University of Georgia Research Foundation Inc

NSF Awards · FY 2025 · 2025-05

This award will provide funding for the “Integers Conference 2025,” which will be held in Athens, Georgia, May 14-17, 2025. The conference is being hosted by the Department of Mathematics of the University of Georgia. The Integers conferences, of which this will be the tenth, serve to bring together mathematicians and students interested in the fields of combinatorics and number theory. The conference will promote interaction among research mathematicians at all career stages ranging from undergraduate mathematics students to internationally distinguished researchers. A significant portion of the funding will support students and early-career faculty. The conference will serve as a catalyst for major collaborative projects among mathematicians representing the areas of additive number theory, multiplicative number theory, combinatorial game theory, probabilistic number theory, enumerative combinatorics, combinatorial optimization, Ramsey theory, the theory of partitions, and other areas of number theory and combinatorics. It will feature twelve plenary speakers and about fifty additional speakers. The conference organizers intend to include a number of student research talks, including talks presented by students who have recently participated in NSF “Research Experiences for Undergraduates” programs. The proceedings of the conference will be published as a special volume of the journal Integers. The following website has been created for the conference: https://sites.google.com/view/integersconference2025 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 award supports the travel of scientists, particularly early-career researchers (including students), working at institutions in the United States to participate in the Plant Genetics and Developmental Biology Symposium that will be held at the University of Pennsylvania on August 22, 2025. The symposium bridges the historical evolution and future trajectory of plant developmental biology. This research field is critical for addressing global challenges such as food security and biodiversity conservation. It is a cornerstone of our understanding of how plants grow, reproduce, interact with other organisms and adapt to their environments. Studying the mechanisms that control plant development at molecular, cellular, and systemic levels has revealed principles that bridge fundamental biology with applied sciences, including agriculture and biotechnology. The symposium will create significant impacts by bringing together scientists across multiple generations. It aims to inspire undergraduate students and other early-stage researchers through interactive sessions and will explore practical strategies for integrating plant science into pre-college education. The event highlights varied professional opportunities, featuring speakers from academia, industry, science communication, and education. The symposium will provide a unique platform to explore the historic evolution of plant developmental biology over the past 50 years which have marked the dawn of plant molecular biology, the rise of Arabidopsis as a model system, and an explosion in functional genomics research. This symposium will celebrate scientific milestones in the field, and will also critically examine the trajectory of the field, offering attendees a rich context to shape its future. To achieve the symposium’s aim of reflecting on historical discoveries to guide future research, it will include three sessions: 1) The early days of plant developmental biology; 2) The rise of Arabidopsis as a model system; and 3) New systems and frontiers. This symposium will unite researchers in this field, from pioneers with decades of contributions to various aspects of plant development to recent graduates developing cutting-edge tools in this field, featuring a multigenerational dialogue about the past, present, and future of plant developmental biology. By embedding contemporary breakthroughs within the context of historical evolution, the symposium aims to inspire innovative perspectives and strategic directions for the field. Through this integrative approach, participants, especially early career researchers will gain not only a deeper appreciation for the discipline's milestones but also a visionary outlook on its future to solve pressing global issues in the decades ahead. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

In the COVID-19 pandemic, we have all come to recognize the importance of promoting well-being in every facet of life, including and especially in higher education. We recognize that promoting well-being among faculty and students must be a central focus rather than an afterthought of professional education. Accordingly, this project will advance cultures of well-being in engineering education by understanding faculty members’ personal experiences of coping with negative emotions and failure within their professional context. Engineering faculty members are highly influential to students who seek their guidance to understand what it means to become an engineer. Faculty are best positioned to influence equitable, inclusive, and healthy cultures of engineering education when their own emotional needs are met. Therefore, this project will examine how faculty members meet their well-being needs and how they use their influence to nurture or inhibit cultures that allow for engineering students to experience well-being. This project aims to transform how faculty relationally connect with students and other faculty and staff by transforming the ways that they understand themselves. In addition to the research plan, which includes intensive interviews with engineering faculty at multiple institutions, this project will also provide direct training to faculty on coping with failures and preserving a positive professional identity. In line with the PI’s career mission, this project will develop and define a scholarship of care within engineering education research that influences national and local policies of well-being through research-informed insights. Specifically, this project will address two significant gaps in extant literature: 1) the role of failure and negative emotions in facilitating or mitigating cultural patterns of well-being; 2) the complex, dynamic nature of the lived emotional experiences of engineering faculty. This project is organized around the following objectives: Objective 1: Examine social and individual experiences of failure and negative emotions in engineering faculty. Objective 2: Characterize the link between faculty’s emotional experience and their surrounding cultures of well-being. Objective 3: Establish a framework to provide training for engineering programs to establish cultures that support healthy strategies for coping with professional failure. This project will use a qualitative mixed-methods approach that embeds an interpretative phenomenological analysis (IPA) study that examines the lived experiences of professional failure and negative emotions in engineering faculty (Objective 1) within a constructivist grounded theory (CGT) analysis that generates a theoretical model of the relationships between faculty emotional regulation and cultures of well-being (Objective 2). The education plan to develop faculty training on regulating emotions related to professional failure (Objective 3) will be interwoven with the research focus to change cultures of well-being (Objective 2). This study will occur at three purposefully selected institutions and involve 10-12 faculty participants for the IPA study, 18-22 participants that are interviewed twice for the CGT study (36-44 total interviews), and a three-module training series to be delivered at four institutions. This project is jointly funded by Broadening Participation in Engineering (BPE) in the Engineering Education and Centers (EEC) Division of Engineering (ENG), 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 2025 · 2025-02

The University of Georgia Research Foundation Inc., Skidaway Institute of Oceanography (SKIO) proposes to support oceanographic technical services on R/V Savannah operated as part of the U.S. Academic Research Fleet (ARF), which is scheduled by the University-National Oceanographic Laboratory System (UNOLS). As part of their basic operations, SKIO will provide shipboard technicians on each seagoing research project to support basic services. Technicians will maintain, calibrate and provide for qualified users, items from their pool of shared-use research instrumentation. Research vessels in the ARF provide support for researchers from a variety of federal and state agencies, as well as some private sponsors. All users (or the appropriate funding agencies) share support costs for basic technical services on the vessel equally, via a day-rate, with each paying a share of the costs based on fractional usage of the vessel. The principal impact of the present proposal is under Merit Review Criterion 2 of the Proposal Guidelines (NSF 23-525). It provides infrastructure support for scientists to use the vessel and its shared-use instrumentation in support of their NSF-funded oceanographic research projects (which individually undergo separate review by the relevant research program of NSF). The acquisition, maintenance and operation of shared-use instrumentation allows NSF-funded researchers from any US university or lab access to working, calibrated instruments for their research, reducing the cost of that research, and expanding the base of potential researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Glacier melting in polar regions can result in large amounts of freshwater being introduced into the coastal ocean, changing nutrient levels at the surface, impacting organisms like phytoplankton and influencing the distribution and composition of organic material in the water. In this project, researchers will participate in an expedition to the West Antarctic Peninsula in early 2025 that will approach several retreat glaciers, starting from the northern tip of the Peninsula and progressing toward its central region. The team will analyze physical, chemical, and biological changes in the area, and relate these changes to differences in melting trends that have been observed along the Peninsula. Results will help predict future impacts on the region due to increased melting associated with continued warming. The program will provide training for a graduate student and disseminate results of the study across a broad audience. This project will examine the effects of meltwater input from tidewater glaciers on dissolved organic matter along the West Antarctic Peninsula. While glaciers near the northern tip of the Peninsula show no changes in grounding line discharge trends, several farther south, near Palmer Station, are characterized by significant changes, with discharge trends surging by a factor of 3 in many glaciers. This suggests a north-south gradient in the response of discharge rates to environmental forcing along the Peninsula, likely influencing the vertical flux of nutrients and affecting phytoplankton and the composition and lability of dissolved organic matter. The PIs will take advantage of a cruise-of-opportunity to the West Antarctic Peninsula in February 2025 to collect samples in transects approaching multiple tidewater glaciers, capturing the north-south gradient in discharge trends reported for the region. Samples of seawater and floating ice from glaciers will be analyzed for temperature, salinity, nutrients, chlorophyll, particulate and dissolved organic carbon concentrations and lability, and dissolved organic matter composition using ultrahigh-resolution mass spectrometry, optics and isotopic analyses. Variations in organic matter composition and lability will be identified and compared along the north-south gradient in discharge trends, which will inform how the system might respond to future increases in glacier melting. Results will also be compared with similar analyses currently being conducted in the Amundsen Sea and the Kerguelen Islands, collectively capturing the influence of meltwater originating from large ice sheets, tidewater glaciers and land-terminating glaciers. The project will support a graduate student who will be trained in the analysis of dissolved organic matter 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

Global sea levels are rising at unprecedented rates and will continue to reshape the coastline of densely populated regions both in the US and globally with implications for housing, transportation, agriculture, wildlife habitability, and tourism. Over the next 50 years, mass loss from the Antarctic Ice Sheet will be a dominant contribution to global sea level, but it is also associated with the greatest uncertainty in sea level rise estimates. Much of this uncertainty results from incomplete understanding of processes that occur near the Antarctic coast where there are close interactions between the open ocean, near-coastal waters whose properties are influenced by interactions with sea-ice, and ocean water that is carrying glacier meltwater originating from the Antarctic ice sheet itself. These regions also happen to be among the most biologically productive of all waters in the Southern Ocean, and the impact of climate-related biogeochemical changes here remain a blind spot in our understanding of a changing global carbon cycle. Current understanding of changes occurring around Antarctica are largely derived from decades of work in the Amundsen Sea. Yet, the melting of ice shelves in the neighboring Bellingshausen Sea is comparably high and pre-conditions the physical and biogeochemical properties of the water that enters the Amundsen. Thus, the role of the “upstream” Bellingshausen Sea in ice sheet mass loss and ocean carbon uptake remains uncertain, although models suggest this region can broadly influence these processes throughout West Antarctica. The Bellingshausen Sea: A Carbon and Overturning Nexus (BEACON) project will collect a broad suite of physical and biogeochemical observations needed to assess the Bellingshausen Sea’s role in the large-scale distributions of heat, meltwater, dissolved iron and other nutrients, and biological productivity. The research team will combine standard and trace-metal shipboard measurements, towed underway observations, and a small fleet of remote autonomous underwater vehicles aimed at capturing key transport pathways associated with narrow boundary currents located along the coast. These observations will capture dynamical processes related to mixing of water properties by ocean turbulence from centimeter to kilometer scales. This information about mixing will then be applied to an inverse-modeling framework to assess how changes in near-coastal processes in the Bellingshausen Sea impact larger-scale ice-shelf melt rates, nutrient supply to the upper ocean, the timing and intensity of seasonal primary production, and the oceanic uptake of carbon dioxide throughout West Antarctica. 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

Emerging infectious diseases, driven by globalization, societal and economic activities, land use changes, and climate change, pose a significant threat to public health. This project addresses these threats by synthesizing data from NSF-funded studies on pathogens like West Nile virus, avian influenza, and MERS-coronavirus. By integrating these data with recent community initiatives, the project will create a comprehensive and interoperable database accessible through an online portal. By leveraging a data-driven approach combining ecology, data science, and mathematical modeling, the project will generate actionable knowledge for public health strategies and policy-making. Emphasizing interdisciplinary collaboration and cultural exchange between the US and UK, it will enhance global pandemic preparedness. The study will lead to new understanding of how diseases emerge and spread, which is crucial for predicting, preventing, and managing future outbreaks. The findings will be made available through user-friendly web dashboards, ensuring accessibility for scientists, policymakers, and the public, ultimately contributing to improved health and welfare for human and animal populations. The goal of the study is to integrate knowledge from two decades of research, advancing understanding of pathogen dynamics, generating actionable knowledge for disease prediction, prevention, and management, and fostering interdisciplinary collaboration. Published data will be reviewed for consistency in format, variable names, and metadata, and then harmonized for interoperability with repositories such as EID239, GLOBI40, and the Verena dataverse. The harmonized data will be archived in Dryad and Figshare, ensuring long-term preservation and accessibility. Additionally, a sophisticated web interface will be developed to enable interactive exploration and analysis of the datasets, providing tools for visualization, filtering, and cross-referencing data points. A conceptual framework will be introduced to guide future research in the macroecology of emerging diseases and pandemics, enabling statistical methods and machine learning algorithms to identify patterns and predict disease emergence. This framework will serve as a foundation for interdisciplinary research, facilitating collaboration across fields such as epidemiology, ecology, data science, and public health. 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 provides funding for the Research Vessel Savannah to conduct oceanographic research missions supported by the National Science Foundation. The oceanographic research vessels of the Academic Research Fleet (ARF), operated by the academic institutions within the University-National Oceanographic Laboratory System (UNOLS) framework are multi-use facilities used to expand knowledge of the ocean environment. The surface work of these ships is complemented by human-occupied, remotely operated, and autonomous undersea vehicles and sensors that provide vital tools to understand the oceans and their resources. These seagoing research and educational facilities enable scientists and students to study natural phenomena and train future scientists while on board state-of-the-art oceanographic research vessels utilizing high-quality instrumentation. The ship operators will also conduct learning activities for students and the general public including hands-on demonstrations of marine science research guided by faculty, students, and ship crew members. 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

This rapid-response research project investigates the impacts of Hurricane Helene on the Oconee River watershed, an essential water source for Athens-Clarke County, Georgia, and surrounding communities. As climate change drives more frequent and intense extreme weather events such as hurricanes, it becomes critical to understand how these storms influence our natural water systems. Hurricane Helene’s heavy rainfall and flooding likely washed pollutants from urban, agricultural, and industrial areas into the watershed, introducing contaminants like pesticides. These pollutants disrupt the natural nutrient cycles and alter microbial communities that play a vital role in maintaining ecosystem balance. By examining the immediate and longer-term effects of this hurricane, the research will reveal how such events impact water quality and ecosystem health. The study holds significant value for advancing our scientific knowledge of climate impacts on natural systems and supports efforts to protect public health by informing better water management practices. The outcomes of this study will contribute to a broader understanding of hurricane-induced changes to watershed ecosystems, especially in relation to climate-induced extreme events. The broader impacts of this research extend to environmental and public health, as extreme weather events increasingly threaten water quality in vulnerable regions. With the North Oconee River watershed supplying drinking water to Athens-Clarke County and neighboring areas, the study’s findings will help to determine whether pollutants mobilized by hurricanes like Helene pose risks to human health and ecosystem stability. By identifying changes in nutrient balances and microbial community structures, the research will aid in developing strategies to protect water resources and enhance watershed resilience. Beyond scientific contributions, this project will foster greater public awareness of the importance of watersheds in safeguarding environmental health and public safety. This research builds upon monthly monitoring of pre-hurricane data on nutrient levels (such as nitrate, phosphate, and sulfate), organic contaminants, and microbial diversity in the Oconee River watershed. Utilizing this baseline, the study will measure post-hurricane changes to assess how Hurricane Helene’s runoff affected nutrient flux, organic contaminants (specifically six organic contaminants: malathion, atrazine, nicotine, naphthalene, acenaphthene, and pyrene), and microbial composition. These six organic contaminants showed elevated concentrations in post-hurricane sampling. These higher concentrations of contaminants can be attributed to stormwater and surface runoff from the surrounding areas and intensified water mixing and atmospheric deposition triggered by the hurricane event. Early post-hurricane findings on nutrient levels, such as a sharp increase in phosphate levels from 0.03 ppm to 0.83 ppm within 48 hours, also highlight the urgency of capturing and analyzing these rapid shifts. By investigating how hurricanes intensify the transport of these compounds and disrupt the watershed’s biogeochemical cycles, this project aims to provide valuable insights into nutrient dynamics, pollutant mobilization, and microbial community changes. 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

This project seeks to answer the question how much fresh (low salinity) water is carried from the Arctic Ocean along the East Greenland Coast into the North Atlantic Ocean and how much this transport may vary as more of the Greenland and Arctic ice sheets melt. For this purpose, an array of six moorings is to be deployed on the Northeast Greenland Shelf to make continuous measurements of temperature, salinity, and current velocities. An exciting new feature of this array includes a variable ballast buoy at the top of one of the moorings, the one closest to the coast, that allows measurements to be made all the way to the ocean surface when the region is ice free, but that prohibits collision of the instruments with sea-ice or icebergs in winter by keeping the mooring line below the ice then. The mooring observations are to be complemented by a modeling study that estimates how the East Greenland Coastal Current evolves over longer time scales. A collaboration with European partners who have a similar mooring array in deeper waters further offshore allows to examine the spatial extent of the current system. Together these efforts will fill a critical gap in our understanding of Arctic-Subarctic exchange, and results will be applicable to a range of scientific fields beyond physical oceanography including climate science, marine biogeochemistry, and fisheries management, among others. The oceanic circulation of the high-latitude North Atlantic is a critical component of our climate system and is potentially sensitive to the release of fresh, surface waters from the Greenland Ice Sheet and the Arctic Ocean. A large gap exists in our monitoring of this freshwater input on the Northeast Greenland Shelf (NEGS). This gap will be filled by measuring the southward-flowing East Greenland Coastal Current (EGCC) on the NEGS for the first time with continuous, direct measurements over an entire year. Based on existing data from summer shipboard sections and satellites, it is hypothesized that the freshwater transport in the EGCC is as strong as the freshwater transport of the better known East Greenland Current (EGC) further offshore at the shelf break. If true, the EGCC would be a major contributor to the total freshwater budget of the Arctic and a key player in Arctic-Subarctic exchange. In addition to the mooring array, it will be analyzed how these data fit into the larger scale NEGS circulation using model simulations, reanalysis products, and satellite data. The new ice-avoiding buoy technology that is to be developed as part of this project has the potential to be widely applicable to a range of environments and is significantly more cost-effective than other similar products. Results from this project will: (1) quantify the volume, heat, and freshwater transports of the EGCC on the NEGS, (2) compare these transports to those of the EGC measured by European partners, (3) identify the physical drivers of transport variability in the EGCC, and (4) assess the long-term variability of the EGCC and its role in the Arctic freshwater budget. 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 National Robotics Initiative (NRI) project addresses the increasing quantity of discarded high-precision electronics such as cell phones, tablets, and laptops. Current recycling methods rely on shredding after battery removal, due to high labor costs for disassembly. As a result, many valuable components are buried in landfills and not recycled. Disassembly, the first step of recycling, is more complex than assembly since there is much more variability in product type and, as a result, remanufacturing is usually not profitable. This award supports research to provide the fundamental understanding needed for the development of a novel robotic system that can effectively perform high-precision disassembly operations and make them practically and economically viable. The work has potential to mitigate labor shortages in recycling industry, reduce electronics waste, and revolutionize the remanufacturing of high-precision electronics. The research involves several disciplines including 3D sensing, deep learning, and robotics. The multidisciplinary research will be integrated into a series of educational and outreach activities which will increase the participation of underrepresented groups in research and positively impact engineering education. Unlike the robotic assembly lines that assemble products, programming robots for repetitive operations is not a feasible solution for disassembly due to the widely varying types of discarded high-precision electronics. Therefore, disassembly of high-precision electronics is significantly more complex than assembly and requires high robotic adaptability, dexterity and accuracy. The research aims to enable a novel robotic system that can accurately see, interpret, and disassemble high-precision electronics through integrated and convergent research on 3D sensing, deep learning, robotic hand design, and high-precision manipulation. In particular, the research team will (1) perform accurate 3D sensing for complex surfaces exhibiting wide ranges of optical properties and reflectivity variations; (2) design and optimize the design of deep learning architectures for 3D point cloud interpretation; and (3) design a novel lightweight cable-driven robotic hand and develop a high-precision manipulation algorithm enabling efficient learning from experience. 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

Today, there is an increasing need for running powerful Artificial Intelligence (AI) models on mobile phones. Many of the latest generation of AI models (including ChatGPT and Gemini) follow what is known as the transformer architecture. Similar to what has been seen in optimizing various workloads on different computing devices, a class of optimizations related to memory hierarchy is extremely important for the efficient execution of transformer-based models on modern mobile devices. This project is based on the premise that features of these workloads and characteristics of mobile devices require not only the application of existing techniques from compiler literature but also the development of new methods. The project’s novelties are in considering such workload and architecture combination and proposing techniques related to choosing new layouts, removing redundant layout changes that slow down execution, performing memory allocation judiciously to improve performance, and dealing with the newest accelerators. The project’s impacts are helping bring the latest advances in Artificial Intelligence (AI) on mobile and edge devices, letting these advances reach more individuals, and contributing to compiler and runtime support literature by developing new methods. In targeting memory hierarchy-related optimizations for transformers, we observe that compared to the previous generation of deep learning-based models, transformers have more data flow splits, shuffles, merges, and transpose/reshape(-like) operations. Thus, various compilation systems targeting deep learning developed in the past decade fall short with respect to memory-related transformations, especially with a global view of the problem. This project builds on top of the investigators’ recent work developing a comprehensive framework for removing relayout operations and delivering significantly better performance for transformer models. Building on this work, the following agenda is being undertaken: Performance (Cost) Models -- a detailed performance model for execution on mobile GPUs is being developed, which will especially be novel in capturing the locality behavior of a 2.5D cache; Formal Approaches to Transformations – more formal approaches for the same set of optimizations (e.g., replacing a relayout operator) are undertaken, including both polyhedral formulation and computation-data graph-based approaches; Layout Transformation in View of New Instructions – as newer processors are increasingly offering matrix (or tensor)-based instructions, which have their own specific data layout requirements, memory performance-related problems in view of these requirements are undertaken; and Memory Management for Dynamic Models --focusing on emerging dynamic models, computation ordering, memory allocation, and memory fragmentation problems are investigated. The investigators are working towards creating more synergy between compiler research (especially memory/cache modeling and tuning) and ML-model development communities. The research on large-scale Machine Learning and Deep Learning transformation/implementation techniques is to be incorporated into courses taught by the investigators. 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

Since the invention of DNA sequencing in 1977, genomic data has grown exponentially due to decreasing sequencing costs. Unfortunately, many bioinformatics systems lag behind in adopting state-of-the-art computing principles, resulting in wasted computing potential. Such adoption is challenging due to domain-specific expertise requirements and the limited resources available for many bioinformatics projects. Exciting research areas, including workload characterization, performance modeling, resource optimization, scheduling, and leveraging advanced hardware accelerators, remain largely unexplored in bioinformatics systems. The All-in-One (AIO) collaborative research project aims to build a next-generation genomic data processing system that incorporates state-of-the-art systems design principles. Toward this end, the project focuses on three key innovations: (1) cluster scheduling policy improvement, which uses the characterization of genomic workloads to build an execution time predictor and guide scheduling design; (2) machinery for independently-scheduled genomic tasks that support resource-aware and failure-aware directed acyclic graph-based (DAG) scheduling; and (3) a meta-compiler for a cloud-and-language agnostic processing system, which allows automated performance tuning for various domain-specific languages and cloud execution environments. The project will transfer expertise from the systems community to bioinformatics, addressing the growing computational demands for genomic data processing. 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 participant support for students and young researchers to attend the Materials-Manufacturing-Machine Learning Nexus (M3X) Conference at the University of Georgia, Athens, Georgia, 18-20 May 2025. Priority is given to participation by women and underrepresented minority groups, which promotes diversity, equity, and inclusion. The M3X conference explores research at the intersection of materials science, manufacturing technologies, and machine learning. U.S. and international researchers, faculty and scholars present their research results on advanced materials, manufacturing and machine learning. The conference impacts the materials, manufacturing and data science communities through discussions of cutting-edge research. This project benefits the nation through the education of a skilled science and engineering workforce, which is better prepared to provide transformative solutions to the challenges in their chosen fields. The conference plays an important role in supporting and sustaining machine learning-enabled advanced material discovery and advanced manufacturing, which have various important applications in many industrial sectors such as microelectronics, energy, healthcare, automotive and aerospace. This participant support is expected to benefit the students’ and young researchers’ professional, scientific, and technical development. Attendance at the conference gives the students and young faculty a broader view of advanced materials, manufacturing and machine learning technologies, their fundamentals and practical applications. The conference provides a venue for presentations and discussions on the integration of advanced computational methods with materials science and manufacturing engineering. Specifically, the discussions focus on using machine learning algorithms to optimize material properties, enhance manufacturing efficiencies, and streamline production processes. At the conference, concepts and challenges at the intersection of advanced materials, manufacturing and machine learning are identified and presented, and attendees chart new paths forward in the field and rally a new generation of researchers toward them. The conference is attended by U.S. and international researchers, which provides an opportunity for a variety of perspectives to be presented and discussed. The conference is an opportunity for participants to showcase their scientific accomplishments and interact with peers and colleagues in academia, government laboratories and industry. 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 focus on improving the ways that engineering students and professionals learn to design solutions that benefit a wide range of people who use these solutions. Engineering students come to learn engineering design through an intensive project-based course known as their capstone course, a process critical to their formation as engineers. In these courses, students often emulate industry practices by designing projects within a team, in a process intended to be similar to what they might experience in the workplace. However, research in engineering education and practice continues to show that much design learning happens after students graduate with their engineering degrees. Might engineering students be better equipped to practice high-quality design in the workplace if they were more engaged in their capstone course? And how might capstone courses be designed in a way that motivates students to learn design with a deeper connection to the process? This project will address these critical questions through an in-depth study of engineering students and professionals and a sustained plan to equip engineering design instructors in preparing students for the workplace. There is little knowledge of the specifics of how engineering students engage in capstone courses and how their engagement is driven by identity and motivation needs. The objective of this project will be to investigate how and why civil and mechanical engineering students and practicing engineers engage with engineering design activity. In particular, this project will be characterized by the following objectives: 1) Develop a model of design activity engagement and identity motives of students and professionals, 2) Expand our model to account for resistance and synergies, alignment and tension between academic and workplace settings and across disciplines, and 3) Advance holistic and authentic engagement of students in multiple academic settings through extensive collaboration with and training of engineering faculty. The capstone design experience is particularly unique because explicit and implicit goals focus on simulating the engineering design experience to the extent possible. How and why students engage in engineering design in capstone courses is therefore foundational to understanding and improving the efficacy of capstone courses. We will use constructivist grounded theory (CGT) to examine the how and why of this phenomenon in four important settings. The rationale for this research is that understanding differentially how students and engineers in different disciplines engage in design is a critical step in enhancing our understanding of engagement in academic and workplace settings and results can broadly inform efforts to enhance student engagement. This project is further characterized by the formation Engaged Design Learning Institute (EDLI), where we will enroll a cross-institutional cohort of 8-10 capstone instructors to support them in their design instruction and to develop contextually relevant insight on how the research findings may be applied by educators. 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 transforms engineering education by leveraging "meaningful failure" as a promising approach to learning and teaching. Failure is an inherent part of human life and learning processes, and early failure is often prerequisite step on the path to successful learning. However, typical engineering education currently punishes failure, which disincentivizes innovation, exploration, and risk-taking, ultimately resulting in engineers who are less prepared to tackle complex global challenges. By understanding students’ unique experiences during moments of academic failure, this project supports students taking risks and learning from setbacks, developing the skills and mindsets to embrace failure as a meaningful experience in their learning. Our research involves the use of biometric data, observations of classroom dynamics, and psychosocial assessments to better understand how each student experiences failure on a physiological, cognitive, and social level. We will use these data to develop new educational tools and strategies that will provide immediate, tailored interventions connected to individual student needs and experiences. This research will support the development of a workforce ready to persist past ubiquitous failure experiences in engineering to address tomorrow’s challenging engineering problems. Further, this research aligns with the goal of creating inclusive and equitable learning environments that can adapt to the diverse needs of all students. The project will explore meaningful failure in engineering education contexts by developing personalized learning strategies and pedagogical tools. The proposed research has three goals: identifying real-time failure profile signals, understanding how learners' responses to failure are individualized, and determining necessary changes in pedagogy and assessment to support personalized responses tolearning from failure. The research involves a multi-pronged data collection approach, including laboratory experiments using video and biosensing modalities (EEG, EDA, ECG), classroom observations, surveys, and interviews with educators and administrators. A convergent team from five institutions, with expertise in cognitive neuroscience, learning sciences, AI, and psychosocial theories of learning and development collaborate to create individualized failure profiles. These profiles will integrate multi-modal data sources to formally represent each learner’s unique cognitive, affective, and behavioral responses to failure. The project will culminate in the development of pedagogical tools and strategies to support personalized learning and resilience – increasing retention and success rates in engineering fields and pioneering a shift in engineering education towards valuing learning from failure. 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

Computational quantum chemistry provides accurate descriptions of molecules, and it has become a standard research tool in chemistry, biochemistry, chemical engineering, materials science, and other fields. However, the equations in quantum chemistry are very complicated. This means that they are very hard to convert into computer programs, and also that those computer programs can take a very long time to run. The long computation times mean that many theoretical chemists are actively engaged in developing new theoretical models that yield faster computations, while hopefully having a minimal impact on accuracy. However, these new methods also tend to involve complicated equations that are hard to implement in computer programs. Because the programs are very complicated, they are difficult to adapt to new computer hardware that could make them run faster. This collaborative project will develop a software framework to make it much easier to implement advanced quantum chemistry methods on emerging hardware. It involves a team of experts in quantum chemistry and computer science from Georgia Tech, the University of Georgia, Virginia Tech, and the University of Memphis. This team will develop a library to efficiently handle the matrices and tensors that appear in quantum chemistry equations, and to make it easy for programmers to implement quantum chemistry methods by writing code that looks more like the equations. It will also develop a library to compute the electron repulsion integrals that are central to quantum chemistry on graphics processing units. These tools will be thoroughly tested by using them to implement several advanced theoretical methods, including coupled-cluster, relativistic, and real-time electron dynamics methods. These implementations will test the libraries and will also provide advanced simulation techniques to researchers. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry. 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

Humans change the behavior of wildfires, storms, diseases, and other disturbances. Altered disturbances can cause vegetation to permanently shift between types, such as from forest to shrubland. However, it is difficult to predict which parts of a landscape are vulnerable to shifts. This research examines how interactions between fires and an emerging plant disease may shift coastal forests to shrublands in the western U.S. This work will identify where and why forests are vulnerable to permanent conversion to inform disease and fire management. The research team will apply findings through relationships with the public, managers, tribal communities, and policymakers. This work will create a network across diverse research institutions to mentor students from underrepresented groups. This project will also support an innovative course that integrates art and science and publicly share a dataset that spans two decades. Although persistent state shifts have been described in many systems, empirical work has largely focused on demonstrating state permanence, rather than determining environmental variation in where transitions are likely. This research integrates an 18-year monitoring network, shrub-tree competition experiments that manipulate soil nutrient dynamics and other resource availability, and epidemiological and forest dynamics models. This integrated approach will: 1) quantify the sensitivity of forest-to-shrubland transitions to repeated fire and disease disturbances; 2) identify biogeochemical and disturbance-related feedbacks that destabilize forests and stabilize shrublands; and 3) examine where and when state shifts may occur across heterogenous, rapidly changing landscapes. This work will quantify the likelihood of persistent state shifts using a focal system that comprises plant traits relevant to disturbance-prone systems globally: coast redwood and mixed evergreen forests impacted by an introduced oomycete pathogen, Phytophthora ramorum. To date, there has been limited experimental evidence that diseases can trigger stable state transitions, despite their acute effects on plant mortality, resource competition, and biogeochemical cycles. This research will generate experimental and simulation-based tests of state shifts mediated by disease, while expanding the scope of a valuable longitudinal dataset in a long-lived forest system. 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 overwhelming majority (approximately 85 percent) of modern polymer materials are petroleum-based thermoplastics that degrade in the environment slowly and release toxic substances. Only a small fraction of plastic waste is recycled, while most is accumulated in landfills. The associated environmental problems are increasing with the global production of plastics. Among the applications that raise environmental concerns are flexible food packaging, fabrics, and commodity plastics. This CMMI-UKRI Engineering and Physical Sciences Research Council (EPSRC) project combines the efforts and expertise of UK and US collaborating research labs to systematically address the need for new manufacturing technologies to develop sustainable bioplastics made of biomass feedstock and industrial wastes. A series of novel approaches to processing and converting biomass to bioplastics using highly efficient ultrasound technology will be explored in the project. The project responds to the current demand in the US and UK for new sustainable manufacturing technologies and reduction of carbon dioxide emissions, which are key priorities for future strategic conversion of the economy to renewable energy sources and biodegradable materials. This research project aims to develop innovative green ultrasonic technology for manufacturing novel biobased copolymers that can be either recyclable or compostable with minimal pollution of the environment. The copolymers are based on the lignocellulosic ingredients (LCI) of biomass feedstock that can be obtained from different resources of agro-food residues, paper, fiber, cellophane, and biofuel refineries' side products. Most present production of LCI involves biomass deconstruction, chemical modification, and depolymerization in harsh conditions with high energy consumption. Once extracted from biomass, LCI is not soluble in most solvents, and in many cases industrial-scale functionalization and processing of the biomass components are conducted as a heterogeneous process. Using the methods being explored in this project, reactions of colloidal components can be efficiently accelerated with ultrasonication so that the LCI can be functionalized and polymerized without their degradation in advance. Ultrasonic treatment is done at reduced temperatures of the bulk reactors (25-50 degrees C) for a much shorter reaction time as compared to traditional high-temperature polycondensation (200-280 degrees C), reducing energy consumption and minimizing side reactions. At the same time, ultrasonication will generate material microstructures on the nanoscale to enhance the bioplastics' barrier and thermo-mechanical properties. The project focuses on producing sustainable bioplastics with much less damaging impact on the environment thanks to energy-saving synthesis and retention of the natural biodegradation mechanisms of their plant-derived ingredients. The developed thermoplastics could replace petroleum-based packaging materials, fibers, and commodity plastics (polyolefins, polyesters, and polystyrene), which are major ocean and landfill contamination sources which create microplastic particles that are not easily recyclable. 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 BioFoundry: Glycoscience Resources, Education, And Training (BioF:GREAT) will develop new research, technologies, and instructional experiences to allow a broader adoption of glycoscience into research environments and education curriculums. Although glycans, also referred to as complex carbohydrates, are one of the four classes of biomolecules found in all living organisms, they have been consistently understudied in the laboratory and undertaught in the classroom. This is despite the fact that biofuel and biomaterial efforts rely heavily on glycan biomass from plants, the vast majority of biologics in medicine are glycoproteins, and glycans are found on cell surfaces of all living cells where they contribute to cell interactions and diverse biological functions. Unlike DNA/RNA and proteins, glycans are rarely linear polymers and are not generated by template-based processes. This complexity has made them difficult to study at the bench and challenging to teach in the classroom. BioF:GREAT will leverage a broad range of expertise, including AI and machine learning, to generate research tools and technologies, while also developing and deploying novel instructional and training strategies, resources, research materials, and automated tools in the field to propel glycoscience into the scientific mainstream and lead to paradigm shifts in glycoscience education. BioF:GREAT discoveries and deliverables are expected to lead to commercial applications in bioenergy, bioengineering, biomaterials, and biomedicine. The BioFoundry: Glycoscience Resources, Education, And Training (BioF:GREAT) will take advantage of the Complex Carbohydrate Research Center (CCRC) at the University of Georgia (UGA), home to one of the largest communities of glycobiologists in the world, coupled with UGA experts in bioinformatics/machine learning and pedagogy/evaluation. The Research team will focus on three synergistic goals: 1) bioinformatic tools/machine learning/artificial intelligence (AI) to predict and define glycoenzymes and glycoproteins, 2) glycoenzyme expression, characterization, and manipulation, and 3) mass spectrometry-facilitated analyses of glycan modifications. The Technology Development team will focus on four themes that will generate 1) expression libraries for glycoenzymes from diverse species sources, 2) fine-tuned protein language models and new user-friendly informatics tools for classifying and predicting glycoenzymes and site-specific glycosylation of glycoproteins, 3) engineered glycoenzymes for generating novel chemical biology tools, and 4) species-agnostic methods for the mass spectrometry-based analyses of glycoproteins. The User Facility will provide hands-on training and service using cutting-edge computational, enzymatic, and analytical glycoscience approaches. Collaboration among User Facility, Research, and Technology Development teams will lead to the deployment of new technologies to catalyze in-house research and technology development efforts. The User Facility will equally focus on external glycobiology research projects spanning the tree of life in partnerships with scientists at R1 and non-R1 schools including minority-, primarily undergraduate-, and EPSCoR-serving institutions. The Education/Instruction team will establish and evaluate a suite of instructional experiences, including small modules for existing chemistry/biology courses, dedicated stand-alone glycoscience courses at the undergraduate/graduate level, and hands-on summer courses for beginners and experts with rigorous attention to best pedagogical practices and evaluation for improvement. Our Platform-Sharing team will facilitate the transfer of deliverables from the bench and the classroom to academic, government, and commercial/industrial research communities using a knowledge graph framework consistent with the Prototype Open Knowledge Network (Proto-OKN). By providing equitable access to advanced infrastructure and resources in glycoscience, BioF:GREAT will advance scientific inquiry and education in biosciences across all kingdoms of life. 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

Engineering design courses are a staple of the undergraduate engineering curriculum. These courses provide students with opportunities to tackle real-world problems before encountering them in the workplace. The courses require students to integrate their creativity, critical thinking, and problem-solving skills. Design courses are crucial aspects of the professional formation of engineers and, therefore, are essential to the competitiveness of the nation’s scientific workforce. The research team will conduct interviews and then develop and deploy a survey focusing on assets that minoritized students bring to the engineering design process. This project provides a perspective that is currently missing from the professional formation of engineers and will help educators improve the engineering curriculum by making it more inclusive for all students, ultimately helping strengthen the workforce. The project will use an exploratory sequential mixed-methods research design to expand community cultural wealth theory for application in engineering design courses. Recruiting through design-based course instructors, the researchers will conduct two ethnographic interviews with approximately 12-15 minoritized undergraduate students at varying stages of their undergraduate studies. The interview series will focus on students’ linguistic, resistant, navigational, familial, social, and aspirational capital and how design experiences allow them to practice these strengths. Researchers will employ inductive and deductive thematic analysis as well as critical counternarrative analysis. We will publish critical counternarratives to elevate the lived experiences of minoritized engineering students in design-based courses from an asset perspective. The thematically analyzed interview results will include a framework of design-based community cultural wealth working definitions. The researchers will seek feedback from 10-12 faculty experts who teach engineering design courses. The researchers will use critical quantitative methods to design and validate a design-based community cultural wealth survey instrument with students at partnering ABET-accredited institutions. First, the team will deploy the survey to a large and diverse sample of 500-800 engineering students and conduct exploratory factor analysis. The following year, they will relaunch the survey with an additional 500-800 engineering students and conduct a confirmatory factor analysis. The final survey instrument and its accompanying ethical-use manual will provide a way for design-based course instructors to understand the extent to which their students believe they have had the opportunities to practice their design-based forms of capital and the impact of these opportunities on their engineering self-efficacy and identity development. Workshops facilitated by the project team provide an opportunity for educators at partnering institutions and others around the country to collaboratively develop plans to use the instrument and address equity gaps in how design courses are taught. The project will help to promote the integration of asset-based approaches into students’ professional formation to combat disparities in engineering education, a necessary step in building a diverse engineering workforce equipped to tackle the complex challenges of the 21st century. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project develops theory about confrontation initiation, escalation, and termination. It generates new data to assess predictions from this theory. This involves the use of state-of-the-art advancements in artificial intelligence (AI) to train a model that uses a large text corpus to identify events and to describe a range of characteristics for each event. The project will shed new light on how confrontations begin, why some minor confrontations become major conflicts, and what can be done to end conflicts. The data allows real-time descriptions of characteristics as confrontations unfold, which will be use for analysis and pedagogy. This project contributes new theory and data to the study of militarized interstate confrontations. It introduces new theory about the conditions under which confrontations emerge, why they increase in intensity, and how they end. To create a new dataset on confrontations, it first develops and cleans a large text corpora. Each element of the corpora with relevant text is attributed a feature or feature of the dataset. This labelled dataset is then used to fine-tune a BERT classifier, which allows the artificial intelligence model to identify and code subsequent entries automatically. The dataset allows testing of propositions derived from the theory developed in the project, can also be used by other scholars to investigate additional hypotheses, and can be updated in real-time for analysis. 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

Sea turtles and whales serve as the only attachment surfaces for approximately two dozen species of barnacles. These stationary crustaceans have evolved to seek out specific hosts as mobile homes because they provide barnacles an easy means of dispersing and feeding, while giving them protection from predators. Barnacles attach to turtles and whales by three mechanisms: there are “gluers” which cement themselves to shell and other firm body parts with minimal to no invasive action; “grippers” which cling to soft epidermis by means of skin-pinching mechanics; and “gougers” which can penetrate tissue, scutes, scales, and sometimes to the underlying bone. This research will map genetic tags from barnacles collected worldwide to 1) study host choice and the evolutionary history of barnacle attachment, 2) describe to what extent barnacles may be a “fingerprint” management tool for tracking sea turtle movements, and 3) establish a baseline distribution of barnacle genetic types for monitoring alterations in species ranges due to climate change. This research will train undergraduate and graduate students in genetics and evolutionary analyses as well as field methods in marine biology via a field course in Costa Rica. Outreach includes the development of museum exhibits and the publication of popular articles for a broad audience. The evolutionary history and species delineations of the coronuloid barnacles (Arthropoda: Crustacea: Cirripedia) is uncertain. Specifically, little is known of their route to epizoism, their degree of dependence on hosts for dispersal and diversification, their pathways to variable attachment mechanisms, and their contemporary phylogeographic distributions. The researchers aim to address these deficiencies through phylogenetic and population genomic analysis with three principle objectives: 1) construct a definitive phylogeny for the whale and turtle barnacles and map to it the evolutionary chronology of their gluing, gripping, and gouging attachment modes; 2) compare global phylogeographic patterns using genome-wide markers for the three most-common barnacle species from the four most-common sea turtle species, assessing population connectivity in the context of oceanographic larval dispersal (geographic localities) vs. host transport (host lineages); and 3) test whether barnacle genetic lineages within species assort with respect to sea turtle species. A network of international collaborators at 16 global localities will help collect, from more than 130 sea turtles, ~2,200 total barnacle specimens for population genomic analysis. 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

Fundamental exoplanet properties, such as mass and semimajor axis, are set by the conditions of their formation in the protoplanetary disk. They may also be determined from observations of those disks, if interpreted with appropriate tools. This project develops DECIPHER: Deep computer vision in the astrophysics of planet formation — a machine learning tool that detects and measures the mass of forming exoplanets in protoplanetary disks from observational data. It is cross-disciplinary, using cutting-edge advances in computational astrophysics, artificial intelligence (AI), and computer science. With a goal of broadening participation in STEM graduate studies, this project actively involves undergraduate students in the development of DECIPHER. Moreover, this project conducts free research-based workshops to train undergraduate students in the fundamentals of programming and data analysis. DECIPHER will be released on a collaborative, open-source basis through GitHub. The AI advances leveraged are semantic image segmentation and generative adversarial networks, which will be accessed through collaborations with forefront AI researchers. The code will be developed through an integrated research-education program, where undergraduate students from the University of Georgia and other universities in the Peach State Louis Stokes Alliance for Minority Participation (PS-LSAMP) lead pivotal sections of the testing and development of DECIPHER. It has the potential to increase the speed of detection and characterization of forming exoplanets by orders of magnitude, without additional computationally expensive, ad hoc simulations, opening analysis of these disks to smaller institutions such as community colleges. Undergraduate students will help to assess DECIPHER’s ability to outperform the current state-of-the art, human-based approach to detecting exoplanets and measuring exoplanet mass from observations of their formation. 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 broader impact of this Partnerships for Innovation - Research Partnerships (PFI-RP) project is to enhance the reliability and security of electrical devices and networks within modern infrastructure including, but not limited to, buildings, manufacturing systems, and hospitals. This PFI-RP project introduces a smart sensor capable of detecting anomalies, pinpointing their locations, and diagnosing issues in electrical devices and networks. The algorithms and designs developed may also contribute to the broader field of anomaly detection and diagnosis beyond electrical signals. The project team will provide training to undergraduate and graduate students, in addition to middle school teachers. Collaborations with the Peach State Louis Stokes Alliances for Minority Participation (LSAMP) and the NSF Research Experiences for Undergraduates (REU) programs will be nurtured to support these efforts. Strategic partnerships with industry leaders offer vital insights and provide educational and leadership opportunities for graduate students and postdoctoral researchers. The project brings together a strong partnership involving academia, represented by the University of Georgia (UGA), and prominent industry leaders, including General Electric (GE), United States Robins Air Force Base (RAFB), Siemens America (Siemens), and NEC Laboratories America (NEC) to explore the commercialization of an electrical sensing technology for scalable anomaly detection and diagnosis in electrical devices and networks. The UGA team has collaborated with industrial partners RAFB and GE successfully in anomaly detection and diagnosis in joint projects. The impact spans from small-scale applications (e.g., homes, buildings, factories) to large-scale scenarios (e.g., distribution networks to transmission networks of main grids). This adaptability facilitates dynamic data processing, allowing the installation of varying numbers of smart sensors. Additionally, the technology offers customized programming and low-cost, flexible deployments, which can be easily installed by plugging into an electrical outlet in residential and commercial settings. 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.