Arizona State University

NSF Awards · FY 2025 · 2025-08

Understanding human responses to change is essential to our knowledge of past, present, and future human behavior. Contributing to questions of human adaptability in long-term human-environment relationships, this study assesses how human social dynamics and strategies and technological innovations relate to environmental shifts. To achieve this goal, the study integrates archaeological and paleoecological data with existing knowledge. The study strengthens USA STEM workforce by training K-12 students on annotated 3D artifact models, immersive site visualizations, and virtual tours. The study also offers training opportunities for students in higher education. This study advances NSF investments in understanding human adoption of biotechnology innovations through its implementation of biotechnology methods in its analysis of stable isotopes and phytolith composition. Excavations are planned using current archaeological information that is integrated with 3D models. Material culture is recovered, provenience is detailed, and all evidence is analyzed applying existing standard methods. Bulk and micromorphological samples are collected from strata and excavation profiles, respectively. Microbotanical remains (pollen and phytoliths) are recovered and classified by taxon. Additional paleoecological information is obtained by applying flotation techniques. Stable isotope information is obtained from unmodified eggshells. Micro and macrofaunal remains are recovered and identified. Cultural and paleoecological data are integrated and interpreted applying existing ecological knowledge. 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 2025 · 2025-08

Propylene is a vital feedstock for producing plastics and chemicals. Propylene feedstocks typically also contain large fractions of propane. Accordingly, propylene/propane separation is an important industrial process. The workhorse separation process of the petrochemical industry is distillation. However separating propylene from propane by distillation is energy-intensive due to their similar boiling points. Membrane technologies are an efficient alternative for this separation. One promising approach is to use advanced materials called metal-organic frameworks (MOFs). MOFs act like sieves that separate molecules based on size and chemical interaction. However, existing MOF membranes show inconsistent performance, poor stability, and difficulty in large-scale manufacturing. This project focuses on the design and synthesis of membranes made from a new MOF material which is more stable and more efficient for propylene/propane separation. This research aims to make propylene/propane separation processes more reliable, efficient, and environmentally friendly. Other benefits of this project include educational opportunities, contributions on membrane technology to a public encyclopedia, and scientific outreach. This project addresses the critical limitations of current metal-organic framework (MOF) membranes for the separation of hydrocarbons. The project will synthesize high-performing polycrystalline membranes with sorption-enhanced molecular sieving selectivity for mixtures of propylene and propane. The focus is on a novel MOF material called ZU-609, which has high adsorption capacity and diffusivity for propylene, but negligible adsorption capacity and low diffusivity for propane. It also shows superior structural stability as compared to other rival materials. These properties make this new MOF material an ideal candidate for membranes with high propylene/propane selectivity (>300) and propylene permeance (>1×10⁻⁸ mol/(m²·s·Pa)), while offering greater reproducibility and scalability than conventional metal-organic framework membranes. The research will synthesize thin, defect-free membranes of the new MOF material on suitable supports by the methods of conventional seeding and reactive seeding with 2D-structured copper oxide substrate, followed by secondary crystal growth. The membranes will then be evaluated for their gas permeation, separation performance, stability, and reproducibility. Comprehensive structural and functional characterization will provide insights into the design of robust, scalable membranes for industrial hydrocarbon separations. Beyond technical innovation, this project will offer comprehensive training for graduate and undergraduate students in membrane science, nanomaterials, and separation technologies through research, coursework, and hands-on lab experience. Additionally, it will conduct public outreach to disseminate information on inorganic membranes, helping to bridge educational gaps and enhance public engagement with science. By improving energy efficiency in chemical separations and promoting education and public awareness of membrane science, this work has the potential for broad scientific and societal impact. 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 2025 · 2025-08

As artificial intelligence (AI) applications become increasingly central to our daily lives, the datacenters that serve these applications are growing rapidly. However, this growth comes with a significant environmental cost. These datacenters rely on computer chips whose design, manufacturing, and operation consume substantial amounts of energy, water, and resources, posing challenges to environmental sustainability. This project addresses this critical challenge by rethinking how we design computer chips that power AI in today’s datacenters. Current chip design methodologies often overlook their full environmental footprint, particularly the water and energy impacts across the chip lifecycle. As AI workloads expand, these factors, especially in chip manufacturing and datacenter cooling, are becoming increasingly important to sustainable computing. This project directly addresses these challenges by introducing three key strategies for a sustainability-aware approach to chip design. First, this project develops new models, tools, and metrics to quantify the environmental footprint of chips throughout their lifecycle - from design and manufacturing to deployment and eventual end-of-life. It includes detailed modeling of water and energy consumption, particularly for chip fabrication and chip cooling. Second, the project introduces a framework for splitting large complex chips into small modular chiplets, and replacing them with reconfigurable chiplets allowing their reuse across multiple different AI applications. This increases their operation lifetime in datacenters reducing wastage and cost. Third, the project builds software to optimize chip floor planning and interconnect design to minimize cooling requirements, fabrication energy and environmental costs. This project supports NSF’s mission to advance American innovation, economic prosperity, and global leadership in emerging technologies. It strengthens U.S. leadership in AI and datacenter infrastructure by developing advanced tools to design more efficient, cost-effective computer chips that power these systems. By reducing unnecessary energy and water use, the project improves operational efficiency and lowers costs which are key priorities for sustainable growth and resilient infrastructure. Additionally, it supports workforce development by engaging K–12 and community college students in applied learning experiences that prepare them for high-demand jobs in AI, semiconductors, and advanced manufacturing. This project aims to develop a framework for designing computing chips for AI datacenters. The overarching goal is to minimize the environmental footprint (EFP), including both carbon footprint (CFP) and water footprint (WFP), across the lifecycle of computing - from design to manufacturing to use. The project is structured around three thrusts: (1) Developing models and metrics for evaluating EFP with a special emphasis of WFP, (2) Disaggregating Systems-on-Chip (SoCs) into chiplet-based SiPs with a focus on integrating Field Programmable Gate Array (FPGA) chiplets, and (3) Physical design of chiplet-based Systems-in-Package (SiPs) considering EFP as a metric of optimization. To support EFP-aware chip design, the project introduces new metrics, such as Performance-per-Unit-EFP, to guide architectural and physical optimizations. The modeling framework will extend beyond traditional CFP-based models by incorporating water usage in both semiconductor fabrication and datacenter cooling. A design-space exploration (DSE) framework will be developed using graph-based partitioning methods to decompose SoCs into SiPs. This DSE process will optimize across multiple objectives, including bandwidth, latency, and power, while minimizing overall EFP. Furthermore, the project will model the EFP impact of physical implementation choices, including placement, routing, and 3D stacking, and integrate these models into traditional place-and-route algorithms to enable EFP-aware physical design. This project pioneers the integration of EFP with tools and methodologies used for the architecture and design of future chips. By incorporating WFP, it expands the scope of current sustainable computing research. The open-source software for SoC disaggregation and EFP-aware physical design will help accelerate the adoption of sustainable design practices in the chip design industry. 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 2025 · 2025-08

A strong U.S. workforce is vital to maintain a standard of excellence and global leadership in science, technology, engineering and mathematics (STEM). However, the pace in which Americans are entering the STEM workforce is too slow to meet the pressing needs of industries like healthcare, military, manufacturing, and construction. New training methods are needed to improve how learners acquire STEM knowledge and skills to enter the U.S. STEM workforce. Immersive virtual reality (VR) as a training platform is a solution that can provide individual training and education for all Americans. VR experiences with high engagement are particularly effective as mechanisms for learning new and complex ideas. However, without real-time instructor feedback, many learners struggle to stay engaged. This project explores how eye tracking and artificial intelligence (AI) can be used to measure and guide learner engagement in VR training. By automatically detecting when learners are focused or distracted, the system can provide visual cues to support attention and understanding. The outcomes of this work will help more Americans gain the knowledge and skills needed for high-demand STEM careers. Also, novel opportunities will be opened for improving training approaches for people with attention or cognitive challenges. Overall, the knowledge generated from this project will enhance STEM training outcomes when using VR. This work will empower more Americans to advance our national welfare, prosperity and security. This project investigates how gaze-based models can be used for estimating engagement and reactively guiding user attention within immersive VR training experiences. Specifically, VR training will take place within construction safety training, as this domain has a rapidly growing workforce similar to other STEM disciplines. Currently, the speed at which construction workers and professionals gain knowledge and skills needed to complete job tasks safely remains inadequate. To perform construction work safely, trainees are required to be highly engaged during short, demanding, complex training interventions to reduce fatalities and injuries in the workplace. The project is structured around three thrusts in the following areas: (1) data collection and modeling approaches, (2) reactive designs to support engagement; and (3) validation with construction professionals. First, the project will develop real-time and post annotation methods to label engagement states. This process will use gaze, engagement, and training performance data from VR training sessions. These data will be used to reduce labeling bias and improve model accuracy. Then, machine learning techniques (e.g., deep-learning, spike, liquid networks) will be used to identify key fixations, saccades, and pupillary features as they relate to engagement and performance for building real-time engagement models. Various time scales, features, and models will be explored to produce optimal real-time estimations of learner engagement. Second, the research team will investigate how different activation functions can be used to trigger visual attention cues in response to user engagement levels (low, medium, high). Human-centered interventions will assess how these functions influence learner ability to initiate, sustain, or regain engagement. Third, the project will study the produced models using VR construction safety training sessions with industry professionals. The effectiveness and transferability of real-time attention guidance will be evaluated to improve VR trainings. Ultimately, these thrusts will advance our scientific understanding of gaze-based engagement modeling to learn construction safety materials, use AI to guide visual attention, and improve VR tools for workforce training outcomes. 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 2025 · 2025-08

Vulnerability discovery for software security poses significant challenges due to the vast program state space in complex, real-world programs. This project tackles the challenge of vulnerability discovery in software systems through the enhancement of binary symbolic execution, a technique that simplifies the vast and complex landscape of software operations. Despite its potential, symbolic execution requires substantial expert intervention to manage its complexity, making the process cumbersome and prone to errors. By advancing the automation of this technique, the project promises to significantly boost the efficiency and reliability of detecting vulnerabilities. Advances will improve security for critical infrastructure and software systems by reducing susceptibility to cyber-attacks. The results of the research will be integrated into teaching, outreach and capture-the-flag competitions. This project introduces an innovative approach to improve the scalability of binary symbolic execution, a technique essential for detecting software vulnerabilities. The research will develop a system, referred to as SE-bot, which automates the detection process traditionally performed by human cybersecurity experts. This involves analyzing strategies used by experts in handling symbolic execution tools. These strategies will be decomposed into tasks that can be automated using machine learning techniques to predict and address performance bottlenecks. The system will not only replicate current expert strategies but will also proactively prevent issues before they arise. The proposed research includes three components: 1) detecting performance bottleneck using a combination of machine learning and heuristics, 2) mitigating the slowdown through a number of techniques such as path prioritization and partial execution, and 3) predicting performance bottlenecks and mitigating them. 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 2025 · 2025-08

Discovering planets orbiting other stars can teach us about how they form, and capabilities now exist to find gas giants orbiting at 5-30AU from their stars (an AU is the distance between the Earth and the Sun), like Jupiter and Saturn do in the Solar System. Unlike commonly employed indirect methods to sense a planet, the technique of infrared direct imaging requires that the planet still glows from its heat of formation. Hence stars that have newly formed, which may be accompanied by newly-formed planets, need to be identified and targeted. Imaged planets can be followed up with spectroscopy to characterize their atmospheres and determine their fundamental properties, and about a dozen have been found to date. Sometimes the youth of nearby stars can be inferred from how they move in groups with other stars, however a very limited number of these stars are currently known. In this work, isolated stars that are less than 1 billion years old will be identified, and their ages determined. Some of those stars will be targeted to search for planets, and the full catalog of stars will be published for follow-on imaging by others. Mentoring programs for undergraduate students in summer research will be done with combination of a traditional in-person mentoring at NMSU and an innovative online experience for a larger number of students at ASU. To produce an all-sky catalog of nearby (<50 parsec), young (<1 gigayear) stars, a combination of multiple spectroscopic and photometric age indicators across a range of spectral types will be interpreted with a robust statistical framework. The team will employ X-ray flux, Gaia-measured space velocity, and TESS-derived rotational periods, then confirm their youth with high resolution optical spectra for lithium abundance, H-alpha emission, and calcium emission. They will also screen the targets for binarity with snapshot Adaptive-Optics observations and radial-velocity monitoring. As the youngest nearby stars are identified, the team will begin a direct imaging planet search with available high contrast AO imagers, including Gemini-North/GPI, LBT/LMIRcam+SHARK, and Subaru/SCEx-AO. Undergraduate researchers will participate in observing runs (in-person or virtually), gain experience in data analysis, and present their results at conferences or in virtual reports/posters at team meetings. Graduate students at both institutions will participate in mentoring. 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 2025 · 2025-08

Teams appear in almost any organization such as universities, corporations, and governments. The importance of teams is even more evident with the work practice has been evolving to a new hybrid mode – a combination of work in office and from home which inevitably changes how people collaborate as a team. Consequently, it presents new challenges to team collaborations, in that it increases difficulty of communications, stifles innovation, and affects collaboration. Despite an organization as well as an individual’s profound dependency on teams and the rapid changing landscape of team-enabled operations, computational models, algorithms and tools to optimize the team collaboration are lacking and lagging. To name a few, how to model the multi-channel, multi-platform team collaboration data? How to foresee the rising or the falling of a team at an early stage? How to form a high-performing team as well as to enhance the performance of an existing team? This project develops data mining models, algorithms and tools to optimize team collaboration facing novel challenges in a new hybrid working environment. It consists of three mutually complementary and synergistic research tasks. The first task models the raw team collaboration data to provide a worldview representation of how complex tasks are conducted by teams in multiple channels and platforms. The second task builds multi-task, multi-target predictive models to forecast the performance of a given team. The third task develops algorithms and tools to optimize teams. Specially, it develops data-driven approaches to form and enhance teams. Based on that, it develops reinforcement learning based methods to proactively optimize teams and game-theoretic methods to interactively optimize teams by incorporating user feedback. This project helps improve team efficacy, and optimize human resource allocation, thereby mitigating the challenges that the post-pandemic age has posed to the workforce. The project team actively seeks to engage under-represented students. The research outcome of this project is disseminated through publications, tutorials and open-source software. 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 2025 · 2025-08

This project aims to broaden U.S. student participation at the 2025 ACM/IEEE International Symposium on Machine Learning for Computer-Aided Design (MLCAD), a premier conference at the intersection of machine learning (ML) and electronic design automation (EDA). Since its inception in 2019, MLCAD has evolved into a key technical forum for researchers and engineers exploring the use of ML techniques in all aspects of electronic system design. Jointly sponsored by ACM and IEEE, and supported by leading industry sponsors, MLCAD has grown in both attendance and impact, becoming a central venue for advancing interdisciplinary innovation. Funding from this proposal will support the travel and registration costs of U.S.-based undergraduate and graduate students, enabling them to attend MLCAD 2025 in Santa Cruz, California. Students will have the opportunity to present their research, participate in a contest, poster sessions, attend invited talks and panels, and engage directly with academic and industry experts. These experiences —delivered in a focused, small-format setting — are critical for career development and for fostering the next generation of ML EDA researchers. MLCAD 2025 will be the seventh edition of the symposium, continuing its mission to advance ML techniques for chip design and EDA. The program includes peer-reviewed papers, special sessions, panels, and structured student-centered activities — all supported by academic and industry collaboration. This NSF travel grant will fund approximately 10 U.S.-based students. Priority will be given to students who are actively participating in MLCAD’s student-focused activities, which include (1) ReSynthAI Contest – a logic resynthesis competition using ML techniques to improve timing under physical design constraints, and (2) Artifact Evaluation (AE) – a reproducibility initiative where students submit code, data, and models of their papers for peer review and badging. Applicants will be evaluated based on participation in student activities, financial need, and academic merit. The travel grant will cover registration, airfare, lodging, and local transportation, enabling students to participate fully and benefit from direct interaction with leaders in ML and semiconductor 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 2025 · 2025-08

Coral reefs, vital ecosystems supporting marine biodiversity, coastal protection, and local economies, face severe threats from warming and acidifying oceans as well as from local human activities. The Arizona State University (ASU) ʻĀkoʻakoʻa Coral Facility in Hawaiʻi is uniquely positioned to tackle these challenges by leveraging its strategic location along the 120-mile West Hawaiʻi reef tract – the largest contiguous coral reef system in the Hawaiian archipelago – and its existing infrastructure, which includes the Pacific’s largest outdoor coral nursery. This project will expand the Arizona State University's Ridge to Reef Restoration Center in Hawai'i research facilities to establish it as a global hub for coral reef research, enabling large-scale, interdisciplinary studies on coral resilience to climate stressors and ecosystem-scale restoration. It also integrates community involvement and education by providing hands-on training in coral restoration techniques to local students, underserved communities, and tourists, fostering reef conservation stewardship. Key advancements to infrastructure include the installation of state-of-the-art seawater systems with precise temperature and pCO2 controls, reproduction systems for mass larval rearing, and ultrafiltration technology to support microbiome research. These improvements will allow scientists to conduct complex experiments at all stages of coral development under near-natural conditions, providing critical insights into coral performance, adaptation, and climate resilience. The enhanced infrastructure will also enable large-scale selective breeding and the annual production of over 200 million stress-tolerant coral larvae for reef restoration along the West Hawaiʻi coast. As such, this initiative will directly aid in the recovery of Hawaiʻi’s coral reefs, safeguarding their ecological and cultural legacy for future generations. The ASU ʻĀkoʻakoʻa Coral Facility is poised to advance pioneering research in coral reef resilience and restoration through the implementation of cutting-edge technological infrastructure. The facility will feature 36 seawater raceways integrated with precision environmental control systems, enabling high-resolution, large-scale, and long-duration experiments to quantify the synergistic impacts of thermal stress, ocean acidification, and other environmental variables on coral physiology, reproductive biology, and microbiome dynamics. Additionally, 50 conical tanks and 40 specialized larval systems will be deployed to facilitate controlled studies on gametogenesis, larval settlement kinetics, and post-settlement ontogeny. Leveraging these advanced systems, researchers will address pivotal scientific inquiries, including the heritability of stress tolerance, microbial contributions to early ontogenetic stages, and ecosystem-level outcomes of assisted reproductive interventions. By synthesizing these technological capabilities with a longitudinal ecological dataset spanning over three decades, the 3RC will serve as a premier research hub, equipping both resident and visiting scientists with unparalleled resources to elucidate mechanisms of coral reef resilience and to engineer scalable, evidence-based restoration methodologies. 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 2025 · 2025-08

This IMPRESS-U project is jointly funded by NSF, Estonian Research Council (ETAG), Latvian Council of Science (LCS), Research Council of Lithuania (LMT), National Science Center of Poland (NCN), and US National Academy of Sciences. The research will be performed in a multilateral international partnership that unites the Arizona State University (USA), University of Tartu (Estonia), Riga Stradiņš University (Latvia), Vilnius University (Lithuania), University of Warsaw (Poland), and Lviv Polytechnic National University (Ukraine). This project examines how uncertainty and crisis drive innovation and change in engineering education, particularly academic departments operating in challenging environments. We ask: What policy, institutional, structural, and cultural factors determine or hinder the successful implementation of innovations in engineering programs during times of crisis? Engineering as a discipline is transforming to be more responsive to regional societal and economic needs. Throughout history, crisis has driven technological innovation, from commercializable products to resilient infrastructure systems. Yet, how programs that train engineers for future careers adapt and/or transform in the face of crisis remains underexplored. Our study investigates the academic engineering ecosystems that shape such innovation including an examination of organizational capacities, faculty and administrative engagement, local economic interests, and policy support structures. We focus on “Research Periphery Countries” (RPC), which face different challenges from more economically advanced countries. These include market and economic volatility, security threats, and other disruptions that hinder an engineering school's ability to succeed. By identifying both successful models and failed attempts, we aim to provide actionable findings to guide organizational and programmatic design strategies in engineering schools. Our findings will support policy, economic, and other local factors in advancing societal resilience, particularly in crisis-affected regions. Ultimately, we seek to inform globally relevant approaches to collaborative research, interdisciplinary education, and public value-oriented innovation in engineering schools. This project uses a multi-country mixed-methods case study design involving academic engineering programs operating in different RPC contexts. The comparative case study methodology enables us to account for cross-national variation in policy, budgetary, and other factors relevant to engineering education. Data collection will focus on five RPCs and include semi-structured interviews and secondary documents like institutional policy and strategy documents. Using qualitative design principles, we will organize cases first by country, then by transformational initiative type (organizational/pedagogical), enabling cross-national comparisons. Transcribed interviews and policy documents will be coded and analyzed, using text analysis software with multiple coders to ensure inter-rater reliability. Thematic analysis will identify key ecosystem characteristics influencing engineering transformations, which will inform the development of a typology of crisis-responsive reforms and related barriers and opportunities. The intellectual merit of our project includes an interdisciplinary research design grounded in crisis science and theories of public value and institutional transformation. Studies of innovation in RPCs have focused on policy-level strategies, but little exploration of RPC-specific higher education and/or engineering-related innovation has been conducted. The project’s broader impacts will be evident in the potentially transformative lesson learned. Findings will identify approaches, institutional support resources, and other ecosystem supports aimed at building more resilient, socially responsive academic engineering systems in challenging 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 2025 · 2025-08

Arizona State University will investigate best practices for designing and implementing instructional coaching for computer science (CS) in the context of a large, urban school district: Clark County School District (CCSD), NV. Using a reciprocal learning approach, CSTransform seeks to advance progress in computer science education to benefit society by focusing on the overarching question: How does a large district leverage instructional coaching to develop the capacity of their high school CS teachers to teach CS in content-rich, and pedagogically engaging ways through a reciprocal learning approach? By expanding CS coaching resources and contextualizing and adapting the needs of a large school district, CSTransform will explore approaches to implement job-embedded CS coaching. The results of this study related to implementing a reciprocal lens to CS coaching and nurturing a coaching community of practice will be expanded to include professional development and implementation guidance. Moreover, this project team will co-construct a community of practice among CS coaches in CCSD while working in partnership with the Computer Science Teachers Association. Together, we will establish a national CS coaching community where instructional leaders and coaches can share their approaches, ideas, and resources with the larger community. This Small CSforAll High School Strand Research-Practice Partnership (RPP) explores the effectiveness of instructional coaching as a form of teacher professional development. Instructional coaching as a form of teacher professional development is just emerging within CS education. Research underpinning the design and implementation of CS coaching is in its nascent stage. Opportunities for CS teachers to refine their teaching and further develop their CS content, knowledge, and pedagogy beyond initial exposure is lacking. Preservice teacher programs dedicated to developing a future generation of CS teachers are also rare. This proposal will contribute to a critical domain of research to explore how a research-practitioner partnership can support and sustain job-embedded CS coaching for high school teachers and inform further scaling of such efforts. 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 2025 · 2025-07

This project addresses critical challenges in applying reinforcement learning (RL) to real-world urban environments, which have yet to achieve the same level of success as RL applications in controlled settings such as games or virtual simulations. The research focuses on developing actionable data analytics tailored to urban decision-making, tackling key issues including noisy and incomplete real-world observations, complex and dynamic urban system behaviors, and the necessity of human-in-the-loop decision-making to ensure interpretability and trust. Additionally, the project emphasizes the development of reproducible and cost-effective benchmarking environments to bridge the simulation-to-reality (sim-to-real) gap. By addressing these challenges, this project aims to advance the progress of science and support sustainable urban development. Technically, this project investigates the sim-to-real gap in a systematic way by addressing gaps in observations, system dynamics, and human interactions in urban decision-making. The research introduces innovative methodologies such as iterative optimization techniques, diffusion policy models, and a grounded action transformation framework enhanced by controllable domain context generation. It also develops uncertainty quantification and rule-based methods to support human collaboration in decision-making tasks. A significant output of this project is the creation of benchmarking environments to evaluate and refine RL policies under configurable settings, enabling more reliable deployment in real-world scenarios. These contributions promise to transform urban data mining and prescriptive analytics, setting the stage for actionable, adaptable, and interpretable methods across disciplines, including transportation and urban studies. 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 2025 · 2025-07

A significant number of individuals face challenges with hand movement due to neurological conditions, aging, or injuries. Accessing physical therapy is often difficult due to a shortage of therapists and other barriers to care. To address this issue, many patients rely on self-directed rehabilitation programs that allow them to exercise at home. While these programs can be beneficial, they often lack professional guidance, increasing the risk of incorrect exercise execution or loss of motivation. This project will enhance the effectiveness and engagement of home rehabilitation by leveraging artificial intelligence (AI) and motion-sensing technology. Beyond improving rehabilitation tools, the project will provide opportunities for students to work at the intersection of engineering and healthcare. Students will gain hands-on experience developing innovative technologies, exploring entrepreneurship, and engaging in public outreach to raise awareness of AI-powered rehabilitation solutions. This CAREER proposal focuses on advancing adaptive, self-directed hand rehabilitation programs through three technical innovations. First, it will develop a computer vision-based recovery monitoring system that integrates motion sensing and muscle activity data to model and visualize hand recovery dynamics. These recovery models will serve as real-time feedback to patients, offering a detailed understanding of their progress. Second, it aims to explore the predictive power of physiological signals and verbalized (think-aloud) data for adherence levels, establishing data-driven insights into patient behavior. Third, the project will design and evaluate an AI-supported rehabilitation program that leverages these insights to provide personalized care. A generative AI module will dynamically adapt multimedia interventions, offering rewards and encouragement based on adherence predictions, to enhance patient motivation and engagement. This work incorporates advanced methodologies, including motion analysis, machine learning, and generative AI, to create an integrated system that bridges recovery monitoring and motivational support. The findings will lead to fundamental advancements in recovery dynamics modeling and adaptive intervention strategies, paving the way for sustainable, tailored self-directed rehabilitation solutions. 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 2025 · 2025-07

This project enables researchers to share research ideas and discuss interesting research problems. In particular, this project will focus on supporting US researchers to share research ideas and discuss interesting research problems in a US–Singapore Joint Workshop on Cybersecurity. It facilitates and fosters collaborations between cybersecurity researchers from both countries. The project seeks support to cover the costs and the travel expenses for invited participants from U.S. institutions to attend the joint research workshop in 2025. The workshop will offer a unique experience for cyber security researchers to engage with each other under a two-day extensive program allowing the sharing of research ideas and the discussion of emerging topics. The project’s broader significance and importance are rooted in its potential to catalyze lasting international partnerships, enhance cross-cultural research competencies, and seed collaborative innovations in cybersecurity. The workshop focuses on emerging research challenges in cybersecurity, trustworthy artificial intelligence (AI), AI for security, resilient networks, and possibly others. Through a combination of keynotes, lightning talks, expert panels, and roundtable discussions, the event cultivates dialogue, identifies shared research interests, and supports the development of joint research collaborations among the researchers. This workshop is expected to result in new research collaborations, influence future research directions, and contribute to the global effort to securing emerging cybersecurity technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Current machine learning methods are not capable of fully analyzing and interacting with satellite remote sensing data, limiting their ability to benefit society through real-world applications. Data collected by Earth-observing satellites differ significantly from image data (e.g., photos taken with a smartphone or digital camera) or text data (e.g., online articles or social media posts). Satellite data captures the Earth’s diverse and dynamic ecosystems, environments, and human activities that are constantly changing. These patterns and changes can be subtle or obvious, large or small, difficult or easy to see with the human eye. Satellites record these patterns in many different wavelengths and sensor types, which hold much more information than the visible color wavelengths humans see. This project will advance fundamental machine learning research methods for analyzing satellite data, unlocking its untapped potential for solving societal challenges including agriculture, conservation, and natural hazards. The project will develop new technologies that improve the performance and accessibility of satellite machine learning models for different applications, thus advancing scientific progress, human and environmental sustainability, and societal welfare. This award will develop (1) a hypermodal geospatial foundation model that accommodates diverse sensor modalities and input formats, (2) a novel algorithm for zero-shot mapping using natural language prompts instead of traditional fine-tuning, (3) a testbed to evaluate model robustness under realistic distribution shifts, and (4) a zero-shot evaluation algorithm that eliminates the need for ground-truth labels. These advancements will minimize the reliance on expensive data labeling and enable flexible and efficient interaction with machine learning models. The research will be iteratively validated through real-world deployments via NASA Harvest, NASA Acres, and other user-facing organizations implementing satellite solutions for global challenges. By treating ML research and deployment as a unified approach instead of siloed steps, this project pushes model evaluation into new regimes not typically explored in machine learning research. Broader adoption of this perspective in ML research will increase end-users’ trust and adoption of machine learning research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The widespread use, virtual nature, and limited regulation of social media enable a range of problematic online behaviors such as cyberbullying that have threatened users' mental health and well-being. Through the integration of computer science and psychology, this project seeks to better understand how problematic online behaviors can be identified and prevented. The research will investigate the connections between specific features of social media and people's well-being. Major components of the work include developing models for the early detection of problematic online behaviors, designing social media features that prioritize users’ well-being, and studying how cyberbullying may elicit bystander intervention. The broader impact of this project includes the creation and sharing of research and educational resources, information for policymakers to raise awareness of problematic online behaviors and effective strategies to address them, and a unique interdisciplinary training experience that helps students develop into scholars who enact meaningful change. The project is structured around three core research aims. The first aim is to study predictive models for early and community-aware detection of problematic online behaviors, with an emphasis on the assessment of severity. It entails the contextualized annotation of social media data and design and testing of machine learning models. The second aim addresses the need for new social media mechanisms that tackle problematic online behavior and promote well-being. It does so through the design of new interaction metrics and visual interfaces and a focus on the detection of anti-bullying interactions. The third aim is to investigate the interrelations among problematic online behavior, bystanders, and well-being. This aim is addressed through online experiments examining how different forms of bystander involvement and mental health factors contribute to the severity and impact of problematic online behaviors. Overall, this project seeks to generate new insights and improve societal outcomes through the synergy of computer and psychological science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The Biomedical and Health Informatics Conference (BHI) is a premier flagship conference sponsored by the Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS), that focuses on informatics and computing in healthcare and life sciences. BHI’25 will take place from October 26 to 29, 2025, in Atlanta, Georgia. The theme of BHI’25 is “Precision Health: AI Tailored to Individuals.” It will provide a unique platform for cross-disciplinary researchers to showcase their research on big data analytics and machine learning, addressing challenges in biomedicine. An important mission of BHI’25 is to promote the participation and engagement of undergraduate and graduate students, particularly first-time attendees. The NSF Student Travel Award will support this goal by providing travel awards to qualified students from US institutions to attend the conference. With NSF support, the investigator expects to provide travel awards to approximately 28 student participants to encourage their attendance at BHI’25. The conference will offer student awardees opportunities to present their research, expand their knowledge, network with world-class researchers, and widen their collaborations. Additionally, participants will have access to keynote speeches from world-renowned researchers, career and technology panels, special sessions, workshops, and tutorials. The investigator will particularly encourage the participation and engagement of first-time undergraduate and graduate student attendees to the conference. 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 2025 · 2025-06

Many fluid flows of practical and scientific interest involve fluids flowing past surfaces with complex shapes or with shapes that change in part due to the flow. Examples include sediment transport in estuaries, biomedical flows such as blood flow in the heart and air flow in lungs, bio-inspired propulsion and flow past moving deformable objects. Predicting the dynamics of these flows is especially challenging when the flow rates are high, but that is often the case of most interest. This project will develop a new method, called Immersed Boundary-Modeled Large Eddy Simulations, to compute flow fields in turbulent flows past complex and deforming surfaces. The method will combine numerical computation with models of turbulence near boundaries to produce accurate predictions of flow dynamics at much lower computational cost than comparable methods. The models will be formulated to handle arbitrary deformations of immersed objects. The project will also support educational initiatives to inspire K-12 students to pursue STEM careers, which will help expand the future science and engineering workforce This project will introduce a novel framework that incorporates Immersed Boundaries and turbulence closures consistently. This framework is formulated using volume-filtering and differs from regular Large Eddy Simulation filtering in the treatment of the solid-fluid interface, which is key to incorporating Immersed Boundaries and turbulence closures consistently. The proposed work includes a priori analyses, modeling, and a posteriori analysis of unclosed terms resulting from volume-filtering the solid-fluid interface. A novel closure of the Immersed Boundary-shear stress with a dynamic-slip model specially formulated for moving and deforming Immersed Boundaries will be developed and characterized. This framework will be demonstrated in a prototypal high Reynolds number Fluid Structure Interaction problem that involves significant topology change: the dispersion of large deformable particles in wall-bounded turbulence. Rich datasets from this project will be leveraged to integrate research and education via two initiatives aimed at improving students’ academic attainment. First, undergraduate researchers will build a digital learning environment for fluid dynamics using Augmented and Virtual Reality. This platform will allow lay users to engage with simulation data from this project intuitively and will be used to inspire K-12 students to pursue STEM careers during an annual outreach event. Second, research will be weaved into modernized undergraduate fluid mechanics teaching using engaging Entrepreneurial Mindset pedagogy. This approach will be tested at Arizona State University, then shared with thousands of educators online. 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 2025 · 2025-06

This doctoral dissertation improvement grant investigates public engagement with science and technology at large scale events such as fairs, expos and trade shows. The study will assess the potential of megaevents to serve as forums for advancing and engaging the public with science and technology in public spaces. The results of this study will be of benefit to decision-makers, planners, science center and museum curators, and event facilitators who convene members of the public to engage in scientific activities and plans. This work will assess the potential of megaevents for public engagement in conversations about the role of science and technology. It will examine the content and techniques used in megaevent exhibits in order to answer the following research questions: 1) What stories are told at megaevents about science/technology?, 2) How is public engagement facilitated?, and 3) Who participates in megaevents? This mixed-methods research project will collect data from promotional materials, interviews, field research, and visitor-uploaded videos to identify effective means for public engagement. 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 2025 · 2025-06

The understanding and containment of epidemics involves many factors, such as how people behave and interact, how they move around, and the decisions made by governments and organizations to control the spread. Because of this complexity, building accurate and timely models to predict and manage outbreaks in near real time is challenging and time consuming. It also requires copious data, which are often unavailable when it is urgently needed. To address these challenges, this project develops PanAX, a new computational system to improve how we prepare for and respond to evolving epidemics. The core idea is to use existing data and models in smarter ways and based on situational awareness, so we do not have to start from scratch every time. The focus is on identifying and leveraging the key underlying patterns and relationships that drive the spread of diseases, allowing models and data to be adapted to new situations more easily. Bringing together experts from computational and data sciences with epidemiologists, PanAX explores the deeper causes of how epidemics spread in different contexts, helping to create models that are more adaptable, accurate and reliable based on real-time conditions. The project develops methods to reuse parts of existing data and models, applying them to new outbreaks in different locations or circumstances. Consequently, new tools will be created to better plan, inform, prepare the public, and respond to outbreaks. The project also trains students and researchers in advanced techniques in data and model driven response to epidemics, equipping them to tackle real-world challenges. In short, the aim of this project is to make epidemic response faster, more efficient, and more effective, using advanced data and machine learning technologies to benefit the society. Epidemics represent complex systems where the dynamics of disease spreading emerge from an interplay of time-dependent factors, including spatially distributed populations, mobility networks, and intervention policies. Modeling these systems accurately is challenging due to their multi-layered, causally interdependent structure and the lack of sufficient, high-quality data during critical early stages of an outbreak. The project develops an innovative framework, PanAX, that addresses these challenges by leveraging a data-driven, causally-informed approach to improve model reuse, generalization, and transferability across epidemic contexts. PanAX aims to disentangle and isolate domain-specific and domain-agnostic components from data and models, enabling efficient knowledge transfer and adaptive modeling. PanAX incorporates multi-layer, multi-scale causal relationships to better capture uncertainties in models derived from observational data and uses causality-based de-biasing techniques to eliminate statistical artifacts, enhancing model robustness and accuracy. It identifies transferable features and models between contexts, facilitating knowledge transfer from data-rich scenarios to emerging outbreaks with limited data. The framework focuses on scalable and effective spatio-temporal modeling, leveraging causal discovery and data management techniques to support counterfactual "what-if" analyses for outbreak preparedness and enhance prediction and intervention modeling by repurposing existing data and models. The project will deliver an open-source software system supporting researchers, public health officials, and policymakers in planning and managing epidemics. It aims to accelerate research, catalyze the development of innovative tools, and train the next generation of computer scientists to tackle cross-disciplinary challenges in causal learning, data integration, and computational epidemiology. By advancing the state of the art in causally-informed domain generalization and knowledge transfer, PanAX will transform how data and computer scientists contribute to epidemic science, offering scalable and generalizable solutions with real-world impact. 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.

CIHR Grants and Awards · FY 202526 · 2025-06

CHILD MENTAL HEALTH; TREATMENT

NSF Awards · FY 2025 · 2025-06

Dmitry Matyushov of Arizona State University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop theoretical models of polarity of interfaces, linear and nonlinear mobility of colloidal particles, and electrical conductivity of protein complexes. Electrified interfaces are critical to a vast number of applications in engineering, chemistry, and life sciences. The response of water to local electric fields at biological interfaces affects how proteins attach to other macromolecules (e.g., DNA) and to lipid membranes. The mobility of proteins critically depends on dynamical correlations of osmotic and electrostatic forces from hydrating waters which are also affected by the properties of cytosol and interfaces. While their mobility is connected to random forces, how these forces contribute to observable diffusion of macromolecules and colloidal particles remains unclear. Similarly, surface water molecules can impact barriers to charge-transfer relevant to photosynthesis and respiration. Matyushov, with experimental colleagues, will pursue theoretical modeling of mechanisms of colloidal and protein mobility, electrified interfaces, and protein conductivity. The results of this work will be available to the community in the form of predictive computational algorithms and microscopic insights into physical mechanisms which are not possible to infer from direct laboratory measurements. Theoretical results will be disseminated through a planned textbook, conference talks, and review articles targeting broad audiences. The proposal seeks to develop formal theories and computational algorithms to address the problem of statistics and dynamics of interfacial fluctuations affecting interfacial screening, colloidal mobility, dynamics of electrolytes, and protein conductivity. Three research goals will be pursued. First, we will develop formal algorithms and perform simulations to address anisotropic dielectric susceptibility of water in the interface of simple solutes, large protein complexes, and lipid membranes. Second, we will apply the formalism of memory functions to model strong dynamical cross-correlations between osmotic and electrostatic forces allowing colloidal diffusion. The formalism will be applied to collective dynamics of low-temperature glass-formers. Third, we will carry out molecular dynamics simulations of redox-active proteins and protein-DNA complexes in confinement to mimic laboratory conductivity measurements and natural conditions. We have shown that protein conductivity requires violation of the fluctuation-dissipation relation (FDR) for electrostatics at protein cofactors and we aim to understand the role of interfacial water in reducing protein-water cross-correlations responsible for the FDR violation. Strong connection of the proposed activities to experiment will help graduate students and postdoctoral fellows to gain a broader view of the discipline and learn the culture of collaborative research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project explores how quantum technology can improve the way we measure and detect small changes in our environment, such as temperature shifts, pollution levels, or even tiny vibrations, across large areas like cities. Researchers at the University of Tennessee at Chattanooga will use a special kind of light, a so-called "squeezed light", to create a network that senses these changes more precisely than current methods allow. By testing this innovative approach on a real-world fiber-optic network in Chattanooga, built in collaboration with industry partners like the Electric Power Board (EPB) and IonQ, Inc., the project demonstrates how quantum science can move beyond laboratory experiments into practical, everyday use. Imagine a system so sensitive it could help monitor air quality in neighborhoods or ensure clocks worldwide stay perfectly in sync; those are the kinds of possibilities this work opens up. This effort funded by NSF will push scientific boundaries while offering real-world benefits. Beyond the technology, the project trains students and professionals in cutting-edge skills, preparing them for future careers in quantum information science and engineering. It also strengthens ties between universities and local industries, showing how federal investment can spark innovation, improve lives, and inspire the next generation to tackle big challenges with creative solutions. This research focuses on achieving sub-shot-noise-limited (sub-SNL) distributed quantum sensing using continuous-variable (CV) entanglement on a commercial metropolitan-scale quantum network. The team will construct a table-top CV-entangled network utilizing two-mode squeezed states, generated through four-wave mixing in atomic rubidium-85 vapor, to measure distributed phase shifts with sensitivity surpassing classical limits. Deep learning, specifically Q-learning, which is a reinforcement learning technique, will be employed to suppress excess noise without requiring pilot tones or training sequences, by adapting similar noise mitigation strategies from CV quantum key distribution (CV-QKD). This approach leverages homodyne detection and real-time phase estimation to optimize local oscillators across the network, addressing noise introduced by beam splitters and environmental interactions. A single-mode squeezed light source at the telecom wavelength of 1570 nm will extend this methodology to the EPB Bohr-IV Quantum Network, a software-reconfigurable fiber-optic infrastructure deployed by IonQ, Inc., featuring a hybrid ring/spoke topology with scalable quantum nodes. The project’s intellectual significance lies in its novel integration of machine learning (ML) with CV quantum sensing, offering the first practical demonstration of sub-SNL distributed sensing on a deployed commercial metro-scale quantum network. Through partnerships with Arizona State University and industry collaborators like EPB and IonQ, Inc., this work advances quantum information science and engineering, providing a scalable framework for future quantum networking applications and contributing to both theoretical and experimental progress in the field. 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 2025 · 2025-05

Critical minerals are essential to the U.S. economy and national security. Critical minerals are used in manufacturing computers, cell phones, solar panels, batteries, and many other electronic devices and advanced technologies. The U.S. imports many critical minerals, which means disruptions in their supply chains pose significant risks. New methods to recover and recycle critical minerals could help mitigate these risks. This project will develop new materials and organisms that can separate, recover, and recycle critical minerals from waste streams. In addition, the project will support education and outreach activities for K-12 students and research opportunities for undergraduates to help stimulate interest in STEM and expand the science and engineering future workforce. The overall goal of the project is to engineer biomaterials that bind critical minerals. The team will focus first on peptides, enzymes, and whole cells that can bind critical minerals. It will identify and validate existing critical mineral binding peptides (CMPs). New CMPs will be developed using rational and combinatorial approaches. Structure-function studies will be performed using a selected group of enzyme variants that display activity as critical mineral reductases (CMRases). Insights will shed new light on how these unique reductases bind to and then reduce critical mineral substrates to their elemental form. Finally, lessons learned regarding CMP and CMRase functions will be combined to develop robust microbial strains for efficient critical mineral recovery. This will be accomplished by developing a genetic screen to facilitate in vivo functional assays and enable directed evolution of improved CMPs and CMRases. Understanding the fundamental mechanisms regarding how biomolecules and microbial catalysts both bind and then reduce critical minerals will allow the selection and design of CMPs and CMRases with enhanced activities, enabling their future deployment within microbial systems for critical mineral recovery. This project involves a collaboration between researchers from the United State and India. It is jointly supported by the US National Science Foundation and the Department of Biotechnology of the Government of India (NSF-DBT). 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 2025 · 2025-05

NON-TECHNICAL SUMMARY Designing new materials often requires extensive computer simulations to explore how a material’s internal structure influences its physical properties. This can be very costly, especially when materials have random or disordered features. While machine learning (ML) can speed up some parts of the design process, it does not always provide clear physical insights, such as why the connectivity of one material phase strongly affects overall conductivity or how certain patterns might enhance stiffness. This project addresses the challenge of making material design both efficient and explainable by focusing on the concept of N-point correlation functions (NPCFs). An NPCF is a statistical measure describing how different regions of a material relate to one another. By systematically learning which NPCFs matter most for a given property, the research will provide both accurate predictions and clear insights about the structure-property relationship. In addition to improving the design of smart composites for soft robotics, the methods developed will lay the foundation for broader applications in biological systems, climate modeling, and other areas where disordered structures play a critical role. The educational activities developed through this project will help K-12 students understand material structure-property relationships through hands-on experiments and contribute to training the next generation of researchers at the intersection of physics, mathematics, and computational science. TECHNICAL SUMMARY This project investigates how to identify concise and complete microstructure representations for two-phase, disordered heterogeneous materials. Since these microstructures can be treated as random fields, the research focuses on N-point correlation functions (NPCFs) as the core representation. Although machine learning models can approximate structure-property mappings, they often do not reveal the underlying physical causes. To address this, the project leverages a strong contrast expansion formalism, which links material properties to microstructure morphology through physics-based governing equations, such as partial differential equations (PDEs). By interpreting linear and nonlinear effective properties as expansions in terms of Green’s functions and NPCFs, the research team will develop new computational algorithms to learn both the necessary Green’s functions and the relevant NPCFs directly from data. These algorithms will provide a physics-driven mechanism for down-selecting which NPCFs are most critical, leading to an explainable and more efficient representation for microstructure reconstruction and property prediction. To demonstrate practical value, the project will apply these methods to the design of a bi-phase composite with specific mechanical and thermal transport properties relevant to soft robotics. By uncovering how different morphological features affect performance, the research aims to streamline the design process for materials that must satisfy coupled mechanical and thermal requirements. Beyond this specific application, the methods and principles developed will be broadly applicable to other random fields where PDE-driven properties dominate, such as multi-phase materials, biological systems, climate science, and cosmology. In addition to advancing knowledge in materials science, the project has a broader impact on data science by prompting new ideas for efficiently representing and modeling random fields. Just as insights into sequence modeling led to transformational language models, and insights into graph structures enabled advanced drug discovery methods, this research explores whether NPCFs can serve as essential building blocks for future architectures that handle large-scale or high-dimensional random fields. The developed educational activities will also contribute to training the next generation of researchers at the intersection of physics, mathematics, and computational science. STATEMENT OF MERIT REVIEW 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.