University of North Carolina at Charlotte

NSF Awards · FY 2025 · 2025-06

Networked Multi-agent Systems (NMS), such as fleets of drones, connected autonomous vehicles, or smart power-grids, consist of multiple plants, controllers, and sensors exchanging data over a shared communication network managed by a network manager. This network manager allocates communication services (CS), such as bandwidth, reliability, and latency, to each agent, enabling sensors to transmit data to their respective controllers. For optimal NMS performance, the problem must be jointly studied at the agent level and the network level. Agents need to design communication-aware controllers, while the network manager must allocate communication services in a control-aware manner. Specifically, agents must develop controllers that proactively incorporate allocated communication services, analyzing their impact on sensor data quality, timing, and resolution. Simultaneously, the network manager must dynamically allocate communication resources to meet agents' evolving needs while ensuring fairness in allocations across all agents. At its core, this problem requires developing an optimal control-communication theory to guide decision-making for both agents and the network manager. The proposal envisions enabling optimal decision-making for NMS across various domains: from enhancing coordination and cooperation in multi-robot systems to optimizing information exchange among connected and autonomous vehicles to voltage and frequency control in power-grids. With advancements in communication and networking technologies, communication-aware controllers are especially important for real-world applications, offering improved performance, reliability, and resource utilization. The educational objectives of this proposal aim to ignite interest in NMS and control systems in general, while the outreach activities are designed to inspire K-12 students to pursue STEM education. This proposal systematically investigates NMS operations at both agent and network levels. The first contribution is a principled control-communication theory that addresses the challenges of designing CS-aware controllers and control-aware CS allocations. This theory enables tradeoff and sensitivity analyses within the CS parameter space, answering questions such as whether one parameter (e.g., latency) can compensate for another (e.g., bandwidth). It provides insights into which parameter has the most significant impact under varying conditions, allowing the network manager to allocate resources effectively. The research extends beyond agent and network levels by examining environmental impacts on CS allocations. A risk-aware controller synthesis framework is proposed to address uncertainties that traditional robust control techniques cannot adequately handle. 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 · 2025-06

This I-Corps project focuses on the development of a privacy-preserving video analytics platform that transforms existing urban camera infrastructure into a tool for generating actionable insights on public space usage and mobility patterns. The technology analyzes anonymized visual data to measure foot traffic, dwell times, pedestrian flow, and interactions with urban infrastructure in real time. These insights support data-informed urban planning by revealing how people move through and engage with parks, sidewalks, transit hubs, and commercial corridors. Unlike traditional approaches that rely on manual observation or invasive surveillance methods, this solution prioritizes ethical design, converting raw footage into de-identified, abstract representations such as heatmaps. The platform enables municipal agencies, design firms, and economic development organizations to assess the effectiveness of public infrastructure investments and optimize the use of shared spaces. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an edge-based artificial intelligence system that captures behavioral and mobility trends while excluding facial, demographic, or personally identifiable information. Through direct engagement with urban planners, transportation agencies, business districts, and civil engineering stakeholders, the project investigates market demand, deployment feasibility, and stakeholder needs across multiple sectors. By quantifying the impact of infrastructure changes, such as sidewalk redesigns, street closures, or event staging, this platform provides a scalable and affordable approach to understanding the evolving dynamics of city 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 2025 · 2025-06

This award will partially support about 50 U.S.-based students to attend the International Symposium on Hardware-Oriented Security and Trust (HOST) to be held in May 2025. HOST is one of the largest events focused on computer hardware security. Secure hardware, and supply chains for designing and making it, are critical to the computing and communication systems that underpin an increasing range of societal applications from autonomous vehicles to smart medical devices. The HOST conference aims to facilitate the rapid growth of hardware-based security research and development by highlighting new results around techniques, tools, design/test methods, architectures, circuits, and applications of secure hardware. Students supported by this award will be exposed to cutting-edge research and connected to experienced researchers and industry recruiters, benefiting both their research and their careers. In particular, the conference seeks to widen the pipeline of students entering the hardware security community, with the goal of benefiting not just the students but the community as a whole. To accomplish this, the selection committee will widely advertise the availability of support to students who might not otherwise be able to attend, including bachelors' and masters' students who often do not receive funding support. Students will be selected based on having a presentation at the conference, being first-time attendees, financial need, and institutional and topical diversity, with the goal of growing and broadening the talent pool of cybersecurity researchers and skilled workers. 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 · 2025-04

When a strong shockwave passes through the interface between a solid and a gas, the solid can melt and form liquid sheets that are released into the gas. Typically, the solid surface will have roughness features that can cause ripples on the interface, leading to the formation of the liquid sheets. This phenomenon is called ejecta and occurs in several engineering applications and natural phenomena such as semiconductor manufacturing, nuclear fusion and weapons, ballistic projectile impact on an armored vehicle, asteroid impacts and supernovae explosions. Once the liquid sheets form, they can undergo further destabilization and break up into ligaments and eventually droplets. The process leading to breakup of the sheets is poorly understood and will be the focus of this study. The research will address this problem through detailed numerical simulations and detonation tube experiments. The findings from the simulations and experiments will be used to develop a ‘lifecycle’ model that can describe all stages of shock-driven liquid sheet evolution from initial formation, to growth, to fragmentation into ligaments and droplets. The project will support training of students in areas vital to national security. The overall goal of this project is to improve our understanding of the hydrodynamic instability mechanisms that govern the breakup of ejecta sheets in a gas, and the effect of such mechanisms on the size and velocity distributions of ejecta particles. Thus, the proposed work will help us understand the complex physics governing the breakup of ejecta into droplets. This is accomplished by investigating the problem at lower strain rates, so that every stage of the flow can be resolved by our detonation tube experiments and numerical simulations. Insights from the experiments and simulations will feed into the development of a ‘lifecycle’ model that will tie breakup dynamics to early-stage, linear instability physics. Having such an end-to-end model will enable control of liquid sheets in several industrial applications, by controlling the surface corrugations on the solid, so that a desired droplet distribution may be achieved. The breakup model developed will find broad usage as a sub grid model in simulations of impulsively driven sheets and jets. The project will help support the defense and security industries and will provide students the training to fill a vital workforce need in areas of national security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Statistics and data science are increasingly necessary for an informed citizenry and for STEM professions. Learning about statistics concepts requires understanding how statistics and data are used in to interpret and describe patterns in real world situations. As middle and high school math classes include more statistics, teachers will need support in developing knowledge and resources for teaching statistical concepts and applications. In any learning context, students draw on their own knowledge and experience to understand new topics and concepts. This project will engage students and teachers in finding meaningful ways to use statistical reasoning to make data-based arguments and reason about patterns they observe in society. This CAREER award is funded by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. 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 proposed projects. The project examines the development of statistical literacy that combines statistical reasoning and thinking. The focus is in designing and refining an empirical theory of change for supporting the development of teachers’ statistical literacy. In this case a critical statistical literacy also includes how students learn about statistics in ways that are grounded in their experience and understanding of the world. Statistical literacy for students and teachers helps interpret and explain social phenomena using data collection and analysis. Students should be prepared to develop data-based arguments for interpreting and explaining real phenomenon. The project will use professional learning communities for teachers to learn about statistical literacy and develop learning experiences for their students. The research will examine teachers’ knowledge development and their use of statistical learning experiences for students. The data collection includes quantitative and qualitative sources. These include video analysis of the professional learning communities, measures of teachers’ statistical knowledge, and evidence from classroom interactions with students. This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). 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

Advances in computing capability have led to huge improvements in the resolution and validity of scientific simulations, which has enabled science breakthroughs that were not possible in the past. With the sheer amount of data generated, analysis has become increasingly cumbersome due to the high cost of data storage, transmission, and computing. Error-controlled progressive data management frameworks have been designed to address such issues but have a number of limitations. To overcome these limitations, this project develops intelligent error control for the progressive data management framework on multi-tier storage systems. The effort advances next-generation data reduction and management techniques for advanced cyberinfrastructures, benefiting scientific users and expediting discoveries. This project develops an efficient progressive data management framework that controls reconstruction errors in fine-grained areas. The framework optimizes the data retrieval mechanism based on the interplay between error control factors and I/O size. Such optimization is designed as a derivation-free heuristic searching algorithm to identify the satisfactory error control factors towards the minimal I/O. The framework supports adaptive tuning of error control factors through a surrogate-based model that can identify the best configurations tailored for every application under the constraints of error tolerance and quantities of interest preservation. 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

A superlattice is a material that is formed in alternating layers to specific thicknesses. Ionic-conducting superlattices offer a novel and efficient approach to enhance the conduction of ions in devices that span a variety of technological applications, including renewable energy, sensors, and microelectronics. However, the complex interactions between structural defects, ions and heat within the superlattice present significant challenges to optimizing these materials. This project seeks to unlock the potential of ionic-conducting superlattices by decoding these intricate dynamics, thereby pushing the functional limits of various technologies. The new insights gained from this research not only will advance fundamental scientific understanding but also will significantly benefit society by improving energy storage and device efficiency. The research outcomes will be incorporated into educational programs ranging from university courses to K-12 initiatives, fostering a diverse STEM pipeline and inspiring the next generation of the science and engineering workforce. The goal of this project is to develop a comprehensive understanding of defect-facilitated phonon engineering in ionic-conducting superlattices, including localization/delocalization, and its impact on ion transport. Such understanding requires combining multiscale structural simulation with transport simulation, with spatial and temporal resolution, which has posed a long-standing challenge. In addition, the research outcome of this project seeks to bridge the gap between thermal transport and solid-state ionics—two scientific disciplines that are traditionally operated separately. This project will 1) Develop and validate a multiscale method for coupling phonon transport with ion conduction, capturing both long-range mesoscale structures and sublattice anharmonic vibrations; 2) Utilize a pre-selected list of geometric configurations to test the hypothesis of defect-induced phonon localization/delocalization; 3) Measure the relationship between mode-specific phonon excitation and ion diffusivity, testing the hypothesis of phonon localization-induced ion hop; 4) Integrate research into education through a variety of project-based activities to promote learning among peers and the general 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 2025 · 2025-01

This project aims to serve the national interest by developing an empirically validated tool to assess a student’s knowledge of computing concepts. Such well-designed knowledge assessments, vetted by rigorous design and psychometric processes, are still relatively uncommon in the computer science education community. Moreover, students learn computing concepts in a variety of increasingly different educational contexts: at home, on the job, in a course offered online, or in a traditional classroom. Thus, there is a pressing need for an empirically validated assessment of computing knowledge that works in a variety of educational contexts and with different programming languages. The goal of this project is to design and pilot such a multi-contextual and multi-language assessment by enhancing the existing Second Computer Science 1 (SCS1) course assessment. The new assessment, referred to as SCS1++, will be designed by addressing known issues with the existing SCS1 assessment while also increasing the number of questions on the assessment. Specifically, the project will (1) construct a coherent argument for validity claims of SCS1++ as a whole; and (2) create subscales aligned with the concepts on the assessment to improve the assessment’s formative values. The project will directly inform the research on equitable CS assessment by updating the current SCS1 with improved questions and rigorous validation using advanced psychometric tools. A design-based research approach will be used, based on theory, and the system will be evaluated in real educational settings. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This 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

Mathematical models, particularly ones that characterize key epidemiological mechanisms such as transmission, enable public health policymakers to estimate epidemic risks, quantify uncertainties, and evaluate policy implications throughout epidemics. This project aims to address deficiencies in current mechanistic modeling paradigms by further integrating the often-neglected feedback loop among various public health policies (such as vaccination and non-pharmaceutical interventions), dynamic public opinions toward these policies during different phases of an epidemic, and critical outcomes such as hospitalization and death. This project establishes a detailed, integrated system encompassing policy, opinion, and epidemic dynamics, supported by robust mathematical methodologies and novel computational opinion mining approaches. This system will serve as a resource for developing, evaluating, and adjusting public health policies. The methodology developed can be applied to mechanistic models beyond the scope of this project, contributing to the broader field of mathematical epidemiology. Additionally, this project seeks to train the next generation of multidisciplinary modeling and public health teams, ensuring more precise situational awareness and policy support, ultimately enabling our society to stay ahead of the curve in future epidemics. This project aims to develop and deliver innovative mathematical models for the co-evolution of public opinions and epidemic dynamics within the framework of mean field games (MFGs), resulting in an integrated system of epidemic MFG equations. The MFG approach captures the complex feedback among public health policies, dynamic public opinions, and epidemic outcomes that are not well captured by the commonly used susceptible-exposed-infected-recovered (SEIR)-type compartment and agent-based models. MFGs will significantly enhance our ability to track the coupled public opinion-epidemic system under spatially and temporally heterogeneous health policies. Additionally, this project will develop robust convexification numerical methods with guaranteed global convergence to accurately infer critical parameters (e.g., transmission coefficient, recovery rate, ...) from observed data, treating these as coefficient inverse problems. Furthermore, advanced natural language processing techniques, including content analysis and sentiment analysis, will be developed to characterize real-time public opinion and estimate compliance with various health policies across time and space. The integrated MFG system will be simulated under various scenarios, such as different public health policies and varying compliance, to predict future epidemic outcomes for policy decision support. This award is jointly funded by the NSF Division of Mathematical Sciences (DMS) through the Mathematical Biology program and Division of Environment Biology (DEB). This project was also co-funded in collaboration with the CDC. 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

Per- and polyfluoroalkyl substances (PFAS) are chemicals that persist in the environment, accumulating in soil, water, and living things. These “forever chemicals” do not break down easily, and scientists have detected their presence far from their source, including in the Arctic. Exposure to PFAS can harm human health, being linked to liver damage, immune system disruption, cancers, thyroid hormone disruption, and developmental issues. Arctic communities, who often rely on subsistence diets, are particularly vulnerable to exposure to these substances. The Arctic region’s unique characteristics, including low temperatures, ice cover, and long-range atmospheric transport, create conditions that favor the accumulation and persistence of these chemicals. Arctic ecosystems also support diverse wildlife and play a crucial role in global climate regulation. As such, understanding PFAS distribution, sources, and impacts on ecosystems and wildlife in the Arctic is essential for data-driven management and policy decisions. To investigate how pervasive PFAS contamination is across the Arctic, the research team will collect samples onboard an icebreaker research vessel that will transit from Alaska to Norway. The potential presence of newest generation of PFAS chemicals will also be investigated by researchers for the first time. Water samples from the surface down to 3,000 meters in depth will be collected and analyzed for the presence and concentration of PFAS contamination. From these data, models will be developed to understand how the distribution of PFAS has spread to predict potential accumulation in the future. The research team will investigate the distribution patterns of PFAS in Arctic Ocean seawater, assess the roles of atmospheric and oceanic transport processes, and reconcile measured data with results from a geochemical model. The research team will also evaluate whether overall PFAS contamination varies across different matrices including water, ice, and animal life in the Arctic. To address these questions, the research team will undertake comprehensive field studies to collect water and ice samples, develop predictive models of PFAS spread in the Arctic investigate of sources and transport pathways, and initiate an assessment of risk to Arctic communities. This research has the potential to inform conservation efforts, policy decisions, and risk assessments related to PFAS exposure in the Arctic, and numerous early career researchers will be trained and mentored throughout the course of the project. 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

IEEE GLOBECOM is one of the two flagship conferences of the IEEE Communications Society (ComSoc), together with IEEE International Conference on Communications (ICC). IEEE GLOBECOM 2024 will take place in Cape Town, South Africa, from 8-12 December 2024. This project funds undergraduate or graduate students from US universities to attend IEEE GLOBECOM. Attending such a high-caliber technical venue is extremely valuable for students. Not only will they be exposed to the state-of-the-art research areas, but they also can have the opportunity to interact with peers from institutions worldwide, meet with leading researchers from both industry and academia, and take part in discussions that may shape the future of the field. For graduating senior students to be on the job market, attending the conference may significantly benefit their career development. Applications are invited from qualified full-time US-based graduate and undergraduate students. Women and underrepresented groups will be encouraged to apply for this travel grant, in order to promote participation in Science, Technology, Engineering, and Mathematics (STEM) fields. The requested support will cover 16 students, leading to a total of $20,000. Applications are invited from qualified full-time US-based engineering graduate and undergraduate students. Women and other underrepresented groups in STEM, beginning graduate students, and first time IEEE GLOBECOM student attendees will be especially encouraged to apply for this travel grant. Please report errors in award information by writing to: awardsearch@nsf.gov. 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

Hurricane Helene, striking the southern United States in late September 2024, caused catastrophic damage to western North Carolina. The damage to infrastructure (electricity, water, cell service, roads), displacement of residents, and the prioritization of basic needs for relief efforts may directly affect citizen participation in the voting process. Ensuring voter access, security, and an efficient process requires a robust response by government officials and non-governmental organizations. Failures to provide these services—or the belief that essential services are lacking—could undermine perceptions that the election results are fair and valid. Consequently, developing an understanding of the administrative responses to a disaster, as well as public perceptions during an election period, is critical. Because North Carolina is a key battleground state in the U.S. presidential election, the impact of the storm raises critical research questions: (1) how will the disaster affect the state's capacity to implement elections and (2) how will the state's responses influence public perceptions of election integrity? These questions are important as the state's ability to quickly address logistical challenges could impact voter turnout and confidence in the election outcome. To analyze Helene's impact on election administration, this research project will collect and analyze four kinds of data: post-election survey data from residents of the state; exit poll data from voters at early voting sites and election day polling places; interviews with state and local election officials; and administrative data on the location of polling places, changes due to the disaster, and official election results. By analyzing these data, the research will evaluate how logistical challenges influence administrative responses, voter behavior, and perceptions of integrity. The project will contribute to the development of knowledge in several areas of social science research. It will enhance the understanding of voter behavior, election administration, disaster management, and the implications of crises on policy implementation. 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) grant will support fundamental research on processing-structure-mechanics relationships in thermosetting nanocomposites containing ultrasmall two-dimensional quantum dot nanoparticles. Thermosetting polymers are used by aerospace, defense, transportation, electronics, and many other industries. Moreover, fiber reinforced thermoset composites dominate high-performance structural applications. Despite their common use, thermosets are brittle. Quantum dot nanoparticles, on the other hand, simultaneously enhance the toughness and strength of the thermosets. Yet, we don’t know how the ultrasmall two-dimensional particles enhance mechanical properties of the thermosets. The researched project will reveal the molecular origins of these enhancements using systematic simulations and experiments. This new knowledge is potentially transformative because quantum dots can enhance all the other larger-scale-filler composites, such as continuous fiber, graphene, and carbon nanotube composites. The education objective is to create open-source virtual reality engineering education tools and cultivate creative engineers. Unique collaborative design projects are planned to enhance student creativity that can impact innovation potential of the next-generation engineers. Outreach activities will also be performed in the elementary/high schools that serve underrepresented groups in the Silicon Valley. This study will improve our understanding of molecular level damage/deformation mechanisms as well as macroscopic fracture behavior of quantum dot nanocomposites. A specific focus is given to unique sub-20 nm structures containing up to three different quantum dot sizes that can react with thermosets and with each other. During this CAREER award, a combination of experimental, theoretical, computational, and informatics approaches will be used to answer: (a) How do quantum dot nanocomposites deform and break? (b) What are their main toughening mechanisms? The research objectives are to (i) reveal the load transfer and debonding mechanisms, (ii) identify the plastic deformation, damage initiation, and damage accumulation mechanisms, and (iii) observe, quantify, and model the toughening mechanisms in thermosets containing multi-modal size quantum dots below 20 nm. To achieve these objectives, molecular dynamics synthesis and mechanical tests will be performed. Positron annihilation time spectroscopy, in-situ Raman spectroscopy, in-situ synchrotron X-ray characterization, and ex-situ mechanical tests will be performed. A molecular dynamics-informed multi-scale model will be used to quantify the toughening behavior. In addition, a new atomistic data-driven predictive framework will be created to quickly discover nanocomposites with high mechanical performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The research-centered CIVIC pilot aims to bring recent advances in Artificial Intelligence (AI) and video analytics to transform Charlotte's city center from a traditional Central Business District (CBD) space into a dynamic Central Activity District (CAD). To this end, this project will leverage privacy-preserving video analytics with integrated AI-assisted solutions to influence public perception and usage of urban spaces, enhancing the urban living experience, improving the quality of life, and reducing service disruptions. More specifically, the proposed pilots aim to (1) create proactive safety measures to enhance safety and perception of safety in uptown Charlotte; (2) address critical information gaps in understanding complex micro walking behaviors and dynamics of pedestrian flows for more efficient and intentional resource allocation; (3) and enable meaningful AI-guided soft changes in city center infrastructure. This planning phase includes a unique, established partnership between UNC Charlotte and Charlotte Center City Partners (CCCP), a leading civic organization, and other diverse stakeholders. The team will also work to get feedback from local businesses, residential communities, and other civic partners, such as local government and private service providers, as well as the larger general public of Charlotte. The proposed pilot aims to redefine the role of Closed Circuit Television (CCTV) cameras from passive surveillance devices to insightful, proactive tools providing actionable insights to stakeholders and civic organizations by leveraging recent breakthroughs in privacy-preserving AI and real-time edge video analytics. This project's research and planning activities are centered around transforming existing passive surveillance devices into insightful tools for community and economic development that can deliver continuous growth, prosperity, and inclusion in cities. The pilot aims to break the barriers in translational research and pave the way for the responsible deployment of AI technologies within the city environment at a large scale, combining technical innovation with social, ethical, and legal aspects for responsible deployment. At the same time, this project establishes a robust community-in-the-loop framework that ensures that insights derived from AI analytics are directly fed back to the community, thus fostering a collaborative ecosystem where real-time data and community feedback inform decisions This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs 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 · 2024-09

Virtual meetings are important for organizational leaders as this context represents a critical activity to share information, develop relationships, and stimulate creativity and innovation. The project collects data and leverages data science to inform the training of leaders to create effective and inclusive virtual meetings. This research has implications for the scientific advancement of leadership theories as well as downstream practical implications for organizations and society. Almost every societal challenge faced by organizations requires effective leadership to overcome it. The application of data science to studying leaders’ and followers’ use of emotional expressions in virtual meetings is necessary. First, those who train leaders will have greater specificity in the knowledge, skills, and abilities required to facilitate leader emergence and effective leadership. Second, leaders who receive this training, and their organization, will benefit from the creation of a more inclusive and engaging virtual environment. Along with what leaders say in meetings, it is also important to understand how they say it in the form of verbal and nonverbal emotional expressions. This project seeks to advance scientific knowledge of how leaders and followers effectively use emotional expressions as signals in virtual meetings. First, the project team conducts a series of online meetings in which leaders and followers work on a component of a strategic plan for a real organization (Task 1: Generate leader and follower verbal and nonverbal data). Second, the project team applies artificial intelligence methods to analyze leaders’ and followers’ verbal and nonverbal (voice tone, pitch, volume) emotional expressions during meetings (Task 2: Develop multimodal AI scores of leader and follower verbal and nonverbal behavior). Third, the project team explores gender differences in how leaders use emotions and how followers rate the leaders (Task 3: Examine the occurrence and outcomes of emotional expressions during meetings). 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

Education researchers have access to more extensive and heterogeneous data sources for their research and assessments, which requires skills in advanced cyber-infrastructures. Artificial intelligence (AI) can help improve the quality of educational research and assessment. This kind of research and assessment is invaluable in advancing national interest by enhancing the ability to answer research questions such as the effectiveness of education policies and pedagogy techniques and closing the achievement gaps. Utilizing AI in education requires additional skills beyond conventional statistics training education researchers, school administrators, and policymakers receive. This project addresses the fundamental issues of training users to use advanced cyber-infrastructure, such as cloud computing systems, to deal with the challenges of working with large quantities of education data. The training materials, software tools, and hands-on project assessments developed as part of this project help prepare future educational researchers in learning analytics to use advanced cyberinfrastructure systems in the cloud. The other potential benefits include expanding the utilization of cyberinfrastructure resources beyond the traditional natural science researchers to involve other social science researchers in education to serve national needs. This project, AI4EDU, aims to develop innovative training materials for education researchers to enable them to utilize AI in educational research and assessment using cloud infrastructures. AI4EDU consists of three integrated thrusts to address this challenge. The first thrust is the development of educational materials that introduce critical aspects of planning, configuring, and utilizing cloud computing resources and frameworks (e.g., Hadoop, federated learning) to support various educational analytical tasks. The second thrust is to develop tools in data quality, cloud monitoring, cloud planning, and configuration to support utilizing cloud services. The last thrust is to design sample projects with accompanying datasets for real-world, hands-on training. In addition, AI4EDU includes a public repository to collect and share machine learning programs and datasets tailored for various educational research tasks to help build up the community of users. The AI4EDU project helps support the AI for Education initiatives by bridging the gap between the analytical techniques taught in the classroom and the tools and skillsets needed to work with data in education. 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 · 2024-08

The CD300 protein family of immune receptors is encoded by a family of genes present in all mammalian species, including humans. CD300 proteins have been implicated in multiple important roles in human health, including the regulation of cancers, inflammatory diseases, and viral infections. By directly binding pathogens and initiating an immune response, CD300’s play a crucial role in the immune response of humans and other animals. However, the number of CD300 genes varies dramatically between species; for example, rodents, dogs, and armadillos encode more CD300 genes than humans. Little is known about the function of these receptors across different species. Results from this project will shed light on the evolutionary history and functional diversification of the CD300 gene family, providing valuable insights into how these receptors contribute to immune function and disease susceptibility in all mammals including humans. This knowledge has the potential to enhance our understanding of rules that govern the emergence of novel immune function while simultaneously informing the development of new therapeutic strategies. Additionally, the interdisciplinary efforts from this project will yield new K-12 educational modules that align with state standards, will be widely available to public school teachers, and will feature gamified elements to enhance STEM education. This award was co-funded by SBS/DEB. The human genome encodes seven structurally similar CD300 genes in a single cluster on chromosome 17 which are presumed to have arisen through tandem gene duplication events. Select CD300 proteins have been shown to bind specific phospholipids and are implicated in pathogen recognition and immune defense. However, mammals and other vertebrate species also encode clusters of CD300 homologs with variable gene content across species. Little is known about the functional diversification and evolutionary dynamics of CD300 genes across these lineages. As a consequence, it remains unknown whether the emergence of novel CD300 genes is associated with the development of novel functions. This project will use a multi-omic approach to fill this knowledge gap by mapping the molecular and functional diversification of CD300 orthologs and paralogs, and experimentally testing ancestral CD300 functions. By using a comparative approach to study CD300 genes, the proteins they encode, and the lipids they bind, this research will provide insights into the mechanisms that drive the generation of immunogenetic diversity across vertebrates, while also creating a critical receptor-ligand framework for novel therapeutic development. Additionally, this project will contribute to broader impacts by integrating research findings into STEM education curricula, particularly benefiting underserved middle and high schools, including the Eastern Band of Cherokee Indian community, and fostering scientific literacy and engagement. 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 · 2024-08

Networks have been used for many years to model and study a variety of phenomena, including social interactions, co-authorship of scholarly work, and financial interactions. More recently, networks themselves have become objects of study. This research project will develop new statistical approaches for comparing and aligning networks, and will include applications of these methods to problems in computational neuroscience, systems biology, and urban planning. This research will advance the state of network analysis by providing new statistical methods as well as theoretical support for these methods. The broader impacts of the project include applications, collaborations with disciplinary scientists, educational outreach from high school to the graduate level, and community outreach. This research project focuses on the statistical analysis of network data, including the design of new methods, the development of rigorous theoretical support for these methods, and the application of these methods in several relevant scientific domains. The project has four specific objectives. First, investigate optimal transport-based distances for Markov embeddings of networks. Second, develop new methods for network alignment and comparison based on these distances, and apply these or new methods to the problems of model fitting, classification, and node feature prediction. Third, establish rigorous theoretical results concerning the properties of optimal transport distances on networks, investigate relationships between distances and different embedding procedures, and provide theoretical support for the associated methods. Fourth, apply the methods to address problems in computational neuroscience, systems biology, and urban planning. This research will bring together ideas from Markov chains and optimal transport in the setting of network analysis, and the applications of this research will involve the development of efficient, scalable algorithms for analyzing network 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 2024 · 2024-08

Human interactions with plants and animals on dynamic and integrated natural and cultural landscapes, have provided the agrarian foundation for civilization. Agrarian responses to environmental flux depend on complex human interactions with the landscape, water management, and choices in plant and animal management, especially in environmentally fragile ecosystems. This project investigates how human civilizations respond, both successfully and unsuccessfully, to pronounced environmental changes. Specifically, this research illuminates the often-precarious relationships between agrarian societies, and their ecological settings over the long term. Profound environmental stress about four thousand years ago has been linked with major societal disruptions across the ancient world. Today, some ecosystems are warming almost twice as fast as the global average, and water demand is expected to double or triple in the next few decades, which will continue to stress already-vulnerable ecosystems, economies, and societies. With the dynamic sensitivity under consideration this investigation of social decision making and agrarian responses to environmental stress in the deep past potentially reveals fundamental implications for human resilience and sustainable agriculture in the present and the future. This project is an interdisciplinary study integrating social and natural scientists. Such studies are particularly well-suited to applying deep time perspectives to elucidate long-term environmental changes and assess how agricultural communities cope with environmental stress. Communication of project results will be facilitated by archaeologically informed artistic depictions of ancient landscapes and agrarian communities. Outreach to popular and university audiences in the United States and beyond will foster a sense of archaeological stewardship, an appreciation of deep human heritage, and an awareness of the varied responses to environmental change implemented by agrarian societies. Project data will be accessioned into the University of California’s Digital Library Merritt repository, where it will be managed by Open Context, and available for broad audiences to investigate agricultural practices during the rise and collapse of ancient civilizations. Project results contribute a new comparative approach for the study of ancient social flux across broad geographic and temporal scales, while offering opportunities for student and popular engagement in scientific research. 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 · 2024-08

The rapid development and attention towards generative artificial intelligence (AI) makes their trustworthiness a critical issue of societal importance. Diffusion models (DMs) are large generative AI models which can generate high quality images from text instructions. There are concerns about the trustworthiness of DMs in terms of privacy, fairness and explainability. It may over-memorize the training data, which causes vulnerabilities to privacy leakages. Without proper guidance, it may inherent social bias from the training data and generate harmful images towards unprivileged groups. It cannot explain why or how the images are generated based on the instructions. This project evaluates the trustworthiness of DMs and provides advanced solutions to address these issues. The results of this project can benefit decision-makers and practitioners in different areas such as health care, media, law, and education to adopt generative models to assist content creation and daily productivity. This project extends the current techniques in Diffusion Models focusing on a single aspect of trustworthiness to achieve multi-desiderata simultaneously including privacy, fairness, and explainability. This project first implements differential private DMs to defend against privacy attacks. It then introduces fair training in private DMs to avoid spurious correlation and stereotyping in generated content. In addition, it explores faithful explanations of DMs with the assistance of attention mechanisms. Most importantly, the projects evaluate the trade-off between these trustworthiness properties and generative quality in a joint trustworthy DM framework. 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 · 2024-07

NON-TECHNICAL SUMMARY: Cells are the amazing machinery in living organisms that enable life by biological function through chemical and physical processes. It all happens within their tiny size of one to ten microns, but these tiny cells are visually transparent. To understand their internal structures and processes in detail while they are doing their work, special agents are needed along with a microscope. One such agent is green fluorescent protein, or GFP, which revolutionized the way biological processes at sub-cellular scale are visualized and understood today. In addition to GFP, countless natural or synthetic proteins are expected to have untapped potential for enabling visualization of live cells as well as for other technological applications. However, identifying a protein structure optimal for a particular technological application is not trivial. The goal of this project is to tackle this challenge and discover new protein materials by characterizing the chemical and physical properties of known protein materials with desired properties. This will be done in combination with the screening of other related proteins aided by machine learning. Finally, this project also provides graduate and undergraduate students with interdisciplinary training in biomaterials field and extends the hands-on research experience to local K-12 teachers through outreach programs. TECHNICAL SUMMARY: This project aims to investigate a new class of luminescent protein materials. Luminescent protein-metal compounds are important materials with demonstrated versatile applications in imaging, sensing, as well as tracking agents for biological and environmental studies. These compounds exhibit luminescence that is stable in wide range of pH, but via mechanisms that are unknown. In this project, multi-protein time-resolved spectroscopic measurements are combined with pan-protein analyses via machine learning using iterative Bayesian optimization for the model training, prediction, and experiments. The combined analyses allow determination of the mechanistic origin of the luminescence and the structure of the luminophore in proteins. The elucidated mechanism and the structural origin of luminescence enable the ability to identify, design, and develop novel biological luminophores. This project provides graduate and undergraduate students with interdisciplinary research opportunities and training as future workforce in biomaterials field and extends the hands-on research experience to local K-12 teachers through outreach programs at the PI’s university. 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 · 2024-06

Discrete graphics processing units (GPUs) are crucial for providing the computing power of today's data centers and high performance computing systems, enabling significant advancements in many disciplines such as climate modeling, nuclear energy, drug design, social networks, deep learning, and artificial intelligence. Programming for GPUs has been laborious and error-prone due to the need to manage data migration between discrete host and GPU memories. As deep learning models and social networks become increasingly larger, and scientific workloads more data-intensive, it is imperative to relieve programmers of such tasks and make GPU programming more productive and portable. Unified Memory (UM) technologies have been developed to meet this need. Nevertheless, current UM technologies cause significant or prohibitive performance degradation. This award establishes a foundation for efficient and intelligent unified memory design, closing the prohibitive performance gap and harnessing the power of advanced GPU accelerators. It leads to reductions in time-to-production and time-to-completion of various scientific simulations and deep learning workloads, enabling them to scale up to larger problem sizes with ease. The award includes rich education, outreach, and broadening participation activities, offering in-class and out-of-class experiences, as well as team-based undergraduate research and engaged learning spanning multiple semesters. It recruits and supports students from underrepresented groups, including people with disabilities, fostering their inclusion and belonging. The comprehensive framework, ACCess Pattern ORienteD (ACCORD), includes abstraction methodologies, cost models, and techniques to enable efficient UM algorithms and systems for various workloads and problem sizes. Its key innovation lies in the abstraction of access patterns, which uses metrics obtainable at the system level to capture the spatial distribution and temporal repetition patterns of massively parallel memory accesses. This abstraction empowers the quantitative assessment of their interaction with UM designs and guides the optimization of algorithms and UM techniques to eliminate performance bottlenecks and optimize data movement effectively. The research objectives include: (1) devising the abstraction of access patterns for UM-based GPU-accelerated systems, (2) developing quantitative methods to analyze the cost of various access patterns and their interaction with UM techniques, (3) designing access pattern-oriented UM techniques for online deployment, and (4) integrating ACCORD into real-world UM systems to support various applications. This project is jointly funded by the Software and Hardware Foundations (SHF) core program at the Division of Computing and Communication Foundations (CCF) 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 · 2024-06

This project explores a proof-of-concept and feasibility evaluation to inform the future development of a centralized data repository to support the privacy research community. The repository will enable tracking and systematic study of privacy harms. Current incident reporting systems are designed to track the occurrence of large-scale data breaches, but there is currently no centralized reporting system to effectively track other types of privacy violations (e.g., online harassment, cyber abuse) that negatively impact end-users. Without access to this information, it is difficult to quantify / qualify how and to what extent different online platforms propagate privacy breaches, as well as how to redesign such systems to be more secure and trustworthy. Therefore, this planning effort aims to (1) solicit the opinions of privacy experts on the design of the repository; (2) prototype the repository and solicit feedback from experts piloting it; and (3) build on these learnings to develop a plan to develop a centralized privacy incident repository. This will ultimately enable researchers to work together to (1) identify and prioritize privacy harms and the factors associated with the incidents; (2) understand how various populations are impacted by these harms; and (3) develop and evaluate potential interventions. This repository is envisioned to support the protection of vulnerable end-users who are disproportionately threatened and harmed by digital privacy violations, addressing the recent R&D budget priority from the White House and the Office of Science and Technology Policy focused on reducing inequities. By identifying evolving privacy risks, we also work towards two other budget priorities -- advancing trustworthy AI technology and maintaining global security and stability. 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.

Fonds de recherche du Québec – Société et culture · FY 2023-2024 · 2023-04

Volet: Bourses postdoctorales; Domaine: Gestion des organisations; Objet: Croissance et cycles économiques; Objet: Contextes familiaux; Application: Structures et relations sociales; Application: Structures organisationnelles; Mots-clés: ENTREPRISES FAMILIALES, FAMILLES EN AFFAIRES, ENTREPRENEURIAT, DYNAMIQUES INTERGENERATIONNELLES, PATRIMOINE FAMILIALE, RELATIONS SOCIALES