Worcester Polytechnic Institute

NSF Awards · FY 2026 · 2026-10

This project establishes a three-year, fully online Research Experiences for Teachers (RET) Site focused on quantum computing and cybersecurity education. The project prepares high school teachers from across the United States to bring emerging topics in quantum information science and cybersecurity into their classrooms. As quantum technologies continue to develop, future workers will need new knowledge and skills to understand how quantum computing affects digital security. However, most high schools do not currently have the resources or training to teach these topics. This project addresses this need by engaging teachers in summer research experiences and supporting them throughout the academic year as they develop and use new classroom materials. By preparing teacher-leaders and providing accessible instructional resources, the project expands access to emerging science and engineering topics, supports national workforce development in cybersecurity and quantum information science. The project engages teachers in educational research focused on developing and evaluating classroom instructional units that teach cybersecurity concepts in the context of quantum computing using game-based and technology-supported learning approaches. Each summer, participating teachers complete a six-week online research experience in which they work with university researchers to develop standards-aligned lesson plans, classroom activities, and laboratory exercises. During the academic year, teachers participate in follow-up activities including biweekly meetings, classroom implementation, curriculum refinement, and sharing of instructional materials and research results. Teachers and students use an online platform that provides access to a browser-based quantum circuit simulator, automated assessment tools, and artificial intelligence (AI) learning support. The project studies how game-based learning and interactive tools can help make complex quantum cybersecurity concepts accessible at the high school level and produces classroom-ready instructional materials that can be adopted nationwide. 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 · 2026-06

Coarctation of the aorta (CoA) is a common congenital heart defect, accounting for 6-8% of live births with CHD. If the CoA is significant and is not diagnosed and treated in a timely fashion, the newborn or young infant may develop cardiogenic shock or may die. Contemporary prenatal screening and diagnosis of CoA rely on anatomic and blood flow conditions acquired by the fetal imaging technique. The limited resolution of the imaging technique confines the anatomical and hemodynamic metrics available for prenatal screening and diagnosis. Both false positive and false negative diagnoses occur frequently. Hence, there is an emerging need for a novel technique that provides new metrics and knowledge of the fetal aorta. Personalized flow modeling offers a cost-effective approach to evaluating high-fidelity hemodynamics in the cardiovascular system. It has led to paradigm shifts in uncovering mechanisms, improving diagnosis and prognosis, and developing and optimizing medical devices and treatments for heart disease. However, few models have been developed for fetal circulation. Therefore, the proposed work aims to develop novel, personalized flow models that assess high-fidelity hemodynamics of the fetal aorta and to use the models to evaluate novel flow metrics for the improved diagnosis of CoA in fetuses, leveraging an existing, large cohort of fetuses (n=250 normal aortas and 250 CoA in fetuses) from the Fetal Heart Society. We aim to (1) develop a novel personalized, computational flow model of the fetal aorta using a clinically routine imaging technique and validate it against in vivo measurements; (2) develop a novel in vitro flow model of the fetal aorta for the validation of novel flow metrics; and (3) evaluate novel flow metrics for the improved diagnosis of CoA in the fetus. The metrics of interest include (1) wall shear stress (WSS), an important flow stimulus for the maturation and remodeling of embryonic and postnatal vessels, and linked to recurrent coarctation after surgical repair in CoA patients, (2) fractional flow reserve (FFR) and flow resistance, well- recognized markers of vascular obstruction. However, these flow metrics have rarely been explored in fetal CoA. This project builds upon our extensive experience in developing and applying blood flow modeling in congenital heart defects, unique clinical expertise in fetal imaging, and access to a comprehensive clinical database (n = 500) from the Fetal Heart Society. The proposed work will address the challenges and gaps in the research on fetal circulation and CHDs in fetuses. The project will yield new data and knowledge on improved diagnosis of CoA in fetuses. Additionally, we will deliver the first-of-its-kind, validated computational and in vitro flow models for the fetal aorta. The findings and technology obtained through this project will lay a solid foundation for future clinical studies that could lead to improved screening, diagnosis, and outcomes for children with CoA. In alignment with the NIH’s mission statement, the proposed work aims to improve lifelong health for CoA patients and reduce the burden on these patients and their families.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Uterine fibroids, also known as leiomyoma, affect 70-80% of women in the United States of reproductive age and carry significant health and economic burdens. Fibroids are benign fibroproliferative tumors characterized by excessive extracellular matrix (ECM) deposition and remodeling that alters the biophysical and biochemical microenvironment, cell interactions, and access to signaling molecules (i.e., hormones, growth factors) and treatments. Yet, knowledge gaps remain regarding how these factors influence fibroid behavior, and there are limited preclinical in vitro models for uterine fibroids that recapitulate 3D tissue architecture, cell interactions, and relevant microenvironmental features. There is a significant need for improved in vitro models for uterine fibroids with pathophysiologically relevant cell interactions, tunable biophysical properties, and biochemical signaling to effectively study fibroid biology and advance therapeutic development. The proposal objective is to leverage two novel in vitro models of uterine fibroids—one with tunable transport properties and one representing intramural uterine fibroids (fibroids surrounded by myometrium)—to study how altered signaling from sex hormones (estrogen, progesterone), obesity, and therapeutics affect fibroid growth. This work supports the long-term goal of developing predictive in vitro models of uterine fibroids—across all subtypes—to study fibroid initiation, growth, persistence, and recurrence. Aims will test the central hypothesis that fibrotic biophysical properties within the fibroid microenvironment and fibroid-myometrium interactions contribute to fibroid growth and treatment response by reducing molecular transport and modulating the activity of sex hormones, obesity-related factors, and therapeutics. The objective will be accomplished by two Specific Aims: Aim 1 – Characterize fibroid cell responses to molecular transport barriers using a novel tunable collagen-alginate interpenetrating polymer network (IPN) model and Aim 2 – Establish a self- assembled myometrial tissue ring model with embedded fibroid spheroids for evaluating fibroid cell responses to hormones, obesity, and treatment. In Aim 1, we will change fabrication parameters for type I collagen-alginate IPN hydrogels to tune stiffness to that of myometrium and fibroid tissue and reduce molecular transport. A tissue model with a fibroid-tissue sphere embedded within an IPN hydrogel will then be used to evaluate how reduced molecular transport of bioactive molecules of varying size influences functional outcomes, including proliferation, metabolic, activity, and apoptosis, using high throughput and content multiplexed analysis. In Aim 2, self-assembled uterine smooth muscle tissue rings with embedded uterine fibroid spheroids (fibroid cells, fibroblasts) will undergo separate exposures: 1) estrogen and progesterone hormone at concentrations representing a normal menstrual cycle (luteal and follicular phase) and pregnancy (three trimesters); 2) conditioned medium from lean and obese adipocytes; 3) clinically relevant dose of Leuprolide—a gonadotropin-releasing hormone agonist that suppresses estrogen and progesterone production. Models will be evaluated on fibroid growth and related processes, primarily proliferation, apoptosis, and ECM production. Collectively, these studies will provide new insights into how the fibroid microenvironment influences fibroid growth and treatment response, while establishing innovative in vitro models that serve as platforms for future mechanistic and therapeutic investigation.

NSF Awards · FY 2026 · 2026-05

Neural networks learn patterns from labeled data to output accurate predictions on new inputs. They are increasingly embedded in critical real-world systems, from healthcare wearables and smart homes to autonomous vehicles. These systems often rely on privacy-preserving implementations that keep user inputs and model details hidden from other parties. However, both the model provider and the user face privacy and security risks. A malicious model provider can insert backdoors during training, while a malicious user can attempt intellectual property theft by reverse-engineering model parameters and architecture. Physical attacks intensify these risks by exploiting device-level observables (e.g., power usage) and faults to bypass conventional security boundaries. The rapid adoption of privacy-preserving neural networks leaves limited time to identify and fix such critical threats. The project's novelties are physical-security-first approaches to privacy-preserving neural network deployments that combat backdoors, leakage of security-critical information, and fault injection within a single system. The project's broader significance and importance are enhancing national security and improving trust in sensitive applications through measurable advances that reduce the risk of model manipulation and theft, as well as violation of users' data privacy. This project offers community dissemination through a public issue database and vulnerability-discovery competitions that train next-generation engineers to build and evaluate secure, privacy-preserving neural network systems. The project develops a vulnerability assessment methodology that characterizes (1) backdoor insertion during training, (2) susceptibility to non-invasive and semi-invasive information leakage during inference, and (3) fault injection combined with machine learning to infer secret parameters and architecture. The technical approach derives threat-specific tests and metrics, then designs low-overhead countermeasures that are native to privacy-preserving implementation and require minimal hardware change. Cross-cutting defenses include hardware-aware randomness generation with provable, composable leakage resistance across modules, and fault-resilient computation structures that limit fault propagation while preserving correctness. The research establishes theoretical and empirical bounds on fault tolerance and validates findings through real implementations on field-programmable gate arrays. This project produces practical guidelines, open toolkits, and datasets. 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

Wildfires are becoming more frequent, intense, and difficult to manage due to the shift in the global climate and expanding development into fire-prone areas. A major challenge in wildfire response is predicting how fires will behave, especially in forested areas where wind, vegetation, and fire interact in complex ways. Current fire models oversimplify forests as static blocks and fail to capture how trees sway, bend, and influence airflow. These oversights can lead to inaccurate forecasts and limit the effectiveness of prescribed burns and emergency planning. This project seeks to change that by developing more realistic, science-based tools to help land managers better anticipate fire behavior. The research team will also engage with fire professionals, students, and educators to ensure that the science gained through this research is applicable in real-world settings. The broader impacts include improving public safety, enhancing wildfire resilience, and training a new generation of interdisciplinary wildfire scientists. This project will develop and validate a new predictive modeling framework that explicitly incorporates canopy biomechanics, aerodynamics, and fire-atmosphere interactions to capture the dynamic motion of real tree canopies and their effects on fire behavior. Using multi-physics modeling and controlled laboratory experiments, the team will simulate how flexible trees interact with wind and buoyant flows during surface fires. These models will incorporate realistic canopy geometries, fire-atmosphere interactions, and turbulence to better understand key processes such as the transition from surface to crown fires. A multi-fidelity modeling approach and high-quality experimental datasets will be used to reduce uncertainties and improve model accuracy. With this, the project offers a new framework to address current limitations in realistic prediction of understory fire behavior. The resulting tools will support proactive fire management, prescribed burn planning, and risk assessment in the wildland-urban interface. Beyond wildfire science, the project advances computational modeling, biomechanics, and environmental fluid dynamics, with potential applications in agriculture, climate resilience, and natural hazard preparedness. 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 NExT CHAMPION program aims to expand access to hydrogen energy careers and support workforce participation from all Americans. Hydrogen is a key enabler of U.S. energy security and resilience, with official reports projecting that the hydrogen sector could support up to 100,000 jobs nationwide by 2030. This initiative builds a regional hydrogen workforce pipeline by connecting two-year college students to advanced training opportunities, industry internships, and job placement services. Through strong partnerships with community colleges, industry leaders, regional workforce organizations, and four-year institutions, participants engage in hands-on learning, receive mentoring, and complete in-situ internships. The program is designed to broaden access and participation by offering mentoring, networking opportunities, and targeted interventions that address common barriers to workforce entry. The long-term goal is to strengthen the energy workforce and ensure that opportunities in hydrogen technologies are open to all Americans. This project supports experiential learning and technical skill development for non-traditional learners pursuing careers in hydrogen energy technologies. Built around the Recruitment–Awareness–Mentorship–Post-internship (RAMP) model, the program is developed and delivered in close collaboration with industry partners who help shape the curriculum, co-design learning activities, and host participants for immersive internship experiences. The training includes intensive modules on PEM electrolyzers, fuel cells, hydrogen safety, and infrastructure, supported by lab activities, simulation tools, and real-world case studies informed by industry input. Internships provide hands-on experience and direct mentorship in operational settings, ensuring that participants gain skills aligned with current and emerging workforce demands. The program’s cognitive apprenticeship framework emphasizes modeling, coaching, articulation, and reflection. Outcomes will be evaluated using both formative and summative methods, targeting participant retention, skill development, and employment placement. Project findings and curriculum materials will be disseminated to support broader adoption in other 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Ventricular tachycardia (VT) and ventricular fibrillation (VF) are major contributors to the approximately 300,000 sudden cardiac deaths occurring annually in the United States. VT, particularly when refractory to ablation and medication, poses a life-threatening risk that necessitates innovative treatment strategies. Cardiac stereotactic body radiotherapy (cSBRT) offers a promising non-invasive alternative; however, its success depends on precise localization of the VT substrate for targeted, high-dose radiation delivery. The PACE-VT (Personalized Automated VT Circuit and Exit Localization) represents a transformative step forward in the management of VT, particularly for patients who are refractory to traditional treatments. This innovative research aims to integrate patient-specific computational models derived from CT imaging with real-time ECG data to enhance the precision of non-invasive VT localization. This integration facilitates targeted, non-invasive interventions such as cSBRT, offering potential improvements in safety and efficacy over existing therapies. The primary objective of the proposed project is to validate the effectiveness of a novel non-invasive VT mapping methodology that combines detailed structural data from CT scans with functional data from ECGs. The project seeks to demonstrate that this PACE-VT approach can accurately identify VT substrate locations, improving the targeting of therapeutic interventions such as cSBRT. The project will leverage existing datasets from catheter ablation procedures to validate the accuracy of the PACE-VT approach against clinically-identified VT circuits and exits. Statistical analyses will assess the spatial concordance between predicted VT substrate and actual VT substrate locations, aiming to substantiate the model’s predictive power. The project also utilizes advanced image processing techniques, machine learning models, and personalized heart digital twins to analyze VT circuits and exit sites. Success in this endeavor could lead to broad adoption of more accurate and patient-friendly management strategies for individuals with challenging cardiac arrhythmias. This project aligns with the NHLBI’s mission to foster innovative research and has the potential to significantly improve clinical outcomes and quality of life for patients with life-threatening heart conditions.

NSF Awards · FY 2025 · 2025-09

This award will support fundamental research to enable aerial robots smaller than 100 millimeters and weighing less than 100 grams to navigate through cluttered surroundings in the presence of smoke, darkness, dust, fog, and snow. To accomplish this in a completely self-contained way, the robots in this project will use sound waves instead of light to sense nearby objects. Sound waves penetrate much farther than light through airborne particles, but are easily confused by propellor noise and are unable to reliably distinguish small features. Through advances in mathematical modeling, neural network design, and sensor characterization, this project will greatly improve the quality of sound-based images, without exceeding size, weight, and power constraints imposed by the limitations of the small aerial platform. The research will result in inexpensive and easily transported drones with the potential to save lives in forest fires, cave rescue, wildlife management, and natural disasters, thereby contributing to the health, well-being, and economic strength of the Nation. The research brings together multiple fields including robot perception, bio-inspired artificial intelligence, sensor fusion, and signal processing, thus germinating new research questions and creating research training opportunities across traditional disciplinary silos. Aerial robot navigation built on multi-modal visual-sonic-inertial (VSI) sensing can overcome challenges in visually degraded and challenging scenes that may confound current sensing systems such as may arise due to darkness, snow, smoke, dust, or fog. This project addresses scientific barriers to widespread deployment of VSI-based systems, through the advancement of representations, mathematical models, and neural architectures. These advances will allow effective extraction and fusion of information from VSI systems, without reliance on any external infrastructure or prior information about the environment, while respecting practical constraints on on-board computation and information processing. The project builds on the following three key ideas: (1) enhancing ultrasonic depth estimation through bio-inspired passive structures, active noise reduction techniques, and physics-informed neural networks that leverage robot motion to overcome the ill-posed nature of sparse sensor trilateration; (2) fusing multi-modal sensor data using uncertainty-aware deep learning models and factor graph optimization to achieve robust depth estimation across varying environmental conditions; and (3) implementing a hierarchical reinforcement learning navigation stack with compositional policies for perception-action synergy, obstacle avoidance, and goal-directed movement. The approach combines mathematical modeling of wave physics with deep declarative networks, employs sim2real2sim methodologies for robust generalization, and utilizes factor graph formulations for temporal consistency. The results will be extensively tested in controlled physical experiments simulating autonomous operations in (1) dense forest with low light and heavy fog, (2) forest fires with corresponding thick smoke, and (3) caves, mines, and collapsed structures subject to high dust and low light. 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 · 2025-09

Project Summary Sudden cardiac arrest is one of the leading causes of death in developed countries, accounting for approximately 350,000 deaths per year in the United States. The majority of those events are caused by ventricular arrhythmias (VA). Implantable defibrillators reduce mortality in high-risk patients, but do not prevent recurrent arrhythmias. Suppression of recurrent ventricular tachycardia (VT) can be accomplished effectively with catheter ablation; more recently stereotactic body radiotherapy (SBRT) has been shown to have a potential role. Accurate identification of the substrate responsible for the VA is key to the success of either of these modalities and may be facilitated using the standard 12-lead ECG to noninvasively identify the site from which a focal ventricular tachycardia (VT)/premature ventricular complex (PVC) arises or from which a reentrant circuit exits the central isthmus to activate the “normal” myocardium. Currently, there is not an automatic real-time non-invasive patient-specific approach that can be used to accurately identify the site of origin (SoO) of VA using the 12-lead ECG. Rapid 12-lead ECG interpretation to identify the SoO of VA requires expertise and could be facilitated with a computerized method to automatically locate the VT exit/PVC origin site in real-time. The ability to accurately identify the VT exit/PVC origin site enables the electrophysiologist to concentrate mapping/targeting to a specific region. To tackle this problem, this research proposes to develop a novel non-invasive 3D mapping technique that relies on the assembly of personalized ventricular surfaces from CT/MRI scans in combination with a statistical estimate derived from a large clinical database to accurately identify the VT exit/PVC origin site from an induced/recorded VT/PVC ECG in real time. The project is interdisciplinary as it combines expertise in biomedical engineering, clinical cardiac electrophysiology, ECG signal processing, image processing, and computational statistical modeling. To this end, the project will include the following two activities: 1) to develop the proposed system in clinically usable software; 2) to assess the accuracy of the proposed software in a prospective case-series study (with the goal of achieving a mean localization error of less than 10 mm). The proposed software delivered by this project will provide significant accuracy improvement in the VT exit/PVC origin site localization, potentially decrease in the time of an invasive VA ablation procedure, and would be helpful to accurately target VT for non-invasive cardiac SBRT. The proposed project is innovative in proposing to bring computational statistical modeling that integrates structure data (CT/MRI imaging), function data (ECG), and a large clinical dataset into the realm of contemporary patient care. At its core, the project is of translational nature, with personalized computational statistical modeling being used for guidance of clinical therapies.

NSF Awards · FY 2025 · 2025-09

For a large range of applications, from civil infrastructure to national defense, predicting the failure of materials and structures is critical. Our ability to predict failure depends on mathematical and computational models, and we need both to be as rigorous and physically justified as possible. Over the last 25 years, there have been significant mathematical advances in this area, which have directly improved computational models, particularly for fracture. However, these advances still have significant limitations - they are mostly restricted to models without applied forces, and existing mathematical models can entangle fracture nucleation and propagation in an unphysical way. The investigator recently formulated a mathematical model for fracture that isolates propagation and is compatible with all applied forces. The first goal of this project is to show existence of evolutions satisfying this principle. Another goal is to formulate models and show existence for evolutions satisfying both this principle and physical criteria for nucleation. The investigator also develops and studies improved computational models based on these mathematical results. The project includes training Ph.D. students. The ability to accurately predict material failure depends on the quality of mathematical models of defects, as well as on understanding basic properties of solutions. While successful in many ways, variational models for static and quasi-static fracture have been based on energy minimization which can force unphysical nucleation and is incompatible with body loads. Recent progress includes formulating a new variational principle that overcomes these limitations. It remains to show that there exist equilibrium states satisfying this principle, and similarly for quasi-static evolutions. Both are goals of this project. The addition of completely independent strength criteria for nucleation is also studied. A second direction is to substantially improve the connection between phase-field fracture implementations and models for sharp fracture. This utilizes the newly introduced variational principle, which can be studied using Gamma-convergence, even though it is completely spatially local. 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 2024 · 2025-09

The goal of this research is to develop a sensor device prototype to rapidly measure an array of diverse salivary biomarkers as input for novel machine learning (ML) methods that can predict periodontitis and monitor periodontal progression. Our long-term goal is to develop a rapid, user-friendly, and low-cost point-of-care (POC) device, for use in either a dentist’s office or at home, that rapidly integrates and analyzes data to support patient management. It addresses the priority area of the Data Science, Computational Biology, and Bioinformatics Program of NIDCR in integrating and analyzing high-volume and diverse data to better understand dental, oral, and craniofacial biology and diseases. According to the CDC, nearly 50% adults have some form of periodontal disease. Perioodontitis is silently progressive and patients often seek professional care only in an advanced stage where advanced, painful and costly procedures are needed to control disease or replace lost teeth. Early detection of periodontal disease at an individual patient level is required and there is growing awareness that multiple biomarkers are valued in predicting risk of disease in individuals. We hypothesize that predictive models can be established based on the measurements of a large set of periodontitis-associated biomarkers in saliva; a sensor device that integrates multi-sensor modalities and the machine learning (ML) models will advance the clinical goal of early diagnosis of periodontitis to enable earlier clinical interventions. Thus, we will develop and apply three distinctive sensor modalities for detecting concentrations of salivary analytes relevant to various stages of periodontal progression, i.e., inflammation, soft tissue destruction or bone destruction (Aim 1). Data from both sensor outputs and clinical examination will be used to train ML models via a novel multi-modal adversarial knowledge distillation ML framework, which promotes accurate early prediction with partial longitudinal data representations (Aim 2). The multi-sensor modalities and the ML models will be embedded in a single microfluidic device, incorporating steps such as sampling, detection, and data analysis as an integrated lab-on-a-chip, and permitting the sensor data preprocessed to transmit only the actionable information to the outside platform to protect the user's privacy (Aim 3). Such a device is anticipated to offer for unobtrusive, accurate, and frequent saliva-based self-monitoring, and provide detailed medical data to support clinical decisions. It will be an effective tool for future personalized medicine and dramatically improve patients' oral health.

NIH Research Projects · FY 2025 · 2025-09

Lead (Pb) poisoning remains a pervasive public health problem in the U.S., causing neurotoxicity in children. Pb exposures are cumulative, and therapeutic elimination of Pb is not feasible for most affected people. Thus, prophylactic strategies are essential for protecting the health of exposed children. The neurotoxic effects of Pb are permanent, thereby adding significantly to the disease burden for affected children and communities. Therefore, there remains an urgent need for methods to protect children from cumulative lead exposure. Most Pb exposures are via ingestion from environmental sources including water, food, dust, or paint chips. A potential solution is to provide an effective prophylactic to protect individuals in high-risk environments from Pb ingestion. Aptamers are short stretches of nucleic acids (<100 nucleotide singlestranded DNA or RNA molecules) that bind specifically to a target molecule or ion. Pb-binding aptamers could be used to bind Pb in the gastrointestinal tract and thereby prevent absorption. However, the use of Pb-binding aptamers as prophylactics for Pb toxicity has not been previously reported. To assess the potential of an aptamer-based prophylactic strategy for Pb toxicity, we developed an in vivo Pb toxicity assay using the model organism C. elegans. Our recently published work (Anwar et al., 2024, New Biotechnol.) demonstrates that morphologic, behavioral, and reproductive phenotypes are adversely affected by Pb in our C. elegans model. Importantly, all three phenotypes are prevented in response to pre-exposure of the animals to Pb-binding aptamers. Our long-term goal is to develop highly efficient and cost-effective strategies for preventing Pb toxicity using aptamers. The objective of this R03 proposal is to determine the mechanism by which DNA and RNA aptamers protect C. elegans from neurotoxicity. We further seek to make initial an initial determination of the safety of aptamer-based Pb-chelation by performing toxicity assessments on the aptamers themselves. To attain our objective, we propose the following specific aims: Aim 1: Determine the mechanism of prophylactic effects of aptamers on developmental neurotoxicity caused by Pb. We will use our Pb-binding aptamers to specifically examine neurotoxicity in dopaminergic (DA) neurons in response to Pb, using the dat-1::GFP reporter system. Aim 2: Determine the mechanism of action and potential toxicity of Pb-binding aptamers. The mechanism of aptamer function will be investigated by measuring intracellular lead levels by atomic absorption spectroscopy. The role of complex secondary structures (G-quadruplexes, G4) that reportedly sequester lead ions will be assessed using competitive G4-binding small molecules. We will also critically assess the toxicity of the aptamers with survival, lifespan, and neurotoxicity assays. The proposed research addresses the lack of interventions for low level Pb poisoning in children, which is a clear public health gap.

NSF Awards · FY 2025 · 2025-08

This project will develop new statistical tools to enhance the power and precision of discovering disease-associated genes. By integrating diverse sources of genomic information and improving how prior knowledge and statistical evidence are combined, the project aims to uncover subtle genetic signals that might otherwise be missed. These innovations have the potential to transform the understanding and treatment of complex diseases, such as neurodegenerative disorders. The project supports national interests by promoting scientific advancement and improving health outcomes. It also fosters education in statistics, data science, and bioinformatics, supports workforce development, and promotes collaboration across disciplines and sectors to enhance the societal impact of statistical research. The research will advance statistical theory, methodology, and computation for integrative association testing in heterogeneous genomic data. It focuses on two core challenges: (1) designing more effective weighting strategies for incorporating prior information when combining statistical significances, and (2) developing new methods to integrate discrete statistics within a general hypothesis testing framework. The project will implement and apply these approaches to harmonized whole genome sequencing datasets, with a focus on amyotrophic lateral sclerosis and related neurodegenerative diseases. It also supports interdisciplinary education and research infrastructure by connecting expertise in statistics, computational biology, and medical genetics. 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

Stemming from human societies, the notion of commitment refers to a decision maker, or agent, making credible and prolonged promises about various aspects of the consequences of its future decisions, thus facilitating cooperation with other agents. Engineering commitments is therefore a promising framework to achieve cooperative artificial intelligence (AI) that equips a group of autonomous agents with the capability of planning and learning to maximize their joint utility. This research project seeks to initiate a paradigm shift that brings the notion of commitment to its full potential by scaling it to various dimensions of complexity in cooperative AI, developing novel methods that promise to significantly and positively impact real-world and large-scale cooperative AI applications. This project integrates an array of education initiatives, playing key roles in PI's classes, the recruitment and training of undergraduate students from underrepresented backgrounds, and extensive activities planned to involve high school students and junior researchers. This research consists of two cohesive thrusts: Thrust 1 redesigns an existing approach for commitment-based distributed cooperative planning with a predefined parameterization for probabilistic commitments, by developing novel algorithms and analyses in planning under constraints and uncertainty, approximate linear programming, and robust planning that address long decision horizon and high-dimensional perception and action; Thrust 2 develops and evaluates a novel approach for distributed cooperative learning with emergent commitment parameterization, which combines the best from the framework of multi-agent commitments and deep reinforcement learning to address all aforementioned dimensions of complexity. Success of the proposed research is expected to significantly increase the applicability of commitment-based planning and learning for large-scale and complex cooperative AI systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Robotic systems have already found widespread adoption in controlled environments such as manufacturing and logistics, where tasks follow well-defined rules. However, they struggle in unstructured, dynamic, real-world settings that demand adaptability and autonomy -- challenges that humans handle with ease. Humans excel at abstract reasoning, allowing them to perform complex tasks without constant attention to low-level details. This research project aims to equip robots with similar capabilities, enabling them to learn abstract representations of their environment and actions through experience. By enhancing the ability of robotic agents to plan and execute tasks in real world settings, this research could drive advancements in automation, assistive care, and disaster response. Additionally, the project intends to contribute to STEM education through outreach programs for high school students and research opportunities for undergraduate students. The findings will be disseminated through leading robotics conferences and peer-reviewed journals, ensuring broad visibility within the research community. Moreover, the results will inform and enhance robotics courses and all software and datasets produced will be openly shared, fostering collaboration and further advancements in the field. This project aims to advance robotic manipulation in unstructured environments by enabling robots to autonomously learn abstract representations of states and actions from sensory and execution data. Existing planning methods have been successful in controlled settings by using task planners to reason abstractly about complex tasks, providing reliability, explainability, and transparency. However, they rely on human-specified representations, which are impractical in real-world scenarios with unknown objects and noisy sensor data. In contrast, purely data-driven approaches can learn directly from raw sensor data, reducing the need for manual specification but struggling with generalization and lacking interpretability, limiting their deployment in dynamic environments. Research funded by this award seeks to address these issues by developing a framework that learns abstract state and action representations from experience and seamlessly incorporating them into existing manipulation planners. Research activities will focus on designing methods for autonomous real-world data collection, designing algorithms to extract structured representations, and adapting decision-making strategies to leverage learned abstractions. Experimental validation will be conducted on real-world robotic platforms to assess the effectiveness of the learned abstractions. If successful, this research will enhance robotic adaptability, transparency, and efficiency that could significantly expand applicability of autonomous manipulation in open-world environments such as household tasks, assistive care, and construction. Robots capable of abstract reasoning will be better equipped to handle long-horizon tasks like rearrangement, packaging, and sorting. The results will be disseminated through leading robotics conferences and peer-reviewed journals, with software and datasets openly shared to support further research and educational initiatives. 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

The National AI Research Resource (NAIRR) provides U.S.-based researchers with high-performance computing, data, and AI tools, fostering broad access to cutting-edge AI research. Training scientific researchers to fully utilize NAIRR capabilities is critical in advancing the nation’s science and technology. This project, a collaboration between Oakland University and Worcester Polytechnic Institute, aims to increase awareness and promote broader adoption of the NAIRR Pilot. The project will organize two 12-workshop series over two years, offering hands-on training for emerging researchers to utilize NAIRR for using AI in three critical domains: AI for Cybersecurity & Trustworthy AI, Edge AI, and Autonomous Driving. These workshops will provide guidance on integrating NAIRR’s computational resources, datasets, and AI frameworks in the R&D efforts to develop secure, efficient, and impactful AI use cases in these three domains. It will equip researchers with the knowledge and tools to develop secure and innovative AI systems, fostering advancements in cybersecurity, autonomous systems, and other interdisciplinary AI applications. The NAIRR resource utilization training will guide participants in using NAIRR resources to further research and prepare students for projected future workforce needs. 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

When administrators, teachers, and families engage together in meaningful STEM research, and students use innovative interventions and technologies in the classroom, everyone benefits. Research Practice Partnerships (RPP) between university researchers and school districts can facilitate the implementation of sustainable and impactful STEM education research, provide relevant and useful information and results to practitioners and families, and improve the quality of instruction provided to students. However, establishing those partnerships takes time. For RPPs to be successful, a shared mission, dedicated time, resources, relationships of trust, and systems and structures need to be put in place first. Through developing a common vision, framework, and RPP network that responds to key needs of members of the community, including researchers, teachers, administrators, and families, this Partnership Development project will enhance partnerships between academic research and K-12 schools, guide future research and development in innovation, and lead to greater participation and engagement by teachers in STEM research and technology as a part of their instructional practice in the future. This project supports the goals of NSF's DRK-12 program by catalyzing research that enhances all preK-12 teachers' and students' opportunities to engage in high-quality learning experiences in mathematics and by promoting collaborative partnerships among STEM education researchers, practitioners, school leaders, and families. This project will develop a sustainable RPP model between the Worcester Public Schools (WPS) and the Learning Sciences Lab at Worcester Polytechnic Institute (WPI). Together, WPI and WPS will build the collaborative infrastructure for conducting impactful STEM education research within WPS. Specifically, the RPP will establish and document shared infrastructural systematic processes and materials, brainstorm and facilitate research ideas that address pressing issues in mathematics education, and build a community of trust among researchers, administrators, teachers, and families to make future research and implementation, innovation, and collaboration more impactful, accessible, and efficient. Project activities include meetings with various stakeholders, focusing on three parallel efforts to develop a sustainable RPP: (1) systems and structures (2) continuous learning, and (3) collaborative processes (6 meetings for each of the three efforts). Members of the WPS community will work equally with the WPI research team to share ideas and perspectives and make progress on each aspect of the RPP pipeline, including the creation of processes and community-facing materials, and identifying relevant research questions and designs for future studies that can address real-world problems in mathematics education. Survey data and feedback will be collected after each meeting to identify benefits and challenges that can inform iterative revision to the RPP processes and ensure strong partnership development for the future. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor V. Sara Thoi of Johns Hopkins University and Professor Fanglin Che of University of Massachusetts, Lowell are studying the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. Organonitrogen species are a ubiquitous class of compounds used in a variety of industries, from agriculture to pharmaceuticals. Urea, for instance, is an important fertilizer, but it is industrially derived from a highly energy-intensive process called the Haber-Bosch process. Electrochemical transformation of abundant carbon and nitrogen molecules, such as carbon dioxide and nitrogen gas, to organonitrogen products thus emerges as an attractive approach. Electrochemical synthesis can be conducted at room temperature, ambient pressure, and in water. Moreover, the rise in renewably generated electricity provides a path towards decarbonization of the chemical manufacturing industry. This project uses a combination of computational and experimental chemistry to design, synthesize, and test new catalysts for forming commercially valuable organonitrogen compounds, such as urea, acetamide, and N-methylamines. The expected outcomes are the fundamental knowledge for activating small molecules to form carbon-nitrogen bonds, the identification of design parameters for synthesizing efficient catalysts, and the broader applications of electrochemical synthesis to a new class of commodity chemicals. Additionally, the educational goal of this project is to engage young students in the STEM field via the publicly accessible video series, “Meet the Chemist,” which highlights the unique and diverse paths of undergraduate students to chemical research. Moreover, a novel theory course on applied machine learning for computational catalysis will be developed for undergraduate and graduate students. The scientific and educational merits of this project advance the frontiers of chemical synthesis, promote public engagement between researchers and young students, and align with our national interest for decarbonization. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor V. Sara Thoi of Johns Hopkins University and Professor Fanglin Che of University of Massachusetts, Lowell are studying the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. This project will focus on a class of metal-organic materials called boron imidazolate frameworks (BIFs) for electrochemical C-N coupling to form commercially valuable products, such as urea, acetamide, and N-methylamines. Owing to their synthetic tunability, BIFs provide facile access to a range of isostructural metal-organic materials to identify structure-function relationships, serving as an ideal materials platform for fundamental insights to catalytic mechanisms. This project has three objectives: i) identify the structure-function relationships between the electronics of the BIFs and C-N coupling, ii) observe key intermediates via in situ vibrational spectroscopy, and iii) develop physics-informed machine learning to identify design criteria for new BIF catalysts. Together, this knowledge will be used to explore the scope of C-N coupling products, using inexpensive and abundant carbon and nitrogen precursors such as carbon dioxide, aldehyde, ketones, dinitrogen, nitrite, nitrates, and amines. Additionally, we will expand an existing video series, called “Meet the Chemist,” to highlight the unique and diverse paths of undergraduate students to chemical research. The videos, which are publicly available, are designed to encourage young students to learn that people of all backgrounds can engage in STEM research. Moreover, a novel theory course on applied machine learning for computational catalysis will be developed for undergraduate and graduate students. Along with these broader outreach goals, this project aims to enhance the utility of electrochemical synthesis for a wide range of industrially relevant compounds, thereby creating opportunities to decarbonize the chemical 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.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Bone stress injuries (BSI) are overuse injuries that result from the accumulation of microdamage in bones. They are among the most common injuries in running athletes and military professionals, accounting for 20% of sports medicine outpatient visits. The metatarsals bones in the feet are a common location, accounting for 38% of BSI in collegiate athletes and up to 58% in the military. Running is a highly repetitive activity involving approximately 1000 footstrikes per mile. Bone adapts to mechanical strain to become stronger. However, when strains are too high, microdamage accumulates faster than it can be repaired and a BSI can result. During the stance phase of running, the metatarsals, which are long and thin, experience high bending moments as the arch of the foot deflects downward and ground reaction forces increase. These bending moments cause high strains on the dorsal surface of the metatarsals. The four layers of plantar intrinsic muscles (PIMs) help to mitigate the bending moments. Wearing minimal footwear, which is flexible and offers no external support of the arch, has been consistently shown to increase the size and strength of the PIMs. In contrast, chronic support of the foot (e.g. from orthotics) results in significant foot weakening. Among the many differences, runners typically use a forefoot strike in minimal shoes and a rearfoot strike in supportive shoes. Because both bone and muscle adapt to habitual mechanical loads, we hypothesize that long-term running in minimal footwear will be associated with improved PIMs and metatarsal strength compared to running in traditional cushioned footwear. We further hypothesize that the biomechanics associated with running in minimal footwear stimulates these positive adaptations to the muscles and bones of the feet. This cross-sectional observational study will evaluate associations between foot biomechanics, foot structure, and foot strength in a population of male and female recreational runners who are habituated to either minimal or supportive shoes. To uncover the mechanisms that explain the biomechanical influence of footwear on risk factors for metatarsal BSI, we will use a combination of quantitative imaging, biomechanics, and modeling. All three Aims will use data from the same participants and experimentally collected data. Aim 1 will evaluate the role of habitual footwear on foot structure and strength in the two groups. Aim 2 will evaluate foot biomechanics, and Aim 3 will quantify the associations between foot muscle strength, foot biomechanics, and metatarsal strength. By investigating differences between runners who are well habituated to either minimal or cushioned shoes, we will clarify the potential role that footwear has on strengthening the muscles and bones within the foot. This will directly inform interventions in the future to prevent injury. Beyond the immediate impact, the unique data will form a foundation for more sophisticated analyses to better understand load transmission within the feet. This has broad applications in preventing and treating other common foot injuries and progressive deformities that have biomechanical pathologies, such as bunion, collapsing foot deformity, and plantar fasciitis.

NIH Research Projects · FY 2025 · 2025-08

Project summary Mycobacteria include important human pathogens such as Mycobacterium tuberculosis, and elucidating the mechanisms by which they regulate gene expression is necessary to understand their mechanisms of surviving drugs and other stressors encountered during infection. However, the mycobacteria are evolutionarily divergent from better-studied model organisms. Small RNA binding proteins have critical roles in mediating sRNA-mRNA interactions and modulating mRNA stability in E. coli. However, mycobacteria lack orthologs of the RNA binding proteins that are best characterized in E. coli. Instead, mycobacteria encode two putative RNA binding proteins, KhpA and KhpB, that have orthologs in some gram-positive bacteria where their functions are poorly understood. Given the almost complete absence of published data on small RNA binding proteins in mycobacteria, we propose to determine the biochemical properties and functional impact of KhpA and KhpB using the non-pathogenic model mycobacterial species Mycolicibacterium smegmatis. The proposed project will address a major knowledge gap in the mycobacterial field while providing in-depth training opportunities for undergraduate students at Worcester Polytechnic Institute. We hypothesize that KhpA and KhpB form both homodimers and heterodimers that bind differing suites of mRNAs and small regulatory RNAs (sRNAs), affecting their stability and function. The objectives of this proposal are to determine the functional oligomerization states of these two proteins; determine the compendia of RNAs that they bind; identify the properties that make these RNAs targets of KhpA/B; determine the impact of khpA/B on RNA degradation rates; and determine the impact of khpA/B on growth and stress tolerance.

NSF Awards · FY 2025 · 2025-07

This Engineering Research Initiation (ERI) project will fund research that investigates the nonlinear behavior of slender beams with localized geometric variations, such as varying curvature, which have the potential to program and tune their mechanical properties. Beams are commonly used in structural systems due to their ability to efficiently distribute loads, and for decades, beam design has primarily focused on global geometric variations to meet key criteria like stiffness, strength, and critical load. However, the vast potential of localized geometric changes has been largely unexplored. This research will attempt to elucidate how localized curvatures influence the nonlinear behavior of beams, providing insights that can drive the development of innovative lightweight structures with tailored mechanical behaviors for applications in biomedical devices, metamaterials, aerospace, robotics, and civil infrastructure. This work looks to address the national need for next-generation lightweight structures that fully utilize material potential. In addition, the project seeks to provide valuable educational opportunities, including the training of graduate and undergraduate students and outreach activities for K-12 students. The objective of this project is to develop a hybrid high-order nonlinear beam model that integrates beam-scale and cross-sectional models to capture large deformations in beam-type structures with arbitrary initial shapes. The research will focus on two key objectives: (1) understanding how localized geometric variations affect the nonlinear mechanical response of slender beams, and (2) examining the interactions between localized and global geometric variations. Experiments will be conducted to validate the numerical simulations, providing insights into how geometric defects from the manufacturing process influence the mechanical response of beam structures. The knowledge gained will looks to lay the foundation for designing more complex structures made up of multiple beam components and improve the assessment of how these structures behave under various loading conditions. 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

It is widely believed by scientists that our universe follows certain symmetry patterns and principles, which lead to profound implications such as conservation laws. Artificial intelligence (AI) can and has already benefited tremendously from exploiting these symmetries. This project seeks to identify and exploit symmetries that are prevalent in cooperative AI tasks, where a group of multiple autonomous sequential decision makers, or agents, plan and learn to maximize their combined benefit. As an example, consider the application of adaptive traffic signal control, where each intersection can be modeled as an agent controlling its traffic signal in a way that adapts to real-time traffic conditions to reduce congestion. There exist certain symmetries when the topology of the road network is regular, e.g., as a 4-connected grid, and the road condition is uniform. When done properly, such multi-agent symmetries can be identified and exploited to greatly improve the efficiency and effectiveness of the current solutions to cooperative AI. This project also integrates the proposed research into an array of education initiatives, playing key roles in the curriculum development and undergraduate research experiences at the PI's university, as well as outreach activities that bridge academia with industry practitioners and community stakeholders. This research will establish a unified framework and develop a set of interdependent methods that formulate, identify, and exploit multi-agent symmetries for cooperative AI tasks. The research first adopts a mathematically rigorous language to formulate the notion of multi-agent symmetry into the framework of symmetric Markov game, revealing its core property which can be exploited by planning and learning methods. Then, the research plan concretizes how to exploit several most common types of multi-agent symmetries, including permutation symmetries, Euclidean symmetries, and hierarchies of multi-agent symmetries of mixed types. Next, the research plan discusses issues that are critical for practice, including identifying and exploiting approximate multi-agent symmetries and dealing with partial observability. Finally, the research features several real-world applications, including adaptive traffic signal control, automated circuit design, and material design, to evaluate and showcase the proposed methodology. This project is jointly funded by Robust Intelligence 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 2025 · 2025-07

Side-channel attacks are a type of cyber attack where the adversary learns about what is being calculated by observing external properties such as power consumption or electromagnetic emissions. Side-channel information leakage of this type is a potent threat to cryptographic hardware security, arising from the physical effects of computation. These unintentional emissions can be exploited to extract sensitive data, even when cryptographic algorithms are mathematically secure. This project addresses this critical vulnerability by bridging the gap between theoretical models of side-channel leakage and practical hardware design. The novelties of this project include in the development of advanced physical probing models, scalable validation tools, and provably secure design methods. The project's broader significance and importance are in its potential to fundamentally strengthen the physical security of hardware systems used in everyday technologies by making side-channel security verification more systematic, predictable, and accessible. The research team develops new probing models that reflect real-world adversary capabilities and integrates them into advanced computer-aided design tools. These tools analyze various forms of side-channel leakage and determine the level of physical detail needed for accurate predictions. The research team validates the new probing models by designing and fabricating a reconfigurable integrated circuit. This hardware serves as a testbed to assess the models and to drive model improvements. The team also explores secure-by-construction design techniques that guarantee resistance to side-channel attacks based on formal proofs. The project links experts in secure hardware and formal modeling across international borders and enhances the scientific reach and impact of the work. Results are expected to advance the state of the art in secure hardware design, influence industry practices, and inform educational activities through tutorials and workshops in cryptographic engineering and design automation. 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

Artificial intelligence has achieved remarkable success in recent years, largely driven by advancements in foundation models, which leverage complex neural networks trained on vast amounts of data in order to perform a variety of tasks, such as question answering, text summarization, and image generation. This project seeks to extend the success of foundation models to sequential decision-making, where an agent--a programmable entity---interacts with an environment, seeking to accomplish a task by taking a series of actions over time, with each action influenced by the outcomes of previous actions. Sequential decision-making commonly arises in situations characterized by uncertainty, limited resources, or dynamic conditions, where each decision can have an impact on future actions. The objective is to select a sequence of actions that maximizes profits, rewards, utilities, or some other well-defined objective. Adapting foundation models for sequential decision-making is challenging, because high-quality data is often lacking and it requires recognizing task-specific structures and optimizing long-term objectives, where minor differences can drastically change optimal solutions. This project will develop novel methods for overcoming these challenges to significantly increase the applicability of foundation models for a wide range of sequential decision-making applications, such as smart manufacturing, multi-agent systems, and human-machine interaction. This project will develop novel techniques and methods to effectively adapt foundation models to multimodal sequential decision-making. The proposed research will be conducted and evaluated on three thrusts with progressively increasing problem complexity. Thrust 1 studies sequential decision-making problems in textual modalities where the decision-maker only needs to look one step into the future when evaluating the consequences of a proposed action, referred to as contextual bandits. The investigators will develop new techniques such as reward-aware text summarization and mixing foundation model-based and online-learned decision rules that leverage foundation models to warm-start the agent while avoiding being locked into pretrained parameters to improve the performance in the long run. Thrust 2 studies sequential decision-making problems that involve long decision horizons (the full reinforcement-learning problem) and are multimodal. The investigators will develop additional techniques that leverage foundation models for multimodal and hierarchical reinforcement learning. Thrust 3 extends the techniques to the cooperative multi-agent setting, where the foundation models are leveraged to facilitate both centralized and decentralized inter-agent communication, which is crucial for multi-agent coordination. In and outside the classroom, this project will conduct a series of educational and outreach activities, including development of course materials related to foundation models and sequential decision-making, undergraduate research mentoring, and public outreach in local communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Fibrosis, or excess tissue production, can occur as a result of injury or inflammation in many tissues throughout the body. If unchecked, fibrosis can disrupt organ function. How specific cells within tissues contribute to fibrosis in certain organs is unknown, which makes it difficult to develop targeted treatments. This project will develop in vitro models to study fibrosis in the pancreas, skin, and uterine fibroids. The pancreatic fibrosis model will study how tissue stiffness, which increases during fibrosis, encourages or prevents cells from participating in fibrosis. The skin model will study how tissue stiffness and stretching influence how cells communicate across skin layers during fibrosis. The uterine fibroid model will examine how hormones and hormone disruptors impact fibroid cell growth. The outcomes of this work will expand knowledge of how cells behave in fibrosis in different organs and how certain cells can be targeted to develop therapies in the future. This award will support STEM activities for high school students as well as community engagement activities to improve patient education and healthcare understanding. Fibrosis contributes to 45% of deaths in industrialized nations through chronic pathological conditions, yet knowledge gaps remain about fibrosis initiation, progression, and resolution in different tissues. A critical barrier to fibrosis treatment is limited understanding of how tissue-specific features of the extracellular matrix (ECM) affect how fibrogenic and non-fibrogenic cells participate in fibrosis. This CAREER project aims to engineer many in vitro fibrosis models—pancreas, skin, and uterine fibroids—that designed to enable control over tissue-specific ECM features (stiffness, microstructure), cellular composition, and/or exogenous biochemical interactions for targeted investigation into fibrotic processes. The pancreatic model uses ECM materials with tunable stiffness—methacrylated type I collagen and hyaluronic acid—to study how stiffness and ECM remodeling direct, protect, or hinder cell fate transformations of fibrogenic (pancreatic stellate cells, macrophages) and non-fibrogenic cells (pancreatic epithelial cells, mesenchymal stem cells). The skin fibrosis model recreates skin’s layered structure to independently alter cellular composition and ECM properties (stiffness, alignment, microstructure) and examine cellular crosstalk across skin layers. A novel intramural uterine fibroid model is used to investigate biochemical interactions—sex hormones and endocrine disrupting agents—as drivers of fibrosis that influence fibroid cell growth, ECM deposition, and lymphatic vascular infiltration. Overall, this project uses distinct, yet complementary, approaches to advance in vitro fibrosis models and lower barriers to resolving fibrosis at a cellular level. Integrated education goals focus on expanding STEM participation and increasing STEM and medical literacy. Activities include a re-designed summer biomaterials course for high school students through Worcester Polytechnic Institute’s Frontiers program and a new outreach partnership with the Epworth Free Clinic in Worcester, MA to promote medical literacy and awareness. 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.