Arizona State University

NSF Awards · FY 2024 · 2024-10

Switched-mode power converters are critical components in modern electrical circuits, enabling the efficient conversion and management of electrical energy for applications such as electric vehicles, power supply for consumer electronics, power grid, locomotive traction and industrial motor drives. Projections indicate that by the year 2030, approximately 80% of electric power will pass through some form of power electronics. The primary components of a power converter are power switches such as field effect transistors and the efficiency of the converter is largely dependent on the power losses occurring within these devices. Although silicon represents the most prevalent commercial technology for power semiconductor devices, the limited breakdown field of silicon (0.3 MV/cm) results in substantial power losses, thereby restricting the efficiency and operation frequency of silicon-based power electronics. Ultra-wide bandgap semiconductors (UWBG) such as β-Ga2O3 are seen as promising platforms for developing next generation of power devices owing to the much higher breakdown field strength. With a large critical breakdown field strength of 8 MV/cm and the availability of cost-effective bulk substrates, β-Ga2O3 devices can significantly outperform Silicon and wide-band gap SiC and GaN, while maintaining low cost. Significant improvements in the performance of β-Ga2O3 devices have been achieved in the past few years particularly for two terminal devices such as diodes, with reports approaching the theoretical material limits of β-Ga2O3. However, the performance of field effect transistors (power switches), which are vital components in power converters, are still far away from the theoretical material limits. The proposed work aims to advance the field of β-Ga2O3 field effect transistors by engineering high aspect ratio β-Ga2O3 FinFETs capable of achieving high breakdown voltage and low-on resistance, surpassing state-of-the-art performance of SiC and GaN. This advancement will facilitate the scalability and widespread adoption of β-Ga2O3 transistors in the medium-voltage and high-voltage power device market. The proposed work aims to design and fabricate β-Ga2O3 high aspect ratio (ASR) FinFET devices, capable of achieving improved device performance by decoupling the relation between breakdown voltage and charge density. The fabrication of tall high aspect ratio fins is made possible by the newly developed ‘in-situ Ga etching’ technique which can be carried out within an MOCVD reactor. This new etching technique simultaneously enables high etch rates, damage free etched surfaces and vertical 90o sidewalls in β-Ga2O3 surpassing the limitation of standard dry and wet etching techniques. The key areas of focus in this proposed work include 1) investigation of anisotropic properties of in-situ Ga etching, 2) development of in-situ etch-followed-by-regrowth approach for engineering high quality ohmic contacts and dielectric interfaces 3) field management for achieving multi-kilo volt class breakdown voltage in lateral high ASR FinFETs using high permittivity dielectrics such as BaTiO3 and 4) investigation of normally off operation. The successful outcome of this proposal will enable demonstration of β-Ga2O3 three terminal devices that surpass the current state of the art wide band gap devices. 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

Enabling innovative applications in autonomous transportation, industrial Internet of Things, and remote surgery imposes new data rate, reliability, energy efficiency, and latency requirements on future wireless communication networks. Therefore, the development of the next generation of wireless networks is a priority for the United States, Finland, and other countries around the world. To meet these requirements, this project harnesses machine learning and data collected from distributed sensing networks to design cutting-edge wireless communication networks. These networks will ensure consistently high data rates and reliable connections over large areas, providing excellent service to communication users. The proposed theories, algorithms, and proof-of-concept prototypes are expected to have impacts in multiple areas. For education, the project creates new course materials for undergraduate and graduate students enriching the wireless communications curriculum and offering hands-on training opportunities to build hardware proof-of-concept prototypes. Additionally, the project is expected to significantly impact technology transfer to industry in both the US and Finland, and facilitate research and development in machine learning based wireless communication networks by making all developed datasets publicly available to the wireless communication community. This collaboration between Arizona State University, USA, and the University of Oulu, Finland, holds promise for substantial benefits for both societies and the broader international community. The primary goal of this project is to enable scalable and reliable large-scale MIMO and high-frequency communication networks using distributed multi-modal sensing information. Toward this goal, this project seeks to develop a novel mathematical framework that balances the communication overhead of distributed sensing data with the benefits for wireless communication tasks. It also designs efficient techniques for extracting, compressing, and merging data across distributed sensing networks. Furthermore, the project develops a new scalable network architecture for distributed sensing-aided communications that optimizes interactions across various network components. It also devises training and learning strategies for large-scale distributed sensing and communication networks that operate within practical constraints of power, complexity, and data availability. Additionally, the project builds the world's first research platform for investigating distributed multi-modal sensing and communication networks using real-world datasets, following the DeepSense 6G framework. These research objectives promise significant advancements in wireless communication, with potential societal and technological benefits that extend globally. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The objective of this Civic Innovation Challenge (CIVIC) project is to support research on designing and piloting innovative AI-empowered solutions to mitigate extreme heat risk in Tempe, AZ. Extreme heat is one of the leading causes of weather-related deaths, more than all other causes (except hurricanes) combined in the U.S. Preparation for and protection against heatwaves require networks of organizations working collaboratively to make resources available during heat emergencies. In Maricopa County, Arizona, which witnessed nearly the entirety of July 2023 with temperatures at or above 110°F, the Heat Relief Network partnership of municipalities, nonprofits, faith-based community, and businesses, provides lifesaving heat relief stations throughout the county. The city of Tempe similarly manages Resilience Hubs for the vulnerable population. To augment their success, the research team intends to introduce technological advances towards proactive heat resilience planning and human-centered energy efficient building operations while improving indoor air quality. A community-driven and multi-sector partnership approach facilitates identification of community practice, gaps, and critical challenges as well as transition of advanced building control into resilience enhancement in Arizona and beyond. In Stage 1, the civic-academic team will: (1) identify community needs in the event of heatwaves; (2) enable convergent research across engineering, data science and AI, and social sciences; and (3) bridge academic research with community practices, with a special attention to under-resourced communities. The team will work closely with heat resilience networks operating cooling/respite centers to assess strategies for toolkit deployment, smart ventilation, and energy consumption monitoring. Key research questions relate to AI-based models to forecast the risk and determine the hours-of-the-safety; AI-empowered sensing technologies to monitor indoor environment quality for occupant wellbeing; and methods for minimizing electricity demands to alleviate potential overload from power grid as well as reduce operational costs of cooling centers. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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 examines how worldviews held by decision-makers at different levels– from local, to regional, state, and federal– play a role in governing rapidly changing Arctic frozen landscapes of ice, snow, and permafrost. Governance is a process that takes rules, norms, and shared strategies and applies them as policies or regulations across diverse social and environmental contexts. Worldviews are grounded in different underlying values and assumptions held by rights- and stake-holders who, for example, steward or manage lands and waters in Alaska. Differences embedded in worldviews are foundational to decision-making and contribute to consensus or disagreement emerging around policy actions. A better understanding of consensus and conflict between decision-makers could support collective action to quickly address thawing permafrost and emerging safety concerns given a less frozen Arctic. This research addresses a need for researchers and policy makers to better engage with diverse ways of knowing, and provides a way for Arctic communities, agencies, and resource practitioners to speak with, instead of past each other. Understanding the role of worldviews in governance processes is a crucial step toward multi-level governance becoming a powerful means of collective - and more equitable - decision making. Future research can build on this knowledge to support diverse decision-makers in a range of social and ecological settings throughout the Arctic, and globally. This project emphasizes collaborative research with the communities of McGrath and Nikolai in the Upper Kuskokwim Region of Alaska, USA. The work is comprised of two inter-related studies. Study 1 uses semantic network analysis to identify and analyze emergent worldview themes and resource decisions from archival documents published by all stake- and rights-holders in the region. The research team will then conduct semi-structured interviews with community members and regional, state, and federal agency decision-makers. This two-step approach will identify connections between worldviews and the governance of frozen commons. Study 2 will work closely with community partners to model critical frozen landscape scenarios and bring worldviews, multi-level governance actions, and social-ecological systems modeling into conversation during two community workshops. This study will co-create knowledge by investigating tradeoffs from a range of decisions in the context of rapidly changing, less-frozen landscapes. Using an iterative, community-driven design and mixed methods approach, this research holistically addresses and communicates how radically different worldviews across rights- and stake-holders play an important role in goal identification and valuation, and whose voices are marginalized or strengthened in processes of decision-making. 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

Reliable and high-rate 5G wireless access has become a global necessity; however, the US has fallen behind in wireless leadership, lacking major radio access network (RAN) or cellular network manufacturers. Furthermore, cellular networks have not been designed for mission-critical communications and have exposed several security vulnerabilities. Consequently, the Department of Defense (DoD) faces challenges in using commercial off-the-shelf 5G products and commercial networks for US military operations. The Zero Trust X (ZTX) team, a consortium of interdisciplinary experts in the field of 5G and security, will research and develop a family of security solutions to establish a Zero Trust Chain (ZTC) that enables end-to-end security and protection for reliable use of 5G networks for DoD use cases. The proposed effort will generate knowledge and research outcomes tailored for use by US industry and DoD. Additionally, the project will train a diverse team of students in research and provide open-source software that facilitates portability, reproducibility, and integration with other Track G solutions of this program. The project's specific goal is to develop the ZTC software that enables military squads to securely share situational awareness in their operations using high-performance, yet often untrusted, 5G networks. The software solution leverages the flexibility of the 5G standard and implements innovative security solutions at different network nodes and layers to empower DoD operators to detect malicious entities in near-real time and establish communication mechanisms to prevent access to or control over DoD traffic. Specifically, through minimal cooperation with 5G network operators, part of the ZTC solution leverages Open-RAN (O-RAN) and 5G core-centric approaches for practical threat monitoring and mitigation. This is complemented by device-centric security enhancements to ensure that DoD devices also implement their own layer of security and do not solely depend on the security protocols of the network provider. Six key features set ZTC apart from other solutions: (i) it builds on the Open Artificial Intelligence Cellular (OAIC) platform for developing O-RAN threat monitoring and mitigation through RAN Intelligent Controllers; (ii) it offers end-to-end secure slicing across the 5G RAN and Core; (iii) it detects threats at user devices in near-real time; (iv) it protects communication through innovation at the application layer rather than modifying existing 5G physical layer protocols and algorithms; (v) it ensures location privacy and resiliency to unknown/unanticipated denial of service (DoS) attacks; and (vi) it does not require modifications to public 5G/O-RAN networks and standards, and only requires installation of low-overhead software modules on 5G user devices and cooperative 5G networks. The ZTX team's work is applicable to commercial and military 5G communication networks and to O-RAN. The ZTX team will implement and experimentally evaluate the proposed ZTC initially on a laboratory-scale integrated 5G/O-RAN testbed, and subsequently on other available testbeds to prepare for commercial transition. The team will apply Convergence Accelerator fundamentals to foster partnerships and to develop a sustainability model with an impact extending well beyond Phase 2 of the program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Melting of ice shelves by warm ocean waters destabilizes glaciers, enhancing ice flow into the ocean and contributing to global sea level rise. Measurements of ocean properties under ice shelves are needed to improve global predictions of sea level rise and to anticipate its societal consequences. Such measurements, however, are challenging to obtain. In this Ideas Lab: Engineering Technologies to Advance Underwater Sciences (ETAUS) project, a proof-of-concept mothership-and-passenger system will be developed to permit the future deployment of a highly capable, autonomous underwater vehicle (the mothership), programmed to travel as far as safely possible under ice shelves to release a swarm of novel, low-cost passenger robots that will coordinate to explore further into the ice cavity. The hardware prototypes, networked communication systems and protocols, and coordination algorithms developed as part of this project’s mothership-and-passenger system will help advance the field of underwater exploration in confined and hard-to-reach environments. The project will also foster the training of future scientists and engineers by engaging youths from small fishing communities in Oregon through presentations at Oregon’s MATE ROV Regional Competition, by employing high-school interns through the Apprenticeships in Science and Engineering (ASE) Summer Academy Program, and by training multiple undergraduate and graduate students at participating institutions. The goal of this project is to develop a mothership-and-passenger sampling system to reach difficult-to-access glacier grounding zones via the open ocean to measure the extent of ice cavities and surrounding water properties. The project will innovate along three main areas of inquiry: 1) passenger robot design, 2) acoustic communication protocols and hardware, and 3) mothership-and-passenger coordination algorithms. Novel, low-cost passenger robots will be conceived that can switch through different operation modes to optimize maneuverability, power consumption, or a combination of both, as needed for various tasks throughout a deployment. Acoustic communication protocols and hardware will be developed to prioritize robust communication between passenger robots over throughput and permit swarm self-localization by utilizing time-of-flight and angle-of-arrival between passengers to estimate relative positions. Swarm coordination algorithms will be designed to estimate flow direction and strength from the passenger robots’ relative positions to optimize navigation and power consumption. The performance of the network will be tested in increasingly challenging environments, i.e., tests will be conducted in a pool, an unfrozen lake, and finally in a frozen lake, while network capabilities with a larger swarm will be modeled to ensure the scalability of the system to ocean deployments. Finally, the software developed for the mothership-and-passenger communication and self-localization protocols will be generalizable and made available open-source to allow other research teams to adapt the system to their own needs. The mothership-and-passenger sampling system will not only advance under-ice-observation capabilities but also have wider oceanographic applications such as detection and monitoring of underwater harmful algal blooms or anoxic events threatening fisheries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The state-of-the-art DNA sequencing technologies could generate Terabytes of DNA sequence data in a single run, and their throughput is expected to increase 3-5 times each year in the coming years. In order to apply these big DNA-data into follow-up complex disease diagnostics/prognostics, such as cancer risk assessment, tailor patient treatment, and prenatal testing, they must be first aligned to a 3.2-billion-length human reference genome. However, the existing software tools for this purpose may need hours or days to align such large amount of DNA sequence data even with very powerful computing systems of today due to the 'memory wall' challenge in state-of-the-art computing architecture that describes the speed mismatch between memory units and computing units. To this end this, project leverages innovations from non-volatile nano-magnet based Magnetic Random Access Memory (MRAM) technology and in-memory computing architecture. If successful, it can achieve up to two orders magnitude higher computing performance, speed and energy efficiency for next-generation DNA sequence analysis system, which enables large-scale fast genomic data analytics to support research on various disease studies and biomedical applications. This project will develop new undergraduate/graduate level course modules on in-memory computing architecture and bioinformatics. This project will follow two main research tracks. The first one explores how to leverage the intrinsic non-volatile MRAM device property to efficiently develop ultra-parallel, reconfigurable in-memory logic required by DNA alignment computation and its big DNA-data Processing-in-Memory (PIM) accelerator architecture. The second research track will investigate how to develop fast DNA alignment-in-memory algorithm based on Burrows-Wheeler Transformation to match with the proposed MRAM based PIM platform and its large-scale genomic analysis application in disease phenotype prediction. Alignments generated will be used to estimate gene expression, and identify single nucleotide mutation events for patient samples, leading to molecular signatures for disease risk assessment. 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 identifying essential competencies for data science literacy and how undergraduate college students learn these data science fundamentals in introductory data science course for diverse, general audiences. The project is significant because it addresses the growing need for data literacy among students who are not majoring in data science, yet will benefit from the ability to interpret and use data in various aspects of their lives and careers. Data science literacy requires a combination of competencies including statistical thinking, critical thinking, computational skills, data management, data visualization, and ethical reasoning. However, it is challenging to cover all these skills in an introductory course. This project will investigate which competencies should be prioritized and carefully investigate the pedagogical choices and learning pathways that support the development of these skills for students who do not plan to pursue a degree with a heavy emphasis on data analysis. The broader significance of this project lies in its potential to advance data science education, making it more accessible and effective for students of all majors. This aligns with NSF's mission to promote the progress of science and enhance STEM education. The research will to provide a comprehensive understanding of competencies and learning trajectories through a mixed-methods design, integrating both qualitative and quantitative data. The integration of these data types will allow for a robust analysis of the current state of data science education. The project will use surveys to gather quantitative data on student experiences and competencies, and semi-structured interviews to collect qualitative insights from instructors and students, which will be analyzed using thematic content analysis. Additionally, content analysis of syllabi will provide context on current curricular approaches. Guided by resource theory, expectancy-value theory, and grounded theory, the research aims to identify core competencies to prioritize in introductory courses and refine learning trajectories for a diverse audience. The findings will contribute to the development of more effective data science curricula and teaching methods, ultimately enhancing student outcomes and expanding access to data science education. This project is supported by the Mid-Career Advancement program that offers opportunities for scientists and engineers to substantively enhance and advance their research program through synergistic and mutually beneficial partnerships. This project is also supported by the NSF IUSE: EDU program, which supports research and development projects to improve the effectiveness of STEM education for all student, and the by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at Hispanic Serving Institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The amount of data required to be analyzed by computing systems has been increasing drastically to exascale (i.e., billions of gigabytes) and beyond. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Network (DNN), there is a need for high performance, efficient, fast, and adaptive AI-based big data processing systems. However, those requirements are not sufficiently met by existing computing solutions due to the power-wall in silicon-based semiconductor devices, memory-wall in traditional Von-Neuman computing architecture, and ultra computation- and memory-intensive DNN-based AI algorithms. This project brings together an interdisciplinary group of researchers, with expertise spanning from material science, device fabrication, integrated circuit design, computer architecture, and AI algorithms to undertake innovative device-circuit-algorithm co-design for developing an AI Processing-In-Memory (AI-PIM) system that could leverage the emerging non-volatile magnetic memory technology to implement efficient AI data processing, as well as situation-aware on-chip continual learning. This project targets to significantly improve the AI data processing energy efficiency, with 100X higher efficiency than that of state-of-the-art Graph Processing Units (GPUs). The project will greatly benefit various application areas, such as autonomous driving, robotics, personalized cognitive speech, and smart connected health, etc. This project will also involve education and workforce development activities, including K-12 STEM outreach, undergraduate/graduate training, curriculum development in semiconductor, semiconductor industry internship mentoring, cleanroom fab internships, advance integrated circuit design courses. It will also encourage broader participation of female and under-represented minorities in the microelectronics and semiconductor chip industry. This project will advance knowledge and conduct cross-layer research spanning from emerging Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) material, device, circuit, architecture, to AI algorithm exploration with three main interweaved thrusts. Thrust 1 will explore unconventional spins in SOT materials, e.g., MnPd3, and novel device geometry to fabricate a new design of 2-terminal SOT-MRAM, which simultaneously delivers unlimited endurance, nano-seconds programming time, very high cell density, deterministic programming without external magnetic field, zero leakage, and non-volatility. Leveraging the developed 2-terminal SOT-MRAM, Thrust 2 will design and tape-out an AI Processing-in-Memory (PIM) chip to implement fully digital ‘in-memory sparse multiplication-and-accumulation (MAC)’ operations that support both forward and backward computations of neural networks. Following a co-design methodology, Thrust 3 will first investigate automated network architecture search methods to construct AI model best suitable for given situation while considering our AI-PIM system constraint. This thrust will further develop novel PIM-friendly, compute- and memory-efficient, situation-aware continual learning algorithms that could minimize the power-hungry on-chip weight update (i.e., memory write) complexity, while learning new situation- and user-specific data. 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 networks (DNNs) have been successfully applied in many domains, including image classification, language models, speech analysis, autonomous vehicles, wireless communications, bioinformatics, and others. Their success stems from their ability to handle vast amounts of data and infer patterns without making assumptions on the underlying dynamics that produced the data. Cloud providers operate large data centers with high-speed computers that continuously perform DNN computations, with huge energy consumption that rivals that of some industries and nations. In addition to being used in solving large-scale problems, DNNs are now being considered for recognition and inference applications in battery-operated systems such as smartphones and embedded devices. Thus, there is a critical need to improve the energy efficiency of DNNs. The main objective of this project is twofold: (1) Design and evaluate a radically innovative energy-efficient hardware/software framework for on-chip implementation of DNNs, and (2) customize this framework for new DNNs that enable real-time signal classification in next-generation wireless systems. By integrating processing elements within memory chips, the energy consumption of a DNN can be significantly reduced, and more computations can be done faster. The hardware-accelerated DNN designs provided by this project will facilitate rapid identification of wireless transmissions (e.g. radar, 5G, LTE, Wi-Fi, microwave, satellite, and others) in a shared-spectrum scenario, enabling better use of the spectrum and facilitating accurate detection of adversarial and rogue signals. To achieve 10x-100x reduction in DNN energy consumption, a holistic approach is pursued, which encompasses: (1) new circuit designs that leverage emerging CMOS+X technologies; (2) a novel near-memory architecture in which processing elements are seamlessly integrated with traditional Dynamic RAM (DRAM); (3) novel 3D-matrix-based per-layer DNN computations and data-layout optimizations for kernel weights; and (4) algorithms and hardware/software co-design tailored for near-real-time DNN-based signal classification in next-generation wireless systems. In addition to its research goals, the project has a comprehensive educational and outreach agenda, which includes graduate and undergraduate curriculum development, an internship program, and engagement of students from under-represented groups. Technology transition is streamlined through interactions with industry affiliates of two existing centers. 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

2420676 (Tong) and 2420677 (Lin). Climate change is threatening water sustainability by causing more droughts and limiting water access to people around the world. For example, the Western United States has suffered from severe droughts and heat waves, and the Colorado River has recently experienced record low water levels. Brackish water desalination (BWD) is a promising approach to produce more freshwater, but it is inhibited by the lack of effective strategies for brine management. The goal of this research is to develop cost- and energy-efficient brine treatment technologies that enable decarbonized BWD for climate-adaptive water supply. This goal is targeted to be achieved through interdisciplinary research that integrates fundamental interfacial processes and thermal transport to achieve a solar driven zero liquid discharge (ZLD) system. The environmental impacts of this system will be evaluated by techno-economic analysis, life-cycle assessment, and assessing public acceptance. Further benefits to society will result from research training of college students from underrepresented groups, curriculum enrichment, and outreach and public engagement activities. The accelerating global effects of climate change have resulted in an immediate need of adapting water supplies to the rapidly intensified drought conditions. The nationwide adoption of BWD as a feasible strategy to augment freshwater supply is hindered by the challenge of brine management. Minimizing brine volume via ZLD is the key to render BWD a practical and viable means to mitigate the adverse impact of climate change on water security and resiliency. The overarching goal of this project is to achieve solar driven ZLD for decarbonized inland freshwater production as part of a strategy to address climate change. Specific objectives of the project are to 1) develop a novel process integrating nanofiltration and reverse osmosis to enable cost-effective brine volume reduction; 2) design an innovative interface enhanced crystallizer for energy-efficient and robust brine crystallization, guided by fundamental understanding of interfacial salt crystallization, 3) develop a novel high-efficiency heat pump to power ZLD with interface enhanced crystallizer; and 4) evaluate the sustainability of off-grid, decarbonized inland BWD with ZLD with concurrent techno-economic, lifecycle, carbon flow, and social acceptance assessments. To achieve these objectives, this project will integrate and converge knowledge and approaches from multiple disciplines including environmental engineering, environmental sustainability, interfacial engineering, thermal transport processes, systems engineering, and social science. The successful completion of this project has the potential for transformative impact through enabling decarbonized ZLD to support the wide adoption of climate-resilient inland desalination that improves water resilience against a changing climate. The project will provide undergraduate and graduate students from underrepresented groups with opportunities of preforming interdisciplinary, convergent research to solve an environmental and sustainability challenge of global concern. 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

With the recent rapid development of wireless communication and advanced sensing technology, rich and complex sequential high-dimensional data are made available for a wide range of threat detection applications, e.g., intrusion detection, anomaly detection, fake news detection, and false data injection detection. However, the reliance on wireless communication and the sparsely spatial distribution of these networked sensors make them vulnerable to adversarial attacks, such as measurement manipulation and false data injection. Moreover, threats are oftentimes caused by human factors, and thus any attempt to improve the performance of threat detection algorithms may result in a dual effort to devise more powerful counter-threat-detection techniques that leave less evidence. In this project, a game-theoretic framework will be developed to investigate the ultimate limits of the dual efforts for quickest threat detection in adversarial networked environments. The investigators will co-organize special sessions at conferences, workshops, and symposia on quickest change detection to disseminate the research outcomes of this project, formalize far-reaching research directions, identify new challenges in this emerging area, stimulate the development of original research ideas, and foster interdisciplinary collaborations. The investigators are committed to broadening the participation of under-represented minorities and women both among the graduate and undergraduate students in STEM education. The investigators will enrich their current courses and further develop new courses on topics related to this project. The project is expected to make new contributions to quickest change detection, adversarial learning, sequential analysis, and game theory. A systematic methodology of developing Nash equilibrium strategies for quickest threat detection in networked adversarial environments will be developed, and their fundamental performance limits at the Nash equilibrium will be theoretically characterized. This project consists of three thrusts. The first thrust focuses on one data stream under adversarial attacks with temporal structure. The second thrust focuses on the case with multiple independent data streams. The third thrust focuses on networks with graphic correlation structure. 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 advancing our understanding of how universities nationwide can improve support for science faculty instructors in providing accommodations for students with disabilities in their classes. While the process of providing formal accommodations in higher education classrooms is initiated by students and coordinated typically by a disability or accessibility center, the actual implementation of accommodations is highly dependent on faculty instructors. Because faculty at different institutions have different responsibilities, resources, and student bodies, the context of providing accommodations likely varies greatly by the type of institution. Additional variables including rising numbers of students with disabilities and an increased use of active learning and hybrid/remote instruction inevitably influences a faculty instructor's experiences and willingness to provide accommodations to students with disabilities. By identifying and understanding the factors that impact faculty instructor motivation to provide accommodations, this project aims to elicit needed information on how to support science faculty instructors in meeting the needs of undergraduate students with disabilities. This project plans to identify personal, institutional, and logistical factors that impact how science faculty instructors administer accommodations to students with disabilities. Because factors including class sizes, accessibility center resources, numbers of students who receive accommodations, faculty instructor responsibilities and expectations, and teaching support resources vary greatly by institution, this project will disaggregate findings by four different institution types (community colleges, primarily undergraduate institutions, comprehensive institutions, and research-intensive institutions). This project includes a nationwide interview study to identify how different factors impact a science faculty member's expected ability and value for the task of providing accommodations for students. To increase the generalizability of this work, the project plans to use findings from the interview study to inform development of three survey instruments, designed to measure science faculty's: (1) motivation to provide accommodations; (2) perceptions of the logistical, situational, and instrumental factors that impact student accommodations; and (2) personal knowledge of disability and accommodations. After instrument validation, the project plans to deploy the three survey instruments to science faculty at institutions nationwide. Data analysis will explore how different factors impact science faculty instructor's motivation and experiences with providing accommodations. Outcomes will be disseminated through publications, presentations, as well as videos, recorded talks and blog postings. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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 award funds a workshop series that will engage multi-sectoral actors involved in research, testing, development, and deployment of Marine Carbon Dioxide Removal (mCDR) with community engagement scholars and practitioners in relevant domains to help co-develop a roadmap for building informed and involved communities through engagement and information sharing. It will leverage past, current and planned projects, initiatives and coordination efforts at the local, regional and national levels to avoid redundancy and ensure the optimal use of knowledge and resources to ensure that the supply of and demand for usable and actionable science are effectively reconciled. The design and development of the workshops input, output and outcomes will be guided by a multi- sectoral advisory board representing principal drivers of mCDR science and technology in government, industry, non-government, philanthropic and academic organizations. The workshop will inform the current mCDR research, education and policy development, coordination and implementation activities undertaken by government, industry, non-government and academic organizations. It will help in the building of informed and engaged communities for responsible mCDR by mobilizing evidence-based resource allocation and capacity development activities. Two workshops are envisioned over a twelve-month period. The first workshop will socialize state of the science on community building and define the challenges. The second workshop will identify opportunities and the solution pathways and map responsibility for each sectoral actor. The results and outcomes of the workshops, including recommendation for collective and coordinated actions will be synthesized in a final report and disseminated widely through different channels, formats and platforms accessible to target mCDR stakeholders in public, private, philanthropic and non-governmental organizations. 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

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. The design of technological infrastructures in many U.S. cities contributes to multiple social, economic, and environmental crises. Where facilities are sited, unfair practices of administration, and inequitable distributions of costs and services have been linked to urban heat, air pollution, housing scarcity, energy insecurity, racial inequality, and environmental injustice. These crises are often place-based and reinforce one another. A key opportunity for innovation and strengthening of American infrastructure is to redesign infrastructures in ways that reduce their contributions to inequality, insecurity, and injustice in the communities that live in and around them. This requires new methods of infrastructure design that allow cities to: (1) reimagine technologies and how they provide services to cities and communities; (2) develop new tools and methods for evaluating the human outcomes of infrastructure design; and (3) create new approaches to infrastructure planning that enable diverse communities and stakeholders to collaborate in the co-design of solutions that address multiple social, economic, and environmental problems. This project advances innovative approaches to design and planning of energy infrastructures, with an emphasis on the Phoenix, Arizona metropolitan region. Energy infrastructure failures currently contribute to many social, economic, and environmental crises facing Phoenix and other U.S. cities. Energy infrastructure redesign offers an opportunity to simultaneously address multiple crises, especially by finding ways to design future energy infrastructures that are carbon-neutral, provide significant benefits to communities, and advance community goals. To accomplish these goals, this project engages communities, municipalities, and energy utilities in co-designing energy infrastructure solutions that are built using distributed solar energy and battery storage technologies and deployed within the city itself. The project maps the geography of potential urban solar deployment in Phoenix and establishes a collaboration with communities and stakeholders to co-identify potential solar infrastructure designs, co-develop evaluation criteria and methods for assessing the community benefits that would flow from those designs, and co-create research insights that inform energy infrastructure planning decisions. 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

Research shows the importance of children seeing themselves as capable of “doing STEM” and developing a positive STEM identity. Yet, children of color are less likely to have access to these informal STEM opportunities. Building on the successful “Science is Fun” (SIF) intervention, the project will revise this program for Hispanic students using a culturally-responsive approach. Facilitated by Hispanic, near-peer mentors, the revised, afterschool program will engage 4th grade students (from schools with predominantly Hispanic enrollment) with several sessions of science activities and their families in in family science explorations. The activities will focus on light and energy, using an approach combining demonstration and inquiry. (A phenomenon may be demonstrated, resulting in questions stimulated by a counterintuitive outcome. Participants then engage in hands-on exploration to explain the outcomes.) A co-design process involving teachers, role models, advisors and students will be conducted, incorporating language and cultural themes in program activities and facilitation. The iterative project research and evaluation aims to observe, assess, and revise the Science is Fun (SIF) program, helping to understand the program’s cultural responsiveness for historically marginalized Hispanic audiences. The major hypothesis of project research is that incorporating a cross-cultural curriculum will have positive impacts on participants’ 1) cultural awareness, 2) STEM interest, self-efficacy, and identity, and 3) perceptions of careers in STEM and who can be a scientist. Data will be collected from focus groups, surveys and observations from students, near-peer mentors, and families. This project will contribute to the research on culturally responsive STEM programs and will present important theoretical and practical implications. 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 access to and 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 · 2024-09

Nearly one-third of the Earth's land surface is covered by forests, which host the majority of terrestrial biodiversity. Accurate mapping and monitoring of forests across large regions and over time is critical for mitigating climate and natural hazards, managing natural resources and protection of vital ecosystems. While traditional ground-based measurements of plant species and size provide the most accurate data on forest structure and above ground biomass, these methods become impractical when covering large areas with high-frequency repeat cycles. Airborne and Space-based remote sensing techniques provide a timely and cost-effective way to assess forest structure and biomass on regional to global scales. Satellite missions from NASA and ESA have sensors that gather data with significantly more frequent repeat cycles compared to in situ measurements or aerial surveys. While these satellite missions offer global coverage, some provide only sparse data on forest structure and need to be combined with other data sources for producing comprehensive and accurate wall-to-wall maps. There is a lack of efficient frameworks that utilize multi-source remote sensing data to produce wall-to-wall forest structure or above ground biomass at temporal and spatial scales necessary for effective forest management or use in hazard mitigation and monitoring applications. Without significant improvements to existing methodologies and looking beyond traditional data sources, efficient and accurate monitoring of forest structure and above ground biomass will remain limited. OpenForest4D will allow a wide range of users to generate on-demand and up-to-date research-grade forest structure and above ground biomass estimates across a range of timescales. This will be achieved by applying novel statistical models and artificial intelligence methodologies on a fusion of multi-source remote sensing data from ground, airborne and spaceborne platforms. Providing these cyberinfrastructure services through easily accessible interactive web-based interfaces, along with educational resources focused on the underlying domain science, will facilitate transformative research in forest sciences and ecology and encourage broad community participation. OpenForest4D's web-based educational resources, published curriculum materials, and live webinars will help develop a diverse, globally competitive STEM workforce. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Biological Infrastructure within the Directorate for Biological Sciences and NSF's National Discovery Cloud for Climate initiative. 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

Arctic sea ice is a key component of the global climate system and a major indicator of ongoing climate change, and consequently there is an appreciable increase in research activity related to sea ice in the Arctic regions. To understand Earth’s changing climate, scientists have engaged with traditional knowledge of Arctic Indigenous peoples through various participatory methods, including co-production of knowledge (CPK). However, Indigenous knowledge has often been treated merely as data to inform environmental decision-making, with Indigenous knowledge holders excluded or not equally involved in the decision-making process. The application of the CPK approach in the context of Arctic research has emerged relatively recently. Prior studies in the Arctic may incorporate co-production principles, yet might not have explicitly been identified as CPK. The project takes an empirical approach to understanding the underlying dimensions of CPK in practice with the goal of unveiling which factors are employed, how, and when through three different approaches and analyses. The first will be a review of existing literature on sea ice knowledge co-production to explore common themes, types of participatory research, key entities and networks. Second, the team will conduct a place-based study in Gambell, Alaska, involving interviews with community members about their experiences collaborating with scientists, the value and importance of research outputs, and how they envision more equitable CPK processes. Third, we will interview project leaders identified through community interviews to discuss their engagement with the community, the strategies implemented, and the challenges they encountered. Community research leads will actively engage in interviews, analysis, and authorship embracing a CPK approach to the project. The intellectual merit of the project includes a community-centered understanding of CPK practice on the ground, which has potential to transform CPK approaches in the Arctic and beyond. This project, informed by social and environmental sciences, aims to advance understanding of co-production of knowledge processes related to sea ice. The results of this study can inform policy making by providing evidence-based recommendations for inclusive and participatory research practices. The project advances an empirical approach with potential to steer future research toward ethical, anticolonial CPK methodologies. The connection to sea ice knowledge offers a unique aspect by analyzing how CPK projects advanced understanding through the integration of Western and Indigenous science. While multiple results dissemination routes are plausible, the final selection will be reached through a collaborative decision-making process with the community. This could include a diverse array of channels, including pamphlets, booklets, posters, Facebook, school presentations, and more to disseminate information. With the community’s consent, the project will share the findings with academic institutions, policy makers, and other relevant organizations to promote broader understanding and application of co-production processes. Concurrently, the project will partner with community research leads to co-author publications amplifying community voice in the peer-reviewed literature. Ultimately, this project aims to contribute to a more nuanced understanding of co-production of knowledge, with implications for enhancing collaborative, equitable research and policy across various contexts. 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 new generation of large radio telescopes are being developed to study how and when the first stars and galaxies formed, understand our evolving universe, and explain the behavior of our sun and solar system. These telescopes will have many more antennas than previous radio telescopes. A new technique has been developed to significantly reduce the data rates from such large telescopes. Under this grant, the investigators will add functionality to the open source pyFHD software package. The investigators will test this software with data collected from several radio telescopes including both traditional and direct imaging systems. These software enhancements will be made widely available to the entire radio astronomy community, facilitating and encouraging additional contributions to the package. In addition, this grant will support EPIC TV, giving the public a real-time view of the radio sky as seen by the Long Wavelength Array in New Mexico. This project will provide research experiences for non-traditional undergraduate students that are not well served by standard research opportunities. This will include enabling these students on a path to graduate school, with a goal to diversify the broader STEM workforce. Fast Holographic Deconvolution (FHD) was developed in the 21cm cosmology community that solves many of the analysis challenges encountered in that research area. It has been used in obtaining leading limits of the 21cm Epoch of Reionization power spectrum, demonstrating that it has the precision required for cosmology, as well as being used to make high-quality catalogs and polarized maps of galactic emission. FHD, originally written in IDL (the Interactive Data Language), shares a mathematical framework with direct imaging, making it well placed to help address the data analysis challenges for direct imaging telescopes. Recently, in a major development effort, the minimal set of FHD functionality required for a standard cosmology analysis was ported to python, resulting in the open source pyFHD package. The functionality the investigators will develop in pyFHD under this grant will enable the community to demonstrate the science performance of direct imaging radio telescopes with a full end-to-end pipeline. This will provide the community with a reference implementation of the software needed to perform science without interferometric visibilities and will facilitate evaluation for future proposals to construct direct imaging radio telescopes. 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

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professor James Allen and Dr. JoAnn Williams of Arizona State University are studying how fundamental biological processes such as respiration and photosynthesis depend on the controlled transfer of protons and electrons in protein complexes. The project is directed towards creating reversible, long-lived electron and proton transfer reactions that mimic the properties of biological processes. An experimental framework will be developed that characterizes proteins modified to gain the capacity to perform proton-coupled electron transfer reactions. Identification of critical interactions among components would aid in predictive algorithms for the management of protons and the accompanying electrons. This convergence of molecular tools should support development of new strategies for the design of proteins. The biochemical expertise of the investigators will be applied to the educational goal of increasing participation in undergraduate research through development of a course that offers a research experience to online students. Because multiple students will be working together in the laboratory course, this powerful and scalable strategy will provide large numbers of students with transferable skills through an enhanced undergraduate experience. Towards understanding the properties of proton-coupled electron transfer processes in biological reactions, this research project seeks to investigate how proteins control these reactions. The experimental approach will involve manipulating bacterial reaction centers, the site of the primary photochemistry in photosynthesis, to contain a four-helix bundle domain that can bind an array of synthetic porphyrins. The unique synthetic compounds include porphyrins with benzimidazole phenol substituents capable of both electron and proton transfer. The four-helix bundle allows probing of the impact of the inhomogeneous protein environment on biological proton transfer. The reaction center provides a light-induced driving force with a range of redox potentials. Modeling of the dependence of the electron and proton transfer rates on the redox properties of the cofactors in the hybrid complex seeks to determine critical parameters concerning the mechanisms. The outcomes should yield new experimental approaches for the design of proteins capable of proton-coupled electron transfer reactions. This project has a goal of increasing the exposure of nontraditional students to scientific research methodology. The proposed approach is to offer a Course-based Undergraduate Research Experience (CURE) that is targeted at the online student population. The course will be supported by a national network of faculty dedicated to using CUREs to engage biochemistry undergraduates in research 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 · 2024-09

This project will reveal whether conservation science is producing the kind of knowledge that has proven to be useful in conservation practice. Conservation practitioners aim to use the ‘best available science’ to make decisions. However, science is a human process influenced by social factors, and so what knowledge is considered to be ‘best’ – and even what knowledge is considered to be ‘science’ – changes over time. This project utilizes the documents produced for the Endangered Species Act to determine the differences between what knowledge was expected to be useful for the conservation of a species (documented in a Recovery Plan or Species Status Assessment) and what knowledge was actually used for the successful recovery of a species (documented in a Delisting Determination). We will also speak to practitioners to determine what knowledge was useful but was not included in the Delisting Determination. This will reveal what knowledge was important for achieving conservation outcomes but was not recognized as such at the start of the conservation process. The project serves the national interest in two ways: it will improve the effectiveness of research into the realms of biodiversity conservation, and it will improve the effectiveness of biodiversity conservation practices themselves. The goal of this project is to explore the dynamics of ‘boundary work’ and paradigm shifts in the discipline of conservation, and how these dynamics affect the discipline’s ability to advance and produce knowledge useful for achieving the goal of biodiversity conservation. Phase 1 will compare the knowledge referenced in the Recovery Plan or Species Status Assessment for each of the 72 species that have been delisted from the endangered species list with the knowledge referenced in the Delisting Determination for that species. These documents will be coded to identify the types of knowledge referenced, both broad (e.g., biology) and specific (e.g., species diet). The researchers will also document the sources of the knowledge that are included in these plans (e.g., scientific publications, practitioner experience, Indigenous and local knowledge). Through this process they will develop a codebook. Phase 2 will consist of semi-structured interviews with those involved in the conservation of a handful of species chosen as case studies, which will be analyzed in a similar manner as the documents in phase 1. The knowledge referenced in the Delisting Determinations and the interviews will be compared to that referenced in the Recovery Plans. This will reveal the current boundaries around what is considered conservation knowledge by the experts who authored the recovery plans, as well as how those boundaries have changed over time and whether they circumscribe the knowledge necessary to actually conserve a species. 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 focuses on the intricate, often overlooked, and unavoidable imperfections within composite electrodes used in all-solid-state batteries. Understanding this “microstructure” is crucial for advancing battery technology with improved safety and energy density. By examining how these features behave during synthesis and electrochemical cycling, the research aims to uncover their impact on lithium diffusion and structural integrity. This study is significant because it fills a critical knowledge gap and advances solid-state battery technology. Beyond the scientific advancements, the project has broad societal impacts by fostering sustainable energy technologies and promoting diversity and inclusivity in STEM fields. By leveraging the diverse demographics of Cornell University and Arizona State University, the project will engage underrepresented communities in materials science through outreach initiatives targeting K-12 students and inter-institutional collaborations. These efforts aim to inspire a passion for STEM, cultivate a diverse future workforce, and enhance the interdisciplinary and inclusive nature of scientific research, ultimately contributing to national health, prosperity, and welfare. The project investigates the microstructure, including grain boundaries, secondary phases, and defects, within composite electrodes composed of solid-state electrolytes and cathode active materials. The research aims to quantify defect formation mechanisms and monitor operando microstructural evolution, and to elucidate to what extent these changes impact the mechanical and electrochemical properties of the electrodes. By combining tailored synthesis, advanced electrochemical characterization, real-time operando x-ray techniques, including single-particle diffraction and coherent imaging, and rigorous modeling, this study promises to unravel the profound influence of microstructural defects on ionic transport, mechanical resilience, and fracture toughness, paving the way for the development of high-performance solid-state batteries. 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

How can disease outbreaks in an increasingly interconnected world be better predicted and responded to? The project tackles this challenge by combining two key sources of information: community wastewater and human behaviors. While current methods often rely on delayed and inaccurate medical reports, our innovative approach analyzes traces of viruses in sewage and incorporates various types of data about human activity. This includes information on people's movements, social interactions, online searches, social media posts, and immune factors. By combining these diverse data sources, the Investigators aim to detect diseases earlier and gain a more comprehensive understanding of how they spread through communities. The investigators will also examine how public attitudes and behaviors evolve during prolonged health crises. Although the initial focus is on COVID-19, the methods to be developed could be applied to other infectious diseases, helping communities worldwide prepare for future health emergencies. Beyond the research, the investigators are committed to training undergraduate and graduate students from diverse backgrounds, nurturing the next generation of public health professionals. Ultimately, this project will provide valuable tools for health officials to make quicker, more informed decisions to protect public health. The goal of this project is to enhance mathematical epidemiological modeling by integrating human behavioral data with wastewater surveillance data, creating a more comprehensive and timely approach to outbreak detection and response. By synthesizing advancements across mathematical modeling, wastewater epidemiology, and geographic information science (GIScience), the research approach innovatively connects human behavior insights with wastewater data to enhance viral transmission understanding and forecasts at the community level. To achieve this, the Investigators will pursue three main objectives: (1) Develop an early-warning system using wastewater and digital and social behavior data; (2) Create a socio-immuno-epidemiological framework to examine the effectiveness of pharmaceutical interventions and the emergence of dominant variants using wastewater surveillance data; and (3) Model the impact of pandemic fatigue social behaviors on viral transmission at the community level. These objectives will be addressed by a interdisciplinary research team, which brings together expertise in applied mathematics, epidemiology, public health, and geography. This approach represents a significant step forward in understanding the complex interactions between human behavior, immune responses, and pathogen spread. Ultimately, the research outcomes will equip health officials with valuable tools for designing proactive, targeted, and adaptable interventions, enabling quicker and more informed decision-making. This award is co-funded by DMS (Division of Mathematical Sciences) and SBE/SES (Directorate of Social, Behavioral and Economic Sciences, Division of Social 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

Through this IRES project, diverse cohorts of undergraduate and graduate students from Rutgers University - Newark, Arizona State University, Cal Poly Humboldt, and University of Hawai’i Hilo gain research experience as interdisciplinary and community-based water scientists. Students conduct research advised by PIs and in collaboration with Peruvian professors, students, and community members. The research focus is on water resources in the source watersheds of two communities of less than 4,000 inhabitants. Working with these communities, the researchers use a variety of field-based techniques to determine how landscape features regulate the flow of water to sustain streamflow vital to municipal and agricultural needs. The collaborative, interdisciplinary, and intercultural team empowers students to be future environmental leaders and co-produce knowledge to guide local and regional sustainable management of the Andean Water Tower. The Vilcanota Urubamba Basin (VUB, ~ 11,000 km2) is a seasonally dry landscape in the Cusco highlands of Perú. Of the more than one million people living in the VUB, the vast majority depend directly on the water tower of puna grasslands for food and water security, hydropower, and cultural practices. Cusco, home to nearly half of the basin’s population, derives 90 % of its municipal water from two puna-dominated sources. In isolated upland catchments peat-forming wetlands, known as bofedales, play a critical role in regulating water to sustain baseflow and may be key landscape features to achieving regional water security. However, the current and future water regulation capacity of puna is poorly constrained. To address current and future knowledge gaps, this project quantifies the hydrologic role of bofedales in two watersheds within the VUB that are representative of Cusco’s primary water sources as well as vital to the immediate downstream communities of Zurite and Phinaya. The research project has three primary objectives. First, to train and empower three diverse cohorts of undergraduate and graduate students in community-based and interdisciplinary water resources research focused in Zurite and/or Phinaya. Second, to collaborate with Peruvian knowledge experts and use hydrogeophysical techniques to quantify bofedal water storage in the communities’ source watersheds. Third, to collaborate with the two communities to co-produce knowledge to guide local and regional conservation to achieve water 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.