George Mason University

NSF Awards · FY 2025 · 2025-04

Studies of human population genetics can reveal patterns of human movements and interactions, advancing knowledge about how individual and group behaviors shape human societies. In this project, the investigators use genomic and isotopic methods and data to reconstruct population histories and evaluate changes in mobility patterns through time. The project advances science outreach as well as graduate and postdoctoral training in STEM fields. The project generates genomic, paleogenomic, and isotopic data to reconstruct population histories and address the following three objectives: 1) evaluate genomic variation and population dynamics, 2) investigate changes in mobility patterns, and 3) assess genomic and isotopic variation and evaluate genomic continuities/discontinuities within groups over time. The research can inform our understanding of the scale of interactions between various populations before and after major events. 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

This I-Corps project is based on the development of immersive telepresence, which is a technology that enables people at different locations to meet in virtual environments using realistic, three-dimensional representations of themselves. Immersive technologies are increasingly important for engaging remote learning solutions, advancing medical training tools and telemedicine, and growing virtual and augmented reality experiences in entertainment, sports, and other areas. Achieving a truly immersive and highly interactive user experience for telepresence requires significant network bandwidth, an ultra-low latency, and real-time processing to support fluid motion and user interaction. The goal of this project is to provide users with a sense of physical presence in remote locations by integrating high-fidelity video, spatial audio, and real-time interactive environments. This technology may provide practical, scalable solutions for real-world applications in an increasingly digital and connected world. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of live immersive content delivery technology. Live immersive content delivery forms the foundation for immersive telepresence, which is a key use case in the envisioned 6G communication landscape. Immersive telepresence enables users to experience a sense of physical presence in remote locations by integrating high-fidelity video, spatial audio, and real-time interactive environments. Unlike traditional communication methods, this technology leverages 3D representations, such as point clouds or meshes, allowing users six degrees of freedom (6DoF) motion, enabling them to move and interact naturally within virtual spaces. The technology builds on extensive research and prototyping efforts to address critical challenges in immersive content delivery, such as the need for high network bandwidth (>1 gigabits per second), ultra-low latency (<100 millisecond), and real-time streaming at a minimum of 30 frames per second. This innovation may have the potential to transform remote collaborations across fields such as education, healthcare, training, and business by overcoming geographical barriers and creating more engaging interactions. 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 fifth East Coast Optimization Meeting (ECOM) will occur on April 17-18, 2025, at George Mason University, Arlington, Virginia. The goal of ECOM is to introduce students and early-career researchers to current trends in optimization as well as to provide a strong networking environment between academia, industry, and the national laboratories. The focus of this fifth meeting will be on the role of optimization for Digital Twins. Digital Twins are expected to have a significant impact on science, engineering, and society. For instance, it is anticipated that they will lead to new developments in identifying weaknesses in structures such as bridges, nuclear plants, and wind turbines. Digital Twins of human organs have the potential to lead to cures of diseases that have long eluded researchers. The variety of topics to be discussed in the meeting such as stochastic optimization, modeling, partial differential equations, and risk averse optimization are also of much wider interest beyond Digital Twins. The meeting will also provide a unique opportunity for graduate students, postdocs and other early career scientists to take courses from two leading researchers in modeling, optimization, and scientific computing. ECOM speakers and participants will study the key question of how to best utilize optimization to combine physics-based and data-driven models. This approach, when carried out for the entire complex physical system, for its lifetime, can be termed a "Digital Twin." One of the critical components of a Digital Twin, which distinguishes it from classical modeling, is the use of data over the entire lifetime of the physical system to update the Digital Twin. Subsequently, Digital Twins bring together several research areas in mathematics, including modeling, analysis, control, optimization, numerical analysis, and scientific computing. New algorithmic developments are expected in these areas and this workshop aims to dive deeper into the topics relevant to Digital Twins: optimization constrained by simulation, optimization under uncertainty, and inexact optimization algorithms. A particular focus of this workshop will be the identification and development of benchmark applications and software implementation. The tutorials and invited talks will focus on consequential problems and will discuss state-of-the-art optimization solvers to handle these problems. As a result, the attendees will be equipped to tackle a new set of challenging problems. More details can be found at the conference website: https://math.gmu.edu/~hantil/ECOM/2025/ 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 Major Research Instrumentation (MRI) award facilitates the acquisition of a high-resolution micro-computed tomography scanner to address the growing educational and research needs at George Mason University and collaborating institutions. This instrument provides detailed three-dimensional images of objects, including their internal structure, at the micrometer scale, and can be used to image a wide range of materials, such as metals, polymers, biological tissues, sedimentary formations, and paleontological specimens. It will enable advanced research across a broad diversity of disciplines, from materials science and biological research to archaeological studies, ultimately resulting in higher-quality products and improved medical care. The instrument will also enhance education across the disciplines of materials science, biomedical engineering, and mechanical engineering, and it will support research leading to master’s and doctoral-level theses. Through outreach to local high school students, the instrument will encourage them to gain a deeper understanding of science, engineering, and mathematics, enabling them to make more informed career choices. The researched micro-computed tomography instrument will include three operational modes, allowing for the inspection of a wide range of materials, including light and dense samples, at a resolution of 3 μm isotropic pixel size and 5 μm resolution in 3D. Initially, faculty from seven departments: Mechanical Engineering; Bioengineering; Geography and Geoinformation Science; Civil, Environmental, and Infrastructure Engineering; Mathematical Sciences; Biology; and Atmospheric, Oceanic, and Earth Sciences, will use this instrument to pursue research objectives, including: (a) measuring manufacturing mismatches between the design and fabrication of additively manufactured cellular metamaterials, ultimately leading to improved part design; (b) imaging internal structural anomalies caused by surface treatments in additively manufactured metals, reducing part failures; (c) characterizing creep mineralization processes at the cartilage-bone interface following injury, contributing to better injury treatments; and (d) measuring functional connectivity in deep-brain structures to investigate neurodegenerative diseases in small-animal models. Instrument time will be allocated by an operations oversight committee consisting of seven members chosen to represent the involved disciplines. The committee will also be responsible for promoting instrument use by collaborating institutions, thereby expanding access to a wider research community. 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

Due to the proliferation of cyber-attacks, the new generation of cybersecurity engineers must develop appropriate, effective, and affordable systems with security engineered from the concept phase through design and into implementation and deployment. This project will establish a new EAGLE SFS program at George Mason University to support a cohort of undergraduate, graduate, and doctoral students. Through scholarships, advanced training, and professional development, students will gain interdisciplinary education, hands-on research experience, and leadership skills to meet the evolving demands of cybersecurity. Graduates will serve in key government roles, advancing national defense, public safety, and technological innovation. Over a five-year period, the SFS program at George Mason will employ an iterative approach to develop a well-managed, student-centered, and sustainable initiative consisting of five main components: (1) recruitment, (2) education, research, and professional development, (3) retention and placement, (4) promotion and outreach, and (5) program evaluation. The project will: 1) support and integrate undergraduate and graduate research in artificial intelligence and autonomous systems security, secure cyber-physical systems and critical infrastructure, next-generation wireless networks, and cybersecurity for smart manufacturing; 2) promote student engagement with federal, state, and local governments through internships, senior-design projects, and graduate research; 3) organize workshops and seminars on cybersecurity focused on real-live applications; and 4) create pathways and advanced opportunities in cybersecurity education for K-12 and 2-year community college students. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support. 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

Quantum information science is a transdisciplinary field spanning physics, math, chemistry, computer science, and most of the engineering disciplines. It involves the study of matter at the smallest scales -- the scale of atoms and subatomic particles -- and its application to a variety of technologies. Technologies based on quantum science are poised to revolutionize many aspects of our current world and industry predicts a need for a rapidly expanding quantum workforce. With the field poised for rapid growth, now is the time to understand and address systemic equity challenges in the field in order to build an inclusive and equitable workforce. Systemic challenges in quantum education and workforce development restrict access to key education and career opportunities, limiting the ability of institutions to meet industry needs. Researchers studying quantum education and workforce development can play key roles in understanding and addressing these challenges. The Quantum Education Research Postdoctoral Fellowship will prepare recent doctoral recipients to become leaders in quantum education and workforce development research. The program is designed to 1) launch the careers and individual research programs of three recent doctoral recipients with diverse personal, disciplinary, and research backgrounds; (2) support the use of a convergence approach to quantum education and workforce development research through a cohort research model that contributes to the establishment of a diverse and equitable quantum workforce; and (3) advance access, justice, equity, diversity and inclusion in STEM education through transdisciplinary research projects. The project aims to advance knowledge and understanding of systemic challenges to equity, inclusion, and access. Through this work, the Fellows can generate knowledge that can help in the creation of an inclusive and equitable workforce. The program will provide wrap-around support in the form of career guidance, mentorship, professional development workshops, and access to leaders in related disciplines to aid in Fellows’ development as leaders in this field. This project is funded by the Science, technology, engineering, and mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the EDU Core Research: Building Capacity in STEM Education Research (ECR: BCSER) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. ECR: BCSER is designed to build the capacity of individuals to carry out high-quality, fundamental STEM education research in STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development. 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 EArly-concept Grant for Exploratory Research (EAGER) project funds research that intends to speed up the development of mixed reality and artificial intelligence technologies to help first responders, aiming to reduce training-related risks and casualties. The research team will work with the Fairfax Fire and Rescue Department to explore how AI can be used in mixed reality tools to improve training and effectiveness for first responders. By adding virtual elements like fires, hazards, firefighters, robots, and people in need of rescue to real-life scenes, these mixed reality scenarios help first responders practice handling real-world challenges through interactive training. The project will also involve a postdoctoral researcher and undergraduate students, including those from underrepresented groups in science and technology fields. The team will share their findings at conferences focused on mixed reality and training. This EAGER project offers a novel interdisciplinary research perspective by integrating concepts and techniques from mixed reality, artificial intelligence, human-computer interaction, and movement science to advance first responder training. The goal is to devise a novel optimization-based generative framework for adapting mixed reality training scenarios to real scenes, which will offer ample training opportunities for first responders to practice, accomplishing different first-responder tasks (e.g., firefighting, search and rescue) via human-artificial intelligence collaboration enabled by mixed reality headsets. To carry out the research, the team will first investigate how artificial intelligence techniques could be integrated with mixed reality devices to provide first response assistance. The team will then devise a generative framework based on optimization techniques for adapting mixed reality training scenarios to real scenes. The team will conduct user studies to evaluate the performance gain brought about by the advanced mixed reality interfaces and the synthesized training scenarios. 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 develop the mathematical foundations for a digital twin (DT) system for individuals with autism spectrum disorder (ASD), focusing on dynamic modeling, prediction, uncertainty quantification, and treatment or intervention recommendation through DT-based optimization. ASD is characterized by challenges in social interaction, communication, and behavior, such as difficulties in forming relationships, understanding nonverbal cues, speech development, repetitive behaviors, and sensory sensitivities. The project will create a unified system integrating clinical and neuro-developmental data, analyzed using a DT healthcare paradigm. The DT technology will enable individualized models, and its predictive capabilities will allow healthcare providers to anticipate progression and adjust treatment or intervention proactively. Additionally, the continuous feedback loop from real-time data will enhance therapeutic outcomes. The developed methods and theories will have broader applicability to other medical areas, improving healthcare efficiency, reducing system burdens, and informing public health strategies. This will ultimately enhance care and promote community well-being. The project will also develop quality cyberinfrastructure to share algorithms, data, and open-source software with the community. Furthermore, the investigators plan to expand scientific impacts through collaborating with medical experts and industry scientists, training undergraduate and graduate students, and integrating research findings into course development. The project will develop a DT framework by modeling brain activities with a unified data structure, linked to behavioral characteristics and interventions aligned with individuals' neuro-developmental processes. This system will integrate multimodal and multi-source data related to human health and development. It will establish foundational models for training and generating synthetic data from DT models, enabling personalized predictions of progression and uncertainty quantification through novel interdisciplinary approaches. The DT system consists of four research modules: (1) Develop computational models based on conditional variational auto-encoders (CVAE) and longitudinal CVAE to analyze brain activities, integrate diverse imaging data, and model neurodevelopmental processes. (2) Create a novel bilevel formulation for multi-distribution fine-tuning techniques on pretrained foundational models and a fast algorithm to learn from heterogeneous data sources to predict ASD outcomes. (3) Develop a model-free conformal prediction procedure to ensemble predictions from multiple models obtained with different modalities and progression simulations, integrating various types of uncertainties into one framework. (4) Develop a DT-based reinforcement learning framework to recommend personalized treatment/intervention plans that significantly improve online learning efficiency and clinical outcomes. The project will address challenges such as multimodality and multi-source data, high-dimensional features, dynamic progression of ASD symptoms, brain functional connectivity, and the need for personalized intervention or treatment recommendations and uncertainty quantification. This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Interconnected environmental processes influence the initiation and propagation of cascading hazards (e.g., hurricanes, extreme rainfall, floods, landslides) that pose significant risks to infrastructure, ecosystems, and human populations. This threat is particularly acute in underserved communities, which lack sufficient resources, infrastructure, and services to adequately prepare for, respond to, and recover from such hazards. Risk responses at the individual and collective level are hampered not just by a lack of data on the science underlying cascading hazards and specific social and physical vulnerabilities in underserved communities, but by the need for inclusive knowledge formation and decision-making processes. This project collaborates with communities to formulate research questions, conceptualize actionable solutions, and co-produce a research program that advances scientific knowledge, builds community capacity, and reduces vulnerability to the impacts of cascading hazards in a changing climate. While our understanding of cascading hazards shapes the research, there is a need for a strategic shift from response and recovery to preparedness, proactive risk management, and the ability to adapt. This planning project establishes the foundations for enhancing the capacity of geographers, geoscientists, social scientists, and community leaders to effectively evaluate and mitigate the evolving impacts and risks of cascading hazards on underserved communities. Broader impacts include building equitable community partnerships to ensure that the research is grounded in real-world applications and responsive to the needs of the communities it serves, as well as providing interdisciplinary scientific and technical training and experience in community engagement for graduate students. The overarching goal of this planning project is to lay the groundwork for collaboratively developing a set of fundamental research questions and establishing long-term collaboration in three communities in Puerto Rico to shape and account for the effective investment of federal and local resources. It advances the science, theory, and practice necessary to equitably co-produce project research questions and solutions, and explore dynamic interactions and couplings among natural and social processes affecting the resilience of Puerto Rican communities. First, it contributes to co-production literature by empirically categorizing differing perspectives on how participants view its processes and outcomes. Second, it contributes to Earth system science literature by assessing how sequential hazards may drive one another and how the consequences of cascading hazards may scale temporally and spatially. Third, it extends knowledge on how co-production outcomes may relate to changes in social capital and impact federal and territorial policies and guidance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The Devonian Period (419 to 359 million years ago) witnessed some of the most important transformations leading to the habitable planet that we have today. Plants and vertebrate animals first colonized the land surface, oxygen levels rose in the oceans and atmosphere, and the planet cooled significantly. There were also a series of mass extinctions related to a temporary loss of oxygen in the shallow oceans. The funded work seeks to understand the timing, causes, and consequences of these Devonian mass extinctions, and how they can be identified in rocks deposited across North America, as well as in Bolivia and Western Australia. Ultimately, this study will provide a new integrated framework of ocean oxygen loss across time and space through the Devonian extinctions, improving our understanding of how our planet came to resemble to modern world. This project also supports two early-career faculty members of mixed-race and African ancestry, expands field geology opportunities for high school students, supports numerous undergraduate and graduate students and a postdoc, and will disseminate results to non-technical audiences in English and in Spanish. The Late Devonian is a unique interval in Earth history during which the proliferation of land plants triggered a cascade of Earth system perturbations, including atmospheric CO2 drawdown and O2 rise, climate cooling, eutrophication and widespread development of anoxia in epeiric seas, and ultimately, a series of mass extinctions that fundamentally altered the trajectory of Earth’s biosphere. This proposal seeks to link key Late Devonian global events in a new genetic framework that ties a refined temporal record of anoxic expansion in epeiric seas across Laurentia and Gondwana directly to the extinction events, determines the effect of epeiric sea anoxia on the global carbon cycle, and then links these records to global carbonate-based isotopic curves. Specifically, this work proposes to: 1) develop a new, integrated geologic framework that ties Late Devonian mass extinctions to the epeiric black shale successions of North and South America using a combination of conodont biostratigraphy, Re-Os geochronology, and redox geochemistry; 2) use these data to refine the open access Macrostrat database for the Late Devonian with the goal of estimating global carbon burial, CO2 drawdown, and O2 buildup; and 3) generate a new uranium and carbon isotope record through Late Devonian carbonates of Western Australia, which will provide a quantitative record of global ocean anoxia and carbon burial. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The United States (U.S.) transportation sector remains a cornerstone of the economy, contributing over 8% to the country's Gross Domestic Product (GDP). Electrification efforts are transforming this sector, aiming to enhance mobility efficiency, reduce operating and maintenance costs, and cut greenhouse gas emissions. These efforts also seek to boost energy independence and security while significantly contributing to employment, particularly in technology and innovation fields. This shift has already placed more than 2.5 million Electric Vehicles (EVs) on U.S. roads, supported by over 70 thousand charging stations nationwide. To manage this advanced and complex cyberinfrastructure (CI), EV operators and vendors rely on cloud-based EV Management Stations (EVMS), crucial for provisioning services such as charging, billing, and authentication. However, the critical nature of EVMS has made them targets for malicious attacks, often state-sponsored, exploiting rarely investigated vulnerabilities. In response, this project establishes a collaborative ecosystem among academia, industry, and the public sector to bolster the resilience of the EV CI. It aims to develop proactive methodologies to identify and analyze Internet-connected EVMS and their software, thoroughly exploring and mitigating related vulnerabilities. This initiative connects several diverse Minority Serving Institutions (MSIs) within the established ecosystem, fostering joint research and providing enriching training opportunities. Through workshops, capstones, curricula material, virtual hands-on labs, professional development, and mentorship programs, the project enhances cross-disciplinary capacities at MSIs and beyond, driving forward the future of resilient, electrified transportation. In this context, this project serves NSF's mission in promoting the progress of science and securing national defense related to this ever-evolving CI. The project pioneers advanced fingerprinting techniques employing automated web scraping, recursive unsupervised learning algorithms, and pattern matching methodologies to identify and cluster Internet-scale EVMS. The primary objective is to detect deployed configurations and their interconnections, while retrieving critical artifacts, such as firmware binaries and compiled software, for comprehensive vulnerability analysis and disclosure. Leveraging robust industry connections, the project acquires auxiliary artifacts, including EVMS source code, through advanced supply chain reconnaissance and reverse engineering methods. This initiative also devises and implements an advanced digital forensic methodology rooted in ensemble techniques and machine learning classifiers. It integrates static analysis, file system forensics, memory forensics using volatility frameworks, data carving with custom heuristics, offensive security tactics, behavioral analysis through dynamic instrumentation, and virtualization methodologies such as hypervisor introspection to meticulously analyze the security posture of EVMS firmware and web endpoints. Furthermore, the project exploits state-of-the-art innovations in Large Language Models (LLMs) to automatically identify vulnerabilities in EVMS source code and suggest tailored and sound code fixes. This is accomplished by creating an unprecedented instruction-based training dataset using supervised fine-tuning, reinforcement learning, and transfer learning techniques. Additionally, the project establishes a large-scale data and threat repository to index discovered threat models, associated vulnerabilities, and retrieved EVMS artifacts. Accessible via RESTful APIs and web-based interfaces, this repository democratizes knowledge by making the harvested EVMS assets available at large, significantly empowering EVMS-centric threat situational awareness while fostering advanced research and development. 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

In this context of virtual reality, creating, perceiving, and editing three-dimensional (3D) shapes are at the core of activities such as product design (creating or evaluating objects for manufacturing or personal fabrication), online shopping (experiencing furniture in a room or trying on clothing), and specialized training (gaining familiarity with a remote tool). Yet, today's approaches for interacting with virtual 3D shapes are strictly visual, requiring precise manipulation and interpretation of digital designs on a screen. This project's goal is to create algorithms and interfaces that make 3D modeling easier and more effective, even in the absence of visual cues: auto-correct for 3D drawing, the ability to hear shapes, and the ability to edit 3D shapes verbally. By using senses that do not require a screen—body awareness and sound—this project aims to untether people from their screens, enabling virtual 3D perception from anywhere. The outcomes of this project are expected to have far-reaching impacts, including making computers easier to use for people with visual impairments, enhancing interface techniques for low-visibility scenarios, and creating new opportunities for human-computer interface research and do-it-yourself fabrication. The research focuses on three main objectives: developing accurate “in-air” 3D drawing tools, designing sonification (conveying information through sound) techniques for non-visual shape perception and editing, and creating verbal 3D shape editing tools and interactions. These aims will be pursued through auto-correct algorithms that account for the limits of proprioceptive (a person’s sense of their body pose and movement) accuracy, techniques to sonify shapes based on hand pose, and methods for verbal shape modification. This research sets the stage for future studies on incorporating sound and speech into 3D modeling, as well as non-visual user interfaces. 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

Language technologies are promising and could have strong impact during disaster responses. they can help to triage text messages in a disaster to determine what aid to provide. Language technologies can translate vast amounts of data related to an ongoing pandemic. Responders can use these technologies to converse with victims during disaster responses. However, advances in language technologies to date are limited. They focus on a few dozen of the more than 6500 languages spoken or signed in the world today. Current language technologies neglect millions of people.  This especially impacts those who are most at risk for experiencing disasters. This project provides an infrastructure for language technology advancements for crisis response. The results will be useful for everyone, no matter the language they speak. This project builds datasets of crisis communications using dedicated data collections and social media harvesting. These datasets will be applicable to curated crisis scenarios. They will use common language scenarios necessary to communicate with vulnerable populations. This approach helps people for whom language technologies are not typically developed. The project will bring together researchers from different disciplines. These include language technology researchers, experts in disaster relief, linguistics, and human-computer interaction.  The project will target representatives from the local speech communities to take part. To coordinate this effort, the project will organize yearly workshops and shared tasks with the communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Three-dimensional, large-size, and long-duration immersive content captured from real scenes will have a significant impact in the foreseeable future. Playing a critical role in holographic communication, immersive content allows viewers to exercise 6-degree-of-freedom (6DoF) motion during playback. Most existing research on immersive content delivery focuses on single-viewer scenarios. This project proposes to enable, for the first time, a large number of co-located viewers over a millimeter wave (mmWave) network that is capable of providing high bandwidth, with a single access point and edge server. It suits numerous use cases such as massive interactive demonstration and immersive classroom education. This project aims demonstrable networking and systems research with a synergy among wireless networking & sensing, multimedia systems, machine learning, and computer vision. It will help bridge the digital divide by reducing the cost of multi-user holographic communication and telepresence. It will also provide a platform to conduct various outreach activities and community services. As streaming emerging multimedia content is playing a key role in the post-COVID world, the project will have a high impact on global societies and economies. To overcome the challenge of supporting multiple users with limited network and compute resources, this project innovates in three key dimensions. First, it will develop an accurate motion prediction model that captures users' collective motion and their interactions, and study how to adapt to changes deviating from training data. Second, this project will leverage mmWave sensing based on FMCW (frequency-modulated continuous-wave) radar to directly incorporate environment reflection profiles into beamforming and mmWave throughput prediction. Assisted by 6DoF motion prediction, this will lead to proactive and fast beamforming, as well as an accurate forecast of mmWave performance that benefits upper layers. To realize environment profiling based on mmWave sensing, the project will design two techniques: collaboratively reconstructing indoor 3D reflectivity maps and building a neural representation of indoor mmWave reflections. Third, this project proposes two approaches to scale up at the application layer: hybrid streaming where certain viewers receive 3D content and others consume content live-transcoded by the edge, and allowing viewers to share a transcoded view. The team will integrate the above thrusts into a holistic framework, implement it on their mmWave testbed with heterogeneous client devices, and conduct extensive evaluations including field trials with real users. 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

Understanding the dynamics of snow water equivalent (SWE) is vital for effective water resource management, especially in the Western United States where snowpack serves as a major water source. Accurate SWE forecasting is a significant challenge due to the complex interactions among snow physics, atmospheric conditions, and varied terrains. This project aims to revolutionize SWE prediction by integrating cutting-edge artificial intelligence (AI) techniques, specifically physics-informed neural networks (PINNs). In addition to advancing scientific knowledge about snow water processes, this project is expected to have positive societal impacts, such as improved water resource management and informed decision-making in response to climate change. The project will also enable inclusivity and education by involving graduate students and underrepresented groups in AI research, fostering a diverse community of future experts in SWE forecasting research. This project will employ an innovative approach that combines graph neural network models with physics-based constraints and partial differential equations. This integration will enable the creation of more accurate and reliable SWE forecasts by capturing the detailed processes of snow accumulation and melt. The GeoWeaver workflow management platform will be utilized for making advanced AI tools accessible to researchers and practitioners. The project also includes a series of hackathon-style workshops providing students and snow researchers with hands-on experience in AI and SWE forecasting. Overall, the project seeks to democratize access to AI research workflows and tools for snow researchers, foster interdisciplinary collaboration, and support sustainable resource management, thereby enhancing our understanding of water resources and contributing to the broader discourse on climate change and water sustainability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Program invariants, which describe properties that always hold at a program location, are essential for program understanding, debugging, and verification. Among existing modern invariant learning work, the DIG tool can discover rich numerical invariants in programs by integrating dynamic inference and symbolic checking. However, while DIG has inspired many research projects and applications, it needs better scalability to support industry settings, and like other invariant research tools, it is generally not accessible to software developers and engineers who may lack the familiarity or time to learn its usage. This project aims to develop DIG-I (DIG-Industry) to make DIG more practical and usable. The project's novelties are optimizations to improve DIG’s performance and scalability as well as integration with artificial intelligence (AI) to learn invariants more effectively. The project's impacts are that the open-source DIG-I tool will enhance the efficiency and usability of invariant learning, benefiting developers in industry and research labs, and will be used to introduce formal methods and invariant generation to students and professionals through courses at George Mason University. This proposal will develop DIG-I to make invariant research more practical and accessible. It focuses on (i) improving performance by transforming expensive matrix and constraint-solving operations in DIG to Compute Unified Device Architecture (CUDA) kernels to be run efficiently on Graphics Processing Units (GPUs), (ii) supporting additional useful invariants and their applications by integrating existing invariant work directly into DIG's base code, (iii) modernizing DIG by adopting large language models (LLMs) to learn invariants more effectively, and (iv) improving the usability and adoption of invariant analysis by developing a Language Server Protocol (LSP) that allows invariant tools to integrate with popular Integrated Development Environments (IDEs) and editors such as Visual Studio (VS) Code. The findings from this project will be used in the investigators' courses, and mentoring and outreach activities. 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

Grid cells, a type of nerve cell in the brain, are important for spatial navigation and create a grid-like map of the environment by being active at specific locations in space that fall on a hexagonal lattice. Furthermore, grid cells are organized in a modular way, so that the grids of the next module span a larger area than the grids of the previous module. Intriguingly, the sizes of grids increase from module to module by the square root of 2. Yet, no mathematical framework exists to date that explains the emergence of geometric patterns in the activity of grid cells from a set of simple rules governing brain activity, applied to the special case of navigating physical and abstract space. The project will identify the set of rules from which grid cells emerge during navigation, how the activity of grid cells on the single cell level gives rise to grid-like activity on the population level, how the identified set of rules predict distortions of grids in multi-dimensional space, and how the rules can be implemented in a biologically plausible simulation. The expected outcomes will help develop computationally efficient and interpretable algorithms for use in artificial intelligence systems. The engagement of local high school students and undergraduate students in the project's activities will foster awareness of and interest in STEM fields. Recruitment efforts by the investigators will focus on female students and first-generation students that constitute almost 40% of the student population at George Mason University. Despite research on the mechanisms underlying the firing patterns of grid cells in the medial entorhinal cortex, no mathematical framework or theory exists to date that sufficiently explains the experimentally observed geometric patterns in grid cell activity and the specific ways these patterns are distorted in asymmetric and multidimensional environments, such as those found in nature. As a result, our understanding of grid cell firing patterns and their computational function in the context of spatial navigation and episodic memory remains superficial. The project will be significant because it will develop an axiomatic framework for sequence coding of trajectories in multidimensional space that will explain geometric patterns in the firing patterns of grid cells as emergent properties. Moreover, the same axiomatic framework will explain the emergence of grid-like activity in humans. This is a significant contribution because it is not known to date how grid-like activity on the population level emerges from the activity of grid cells on the single cell level. The developed framework will thereby bridge a gap between animal experiments and human relevance. Furthermore, the framework will make it possible to develop algorithmic implementations of a brain-inspired sequence code of trajectories for artificial agents. Finally, a unifying framework explaining geometric organization in the grid cell system and its implementation in biologically plausible spiking neural networks will advance our understanding of how the mammalian brain performs navigational computations and higher cognitive functions such as episodic memory. This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences and the Neural Systems Cluster in the Division of Integrative Organismal Systems in the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This interdisciplinary project in close collaboration with partners in Philadelphia, PA, is designed to reduce the vulnerability of underserved communities to the social and environmental impacts of urban nuisance flooding events. Challenges from nuisance flooding, or frequent but small-magnitude flood events, are being exacerbated as increasingly frequent and severe rainfall coincides with intense urban growth. Urban nuisance flooding events stress transportation infrastructure, shutting down portions of the transportation network and making transportation facilities temporarily inaccessible or difficult to access. This is particularly acute in underserved communities where: (a) residents rely on modes of travel (e.g., buses, trains, bicycles, pedestrian paths) that are impacted by nuisance flooding; (b) urban flood events tend to impact the same locations repeatedly; and (c) residents, already burdened by a combination of socioeconomic, environmental, and mobility barriers, may find it challenging to adapt their travel patterns. While our understanding of flood-induced mobility patterns is beginning to take shape, we lack a systematic framework to understand the mobility barriers and disparities induced by nuisance flooding. Broader impacts include training for residents to be community educators to disseminate information and resources related to nuisance flooding, as well as interdisciplinary scientific and technical training and experience in community engagement for graduate students. The overarching goal of this project is to reduce the vulnerability of underserved communities to the social and environmental impacts of urban nuisance flood events and develop and implement a science-informed, community-based solution in underserved communities to ameliorate equity discrepancies resulting from the impacts of nuisance flooding. This is achieved through three research activities co-produced in close collaboration with nuisance flood-prone communities in Philadelphia, PA. First, the spatiotemporal extent of nuisance flood risk is identified for a range of storm events. Second, novel strategies are identified to reduce the mobility barriers and disparities induced by urban nuisance flooding in these communities. Third, a science-based, community-centered interactive strategic planning approach is constructed, drawing on cross-disciplinary science to achieve equitable adaptation to the evolving risk of nuisance flooding. The research activities incorporate equity into urban nuisance flood mitigation strategies by analyzing the socio-environmental system comprising the two elements of human mobility and nuisance flood, which interact through the built-environment parameters. The outcomes add important nuance to knowledge on urban mobility by focusing on the impacts of an increasingly common environmental hazard. 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

Physics, which comprises a combination of theoretical concepts and practical experiments, could be a challenging STEM subject to learn, especially for non-science majors. This project investigates a multi-user virtual reality classroom framework to promote collaborative experiential learning and teaching of introductory physics. Through this framework, teachers can easily author and conduct physics classes in virtual reality, while students engage in collaborative experiential learning, leading to a deeper conceptual understanding of physics and more sophisticated experiment skills. This project offers a novel, interdisciplinary research perspective by bringing together concepts and techniques from virtual reality, human-computer interaction, multimedia, networking, astronomy, physics, learning technologies, and science learning. The team will publicly disseminate the research findings, system designs, and software toolkits. They will collaborate with the George Mason University’s teacher preparation programs and the observatory to adopt virtual reality for physics education. They will also mentor undergraduate students from underrepresented backgrounds to participate in virtual reality research, as well as organize outreach activities to showcase multi-user virtual reality physics classes to students, educators, and the general public. This project focuses on four research aims. First, the project team will investigate system-level techniques such as efficient remote rendering and viewport adaptive streaming to enable scalable multi-user virtual reality physics classes. Second, the project team will create interactive annotation, communication, and visualization tools to support collaborative experiential learning and teaching of physics in virtual reality. Third, the team will develop handy authoring tools for content creators and teachers to quickly generate virtual reality physics classes adaptive to students’ learning progress. Fourth, the team will conduct user studies to examine the impact of the proposed virtual reality classroom framework on students’ enjoyment, engagement, teamwork, confidence, and knowledge retention compared to traditional teaching approaches. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Autonomous vehicles offer profound societal benefits, promising increased productivity and enhanced quality of life by reducing traffic congestion and improving transportation accessibility. Ensuring the safety of autonomous vehicles is paramount, given their operation on public roads and interaction with human beings. The project aims to develop MELIOREM, an automated tool designed to enhance the safety of autonomous vehicles. By utilizing our nation's high-performance computing infrastructure, MELIOREM will conduct rigorous testing to identify and address potential safety issues before they impact public roads. This initiative ensures that autonomous vehicles are dependable and safe for all road users. Using advanced search techniques, MELIOREM will simulate various driving scenarios to assess how well these vehicles perform under different conditions, leveraging extensive computational power for complex calculations and analysis. By bolstering the safety of self-driving technology, this project not only advances transportation safety but also provides a valuable resource to academia and industry, contributing to the broader professional community. It also creates educational opportunities by training students from diverse backgrounds in higher education. Autonomous vehicles (AVs) promise vast societal benefits of increasing productivity and improving quality of life, from reducing traffic congestion to improving access to transportation. Ensuring AV safety is critical to success in the marketplace, and an essential aspect of AV development to ensure safety is testing. Existing techniques incorporate computerized simulation-based iterations, where the AV under evaluation is stress tested by perturbing traffic parameters and AV internal states to generate safety cases for analysis, identify AV vulnerabilities, and mitigate safety hazards. This process largely involves using high-performance computing (HPC) infrastructure given the enormous amount of computation resources demanded by the simulations. However, current approaches often face state space explosions due to the large search spaces in both internal program executions and external environment parameters when searching for safety cases, making existing tools far from being comprehensive and efficient in HPC. Furthermore, due to the complicated structure of AV software stack, error resilience is not yet well understood, making diagnosis and protection extremely time consuming. This project will develop an efficient and comprehensive testing infrastructure, MELIOREM, for characterizing, assessing, and identifying vulnerabilities in AV software systems in evolving traffic situations. The core purpose of this work is practicality, enabling domain scientists to generate safety cases for characterizing and understanding AV safety, and AV developers to identify AV safety vulnerabilities using existing HPC infrastructure. This project will develop a series of algorithms to optimize test coverage, emulation efficiencies, and identify representative safety cases for an AV under test. This work will resolve these AV development issues with respect to their practical analysis by applying MELIOREM in intelligent cyber-systems in transportation and crash analysis research domains. This project is jointly funded by the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program and the Division of Information and Intelligent Systems (IIS). 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

Galaxies are observed to come in many sizes and luminosities. Of particular interest are galaxies that, for their mass, are large and dim, called Low Surface Brightness (LSB) galaxies. These galaxies are everywhere, making up nearly 50% of the. This proposal will help astronomers understand why LSBs look the way they do, including understanding the matter we can’t see: dark matter. This investigator will use large-scale computer simulations, which are able to model the universe from the big bang to present day. This award will also support GMU’s Women Leaders in Stem (WLIS) by funding expert education and STEM speakers to be hosted on campus for events. This will support the WLIS’s efforts to support undergraduate students through their STEM journeys, by providing professional development, networking strategies, study skills and future job resources. Understanding how galaxies form and evolve is a fundamental goal in astronomy. One particular class of galaxies, Low Surface Brightness (LSB) galaxies, is especially challenging to understand as LSB galaxies seem to have followed a different evolutionary path from their high surface brightness (HSB) counterparts. This investigator and her collaborators will carry out a comprehensive study LSB galaxies in order to identify their formation channel(s), explain their evolution, and understand their dark matter (DM) content and distribution in the context of Cold Dark Matter (CDM). In particular, they will (1) statistically study the formation of LSB galaxies as a function of mass and environment using the existing large-volume simulation Romulus25, (2) study classical LSB galaxies in detail by creating zoom-in simulations capable of resolving the interplay between baryonic physics dark matter distribution and, (3) use the Genetic Modification Technique (GM) technique on the zoom-in simulations in order to understand the role of angular momentum in 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

Data literacy plays a pivotal role in understanding real-world problems, making it an increasingly important topic in mathematics education. Preparing young learners to use data to answer questions and solve problems empowers them to participate in society as informed citizens and opens doors to 21st-century career opportunities. For many learners underrepresented in STEM, developing data literacy through innovative technologies requires personally meaningful experiences working with data. The Framework for Integrating Technology for Equity (FIT for Equity) is a Developing and Testing Innovations (DTI) project that will engage 24 teachers in co-designing technology-enhanced data literacy lessons and including students and community members as co-authors. This inclusive lesson study approach advances equity in math classes by supporting the critical data literacies necessary to participate in today’s workforce as informed citizens. FIT for Equity will cultivate design principles that bring together teachers, students, and community members in this innovative capacity building effort that may lead to more equitable learning opportunities. The project team will also produce a collection of data literacy mathematics lessons featuring transformative technologies to address community-based challenges, co-authored by elementary teachers, students, and community members in four distinct geographic locales in Virginia, Ohio, Tennessee, and Michigan. Through equity frameworks in mathematics education, this project will develop and test design principles for planning, observing, and reflecting on technology-integrated mathematics lessons. Researchers will use a design-based research approach to answer three research questions: 1. How do technology-enhanced data literacy lessons develop students' data literacy, understanding of community issues, and attitudes towards STEM? 2. How do the project’s design principles for technology-enhanced data literacy lessons promote teachers’ practices for culturally responsive mathematics teaching? 3. What are the affordances and constraints of Inclusive Lesson Study in expanding the integration of technology for data literacy towards equity? Iterative implementation cycles will be used to develop and test the inclusive lesson study cycles. Data will be collected through inventories and document analysis of lesson study artifacts, including student work, annotated classroom lessons, and lesson study meeting recordings. Additionally, data will be gathered using the Culturally Relevant Mathematics Teaching (CRMT2) Classroom Observation Tool, the Equity-centered Transformative Technology Lesson Analysis Tool, and interviews with participating teachers, students, and community members. Pre- and post-surveys will be administered to measure changes in students' STEM self-efficacy and career interests. Deliverables will include a repository of research lessons and video vignettes highlighting FIT for Equity lessons. Research findings will be disseminated through a project website, conference presentations, and journal publications. All program materials will be made free and publicly accessible, allowing other educators, designers, and researchers to replicate or modify them to foster innovative approaches to promoting inquiry topics that are both meaningful and applicable to underrepresented learners’ real-world contexts. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that increase students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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 aims to address racial equity in engineering education by enhancing the awareness and knowledge of engineering faculty to support Black engineering graduate students. Black students in engineering programs often experience microaggressions, lack of support, and systemic barriers, which impede their academic success and well-being. This initiative aims to disrupt these cycles by equipping faculty with the tools and understanding necessary to be positioned to be actionable in cultivating a more inclusive and supportive environment. By focusing on the three phases of awareness, knowledge, capacity building, and community, the project will provide faculty with empirical data on the lived experiences of Black students, engage them in professional development to build cultural competence, and establish a supportive community of practice. This comprehensive approach not only seeks to improve the academic outcomes and mental health of Black engineering students but also serves as a model for fostering antiracist educational environments across disciplines and institutions. The anticipated outcomes include greater faculty awareness of racial inequities experienced by Black students, improved faculty-student rapport and relationship, and a reduction in the harm done to Black engineering graduate students. This project has the potential to advance social justice, contribute to a more equitable academic landscape, and inspire similar initiatives in other fields and institutions. To address systemic racial inequities faced by Black engineering graduate students, this research is situated in the theory of racialized organizations and seeks to develop a comprehensive professional development program that increases faculty awareness of the unique challenges faced by Black scholars. Using a multimodal, mixed-method approach, the project will compare three educational modalities (e.g., case study, 2D-video, and immersive virtual reality simulations) to determine the most effective method for fostering faculty awareness and resonance of the lived experience of Black graduate scholars in engineering. Conducted with engineering faculty at Arizona State University (ASU) and George Mason University (GMU), cohorts will participate in the Positioning Faculty for Antiracist Orientations (PFAO) program anchored in the High Impact Cultural Competency framework. This program is designed to build cultural competency while establishing a supportive, longitudinal community of practice of Engineering faculty committed to racial equity. The project will leverage previous NSF funded work centering Black students as experts of their own experiences in applying their insights to inform the development of educational content. Over five years, the project will directly impact 90 engineering faculty, a novel and significant effort focused on the gatekeepers of engineering culture. The study's findings have potential implications for higher education, providing a model for capacity building and positioning antiracist orientations that can be adapted to support other minoritized groups. This work is supported by an interdisciplinary team and aims to contribute significantly to the fields of Engineering, Education, Psychology, and Computing. This collaborative project is funded by the EDU Racial Equity in STEM Education activity, which is supported by the Directorate for STEM Education (EDU). This activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

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 · 2024-09

The broader impact of this I-Corps project is based on the development of a Generative Pre-trained Transformer (GPT) system specifically designed to help potential kidney donors access personalized answers during their online information-seeking journey. This technology can help transplant centers attract more live kidney donors, which is essential for achieving superior transplant outcomes for patients. This technology could prove to be a significant cost-effective strategy for Medicare as the main payer of kidney care in the country, while enabling doctors and hospitals to achieve better outcomes for patients. The lack of comprehensive and personalized online material about kidney transplant and kidney donation has been a major barrier for potential kidney donors to complete the donation process. This project aims to lower this barrier by introducing accurate and traceable generative artificial intelligence (AI) patient support, coupled with live-donor mentors offered by transplant centers, to increase the live kidney donation rate and save more patients. 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. The solution is based on the development of a well-trained Generative Pre-trained Transformer (GPT) AI system with a comprehensive dataset encompassing all kidney transplant and donation related topics. Existing GPT models are not specialized in the field of kidney donation and this technology would change that. Online information from kidney transplant centers and other professional transplant-related organizations will create a well-trained GPT model with a comprehensive dataset. A context-aware prompts-generation (CAPG) model enables personalization of the GPT responses. 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.