George Mason University

NSF Awards · FY 2024 · 2024-09

This National Science Foundation (NSF) and US-Israel Binational Science Foundation (BSF) Collaborative Research Opportunities (NSF-BSF) grant will support research that will contribute new knowledge related to social robot navigation, promoting the acceptance and adoption of mobile service robots in crowded public spaces and advancing national prosperity and welfare. Social robot navigation techniques autonomously move mobile robots from one point to another, serving people by, for example, delivering food or packages. When these autonomous robots move in crowded public spaces, they not only need to share the same space and avoid surrounding humans, but also observe unwritten social norms, e.g., leaving enough social space, following the flow, and yielding to the right. However, such unwritten social norms vary from culture to culture, making a mobile robot developed for one culture difficult to adapt to another and therefore hindering the acceptance of mobile service robots in the new culture. This award supports fundamental research to develop social robot navigation techniques that can easily adapt to different cultures. While providing mobile robot services, such techniques will enable robots to efficiently navigate through dense human crowds without any collision and simultaneously follow culturally dependent social norms. A smooth integration of mobile service robots into daily lives will free people’s burden from repetitive and laborious tasks and increase the prosperity and welfare of people from different cultures. Therefore, results from this research will benefit the US economy and society. This research involves several disciplines including artificial intelligence, machine learning, human-robot interaction, and human factors. The multi-disciplinary approach will help broaden participation of underrepresented groups in research and positively impact engineering education. The cultural adaptiveness developed from this grant can overcome several limitations that existing mobile robot navigation techniques suffer from, including the "frozen robot" problem that impedes efficiency, moving too close to walking humans which compromises safety, and behaving in a way that does not conform with the underlying social norms therefore causing public resistance. This research will fill such a knowledge gap using data-driven approaches. The research team will collect a large-scale cross-cultural social robot navigation demonstration dataset, devise new algorithms to first comply with one underlying culture while maintaining efficiency and safety, design evaluation benchmarks and metrics to assess robot social compliance, and finally develop techniques that leverage commonality within different available cultures and quickly adapt to a new culture. 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 tool that improves the understanding and documentation of software design. As technology advances, the demand for efficient and maintainable software grows; This tool addresses the critical need for developers to understand unfamiliar code. Working with unfamiliar code is a significant challenge for engineers when onboarding to a project, working with old legacy code, and when collaborating with globally distributed teams. By enabling developers to comprehend and adapt to codebases rapidly, this tool enhances collaboration, accelerates development timelines, and reduces errors, ultimately lowering costs for businesses. The potential societal and commercial impact include reducing the tedious and challenging work with unfamiliar code that slows the development process and increasing the ability of companies and organizations of all sizes to release software and new features to their users faster and at lower cost. 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 an innovative tool that offers a new form of documentation for software engineers working with unfamiliar code. Traditional documentation methods often fail to keep up with changes in the codebase, leading software documentation to be distrusted or unused by engineers in practice. This tool integrates static analysis tools into developers’ workflows to enable the design to be synchronized with the code. Built on top of a new way of documenting code through design rules that are checked against the code, the tool offers documentation that is always up-to-date, offers information when developers need it, and guides developers in writing code. 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 problem of real-time monitoring and detecting changes in the statistical properties of multi-stream data has many applications in science and engineering. These include monitoring public health for possible pandemic onset and recognizing abnormal events in multi-camera video surveillance. Often the data of interest is non-stationary, i.e., its statistical properties that change with time. Traditional change detection algorithms are not optimized to process non-stationary data. This project will develop algorithms that are provably robust against uncertainty in data distribution and easily implementable in practice. The algorithms will be further developed to apply in settings where privacy, energy efficiency, and high-dimensionality of data come into play. The developed algorithms will be applicable to solve a wide class of spatiotemporal change detection problems in public health and cyber-physical systems. The algorithms will be validated on several publicly available datasets and the code will be made publicly available. Students will have opportunities to participate in the research and efforts will be made to recruit participants from underrepresented groups. The algorithms developed in this project will be optimized to detect changes in the statistical properties of multi-stream non-stationary data with the minimum possible delay, subject to a constraint on the rate of false alarms. These quickest change detection algorithms will be designed to be robust against uncertainty in the distribution of data before and after the change. The project is divided into four technical thrusts. The first thrust will develop robust algorithms for quickest change detection when there are multiple streams of non-stationary data with unknown pre- and post-change distributions and the change can occur in any subset of the streams. The algorithms will also be designed to identify the affected stream, at the time the change is declared, and an alarm is raised. The algorithms will be based on the least favorable pair of distributions in the pre- and post-change uncertainty classes. Procedures to analytically characterize or numerically calculate the least favorable pair will also be provided. The second thrust will develop robust algorithms for non-stationary high-dimensional data. The algorithms will be based on the gradient of the logarithm of the density of the data which can be learned using deep neural networks. The third thrust will develop algorithms for data-efficient quickest change detection in non-stationary data. A data-efficient procedure utilizes adaptive sampling techniques to control the average number of observations used before the change. The fourth thrust will develop optimal algorithms for some special classes of non-stationary processes encountered in traffic safety, satellite safety, and public health applications. 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

As human-emitted greenhouse gas pollution warms the planet and changes the dynamics of the climate system, losses due to weather extremes and their impacts on human life and property has become a significant and costly challenge. Reliance on historical records with outdated climate states, coarse model resolution, and incongruency between the spatiotemporal scale of impacts exacerbates the problem and presents serious difficulties for the insurance and finance sectors that rely on accurate assessment of natural perils and the corresponding uncertainties around their frequency, intensity, and duration. This knowledge is required to cover climate disaster related losses that, annually, reach well into tens to hundreds of billions of dollars. To address this challenge, three institutions: George Mason University, the Massachusetts Institute of Technology, and the City University of New York have come together to plan an industry-university cooperative research center that addresses the critical, high priority needs of the insurance and finance sectors, both of which are wrestling with uncertainties in assessing risks and damages due to climate-related disasters. Center research thrusts include: (1) improving climate predictions at spatiotemporal scales needed by the insurance and finance industries; (2) modeling the catastrophic impacts of natural perils to critical infrastructure systems; and (3) quantifying how the local environment modifies the frequency, intensity, and impact of weather-related natural perils on people and property. Broader impacts of the Center would include increased national economic stability by providing better and more reliable tools for assessing climate risk; training the next generation of climate science, engineering, and policy professionals able to tackle the challenges that a changing climate poses to the nation; and broadening the diversity of underrepresented groups in climate disaster modeling field. Research conducted by the Center for Climate Risk Applications, now in the planning phase, will focus on addressing existing gaps on the impact of climate change on a range of natural perils by analyzing state-of-the-art climate model ensembles, improving existing models, and advancing the science of integration between climate modeling and asset-scale risks. Research will analyze and improve the output of climate models at the actionable spatial and temporal scales required by the insurance and finance sectors of the economy. The Center will also develop new methods for downscaling hazard information to asset-scale granularity, while quantifying uncertainties of year-to-decadal climate predictions. Additional work will address the sensitivity of interconnected infrastructure systems to a changing landscape of natural perils and the potential for disruption of critical services and supply/value chains. Natural disasters impact people, not just infrastructure; thus, the Center, presently in the planning stage will also focus on how public policy and regulation impacts the insurance of properties, as well as how existing frameworks for decision-making around these perils inform resilience efforts in the private and public sectors. The Center's education and outreach activities will help enable and maintain healthy insurance and reinsurance markets to promote economic stability and growth in the face of severe threats from climate change to life and property. It will also develop a diverse, knowledgeable, and capable workforce necessary to quantify risks of climate change for those owning assets that need protection as well as the need to improve their ability to understand and predict risks and create policies, standards, and incentives that reduce the risks of loss due to climate change. The George Mason University contribution to the Center brings its leadership in climate dynamics, infrastructure risk, and resilience that complements the center-wide research agenda that advances the science connecting climate modeling with risk and loss models used in the private and public sectors. 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

Advancements in modern technology have exponentially increased the availability and complexity of spatial and spatiotemporal data across various fields, presenting unique challenges and opportunities. This project aims to develop scalable and efficient quantile learning techniques to unlock valuable insights from large-scale, heterogeneous spatial-temporal data, overcoming limitations in handling dynamic patterns and spatial variations while accounting for uncertainty. These new analytical techniques will have wide-ranging applications, revolutionizing our understanding of spatial and temporal variations in critical areas. For example, they can help identify communities facing disproportionate risks from environmental hazards, health crises, or crime, enabling more targeted and effective interventions. By making these techniques widely accessible through public software releases, the project will empower researchers and policymakers to leverage vast amounts of spatial-temporal data and address pressing societal issues more effectively. The project will also contribute to STEM education by engaging both undergraduate and graduate students in hands-on learning and incorporating research findings into course development. The project will develop scalable and efficient quantile learning methodologies, algorithms, and theories to address challenges in analyzing large-scale spatial-temporal data through three main research aims. First, the investigators will introduce a flexible quantile spatial model framework that simultaneously captures spatial nonstationarity and heterogeneity via spatially varying coefficients. Second, they will develop a scalable distributed learning procedure using domain decomposition computing to efficiently handle large spatial datasets across complex domains, including a communication-efficient aggregation method for estimating constant coefficients to ensure optimal efficiency. Third, the research will expand analysis from 2D to 3D to tackle complex and heterogeneous dynamics of extremely large spatiotemporal data, introducing a class of quantile spatiotemporal models and developing a robust, scalable estimating procedure to meet substantial computational demands. These advancements will significantly impact multiple areas of statistics, including large-scale computing, inference, optimization, and nonparametric approximation theory. 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

Mathematical modeling involves the cognitively demanding process of translating real-life situations into mathematical notations and is a fundamental skill for students pursuing science, technology, engineering, and mathematics (STEM). Cultivating mathematical modeling through collaborative learning can be particularly effective, but orchestrating such collaborative learning tasks requires significant efforts from teachers to oversee group discussions. This could be particularly challenging for marginalized communities that lack teacher resources. This project will explore the development of generative Artificial Intelligence (AI) techniques for creating a virtual classroom platform that supports collaborative learning of mathematical modeling for middle-school students. Through use of the platform the project aims to increase the opportunities for students from under-resourced communities to receive effective mathematics education, supporting more equitable learning. The project will also use the platform as a lens to understand the opportunities and risks of Generative AI techniques and provide insights for future researchers and educators. The virtual classroom platform will include multiple Large Language Model (LLM)-simulated agents/students with which human students can practice collaborative mathematical problem-solving. The project has three research goals, implemented by the interdisciplinary project team with expertise in AI, natural language processing, human-computer interaction, and mathematics education. First, it will address the AI/LLM grounding challenge in the platform development through a neuro-symbolic approach and a modular architecture design. This includes exploring methods for simulated students to behave cohesively in context, aiming to replicate the collaborative behavior of real-life middle-school students during mathematical tasks. Second, it will enhance the platform to serve as an equitable learning environment by conducting participatory design with student users, gathering their input, and leveraging the collected data to refine the platform. Finally, the project involves conducting a series of research studies to understand the efficacy of the platform in fostering students’ mathematical modeling competencies and provide insights into effective ways of applying generative AI in the future of teaching and learning. 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.

NIH Research Projects · FY 2024 · 2024-08

Project Summary/Abstract Studying the anti-HIV mechanisms of restriction factors is central to understanding virus-host interaction and for developing novel therapeutics. Recently, PI Wu's lab has co-identified a new HIV restriction factor, PSGL-1 (P-selectin glycoprotein ligand-1), that inactivates the infectivity of HIV virions released from HIV producing cells. Wu and his collaborators have further demonstrated that PSGL-1 acts through a novel mechanism of virion incorporation of PSGL-1 that sterically hinders particle attachment to target cells. PSGL-1 is a dimeric mucin-like 120-KD glycoprotein that is primarily expressed on the surface of lymphoid and myeloid cells, and binds to P-, L-, and E-selectins for leukocyte rolling and transmigration. Structurally, PSGL-1 has a relatively rigid and elongated extracellular domain that extends nearly 60 nm from cell surface. A large structural part of the extracellular domain also consists of 14-16 tandem decameric repeats (DR), which are characterized by repeated stretches of 10 amino acids with numerous O-glycosylated threonines (30%) and prolines (10%). Our preliminary studies discovered that DR plays a pivotal role in PSGL-1's anti-HIV activity, and a single DR possesses basic anti-viral activity. Nevertheless, individual DR shows amino acid sequence variation and varying degrees of anti-viral activity. We hypothesize that the structural rigidity and glycosylation of DR affect its anti-HIV activity. The proposed research integrates molecular dynamics simulations, machine learning, and laboratory experiments to study the anti-HIV properties of DR. The proposal has two aims. Aim 1 is to determine the structure-function relationship of DR via the integration of In silico molecular modeling, DNA mutagenesis, and functional anti-HIV assays. We will apply all-atom replica exchange with solute tempering (REST) molecular dynamics simulations to examine DR glycosylation, rigidity, and extension. We will also apply machine learning to feature datasets extracted from molecular simulation of DR variants. We expect that the integration of our studies from molecular modeling, DR mutagenesis, and functional anti-HIV assays will guide the design of novel PSGL-1 variants with greater anti-HIV activity. Aim 2 is to test and validate the anti-HIV activity of PSGL-1 in vivo in a humanized mouse model for control of HIV replication in the absence of ART. The proposed work will identify how DR residue positioning and composition quantitatively correlate with PSGL-1's anti-HIV function. Our study will also help developing novel therapeutics, based on PSGL-1's anti-HIV activity, for a functional cure of HIV infection.

NIH Research Projects · FY 2025 · 2024-08

The goal of this project is to investigate changes in the temporal dynamics of cholinergic modulation during aging and the extent to which these changes contribute to alterations of network activity in hippocampal area CA3 that can result in age-related memory loss and cognitive decline, the hallmarks of Alzheimer’s disease. A major deliverable of the project is a biologically realistic, full-scale spiking neural network model of hippocampal CA3 that will include simulations of temporal dynamics in cholinergic modulation as a function of animal behavior in the context of encoding and retrieval of behaviorally-relevant information. To achieve this goal, we will acquire a high-quality experimental dataset on neural firing in area CA3 and temporal dynamics of cholinergic modulation using parallel recordings of multiple single units in CA3 and fiber photometry recordings of septo-cholinergic projection neurons or acetylcholine release in the hippocampus in freely behaving mice performing a hippocampus-dependent spatial memory task. Tightly interrelated computational modeling will implement a data-driven, biophysically detailed, real-scale spiking neural network simulation of the CA3 circuit at the level of individual neurons and synapses to investigate encoding and retrieval dynamics during distinguishable behavioral activities requiring the acquisition or recall of information. Experiments in aged mice and quantitative analysis of in silico recordings using data from young and aged mice will generate testable hypotheses on the functional role of distinct neuron types for specific network patterns associated with spatial memory and pattern completion that will allow the mechanistic investigation of the effects of cholinergic modulation on neural activity underlying encoding, storage, and retrieval of memory traces and their changes during aging, the major risk factor for developing Alzheimer’s disease. The in silico model will also explore quantitative effects of muscarinic and nicotinic receptors on individual neuron types within CA3 and mechanisms such as runaway synaptic modification and structural plasticity possibly underlying the changes in memory encoding and retrieval observed in aged mice. The data-driven biologically realistic CA3 circuit simulations will foster the formulation of novel, specific, and testable hypotheses of memory impairment that will be tested and quantified in closed-loop optogenetic manipulations of temporal dynamics in cholinergic modulation in freely behaving mice that aim at either disrupting or restoring hippocampal network patterns in young or aged mice, respectively. We expect the results of this interdisciplinary collaborative project to advance our understanding of temporal dynamics in the encoding and retrieval of spatial memories, the contribution of fast cholinergic modulation to this process, and how changes in temporal dynamics can result in maladaptive changes on the synaptic, cellular, or network level that may contribute to further memory loss and cognitive decline during aging, preceding and potentially causing the development of Alzheimer’s disease.

NSF Awards · FY 2024 · 2024-08

This project aims to improve the understanding of how humans perceive and are aware of time, a key aspect of everyday life. Whether it is talking with friends, playing music, or performing tasks, a sense of timing is crucial. Remarkably, one can also judge one's own timing accuracy, such as knowing if one is early or late and by how much. This awareness helps to learn and improve timing skills. However, little is known about how these two processes, timing and self-awareness of timing, work together through learning. The objective of the project is to conduct a series of experiments to uncover which brain areas are involved in the awareness of time passing. Findings from this work may have implications for conditions in which timing perception is disrupted. Furthermore, the project includes cross-disciplinary research experiences for trainees, and scientists will engage in research-related outreach to local communities. Technically, the project aims to investigate the neural mechanisms behind interval timing and metacognitive awareness in time perception, areas that are not well understood in cognitive neuroscience. To examine how humans recognize and learn from their own timing errors to improve performance, the project uses a combination of electroencephalography (EEG), functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and computational modeling to identify the brain regions responsible for these processes. The range of cognitive neuroscience methods provides trainees with valuable skills in experimental design and computational analysis. This research not only aims to expand knowledge of how humans learn and adapt to time intervals but also has potential implications for understanding and treating various mental health conditions where time perception is affected. 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

Local, state, and nonprofit institutions typically follow a range of different prioritization practices for the allocation of scarce resources. Communities also have to make decisions about how to allocate human resources across space and time (for example, police officers across a district for crime prevention). This project aims to understand how algorithmic techniques for prioritization and for resource allocation can best be used for benefit in such domains. This project will produce algorithms, models, and insights that are of broad interest to researchers studying artificial intelligence (AI), algorithmic models of strategic interactions (i.e., game theory) and mechanism design, multiagent systems, and human-AI interaction. In addition, the work will impact policy through collaborations with community partners and support the training of graduate students. At a technical level, the project will focus on several research problems important to the development of trustworthy AI. These include: (1) The design of algorithmic techniques for facilitating individualized deployment of scarce societal resources based on (potentially poorly calibrated and semantically ambiguous) risk scores, using rank information and/or learned transformations of cardinal risk scores. (2) Developing foundational models for appropriate and efficient deployment of human resources (e.g., police officers, schoolteachers and specialists) across space and time. (3) Use of interpretable machine learning to characterize current human decision-making in public-facing positions and analyze the efficiency of current approaches versus algorithmic ones. (4) Elicitation of reliable information in order to improve societal decision-making, using ideas from mechanism design and audit games. (5) The design of algorithmic decision support tools that can align the incentives of agents with the local agencies they represent while allowing continued use of discretion. 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

Optimization problems constrained by physics are ubiquitous. These problems are nonlinear, nonsmooth and contain unknown parameters. The physics describing the constraints are partial differential equations (PDEs) which are multiscale, multiphysics, and geometric in nature. They capture many realistic scenarios: control of pathogen propagation like COVID-19, blood flow in aneurysms, determining weakness in structures, and vortex control in nuclear reactors. This project will study optimization problems constrained by PDEs that can incorporate data to make decisions that are resilient to uncertainty. The proposed methods will provide new insights into nonsmooth nonconvex optimization, and they will be applied to applications such as identifying weakness in structures (e.g., bridges and nuclear plants). The results of this research will be shared with the community via publications and research talks. The outcomes of this research will benefit scientists working in multiple research areas such as numerical analysis, optimization, structural engineering and bioengineering. A PhD student will be fully supported by the project. Particular focus of the project is on risk-averse optimization problems where the PDEs contain uncertainty arising from modeling unknown quantities (coefficients, boundary conditions, etc.) as random variables and dynamic optimization problems. The project will develop: (i) Inexact adaptive Semismooth Newton and Trust-region methods to solve these optimization problems; (ii) Primal dual methods for risk-averse optimization problems with general constraints; (iii) Applications to problems where inexactness arise from finite element discretization. Thrusts (i) and (iii) will enable interaction between finite element discretization and optimization solvers leading to structure preserving algorithms. Additionally, Thrust (ii) will lead to different penalty parameters for different constraints and will allow inexact solves at each iteration which is essential for large systems. This will enable a new paradigm for widely used penalty-based methods. Algorithms for high-dimensional nonsmooth risk-averse optimization will help overcome curse of dimensionality for similar problems. 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 2025 · 2024-08

Alcohol use is common in the United States and more frequent use is associated with individual and societal consequences. The use of ecological momentary assessment (EMA) approaches is useful in alcohol research to identify strategies that may reduce alcohol use and alcohol-related consequences in the moment they would otherwise occur. The role of stress in alcohol use has received considerable attention, and some EMA studies have found momentary associations between stress and alcohol craving and use. Parenting is one common source of stress for adults, and mothers in particular may be more likely to drink in response to stress. Both parent stress and parent alcohol use can impact children’s well-being and alcohol use. Therefore, it is important to understand these associations in mothers, in the moments they occur, as they have potential to impact the health and well-being of both mothers and their children. It is also important to understand moderators that might disrupt associations between stress and drinking to inform alcohol interventions. Mindfulness is broadly associated with stress reduction and less alcohol use. Therefore, mindfulness may be one useful target for alcohol interventions that seek to disrupt stress-related drinking. However, no studies to date have tested mindfulness buffering against stress-related drinking in EMA, and no studies have looked at this in mothers. The proposed study will address these gaps by investigating momentary associations between stress and alcohol craving and use in the daily lives of mothers and whether mindfulness moderates this association using EMA. This study will leverage an ongoing R01 study of highly stressed parents (R01DA052427) by recruiting a subsample of 75 mothers to complete an additional EMA component. EMA surveys will assess momentary stress and stressors, alcohol craving, alcohol use, and mindfulness in mothers’ daily lives across two weeks. Knowledge from the proposed F31 study will elucidate processes underlying stress-related drinking in mothers that can be used as targets for future interventions, including smartphone-based just-in-time interventions that are sensitive to sex and social context (e.g., parenting). The proposed study will be completed within a research training plan that provides training in design and implementation of EMA studies, analysis of intensive repeated longitudinal data, knowledge of stress and alcohol and alcohol measurement, intervention science, and research dissemination. This training will be supported by a mentorship team with expertise in EMA, stress and alcohol use, alcohol measurement, mindfulness-based intervention, and longitudinal statistical methods. Completion of the proposed research project will provide in-depth conceptual knowledge and hands-on training that would prepare me to independently conduct EMA studies of alcohol use after the completion of the F31 award. Altogether, the training and mentorship afforded through the proposed study would greatly support my development toward a career as an independent alcohol researcher, integrating cutting edge methods to contribute meaningful knowledge to the field that can inform future alcohol interventions.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Schizophrenia is characterized by discordant thought processes, perceptions, emotional responsiveness, and social interactions impacting up to 0.7 % of the US population (National Institute of Mental Health, Health Statistics). While rates of occurrence are low, schizophrenia ranks in the top fifteen leading causes of work disability worldwide due to the severity of the symptoms. First episodes of schizophrenia typically occur in the late adolescence to early adulthood. It is believed that interactions between genetic factors and aversive early life experiences lead to abnormal brain development which produces the schizophrenia symptoms. While widespread changes in the brains of schizophrenic patients have been documented, it is thought that the cognitive disorganization relies on pathological processes within the hippocampus. As evidence, aberrant network oscillations in the hippocampus in association with a selective spatial memory deficit are prominent in schizophrenic patients. Within the N-methyl-D-aspartate receptor (NMDAR) hypofunction model of schizophrenia, GluN2B subnits have been implicated in changes in theta and slow gamma oscillations, cross frequency couping, and impaired spatial memory. However, this situation is made more complex by the fact that GluN2 subunits regulate ionotropic and direct intracellular signaling and these factors have not been addressed fully with respect to the regulation of network oscillations and memory retrieval. We created transgenic mice expressing chimeric GluN2 subunits in the forebrain to separate the ionotropic and direct intracellular signaling processes downstream from NMDAR activation and discovered that heightened GluN2B- type intracellular signaling enhances long-term spatial, but not non-spatial memory performance. Since slow gamma oscillations in the hippocampus provide the functional network organization for spatial memory retrieval, it is possible that GluN2B-type intracellular signaling regulates slow gamma oscillations to mediate its effects on long-term spatial memory. We propose that inappropriate GluN2B-type CTD signaling is a critical factor in the alterations in slow gamma oscillations and spatial memory in schizophrenia. We will test this idea and extend the question to specific synapses by analyzing theta and slow gamma oscillations, cross frequency coupling, and spatial memory performance in transgenic mice expressing chimeric GluN2 subunits in the forebrain or limited to hippocampal principal cells or parvalbumin-positive interneurons. We predict that heightened GluN2B-type CTD signaling at excitatory synapses onto principal cells or interneurons will accentuate slow gamma function during planning of paths to know goal locations and improve spatial memory. These findings would support GluN2B-type CTD signaling as a powerful factor in the regulation of hippocampal network dynamics and spatial memory. Outcomes from this study will transform our understanding of the neurobiological bases of schizophrenia and provide a novel framework for future research.

NSF Awards · FY 2024 · 2024-07

Oceans play an important role in the climate system, having already taken up around one-third of anthropogenic carbon released into the atmosphere since the Industrial Revolution. However, as temperatures continue to climb, the extent to which oceans will continue to mitigate rising atmospheric carbon remains to be fully constrained. Yet quantifying this atmospheric carbon sink is critical to projecting the future response of the climate system. To narrow this gap, researchers in this study are investigating changes in carbon uptake and storage in the Pacific Ocean during the Pliocene, an interval of warmth around 3 million years ago that is commonly used as an analog to investigate the response of the climate system to modern warming. Using both data and models, the aim of this study is to quantify the marine carbon response to two specific temperature-sensitive mechanisms within the ocean with the goal of better predicting carbon storage during future warmth. This collaborative project is also advancing public understanding of climate science through the development of a new exhibit for the Central Gallery of the Yale Peabody Museum showcasing how climate signals are measured from fossil plankton in ancient oceans. Additionally, the project is supporting participation in George Mason’s Summer Undergraduate Research Experience (S.U.R.E) Program, doctoral students at Yale and Mason, and engaging high school and undergraduate students in the translation of core science into a publicly accessible display During warm climate conditions, such as the Pliocene, marine carbon cycling was likely affected by changes in circulation and temperature-dependent rates of biological processes. Changes in these levers are predicted to have cascading effects on the relative amount of short- and long-term marine carbon storage, and through subsequent feedbacks, the climate system as a whole. Although both circulation and temperature-dependent biology have been argued to dominate carbon cycle changes in warm climate states, they have yet to be directly compared in state-of-the-art climate models and model-data comparisons. This study addresses this gap using a series of Community Earth System Model experiments designed to examine each lever individually, and in combination, to quantify the associated model-predicted changes in carbon storage. These predictions are also being tested in the Pliocene using geochemical proxy data for ocean pH, dissolved inorganic carbon, and temperature at four Pacific Ocean sites. This study provides a valuable assessment of the potential strength and interaction of circulation and temperature-dependent remineralization on marine carbon cycling and serves as a testbed for how well climate models simulate carbon cycling and other key elements of ocean biogeochemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Autonomous vehicles, such as self-driving cars, are complex and advanced cyber-physical systems that are part of a significant economic sector. With the goal of moving towards full autonomy without human drivers, the reliability and safety of these systems are of highest importance. The safe operation of autonomous vehicles depends not only on their physical components, such as sensors and brakes, but also on the proper operation of their autonomous control software and machine learning components. System faults such as transient faults (soft errors) in these components may lead to safety hazards and accidents. This project aims to enhance the safety of autonomous vehicles by thoroughly examining and improving their controller and machine learning components. This project brings classical reliability research methodologies to autonomous driving systems and improves them to understand the reliability behavior of autonomous vehicles with complicated software and hardware. This understanding will be used to identify vulnerabilities (that could lead to hazards) and to improve the reliability of autonomous vehicles. The involved research spans a broad range of theoretical and experimental approaches that are also applicable to other complex cyber-physical systems. This project will combine model-based and data-driven approaches for end-to-end strategic resilience assessment and multi-level selective resilience enhancement in autonomous vehicles through a holistic focus on temporal and spatial aspects of vulnerabilities within the software and hardware components. The project will start with a spatial vulnerability assessment to pinpoint critical fault locations inside the vast software space in autonomous vehicles, hence accelerating the process of identifying vulnerabilities in their machine learning models and the vulnerable functions and variables in the controller. Meanwhile, temporal vulnerability assessment will be performed to identify the underlying system contexts that are critical in the activation and propagation of faults and safety violations for the purpose of bridging the gap between in-lab reliability assessment and practical system development in the real world. Based on the spatial and temporal vulnerability assessment, the project will explore mitigation techniques through efficient selection protection to enhance the resilience of autonomous driving systems based on the knowledge of spatial and temporal criticality of vulnerabilities to address the challenges of real-time requirements and resource constraints in AVs. The research in this project will be tested and validated through the integration of strategic fault injection and selective protection mechanisms with end-to-end AV testbeds, comprising realistic control software, driving simulators, and safety intervention simulators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Nontechnical Abstract: Future technologies based on quantum materials and devices are expected to have applications with significant practical impact on everyday life and to provide help in solving many of today’s global challenges. This project aims to address some of the important fundamental questions about the complex phases in graphene materials used to make quantum devices. The complex special properties of these materials make them potential building blocks for future quantum computers. Various physical observables which can reflect the topological properties found in these materials are explored, with a particular focus on entropy and the way charges flow. This research helps to provide the critical fundamental knowledge required for the advancement of future topological quantum computing schemes. These new quantum technologies need an educated labor force to properly utilize the breathtaking possibilities. Therefore, the educational plan in this project focuses on creating a new generation of quantum material scientists by promoting outreach and educational opportunities for students and underrepresented groups and enhancing community’s knowledge of quantum nanoscience and nanoelectronics by a mixture of training, designing new courses and seminar series. Technical Abstract: Understanding quantum correlated phases in two dimensional systems is important for both fundamental physics and quantum-based applications. Specifically, the fractional quantum Hall effect (FQHE) is an exemplar of a topological phase of matter which provides a rich platform to study emergent phenomena and quantum statistics. While various techniques have been applied to tackle the issues of statistics and topology in FQH states over the last decade, the critical fundamental knowledge required for applications is still lacking. The topological properties of FQH states can be reflected in the entropy carried by their emergent quasiparticles, the internal structures of their edge channels, and the conductance across their specific interfaces. This project is focused on utilizing less explored approaches to measure these physical observables which can give valuable insights into the underlying topology and statistics of the FQH states. This research relies on the fabrication of clean graphene heterostructures and engineering of gate-tunable interfaces, where topologically distinct FQH phases can be interfaced within a single device. The FQH physics in these systems is investigated by a combination of thermopower, electrical transport and local probe measurements. These studies address some of the most important fundamental questions in this field. For example, what is the thermodynamic entropy carried by emergent quasiparticles of FQH states and how it can be used to distinguish Abelian and non-Abelian states? Can interfacing various FQH states be used to identify the topological order of these states or help to resolve the black hole information paradox? Addressing these questions can have important implications for the development of topological quantum computing schemes and may provide valuable insights into one of the long-standing problems in cosmology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Public safety has become an increasingly important issue in the United States due to the potential threat posed by hidden weapons and homemade bombs in public places where extensive security checks are not available. Traditional security systems, such as X-ray machine screening, are expensive and primarily deployed in high-security areas like airports and government buildings. This project proposes to leverage the prevalent WiFi infrastructure in many public spaces to enable hidden object detection. The project team utilizes extracted WiFi signal features to identify the shape of hidden objects and determine their materials, and subsequently be able to detect suspicious items. The success of this project will greatly enhance public safety by offering easy to deploy and low cost detection systems at public venues (e.g., schools, theme parks, and sports stadiums), thereby addressing the urgent need for better safety in everyday public spaces. Building upon the team's previous foundational work, this project investigates using received WiFi signal features to determine the types of materials of objects inside bags. Target identification models and domain adaptation frameworks based on deep learning techniques are designed to ensure a good identification accuracy in diverse environments. Robust shape reconstruction algorithm helps to recognize suspicious objects. Additionally, new mechanisms using directional antennas are developed to mitigate the impact of the bag carrier's movements. The TTP project will create a prototype system and validate the system's functionality, accuracy, and robustness. The project team seeks to integrate the project's research efforts with educational activities such as developing graduate and undergraduate curricula. The team will also recruit underrepresented students into the project. The team will work closely with technology collaborators for field trial and potential deployment into an operational environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This Smart and Connected Community (S&CC) project supports research that aims to foster a Community-Responsive Electrified and Adaptive Transit Ecosystem (CREATE) to tackle interrelated challenges that arise in the planning, operations, and management of public bus fleet electrification. Public bus fleets, including transit and school buses, represent a prime opportunity for transportation electrification, and associated improvements in environmental quality and health benefits in impacted communities. Widespread adoption of electric buses has been hindered by an array of complex and interrelated planning, operational, and managerial challenges. Some major ones are range limits, long charging time, high capital expenses, low bus utilization ratios, equipment downtime, underdeveloped workforce, and diverse stakeholder interests and priorities. To overcome these hurdles, this project adopts a holistic approach by integrating intelligent technology development with community needs, while prioritizing environmental justice (EJ) and transportation justice (TJ) in research and solution design. A suite of intelligent decision support tools will be developed and deployed to support a scalable, transferable, and sustainable path for electric bus transition. This project will also assess collaborative governance in public bus fleet electrification planning and policymaking. An advisory council consisting of key stakeholders in public bus fleet electrification will be established to guide all stages of the project. In collaboration with industry and community partners, this project will further contribute to the development of the workforce to facilitate a sustainable future for electrified public bus transportation. Outputs from this project will include a report and clearinghouse on lessons learned, resources and best practices to guide other public transit agencies and school systems in their fleet electrification efforts, thereby accelerating the nationwide transition to electric buses. The project outcomes will advance EJ and TJ, benefiting marginalized communities that have long been harmed by diesel-exhaust pollution. The overarching research goal of this award is to tackle interrelated challenges that arise in the planning, operations, and management of public bus fleet electrification. The project will be conducted in close collaboration with local transit agencies, public schools, local municipalities, utility companies, electric bus distributor and maintenance service providers, and national laboratories. The multidisciplinary team will devise innovative technologies to support analytical and practical needs of community partners. Strategic planning for electric bus fleets differs fundamentally from conventional fleet planning because decisions such as charging capacity and charger locations profoundly influence fleet performance due to the much-reduced range and longer refueling time. A holistic approach is thus devised to integrate operations into the strategic planning for bus fleet electrification. It will develop an integrated strategic and operational planning framework guided by EJ and TJ. Innovations in graph machine learning and learning-enabled stochastic optimization will enable stakeholders to address the complexity, nonlinearity, uncertainty, and data scarcity challenges associated with bus electrification. Daily operations of electric bus fleets present hurdles to achieving high utilization of electric buses and chargers while maintaining optimal state of charge ranges, all without compromising the reliability of bus services. Adaptive operations decision support capabilities will be developed to address those operational challenges. Those include new smart predict-then-optimize, digital twin-based real-time decision making, and transfer learning algorithms to dynamically optimize fleet charging, incident response, and maintenance, thereby achieving the overarching goal of high vehicle utilization and competitive total ownership cost. The project will culminate in a pilot program to deploy CREATE Suite, a set of intelligent decision support tools, to facilitate public bus fleet electrification efforts at the project’s public transit systems and school districts partners. At the conclusion of this project, an electric bus clearinghouse will be established as a centralized platform to support bus electrification. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award will support about 15 U.S.-based students' travel to the 2024 IEEE Conference on Communications and Network Security (IEEE CNS), which will be held in Taipei, Taiwan from September 30-October 3, 2024. The IEEE CNS conference is a premier venue for communications and network security researchers to present their latest research in a number of areas related to security and privacy. Student attendees will be able to present their ideas and projects to other attendees; this can them develop their communication and presentation skills, receive valuable feedback on their research from experts in the field, and expand their professional networks with researchers and industry professionals to get their insights on the latest technologies and challenges. Student attendance also enriches the conference itself, bringing new ideas and experiences into the community. This travel award will provide career development and learning opportunities in IEEE CNS-related fields for U.S.-based students who would otherwise be less likely to be able to attend. Criteria for selection include a demonstrated interest in the field of communications and network security, as shown through research output, coursework, and/or project experience; need for financial support; and diversity of perspectives and backgrounds. The organizing team will widely advertise the availability of funding to increase the chance of reaching potential attendees from groups historically underrepresented in computing, with the twin goals of increasing the breadth of thoughts and perspectives available to conference attendees and developing the next generation of security and privacy researchers and practitioners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Immersive computing blends digital information and content into physical environments through virtual, augmented, and mixed reality. As immersive computing unfolds, it presents new research challenges, such as optimizing data transfer, reducing end-to-end latency, improving system scalability, preserving user privacy, and ensuring seamless interactions in increasingly complex environments. This workshop will identify and explore the most promising frontiers for tackling networking and systems challenges in immersive computing. The objective is to further catalyze research progress in this exciting area to enable a future where immersive computing positively impacts domains ranging from healthcare and education to entertainment and beyond. This workshop will bring together experts from diverse backgrounds, fostering interdisciplinary collaboration to delve into cutting-edge research topics and encourage knowledge exchange. The technical program revolves around four key themes: (1) network architecture and protocols, (2) sustainability and scalability of systems, (3) applications and user experience, and (4) optimizations driven by machine learning/artificial intelligence. Participants will be tasked with pinpointing obstacles and opportunities for advancing immersive computing within these themes, drawing on concrete examples from various application domains. Through interactive breakout sessions, these insights will be collected and organized. The outcomes of the workshop, including research agendas, identified challenges, and collaborative initiatives, will contribute to the intellectual landscape of the field and guide future investigations in the pursuit of transformative advancements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Energetic electron precipitation (EEP) occurs when the high-energy electrons trapped in Earth's radiation belts enter the atmosphere and collide with atmospheric particles, depositing energy in the atmospheric system. EEP is one of the main processes contributing to the loss of energetic electrons and has important implications in the interconnected atmosphere-ionosphere-magnetosphere system (e.g., changes in atmospheric chemistry, ionization, and conductance) and in space weather (e.g., satellite radiation monitoring, satellite drag, etc.). These energetic electrons are primarily scattered by plasma waves; however, due to limited data coverage, our comprehensive understanding of EEP is limited. In this project, by developing a machine learning (ML) model, the team will characterize and parameterize the EEP phenomenon's properties and dynamics. Modeling the spatial and temporal evolution of regions where EEP occurs will lead to a better understanding of the evolution of the radiation belt dynamics and temporal dependence of the energy input to the atmospheric system. The project is highly interdisciplinary, as our understanding of EEP directly impacts several fields, from the atmosphere and ionosphere system to the magnetosphere, and even potentially provides helpful information for space weather monitoring of electron radiation in the near-Earth environment. The project has the potential to support collaborative efforts across all these communities. The ML model and its outputs will be released to the public, enabling follow-up research projects. The lack of global observations of EEP is a major limiting factor in advancing our knowledge on EEP. The team suggest to parameterize EEP by developing global EEP maps through the use of machine learning (ML) techniques. These maps will be based on measurements from the long-lived NOAA's POES/MetOp satellites and will be produced given a time history of geomagnetic activity. The project will address the following science questions: How does the global electron precipitation evolve in time and space with geomagnetic activity? Which plasma waves correspond to the observed enhanced electron precipitation? How does the improved spatial coverage impact the estimates/constraints on the spatial size, duration, and flux intensity of the electron precipitation regions? Analyzing the spatial and temporal evolution of regions where EEP occurs will lead to a better understanding of the evolution of the radiation belt dynamics and temporal dependence of the energy input to the atmosphere. Additionally, the team will explore whether there is a clear cause-effect correlation between the location and energy of EEP and two main plasma waves. This provides a more definite understanding of the causal relationship between the wave modes and EEP, possibly demonstrating that EEP maps can serve as a proxy for wave activity. Finally, by estimating the size and flux of EEP regions, the team will quantify the electron loss of the outer radiation belt and the EEP input that contributes to variations in atmospheric chemistry and ionization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This planning project will engage the Computer and Information Science and Engineering (CISE) community on millimeter-wave (mmWave) and advanced wireless research to gather feedback and gauge support for a new research infrastructure that introduces the principles of open, disaggregated, and “softwarized” radio access networks to mmWave systems. This planning project will analyze research priorities, platforms, and interfaces to inform the development of an experimental research infrastructure combining flexible radio capabilities and control loops based on Artificial Intelligence (AI) within mmWave systems. The intent is to enable adaptive wireless experimentation over high-frequency systems, offering new opportunities for creating, training, evaluating, and improving mmWave systems on realistic, over-the-air scenarios. The goal of this planning effort is to (1) understand the needs of the CISE community; (2) scope enabling technologies and architectural building blocks; and (3) lay out the design for an adaptive mmWave system that would spur experimental research and development exploring high-frequency bands for 6G and beyond. This planning project will provide an initial assessment of the need and potential for an adaptive mmWave research infrastructure, adopting the principles of open and softwarized radio access networks to mmWave systems. The planning activities involve (1) mapping and interviewing relevant stakeholders, including members and officers of the O-RAN Alliance, the Next G Alliance, and PAWR (Platform for Advanced Wireless Research) facilities; (2) conducting community surveys across academic, industry, and government participants to understand their research priorities, needs, and pain points; and (3) visiting existing wireless testbeds to gather insights into their capabilities and limitations, as well as to identify enabling technologies and architectural building blocks. This project will co-design the research vision and infrastructure architecture with the CISE research community and contribute back by disseminating its findings, potential use cases, and designs to help support and motivate additional research and important standardization and regulatory decisions related to high-frequency bands for 6G and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The goal of this project is to investigate the direct harms to science wrought by structural racism and the benefits derived by the inclusion of people of color and other historically marginalized groups in the scientific workforce. Specifically, this work seeks to (a) quantify the participation of people of color and members of historically marginalized populations in the production of science, (b) elucidate their role in propelling intellectual innovations, and (c) understand how the distribution of labor and composition of scientific teams creates barriers and pathways to their scientific success. The project will support the mission of open science, by making the algorithms and publications openly available to propel this area of research. Finally, the PI team will recruit a cohort of a dozen student Fellows from a variety of disciplines and countries to discuss the ways in which they incorporate their lived experiences into research design and the challenges and barriers to this process. Priority will be given to doctoral students of color, or who identify as a member of a historically marginalized population within their country of affiliation. The goal of the fellowship is to empower students to navigate academic spaces by suggesting new topical directions with advisors, to cultivate change in terms of how authors are distributed in scientific publications, and to examine what and how science is conducted. Our research aims to empirically examine the degree to which diversity in the scientific workforce creates a more innovative and robust scientific system. The research has strong implications for all sectors of society. This research builds upon previous quantitative analyses to construct more robust and equitable algorithms that take into consideration contextual factors that influence the performance of the algorithm. To address our primary aim we use articles’ abstract, title, and keywords to train a Latent Dirichlet Allocation (LDA) model to infer the topics within a corpus of papers and the distribution of topics within each article. Data sources include millions of articles and distinct authors indexed in the Web of Science (WoS) database. To address our primary aim we will use articles’ abstract, title, and keywords to train a Latent Dirichlet Allocation (LDA) model and to extend our work on intersecting race, ethnicity, and gender inequalities in the US research landscape to citation and collaboration patterns, the role of institutional affiliation and changes over time; infer the topics within a corpus of papers, and the distribution of topics within each article. Our second aim is to determine if variation by race, ethnicity and gender identified in the US context translates to other national contexts. To address this second aim we will replicate and expand our methodology to two other scientifically productive, diverse societies. Comparison across all three nation states will allow for the identification of potentially generalizable characteristics, mechanisms that can be used to improve equity in science across the globe, and knowledge of how topicality of research in different countries is affected by the racial composition of teams. This research will provide a scalable methodological contribution that extends beyond the confines of this single research project and will allow other researchers to analyze race, ethnicity, and gender in any dataset that includes individual names. 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 · 2024-01

The tropics have profound influence on the global climate system, but the region’s response to changing climate remains unclear and the predictions of tropical precipitation and water cycling remain highly uncertain. This project aims to improve the ability of future climate projections in this region by using numerical model simulation and proxy derived records of water tracers of tropical hydroclimate to identify pan-tropical and regional responses to climate drivers under different boundary conditions. The investigation will focus on the last deglaciation period which is characterized by large shifts in atmospheric greenhouse gases concentrations, seasonal insolation, ice sheets, and sea level. Thus, the configuration of the last deglaciation creates a unique opportunity to study the tropical hydroclimate changes under different boundary condition than today. The overarching goal of this project is to gain new insight into the large-scale dynamics and controls on tropical hydroclimate through (1) syntheses of tropical water isotope records to characterize the spatiotemporal changes of tropical isotope hydrological records during the last deglaciation, and (2) testing critical hypotheses about the impact of external forcings and internal feedbacks on tropical rainfall. To accomplish this goal, the researchers propose a comprehensive pan-tropical data model comparison through integrating water-isotope based proxy-data, proxy system models, and new simulations with a state of-the-art isotope enabled transient climate model (iTRACE). Further, the researchers will perform new sensitivity experiments under different boundary conditions with isotope-enabled Community Earth System Model to test the influence of land-ocean configuration on orbital and millennial-scale variability across the tropics. The potential Broader Impacts include a better understanding of the spatiotemporal changes and regional drivers of tropical hydroclimate during the last deglaciation. The proposed research will assess the influence of precipitation amount relative to atmospheric circulation changes on precipitation thus potentially enhancing the interpretation of regional paleoclimate records. The project will support the professional development of an early career scientist at Brown University and the education and research training of undergraduate students from minority-serving-institutions through the Leadership Alliance-NSF-REU partnership at Brown University. Additionally, the researchers will develop a new seminar course on climate data analysis and data-model comparison for undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2023 · 2023-10

The project serves the national need of recruiting and retaining highly effective Black male high school STEM teachers. This research project will investigate factors influencing the effectiveness and retention of Black male STEM teachers in high-need school districts. The study will examine the academic and career decision-making processes and experiences of Black male STEM teachers as well as the culturally responsive practices they use in their classrooms to motivate and engage ethnically and racially diverse students. Using a mixed methods approach, the first phase will examine how Black male teachers’ educational backgrounds, professional characteristics, dispositions, and experiences, and school environments lead to their academic and career persistence as STEM educators as well as what contributes to their effectiveness as teachers. The second phase will examine the influences that Black male STEM teachers have in the classroom with their students. The project outcomes will highlight the voices, knowledge, and experiences of both Black male STEM career academy teachers and ethnically and racially diverse students to provide critical insights and perspectives into how instructional strategies may engage diverse learners and promote their STEM college and career interests. This project at Florida State University and The Ohio State University will focus on a national network of 211 Engineering and Information Technology career academies through collaboration with NAF (formerly known as the National Academy Foundation). The network involves 173 urban high schools, serving 46,719 students. A subset of Black male teachers and students from these academies will serve as the research subjects. Project goals include uncovering the pedagogical practices of Black male STEM teachers and their impact and effectiveness with ethnically and racially diverse STEM learners’ achievement, engagement, interests, learning, and persistence in STEM. The project will be informed by culturally responsive pedagogies and ethnic matching frameworks. Using a mixed methods approach, the project will investigate seven research questions: factors that contributed to Black males’ decision to pursue a career in teaching STEM; practices for improving the recruitment of the next generation of Black male teachers; challenges and barriers that Black male STEM teachers face in the school/classroom; culturally responsive pedagogical practices used by Black male STEM teachers to effectively engage ethnically and racially diverse students; perceptions of Black male students of their Black male STEM teachers; experiences, interactions, connections, and opportunities that have the most positive influence on ethnically and racially diverse students’ academic engagement and experiences in the STEM classroom; and effectiveness of Black male STEM teachers in high-need school districts associated with improving the student learning of ethnically and racially diverse students. Findings will be published in peer-reviewed journals, policy briefs, research zines, and infographics to participating schools as well as interactive webinars. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high- need school districts. 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.