Northeastern University

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY Inflammation is a fundamental biological process that coordinates immune responses to infection, injury, and disease. Understanding the dynamics of inflammation is essential for deciphering how immune responses shift from protective to pathological states. Our research aims to provide mechanistic insights into these processes to uncover therapeutic opportunities for modulating the immune system. Macrophages, due to their phenotypic plasticity and ability to regulate inflammation, play a pivotal role in this context. Additionally, macrophage trafficking—encompassing their movement and localization within tissues—is critical to understanding inflammation dynamics. A core focus of our laboratory is to develop non-invasive imaging and control techniques that allow for real-time visualization and modulation of macrophages. With support from this MIRA, we will develop and validate a new class of engineered macrophages capable of both tracking and modulating inflammation in vivo. By equipping macrophages with markers detectable across multiple imaging modalities— including ultrasound, MRI, and optical imaging—we aim to achieve a comprehensive, multimodal view of macrophage behavior at different spatial and temporal scales. This innovative approach will significantly enhance our ability to probe inflammation dynamics and pave the way for targeted interventions that precisely modulate macrophage activity. Furthermore, we will employ focused ultrasound technology to control the functional responses of engineered macrophages in remote control, establishing a novel framework for understanding and managing inflammation in living systems. This integrated strategy not only aims to enhance our understanding of macrophage dynamics within various disease settings but also serves as a foundation for innovative macrophage-based therapeutics, specifically aimed at reprogramming macrophage functions for restoring immune balance in diseased environments. Our ultimate vision is to create a theranostic platform using living cells as both therapeutic agents and imaging probes, enabling compatibility with bioacoustic, biophotonic, and biomagnetic tracking methods and setting a new standard for the precision treatment of inflammation-related diseases.

NSF Awards · FY 2025 · 2025-08

Many safety-critical applications depend on the robustness of machine learning (ML) algorithms, i.e., their ability to make good predictions when exposed to previously unseen inputs. These safety-critical applications, such as autonomous vehicles, medical applications, wireless networks, and smart cities, often involve "edge devices" such as phones, sensors, and Internet-of-Things devices (e.g., wearable and smart home technology). These edge devices have computational, storage, and power limitations that raise new challenges for machine learning robustness against attack. These limitations make it hard to use common methods designed to make ML algorithms more robust against attackers, such as, previously unseen input designed to confuse the algorithms. Previous techniques designed to address these limitations, such as simplifying ML models to make them smaller, may also harm robustness. This project will develop new algorithmic techniques that achieve favorable tradeoffs between the robustness of machine learning algorithms and compression, thereby enabling the deployment of robust machine learning algorithms at the edge. The project team will design new robustness-enhancing training regularization penalties and adversarial training techniques, and will apply them to last-generation, large-scale transformer architectures fine-tuned to new tasks. It will also combine them with model compression techniques such as pruning, quantization, distillation, and neural architecture search, to enable robustness at the edge, and with continual learning approaches, to enable dynamic adaptation. These approaches will be tested and evaluated on large foundation models and applications of machine learning over wireless signals. Graduate and undergraduate students will be trained and acquire skills in diverse areas including machine learning, networking, and systems, while the research activities will be integrated with both educational and outreach programs in the researcher team's host institutions. 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 There is a technology gap in currently developed tools that simultaneously monitor, compute, and respond to both coding and non-coding RNA in real-time within living cells or patients. The continued existence of this gap represents an urgent unmet need because, until it is filled, the accuracy of RNA-based therapeutics remains limited in complex and evolving biological systems like differentiation or cancer. The long-term goal of this proposal is to develop safe, universal, and programmable synthetic biology tools that using both coding and non- coding RNAs as disease marker inputs and program outputs to trigger therapeutic responses in patients. The objective of this particular application is to develop an RNA-based sensor (using mRNA as the delivery modality) that detects integrated changes in both mRNA and miRNA for in situ therapeutic responses within living cells and mouse models, given the crucial role of ncRNAs, especially microRNAs (miRNAs), as key regulators of post- transcriptional gene regulation, which allow only the correct set of genes to be active in each cell type. The central hypothesis is that an RNA-based sensor integrating both mRNA and miRNA inputs, using Boolean logic gate computation, can improve the specificity of cell type identification in complex biological systems. This proposed work builds on our and other’s recent works on sensing individual RNA species like mRNA in live cells. The rationale for the proposed research is that a deeper understanding of disease progression, derived from the vast RNA sequencing resources now available in user-friendly databases, creates a timely and unique opportunity for synthetic biologists to develop tools that can precisely identify diseased cells based on their RNA species and levels in living cells or even in patients. This allows for the development of treatments that specifically target diseased cells while minimizing off-target effects on healthy cells. Additionally, the success of COVID-19 mRNA vaccines using lipid nanoparticle delivery systems highlights the potential to translate RNA-based genetic circuits into practical medical applications. Given these advances, we plan to develop two independent and complementary aims for in situ cell state sensing using endogenous mRNA and miRNA as inputs: AND logic gates (requiring both inputs for an output) in Aim 1 and NOR logic gates (requiring neither input for an output) in Aim 2. This platform has broad biomedical potentials. As a proof of concept, we will demonstrate its ability to distinguish breast cancer cells from normal breast epithelial cells, evaluating its translational potential using a syngeneic mouse model of triple-negative breast cancer, which lacks key cell surface targets in current therapies. The proposed platform is innovative because it develops new platform by integration of existing miRNA sensing and RNA detecting approaches in a previously unproven combinatorial logic computation format to address a significant unmet need for accurate cell type identification for basic and translational applications. The proposed research is significant, because in situ monitoring and intervening based on endogenous RNAs will be key to addressing this unmet need, transforming disease detection and treatment.

NSF Awards · FY 2025 · 2025-08

Recent advances in sequencing technologies have enabled the collection of large-scale, multi-cohort genomics and genetics datasets. Among these, spatially resolved transcriptomics (SRT) offers the ability to measure gene expression across tissue sections while preserving spatial context, providing critical insights into cellular organization, and disease mechanisms. To fully realize the scientific potential of these datasets, integration of multi-cohort genomic and genetic data from different institutions is essential. Individual studies typically lack the breadth of biological and technical variation required for comprehensive analysis or robust model development. By jointly analyzing data from multiple sources, researchers can detect reproducible molecular patterns, strengthen the reliability of computational methods, and uncover signals that may only emerge in larger combined cohorts. Such Integrative studies also promote reproducibility through cross-validation across datasets and enable researchers to uncover new insights by reanalyzing existing data, thereby maximizing its value and reducing redundant collection efforts. As such, integrative analysis has become a cornerstone of modern biomedical research. However, current approaches to data integration often rely on centralized data sharing, which poses significant privacy and regulatory challenges. Both genomic and genetic datasets, such as those used in SRT and polygenic risk score (PRS) modeling, can contain sensitive information related to individual traits, health conditions, and ancestry. Sharing such data across different institutions raises serious concerns about confidentiality and compliance with data governance policies. Moreover, differences in infrastructure and access further limit the feasibility of centralized analysis. These challenges hinder the scale, consistency, and accessibility of collaborative studies, particularly in applications such as multi-omics data integration, and PRS prediction, where large and heterogeneous datasets are essential. Addressing these limitations requires new computational frameworks that enable collaborative analysis without exposing sensitive data. This project introduces FLAG (Federated Learning for Advanced Genomics), a federated learning framework for secure, scalable analysis of multi-institutional genomic and genetic datasets. The research includes three aims. First, the team will develop federated spatial representation learning methods that preserve fine-scale tissue structure and extract low-dimensional molecular features across institutions without data sharing to protect privacy. Second, the project develops federated Bayesian models to improve the accuracy and generalizability of PRS predictions from genetic data across heterogeneous cohorts. These methods incorporate hierarchical priors and uncertainty quantification to optimize model robustness across populations. Third, the project will release a user-friendly software platform that enables decentralized analysis workflows, allowing institutions with limited computational resources to participate in federated modeling without requiring centralized infrastructure. The proposed methods are grounded in rigorous statistical principles and tailored to the privacy, scalability, and structural demands of high-dimensional biomedical data. By enabling secure cross-institutional analysis without compromising confidentiality or requiring data centralization, FLAG offers a robust foundation for collaborative research in genomics and precision medicine. The framework is also applicable to other biomedical domains, including electronic health records and pathology imaging. Ultimately, this project will provide the research community with practical tools for privacy-aware genomic and genetic discovery, advancing reproducible science and enabling broader collaboration in data-driven biomedical innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to make the Internet more reliable by giving operators more control over how data reaches their network from other parts of the Internet. Today, network operators rely on trial and error, and often settle on subpar outcomes, because they do not have direct control over or visibility into which geographic locations and other networks the data will traverse to reach their network. This project aims to change that by creating a system where operators can simply specify their goals, such as improving performance or avoiding particular countries, and then the system figures out how to make that happen. This collaborative project brings together investigators from Columbia University, Northeastern University, and Federal University of Minas Gerais (UFMG) in Brazil. The project envisions a system that allows a network operator to describe the policy for how to set preferences for possible ingress routes (which the project calls “intents”), then automatically configures routing announcements to achieve the most preferred (feasible) outcome. To realize this vision, the project will address the following research questions: 1) What intents are desired, and how can it be made easy for operators to express them? 2) How can a system predict the routes and traffic engineering metrics that will result from an announcement? 3) How can a system automatically learn which configurations are possible and what their semantics are? 4) What are efficient ways to search through large numbers of configurations spanning multiple networks to satisfy general intent? The project will enable new ways for Internet providers and cloud services to improve the reliability and performance of Internet services on which society increasingly relies. For example, our project can help Internet providers maintain service during and after natural disasters, as well as identify and block Internet attacks. It will help improve performance for a wide range of services that include online educational technologies, telemedicine, remote work, and/or various forms of e-commerce and entertainment. The research outcomes will also serve as a foundation for future academic and industrial innovation in Internet routing. Project updates and outcomes will be published at https://ingress-routing.ee.columbia.edu/, with the plan to maintain the site for at least three years beyond the award period. 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 Childhood apraxia of speech (CAS) and dysarthria are two neurological motor speech disorders that can have long-term negative effects on communication and participation for 1-2/1000 children in the general population and up to 80% of children with other neurodevelopmental, genetic or metabolic disorders. Although CAS and dysarthria have distinct neural bases, they can be challenging to differentially diagnose because several of the speech features used for diagnosis overlap between the disorders and can also be present in young children with typical development (e.g., slow rate, consonant distortions). Currently, no validated assessments exist that speech-language pathologists can use to reliably make these diagnoses. As such, many children remain un- or misdiagnosed and therefore unable to receive early and appropriate treatment, contributing to gaps in services and life-long negative impacts on social-emotional, academic and vocational outcomes for these individuals. The long-term goal of this work is to develop and validate a clinically feasible, norm-referenced assessment protocol to identify children with speech features that are atypical for their age and facilitate early and accurate differential diagnosis of CAS and dysarthria. The proposed project represents the first step towards this goal, using a rigorous approach to quantifying auditory-perceptual features of motor speech disorders in children and systematically considering the overlapping and unique features of CAS and dysarthria in the context of typical development. The goals of this project are three-fold. Aim 1 will use a cross-sectional design to establish growth curves for development of auditory-perceptual speech features associated with motor speech disorders in 3-6-year-old children with typical development. This information will provide the normative framework needed to identify speech features that indicate atypical motor speech function in young children. Aim 2 will use a random forest approach to identify speech features that best classify children with motor speech disorders into clinically validated groups (i.e., CAS and dysarthria), adjusting for age. Aim 3 will use exploratory factor analyses to identify data-driven clusters of motor speech features (latent classes) that will be compared to feature profiles of the clinically validated diagnostic groups. Together, results of Aims 2 and 3 will yield objectively determined sets of speech features with high diagnostic utility for identifying and differentiating children with CAS and dysarthria. Results of this work will provide the scientific foundation for development of a validated assessment tool that will improve early and accurate motor speech diagnosis, leading to enhanced communication and quality of life outcomes for millions of children with motor speech disorders and their families, consistent with the mission of the NIH.

NSF Awards · FY 2025 · 2025-08

Technical Summary This proposal aims to develop a platform for the rational design of polymer-based oligonucleotide delivery vehicles using digital bottlebrush architectures. The central innovation is the design of sequence-defined polymer backbones that allow precise tuning of physiochemical and biological properties. The system—termed pacDNA—consists of antisense oligonucleotides conjugated to the backbone of bottlebrush poly(ethylene glycol) (PEG). Earlier studies showed that slight variations in backbone composition (e.g., polynorbornene vs. polyphosphodiester) cause significant differences in biological behavior, despite accounting for less than 5% of total molecular weight. This observation motivated the 'backbonomics' approach: systematically modifying backbone structure to study and predict delivery efficiency, biodistribution, and immune evasion. The project has two main goals: (1) synthesize and characterize a library of ribose-based digital bottlebrush polymers with controlled hydrophobicity and charge, and (2) study their biodistribution, pharmacokinetics, immune response, and therapeutic efficacy in vitro and in vivo. Experimental results will be used to train machine learning (ML) models for predictive design. Preliminary studies demonstrate feasibility in synthesis, molecular simulation, and biological evaluation. Key advances include identifying spacing patterns of hydrophobic units that enhance cellular uptake and using pacDNA to suppress IL-17RA expression in a murine psoriasis model. Overall, the proposed research integrates synthetic chemistry, biomaterials science, molecular biology, and machine learning to expand oligonucleotide delivery capabilities. Non-Technical Summary This research aims to improve the way genetic medicines are delivered into the body. Many promising drugs made from DNA or RNA struggle to reach their targets inside our cells due to poor stability and limited absorption. The proposed project will develop new delivery vehicles made of polymers—large molecules that can be engineered to carry and protect these drugs. Specifically, the team will use a ‘bottlebrush’ design, where many protective chains are attached to a central backbone. By changing the chemical makeup and spacing of units along the backbone, we can fine-tune how these particles behave in the body. The project also uses machine learning to find patterns between structure and function, helping to predict what designs work best. The ultimate goal is to create a delivery platform that helps genetic medicines reach difficult tissues like skin and muscle more effectively and with fewer side effects. In addition to scientific innovation, the project includes educational outreach to high school and college students and course development to train the next generation of scientists in this field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Anomia (i.e., impaired word retrieval) is one of the most common and debilitating deficits experienced by stroke survivors with aphasia, an acquired disorder characterized by impaired language. Although many anomia treatments exist, treatment response is modest and inconsistent across studies, and the extent of generalization to everyday contexts—the therapy gold standard—is largely unknown. The critical gap between actual outcomes and the therapeutic potential of naming therapy is tied to problems with current treatment delivery practices (i.e., low/unknown dosage, massed practice delivery, and decontextualized training). In this research, we aim to demonstrate that a novel smartwatch-based ecological momentary intervention (EMI) that delivers high-dosage semantic feature training throughout daily life has the potential for overcoming these issues. Every day for six weeks, 20 PWA will complete 24 trials of semantic feature EMI distributed over the day (from 10am-8pm) and delivered by a smartwatch. The EMI trial flow will include five steps: 1) a prompt alert and audiovisual “Ready to name a picture?” cue, 2) a first naming attempt after a YES response, 3) the repetition of an auditory model, 4) semantic feature verification via yes/no questions, and 5) a second naming attempt. To contextualize training further, trained items will be clustered into location categories (e.g., home, pharmacy), and we will use the mobile device’s location detection to match PWA’s location and item category for ~50% of trials. In Aim #1, we will establish preliminary efficacy of this EMI by determining the extent to which semantic feature EMI improves naming of trained items and generalizes to untrained, related contexts. We predict that the EMI will significantly improve 1) naming of trained items and semantically related, untrained items relative to semantically unrelated items and 2) subjective and objective measures of functional communication effectiveness. Given the novelty of our EMI, the second two aims will focus on identifying key factors of semantic feature EMI user experience (Aim #2) and trends between therapy response and the treatment delivery factors of dosage, trial spacing, and item- location congruence (Aim #3). The proposed research is innovative because of the novel use of EMI for aphasia, our combined application of distributed practice and context-dependent training, and the future promise of extending our system to other types of evidence-based treatment and to other clinical populations, as well as to in-situ “just-in-time” interventions that can intervene in real time during the moment of communication breakdown. The significance of this research lies in its ability to provide technology that automatically tracks dosage metrics; its potential for increasing knowledge regarding which therapy delivery factors impact outcomes; and its promise for providing PWA alternative paths to receive therapy. Successful completion of this research will lead to an R01 application aimed at establishing the efficacy of this novel therapy compared to gold-standard, clinician- provided treatment and determining which active treatment ingredients contribute to outcomes.

NSF Awards · FY 2025 · 2025-08

Artificial intelligence (AI) is rapidly transforming many areas of technology and day-to-day life, yet its success often depends on massive amounts of labeled data, something not readily available in scientific domains like robotics, materials science, and fluid dynamics due to cost and time constraints. This project addresses a critical challenge: how to make machine learning more efficient in real-world scientific and engineering settings where data is sparse or imperfect. The research program focuses on incorporating symmetry, an organizing principle in nature, into machine learning systems in a way that is flexible and adaptable to noisy, real-world data. This flexibility will make models more efficient and applicable across a range of real-world problems. The outcomes will benefit applications such as material discovery, robotics, and climate modeling, and include an online course, an interdisciplinary workshop aimed at creating partnerships across academia and industry and between AI researchers and domain experts, and outreach programs aimed at engaging local high school students in AI research. This project will develop and study relaxed equivariant neural networks, models that learn from data while respecting approximate symmetry in physical systems through relaxed mathematical constraints. The research has four main thrusts. First, it will design new machine learning architectures that enforce symmetry constraints flexibly during training. Second, it will establish mathematical guarantees about the performance and stability of these models. Third, it will explore how relaxing symmetry constraints can make optimization more effective, improving the ability of neural networks to train even when the task is fully symmetric. Finally, the project will apply these methods to problems in robotics and materials science where real-world systems deviate from ideal symmetries. Together, these contributions will lead to a deeper understanding of how geometric structure can be used to make machine learning more efficient and applicable in scientific domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project will evaluate how communities have responded to recent floods and provide new tools to help those communities build resiliency to flooding hazards. River flooding costs the United States nearly $500 billion each year due to damaged infrastructure and disrupted businesses and lives. These costs continue to rise due to growing populations in flood-prone areas, sea-level rise, and stronger precipitation events. Yet, scientists still struggle to predict how floods will impact communities, and how best to communicate those findings to community leaders and stakeholders to guide decision making. To address these issues, the researchers will collaborate with community partners to evaluate and communicate changes in flood hazards. The project will bring community knowledge together with earth system observations and simulations to understand what drives flooding at the local scale, and how flood hazards evolve under different scenarios. The researchers will examine how communities have responded to recent floods and use that information to inform planning needs for future flood events. Working with community members, the research team will co-develop a public facing decision-support tool. The tool will help communities define and visualize local conditions under different community-defined scenarios. The project will enhance the resilience of floodplain communities by increasing understanding of the causes and projections of riverine flooding at the local level. Researchers will work with community members to ensure the results address long-standing issues around uncertainty, social trust, and stakeholder utility. The approach leverages both community-level knowledge of flood hazards along with state-of-the-art tools in earth system science to address long-standing shortcomings of traditional flood hazard assessments. By including community members throughout the process, the framework ensures that science outcomes directly address local needs. The project will provide leading-edge flood inundation projections and visualizations to municipal decision makers to enhance resilience planning decisions. From an earth system science perspective, the proposed research addresses critical uncertainties in how riverine flood hazards are changing and will continue to evolve at local (municipal) scales, where community efforts to enhance resilience are hampered by shortcomings in traditional approaches to flood hazard assessment. The proposed community-oriented approach will be transferable to other communities concerned with changing flood hazards. 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

Wireless networks are complex systems that rely on integrating different software components to work correctly. This project brings people and communities together to build better wireless networks that everyone can use. The effort combines different open-source tools (free software that anyone can modify) to make it easier to create new types of wireless networks. This solution helps researchers, businesses, and communities access and develop better ways to provide wireless connectivity. The project aims to improve how people communicate and creates new opportunities for learning and starting businesses. In this project, funded by the Pathways to Enable Open-Source Ecosystems program, the challenge is to integrate fragmented open-source efforts by combining mature projects into one ready and user-friendly solution. This integration creates a complete, open-source reference architecture for 5G and future networks. The project uses test beds to continuously host and test the software and includes a suite of tools that help open-source developers build and test robust wireless network code. Additionally, the project focuses on community building through outreach, clear documentation, and hands-on workshops that attract new developers and users. This coordinated strategy enables the smooth application of advanced artificial intelligence and machine learning methods within next-generation networks, paving the way for innovations that move quickly from research to real-world use. 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

Microcontrollers (MCU) are tiny computers found in everything from medical devices and home appliances to cars and factory equipment. As these systems become more connected to the internet and each other, they also become more vulnerable to cyber threats. Unfortunately, most existing security protections were designed for more advanced computers and do not work well on these smaller, more limited devices. This project will develop new, practical solutions to protect MCU-based systems from cyberattacks, helping ensure that the technology we increasingly rely on remains trustworthy. These advances will support national priorities in healthcare, energy, transportation, and public safety. To broaden impact, the project will create new educational resources, including hands-on training platforms and classroom materials, to make cybersecurity more approachable for students. The PIs will share learning content publicly and mentor undergraduate, graduate, and high school students through cybersecurity competitions and outreach activities. Together, these efforts will expand participation in STEM fields. The project will realize its research goal by leveraging novel MCU-specific hardware functionalities (hence, hardware-assisted) for memory management, tracing, and cryptographic signatures. If successful, the project will represent a significant advancement in algorithm, mechanism, and system design for safeguarding the control-flow of MCU systems. Specifically, the project initiates by delving into the realm of cross-state control-flow hijacking, seeking to both comprehend and counteract this phenomenon to secure control-flow from TEE to rich execution environment. Then, the project leverages the capabilities of the secured TEE and hardware tracing units to develop comprehensive control-flow violation detection mechanisms on the entire MCU software. Finally, the project capitalizes on the secured TEE and the pointer authentication hardware functionality to devise a space-efficient algorithm to attest the control-flow integrity of a significant portion of the MCU software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Radio signals power many everyday services such as wireless internet, Global Positioning Systems (GPS), weather and climate forecasts, and navigation systems for ships and planes. This project explores smarter methods for different services to share radio waves and the devices that use them. Its goal is to ensure that everyone can benefit from these services while making the best possible use of the radio signals. Overall, the project strives to make communication systems work more reliably, safely, and easily for all. The project introduces the concept of Open Spectrum by extending the idea of open and programmable Radio Access Networks to manage shared spectrum systems beyond cellular networks, for example radar and radio sensing systems. Its core idea is to pool spectrum, infrastructure, and, when possible, services so that access and deployment are automated and coordinated through dynamic deconfliction policies. The work is organized into four main thrusts. The first thrust focuses on designing a flexible architecture for spectrum management. The second thrust involves sharing heterogeneous services, such as sensing, using automated systems built on cellular infrastructure. The third thrust develops algorithms and frameworks for policy and conflict management to maintain quality of service. The fourth thrust creates testbeds and dynamic research platforms to validate the ideas. The broader impact of the project is its potential to transform how radio spectrum and network resources are shared and managed. The project is set to pioneer techniques that can improve public safety communications, emergency response systems, and commercial wireless services. It will produce open-source software, openly accessible data sets, and educational resources that benefit both industry and academic researchers. This initiative will help develop a future workforce skilled in sophisticated spectrum engineering and policy and is expected to influence regulations by demonstrating that advanced, efficient, and secure spectrum sharing is achievable for a more connected and sustainable society. The project website can be found at https://openspectrum.dev. This site provides links to the repositories, tutorials, and other resources developed during the project. The software, data, and other research outputs will be hosted on digital archival platforms and will remain accessible beyond the duration of this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This Computational and Data-Enabled Science and Engineering (CDS&E) project aims to advance national prosperity and workforce development by enabling research that seeks to develop more efficient and cost-effective design of mechanical systems through automation. Traditional engineering design relies heavily on human expertise and extensive training, making it labor-intensive and expensive - especially in the context of preparing the next-generation workforce for the manufacturing sector. This research supports a shift toward data-enabled, computer-aided design by addressing a fundamental challenge: quantifying the amount of useful information obtained from a single mechanical test. By developing a theory that measures how much information each test provides, this project looks to lay the groundwork for automated design, testing, and analysis of materials and structures. The outcomes will benefit both industry and education by lowering training costs and enabling smarter, faster decision-making through artificial intelligence. The research project seeks to introduce a new concept called mechanics informatics - a theoretical foundation for learning material properties from a single, optimized test instead of many. Using advanced information engineering methods, the research team will design specimens that produce the most informative data under complex loading conditions. A new inverse learning algorithm will be developed, combining finite element simulations and Bayesian optimization to iteratively identify unknown mechanical parameters. The approach also includes uncertainty quantification using Gaussian processes to account for variations in measurements and model choices. Initially, the framework will be applied to aluminum alloy sheets to learn their plastic deformation behavior, then extended to other sheet metals produced by new manufacturing technologies and fiber-reinforced composites to study elastic properties under uncertain conditions. This work seeks to open new avenues for integrating AI into engineering, while promoting hands-on learning and collaboration with industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The workshop: "Workshop on Physics of Biological Aging" will explore the role of physical science in advancing the field of biological aging. The field is in need of new, integrative concepts and bringing together physicists and biologist will bring novel ideas in this important research effort. The workshop would have approximately 22 participants (21 confirmed) chosen from both sides of the biological science/physical science divide and meet in Paris for several days in September 2025. This effort would be the latest in a series of such workshops aimed at extending the reach of the Physics of Living Systems community and providing a publicly available perspective for students (and faculty members) for getting started on aging research. The meeting is scheduled to occur in early September with approximately 22 participants. These will be drawn both from leaders in the biology of aging research community and from the PoLS community of physical scientists. The meeting will last for two days and explicitly consider how to move forward using advanced ideas from physics, mathematics, and computer science (including of course on AI using deep learning). The organizers plan for a follow-up article presenting the results of the discussions to the NSF, the physics and biology community as well as to the general public. 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

Personal computing devices, such as smartphones and wearable technology, are transforming care for mental and behavioral health. These devices can track various aspects of daily life, including activity, sleep patterns, location, and physiological signals such as heart rate. This tracking enables the design and development of intervention systems that can deliver timely, personalized support. The systems will help a person to better manage their health outcomes, for example, reducing stress or improving physical activity. However, knowing when a person can receive, process, and use the support in their daily lives to enable long-term sustainable engagement is a major challenge. Factors such as a person's current activity, location, emotions, motivation, and even how much effort they think it will take to engage can influence how willing they are to interact with an intervention. This project aims to address these challenges by creating smart systems that optimize how interventions are delivered and by determining the best time, type, and device for providing support. The project will thus improve engagement and promote sustainable behavior change. The project proposes new approaches to understanding a person's behavioral states using smartphones and wearable technology. The project will also evaluate how these states impact how a person interacts with interventions. The findings and tools from this project will enable behavioral scientists and intervention designers to create new types of personalized digital health interventions. Research activities will include training graduate students, integrating findings into courses on data science and personal health informatics, and hosting hands-on workshops for intervention designers. This project advances the field of digital health by combining human-centered computing, machine learning, and mobile sensing to enhance just-in-time adaptive interventions (JITAIs). The project includes three key thrusts: The first part focuses on developing a comprehensive framework to represent the context of a person as a multivariate state that includes both physical (for example, location and activity) and emotional states (for example, arousal or stress). The project will also develop a hybrid approach of Dynamic Bayesian Networks and Hidden Semi-Markov Models to model current context and predict expected future contextual states. The second thrust of the project seeks to understand and predict the impact of contextual states on receptivity to interventions and develop a multi-objective reinforcement learning algorithm to leverage the context, receptivity, intervention burdens, and expected effectiveness of the intervention to determine "what," "when," and "how" to deliver interventions to maximize intervention engagement and effectiveness. Finally, the project will develop an extensible state-of-receptivity framework that enables behavior scientists to design and implement effective adaptive interventions by leveraging the optimization algorithms and balancing intervention burden and effectiveness across diverse platforms. The project will evaluate the performance, efficacy, and usability of all tools, methods, and algorithms through human subject studies and release them as open-source resources to contribute to the broader digital health, ubiquitous computing, and human-centered computing 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 · 2025-07

Bacteria can grow using a variety of carbon sources, referred to as substrates. Most bacteria grow well on glucose, which contains 6 carbon atoms per molecule (C6). Certain bacteria, referred to as acetogens, can grow on single-carbon (C1) molecules. For example, acetogens can grow on carbon monoxide, or methanol. These compounds can be produced from carbon dioxide. There is a catch; acetogens grow very poorly or not at all in the presence of oxygen. Also, because C1 compounds are not energy-rich, acetogens generally have limited synthetic capabilities. Acetogens could be useful in the renewable production of fuels and chemicals. Achieving this will require reengineering certain aspects of their metabolism. There are two key objectives of this project. One is understanding the regulation of carbon metabolism. The second is to use the insight gained make acetogens a more effective producer of valuable fuels and chemicals. In parallel with these technical objectives, outreach to local high-school students will introduce them to the concept of microbial cell factories. Undergraduates will be provided an opportunity for hands-on experience with bioprocessing in a unit operations lab course. The objective is to equip them for future careers in a robust US bioeconomy. Acetogens are obligate anaerobic bacteria that dwell in energy-poor environments near the thermodynamic limit of life, making a living by converting C1 substrates into acetate through the Wood-Ljungdahl pathway. The high energetic efficiency of this pathway, coupled with recent progress developing advanced genetic tools for these organisms, have made acetogens attractive microbes for the renewable production of biofuels and biochemicals. The major challenge is that, as anaerobes, acetogens growing on a single C1 compound are severely energy limited. Thus, while they can produce low-value compounds, efforts to expand the product profile to higher value products from pathways that demand more cellular energy have been met with minimal success. Some acetogens can simultaneously consume multiple different C1 substrates, or a C1 substrate and sugars. Interestingly, even when the second substrate is provided only at low levels as a ‘dopant’, this can result in drastic improvements in growth rate and productivity. The overall research goals of this CAREER project are to i) understand the metabolic and regulatory mechanisms by which acetogens synergistically leverage multiple substrates simultaneously to overcome growth limitations, then ii) learn how to harness this to direct C1 flux to higher-value products than have previously been possible. An approach that combines isotopic tracer experiments, cofactor perturbation, and transcriptomics will be applied to probe the mechanisms underlying substrate doping. 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

Biomedical engineers and bioengineers (BME) address today’s most pressing healthcare challenges by applying engineering principles to biological science. Graduates go on to work in a variety of healthcare fields, including medical devices, therapeutics and diagnostics, and clinical practice. BME faculty are subject matter experts in the science and engineering of biology but are often not formally trained in the science of learning and education research. The purpose of this project is to train a cohort of 15 BME faculty in education research tools that they can use to better support the education and professional formation of future biomedical engineers and bioengineers, a specific goal of the NSF’s Research in Formation of Engineers (RFE) Program. With this expertise, we will build a community of reflective BME educators who possess the training to evaluate and assess their educational approaches. This project will enable a BME Education Research workshop that will broaden and deepen education research participation in the community. The workshop is specifically designed to give participants the space to cultivate their education research ideas in a scaffolded environment with resources to advance their work in real time. Two of the biggest barriers to engaging discipline-based faculty in education research is time and access to the appropriate educational research tools. For this workshop, participants will be mentored by education researchers and implement what they learn. The goal is for participants to use the time and support to develop research plans that can be put into action after the workshop. We will focus on fundamental topics–such as research question development, quantitative and qualitative data collection procedures, and data analysis approaches that are critical for conducting educational research. The goal is to facilitate active engagement with education research content and utilize a peer-learning environment to develop education research projects. To achieve this goal, the following structure will be implemented: 1. Participant cohorts will be based on shared commonalities in research agenda as defined by a pre-survey; 2. Cohorts will work closely with the mentor team in small groups; 3. Cohorts will also come together throughout the workshop to discuss the fundamental engineering education concepts being introduced. This workshop is designed to prototype an effective multi-day, experiential engineering education research workshop. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This collaborative project will create computer simulations that will help scientists study how organic molecules form crystals when mixed with a liquid and placed inside materials with tiny holes. This is important because the way crystals form can affect how well medicines work and how certain chemicals are used in defense technology. For example, in drug-making, specific crystal shapes of a medicine's active ingredient can make it more effective. The same idea applies to creating special chemicals for other uses, like materials for the military. By learning how tiny spaces (measured in nanometers) affect crystal formation, scientists can control the process better. This could lead to new medicines and stronger materials that help keep people safe. The computer tools and programs created in this project will be shared with other scientists and companies through websites like GitHub and nanoHUB. The project will also train students, from high school to PhD level, in using computer simulations. They will work with industry experts and create fun science comics to get more early career researchers excited about STEM fields. This project will help understand how confinement in nanopores affects crystal nucleation of solutes in solution. Most studies of nucleation in confinement have been experimental, with the results explained using classical theories that break down for nanometer-pore sizes. Molecular simulations in this field require the use of rare event methodologies in the Grand Canonical Ensemble (GCE), as the modeled confined solution is in equilibrium with a bulk phase with experimentally accessible properties. However, most rare events methods are based on molecular dynamics, which is difficult and computationally expensive to extend to the GCE. Simulations require challenging insertions and deletions of molecules in a dense solution confined inside nanopores. These challenges will be addressed by developing a new simulation method that combines the String Method in Collective Variables (SMCV) and Voronoi Milestoning with the continuous fractional component Monte Carlo method, all in the GCE, to model solute precipitate nucleation in confinement. These computational tools will then be used to determine how surface wall solvophobicity, pore size and pore geometry, and the local structure of the solvent near the pore walls, affect the solute nucleation rate and formation of crystal polymorphs. 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

The two Maurice Auslander Distinguished Lectures and International Conferences will be held in April 2025 and April 2026 at Woods Hole Oceanographic Institution. There are two main objectives of the conferences: The first is to share knowledge about recent developments in the chosen topics, two topics each year. The second is to create new connections between the topics. The topics are cluster theory and Hochschild cohomology in 2025 and triangulated categories and topological data analysis in 2026. The conferences will include leading researchers in the topics under consideration as well as interested researchers at different career stages, including graduate students and post-doctoral scholars. In more detail, cluster algebras were introduced at the turn of the century by Fomin and Zelevinsky, and they immediately became a very broad and influential topic in many areas of mathematics. One of the central problems is to construct categorifications of cluster algebras. This will be one theme of the 2025 conference. Hochschild cohomology is the other theme of the 2025 conference. Experts in the field will give talks on the latest developments, with a focus on the categorification of cluster algebras given by quivers with potentials, where the potentials are Hochschild cycles. More information is available on the conference website: https://sites.google.com/view/madlconference/home. 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

Manifold optimization is instrumental in control and engineering applications, ranging from trajectory optimization in robotics and safe reinforcement learning to subspace estimation, geometric deep learning, and adaptive fine-tuning of large language models (LLMs). Despite recent advances, most of the research on manifold optimization is limited to centralized approaches not implementable in multi-agent systems. This CAREER project takes a substantial step towards the development and adoption of decentralized manifold optimization (DMO) in large-scale, multi-agent optimization by overcoming three fundamental challenges: scalability, efficiency, and adaptation to dynamic environments. The PI proposes three integrated research thrusts to address these challenges. (i) Thrust 1 will fundamentally advance the scalability of DMO by innovating decentralized retraction-free methods that are computationally efficient, and it will also extend the theory of retraction-free methods to bilevel DMO. (ii) Thrust 2 will provide a systematic understanding of acceleration to improve the iteration complexity of DMO algorithms. This research will establish provably fast convergence guarantees for accelerated DMO and further expand this theory to the zero-order setting using smoothing techniques to generate high-fidelity gradient estimators. (iii) Thrust 3 will pioneer online DMO to contextualize multi-agent Riemannian optimization in dynamic, unpredictable environments. It will investigate projection-free methods to replace costly projection operations with efficient oracles and further extend the study to functional constrained online DMO by developing decentralized Riemannian augmented Lagrangian methods. The proposed research in this CAREER project will produce and draw upon novel scientific tools in Riemannian optimization, multi-agent systems and control, high-dimensional statistics and concentration inequalities. The research agenda also includes a concrete evaluation plan of the proposed algorithms on fine-tuning of LLMs and active mapping/planning with robot teams. The PI has an integrated education plan to engage high school and undergraduate students in research and organize STEM field trips for middle school students. 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

Rivers and deltas of the world have been extensively engineered for centuries. Past engineering projects have, collectively, enabled the prosperous economies of deltaic regions that are enjoyed today, but have also created conditions that limit the regions in the future. In many places, including along the United States Gulf Coast, planners and governments are implementing new projects that aim to restore coastal changes, enhance economic and environmental support, and mitigate risk to human lives and livelihoods. However, there is not a robust understanding of how past delta management has influenced human decisions and outcomes. This project uses novel numerical and computational modeling approaches to study how human engineering decisions cascade through space and time over centuries of landscape change to create system conditions that limit or enhance the portfolio of management decisions available at future times. This work develops tools with coastal planners to determine the portfolio of projects that maximize coastal survival. Delta engineering projects induce geomorphic change across space and time scales that impacts human lives and society. This project integrates cascading human decision making into landscape evolution models with three modeling approaches that inform one another and have complementary strengths: agent-based modeling, dynamical system modeling, and participatory modeling. Project research focuses on two testable hypotheses towards the tools and quantitative understanding of cascading decision making that are needed for delta planning: (1) local-scale engineering interventions can lead to less geomorphologically stable delta landscapes, when compared to a few larger system-scale interventions, and (2) human decisions can push the coupled system across tipping points, to both system benefit and detriment. Finally, research and development across all modeling approaches in this project is guided by community engagement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

PROJECT SUMMARY There is rapid growth in the aging HIV population in the United States, who are at increased risk for cognitive impairment. Loneliness is a risk factor for cognitive deficits, and older people with HIV (PWH) report significant loneliness. Despite well-established associations between loneliness and cognition, findings specific to older PWH address only overall cognition and are cross-sectional, limiting understanding of longitudinal effects on specific cognitive domains over time. The ecological theory of aging posits that well-being is shaped by the dynamic interplay of individual- and environment-level factors; social and physical environments may therefore mutually reinforce loneliness and exacerbate cognitive health, yet this has not been empirically studied. My long-term goal is to enhance health and well-being of the aging HIV population by identifying and intervening on modifiable, multilevel factors. Building on prior cross-sectional work, this proposed mixed-methods study will examine how specific, measurable social (including community integration, social support, social isolation) and physical environment-level factors (geocoded data informing area deprivation, neighborhood-level resources, greenspace) influence the relationship between loneliness and cognition in older PWH, and will elicit qualitative perspectives to add rich description to these associations by leveraging an ongoing prospective R01 nested within the MACS/WIHS Combined Cohort Study. Guided by multidisciplinary mentors with complementary and synergistic expertise, I will pursue the following research aims among older people with HIV (≥50 years old): 1) determine the longitudinal patterns of loneliness (measured by De Jong Gierveld Scale) and cognitive performance across specific domains over time; 2) identify social and physical environment-level factors related to loneliness and cognition; and 3) elicit perspectives of how individual- and social/physical environment-level factors affect cognitive health and how narratives of loneliness vary. Complementing this research, a detailed training plan will include 1) the science of cognition within the context of HIV and aging; 2) measurement and analysis of social and physical environmental factors that influence health ; 3) longitudinal data analytic skills; and 4) mixed-methods and community-engaged research. The protected time afforded by K01 will equip me to launch an independent research career incorporating measurable social and environmental determinants into the study of HIV and aging, informing subsequent R-level grants and ultimately strategies to improve health and well-being of older PWH.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development Program (CAREER) grant will contribute to the progress of science and the advancement of national welfare by supporting research to more effectively combat human trafficking via improved understanding and disruption of the networks that enable trafficking. Despite significant efforts, current anti-trafficking interventions are often fragmented and lack coordination, limiting their effectiveness. Moreover, the success of interventions to disrupt trafficking networks and the availability of support services for survivors are deeply interconnected: access to services influences the effectiveness of interdictions which, in turn, affects the number of survivors seeking services. This project will develop novel analytical decision models to stimulate enhanced collaboration among stakeholders, address the interdependencies between interdictions and survivor services, and guide the optimal allocation of limited resources. The project emphasizes the importance of conducting research in partnership with community members and individuals with lived experience, such as trafficking survivors, to ensure the research is contextually nuanced. As such, the educational initiatives include research fellowships for trafficking survivors and training researchers on how to build and sustain community-engaged research partnerships. These efforts aim to foster a new generation of leaders equipped to address pressing societal issues. This project intends to advance fundamental knowledge in bi-level and two-stage stochastic optimization with endogenous uncertainty while contributing to the growing field of operations engineering models focused on illicit networks. The project is structured into three key components: (1) developing a new class of multi-path network evasion interdiction models to optimize collaboration among multiple interdictors and minimize the probability of traffickers evading detection; (2) creating explainable policies to schedule survivors to services more effectively within existing capacity and determining cost-efficient ways to expand capacity to meet their needs; and (3) integrating these models into a systems framework to analyze the dynamic interplay between interdiction and survivor support, where effective interdictions increase the number of survivors needing support, and improved survivor services enhance interdiction success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

NONTECHNICAL SUMMARY This CAREER award supports research and education activities towards advancing our understanding of an unusual phase of matter known as the "fracton" phase. Fractons are unique particle-like responses in certain materials that have distinctive behavior: Unlike familiar particles like electrons or protons, they cannot move freely and often require binding in groups in order to move. This intriguing property is transforming our understanding of matter and sparking interest across diverse fields such as quantum materials, quantum field theory, and quantum information science. The restricted mobility of fractons also offers exciting potential for breakthroughs in emerging technologies, especially in developing quantum hardware and efficient quantum computers. The research will focus on three main objectives: 1) developing theoretical approaches to understand the collective behavior of fractons, 2) investigating fractons in open quantum systems, where interactions with the environment introduce challenges like noise and interference, and 3) designing algorithms that enable control of fracton phases in dynamic, out-of-equilibrium settings. Progress in these areas is key to leveraging fractons for quantum error correction and information processing technologies. This CAREER award also supports educational and outreach activities to train, mentor, energize, and retain students in STEM by providing immersive learning experiences. The PI will expand outreach to encourage female students and postdocs in STEM through the Women in Quantum Era seminar series and inspire future STEM aspirants through the Science Inspired by Art workshop, which will employ modular origami and decorative knots to interactively explore geometry and related scientific ideas. TECHNICAL SUMMARY This CAREER award supports theoretical research and education to understand the interplay between symmetry and decoherence in fracton and topological phases under both equilibrium and non-equilibrium conditions. The project aims to study fracton and topological states by constructing microscopic models and developing hybrid fracton field theories, allowing for the exploration of various correlated phases and phase transitions. A key component of this research involves open quantum systems, investigating whether fracton states can retain quantum coherence and entanglement in noisy environments, and exploring if decoherence can give rise to unique mixed ensembles absent in thermal equilibrium. Expected outcomes include advancements in quantum field theory frameworks with generalized symmetries and insights into dissipation and decoherence in dynamical phase transitions. More broadly, this research connects the fields of condensed matter and quantum information theory, with potential applications in scalable quantum simulators and robust quantum information processing. This CAREER award also supports educational and outreach activities to train, mentor, energize, and retain students in STEM by providing immersive learning experiences. The PI will expand outreach to encourage female students and postdocs in STEM through the Women in Quantum Era seminar series and inspire future STEM aspirants through the Science Inspired by Art workshop, which will employ modular origami and decorative knots to interactively explore geometry and related scientific ideas. 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.