Arizona State University

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is the development of automated manufacturing of fiber-reinforced polymer composites (FRPCs). The proposed technology may be used to develop cost-effective and lightweight FRPC parts for use in a wide variety of applications and industries including aerospace, energy, automotive, and biomedical. Traditional manufacturing of FRPCs relies on expensive tooling and molds, long heating cycles in ovens and autoclaves, and labor-intensive processes, making the production of FRPCs slow and cost prohibitive. The proposed technology is an advanced manufacturing method based on the automated handling and placement of composite materials using robotic platforms and rapid rigidization of the material during the manufacturing process. The proposed manufacturing method allows for rapid, cost-effective, and energy-efficient production of customized composite parts. In addition, the proposed mold-free manufacturing method enables the design of complex composite structures that cannot be produced using traditional approaches. The new design capabilities may support the development of lighter ground and aerial vehicles as well as larger wind turbines for more efficient energy conversion. This I-Corps project is based on the development of an advanced manufacturing platform for the rapid and automated manufacture of high-performance fiber-reinforced polymer composites. The proposed technology relies on the placement of carbon fiber reinforcements impregnated with a thermoset resin along desired 3D paths using a robotic platform followed by in-situ, instantaneous curing and rigidization of the composite material. Controlled placement of the material to construct the desired structure eliminates the need for molds and tooling commonly used in the composite industry. Controlling the process parameters allows for manufacture of FRPC parts with a low void content and high concentrations of carbon fiber reinforcements. In addition, the ability to cure in-situ and rigidize composites enables freeform, support-free manufacturing in midair and achievement of three-dimensional reinforcements. The proposed technology may cut down the energy use compared to traditional approaches, leading to more sustainable practices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The demand for regional scale thinking and planning by leadership organizations is now an essential element in rebuilding research infrastructure. The cyberinfrastructure needed to deploy vast and complex sensor networks, adopt high capacity middle mile services for remote instruments such as telescopes, and expand connectivity into isolated, rural, and expensive locations where data acquisition is needed to better understand fire, water, and climate impacts is an essential element of the research ecosystem. SHEKATE’s collaborative planning events introduce and connect researchers from the region’s research universities to cross state and international boundaries and work with federal and state broadband initiatives to establish a new research core across the region. Led by Arizona State University (ASU), the Sun Corridor Network (SCN), NevadaNet, the Nevada Higher Education Network (NSHE) and the Utah Education and Telehealth Network (UETN) as planning leads, SHEKATE conference events focus on researcher enabled solutions in the field requiring new levels of collaboration and the use of innovative technologies to bring networks, high performance computing, and improved science workflows to isolated (and often expensive) locations in rural and urban areas. Science in the American West regularly includes significant multi-state studies in water, fire, agriculture, land use, climate, and geology. Field research is generally an ad hoc cyberinfrastructure supported by faculty, graduate students, and rarely systemically by central information technology organizations. SHEKATE’s regional workshops continue to focus discussions on gaps in the use of technology, the lack or deficiency of staffing, and the types and deficiencies of research centric interconnections needed for projects that cross state or international boundaries. SHEKATE continues to discover, engage, and reconcile the need for regional scale solutions for intra state research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is based on the development of an innovative software package designed for the discovery of new materials aimed at advancing sustainability efforts. This technology leverages state-of-the-art quantum computing to identify materials that can address a wide range of sustainability issues, such as carbon dioxide capture, hydrogen transport and storage, and the development of new semiconductors for solar energy conversion or next-generation chips. The software integrates quantum machine learning for initial material screening and quantum chemistry simulations for detailed analysis, significantly accelerating the discovery process compared to traditional methods. This solution has broad commercial applications across multiple sectors, including environmental sustainability, energy, and advanced materials, supporting global efforts to mitigate climate change and reduce carbon emissions. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the proposed technology. It is based on the prior development of an innovative software package designed for the discovery of new materials aimed at advancing sustainability efforts. This solution utilizes two primary functionalities: quantum machine learning and quantum chemistry simulations. The quantum machine learning component utilizes quantum-enhanced artificial intelligence algorithms for the initial screening of potential materials, efficiently navigating the vast material design space. The quantum chemistry simulations component performs detailed analysis of promising materials identified during the initial screening, calculating crucial material descriptors such as binding energies and diffusion coefficients. This dual approach allows for rapid and accurate assessment of material candidates for sustainable technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The broader impact/commercial potential of this I-Corps project is the development of an injection-molded, organ on-a-chip microfluidic platform engineered for high-throughput usage in oncology drug screening and cancer disease modeling applications. Microfluidic and organ-on-a-chip technologies are poised to replace ineffective preclinical platforms (e.g., animal models and two-dimensional cell culture systems) traditionally used within pharmaceutical drug research and development. However, current soft-lithography microfabrication techniques are time-consuming and labor-intensive. Moreover, the primary polymeric material, polydimethylsiloxane, is prone to drug adsorption and leaching. These factors ultimately hinder the mass production of microfluidic technology for disease modeling and drug screening development applications. By leveraging precision micro injection molding, the proposed technology aims to address the critical need within the cancer disease modeling and oncology drug development sectors for more cost-effective, time-efficient, and physiologically relevant tools for oncology disease modeling and drug screening. The need for an innovative human tissue platform applies to a variety of other biotech industries and academic biomedical research laboratories outside the area of cancer research. More broadly, the proposed technology has significant potential to assist researchers in obtaining physiologically relevant and clinical data for human tissue and disease modeling, drug screening efforts, and the development of personalized medicine. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of human disease on-a-chip technologies that have been designed and extensively validated to accurately recapitulate the human tissue and tumor microenvironments. More specifically, the proposed technology builds upon foundational research in human organoid technologies that have resulted in a better understanding of the biological mechanisms of complex diseases and cancers such as glioblastoma and metastatic breast cancer and have led to the discovery of novel pathways and genes which could benefit patients' treatment outcomes and survival. The technology comprises modular microfluidic devices, injection-molded in a biocompatible, optically clear thermoplastic material, that houses a biomimetic, three-dimensional, tumor microenvironment for accurate recapitulation of physiologically relevant human tissue and organs of interest. The modular devices are accompanied by a custom microplate, designed per ANSI microplate dimension standards, for ease of handling, storage, and high-throughput experimentation. Initial prototyping phases of the proposed platform demonstrated that the platform is suitable for cell culture and its design is compatible with precision micro injection molding design-for-manufacturing requirements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Chronic pain affects up to 20% of all people in the United States and involves changes in peripheral tissues (i.e. skin, muscle), the spinal cord, and the brain. Current models of chronic pain fail to replicate the complexity of the body and thus fail to develop new treatments that translate to patients. This project seeks to engineer new bench top models that accurately mimic the features of chronic pain in the periphery; and increase understanding of how chronic pain develops, is maintained, and can be resolved. This research will also explore how the immune system can help resolve chronic pain. Project implementation includes integrated education/outreach activities designed to increase interest in science among diverse populations and across students of multiple ages. The team will incorporate primary neuronal culture into the Tissue Engineering curriculum at the University of Nebraska-Lincoln to lower the barrier to entry in the neural engineering field. Further, the team will develop and implement fun, interactive lessons and labs to teach middle school students and Osher Lifelong Learning Institute members (serving people over the age of 50) about biomedical engineering and chronic pain. The goal of this research is to create in vitro models that replicate the physiological complexity in peripheral tissue present during chronic pain to increase mechanistic understanding and identify novel targets to treat peripheral pain. The most common peripheral feature of pain is a lowered stimulation threshold of painful neurons termed nociceptor hypersensitivity. The high rate of translational failure of pain therapeutics in clinical trials that have demonstrated efficacy in animal models motivates the need for more effective tools and mechanistic knowledge. In vitro models of primary sensory neurons and associated cells that accurately mimic the peripheral features of chronic pain hold great promise to understand mechanisms of chronic pain and develop new treatment targets. However, to date, most in vitro models of lowered neuronal thresholds or nociceptor hypersensitivity have limitations. Objective 1 will engineer a physiologically relevant multi-compartment model of nociceptor hypersensitivity and validate its response using noxious stimuli. Objective 2 will establish a neuroimmune-specific model of nociceptor hypersensitization to test anti-inflammatory macrophage mechanisms of resolution by co-culturing macrophages of varying phenotypes in the neurite compartments. In vitro models of peripheral pain features hold great promise to enhance understanding of the mechanisms driving pain and translational efficacy of treatments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Membrane-based technologies such as reverse osmosis (RO) are increasingly being utilized to provide clean water in the United States and worldwide under a rapidly changing climate and growing water scarcity. Currently, RO is the best available commercial technology for extracting and recovering clean water from a wide range of impaired water sources including seawater, inland brackish water, and municipal/industrial wastewater. However, the formation and precipitation of inorganic scales at the surface of RO membranes severely limit their water recovery and adversely impact the overall process efficiency and cost of water produced by RO desalination and water reuse plants. Compared to organic/biological membrane fouling, the mechanisms of formation and precipitation of inorganic scales at RO membrane surfaces are poorly understood as they involve complex chemical reactions and nucleation phenomena at polymer-mineral-water interfaces. The overarching goal of this CAREER project is to advance the fundamental understanding of mineral scaling at the surface of RO membranes. The successful completion of this project will benefit society through the development of new fundamental knowledge to advance the development and implementation of more efficient and cost-effective solutions to control and mitigate mineral scaling in RO desalination and water reuse systems. Further benefits to society will be achieved through student education and training including the mentoring of a graduate student and an undergraduate student at Colorado State University. Membrane scaling remains an important and unresolved challenge that limits the water recovery and overall system efficiency of commercial reverse osmosis (RO) desalination and water reuse plants. There are still critical knowledge gaps in the fundamental understanding of RO membrane scaling. First, the key physical/chemical processes and factors that control the extent of mineral scaling in RO membranes are not well understood. Second, the utilization of surface modification to mitigate mineral scaling in RO membranes has met with only limited success. Third, there is a lack of fundamental knowledge and principles to guide the design of antiscalants to mitigate the formation and precipitation of amorphous silica scales in RO membranes. This CAREER proposal will address these critical knowledge gaps. The guiding hypothesis of the proposed research is that polymers used as RO membrane surface modifiers or antiscalants can control the extent of mineral scaling by altering the thermodynamics and kinetics of scale nucleation events at membrane-water interfaces and the subsequent attachments of nascent mineral scales to membrane surfaces. Two key goals of the research are to: (1) Characterize and unravel the roles of scale nucleation thermodynamics, kinetics, and mineral-membrane affinity on the extent of RO membrane scaling and (2) Develop structure-property-performance relationships to guide and inform the design of scaling-resistant RO membranes and polymeric antiscalants to minimize and prevent RO membrane scaling. The successful completion of this project has the potential for transformative impact through the generation of new fundamental knowledge to advance the development of more effective strategies to control and mitigate membrane scaling in RO desalination and water reuse plants. To implement the educational and training goals of this CAREER project, the Principal Investigator (PI) will work with the Native American STEM Institute of Colorado State University (CSU) to develop and implement lectures and hands-on experiments for Native American high school students to learn about the critical science and engineering of challenges around water sustainability including the desalination of seawater and brackish water. In addition, the PI plans to partner with the ENpower Bridge program of CSU’s Engineering College to encourage and recruit students from underrepresented groups to pursue undergraduate/graduate education in Environmental Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Many water treatment and industrial processes generate significant amounts of high-salinity brines including seawater desalination, inland brackish water desalination, and oil and gas production by fracking. The management of hazardous high-salinity brines from water desalination plants and oil/gas production wells has emerged as a global environmental challenge. Membrane distillation (MD) is a promising technology for the treatment of high-salinity brines that could reduce the amounts of brine that need to be disposed of while generating a purified water permeate to support industrial and agricultural usages. During the last decade, significant progress has been made toward the development of more efficient MD membranes with high wetting and scaling resistance for high-salinity brine treatment. However, these membranes are typically prepared by modifying their surfaces with long-chain per- and polyfluoroalkyl substances (PFAS), which have become priority pollutants due to increasing concerns about their persistence in the environment, stability, and toxicity to humans and living organisms. The overarching goal of this project is to explore the design and fabrication of wetting- and scaling-resistant MD membranes for brine treatment without the use of PFAS. Inspired by the unique repellency of springtails towards low surface tension liquids, the Principal Investigators propose to test the hypothesis that efficient MD membranes, with both high wetting resistance and high scaling resistance, can be fabricated by covalent attachment of springtail-inspired supracolloidal structures onto the surface of a hydrophobic flat sheet membrane. The successful completion of this project will benefit society through the development of new fundamental knowledge to guide the design and fabrication of PFAS-free membrane materials for robust and efficient brine treatment. Additional benefits to society will be achieved through outreach and educational activities including the mentoring of one graduate student at the University of Tennessee, Knoxville and one graduate student at Colorado State University. The effectiveness of membrane distillation (MD) as a brine treatment technology is limited by both the intrusion of brines into membrane pores (membrane pore wetting) and the precipitation of minerals on the membrane surfaces (membrane scaling). The goal of this project is to design and fabricate a new family of biomimetic wetting- and scaling-resistant MD membranes without using PFAS building blocks. To advance this goal, the Principal Investigators (PIs) proposal to explore new strategies to modify the surface of a commercially available hydrophobic flat sheet membrane by covalent attachment of supracolloidal structures that mimic the overhang texture of springtails and their unique capability to repel low surface tension liquids. These supracolloidal structures will be formed through controlled assembly of smaller colloids onto the surfaces of larger colloids that have overhang structures with negative curvature and non-fluorinated ligands. The specific objectives of the research are to: 1) Elucidate the design criteria of supracolloidal structures for wetting resistant membranes, 2) Characterize and unravel the mechanisms of scaling resistance of the new biomimetic MD membranes, and 3) Evaluate the treatment effectiveness of the new MD membranes using model brine mixtures and a high salinity produced water from an oil and gas production field in Colorado. The successful completion of this project has the potential for transformative impact through the generation of new fundamental knowledge and functional materials to advance the development of PFAS-free MD membranes for efficient and cost-effective treatment of high salinity brines. To implement the educational and training goals of this project, the Principal Investigators (PIs) plan to integrate the findings from this research into existing undergraduate and graduate courses at the University of Tennessee, Knoxville (UTK) and Colorado State University (CSU). In addition, the PIs propose to leverage existing programs at UTK and CSU to launch outreach activities to recruit and engage high and middle school students from underrepresented groups with a focus on the utilization of bioinspired materials to improve water sustainability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Climate change and anthropogenic pollution are contributing to the increasing scarcity of freshwater resources in many regions around the globe. In the United States, water stress in arid and semi-arid regions poses a threat to food production, energy generation, and ecological and human health. Desalination technologies can harvest purified water from seawater, saline groundwater, and wastewater and are an important tool to combat water scarcity. Reverse osmosis (RO) is a commercial desalination technology that relies on the permeation of freshwater through a dense membrane under an applied pressure. Despite its widespread application, the RO process is vulnerable to performance decay caused by fouling, or the unwanted deposition of substances on the membrane surface. This research aims to understand the interplay between two common types of RO fouling: organic fouling caused by the adsorption of organic matter and inorganic scaling caused by the precipitation of minerals. The investigators will integrate experimental measurements with computational simulations to reveal how organic foulants and inorganic scale-forming substances interact with each other during RO desalination. The investigators will lead research-related public engagement and outreach activities at both George Washington University and Colorado State University. Water sustainability-themed workshops will be hosted for students from local communities in Washington, D.C., and Colorado. Reverse osmosis (RO) is currently the state-of-the-art desalination technology due to its exceptional energy efficiency. Although the existence of inorganic scalants and organic foulants is known to greatly constrain the performance of RO, the combined effects of inorganic scaling and organic fouling are not well understood. The overarching goal of this research project is to elucidate the interactions of inorganic scalants with organic foulants at the membrane-water interface. The investigators will study the performance of thin-film composite polyamide membranes under combined inorganic scaling and organic fouling in RO and unravel the mechanisms by which organic foulants impact mineral scaling. Advanced modeling approaches will be employed to simulate nucleation kinetics of mineral scales in the presence of organic foulants, elucidating the role of organic foulants in controlling mineral nucleation at the molecular level. The performance of anti-fouling membranes under combined scaling and fouling will also be examined to inform membrane design. The project will close a fundamental knowledge gap by (i) elucidating the effects of combined scaling and fouling on RO membrane performance, which cannot be predicted by existing knowledge of individual scaling or fouling, (ii) advancing mechanistic understanding of how organic foulants regulate mineral nucleation and growth at engineered membrane surfaces, and (iii) demonstrating how functional membrane surfaces that are intended to mitigate organic fouling will respond to combined scaling and fouling. Educational and outreach aspects of the project will incorporate research findings into undergraduate and graduate course materials, introduce water sustainability-themed workshops in local communities, and promote the participation of underrepresented students in research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Progress in science is motivated and directed by uncertainties. Uncertainty is unavoidable in an endeavor that strives to match theoretical models of the natural world with empirical data. Yet even though uncertainty is a crucial fulcrum for scientific thought, school students are taught science within an overarching assumption about the opposite—that scientific knowledge is certain. This project explores the intellectual leverage of enabling middle school students to experience how scientific work grapples with uncertainty. The overall goal of this project is to understand how teachers can create equitable learning environments for culturally and linguistically diverse learners using Student Uncertainty for Productive Struggle as a pedagogical model in middle school science classrooms. The project has several goals. It is creating an evidence-based and equity-oriented professional learning program that includes teacher engagement to adapt lessons for equity-oriented sensemaking in their classrooms through curricular resources to foster productive struggle and practice. It is authoring reliable and valid measurements of student disposition toward uncertainty and its navigation, an observation protocol of classroom activities, and measures of engagement in learning and student achievement. Finally, it is making recommendations for implementation across a broad range of learning contexts and diversity of student populations, including a framework to guide teachers in designing uncertainty to support student struggle productively. This four-year project directly involves 25 teachers and indirectly their students in middle schools serving historically underserved communities in Arizona. The pedagogical approach guides teachers in designing, developing, and implementing science classes activities where scientific uncertainties are purposefully incorporated so students experience productive struggle. The project uses a Professional Learning Program to meet this goal. Professional Learning Programs support middle school teachers in gaining the necessary experience, orientation, materials, and tools to utilize the pedagogical approach for productive struggle and to create an equity-oriented sensemaking environment. The science content of the Professional Learning Program includes thematic-focused, solar energy-related phenomena to help teachers develop core concepts and practices across different science subjects (e.g., physics, chemistry, life science, earth science) and understand their technical application. Each summer program begins with examination of a local, relevant phenomenon related to the students’ neighborhoods, identified with the help of an industrial or institutional partner. Then teachers reflect on and design three meaningful lessons so that students connect science content to their lives. The first sequential lessons pertain to the features, functions, and uses of photovoltaic panels. In the subsequent year, they focus on design of solar panels for generating electricity and electromagnetic force. Finally, the curriculum addresses application of semiconductors to the generation of solar power for students’ families and communities. Deliverables of the project include (a) an equity-oriented teaching approach so culturally and linguistically diverse learners become competent in understanding STEM, (b) evidence of changes in teaching perceptions and practices, and (c) an understanding of the impact of teacher pedagogical change on students’ equitable learning opportunities and learning outcomes. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Over past decades, there have existed grand challenges in developing high performance and energy-efficient computing solutions for big-data processing. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Networks (DNNs), such big-data processing requires efficient, intelligent, fast, dynamic, robust, and on-device adaptive cognitive computing. However, those requirements are not sufficiently satisfied by existing computing solutions due to the well-known power wall in silicon-based semiconductor devices, the memory wall in traditional Von-Neuman computing architectures, and computation-/memory-intensive DNN computing algorithms. This project aims to foster a systematic breakthrough in developing AI-in-Memory computing systems, through collaboratively developing a hybrid in-memory computing (IMC) hardware platform integrating the benefits of emerging non-volatile resistive memory (RRAM) and Static Random Access Memory (SRAM) technologies, as well as incorporating IMC-aware deep-learning algorithm innovations. The overarching goal of this project is to design, implement, and experimentally validate a new hybrid in-memory computing system that is collaboratively optimized for energy efficiency, inference accuracy, spatiotemporal dynamics, robustness, and on-device learning, which will greatly advance AI-based big-data processing fields such as computer vision, autonomous driving, robotics, etc. The research will also be extended into an educational platform, providing a user-friendly learning framework, and will serve the educational objectives for K-12 students, undergraduate, and graduate students. This project will advance knowledge and produce scientific principles and tools for a new paradigm of AI-in-Memory computing featuring significant improvements in energy efficiency, speed, dynamics, robustness, and on-device learning capability. This cross-layer project spans from device, circuit, and architecture to DNN algorithm exploration. First, a hybrid RRAM-SRAM based in-memory computing chip will be designed, optimized, and fabricated. Second, based on this new computing platform, the on-device spatiotemporal dynamic neural network structure will be developed to provide an enhanced run-time computing profile (latency, resource allocation, working load, power budget, etc.), as well as improve the robustness of the system against hardware intrinsic and adversarial noise injection. Then, efficient on-device learning methodologies with the developed computing platform will be investigated. In the last thrust, an end-to-end DNN training, optimization, mapping, and evaluation CAD tool will be developed that integrates the developed hardware platform and algorithm innovations, for optimizing the software and hardware co-designs to achieve the user-defined multi-objectives in latency, energy efficiency, dynamics, accuracy, robustness, on-device adaption, etc. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Urban societies throughout the world have come to depend on access to reliable and high-speed wireless, as well as the intelligent data-driven services they can deliver to end users. Low population densities and sparse infrastructure in rural areas, however, make them less attractive for investment in network connectivity. Fifth generation (5G)’s support for vertical industry integration has further magnified the broadband access inequalities between urban and rural environments. Technologies that have been proposed to provide rural broadband access are not designed to address the non-uniformity in demand for communication and computation resources that are found throughout these environments; for example, many automated farming services will have stringent delay requirements for short periods during the day while residential areas exhibit traditional diurnal patterns. This project aims to address this challenge by establishing a core set of methodologies that synergize the design of rural connectivity and computing. Emerging massive multiple-input multiple-output (MIMO) technology is integrated with the design of hierarchical data processing architectures across the rural infrastructure to adapt based on spatial and temporal demand variations. Extensive evaluations are conducted based on a massive MIMO platform and wireless device equipment available from the industrial partner. The project contributes to the development of a diverse workforce in rural broadband and intelligence through the formation of research teams integrating undergraduate and graduate students across institutions. Project research activities are organized into three thrusts. Thrust 1 develops agile communication techniques under non-uniform rural settings, addressing the needs for multi-user management and resource allocation for massive MIMO infrastructure. This includes the development of novel network control algorithms with scheduling policies that are customized for heterogeneous rural connectivity needs. The developed algorithms are further enhanced by reducing scheduling overhead through position-aided channel representation techniques. Thrust 2 investigates the orchestration of computation tasks under rural non-uniformity, focusing on establishing intelligent rural data processing architectures leveraging the broadband connectivity provided by Thrust 1. Techniques for distributing data processing among clusters of rural devices aiming to execute collaborative tasks rapidly are developed via joint optimization of device-to-device communications and task utility metrics. The orchestration of data processing and intelligence synchronization steps along the rural network hierarchy is also considered to adapt based on task demand variations. Thrust 3 consists of proof-of-concept implementation and testing of the methodologies in Thrust 1&2. This employs a testbed developed leveraging a commercial-grade hybrid massive MIMO base station system engineered by the industrial partner. This project is jointly funded by CNS and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Over the last few years, discussions oriented toward defining sixth generation (6G) requirements and possible technologies have started to circulate within the wireless community. One of the key ideas will likely be to take steps to remove the conventional cell boundaries and facilitate enhanced joint uplink and downlink processing using many dispersed access points (APs). These ideas fall within the academic definition of cell-free massive multiple-input multiple-output (CFmMIMO). It alleviates the existing cell-edge and handover problems and improves energy efficiency. The primary limiting factor is achieving cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. This poses many important research questions that must be explored systematically and in-depth. This project firstly develops scalable artificial intelligence (AI)-based solutions. Together with the appropriate (cost-efficient) AP deployment planning tools (e.g., where to put the APs), these developments constitute a significant step toward enabling the low-latency and uniformly reliable wireless services at a lower cost. Given the international nature of the project, the project contributes to the development of a diverse workforce in AI and 6G wireless networks through the formation of international research teams integrating undergraduate and graduate students. Project research activities are organized into three thrusts. Thrust 1 develops scalable AI-based resource allocation solutions enabling the implementation of large-scale CFmMIMO. The developed solutions are further enhanced by exploring AI architectures applicable to large networks. This includes the security aspects, especially in the context of AI algorithms and architecture, and the cloud radio access network. Thrust 2 focuses on establishing network planning and waveform constraints to address scalable deployment solutions. This includes the development of infrastructure-aware minimum-cost AP deployment methodologies by taking into account the QoS requirements and available transport infrastructure. The developed methodologies are further augmented by developing a network-wide user signal detection method, accounting for the fronthaul capacity and the quantization resolution at each AP. This task also investigates how CFmMIMO can address many of today’s most challenging spectrum policy issues. Evaluation Thrust evaluates and analyzes the methodologies developed in Thrusts 1&2. This employs the existing US and European experimental testbeds and provides a continuous feedback cycle between theory and experimentation. The US team will build upon the prior experience with Colosseum. On the European side, the team will experiment with the Open Air Interface (OAI). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Solutions to water resources management and water security require not only fundamentally sound science, but also a diverse coalition of trained scientists that recognizes and is aware of the needs and knowledge of local communities. Through our IRES award, we will train three cohorts of ten U.S. students from the Hispanic Serving Institutions of Humboldt State University (HSU) and Rutgers University, Newark (RUN) in applied, interdisciplinary and community-based water development. Each cohort will participate in a yearlong program consisting of three components: (1) a spring preparatory course which will link students from HSU and RUN via videoconferencing to their Peruvian peers; (2) a five-week hydrogeophysical field campaign centered in two communities in the Cusco region of Perú and led by an interdisciplinary team of U.S. and Peruvian scholars; and (3) a fall semester of independent research. The experience will empower a diverse cohort of students to be interdisciplinary and community-minded scientists as well as train members of the local communities to collect hydrologic data in order to guide best practices in water management and increase local water security and resiliency. The puna biome, a seasonally dry grass and shrub ecosystem at the altitudinal limits of plant growth in the central Andes, provides the primary source of water to many small agrarian communities and large cities, such as Cusco. Despite its importance for water resources, however, the hydrology of the puna is poorly understood, especially with regard to storage capacity and seasonal runoff dynamics. Communities relying on puna derived water sources are thus potentially vulnerable to changes in water supply and uninformed water management decisions. We will guide students in a hydrologic, geologic and geophysical investigation to quantify water resources and flow pathways in two small (~ 2.2 km2) ‘end-member’ puna catchments. In one catchment, slow drainage from peat forming wetlands, known locally as bofedales, sustains perennial streamflow which supports community agriculture. In the other catchment, devoid of bofedales, stream flow is ephemeral, however the community harvests fracture flow for agricultural and municipal water use via a 140 m long tunnel. Our results will quantify water resources in puna landscapes with and without the influence of bofedales, inform model predictions of future changes in water resources, and guide community-based best practices in land and water management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Natural and man-made disasters pose a serious threat to individuals, assets, and society. As such, public safety organizations and first responders are increasingly reliant on Information Communication Technology (ICT) to perform their duties. Disaster management requires a set of capabilities, which includes resource management, access to relevant data and information, and robust and resilient communications. In support of these needs, the RESCUE project will design a three dimensional (3D) networks architecture that exploits the advances in the field of next generation wireless networks to provide a prototype solution such that an integrated communications system for disaster relief operations is realized. Additionally, an innovative massive multiple access mechanism and a dynamic spectrum sharing model will be introduced to increase the public safety system’s capacity and improve the spectrum utilization, respectively, during the relief operations. A new Positioning, Navigation, and Timing (PNT) solution will be proposed to support scenarios of Global Positioning System (GPS) denial by utilizing the next generation networks technology of Reconfigurable Intelligent Surfaces (RIS). The outcomes will have long-lasting benefits for the communications, and in turn the well-being, of the victims and first responders during disaster relief operations. Furthermore, the project will provide unique training for graduate and undergraduate students at the crossroads of reinforcement learning and next generation networking technologies. The RESCUE project will introduce a novel 3D networks architecture that exploits terrestrial and aerial base stations to provide the necessary redundancy of communications during disaster relief operations. A novel massive multiple access mechanism will support the victims and first responders’ connectivity, and a robust dynamic spectrum sharing model will improve the spectrum utilization during disaster scenarios characterized by increased traffic demand. Innovative mechanisms to support the extended communications coverage, the mobility management, and the efficient resource management of the limited communications resources will be designed via utilization of the next generation wireless networks’ technologies of Reconfigurable Intelligent Surfaces, Intelligent Omni-Surfaces, and the Integrated Access and Backhaul. A new positioning, navigation, and timing solution will support the disaster relief operations in cases of GPS-denial scenarios or indoor environments by exploiting the next generation wireless networks’ technologies. A thorough testing and evaluation of the proposed 3D networks architecture and the supporting modules will be performed by following a simulation, emulation, and in-field iterative testing approach. The novelty of the RESCUE project lies in the synergistic, integrated, and pragmatic approach to efficiently utilize the next generation wireless networks’ technologies to design an operational prototype that will support the connectivity of victims and first responders in public safety scenarios. The research outcomes of this project have the potential to support activities of Emergency Control Centers, such as in the City of Albuquerque. This project is jointly funded by the CISE MSI program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Many researchers have established that groups Historically Minoritized and Marginalized (HMM) in STEM (e.g., women, Hispanic, Indigenous, Black) tend to place greater importance on social/communal, cultural, and altruistic values when making education/career decisions. However, their perceptions of stereotypical STEM culture are incongruent with such values and discourages consideration of STEM education/careers. This is commonly referred to as Goal Congruity Theory. Not only does this incongruity discourage consideration of STEM education/careers, it also thwarts efforts to broaden participation of HMM in STEM. In fact, despite the countless resources invested to broaden participation in engineering, women and Hispanic, LatinX/e/a/o, XicanX/e/a/o peoples, inclusive of their intersectionalities (HLX+), represent only 16% and 9% of the college educated engineering workforce, respectively. Although, the efforts have resulted in an increased share of engineering Bachelor’s degrees awarded to women (24%) and HLX+ (14%) over the years, women and HLX+ are still unacceptably underrepresented in the college educated engineering workforce. Considerable research has explored engineering identity development as a potential solution to address many of the issues facing engineering education. However, critiques of the narrow focus of the literature (e.g., identification with the profession itself, historical definitions of the profession) disclose that none ask students to connect their beliefs, values, or other aspects of identity to engineering. Engineering identity may support recruitment and retention efforts, but it is wanting if the central goal is to broaden participation and increase diversity because individuals’ backgrounds (e.g., ethnic, racial, cultural, gender, sexual preference, historical) and their associated beliefs and values are not even considered in many of the engineering identity constructs. Therefore, in alignment with the National Science Foundation’s Broadening Participation in Engineering program, this project intends to enable and encourage the participation of all citizens in the engineering enterprise by challenging these patterns. This project will take on a holistic approach to enhance “servingness” at HSIs and beyond by creating an identity affirming culture in engineering education. Hispanic, LatinX/e/a/o, XicanX/e/a/o undergraduate students, inclusive of their intersectionalities (HLX+), in engineering will participate in a program that nurtures holistic identity development and empowerment informed by Anzaldua’s Path to Conocimiento (i.e., Conocimiento). Path to Conocimiento is framework that contributes to their making meaning of the transitional journey that naturally occurs during higher education. Students will engage in autoethnographic inquiry to make meaning of their experiences as HLX+ in engineering education and then identify where and how they can dismantle cultural barriers or transform hegemonic structures. The Conocimiento will also provide the context to explore HLX+ Cultural, Community, Racial, Ethnic (CCoRE) values. The research component, using a combination of qualitative and participatory research methods, will seek to answer: (1) What are the salient CCoRE values that HLX+ undergrads bring with them into their engineering education?; (2) Where and how do HLX+ undergrads experience conflicts with their CCoRE values during their engineering education?; and (3) How do HLX+ undergrads navigate conflicts with their CCoRE values when empowered with their own conocimiento? This new knowledge will be used to develop a workshop to inspire and aid faculty to create an identity affirming culture within their research teams and/or in their classes, departments, and beyond. Ultimately, this project seeks to (1) empower students to recognize and disrupt hegemonic engineering education structures and, by extension, the profession, (2) dismantle cultural barriers that dissuade historically minoritized and marginalized people from participating in engineering education/careers, and (3) transform the dominant engineering culture by inspiring radically inclusive values, particularly among faculty and HLX+. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

For many rural and indigenous locales, the conditions for technical innovation in university and industry labs are often unavailable or are out-of-step with everyday life. Nevertheless, despite the obstacles, technologists working for sovereign Native Nations and Indigenous communities worldwide apply meaningful and responsive design methods to sustain innovation. The challenge is that many of these technologists are far removed from each other, working across diverse urban, rural, and remote locales as field practitioners, artists, entrepreneurs, and lone scientists with small teams. Through a series of convenings over one year, Indigenous Approaches to Computational Futures will bring these local experts together to: 1) characterize common conditions for responsive innovation; 2) identify helpful institutions; and 3) publish insights and findings that have been otherwise sustained through small group conversations and niche conferences. The goal is to create trusted networks of senior scientists, entrepreneurs, policy-makers, and community-centered creatives to amplify scientific understanding of how to effectively leverage artificial intelligence (AI) and machine learning, advanced computational hardware and software, and telecommunications and immersive technologies toward compelling people-centered, environmentally-conscious futures, futures centered in the lived experiences of highly creative Indigenous peoples. These convenings are guided by a set of questions: What are the conditions for infrastructural augmentation and innovation across Indigenous geographies? How do technologists ideate, pilot, and advance systems through such circumstances? How do experienced researchers, entrepreneurs, and seasoned tech practitioners frame productive and meaningful community-centered design in these contexts? What can infrastructures built on Indigenous community strengths teach us about new modalities for innovation and incubation in comparable contexts? Over the course of one hybrid four-day convening and two online convenings, over a hundred notable experts in indigenous technologies will contribute to a set of white papers, publications in high-ranking ACM journals, a directory of experts, and working lists of theoretical frameworks, recommended readings, and noteworthy labs and institutions. The practical goals are three-fold: 1) to generate a trustworthy network of support for junior technologists in the field; 2) to encourage collaboration between researchers and practitioners across many domains; and 3) to advance the state-of-knowledge in community-centered computational systems design fields, including from law and policy vantage points. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Alongside the rapid growth of cloud-computing market and critical developments in machine learning (ML) computation, the cloud-FPGA (Field Programmable Gate Arrays) has become a vital hardware resource for public lease, where multiple tenants can co-reside and share an FPGA chip over time or even simultaneously. With many hardware resources being jointly used in the multi-tenant cloud-FPGA environment, a unique attack surface is created, where a malicious tenant can leverage such indirect interaction to manipulate the circuit application of other tenants, e.g., intentionally injecting faults. It has been demonstrated in prior research that small, but carefully designed, perturbation of the ML model parameter transmission between off-chip memory and on-chip buffer could completely malfunction ML intelligence, even under black-box attack scenario, posing an unprecedented threat to future ML cloud-FPGA system. This project (1) targets to understand the vulnerability of multi-tenant ML cloud-FPGA systems and explore defensive approaches, which are crucial and timely for both industry and academia in the cloud-FPGA computing domain; (2) advances the security of ML cloud system against hardware-based model tampering on off-chip data transmission in multi-tenant cloud-FPGA computing infrastructure; and (3) integrates the research outcomes with education in terms of new curriculum development, undergraduate and graduate student training, as well as promoting women and underrepresented minorities in STEM through K-12 outreach programs. This project integrates ML algorithm security and FPGA hardware security to follow a software-hardware co-design mechanism, exploring novel solutions that improve the security of multi-tenant ML cloud-FPGA system. It consists of three research thrusts. Thrust-1 systematically studies, models, and characterizes an adversarial weight duplication hardware fault injection method, which leverages aggressive power-plundering circuits in malicious tenant to inject fault into the victim tenant's ML model. Thrust-2 explores various ML algorithmic methodologies to enhance the intrinsic robustness and resiliency of ML model against adversarial fault injection into model parameters during the transmission from off-chip memory to on-chip buffer. Thrust-3 investigates FPGA system-level tamper-resistant approaches to further provide comprehensive solutions to improve the ML-FPGA system security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Deep neural network (DNN) is widely deployed for a variety of decision-making tasks such as access control, medical diagnostics, and autonomous driving. Compromise of DNN models can severely disrupt inference behavior, leading to catastrophic outcomes for security and safety-sensitive applications. While a tremendous amount of efforts have been made to secure DNNs against external adversaries (e.g., adversarial examples), internal adversaries that tamper DNN model integrity through exploiting hardware threats (i.e., fault injection attacks) can raise unprecedented concerns. This project aims to offer insights into DNN security issues due to hardware-based fault attacks, and explore ways to promote the robustness and security of future deep learning system against such internal adversaries. This project targets one critical research topic, namely securing deep learning systems against hardware-based model tampering. Recent advances in hardware fault attacks (e.g., rowhammer) can deterministically inject faults to DNN models, causing bit flips in key DNN parameters including model weights. Such threats can be extremely dangerous as they could potentially enable malicious manipulation of prediction outcomes in the inference stage by the adversary. The project seeks to systematically understand the practicality and severity of DNN model bit flip attacks in real systems and investigate software/architecture level protection techniques to secure DNNs against internal tampering. The study focuses on quantized DNNs which exhibit higher robustness against model tampering. This project will incorporate the following research efforts: (1) Investigate the vulnerability of quantized DNNs to deterministic bit flipping of model weights concerning various attack objectives; (2) Explore algorithmic approaches to enhance the intrinsic robustness of quantized DNN models; (3) Design effective and efficient system and architecture level defense mechanisms to comprehensively defeat DNN model bit flip attacks. This project will result in the dissemination of shared data, attack artifacts, algorithms and tools to the broader hardware security and AI security community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This EArly-concept Grants for Exploratory Research (EAGER) project develops a framework to measure the dynamics, intermediate outcomes, and broader socio-economic and environmental impacts of regional innovation programs. Large scale investments like the NSF Regional Innovation Engines program require more inclusive and comprehensive attention to the participants and the range of activities, including the development of supply chains. This project engages novel interdisciplinary perspectives to guide assessment and involves state of the art curation of data sources to manage and assess such investments in a timely manner. The team will design an evaluation system and data infrastructure to track outcomes from NSF Engine investments; this includes short-term enabling outcomes and intermediate outcomes that become inputs into building local capacities that will ultimately translate into the desired social and economic results. The team will design and begin to implement a public dashboard, which will help scholars, practitioners, policymakers, and the public deepen their commitment and engagement in economic development. This EAGER project develops a data intensive, multi-level analytical framework to determine the dynamics, intermediate outcomes, and broader socio-economic and environmental impacts of regional innovation programs. Such large-scale government investments defy standard geographic classifications requiring the need to geocode organizations, activity, and firms to situate them in a delineated catchment area where impact may be observed. This project engages novel interdisciplinary perspectives across the fields of public policy, economics, geography, and management to build the conceptual foundation guiding assessment and involves state of the art curation of myriad data sources to manage and assess such investments in a timely manner. The team will assemble a novel panel dataset of firms and organizations; these data will be geocoded to precise address location to understand the concentric zone of investment impact. The approach centers on tracing the micro-foundations of ecosystem activity, encompassing employment dynamics, supplier-buyer networks, and financial, innovative, and societal performance. The team will design and begin to implement a public dashboard, providing communities with immediate access to essential economic development metrics. This instrument will facilitate strategic adjustments informed by empirical insights and augment the capacity of communities to articulate their development narratives effectively. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In the realm of fully autonomous data-driven manufacturing, generalizability of solutions facilitated by Artificial Intelligence (AI) is critical for scalable solutions. While significant progress has been made in industrial automation, transitioning from engineering requirements that begin with the manufacturing process plan specification to specific robotic control for manufacturing operations still requires significant manually configured input parameters. These manually configured steps limit advances in data driven autonomous manufacturing, particularly within extreme environments, such as in outerspace, underwater, biological and radioactive environments. This project establishes a systematic and generalizable methodology to integrate vision language models and robotics to fully automate manufacturing assembly operations. The project will provide solutions for the automated extraction of process task descriptions from engineering documentation, an integrated multi-agent task planning algorithm which assigns tasks and commands to robots with a digital twin guided real-time feedback evaluation system. The project will facilitate the integration of engineering design specifications, manufacturing process requirements, and robotic systems to improve productivity, especially in extreme environments, aligning well with the US National Strategy on the development and use of Artificial Intelligence in Manufacturing. The integration of education and research will broaden participation in manufacturing by training the next generation of engineers and researchers at the intersection of manufacturing processes and robotics systems. This project investigates an end-to-end generalizable task planning framework for robots in manufacturing environments by filling the knowledge gap between abstract process engineering design instructions and low-level robot control, leveraging the capability of vision-language models (VLMs) for high-level multimodal reasoning, and developing a customized task planner paired with a digital twin (DT) for iterative evaluation. The project consists of three highly integrated thrusts. First, it involves interpreting Product Manufacturing Information and process instructions to derive high-level task sequences from various input forms such as text prompts, technical engineering drawings, 3D layouts, and structured data. This information is processed using a vision-based language model, which integrates computer vision and natural language processing to generate task sequences. The second thrust addresses automated sub-goal planning in single or multi-robot manufacturing scenarios through a language-integrated task planner. This planner selects optimal sub-goals and assigns task primitives to robots. Finally, the project focuses on validating and correcting plans using the digital twin, which provides sensor feedback and evaluates trajectories before physical realization. Advanced machine learning methods will be employed to ensure the validity of instructions sent to the manufacturing environment's physical asset endpoints. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Heat is a leading weather-related cause of deaths and illnesses globally, but most heat-related health outcomes are preventable. Policy-relevant targeted interventions based on hazard-specific vulnerability assessment can lead to improved disaster risk strategies. This project seeks to enhance heat vulnerability assessment for targeted responses to save lives and protect against preventable illnesses. The overriding research question is: how can hazard-specific, place-specific, and time-variant heat vulnerability representative variables enable spatially targeted interventions that are policy-relevant? The research is critical to advancing theories toward heat-specific and place-specific vulnerability representations. Heat vulnerability studies typically use general, all-hazard conceptual models that provide broad frameworks for understanding various types of hazards. However, general conceptualizations do not sufficiently promote tailored heat-specific strategies, undermining effective responses. This project investigates the decision criteria underpinning heat vulnerability including the selection of input variables, modeling approaches, statistical considerations, and geospatial science issues, toward the development and refining of consistent theories and conceptual frameworks that are heat-specific and place-specific. This project works to develop a generalizable approach for testing hypotheses and theoretical frameworks of heat vulnerability, which is crucial for instituting mitigative and adaptive protections against preventable health outcomes. Knowledge gained from this research can inform local and federal agencies in the coordinating, planning, and implementing of heat relief activities, toward enhancing community resilience. The study findings contribute a basis for nationwide or global heat response activities, such as the placement of hydration stations, cooling centers, and personal heat relief 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 · 2024-10

This project aims to serve the national interest by exploring strategies to support student retention of important mathematical skills from key introductory courses. This project centers on the Keep in School Shape (KiSS) program at Arizona State University, which provides students with opportunities to regularly engage with foundational course material over academic breaks or unplanned disruptions in their schooling. Keeping key skills up-to-date during these periods will help students maintain critical skills over times when school is not in session. The program uses existing, readily available online survey software to send students daily review activities, resources, hints, solutions, and empowering feedback messages via text or email. In the context of an introductory calculus course sequence at the university level, this project explores the outcomes of participation in the KiSS program. The approaches used to support students through this project are cost-effective and have potential to inform other efforts to support students outside of traditional periods of instruction in disciplines outside of mathematics. The goals of this project include an exploration of how engagement with review activities impacts student outcomes, how students respond to various incentive systems, and how students engage with support opportunities over academic breaks. Project research will randomly allocate participants to treatment conditions and compare their outcomes to comparable nonparticipants. By exploring short- and long-term outcomes of the review program, this project hopes to set the stage for understanding how best to design outside-of-class supports that both appeal to students and help them achieve success in their chosen field of study. The project will also investigate how individual accolades and varied incentive structures can impact students' motivation and engagement. Research and project evaluation outcomes will be disseminated broadly to communities of interest, with an emphasis on adapting the KiSS program to other institutions and disciplines. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project presents intelligent anonymization methods for preserving the privacy of clients’ bio-signals while retaining data utility for clinical purposes. Bio-signal anonymization methods proposed in this project safeguard clients’ personal data against potential stigmatization, judgment, and discrimination. This fosters patients' participation in healthcare and research studies without fear of identity exposure, thereby enabling the development of efficient data-driven AI models for smart healthcare. This project develops modular and scalable anonymization methods that are suitable for both bio-signals from clinical settings as well as bio-signals acquired from wearable devices in everyday settings. Bio-signal anonymization models proposed in this project are highly adaptable and can be customized for clients across diverse demographics and existing health conditions, achieving a ubiquitous coverage of clients. This project directly impacts the healthcare sector by minimizing regulatory costs, improving trust and confidence between clients and healthcare providers, and delivering high-quality smart healthcare services. This project will also train the next generation of digital healthcare providers in curating clients’ data and developing fundamentally secure smart AI models for healthcare. The first technical thrust develops anonymization models for multi-channel bio-signals through reinforcement learning guided generative deep models. This thrust will design reinforcement learning models to understand critical details of bio-signals for adaptive pruning of anonymization models for different health conditions. This approach balances the competing objectives of obfuscating re-identifiable information while preserving the structural characteristics of bio-signals critical for diagnosis. The anonymization models use conditional and multi-view generative adversarial networks to generate multi-variate bio-signals by sanitizing the original signals. The second technical thrust develops a privacy assessment and evaluation framework for updating anonymization models subject to different attacks. This thrust develops an evaluation framework comprising utility and anonymity metrics to provide feedback on updating bio-signal anonymization models based on utility-anonymity analysis. This enables bio-signal anonymization models to be customizable for clients across diverse demographics and pre-existing health conditions, ensuring both fairness and utility-privacy guarantees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Graphene, a two-dimensional carbon material, is ultra-strong, flexible, and lightweight. These mechanical advantages can be combined with conventional metal to manufacture graphene-metal composites with enhanced mechanical performance. This project will achieve a fundamental understanding of the strengthening mechanisms of such composites by developing and utilizing bi-continuous graphene-nickel composites as a model. The results of this project will also guide the development of new theoretical models and innovative designs of graphene-based composites. High-performance composites, characterized by both strength and deformability, are relevant to a wide range of applications, including automotive/aerospace composites, sports equipment, protective armor, and high-strength cables. This project will integrate research and engineering training opportunities by fostering interdisciplinary research, education, and outreach opportunities across individuals from K-12 to undergraduate and graduate students. This project aims to deepen our understanding of the deformation mechanisms in axially bi-continuous graphene-metal composites. Graphene (Gr) offers excellent mechanical strength far beyond conventional metal and, therefore, is often dispersed in a metal matrix to develop macroscopic Gr-enhanced metal matrix composites for structural applications. However, such composites suffer from the intrinsic trade-off between strength and ductility due to weak Gr-metal interfaces. In this work, fine nickel wires coated by axially continuous graphene structures are used to break the intrinsic trade-off and achieve excellent combined strength and ductility. However, the exact mechanism(s) responsible for the observed enhancements are not well understood mainly due to the complex coupling of various size-dependent strengthening mechanisms. This project will reveal the underlying mechanisms by developing and utilizing new laser-based material processes and microdevice-based characterization methods. This project will provide valuable scientific knowledge on the intricate relationship between graphene structures, grains, and dislocations in such bi-continuous composites and their effect on mechanical behavior. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices. The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.