North Carolina State University

NSF Awards · FY 2025 · 2025-03

Optimal transport (OT) is a powerful tool for comparing probability distributions and computing maps between them. Simply put, optimal transport is the minimum-cost plan to transport mass from one distribution to the other, where the cost of transporting one unit of mass between two locations is the ground distance between the two locations. OT has been studied extensively in mathematics, engineering, physics, economics, operations research, and computer science because of their numerous applications. Despite extensive work, computing OT plans has remained a computationally challenging problem, and there is a large gap between the theory and practice of OT algorithms. The need for fast OT algorithms is becoming even more urgent with the proliferation of machine learning and algorithmic decision making in all disciplines. The scarcity of scalable algorithms that compute high quality transport plans has limited the applicability OT to many applications. The main goal of this project is to advance the theoretical underpinnings of OT and to bridge the gap between the theory and practice of OT algorithms. By exploiting combinatorial, geometric and statistical properties of OT, leveraging new approaches for min-cost flow, and exploiting approximation and probabilistic techniques, simple and scalable algorithms will be developed for computing high quality OT plans of both discrete and continuous distributions whose supports are compact regions in Euclidean space. The emphasis will be on designing combinatorial algorithms that not only have good worst-case running time but that have better expected running time on stochastic or semi-stochastic inputs. The project will also explore techniques to circumvent the curse of dimensionality, which arises in the OT of high-dimensional distributions. Building on these OT algorithms, new algorithms will be developed for data analysis (e.g. clustering, training neural networks) on a family of distributions in Wasserstein space, i.e., using OT as the distance between a pair of distributions; for quality assessment of algorithms that return a probability distribution (e.g., flood-risk-analysis algorithms that return a distribution of water over a region). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This Faculty Early Career Development (CAREER) grant focuses on supporting research that intends to advance sustainable textile manufacturing through innovative investigations of hemp fibers. Hemp is emerging as a promising alternative to traditional textile fibers, offering an eco-friendly option to reduce dependence on cotton and synthetic materials for textiles. However, processing hemp into high-quality textiles presents challenges due to their stiffness, lack of crimp, and limitations in existing spinning technologies. This research project aims to overcome these barriers by introducing a modified spinning process that leverages wetting to improve fiber cohesion and adjustments to the spinning triangle for enhanced yarn structure. These advancements contribute to the broader goals of sustainable textile production by minimizing environmental impacts and reducing reliance on synthetic fibers. The overarching goal is to advance the knowledge base in sustainable textile manufacturing and integrate this knowledge with STEM education programs, spanning middle school to doctoral levels, through curriculum development, outreach events, and hands-on research experiences. This project serves the national interest by contributing to the progress of science, advancing environmental sustainability, and preparing a technically skilled workforce. The technical objectives of this project involve developing a comprehensive framework to understand the relationships between the structural, mechanical, and environmental properties of hemp yarns and textiles. Specific aims include: (1) investigating the interplay between hemp fiber surface topography, water capillary adhesion, and fiber cohesion to enhance yarn quality; (2) implementing automated methods for analysis of the spinning triangle using transparent rollers and deep learning algorithms; (3) exploring fiber dynamics in hemp yarns through AI-assisted micro-CT segmentation for detailed structural insights; and (4) performing a cradle-to-gate life cycle assessment to evaluate the environmental impact of hemp yarns and textiles. These efforts address fundamental questions in fiber science, such as how surface characteristics and processing conditions affect fiber cohesion and yarn performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Gene therapies are poised to revolutionize medicine by addressing diseases previously deemed incurable. Viral vectors that deliver therapeutic genes to target organs are central to these therapies. However, current production systems to generate functional vectors also generate by-products such as empty or incorrectly filled viruses. Detecting these defective particles is critical for ensuring the efficacy and safety of gene therapies. However, available analytical tools are labor-intensive and costly. This project leverages the discovery that the surface structure of a virus depends on the genetic material it carries. The project will use molecular fingers called "ligands" that are engineered to bind to the surface of functional viruses differently than they bind to defective ones. This binding will be analyzed using label-free biosensing technologies with high sensitivity and accuracy. This research has three aims: first, to study ligand interactions with full and empty viruses; second, to integrate these ligands with Extended-Gate Field Effect Transistor (EGFET) biosensors, creating an "electrochemical dictionary" for interpreting the ligand-to-virus binding events; and third, to validate this technology with therapeutic viruses. This approach could be extended to detecting disease-causing viruses and engineering sensors for environmental or industrial applications. Biotech and biopharma communities will be engaged through professional presentations, academic meetings, and student involvement, fostering the adoption of this biosensing technology in research and industry. Quantifying viral titer and gene loading is critical for assessing viral infectivity and ensuring the safety and efficacy of gene therapies utilizing viral vectors to deliver therapeutic transgenes. Recent findings on the relationships between viral capsid biomolecular features, genetic payloads, and virion transduction activity have significant implications for viral vector manufacturing and diagnostic analysis. Adeno-Associated Viruses (AAVs) exemplify this potential, as their capsid surface signals the presence of therapeutic genes. However, AAV production often yields heterogeneous mixtures, including empty and misloaded capsids that can induce genotoxicity and immunogenicity. Current analytical assays for quantifying the titers of capsid, encapsulated transgenes, and infectious units are laborious and inaccurate, causing delays and uncertainty in treating patients. To address these challenges, this project will develop "Bio-Censors" that use affinity ligands recognizing capsid features specific to gene-loaded versus empty AAVs to simultaneously quantify capsid and transgene titers. The research approach integrates peptide ligands with pH-controlled affinity for AAVs, microfluidic devices for pH tuning, and multiplexed EGFET biosensors. In Aim 1, peptide ligands' differential recognition of AAV capsids using surface plasmon resonance-electrochemical impedance spectroscopy will be investigated. Aim 2 focuses on engineering EGFET Bio-Censors to enhance detection and discrimination. In Aim 3, the Bio-Censors’ performance will be validated with therapeutic AAVs in complex fluids. 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

Saltwater intrusion and sea-level rise (SWISLR) present major challenges for rural and small coastal systems whose residents often need economic and political aid to ameliorate SWISLR issues. Rural and unincorporated areas often do not have the same support as more populated regions due to the lower population densities and constrained planning capacity. Many small coastal communities are economically, culturally, and spiritually connected with their location, making adaptation to SWISLR crucial to maintaining the capacity of these communities to coexist with coastal change. This project works across research institutions, local communities, tribal governments, and municipalities to establish partnerships for understanding local flooding hazards and co-develop science priorities that will directly lead to actionable solutions. The core activity for this planning grant is to conduct a series of surveys, community workshops, and interviews with stakeholders across Florida (FL) and North Carolina (NC). The goal is to identify specific earth system science problems related to coastal flooding that will lead to detailed, local information for coastal communities. Ultimately, this project will increase the ability of small coastal communities to develop and implement adaptation strategies, while also learning from other communities across the Southeast who are facing similar challenges. This grant advances earth systems hazard research through capitalizing on innovations in high resolution data (remote sensing, machine learning, flood modeling). With these data, this project answers localized questions about the variability of flooding and coastal hazards, moving beyond regional projections to inform local flooding hazards and determine the consequences of a changing system. Bringing new knowledge to overlooked systems and communities will allow understanding of the variability of flooding hazards, determine how understudied areas might contribute new insights to our understanding of SWISLR, and include underserved voices (Indigenous and local communities) in the coastal solutions discussion. Through developing a repository of successful coastal projects and initiating a series of workshops, this project will increase the communication and dissemination of information and stories beyond state lines. This project connects to communities through a co-production process to enhance resilience to SWISLR hazards. Collaboration through FL Sea Grant, NC Sea Grant, and SWISLR Research Coordination Network extends the results of this project throughout municipalities in the southeastern coastal plain. The project deliverables will be openly available and archived as open pre-prints or deposited into open repositories using FAIR data practices. 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

Plankton significantly alter elemental and energy cycling in the ocean, as well as maintain marine food webs and impact ecosystem health. To optimally predict plankton stocks and activity, there is a need for understanding factors that control them and how they are altered by anticipated environmental changes. B-vitamins, specifically vitamin B1 and related compounds, need to be externally supplied to key plankton for them to survive in the surface ocean yet have not received sufficient attention in part due to uncertainty about supply and demand in-situ. This study tackles the question: What processes are crucial in supplying B1/vitamers to seawater? To address this question, culture-based experiments and recently developed chemical analyses are conducted to estimate the amount of vitamin B1 and related compounds that come from bacteria themselves but also viral infection of bacteria in the surface ocean. In addition, fluxes of these compounds are evaluated with respect to temperature, an influential variable on biology that is predicted to change in the future surface ocean. Interdisciplinary training for two PhD students at collaborating institutions is supported by this project, as well as the training of undergraduates from underrepresented groups recruited as summer research interns. In collaboration with the NCSU Science House, whose staff are experts in pedagogical methods and learning assessment, lesson plans related to the ‘life and death’ of marine plankton are developed to introduce young students from middle school and early high school in rural or low-income regions of NC to marine plankton, their global importance, and connections between nutrients and activity of plankton. Bacterioplankton and phytoplankton are cornerstones of the marine food web and impact oceanic elemental cycling, productivity, and ecosystem health. Most marine bacterioplankton and key phytoplankton lineages rely on exogenous vitamin B1 (thiamin; B1 herein) or vitamers (B1-related compounds) to survive. B1 & vitamers must come from cells, presumably via cell secretion/release and mortality, but the relative importance of these processes as well as the importance of certain taxa as sources and impact of environmental change (especially temperature) on flux are unknown. This project assesses the release of B1/vitamers by representatives of key de novo B1-producing bacterioplankton (picocyanobacteria and heterotrophic bacteria) in axenic cultures and in the context of temperature change. Moreover, tests are carried out to study whether viral infection and lysis results in unique B1/vitamer flux compared to release by model bacterioplankton alone and to assess how temperature change alters these processes – reaching beyond consideration of temperature as only an abiotic degradative factor. Overall, results of these laboratory experiments provide flux data enabling: the first estimation of in-situ fluxes in the surface ocean, tests of genome-based prediction of high vitamin providing bacterioplankton, and linkage of flux data to fundamental biochemical and physiological measurements (biomass, growth rate, lysis rate) useful for ocean models predicting biochemistry, community composition, and productivity. 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

Personalized learning has proven effective in improving students' learning outcomes and is essential for closing the learning gap among students with varying backgrounds and preparation levels. The emergence of advanced Artificial Intelligence (AI) technologies, including generative AI, creates an opportunity for enhancing the effectiveness and quality of personalized learning. Yet, existing tools are not tailored for educational purposes and generate responses that might not be suitable for students' knowledge level, are inaccurate, and/or are not helpful for students' learning. This project will tailor Large Language Models (LLMs) to account for students' current state of knowledge and learning practices, the learning context, and their perception of the helpfulness of the support they have received in prior interactions with the system. The research will advance the state-of-the-art in modeling students' problem-solving strategies and algorithmic thinking in computer science education. Through implementing the techniques in existing intelligent learning environments, classroom studies, and outreach work, thousands of students at different levels will be able to benefit from these tools, improving their programming knowledge and skills and broadening participation in computing fields. The recent wide availability of LLMs has incentivized different disciplines, including education, to improve existing processes and practices. One key area that is actively being studied is how to tailor conversations toward maximized alignment with user preferences for optimized task completion. In education, this alignment comes from offering adaptive instructional support by modeling students' knowledge state and competencies. This project will develop and evaluate novel AI-based methods for student modeling to trace students' competencies within a range of abstraction levels through (1) integrating fine-grained process data to model students' competencies related to problem-solving strategies, (2) identifying effective and harmful learning patterns, and (3) understanding the consequences of learners' patterns of interactions with the intelligent learning systems on their competence. The project team will use these findings to develop LLM-based systems for generating learning scaffolds -- feedback, worked examples, and suggested next problems -- using a dual-strategy approach that combines the fine-tuning of LLMs with advanced Reinforcement Learning with Human Feedback (RLHF). The goal is that the learning scaffolds generated by the fine-tuned LLM plus RLHF-based agent are more pedagogically relevant for the learner than scaffolds generated by other state-of-the-art models. Output quality will be assessed on three main factors: relevance to classroom content, current competency of the student, and helpfulness of the response from a pedagogical standpoint. 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

Efficient and high-performance control of chemical processes is critical to the safety, sustainability, and economic interest of industrial sectors. Some processes, called nonlinear processes, are more difficult to control because they exhibit more complex behaviors. Additionally, establishing accurate equations completely from physical and chemical principles is unrealistic in these situations. This project aims to develop direct data-driven, namely model-free, methodology for nonlinear control, based on the principle that control-relevant information underlying the nonlinear dynamics can be statistically learned from process data. The focus of this project is on the combination of learning and control theories underpinning (1) the observation of unmeasured (hidden) states, (2) the analysis of unknown dynamics, as well as (3) the synthesis of optimal controllers, in a generic, physically meaningful, and performance-guaranteed framework. The algorithms and methods of this project will be tested with benchmark systems and finally developed into computer codes for practical implementation. Outcomes of this project are expected to facilitate a “big data” transformation of industrial control technology that features time-flexible workflows and increasing workforce diversity. The research outcomes will also be used in the design of a new graduate-level course on machine learning for chemical engineers and incorporated into the existing undergraduate-level process control course, both aiming to improve the “data literacy” of chemical engineering students. In addition, the project will involve outreach activities, including the PI’s lectures at local high schools and NC State’s Engineering Summer Camp, to motivate younger generations to pursue higher studies and careers in STEM. State-space descriptions of nonlinear dynamics should be used for data-driven control to guarantee physical interpretability and achieve optimal control. The goals of this research program include the following aspects: (1) the development of model-free state observers, i.e., dynamical routines that reconstruct the hidden state trajectories based on the input and output measurements, through machine learning over typical nonlinear observer structures, (2) the analysis and prediction of state-space dynamical behaviors, e.g., bifurcation, chaos, and conservation laws, through a global linearization of nonlinear dynamics as Koopman operators defined on function spaces, and (3) the learning of input-output dissipative properties, where physical constraints are used to enforce conformity to first principles and conic optimization methods are leveraged to synthesize stabilizing and rigorously performance-guaranteed controllers. This data-driven framework is end-to-end (i.e., from data processing to the final control) and thus suitable for implementation in real-world processes. To verify the practicality of the proposed technical approaches, three representative systems – a computational fluid dynamics (CFD) reactor simulator, a Belousov-Zhabotinsky reactive system with video data, and a hydrogel manufacturing device with lab measurements – will be used as benchmarks. 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

Rotating detonation combustors (RDCs) coupled to highly diffusive mixers enable compact, green, and efficient energy production. RDCs operate through the injection of an air-fuel mixture which is detonated through a reactive shock wave rotating at supersonic speeds and fed through the mixer to cool and slow the flow before it reaches a turbine which ultimately harnesses the energy. RDC-mixers hold great promise to revolutionize power and propulsion systems, but they are difficult to model/optimize due to unsteady mixing, extreme temperatures, and high-speed diffusion. This collaborative project aims to develop models and methodologies that enable optimization of the RDC-mixer for maximal fuel efficiency. The investigators will leverage a three-pronged meta-modeling framework featuring an innovative digital twin, a novel statistical surrogate model, and a physical experiment involving a high speed wind tunnel in which the mixer will be assessed through high-frequency optical and probe-based measurement techniques. RDC-mixer-turbine systems are directly impactful to clean energy and heat production, but their potential impact is even broader. Diffusing elements and mixers are used in a variety of applications, ranging from aviation, aerospace, agriculture, refrigeration cycles and heat exchangers. The mathematical modeling foundations developed in this project will be widely applicable to computer simulation experiments and digital twins. This project is organized into three aims. First, motivated by the complexities of the digital twin, a gradient-enhanced Bayesian deep Gaussian process surrogate will be developed to provide non-stationary flexibility, uncertainty quantification, gradient-enhancement for improved accuracy, and gradient predictions to facilitate Bayesian optimization. Second, the digital twin of the RDC-mixer will be developed at reduced computational costs as existing simulations of RDC-mixers require weeks of compute time. Tailored unsteady boundary conditions are proposed to separate the computational fluid dynamic simulations for the combustor and mixer, which will enable faster computation. The digital twin will incorporate steady and unsteady flows, meshing, and adjoint solvers to provide gradient information at minimal cost. Third, a novel calibrated Bayesian optimization framework will be developed to first optimize calibration parameters of the digital twin, then use these with a bias-correction model to sequentially optimize the physical experiment. The physical model will be used in the calibration feedback loop to train the bias-correction model and to test and validate the best designs. Collectively, the surrogate model, digital twin, and physical experiment will enable effective optimization of the RDC-mixer design for optimal fuel 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

Proteins are the catalytic agents in the cell and carry out most cellular reactions. Control of the abundance or activity of cellular proteins can be used to modify cellular pathways and, by extension, control the health and viability of an organism. While systems for targeted destruction of proteins of interest are available for research and therapeutics in animals, very few tools exist to control protein abundance in plants. Instead, plant scientists are currently reliant on methods that regulate protein abundance at the level of mRNA expression, which are inherently slow. This research will enhance the capabilities of a recently developed tool to control protein degradation in plants called E3 DART, thus providing the opportunity to control the abundance of a protein target at the protein level. Specifically, this work will expand the mode of activation of E3 DART and widen its applicability to multiple plant species of research and commercial importance. The Broader Impacts of this work include its intrinsic merit as the optimized tool will enhance fundamental plant biology research and may be deployed in the future for applied agronomic innovations. Agronomic industry innovations that could benefit include developing novel herbicide resistance traits, engineering pathogen resistance by degrading pathogen effectors or design of new haploid technologies for faster breeding. Additional activities include outreach and development of teaching tools for museum activities, high school and/or undergraduate courses, and plasmid designs and plant lines deposited in public repositories. The research team will continue to mentor young scientists to develop a strong workforce in STEM. Inducible protein degradation systems are an important, but untapped resource for the study of protein function in plant cells. The recently developed E3-targeted Degradation of Plant Proteins (E3-DART) is a protein degradation system based on the activity of a Novel E3 Ligase (NEL) from Salmonella. The goals of this work are to optimize the E3 DART system such that it can be chemically controlled and, combined with other recombinant strategies, used in proof-of concept experiments to test the function of specific endomembrane proteins. This complementary set of tools, which are lacking in model plant systems, will provide deeper insights than previously possible into the highly dynamic, temporal, and spatial molecular mechanisms of organelle biogenesis and endomembrane trafficking. The specific aims of this research are to: 1) Develop a ligand-inducible E3-DART system; 2) Control E3-DART activity with novel recombinant tools; and 3) Develop proof-of-concept methodology with E3-DART to study endomembrane protein function and synchronized secretory protein trafficking. A robust system to control protein degradation will have a significant impact on plant biology. Key for the development of such systems is to engineer plant lines in which the degron-tagged protein of interest functionally complements a mutant, and the E3 DART activity and target protein degradation are controlled in a tunable and reversible manner. Such capability will allow for future characterization of the function of essential proteins involved in dynamic cellular processes in plants in ways not achievable with existing tools. 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

The broader impact of this I-Corps project is the development of an economic method to autonomously organize semiconductors precursors into three dimensional (3D) hierarchical structures. These structures can then be converted into semiconductor devices with functions similar to modern day microelectronics devices. This method has the potential to improve microelectronic fabrication so that it avoids the expensive lithographic processes common in modern day fabrication laboratories. The ability to fabricate various microelectronic devices at low capital costs and at lower workforce skill levels will reduce both supply challenges and the dependency on imports. Successful commercialization of the technology could contribute to building a new generation of foundries with a decrease in the carbon footprint of microelectronics devices. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a guided self-assembly fabrication process of semiconductor arrays. Target atoms are harvested from a reservoir and transported to the growing front of a wire. Using symmetry and the energy-driven orientation of chemical bonds, the chelated atoms are organized into 3D assemblies, enabling fabrication across nano- to micrometers. By using the spacing and capillary bridges on deposited arrays, the technology enables deposition of new wire arrays on top of existing ones to create a hierarchical assembly. Given the controlled bottom-up approach, the composition of the arrays can be tuned across the length of the wires or across the layers enabling fabrication of diodes, gates/transistors at any location of the array. The use of fluids as carriers and assembly vehicles enables simultaneous or in tandem fabrication of arrays of complex shapes and sizes within the same array. Hierarchical assembly has the potential for use in the fabrication of 3D chips and high-density processors. 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

In today's rapidly changing environmental landscape, developing a skilled workforce adept at utilizing advanced cyberinfrastructure is critical for sustainable and transdisciplinary environmental science research. The EcoTern project addresses this need by pioneering the training of the next generation cyberinfrastructure workforce to be capable of integrating artificial intelligence and machine learning technologies into environmental and computer science and engineering research. This collaborative effort, involving Florida International University (FIU), North Carolina State University (NCSU), and the NSF-funded Artificial Intelligence for Environmental Sciences (AI2ES) Institute, aims to develop comprehensive training activities, including new degree programs, curriculum enhancements, reusable course content, summer bootcamps, seminars, and interactive hands-on exercises. These activities will provide trainees with the necessary skills to utilize cyberinfrastructure for predicting and mitigating environmental impacts, such as coastal flooding, hurricane disasters, and marine ecological changes. By promoting the progress of science and supporting national health, prosperity, and welfare, this project serves the national interest by preparing a diverse and knowledgeable workforce to address environmental challenges resulting from a changing climate and other causes. EcoTern’s innovative approach involves weaving cyberinfrastructure training into the new undergraduate Data Science program at FIU and integrating cyberinfrastructure, artificial intelligence, and environmental science training into nine existing graduate courses at FIU and NCSU. Course materials will be shared broadly, and the project will cultivate a network of collaborating institutions engaged in the overlap of environmental science, artificial intelligence, and cyberinfrastructure education. The project will host a two-week summer bootcamp, providing intensive instruction and interdisciplinary research opportunities. A series of specialized workshops and invited lectures from cyberinfrastructure and artificial intelligence experts will further enhance the training program. An online platform will be developed to offer personalized hands-on exercises and real-time learning progress tracking. Research objectives include advancing interdisciplinary environmental and computer science and engineering research, preparing a better scientific workforce for cyberinfrastructure-enabled research, and creating a ubiquitous and scalable educational and training ecosystem for online, dynamic, personalized lessons and certifications. By democratizing access to advanced cyberinfrastructure resources and promoting transdisciplinary collaboration, EcoTern aims to cultivate a diverse, knowledgeable, and skilled community capable of driving innovation and addressing emerging environmental science and engineering challenges. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering. 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

The bacterial phytoplankton Synechococcus is a key driver of the carbon cycle, and the carbon they produce can be removed from the atmosphere and exported to the deep ocean, so it is important to understand their role as we seek to limit the impacts of climate change. Natural limitations in the resources necessary for phytoplankton growth, such as the micronutrient iron, can limit the amount of carbon removed from the atmosphere by these organisms. Likely as an adaptation to iron limitation, the newly discovered Synechococcus in the iron-limited northeast subarctic Pacific (NESAP) has lost the genes for converting nitrate into ammonium, which is needed for protein and DNA synthesis. These are the first members of this group known to have lost this pathway, and it effectively restricts these strains into a “recycling” state, in which carbon can only be fixed along with ammonium that is released from organic matter degradation and carbon emission, resulting in no net gain of fixed carbon. Recent global marine genetic data analysis suggests this adaptation by Synechococcus is occurring in all major iron-limited ocean regions, and because climate change will alter iron availability in the ocean, it is critical to understand the impact this adaptation will have on the marine carbon cycle. This project aims to study this nitrate utilization loss adaptation by culturing these new NESAP strains, studying their physiology, and using genetic techniques across multiple sites in the NESAP to determine their range and abundance in this part of the ocean. This project will support the training of a postdoctoral scholar, train two undergraduate STEM students, and include public scientific outreach via the North Carolina Poetry Society. This project’s goals are: (1) collect the NESAP nitrate-utilization loss Synechococcus ecotype, (2) determine the seasonal and spatial dynamics of different Synechococcus nitrogen-utilization ecotypes in the NESAP, and (3) calculate and compare the metabolic states of the nitrate-utilization gene loss ecotype to that of other Synechococcus nitrogen-utilization ecotypes. Field work will be done in collaboration with the Canadian Line P program, in which seawater will be collected from each Line P station and incubated under various nutrient regimes (high vs low iron, and nitrate, nitrite, or ammonium as the nitrogen source) in order to cultivate the new Synechococcus ecotypes. DNA samples will be collected seasonally at each Line P Station, which transition from nitrogen limited coastal stations to increasingly iron limited open ocean stations, and metagenomes generated from these samples will be used to determine the range of the nutrient utilization gene loss phenotype and temporal population dynamics of each ecotype due to seasonal nutrient fluxes. This data can then be extrapolated to map out the range of other strains exhibiting this phenotype in other iron-limited regions. Finally, Synechococcus strains collected from Line P will be compared to Synechococcus reference strains with complete and partial nitrate utilization pathways under replete and limiting iron conditions, with growth rates, proteomic profiles, and iron quotas per cell being measured in each treatment. Overall, this project’s results will provide new Synechococcus isolates for study by the oceanographic science community, expand our understanding of carbon cycling in iron-limited ocean regions, and contribute vital data to inform climate change marine carbon cycle models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The purpose of this project is to organize and host the 2025 Principal Investigators Meeting for National Science Foundation (NSF) Community Infrastructure for Research in CISE (CIRC) and also for Major Research Instrumentation (MRI) awardees in NSF's Computer & Network Systems Division (CNS) in Raleigh, North Carolina. The meeting is expected to be held for two days starting March 10th, 2025. It will involve participants, including Principal Investigators (PIs), NSF Program Directors, and the organizing team. The 2-day program will include a keynote, presentations by the PIs of featured projects, panel discussions, poster presentation, a reception, and “office hours” with Program Directors. The meeting will be where PIs, Program Directors, and others meet to present and exchange information about projects supported by the NSF’s CIRC and CNS's MRI Programs, discuss research infrastructure opportunities and challenges, and explore new ideas and partnerships for future work. It is also an opportunity for the academic community to interact with government agencies interested in research infrastructure developments and advancements. The Meeting series has played a major role in growing the community across a broad range of sectors and technologies, as well as in performing outreach to parties who have an interest in learning about the program and participating as future proposers, transition partners, or sponsors. 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 aims to serve the national interest by integrating the study of software performance throughout the CS curriculum. There is a growing recognition of the importance of software performance, however, learning how to code efficiently remains a critical missing piece in CS curricula, as for decades the primary focus has been on achieving functional correctness (and this trend continues). This project intends to develop EduPerf, an education-centric software platform, to integrate software performance analysis into different levels of CS courses to help students and instructors. The project has the potential to cultivate a highly skilled cohort of software engineers adept in performance analysis and proficient in writing efficient code. The project team plans to develop interventions and novel measurement-based techniques to enable instructors to assess student learning effectively. The team plans to use formative and summative assessment methods to measure the student learning outcomes and answer several research questions. The project results will be disseminated at several major CS education conferences. The team will provide detailed user and developer guides to EduPerf in PDF, HTML, video, and other comparable presentation formats. The project outcomes will not only benefit the technology industry in Silicon Valley in California, the automotive industry in Michigan, and Research Triangle Park in North Carolina but also advance the nationwide scientific endeavor. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This project is also supported by the NSF IUSE: HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM. 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

American Institutes for Research (AIR), North Carolina State University (NCSU), Partner to Improve (PTI), and educators from rural North Carolina school districts aim to establish and nurture a research-practice partnership (RPP), AI by 8, with a mission centered on the belief that every child, regardless of geographic location, deserves to be educated on the fundamentals of artificial intelligence (AI) by 8 years of age. This project highlights a culture of shared learning around early AI education, continuous improvement of the project, and a collaborative environment where researchers and practitioners support and value each others’ contributions and expertise. The project will directly impact 30 teachers and approximately 600 students across rural areas of North Carolina. The project holds significant transformative potential for (1) understanding the factors that impact K-2 teachers’ design and adoption of unplugged AI language arts instruction, (2) formulating emerging practices from teacher professional development to create and locally enact AI-focused English Language Arts (ELA) lessons, and (3) producing theoretical and practical advances in developing a set of K-2 AI lessons, especially for students from rural communities. AI by 8 will build intentional RPP that introduces AI concepts, practices, and perspectives into K-2 elementary classrooms in rural North Carolina communities. Ten rural educators will participate and partner with researchers from AIR and NCSU to learn about the fundamentals of AI and design unplugged ELA lessons. Twenty additional educators will engage with the RPP as AI Implementers, testing the lessons in their classrooms and providing feedback for iterative improvement. The project will tailor its approach to the unique assets of rural districts to collaboratively design at least 20 unplugged lessons for K-2 students that demystify AI concepts. The research questions explored in this project center around (1) facilitators and barriers engaged by K-2 teacher in incorporating AI into their ELA instruction; (2) student engagement within these AI-focused ELA lessons; (3) self-efficacy for teaching AI to K-2 students; and (4) professional development adapted to the needs and assets of the communities served. This project is funded through the Computer Science for All: Research and RPPs program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Encryption services are crucial because they ensure the confidentiality and security of data by transforming it into a secure format that can only be decoded by authorized parties, thereby protecting sensitive information from unauthorized access and cyber threats. Custom hardware design for encryption services is useful because it provides specialized, high-performance and low-energy solutions tailored to specific security needs, significantly enhancing data protection and processing capabilities compared to general-purpose hardware. This project develops custom hardware that can efficiently support multiple encryption algorithms. The novelties of this project are designing unified arithmetic functions and hardware units to support the operational and security needs of multiple algorithms. This project strengthens research collaborations and cryptography adoption across the United States and Germany and attracts students to hardware security research. This project develops a unified custom hardware design to efficiently execute post-quantum cryptography algorithms. To that end, the research team designs new hardware to jointly support critical computational blocks including arithmetic units, cryptographic primitives, and algorithm-specific steps such as sampling, encoding, and compressing. Moreover, the project explores new potential side-channel vulnerabilities of such hardware that can leak information and develops effective countermeasures using randomization techniques in hardware. The research team organizes a session at an international conference in applied cryptography and integrates the research findings into graduate and undergraduate curricula to help train the next generation STEM researchers and practitioners with the necessary skills in hardware security. 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

On-the-job teacher learning is where most teacher learning occurs. Professional learning communities (PLCs) are one common model for teachers to collaborate and learn from one another. Often, PLCs are organized by grade level to meet regularly for teacher collaboration. When teachers discuss mathematics in PLC meetings, they may be planning instruction, considering assessment results, or reflecting on their students' learning. As the PLC works together, teachers' expertise informs the ideas that are discussed and how the teachers collaborate. In addition, the PLC is one part of the school and district context where teachers focus on mathematics learning. The goal of this study is to understand how teachers' expertise is positioned in the PLC and the larger system of the school and district to inform mathematics teaching and learning. This should help schools and districts understand the features of PLCs that are important for supporting teachers as they collaborate and learn. This study takes a complex learning systems perspective on teachers' expertise by examining PLCs in the context of the school, district, and state-level activities. The first overarching research question is: How do upper elementary school and middle school teachers experience mathematics-focused PLC meetings that are part of a broader teacher learning system? The second question is: What are the perspectives of various stakeholders within the system on the role of mathematics-focused PLCs in schools? These two questions will be studied via observation data from the PLC meetings, interviews, and surveys. Participants in the study will include elementary and middle school teachers as well as school, district, and state-level leaders to understand their perceptions of the goals and work of the PLCs. Document analysis of artifacts about PLC work in the school and district will be used in conjunction with the interview, survey, and observational data. Epistemic network analysis and social network analysis will be used to understand the connections between individual educators' understandings, the discourse within PLCs, and the collaborative connections amongst PLC members. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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

Human-centric cyber-physical systems (CPS) such as assistive driving and exoskeletons aim to augment human capabilities instead of replacing humans. Humans can collaborate with these machines to overcome corner cases and demonstrate the correct action under safety-critical situations. Such human collaboration enables the human-centric CPS to achieve a better outcome than either could achieve alone. In this project, investigators will develop an efficient human-in-the-loop learning framework for human-centric CPS. During training, the machine will learn to make decisions in an uncertain environment, while the human will oversee the machine and actively intervene when anomalous or unsafe behavior occurs. The human will then demonstrate the correct action to the machine. The project’s novelties are incorporating a human subject to guard the learning agent, where the human can actively intervene in unsafe situations and demonstrate the correct actions to the agent during training, and developing a reward-free learning approach that substantially encourages learning efficiency, safety, and AI alignment. The proposed human-in-the-loop learning framework is being instantiated in two human-centric CPS including assistive driving and exoskeleton. The project's impacts are facilitating harmonious human-machine collaborations and enabling CPS's efficient and safe autonomy. This research contributes to establishing best practices and standards applicable to various industries where it is essential to integrate humans in the operation of CPS, including automotive, package delivery, and rehabilitation. The team is creating research and training opportunities for high school, undergraduate, and graduate students in machine learning, robotics, control, and biomechanics. The project breaks away from the prevailing paradigms of model-based control and safe reinforcement learning through three research thrusts. 1) Development of a human-in-the-loop learning framework that incorporates a human subject to guard the learning agent, where the human can actively intervene in unsafe situations and demonstrate the correct actions to the agent during training. This approach is reward-free and encourages learning efficiency, safety, and AI alignment. 2) Creation of digital twins of task-specific human behaviors for evaluating the proposed learning method in each targeted CPS, with focus on developing a simulated environment of human behaviors in driving and exoskeleton. 3) Empirical evaluation and real-world experimentation of each targeted CPS to train and evaluate the proposed learning methods against various scenarios in simulation and real-world settings to validate their performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Despite advancements in wireless technologies like cellular and WiFi, issues such as spectrum scarcity and inefficiency persist and are limiting connectivity for millions. Current systems often restrict access and hinder collaboration among stakeholders, especially in marginalized communities. This project aims to develop technical solutions for a more open wireless access ecosystem. By leveraging open-source technologies and decentralized models, it seeks to promote competition and collaboration among service providers. This approach aims to enhance spectrum efficiency, expand Internet availability, and improve reliability. Key goals include fostering trust through transparent contracts, ensuring verifiable service and spectrum sharing, and encouraging active participation from diverse stakeholders. Beyond technical advancements, the project aims to revolutionize wireless access for sectors like national security and smart communities, and support emerging applications such as autonomous agriculture and connected healthcare. It emphasizes openness and collaboration through publications, presentations, and partnerships with industry leaders like IBM, Google, and Verizon. All developments are open-sourced, ensuring accessibility and fostering innovation. This project also enriches curricula at Colorado School of Mines and NC State University, focusing on networks, Internet protocols, and incentive mechanisms, and fosters diversity in STEM through mentoring and engagement with underrepresented minorities. This transformative research designs Opennect, a framework for democratized wireless access. It yields several advances: 1) a robust economic and trust foundation via peer-to-peer and multi-party decentralized contracts including a robust contract network driven by economic dynamics, efficient methods to establish on-demand contracts, and highly robust and sustainable operations of the contract network, 2) a suite of methods for verifiability in decentralized environments by developing transparent data usage accounting, quality-of-service provisioning, and spectrum monitoring mechanisms, and 3) a sustainable market that encourages active participation in Opennect, including on-demand spectrum leasing, decentralized pricing for data plans, and crowdsensing-based spectrum access verification. The system is evaluated using large-scale simulations with real data, local testbed implementations, and scaled demonstrations on the NSF/PAWR-funded AERPAW testbed at NC State University. Overall, the project aims to advance the field of democratized wireless access by fostering trust, enabling verifiability, and promoting active participation through innovative market mechanisms. Its potential contributions include enhancing spectrum efficiency, expanding access to reliable Internet services, and supporting diverse applications in smart communities and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This award provides travel and registration support to graduate students to participate in the 2025 Institute of Electrical and Electronics Engineers (IEEE) International Conference on Nanotechnology (IEEE NANO) that will be held in Washington, DC, July 13-16, 2025. IEEE NANO is the flagship conference of the IEEE Nanotechnology Council (NTC). This annual conference rotates between Europe, Asia, and North America, bringing together researchers, educators, and students working on various aspects of nanotechnology for electronics, computing, materials, photonics, magnetic, acoustics, sensors, robotics, and biomedical applications. The technical program will feature plenary presentations, parallel technical sessions (including both oral and poster sessions), and special sessions and workshops in selected areas of emerging technologies. The award will support 20 graduate students in the form of IEEE Nano Fellowships. The Call for Fellowship Application will be widely disseminated to the graduate students at US institutions through the large network of IEEE NTC Chapters and Women in Nanotechnology (WIN) program and Young Professional Networking program, as well as direct messages to the past participants of the IEEE NANO conferences. An ad-hoc Fellowship Selection Committee will be formed to evaluate the fellowship applications. In addition, the support will help defray the cost for an Early-Career Advice Panel. The panel will consist of members from academia, industry and national labs at different career stages, who will share their experiences in the Graduate School and for the transition from the Graduate School to their respective careers. The Fellowship support and the professional development activity are designed to support the workforce development for the Unites States of America. 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

From autonomous driving to the metaverse, from digital democracy to intelligent healthcare, our next-generation revolutionizing applications are faced with an unprecedented shortage of frequency spectrum resources to meet their high demands from wireless technologies. This calls for a fundamental paradigm shift of the wireless ecosystem, from the current exclusive usage of licensed spectrum to dynamic, market-driven spectrum utilization that allows free spectrum trading and maximizes spectrum efficiency for a better digital future. As a pillar technology of the new spectrum market, intelligent algorithms based on advanced artificial intelligence and machine learning are expected to provide the ever-needed scalability and adaptability for managing and decision making in the new dynamic and complex environment, working in conjunction with or even replacing traditional algorithms used in the current wireless ecosystem. Building on top of the latest advances from machine learning, robust optimization, economic markets and wireless system design, this project designs robust intelligent algorithms for wireless stakeholders to ensure a reliable and resilient wireless infrastructure and ecosystem that can meet the critical requirements of spectrum access for current and future applications. Besides intellectual merit, the research helps develop the future wireless workforce by actively involving and broadening participation from high school and undergraduate students in spectrum-related research. The key innovation of this project is a suite of models and algorithms that fundamentally robustify modeling, optimization, and decision making in dynamic spectrum access using predictive intelligence. Specifically, the proposed research seeks to answer several key questions: how to make sure spectrum management and access is robust against uncertainty from spectrum data and predictive models; how to robustly monitor spectrum activities when spectrum ownership frequently changes; how to ensure a manipulation-free spectrum market with intelligent mechanism design. To answer these questions, the expected contributions of this project are: (1) a suite of techniques and algorithms for quantifying and integrating data and model uncertainty in automated dynamic spectrum access; (2) a new framework to achieve trustworthy spectrum monitoring model re-calibration during spectrum handovers; (3) a set of learning-based spectrum market mechanisms that maximize market efficiency or revenue while being robust against traditional and learning-oriented market manipulation. The outcomes of this research bridge the fundamental gap between the lack of robustness guarantee in current predictive intelligence models and algorithms, and the critical need for robustness in future dynamic spectrum access systems. 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

Computer simulation is a widely used but computationally intensive method for predicting the transient stability of power systems following large disturbances. Currently, simulation performance lags behind industry demand for online, real-time applications and research need for data-driven applications, particularly for practically sized systems with high penetration of renewable energy. This NSF project aims to develop a high-performance framework that enables data, task, and job parallelisms for transient stability simulations to scale to the full capacity of contemporary and future parallel computing hardware. The proposed framework will bring transformative changes to the understanding of how power system models should be represented and how computational workflows should be structured to take advantage of modern parallel computers. The intellectual merits of the project include a) accelerating the building and solving phases of differential-algebraic equations (DAE) through the design of parallel-enabled software representations of power system models, and b) the identification and utilization of computational methods and hardware devices based on the characteristics of simulation test cases. The broader impacts of the project include the dissemination of research findings via open-source software and publications, integrated research and education activities, and the potential to enhance the stability of the power grid infrastructure. Three tasks have been identified to accomplish the goal. Task 1 will create software representations of power models and computational workflows to enable staged data and task parallelisms for the DAE building process on CPUs and Graphics Processing Units (GPUs). It will ensure correct results from concurrent executions by coordinating the updating of equations shared across models while optimizing caches. Task 2 will develop adaptive dispatchers to identify and apply the most efficient hardware and solution algorithms for given power system cases, considering system size, acceleration techniques, and practical constraints. Task 3 will investigate pipelining algorithms for parallelizing multi-scenario jobs on heterogeneous hardware to build and solve DAEs for maximized hardware utilization. Upon successful completion, the project is expected to have established a novel, high-performance framework for modern computing hardware that will markedly accelerate the simulation of power system dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The PowerCyber project aims to address the underutilization of advanced Cyberinfrastructure (CI) in the domain of power and energy engineering. Despite the significant advancements in CI, its adoption in power engineering has been limited due to a lack of quality training materials and the historical reliance on limited tools. This project seeks to fill the gap by creating an online, modular, and open-access training workshop tailored for researchers in power engineering. The project will democratize access to high-quality research training, benefit a diverse population, and foster collaborations between the power and CI communities. Also, the project is expected to equip the research workforce with an understanding of advanced CI software and hardware and help accelerate interdisciplinary research for the clean energy transition. In this project, the investigators will develop an online, modular, and openly available PowerCyber training to prepare power engineering researchers with a comprehensive understanding of advanced CI software, hardware, and emerging CI technologies. The tasks include a) developing high-quality, interactive, and on-demand training modules to cover advanced CI in software, hardware, and emerging techniques and demonstrating, by research examples, their potential to transform power engineering research; b) offering virtual PowerCyber training workshops; and c) incorporating the training materials into the curricula at the home institution of the investigator. Upon the completion of this project, we expect to have demonstrated a pilot training workshop that provides researchers with advanced CI capabilities for solving power-domain problems. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Established Program to Stimulate Competitive Research (EPSCoR). 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

Unmanned Aircraft Systems (UASs), or drones, have tremendous scientific, military, and civilian potential for data collection, monitoring, and interacting with the environment. These activities require high levels of reasoning, perception, and control, and the flexibility to adapt to changing environments. However, like other automated agents, UAS don't possess the ability to refocus their attention or reallocate resources to adapt to new scenarios and adjust performance. This project will provide a new class of control and planning algorithms capable of adjusting performance as computing resources are continually reallocated, such as when transitioning from waypoint navigation to environmental sample collection. A computing framework to make use of freed resources will be developed allowing autonomous agents to focus attention where it is needed, for example, away from navigation and to perception. Together, these will provide a blueprint for making use of similar algorithms with adjustable performance (e.g., anytime algorithms) which can be adapted to other robotics platforms, as well as water, space, or ground vehicles. These technology innovations will improve the ability of agents to learn more, perceive more accurately, collect better data, and respond more appropriately to changing environments and mission objectives. Specific to UAS, this project will help maintain U.S. air superiority goals through agile planning, targeted and persistent Intelligence, Surveillance, and Reconnaissance (ISR), and flexibility and adaptability. The project goals are coupled with outreach and educational activities focused on increasing the understanding of rural populations of the value of investing in scientific and technological research. The educational efforts, targeted at K-12, undergraduate, graduate, and adult engagement are designed to dramatically increase the CPS educational pipeline in the Midwest. The project focuses on achieving its goals by providing a complete framework for a class of performance-adjustable, resource-aware algorithms called "co-regulation." First, a new modeling and analysis framework, Co-regulated Hybrid Systems (CHS), will provide a mathematical foundation for optimal control, control synthesis, and performance analysis for systems that can dynamically vary sampling rate and other computational resources to adjust performance. Next, using the CHS formalism, computational workload is predicted forming the basis for a novel Co-regulated Real-Time Kernel (CRTK) to dynamically reallocate computing resources while guaranteeing real-time schedule feasibility. Finally, a co-regulated Markov Decision Process (MDP) forms the planning portion of a resource-aware autopilot for adaptable UAS. The system will be implemented in a multi-agent, rainforest monitoring scenario requiring periods of surveillance, sampling of plants, and emplacement of sensors. 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

Methane is second only to carbon dioxide in its contribution to human-induced climate change due to its global warming potential, which is 34 times greater than that of CO2. Microorganisms in wet landscapes tend to release methane, whereas those in dry ones tend to take up the gas from Earth's atmosphere. Researchers at the Howland Research Forest in Maine have been measuring methane fluctuations across this sub-boreal forest since 2012. Their studies have found that the forest usually serves as a methane "sink" due to microbial consumption, although occasionally, under extremely wet conditions, the reverse can be true. This research site provides an ideal opportunity to study the conditions under which a forest would switch from a net sink to become a source of atmospheric methane. Under future climate change scenarios, the region is expected to become warmer and wetter, conditions that may induce a shift from methane sink to source, with the potential to have an impact on atmospheric methane concentrations at regional to global scale. This project will examine how forest soil microbial communities will change in response to climate warming, to identify the conditions that may lead forests to switch from being a methane sink to more of a source. The project will also support the cross-disciplinary training of graduate and undergraduate students and postdoctoral research scholars, including those from underrepresented groups in science. A series of public talks will be convened, and short videos and StoryMaps focused on science outreach will be paired with “scientist in the classroom” visits to local high schools. The project will host an open house for students and the public at the Howland Research Forest to learn about this important research. This study aims to identify - through the integration of field observations, laboratory analyses, and modeling - the conditions and mechanisms driving methane sink vs source activity in forests, using the Howland Research Forest in Maine as a case study. The project's novel approach focuses on three key areas to improve understanding of methane in such habitats: 1) identify the roles and response of soil microbial communities, specifically, methanogens and methanotrophs (and their functional guilds), in driving methane flux across environmental gradients; 2) understand and quantify how wet vs dry landscape microsites, and belowground vs. aboveground components within a forest contribute to seasonal and annual methane fluxes; and 3) integrate knowledge gained from field and laboratory analyses to inform and improve ecosystem process models. A suite of in-situ and lab-based experimental measures of methane production and oxidation, stable isotopes, and profiles of microbial community composition and function will be used to understand the mechanisms, processes, and feedbacks driving methane sink/source activity from site to landscape levels. At the site level, multi-scale observations of soil and aboveground methane fluxes, microbial traits, and associated in-situ environmental conditions will be obtained. To further understand and quantify methane response, in-situ and laboratory manipulation experiments to identify the role of functional guild activity, under changing environmental conditions, in regulating methane production/oxidation and ultimately net methane flux to and from the atmosphere will be employed. Finally, these data, integrated with project data-enhanced Microbial Model for Methane Dynamics-Dual Arrhenius Michaels Menten (M3D-DAMM) and Community Land Model-Microbe (CLM-Microbe) process models, will allow researchers to identify seasonal and annual methane sink/source activity at the landscape level within Howland Forest from the present to 2100. The research will include training at the undergraduate, graduate and postdoctoral levels, as well as a variety of outreach activities to engage high school students and the public. 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.