Lehigh University

NSF Awards · FY 2026 · 2026-07

Quantum computing has the potential to transform scientific discovery and technological innovation in areas such as artificial intelligence, molecular simulation, biotechnology, and secure information processing. Variational quantum algorithms are among the most promising approaches for near-term quantum computing, but existing optimization methods used to train these algorithms often become unreliable when quantum measurements are highly noisy or when problems become very large. These limitations present major difficulties to realizing the practical benefits of quantum computing technologies. This research addresses these challenges by developing mathematically rigorous and scalable optimization frameworks that remain effective under severe noise and computational uncertainty. The project advances the mathematical and computational foundations needed for trustworthy quantum algorithm training and evaluation, while also enabling systematic assessment of when quantum computing can provide advantages over classical methods. By strengthening core capabilities in quantum computing, the research supports national priorities in scientific innovation. The project also contributes to workforce development through interdisciplinary training opportunities for undergraduate and graduate students in computational mathematics, optimization, and quantum computing. Research outcomes, including open-source software, benchmark test problems, and educational materials, will be broadly disseminated to accelerate adoption by the scientific community and support the emerging quantum technology workforce in the United States. This project studies large-scale unconstrained and constrained quantum-specific optimization problems arising in variational quantum algorithms. The research develops advanced noise-aware, derivative-free, and factorization-free optimization algorithms that rely solely on noisy objective and constraint evaluations, avoid costly matrix factorizations, and accommodate realistic quantum hardware conditions, including stochastic and potentially unbounded measurement noise. Rigorous convergence, complexity, and robustness analyses establish theoretical guarantees for algorithmic performance, scalability, and resource efficiency. The research also integrates modern linear-system solution techniques to improve scalability while maintaining convergence behavior comparable to leading factorization-based methods. The resulting frameworks will be validated across a broad range of quantum computing applications, including quantum approximate optimization algorithms, variational quantum linear solvers, and quantum neural networks, with potential downstream applications in artificial intelligence, molecular design, and computational biotechnology. The resulting methodologies are also broadly applicable to large-scale stochastic optimization problems arising in science, engineering, and advanced computational 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 2026 · 2026-04

This NSF CAREER project aims to develop a new way to control the growing number of distributed energy resources (DERs) such as solar panels, wind turbines, batteries, and small generators, that are transforming the nation’s power grid. As these devices become more widespread, traditional model-based control methods struggle to keep pace with the grid’s increasing complexity and data volume. The project will bring transformative change by introducing a geometry-based control paradigm that learns how these systems behave directly from data, rather than relying on detailed physical models that are often difficult to obtain and maintain. This will be achieved by identifying low-dimensional patterns, or “manifolds,” hidden within system measurements and using them to design scalable, real-time control strategies. The intellectual merit of the project includes advancing fundamental knowledge at the intersection of nonlinear dynamics, machine learning, uncertainty quantification, and distributed control. This will enable resilient and adaptive operation of DER-rich power systems. The broader impacts of the project include improving grid reliability and energy resilience, integrating research into undergraduate and graduate education, offering industry internships and workforce training opportunities, and preparing U.S. students to lead in the modernization of electrified communities. The project integrates nonlinear manifold learning with predictive control to regulate voltage, frequency, and power sharing in microgrids that can operate independently or connected to the main grid. Instead of building full mathematical models of each device, the research extracts governing dynamics from time-series measurements and constructs reduced-order representations in a learned latent space. These representations are used to design predictive control algorithms that compute optimal control actions in real time. To address noise, missing data, and limited sensor availability, the project develops probabilistic latent-space models that estimate uncertainty and incorporate it into control decisions through stochastic optimization. A distributed coordination layer enables multiple DERs to cooperate using only local communication, avoiding centralized computation and improving scalability and cyber resilience. The framework will be validated on device-level and microgrid testbeds and is designed to generalize to larger power networks. By combining geometry-based learning with real-time control, the project will establish a new data-centric foundation for managing complex electrified infrastructure systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY The inability to build physiologically relevant in vitro tissue models greatly limits both research capabilities and regenerative therapies. Hydrogels are frequently used to mimic the extracellular matrix (ECM) which surrounds cells in tissue, and the physical properties of these scaffolds can be tailored to individual cell types. These scaffolds are often made from polymers crosslinked by peptides that are substrates for cell-secreted proteases to enable the encapsulated cells to spread and migrate within the matrices. A challenge for these systems is that each cell type within a tissue can have a unique set of ideal matrix parameters. For instance, most tissues are highly vascularized, but endothelial network formation is optimized within matrices that are very soft, while other physiological processes, such as osteogenic differentiation, are typically optimized within stiffer, more highly crosslinked hydrogel matrices. This highlights the need for making hydrogels with specific niches for each cell type. To address this need, we propose fabricating scaffolds in which cell-specific protease activity creates tailored microenvironments around individual cell types. Each cell type expresses a unique combination of proteases, and we have developed novel methods to identify peptides that are specifically cleaved by individual cell types. We are also able to determine whether these peptides are cleaved near the surface of the cell or by soluble proteases that induce bulk matrix degradation. Using a "split-and-pool" peptide synthesis technique, we can generate more than 300 variants of protease-substrate peptides to tune the degradation rates to desired values. We hypothesize that hydrogels crosslinked with peptides with optimal spatiotemporal degradation kinetics will have increased biological performance over existing crosslinking peptides. We will test this hypothesis in two Aims: In Aim 1, we will use a split-and-pool synthesis technique to identify hydrogel crosslinking peptides whose degradation kinetics are optimized for either osteogenic differentiation of human mesenchymal stem cells (hMSCs) or vasculogenesis of human umbilical vein endothelial cells (hUVECs). We will also make peptides which are conjugated with chemically-labile bonds that will enable quantification of the fraction of crosslinks cleaved during culture, which will couple physiological behavior in gels to dynamic changes in hydrogel structure. In Aim 2, we will develop co-culture hydrogels that contain both hMSCs and hUVECs to identify a single peptide that supports both osteogenesis and vasculogenesis within hydrogels. This will pioneer the use of hydrogel crosslinking peptides to simultaneously promote multiple physiological processes within a single system. The proposed research plan combines biomaterial synthesis, analytical chemistry, and cell culture to develop a versatile platform that can be used across tissue systems to improve our ability to model tissues in vitro and regenerate them in vivo.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY Biomaterials for tissue engineering must simultaneously provide mechanical support at the tissue level and local biochemical and physical cues at the cellular level to promote functional tissue regeneration. However, these multiscale requirements often conflict with each other. For example, hydrogels designed to mimic cartilage extracellular matrix typically lack sufficient mechanical strength to withstand forces applied in vivo. Solid scaffolds with load-bearing capability are significantly stiffer than native cartilage. Both examples result in unwanted changes in cellular response that leads to the formation of functionally inferior scar-like tissue. These challenges highlight the critical need for biomaterials with properties that can be independently tuned across multiple length scales to direct functional tissue regeneration. To address this need, a versatile 3D printing approach has been developed to independently control biochemical and physical properties within a single biomaterial. Prior work demonstrated that printing inks containing peptide-functionalized polymer conjugates enabled control of bioactive peptide concentration on the surface without altering scaffold modulus or architecture. In addition, printing with different ratios of polymer molecular weight resulted in scaffolds with significantly different mechanical properties without affecting scaffold architecture, surface chemistry, or crystallinity. Relevant to the proposed work, human mesenchymal stromal cells (hMSCs) cultured in high stiffness scaffolds under chondrogenic (cartilage-promoting) conditions differentiated towards unwanted hypertrophic and osteogenic (bone) lineages while low stiffness scaffolds promoted more stable chondrogenesis. These findings underscore how biochemical and mechanical cues can have competing or synergistic effects and must be optimized independently to direct stem cell fate. The proposed project aims to expand and refine this biomaterial platform using hMSC differentiation toward cartilage as a model system. Specifically, a new approach will be developed to functionalize the surface of 3D-printed solid scaffolds with soft, hydrophilic peptide-polymer bottlebrushes to independently control surface and bulk properties across length scales within a single construct. It is hypothesized that the surface-grafted bottlebrushes will create a soft, hydrogel-like microenvironment for cells without compromising bulk scaffold modulus, and that including bioactive cartilage-promoting peptides will synergistically enhance hMSC differentiation into cartilage cells. This hypothesis will be tested through two Specific Aims: (1) demonstrate that surface properties can be tuned independently of bulk scaffold modulus, and (2) demonstrate that surface-grafted peptide-polymer bottlebrushes enhance hMSC differentiation. This work will provide a powerful and adaptable platform for future biomaterial designs by enabling independent control of cell- material interactions at multiple length scales. The proposed strategy can be broadly applied to other tissue applications by varying peptide sequences, bottlebrush compositions, and bulk scaffold materials.

NSF Awards · FY 2026 · 2026-02

Every year, about 8000 babies in the United States are born with serious heart defects, known as critical congenital heart diseases (CCHDs). These defects can change the normal flow of blood through the heart and can be life-threatening. It is important to find these defects soon after birth. In the U.S., all newborns are checked for CCHDs before they leave the hospital. This is done using a method called pulse oximetry which measures how much oxygen is in the blood. However, this method doesn’t always find some types of CCHDs. This project will work on a new way to find these heart defects. It is based on the idea that the small movements of a baby’s chest caused by heart activity are different in babies with CCHDs. The project will focus on finding the specific patterns of these chest movements that could indicate a CCHD. This could offer a new way to check for these heart defects. The knowledge and tools created by this project could help improve care for newborns with these heart diseases and reduce healthcare costs. The project will also include activities to teach people about CCHDs and how they are diagnosed, especially among groups who are more likely to be affected by these conditions. These activities include outreach to K-12 students, mentoring K-12 and undergraduate students in CCHD-related research, creating and publicly sharing short educational videos, and developing a mobile app to enhance parental awareness of congenital heart defects. The overall goal of this CAREER project is to enhance our understanding of the impact of major types of CCHDs, including coarctation of the aorta, tetralogy of Fallot, and patent ductus arteriosus, on the patterns and characteristics of chest surface vibrations recorded by seismocardiography (SCG) and gyrocardiography (GCG), and therefore, evaluate the clinical value of these signals in detecting CCHDs. The project’s central hypothesis is that these vibrations vary between newborns with CCHDs and those without cardiovascular diseases, and thus, they hold significant diagnostic potential for identifying CCHDs of interest. The goal of this project will be attained by (i) identifying indicators of CCHDs by evaluating the signatures of SCG and GCG signals in the presence of CCHDs, using machine learning and statistical models, (ii) understanding the spatiotemporal distribution of chest vibrations in the presence of CCHDs by creating and validating a novel vision-based pipeline, and (iii) investigating the genesis of CCHD indicators through developing and validating multiscale computational models. This project is jointly funded by the Engineering of Biomedical Systems Program (EBMS) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Volcanic eruptions are a major natural hazard in the United States, where there are about 169 potentially active volcanoes, many of which are located in areas with rapidly expanding populations. Although monitoring volcanic unrest is undoubtedly important to eruption forecasting, accurate interpretation of monitoring data and prediction of future volcano behavior require a scientific understanding of the processes that transport magma out of storage regions in the Earth’s crust and towards the surface. This project will enhance knowledge about what causes magma to move in different directions through the subsurface under large stratovolcanoes such as Mount Rainier and Mount Hood. The work will test how the physical properties of magma and the size of the volcanic edifice influence eruption location, which is currently poorly understood. The project supports interdisciplinary training of graduate and undergraduate students, as well as the development of educational materials that will be implemented in university classrooms and made available to the public for broad classroom and online educational use. The primary objective of this project is to determine what controls the pathways of magmatic dikes under stratovolcanoes. Stress perturbations in the crust beneath a stratovolcano are thought to impact the propagation direction of ascending dikes, potentially arresting propagation or deflecting dikes toward the flanks and causing eruptions at lower elevations that can be hazardous to local communities. The proposed three-year project will employ a synergistic approach that integrates geologic field data, analogue gelatin experiments, and numerical models to investigate how the stress field generated by the load of stratovolcanoes and variations in magma properties impact the geometry and propagation of dikes ascending from crustal sources. The proposed work includes geologic mapping and sample collection from the radial dike sequence exposed at Summer Coon, an eroded stratovolcano in Colorado, to measure dike outcrop geometries, flow fabrics, and physical magma properties like density and vesicularity. Using a novel experimental setup and input parameters constrained by field data, gelatin experiments will include injecting magma analogues beneath a simulated edifice to investigate the impacts of variable edifice height/slopes, magma densities, and dike injection depths on dike propagation. Finally, field and experimental results will inform new numerical models designed to evaluate crustal stresses beneath a volcanic edifice and investigate how in-plane dike propagation directions vary as a function of edifice geometry, magma buoyancy, initial magma pressures, and dike injection depths. 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 2026 · 2026-01

NON-TECHNICAL SUMMARY Synthetic polymers, including rubber and plastics, are used in nearly every aspect of daily life. The dominance of synthetic polymers is largely driven by their excellent stability and versatile mechanical properties. However, due to their high durability, waste materials composed of these polymers have accumulated in the land and oceans, causing serious concerns for the ecosystem. In addition, since over 90% of these polymers are derived from finite fossil feedstocks, the production of these materials is unsustainable if they cannot be recycled and reused. This project seeks to address these challenges by developing recyclable polymers that can be broken down into the constituents (monomers) from which they are made. The recycled monomers can be reused to produce the polymers, allowing for a circular use of materials, which not only helps to preserve the finite natural resources used in plastics production, but also addresses the issue of unwanted end-of-life accumulation of plastic objects. Besides demonstrating the recycling of the polymers, the PI plans to tune the thermal and mechanical properties of the polymers to meet the needs of a variety of applications. This project will also contribute to education and broadened participation in materials science and sustainability through developing undergraduate and graduate courses, recruiting underrepresented minority researchers, and reaching out to local high schools and museums. A multimedia exhibition called “Polymers and Life”, which will include cards, videos and hands-on projects, will be developed in collaboration with the Akron Children’s Museum. TECHNICAL SUMMARY A promising solution to address the challenges in plastics sustainability is to replace current polymers with recyclable ones in order to achieve a circular use of materials. Despite the progress made thus far, few recyclable polymers exhibit the excellent thermal stability and high-performance mechanical properties of traditional polymers. This project aims to address those challenges by developing polymers that can undergo catalytic depolymerization to yield their constituent monomers. When the catalyst for depolymerization is absent or removed, the polymers will be highly stable and their thermal and mechanical properties can be tuned to meet the needs of various applications. The objectives of the research include design and synthesis of the polymers, demonstration of depolymerizability and stability, and establishment of structure–thermal property relationships and structure–mechanical property relationships. Research results will be incorporated into undergraduate and graduate courses. These courses are expected to bridge the knowledge gap between fundamental physical organic chemistry and materials science, integrate experimental and computational research, and enhance student awareness of sustainability. The proposed research will also provide opportunities to attract and nurture a diverse group of future scientists who can participate in research activities on sustainable polymers. In addition, demonstration materials based on the research findings will be developed for use in outreach efforts to local high schools and museums in order to inspire an interest in science in the younger generation. . 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 · 2025-12

On April 29, 2025, scientists observed molten lava erupting on the deep seafloor at the East Pacific Rise. This RAPID project will investigate the hydrothermal vent system at the eruption site. Field work includes high-temperature fluid and temperature sampling. These measurements will extend an existing time series (6 expeditions from 2018-2025) with post-eruption (within one year) data. The project will result in a unique data set for understanding hydrothermal vent evolution. Broader impacts include training opportunities for early-career scientists and a live at-sea webinar with K-12 classrooms. The work leverages a scheduled field program at the study site in January 2026 that will include geology and biology science teams. This project benefits the US public by helping to understand volcanic eruptions and helps to build a workforce knowledgeable in cutting-edge deep-sea exploration technology. This project will advance understanding of how submarine eruptions reorganize subsurface permeability, fluid pathways, and chemical exchange between the crust and the ocean. High-temperature fluid sampling will be paired with in situ temperature measurements to provide a multidisciplinary view of post-eruptive vent evolution. These samples will constrain post-eruption conditions, including phase separation, redox, and volatile budgets, while geothermobarometric and isotopic data will link these changes to permeability shifts. Integrating these data with long-term temperature and permeability records will improve models of magmatic-hydrothermal coupling and refine the ability to predict mid-ocean ridge eruptions and their impact on the ocean. 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 · 2025-10

Bone fractures happen to 9.6 million Americans each year. A quarter of all lower-limb fractures are delayed healers and about 1 in 10 are nonunions—fractures that do not heal without another major surgery. The long-term goal of this US NSF-Swiss NSF (NSF-SNSF) project is to improve health outcomes for people with poorly healing fractures by developing computer simulation tools that can predict how bones heal over time. The simulations will use medical imaging to create digital twins – virtual models of real bones – that give insight into how an individual person’s healing may progress with time. Better predictions for healing outcomes will help doctors and patients make more informed decisions about what to do when bone healing goes poorly. The project will also provide a unique training opportunity for a mechanical engineering graduate student. The student will have the opportunity to do an international research exchange in Switzerland at the AO Research Institute, a global leader in biomechanics and bone fracture care. This research will address fundamental knowledge gaps in biomechanics and mechanobiology that are of broad interest to engineers, biologists, and clinicians working in the field of fracture healing. Objective 1 will define the limiting conditions for callus formation by performing spatial colocalization of strain from subject-specific finite element models and mineralization from longitudinal imaging. Objective 2 will measure rate constants for bone remodeling and add this process to the simulation framework. Objective 3 will reduce the computational cost of prognostic bone healing simulations with a goal of achieving 100x reduction in compute time. Objective 4 will introduce a novel probabilistic model for normal and compromised healing and perform fracture healing simulations in human digital twins. This project will advance computational mechanobiology in orthopedics by developing credible simulation tools that have been calibrated and robustly validated using imaging of large animal and human fracture healing. Fundamental discoveries from this project will be disseminated through OSapp (Osteosynthesis app), a free online educational platform maintained by the AO Foundation. OSapp teaches the biomechanical principles of fracture repair to a global audience of orthopedic surgeons and trainees through interactive simulation-based case 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 · 2025-10

This project aims to serve the national interest by engaging facilitators of biology course-based undergraduate research experiences (CUREs) in the creation and evaluation of novel professional development resources and opportunities designed for laboratory coordinators and teaching assistants (TAs) who are, themselves, responsible for leading CUREs. The research-oriented nature of a CURE presents a unique set of instructional challenges for those that teach them, requiring distinct professional development (PD) opportunities. Given that no community currently exists that focuses on CURE TA PD and the literature on CURE TA PD is limited, this project aims to establish the CURE Teaching Assistant Professional Development to Enhance Scientific Teaching, Research, and Mentoring Capacity (TAPESTRy) network, with the primary goal of advancing CURE TA PD knowledge and best practices. Specifically, this project will: (1) establish the CURE TAPESTRy network and cultivate growth in the network through recruitment of diverse individuals interested in professional development for laboratory coordinators and TAs PD; (2) characterize the current landscape of PD opportunities for CURE TAs through a literature review and national survey; (3) create, implement, and evaluate an innovative year-long fellowship program for CURE TA PD facilitators, in which fellows will create and assess CURE TA PD resources; (4) generate a curated repository of field-tested CURE TA PD resources, which will be freely available to practitioners and researchers; and (5) develop, launch, and assess an edX “train-the-trainer” massively-open online course (MOOC) for CURE TA PD facilitators. By promoting PD for CURE TAs, the CURE TAPESTRy network will contribute to the professional growth of TAs while simultaneously cultivating the pedagogical knowledge and intellectual capital needed to assist undergraduates in developing their research skills. This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for Education and Human Resources, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/).This project is also supported by the NSF IUSE:HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees 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 · 2025-10

This award funds a research project that will study the effects of governance and trust in determining autonomous growth of local economies. It compares variation in technology, finance, business structures governance, social identities, and trust among local people between and across two local economies. This research is designed to better understand technology, finance, existing Small, Medium, and Micro Enterprises (SMMEs), and the extent to which these entities are influenced by governance, social identities and networks, and localized trust. The project will use a three-stage research design to identify drivers of growth. We will draw a combination of secondary data, direct observation, key informant interviews, and a rapid household-level survey in two contrasting sub-localities in selected townships to build a profile of each, covering its history, land use, demography, and sectoral economic activity. The first stage is to conduct a baseline rapid survey and mapping of the major businesses, investors, or other major economic players in each township. The data aims to create a profile of the selected townships and document the resources available for effective economic interaction. In the second stage, using the township profiles constructed in the first stage, we will identify key groups for quota samples of open-ended qualitative interviews using an adapted version of the Qualitative Impact Assessment Protocol (QuIP). The QuIP is a methodology designed to facilitate narrative explanations of the drivers of change working backwards from perceived changes in selected domains of respondents' lives and livelihoods. We will aim to understand how various relationships and associations intermediate between economic activity, institutions and trust in other people and government and how adherence to formal and informal institutions of economic governance shape hope for the future. We will use the trust lens to understand respondent’s perceptions of tangible outcomes like income, employment, business activity and education looking both backwards and forwards. We seek to engage with various demographic groups in each township. In the final stage, these tools will be used in ‘sensemaking’ activities with selected stakeholders to get participatory feedback on causes identified by the participants. 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 · 2025-10

The ability to reliably order our groceries or takeout, enjoy rapid package delivery, check the weather forecast, and navigate with GPS is all a part of the United States’ ever-growing space economy. One promising breakthrough in propulsion technology that could enable more affordable and efficient access to space is the Rotating Detonation Engine (RDE), a revolutionary engine concept now under active development. An RDE generates power through sustaining a circulating detonation wave in an annular chamber at thousands of meters per second, achieving power levels orders of magnitude higher than conventional engines, while providing higher efficiencies, more compact designs, and higher thrust-to-weight ratios. While the RDE technology is advancing rapidly, the lack of established material solutions to contend with the extreme thermomechanical loadings associated with detonation remains a critical barrier to deployment. Current testing has reported material failures after only a few engine cycles, with no clear consensus on ideal materials. Metals, composites, and ceramics are all being explored. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to tackle this materials barrier by establishing a synergistic platform informed by the Materials Genome Initiative. It will integrate industry collaboration and a partnership with the Air Force Research Laboratory to accelerate the design of high-performance copper alloys and testing protocols for the kinds of extreme dynamic conditions found in RDEs. Through a strategic combination of (i) multidisciplinary research encompassing materials science, materials informatics, mechanics, aerodynamics and combustion, (ii) organization of workshops and conferences with academic, government and industrial participation, and (iii) educational and outreach initiatives to undergraduate students and the K-12 community, this project will establish a new hub of materials discovery and design for extreme aerospace environments and help train the next-generation workforce. This DMREF project will leverage a generative AI multi-agent framework to enable closed-loop design of copper-based alloys for extreme dynamic environments, using both cold spray and directed energy deposition additive manufacturing. This framework will coordinate experimental and simulation activities through an uncertainty-responsive model, guiding material design through iterative learning. A unique aspect of the project is the development of a first-of-its-kind miniaturized RDE material testing platform, specifically designed and instrumented to rapidly screen candidate materials under realistic RDE conditions. In addition, this project will integrate reduced-order models for cyclic and high-strain-rate material performance and generate damage and failure mechanism regime maps, providing fundamental insights into how composition, microstructure, and gradation may mitigate high-frequency, high-amplitude thermomechanical loads. The resulting knowledge will not only advance copper-based alloys but also provide transferable principles for designing broader classes of structural alloys via additive manufacturing and coated materials systems for propulsion and power generation. This project is supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG), and the Division of Materials Research (DMR) of the Directorate for Mathematical and Physical Sciences (MPS). 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 · 2025-09

Fire investigation training programs aim to equip investigators with the skills to identify fire origins and causes, but the chaotic nature of post-fire scenes presents substantial challenges. Investigators must connect evidence and scene features to the fire dynamics that shaped a scene, which requires strong spatial-temporal reasoning skills. Immersive training in realistic environments is essential to help investigators piece together evidence, analyze fire progression, and accurately trace fire origins. However, most training programs in the U.S. rely on lectures and 2D visuals, lacking the immersive experience needed to develop these crucial reasoning skills. Further, many investigators lack formal education in fire science, which is essential for understanding fire behavior. This project seeks to create a multimodal embodied training platform that advances fire investigation training through adaptive deliberate practice and learning analytics, focusing on the spatial-temporal reasoning skills needed to reconstruct fire development from observed fire damage and scene features. This new training approach will improve the quality and effectiveness of fire investigation practices, benefiting public safety by enabling more accurate identification of fire origins and causes. Many of the ideas can be extended to related fields such as crime scene investigation and other STEM areas requiring advanced spatial-temporal reasoning skills. To achieve these goals, the training platform will incorporate an AI-driven, physics-informed 3D fire modeling system that dynamically generates and visualizes fire scenarios based on learner-selected fire origins. Learners will identify and analyze scattered evidence, reconstruct fire progression, test interpretations, and explore variations in fire dynamics relative to observed damage patterns. Multimodal sensors will track learner interactions, enabling adaptive instructional approaches, enhancing engagement, and fostering seamless interactions between learners, instructors, and virtual fire scenarios. A deliberate practice pedagogical model will integrate structured skill-building exercises, multimodal analytics for performance assessment, and personalized adaptive training tailored to individual learner profiles. The platform's effectiveness will be evaluated in three phases: iterative expert reviews, student prototype assessments, and nationwide testing by early-career fire investigators, ensuring robust skill development in spatial-temporal reasoning for fire investigation. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Computation and Data-Enabled Science and Engineering (CDS&E) project seeks to support research the explores computationally efficient generation of the process-structure-property (PSP) map linking manufacturing process parameters to the resulting solidification microstructure. This understanding, which has a strong influence on the properties of additively manufactured materials, is critical to the design of metals with superior properties and can also be leveraged in an inverse problem framework to optimize process parameters for desired properties. However, the vast range of length and time scales inherent in the manufacturing process makes constructing PSP maps computationally prohibitive. This research seeks to address this challenge by developing a computationally efficient surrogate model for solidification, significantly accelerating both forward and inverse problems on the process-structure (PS) linkage. By enhancing the computational efficiency of manufacturing process parameter optimization, this looks to drive technological innovation, strengthen the US economy, and support workforce development by engaging graduate and middle-school students in STEM learning, cultivating the next generation of engineers and scientists. This project seeks to develop a physics-based, data-driven reduced-order model (ROM) for predicting microstructures evolution in binary alloy solidification. The proposed non-intrusive ROM reduces the computational cost of high-dimensional models by projection-based model reduction utilizing nonlinear manifolds and sparsity-inducing operator learning to capture transport- dominated phenomena across the solid-liquid interface. The research will: (1) develop a model reduction framework tailored for nonlinear transport-dominated processes, (2) construct a parametric ROM that maps process parameters to microstructure attributes, and (3) establish an inverse problem framework for optimizing process parameters. By accelerating the Process-Structure linkage in PSP maps, this work looks to advance the design of high-performance materials, and has broad applicability to other engineering problems, including crack propagation, solute distribution and wave propagation. 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 · 2025-09

Information processing in the brain has inspired advances in artificial intelligence. However, some aspects of information processing in the brain have been difficult to translate to conventional hardware and software. Neurons in the cortex of the brain can process temporal and spatial information simultaneously, unlike artificial neural networks. This may explain why human brains can make sense of moving visual scenes accurately and without significant effort. This award will support a project to explore the use of layered biological neural networks for processing dynamic visual information. Connections between layers will be guided by harnessing natural development to mimic the visual pathway in the brain. Fully developed and trained, these layered biological neural networks will be used for identification of moving objects in videos. Ethical, legal, and social implications (ELSI) of the use of human-derived neurons to create organoids will be examined. Technology developed during the project may have significant impact on national needs such as improving energy efficiency of artificial intelligence and endowing autonomous systems with improved vision. Research findings will be incorporated into courses, and undergraduate students will be recruited to participate in the project to promote their interest in research. K-12 outreach will also be undertaken to promote student interest in higher education and engineering. This project will address long-standing challenges in computation with living neurons through the development of functional and structural networks in multilayer organoids. These challenges include transfer of two-dimensional information, aggregation and compaction of neurons, undesirable spontaneous activity, random connectivity, and synaptic scaling. Networks of neurons will be organized into stacked layers. Inter-layer connectivity will be retinotopic, while intra-layer connectivity will provide recurrent connectivity. This network structure is optimized for processing of spatiotemporal information. It takes advantage of the rich repertoire of intracellular dynamic processes and energy-efficient recurrent connectivity of biological neurons that endows them with complex temporal properties. Creation of layered organoids is made possible by advances in 3D-printed scaffolds that stabilize three-dimensional neuronal cultures and address aggregation and synaptic scaling challenges. An all-optical interface will be used to encode and decode information and to prevent spontaneous activity. Neuronal activity will be harnessed to develop desired connectivity. Computational models will be created to assist with the development of encoding, decoding, and training algorithms, as well as with secure information processing. Performance of structured organoids on optical flow and moving object segmentation tasks will be assessed. This project should lead to the creation of fundamental new knowledge in computer engineering, bioengineering, and neuroscience. 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 award supports research into the fundamental building blocks of matter by investigating a new state of matter known as the quark-gluon plasma (QGP), which filled the universe just microseconds after the Big Bang. The QGP can be creating in the laboratory by colliding atomic nuclei at nearly the speed of light, generating droplets that are so hot that the quarks and gluons, normally confined inside protons and neutrons, are briefly freed. The PI and her team will study this plasma using jets, which are narrow sprays of particles created when quarks or gluons are scattered in the initial collision with high energy. As these jets travel through the QGP, they lose energy in a way that reveals information about the medium, much like a CT scan can reveal the internal structure of the human body. Understanding the structure of the QGP will improve the field’s understanding of quantum chromodynamics (QCD), the theory describing the strong nuclear force. This award will also give opportunities for training students in nuclear physics, instrumentation, and data science. The PI and her graduate students will also apply modern tools such as artificial intelligence to improve techniques for subtracting background signals from jet measurements, which remains one of the greatest challenges of this class of measurements. To support hands-on learning, the PI will construct a small electronics test stand that can be used to understand how different detector components operate. This will allow undergraduate students at the PI’s institution or other local institutions to gain practical experience in experimental nuclear physics and develop the skills essential for future scientific careers. This award will allow the PI, her graduate students and postdoc to investigates how the QGP modifies high-energy jets, focusing on the temperature, density, and path-length dependence of jet quenching. They will use the STAR and sPHENIX detectors at RHIC to measure dijet momentum imbalance and jet azimuthal anisotropy (v2) across multiple collision systems, including p+p, Au+Au, Ru+Ru, Zr+Zr, and potentially O+O. Advanced analysis techniques such as Event Shape Engineering (ESE) and Jet Geometry Engineering (JGE) will be used to isolate the geometric dependence of jet energy loss and enhance the sensitivity of the jets to the spatial and dynamical structure of the QGP. The STAR and sPHENIX Event Plane Detectors (EPDs), which are essential for determining the collision geometry, were both designed, constructed, and maintained by the PI and her group. This provides a unique opportunity to cross-calibrate global event properties between the two experiments, reducing systematic uncertainties in RHIC jet measurements. Jets produced at RHIC energies interact with the QGP over longer timescales than those at the LHC, making them well-suited to probing medium-induced modifications, though they are also more vulnerable to background contamination. To address this, the PI and her group will apply AI-driven techniques to disentangle medium response and background contributions from the intrinsic, but modified, jet structure, improving the precision of both the dijet and v2 measurements. This award will also support continued calibration and data production efforts for the EPDs, contributing directly to the broader RHIC physics program. Together, these efforts aim to enable tomographic imaging of the QGP and establish essential methodologies for future jet studies at the Electron-Ion Collider. 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 Pathways to Enable Open-Source Ecosystems (POSE) project involves the creation of an open, community-driven framework to evaluate and enhance the trustworthiness of large language models (LLMs). Large language models are increasingly utilized in sectors such as healthcare, finance, education, and national security, yet concerns about their reliability, safety, and transparency remain significant. This project establishes a collaborative ecosystem that enables stakeholders to assess trustworthiness using open standards and transparent processes. By promoting confidence in artificial intelligence technologies, this project advances national health, economic growth, and benefits all Americans. The effort contributes to scientific and technological understanding by promoting rigorous evaluation practices and facilitating education around trustworthy artificial intelligence development. The project strengthens United States leadership in artificial intelligence safety and reliability, benefiting academic researchers, industry professionals, government agencies, and the broader public through more dependable artificial intelligence applications. This Pathways to Enable Open-Source Ecosystems (POSE) project revises an existing trustworthiness evaluation platform into a sustainable open-source ecosystem named OpenTrustLLM. The project addresses the challenge of fragmented trustworthiness evaluation methods by constructing a unified infrastructure with distributed community governance. Key objectives include refactoring the current framework for modular contributions, developing continuous integration workflows, establishing a long-term governing committee, and expanding an engaged user and developer community. The project integrates multiple evaluation tools to cover critical trustworthiness dimensions such as robustness, privacy, and safety. Technical approaches include open-source software engineering best practices, black-box evaluation protocols applicable to both proprietary and open-source large language models, and proactive community education. The outcome is a scalable and sustainable ecosystem that enables systematic trustworthiness assessment for a wide range of large language models deployed in real-world applications. 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

NON-TECHNICAL SUMMARY: In recent years there have been significant efforts to develop new polymers that dissolve in water based on materials from biological systems to address the demand for a circular economy. However, knowledge about how these new bio-based polymer products are degraded by microorganisms, which determines their fate in the environment, remains fragmented. This GOALI research will synergize academic and industrial expertise to investigate the chemical and biological contributors to the biodegradation of water soluble polymers (WSPs). Knowledge about how polymer biodegradability depends on various aspects of their chemistry will ultimately guide the design of new biopolymers. In addition, understanding the types and combinations of microorganisms that degrade biopolymers will guide the design of engineered combinations of microorganisms that can more efficiently degrade biopolymers. This project will promote the progress of science and greatly contribute to workforce development through use-inspired research, integrated academic and industrial research experiences, as well as joint academic and industrial advisors. The project will broaden STEM participation through various outreach and educational activities at the K-12 and community college levels. TECHNICAL SUMMARY: This project will provide a deep and systematic understanding of the chemical and biological contributors controlling the fate of water-soluble polymers. Different stages of the bi-directional influences between selected water-soluble cellulose derivatives and bacterial strains will be investigated, including the following: (1) initial polymer binding, (2) subsequent cell responses such as growth, enzyme production and metabolic changes, (3) polymer biodegradation processes, and (4) impact of the polymer degradation products on the cells. Systematic understanding of the connection between the polymer chemistry and biodegradability will be captured by investigating cellulose derivatives with a spectrum of molecular weights as well as modification types and degrees of substitution. Cell responses contributing to the polymer biodegradation processes and outcome will be characterized in culture systems of single microbial strains and rationally designed synthetic consortia of bacteria to identify strategies for promoting cell synergy and efficient polymer biodegradation. Parameters describing the polymer properties, polymer-cell interactions, cell responses and the polymer outcomes will be correlated to direct the design of polymer chemistries, formulations and remediation processes for desirable degradation profiles. The research lies at the intersection of polymer science, biodegradation, environmental science and microbial ecology, and is expected to push the boundaries of knowledge in each field. This project will promote the progress of science and greatly contribute to workforce development through use-inspired research, integrated academic and industrial research experiences, as well as joint academic and industrial advisors. The project will broaden STEM participation through various outreach and educational activities at the K-12 and community college levels. 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

Computational techniques for solving problems in image processing, statistical learning, robust control, distance geometry, and other areas require that large-scale unstructured optimization problems be solved. To obtain solutions that translate best into real-world settings, these problems should involve models of the real world that are as accurate as possible, which in a mathematical context means that the problems may need to involve a large number of decision variables and complicated formulas with features such as nonsmoothness. Thanks to the thriving field of mathematical optimization, there exist algorithmic methodologies for solving certain instances of problems of this type. However, contemporary methodologies also have limitations that preclude their use for solving complex problems robustly and efficiently. for example, it may be that with respect to an objective that is optimized, only the consequences of a certain set of decisions can be estimated (in terms of a cost, error, or other measure). This can occur, when the value can only be determined through a computer simulation or as a statistical prediction over only partially observed data. Such a context demands that contemporary approaches be extended so that they can intelligently handle noisy or stochastic (i.e., randomized) estimates of the value of decisions. This project aims to design such algorithmic extensions for solving important classes of optimization problems arising in these prevalent and challenging application areas. This project will involve the design, analysis, and implementation of gradient-sampling-based algorithms for solving locally Lipschitz optimization problems, specifically those that have nonconvex and nonsmooth objective functions. The main scope of the project is to extend contemporary gradient-sampling-based algorithms for settings when function and derivative evaluations are corrupted by computational and/or stochastic noise. In settings with such noise, fundamental properties on which gradient-sampling methods rely for their convergence guarantees break down, meaning that these fundamental properties need to be revisited and enhanced for settings with computational and/or stochastic noise. These, in turn, will necessitate careful redesigns of complete algorithmic methodologies along with their corresponding convergence theories. The real-world impact of the project will be enhanced by the fact that it will translate into enhancements to the state-of-the-art software developed by the principal investigator. 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.

NIH Research Projects · FY 2025 · 2025-08

Successful sexual reproduction relies on gametes, like sperm and eggs, having exactly half the genome of their somatic progenitor cells. This is accomplished through meiosis, a cell division process that accurately segregates the two copies of homologous chromosomes inherited from both parents. During meiosis, homologous chromosomes must pair up along their lengths to enable genetic crossover, which is essential for accurate segregation. Physical pairing between homologs is usually stabilized by synapsis, where a protein complex called the synaptonemal complex (SC) assembles between homologs along their lengths. Failure in synapsis can lead to chromosome missegregation and aneuploidy, a major cause of birth defects such as Down Syndrome. In diverse organisms, defects in synapsis can trigger apoptosis to eliminate the affected meiotic cells as a quality control measure. However, the mechanisms by which meiotic cells detect and respond to synapsis failure remain unclear. My research program will address this fundamental gap using the highly tractable Caenorhabditis elegans germline as a model system. In C. elegans, defects in synapsis during meiosis promote apoptosis of oocytes – precursors of egg cells. Recently, we discovered a critical role of the oocyte nuclear envelope (NE) in apoptosis when synapsis fails. By developing and deploying a new chemically induced proximity (CIP) tool, we discovered that following synapsis failure, the Polo-like kinase PLK-2 is recruited to the oocyte NE to phosphorylate and destabilize the nuclear lamina. Unexpectedly, we found that the mechanosensitive ion channel Piezo, which typically functions at the plasma membrane, also localizes to the oocyte NE and is required to transduce this signal to promote apoptosis. This is the first evidence of mechanosensitive ion channels in transducing a signal that originates in the cell nucleus. Now, by combining cell biology, genetics, engineering, and other approaches, my lab will elucidate how the dynamic NE integrates mechanical and chemical signals to regulate quality control in developing oocytes, focusing on three distinct but complementary projects. First, we will determine the mechanisms by which Piezo channels function at the NE. Second, we will interrogate how different signaling pathways are coordinated at the NE to regulate oocyte development, leveraging our recent understanding of the mechanics of meiotic nuclei. Third, we will explore the evolutionary conservation and divergence of NE-based meiotic quality control using Pristionchus pacificus, a nematode with meiosis more similar to mammals than C. elegans. By studying nematode meiosis as a model, the questions we seek to address are at the nexus of basic cell biology, genetics, and development. As PIEZO2 is enriched in human oocyte-containing follicles, we expect that our research will uncover conserved, fundamental principles underlying the accurate transmission of genetic material during sexual reproduction. Our findings will also impact a wide range of cell biology problems related to nuclear envelope dynamics. Additionally, new tools we develop will be broadly applicable to probe diverse cellular processes in vivo.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Each major neurodegenerative disease is associated with the loss of specific populations of neurons, yet very little is known regarding why these neurons are rendered vulnerable while others remain relatively resistant. In Parkinson’s Disease, for example, dopaminergic neurons within the Substantia Nigra are primarily lost while other nearby neurons remain unaffected. Understanding the mechanisms underlying this selective vulnerability should help to generate strategies for protective these vulnerable neurons from disease. Thus far, a major hurdle in the field has been the inability to assess the vulnerability of individually identifiable neurons in vivo. To address this, we have developed a proposed research plan to investigate the mechanisms responsible for maintaining Dopaminergic (DA) neuron viability within the Drosophila brain. Our guiding hypothesis is that conserved factors render specific populations of neurons vulnerable to Parkinson’s Disease while others remain resistant. To test this hypothesis, we propose to study the selective vulnerability of Drosophila DA neurons by: (1) Creating a vulnerability map of individual DA neurons in an environmental model of Parkinson’s Disease, (2) Manipulating the vulnerability status of individual DA neurons, and (3) Comparing vulnerability maps of different Parkinson’s Disease models. By investigating the cellular and molecular mechanisms underlying selective neuronal vulnerability, our proposed research has the potential to reveal why certain populations of neurons are rendered vulnerable. Understanding the differences between vulnerable and resistant neurons is critical for identifying potential therapeutic targets and developing strategies to help maintain vulnerable neurons and increase their resistance to disease.

NIH Research Projects · FY 2025 · 2025-08

Summary The zebrafish regenerating fin is an important model system to understand mechanisms underlying regeneration, skeletal development, and skeletal disease. Although overt phenotypic sex-differences during fin regeneration have not been observed, not all sexually dimorphic traits are evident. Thus, the possibility that there are measurable sex-differences cannot be ruled out. Indeed, one rationale for the 2016 NIH policy to consider sex as a biological variable (SABV) was that important biological information could be missing from studies that relied on a single sex, or that ignored sex when reporting cell/animal data. Importantly, while the inclusion of both sexes is appropriate under the SABV policy, this may not be sufficient over the long-term to reveal the complete biology underlying regeneration. Therefore, the long-term goal of this project is to improve the consideration of SABV for research involving fin regeneration. The overall objectives for this proposal are to collect, analyze, and report, phenotypic and transcriptomic data by sex during fin regeneration. The central hypothesis is that while the majority of experimental studies during zebrafish regeneration includes both sexes, without collecting/analyzing data separately, relevant sex-dependent differences may be lost. The rationale is that completion of this project will reveal both sex-dependent and sex-independent outcomes. In turn, these data will better inform future research by identifying specific characteristics of fin regeneration better served by collecting/analyzing/reporting data by sex. The overall objectives will be achieved using two specific aims. The goal of Aim 1 is to reveal sex-dependent phenotypes during fin regeneration. Fins at different stages of regeneration will be analyzed for differences in commonly measured parameters including total regenerate length, fin ray regenerate length, segment length, and cell proliferation. The goal of Aim 2 is to reveal sex- dependent differential gene expression during fin regeneration. Fins at different stages of regeneration will be analyzed to identify all differentially expressed genes. This proposal is innovative because completion will generate resources that will facilitate the improved application of SABV during fin regeneration research. This proposal is significant because these resources will drive better informed decisions about collecting/analyzing/reporting data based on sex, and will therefore positively impact future experimental design.

NSF Awards · FY 2025 · 2025-07

Quantum materials in which electrons interact strongly have unique properties that hold promise for future technologies. Example applications include quantum computing, more efficient electrical power transmission, and improved thermoelectric cooling. In this project, the team will study gases of strongly interacting atoms to better understand the physics of quantum materials. These atoms, like electrons, carry spin, allowing them to mimic the behavior of electrons. At low temperatures, electrons of opposite spin form pairs, leading to superconductivity. However, strongly interacting electrons can also pair above the superconducting transition temperature. A better understanding of this type of pairing may help to explain why some superconductors work at higher temperatures, allowing researchers to design superconductors with higher transition temperatures. The team will use a gas of atoms in a trap made of laser light to study how spin and heat flow between different regions of the trapped atomic gas. These measurements will give insight into pairing and conduction in quantum materials, contributing to ongoing efforts to better understand and design materials for technological applications. Graduate and undergraduate students involved in the project will gain valuable scientific and technical skills that prepare them for careers in quantum science and technology. Strongly interacting fermions lie at the heart of exotic quantum many-body systems, including quantum materials such as high-temperature superconductors, and nuclear systems such as neutron stars. The transport properties of many-body systems determine their dynamics under small deviations from equilibrium. Transport properties also serve as a fundamental tool to characterize many-body systems. This project investigates the transport of spin and coupled spin-heat transport in gases of strongly interacting fermionic atoms. The PI and students will carry out measurements on gases of lithium-6 atoms in a multi-region optical trap. The multi-region trap will enable preparation of non-equilibrium initial spin and density distributions. Removal of the optical barriers between the regions after initialization will allow the system to evolve towards equilibrium. These measurements will provide precise new benchmarks for testing many-body theories, illuminate the nature of the pseudogap in the unitary Fermi gas, and test proposed quantum bounds on transport based on quantum critical scaling laws. 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

Rapid, portable, and accurate diagnostic testing is of great importance to improve the quality and accessibility of healthcare. However, the performance of existing rapid and portable testing methods is often not sufficient for complex diseases such as infectious diseases, cancer, and cardiovascular diseases. The major goal of this CAREER project is to develop a new digital testing platform that offers high detection sensitivity and quantification accuracy without sacrificing the simplicity and the portability. The new testing platform is enabled by the unique fluid phenomenon induced by a vibrating capillary and 3D printed microdevices. Upon the successful completion of this project, integrated devices that can detect low abundance nucleic acid and protein biomarkers without the need of dedicated instrument and laboratory space will be developed. Such devices will have great potential to deliver high quality medical testing to a broad population including underserved communities and resource limited regions. This project is highly multidisciplinary and will produce low cost, and compact instrumentation. It therefore creates unique educational opportunities for students with different backgrounds. The proposal will enable 1) improved learning outcomes for students in quantitative chemical analysis lab through implementing project- and problem-based learning concepts to the curriculum; 2) novel microscale analytical chemistry experiments for undergraduate students, e.g., vibrating sharp-tip powered microfluidic enzymatic assay; 3) a research program that trains students to solve research problems using multidisciplinary approaches. This project is to develop an integrated solution to perform complete digital bioassays under a point-of-care (POC) setting thereby addressing the unmet need of developing high performance POC tests. The proposed method is enabled by controlling the vibration of sharp tips, which can generate localized and individually addressable acoustic streaming in microchannels for fluid control and droplet generation. To achieve the proposed impact, three aims will be pursued: 1) Elucidate the sharp-tip droplet generation process through numerical modeling and streaming analysis and demonstrate a POC nucleic acids detection system; 2) Develop a simple dual flow droplet generation system for performing digital ELISA; 3) Demonstrate an integrated the sample processing system enabled by vibrating sharp-tip and composable microfluidic plates. This project will lead to a flexible biosensing platform that can be easily adapted to measure either low abundance nucleic acids or protein biomarkers. It will also advance the fundamental understanding of the acoustic streaming in immiscible fluids and expand the utility of acoustic streaming for complex fluid, droplet, and particle manipulations. 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

Automated agents, such as self-driving vehicles, drones, and robotic assistants, have the potential to transform transportation, delivery services, health care, and various other industries by offering more efficient and safer services. These applications are powered by advances in hardware, artificial intelligence (AI), and machine learning that are leading to a proliferation of new agent capabilities to sense their surroundings more accurately, see and understand the world better, navigate and maneuver with dexterity, and engage in complex reasoning. As AI continues to make rapid progress, these capabilities will also give rise to an increasing number of designs and functions of automated agents tailored to specific tasks. The expected increase in capabilities has the potential to enable automated agents to carry out highly complex tasks, significantly improving safety, reliability, and efficiency across various industries. However, one of the key challenges is ensuring that agents with different capabilities can work effectively as a team. Toward this objective, the project focuses on how to systematically characterize the capabilities of agents, assign tasks to a large team of agents with different capabilities, and what actions the agents should take so that the team completes the overall mission as safely and as efficiently as possible. For example, knowing how well delivery vehicles navigate a city during the day or at night and on large roads or narrow streets would allow users to anticipate the volume of deliveries the fleet can handle logistically and cost effectively. Formally, characterizing the capabilities of agents would allow effective matching of agents to tasks, efficient overall planning for teams of agents, and detection of problems during their deployment. The project focuses on reasoning about the capabilities of agents with learning-based components and planning for teams of agents. It will enable the integration of automated agents into existing planning and decision-making frameworks that include both traditional and learning-based components. Approaches that rely exclusively on machine learning are prohibitively expensive since they require large amounts of data, time and energy to train, and are unsafe due to their opaque nature that precludes explanations of their behavior. This work addresses planning with learning-enabled agents by providing formal guarantees for the capabilities and performance of agents. Capabilities are determined by on-board hardware and software, while performance is dependent on the time, location and situation of deployment. Reasoning about agent capabilities enables the integration of learning-based and conventional planning components, provides guarantees and interpretability on agent behavior, and increases effective and efficient use of agents. The three main tasks of the project are developing: (1) A formal framework to describe, reason about and learn agents’ capabilities and performance; (2) Planning methods for teams of agents that match agents to complex tasks based on temporal, spatial and semantic contexts; and (3) Mechanisms for the detection and recovery from failures during deployment stemming from errors in capturing capabilities and estimating their performance. Additionally, the educational activities will prepare students for careers in the interdisciplinary field of planning and decision-making for learning-enabled agents. 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.