions that are more energetically (thermodynamically) favorable than for the functional groups present in many other types of organic chemicals. This reactivity is perhaps most familiar in the form of an acetylene (ethyne, the simplest alkyne) torch, which gives off considerably more heat than, say, methane (natural gas) when combusted with oxygen to give carbon dioxide and water. This energetic driving force can be used to design reactions using more complex alkynes to fuel the formation of reactive organic intermediates that undergo further reactions to give structurally interesting new molecules. These scientific advances have the possibility of opening new avenues for synthesis of compounds of value to other researchers engaged in the discovery of new compounds that have beneficial societal impact (e.g., pharmaceuticals, agrochemicals, or electronic/photonic device components). The student researchers who will engage in these studies will gain valuable skills from which they will launch their own careers. Dr. Hoye and his research group at University of Minnesota will capitalize on the potential energy housed by the alkyne functional group in designed organic reactants to serve as the thermodynamic driver to allow access to transient reactive intermediates. In addition to several ongoing investigations of aspects of triyne to benzyne conversion (the hexadehydro-Diels-Alder or HDDA reaction), they will exploit a recently discovered, two-alkyne-to-carbene reaction. Furthermore, unknown transformations of alkynes to produce, for example, nitrenes, highly strained ring systems, and cyclic 1,2-dienes or 1,2,3-trienes will be explored. The discovery of new reactions will continue to be the most important 'products' to emerge from this program. New and fundamentally important mechanistic knowledge will be produced in parallel with the synthetic advances. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

A research and education partnership in astronomy between two University of Minnesota campuses, at Morris (UMM) and at the Twin Cities (UMTC) and New Mexico Tech (NMT) will be developed. It is aimed at building a robust pathway for students at the institutions, in particular those at UMM, to enter and succeed in the field of astrophysics. The program will interweave the courses, workshops and research strengths at UMM, UMTC and NMT campuses. The projects will be data-intensive, leveraging the UMTC astrophysics faculty’s access to the most modern astrophysics datasets and computing resources, UMM faculty’s expertise in data science, and NMT’s access to the state-of-the-art facilities at the Magdalena Ridge Observatory. Both faculty and students will have opportunities for professional development including workshops and other activities designed to develop communication skills, as well as provide leadership, teamwork, and management skills. This partnership will provide a successful template that could be replicated in other programs and disciplines. Astrophysics has witnessed remarkable discoveries over the last decade, including the discovery of gravitational waves generated in mergers of binary systems of black holes and/or neutron stars. A series of such astrophysics research projects ranging from using gravitational wave-kilonova analysis to measure the accelerated expansion of the universe, to using the TURBO instrument at the Magdelana Ridge Observatory for gravitational-wave follow-up, to projects in space physics that will identify and follow solar flares, will be pursued by participating UMM students, mentored by interdisciplinary faculty and hosted at both UMTC and NMT campuses. The students will have the opportunity to spend multiple weeks at the Magdalena Ridge Observatory and visit other important astronomical sites and facilities. In education, a new Astrophysics Area of Concentration at UMM will be established at the beginning of the partnership. The project will also support post-baccalaureate and graduate students, develop summer camps and workshops for high school students, and pursue a series of activities aimed at promoting astrophysics (and science, more broadly) to the local communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Many naturally occurring microorganisms can produce soap-like substances, called biosurfactants, that alter how fluids and other substances in the liquids move through porous spaces such as soils and biological tissues. However, scientists still do not know how far and how fast these biosurfactants spread or how they redirect substances ranging from nutrients and pollutants to disease-causing bacteria. Yet, these microbial agents play important roles in the health of soils, plant roots, and even human lungs. This project tackles this knowledge gap by using laboratory models that mimic real soils to observe the microbes and biosurfactants in action and quantify their influence on the transport of fluids and dissolved chemicals. Outcomes from the project could support improved soil remediation strategies, more sustainable agricultural practices, and new methods to manage the spread of harmful bacteria. The project also promotes national goals in science and education by training undergraduate and graduate students, supporting interdisciplinary collaboration, and conducting outreach activities to help K-12 students appreciate fluid mechanics and microbiology and their relevance to daily life. This project integrates multiscale experiments and physics-based modeling to investigate how biosurfactants and the microbes that secrete them alter mass transport in unsaturated porous media. The goals are to: (1) quantify how time-dependent biosurfactant production alters transport behavior in two-dimensional porous systems; (2) characterize feedback loops between biosurfactant-induced flow, solute transport, and bacterial migration; and (3) measure and model biosurfactant-driven transport processes in three-dimensional porous media. Innovative visualization experiments paired with advanced multiscale models will reveal the intertwined dynamics of bacteria, biosurfactants, and transported substances. Model-data comparisons will guide the development of predictive tools that can be applied to environmental systems ranging from contaminated soils to biological tissues. The findings will provide a mechanistic framework for understanding biosurfactant-mediated mass transport, improving our ability to forecast chemical and microbial movement in complex, unsaturated porous media. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project uses long-term data and experiments to predict how ecosystems will respond to environmental change. This includes changes to the chemical makeup of the atmosphere and soils, the frequency of events like fire and drought, the rate at which species go extinct, and the spread of weeds, pests, and pathogens. Many of these changes directly affect ecosystems and their ability to provide important services on which humans depend. The effects of environmental change occur over many decades and may vary through time. Predicting the effect of these changes depends on long-term experiments. Long-term feedbacks between organisms and the environment can make these changes stronger, weaker, or even reverse them. For example, soils under retired farmlands keep getting more fertile and store more atmospheric carbon for decades, but how fast this happens depends on the diversity of plant species in the area. Researchers at the Cedar Creek Long-Term Ecological Research Program (CDR) will collect long-term data in retired agricultural lands, prairies, savannas, and forests. These data will be combined with mathematical models to better understand how ecosystems respond to many environmental changes happening at the same time. CDR’s education and outreach team along with the Cedar Creek field station staff will continue to work with community partners to recruit, train, and support a STEM workforce. The project will create opportunities for K-12 students, undergraduate and graduate students, educators, policy makers, and the public. These opportunities include school field trips, a high school internship program, and onsite apprenticeships that provide training in science education. Researchers at CDR LTER will use long-term observations and experiments, theory, and models to achieve a mechanistic and predictive understanding of the role of plant biodiversity change, including lags and feedbacks, in determining ecosystem responses to a changing environment. This understanding will be achieved by leveraging CDR’s long-term research platforms that, taken together, manipulate or observe plant biodiversity under ambient and experimentally altered environmental conditions in a range of terrestrial ecosystems to document responses across scales, from leaf-scale physiology to the whole ecosystem scale. This hypothesis-based work will advance knowledge by maintaining CDR’s many research platforms and by starting four new experiments strategically positioned to resolve critical knowledge gaps about community assembly and organism-environment feedbacks. CDR scientists also will start collecting new data on carbon dynamics in directly comparable treatments spanning existing research platforms. In addition, researchers will use ecological theory, modeling, multi-site experiments, and synthesis activities to increase the generality of the insights arising from CDR site-based studies. Taken together, these projects will reveal when and why multi-decadal lags and long-term feedbacks occur as well as the role of biodiversity change on lags and feedbacks to ecosystem functioning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The remarkable progress of Artificial Intelligence (AI) in recent years is starting to greatly influence research across a wide range of disciplines. As Numerical Linear Algebra plays a crucial role in Deep Learning models, this trend presents unprecedented opportunities for experts in numerical analysis and linear algebra to contribute to ongoing AI research. This proposal represents a step toward capitalizing on this opportunity. The focus of the proposed work is not on applying AI to solve a specific problem, but rather on enhancing AI methods themselves by exploiting insights from numerical methods to optimize the Deep Learning process. This process is time-consuming, energy-intensive, resource-demanding, and overall very costly. Therefore, any improvements that can speed up the process are likely to have a significant impact. The investigators will leverage their experience in numerical methods to develop a number of techniques for accelerating the training of large AI models. The project aims to develop techniques that exploit both accelerators and preconditioners to speed up iterative procedures used in training deep learning models. The same combination of preconditioning and acceleration techniques is central to the effectiveness of iterative solution methods for linear systems. Acceleration methods such as Anderson/Pulay mixing or the Reduced Rank Extrapolation method, among others, have had immense success across various fields of science and engineering. However, in the context of deep learning, these methods face challenges, particularly since they were not developed for stochastic sequences common in deep learning. The team will investigate the relationship between mini-batching, a technique used for sampling subfunctions in stochastic methods, and its impact on both the convergence speed and the accuracy of the resulting models. Simple diagonal preconditioning methods have already been incorporated into optimization techniques in deep learning. The research team will explore more advanced preconditioning methods based on various approximations to the Fisher information matrix, a matrix that measures the amount of information that the observed data provides about the parameters. It has been shown that replacing the Hessian in second order methods by the Fisher matrix yields a more meaningful form of scaling of the variables, leading to better convergence and generalization. The investigating team will consider various methods for obtaining inexpensive approximations of the Fisher matrix and for combining them with accelerators. The proposed work is expected to benefit research in "scientific machine learning" by promoting participation of numerical linear algebra specialists in AI research. The PIs plan on several specific activities to promote the dissemination of knowledge in machine learning, such as writing a book on the topic of numerical methods in machine learning or offering tutorials and short courses. These activities will stimulate the interest of students in a field of increasing importance and that it will help with the immersion of those students from other areas, e.g., mathematics, into data-related disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Phononic crystals are engineered materials with repeating internal structures that allow for the control and manipulation of sound and vibration in ways that go beyond traditional materials. These structures have a wide range of applications, including vibration control, noise reduction, energy harvesting, sensing, and communication technologies. However, a major challenge limits their practical deployment: when these materials are manufactured as real, finite components, as opposed to idealized, infinite structures, surface waves develop along their boundaries that are poorly understood and difficult to predict. The research funded by this award seeks to bridge a critical gap in scientific understanding by creating a new theoretical framework to describe, manipulate and ultimately harness the sensing potential of surface waves in phononic crystals. This project serves the national interest by advancing non-destructive evaluation techniques for infrastructure safety and aerospace applications, improving quality control of next-generation composite materials, and enabling more efficient acoustic devices for medical diagnostics. The project will also support workforce development by training graduate students in advanced computational and experimental methods, while generating fundamental insights that will benefit a broad scientific community working in wave physics, material characterization, and additive manufacturing. Building on preliminary work focused on scalar waves, this research aims to establish a comprehensive framework for analyzing, controlling, and harnessing surface-bound waves in phononic crystals through three interconnected objectives. First, the mathematical concept of surface Bloch waves will be formulated as the boundary layers native to traction-free surfaces of phononic crystals, which will lead to the development of a unit-cell-based, reduced-order model that predicts their dispersion, waveforms, power flow, and spatial decay (or “skin depth”). Second, this research will explore how engineered geometric design of the traction-free surfaces, through controlled orientation, elevation, and periodic undulation of surface cuts, can be leveraged to manipulate the surface Bloch waves. The reduced-order model will support efficient parametric exploration of the geometric design space. Third, an experimental study using laser Doppler vibrometry will be conducted to (i) verify the theoretical framework, and (ii) develop a non-destructive evaluation methodology that extends widely used spectral-analysis-of-surface-waves (SASW) technique to as-fabricated phononic crystals. This project is expected to yield new insights into the physics of surface waves and boundary layers in periodic media, while delivering practical tools for inverse characterization of additively manufactured phononic crystals. This study seeks to create the foundational knowledge enabling further exploration of interfacial wave phenomena and topological effects in structured solids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

As our planet continues to warm, we are facing a future climate unlike anything humanity has ever experienced. To better understand and prepare for what might happen, this project examines periods in Earth’s distant past when global temperatures were much higher than today. While previous studies have focused on single warming events, this research will analyze more than ten consecutive events, providing more accurate and dependable insights into how the Earth responds to extreme warming. Using new, cutting-edge methods, the team will measure changes in rainfall intensity, flooding, and other climate shifts in ways that were not possible before. The knowledge gained will improve our ability to anticipate and manage risks such as flooding and extreme rainfall, strengthening our nation’s resilience and protecting public welfare. Beyond advancing the frontiers of climate science, the project will help to build a strong, competitive STEM workforce by integrating research findings into K–12 outreach and university teaching materials, thereby directly contributing to national priorities. This project aims to advance our fundamental understanding of how rainfall patterns and river systems respond to a changing climate by collecting new and unique data on extremely warm climates of the past. The team expects to generate novel insights, as this will be the first terrestrial dataset to (1) quantify both climate changes and Earth system responses across more than ten consecutive global warming events, and (2) apply innovative reconstruction and analytical methods to measure rainfall intensity and intermittency—key information that is difficult to obtain using traditional approaches. Combining these new methods with the traditional state-of-the-art stable isotopic and geochemical methods will allow the team to collect new and original data on precipitation changes and extremes, and determine whether these changes occurred gradually or involved sudden "tipping points" that lead to dramatic shifts. To reduce uncertainty typically associated with sedimentary record-based climate reconstructions, the study will focus on a single paleo-river system. Together, these efforts promise to deliver transformative insights into Earth’s past, providing critical context for understanding our planet’s future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Remotely sensed spectral images, such as hyperspectral images (HSIs) and multispectral images (MSIs), are widely used across science and engineering fields, including agriculture, oceanography, forest monitoring, mineral discovery, and space exploration. These image modalities involve an inherent trade-off between spatial and spectral resolution: HSIs provide fine spectral detail but coarse spatial resolution, whereas MSIs offer the reverse. Spectral image fusion techniques seek to combine the strengths of both by integrating an HSI and MSI of the same region to produce fused images with high-resolution information in both domains, supporting various tasks such as pixel classification, target identification, and change detection. However, many existing fusion methods operate under the assumption that the spectral images are co-registered (i.e., covering the same region and sharing the same coordinates), whereas in practice the data are often spatially misaligned by pixel shifts, rotations, and other distortions (collectively referred to as “unregistered”), typically arising from differences in sensors or imaging platforms. Despite its fundamental practical importance and considerable interest, the fusion of unregistered spectral images still lacks rigorous theoretical underpinnings and reliable algorithms. This project addresses these gaps by developing new analytical and computational methods to establish a solid theoretical and algorithmic foundation for this long-standing and practically significant problem, enabling performance-guaranteed fusion of unregistered spectral data in real-world scenarios. Beyond remote sensing, the outcomes are expected to benefit areas such as cross-platform medical imaging and domain adaptation/transfer in machine learning. The project also offers undergraduate research opportunities, providing students with training in machine learning, optimization, and image/signal processing. This project develops a unified, unsupervised framework for fusion of unregistered spectral images with provable guarantees, tackling key challenges including spatial misalignment, lack of training data, and nonrigid deformation. Thrust 1 focuses on establishing theoretical foundations by integrating spectral unmixing with adversarial learning through diversified distribution matching in a latent spatial domain, enabling provable spatio-spectral super-resolution under practical, unregistered scenarios. Thrust 2 extends this framework to more complex real-world cases such as those involving unknown and potentially large nonrigid deformations. Thrust 3 develops stable and efficient optimization algorithms for the proposed fusion formulations, tailored to adversarial learning in latent domains and addressing the limitations of standard optimizers. Validation on semi-realistic and real-world datasets is used to assess the robustness and generalizability of the proposed methods. Expected outcomes include new theoretical insights, practical algorithms with convergence guarantees, and reproducible benchmarks to advance unregistered spectral image fusion and its applications in machine learning, signal processing, and scientific imaging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Aaron Massari at the University of Minnesota-Twin Cities is investigating the roles of molecular interactions in the charge transport characteristics of thin films for the development of efficient molecular electronic materials. Many promising thin film systems are highly heterogeneous with transport characteristics that vary wildly with position and length. Prof. Massari and his students will prepare polycrystalline and molecular thin films with systematically controlled structures and use two-dimensional IR (2D-IR) spectroscopy to measure their charge transport characteristics. Their studies will advance the understanding of how molecular structure and interactions in a thin film control the movement of electrical charges. This work will inform the design of electrical materials that benefit society as a whole by leading to devices that are less toxic and require less energy to produce. Prof. Massari is the Director of the Energy and U Show; a high-octane stage show that brings science to over 10,000 3rd–6th graders to the U of MN campus each year to learn about the First Law of Thermodynamics and college education. This work will leverage the nonlinear nature of 2D-IR spectroscopy to extract charge transport dynamics from the naturally heterogeneous environments of thin films while electrically mobilizing charges with an AC voltage. The emphasis is on ground state electron transport though doped organic films, and the studies will specifically address the role of polymorphism, donor-acceptor spacing, degree of charge transfer, and covalent attachment of dopants in facilitating charge transport. Experimental studies to directly support (or disprove) the role of these structural parameters in molecular electronic films will advance the level of understanding needed to modify current models of charge conduction. The outcomes of these experiments will eliminate assumptions about the molecular structures through which charges are transported efficiently and could be used to make existing models more accurate and predictive. This will advance the level of understanding in the area of molecular design and synthesis by prescribing the features that need to be built into next generation molecules to impart efficient thin film performance. The interdisciplinary nature of these projects will support a training environment for undergraduate and graduate students that produces next-generation scientists who are skilled in benchtop chemistry, but also electrical, microscopic, and spectroscopic characterization techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Liquid crystals are a phase of matter with properties somewhere between liquids and crystalline solids, with applications in display technologies, optical devices, and biosensors. This project studies theoretical frameworks for liquid crystal systems which require the development of advanced mathematical techniques. The research focuses on two areas: ferroelectric nematic liquid crystals, which exhibit switchable electric polarization and show promise for improved electro-optical devices, and liquid crystals with very high symmetries that require mathematical tools like tensors for an accurate description. The mathematical methods proposed in this project should improve computational simulations and help understand structural ordering and defects in these materials. By developing new theoretical models and computational methods, this work supports advances in sensing and simulation technologies. This project also contributes to education and workforce development in science and engineering, involving the training of both graduate and undergraduate students in this research area. This proposal presents two research directions investigating novel structures in nematic liquid crystals. The first focuses on ferroelectric nematic liquid crystals, a recently observed phase that was long predicted theoretically and enables polarity switching. These materials develop singular structures similar to domain walls in ferromagnetic systems, occurring in regions where polarity transitions sharply between states. The second research area examines nematic liquid crystals with symmetries beyond the typical head-tail symmetry, including tetrahedral and cubic symmetries that can be characterized using appropriate order parameters. For these high-order symmetries, higher-order tensors provide a more natural description than the second-order tensors used in classical Landau-de Gennes theory. Both research topics have practical relevance for sensor development and computational mesh generation for scientific simulations. The work requires developing new mathematical tools from tensor analysis, calculus of variations, and partial differential equations to characterize the complex structures observed in physical experiments and numerical simulations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project is focused on the study of incompressible Navier-Stokes and Euler equations, which are the fundamental equations modeling the flow of air (at subsonic speeds) and water, among other fluids. Due to the inherent complexity of fluid dynamics, many questions about these equations are still open. For example, whether the fluid velocity at a small volume could become uncontrollably large, even when the average fluid motion is mild, remains a central open question. In nature, we often observe coherent structures in fluid flows such as vortices (eddies) generated behind an obstacle in a stream of fluid flow, and vortex rings (such as smoke rings) and vortex columns trailing airplanes. Mathematically, these coherent structures represent large (approximate) solutions to the Euler and Navier Stokes equations. A natural question is how stable these solutions are, and what are the precise dynamics of nearby solutions. The main goal of the project is to address these important questions. Theoretical understanding of the Navier Stokes and Euler equations are useful in precise computations of the solutions and in the design of efficient numerical algorithms, which are essential for many scientific and engineering applications. The project provides training opportunities for graduate students, who will learn to use tools from spectral analysis, Fourier analysis, dynamical systems, nonlinear partial differential equations, and numerical simulations in the study of physically significant problems. The investigator aims to extend the precise linearized analysis for the Euler equations around shear flows and vortices to more general steady states. The essential new difficulties are the complicated Hamiltonian dynamics associated with the steady velocity field, including the presence of elliptic and hyperbolic critical points, and the genuine two-dimensional nature of the problem. The project develops new and robust methods to treat these difficulties. A second important goal is to establish sharp nonlinear stability results for both the inviscid Euler equations and slightly viscous Navier-Stokes equations around monotonic vortices. Another important goal of the project is to study the regularity of solutions to the three-dimensional Navier-Stokes equations under various symmetry or critical bounds assumptions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Catalysis Program of the Division of Chemistry, Professor Hoover of the Department of Chemistry at the University of Minnesota-Twin Cities is studying new catalysts to convert carboxylic acids to value-added compounds. Current routes to synthesize molecules used in medicine, agriculture and materials applications rely on expensive starting materials and involve lengthy multi-step syntheses. In contrast, carboxylic acids are abundant starting materials that can be obtained from renewable sources. The development of new catalysts to utilize these precursors will improve the efficiency and sustainability of these processes by reducing the number of synthetic steps required and minimizing the formation of waste byproducts. The broader impacts of the project will extend to current and future high school teachers who will participate in summer research opportunities with the Hoover lab. This program will promote careers in science and engineering and aid in developing the STEM workforce by encouraging and enabling research participation by Minnesota’s current and future teachers. This project will establish two new classes of oxidant-controlled reactions of carboxylic acids. Although oxidative decarboxylation reactions are an attractive approach to generate a variety of value-added targets from simple and abundant precursors, there are limited examples of such reactions that operate with high efficiency and selectivity. The approach will leverage distinct oxidant-based conditions to achieve disparate but related decarboxylative transformations. First, copper-catalysts paired with nitroxyl radical oxidants will enable selective dehydrogenation while mitigating decarboxylation to access unsaturated carbonyl compounds. Second, a new metal-free oxidant-mediated decarboxylative amination will generate primary amines from heteroaromatic carboxylic acids. Mechanistic studies will provide a fundamental understanding of the influence of the oxidant on the decarboxylation steps and will lay the groundwork for establishing a library of reliable and predictive metal-catalyzed and metal-free decarboxylation reactions of (hetero)aromatic and aliphatic carboxylic acids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

By sequencing the genome or transcriptome of a tumor, scientists and doctors can gain detailed insights into the tumor's genetic history. For example, how many mutations drove the tumor's progression, and what specific mutations were most important? Doctors can use this information to determine the best courses of treatment and the frequency of surveillance tests. In recent years, scientists have developed novel sequencing technologies -- including single-cell sequencing, multi-region sequencing, and sequencing of circulating tumor DNA (ctDNA). In this project, the investigators will develop new mathematical and statistical tools to analyze the large amounts of data generated by these new sequencing technologies. Their methods will leverage the latest knowledge of biological mechanisms driving cancer progression to develop constrained statistical models that maximize insights from cancer sequencing data. The investigators will incorporate real-world limitations of cancer sequencing data availability, e.g. limited time points and samples. This project will support the training of graduate students in inter-disciplinary science that combines mathematics, statistics, and bioinformatics. The mathematical tools will be built upon the underlying principles of population genetics which govern tumor progression. Due to the rapid expansion of tumor cell populations, work will primarily be done with branching process models; however, variants of the branching process model that incorporate finite carrying capacity will also be used, allowing for the accurate representation of real-world observations of tumor cell growth rates that decrease over time. Within the context of these stochastic models, the investigators will study several quantities related to new genomic sequencing technologies. For example, they will study the behavior of the site frequency spectrum in a branching process model with selection. This study will enable the development of tools to better detect the presence of so-called driver mutations in the genetic history of tumors. The investigators will also develop techniques based on multi-type branching processes for understanding data generated from multi-region sequencing. Additionally, techniques for utilizing single-cell sequencing to identify the mutations responsible for driving tumor progression will be developed. Finally, the investigators will study how ctDNA can be used to predict cancer recurrence and quantify the population dynamics of recurrent tumors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Environmental Chemical Sciences (ECS) program in the Division of Chemistry, Professors R. Lee Penn and Rene Boiteau at the University of Minnesota investigate the stability of organic matter (OM) and minerals in soils and sediments. Results will elucidate and improve predictions of OM accumulation and degradation. Assemblages of iron oxide minerals and organic matter, hereafter referred to as Fe-OM, are major regulators of global carbon and nutrient cycles, with OM often protected by minerals through complex and interconnected chemical processes. Using model compounds and whole root exudates, the team will examine how plant exudates transform Fe-OM and impact the distribution of OM within the solid and liquid phases and at their interfaces as minerals change over time. Findings will elucidate mechanisms of OM stabilization and disruption that can shed light on how plant and microbial processes affect OM stability, potentially leading to new strategies for increasing soil carbon and supporting sustainable land use. The project will also develop open-source machine learning algorithms to predict how organic molecules react with minerals in soil, with applications to understanding the fate of various chemicals, including pollutants. This project aims to test the hypothesis that plant exudate molecules that act as both reductants and chelates may be particularly effective at promoting the release of Fe and OM from Fe-OM, and that certain Fe-OM materials resist transformation. It combines experiments using varied model organic compounds and root exudates and synthesized Fe minerals with a broad suite of experiments and analyses. The three primary aims are (1) quantify the distribution and fractionation of organic matter during Fe(II) catalyzed iron oxide transformations; (2) identify which exudate model compounds and whole root exudate molecules disrupt Fe-OM, releasing dissolved OM and Fe(II) and promoting phase transformations, and understand the relevant modes of action; and (3) characterize the impact of chemical gradients on OM redistribution upon introduction of Fe(II), model exudates, and whole root exudate using flow-through reactors to better mimic field conditions. The three tasks will focus on progressively narrower Fe-OM materials and exudate chemistries that exhibit the greatest differences in reactivity to ultimately reveal mechanisms and determine which Fe-OM are most susceptible and resistant to those processes. The resulting large data set relating organic molecule structural characteristics to their distribution within the solid and liquid phases and at their interfaces will be leveraged to develop a predictive machine learning model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Communities across the United States are increasingly experiencing the severe consequences of shifting weather patterns, increased impacts from droughts, floods, and other extreme weather events, and rising sea levels. These strain vital infrastructure, jeopardize public health, and disrupt economies. There is an urgent national need for response solutions that address some of the most pressing resilience challenges, and these require a central hub to coordinate efforts, bridge scientific understanding, and disseminate actionable strategies for resilience. This NSF grant will provide support for a National Office to coordinate a network of Regional Resilience Innovation Incubator (R2I2) projects addressing regional resilience challenges. The National Office will facilitate collaboration, coordinated data management, and effective knowledge sharing among regional resilience initiatives by creating a unified network of NSF supported R2I2 projects. The office will enhance scientific and technological understanding of R2I2 projects by ensuring insights and innovative practices flow efficiently between regional projects and communities nationwide, thus transforming knowledge into impactful action. This R2I2 National Office will create a National Research Network and Hub to support the Regional Resilience Innovation Incubator (R2I2) teams. The project will launch in two stages, beginning by forming an advisory board and creating shared understanding, analysis, and participatory action planning. Subsequently, a project implementation plan will be established with Key Performance Indicators and iteratively refined through adaptive management and organizational learning approaches. Key activities include co-developing and implementing a FAIR (Findable, Accessible, Interoperable, Reusable) knowledge and data sharing strategy that is coupled with a FAIR data repository and establishing a centralized project website and knowledge exchange hub. The National Research Network and Hub will function as a community of practice through quarterly virtual meetings, an annual meeting of regional teams, and regular newsletters to promote knowledge sharing and peer-learning. Anticipated technical results include a robust research network fostering collaboration, a publicly accessible knowledge sharing and training library, enhanced team capacity in convergence research, a pipeline of solutions-driven scientific leaders, and widespread dissemination of R2I2 findings to benefit local, state, and national policymakers, for-profit and non-profit organizations, entrepreneurs, and all U.S. communities. By enabling research translation and equipping regional resilience incubator projects with the necessary knowledge, data, tools, and partnerships to confront resilience challenges, this project will promote scientific progress, serve national and economic security, advance public health, prosperity, and welfare. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to break the low latency performance barrier in today’s fifth generation (5G) networks that hinders progress and adoption of remote driving industry (the “vertical” application). It advances an innovative “vertical-aware” framework to optimize both 5G networks and the vertical application. Despite tremendous progress, today’s “self-driving” cars may encounter many situations where they cannot drive themselves safely. Examples include construction zones and traffic accidents on the road. By ensuring low latency needed for remote driving, the developed solutions will allow a human teleoperator to remotely steer a “connected and autonomous” vehicle (CAV) through complex situations as if sitting in the driver seat. Technological advances enabled by this project will help (re-)establish the U.S.’s leadership in next-generation (NextG) wireless telecommunications and major vertical industries such as automotive and robotic automation. This project also provides a unique educational platform to train students and expand the STEM (Science, Technology, Engineering & Mathematics) workforce. Two major hurdles in ensuring low latency over 5G networks are i) high mobility of vehicles leads to poor radio channel conditions, causing data delivery errors; ii) frequent handovers among radio base stations further prolong data delivery. The project will develop a novel Open Radio Access Network (O-RAN) enabled, vertical-driven framework with mobility-aware, proactive mechanisms to reduce impacts of high mobility and handovers on the tail latency performance of the target vertical application. This is achieved by enabling 5G networks to utilize information (e.g., vehicle trajectory and speed) provided by remote driving applications to make intelligent decisions to speed up the delivery of sensor and command-and-control data that are critical to remote driving, whereas CAVs can also take advantage of vertical-aware predictions made by 5G networks to decide when and how to transmit data. Additional innovations include incorporation of integrated 5G and cellular vehicle-to-everything (C-V2X) technologies for cooperative situation awareness to further ensure safe remote driving operations. The phased approach to developing the proposed solutions and demonstrating their capabilities will ensure a high chance of successful execution, truly moving the needle with transformative impacts on relevant industrial sectors. The project represents close collaboration across three academic institutions and two industry leaders in key relevant sectors providing an accelerated pathway to technology transition. By demonstrating the value of vertical-aware advanced 5G/NextG networks in support of remote and cooperative driving and other industrial use cases, this project will help create new opportunities and business models for both mobile network operators and network equipment vendors for sustained investments in network innovations. It will also help accelerate adoption of autonomous driving with teleoperation capabilities. 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.