Virginia Polytechnic Institute and State University

NSF Awards · FY 2024 · 2024-09

Biological computing seeks to harness the complex, rapid, and energy-efficient performance of living systems. Most biocomputing strategies use neurons. They are the core processors in the human brain, and they can be easily interfaced electrically. Electrical signaling is not the only means of communicating with living systems. This project will take advantage of the responsiveness of pericytes to mechanical stresses to construct a computing system. Pericytes wrap around capillaries and respond to neural signals to contract and relax, altering blood flow. Clusters of these cells, referred to as spheroids, will be placed on a fiber network. Mechanical forces will be applied to the fibers, and the response of the spheroids will be monitored and modeled. The mapping of inputs to outputs can ultimately be applied to problems of speech and image recognition. The project will also address ethical issues surrounding the use of biological material to perform computations. Efforts will be made to identify what unit of organization in this system (cells, spheroids, networks of spheroids) exhibits computational ability, and whether that unit exhibits a level of awareness that might be considered consciousness. These determinations will guide discussions and action regarding the care and use of these systems. The reservoid platform design draws inspiration from two biological entities in nature. First, the extracellular matrix (ECM), a fibrous environment that cells produce and use to organize themselves into three-dimensional (3D) tissue. Second, 3-D cell clusters (spheroids), which are powerful emerging tools for modeling and studying biology and developing bioengineered systems. The project will create a computing paradigm of reservoids using biological rules established by cell-cell, spheroid-spheroid, and spheroid-fiber interactions on ECM fibrous networks of varying fiber diameters and architectures. The envisioned reservoids will dynamically evolve to enhance performance, efficiency, and scalability with changing environmental conditions and computing demands. The team will identify and apply spheroid-merger biological rules to generate a numerical emulator that quickly iterates reservoid designs and optimizes its computing performance over time. Systematic integration of philosophical and ethical studies with early-stage bio-computing research will unveil how biological components in a reservoid represent practical, moral, and ethical considerations and obtain a new understanding of the potential consequences from the widespread deployment and use(s) of reservoids. This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI) and the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Non-technical Summary: In this project, supported through the Solid State and Materials Chemistry program in the Division of Materials Research, Professors Amanda Morris and Alan Esker of the Department of Chemistry at Virginia Polytechnic Institute and State University investigate the electronic and ionic transport properties of metal-organic frameworks. The project advances our knowledge how to control these properties and aid in developing next-generation smart windows, batteries, sensors, and catalysts. The project addresses critical, present-day energy technology challenges and, thus, holds the potential for genuinely transformative economic benefits. The research also serves as the inspiration for a permanent exhibit installation at a local children's museum for K-5 students and educational enrichment opportunities at Virginia Tech. The PIs also commit to recruiting and mentoring undergraduate and graduate students through this interdisciplinary STEM project. Technical Summary: Commercial applications for metal-organic frameworks (MOFs) have now been realized in gas sorption and catalysis, with more on the horizon in water harvesting and electrochemical applications, e.g., supercapacitors. Regarding the latter, the field has extensively studied the mechanisms of electrochemical charge transport and uncovered three primary modes: through-bond, through-space, and redox-hopping. The Morris group was among the first to propose an operative redox hopping mechanism for charge transport in MOFs. This project, supported through the Solid State and Materials Chemistry program in the Division of Materials Research, builds on this prior knowledge. The principal investigators carry out a detailed study on redox hopping-driven ion transport in MOFs using precision electrochemical investigations and modeling expertise to answer the following fundamental questions: What are the dominant pathways for ion insertion in redox hopping MOFs under non-Faradaic (migration) and Faradaic (catalytic) control? What synthetic handles can be used to manipulate ion transport in MOFs? Additionally, the research serves as the inspiration for a permanent exhibit installation at a local children's museum for K-5 students and educational enrichment opportunities at Virginia Tech and provides research opportunities to 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 2024 · 2024-09

This incubation project will advance integrated approaches to artificial intelligence (AI) ethics education in undergraduate science, technology, engineering, and mathematics (STEM) programs. The research team will seek to transform materials from the ethics bowl competition, which is traditionally an extracurricular activity focused on research, consultation, collaboration, and debate, into structured classroom tools. This will help instructors to cultivate the understanding that students have of AI ethics. The project aims to build a strong community foundation and develop capacity for larger educational initiatives. The project will create and evaluate instructional resources like case studies, instructor guides, and active learning assignments. These tools will provide practical, scenario-based learning experiences to enhance student skills in ethical reasoning, teamwork, and communication. The team will develop and pilot sample resources with participants during a professional conference, workshop the resources at a two-day meeting, and refine and disseminate the resources after the workshop. The project focuses on STEM ethics education, specifically AI ethics. Project goals are to (1) recalibrate STEM education to incorporate ethical reasoning with professional competencies like teamwork, research, and communication, addressing a critical gap in AI education; (2) equip STEM faculty with a new pedagogy to engage students in ethical discourse and analysis; and (3) set new benchmarks in AI ethics education by providing a replicable model for integrating ethical decision-making into STEM disciplines. The impacts of the project include enhancing AI ethics understanding among students, expanding the reach and inclusivity of ethics education by co-creating materials with a broad collection of institutions, and producing deliverables like pedagogical materials, online resources, and community engagement platforms. The project will involve a collaboration with government, industry, and community partners. The curriculum produced will address current ethical challenges in AI and equip students with relevant skills for various professional settings. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

An important problem in human-robot interaction with multiple robots is the ability to cooperatively manipulate objects while navigating complex environments. This scenario involves a collaboration between a human leader and a group of teleoperated and semi-autonomous agile-legged robots (i.e., synthetic actors) working together to manipulate and transport complex objects. This research project aims to enable these human-robot teams to achieve intelligent and robust cooperative manipulation and transportation in challenging settings like factories, homes, and offices. The project fosters a bidirectional sensorimotor interaction in which haptic gloves convey force feedback experienced by the robotic agents to the human, while electroencephalogram (EEG), electromyography (EMG), and hand position signals from the human are transferred to the robots' control algorithms for embodied reasoning. The project's overarching research goal is to establish a formal foundation to deploy a distributed planning and control approach for co-learning and co-adaptation of human-robot interaction with multiple robot agents in cooperative loco-manipulation. This work will have important societal impacts by deploying semi-autonomous legged robots that can effectively work together with humans to accomplish labor-intensive tasks, such as assembly and manufacturing. The developed co-learning and co-adaptation algorithms will allow teams of humans and synthetic actors to manipulate and transport heavy objects in challenging environments. Moreover, incorporating EEG and EMG into the researched data-driven models could lay the groundwork for developing multi-agent legged assistive devices to allow paralyzed individuals to perform daily activities. The integrated educational plan will have a profound impact by 1) creating hands-on educational activities on robot locomotion and programming for K-12 students and underrepresented minorities and 2) sponsoring senior design projects for undergraduate teams to be involved in research and experiments. The research project will advance knowledge in the largely unexplored field of formation control and embodied reasoning (planning and control) of complex models of loco-manipulation in multi-agent human-robot interaction systems through four objectives: 1) Creation of data-driven dynamic prediction models for human intention based on deep learning techniques; 2) Creation of data-driven dynamical models for the complex network of multi-agent legged robots and the human operator (Co-learning); 3) Creation of human dynamics-aware and distributed data-driven predictive control algorithms for optimal control of the network of synthetic actors with the human in the loop for loco-manipulation (Co-adaptation); and 4) Experimental validation on a team of advanced quadrupedal robots for cooperative loco-manipulation and transportation tasks in the Principal Investigator's laboratory. This award has been co-funded by the Mind, Machine and Motor Nexus program and the Dynamics, Controls and System Diagnostics programs. 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 Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry supports the research groups of Prof. Rong Tong and Prof. Hongliang Xin at the Virginia Polytechnic Institution and State University to develop new chemistry for the synthesis and recycling of stereocomplex poly(lactic) acids (sc-PLA). With the co-funding support from the Official of Strategic Initiatives of the Directorate for Mathematical and Physical Sciences, computation-assisted approaches will be used to help identifying efficient catalysts for the polymerization process. Poly(lactic) acid (PLA) is a biodegradable and biocompatible polyester. The goal of this project is to improve understanding of the critical catalyst structural features affecting polymerization enantioselectivity and to enable efficient synthesis of sc-PLA, which is anticipated to have enhanced thermal-mechanical properties. Undergraduates and graduate students will receive interdisciplinary research training at the intersection of catalysis, polymer science, and data science. The accomplishments will be communicated to the public and scientific community, with a commitment to the open-source release of all datasets and models. Efficient enantioselective catalysts for scalable synthesis of recyclable sc-PLA from racemic lactide remain elusive. The two research teams aim to establish an integrated experimental and computational framework to identify efficient catalysts and to improve understanding of the structural features affecting polymerization enantioselectivity and mechanism. This research will focus on studying enantioselective ring-opening polymerization mediated by bimetallic chiral complexes. A combination of density-function theory (DFT) computations and machine learning models will be employed to assist the discovery of efficient catalysts. The effectiveness in the synthesis of sc-PLA from industrial monomer mixtures of racemic lactide and meso-lactide as well as the recyclability of sc-PLA will be evaluated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges. The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project supports foundational research to create transformative structures and materials with multi-faceted intelligence in the mechanical domain (aka. mechano-intelligence). Such mechano-intelligence will be embodied and synergized throughout the physical body of these structures and materials to execute highly autonomous engineering tasks, such as acquiring information from the surrounding environment, memorizing it, and deciding on an action plan. The novelty of this project is the use of multi-functional origami as a mechanical neural network so that one can harness its computing power as the core foundation for creating and integrating essential intelligent elements. The impact of this project will be the advancement of many intelligent engineering systems widely applicable in different industries, with less power requirements, more direct interaction, and more resilience against harsh environments and cyberattacks. In addition, this project will integrate its research outcomes into new teaching curricula, outreach activities, and lab demonstrations, cultivating diverse students’ interest in STEM pursuits under the inspirational theme of physical intelligence. The vision of this collaborative research effort is to create structures and materials with multi-faceted intelligence embodied in the physical body. Although some studies have attempted to distribute (offload) intelligence to the mechanical domain, there is still a lack of a broad and systematic foundation for constructing and integrating the different elements of mechano-intelligence, such as information perception, memorization, and decision-making. To this end, the investigators will bridge this crucial gap by harnessing multi-functional origami as a mechanical neural network and leveraging its physical reservoir computing power as the needed foundation for creating, measuring, and designing the essential elements of intelligence. More specifically, the investigators will 1) explore the extent of complexity and sophistication that mechano-intelligence can attain on physical platforms, 2) formulate performance metrics to quantify and measure mechano-intelligence, 3) correlate the design and mechanical properties of the physical platform to the corresponding intelligence performance, and 4) create a systematic design method for integrating mechano-intelligence with engineering functions. 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

Typical university classrooms use traditional grading practices where points are allocated to assignments, mistakes result in point deductions, and assignment scores are combined using some form of weighted averaging to determine grades. Often, student behaviors are assessed within grades as well, including timeliness, effort, and conformity to standards. Several alternative and equitable grading practices have been proposed to combat the implicit bias often embedded in these practices. However, a significant barrier to adoption of these equitable grading practices has been the lack of tool support for educators. Virginia Tech and University of Buffalo will develop and integrate educational tools to deploy equitable grading practices (EGP), which have been shown to engage students more deeply with the learning process, to provide more equitable feedback to students about their learning. The project will bring equitable grading practices into the hands of many more educators because of the integration with already popular online learning tools and the most common learning management systems (LMS). Because these learning management systems are used by all levels (high school, community college, university), this technology integration will make adopting these practices much easier at all levels. This project will provide educators with the tools necessary to transform grading practices in their classrooms towards equitable grading practices and thereby better support student learning and achievement of course outcomes. The current set of tools available for instructors to use when adopting equitable grading practices is lacking in several respects. The project will resolve these technology issues by (1) designing a concise and expressive way for instructors to describe the way they want results from learning tools to be mapped into their chosen grading strategy; (2) developing a Learning Tools Interoperability (LTI) “proxy” for equitable grading practices that can serve as a software adapter that uses the instructor description to map results from any LTI-based educational tool into an LMS in an EGP-supportive way; (3) devising corresponding practical strategies for using learning management systems (Canvas) as the umbrella to integrate equitable grading practices seamlessly into existing courses; and (4) building a pathway for learning tools to directly support these approaches, research and develop proof-of-concept integrations of EGP-based grading and feedback processes into a selection of existing community learning tools for practice exercises, automatically graded assignments, and electronic textbooks. In addition, this project will address the following educational research questions that investigate the impact of equitable grading practices as a teaching innovation [RQ1] How does student engagement with and use of educational learning tools (such as autograding tools, homework practice tools, e-textbook tools, etc.) change with the use of EGP integrated tools? [RQ2] Are EGP-driven changes in the feedback given to students on assignments from these tools associated with any changes in student behavior or achievement? [RQ3] Is achievement of course learning objectives measured more consistently and uniformly under EGP supported by tools, compared to traditional grading practices? This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Medical errors are one of the leading causes of death in the United States. Healthcare professionals (HPs) perform critical tasks in complex environments, facing incomplete and inconsistent information, fluid team dynamics, and complex interactions with technology, among other distractions. Consequently, healthcare presents a high-risk setting for errors. Errors are driven by multiple interrelated factors, such as patient acuity, human factors, team composition, and clinical context. Addressing these factors in isolation fails to capture their interconnected nature. This project adopts a proactive systems approach, leveraging artificial intelligence (AI) techniques to analyze diverse data sources, including cognitive and emotional states, behavioral patterns, and environmental conditions, to predict errors. Error prediction will leverage AI to determine assistive interventions for HPs that will contribute to the avoidance or reduction of medical errors. This project aligns with the National Science Foundation's mission to support innovative research that benefits society by advancing national health. It builds on ongoing interdisciplinary research at the nexus of human performance, sensing technologies, and human-AI collaboration. Additionally, it offers opportunities for graduate and undergraduate students to gain critical research skills for advancing smart and safe healthcare delivery in the age of artificial intelligence. The outcomes of this project may also impact other high-stakes fields like aviation, military training, and emergency response. This project will develop a novel system to identify cognitive and affective states, behavioral patterns, and contextual factors contributing to medical errors. The system will provide a transparent risk assessment of medical errors and prevent them through AI collaboration with HPs. The research team will implement multi-modal machine-learning algorithms leveraging data from wearable sensors, audio, video, and other contextual sources to predict medical errors. The research involves three main thrusts: (1) investigating the impact of neurophysiological processes on medical error rates, (2) developing multi-modal representation learning to predict healthcare professionals' cognitive states and potential errors, and (3) designing adaptive AI interactions considering human readiness and cognitive resources. A key component is human subject research (HSR), collecting multi-modal data (facilitating system testing and validation) from HPs across various simulated emergency scenarios with varying distractions and error-inducing conditions. The HSR will also compare how healthcare professionals perform under three conditions: (i) no AI support, (ii) constant AI support, and (iii) context-aware AI support based on predicted errors. This strategy will yield quantitative and qualitative data to assess the effectiveness of the context-aware, adaptive Human-AI teaming framework developed in this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Understanding the long-term history and coastal impacts of earthquakes and tsunamis along the Alaska-Aleutian subduction zone (AASZ) is critical for preparing communities for future natural disasters. Despite the AASZ experiencing a series of significant earthquakes in the 20th century, this is only a brief observational window, and the documented events may not represent the full potential of the subduction zone in the future. This research aims to fill the knowledge gap between observed events and prehistoric events by using innovative methods to build thousands of year-long records of past earthquakes and tsunamis. By examining coastal sediments and microfossils, and simulating tsunamis, the researchers will reconstruct the patterns, timing, and size of earthquakes and tsunamis over the last 2,000 years. This work not only advances scientific knowledge but also provides critical data for improving seismic hazard maps used to protect coastal communities in Alaska, the west coast of the United States, and Hawaii. Furthermore, the researchers are committed to sharing their findings with the scientific community, educating students, and increasing public awareness through outreach programs in collaboration with local educators and the NSF-funded Alaska Native Geoscience Learning Experience (ANGLE). The goal is to enhance resilience to future geohazards by fostering a deeper understanding of earthquake and tsunami science. This project will employ innovative microfossil-based paleogeodetic methods and tsunami simulations to reconstruct the patterns, timing, and magnitude of strain accumulation and release during past AASZ earthquakes on the western edge of the 1964 CE rupture. Trench-parallel and trench-perpendicular site transects will establish the lateral and down-dip extent of past ruptures, addressing a significant limitation of most existing studies at global subduction zones. At new and existing sites, the researchers will (1) quantify vertical deformation over the past ~2,000 years using a new diatom-based Bayesian transfer function technique never applied along the AASZ; and (2) map the spatial distribution of tsunami deposition and conduct a systematic exploration of which kinds of slip distributions and subsequent tsunamis best match the spatial pattern of coastal deformation and tsunami deposit extent. This work will be the first to combine geologic evidence of vertical deformation and tsunami deposit extent with a suite of forward models of possible tsunamigenic earthquake locations to evaluate the down-dip and lateral variability of past ruptures along the AASZ, providing important inputs for the advancement of USGS National Seismic Hazard Maps. 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

Teamwork is an integral part of engineering and computer science curricula. However, underrepresented students, particularly Black and Latinx students, especially those of lower socioeconomic status, tend to encounter adverse team experiences beyond those generally encountered by all students. A team-based learning environment that values each individual student’s assets can potentially decrease occurrences of negative team experiences rooted in racial bias, increase belongingness, and provide students with teamwork skills to succeed in the increasingly global job market. The goal of this collaborative project is to identify and understand pedagogical strategies that promote equity in team experiences for Black and Latinx students in engineering and computer science classrooms. The research team will use an asset-based approach drawing upon students’ cultural, behavioral, and cognitive assets to inform team compositions that will foster cooperation, collaboration, and inclusion leading to equitable outcomes in team-based assignments. Additionally, the research team will couple this novel approach to team formation with training that educates faculty and students about conscious and unconscious bias, intercultural conflict, and culturally responsive communication to improve team dynamics. Enhancing the persistence of Black and Latinx students to degree completion and subsequent entrance into the STEM workforce can increase the diversity and global competitiveness of the STEM workforce in the U.S. which, in turn, promotes national economic prosperity. The research team will perform a quasi-experimental, quantitatively driven, sequential, mixed methods design in three phases guided by a socioecological framework. The unit of analysis will focus on undergraduate teams formed in engineering and computer science courses that assign team-based assignments at the University of South Florida, Virginia Tech, and West Point. Undergraduate Black and Latinx students will partner with the PIs and co-PIs to make decisions about the research design, data collection and analysis, and dissemination of research results. The intellectual merits of this study will provide insights regarding the use of cultural, behavioral, and cognitive assets in the formation of equitable engineering and computer science student teams. By leveraging the new insights, the research impact will be to create more inclusive and equitable classroom environments to help alleviate challenges encountered in team-based undergraduate assignments. This project is a step toward transforming the STEM higher education system by illuminating the cultural assets that Black and Latinx students bring to the classroom and by providing inclusive team training to establish better team working environments and pedagogical strategies to improve overall learning experiences. This collaborative project is funded by the EDU Racial Equity in STEM Education activity, which is supported by the Directorate for STEM Education (EDU). This activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Center for Community Empowering Pandemic Prediction and Prevention from Atoms to Societies (COMPASS) center is a partnership among Virginia Tech, Cornell University, Meharry Medical College, the University of Michigan, and the Wake Forest University School of Medicine that seeks to address the challenges and gaps in our understanding of how pathogens may cause pandemics. COMPASS researchers will develop computational and experimental methods relevant to pandemic prediction and prevention and strive to effectively communicate scientific findings to the public. Computational models will seek to predict the barriers that prevent a pathogen from infecting a new host. In parallel, the center will seek to create new methods for designing tissue-mimetic organoids that will enable detailed investigations of how infections can harm organs while also facilitating the development of new drugs. Additional computational models developed in the center will have the ability to predict how well a pathogen survives in different environments, its potential to spread rapidly, and what disinfection strategies are most effective. COMPASS activities will tie all scientific endeavors with efforts to effectively communicate pandemic science with non-scientists and policy makers. Development of bidirectional feedback loops between researchers and community members will underlie the research conducted in the center. The center’s educational efforts will include increased participation by trainees at multiple levels to address a global challenge, and the COMPASS Center hopes to achieve a higher level of public acceptance in the knowledge it gains and disseminates. The COMPASS center seeks to forecast and control future pandemics by addressing the grand challenge of uncovering the genetic, molecular, cellular, and chemical rules of life underlying pathogen-host interactions through community-based and ethically grounded research. COMPASS researchers will create foundation machine learning models that address how a pathogen may lower host barriers to infect a cell, how it persists in the environment, and how drugs that have already been approved may be utilized to treat infections. In parallel, COMPASS scientists will generate novel organoid systems to serve as robust platforms to study pathogen life cycles and to test therapies. The focus on ethically grounded research will result in COMPASS scientists who can effectively communicate complex research to non-technical audiences, work with vulnerable groups to identify key ethical and equity concerns of pandemic research and use community-academic feedback to uniquely reflect reciprocal knowledge exchange between researchers and the public. The outcomes of COMPASS foundational research will inform a diverse set of use-inspired research projects across industry, federal agencies, and international organizations, resulting in a robust public-private ecosystem to provide solutions to diverse problems in pandemic science. The COMPASS Center will incorporate robust education and training plans to actively build the next generation of talent for a workforce ready to deal with pandemic threats. Through multiple innovative approaches, COMPASS will empower varying age groups of future professionals and the public in pandemic science. 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

High-latitude and polar regions present a unique set of challenges for continuous observations because of their remoteness and extreme and harsh environments. This project seeks to develop the next generation of small, low-power, autonomous, multi-instrument adaptive, ground-based geospace observation arrays, named AUtonomous Remote Geospace Observation and Research Array (AURORA). It is designed to fill large gaps in the currently existing ground-based instrument arrays in the high-latitude and polar regions. With advanced technologies in solar panels, batteries, bidirectional satellite communication, low-power sensors (fluxgate and searchcoil magnetometers, radio receivers, etc.), and high-performance single-board computers, AURORA will enable year-round observations with cost-effective, multiple instruments in these remote logistically challenging locations. This will significantly improve the ability to study (1) interhemispheric asymmetries from the viewpoint of geomagnetic and ionospheric variability, (2) the mesoscale of solar-wind - magnetosphere - ionosphere coupling in high-latitude and polar regions, as well as other space weather phenomena. Deep-field autonomous observatories have the potential to co-locate instruments across disciplines in polar science and facilitate international collaborations. This project will also contribute to training the future workforce. It will support two early-career researchers including a female early-career scientist. Graduate and undergraduate students will participate in the research, assisting with instrumentation design and testing. 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 current development cycle of Artificial Intelligence (AI) methods and tools typically involves collecting data, using the data to construct a representation (known as feature spaces), and applying machine learning models to categorize examples using this feature space representation. Building an optimal and appropriate feature space is essential as it can be used to reconstruct distance measures, reshape discriminative patterns, and enhance the AI readiness (structural, predictive, interaction, and expression levels) of the data. Appropriate features are extremely important for real-world deployments across both scientific and industrial applications. This project seeks to create a more automated and generic framework, along with effective tools, to distill fundamental knowledge of feature spaces and build an AI-ready feature space. Artificial intelligence has the potential to deliver far better features than human engineers can. This project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search methods. The educational component of this project includes developing a new curriculum of data centric AI and provides students from under-represented groups with opportunities to participate in research. This project addresses an important problem: feature space construction learning. The unique perspective is to view feature space construction as a cross-sequence feature-generation task. The project proposes new techniques for feature learning, generalization, and supporting robustness to data imperfections. Specifically: 1) This project proposes a principled deep EOG (embedding-optimization-generation) framework to distill feature knowledge, convert discrete search in feature space into efficient continuous optimization in embedding space, and reduce feature space reconstruction to sequential generation; 2) This project develops generalization strategies to achieve task-agnostic, label-free learning, transferability, and distribution shift awareness in generative feature transformation; 3) This project develops graph topology-aware generation, reinforcement augmentation, variational smoothing, and adversarial robustness to handle complex attributed graphs and weak training data, ensuring data-efficient and robust learning. Finally, this project incorporates the proposed methods into systems for modeling material formula interactions and for composing and reconstructing polymer configuration indicators for screening polymer performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Ecosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) grant supports research that contributes new knowledge related to a novel manufacturing process called magnetically enhanced laser induced plasma (M-ELIP) micromachining, promoting the progress of science, and benefiting US industries. This manufacturing process uses plasma generated by a laser to create complex micro-components. Such micro-scale components are critical for semiconductor and optical electronics, biomedical implants, wearable devices, solar cells, and electromechanical aerospace and defense parts. Compared to existing techniques, the process planned in this project can increase the geometric resolution of such components by at least three-fold while increasing their production speed by two and a half times. This award supports fundamental research to understand laser-plasma-magnetic field-workpiece interactions. Given the above applications, the results from this research impact the production and operational functionality of components that are foundational to the Nation’s prosperity and security. This collaborative multi-institutional project involves several disciplines including manufacturing, modeling, sensing, and machine learning and broadens the participation and education of women and underrepresented minority students in manufacturing and engineering. Current limitations of the state-of-the-art micromilling methods such as laser-induced plasma and direct laser ablation are limited in planar resolution and aspect ratio. The enhanced magnetically assisted laser induced plasma (E-MLIP) micromilling process involves focusing a pulsed laser inside a dielectric liquid to create a plasma at the focal spot, simultaneously shaping the plasma via an external magnetic field, and bringing the plasma in contact with the workpiece located inside the liquid to remove material. E-MLIP overcomes the limitations of planar resolution and aspect ratio of the machined features in laser-induced plasma (LIP) and direct laser ablation (DLA) micromilling. This research addresses the knowledge gaps on the physical mechanisms of material removal in E-MLIP. The research team performs experiments to characterize material removal and defect formation, develops physics-based models to explain and predict the mechanisms that drive such material removal and flaw formation, and uses a combination of in-situ acoustic-based sensing and electromagnetic finite element analysis to detect and correct defects during the process. Together, these tasks enable a physics- and sensor-data informed defect correction paradigm for understanding and scaling the magnetically enhanced laser induced plasma micromachining process in a cost-effective manner. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This CAREER project will explore how engineering faculty members perceive and integrate generative artificial intelligence (GAI) technologies into their teaching practices, particularly regarding assessments. Generative AI models are rapidly transforming many sectors in society, including education. As these GAI models become more capable of performing tasks typically required in engineering classrooms and assignments, it is crucial for educators to adapt their teaching and assessment strategies. Those adaptations can ensure that engineering students appropriately learn fundamental concepts in their fields while also learning to work effectively alongside these advanced tools. To realize that vision, it is important to understand how faculty members view these technologies and make informed assessment decisions. Based on that motivation, this project addresses the pressing need to initially characterize and then improve faculty members’ mental models about GAI. In turn, more realistic mental models can help faculty members make better informed decisions about how to adapt their assessment approaches and integrate these technologies into engineering curricula. Through research and education activities the project will help engineering faculty members develop their mental models and assessment practices while also offering the additional benefit of supporting broader efforts to prepare future engineers who are adept at leveraging GAI in their professional lives. This project aims to understand and improve the mental models of GAI held by engineering faculty members and how these mental models influence their instructional decisions, particularly in assessment strategies. The project is grounded in the mental models approach from risk communication and system dynamics to understand perspectives of these technological systems along with the theory of planned behavior as a lens toward understanding faculty member intentions and behaviors. Together, the comprehensive framework will provide a conduit for examining the cognitive and social factors shaping faculty members’ intentions and behaviors related to adapting their assessment strategies in response to GAI model capabilities and availability. The research activities of the project will employ an exploratory sequential mixed-methods design. The initial phase will involve in-depth, semi-structured interviews with two groups of participants: engineering faculty members across multiple disciplines and GAI experts. These interviews will explore current mental models and assessment adaptation strategies. Qualitative findings from phase one will inform the design of an annual cross-sectional trend survey administered to a nationally representative sample of engineering faculty throughout phase two. Activities in this quantitative second phase will support inferential claims about associations between faculty and institutional characteristics and mental models. Likewise, large-scale qualitative data analysis of open-ended items on these annual surveys will support investigations into systematic associations between groups, mental models, and assessment decisions. Administering the survey at multiple time points will also enable identification of how mental models and practices evolve as the relevant technologies continue to change. The expected outcomes from this research include a nuanced understanding of faculty attitudes towards GAI and assessment, development of tailored faculty development initiatives, and creation of educational resources to support effective GAI integration into engineering education. Building on those findings, education activities associated with the project will involve faculty workshops and communities of practice to support informed engagement with these technologies and appropriate assessment adaptations. Partnerships with multiple universities and professional organizations will ensure broad dissemination and impact, contributing to a more informed and prepared engineering faculty and, ultimately, a workforce equipped to harness the potential of GAI. 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

Advances in artificial intelligence (AI) have the potential to rapidly transform jobs, organizations, leisure, social life, health care, education, industry, domestic politics, and international relations. American colleges and universities are developing a variety of courses and modules to ensure that students gain not only the technical competencies needed to develop, understand, deploy, and use AI but also the ethical competencies needed to ensure that these advances are used wisely to contribute to a more productive workforce and a stronger, fairer, and more prosperous nation. Despite the rapid expansion of AI ethics education interventions across various institutions, there is a notable absence of empirical research systematically mapping or comparing these interventions. To address this gap, this project aims to conduct the first-of-its-kind national survey on the state of AI ethics education interventions and how faculty and administrators, as well as their institutions, approach AI ethics education. A key aspect of the research is the development of meaningful collaborations between the three R1 universities and regional institutional partners with diverse stakeholders. The research will be conducted through three regional networks, each anchored by an R1 institution that connects area higher education institutions (HEIs) such as (minority-serving institutions (MSIs), community colleges, and research-intensive universities) and actively engages them in the design, implementation, and dissemination of research. Using a variety of methods (e.g., quantitative surveys and qualitative interviews with faculty and administrators, as well as natural language processing analysis of survey and interview data), the project team will analyze the state of AI ethics interventions in diverse institutions across the United States by (a) mining existing interventions to produce a comprehensive overview of current and planned AI ethics education; (b) developing a framework for describing the ways in which the faculty perceive and conceptualize AI ethics education; (c) exploring the factors that affect the decision- making of instructors while proposing, designing, and offering various AI ethics-related interventions; and (d) identifying institutional capacity and needs to support effective AI ethics education. Overall, the research will allow STEM faculty and educational researchers to craft curricula and administrators to develop institutional initiatives that generate AI ethics competencies tailored to the needs of their students, their employers, and their communities. This project is jointly funded by the Directorate for Engineering, the Directorate for STEM Education, and the Directorate for Computer and Information Science and Engineering, and is managed by the Division of Engineering Education & Centers on behalf of the ER2 Program of the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The underlying geophysical connections linking great earthquakes with plate tectonics will be addressed using sophisticated models. These seismic events are the largest earthquakes with magnitudes of nine or higher and are among largest sources of natural hazard affecting many countries, including the United States. Scientists have long known that these earthquakes are linked to plate tectonics and occur where an oceanic plate dives into the earth’s interior at a subduction zone, like the one along the coasts of Washington State, Oregon and northern California. Unfortunately, fundamental issues remain as to the reasons where and when they occur. This interdisciplinary team will link the long-term physics driving and resisting plate tectonics with that of great earthquakes. The outcome of the models will be a deeper understanding of earthquake occurrence. Currently, the ability to solve the set of equations describing the physics of this coupled problem is beyond the ability of mathematical methods. Consequently, this project brings together mathematical scientists with earth scientists to attempt to solve this problem collaboratively. Mathematically, problems like this are solved on the largest supercomputers and are described by many equations. For the plate tectonics problem by itself, the equations change only a small amount from one moment of time to the next in the computer model, but in this coupled problem only a set of the equations change when an earthquake happens and so the mathematicians will discover and implement new ways to solve such large sets of equations. Meanwhile, the earth scientists will use the new methods to understand the basic physics of the coupled earthquake and plate tectonics problem, ultimately tailoring the methods to models of individual plates and faults, such as the Juan de Fuca Plate which subducts below the Pacific northwest. The algorithms are expected to efficiently use the largest supercomputers now in the planning stage, including the NSF-planned LCCF (Leadership-Class Computing Facility). Moreover, the computer software, called Rhea, will be distributed open-source and will be well-engineered and documented. The team will collaborate with the Computational Infrastructure for Geodynamics, supported by the NSF, for the distribution of Rhea to the broader scientific community. The PIs will train graduate students at Caltech, Virginia Tech, and NYU at the boundary between the mathematical sciences and science applications. The team will participate in outreach programs: In California through a program that brings geophysical science to local Title I schools; in Virginia, through one that provides outreach projects for local high schools; and in New York City, through a program that focuses on exposing undergraduates to mathematical research. The forces controlling plate tectonics and the conditions leading to great earthquakes are currently treated as separate, fundamental problems, but in this project, they will be linked with a focused effort to develop and apply a new generation of finite element methods with solver adaptivity that will scale on the largest computers. The activity will involve major advances in mathematical and computational algorithms for multi-physics problems, the team will bridge the space–time divide and self-consistently compute the long-term motions of tectonic plates and the intervening space–time evolution of stress within and adjacent to plate boundaries. This undertaking is beyond currently available methods and mathematically requires new concepts to allow tracking the shifting—but localized—regions of enormous computational need during earthquakes. The team will expand the notion of space and time discretization adaptivity towards solver adaptivity. Solver adaptivity will use equation residuals to focus computing resources towards the most efficient solution of large linear and nonlinear systems of equations. Since the system arising by discretizing the equations in the earthquake–plate tectonic problem typically has tens and hundreds of millions of unknowns, solvers based on matrix factorization are out of question and one must rely on iterative solvers that also allow parallelization. The algorithms are expected to scale on the largest anticipated supercomputers with distributed memory and computational elements, such as the NSF-planned LCCF (Leadership-Class Computing Facility). As such, the scalable algorithms will fill an important need and demonstrate the efficient use of future LCCF machines. The methods will be incorporated into the highly scalable Stokes solver, finite element package Rhea. Visco-elasticity and frictional material models will be implemented into Rhea. The science and mathematical challenges will be addressed with an interdisciplinary team consisting of a geophysicist who works on the dynamics of plate tectonics, a mechanician who works on the physics of earthquakes, and applied mathematicians who work on linear and nonlinear scalable PDE solvers. The team will apply the methods to understand the coupled physics generically, first in two dimensions and then in three dimensions. Then, using models regionally tailored by the explicit incorporation of seismic, geologic and fault structure, they will simulate Cascadia and the northwestern Pacific subduction systems. This project is jointly supported by the Computational and Data-Enabled Science and Engineering in mathematical and Statistical Sciences program in the Division of Mathematical Sciences and the Geophysics program in the Division of Earth Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Non-Technical Abstract: Cell membranes are sophisticated soft material assemblies that are crucial to biological function and technological applications. They exhibit diverse functionalities that are shaped by the molecular architectures of their lipid and sterol building blocks. To understand membrane functions and design related technologies, it is essential to determine how variations in molecular compositions govern membrane behavior across different functional scales. In this project, a team of researchers combine experimental and computational methods to determine how molecular forces – dictated by lipid and sterol chemistries – control the material properties of membranes on various scales of size and time. The broad aim is to simplify the complex relations between membrane structure and behavior by discovering physical laws that capture non-intuitive and often contradictory observations. This research has important implementations in understanding the role of membrane properties in health and disease and driving new innovations in artificial cells, biosensors, and drug delivery methods. Technical Abstract: Replicating the multifunctionality of cell membranes is a significant focus in synthetic biology, artificial cell technologies, and biosensing applications. To achieve this goal requires knowledge of how lipid and sterol variations – such as modifications in sterol structures and changes in lipid headgroups or acyl chains – affect molecular interactions and membrane dynamics. In addressing such pressing scientific needs, the project aims to uncover the physical principles underlying structure-property relationships in biomimetic cell membranes across multiple spatiotemporal scales. It integrates experimental methods including neutron spectroscopy, solid-state deuterium NMR relaxometry, and Flicker spectroscopy with molecular dynamics simulations to establish the connection between structural descriptors, emergent dynamics, and viscoelastic properties on molecular, mesoscopic, and macroscopic levels. By specifically focusing on mesoscopic dynamics, which remain poorly understood, the project fills a critical information gap in membrane biophysics. Quantifying how various dynamic modes are affected by lipid architecture or sterol content is directly relevant to biological functions and bioengineering. These findings promise to enrich our understanding of membrane evolution, guide the design of lipidic materials with tailored functionalities, and advance computational biophysics by providing training data sets for simulation tools and machine-learning algorithms. 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 common and fundamental property of real-world data is the long-tailed distribution; that is, where the majority of examples come from a few key categories (majority classes), while the rest of the examples belong to a massive number of tail categories (minority classes). This data description fits across a wide range of domains, including financial fraud detection, e-commerce recommendation, scientific discovery, and rare disease diagnosis. Although there has been considerable research on long-tailed learning, the vast majority has been conducted in an artificial, closed environment with predefined domains, data distributions, and downstream tasks. A natural and fundamental research question largely remains nascent: How can we take this research one step further to enable Open-World Long-Tailed Learning (OpenLT), where the domains are heterogeneous, open-ended, and evolving over time? Building upon the existing observatory work, this project aims to develop fundamental theories and algorithms for OpenLT. To be specific, there are three research thrusts. The first thrust aims to develop fundamental theories for a better understanding of the OpenLT problem. The second thrust aims to create a generic computational framework for heterogeneous long-tailed data in the wild. The third thrust systematically validates and verifies the theories and techniques from the first two thrusts on high-impact applications, including financial fraud detection and rare disease diagnosis. Upon completion, this project will advance the state of the art in long-tailed learning in two key dimensions. First, it will establish theoretical foundations for OpenLT, encompassing the unification of long-tailedness measurements, reliability analysis, and generalization bound analysis, most of which are currently absent in the existing literature. Second, it will lead to a generic OpenLT computation framework with novel pre-training, fine-tuning, and adaptation techniques, which is anticipated to exhibit substantial improvements in open and dynamic environments. The research outcomes will be integrated into a variety of educational activities during and beyond the course of this project. Leveraging various supporting programs at Virginia Tech, the investigator will ensure that students at different levels (e.g., K-12, undergraduate, and graduate students) have the opportunity to learn from and participate in the advancements brought forth by this research. The research findings will be integrated into the machine learning and data science courses taught by the investigator and disseminated through various channels, including paper publications, conference tutorials, workshops, and potential technology transfers.    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 research project will enhance data science by developing new methodologies for uncertainty quantification and decision-making in large-scale, streaming data with complex structures. These novel statistical tools will improve real-time analysis in fields such as mobile health, infectious disease surveillance, and neuroscience. In healthcare, the developed methods will advance diagnostic accuracy and treatment precision across various medical conditions like diabetes and Alzheimer's disease, enabling timely interventions and personalized care strategies based on real-time data analysis. Additionally, the project will integrate its research into K-12 educational programs and offer training opportunities for graduate and undergraduate students, focusing on engaging underrepresented groups in STEM. This initiative aims to cultivate the next generation of data scientists and statisticians, equipping them with the vital skills needed to address future challenges in data-driven fields. The research will establish a unified framework for nonparametric online statistical inference using an online multiplier bootstrap approach combined with functional stochastic gradient descent (SGD) algorithms. This framework will include local and global confidence intervals and bands, pattern and signal detection via hypothesis testing, and real-time decision-making strategies for nonparametric regressions. These methods will be applicable to various data scenarios, from independent to dependent data. The project will characterize the non-asymptotic behavior of the functional SGD estimator, validate the consistency of the multiplier bootstrap method, establish honest confidence bands, and demonstrate minimax optimal testing consistency of the proposed inference tools. By developing a solid foundation with accompanying software for nonparametric online inference, this research will advance methodologies in online data-driven decision-making, with broad applications ranging from mobile health to financial markets. 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

Overnight outdoor education programs are a form of informal learning that emphasize learner choice, hands-on, collaborative experiences and extended opportunities for learners to engage behaviorally, cognitively, and emotionally with real world STEM phenomena outside the classroom. There has been strong evidence that these programs can have positive benefits for learners, improved social, scientific, and cognitive skills; personal development; and improved interpersonal relationships, but historically the availability of these opportunities has been limited. This is changing, with two states (Washington and Oregon) passing legislation to provide an overnight informal STEM outdoor educational experience for all 5th and 6th grade students enrolled in public schools. This project is filling a needed gap by studying how to develop a community of practice and systematic evaluation system for overnight outdoor education programs, using the state of Washington as a testbed. The organizations providing these programs vary widely in terms of the populations they serve, their organizational size and budget, their urban or rural geographic locations, and the pedagogical design of their programs. The lessons learned and shared from this project will provide empirical evidence about more effective approaches for achieving positive outcomes for students and serve as a model for developing effective communities of practice to facilitate high-quality evaluation and the continuous improvement of outdoor education programs. As additional states pass legislation to make outdoor education programs more broadly available, these lessons will directly impact the quality of these programs for all audiences. The project will conduct the work via several inter-linked activities aimed at meaningfully and sustainably integrating research and practice: (1) developing shared outcome measures by enlisting 20-30 overnight education providers in a participatory approach that is sensitive to local contexts; (2) setting up an evidence-based learning network to support practitioners within a community of practice as they collect data, reflect on the research, suggest ideas, and improve their programs; (3) enacting three cycles of systematic program evaluation and revision; and (4) developing case studies of eight programs purposively selected to represent a range of populations, geographic locations, and organizations. The shared outcome measures will be used to conduct research to identify effective practices within programs, and the learning network will be evaluated via surveys, focus groups, and interviews. Results about effective outdoor education practices learned through systematic evaluations and comparative case studies, and insights into the ways evidence-based learning networks can effectively support and sustain continuous evaluation practices among outdoor education providers will be disseminated via academic and practitioner channels. This Integrating Research and Practice 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 multiple pathways for broadening engagement in STEM learning experiences. 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

Mental health and well-being are rising concerns nationally. University faculty are no exception, as academia is known as a high stress environment. This is a national study of the prevalence and severity of mental health problems among faculty working in STEM disciplines. The project will examine specific factors that positively and negatively impact STEM faculty mental health and well-being and how academic systems affect these factors for different demographic groups. In doing so, the project will ultimately contribute to improved support for faculty by informing practices and policies that can promote well-being and more inclusive academic environments. The project leverages an exploratory mixed methods design informed by the Job-Hindrance-Support-Control model, specifically the hindrance appraisal component of the model, and Collins and Bilge’s model of multiple strata interacting to create unique hindrances for some. Results will confirm, extend, or modify the Job Hindrance-Support-Control model, thereby expanding occupational well-being literature in academic contexts. Collins and Bilge describe a model of systems of power within an organization and highlights structural, disciplinary, cultural, and interpersonal domains of power. The qualitative phase of the project will include exploratory interviews with 60 STEM faculty and 20 administrators at U.S. institutions. These interviews will be leveraged to develop a novel survey validated through cognitive interview and pilot data collection phases. Once distributed nationally to an estimated 1,244 STEM faculty members through institutional partnerships, the project will become the largest study to date on faculty mental health and well-being. Through this large-scale data collection, the project will identify stressors for faculty strata that are often excluded (e.g. non-tenure track) and contribute to understanding how multiple faculty identities impacts their mental health and well-being. As part of the partnership agreement, the project will return customized reports to partner institutions in addition to workshops reviewing the institutional findings and research-based workshops on supporting faculty. The project results will be shared broadly with research communities and the public, including but not limited to scholarly publications, popular media, and a project website that hosts community resources. 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

Collaborative Research: Designing Intelligent Industrial Robots for STEM Inclusion by Leveraging Self-Determination Theory to Foster Autistic Talent in Manufacturing Work Workforce diversity is crucial in today's rapidly changing world. Autistic adults, with their unique perspectives and skills, can significantly contribute to workplace diversity. However, compared to similarly qualified peers, they often struggle to find and retain jobs, including in STEM fields where the U.S. faces an increasing skills gap. Autistic adults comprise at least 2% of the U.S. population, so increasing their employment rate could meaningfully expand and enhance the U.S. manufacturing and STEM workforce. This project aims to address this issue by developing smart industrial robots that provide personalized support for autistic employees in manufacturing and STEM work environments. By creating more supportive and inclusive workplaces, we seek to improve job retention, income, and independence for autistic employees. Furthermore, this initiative will help bridge the skills gap in manufacturing and boost economic growth. The advancements from this project will also enhance educational opportunities and improve employment prospects for autistic adults, fostering more neurodiverse and productive work environments that drive innovation in the U.S. manufacturing sector. This project focuses on developing smart industrial robots that offer personalized support for autistic employees in STEM and manufacturing jobs. Our approach combines the co-design framework of mutual shaping with the principles of Self-Determination Theory (SDT). We will engage key stakeholders, including autistic adults and industry experts, throughout all development cycles in an iterative design process to advance industrial robot intelligence. The primary objectives of this project are twofold: (1) to co-create support approaches based on SDT that address fundamental psychological needs (i.e., autonomy, competence, and relatedness) through interviews, focus groups, and human-in-the-loop simulations, and (2) to enhance robot intelligence for accurately identifying and meeting workers' psychological needs in manufacturing settings, resulting in adaptive and personalized support. By integrating SDT-based support into industrial robot design, we anticipate increased motivation, work quality, and job satisfaction for all employees. This neuro-affirming work environment will, in turn, promote inclusion, productivity, and innovation in the STEM workforce. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). This project is funded by the Advancing Informal STEM Learning (AISL) Program in the Division of Research on Learning in Formal and Informal Settings (DRL) in the Directorate for STEM Education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Dynamic landscapes represent a network of hydrologic, environmental, and anthropogenic features that work in tandem to confer ecosystem benefits and provide for societal demands. Increasingly, landscapes are at risk under the growing pressures of land use alteration and climate change. Understanding how landscapes dynamically connect the transfer of water, sediment, and nutrients to rivers and the role humans play in modulating this connectivity is crucial if we are to sustainably manage our shared water resources. Thus, the driving questions behind this work are “how have humans changed the landscapes around us for the worse and how are we able to manage them for the better?” This project will answer these questions and advance the frontiers of research and education for sustainable water management by coupling agricultural, municipal, and stormwater expertise together with high-frequency aquatic sensing, deep learning modeling, and large-sample water quality datasets. This research will generate fundamental scientific advances to identify the magnitude, duration, and extent of landscape loading to river systems across climatological, geomorphic, and anthropogenic settings. The education of today’s students, who will become tomorrow’s stakeholders, is deeply embedded in this project through hands-on experiences that will equip them with the confidence and communication skills to handle big data and tackle society’s grandest water challenges. Contemporary research in hydrologic sciences recognizes the importance of connectivity in most aspects of the water cycle; however, despite its ubiquity, connectivity is often assessed either qualitatively or in a static, structural context. The proposed research has the potential to be transformative in moving toward a dynamic assessment of connectivity. This project will quantify dynamic connectivity through time and across space for the United States. This will be achieved by leveraging high-frequency aquatic sensors for nitrate and turbidity from over 150 rivers, which serve as training data for a deep learning model. Further, a mathematical description of dynamic connectivity will inform dominant pathways of connection. Explainable machine learning techniques will link how dynamic landscape attributes lead to riverine water quality impacts. Thereafter, the potential to use dynamic connectivity as a management tool will be assessed through a web application developed for practitioners. The outcomes will lead directly into the education and training of the stakeholders-of-tomorrow, including through building big data confidence in high school settings and science communication skills in college students. This project is jointly funded by Hydrologic Sciences and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.