University of Maryland, College Park

NSF Awards · FY 2025 · 2025-01

Firebrand shower, also known as ember attack, is the ignition of spot fires due to the generation, transport, and accumulation of embers over vegetation or structural components away from the active fire lines. Although spot fires contribute to rapid wildfire growth and are known to be responsible for significant damages incurred at the Wildland Urban Interface incidents, the current understanding of the heat (energy) transfer mechanisms from firebrands to the exposed surfaces is incomplete. The primary aim of this project is to establish a fundamental knowledge of the heat (energy) transfer from firebrand depositions to the recipient surfaces. The outcomes of this project directly inform wildfire simulators by enabling a physics-based spot fire ignition module leading to more accurate forecasts and estimation of wildfire risk. The project will also encompass educational activities such as mentoring and training undergraduate and graduate students, engagement with stakeholders and practitioners, and creating collaborations between different engineering/scientific disciplines to address challenges in fire science. The main objective of this project is to quantify the heat transfer from the accumulated firebrands to the recipient surface using a systematic, scalable, and accurate quantitative approach. Specifically, the project performs the following tasks: First, conducting numerical simulations using a four-way coupled method to identify the effects of firebrand properties and their interactions within the boundary layer on the deposition patterns over an inert flat surface. Second, incorporating surface fuel morphology into the computational domain, using X-Ray Computed Tomography (XCT), and quantitatively discern its influence on firebrand deposition patterns and heat transfer. Third, leveraging the generated data from high-fidelity simulations, developing a data-driven heat transfer model, and testing its accuracy. The developed model makes the findings applicable for large-scale (operational) wildfire simulators, culminating in the development of a physics-based spot fire ignition module. The results are expected to provide insights into dominant heat transfer mechanisms involved in the spot fire ignition process and establish a computational framework for quantifying the energy transfer from reacting particles to the surrounding walls. 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

This project serves the national interest by holding two workshops to develop and refine a self-assessment tool for mathematics departments at research universities. The project-developed assessment will enable departments to increase their student success and graduation rates by improving teaching and learning in undergraduate mathematics courses. This tool will be developed in partnership with teams from three large Hispanic-Serving Institutions (HSIs): Florida International University, San Diego State University, and the University of Texas San Antonio. The mathematics departments at these partner institutions will also serve as pilot and testing sites for the new tool. The project has three phases. First, through the initial workshop and virtual collaborations, an expert writing team will work with departmental teams to adapt existing assessments that have been used effectively in other disciplines and at other types of institutions. Next, departmental leaders from the three partnering HSIs will use the self-assessment to explore how several proven strategies are being utilized in undergraduate mathematics courses at each site. Finally, the project team and participants will come together for a closing workshop to discuss lessons learned, revisions to the tool, and next steps. The current project is part of the larger Eliminating the Math Barrier through Evidence-based Reforms (EMBER) initiative which seeks to engage 100 institutions in deploying established student success strategies. The planned self-assessment will examine placement, course structures, course coordination, active pedagogies, student support, creating community, advising, technology, data on student progress, student assessment, classroom teacher development and client department needs. While this project will explore specific aspects of effective self-assessment at Hispanic Serving Research Universities (HSRUs), the tools developed will be applicable across institution types. Development of the self-assessment will build on existing tools and self-assessment practices, including those used in American Mathematical Association of Two-Year Colleges’ (AMATYC’s) Teaching for Prowess, Effective Practices for Physics Programs, the Departmental Action Team model, the MAA's Guidelines for Programs and Departments, and American Association for the Advancement of Science (AAAS) STEMM Equity Achievement (SEA) Change initiative. The three collaborating HSIs will examine their campus student success as part of the program self-assessment exercise. The department teams will disaggregate data whenever possible, allowing for the identification of challenges and gaps within and across groups of students. Key to the work will be teaching-focused faculty, who now coordinate instruction for most introductory mathematics courses at research institutions. As part of the piloting process, each institution will subsequently consider an action plan. The closing workshop will showcase each team's work, continue building collaboration, and gather feedback about the self-assessment with the goal of further developing the self-assessment for broader use. Lessons learned will be shared with the mathematics community and other disciplinary groups interested in exploring similar vehicles for departmental assessment and change. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The broader impact of this I-Corps project is the development of an artificial intelligence (AI) platform to help students learn to play the violin more effectively and affordably. Currently, many students struggle to get private music lessons due to their high cost and limited availability. This platform solves that problem by using regular webcams and microphones, making it easy for anyone to access with a computer. The system provides real-time feedback on how students are playing, helping them improve their posture, bowing, and sound quality. In addition, the technology helps them avoid common mistakes and progress faster while reducing the risk of injuries. Teachers also may benefit from this technology by getting detailed insights into their students' practice habits, allowing them to offer more personalized support. This AI-driven tool may expand access to music education, making it available to more students regardless of where they live or their financial situation. The solution also may appeal to schools, music teachers, and students looking for affordable ways to improve their skills, whether they are learning online or in person. This technology has the potential to keep students more engaged in their musical journey and help them achieve their goals faster. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of advanced artificial intelligence (AI) algorithms that analyze how violin students play by using both visual and audio inputs. The technology evaluates students' posture and bowing technique, providing instant feedback for improvement. Additionally, a unique collection of recorded violin exercises has been digitized to assess students' skills and suggest tailored practice materials. The project aims to enhance online music education by offering personalized tools that help students develop the motor skills essential for playing an instrument. The solution also advances research on how sound and movement interact, creating educational modules customized to individual learners. This project may improve music education and also contribute to scientific knowledge in areas like causal modeling and computational analysis of motion and sound. The data and tools developed may be applied broadly to understand how perception and physical actions are linked, providing valuable insights into the science of learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The global biodiversity crisis is a central problem facing humanity, yet we lack the means to assess biodiversity at the pace of the Earth's changing environment. For this project, rapid assessment technologies will be integrated at the landscape and island level to forecast unseen change in high-impact insect and spider populations tracked by their DNA. The project goal is to infer processes that shape biodiversity and its decline, and how these processes might be captured remotely across different scales and degrees of human impact. In the Hawaiian Islands, the velocity and extent of non-native plant invasions in ecological landscapes will be measured using satellite, helicopter, drone, and ground-based monitoring systems. Those metrics will be combined with assessments of insect biodiversity at the same sites generated from rapid, high-throughput environmental genomic analyses. Outcomes will aid land managers with actionable solutions, building on the ongoing activities of the research team and working with the Pacific Regional Invasive Species and Climate Change (Pacific RISCC) Management Network. Results will be translated for the general public through the web-based ESRI ArcGIS StoryMap. The researchers will provide mentoring for undergraduates, graduate students and a postdoc at the University of California Berkeley, the University of Maryland, and the University of Hawaii Hilo, the latter of which is a primarily undergraduate serving institution that helps meet the needs of Pacific Islanders. Products will include the development of a learning module and toolkit for students to adopt new skills of data analysis and visualization for communicating biodiversity and remote sensing data. Using the model system of the Hawaiian Islands, the project will couple high-throughput arthropod biodiversity sequencing and remote sensing imagery to examine correlated shifts across two orthogonal gradients set within the same native forest type. The first gradient is a geological chronosequence, from 0-5 million years, across which arthropod communities increase in diversity and become more ecologically specialized. The second, intersecting, gradient is composed of a landscape matrix that runs from native to heavily invaded forest habitats on each island. At plot scales, whole arthropod communities will be sampled using genetic signatures from high-throughput sequencing to test models of community assembly over extended ecological-to-evolutionary time, and hence infer the changing roles of key processes of filtering, competition, and neutrality, through time. The models will predict trajectories of disassembly in the face of rapid biotic change. Arthropod community analyses will be coupled with remote sensing imagery at scales ranging from regional (archipelago; satellites), to area (leeward slope of one mountain; helicopter), to plots within heterogeneous landscapes (drone imagery and airborne and ground lidar). The different remote indicators of change in the ecosystem (spectral properties, leaf and water content, nitrogen content, plant stress) will be integrated by using structural equation models (SEMs) to identify candidate parameters that reflect arthropod community dynamics in rapidly changing island forest systems. Joint species distribution models will be used to integrate data across scales. This research will test the predictability of remote sensing data for explaining the spatio-temporal variability of biodiversity and its resilience to anthropogenic modification. In addition to training at the undergraduate, graduate and postdoctoral levels, products will include the development of a learning module and toolkit for students to adopt new skills of data analysis and visualization for communicating biodiversity and remote sensing data. 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

Parallel computing, with proper engineering, has the potential to become the dominant computing paradigm, as demonstrated by the success of graphics processing units (GPUs) in the gaming application domain and more recently in the artificial intelligence (AI) application domain. In order to expand the horizon of parallel computing to more general-purpose application domains, it is imperative to resolve the existing mismatch between current parallel hardware and the type of parallelism present in these applications, which is often referred to as “irregular”. The core idea behind this project is to develop and study a novel parallel architecture that is geared for harnessing the difficult, irregular parallelism present in many applications. Successful completion of this project will be a significant step towards prototyping a parallel hardware system that serves applications in such diverse fields as hardware/software verification, automated reasoning, and more generally, computer security. Parallel processing of an application that has irregular parallelism such as satisfiability (SAT) solvers (where distributions of work and data defy pre-runtime characterization) is an extremely challenging task. This project attacks this grand challenge on multiple fronts—algorithms and data structures, parallel programming, and hardware architecture. While the software development part will rewrite applications using irregular threads and nested spawns, the novel hardware framework will use two different components to target different types of parallelism: (1) a sophisticated general-purpose processor that exploits instruction-level parallelism and (2) an integrated parallel processing accelerator that exploits irregular parallelism by providing mechanisms for low-overhead thread spawning and nested thread spawning. While inherently serial portions of the application will run on the general-purpose processor, the parallel portions of the application will run on the accelerator. An integrated accelerator that supports low-overhead nested spawning of threads will be designed and evaluated. Specifically, microarchitectural modeling and cycle-accurate simulation will be used to evaluate performance scaling for SAT solvers, a core routine that does the computational heavy lifting in a vast majority of automated reasoning technologies. Preliminary assessments of system-level integration of the processor and the accelerator with a low-latency, high-bandwidth memory system will be conducted. 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

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research grant supports fundamental research to enable automatic discovery of constitutive laws for soft functional composites using symbolic artificial intelligence (AI). Soft functional composites are essential to emerging technological and economic areas such as stretchable and wearable electronics, soft robotics, and sensing and actuation. Conventional methods for constitutive law discovery are usually time-consuming, inefficient, and often ineffective for complex cases. This project will significantly accelerate the constitutive law discovery process by leveraging symbolic AI technology not only for soft functional composites but also other material classes. The software tools developed will be made available to the research and education communities through a website. This project will also train undergraduate and graduate students, including underrepresented groups, workforce in the transdisciplinary area of AI, mechanics, and materials science. The project will establish a symbolic AI framework for discovering physically interpretable constitutive laws of soft functional composites, involving general tensor functions of constitutive laws, both scalar and tensor-based operators, and statistical analysis for noisy data. The project will establish general tensor functions of constitutive laws for different anisotropic materials using modified representation and symmetry theories. The symbolic-AI architecture will be built upon genome representation, physics constraints, symbolic tree search, and statistic modeling, and will be applied to discover and interpret mechano-physical constitutive laws of soft functional composites with various microstructures. The tool and the insights it provides will expedite the design and applications of soft functional composites, and eventually other material classes. 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

Prime editing (PE) is a recently developed, cutting-edge CRISPR/Cas gene editing technology that can accurately replace, add or remove specific pieces of DNA in an organism. Due to its high accuracy and versatility for gene editing, PE offers significant advantages over the conventional CRISPR/Cas technology and is a highly promising tool for basic plant research and applied crop breeding. This project aims to develop highly efficient and robust PE systems in rice, tomato, and poplar plants. The resulting PE tools are expected to facilitate fundamental studies in plant biology and genetic improvement of agricultural crops for important traits such as high yield, superior quality, disease resistance and abiotic stress tolerance. This project will also provide multidisciplinary research training in plant biology, functional genomics and genome engineering to postdoctoral fellows as well as graduate and undergraduate students. Outreach activities will involve local K-12 students, growers, and the general public through already established programs or newly created workshops or activities at Penn State, University of Maryland, and the National Rice Research Institute in India. Prime editing is a revolutionary and advanced CRISPR/Cas genome engineering technology that enables almost all forms of precise gene editing, including base substitutions, insertions and deletions within the genome. This versatility makes PE a highly valuable tool for precise editing of plant genomes, with broad applications ranging from basic research in plant biology (e.g., epitope tagging of endogenous proteins) to practical usage in crop breeding (e.g., creating desirable alleles). Despite its successful applications in the mammalian cells, high efficiency PE in plants has only been demonstrated in rice. To fully realize the potential of PE and meet the urgent need of plant biology and crop breeding communities for precise genome editing, it is imperative to develop highly efficient and robust PE systems in plants. This project aims to optimize prime editor proteins to increase their activity and editing efficiency, improve pegRNA structure and expression for single and multiplex prime editing, and modify plant DNA repair systems to increase PE efficiency. By incorporating these innovative features and extensively testing them in rice, tomato, and poplar, highly efficient next-generation PE systems are expected to be developed for precise genome editing of both monocot and dicot plants. All data and project outcomes including plasmid vectors will be made available to the broader research communities through publications and public repositories. 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 Devonian Period (419 to 359 million years ago) witnessed some of the most important transformations leading to the habitable planet that we have today. Plants and vertebrate animals first colonized the land surface, oxygen levels rose in the oceans and atmosphere, and the planet cooled significantly. There were also a series of mass extinctions related to a temporary loss of oxygen in the shallow oceans. The funded work seeks to understand the timing, causes, and consequences of these Devonian mass extinctions, and how they can be identified in rocks deposited across North America, as well as in Bolivia and Western Australia. Ultimately, this study will provide a new integrated framework of ocean oxygen loss across time and space through the Devonian extinctions, improving our understanding of how our planet came to resemble to modern world. This project also supports two early-career faculty members of mixed-race and African ancestry, expands field geology opportunities for high school students, supports numerous undergraduate and graduate students and a postdoc, and will disseminate results to non-technical audiences in English and in Spanish. The Late Devonian is a unique interval in Earth history during which the proliferation of land plants triggered a cascade of Earth system perturbations, including atmospheric CO2 drawdown and O2 rise, climate cooling, eutrophication and widespread development of anoxia in epeiric seas, and ultimately, a series of mass extinctions that fundamentally altered the trajectory of Earth’s biosphere. This proposal seeks to link key Late Devonian global events in a new genetic framework that ties a refined temporal record of anoxic expansion in epeiric seas across Laurentia and Gondwana directly to the extinction events, determines the effect of epeiric sea anoxia on the global carbon cycle, and then links these records to global carbonate-based isotopic curves. Specifically, this work proposes to: 1) develop a new, integrated geologic framework that ties Late Devonian mass extinctions to the epeiric black shale successions of North and South America using a combination of conodont biostratigraphy, Re-Os geochronology, and redox geochemistry; 2) use these data to refine the open access Macrostrat database for the Late Devonian with the goal of estimating global carbon burial, CO2 drawdown, and O2 buildup; and 3) generate a new uranium and carbon isotope record through Late Devonian carbonates of Western Australia, which will provide a quantitative record of global ocean anoxia and carbon burial. 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

Collaborative Research: ENG-SEMICON: Integrating Magneto-ionic and Ferroelectric Control of 2D Magnets for Energy-efficient Skyrmion-based Memory Non-technical abstract: Magnetic materials are widely used in today’s society. In conventional magnetic materials, their magnetic moments prefer to line up in a parallel fashion. In certain special magnetic materials, their moments wind up into a twisting pattern named “magnetic skyrmions”, which contain topological characters. Such magnetic skyrmions are considered as potentially robust information carriers for the development of novel memory devices for data storage and computing. Hence, low-voltage control of magnetic skyrmions becomes a scientifically intriguing and technologically relevant topic. This project aims to build low-power skyrmion memory based on the studies of the effects of adjacent materials such as ionic materials and ferroelectric materials on atomically thin magnets such as two-dimensional van der Waals magnets. When a small voltage is applied to either mobilize the ions or switch the ferroelectric polarization, properties of the neighboring magnets will be altered, potentially leading to the energy efficient control of magnetic skyrmions in two-dimensional magnets. When the ionic materials and ferroelectric materials are stacked together, the synergy of these two materials may enable new types of energy-efficient skyrmion memory. Graduate, undergraduate, and high-school intern students, from all backgrounds, are trained with a rich set of expertise in two-dimensional materials preparation, thin film deposition, nanoscale device fabrication, and a variety of optical and electron-beam microscopies and spectroscopies. This project can help prepare the future workforce for the semiconductor device science and technologies in the U.S. and raise the public literacy of microelectronics by new course development and local educational activities. Technical abstract: Magnetic skyrmions are topological spin textures that have been envisioned to circumvent local defects, in contrast to domain walls that are more susceptible to defect pinning, for efficient and reliable information storage and transmission. Creation and manipulation of skyrmions in ultrathin material platforms may enable energy-efficient ultracompact spintronic devices. However, traditional magnetic thin films inevitably contain defects and structural nonuniformities, hindering the development of high-performance skyrmionic devices. In stark contrast, the emergent two-dimensional van der Waals magnets exhibit single crystallinity with minimal defects, holding unique promise for exquisite control of skyrmions towards practical devices. This project aims at achieving energy-efficient skyrmion-based memory by creating, manipulating, and annihilating skyrmions in two-dimensional van der Waals magnets using magneto-ionic and ferroelectric means. First, a magneto-ionic gate will be used to locally tailor the Dzyaloshinsky-Moriya interaction and magnetic anisotropy in magnets through ionic migration. Second, heterojunctions of ferroelectrics and two-dimensional magnets will be implemented to globally engineer the atomically thin magnets for skyrmion control through polarization-tunable Dzyaloshinsky-Moriya interaction and magnetic anisotropy. Third, the magneto-ionic and ferroelectric control will be integrated onto van der Waals magnets so that (1) the electric field effect in ionic layers can be amplified by ferroelectrics, (2) the ferroelectric coercivity can be lowered by ion-modulated domain wall nucleation, and (3) as a result, the voltage controlling efficiency of skyrmions can be largely enhanced, potentially enabling low threshold voltage switching of the skyrmion phases for non-volatile memory with ultralow energy consumption. This project will have broad impacts on the understanding of magnetic skyrmions in low-dimensional systems and the development of unconventional, energy-efficient memory devices, and will serve to prepare the workforce with expertise in energy-efficient nanoelectronic devices for the microelectronics industry in the U.S. 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 investigating how athletic contexts can be leveraged to advance data literacy, strengthen STEM identities, and expand participation in science, technology, engineering, and mathematics (STEM). College athletes across the United States consistently engage with data practices such as reviewing statistics, projecting outcomes, and making evidence-based decisions. However, these data-rich experiences are rarely recognized or integrated into formal education, creating a missed opportunity to connect existing practices with STEM pathways. DataGOAT (Greatest Of All Time) will co-design an innovative educational infrastructure that connects sport performance and health data to academic coursework in data science. Students will learn to collect, analyze, visualize, and interpret data through applied experiences grounded in athletics, helping them to recognize themselves as data practitioners while developing important STEM competencies. The project will include new university courses, experiential learning opportunities, and a customized data analysis platform to support teaching and learning. The research will address three central questions: (1) What structural and socio-cultural factors influence athletes’ engagement with data literacy in higher education? (2) How can technology-integrated courses be designed to strengthen data literacy and STEM identity among student populations? and (3) What models of curricular integration most effectively bridge applied data practices in sport with broader academic and career pathways in STEM? Through longitudinal interviews, ethnographic observation, co-design workshops, and quantitative surveys, the project will generate new knowledge on how data literacy develops in applied, non-traditional learning environments. Outcomes include the creation of openly licensed curricula, a data analysis environment, and dissemination of findings to higher education institutions and K-12 partners. The project is expected to directly engage more than 350 students across multiple universities, with potential for broad replication nationwide. By linking the cultural relevance of sport with data science, DataGOAT has the potential to expand STEM participation, prepare our future workforce, and contribute to national competitiveness in 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 · 2024-10

Clustering algorithms are one of the most important modern tools for understanding data. Given data on various entities, clustering algorithms group entities into sets or "clusters" such that similar entities are likely to end up in the same cluster while dissimilar entities tend to end up in different clusters. For example, clustering algorithms can be used to group images together according to the contents of the image. However, modern datasets are so large that many existing clustering algorithms cannot be feasibly used. This project aims to systematically address this situation by way of new clustering algorithms that scale to massive datasets with billions of entities. Clustering is widely used by scientists, companies, and government agencies. The toolkit developed in the project will be open-sourced and will make scalable, high-performance clustering more broadly accessible to scientists and practitioners by improving the efficiency and programming productivity of their clustering tasks. Results from the project will be integrated into courses that the investigators teach, and the researchers will recruit undergraduate students to participate in the project. This three-institution collaborative project investigates a new approach for clustering pointsets by constructing sparse graphs that preserve relevant properties of the pointset. By carefully leveraging high-quality near-linear work graph clustering algorithms, very large datasets can be clustered in time that is nearly linear to the number of objects in the input with high accuracy. Particular attention will be paid to new algorithms for graph clustering and construction that utilize structure observed in practice, exploit parallelism, and enable dynamism with provable accuracy guarantees. A major contribution of the project will be an end-to-end clustering toolkit for graphs and pointsets that enables clustering to be scaled to inputs with billions of objects. The investigators will collaborate through regular remote meetings and seminars, student visits, joint publications, and annual in-person workshops. 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 project team leverages rich historical job-postings datasets to construct a new measure for organizations’ involvement in environment, social and governance (ESG)-related activities. In recent decades, there has been growing societal demand for organizations to attend to their ESG-related responsibilities. While organizations are under intense evaluation for their involvement in ESG, most of the popular ESG ratings used by both academics and the investor community are produced by third-party rating agencies. These ratings are largely a black box, with little transparency of the underlying input data or the methodologies that have been employed to produce these ratings. It is thus risky for the academic and the broader investor communities to over-rely on such third party-generated ratings due to the lack of accuracy, transparency, or reproducibility. As a result, constructing a new ESG measure with improved quality and transparency is desired, which motivates this project. With the assumption that organizations need human capital to get the work done, jobs offered by organizations reveal important information about organizational priorities. This project leverages recent availability of 250 million job-postings from 2008 to 2023 (and are still being updated) for 60,000 companies of various sizes to compute a job-posting-based novel ESG measure. The team employs the pre-train and fine-tune paradigm of text representation learning, upon which a fine-tuned large-language model (LLM) coupled with a neural network-based classifier is developed and applied to the textual job postings for constructing the ESG measure. The project aims at delivering three sets of output: (1) a new measure of organizational ESG engagement (and relevant scores in sub-categories), (2) the generic framework and methodology underlying the construction of this new measure, and (3) a report and a re-evaluation study of a widely-cited research article on ESG. All three sets of products contribute to organizational research on ESG and will be shared with 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.

NSF Awards · FY 2024 · 2024-10

Forest ecosystems play a critical role in the Earth system as major carbon sinks, which are essential for carbon neutralization and climate change mitigation. However, significant deforestation and forest degradation could push the Earth to climate change tipping points. As such, there is a growing interest in forest carbon sequestration through afforestation and reforestation initiatives from local to global scales. These developments have led to a strong demand in advancing the scientific understanding of the impact of forest carbon dynamics in the carbon cycle. Specifically, a new generation of models has emerged to connect individual plant level processes with the global carbon cycle. In addition, advancements in remote sensing have generated unprecedented new high-resolution measurements at the global scale. Despite these opportunities, several key challenges persist in understanding forest carbon dynamics, including the lack of understanding of fine-scale but widespread disturbances such as tree mortality in existing remote sensing products, the computational bottlenecks of the theory-based models for global scale analysis, and the limited flexibility of the models in enhancing the prediction quality using new observations. This project aims to develop new capabilities to bridge these research gaps and significantly advance the monitoring and understanding of forest carbon dynamics in the Earth system. The enhanced understanding can provide necessary information for estimating carbon budgets and realizing carbon neuralization goals. The research results will be used to develop materials for both undergraduate and graduate courses in AI and geosciences. The project will also engage students from underrepresented groups in the research activities and partner with K-12 schools to promote education on topics intersecting AI and geosciences. This project will result in several advances of artificial intelligence techniques with the potential to further the understanding of how forest carbon influences the Earth system’s carbon cycle under climate change and what terrestrial ecosystems’ capacity is in climate change mitigation. First, the project team will develop cross-platform and cross-region learning frameworks to enable fine-scale carbon dynamics monitoring at large geographic scales. Second, the team will create high-fidelity fast approximations of the theory-based carbon forecasting model by developing new theory-guided meta-learning and invertible frameworks to enable global-scale capabilities under diverse climate change scenarios. Finally, the team will develop new theory-guided diffusion methods to significantly enhance the ability of theory-based models in improving predictions by leveraging observations enabled by new sensing platforms. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences 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 · 2024-10

This Faculty Early Career Development (CAREER) award supports research that will elucidate fundamental phenomena behind the geo-hydrological issues contributing to spring floods, particularly how hydraulic hysteresis due to freezing and thawing impacts frozen soils and changes water infiltration. This is crucial today due to the quickly increasing effect of climate change. In cold regions, frozen soil controls the partitioning of snowmelt flux between surface runoff and infiltration. The primary mechanism controlling the ground heat flux is thermal conduction associated with freezing and thawing. However, flowing water can transfer substantial heat by advection during water infiltration induced by snow melting. In the long term, this project will contribute to cost-effectiveness and sustainability for multiple stakeholders (e.g., environmental agency regulators and governments, impacted communities, and nongovernmental organizations), promote science’s progress, and advance national health, prosperity, and welfare. The educational activities will also significantly impact education and public knowledge. This work will result in a sustainable outreach module for K-12 students that could be implemented in other settings and undergraduate and graduate courses, course modules, and research training experiences. The research objectives of this CAREER award are to: 1) understand microscale interfacial behavior of a soil particle and water-ice matrix in the unsaturated state during the soil-freezing process and water infiltration in freezing-thawing cycles; 2) determine the hysteresis of the Soil-Freezing Characteristic Curve and the Soil Water Characteristic Curve of selected soils associated with pore distribution, thermal properties, and pre-freezing moisture; 3) elucidate the dynamics of unsaturated water infiltration into frozen soils during temperature cycles; and 4) enhance an existing hydro-thermal coupled modeling scheme with laboratory experiment results to explicitly predict water flow through soils and identify the sensitivity of selected variables. This award will support the first comprehensive work to integrate pore-scale observation/modeling, bench-scale lab experiments, and multiphysical modeling of water infiltration into frozen soils to elucidate interrelations among the multiple components forming a soil-water-ice-pore matrix. The work will enhance the assessment and analysis of water infiltration through a vadose zone system to expedite its adoption in practice, thus promoting flood prediction, sustainable agriculture, and mitigation of climate change impacts in the mining, natural hazard, and erosion fields. This project is jointly funded by the Engineering for Civil, Mechanical and Manufacturing Innovation (CMMI) Division 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

Night shift workers play a crucial role in a continuously operating economy. However, they also encounter unique challenges such as limited transportation options and increased safety risks during nighttime hours. These issues are particularly pronounced in Baltimore City, Maryland, which grapples with crime rates in the evening, inadequate nighttime transit coverage, and mismatches between job locations and residential areas for night shift workers. The recent collapse of the Francis Scott Key Bridge has exacerbated commuting pressures further. The primary objective of this CIVIC Stage 1 award is to improve mobility and safety for night shift workers in Baltimore. This project will investigate the mobility and safety needs of nigh workers and the spatial job mismatch problem in Baltimore. By working closely with local communities, transportation authorities, and the local mobility industry, this project will identify effective and sustainable solutions, such as redesigning transit routes, adjusting schedules to meet nighttime demand and exploring flexible transportation services. In Baltimore City, Maryland, persistent high crime rates, particularly at night, present significant challenges for night shift workers, especially in economically disadvantaged neighborhoods. This issue is compounded by a spatial mismatch between the locations of night-shift jobs and residential areas, which restricts mobility options and heightens vulnerability to crime, exacerbated further by the recent collapse of the Francis Scott Key Bridge. To address these issues, our objectives are fourfold: first, to investigate the spatial mismatch problem in Baltimore City, focusing on night shift jobs; second, to analyze the mobility needs of night workers and develop a tool to assess vulnerability in high-risk areas; third, to identify deficiencies in public mobility services and support Maryland Transit Administration (MTA) in improving transit systems, while exploring alternative transportation solutions; and finally, to cultivate partnerships with local industries to enhance public transport options as necessary. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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 will contribute to the national need for well-educated scientists, mathematicians, engineers, data scientists, and technicians by supporting the future retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Maryland (UMD), the University of Maryland Eastern Shore (UMES), and Bowie State University (BSU) in the University System of Maryland. UMD is the flagship university and UMES and BSU are HBCUs. Over its one-year duration, this Collaborative Planning Grant will prepare the foundation for a Track 3 S-STEM proposal to support S-STEM M.S. students in Engineering (UMD), Chemistry (UMES), and the Internet of Things and Internet Technology (BSU). These disciplines fill local and national needs and represent areas of strength and strong student interest at each University. In addition, they are areas where students can move between consortium partners to earn M.S. degrees. Institutional data, surveys, and focus groups will be used to analyzed to identify how the consortium can best attract, support, and graduate talented scholars and help them launch careers. The long-term goal is to develop a systematic model to successfully couple HBCU institutions with research-intensive universities. The project model would draw on each institution’s strengths to develop a program that graduates low-income M.S. students prepared for the workforce. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving graduate students with demonstrated financial need. The project goals are to develop the knowledge base and approach to support development of a Track 3 S-STEM proposal and program for M.S. students at the three collaborating universities. The project team has identified three research questions to support this effort: 1) How does financial support impact the decision of low-income students to pursue M.S. degrees?; 2) What types of financial aid are most effective in supporting low-income students through their M.S. degrees?; and 3) How can institutions work together to develop a pathway for successful S-STEM scholars’ education? These questions will be addressed using information gained through analysis of institutional data, along with surveys and focus groups of potential S-STEM students, financial aid officers, graduate admissions counselors, and team leaders at each institution. Results will be disseminated by presentations and proceedings at STEM education conferences, journal articles, and sharing of anonymized data that other universities can use to adapt this model. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Artificial intelligence (AI) works by learning from patterns in data. Building AI technologies depends on acquiring personal data for training models. Responsible development of AI as part of public interest technology (PIT) requires building AI that benefits the public interest while safeguarding personal data used to power AI systems. Safeguarding data require tradeoffs between the level of protection provided and the usefulness of the models created with the data. These tradeoffs create a tension that PIT organizations must resolve. This project engages a multi-disciplinary team across sectors in a combination of ethnographic and computational research to develop novel approaches that can support PIT organizations in deploying data safeguards to build AI. The project uses disclosure limitation techniques to protect the privacy of sensitive information in AI training data. Deploying these techniques, including newer techniques like differential privacy (DP), require making tradeoffs that affect stakeholders in the AI lifecycle. For example, strong privacy protection reduces statistical accuracy, which may ultimately reduce the model usefulness. The project will develop novel methods and best practices for navigating these aspects for PIT organizations. The project will: (1) use ethnographic approaches and qualitative inquiry to identify socio-technical decision points and challenges at PIT organizations; (2) create and evaluate novel approaches to participatory engagement of stakeholders in the deployment process; and (3) build software and communication tools for evaluation and transparency of AI systems that use differential privacy. 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

Various disparities in STEM higher education have been the focus of many programs for decades. Practices typically emphasize preparing historically excluded populations and overlook or minimize preparing the institutional environment to be inclusive. The Bowie State (BSU)/University of Maryland Eastern Shore (UMES)/University of Maryland (UMD) Network proposes the development of a Culturally Responsive Community of Practice to facilitate the adaptation of culturally responsive practices in graduate education and mentoring. Culturally relevant practices have recently begun to be incorporated into secondary education; however, these practices may also be effective in addressing the gaps observed in STEM graduate education. Previously funded work by the BSU/UMES/UMD Network evaluated how historically excluded populations at the Network institutions viewed the graduate school at UMD. The survey results from students, faculty, staff, alumni, and administration informed the focus and approach to be utilized in this project. The Community of Practice is designed to provide faculty who have expressed interest in working with the Network with an opportunity to adapt culturally relevant methods to teach and mentor historically excluded populations. Additionally, the proposed project intends to help student participants become familiar with graduate school opportunities at the various institutions, especially UMD, and engage them in necessary research training to ease their transition into graduate education in STEM fields. This novel approach addresses disparities for historically excluded populations in STEM graduate education and has the potential to be adopted by other schools in Maryland and the nation. Culturally relevant pedagogy in STEM refers to approaches that incorporate diverse backgrounds, experiences and perspectives into teaching. Culturally relevant pedagogy is supported by cultural awareness, cultural competence and cultural responsiveness of the educator. This pedagogy has been introduced to secondary educators, postdoctoral students, and graduate students; however, graduate STEM faculty have had limited exposure. The goal of this proposal is to conduct four trainings in culturally relevant pedagogy for STEM faculty and implement an online mentor training program. The training and mentoring program are intended vehicles in developing a Culturally Responsive Community of Practice. The intended structure of the Culturally Responsive Community of Practice is to allow participants to share culturally responsive research opportunities and instructional strategies as well as facilitate continuous engagement among regional colleagues in similar fields. The aim is to implement the culturally relevant practices in a 10-week summer research program held at the three partnering institutions for undergraduate students from historically excluded populations. In addition to the 10-week summer research program, plans include each Network institution hosting professional development workshops for students, which will culminate in a joint symposium and students having the opportunity to travel and present their research, extending their connection with mentors and lab mates beyond the summer. Project activities, including an instrumental case study approach employing mixed methods, are also designed to address the following research questions: 1) What is the impact of a cross-institutional culturally responsive Community of Practice on cultural awareness, responsiveness, and competency among STEM faculty; and 2) What is the impact of a cross-institutional culturally responsive Community of Practice on interest in STEM research among undergraduate students from historically excluded populations. This collaborative project is funded by the EDU Racial Equity in STEM Education activity, which is supported by the Directorate for STEM Education (EDU). This activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Mathematics education research has emphasized instruction that asks teachers to use approaches that center students’ mathematical thinking. A significant part of this is how teachers notice, or focus on, analyze, and decide how to respond to, mathematics thinking. One common professional development method is to use videos of mathematics teaching to help teachers understand what is possible for students' learning. However, professional development facilitators need to develop skills and practices to support teacher learning using classroom video. This exploratory project aims to understand how facilitators of video-based teacher professional development learn to help mathematics teachers of middle and high school students notice student mathematical thinking. The work uses an online context because it provides more opportunities for facilitators who might be limited by location or resources to participate. The project consists of two strands of work. The first strand is focused on the design and enactment of an online video-based professional development for video club facilitators of mathematics teachers. The second investigates facilitator learning and ways to support it. The project will iteratively design resources for facilitator educators that can guide and support the future development and enactment of learning experiences for facilitators of video clubs and video-based professional development, something that is currently missing in the field. The research questions are: (1) How can an online, video-based professional development support facilitators in learning to lead teachers in a video club? (2) In a facilitator professional development, how do facilitators learn to support teachers’ noticing? An important part of the facilitator professional development design includes helping facilitators learn to use teacher video annotations as a window into teacher thinking during video clubs. The project will use a design-based research framework to qualitatively analyze facilitators' tagging of videos, recorded professional development sessions, interviews, and other artifacts of the facilitators' work. 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 · 2024-09

Nontechnical Description Processing and memory take place in different parts of traditional computers. Data is transferred from long term storage into memory, and then passes back and forth between memory and the CPU. These processes inevitably limit the information processing speed, and increasingly consume greater amounts of energy. Given that data centers consume more than 1% of electrical power worldwide, there is a pressing need for fast, energy efficient computing. Such a challenge triggers the widespread interest in developing logic-in-memory hardware, where the latent time in data processing can be effectively minimized. For this quest, the team seeks to realize an energy efficient logic-memory bifunctional device. Ferroelectric tunnel junctions (FTJs) are a critical class of non-volatile memory devices holding unique promise because they can be seamlessly integrated with the contemporary silicon chip platform. The overarching goal of this project is to develop high-performance, energy-efficient FTJs for in-memory computing, based on the co-designed two-dimensional (2D) van der Waals ferroelectric materials, interfaces, and multilayer stacks. To realize this vision, three main figures of merit for the FTJs need to be realized in the same device: low switching voltages, high ON/OFF ratios, and high ON-state current densities. The team applies first-principles calculation, quantum transport simulation, high-quality bulk crystals and thin films synthesis and characterization, and FTJ nanodevices fabrication and characterization, to co-design 2D ferroelectrics with low coercive field and optimized metal/2D ferroelectrics/graphene FTJ stacks with high ON/OFF ratio and high ON-state current density. The team employs a co-design philosophy, with a strategic plan to develop the materials and devices through scalable synthesis and manufacturing techniques. This brings the research outcomes of this project closer to industry-compatible realizations. The team conducts workforce training through workshops (e.g., “Quantum + Chips” summer program), and trains students in device physics and fabrication expertise that are critically needed for a greater microelectronics workforce in the U.S. The team’s education plans also include course material development out of this unique material/device co-design research and the continued outreach to students of underrepresented groups both in co-PIs’ three regions and nationally. Technical Description Ferroelectric tunnel junctions (FTJs) are critical non-volatile memory devices that can be seamlessly integrated with the silicon chip platform. This integration of FTJs into silicon microelectronics can facilitate the physical proximity between memory and logic units, thereby reducing the cross-hardware latent time and enabling high-speed data processing. If an FTJ memory can also function as a logic device such as a tunneling field effect transistor, it would then be memory-logic bifunctional. Such a technological breakthrough would be monumental for in-memory computing. To achieve this goal, compelling advances in FTJs are needed. Specifically, three main figures of merit in performance and energy cost should be satisfied: low switching voltages (e.g., < 0.2 volt), high ON-OFF ratios (e.g., > 10,000,000,000), and high ON-state current densities (e.g., > 100,000 ampere per centimeter square). To achieve these metrics, the team plans to co-design 2D vdW ferroelectric materials, interfaces, and multilayer stacks, potentially leading to three aspects of knowledge advances. First, the team plans to develop a new synthesis approach for ferroelectric In2Se3 with “negligible defects,” and use these crystals to explore the intrinsic properties (e.g., coercivity) of 2D ferroelectric crystals and the intrinsic electron tunneling behavior in 2D FTJs. Second, this project aims to elucidate the fundamental relationship between 2D FTJs’ ON/OFF ratio and basic material parameters including bandgaps of 2D ferroelectrics, graphene layer numbers and doping levels, and metal work functions. Third, this project can experimentally map out the band alignment information between conventional metals and 2D ferroelectrics, revealing the relationship between the metal/ferroelectric band alignment and the resulting ON-state current density. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

While our atmosphere is transparent to visible and radio wavelengths, it is difficult to observe the near-infrared (NIR) from the ground, due to the extreme brightness and variability of the night sky at these wavelengths. The atmospheric emission in the NIR arises almost entirely from a forest of extremely bright, very narrow emission lines from OH molecules that vary on timescales of minutes. This team has recently demonstrated a technique to filter out these emission lines using technology borrowed from photonics. This project will use this photonic OH suppression filter in combination with the RIMAS instrument on the Lowell Discovery Telescope (LDT). The research team will use this new instrument to follow up extremely luminous but transient explosions of massive stars (gamma-ray bursts or GRBs). They will train graduate and undergraduate astronomy students in instrumentation and broad scientific practices at the interface of photonics and astronomy. Some of the undergraduate students will be from neighboring minority-serving institutions as part of Maryland's GRAD-MAP initiative. The Maryland OH Suppression Infrared System (MOHSIS) has the potential to change the way NIR spectroscopy is done from the ground. The unique approach of MOHSIS relies on using fiber Bragg gratings, fabricated by imposing about 100,000 tiny (relative amplitude of 0.0001) variations of the refractive index along individual optical fibers. MOHSIS will be more powerful than the team’s first-generation instrument, called PRAXIS, for several reasons: (1) MOHSIS will remove the 200 strongest OH sky lines at 1.1 - 1.7 microns, covering the critically important J band between the visible and H-band spectroscopic observations. (2) MOHSIS will have lower background noise. (3) LDT will deliver image quality at least twice as good as the host telescope of PRAXIS. Moreover, LDT is ideally suited for GRB follow-ups as it allows for a fast (about 1 min) switch of instruments and simultaneous optical and NIR observations. The low-resolution spectra of optically dark GRBs will be combined with simultaneous optical photometry to derive dust extinction and accurate redshifts based on the position of the Ly-alpha break. Space Variable Objects Monitor (SVOM), which is specifically designed to find distant GRBs out to the era of the first generation of stars, will provide high-redshift GRB candidates starting 2024-25. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Uncertainty quantification—determining how much a prediction can be trusted—is a fundamental challenge in geoscience and is central to cost-effective decision-making, mitigation of extreme weather hazards, and adaptation to a changing climate. Similarly, inequity quantification—measuring how well a predictive model serves different populations—is critical to ensuring that historically marginalized communities, disproportionately vulnerable to extreme weather and climate change, are adequately served by weather and climate models. The growing use of artificial intelligence (AI) models in geoscience has made uncertainty and inequity quantification more important, and difficult, than ever. This project supports a partnership between geoscientists and computer scientists to co-develop novel AI-based approaches to quantify and explain uncertainty and inequity in geoscience. By considering social inequities in geoscience, this project will help shape the future of geoscience through a more interdisciplinary understanding of the Earth system. This project will also build capacity for cross-discipline collaboration and education between computer science and geoscience students, helping meet workforce demands for scientists with experience in both AI and geoscience. Three algorithmic innovations will be developed as part of this project. The first innovation develops a computationally efficient framework capable of producing easily interpretable estimates of aleatoric and epistemic uncertainty in geoscience. The second innovation develops generalizable metrics that quantify inequities in Earth system model performance. The third innovation develops a novel AI-ready multimodal (text and image) geoscience dataset that will be used to fine-tune a large multimodal model, capable of describing geoscience imagery and associated uncertainties and inequities. Collectively, these innovations will enable the contextualization of several sources of uncertainty and inequity in geoscience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Researchers working in computer, information, and data science regularly extract huge amounts of data from online platforms like YouTube, Facebook, and Reddit to study people and their activities. We call this pervasive data—rich information generated about people through their digital interactions with social and mobile media, wearables, and more. Pervasive data spans multiple domains of people’s lives and is often gathered through digital interactions hidden from end users’ full awareness. Notably, the collection and use of pervasive data often does not fall into the category of “human subjects research,” meaning it is often not overseen by university ethics review boards. This leaves researchers on their own to make decisions about appropriate data collection, storage, sharing, and analysis practices. At the same time, many researchers have not received much—if any—training on how to address ethical questions related to these practices. The collaborative project “Developing Educational Resources for the Ethical Use of Pervasive Data” addresses these issues in two phases. In Phase 1, the research team will survey and interview current computing students and junior researchers at US universities, and complete a systematic evaluation of existing computing and Responsible Conduct of Research (RECR) programs. Goals of Phase 1 are to identify current practices, challenges, and opportunities for developing ethics training that address the unique issues with using pervasive data. Phase 2 builds on these findings. The research team will develop and evaluate various curricular materials, tools, and resources to support research ethics training, including (1) adapting a previously developed ethical decision-making tool, (2) creating training materials for students and junior researchers, and (3) developing "train the trainer" modules to help more-experienced researchers confidently mentor others in pervasive data ethics. The intellectual merit of this project lies in its potential to enhance research ethics education by creating innovative teaching materials based on case studies and interactive modules. It aims to integrate content on ethics and responsible computing into broader coursework and contribute to the field of data and research ethics by addressing knowledge gaps among computing students. The broader impact of the project includes promoting ethical awareness and responsible research practices among students and researchers. The project will disseminate its resources widely to computing educators, students, university programs, and other stakeholders through various channels, including a project website, social media, and webinars. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

When studying complex systems, having a simplified description is crucial. Often, this simplification comes from focusing on a select few factors; however, factors that initially seem insignificant can be critical over longer time scales. Thus, a deep understanding of interactions across scales is essential for more effective models of complex systems. This project will address issues in the asymptotic behavior of infinite-dimensional systems that are governed by stochastic partial differential equations (SPDEs) with multiple scales, focusing on SPDEs with conservation laws—a field that remains largely unexplored for systems of infinite dimensions. The planned work will bridge significant gaps in theory and introduce new approaches to the analysis of these multiscale systems. Graduate students and postdocs will participate in the research, and the awardee will develop graduate courses and contribute to the broader mathematical community through lectures, organization of events, and editorial service. Central to this research is the analysis of the Smoluchowski-Kramers diffusion approximation for stochastic systems with an infinite number of degrees of freedom. We aim to prove the validity of the small-mass limit for stochastic damped geometric wave equations, initially concentrating on stochastic wave maps in one dimension and expanding to more complex systems with state-dependent friction. Both non-local and local friction coefficients will be explored, studying their implications on the trajectories over finite time intervals and on stationary solutions. Further, the project plans to develop an infinite-dimensional version of the classical Friedlin-Wentcell averaging theory for random perturbations of PDEs with conservation laws. This includes constructing SPDEs that live on the level sets of specific functional, establishing the existence invariant measures for these processes, and proving their unique ergodicity and averaging limits. Through these endeavors, the proposal aims to understand better the long-term effects of small stochastic and deterministic perturbations on complex systems. By achieving a deeper understanding of these interactions, the research not only contributes to the fundamental theories in mathematical physics and applied mathematics but also provides robust tools for addressing similar phenomena in various scientific 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 2024 · 2024-09

The Epoch of Reionization (EoR) begins when the first luminous objects in the Universe form and their intense ultraviolet emission starts to reionize the neutral hydrogen of the intergalactic medium (IGM). With continued emission of ionizing radiation, reionization fronts form gradually expanding bubbles around the luminous sources. The growth of these ionized bubbles is patchy in both time and space, but the bubbles eventually merge and the EoR ends. Astronomers have yet to study the EoR through the most direct method: tracing the neutral IGM itself through detection of the redshifted 21 cm line. The CO Mapping Array Project is a Line Intensity Mapping experiment using a spectral line other than 21 cm. A collaborative project between California Institute of Technology, University of Miami, and University of Maryland will duplicate the existing Pathfinder receiver and use it to perform a survey of rhe carbon monoxide (CO) line across the sky. The research team will work with the Caltech Education Office to develop material to teach students in underserved high schools about coding and astronomy. During the summer, the project will provide training to teachers to deliver this material and will support the teachers with classroom visits during the school year. The field of 21 cm cosmology has the potential to probe the structure and evolution of the inter- galactic medium, from the Cosmic Dark Ages through to Cosmic Dawn, the Epoch of Reionization and beyond. There are many challenges to this work: a foreground-to-signal ratio spanning five orders of magnitude, strong radio frequency interference (RFI), and subtle instrumental systematic errors. The COMAP project expects to overcome these challenges. After the COMAP survey, the team will cross correlate the resulting CO temperature cube with observations of the same field by a 21 cm cosmology experiment, the Low Frequency Array. These experiments have very different systematic errors, RFI environments and foreground levels, and the planned cross-correlation will therefore be insensitive to these effects. The investigators forecast that the resulting constraint on the CO × HI power spectrum will be 30 times better than the best current limits on either CO or HI. This provides a path to the first unambiguous confirmation of the EoR-era 21 cm signal and a route to tighter constraints on the HI autocorrelation power spectrum alone. 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.