College Of William And Mary

NSF Awards · FY 2026 · 2026-08

Modern scientific progress in fields such as fusion energy, materials research, climate science, and biomedical imaging depends on researchers' ability to find and reuse the vast amounts of data produced across the national research ecosystem. National investments such as Globus and domain-specific data repositories have made scientific data transfer and storage efficient and reliable, but they were not designed to help researchers find data by its scientific meaning, and they do not provide the descriptive information that artificial intelligence tools need to interpret datasets and recommend related work across disciplines. As a result, much valuable scientific data remains underused. By laying the groundwork for an intelligent, AI-ready scientific data discovery ecosystem, this project advances the progress of science and supports national prosperity through faster, more open scientific discovery. The project will assess current data discovery practices and requirements across diverse scientific communities, and identify the metadata and semantic information needed to support concept-driven and AI-driven data discovery. The project will develop a community-informed architectural plan for a scalable, AI-ready metadata service that interoperates with existing software and hardware ecosystems. The plan will define representative scientific use cases, technical requirements, design principles, and a governance model. The findings are expected to benefit not only the engaged communities, but the broader scientific data ecosystem for advancing intelligent, AI-ready data discovery at national scale. 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.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY The amygdala is present in all vertebrate animals because it influences common behaviors that pertain to threat assessment in the natural world (as well as fear, pain, and reward). Neuroscience well understands how the amygdala interacts with neocortical, subcortical and midbrain sites. However, potential links between the amygdala and brainstem respiratory control sites remains mysterious. This proposal will address that knowledge gap by examining how the central amygdala exerts inhibitory control over the breathing core oscillator site, the preBötzinger complex (preBötC) of the lower medulla. Output from the amygdala largely depends on GABAergic neurons of its central subdivision (CeA). Rhythmic breathing movements depend inexorably on the preBötC. Therefore, a CepreBötC projection would be inhibitory and therefore potentially able to perturb or stop breathing. That microcircuit might be important. Why? First, perceived threats, like the presence of a predator, cause arousal in conjunction with arrest of locomotion. Sometimes freezing behavior is accompanied by bradycardia and diminished breathing: bradypnea or apnea. Whereas the microcircuits for vigilance, locomotor arrest and bradycardia are well understood, the mechanisms that diminish breathing are unknown. We propose an explanation that involves – at least in part – CeA neurons that directly inhibit the preBötC. Second, SUDEP (sudden unexpected death in epilepsy) may occur when seizures invade the lateral or basolateral amygdala, which connect to the much smaller CeA and cause long-lasting apneas. Seizure-induced apneas suspend oxygen delivery yet paradoxically fail to cause panic, dyspnea or air hunger in human patients. We hypothesize that seizures invading the lateral or basolateral amygdala activate the CeA preBötC inhibitory pathway, which can stop breathing. The first Aim of the project tests the hypothesis that CeA GABAergic neurons project directly to excitatory preBötC neurons by installing Cre-dependent optogenetic proteins in CeA neurons of VgatCre adult mice and studying the biophysical properties of their synaptic drive onto core preBötC neurons in adult brainstem slice preparations. The second Aim tests that hypothesis that CepreBötC inhibitory synapses can transiently diminish and/or stop breathing. In this context, we photostimulate the CeA with a graded range of intensities during breathing behavior in awake intact adult mice to evaluate its ability to perturb and/or fully stop breathing. Although we acknowledge that the Aims are adversely interdependent, the abundant pilot data in support of Aim 1 make it unlikely to fail and thus undercut Aim 2. This project will reveal a heretofore unknown microcircuit between 2 key nuclei: the central amygdala and the preBötC. Their connection may help explain ethological behaviors like threat assessment common to all mammals and SUDEP (rare but fatal), which can be leveraged for treatment and prevention strategies.

NSF Awards · FY 2026 · 2026-02

The objective of this Civic Innovation Challenge (CIVIC) project is to support research on building and piloting a prototype system that uses artificial intelligence and sensor data to automatically detect unpermitted closures. It seeks to enable real-time monitoring of road closures, guide inspectors with optimized routing tools, and support permit staff with better data for future planning. Right-of-way closures—such as the blocking of streets, sidewalks, and bike lanes for construction, delivery, or special events—are an everyday reality in cities. When these closures are not properly permitted or monitored, they disrupt traffic, endanger pedestrians and cyclists, and negatively affect small businesses. While cities like Nashville are working to improve enforcement, they face challenges due to outdated systems, limited inspection staff, and the sheer number of closures. By combining technology with direct input from city officials, field staff and civic organizations, the project seeks to produce a practical, tested system that improves how cities manage public space. Its deployment would support compliance of existing right-of-way closure procedures, recuperation of lost revenue from missing permit application fees, and improve traffic flow and the safety of urban transportation. Importantly, the tools seek to be designed for reuse by other cities and other sensor modalities. The project will release open-source software, deployment guides, and training materials for public access. The project seeks to advance the automation and optimization of right-of-way closure enforcement through new contributions in machine learning, optimization, and systems engineering. The novelty lies in: 1) the joint integration of anomaly detection and multi-objective inspection routing to support real-time, scalable enforcement; 2) the use of uncertainty-aware learning and human-in-the-loop active validation to improve robustness to noisy, incomplete, or imbalanced urban sensor data; 3) the co-design of a web-based inspector and permit manager interface for operational deployment; and 4) the creation of a generalizable, open-source civic AI framework that can be adapted to infrastructure-constrained transportation departments across the United States. 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 · 2025-09

Modeling is a key scientific practice emphasized in the Next Generation Science Standards (NGSS), but students often need significant support to engage in the practice meaningfully. This project aims to develop an augmented reality (AR) learning environment integrated with a large language model (LLM)-powered pedagogical agent to guide middle school students' modeling practice. It will bring together computer scientists, human-computer interaction researchers, science education researchers, and K-12 teachers to co-design the learning environment. The AR environment will allow students to interact with simulated objects and phenomena as they develop their models. The LLM-powered agent will provide timely assessments of students' diagrammatic models, offer personalized feedback, and share insights with teachers to inform instruction. This project has the potential to transform the way students engage in modeling practice in K-12 science classrooms. Over three years, it will directly impact approximately 10 middle school teachers and 400 middle school students. The project is guided by three core objectives: 1) to build AR simulations for modeling tasks, 2) to develop an on-device multitask LLM to assess diagrammatic models, and 3) to investigate student modeling practice within the AR-LLM learning environment. A key innovation of the project is the closed-loop feedback system: students first receive suggestions for model revisions from the LLM and revisit AR components, and the LLM agent then provides feedback on students' performance to teachers to inform instructional decision-making. The project will investigate students' perceptions of their modeling experiences and how the AR elements and LLM-generated feedback support their model development respectively. Primary data sources will include classroom video recordings of student consensus model building, screen recordings of individual modeling activities, and semi-structured focus student interviews. A multiple-case study approach will be used to investigate student learning. Research products will be widely shared with practitioners, teacher educators, and researchers through publications, conference presentations, teacher workshops, and publicly accessible teacher resources. 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 2025 · 2025-09

Understanding how and why animals move through different environments and habitats is a crucial challenge in ecology, conservation biology, and wildlife management. Though models of random particle motion provide ubiquitous and useful modeling tools for data analysis, real animal movement is influenced by both environmental and cognitive factors, such as perception, memory, and learning. Recent advances into mathematical models have provided valuable insights into how species are geographically distributed and how their population abundances change over time. However, applying these mathematical models to real-world data on GPS tracked animals remains a significant challenge, because most mathematical models are constructed in terms of population density--the number of animals per unit area—rather than the tracked locations of individual animals equipped with GPS tags and collars, even though GPS tracking data are the most informative data for understanding cognitive processes. This project aims to enhance our understanding of animal movement and its ecological implications by developing novel mathematical approaches that bridge the gap between theoretical models and empirical data. Software will be developed for direct application to conservation and wildlife management. The project will foster interdisciplinary collaboration between mathematicians, ecologists, and data scientists, while providing training opportunities for graduate and undergraduate students in applied mathematics and ecological modeling. Whereas dynamical partial differential equations (PDEs) have been at the forefront of modeling cognitive factors in animal movement, they do not straightforwardly provide a likelihood function that can be fit to animal tracking data with the substantial temporal autocorrelation that both typifies modern tracking data and informs cognitive processes. This project aims to develop novel mathematical approaches that integrate PDE and stochastic-process models with real-world tracking data. In contrast to location-based PDEs, continuous-velocity stochastic process models, such as the integrated Ornstein-Uhlenbeck process, and associated higher-dimensional Fokker-Planck equations offer a promising alternative that better aligns with available data. We will work from these higher-order stochastic differential equations and higher-dimensional PDE models to analyze their mathematical behavior, to solve for their transition probabilities and likelihoods, and to implement them in statistical software. 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 · 2025-09

Artificial intelligence (AI) technologies are rapidly reshaping daily life; yet older adults--one of the fastest-growing U.S. demographics--have limited chances to understand, critique, and influence AI technologies. Meanwhile, older adults bring a wealth of life experience, and a willingness to share knowledge across generations. Their shared experience and knowledge can have a great impact on human and technology advances. This project aims to address this gap by designing, developing, and evaluating StoryBridge, a web-based intergenerational digital-storytelling platform that enables dyads of older adults and youths to co-create multimedia narratives about how AI affects community life. By embedding AI-literacy concepts in life stories and fostering dialogues across generations, the project seeks to strengthen AI literacy, social connectedness, and resilience among older adults while cultivating responsible AI awareness in youth, and thus, to promote the progress of science and serve the national interest. Through multiple focus groups and participatory-design sessions with older adults, youths, and community partners, the team will co-design StoryBridge that supports intergenerational digital storytelling for AI literacy. The project is guided by three research questions: 1) How can intergenerational storytelling enhance older adults' AI literacy? 2) In what ways does collaborative storytelling impact social connectedness and resilience in older adults and responsible AI awareness in youths? 3) How do platform design features influence participants' engagement and learning outcomes? The project employs a mixed-methods approach to investigate these questions with 50 dyads of older adults and youths. Quantitative data from surveys and user activities as well as qualitative data from in-depth interviews will be analyzed to assess the acceptability, feasibility and efficacy of the platform. Some specific designs of the platform include: 1) idea cards rooted in local traditions, community events, and everyday scenarios narratives; 2) an "AI Whisper" widget that offers plain-language tooltips or audio snippets that demystify how the system interprets prompts; 3) multimodal input and output, which enables oral stories automatically transcribed and read aloud by text-to-speech, and enables participants with vision, typing, or connectivity constraints to shape rich content; 4) a reflective toolbox that preserves version histories and provides prompts to guide pairs to discuss conceptual understanding, practical use, evaluation, and ethical questions. This project is a pioneer in documenting intergenerational narratives teaching AI concepts to older adults and lays the groundwork for future research in intergenerational learning to enhance AI literacy. This AISL Integrating Research and Practice (Project Type 4) project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing 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 2025 · 2025-09

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professors Kristin Wustholz and Elizabeth Harbron of the Department of Chemistry at the College of William and Mary are developing new fluorescent dyes with unique properties for single molecule spectroscopy (SMS) applications. SMS is renowned for its ability to visualize complex materials and biological structures that are important in technology and medicine. Current SMS studies focus on brightly fluorescent materials that emit light continuously until they fail and become dark. However, some fluorescent dyes can switch between bright, dim, and dark states, with these fluctuations containing important information about the molecule, its location, and its environment. By combining expertise in chemical synthesis, SMS, and advanced statistical analysis, the team of Wustholz and Harbron is creating new dyes engineered to produce dim states and deploying them to reveal otherwise inaccessible structural and dynamic information at the single-molecule level. Students supported by this project gain a range of technical expertise. Considerable effort has been devoted to designing fluorophores that exhibit ideal fluorescence properties such as high quantum yield, which requires minimizing nonradiative deactivation pathways. Minimizing twisted intramolecular charge transfer (TICT), where an internal donor-acceptor charge transfer occurs upon molecular twisting, has improved quantum yields in rhodamines and other common dyes. Conversely, maximizing TICT creates non-emissive dyes that can be useful as fluorogenic probes. While dye design efforts in these highly fluorescent and fluorogenic regimes have led to significant advances in nanoscale imaging, they also discard useful information. This proposal harnesses this untapped wealth of information in an intermediate “diagnostic” regime for single molecule imaging. The team is making a series of TICT-active fluorophores, determining how TICT manifests in single molecule intensity fluctuations (blinking dynamics), and establishing quantitative metrics that contain diagnostic information. Additionally, by deploying TICT dyes to image biological systems, this project is examining one-color multiplexed imaging that reports more fully on environmental spatial and temporal heterogeneities. Collectively, this project generates new probe design strategies and advances fluorescence studies of complex materials and biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Understanding the internal structure of protons and neutrons—collectively called nucleons—is a long-standing challenge in nuclear physics. This project seeks to create a three-dimensional map of how quarks and gluons, the fundamental constituents of matter, move and interact inside nucleons. By analyzing data from high-precision experiments at Jefferson Lab and preparing for measurements at the future Electron-Ion Collider (EIC), the research will investigate how quark and gluon spin and motion contribute to nucleon structure. To interpret these complex datasets, the project applies advanced artificial intelligence (AI) tools—specifically deep learning and statistical methods—to improve measurement accuracy and quantify uncertainty. Beyond advancing fundamental knowledge, the project has broad societal impact. In alignment with the Nuclear Science Advisory Committee’s recommendation to integrate AI in nuclear research, it will train students in physics and data science, develop reusable tools for analyzing complex data, and generate accessible educational resources through workshops and tutorials. These efforts will help cultivate the next generation of scientific leaders. Notably, AI methods developed by the team for nuclear physics have also been applied for other data-intensive applications, demonstrating their versatility and broad relevance. This project advances national priorities in nuclear science and AI while fostering discovery and education. The project aims to study the three-dimensional partonic structure of nucleons through the analysis of semi-inclusive deep inelastic scattering (SIDIS) data collected with the CLAS12 detector at Jefferson Lab, focusing on kaon electro-production from unpolarized and polarized targets. By detecting final-state hadrons alongside the scattered lepton, SIDIS provides sensitivity to the transverse momentum of struck quarks. The central objective is to extract transverse momentum-dependent parton distributions (TMDs), which extend conventional parton distribution functions by incorporating transverse momentum in addition to longitudinal momentum fraction. These observables enable studies of spin-orbit correlations, such as the Boer-Mulders effect, offering insight into how quarks and gluons contribute to nucleon spin. The analysis will leverage two advanced deep learning tools developed by the PI’s group: ELUQuant, for event-level uncertainty quantification, and Deep(er)RICH, for reconstructing images from Cherenkov detectors and improving particle identification. Bayesian inference will be employed for robust extraction of physics observables, complementing traditional unfolding methods. The team will also conduct sensitivity studies for the ePIC detector at the future EIC. This project will advance multidimensional nucleon tomography, addressing key questions such as the proton spin puzzle and the role of orbital angular momentum. It supports both precision nuclear physics and AI-driven 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 · 2025-08

Artificial intelligence (AI) models are increasingly used to process large-scale structured data, yet their ability to memorize and regurgitate information presents both opportunities and risks. This project advances science and technology by developing methods to measure and control memorization in text-attributed graphs (TAGs), which are widely used in social networks, citation networks, and biological systems. While memorization in machine learning can enhance recall of frequently used information, it also raises concerns related to privacy and security. Existing research lacks a comprehensive framework for evaluating and regulating memorization in text-attributed graphs, particularly concerning how graph structures influence memorization patterns. This project will establish new techniques for assessing memorization, develop methods for dynamically adjusting memorization levels, and create the first benchmark for studying memorization effects in text-attributed graph-based learning. These innovations will strengthen the reliability, privacy, and interpretability of AI models used in critical applications such as healthcare, cybersecurity, and knowledge discovery. By ensuring that AI models can retain useful information while preventing unintended leaks of sensitive data, this work will contribute to the development and deployment of responsible AI technologies. This project develops a rigorous framework for measuring and controlling memorization in TAGs through three key research thrusts. The first thrust introduces a novel dynamic prompting strategy that adapts to input variability, enabling more precise measurement of memorization rates. The second thrust proposes a new dynamic pruning framework that allows for fine-grained control over memorization, ensuring that models can be optimized for either enhanced recall or increased privacy. The third thrust establishes a benchmark for memorization in TAGs, systematically evaluating how memorization is influenced by graph topology, such as node connectivity and long-distance relationships. The project’s methodologies integrate insights from graph neural networks and large language models, bridging gaps in understanding between structured and unstructured data representations. By addressing fundamental challenges in memorization, this research will provide practical tools and insights that benefit AI developers, regulatory bodies, and industries that rely on trustworthy machine learning models. The findings will be disseminated through open-source tools, benchmark datasets, and academic collaborations, fostering broader impact in the AI 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 · 2025-06

In recent years, software engineering has undergone a significant transformation with the integration of artificial intelligence into software development workflows. As part of this evolution, large language models have proven to be powerful assets, enabling the automation of various software engineering tasks. Collectively known as Large Language Models for Code (LLMc), these models have been effectively utilized to assist developers in fixing bugs, code generation, software documentation, software testing, and code review, among other practices. The success of LLMc is largely attributed to advancements in computational hardware and the growing availability of large-scale training datasets. However, the increasing reliance on LLMc has also brought to light significant concerns regarding sustainability and environmental impact. Training and deploying LLMc demands extensive computational resources, resulting in significant energy consumption, high costs, and substantial carbon emissions, posing challenges to their long-term sustainability. To address these challenges, this project aims to lay the groundwork for developing sustainable and cost-effective artificial intelligence methods in software engineering automation by enhancing the efficiency of LLMc. The project will integrate its research findings into computer science academic courses, which will help equip future software engineers with the knowledge and tools necessary for sustainable adoption of LLMc in software engineering practices. The proposal focuses on two key strategies: (i) optimizing training data by filtering out low-quality instances using software engineering task-specific metrics, thus reducing computational costs while preserving learning capabilities, and (ii) applying model compression techniques, particularly quantization, to significantly decrease model size and resource consumption without compromising performance. Preliminary research has shown the effectiveness of these methods in improving efficiency for code-related tasks such as code generation and summarization. Building on these insights, this project will expand such optimizations to a wider range of software engineering automation tasks, ensuring their applicability across various scenarios. By establishing a structured methodology to improve LLMc efficiency, this research will offer practical implementation strategies, technical recommendations, and a comprehensive assessment of sustainability-focused optimizations for artificial intelligence-driven software engineering 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 · 2025-05

This research examines the long-term management of oyster fisheries and forests by Native communities in the Mid-Atlantic region. Residents sustained ecological systems while transitioning from a dispersed creek-based village to a political center within the Powhatan paramount chiefdom. The project provides a unique lens into how traditional ecological knowledge shaped resource management practices over fourteen centuries by integrating archaeological, paleoenvironmental, and historical data. The findings offer insights into the dynamics of collective action and political complexity in Native North American societies while informing contemporary conservation efforts in the Chesapeake Bay region. Through collaboration with Virginia Indian tribes, the research also supports cultural revitalization initiatives and emphasizes the stewardship of threatened archaeological sites in areas facing rising sea levels and coastal erosion. Additionally, the project provides interdisciplinary training for undergraduate researchers and fosters partnerships across multiple academic fields and tribal organizations. Guided by collective action theory and common pool resource management, the project addresses two central questions: whether oyster harvesting practices varied between communities while remaining stable over time and whether forest management changed alongside subsistence shifts but maintained consistency across localities. Analyses of oyster shells reveals harvesting practices, seasonality, and spatial patterns of resource use. Sediment core analyses help reconstruct fire histories, while plant remains document changes in forest composition and the role of wild comestibles. These datasets document landscape management strategies that balanced ecological stewardship with the demands of growing populations. Remote sensing and archaeological surveys explore settlement organization. By integrating these methods, the study provides a perspective on resource sustainability and cooperation, offering broader implications for understanding the interplay between ecological management and political authority in past societies. The research also contributes to contemporary resource management frameworks, highlighting the value of traditional knowledge and long-term ecological strategies in addressing today’s environmental challenges. 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 · 2025-05

Nearly one-third of the global population experiences unreliable electricity access, and a U.S. Department of Energy report estimated that the total cost of power outages to American businesses is around $150 billion every year. As power grid simulations grow in complexity and scale, there is an urgent need for more efficient computational models to meet real-time decision-making demands. Traditional simulation approaches struggle to parallelize efficiently, especially for large systems like the Eastern Interconnection with over 70,000 buses. The emergence of Graphics Processing Units (GPUs) and artificial intelligence (AI) models offers promising alternatives for accelerating complex simulations. The main idea is to train neural network surrogates of numerical models, and once pre-trained, the networks can generate simulations with much faster speed and efficient scaling. This project develops a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The resulting framework will lower barriers for power grid engineers to adopt AI surrogates, enabling interdisciplinary research and education between power systems and computer science domains. The project will deliver three key innovations: (1) program-behavior analysis to identify optimal code regions for AI surrogate replacement; (2) semi-automatic AI surrogate construction that incorporates domain-specific physical knowledge; and (3) heterogeneous computing with multi-fidelity modeling that dynamically balances AI surrogates and traditional models across computing resources. The methodological approach combines static and dynamic code analysis, neural network training with physical constraints, and adaptive scheduling algorithms for CPU/GPU resources. The project aims to transform AI surrogates from auxiliary tools into essential elements for power grid planning. 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 project develops innovative tools and techniques to enhance computer architecture analysis, a critical need given the complexity of modern chip designs. Computer architects often struggle to identify bottlenecks, understand underlying issues, and implement efficient improvements. Addressing these challenges, the project introduces advanced visualization, collaborative sensemaking, and generative artificial intelligence techniques to provide deeper insights into system performance, facilitating designing more efficient and sustainable computing systems. Highlighting human-in-the-loop and human-over-the-loop methods, the project fosters a flexible analytical workflow that empowers architects to balance manual insights with automated analysis, significantly improving the efficiency and scalability of performance diagnostics. The tools developed in this project will also serve educational purposes, supporting students in understanding complex computer architecture mechanisms and reducing learning barriers. Existing solutions for performance analysis usually focus on technical capabilities, emphasizing what tools can accomplish while paying limited attention to user experience and the capabilities or limitations of the users themselves. Instead, this project adopts a human-centered approach to design highly usable tools with enhanced user experience, significantly improving developer efficiency and capability. It focuses on three key methods. First, this project designs visualizations tailored for computer architecture systems to pinpoint reasons for slow execution. Second, this project enhances collaborative sensemaking by introducing tagging, annotation, and semantic coding to support multi-user analysis and discussions. Third, it leverages generative AI for automated analysis, identifying points of interest, diagnosing root causes, and suggesting potential improvements. The developed tools will be integrated into the existing tools developed under the PI’s lead, which are already widely used by the community, to accelerate innovation in the computer architecture domain further. 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 proposal is to seek travel support for up to 20 students to attend the 2024 ACM/IEEE Symposium on Edge Computing (ACM/IEEE SEC) in Rome, Italy. This leading technical conference is the primary venue for presenting new research results in the area of "edge computing", a new paradigm in which the resources of a small data center are placed at the edge of the Internet, in close proximity to mobile devices, sensors, and end users, and the emerging Internet of Things (IoT). The conference will consist of technical paper presentations, panels, PhD forums, posters, and demonstrations. It will also feature keynote speeches from leading researchers and practitioners in the field. Participation in SEC 2024 and similar conferences are valuable and important activities of the graduate school experience. It provides students with the opportunity to interact with more senior researchers, and exposes students to leading work and practical industry practices in this important area of research. This may have an important impact on career development. The goal of the funds is to support approximately 20 student participants at recognized US institutions of higher education. Funds will be dispersed with preference given to students who would not otherwise be able to attend the conference and students who are not already scheduled to present a paper, paying particular attention to diversity and relevance of the student's research interest. 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

Data plays an essential role in shaping social decisions and scientific conclusions. With the abundance of data available, many data-intensive applications involve data with an underlying structure. Graphs provide a natural mathematical language to precisely describe this structure. Graph data have a ubiquitous presence in our daily lives, found in hydrological systems, transportation networks, cellular networks, social media, and the Web, among many others. Graph Learning (GL) is a crucial research area that focuses on processing graph signals and building predictive models on graph data, and has become a key topic in statistical modeling, data science, data mining, machine learning, and computer science in general. Despite considerable progress, traditional GL algorithms commonly assume that the important factors of the graph data remain unchanged during the learning process. Such static and closed assumptions tend to offer an overly simplified abstraction of complicated tasks in the real world, making GL models fail to characterize and express the data generated from natural or societal phenomena that constantly evolve. The project’s overarching goal is to provide generic solutions to these core issues. Specific applications studied in this project include the development of better approaches for monitoring waterbody impairment and detecting malicious behaviors and cyber-attacks in a timely manner. This project will also provide training opportunities for both graduate and undergraduate researchers in computer science. There will be a specific emphasis on gender diversity and participation of underrepresented groups, allowing individuals from diverse backgrounds to contribute to the advancement of GL research. This collaborative project aims to build a new, holistic, and standardized Graph Learning (GL) framework. The project focuses on open-world and streaming network (OWSN) learning, which considers the evolution of graph data over time in four critical factors: nodal features, topological structures, target labels, and graph domains. To achieve this goal, the project seeks to address fundamental challenges and answer research questions aligned in two threads. The first thread is Graph Representation, which aims to answer fundamental questions such as how to characterize nodes with complex and ever-growing contents using vector representations, and how to delineate the underlying process that drives the evolution of graph topologies. The second thread is Graph Predictive Modeling, which addresses how a graph learner can identify the emergence of new and unknown classes and adapt to them without sacrificing performance on other known classes, and how to generalize to other disparate graph domains in an unsupervised manner. To address these questions, the project integrates tools and advances from diverse areas, such as online optimization, uncertainty quantification, variational analysis, and decision theory. The aim is to deepen the understanding of graph data analysis and shed new light on related questions in these areas. Real-world data from engineering applications, including hydrological system data and computer network data, will be used to extensively evaluate progress in each of the above themes. Collaboration with domain experts in the specified application areas will ensure that the new theory, tools, and software emerging from this project lead to meaningful societal benefits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This award provides support to 25 US-based graduate students to attend the 2024 Conference on Computer and Communications Security (CCS), to be held Oct 14-18 in Salt Lake City, Utah. CCS is the flagship conference of the Special Interest Group on Security, Audit and Control of the Association for Computing Machinery (ACM). The conference brings together information security researchers, practitioners, developers, and users from all over the world to explore cutting-edge cybersecurity ideas and results. CCS 2024 will include multiple technical tracks across a wide spectrum of cybersecurity topics, with over 240 papers, pre-conference and post-conference workshops, tutorial and poster sessions, and panel discussions. Participation in the conference is a valuable opportunity for students and is an important part of graduate education in cybersecurity. Students will have the opportunity to observe high-quality presentations and interact with senior researchers in the field both in the main conference and the associated workshops. This can lead to community-based research initiatives, knowledge sharing, and positive social impacts beyond academia. The conference will involve researchers, scholars, and professionals from diverse backgrounds, allowing students to build valuable connections and collaborations. Criteria for selection include evidence of a serious interest in the field as demonstrated by an application letter, a resume showing research outcomes, and/or a support letter from the advising faculty member. The project especially encourages participation from underrepresented groups, to broaden both perspectives and participation in the discipline. 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

Marine animal tracking, at both individual and group levels, is crucial for wildlife conservation. It provides essential information and invaluable insights into population dynamics, health, risks, and vulnerability, all of which help shape conservation policies, management decisions and strategies. Traditional tracking methods face significant challenges in balancing cost and precision. They either require attaching transmitters to animals that communicate with radio receivers or satellites (high accuracy but expensive and invasive) or rely on manually produced sketches from photos of distinctive features such as scars (low accuracy and labor-intensive). The overarching goal of this project is to optimize this cost-precision trade-off by designing and delivering an artificial intelligence (AI)-driven system for individual photo-identification and tracking in conservation studies of Florida manatees, a threatened species. The system aims to streamline the creation, maintenance, query, and behavior analysis of manatees using photo-identification. This project will train several graduate students, and will advance collaboration between AI researchers and conservation scientists. In order to bring transformative advancements to current conservation capabilities, emphasizing cost-effective, evidence-based conservation planning, the project will 1) develop new algorithms grounded in explainable AI to identify and track individual manatees by their distinctive features, such as scars and markers, which serve as interpretable evidence for tracking; 2) support long-range spatio-temporal tracking by representing each animal as a series of sketch images throughout their lifespan, annotated with timestamps, geographic information, and metadata on life encounters; and 3) craft a framework for region-based conservation resource planning and management that models evolving patterns in local regions, including both natural and human-caused disturbances, to assess how local animal populations react to these regional changes. The collaborative research team will also extend approaches to additional threatened or endangered marine species (sea turtles, whales, rays). This project will have a lasting impact on the research community and education sectors by highlighting critical needs and showcasing viable design ideas in both conservation and computer science, and in their nexus. This project is jointly funded by the Division of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice 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 · 2024-09

A vast spectrum of real-world decision-making problems in domains such as public health can be formulated as combinatorial optimization problems (COPs). For example, a program may need to allocate screening services to a subset of people with symptoms in a community or a homeless youth shelter may need to invite a subset of people to participate in an educational program. Both problems require identifying the most effective and timely subset of a group for inclusion. Solving these real-world problems is difficult as they involve large amounts of data. Combinatorial Optimization is a field that attempts to find solutions to these complex problems. This project will build innovative technologies to efficiently solve the real-world COPs in public health. The main novelty is an Artificial Intelligence (AI)-based framework that addresses complex real-world COPs, which has been an open challenge for traditional non-machine learning based methods. The developed technologies will be able to assist the stakeholders to make more informed decisions about resource allocation and task scheduling, in a wide range of societal problems in public health, conservation, and more. As an interdisciplinary research project, the research outcomes of this project will promote broader participation of AI research for communities outside of AI, and beyond academia. This project will get the AI research community more exposed to real-world societal problems and inspire more AI researchers to get involved in AI for social good research. Moreover, this research will provide interdisciplinary experiential learning experiences to a cohort of graduate and undergraduate students under direct mentoring of the investigator, as well as the development of courses on the theme of Data Science and Society. Due to the computational hardness of COPs, traditional algorithms for COPs rely on handcrafted heuristics to construct a solution. Such heuristics require domain knowledge and may be suboptimal. Recently, there have been increasing investigations that use reinforcement learning (RL) as an alternative approach to automate the search of these heuristics. Despite the initial advancements, there is still a substantial gap when it comes to deploying RL for COPs in real life. This project identifies the following open challenges that are motivated by the above two problems in public health: (i) Hard objective functions (e.g., ill-shaped, or implicit); (ii) Uncertainty in problem parameters (or sim-to-real gap); and (iii) Multi-shot COPs. The primary objective of this project is a fundamental RL system that addresses these open challenges. The project comprises three major research tasks, each with innovative RL solutions that target the corresponding open challenge. Research task #1 proposes the idea of physics-inspired (task-aware) graph representation learning to enhance function approximation in the RL framework for ill-shaped and implicit objective functions. The new graph representation learning framework will incorporate the problem structure information into the design of the graph neural networks that better approximate the objective functions. Research task #2 designs a robust RL algorithm that deals with the uncertainties in the system parameters / sim-to-real gap, building on ideas from adversarial training and game theory that aims to better balance the trade-off between average-case and worst-case scenarios. Research task #3 introduces the idea of a new hierarchical RL algorithm that jointly decides the budget allocation on the high level, and node selection on the low level. The two levels of RL are interdependent and will be trained interactively. These technical innovations will not only advance research on RL for COPs, but also research on deploying RL for real life in general. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for the presentation of original research results and the exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. Student travel awards permit full participation by those who are primary authors on accepted papers. A PhD forum and a Women in Science Research Forum are part of the agenda, and will help early career researchers to learn and exchange cutting-edge research ideas and help them communicate on different aspects of career development. Data mining and machine learning are now being broadly applied to nearly all disciplines, transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The award will be used to provide travel support for students and early career researchers, with a special focus on women and minorities, for the following activities: 1) To help fund the travel of Ph.D. students who are primary authors of full papers that have been accepted to the technical program; 2) To help fund the travel of Ph.D. students who are participating in the Ph.D. Student Forum; and 3) To help cover the travel expenses of women researchers to participate in the Women in Science Research Forum. This proposal aims to provide the crucial funding needed to support the participation of graduate students and early career researchers who will become future leaders in the science and engineering field. As an effort to engage young researchers, the IEEE ICDM 2024 will involve them in the meeting organization and include mentoring activities in the conference 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 · 2024-08

In today's world, the real-time generation of enormous amounts of data has become commonplace, spanning domains such as e-commerce, social media, environmental science, urban disaster and pandemic monitoring, and many others. Such streaming data necessitate data mining (DM) models that can analyze them in time as they emerge, derive actionable insights, and make adjustments on the fly. For instance, predicting crowd movement due to public events (such as concerts, games, parades, and protests) based on data streaming from social media and city sensors can aid in reducing the traffic by steering clear of overcrowded areas. However, as DM models become more prevalent in practice, interpretability has emerged as a vital issue. User comprehension and trust in DM model outputs are critical for their acceptance in daily routines and workflows. Nonetheless, existing research on data streams has focused mainly on model accuracy, producing models that are too complex for human interpretation. This gap between DM researchers and practitioners calls for new research that optimizes model accuracy and interpretability simultaneously. This project aims to bridge the gap by developing novel online algorithms that are transparent to human users and can provide a complete explanation of the logic behind each prediction, earning the trust of human operators and increasing legal defensibility when used to support decision-making in crucial domains such as healthcare, economy, security, and social goods. The overarching goal of this project is to advance interpretability research of online DM models through three research objectives: (1) understanding the dynamism of varying feature spaces and its impact on model structure; (2) quantifying model prediction uncertainty in the absence of adequate supervision labels; and (3) indexing and elucidating model inference paths. To achieve these objectives, the project will focus on four research thrusts. The first thrust will develop novel algorithms that capture and model the variation patterns of feature spaces through an expository feature correlation graph, allowing for joint learning of graphs and predictive models. The second thrust will focus on developing unsupervised methods to quantify the uncertainty of model predictions and identify geometric manifolds underlying data streams with memory-efficient structures. The third thrust will devise new systems to index, track, and illustrate the complete generation process of online predictions. The fourth thrust will establish evaluation metrics and protocols to standardize interpretability measurement in streaming data contexts. The project aims to contribute to interpretable data mining and machine learning research, which will help bridge the gap between data scientists and domain-specific forecasting experts. The educational component of the project will involve mentoring and educating researchers interested in pursuing DM careers in academia or industry, with a particular focus on underrepresented, financially disadvantaged, or disabled undergraduate students in computer science research. The project will also pioneer new classes at the forefront of data mining research and organize workshops at city libraries to engage with the broader public. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Neutrinos are the most abundant matter particle in the universe. As a fundamental particle, they can not be divided into smaller pieces. They also interact only via the Weak Force, meaning they can penetrate astronomical amounts of material without leaving a trace. These three facts make neutrinos a unique and powerful probe to understand how nature works at the smallest scales. Neutrinos are also emerging as a new way to study objects in our universe that are either very far away or obscured by matter. The Standard Model of particle physics classifies the elementary particles into groups of related particles with similar quantum properties and describes the interactions amongst those particles. With the discovery of the Higgs boson in 2012, physicists have found all the particles predicted by the Standard Model, and no other particles have been discovered. However, the model leaves open many questions about the universe, including the existential question of why an excess of matter over antimatter survived the early universe to form the structure of the observable universe. The Standard Model also assumes neutrinos are massless, but we now know that neutrinos do have mass and because they do, they can change from one type to another. This observation tells us there must be more to the Standard Model. This award will provide detailed measurements of these changes as well as others form one of the most promising ways to look for new physics beyond the Standard Model. This award will study neutrinos with data from the NOvA and MINERvA experiments. The group is also engaged in R&D for DUNE, the next generation, long-baseline neutrino experiment with detectors at Fermilab in Illinois and the Sanford Underground Laboratory in South Dakota. By engaging students in this research, we provide graduate and undergraduate students a vital component of their education and contribute to the development of the future technical workforce. Beyond promoting science education at the college level, the group operates a successful Emerging Scholars program at a local elementary school. This ten-week, after-school program exposes students who are from communities historically underrepresented in the sciences to practicing physicists and exciting science 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 · 2024-08

Software developers spend much of their time reading and understanding (“comprehending”) code, because it is a prerequisite for adding new features, correcting defects, improving existing functionality, and performing other changes to software systems. Prior research has proposed many syntactic metrics that claim to measure code complexity, but the correlation between these metrics and measurements of code comprehension effort from humans is weak, at best. Developers still have limited support for assessing and reducing the complexity of their code to decrease code comprehension effort. This project will investigate new semantic metrics of code complexity derived from existing automated reasoning tools (“verification tools”). These new metrics will be semantic (i.e., based on the program’s meaning) rather than syntactic (i.e., based on the program’s textual representation). If these new, semantics-based metrics correlate better than prior syntactic metrics with human comprehension effort, they will help guide software developers to write code that is easier to understand and modify, thereby improving software quality and reducing software development costs. The key insight that inspires this project is that the output of verification tools contains useful information about a program’s semantic complexity. Verification tools try to prove that a program does or does not have a particular property, such as “cannot dereference a null pointer” or “eventually halts." Because these kinds of semantic program properties are undecidable, verification tools are always approximate: they conservatively answer “I don’t know” when they cannot construct a proof. Such “I don’t know” answers from verification tools may be a useful source of information about a program’s semantic complexity, and a preliminary study found a correlation between such “I don’t know” answers and human comprehension effort. Intuitively, the fewer facts that a suite of verification tools can prove about a program, the more complex that program probably is. This project will investigate three research directions based on this insight: (1) validate the hypothesis that the success or failure of verification (i.e., verifiability) is correlated with code complexity and human-based code comprehension effort (i.e., comprehensibility), and establish whether a causal relationship exists between verifiability and comprehensibility or whether they have some mutual cause; (2) use the semantic code information encoded in the output of verifiers to improve the performance of existing predictive models of comprehensibility that currently rely on syntactic features only; and (3) “verifier-guided” code refactoring to reduce complexity and comprehensibility in an automated way, using information about the parts of programs that verifiers struggle with as a guide for where and how to apply refactorings. 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

A central goal of contemporary fundamental physics is to find evidence for new physics, beyond the scope of the Standard Model of particle physics, and to characterize its features once such evidence is found. One avenue to search for new physics is the "Precision frontier" where precise measurements are made of quantities that are precisely predicted by the Standard Model - a deviation from this prediction would signal new physics. This project supports one such measurement, the MOLLER experiment, which will determine the electron-electron weak interaction to unprecedented precision, using the electron accelerator at the Thomas Jefferson National Accelerator Facility. The team will construct and commission several components of the particle detection system and will develop the particle-tracking software to be used in the data analysis. Graduate students and a postdoctoral researcher will actively participate and thereby develop skills in precision nuclear physics experimentation and in the analysis of large and complex data sets. The MOLLER experiment will use parity-violating electron-electron scattering to extract the electroweak mixing angle to a precision comparable to the best available high-energy measurements, through a measurement of the weak charge of the electron. The expected 35 ppb parity-violating asymmetry in the scattering cross section will be determined to a precision of 0.5 ppb, five times smaller than the only previous measurement. The William & Mary group will provide the detector system used to characterize one of the most important background processes, electro- and photo-production of pions, and will lead the effort to create and commission the particle-reconstruction software used in the calibration of the spectrometer acceptance and to correct for various backgrounds. 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

This award funds the research activities of Professor Christopher Carone at William & Mary. The standard model of particle physics is a theory that provides an extremely successful description of the known elementary particles and their interactions. However, the standard model also leads to mysteries that currently remain unresolved and are likely related to physics at extremely small distance scales. Professor Carone will explore a range of novel possibilities for the particle physics in this regime. These may address a number of mysteries, such as why there are stable hierarchies between the widely different energies and distance scales found in nature, why the masses of known elementary particles have the inexplicable range of values that are observed, and whether the ultimate theory describing elementary particles may be of a special type that can be extrapolated sensibly to distances that are infinitely small. Such investigations advance the national interest by furthering the progress of science in the US via our understanding of the basic building blocks of the universe. Professor Carone will work with doctoral students on the project, providing them with training that will equip them to be valuable additions to either the academic or the broader STEM workforce. Professor Carone will also participate in outreach events that enrich high school teachers by giving them an awareness and exposure to particle physics research. More technically, Professor Carone will study novel higher-derivative quantum field theories that interpolate between Lee-Wick and nonlocal theories and that provide a new solution to the hierarchy problem. He will construct new models of elementary fermion masses that involve modular flavor symmetries, a possibility that is motivated by string theory. He will consider the constraints on well-motivated gauge extensions of the standard model that follow from asymptotic safety, the condition that couplings flow to nontrivial fixed points in the ultraviolet. He will also follow up on his recent work on regular black hole metrics, to understand a variety of astrophysical consequences that follow from the assumed nontrivial mass dependence of the regulator. 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.