University Of Massachusetts Lowell

NSF Awards · FY 2026 · 2026-09

Most unwanted clothing is discarded, even though much of it could be reused or recycled. The benefits of clothing donation, resale, repair, repurposing, and recycling depend on local infrastructure. Tradeoffs between potential benefits and energy use or environmental impacts often pose challenges for businesses, communities and policymakers. This CAREER project will develop evidence-based tools to help decisionmakers find strategies that keep textiles out of landfills, reduce gaseous emissions and conserve water and other resources. The project will demonstrate these tools on test cases involving reuse pathways in Massachusetts and a reuse system at the University of Massachusetts Lowell. The project will also combine research with engineering education, engage students in data collection and analysis, and participate in public events focused on thrifting and repair. The Sustainable Textile Industry Through Circular Handling project is a comprehensive modeling and assessment approach and initiative that aims to transform the textile industry. The project will integrate circular-economy principles with advanced industrial engineering models. The research will combine life-cycle assessment with machine learning and probability-based modeling to estimate, compare, and stress-test the environmental benefits and trade-offs of reuse, repair, and recycling strategies. The project will create time-dependent inventories that track repeated use, changes in product function, and regional differences in collection and processing. A probability-based state-transition model will estimate garment lifetimes and the likelihood that items move from reuse to repair, recycling, or disposal. To ensure that claims about circular solutions reflect physical limits, the research will incorporate thermodynamic measures that capture unavoidable losses in material quality and energy usefulness across repeated processing. Machine learning methods will infer difficult-to-measure quantities, such as how quickly garments degrade and the likelihood of a given pathway. Machine learning will also quantify uncertainty and improve predictions of garment lifetimes and end-of-life routing under different policies and business scenarios. The method will be demonstrated through two case studies: statewide reuse pathways shaped by the Massachusetts Textile Disposal Ban and an institutional reuse system at the University of Massachusetts Lowell. These case studies will produce publicly available models, datasets, and decision-support guidance that can be transferred to other resource-intensive products. 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 · 2026-08

The project will investigate the feasibility, community acceptance, and design requirements for a federated, cross-domain, AI-ready data cyberinfrastructure. The proposed concept of a national data system serving domains of advanced microscopy, imaging, materials, biology, and environmental sciences connects workflows across institutions without requiring all raw data to be centralized. This effort will help transform isolated local repositories into reusable national resources for more reproducible, AI-enabled discoveries, while maintaining local organizational policies and control of data. The project will examine a federated approach, where data remain close to the instruments and institutions that produce them, while shared services support discovery, provenance, trust, and AI-enabled reuse, with a goal to provide a more scalable and sustainable national infrastructure. The planning work will use a Teach–Explore–Design framework: first helping scientists and cyberinfrastructure stakeholders imagine what such a future system could enable, then studying their workflows, concerns, and constraints, and finally co-designing candidate architectures, operating models, and responsible-AI practices with them. The project will produce community-vetted evidence and design artifacts, including candidate federated architectures, operating models, trust and provenance concepts, and responsible-AI workflow patterns, to guide a future national-scale cyberinfrastructure effort. 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 · 2026-07

Artificial intelligence systems that generate text now influence how people search for information, learn new skills, obtain health advice, and communicate online. Yet these systems can behave in ways that are hard to predict. They can copy misleading patterns from their training data, produce text that is too complex for a reader's needs, and retain private, outdated, or incorrect content. This project studies how these systems learn, keep, and forget language patterns over time, and uses that knowledge to build systems that are easier to control and safer to use. The results can help create text at appropriate reading levels for second language learners, patients reading health information, and people with communication or cognitive challenges. The project also develops methods to remove harmful patterns without weakening a model's general ability to generate useful text. The project advances reliable artificial intelligence, improves access to understandable information, and trains students through coursework, mentoring, freely available tools, and interdisciplinary workshops to support science and public well-being. The project develops a linguistically grounded framework for robust and interpretable neural language models. It creates methods for controlled text generation and paraphrasing that allow models to follow user-defined lexical, syntactic, and discourse constraints. These methods combine instruction tuning, explicit control signals, multi-objective optimization, and iterative test time refinement to satisfy several user-defined constraints simultaneously. The project also studies how language models learn over time during training by measuring performance on words and text spans with linguistic annotations. This creates learning timelines that can show when specific language properties are learned, help better align training data with model abilities, and support more efficient training. In addition, the project develops methods to remove harmful patterns while preserving overall language ability. These methods use input perturbations to expose shortcut behavior, contrastive learning to reduce reliance on spurious cues, and smooth low-loss update paths to control forgetting. The methods will be evaluated on benchmark tasks for controllability, linguistic generalization, paraphrasing, and forgetting. Expected outcomes include new models, diagnostic methods, data resources, and publicly available tools for more reliable language 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 · 2026-06

This project presents a groundbreaking approach to developing a large-scale terahertz (THz) reconfigurable reflectarray (an antenna focusing beams like a parabolic dish antenna but with an array of unit cells on a flat surface) for advanced beam steering capability in next-generation wireless communication networks. The approach can potentially revolutionize how data is transmitted and received, enabling faster, more reliable, and adaptive wireless networks that can meet the fast-growing demands of a digitally connected society. By using innovative optical control methods, the project aims to eliminate traditional limitations in high-frequency communication systems, such as signal loss and integration complexity. The societal and national benefits are substantial: improved wireless infrastructure can support smart cities, enhance connectivity in rural and underserved areas, and drive economic growth through new technology platforms. Beyond communications, the research has broad impacts across multiple scientific and engineering fields by offering new tools for medical imaging, security screening, and chemical/biological sensing, enabling new discoveries and new applications. The project also contributes to education and outreach by integrating its research findings into university courses, involving students at all levels in research, and promoting STEM engagement in local schools, thereby fostering the next generation of innovators and engineers. The research of this project aims to investigate and demonstrate a novel method for achieving extremely large-scale THz reflectarrays using photonically-driven unit cells based on enhanced spatially resolved photoconductivity modulation. The innovative approach utilizes a closely coupled micro-LED array to modulate the phase of each unit cell in a hybrid Au-Ge mesa-array semiconductor structure. By adjusting computer-generated light patterns, the system enables pseudo-continuous phase modulation across a full 360-degree range, allowing for real-time synthesis of arbitrary two-dimensional phase profiles to control reflected THz beams. This eliminates the need of electrical wires for biasing or control, mitigating parasitic effects and enabling highly scalable, dense array implementations. The project scope includes device-level design, fabrication, and characterization, as well as system/network-level analysis, simulations, and prototype demonstrations of adaptive high-speed THz wireless links. The approach overcomes limitations of conventional methods, which have been constrained by signal losses and design complexity at frequencies above 100 GHz, and enables advanced functionalities such as beam bending, curving, and multiple-input multiple-output (MIMO) operation. The international collaboration team consists of experts in semiconductor physics, THz technology, electromagnetics, antenna design, and wireless communications to carry out the research tasks and advance knowledge in both semiconductors and wireless technologies. This project was submitted under the United States-Ireland-Northern Ireland R&D Partnership and is a collaboration between researchers at the University of Notre Dame, University of Massachusetts Lowell, Tyndall National Institute, and Queen's University Belfast. 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

Project Summary: Chronic pain, widely considered the United States’ #1 public health crisis, is the most common cause of long-term disability in the world, affecting more than 25 million people in the US and nearly one third of the world’s population overall. Current pharmaceutical treatment options are broadly ineffective and often contribute severe side effects, including anxiety, heart disease, liver disease, and/or addiction. To address the enormous unmet public health challenge of peripherally-mediated chronic pain, novel phenotypic screening tools and new, non-addictive chemical entities that serve as pain therapeutics are urgently needed. Here, we propose a new collaboration that aims to discover novel, non-opioid and non-addictive analgesics from marine microbial resources and advance them to enter the Pain Therapeutics Development Program (PTDP). Our proposal exploits a recently developed phenotypic assay, developed through the HEAL Initiative, that leverages human induced pluripotent stem cell (hiPSC) sensory neurons and glia cultured on multi-well microelectrode arrays (MEAs). This allows long-term and high- content characterization of sensory neuron electrophysiology under baseline (spontaneous) and evoked (thermal and electrical stimulus) conditions. This moderate throughput, pain-physiology-relevant system for hit identification represents a unique approach to compound screening that has yet to be applied to natural product-derived analgesic discovery outside of the preliminary data provided in this proposal. We will employ this unique assay to screen an existing library of purified and structurally characterized marine microbial natural products and extract fraction libraries generated from a diverse and chemically rich culture collection of marine bacteria. We further exploit a new approach called Small Molecule In Situ Resin Capture (SMIRC) to access chemical space that is not available using culture dependent techniques. The hiPSC assay will be used to identify high-priority hits and guide the isolation of active compounds, which will be structurally characterized and tested for toxicity and addiction phenotypes to inform prioritization. A carefully planned workflow, including collaboration with a small biotech company focused on analgesic lead optimization, will maximize the discovery potential of this program.

NIH Research Projects · FY 2026 · 2026-05

Project Summary The oral mucosa, and specifically, the periodontal sulcus, is considered a defense barrier between specialized immune cells and polymicrobial communities. In healthy condition, balanced interactions among the epithelium, microbiota, and immune cells are maintained within the depth of the periodontal sulcus. However, persistent inflammation within the sulcus disrupts the equilibrium, facilitating the growth of pathogenic bacteria and leading to periodontal diseases (gingivitis, periodontitis). At present, the ways in which polymicrobial community influence host physiology and how the innate immune system balance host-pathogen interactions in healthy and disease state are not yet well understood. Moreover, disease trajectory studied by using clinical, animal, or in- vitro models are limited. Specifically, research strategies have failed to mimic key elements of the gingiva, such as anatomical complexity (i.e, sulcus depth at different stages of the disease), polymicrobial native conditions (oxygen and pH levels), and immune components. Thus, there is a compelling need to improve current culture technologies to provide a more sustained in vivo-like environments to investigate host-pathogen interactions in acute and chronic conditions. Therefore, I am proposing a sustained in vitro gingival tissue model, resembling the gingival sulcus anatomy, capable of recreating different periodontal states (healthy, gingivitis, periodontitis), physical properties (i.e., oxygen gradient), and metabolic conditions. Acute and chronic states will be studied with the addition of primary human neutrophils to investigate early dysbiosis clinical fingerprints and monitoring the cytokine profiles in comparison to gingival exudates from patients.

NSF Awards · FY 2025 · 2025-12

The objective of this project is to establish a multi-university, Phase III I-UCRC (Industry-University Collaborative Research Center) for wind energy research, education, and outreach. The effort is based on the successful ten-year operation led by two university sites (UMass Lowell and the University of Texas at Dallas). Together these two universities have conducted wind energy research, established long-term partnerships within the wind industry, trained undergraduate and graduate students to perform state-of-the-art industry relevant research, and engaged in outreach to K-12 students and the international wind energy community. The Center contributes to the nation’s research infrastructure and enhances the intellectual capacity of the renewable energy workforce. An experienced group of scientists, engineers, and practitioners will execute a program of research and education focused on the design, operation, and maintenance of land-based and offshore wind energy systems for electricity production. The Center will be aimed at: (a) enhancing national excellence in wind energy research and development that has direct relevance to industry, and (b) developing a cadre of diverse undergraduate and graduate students with world-class training who will support and eventually lead in the analysis, design, manufacture, and successful operation of wind energy systems. This Phase III I-UCRC integrates engineering with fundamental research to support the development of low-cost and high availability wind energy systems. The partners will engage in cooperative research and education in the following thrust areas: (a) Composites Blade and Rotor Design & Manufacturing, (b) Structural Health Monitoring and Non-Destructive Inspection, (c) Wind Plant Modeling and Measurements, (d) Control Systems for Wind Turbines and Wind Plants, (e) Energy Storage and Grid Integration, (f) Foundation and Towers, and (g) Topics Beyond Levelized Cost of Energy. Examples of industrially relevant research led by the UML site are expected to result in: (1) a better understanding of how computer vision and acoustic sensors can be used to identify wind turbine structural damage and operating conditions; (2) identification of innovative chemistries and cost-effective environmentally benign processes towards recycling epoxy composites, thermos plastic resins, and glass-fiber reinforced polymers into high performance secondary feedstock materials; (3) new methods to predict damage progression during fatigue loading in fiber reinforced composites. Other topics will serve as the basis for conducting fundamental research including: offshore wind energy, electrical grid integration, energy storage, power-to-X, manufacturing of larger blades/towers, advancements in material technologies, understanding how wind turbines impact wildlife, and improving coupled turbine-turbine performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Abstract Title: ERI: Low Pressure CVD-Based Growth and Etching of Gallium Oxide for High Aspect Ratio Vertical Trench Schottky Barrier Diodes Abstract: As electricity demand continues to grow across diverse sectors including electric transportation, smart grids, renewable energy, aerospace, industrial automation, and data-intensive computing, the need for efficient, compact, and reliable power management has become increasingly critical. Electricity is emerging as the dominant form of energy consumption, and by 2030, nearly 80 percent of all electric power in the U.S. is expected to pass through power electronic devices. These systems regulate how energy is generated, transmitted, and consumed, making their efficiency and reliability vital to national infrastructure. However, today’s power electronics rely largely on silicon-based devices, which are now approaching their physical and thermal performance limits. These limitations result in significant energy losses and restrict the scalability of power systems in high-demand applications. While commercial adoption of wide bandgap materials such as silicon carbide and gallium nitride has enabled performance gains in select applications, transformative improvements are still needed to realize their full potential at scale. Among these, beta gallium oxide (beta-Ga₂O₃) offers superior voltage handling capability than existing wide bandgap semiconductors, enabling smaller, lighter, and more energy-efficient high-power devices. Despite its promise, key challenges remain in synthesizing high-quality material, processing it without damage, and translating it into functional, high-voltage devices. This project will address these challenges by advancing the materials and processing foundations necessary to enable the next generation of high-voltage diodes. This research spans the full development pipeline, from high-quality materials synthesis to damage-free processing and fabrication of multi-kilovolt class power diodes optimized for low-loss and high-voltage operation, addressing a critical need for compact, robust, and scalable components in future power systems. Additionally, the project will provide hands-on research experiences for undergraduate and graduate students, helping to build technical expertise in semiconductor processing and support national efforts to expand the domestic microelectronics workforce. The goal of this project is to build a foundational understanding of gallium oxide material synthesis, processing, and device-level integration to enable high-voltage power diode applications. The project centers on three key, interconnected goals. First, the project will establish a scalable low-pressure chemical vapor deposition (LPCVD) growth platform optimized for high growth rates and excellent crystal quality to enable the development of thick, lightly Si-doped beta-Ga₂O₃ drift layers with high electron mobility, ultra-low background carrier concentration, and minimal compensation. Second, it will introduce an in-situ, plasma-damage free, and anisotropic etching method using solid-source metallic gallium within the LPCVD environment to fabricate high-aspect-ratio vertical trench structures. This approach leverages crystallographic orientation-dependent etch behavior to achieve smooth sidewalls and pristine etched surfaces, enabling the fabrication of complex 3D structures including trenches, fins, and nanopillars. Directional dependence of fin orientation, substrate crystallography, and their impact on etch rate, surface morphology, and dopant segregation will also be investigated. Third, vertical trench Schottky barrier diodes will be fabricated and evaluated, integrating the optimized grown and etched materials to demonstrate high device-level performance, including high breakdown voltage, low on-resistance and minimal leakage, and to validate the effectiveness of the underlying material and process innovations. Comprehensive structural, electrical, and surface characterizations will be performed to systematically correlate growth and etching process conditions with material properties and device performance. The project's intellectual significance extends to advancing fundamental understanding of carrier generation and mass transport, doping behavior, and etch chemistry in beta-Ga₂O₃, while also demonstrating a scalable path to ultra-wide bandgap power diodes that meet the stringent requirements of future high-voltage, high-efficiency power electronics 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 · 2025-10

Collaborative Research: Radiation-Tolerant and Thermally Managed High-Voltage Gallium Oxide Power Diodes for High-Power Space Electronics As modern technologies in space, defense, and energy systems continue to evolve, there is a growing need for high-voltage, compact, and energy-efficient power electronics that can operate reliably in extreme environments, particularly those involving high radiation exposure and elevated temperatures. These demands are especially critical for spacecraft and satellite platforms, where power devices must endure harsh conditions while meeting strict requirements on size, weight, and power efficiency. Conventional semiconductors such as silicon (Si), silicon carbide (SiC), and gallium nitride (GaN) have enabled significant progress in power electronics, yet their performance can be compromised when exposed to intense radiation or high temperatures, often requiring additional shielding to ensure reliability. Beta-gallium oxide (β-Ga₂O₃), an emerging ultra-wide bandgap semiconductor, offers unique advantages for such applications, including the ability to sustain high voltages, resist radiation damage, and support compact device designs. However, its broader adoption remains limited by challenges in producing high-quality materials, managing heat effectively, and developing power devices that can maintain stable performance during prolonged exposure to high-radiation and high-temperature conditions. This project aims to address these limitations by advancing the synthesis of high quality β-Ga₂O₃ materials, integrating diamond layers to improve thermal performance, and developing high-voltage vertical power diode structures optimized for reliability in harsh environments. The results will support the development of next-generation power systems for space missions, defense platforms, and nuclear energy applications. In addition to its technical contributions, the project will advance national priorities in microelectronics and aerospace, creating hands-on research and training opportunities for students, developing new educational content in radiation-hardened electronics, engaging with K-12 and community college learners, and sharing research outcomes through open-access publications and partnerships with industry and national laboratories. The project will deliver a new class of vertical β-Ga₂O₃ power diodes that combine thermal management and radiation resilience through integrated innovations in materials synthesis, device design, and performance validation under extreme conditions. First, thick, low-defect β-Ga₂O₃ layers will be grown using low-pressure chemical vapor deposition, incorporating n-type dopants to investigate their effects on carrier transport, compensation, and susceptibility to radiation-induced defects. In-situ plasma-free etching technique will be used to define high-aspect-ratio fin or trench geometries without damages. Second, polycrystalline diamond layers will be deposited using microwave plasma chemical vapor deposition to enhance heat dissipation, using engineered interlayers to reduce thermal boundary resistance. Third, vertical diode structures will incorporate p-n heterojunctions and high-permittivity dielectric field-management layers to improve electric field distribution, support high breakdown voltage, and enhance radiation resilience. These devices will be subjected to radiation exposure and high-temperature electrical testing to evaluate their degradation mechanisms and overall reliability under extreme operating conditions. Modeling and device simulations will be used to guide design improvements and evaluate long-term behavior under coupled stress conditions. This work will advance fundamental understanding of dopant-defect interactions, electro-thermal transport, and radiation effects in ultra-wide bandgap β-Ga₂O₃, enabling scalable high-performance power electronics for mission-critical applications in harsh environments. 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 Faculty Early Career Development (CAREER) award will support research that looks to derive a fundamental understanding of molecular interactions and toughening mechanisms in inorganic minerals for tailoring tough and durable structural materials. A key innovation is anticipate to lie in turning ubiquitous calcium carbonate into a monolithic binder as a potential alternative to cement and concrete, rather than a raw material thermally decomposed in traditional cement manufacturing. Towards this end, novel synthesis and strengthening pathways will be explored to address the fundamental challenges in constructing continuously structured inorganic monoliths and tackling the poor fracture toughness and low tensile strength of crystalline minerals. By redefining the synthesis process and improving the properties of materials and structures in a truly sustainable and cost-effective way, the anticipated project outcomes could ultimately shed light on multiple research areas and industrial sectors, including civil engineering, materials science, mechanical engineering, advanced manufacturing, and the utilization of abundant and renewable resources with long-term economic and environmental benefits. The research efforts will be integrated with educational activities, including the BRIGHT (Build Resilient, Innovative, and Green Homes on Terra), Science Playground, and SEED (Sustainability Exploration, Engagement, and Discovery) programs, to offer interactive, hands-on learning experiences for future engineers and scientists. The principal hypothesis of this research is that inorganic carbonate minerals can be turned into polymerizable phases capable of forming monolithic binders if stable clusters with controlled sizes can be tailored and regulated to trigger non-classical nucleation and crystallization. This hypothesis will be tested by computationally and experimentally investigating polymer-like precursors, refining reaction pathways, and regulating molecular-scale interactions. Inspired by the mineralization pathways in living organisms, the unique non-classical strategy looks to provide a thermodynamically favored pathway for tailoring mineral-based binders under ambient conditions by bypassing the critical free energy that must be overcome in conventional approaches. To enhance toughness and tensile strength, multiple bio-inspired toughening mechanisms, including copolymerization with organic monomers to tailor hybrid molecules, incorporation of coherent nanodomains to trigger pre-strained crystal lattices, and construction of packed and aligned nano-reinforcement, will be tailored and integrated across multiple length and time scales. The fundamental knowledge and insights gained from this project look to advance the reimagining of structural materials design by unlocking a pathway that is prevalent in nature yet rarely replicated through artificial synthesis, offering transformative material solutions for future civil infrastructure. 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

The National Science Foundation (NSF) named Dr. Lara A. Thompson, Associate Professor of Mechanical Engineering at the University of the District of Columbia, one of three 2022 Alan T. Waterman Award recipients. This award is NSF's highest honor that annually recognizes an outstanding researcher who is 40 years or younger or not more than 10 years beyond receipt of the Ph.D. degree, by December 31 of the year of the nomination. The award funds the recipient's research in any field of science or engineering. This year, each awardee will receive a $1 million grant over a five-year period for further advanced study in his or her field. Dr. Thompson is recognized for her innovative research that combines multidisciplinary approaches in engineering, sensor technology, and sensory-motor physiology to investigate and improve clinical diagnostics on human posture, gait, and balance for those impaired by age, trauma, and disease. The revolutionary approaches developed by Thompson are advancing the field of rehabilitation engineering and biomechanics and improving the quality-of-life for mobility impaired populations. Dr. Thompson has trailblazed new research initiatives and educational programs and spearheaded new infrastructure tied to biomedical engineering. In many of her 59 publications, undergraduate research assistants are co-authors. She received a Diverse Issues in Higher Education Emerging Scholar National Award in 2017 and a Black Engineer of the Year STEM innovator Award in 2019. She was a featured alumna in the Fall 2019 edition of Harvard Otolaryngology magazine: "Finding the right balance between research and medicine." She was a featured scientist in three U.S. National Science Foundation events: 1) a 2021 distinguished panel discussion titled "Black Scientists & Engineers at Our Nation's HBCUs: Making American History Now;" 2) the 2020 Presentation to Congress: Broadening Participation in STEM sponsored by the United States House Committee on Science, Space and Technology; and 3) the 2019 "Science Nation" video "Research immerses HBCU undergrads in biomedical engineering." Dr. Thompson earned her Bachelor of Science degree in mechanical engineering from the University of Massachusetts Lowell, followed by a Master of Science degree in aeronautical and astronautical engineering at Stanford University and a doctoral degree in biomedical engineering from the Harvard-MIT Program in Health Sciences and Technology. Her doctoral research was the first to demonstrate that a vestibular-based sensory prosthetic can improve the ability to maintain balance. 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

Efficient control of light and interaction of light with materials is essential for advances in imaging, sensing, communications, and quantum technologies. However, a major challenge is the scale mismatch between nanoscale quantum emitters and micrometer-scale spatial confinement of light, which limits the interaction efficiency. This scale mismatch is one of the fundamental roadblocks in the development of future communications and quantum devices. In this program, an international team comprising Duke University, the University of Massachusetts Lowell, and King’s College London aims to utilize nano-structured composite media to design a fundamentally new generation of devices capable of manipulating complex light beams simultaneously at small spatial (nanometer) and fast temporal (sub-nanosecond) scales. The new class of developed devices will be applied to manipulate important quantum transitions in molecules, which are difficult to access otherwise (dipole-forbidden transitions). The program aims to advance computational modeling, machine learning, advanced nanofabrication and engineering, and novel characterization methods, while preparing a new workforce that is ready to address complex interdisciplinary challenges in photonics and quantum engineering. Technical Description: The interdisciplinary team of researchers aims to design and realize a new transformative class of metamaterial-based devices capable of creating and manipulating optical angular momentum (OAM)-carrying beams at the subwavelength spatial and ultrafast time scales. These devices leverage the extreme anisotropy of hyperbolic metamaterials combined with the design flexibility of metasurfaces to enable unprecedented control of light-matter interactions at the molecular scale. The program integrates expertise across engineering, computational science, machine learning, nanofabrication, advanced characterization, materials science, and photonics. Specific aims include the development of theoretical tools to describe light interactions with complex hyperbolic metamaterial/metasurface systems, the development of reliable fabrication protocols, and the advancement of spatial and temporal characterization techniques for highly confined OAM beams. These meta-devices will be applied to explore and control forbidden optical transitions, addressing the fundamental scale mismatch between macroscopic optical fields and nanoscale quantum emitters. In addition to basic science and novel device platforms with applications in sensing, imaging, and quantum technologies, the program incorporates educational, mentoring, and outreach activities to foster interdisciplinary training and enhance the technology workforce pipeline. This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI), where NSF funds the U.S. investigator and EPSRC funds the partners in the U.K 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 Level 1 IUSE Engaged Student Learning project from the University of Massachusetts Lowell serves the national interest by developing AI-driven educational technology tools to support student learning in undergraduate computer science courses. Specifically, this project will explore how large-language models (LLMs) can be customized to enable tailored, interactive, and reflective learning experiences. The project-developed LLM will draw from student notes and course materials in tandem with existing data sets to personalize their learning experiences. The project tool will be iteratively improved through a collaborative process driven by students and will be used to simulate virtual students that will prompt users and encourage active learning. This project will explore how LLM tools can be better developed to support individualized learning and reflection. The project goals are: (1) to examine how LLMs can provide personalized, content-specific support for STEM students; (2) to examine how LLMs can facilitate collaborative learning environments; and (3) to investigate how LLMs can support the design of active learning experiences. The project will use retrieval-augmented generation techniques to align LLM outputs with course materials and student work to build engagement and reduce the incidence of generic responses. The project will generate knowledge through a rigorous evaluation plan that will utilize a mixed-methods approach to explore the LLM development and refinement process and capture impacts on student users. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY The human body hosts many distinct, interconnecting microbial populations that exert long-lasting, systemic effects. Due to strong correlations of an imbalanced microbiome and disease prevalence (e.g., periodontitis, arthritis, and Alzheimer’s), studies of host-microbial interactions have increased in importance. In particular, the oral microbiota is well known to have evolved mutualistically with the host, is affected by the distinct architecture of the mouth, and is the primary modulator of oral diseases (e.g., gingivitis and periodontitis). This correlation strongly supports the need to understand the interactions between the oral tissue niche, the oral microbiome, and the host tissue immune response; thus, offering long-term opportunities to identify predictive disease biomarkers and to develop interventional strategies that promote oral and overall health. We have recently reported the development and validation of a 3D oral tissue model based on human primary cells that mimics the native tissue organization and native oxygen gradient within the gingival pocket. These features enabled the long- term culture and characterization of human microbiome with a physiologically relevant level of microbial diversity under healthy and inflammatory conditions (i.e., gingivitis). Gingivitis, the highly prevalent immunoinflammatory precursor to periodontitis, initially presents neutrophils, continuously recruited into the gingival tissue to clear pathogenic microbes. The underlying immune-driven events play a major role in the homeostatic balance between host and microbial communities, providing stability in healthy conditions, while contributing to immune-inflammatory progression in periodontal disease states. Our preliminary data further suggests important immunomodulatory roles for mucin glycans, lipoxins, and resolvins in homeostatic maintenance and the prevention/progression of dysbiosis. However, these observations, to date, have been generated using in vivo animal models, lacking mechanistic nuance that would allow subsequent hypotheses regarding diagnostic/therapeutic roles. Leveraging our team’s expertise in human in vitro modeling, microbiology, mucin biophysics, biogeography, and clinical translation, we aim to leverage a 3D human oral tissue model to provide a mechanistic framework of oral host-microbial interactions and modulation in early dysbiosis to support future therapeutic interventions. Built on our published preliminary data, we propose to: (1) define clinically-relevant profiles of early dysbiosis by monitoring host phenotypic changes as well as cytokine profiles in an immunocompetent OTM compared to clinical gingival tissues; (2) correlate the modulatory role of mucin glycans on the oral microbiome to the host tissue response; (3) deciphering the role of Lipoxin A4 and resolvin E1 on the resolution of inflammatory host response in vitro.

NIH Research Projects · FY 2026 · 2025-08

Project Summary The human body houses many distinct, but interconnected microbial populations which can exert long-lasting systemic effects. Understanding how microbial communities interact with the host and how these interactions influence host physiology is critical to defining their roles in health and disease states. In particular, the oral microbiota serves as a reservoir for opportunistic pathogens, potentially contributing to a wide range of oral and systemic diseases. My lab has been focusing on developing benchtop experimental tools to provide a mechanistic framework of oral host-microbial interactions and modulation in early dysbiosis to support therapeutic interventions. The bidirectionality of these interactions has been hypothesized to be attributed to microbial metabolites, diet, immune, and endocrine factors. Specifically, sex as a biological variable has been historically omitted from preclinical and clinical studies, resulting in a knowledge gap regarding how biological systems respond to hormones and their fluctuations and, potentially, how therapeutic regimes can be tailored to account for hormonal alterations. In particular, clinical studies have suggested that several microbiome-related diseases display a sex bias, some of which have been shown to be associated with sex hormones. Specifically, clinical evidence in common oral signs has been observed during menstruation, pregnancy, and hormonal contraception use, however, mechanistic links between hormonal fluctuation, oral dysbiosis, and host tissue health still need to be elucidated. Therefore, underscoring how sex hormone signaling affects disease progression in the context of host-microbiome interactions is critical to support fundamental studies of disease pathogenesis and to open the door to therapeutic strategies targeting both the microbiome, sex hormones and the bidirectionality of those interactions. The mission of my research program is to address these fundamental gaps in bidirectionality of host-microbiome cross-talks and underpinning the role of sex-bias in these interactions and how it affects the onset and progression of inflammatory diseases, in a tractable in vitro model of the oral mucosa. Over the next five years, I will expand the scientific knowledge over the following three themes of research: (1) profiling human host tissue baseline as a function of sex difference and investigating tissue responses to sex hormones changes, (2) underscoring the causal relationships between sex difference and fluctuations of sex hormones and microbiome diversity and spatial organization, (3) dissecting the bidirectionality of human host-microbiome interactions accounting for sex differences, hormonal changes under healthy and inflamed clinically relevant conditions. The outcomes from this research program will provide insights into the role of sex differences and hormonal fluctuations on human host-microbial interactions, opening the doors to therapeutic approaches targeting both the microbiome, endocrine factors, and their mutual interactions.

NSF Awards · FY 2025 · 2025-06

Heating and cooling buildings accounts for over 40% of U.S. energy use. The high energy demand in urban Massachusetts and 2050 net-zero target underscore the urgent need for scalable, sustainable energy alternatives. One promising yet understudied solution is urban networked geothermal systems. These systems use ground-source heat pumps and underground loops to share thermal energy between buildings. This approach can improve efficiency and reduce reliance on fossil fuels, thereby cutting energy costs and offering reliable energy to communities. However, the absence of standardized tools to evaluate their long-term environmental, economic, and social impacts limit its implementation. This research project will develop a novel method to assess the sustainability of these systems in urban settings. The findings will inform local and regional energy planning, promote energy access, and serve as a blueprint for cities nationwide to transition to renewable energy. The objective of this research is to develop a regionally adaptable framework for evaluating the environmental, economic, and social sustainability of urban networked geothermal systems. The project will implement a dynamic model with spatial and temporal resolution to incorporate real-time performance data from pilot projects in Massachusetts, regional climate and grid characteristics, and community-level social data. The model will assess impacts across the full system life cycle, from installation to decommissioning, including greenhouse gas emissions, life cycle costs, and social factors such as job creation, energy distribution, and public acceptance. The method will be tested and refined across thirteen planned geothermal sites in Massachusetts, enabling comparisons with conventional heating and cooling systems and application across residential, commercial, and mixed-use building types. This project contributes to advancing sustainability modeling of complex energy infrastructure and offers data-driven insights for system optimization. The results of this project will guide utilities, municipalities, and community organizations in designing and scaling energy systems to expand access to sustainable heating and cooling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development Program (CAREER) grant supports research aimed at transforming the monitoring of critical infrastructure, such as wind turbines and bridges, by developing new methods to measure three-dimensional structural vibrations using aerial stereovision techniques. This approach seeks to overcome the limitations of traditional structural health monitoring methods, which typically require sensors to be directly placed on the targeted structure and rely on stationary camera setups. By utilizing drone technology paired with advanced computer vision and image processing techniques, this research intends to enable detailed and remote assessments of the dynamic response of large-scale structures. The resulting metrological framework intends to allow for precise quantification of displacement, deformation, and vibrations, enabling early identification of potential damage and extending the operational life of critical infrastructure. The grant also supports educational goals by offering hands-on training, mentorship, and workshops for undergraduate students and individuals seeking to transition into engineering-related careers. These initiatives are designed to create opportunities for all interested Americans by developing practical skills in advanced sensing technologies and structural health monitoring in support of workforce readiness and career advancement. This research aims to advance and validate a framework for drone-based stereovision measurements in structural dynamics, addressing fundamental challenges in stereo camera calibration, feature extraction, and camera motion compensation. Key contributions include (1) developing a correlation function to track inherent structural features—such as bolts or rust—between the left and right views of the stereo cameras with sub-pixel accuracy; (2) reformulating calibration procedures to accommodate time-varying camera positions; and (3) expanding motion magnification techniques to capture subtle displacements in three dimensions. These innovations intend to enable precise structural health monitoring in real-world scenarios where both the structure and the stereo cameras are in motion, enhancing the ability to capture the three-dimensional dynamic behavior of large systems and leading to improvements in engineering practices for condition monitoring and maintenance. Beyond structural health monitoring, this research has potential applications in environmental sciences for tracking geological phenomena (e.g., volcanism, landslides, and glacier ablation) and in medical diagnostics (e.g., detecting subtle tremors in neurological 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.

NIH Research Projects · FY 2025 · 2025-05

Project Summary Per- and polyfluoroalkyl substances (PFAS) are now a part of the everyday lives of most Americans. PFAS are strongly associated with adverse human health effects including, increased serum low density lipoprotein cholesterol, reduced antibody responses to vaccination, altered liver serum biomarkers, pregnancy-induced hypertension, and decreased birth weight. An important toxicokinetic feature of PFAS is their long half-lives of elimination in humans, which contributes to bioaccumulation and increased risk of adverse health outcomes. It is hypothesized that differences in biliary/fecal or urinary elimination between humans and other species are driving the long human PFAS half-lives. What we do not know is the extent to which reuptake of PFAS in the gut prevents PFAS from being eliminated in feces. PFAS are amphipathic, and the majority of molecules are ionic at physiological pH. As such, PFAS are absorbed/reabsorbed by enterocytes and secreted by and reabsorbed by renal epithelial cells via the action of transporter proteins. The balance of uptake and efflux transport of PFAS in the gut and kidney will dictate the overall rate of elimination from the body in feces and urine. PFAS have been measured in urine and bile of people, but not feces. In animal models, PFAS are typically measured in urine and feces, but not bile. A critical gap in our ability to assess the extent of enterohepatic recirculation of PFAS is the lack of data on matched PFAS concentrations in urine, bile and feces. Here, we propose to test the overarching hypotheses that, in addition to slow urinary elimination, enterohepatic recirculation of PFAS is a driving factor in maintaining PFAS body burdens. We will take advantage of samples that have been generated in a mouse study designed to test the hypothesis that consumption of gel-forming natural dietary fibers will reduce PFAS absorption and reabsorption in the gut and increase PFAS elimination (TX220007, PI: Schlezinger, Co-I: Bello). To establish proof of principle that enterohepatic recirculation is critical to maintaining high body burdens of PFAS, we propose the following Specific Aims: Aim 1. To define the urinary, biliary and fecal excretion of PFAS in tandem in the context of different dietary conditions and Aim 2. To define the effect of PFAS on transporter expression that could modify PFAS toxicokinetics. We will achieve these aims by analyzing samples collected from humanized PPARα mice that were exposed in drinking water to seven PFAS commonly measured in people and fed diets based on the What We Eat In America analysis in NHANES for 6 weeks. The diets were supplemented with dietary fibers with different characteristics or cholestyramine. We will test the hypotheses that a) excretion of PFAS in bile is equal to or greater than excretion in urine but that reuptake in the gut is a major limiting factor in PFAS leaving the body in feces and b) the balance of excretion of PFAS in urine and bile is influenced by changes in transporter gene expression induced by PFAS and dietary fiber. The coordinated analyses of our TERP grant and this R03 proposal will generate the toxicokinetic, physiological and molecular evidence needed to define the role of enterohepatic recirculation in PFAS toxicity.

NIH Research Projects · FY 2026 · 2025-02

Project Summary/Abstract Alzheimer’s disease and related dementias (AD/ADRD) is a growing public health burden, further accentuated by the rapid aging of the US population and increased life expectancy. The gut microbiome, sometimes called the ‘second brain’ engages in bi-directional communication with the Central Nervous System (CNS) and is thought to contribute to neurodegeneration and AD/ADRD risk. The microbiome is dynamic, responsive to external stimuli and modifiable, creating the possibility for AD/ADRD prevention and treatment. Prior human studies on GMB and ADRD have had limitations such as cross-sectional design, small sample size, and lack of detailed GMB characterization. Notably few GMB and no GMB – AD/ADRD studies have focused on Latinos, a high-risk population, with distinct GMB profiles and over double the risk of AD/ADRD compared to non-Latino Whites. The proposed project will elucidate the microbial origins of AD/ADRD in SOL and examine whether the microbiome is a potential mechanism that links diabetes and AD/ADRD risk. We will use the SOL cohort’s AD/ADRD sub-study, which over ~15 years followed 6,377 individuals with longitudinal cognitive function assessments, structural brain MRI, plasma amyloid, Tau, and neurodegeneration (ATN) biomarkers (amyloid beta [AB40-42], p-tau181, neurofilament light chain [NfL], glial fibrillary acidic protein [GFAP]), and plasma metabolomics. GMB metagenomic profiles are available among 2204 participants and longitudinal stool metagenomic and serum metabolomic profiles will be generated by this study. The study will benefit from careful assessment of key microbiome covariates, notably, medication, diet, anthropometric variables, physical activity, social and psychosocial factors. The proposed work will pave the path for GMB-based early detection, prevention and/or treatment of AD/ADRD among high-risk Latinos.

NSF Awards · FY 2025 · 2025-01

Cancers are among the leading causes of death around the world, with an estimated annual mortality of close to 10 million. Despite significant efforts to develop effective cancer diagnosis and therapeutics, the ability to predict patient responses to anti-cancer therapeutic agents remains elusive. This is a critical milestone as getting the right choice of therapy early can mean superior anti-tumor outcomes and increased survival, while the wrong choice means tumor relapse, development of resistance, side effects without the desired benefit, and increased cost of treatment. An cyber-physical system that allows an accurate prediction of patient tumor responses to anti-cancer therapies; that is, enable real-time precision medicine, can have a transformative effect not only on health outcomes, but also on the costs of treatment. The goal of this project is therefore to develop an engineered cyber-physical system that combines advanced biological models with state-of-the-art artificial intelligence methods for predictive, automated screening of anti-cancer drugs and optimizations of their dosing. This will move science towards realizing the long-desired precision medicine paradigm leading to significant social impacts. The project has additional social impacts, including minimizing the exponentially growing ethical issues surrounding the use of animals in the past years through increased adoption of the engineered human cancer and heart tissue model systems. The project will provide opportunities to promote STEM education for K-12 students, train students, especially those from under-represented groups, and disseminate science and engineering knowledge to the public. The investigators will leverage their expertise in biofabrication, tissue engineering, microfluidics, bioanalysis, and artificial intelligence to develop a generalized, self-dose-optimizing "multi-sensor-integrated multi-organ-on-a-chip" platform, which can be used to accurately predict both efficacy and safety of anti-cancer regimens in this project. The first innovation is the adoption of three-dimensional bioprinting for generating the vascularized ductal carcinoma model and vascularized cardiac tissue model, leading to the construction of a truly biomimetic human myocardium for evaluating drug toxicity. The adaptation of both of the bioprinted models to microfluidic systems is also a major innovation. Additionally, the real-time yet non-invasive monitoring of key biophysicochemical parameters will generate large-scale multi-dimensional data to enable accurate data-driven predictive modeling. Moreover, the platform will enable self-dose-optimization on the chips through a novel joint Bayes modeling implemented by two deep learning models capable of addressing multiple-instance learning, and dependency in sequences of multi-dimensional data, respectively. The project will use a range of commercially available cells to construct models and pursue the initial platform development and optimizations. Extensions are anticipated for human specimens in future iterations and other cancer treatment, drug combination, and dose optimization in anti-cancer regimens as a rapid and safe testing-bed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses in a wide range of applications, such as cybersecurity, industry, finance, economics, and scientific discovery. As a critical step in machine learning systems, representation learning is meant to be robust in its capacity, regardless of the mutation of raw data due to noises or the variations of raw data caused by capture devices. In the era of big data, representation learning techniques are confronted with new challenges. Massive data collected from different sensors (e.g., the multi-view camera system) or presented in different modalities (e.g., audio-visual-text) have overloaded existing representation learning techniques. In addition, streaming data received from the Internet and sensitive data accumulated over time, such as personal albums and electronic health records, require the established representation learning model to adapt and account for incoming data. This project will develop a robust continual representation learning model to address these challenges. In real-world scenarios where data access is restricted (e.g., sensitive data) or the processing power of devices is limited (e.g., edge and mobile devices), stakeholders will benefit from the adaptive representation learning techniques to enable continual data analyses. This project seeks to advance the fundamental understanding of continual multi-view robust representation learning by integrating machine intelligence and human knowledge in AI-enabled security contexts. There are three unique contributions. First, the project will investigate multi-view consistency pursuit to fuse knowledge and generate a view-invariant representation robust to domain shifts frequently encountered in real-world data. Second, this research will revisit and explore adversarial learning in multi-view contexts to enable new attack modes, including iterative, cross-view, and induced modes. Generated adversarial samples and training procedures will benefit and empower the acquired multi-view representation learning models to mitigate various forms of artificial noise. Third, new continual learning models will be created through a novel Memory Bounded Search Tree to enable the evolution of multi-view representation learning despite continual streams of data. Furthermore, to reduce the search space and uncertainty related to the data, this research will leverage human knowledge to acquire critical annotations and empirical strategies for the proposed continual learning models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Graphs, which can be thought of as a collection of nodes, called vertices, and connections between certain of these nodes, called edges, play an important role in the theory of networks, communication, scheduling, and optimization. Edge coloring and edge packing can be considered partitions of the edges of a graph under certain restrictions. For example, a proper edge coloring partitions the edges into sets such that no two edges in the same set share a common endpoint, while an edge cover packing partitions the edges into sets such that the edges in each set cover all the vertices. Optimal or near-optimal solutions to these problems are important for the above mentioned applications. The PI plans to explore both the theory of edge coloring and packing, as well as to develop corresponding efficient algorithms. Graduate students will be involved in this project. Several open problems in this field are related to the graph parameter called density. Density measures the 'densest' part of a graph, which is often the main concern when solving edge coloring and packing problems. With density-related techniques such as the generalized Tashkinov tree, developed from solving the Goldberg-Seymour conjecture, and the generalized Kempe change method, developed from solving the Core conjecture of Hilton and Zhao, the PI proposes to work on (1) the Berge-Fulkerson conjecture, (2) Gupta’s co-density conjecture, (3) Goldberg’s generalization of the total coloring conjecture for multigraphs, (4) the Overfull conjecture, and (5) finding efficient algorithms to color graphs with the optimal number of colors as stated in the aforementioned conjectures. 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 · 2025-01

PROJECT SUMMARY Data on osteoporosis (OP) among adults living in the US territory of Puerto Rico (PR) is limited. However, evidence suggests that adults in PR may be at high risk of OP (12% for lumbar spine, 8.7% for femoral neck), although the few studies that exist are small, outdated, or rely on self-report. This is supported by data in Puerto Rican (PR) adults from the US mainland showing increased risk for OP compared to non-Hispanic White adults. OP is a serious musculoskeletal disease increasing risk of fragility fracture, morbidity, and mortality. Diet is a known modifiable preventative factor for OP, but evidence for recommendations remains unclear. Psychological stress has been proposed as a novel risk factor for bone health through dysregulation of the hypothalamic-pituitary adrenal and sympathomedullary axes, but most evidence is from animal studies. This relationship may be further modified by diet, which is related to stress and OP. Trabecular bone score (TBS), a low-cost indicator of bone quality, has not been examined in adults in PR. Adults living in PR may be susceptible to poor bone health, as this population has low dietary quality and high psychosocial stressors due to social and environmental inequities compounded by climate-related disasters. A Dietary Approaches to Stop Hypertension (DASH) diet has been shown to be most protective against OP in PR adults from the US mainland. Adults in PR consume both healthy (legumes, dairy, locally sourced fruit/vegetables, seafood) and unhealthy foods (sugary beverages, refined grains, French fries, SPAM), which may differ from PR diets on the US mainland. Developing culturally relevant, effective prevention strategies for OP has been hindered by a major gap in PR: the lack of rigorously collected data on OP and risk factors. Thus, our central hypothesis is that OP is prevalent and that psychophysiological stress and dietary factors are related to bone health among adults in PR. The proposed ancillary study will build on the Puerto Rico Osteoporosis Study of Psychosocial, Environmental and Chronic Disease Trends (PROSPECT), an ongoing investigation of risk factors for CVD in adults 30-75 y in PR. Adults (n=1000) aged > 45 y, who complete their 4-y PROSPECT visit, will be invited to complete additional measures including bone mineral density (BMD), TBS, and questionnaires. Our project aims to: 1) quantify BMD and TBS in PROSPECT, and to compare prevalence of OP to national data and data for US mainland PR adults; 2) assess dietary intake and biomarkers in relation to bone outcomes; and 3) quantify measures of psychophysiological stress and assess their relationships to bone outcomes in PROSPECT. This ancillary study is time sensitive, as PROSPECT will start its 4-y follow up in May 2024, providing a unique opportunity to add OP as an outcome, capitalizing on existing data from the parent study. The proposed study will provide timely data to inform clinical and public health efforts to prevent bone loss and OP for the population of PR. This study will answer new lines of inquiry on the interconnections between diet, stress, and bone outcomes that will serve as a foundation for populations with high prevalence of OP.

NIH Research Projects · FY 2025 · 2024-09

Project Abstract: Increasing racial and ethnic diversity, equity, and inclusivity within the addiction research community is a priority. Minority serving institutions (MSI) can serve as an important resource in creating such an effective and diverse workforce. The University of Massachusetts Lowell (UML), a public MSI located in Massachusetts’ Merrimack Valley, is uniquely positioned to address this challenge through its proposal Enhancing Opportunities for Addiction Research in the Merrimack Valley. For the past two years, UML has taken advantage of federal funding from the Office of the National Coordinator for Health Information Technology (ONC) to develop a regional program for creating “a continuous pipeline of diverse public health informatics and technology professionals.” We now propose to build on this Public Health Informatics and Technology (PHIT) program by creating the PHIT-AR (addiction research) track, with the goal of recruiting motivated undergraduate students and providing them with the data science skills necessary to successfully pursue doctoral training in the field of drug addiction research. To achieve this goal, we have the following four aims: 1) Expand our undergraduate PHIT program with a new focus on applying data sciences to drug addiction research; 2) Recruit a highly diverse and talented student body into this new program; 3) Ensure student success through a supported mentored research experience with diverse partners; and 4) Disseminate knowledge on best practices in creating a diverse and effective drug addiction research workforce with an emphasis on the data sciences. These aims will be achieved through the creation of new undergraduate training opportunities in addiction research and by providing mentored research opportunities with partners including University of Massachusetts Chan Medical School, the Commonwealth of Massachusetts, the Department of Veterans Affairs, and Boston University School of Medicine. Students will be recruited both from UML and from other MSIs in the Merrimack Valley, and they will be supported in their research by an experienced and diverse team with expertise in research, addiction sciences, data sciences, and undergraduate education. Through this program, we expect that students will gain the research skills necessary to further their academic training in a research-intensive doctoral program and eventually gain the full array of tools needed to improve health in communities similar to those from which they come from.

NSF Awards · FY 2024 · 2024-09

Edge computing is an emerging paradigm that extends cloud computing by allocating a range of (edge) servers at or near the user to provide necessary networking, storage, and computing services. Cyber-physical systems (CPS) connect physical devices to the Internet and to each other. This research explores the marriage between edge computing and CPS, namely edge-based CPS. It has attracted a tremendous amount of attention from both academia and industry because it is expected to fundamentally change the way people interact with engineered systems. One essential element –the study of fault-tolerant distributed primitives– is still missing in the literature. This research is focused on distributed consensus primitives that allow a collection of nodes to work as a coherent entity in the presence of various failures and cyberattacks. Concretely, there are three interrelated tasks: (i) identifying fundamental properties, limitations, and trade-offs in the context of edge-based CPS; (ii) designing algorithms that automatically adapt under changing conditions and varying cyberattacks to maintain good performance; and (iii) designing and implementing a realistic simulator to study performance under practical scenarios. The expected outcomes will enable efficient and fault-tolerant services for edge-based CPS, allowing more innovation in CPS applications. This research is expected to advance the state of knowledge by identifying a set of principles, fault-tolerance tools, and analyses for designing distributed fault-tolerant primitives. The work will emphasize models and primitives that allow for practical implementation, with plans for realistic demonstrations of their fault-tolerance and performance advantages. Edge computing and CPS are expected to be key components of next-generation infrastructure, so making edge-based CPS fault-tolerant can yield large-scale benefits. Conversely, missed opportunities for improvements caused by a lack of foundational understanding could have significant performance and fault-tolerance consequences. This research helps address these issues by identifying trade-offs and limitations. The proposed research also plans to develop new pedagogical material on distributed systems that the PI plans to teach, both through courses at Boston College and in online tutorials made available to the public. In addition, the research and outreach work will benefit society by providing principles to help industry and academia design fault-tolerant primitives for edge-based CPS; increasing partnerships between academia and industry; involving women, persons with disabilities, and underrepresented groups in STEM; and increasing public scientific literacy and public engagement with science and technology. 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.