Arizona State University

NSF Awards · FY 2025 · 2025-05

Partial differential equations (PDEs) form the foundation of mathematical models that describe the complex physical processes driving technological developments in energy, transportation, and manufacturing. These equations govern everything from fluid flow in pipelines and aerodynamics in high-speed transportation to plasma control in nuclear fusion and material behavior in advanced manufacturing. However, processes described by nonlinear PDEs are notoriously difficult to analyze and control using existing methods, requiring deep mathematical expertise and significant computational resources. This CMMI-UKRI Engineering and Physical Sciences Research Council (EPSRC) project combines the efforts and expertise of UK and US collaborating investigators to attempt to overcome these challenges by developing new computational methods and algorithms that enable the solution, analysis and control of nonlinear PDEs. The key focus of this collaborative project is ensuring that developed tools are accessible to researchers and engineers without requiring extensive mathematical expertise. By making these techniques more practical and widely available, this project looks to enhance the ability of data-based models to drive improvements in reliability, efficiency, and safety across sectors including energy, industry, and defense. The sum-of-squares polynomial optimization framework allows one to computationally verify stability properties of nonlinear ordinary differential equations (ODEs) by searching for positive polynomial Lyapunov functions. This research project aims to develop an equivalent of the sum-of-squares framework for analyzing nonlinear partial differential equations (PDEs). The first step seeks to construct a suitable parameterization of polynomials on the spatially distributed Hilbert space of Lebesgue-integrable functions. The next steps seeks to reformulate the nonlinear PDE using this distributed polynomial parameterization. This step is anticipated to involve applying a state transformation and the partial integral equation (PIE) framework to eliminate partial derivatives and boundary conditions, ensuring a well-posed representation of the system. Once reformulated, the next step seeks to parameterize positive polynomial Lyapunov functions in a distributed space using positive partial integral operators, enforcing a sum-of-squares-type condition. It is expected that the Lyapunov stability conditions will be expressed as a convex optimization problem, allowing for an efficient computational solution. To support broad applicability, specialized software intends to be be developed to automate each step of this process, enabling analysis across broad classes of nonlinear PDEs. This software looks to feature user-friendly interfaces to facilitate adoption by non-specialists for rapid prototyping of complex data-based models. Finally, resulting algorithms will be applied to problems of fluid flow with transition to turbulence, as well as to data-based models of magnetohydrodynamic plasma in Tokamak nuclear fusion reactors. This research is a collaborative effort under the NSF Directorate for Engineering - UKRI Engineering and Physical Sciences Research Council Lead Agency Opportunity (ENG-EPSRC), NSF 20-510. 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 · 2025-04

The turnover of organic carbon in the ocean plays an important role in regulating the ocean carbon cycle. The oceanic cycles of iron and carbon are tightly coupled. The supply of dissolved iron regulates ocean biology while organic carbon impacts the solubility and biological availability of iron in seawater. We strive to better understand the mechanisms and linkages between pools of iron and organic carbon in the oceans and to predict their sensitivity to future environmental and climatic changes. In this collaborative project, jointly funded with the U.K. Natural Environment Research Council, scientists from the U.S. and U.K. will combine field data from the Bermuda Atlantic Time-series Study (BATS) region and from the Eastern North Atlantic with targeted experimental studies and a state-of-the-art ocean biogeochemical model to better characterize organic carbon - iron linkages and their roles in past, present, and future changes in ocean biology and chemistry. The project will support the education and training of undergraduate, graduate, and postdoctoral researchers, and will connect rural K-12 and undergraduate students with the research through outreach activities. Field observations from the BATS and Cape Verde Ocean Observatory regions will be integrated with experimental studies targeting iron-organic carbon interactions across seasonal and spatial gradients. An ocean biogeochemical model will be used to constrain the processes that modulate interactions of iron with dissolved and particulate organic matter. Specifically, this project will examine the a ‘colloidal shunt’ mechanism, whereby a portion of the dissolved iron pool in the colloidal size range is not stabilized by complexation with organic ligands. This iron instead forms aggregates with organic carbon to form particulate matter that sinks out of the upper water column. The research will focus on the role of dissolved organic carbon and iron-binding organic ligands in mediating the colloidal shunt, the association of organic matter with thus-formed authigenic particulate iron phases, and the dissolution of these phases in the ocean interior as a function of oxygen. Potentially transformative implications of this research are that the colloidal shunt might vary in response to climate driven changes in ocean oxygenation, and that this process may provide a conduit for the vertical export of both particulate iron and organic carbon that augments the biological carbon pump in the subtropical and tropical oceans. 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 · 2025-04

The Industry-University Cooperative Research Center for Digital Twins in Manufacturing (IUCRC-DTM) will address fundamental research challenges for developing, deploying, maintaining, updating, and evaluating Digital Twins in manufacturing domains. A Digital Twin is a purpose-driven replica of some physical component, such as a machine or a process. A Digital Twin collects real-time data from its physical counterpart, and uses this data, together with one or more models, to estimate or make predictions about important metrics, such as system health and product quality. Using these estimates or predictions, decisions can be made to improve quality and cost-effectiveness through reduced downtime and wasted raw materials. For example, machines in a factory may degrade slowly, resulting in inferior product quality, but such degradations may not be noticed right away. Predictions from Digital Twins can be used to alert manufacturers to degrading health of their machines, before significant quality issues arise. The mission of the Center is to generate pre-competitive research outcomes for its members (and the manufacturing industry in general) towards the advancement of Digital Twin technology, and consolidate many current Digital Twin solutions around a common framework to advance extensibility, reusability, and interoperability of the solution components across different manufacturing settings. To empower the manufacturing workforce, materials will be developed for education, training, and workforce development. The IUCRC-DTM will initially propose three thrust areas. (1) Digital Twin Frameworks and Standards: development of a Digital Twin framework that is maintainable and extensible, incorporates existing solutions and solution threads, is reusable (within and across domains), and exhibits inheritance and generalization, interoperability and exploration into new domains. (2) Digital Twin Applications: improving key components of Digital Twins such as reconfiguration, aggregation, and human interfaces, as well as identification of key use cases of Digital Twins models in various manufacturing sectors. (3) Digital Twin Tools and Workforce Development: creation of software tools, workforce development materials/programs, and high-quality datasets to prepare future workforces and successfully deploy Digital Twins in the manufacturing industry. Through integration of research projects across these three thrusts, the Center seeks to advance several transformative concepts in Digital Twins, including novel knowledge representations, aggregation and composition methods for Digital Twins, and automatic generation and maintenance of Digital Twins. The Arizona State University (ASU) Site will leverage existing testbeds including a connected robotic manufacturing system and wire-laser metal deposition system. The ASU affiliated faculty brings in synergistic expertise on multi-physics simulation, stochastic modeling, robotics, and machine learning, with projects addressing the needs of aerospace, semiconductor, and energy sectors. 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 · 2025-04

This project provides three years of continuing support to the Tudor Hill Marine Atmospheric Observatory (THMAO) on the island of Bermuda. Because of its location in the western North Atlantic Ocean, Bermuda is an important location for studies of the marine atmosphere. The observatory is well equipped for carrying out research, with field laboratories and a 23-meter high sampling tower at Tudor Hill. This allows scientists to study the chemistry and physics of the atmosphere over the oceans. This part of the atmosphere plays an important role in the transfer of moisture, chemicals, and energy between the ocean and the atmosphere. Routine work at the site includes the observation and recording of weather conditions, and sampling of rain and air. Data and samples are made freely available to other users. Additional samples and data are collected for research partners, including projects funded by NSF, NOAA, NASA and other agencies. In addition to the impact on research infrastructure and a broad array of marine and atmospheric research, the project facilitates research by students and the team participates in public outreach activities. The specific objectives for this three-year renewal project are: 1) To continue to operate and maintain a state-of-the-art marine atmospheric sampling and observing facility at Tudor Hill, Bermuda, available for use by the wider U.S. and international research community; 2) To continue the collection of continuous atmospheric data and samples, which will be archived at ASU BIOS and made freely available to other researchers; 3) To collect additional atmospheric data and samples for other investigators (primarily in longer-term time-series programs), and to provide for and support the use of the facility in person by other investigators (primarily in shorter-term intensive programs). The THMAO site will enable research to be undertaken that is central to international initiatives such as IGAC, SOLAS and GEOTRACES. In a regional context, the Tudor Hill facility will complement ongoing oceanographic time-series research in the Sargasso Sea, including Hydrostation S, the Bermuda Atlantic Time-series Station, and the Oceanic Flux Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Additive manufacturing (AM), also known as 3D printing, is revolutionizing how materials and components are designed and fabricated. Unlike traditional methods, AM allows for unparalleled geometric freedom and the integration of advanced functionalities, such as functionally graded materials, shape-memory materials, and materials with enhanced properties. These breakthroughs rely on controlling the internal structure of the material, called the microstructure, which directly impacts its mechanical and thermal behavior. However, determining the optimal AM process conditions to achieve specific microstructures is a significant challenge due to the high costs, time, and resources needed for experiments and simulations. This project aims to drastically reduce the time required to predict and optimize microstructures from days to minutes, enabling faster, more efficient design and manufacturing of advanced materials with targeted properties. The anticipated impact includes accelerating the discovery of new materials, enhancing the quality of manufactured components, and broadening the application of AM in critical industries such as aerospace, healthcare, and energy. The technical goal of this project is to develop a computational framework that accelerates microstructure prediction in AM by leveraging cutting-edge physics-informed machine learning techniques. Specifically, this project will involve the design of a new class of graph neural networks (GNNs) tailored for modeling the evolution of microstructures under various process conditions. These networks will incorporate physical laws directly into their training process, ensuring accurate and generalizable predictions. The project comprises two main tasks: (1) designing novel loss functions and training methods to improve the GNNs’ performance and transferability across different materials and geometries, and (2) developing techniques to speed up the training and inference of these networks by utilizing localized updates and adaptive simplifications of the microstructure graphs. Together, these advancements will establish a faster, scalable, and more efficient approach to microstructure modeling, transforming the landscape of AM and materials discovery. 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 · 2025-04

People’s ability to revise their beliefs by considering evidence is vital for the quality of both individual and collective decision-making in economics, health, politics, and day-to-day life. Yet despite the importance of forming veridical beliefs, there is considerable debate about whether human belief revision is fundamentally rational, or whether beliefs are frequently distorted by non-rational influences and motivations, such as the desire to feel positively or to maintain one's social identity. This project examines how individuals differ in their susceptibility to different influences, and what makes people revise their beliefs more rationally. Insights from this project promise to improve scientific and health communication as well as promote better individual and collective decision-making. This project seeks to understand the latent associations and cognitive processes driving people's belief revision by quantifying four types of belief revision distortions: valence-driven distortions (i.e. “good/bad news”), identity-driven distortions, self-serving distortions (e.g. beliefs about one’s own intelligence), and conservatism (too-weak updating). Prior studies examining these influences have often focused on estimating an average effect of a single form of distortion in flawed or contentious behavioral paradigms. Advancing over this prior work, the current project quantifies distinct distorting influences at the level of individual participants, with independent verification of these measurements across multiple, controlled tasks supporting comparison of human behavior against a well-defined rational standard. By measuring at the individual level, and with verification across tasks, this project hopes to determine whether individuals reliably differ from one another in the rationality of their belief revision and explore the latent factor structure of these differences. Through these advances in measurement, the project develops a platform for the rigorous examination of the correlates of rationality in belief revision, and for examining the evidential and cognitive processes underlying belief revision. 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 · 2025-03

Forensic experts often communicate whether a suspect is linked to crime scene evidence by reporting a categorical forensic decision, such as identification (match), elimination or exclusion (mismatch), and inconclusive (neither a match nor a mismatch). This project will test how categorical forensic decisions may contribute to wrongful convictions and identify ways to reduce their harm to the innocent. Wrongful conviction is a serious problem within the US criminal legal system, with far-reaching societal and economic consequences. Invalid forensic analysis is one cause of the problem; however, even valid forensic analysis can contribute to miscarriages of justice if forensic experts unwittingly miscommunicate the meaning of their findings to legal decision makers. Categorical forensic decisions, though seemingly straightforward, have the potential to convey more or less culpability than the physical forensic evidence actually supports. When this happens, innocent people can be convicted and guilty people can go free, eroding trust in the legal system, threatening public safety, and wasting valuable economic resources. This project will promote the fair administration of justice by helping to transform how forensic decisions are reported, understood, and used throughout the criminal legal system and provide opportunities to mentor students and an early career researcher and to foster the engagement of a variety of students in the research process. This three-year project includes five experiments and a continuing legal education component designed for attorneys and judges. Grounded in sender-receiver models of the communication process, the laboratory experiments will (1) test whether categorical forensic decisions encourage miscommunication between forensic experts and prosecutors, defense attorneys, and judges, (2) demonstrate whether an alternative forensic reporting metric can help forensic experts to more effectively communicate their findings to legal decision makers, and (3) examine the effectiveness of expert testimony for clarifying the meaning of categorical forensic decisions for jurors and juries. The project will also create cutting-edge, on-demand, continuing legal education modules to improve the knowledge base of attorneys and judges regarding the validity and probative value of forensic evidence. 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 · 2025-03

This award provides support for participants in the Rocky Mountain Mathematics Consortium (RMMC) Summer School on Analysis and Partial Differential Equations, to be held June 17-20, 2025 at the University of Wyoming. The Summer School will bring together leading experts in analysis, partial differential equations, and their applications - at various stages of their careers - to present recent advances, exchange ideas on open problems, and explore new directions for research. The Summer School will feature a blend of introductory short courses and more advanced lectures, designed to introduce early-career researchers to the broader mathematical community and facilitate professional connections. The mini-courses will help prepare students to engage with the more specialized components of the program, as well as other research conferences in the field. The RMMC Summer School will highlight recent developments in Analysis and Partial Differential Equations, two areas at the forefront of international research and closely aligned with the research activities of many leading U.S. mathematics departments. The program will feature two principal lecturers, each delivering four-hour mini-courses aimed at graduate students and recent PhDs. The mini-courses are "The frequency function method in the theory of elliptic equations," presented by Eugenia Malinnikova (Stanford University), and "Schauder and boundary Harnack principles via degenerate elliptic equations," by Susanna Terracini (University of Turin, Italy). Additionally, approximately ten invited participants - primarily early and mid-career researchers - will give one-hour lectures. Graduate students and recent PhDs will also have the opportunity to contribute 20-minute talks, with around ten speakers chosen through a call for applications. More information on the Summer School is available at the web page https://math.asu.edu/rmmcss2025. 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 · 2025-03

NON-TECHNICAL SUMMARY The electric storage capacity of Li-ion battery technology is limited, leading to issues such as cell phones that cannot retain enough charge for a full day and electric vehicles that cannot travel for more than 300 miles before they need to be plugged in. Graphite, currently used as a lithium reservoir in Li-ion batteries, has limited storage capacity rate capabilities. Lithium is stored inside the atomic structure of graphite, which minimizes the formation of lithium metal filaments, or dendrites, that can cause the battery to short-circuit. However, if charging is done too quickly, there is insufficient time for the lithium ions to be transported into graphite, resulting in lithium accumulation on the surface of the graphite, raising safety concerns. In principle, if lithium and other alkali metals such as sodium could be directly electrodeposited onto the surface of a suitable substrate without the formation of dendrites, both the storage capacity and charging rate could improve significantly. The advantage of sodium is that it is much more earth abundant than lithium, which would significantly reduce costs. By taking advantage of analogies between electrochemical and physical vapor deposition, this research is developing transformative electrodeposition methods to enforce the evolution of atomically flat surfaces that prevent dendrite formation. This knowledge is being used to guide models that inform the design of the next generation of alkali metal batteries. The project involves a range of activities to increase the number of students involved in science and will introduce the next generation of materials scientists and engineers to the varied skills needed to maintain U.S. agility in battery technology. These efforts are contributing to and leveraging investments by the State of Arizona through its New Economy Initiative, which seeks to address local workforce needs and foster growth of industries through establishing the state-funded science and technology center on Advanced Materials, Processes and Energy Devices. TECHNICAL SUMMARY The electrochemical deposition of virtually all metals can result in the evolution of dendrites, where growth occurs at the tip of a protrusion involving tip-splitting phenomenon. Growth of a protrusion can also occur from its base, by an extrusion process at high enough homologous temperature, as a result of compressive stresses that evolve during electrodeposition. This type of growth is called a whisker. The ambient temperature electrodeposition of both Li and Na often results in the development of dendrites and whiskers, which is the major issue in developing practical metal batteries, since this can lead to short circuiting of the batteries. The key issue this project addresses is the identification of electrochemical parameters that result in two-dimensional and atomically flat electrochemical growth of Li and Na without the development of dendrites or whiskers. This project focuses on applying concepts derived from manipulating the kinetics of thin-film growth modes in physical vapor deposition to obtain atomically flat overlayers for film-substrate systems that conventionally result in three-dimensional Volmer-Webber growth. One of the approaches used is called Defect Mediated Growth and involves the use of potential pulsing routines and specific additives in the electrolyte referred to as mediator metals. The other approach, called Surfactant Mediated Growth, involves an additive that floats on the surface of the depositing Li or Na and facilitates an interlayer exchange process promoting the development of flat electrodeposited films. Physical vapor deposition is used to identify the surfactants subsequently used in electrochemical deposition-dissolution cycling of Li and Na to obtain dendrite-free films. Kinetic models of physical vapor deposition verify the role of the surfactants in the experiments and mechanism for formation of atomically flat overlayers. The surfactant-based approach also addresses pitting during the dissolution process and how preferential deposition inside a pit can be achieved without the occurrence of dendrites. 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 · 2025-03

The science of ecology is deeply concerned with how “patchiness” – the spatial patterns of plants, animals, microbes, and larger ecosystems – controls the composition, functioning, and sustainability of many ecological systems on which society depends. Individual research projects often use the concept of spatial patchiness as a jumping off point for data collection and modeling. However, it is important to discover how the ideas and data about spatial patchiness develop to help guide future research, shape the growth of ecological science, and facilitate the practical use of the resulting ecological information. This project integrates ecological theories and concepts into a broad synthesis. This is important because it provides an understanding of how synthesis has helped translate ecological knowledge for the public good. Additional broader impacts of the products focus on: 1) The relevance of applying science to urban design, urban planning, and conservation; 2) An improved ability to assess disturbance in social-ecological-technological systems given changing environments and changing human vulnerability; and 3) Support of ecological education and mentoring efforts and programs. Products will be broadly useful for public communication and education, and will include a multi-media collection of resources that will make the results of the synthesis as widely accessible as possible. This project aims to study how such a fundamental ecological concept as patchiness has progressed by synthesizing insights from an exemplary scientific career. The research will focus on how key concepts from community ecology, ecosystem ecology, and urban ecology can be integrated using the theory of patchiness, or heterogeneity, into a cross-disciplinary science of societal importance. The approach is journalistic and interview-based, where the Lead PI will be interviewed by the Co-PI. The synthesis is organized around five thematic modules extracted from the PI’s published record, with each module building on the previous ones while highlighting how new insights and understanding have emerged. This multi-decadal and multi-thematic exploration of the career accomplishments of the Lead PI will reveal cross-cutting approaches and strategies that are not apparent in the collection of individual products. In short, this project will demonstrate the deep intellectual value of emergent synthesis. 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 · 2025-03

This award will support the participation of about 60 selected students and recent graduates studying STEM disciplines in US institutions in the 2025 co-located International Advanced Manufacturing Conference (NAMRC/MSEC/LEM&P), including the 53nd North American Manufacturing Research Conference (NAMRC52) sponsored by the North American Manufacturing Research Institution of the Society of Manufacturing Engineers, the International Manufacturing Science and Engineering Conference (MSEC 2025) sponsored by the American Society of Mechanical Engineers, and the International Conference on Leading Edge Manufacturing/Materials & Processing (LEM&P 20245 sponsored by the Japan Society of Mechanical Engineers, to be held at Clemson University in Greenville, South Carolina on 23-27 June 2024. These co-located conferences provide an exceptional research forum, with topics including conventional and emerging manufacturing technologies, additive manufacturing, nano-/micro-/meso- manufacturing, biomanufacturing, manufacturing equipment and automation, manufacturing systems, quality control and reliability, advanced materials processing, applications of machine learning and artificial intelligence, sustainable manufacturing, life cycle engineering, and engineering education and many other cross-cutting research. As such, the event provides a global bridge between industry, government laboratories, and academic institutions. Participation of undergraduate and graduate students and recent graduates from US institutions at national manufacturing conferences is crucial to expanding the manufacturing research base in the United States. This award will help lower the barrier for conference participation for selected students by defraying the costs of registration and accommodation. Their attendance will provide opportunities for them to exchange ideas with leading researchers and engineers from industrial and governmental facilities around the world, as well as with students and faculty from both domestic and international universities. The call-for-award applications will be disseminated through postings on main conference websites and directly by conference organizers, track chairs, and symposium organizers, etc. Submitted applications will go through a selection process by the conference committee that will prioritize applicants based on their prior conference involvement, educational achievements, and personal background. During the conference, the awardees will be announced in the conference program. This award will enable selected participants to improve their knowledge of advanced manufacturing research and help them to advance their careers as the next generation of U.S. manufacturing engineers and researchers. 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 · 2025-03

The Compact X-ray Free Electron Laser Facility at Arizona State University features two groundbreaking, first-of-their-kind pulsed X-ray sources. This new Research Experiences for Undergraduates (REU) Site offers a unique opportunity for undergraduate students from diverse disciplines across the U.S. to contribute to the design, construction, and commissioning of these cutting-edge X-ray sources. Participants will also engage in the first scientific experiments exploring applications in quantum materials, biochemistry, atomic and molecular science, optical science, and medicine. This program aims to increase the national STEM talent pool by recruiting participants from a broad range of institutions, including minority-serving institutions, local community colleges, and tribal colleges. Beyond the research experience, students will attend project management meetings and participate in professional development workshops and scientific seminars. By the conclusion of the program, participants will have developed critical skills, gained confidence, and contributed to advancing the field, all while preparing for advanced studies and careers in STEM. Over the course of a 10-week summer program, participants in this REU Site will gain hands-on experience constructing and commissioning novel X-ray sources and planning and executing scientific experiments. The program’s educational goal is to develop independent researchers who can apply scientific principles across various disciplines. Due to the diversity of expertise available among faculty mentors, students will be recruited from fields including physics, engineering, computer science, chemistry, and biology. Science questions increasingly focus on understanding events, changes, and structures at the smallest atomic scales across a wide range of disciplines. The two Compact X-ray Light Sources produce sub-femtosecond X-ray pulses, which enable advanced studies to directly observe, manipulate, and control quantum dynamics in complex materials, molecules, and interfaces. These capabilities will reveal novel biological processes, enable new energy-efficient technologies, and decode quantum behavior that can drive new computing methods and novel materials. Students will work on projects aligned with their interests, contributing to the advancement of these multidisciplinary X-ray sources and the broader field of ultrafast X-ray science. They will also gain valuable insights into project management within the context of a large-scale academic research initiative. The program concludes with a dedicated symposium where participants present their research findings. Students are further encouraged to share their work through peer-reviewed publications and at national conferences. This unique environment offers undergraduates valuable exposure to the interdisciplinary nature of large-scale research facilities, preparing them for advanced degrees and careers in STEM. 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 · 2025-03

This research project focuses on enhancing the performance and programmability of Field Programmable Gate Arrays(FPGAs), electronic devices whose behavior can be modified after manufacturing. Within the FPGA arena, the project further focuses on Compute-In-Memory (CIM) technology, which allows faster computing by merging memory and computer processing. The project’s novelties are the integration of CIM into specific memory blocks of FPGAs, which significantly increases computing power and energy efficiency. The project's broader significance and importance are in addressing the critical challenge of programmability in FPGAs, making them more accessible to a wider range of users, and in demonstrating the principles of open-source hardware design through prototyping CIM-enabled FPGAs. This research aims to accelerate the adoption of advanced FPGA architectures, ultimately leading to more energy-efficient and high-performance computing solutions, for processing modern workloads such as Machine Learning (ML) which require processing vast amounts of data, traditionally involving significant data movement between memory and processing units. Technically, the project employs a holistic approach that includes applications, programmability, architecture, and prototyping. It integrates CIM support into High-Level Synthesis (HLS) design frameworks, allowing for the use of high-level programming languages like C/C++ to program CIM blocks on FPGAs. Additionally, the project involves the physical design and prototyping of a CIM-enabled FPGA chip using open-source tools and technology, providing a proof-of-concept that can persuade industry vendors of its viability. Architectural enhancements in CIM blocks on FPGAs and additional applications for CIM-enabled FPGAs are also to be explored. The expected advances include a transformative impact on FPGA computing, making it a more viable alternative to accelerators like Application Specific Integrated Circuits (ASICs) for parallel workloads such as ML. The results of this research will be disseminated through educational modules, conference proceedings and journal papers, and with the release of open-source code. 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 · 2025-03

This award supports participation in the Southwest Local Algebra Meeting at Arizona State University, which will be held March 1–2, 2025. The research presented at this conference will focus on recent advances across the broad mathematical field of algebra, and on applications of the field. The conference talks will aim to be understandable to the graduate student participants, exposing then to techniques from neighboring mathematical areas. These student participants will also be given the opportunity to present their own results at scheduled poster sessions. The wide range of topics and the event schedule are designed to promote mathematical discussion and collaboration, including among researchers in the southwest and southern United States. The conference speakers will present research in algebraic and enumerative combinatorics, algebraic geometry, and both commutative and noncommutative algebra. The speakers are experts in, e.g., the study of singularities in positive characteristic, hyperplane arrangements, combinatorial free resolutions, graded Hecke algebras, and symbolic powers. More information can be found on the conference webpage: https://www.math.ttu.edu/~lchriste/slam2025.html. 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 · 2025-02

Opioid use disorder (OUD) is a chronic condition and a leading public health problem in the U.S. The risk of overdose is particularly high following a period of abstinence leading to drug-related deaths. OUD includes physical dependency and neural adaptations in brain circuits of reward and motivation, self-regulation, and stress reactivity that can persist years after drug discontinuation. Substance craving is one of the primary causes of OUD patient's relapse. Studies have shown psychological cues such as stress, anxiety, and arousal can precipitate the cultivation of drug craving. Research has found that mindfulness-based strategies reduce cravings, psychological cues and prevent relapse. Mindfulness-based interventions (MBIs) bring about clinically relevant changes to physiological arousal, stress, and addictive behavior through cognitive behavioral skill development. This project focuses on developing and testing innovative technologies to aid sustainable recovery of OUD with wearable and in-home physiological monitoring and generation of adaptive, personalized, and just-in-time MBIs. While the research is focused on OUD, the principle and the outcomes can be expanded to include other substance use disorders. The project includes several education and outreach activities such as machine learning course for medical professionals and annual workshops for middle school girls. This study focuses on opioid use disorder (OUD), related cognition, and behaviors associated with a) reward, b) self-regulation, c) stress reactivity, d) opioid craving, e) physical opioid withdrawal symptoms and MBIs known to be impacted by OUD and post-acute withdrawal from opioids. In particular, the research tasks focus on there areas. First, effective physiological feature identification and extraction to detect craving that is generalizable across large OUD populations and consider the external factors such as age, gender, drug use habits, etc. Second, development of an effective multi-modal sensing integration approach to capture psychological craving cues (e.g., stress, arousal) from a combination of acoustic and physiological sensing. This will include novel multiple instance (MIL) multitask learning based classification techniques that are scalable with near real-time performance. The study will address the fundamental gaps of indoor craving-relevant sensing where only a small fraction of a long signal may convey information relevant to the targeted emotional state/class. The last task will include development of a craving context-aware MBI recommender system that models the dynamic nature of OUD subjects craving-interventions and feedbacks. The system will be formally validated to ensure safety against adverse outcomes. Successful execution of the research will begin to test the effectiveness of integrating passive sensing, adaptive artificial intelligence (AI), and mindfulness interventions on regulating drug craving. 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 · 2025-02

Dark matter is an invisible substance that makes up more than eighty percent of the matter content of the universe. This research project aims to directly detect dark matter particles by exploring a broad range of possible dark matter masses. Recently, the fuzzy dark matter model has emerged as a promising candidate. In this model, the particles that make up dark matter are so light that they spread out over large areas, rather than clustering in small groups. This characteristic could help explain the size of our galaxy and why there seems to be a minimum size limit for galaxies in general. The project is driven by interests in high-energy physics, but it presents considerable experimental challenges. The research team plans to develop a new system designed to cast a wide net in the ongoing laboratory search for dark matter. The innovative techniques they create could not only help in the search for dark matter but also facilitate the discovery of new forces and enhance our ability to measure energy levels in quantum systems. The research team will build a system to search for ultra-low mass dark matter by examining how it interacts with nuclear spins. Nuclear spin energy levels will fluctuate based on how they align with the flow of dark matter. As the Earth rotates and the dark matter oscillates, these energy levels will change, creating a unique pattern over time. The most precise measurements of the energy differences between quantum states—measured in absolute terms—have been achieved using groups of optically “pumped” nuclear spins. This technique involves using light to control the orientation of a nucleus's spin, effectively aligning the nuclear spins in a specific direction through the manipulation of light. There is still significant potential to enhance energy sensitivity using current technology. The research team's goal is to achieve a 30-fold improvement over the current best methods, with data readout conducted by a SQUID (Superconducting Quantum Interference Device). The team will conduct two distinct dark matter searches: one targeting dark matter with a frequency higher than the Earth’s rotation rate, and another for dark matter with a mass lower than this rotation rate, that will include investigating the “Fuzzy” dark matter scenario. 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 · 2025-02

Modern data centers are evolving to become more environmentally sustainable and efficient by adopting resource-disaggregated infrastructure, known as disaggregated data centers. This approach separates computing, storage, and networking resources to improve scalability, flexibility, and energy efficiency. Persistent key-value stores (PKVS)-which are essential for storage systems, databases, data deduplication, and big data analytics-are widely used in today's technology landscape. As cloud computing, big data, and large language models (LLMs) continue to grow rapidly, it is becoming crucial to adapt PKVS for use in disaggregated data centers. However, current PKVS face significant challenges in this environment, such as managing diverse hardware resources, scaling effectively, and handling risks like system failures, data corruption, and security threats. This CAREER proposal aims to create a flexible framework to redesign and optimize different types of PKVS for disaggregated data centers. The project will focus on better use of mixed hardware, scalable performance, and stronger reliability and security. Ultimately, this work will lead to greater resource and energy savings, extended hardware life, and better performance-to-cost balance for large-scale data systems. These improvements will benefit applications in cloud computing, machine learning, and scientific research. This project will devise new techniques to achieve key-range- and hotness-aware compute and data tiering with LLMs-assisted management, component-level scaling up and instance-level scaling out, and decentralized handling of execution failures and data protection with multi-level checksums and encryption. The proposed methodologies, system designs, and implemented components will benefit the memory and storage research communities in further developing storage systems and data infrastructures in disaggregated data centers. The project also tightly integrates research with education through various educational activities, including curriculum development and updates with the concept of "disaggregation," expanding winter and summer research camps to engage more students for cutting-edge research, especially undergraduates students, and enhancing interactions and collaborations with 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 · 2025-01

The evolution of database architectures has prioritized speed, capacity, scalability, and flexibility over privacy considerations, creating challenges in protecting sensitive data like healthcare, biometrics, financial, and educational records. Privacy policies and regulations often fail to integrate seamlessly with data-management practices, complicating the task of ensuring privacy across end-to-end workflows. In addition, decoupling decentralized data management and decentralized machine learning (ML) over federated data silos (i.e., federated learning) causes difficulties in detecting malicious clients and handling diverse data distributions. This project aims to address these shortcomings by developing a database architecture to integrate the privacy regulation and compliance process, enhance federated learning with decentralized data-management functions such as data synthesis and profiling, and automate privacy-model configuration in artificial intelligence (AI) workflows. The project will be evaluated using real-world healthcare and biometrics workloads to demonstrate policy consistency, better privacy-utility tradeoffs, and improvement of productivity in configuring privacy-preserving mechanisms. This project will leverage the partnership between Texas A&M University - Central Texas and Arizona State University to train underrepresented students in both universities at the intersection of AI/ML, privacy, and database systems. The project aims to provide a data-architecture design that enables unified privacy policies, enhance federated learning with advanced data management, and optimize privacy-aware query and storage mechanisms. This project consists of the following research thrusts: (i) developing a database system that integrates AI/ML capabilities, facilitating the coordination of data-privacy policies, AI/ML workflows, and regulatory compliance as well as ensuring that data management and privacy regulations are seamlessly aligned with AI/ML processes; (ii) building a federated data-management framework for federated learning to enhance incentive mechanisms, detect malicious gradients, and balance non-independent and identical distribution to improve the accuracy and robustness of federated learning; and (iii) creating query and storage optimizers that automatically select the appropriate model architecture for privacy-preserving training requests, as well as the storage scheme for the resulting model, thereby ensuring that user utility and privacy objectives are met and enhancing both the effectiveness and the privacy of end-to-end AI/ML workflows. This project will also include educational activities targeting students from underrepresented populations in both universities, including, but not limited to, an annual AI Day activity, students exchange between the two partner universities, and curriculum innovation leveraging the research products of this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Quantum computers, with their potential to revolutionize computation, are on the brink of addressing problems that are currently unsolvable by classical computers. Their unique ability to simulate molecular and atomic behavior for understanding material properties would enable researchers to accurately predict characteristics. However, the current utilization of quantum computing in materials science and chemistry is limited to a few researchers. For utility-scale quantum computing, it will be imperative to train the next generation of materials scientists and chemists to harness the power of quantum computing. This will open new avenues for envisioning and discovering materials, including those beneficial for quantum bits (qubits), the fundamental units of quantum information. The development of a diverse, quantum-ready workforce is a key priority in the National Quantum Initiative. It will prepare more individuals for jobs in quantum information science and engineering (QISE), enhance STEM education at all levels, accelerate the exploration of quantum frontiers, and expand the talent pool for future industries. This pilot project is creating a quantum computing training curriculum for undergraduate, graduate students, and researchers in materials science and chemistry. The curriculum covers quantum computing fundamentals, quantum simulations, and quantum machine learning utilizing both IBM quantum hardware and NVIDIA CUDA Quantum GPU-based simulators powered by high-performance computing (HPC), along with classical and quantum simulation tools for atomic-scale structures and material properties. Each summer, the team will host an in-person training workshop at either Purdue University or Arizona State University. Additionally, the project will provide virtual training tutorials throughout the academic year, reaching participants via NSF's ACCESS (Advance Cyberinfrastructure Coordination Ecosystem: Service & Support) program and other channels. The project offers multi-faceted impactful benefits, particularly in its outreach and educational initiatives. It will significantly enhance diversity in the QISE field by specifically targeting underrepresented undergraduate and graduate students, as well as researchers from Minority Serving Institutions (MSIs), including ASU. The project also includes a public forum as part of its summer schools, serving as an important platform for open discussion and knowledge exchange. The project also catalyzes the spread of QISE knowledge as trained researchers and students teach their communities, furthering the science beyond material science and chemistry applications, and an NSF ACCESS affinity group will promote sustained collaboration and growth in QISE. 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 · 2025-01

Public Safety Systems (PSS) deal with victims, first responders, and emergency control centers (ECCs) that coordinate the rescue missions during and aftermath of a disaster. Currently, there is limited understanding of how humans make decisions to utilize personal and shared resources in the wake of disasters; moreover, the existing models are mostly qualitative. Limited information availability in disaster scenarios and resource uncertainty in PSSs pose two major challenges: (1) how to achieve the victims’ satisfaction accounting for their risk-aware (autonomous) decision-making characteristics about dynamic resource orchestration? and (2) how to design a resilient disaster response system incentivizing the victims’ participation in crowdsourcing? The innovative SOTERIA project addresses these challenges by proposing a novel behavioral decision-making model that captures humans’ decision-making characteristics under risk, stemming from uncertain availability of resources. It introduces a new field in game theory, called Satisfaction Games that aims to satisfy the victims’ minimum service requirements rather than maximizing their overall satisfaction, addressing the resource management problems in PSSs. A novel bio-inspired Disaster Response Network (DRN) is also proposed that mimics the inherent robustness of biological networks of living organisms, to support the victims’ participation in reliable crowdsourcing, while the truthfulness and quality of the collected information are evaluated based on the newly invented Bayesian Prospect Theory. The proposed methods and the system will be evaluated using real-world field data. The SOTERIA project will create new distributed control, optimization, and scalable resource management techniques in PSSs. The novelty lies in the integrated approach to efficiently managing resources considering human behavior and Tragedy of the Commons phenomena about shared resources, thus enhancing the victim’s satisfaction instead of utility maximization. To deal with incomplete information about resource availability, the proposed reinforcement learning techniques will enhance the victim’s decision-making capability in real time. Research outcomes of this project have tremendous potential for supporting ECC activities and saving peoples’ lives and infrastructures during and after disasters, by designing robust situation-aware disaster response networks and advancing state of the art research in Prospect Theory, Tragedy of the Commons, and Satisfaction Games. The project will train undergraduate and graduate students and the research findings will be disseminated via a project website and high-quality publications. 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 · 2025-01

Flooding is a significant challenge with impacts that are unevenly distributed across communities in the United States. This Research Coordination Network (RCN) addresses the urgent issue of flood injustice by examining the social, economic, and environmental factors that contribute to unequal flood risks and recovery outcomes. Communities of color and those of low socioeconomic status are often disproportionately affected by flooding due to historical inequities and inadequate infrastructure. This project aims to bring together researchers, policymakers, and community organizations to identify and address flood injustice through collaborative research and action. By focusing on interactions between the built environment, natural systems, and social vulnerability, the project seeks to inform public policy and create more equitable flood management strategies, benefiting communities nationwide. It also aims to enhance public understanding of flood risks and empower communities through education and engagement by creating educational opportunities and collaboration among students, researchers, and community members. The network will synthesize data and insights, catalyze action, and advance knowledge on how flood injustice is shaped by interactions between social, natural, and engineered systems. The project will focus on two main research themes: (1) mechanisms of urban development and climate change that shape flood risk futures; and (2) broadening participation and co-production of place-based research to address flood justice. Key activities include hosting workshops that leverage the thematic expertise of steering committee members, fostering collaboration and knowledge sharing among participants. The network will engage diverse community perspectives by incorporating non-profit organizations and government representatives on the steering committee, facilitating workshops that develop strategies for flood justice. The network will also actively involve graduate students, providing opportunities to engage in convergence research and develop skills necessary for addressing complex social and environmental issues. By advancing the integration of justice into Earth System Science, this project will contribute to more equitable flood mitigation and recovery policies, promoting social equity and resilience in flood-prone communities across the United States. Additionally, by focusing on the role of natural infrastructure in mitigating flood risks and examining the financial and social dynamics of flood risk futures, the project will inform equitable urban planning and policy decisions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Non-technical summary A team of professors (Arizona State University and University of Arizona) are organizing the second MateriAlZ Winter School to be held in January 2025. They represent key STEM fields ranging from chemistry and materials to manufacturing and engineering and are also the founders of the successful MateriAlZ Seminar series that is accessible remotely every Friday within the school year as well as permanently on the YouTube channel. The team recognizes the importance of Materials Science and Engineering (MSE) topics in research but also in education and science communication. To reach the next generation of trained professionals in these areas, the Winter School covers broad and timely topics, such as synthesis and manufacturing, artificial intelligence, and quantum and energy materials. Aside from these core tutorials, the school also includes interactive soft skill training sessions, panel discussions as well as networking opportunities with industry representatives. Students who are interested in applying for graduate studies will be recruited giving priority to underrepresented groups. The organizers will cover travel and lodging (at Biosphere, Oracle, AZ) for the students. A long-term benefit for all participants will be building a reliable MateriAlZ network that is accessible beyond the meeting dates and will be a valuable resource for students, speakers, and industry/government representatives alike. Technical summary This event is the second MateriAlZ Winter School organized by a team of professors at Arizona State University and the University of Arizona. The school will be held in January 2025 and slight adjustments are made based on 2024 student feedback (e.g. 1 additional day, more breaks). In the theme of the popular MateriAlZ Seminar series that the team is organizing, the five-day winter school will cover broad topics from the key areas of chemistry, engineering, and manufacturing of materials. The event is free of charge for (junior and senior) undergraduate students and will promote the US Southwest as a growing hub for materials research, showcase large-scale activities at both schools, serve as a networking platform for students interested in US graduate programs and promote careers in high-tech industries throughout the Southwest region. The school will include tutorial sessions on broad materials science and engineering (MSE) topics ranging from quantum materials, semiconductors and energy materials to syntheses and manufacturing and artificial intelligence taught by local and external speakers. The program also consists of soft skill courses (e.g., on networking and making efficient figures) as well as panel discussions on graduate programs and funding mechanisms. The school provides an excellent window into MSE research topics and the general graduate school experience, which is beneficial for undergraduate students as they apply to different graduate schools. The immediate impact will be in providing information and guidance that many undergraduate students lack, especially first-generation students and those that belong to an underrepresented group in science. Moreover, the team will provide sustainable long-term benefits for all participants, even beyond the actual meeting dates that include face-to-face time. This will be accomplished within an online space (MateriAlZ website) where brief summaries of the meeting content will be collected, and photo and video materials will be posted. On top of this, there will be an “Alumni corner” for future interactions and events to provide all participants with a reliable and long-term MateriAlZ network. 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 · 2025-01

This grant provides support for students to participate in the 2025 Institute of Industrial and Systems Engineers (IISE) Annual Conference and Expo to be held 31 May to 3 June 2025, in Atlanta, Georgia. The IISE Annual Conference & Expo is interdisciplinary, bringing together engineers from different disciplines which can foster innovation and increased engagement from students with diverse interests. This conference aligns with the scientific areas supported by NSF CMMI, addressing the opportunities and challenges posed by digital technologies in the next industrial revolution, with a focus on integrating design and manufacturing within the broader life cycle ecosystem. Travel support to attend the IISE Annual Conference & Expo can broaden participation in engineering by providing students, especially those from groups historically underrepresented in engineering, with access to a professional network, mentorship opportunities, and exposure to cutting-edge research and industry practices. Engaging with a diverse community of professionals and peers at various career stages helps students build valuable connections, gain insights into career pathways, and develop a sense of belonging within the engineering field, ultimately encouraging their continued growth and participation in the profession. The diverse range of topics presented at the conference will provide students with opportunities to learn more about the methods and tools needed to create globally competitive industries focused on the creation of manufactured products and systems. This grant will support students' conference registration and travel costs with the goal of promoting student participation at the conference, especially among students from groups often underrepresented in engineering. The selection process of the awardees will prioritize students who do not otherwise have sufficient funds from other sources (e.g., advisor, department, other travel awards) to attend the conference. The conference offers career development opportunities through special sessions, workshops, networking activities, and mentoring from experienced professionals representing a variety of career stages, institutions, and geographical regions. Participation is open to all, and the diverse lineup of speakers and organizers fosters an inclusive environment, empowering students from underrepresented groups to engage meaningfully in discussions on the future of manufacturing. 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 · 2025-01

This project explores the gaps in knowledge, skills, and experiences that students may need to gain outside formal learning environments in computer science education and seeks to understand how these gaps impact students' success. By evaluating students' success as they navigate both formal (classroom) and informal curricula (e.g., makerspaces, internships, extracurricular clubs), we will develop learner-centered solutions to support their understanding of computing concepts and their gain of skills. The significance of this project lies in its potential to make CS education more comprehensive. In addition, this project addresses growing impact of artificial intelligence (AI) in education by examining the relationship of exposure to AI (e.g., large language models) outside of the class and student success in programming environments in the classroom. Our findings will benefit society by understanding and improving the educational experiences of all students and enhancing their success in computing programs. The three-year research program will investigate gaps in CS education through three primary strands: (1) identify factors from the formal and informal curricula, which when not available to students, could pose risks to students' mental health, such as anxiety, depression, and the impostor phenomenon; (2) study students' interactions with programming environments and large language models (LLMs) outside of class to characterize effective scaffolding strategies and address technical challenges in the classroom; and (3) evaluate the impact of makerspaces on students' creativity, exploratory skills, and sense of belonging. The project's methodology combines qualitative ethnographic methods, participatory design, and quantitative experimental and quasi-experimental approaches. This project will emphasize the experiences of all students in computing, aiming to create robust learning environments. The research will provide valuable insights and guidelines to improve CS education, ultimately reducing dropout rates and enabling students' success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Digital twins mimic actions and processes of physical assets in real-life executions. This research project concerns with development of a framework for learning digital twins of physical systems capable of incorporating real-world data into first-principles based mathematical representations. Learning digital twin from real data is a novel capability which can help enable effective strategic planning in various domains such as space exploration, autonomous transportation, sustainable water future, smart manufacturing, critical mineral mining, alternative power generation, and healthcare to name a few. This project focuses on applications to important problems in healthcare sciences related to data-informed decision-making exploiting virtual representations of human physiology and has implications for the development and evaluation of new therapies and treatments. One compelling example application is glucose metabolism in people with Type 1 diabetes (T1D). Patients with T1D must replace insulin exogenously as determined by multiple daily measurements of the blood glucose concentration, to maintain glucose homeostasis and avoid hypo / hyper-glycemia and life-threatening diabetic ketoacidosis. As a result, the person with diabetes has to make multiple, complex decisions each day based on food composition, exercise, hormonal cycles and other behavioral factors. Personalized glucose metabolism digital twins developed through this award will be used to devise new ethical treatment modalities and evaluate safety and effectiveness of automated insulin delivery systems without risk factors. Digital twins as such can also feed essential knowledge about system safety and effectiveness to regulatory agencies through assurance cases and advance regulatory science in profound ways. Both study sites University of Houston and Arizona State University are Hispanic serving institutions and the research is integrated with educational and outreach activities to create awareness, especially among youths, and understanding of diabetes and its management, broaden participation of groups traditionally underrepresented in STEM and contribute positively to engineering education. The first-principles informed data-enabled framework seeks to advance foundational techniques underpinning the development and use of digital twins and synthetic data in biomedical and healthcare domains, by combining advances across mathematical modeling, machine learning (ML) and systems’ theory with human physiology. This research will (1) develop advanced structures based on neural networks (NNs) for the recovery of an underlying physics-based model that are capable of operating in real-world conditions characterized by limited data availability, low and non-uniform sample rate and spatial and temporal noise, (2) develop novel parametrizations of black-box dynamics using NNs from a class of models with "built-in" properties of stability and robustness to perturbations, (3) integrate real-world physical twin generated data which are heterogeneous, scarce and noisy, into its virtual first-principles based mathematical representation, (4) develop a novel framework for learning unmodeled dynamics due to e.g., unaccounted for inputs, inter- and intra-individual variability. Extensive evaluation of this methodology will be conducted using publicly available datasets specially for T1D patients. 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.