Arizona State University

NSF Awards · FY 2024 · 2024-09

Reinforcement Learning (RL) is a machine learning paradigm that strives to make optimal decision-making based on experience acting in an environment. In many cases, the "environment" refers to a simulator in the training stage and refers to the real world in the deployment stage. Training in the simulator brings a lot of advantages: lower cost, more safety, and more flexibility. However, it is almost impossible to design a perfect simulator that is identical to the real world. Thus, a decision-maker trained in the simulator may not function well in the real world. The discrepancy between the simulator and the real world is called the simulation-to-reality (sim-to-real) gap. This project will build new technologies to close the sim-to-real gap in both the training and the deployment stages. The research outcomes will benefit the development of next-generation RL techniques, which can improve the availability, applicability, and generalization of RL, and minimize the gap of RL between common practices and real-world practices. This project proposes to close the sim-to-real gap in reinforcement learning by three mechanisms: randomization, alignment, and derivation. Specifically, 1) the randomization mechanism generates a set of homogeneous simulators by original simulator parameter randomization. The simulator set will cover a wider range of state-action regions than the original simulator, have a larger overlap with the real-world environment, and thereafter result in a smaller sim-to-real gap. This mechanism is especially useful when the sim-to-real gap is large and the simulator is only accessible for training the simulator-optimal policy, but not accessible during the sim-to-real transfer process. 2) The alignment mechanism makes the simulator more like the real world during the transfer process. The alignment mechanism not only closes the sim-to-real gap but also is low-cost and high-efficiency, thus, accelerating the transfer process. This mechanism is especially useful when the sim-to-real gap is relatively small and the simulator is accessible in both simulator-optimal policy training and sim-to-real transfer. 3) The derivation mechanism directly derives an optimal policy from real-world offline data without any simulator. It first estimates state-action values from offline data and then derives the policy by function approximation. This mechanism is especially useful when offline data has been collected, but the real-world dynamics are unknown so it is unlikely to build a faithful simulator. 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

Drylands cover more of the Earth’s surface than any other biome and nearly half of the United States. They are characterized by low rainfall amounts, yet high variability in rainfall. This makes drylands unique from wetter locations, yet research is lacking that helps us understand how rainfall variability impacts ecosystem services such as removal of carbon from the atmosphere and the ability of landscapes to store water during drought. In this project researchers will leverage high rainfall variability at the Santa Rita Experimental Site to explore the production and decomposition of plant litter, which influences ecosystem services. Researchers will collect high resolution airborne images and surface elevation data to explore spatial and temporal patterns in plant litter. This funding will enable a multi-year investigation of variability by filling a gap in annual remote sensing data collection. The data will be made available to the public by the National Ecological Observatory Network (NEON) to enable a variety of research related to understanding the ecosystem outcomes of high rainfall variability. Researchers recognize that dryland litter decay differs from that of mesic systems in part due to higher heterogeneity and temporal variability in litter inputs, litter distribution, and environmental conditions across time and space. The unvegetated interspaces characteristic of drylands allow litter transport by wind and water until restricted by surface features (e.g., plant bases, rocks). Environmental conditions (e.g., moisture, temperature, solar radiation) differ greatly among the microsites where litter accumulates, strongly affecting decomposition rates. Accordingly, quantifying decomposition across drylands is a microbial-to-macroscale problem of integrating fine-scale process controls across spatial-temporal heterogeneity in environmental conditions. Researchers will integrating fieldwork, modeling, and remote sensing approaches across a hierarchical range of scales to capture the distribution of litter across a range of environmental conditions. The project will fund a survey by the NEON Airborne Observation Platform at the Santa Rita Experimental Site that will provide an annual record of imaging spectroscopy and lidar data, enabling landscape-scale multi-temporal analysis of ecosystem dynamics. 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

Per- and polyfluoroalkyl substances (PFAS) are a group of manmade chemicals that are used in many consumer products and industrial processes due to their unique chemical properties. However, their persistence in the environment poses a significant threat to the drinking water supply of roughly one in three people in the U.S. One promising method for PFAS removal from contaminated water is nanofiltration (NF), a technique that removes nanoscale particles from a liquid using membranes as a filter. Yet, several critical challenges must be addressed to make NF a viable part of PFAS cleanup efforts. First, the effectiveness of NF in removing the wide variety of PFAS types, especially (ultra)short-chain PFASs and those found in complex mixtures, remains unknown. Second, a better understanding of how various forms of PFAS interact with NF membranes at a molecular level is needed. Third, the lack of predictive models to identify key factors that affect PFAS passage through NF membranes hinders rational membrane design and selection. This research aims to address these knowledge gaps by combining experiments and computer simulations, integrated with specialized modeling techniques such as machine learning, to investigate how NF removes PFAS from contaminated water resources. The fundamental knowledge gained through this work will advance membrane-based technologies for remediating PFAS-contaminated water. In addition, this project will include public engagement and educational activities such as developing a new educational module, training students from underserved groups, and hosting outreach activities for PreK-12 students to increase PFAS scientific literacy and awareness. The overarching goal of this research is to use an innovative integration of experimental and computational studies to elucidate the performance and mechanisms of (ultra)short-chain PFAS removal by NF. To achieve this goal, the NF removal performance for (ultra)short-chain PFAS of varied structural features will be evaluated, and the structure-property-performance relationship of PFAS removal in NF treatment will be established using machine learning techniques. The investigators will use non-targeted chemical analyses to further assess the NF performance in removing diverse PFAS from complex aqueous film-forming foam-impacted water. The interactions and transport of PFAS at the water-membrane interface and within polyamide NF membranes will be probed theoretically using molecular dynamics simulations to gain mechanistic insights into the experimental results. The findings of this research will generate fundamental knowledge to inform rational design strategies for developing more effective NF membranes tailored to remediating PFAS-contaminated water. 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

Rapid population growth and rising living standards have caused the depletion of global water sources and other valuable resources. Separation technologies that can purify saline or contaminated water and recover valuable components are urgently needed. Separation using nanofiltration (NF) has been widely used for water purification and desalination, but emerging challenges such as the extraction of lithium from brines to satisfy the booming lithium-ion battery market go beyond the capabilities of current NF membrane technology. Thus, the overarching goal of this collaborative project is to explore a novel mechanism known as ion dehydration that can be applied using modified NF membranes to carry out challenging separations. This project leverages expertise in computational simulation, laboratory experimentation, and NF membrane fabrication in an international collaboration with researchers at the Technion - Israel Institute of Technology. Successful completion of this project will advance our understanding of NF separation to address pressing societal needs. Beyond the technical focus, the project will benefit society by educating the public through outreach activities to increase scientific literacy and awareness of water and resource sustainability. NF membranes have been used for water purification and desalination processes for many years. Recently, there has been increasing demand for high membrane selectivity between solutes to enable energy-efficient separation for water purification and resource recovery. However, achieving precise separation between similarly sized and charged ions using current polyamide NF membranes remains a significant challenge. Addressing this challenge requires leveraging mechanisms beyond those prevailing in current water-solute separation. Accordingly, the project will pursue three primary thrusts to regulate the transport and selectivity of monovalent ions in polyamide NF membranes: 1) investigate the role of ion dehydration on the transport and selectivity of ions in state-of-the-art NF membranes; 2) delineate the effect of membrane surface hydrophobicity and charge on ion dehydration using self-fabricated membranes with tunable surface properties; and 3) use molecular dynamics (MD) simulations to support the results of ion dehydration and membrane selectivity experiments. Thrust 1 will utilize a custom-made diffusion cell to probe the ion-ion selectivity for a series of ions with distinct hydration properties at different temperatures, pressures, and solvent types. Thrust 2 will focus on fabrication of thin-film composite polyamide (TFC-PA) NF membranes with systematically altered surface hydrophobicity and surface charge. Thrust 3 will apply MD simulations of water and solute ion transport through a TFC-PA NF membrane, which can be used to understand the relationship between the membrane structure and ion transport/rejection. Beyond the direct technical thrusts, the project will include outreach and educational activities that broaden its impacts by providing research training opportunities to graduate and undergraduate students, especially those from underrepresented groups. The team will also perform outreach activities for K-12 students from local communities of Colorado and Wisconsin to increase scientific literacy and support the Nation’s STEM workforce. 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

Future applications of national importance, such as healthcare, critical infrastructure, transportation systems, and smart cities, are expected to increasingly rely on machine-learning methods, including structured learning, supervised learning, and reinforcement learning. In many of these applications, the probabilistic distribution governing the data may undergo variations with time and location, and data could be corrupted by faulty or malicious agents/sensors. Such model deviation and data corruption could result in significant performance degradation. The goal in this project is to explore new ways to design learning and inference methods that are robust to distributional uncertainty and data corruption. This project is bridging and further advancing research in areas of statistical learning, optimization, control theory, network science, reinforcement learning, statistical signal processing and information theory. The methods developed are likely to have significant impact on a wide range of applications in areas of societal importance such as healthcare, transportation systems, smart grids, and smart cities. The investigators are co-organizing special sessions at conferences, workshops and symposia on robust learning and inference 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 participation of under-represented minorities and women both among the graduate and undergraduate students in computing and engineering. The investigators are enriching their current courses and further developing new courses on topics related to this project. This project is expected to make new contributions to the theory and practice of robust learning and inference. Several emerging directions are being investigated, including robust sketch-based learning, robust mean estimation, synthesis of confusing inputs to machine-learning models, robustness to distributional uncertainty at inference time, and robust model-free reinforcement learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Earth’s core-mantle boundary (CMB), where the solid silicate mantle meets the molten iron-rich outer core, is associated with a variety of anomalous structures, including ultra-low velocity zones (ULVZs). Typically, ULVZs are associated with reduced seismic wave velocities and sometimes increased density, but the wide range of ULVZ characteristics reported by previous studies and limited seismic coverage of the lowermost mantle have led to many questions regarding ULVZ origins. Using data recorded by stations in Antarctica, the study will examine a variety of core-reflected seismic waves that sample the CMB beneath the southern hemisphere. This region is a unique portion of the lowermost mantle that is located away from large-scale mantle upwellings and downwellings. The researchers will also perform laboratory experiments and develop numerical models of mantle flow to evaluate what ULVZ characteristics would be expected from different potential sources and how those characteristics may vary both in time and space. By combining results from these complementary investigations, they will determine consistent models of ULVZ structure, which will be used to determine the origins of these deep Earth anomalies and the role they play in the evolution of our planet. Both participating universities are Minority-Serving Institutions, and through collaborations with the American Geophysical Union and the EarthScope Consortium, the project will provide multi-mentored research opportunities for students underrepresented in the geoscience. By working with scientists from different fields, who are collaborating to solve geologic problems, the students will gain valuable training that will help prepare them for their future careers. Ultra-low velocity zones (ULVZs) are anomalous structures along the Earth’s core-mantle boundary (CMB) that are characterized by significantly reduced seismic velocities and, in some cases, increases in density. Given limited geographic sampling of the lowermost mantle as well as modeling trade-offs between different ULVZ properties, many questions persist regarding ULVZ origins, their distribution, and the role they play in the evolution of our planet. Using seismic data recorded by stations in Antarctica, this study will provide the first multi-phase, frequency-dependent assessment of ULVZ characteristics, with a focus on the lowermost mantle beneath the southern hemisphere. This portion of the CMB is unique because it is located away from current subduction systems and from the Large Low Velocity Provinces beneath Africa and the Pacific. Mineral physics analyses and geodynamic simulations will also be performed to evaluate what lowermost mantle properties would result from different potential ULVZ sources and how those properties would vary in both time and space. The complementary approaches will be used to create new, internally consistent maps of ULVZ structure beneath the southern hemisphere, thereby allowing the researchers to determine which lowermost mantle processes critically contribute to ULVZ origins. Additionally, through collaborations with the American Geophysical Union and the EarthScope Consortium, they will provide education and research opportunities for students underrepresented in the geosciences. By working with scientists in different Geology disciplines, who are collaborating to solve Earth structure problems, the students will gain valuable training that will help prepare them for their future careers. This project is jointly funded by Cooperative Studies of the Earth’s Deep Interior (CSEDI), the Established Program to Stimulate Competitive Research (EPSCoR), and Office of Polar Programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Existing reinforcement learning (RL) approaches usually assume that a learned policy will be deployed in the same environment as the one it was trained in. Such an assumption is often violated in practice, due to e.g., adversarial perturbations, modeling error between simulator and real-world applications, non-stationary environment, and limited amount of training data. The discrepancy between the training and test environments gives rise to a model mismatch, which lead to a notable decline in performance and restrict the suitability of RL in crucial domains, e.g., healthcare, critical infrastructure, transportation systems, and smart cities. To address the above challenge, there have been noteworthy efforts to develop distributionally robust RL approaches. This CAREER project aims to advance the fundamental algorithmic and theoretic limits of distributionally robust RL. The research outcome of this project holds the promise to push the algorithmic and theoretical boundaries of robust RL, and will deliver provably convergent, efficient and minimax optimal robust RL algorithms. The project will have a significant impact on theory and practice of sequential decision making in various domains, e.g., special education, intelligent transportation system, wireless communication networks, power systems and drone networks. The activities in this project will provide concrete principles and design guidelines to achieve robustness in face of model uncertainty. The integration of research work into education and outreach will target K-12 educators, graduate, undergraduate and underrepresented students with efforts on (i) Artificial Intelligence (AI) summer camp for K-12 educators; (ii) Buffalo Day workshop; (iii) curriculum development; (iv) student supervision. The research efforts are organized around three complimentary thrusts: (i) Thrust A focuses on developing theoretical and algorithmic foundations for distributionally robust RL under the long-term average-reward criterion. (ii) Thrust B focuses on developing a unified framework of distributional robustness for learning (robust) policies from offline dataset without active data acquisition and exploration, and further uncovering their fundamental limits; (iii) Thrust C focuses on constructive approaches and fundamental limits of robust RL under constraints, i.e., optimizing reward while simultaneously guaranteeing constraints under model uncertainty. This project will develop fundamental understandings of robust RL, minimax optimal robust RL algorithms and novel technical convergence and complexity analyses. The research outcome will significantly improve the robustness of RL algorithms and will be of interest to a broad range of communities, e.g., machine learning, statistics, information theory, networking, communication, power, and education. The proposed work will also foster new interdisciplinary research directions across these research 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.

NSF Awards · FY 2024 · 2024-09

The current development cycle of Artificial Intelligence (AI) methods and tools typically involves collecting data, using the data to construct a representation (known as feature spaces), and applying machine learning models to categorize examples using this feature space representation. Building an optimal and appropriate feature space is essential as it can be used to reconstruct distance measures, reshape discriminative patterns, and enhance the AI readiness (structural, predictive, interaction, and expression levels) of the data. Appropriate features are extremely important for real-world deployments across both scientific and industrial applications. This project seeks to create a more automated and generic framework, along with effective tools, to distill fundamental knowledge of feature spaces and build an AI-ready feature space. Artificial intelligence has the potential to deliver far better features than human engineers can. This project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search methods. The educational component of this project includes developing a new curriculum of data centric AI and provides students from under-represented groups with opportunities to participate in research. This project addresses an important problem: feature space construction learning. The unique perspective is to view feature space construction as a cross-sequence feature-generation task. The project proposes new techniques for feature learning, generalization, and supporting robustness to data imperfections. Specifically: 1) This project proposes a principled deep EOG (embedding-optimization-generation) framework to distill feature knowledge, convert discrete search in feature space into efficient continuous optimization in embedding space, and reduce feature space reconstruction to sequential generation; 2) This project develops generalization strategies to achieve task-agnostic, label-free learning, transferability, and distribution shift awareness in generative feature transformation; 3) This project develops graph topology-aware generation, reinforcement augmentation, variational smoothing, and adversarial robustness to handle complex attributed graphs and weak training data, ensuring data-efficient and robust learning. Finally, this project incorporates the proposed methods into systems for modeling material formula interactions and for composing and reconstructing polymer configuration indicators for screening polymer performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Earth's core is the source of the magnetic field and is composed mainly of iron with a smaller amount of light elements. Despite the core’s significance, the extreme pressure and temperature conditions in the region make it challenging to study the constituent materials under relevant conditions. This research project, a collaboration among scientists from Arizona State University, Carnegie Institution, and University of South Florida, will investigate the properties of iron-sulfur (Fe-S) alloys under the pressure-temperature conditions of the core for their crystal structures, melting, and crystallization processes. The team will perform dynamic compression experiments to the necessary pressures and temperatures, paired with molecular dynamics simulations to aid in the interpretation of the results. One of the fundamental questions this project seeks to answer is how sulfur, an important light element in the Earth's iron core, influences the melting behavior and crystal structures of iron alloys at extreme conditions. This information is vital for understanding the complex structures observed in the Earth's core and could reveal the origins of its heterogeneities. Beyond its scientific goals, this project has significant educational and societal impacts. It provides interdisciplinary training for early career researchers and offers hands-on research opportunities for undergraduate students, particularly those underrepresented in the geosciences. The project's findings will be integrated into educational materials, enhancing science education through accessible, high-quality resources. In this two-year collaborative research project, the team will perform dynamic-compression experiments and molecular dynamics simulations to study the crystal structure, melting, and crystallization of iron-sulfur (Fe-S) alloys at the pressure-temperature (P-T) conditions of the Earth’s core. This work aims to provide essential data for understanding the temperature and structure of the Earth’s core. The Earth's metallic core, generating a magnetic field, presents complex structures revealed by recent seismic studies. However, the extreme P-T conditions pose significant challenges for experimental investigations of core constituents. These dynamic compression experiments will be enabled by sample synthesis in ASU’s FORCE facility. Using large laser facilities, the project will access a range of compression pathways, enabling in-situ X-ray diffraction for monitoring the phase changes in the iron-sulfur alloy system. More specifically, shock-ramp experiments enable an initial shock melts the sample, followed by isentropic compression to re-solidify the sample at high P-T, directly observing the crystallization of Fe-S liquid into stable alloy phases. Complementary machine learning molecular dynamics simulations will model Fe-S behavior under dynamic compression, providing insights into phase transitions and crystal structures. This research addresses key questions: What crystal structures are stabilized in the Fe-S system at core conditions? How does sulfur influence Fe melting? Can Fe-S crystallization explain inner core heterogeneities? The project supports three PIs’ collaborative mentoring of three early career researchers (two postdocs and one Ph.D. student), offering interdisciplinary training in dynamic compression, static compression, and molecular dynamics. Data analysis and simulation codes developed will be included in Jupyter notebook teaching modules, shared for educational and research purposes. Outreach will involve public presentations at ASU and Carnegie Institution, highlighting the Earth's core's significance. This project is co-funded by the Geophysics and Petrology and Geochemistry Programs in the Division of Earth Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the rapid growth of cloud computing, artificial intelligence (AI), machine learning (ML), and scientific computing, a massive amount of unstructured data is being created. To store and access unstructured data, Log-Structured-Merge tree-based Key-Value Stores (LSM-KVS) have become essential data storage systems, widely deployed by most of the large IT service companies. As application workloads vary and continuously scale up, existing LSM-KVS systems, designed based on monolithic servers and shared-nothing architectures, face numerous issues, including resource inefficiency, difficulty in load balancing, low scalability, and poor elasticity. This project aims to develop and optimize LSM-KVS-based systems within disaggregated infrastructure environments, consisting of multiple compute servers and heterogeneous memory and storage farms connected via fast networks. The project will address several fundamental issues, including heavy network traffic between resource pools caused by compaction and shard-migration, memory limitations of read and write buffers, tightly coupled control of otherwise decoupled resource-intensive modules by LSM-KVS, and more frequent and complex transient errors. The goal of this project is to redesign and optimize an LSM-KVS architecture for disaggregated infrastructure, called Decoupled-LSM, to achieve higher performance, better resource utilization, and improved management. The Decoupled-LSM project will devise new techniques that decouple data-intensive modules from the control of LSM-KVS, execute the modules in different resource pools efficiently, and attain high performance, better resource utilization, and greater flexibility in disaggregated environments. Decoupled-LSM will lay the foundation for a new LSM-KVS architecture optimized over disaggregated infrastructure for many critical applications, such as cloud computing, AI, ML, and scientific computing, which impact daily life. Overall, this project can help to better store, use, and manage extremely large-scale unstructured data, used by government, industry, and individuals. The proposed methodologies, system designs, and implemented components will benefit the storage and networking research communities in further developing storage systems for disaggregated infrastructure and cloud computing. The project will also involve students from underrepresented groups and outreach to high schools, along with collaboration with industry partners. 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 aims to address racial equity in engineering education by enhancing the awareness and knowledge of engineering faculty to support Black engineering graduate students. Black students in engineering programs often experience microaggressions, lack of support, and systemic barriers, which impede their academic success and well-being. This initiative aims to disrupt these cycles by equipping faculty with the tools and understanding necessary to be positioned to be actionable in cultivating a more inclusive and supportive environment. By focusing on the three phases of awareness, knowledge, capacity building, and community, the project will provide faculty with empirical data on the lived experiences of Black students, engage them in professional development to build cultural competence, and establish a supportive community of practice. This comprehensive approach not only seeks to improve the academic outcomes and mental health of Black engineering students but also serves as a model for fostering antiracist educational environments across disciplines and institutions. The anticipated outcomes include greater faculty awareness of racial inequities experienced by Black students, improved faculty-student rapport and relationship, and a reduction in the harm done to Black engineering graduate students. This project has the potential to advance social justice, contribute to a more equitable academic landscape, and inspire similar initiatives in other fields and institutions. To address systemic racial inequities faced by Black engineering graduate students, this research is situated in the theory of racialized organizations and seeks to develop a comprehensive professional development program that increases faculty awareness of the unique challenges faced by Black scholars. Using a multimodal, mixed-method approach, the project will compare three educational modalities (e.g., case study, 2D-video, and immersive virtual reality simulations) to determine the most effective method for fostering faculty awareness and resonance of the lived experience of Black graduate scholars in engineering. Conducted with engineering faculty at Arizona State University (ASU) and George Mason University (GMU), cohorts will participate in the Positioning Faculty for Antiracist Orientations (PFAO) program anchored in the High Impact Cultural Competency framework. This program is designed to build cultural competency while establishing a supportive, longitudinal community of practice of Engineering faculty committed to racial equity. The project will leverage previous NSF funded work centering Black students as experts of their own experiences in applying their insights to inform the development of educational content. Over five years, the project will directly impact 90 engineering faculty, a novel and significant effort focused on the gatekeepers of engineering culture. The study's findings have potential implications for higher education, providing a model for capacity building and positioning antiracist orientations that can be adapted to support other minoritized groups. This work is supported by an interdisciplinary team and aims to contribute significantly to the fields of Engineering, Education, Psychology, and Computing. 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

This EArly-concept Grant for Exploratory Research (EAGER) project will support research that intends to create a new modeling and learning framework to enable a human and robot to coordinate efficiently through sensorimotor interactions in urgent and safety-critical tasks. Human-robot sensorimotor interactions have shifted the paradigm in manufacturing, transportation, and healthcare over the past decade. However, most existing robots can only function in human-led, highly repetitive, and slow tasks since there is a lack of understanding of human decisions in urgent and dynamic tasks. This project integrates ideas from behavior economics, game theory, optimal control, and human factors to model human decisions under urgency and uncertainty so the robot can leverage such models for safe and fast co-adaptation with the human, with applications in a balancing task when a human rides a robotic bicycle. This research project will promote the progress of science and advance national health by enabling a human and robot to effectively coordinate in urgent and risky situations, which contribute to many failure cases of existing human-robot systems. Intended outcomes from this project will not only improve safety and productivity of future human-robot teams, but also contribute to improved and calibrated human trust to new robotic technologies. The impact of this project will be broadened by new curriculum and undergraduate research opportunities on human-robot sensorimotor interaction, and by establishing an interdisciplinary research community on cycling safety, biomechanics, and assistive technologies. This research project will develop a mathematical framework for human-robot mutual learning and adaptation by studying a novel balancing task jointly performed by a human rider and robotic bicycle. Specific objectives of this project are to 1) model human responses to balancing perturbations when riding a bicycle, 2) develop a game-theoretic robot controller to safely co-adapt with the human rider, 3) understand how the task performance varies with different task conditions originated from the human, robot, and environment, and 4) understand how humans develop reliance on the robot assistance and how that affects their riding behavior in the long term. This EAGER award has been co-funded by the Dynamics, Controls, and System Diagnostics and the Mind, Machine, and Motor Nexus Programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The project’s objective is to continue to nurture the next generation of law-and-science scholars by administering a Law and Science Dissertation Grant program. The current project continues a program that facilitates the production of high-impact, interdisciplinary law-and-science scholarship as well as aids in student retention, dissertation completion, and student’s subsequent performance in job procurement, publications, and grant success. Using prior NSF funding (2016661), the Law and Science Dissertation Grant Program, administered by Arizona State University, accepted its first proposals in Spring 2021 and over the next three years received 168 proposals from students at 63 different universities. Between the start of 2021 and the end of 2023, the program made 32 awards to graduate students at 23 different universities. The current project continues and enhances that program by issuing approximately 6 to 8 awards in each of two cycles a year for the next four years. Each award supports scientifically rigorous research activities by a graduate student in one or more of the fields that constitute the law-and-science research community. Support for a well-trained stream of junior scholars is essential to the overall health of any STEM discipline and the current project enables junior scholars to conduct the innovative research that sets them on a successful career trajectory. In addition, the collaborative nature of the project--including the many disciplines that constitute the law-and-science research space in an oversight role -- strengthens the connection among the professional societies that comprise the law-and-science research community. The principal broader impact is in terms of improved STEM education by facilitating the more effective training of graduate students. The funding will enable awarded students to engage in meritorious and unique scientific research that they might not be able to conduct otherwise. 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

Engineering design courses are a staple of the undergraduate engineering curriculum. These courses provide students with opportunities to tackle real-world problems before encountering them in the workplace. The courses require students to integrate their creativity, critical thinking, and problem-solving skills. Design courses are crucial aspects of the professional formation of engineers and, therefore, are essential to the competitiveness of the nation’s scientific workforce. The research team will conduct interviews and then develop and deploy a survey focusing on assets that minoritized students bring to the engineering design process. This project provides a perspective that is currently missing from the professional formation of engineers and will help educators improve the engineering curriculum by making it more inclusive for all students, ultimately helping strengthen the workforce. The project will use an exploratory sequential mixed-methods research design to expand community cultural wealth theory for application in engineering design courses. Recruiting through design-based course instructors, the researchers will conduct two ethnographic interviews with approximately 12-15 minoritized undergraduate students at varying stages of their undergraduate studies. The interview series will focus on students’ linguistic, resistant, navigational, familial, social, and aspirational capital and how design experiences allow them to practice these strengths. Researchers will employ inductive and deductive thematic analysis as well as critical counternarrative analysis. We will publish critical counternarratives to elevate the lived experiences of minoritized engineering students in design-based courses from an asset perspective. The thematically analyzed interview results will include a framework of design-based community cultural wealth working definitions. The researchers will seek feedback from 10-12 faculty experts who teach engineering design courses. The researchers will use critical quantitative methods to design and validate a design-based community cultural wealth survey instrument with students at partnering ABET-accredited institutions. First, the team will deploy the survey to a large and diverse sample of 500-800 engineering students and conduct exploratory factor analysis. The following year, they will relaunch the survey with an additional 500-800 engineering students and conduct a confirmatory factor analysis. The final survey instrument and its accompanying ethical-use manual will provide a way for design-based course instructors to understand the extent to which their students believe they have had the opportunities to practice their design-based forms of capital and the impact of these opportunities on their engineering self-efficacy and identity development. Workshops facilitated by the project team provide an opportunity for educators at partnering institutions and others around the country to collaboratively develop plans to use the instrument and address equity gaps in how design courses are taught. The project will help to promote the integration of asset-based approaches into students’ professional formation to combat disparities in engineering education, a necessary step in building a diverse engineering workforce equipped to tackle the complex challenges of the 21st century. 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

Our world is at a critical juncture facing environmental and societal challenges that demand innovative solutions from the engineering sector. This project seeks to transform engineering education by deeply integrating sustainability concepts into the curricula across three diverse universities: Arizona State University, Kennesaw State University, and Villanova University. This transformative initiative aims to fundamentally reshape how future engineers are trained, emphasizing the crucial role of sustainability in engineering practices. This project aligns with the goals of the National Science Foundation and The Lemelson Foundation who both strive to advance science and engineering education, addressing environmental and social sustainability, thereby enhancing the capability of the next generation of engineers to drive sustainable development. In this project, we will pioneer a network that promotes systematic improvements in engineering educational practices to prepare students to be systems thinkers and sustainable in their approaches. Simultaneously, this network will offer faculty a community that they can rely on in their continuous practice of learning, analyzing, and integrating evolving sustainability concepts into their courses. This initiative serves as a public testament to the value of investing in educational transformations that prepare engineers to tackle environmental and social challenges. The project will be executed in three phases over a period of two years, employing a dual-layered approach to engage both faculty and students in the transformative educational process. The initial phase involves establishing a network and developing collaborative action research projects, which enable faculty members from three institutes to explore and refine how sustainability concepts are implemented in their classrooms. The subsequent phase focuses on the continuous refinement of these practices in a collaborative community of practice through iterative action research cycles—planning, acting, observing, and reflecting. This approach not only aims to improve engineering teaching methods but also to deepen our understanding of the dynamics influencing faculty motivations and challenges in integrating sustainability into engineering education. The final phase focuses on dissemination, aiming to scale successful practices and foster a broader adoption of sustainability-focused education in engineering. Research questions will uncover the curricular and instructional changes that promote student learning of sustainability and the factors affecting faculty efforts of sustainability integration across diverse institutional contexts. Anticipated outcomes include the documentation of effective instructional strategies that enhance student understanding of sustainability in engineering problems, the advancement of faculty teaching methods, and better understanding of the processes that aid engineering faculty in integrating sustainability into their teaching. Ultimately, this project seeks to foster a cultural shift towards sustainability in engineering education, thereby equipping future engineers with the knowledge and skills necessary to lead societal progress towards a more sustainable and equitable world. 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 Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Over-reliance on fossil-derived compounds to generate energy and chemical products has led to substantial emissions of carbon dioxide (CO2) and other greenhouse gases (GHGs). Immediate steps are needed to create a circular carbon economy, where CO2 is effectively reused and valorized into new products. This project advances the science and technology of reusing CO2 captured from air into potentially valuable products, specifically into biofuels. The overarching goal of this project is to achieve full CO2 utilization towards renewable-fuel production via an integrated nanomaterials light-based (photocatalytic) process combined with bioprocesses. The project will develop a core fundamental-level understanding of how nano-scale catalyst structure can controllably result in photocatalytic CO2 reduction to produce the one-carbon compound carbon monoxide (CO), which serves as an intermediate chemical converted to biofuels by bacteria. The project also will help meet the demand for a skilled workforce and accelerate STEM education. The project will train and encourage motivated teachers and students from local high schools and community colleges, particularly those from underserved groups, to participate in the hands-on summer research experience. The experiences will nurture nanotechnology and biotechnology principles in high school science laboratories. The project also will stimulate public interest by disseminating scientific findings through social media supported by the NSF Southwest Sustainability Innovation Engine, Global Center for Water Technology, and the Biodesign Institute at ASU. The research will develop the science and engineering foundations for a new, synergistic system capable of advancing the circular carbon economy. One of the project’s fundamental insights includes advancing reactor design by improving the efficiency of using light energy by employing nanoscale-photocatalysts coated on optical fibers and by understanding how water chemistry affects CO2 reduction. Nanotechnology will be used to optimize the material structures of the catalysts. Novel techniques to apply nanomaterials to optical fibers that side-emit light will eliminate reliance upon slurries of catalysts. The optical fibers also optimize utilization of light supplied into the reactors. To maximize CO2-delivery efficiency, the gas-permeable fibers can prevent bubble formation during gas delivery and achieve 100% transfer efficiency. The combined optical fibers plus gas-permeable fibers in one reactor are a unique technological advance that will be optimized to convert CO2 into CO. Then, the CO will be converted into longer-chain organic compounds via microbial chain elongation using a model bacteria strain. Key to chain elongation will be the ability to manage bacterial biofilms of a synthetic Clostridium coculture through optimized CO delivery and utilization that relies on the control of inter-strain metabolic interactions. The synergistic photocatalytic-bioprocessing platform will achieve efficient CO2 conversion and valorization while promoting environmental and economic 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 · 2024-08

This project aims to serve the national interest by enhancing conceptual learning in quantum and semiconductor physics using interactive visualizations and simulations. This Engaged Student Learning, Level 3 project will conduct the first large-scale investigation into the design of educational tools and design features that can significantly improve conceptual understanding in quantum and semiconductor physics in particular, and STEM in general. The project will develop more inclusive tools for a diversity of use cases and assess their utility and effectiveness across different educational environments. While interactive visualization and simulation tools have made progress in mitigating certain student misconceptions, there is a lack of comprehensive studies examining the design efficacy of these tools. Existing research in this area is constrained by both limited size and scope. This project will expand and refine the interactive visualizations and simulation tools previously developed by the team with a focus on aiding the conceptual understanding of quantum mechanics and semiconductor principles among undergraduate students. Additionally, the project will carry out extensive studies on the effectiveness of these tools across diverse educational environments. As a consortium of researchers spanning five diverse universities and colleges, the team will investigate the following research questions: 1) To what extent can such tools change undergraduate students' conceptions of Quantum Mechanics and Semiconductor Physics and 2) How does the design of such tools affect students’ conceptions of these topics? The team will conduct two large-scale mixed-methods controlled studies using a concurrent triangulation design. Both quantitative and qualitative data will be collected and analyzed. Analysis methods include multilevel modeling, repeated measures multivariate analysis of covariance, and qualitative content analysis. 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 · 2024-08

Recent advances in deep reinforcement learning (RL) have shown impressive results across a variety of applications. However, the broader application of RL often faces significant challenges, particularly in real-world scenarios such as robots interacting with the environment or autonomous driving. The challenges in these complex environments are due factors such as the intricacy of the task requirements and/or few opportunities for the system to know the response is correct (i.e., sparse reward functions). Moreover, extensive physical interactions with the environment are costly because they take considerable time and staffing and sometimes even unsafe due to potential physical interactions during exploration. Leveraging the principles of active learning and curriculum RL, the project seeks to enhance the performance of RL systems in completing difficult tasks in complex environments, while optimizing resource allocation and reducing the need for expensive environment interactions. This project intends to fundamentally reshape the RL landscape by developing task and environment representations specifically for active design in RL. More concretely, this project is structured around four interconnected thrusts. First, Active Environment Design for RL (ACED-RL) aims to identify a sequence of auxiliary environments that best facilitate learning in the target environment. Second, active task design for RL seeks to establish a scalable active task selection strategy, allowing the learner to be trained sequentially in these tasks, facilitating RL, and transferring the acquired knowledge to the target problem. Third, active joint task and environment Design combines active task and environment design to generate RL curricula. This approach extends to settings involving multiple agents, accounting for challenges posed by simultaneous learning and non-stationary agent behaviors. Finally, the project will evaluate the proposed approaches across various high-impact machine learning applications, including standard RL benchmarks, autonomous driving, robotic manipulation, and scientific experimental design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This multi-method, interdisciplinary, research project investigates the prevalence of demographically coded language in opposition to multifamily housing (MFH), and its effects on housing/zoning decisions. Textual data on MFH decision making are taken from case transcripts from federal and state fair housing lawsuits and recently decided cases in Arizona. The study more generally draws upon data that include meeting minutes, legal decisions, news articles, and administrative data. The research methods include manual textual and thematic analysis of textual data on MFH decision making, machine learning textual and thematic analysis of such data, an online cultural consensus theory-based survey, and qualitative comparative analysis of interview data. Stage 1 of the research collects publicly available textual data on MFH decision making from federal and state fair housing lawsuits and Arizona (AZ) news media and public meetings between 2021 and 2024. Manual and machine learning (ML) textual and thematic analysis, a meaning decoding test, and descriptive statistics are used to identify the prevalence of demographically coded opposition to MFH and to develop a preliminary code words corpus and decoding guide. Stage 2 deploys in-depth interviews and a cultural consensus theory (CCT) analysis with an estimated 100 MFH stakeholders in a subsample of 20 lawsuits and AZ cases to help verify the corpus with a second application of the “decoding test” and refine the guide. Manual and ML thematic analysis, qualitative comparative analysis, and ML textual and statistical analysis are then employed to investigate the qualities and dynamics of demographically coded opposition. The research contributions will include a refined theory of coded demographic language in MFH decision making, a corpus and decoding guide to assist future research on use of demographically coded language in housing settings, insights on the benefits and drawbacks of using ML to study demographically coded language, and fair housing agency data collection, training, and public education 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 · 2024-08

The giant planets (Jupiter, Saturn, Uranus, & Neptune) contain more than 99% of the mass in the solar system except for the Sun. However, our understanding of the composition and the interior of these planets is still limited. While Jupiter and Saturn contain mainly hydrogen (H) and helium (He), Uranus and Neptune have thinner H-He envelopes, making them key to understand the structure and the composition below the gas envelope in the context of planet formation and origins of the solar system. This project will systematically map fluid-silicate interactions over an array of pressure, temperature, and H2/(H2+H2O) ranges, and then couple the experimental results with interior evolution models to understand the formation of Uranus and Neptune. This project will provide a unique collaborative research opportunity between mineral physics and planet evolution theory for an undergraduate researcher and a postdoctoral researcher, training early career scientists in multi-disciplinary research that is particularly relevant to exo-planet science. Recent pilot experiments found that H2/H2O ratio influences fluid-silicate reactions and therefore this ratio is key for understanding the interiors of Uranus and Neptune. Even though the pressure expected for the region (10–200 GPa) is accessible in experiments, little is known about the behavior of materials in various H2-H2O rich conditions. This project will experimentally measure reactions between H2-H2O fluid and silicates at high pressure -temperature conditions and will determine how the metallization of hydrogen and the superionicity of H2O above 100 GPa can change the fluid–silicate reaction. The experimental results will be coupled with planetary interior evolution models to characterize the composition profiles and thermal evolution of Uranus and Neptune. Results from this project will also benefit the exoplanet community by develop a more thorough understanding of the behavior of materials and reactions realistic for sub-Neptune exoplanets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The reionization of the Universe is a cosmological event driven by young galaxies that resulted in the ionization of 99.99% of the hydrogen in the intergalactic medium (IGM) by a redshift of ~6. Identifying the population of galaxies responsible for reionization has been very difficult, because ionizing flux from these galaxies is absorbed in the neutral IGM. The team will address this by studying galaxies at lower redshift that resemble those at z > 6 (Lyman Continuum Emitters, LCEs). The team will also create two 40-minute planetarium shows as part of the department’s outreach program. The shows will be hosted at the Marston Planetarium at Arizona State University. They will incorporate these shows into the K–12 field trip program that brings in 10,000 middle-school students from the Phoenix metropolitan region every year. The team obtained 221 hours of VLA observations to perform the first radio continuum (RC) study of a statistical sample of 80 confirmed LCEs with well-measured escape fractions. RC is a powerful dust-independent tracer generated by thermal (free-free) and non-thermal (synchrotron) processes. However, it depends on the number of free electrons and the magnetic field. Therefore, RC complements UV or optical traces and adds a new dimension for testing feedback models. The new RC data, when combined with ancillary data from other wavelengths, will yield constraints on theoretical models and physical conditions, e.g., age of the stellar population, radiation pressure-driven feedback, stellar winds, supernova feedback, etc., that results in the leakage of ionizing flux from these systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Semiconductors are fundamental to modern technology, driving advancements in fields such as communication, healthcare, and national security. To maintain global competitiveness in the semiconductor industry, the U.S. must cultivate a skilled and diverse workforce. However, many young individuals, particularly those who are neurodivergent, lack opportunities to engage with the foundational elements of electronics in a hands-on and creative manner. This project will investigate how neurodivergent youth can stimulate community-centered learning of electronic circuits through playful arts-based practices in underserved public spaces. The project aims to amplify engagement with electronics and semiconductors by integrating musical circuit bending, which involves creatively manipulating circuits to produce new sounds or effects, into informal learning settings during public events where music and technology are interdependent. Recognizing neurodiversity as a baseline, the project seeks to generate informal teaching and pop-up learning practices that cater to the unique characteristics of all learners. By introducing practical skills through an early-intervention STEAM-based approach, the project will create multiple pathways into the world of circuits and semiconductors. Utilizing the HOMAGO (Hanging Out, Messing Around, Geeking Out) framework, the project will connect youth through hands-on, minds-on, hearts-on, and social-on experiences. This initiative will directly impact 12 neurodivergent youth community educators who will participate in a one-week intensive course on electronic circuits and subsequently facilitate workshops on circuit bending at two community pop-up events, engaging at least 100 pop-up attendees. This effort aligns with the goals of the CHIPS and Science Act to increase interest in STEM fields by supporting the growth of the semiconductor workforce in Arizona. Partnerships with local community colleges offering accelerated semiconductor workforce training programs will further reinforce this project. This EAGER project will introduce circuits and semiconductor technology to neurodivergent youth community educators using arts-based informal learning methods in underserved public spaces. The study will employ a STEM-rich tinkering approach, focusing on experiential learning and the HOMAGO framework to enhance meaningful and multisensory learning. Circuit bending offers an innovative, cost-effective approach to engaging with electronics, fostering creativity and problem-solving. The project will involve 12 neurodivergent youth community educators who will receive training to facilitate pop-up learning events, fostering personalized learning that adapts to the individual needs of neurodiverse learners and encouraging expression and social interaction. Additionally, up to 20 students in total will participate in the circuit bending course for free. The project will host two pop-up events, each engaging at least 50 attendees per event with the potential to impact many more, providing participants with circuit bending kits and educational materials. The research team will utilize mixed methods research design to collect both quantitative and qualitative data, analyzing engagement patterns and the development of STEM identities among participants. Data collection will include video journals, interviews, quizzes, surveys, and video recordings of pop-up events. Anticipated project outcomes include increased interest in STEM careers, enhanced leadership skills among neurodivergent youth, and a greater public understanding and appreciation of semiconductors and electronics. This project will promote inclusive, diverse learning environments and contribute to workforce development in the semiconductor industry. This project is funded by the Advancing Informal STEM Learning (AISL) program, which supports projects that: (a) contribute to research and practice that considers informal STEM learning's role in equity and belonging in STEM; (b) promote personal and educational success in STEM; (c) advance public engagement in scientific discovery; (d) foster interest in STEM careers; (e) create and enhance the theoretical and empirical foundations for effective informal STEM learning; (f) improve community vibrancy; and/or (g) enhance science communication and the public's engagement in and understanding of STEM and STEM processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project addresses alarming concerns about growing plastic pollution observed in rivers around the world. Microplastic particles, with sizes typically below 100 microns, account for over 80% of the plastic waste in rivers. A large amount of microplastic particles ends up within the pores of the bottom riverbed. This leads to the contamination of aquatic habitats and the food chain, as the small size of these particles (about the same size as plankton) makes them easily ingested by fish, oysters, and other animals. Despite these concerns, the processes controlling the trapping of microplastic particles in riverbeds are not fully understood. While effects related to turbulence at the interface between river stream and sediment bed, particle inertia, and biofouling-induced sticking are expected to have a large impact on the retention of microplastic particles within riverbed pores, there are limited studies that investigate these effects systematically. These knowledge gaps will be addressed in this research. Using high-fidelity numerical simulations and theoretical modeling, this project will reveal the physical processes involved in the trapping of microplastic particles in riverbeds and build a reduced-order model to predict the trapping rates efficiently and accurately. This research will enable a more accurate assessment of the impact of microplastic pollution on ecosystems and inform potential remediation strategies, such as new filtration methods implemented near sources of pollution (e.g., in wastewater treatment plants). Further, by involving undergraduate researchers hired from the diverse pool of students at Arizona State University (a Hispanic-serving institution) and Iowa State University, this project will support diversity in engineering and promote teaching, training, and learning. The goal of this project is to reveal processes that control the trapping of microplastic particles (MP) in riverbeds and model the trapping rates. The research leverages synergistic Pore-Resolving Direct Numerical Simulations (PR-DNS) and theoretical modeling. PR-DNS of MP-laden turbulent flow over a model riverbed will be carried out to reveal the microscopic effects at the pore-level. Scaling laws for the MP effective diffusivity that account for stream turbulence, MP inertia, and sticking will be extracted from simulations. Predictive models for the MP retention rate will be derived using Population Balance Modeling and simulation data. These models will incorporate turbulence and inertial effects from first principles. The combined numerical and modeling work will make it possible to predict the retention rates of MPs for a wide variety of flow, bed, and particle configurations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project investigates changes in prehistoric human foraging, mobility patterns, and population dynamics in response to environmental change during the later Pleistocene. The research provides broader context to occupations in a coastal region by comparing the results of new archaeological data analyses to detailed models of the paleo-environment and human foraging patterns on the landscape. This project represents a further step towards understanding how foraging human populations use entire landscapes to extract resources and leave material traces of their behavior, even if only small portions of that material culture are ever recovered through excavation. Agent-based computer simulation modeling bridges the conceptual gap between a robust understanding of foraging decisions, from optimal foraging theory and human behavioral ecology, to the long-term accumulation of stone, plant, and animal remains in the archaeological record. Through a collaboration a multi-institutional collaboration the project expands a partnership with an institution that services mostly students from previously disadvantaged groups. Researchers plan to recruit students to join field teams to work at an archaeological site and to work as research assistants to learn faunal analysis of excavated assemblages. The project will also improve public engagement with science by helping to design an exhibit at a world cultural heritage site. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project will develop novel pedagogies based around use of storytelling visualizations to teach data privacy. As society is adopting data-driven decision making across many aspects of life, there is growing concern regarding the privacy and security of data collected for these purposes and an increasing need for a STEM workforce that is well-versed in privacy protection. There are significant opportunities to make privacy education more accessible to a broader population via the use of technology enhanced learning tools and curriculum. Specifically, the use of storytelling and interactive visualizations have been shown to inspire engagement with content, creative thinking, and a deeper understanding of the subject matter. The goal of this project is to develop these artifacts, deploy them in classrooms, and assess their impact on student learning outcomes as well as instructor educational practices. This project will analyze collected data to create a novel pedagogical framework for privacy education based around the implementation of storytelling visualization techniques. The project has two primary objectives: (1) It will develop a novel plugin for the Common Online Data Analysis Platform (CODAP), a widely used platform for teaching STEM and data science topics using visualizations. The plugin will support visualization of data privacy concepts for teaching and create a set of associated learning modules that can be integrated into privacy curriculums. (2) Both the plugin and the findings will be integrated into empirically driven pedagogical practices for teaching data privacy using a storytelling visualization approach. In addition to making all learning modules, artifacts, and research findings public, the project team will conduct training workshops and provide ongoing support to help other instructors adopt the plugin and learning modules as well as create new modules. The team will work towards building an active community of instructors around visualization-based privacy teaching. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. Support is also provided by the Improving Undergraduate STEM Education (IUSE:EDU) program. The IUSE:EDU program supports research and development projects to improve the effectiveness of STEM education for all students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.