Northeastern University

NSF Awards · FY 2026 · 2026-06

This Pathways to Enable Open-Source Ecosystems (POSE) project addresses the increasing demand for tools that handle fast changing 3D and 4D visual information used in robotics, extended reality, artificial intelligence, autonomous systems, and similar areas. Current resources are scattered and incompatible, slowing progress and limiting access. This project addresses these challenges by planning a shared, open ecosystem for dynamic 4D geometry data, enabling common standards, tools, and workflows that can be used across domains. The project supports broader participation in spatial computing and accelerates progress in applications such as immersive education, remote operation, and intelligent automation. This effort directly serves the national interest by strengthening the data infrastructure underlying next-generation computing systems. Advances in interoperable spatial data processing can improve the reliability and responsiveness of systems used in areas such as disaster response, critical infrastructure monitoring, and defense and surveillance operations, where timely and accurate spatial understanding is essential. At the same time, the project contributes to economic growth by enabling scalable data pipelines for autonomous systems and spatial computing platforms, supporting innovation across industry sectors. This project scopes and designs an open-source ecosystem for 4D dynamic geometry, building on an existing research artifact as a foundation. The effort focuses on identifying shared abstractions, interoperable data representations, and system-level requirements that enable extensible development across domains such as extended reality (XR), robotics, and autonomous systems. The team will study existing 4D geometry tools, identify common needs, and outline a unified ecosystem that supports shared formats and development infrastructure. The team interviews developers and users to define requirements for interfaces, benchmarks, and governance. The project also evaluates risk, licensing needs, and technical constraints to ensure long-term sustainability. The outcome will include a blueprint for a community-driven open-source framework for dynamic geometry. In addition, the project analyzes fragmentation across current pipelines to identify opportunities to standardize data models and workflows. It examines trade-offs in performance, scalability, and cross-platform compatibility, and assess security and robustness considerations for shared infrastructure. The resulting open-source ecosystem will include a reference architecture, governance model, and evaluation methodology to guide the future development and adoption of scalable 4D geometry processing 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 2026 · 2026-06

This is an education-focused project to incorporate the Clemson University Single-Particle Soot Photometer (SP2) into Northeastern University’s mobile air quality laboratory. The SP2 instrument provides fast data on black carbon in the air. Students will conduct mobile measurements across urban and suburban neighborhoods to gain hands-on experience with research-grade instrumentation. The project will train undergraduate students from Environmental Engineering, Chemical Engineering, and Environmental Sciences majors. The skills gained during the project will be valuable for future research and employment opportunities. The Clemson SP2 will provide real-time, high-resolution data on black carbon mass, number concentration, and size distribution. The SP2 will be incorporated in the Northeastern University mobile lab which currently houses a variety of atmospheric chemistry instruments, but relies on filter-based systems for the measurement of black carbon. One key activity for the students will be to analyze the trade-offs between an inexpensive instrument with larger uncertainty and slower time response, and an expensive research-grade instrument. Teams of students will be trained on the instrument use, design mobile sampling routes, and participate in the field activities. Students will be expected to learn fundamental principles of particle detection mechanisms, exercise critical thinking skills for environmental monitoring, assess measurement requirements and uncertainty tolerances within practical budget constraints, and practice key skills in data synchronization, spatial aggregation, and interpretation. 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/Abstract Higher amounts of physical activity (PA) are linked to multiple health benefits, increased cognition, academic success, and improved classroom behavior for children. These relationships begin to emerge early in childhood during the preschool years. Additional research is needed to determine the rates of PA among children with autism spectrum disorder (ASD); however, research suggests that compared to their typically developing peers, children with ASD participate in fewer types of physical activities, often have delays in a range of motor skills, and face PA challenges including behavior problems, social communication challenges, and adults’ uncertainty about how to modify physically active games to include them. Young children need adult guidance and opportunities to be more active, and early care and education programs are a critical setting for PA promotion. Our team developed and pilot tested WE PLAY (Wellness Enhancing Physical Activity for Young Children), a teacher training, that can be accessed online at no cost. WE PLAY was designed to promote PA at the child- level, to promote knowledge, confidence, and skills to lead PA at the teacher-level, and to impact social and environmental challenges at the program-level. Our three pilot studies demonstrated that WE PLAY was efficacious in increasing PA when implemented with preschoolers with and without ASD and it was viewed as feasible, understandable, and acceptable by teachers. The current proposal aims to test the effectiveness of WE PLAY in children with ASD using a properly powered randomized controlled trial (RCT) and to identify malleable teacher factors that moderate and mediate the relationship between WE PLAY and child PA levels. We hypothesize that at post-intervention and 4-month follow up, children in the WE PLAY group will engage in higher levels of PA in school relative to children in the control group. Further, we will explore the effects of malleable teacher and system-level factors between the intervention and child PA. We hypothesize that malleable teacher and system variables will mediate and moderate the relationship between study group and child PA levels. A national sample of teacher-child dyads (N = 148) from preschools across the country will be randomized into two conditions (WE PLAY vs. Control). WE PLAY teachers will be asked to complete the WE PLAY training. Control group teachers will complete a different online training that is not related to PA. Children with ASD (ages 2.9 to 5.11) will wear accelerometers during waking hours for 5 consecutive days at pre-intervention, post-intervention, and 4-month follow up. Teachers will complete online surveys at each data collection period. The proposed study addresses a critical need for young children with ASD. If WE PLAY is determined to be effective on a large scale it would lead to interventions that could be integrated in a variety of settings, including early care and education programs and community settings, which could improve PA levels in young children, setting them up for greater success throughout their lives.

NSF Awards · FY 2026 · 2026-06

Creativity has become one of the most essential skills in the modern workforce. Digital tools that support creative collaboration, such as digital whiteboards and collaborative document editors , are now central to how teams brainstorm, design, and write together. Yet these tools fall short for the millions of Americans who rely on nonvisual assistive technologies like text-to-speech screen readers. For example, a screen reader can read out sticky notes one by one from a whiteboard, but cannot convey which notes are grouped together, who is editing what, or where a user’s own contributions land on the shared canvas. Sighted users, in contrast, can easily track each other's color-coded cursors and draw inspiration from images and animations on the whiteboard. Blind individuals face a 47.7% unemployment rate, nearly double that of their sighted peers, and visually-oriented creative tools deepen that gap. This project will develop AI-augmented, nonvisual creativity support tools that allow blind users to generate, express, and refine ideas alongside sighted colleagues on equal footing. The project utilizes AI to move beyond visual-centric paradigms to augment nonvisual ideation. Making these tools work well for nonvisual interaction benefits a far broader population, including people with lower literacy levels and anyone who stands to gain from more flexible, audio-friendly design. By advancing nonvisual creativity support tools, this project will expand the US workforce in fields where creative problem-solving is increasingly central to economic competitiveness. This project deepens empirical, technical, and conceptual knowledge of creativity support tools in the nonvisual paradigm through three interconnected research threads. First, interviews, field observations, and content analysis of user-generated artifacts will produce a taxonomy of barriers and facilitators of blind individuals' creative practices, along with design guidelines for appropriate creativity support tools. Second, this taxonomy will inform the design and implementation of two novel systems targeting digital whiteboarding and document editing respectively. These systems will introduce new algorithms to detect and restructure complex visuo-spatial content into screen reader-compatible formats, a suite of interaction mechanisms such as modular hierarchical navigation and custom audio cues to support content creation and collaboration, and generative AI-powered features including scaffolded prompting and an idea repository to augment nonvisual creative thinking. Third, a series of lab-based and longitudinal field studies will evaluate the systems' effects on individual creativity, collaborative creativity, and creative self-efficacy. Ongoing refinement of the findings will yield a conceptual framework that paves the way for future researchers to catalyze creativity across a wide spectrum of 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 2026 · 2026-05

Spatial intelligence enables computing systems to perceive and interact with the physical world. Emerging spatial intelligence technologies such as smart glasses and extended reality systems are beginning to bring digital information directly into everyday physical spaces over the consumer Internet, helping people perform complex tasks in education, healthcare, manufacturing, and logistics more efficiently. Strategic adoption of these technologies could yield billions of dollars in savings. However, today’s Internet is designed primarily for 2D images and video rather than for rich, dynamic 3D, i.e., 4D spatial information. As a result, current systems struggle to efficiently send 4D spatial data on the Internet, limiting the reliability and scalability of these technologies. This research will enable smart glasses to understand and communicate 4D environments in real time, making immersive technologies more practical, accessible, and reliable. The project also expands educational opportunities by introducing students to spatial computing through hands-on experiments, open-source tools, and outreach programs that engage learners from high school through graduate school. The project contributes to workforce development and strengthens national leadership in emerging computing technologies by building accessible platforms and training the next generation of engineers and researchers. This award develops a systems and networking foundation for spatial intelligence by advancing methods for representing, transmitting, and programming 4D spatial data in resource-constrained devices such as smart glasses. The research introduces a hybrid spatial representation that combines structured-mesh geometry with Gaussian-splat-based appearance models to enable real-time spatial perception, rendering, and analytics such as localization and tracking. Building on this representation, the project develops compression and streaming techniques that exploit temporal redundancy in dynamic meshes and allocate network bandwidth efficiently between geometry and texture components. These techniques include 4D mesh compression methods, predictive bitrate control models, and adaptive streaming strategies that jointly optimize visual fidelity and network performance. The project further investigates networked spatial systems through an open hardware and software platform for experimental smart glasses. The platform exposes tunable components across the sensing, networking, and computational layers, enabling researchers and developers to study cross-layer trade-offs among latency, energy consumption, and visual quality. The resulting system will support reproducible research in networked spatial computing and provide new tools for evaluating real-time perception, streaming, and multi-user spatial applications. Additionally, the award will lead to educational activities, including the development of hands-on courses and virtual laboratory environments that use spatial computing systems to illustrate complex systems concepts. The research outcomes will allow students to observe and interact with abstract computing processes in real time, creating new opportunities for experiential learning in networking and distributed 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 2026 · 2026-05

This grant provides funding for a workshop entitled The Future of Pharma Supply Chain: Addressing Real-World Challenges, being held in Boston, Massachusetts, Boston, 27-29 May 2026. The goal of the workshop is the progress of science, advance national health and prosperity, and strengthen societal well-being by convening a three-day interdisciplinary workshop addressing the growing scientific and operational complexities of modern pharmaceutical supply chains. These supply chains are critical to national health and economic prosperity, but are increasingly strained by the need to support a diverse portfolio of products, from traditional small-molecule drugs to biologics and personalized therapies. Ensuring that these systems remain efficient, resilient, and responsive is essential for timely access to life-saving medications and sustaining the nation’s capacity for scientific and economic growth. The workshop will convene leading researchers in Operations Research (OR), Artificial Intelligence (AI), Chemical and Biological Engineering (CME), and Biomedical Engineering (BME), together with industry and regulatory stakeholders, to define the fundamental scientific challenges shaping pharmaceutical supply chains. Through structured dialogue and collaborative problem-solving, the workshop will generate an open-access research roadmap that connects scientific inquiry with real-world implementation. In doing so, it will contribute to the national interest by advancing scientific understanding, improving patient access to therapies, and supporting economic growth through stronger manufacturing and logistics capabilities. The technical goal of the workshop is to develop a forward-looking research roadmap that systematically links real-world pharmaceutical challenges with emerging analytical, computational, and engineering methods. The workshop is structured around five stages of the pharmaceutical value chain: discovery and preclinical development, clinical trials, manufacturing, supply chain logistics and distribution, and market access. Each session follows a challenge-driven format in which speakers present concrete operational problems, followed by complementary perspectives from OR/AI/CBE/BME experts and practitioners. Structured breakout discussions will identify methodological gaps, data needs, and opportunities for interdisciplinary collaboration. Key discussion themes include adaptive trial design, data-driven manufacturing, personalized medicine, rare diseases, cold-chain logistics, and innovative pricing and incentive strategies informed by real-world evidence. The primary outcome of the workshop will be a research roadmap, which will be disseminated in an open-access format to support future interdisciplinary research. Post-workshop activities will be organized to ensure continued collaboration and community building, fostering an open dialogue between research and practice. 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 · 2026-05

State estimation, a foundational problem in modern science and engineering, is the process of determining the internal state of a system when only indirect, noisy, or incomplete measurements are available. Nearly every intelligent technology depends on this capability, including autonomous vehicles, robotics, medical imaging, advanced manufacturing, energy networks and financial analytics. In artificial intelligence and data-driven decision systems, state estimation underlies the ability of machines to interpret data and make reliable predictions in real time. However, real-world systems often violate the ideal mathematical assumptions on which classical estimation methods are built. Measurements may contain frequent outliers or unexpected disturbances, and system dynamics may be nonlinear or poorly modeled. In these settings, existing approaches can become unreliable or computationally burdensome. This award supports research that develops a new mathematical framework that reformulates state estimation as a structured optimization problem, enabling more accurate and computationally efficient estimation even under complex and non-ideal conditions. By strengthening the reliability of intelligent technologies that depend on accurate state information, this research supports innovation, economic competitiveness, and public safety. The project will also contribute to education and workforce development by training graduate and undergraduate students, integrating research results into advanced coursework, and engaging students in hands-on research experiences. This research reformulates state estimation as a problem of maximum a posteriori optimization problem and develops a dynamic programming recursion analogous to that used in optimal control. In the classical linear Gaussian case, this framework recovers the standard Kalman filter, providing a unified perspective. For systems with non-Gaussian noise (Aim I), the research will develop new recursive estimators by locally approximating log-likelihood functions and solving the resulting optimization problems using Newton-type and online optimization methods. The research will analyze robustness to heavy-tailed and multimodal noise distributions, develop algorithmic modifications to enhance numerical stability, and establish theoretical guarantees of convergence and stability using tools from convex optimization and nonlinear control. For systems with nonlinear dynamics (Aim II), the research aims to generalize the optimization-based recursion to nonlinear state and measurement models, exploring both perturbation-based analyses and higher-order or polynomial approximations that trade computational effort for improved accuracy. Throughout, the estimators will be evaluated on a comprehensive suite of numerical benchmarks involving nonlinear dynamical systems that arise in manufacturing and medical applications. Anticipated outcomes include computationally efficient state estimation algorithms with provable properties, stronger theoretical connections between estimation and control, and a unified optimization framework for nonlinear and non-Gaussian filtering. 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 · 2026-05

Stuttering affects approximately 80 million people worldwide and, for many, significantly hinders communication. However, treatment outcomes are variable, which is due in large part to significant behavioral and neurological heterogeneity. Current theories of motor control posit that stuttering develops due to weakened feedforward (FF) commands (i.e., automated adjustments in anticipation of errors) and/or aberrant feedback (FB) control (i.e., monitoring and correcting for errors). While these models agree on the general role of FF and FB systems, they differ in how much each system is affected across individuals who stutter. Furthermore, recent evidence suggests that there may be distinct subgroups of adults who stutter (AWS) characterized by different patterns of motor control. The proposed project will test the hypotheses that distinct stuttering behaviors are associated with behavioral and neural correlates of FB and FF control (Aim 1), and that distinct clinical phenotypes of FB and FF control can be identified in AWS (Aim 2). For Aim 1, we will conduct three linear mixed effects models, with rates of (1) repetitions, (2) prolongations, and (3) blocks as the outcome variables, both reflexive and adaptive responses as the predictors, and participant as a random intercept. We will also conduct three LASSO regressions with rates of (1) repetitions, (2) prolongations, and (3) blocks as the outcome variables and all ROI-to-ROI connections from the FB and FF control networks of the DIVA/GODIVA models as predictors. We expect repetition and prolongation rates to be associated with FB- based behavioral and neural mechanisms (i.e., reflexive response and functional connectivity in the FB control network). In contrast, we expect block rates to be associated with FF-based behavioral and neural mechanisms (i.e., adaptive response and functional connectivity in the FF control network). For Aim 2, we will use K-means clustering to group individuals based on patterns across stuttering behaviors, perturbation responses, and functional connectivity metrics. We will then characterize each cluster by input-variable means and test whether behavioral and neural measures covary with stuttering as predicted by FB- and FF-dominant phenotypes. We expect to find a FB-dominant cluster characterized by elevated repetition and prolongation rates, heightened reflexive responses to auditory perturbation, and stronger functional connectivity within the FB control network—relative to other clusters. Conversely, we expect to find a FF-dominant cluster characterized by increased block rates, reduced adaptive responses, and weaker connectivity within the FF control network—relative to other clusters. Results will have an important scientific and clinical impact by (1) strengthening our mechanistic understanding of stuttering and (2) deepening our clinical insights into factors that may influence therapy outcomes. This project falls under the 2023-2027 NIDCD Strategic Plan’s Theme 3 to “promote a precision medicine approach to prevention, diagnosis, and treatment”, as it investigates subtypes of a source of heterogeneity in AWS that may inform individualized treatments.

NSF Awards · FY 2026 · 2026-05

This proposal seeks to fund U.S.-based researchers to participate in a one-day NSF-NICT workshop on Beyond 5G/6G Research, to be held in Tokyo, Japan, on May 22, 2026. Immediately following IEEE INFOCOM 2026, the workshop aims to align U.S. and Japanese research and development timelines regarding 6G architecture, use cases, and performance metrics. Key focus areas include the development of open research infrastructures and the exploration of Beyond 5G/6G technologies such as AI-RAN, All-Photonics networks, quantum networking, and Non-Terrestrial Networking. By fostering a shared understanding of 6G toolkits and standardization from different national perspectives, the activity accelerates the transition from theoretical research to practical implementation. This workshop strengthens the bilateral U.S.-Japan cooperation initiative by providing a critical platform for leading U.S. and Japanese researchers to present and synthesize findings that will define the design and operation of future wireless networks. Results will be disseminated through a publicly accessible workshop report and a dedicated project website, providing a strategic roadmap for both academia and industry to contribute to the global 6G ecosystem. 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 · 2026-05

Non-Technical Description: An optical frequency comb is a light source whose frequency spectrum comprises many evenly spaced frequencies that form a comb-like structure. These sources underpin a wide range of technologies, including precision time and frequency measurements, global positioning systems (GPS), terabit-scale transmitters for optical communications, light detection and ranging (LiDAR) for autonomous vehicles, and energy-efficient all-optical computing. While recent progress in generating combs using on-chip integrated photonic resonators has enabled compact and scalable platforms, existing devices remain limited in efficiency, tunability, and flexibility for emerging applications. This project will leverage large arrays of coupled resonators to create a new class of optical frequency combs that enable application-specific comb engineering with substantially improved efficiency and agile tunability. The research is tightly integrated with STEM education, outreach, and workforce development activities that provide experiential learning opportunities for K-12, undergraduate, and community college students. Technical Description: The use of chip-integrated photonic resonators with Kerr nonlinearity has enabled a compact, scalable route for generating broadband optical frequency combs. However, most existing on-chip Kerr comb sources rely on single resonators, which severely limit their efficiency, agile tunability, minimum achievable line spacing, and the ability to engineer comb spectra for application-specific needs. This project overcomes these limitations by leveraging large arrays of on-chip coupled nonlinear resonators to realize unconventional comb states that are inaccessible in conventional single-resonator platforms. Guided by condensed-matter-physics-inspired design principles, including topological, Floquet, and non-Hermitian engineering, the project will demonstrate new comb paradigms, including (1) incommensurate combs that, unlike conventional combs, are not uniformly spaced yet remain mode-locked; (2) mode-locked nested combs that form a comb within a comb structure, enabling high spectral resolution for spectroscopy; and (3) quantum combs that provide enhanced quantum resources. Collectively, these architectures will enable tunable comb line spacing, reconfigurable spectral structure, and significantly improved conversion efficiency, while allowing the comb spectrum to be tailored to specific target applications. The project will train a workforce with interdisciplinary expertise spanning optical, electrical, and quantum engineering, and support the advancement of next-generation photonic technologies for classical and quantum applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY/ABSTRACT Minimally invasive, image-guided interventions have been demonstrated to reduce mortality, morbidity, recovery times, and procedure-related risks. Consequently, they are increasingly replacing invasive surgical procedures in the treatment of major diseases, ranging from cardiovascular disease to cancer. Current practice widely uses X-ray fluoroscopy for image guidance. However, this technique suffers from poor soft tissue contrast, hampering its use in complex procedures that require precise tissue localization, such as myocardial biopsy. It also exposes the patient to ionizing radiation, raising health concerns, particularly for patients who require multiple procedures over time. Unlike X-ray fluoroscopy, magnetic resonance imaging (MRI) has recently become the gold standard for image-guided interventions due to its ability to provide excellent soft tissue contrast while using non-ionizing radiation. It also provides functional information, such as blood flow velocities, perfusion, and diffusion. However, there has been very limited success to date in harnessing the power of MRI for procedural guidance. This is due to the unavailability of interventional devices such as catheters and needles that are safe and visible under MRI. Passive devices have shown shortcomings due to their imaging artifacts and poor contrast. On the other hand, most active devices based on coil or dipole antennas to detect radiofrequency (RF) signals for device localization under MRI use long conductive transmission lines. These devices, however, suffer from RF-induced heating due to RF energy deposition into these conductors during MRI, creating a safety hazard; this issue gets exacerbated at high magnetic fields (e.g., 3 tesla (T)). Although several methods have been proposed to mitigate RF heating, none has resulted in a clinical-grade device achieving a signal-to-noise ratio (SNR) sufficient for real-time device tracking. To address these limitations, we need to break away from the conventional scheme of relaying signals on conductors. Here, we propose a disruptive approach combining the latest advancements in nanofabrication and optics to develop an innovative clinical-grade electro-optic sensor for real-time position tracking of devices with virtually zero RF-induced heating under MRI. The sensor core element is a miniature probe, in which a coil antenna coupled with an optical microresonator (OMR), detects and converts an RF signal into an optical signal. The ultra-high quality factor OMR enhances the detected RF signal. An optical fiber carries the resulting signal to a module outside the body for sensor integration with a clinical MRI scanner. Unlike existing approaches, our novel design approach greatly enhances SNR and enables routing on an optical fiber instead of a long conductive transmission line, eliminating RF-induced heating and electromagnetic interference. We will package our sensor onto a commercially available MRI-compatible catheter to validate its clinical utility in tissue-mimicking phantoms and ex vivo in swine via MR imaging experiments at 1.5T and 3T. Successful demonstration of our electro-optic sensor as an active MRI marker can transform the field of MRI-guided interventions by opening the door to safer, novel, more complex, more effective, radiation-free, and minimally invasive interventions in various clinical fields.

NSF Awards · FY 2026 · 2026-04

This Faculty Early Career Development Program (CAREER) grant aims to advance national prosperity and secure national defense by developing new ways to understand, monitor, and manage complex energy storage and conversion systems. Today’s energy systems—such as batteries in electronics, the power grid, and defense technologies—are increasingly complex, yet they are often monitored using only electrical signals. This limited perspective makes it difficult to detect early signs of failure or to fully understand how these systems behave under real-world conditions. The proposed research introduces a new paradigm that integrates multiple types of information, including electrical, mechanical, and thermal signals, and uses artificial intelligence to interpret them together as a complete system. By enabling real-time health monitoring and early fault warning, this approach will improve the safety, reliability, and resilience of energy technologies. The outcomes of this project will support the energy, transportation, and defense sectors through the development of next-generation monitoring hardware and software, while also educating a new generation of engineers with interdisciplinary skills critical to the U.S. manufacturing and energy workforce. The intelligence and resilience of advanced energy systems depend on the ability to manage complex interactions among electrical, chemical, and mechanical processes. However, few existing methods can directly analyze nonlinear and irreversible interactions across multiple physical fields. The long-term goal of this CAREER project is to establish a scientific foundation for understanding multi-field dynamic systems and to address a central question: whether the state and evolution of one physical field can be inferred, diagnosed, and controlled through measurements of another. To achieve this goal, the project will integrate experiments, theory, and computation to develop a unified reduced-order framework for electrochemical and mechanical coupling in porous systems. A new analytical methodology, mechano-electrochemical impedance spectroscopy, will be developed to directly measure coupled electrochemical–mechanical responses under controlled perturbations. Microstructure-resolved simulations will be employed to investigate how heterogeneity, nonlinearity, and irreversibility emerge from evolving porous architectures. These physics-based insights will be combined with machine learning techniques to enable system diagnosis and health assessment without reliance on long-term historical data. In parallel, the education and workforce development activities will tightly integrate research and teaching by introducing cross-linked instructional modules in dynamics and machine learning, aligning curricular content with industrial needs through partnerships and cooperative education, training students in entrepreneurship and technology translation through a student-led venture, and engaging the public through a community-facing initiative focused on battery safety, reliability, and end-of-life management. 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 · 2026-03

This award provides student travel and subsistence for the 2026 International Programming Language Implementation Summer School (PLISS). This summer school provides an important and valuable educational opportunity for students to study topics related to designs and implementations of programming languages. A proper understanding of such topics is crucial to the implementation of tools and toolchains that use artificial intelligence (AI) and machine learning components. The award's broader significance and importance include building international community to create and implement tools and tool chains and enhancing education of US students. The school also provides students exposure to and multiple opportunities to interact with leading-edge research and researchers. By supporting US-based students, the school thus imparts training to the next generation of researchers in design and implementation of programming languages in both industry and academia, as well as to future application developers. 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 · 2026-03

SUMMARY This project seeks to support Research Software Engineer (RSE) Katherine Acevedo’s efforts to develop open science tools to better measure and train cognition. Acevedo is currently the Director of Development at the Brain Game Center for Mental Fitness and Well-Being and in this role supports a range of NIA-funded projects aimed at developing both assessment and interventional materials to advance the understanding cognitive aging, identifying early-stage behavioral biomarkers of ADRD, and promoting cognitive reserve. This work is demonstrated in the following funded projects: U19AG066567 (CoI: Seitz), R01 AG076157 (PIs: Green, Jaeggi & Seitz), R21AG069428 (PIs: Jaeggi & Seitz), R01MH111742 (PIs: Jaeggi & Seitz), R01AG077725 (PIs: Koener & Seitz), R01AG063952 (Green, Jaeggi, & Seitz), R61/R33AG073668 (Jaeggi & Seitz), R01EY031226 (PIs: Seitz & Green), and R21/R33AG074497 (Anguera, Jaeggi, & Seitz). Acevedo leads development of the Portable Adaptive Rapid Testing (PART) software application that supports these projects. The PART system is available on the Apple and Google Play stores, and currently has over 100 measures that address hearing, vision, cognitive control and executive functions, and decision making. It includes measures that are typically used in basic research, but it also includes standard neuropsychological measures and those that are used for clinical research studies. PART contributes a number of novel measures that don’t exist in platforms (such as NIH Toolbox, TabCat, or Mobile Toolbox), particularly measures of central auditory and visual processes. Further, a unique strength of the PART platform is its high degree of configurability, which facilitates the development of new tasks, refinement of existing tasks, adapting tasks to diverse populations, and also allows testing how variants of task structure impact psychometric properties of common tasks. PART is rapidly emerging as a key tool to support open science and Acevedo has been designing systems to promote the ability of researchers and students to more easily, flexibly, develop their own studies, and even tasks in PART. As such PART plays a unique role in cognitive tool sharing as most other cognitive assessment or intervention systems have limited customizability, whereas with PART is one can change the look, feel, language, and many task parameters in order to reach diverse population that have different cognitive needs. Securing dedicated funding for the Acevedo will provide critical support to focus development efforts on advancing the software architecture of the BGC’s apps to promote platform sharing and to promote open science.

NIH Research Projects · FY 2026 · 2026-03

Integrated mass spectrometry strategies to decipher protein-metabolite cross regulation Cellular metabolism relies on protein enzymes to convert substrates to products, working in interconnected networks to generate important biological precursors, remove toxic waste, balance redox and defend energy potential. However, the study of metabolic regulation in both healthy and disease states has proven challenging in part due to the intricate cross-regulation between metabolites and proteins. Current strategies to characterize metabolic regulation have generally focused on characterizing metabolites or protein enzymes, but not both. Consequently, how metabolites and proteins interact in the cellular context remains an open question in the field of metabolism and cellular biology. To bridge this gap, the Skinner lab is focused on characterizing the interactions between metabolites and proteins in the cellular context by using mass spectrometry to integrate existing multi-tiered methods in addition to developing new techniques to enhance the characterization and scope of both protein and metabolite measurements. Our primary research focus is on the regulation of proteins and metabolism by redox cofactors through signaling of sulfur-containing metabolites (Area 1) and the effect of vitamin B6 on metabolic and redox regulation as it is converted to protein cofactor pyridoxal-5-phosphate (PLP) (Area 2). These two areas each represent critical intersections of metabolome and proteome that play a key role across a variety of disease states. To better understand these two ranges of metabolite and protein cross-regulation, we will apply top-down proteomics and native mass spectrometry analyses of intact protein complexes coupled with full-scan and stable isotope labeling metabolomics to simultaneously probe metabolites and proteins. In Area 1, we will characterize how the abundance and redox state of specific thiol metabolites and cellular nicotinamide adenine dinucleotide phosphate (NADP+) redox state can modulate glycolysis and other core metabolic pathways Protein thiols have been demonstrated to be covalently modified at high stoichiometry with a variety of different groups, making this a likely axis for metabolic regulation. In Area 2, we will examine another key intersection between metabolite and protein regulation; the metabolism of vitamin B6 to its active form PLP prior to acting as a cofactor for 53 human enzymes. Specifically, we will characterize how some PLP-containing enzymes seem more resistant to extended B6 deficiency than others and how changes in subcellular nicotinamide adenine dinucleotide (NAD+) redox state can affect PLP levels and vice versa. This project integrates metabolomics with native mass spectrometry and proteomics and applies it to two pressing questions in the field of systems biology and regulation that lie at the intersection of metabolome and proteome.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Type 2 diabetes mellitus (T2DM) is a chronic condition characterized by impaired blood glucose control ultimately leading to cardiovascular, renal, and cognitive dysfunction. T2DM prevalence continues to rise in the U.S., with massive health and financial consequences. Current diet and exercise recommendations often fall short in helping adults with T2DM achieve long-term glycemic control because they inadequately address common psychological barriers (such as chronic stress) that undermine adherence to lifestyle behavior change. Mindful eating and yoga have emerged as promising strategies for T2DM management by combining mindfulness practice with physical activity, dietary habits, and diabetes self-management. Both mindful eating and yoga have independently shown promise for improving glycemic control in adults with T2DM. Mindful eating may help improve emotional regulation, strengthen awareness of internal hunger and satiety cues, and promote increased consumption of nutrient-dense foods, all of which can contribute to improved blood glucose management. Similarly, yoga has shown promise for reducing stress, reducing inflammation, and indirectly supporting T2DM management by fostering healthier attitudes toward lifestyle changes. However, current research has several limitations, including a limited understanding of the mechanisms driving these effects, inconsistent use of yoga types across studies, a scarcity of studies conducted in the U.S, and few studies assessing the combined impact of mindful eating and mindful movement for adults with T2DM. Therefore, rigorous and culturally diverse research is needed to evaluate the combined impact of mindful eating and yoga on T2DM management. We propose a 12-week pilot randomized controlled trial (RCT) to assess the feasibility, acceptability, and early efficacy of a combined mindful eating and yoga intervention to lay the groundwork for a larger efficacy trial. Using a single-blind, two arm RCT, 60 adults (>18 years old) will be assigned to either: a mindful eating and mindful movement group (MEMO) or a standard of care exercise and diet group aligned with American Diabetes Association recommendations. Both groups will engage in hour-long exercise sessions 3x/week and group diet counseling sessions 1x/week. As the primary outcome, a comprehensive battery of feasibility and acceptability measures will be used to determine if a combined mindful eating and yoga program can be successfully delivered to this population. To quantify the impact of mindfulness on key health and clinical outcomes, we will use state-of-the-art measures, including biomarkers (cortisol and HgbA1c), accelerometer-measured physical activity levels, validated dietary assessments, and psychological health scales. Although this pilot study is not designed to establish definitive effects, these measures will provide valuable preliminary insights into how mindfulness delivered via mindful eating practices and yoga may influence metabolic, behavioral, and psychological outcomes in T2DM.

NSF Awards · FY 2026 · 2026-02

Entangled materials—such as polymer networks, textiles, and steel-cable structures—are found across length scales and exhibit remarkable mechanical properties driven by both fiber properties and the complex ways fibers entangle and self-contact. However, their behavior remains difficult to predict and design due to the lack of simple models capturing their intricate geometries and physical interactions. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will address that gap by developing quantitative metrics of entanglement through experiments and microscopy across scales. These metrics will connect entanglement geometries to physical properties, enabling the creation of simplified digital network representations of complex entanglements. These representations will guide the design of future entangled materials with user-defined properties. The project will provide open-source tools and data to support scalable design and optimization of fabrics, textiles, and knits, particularly at industrial scales. Broader impacts include educational integration of network science across institutions and a public art exhibit that will focus on visualizing networks, aiming to raise awareness of network-science-driven materials engineering. The project will establish a closed-loop framework for describing entangled matter using physical networks, correlating structural features with mechanical performance, and using these insights for targeted design. It consists of three unified thrusts that combine theory, computation, and experimentation. To span multiple length scales, the team will use testbeds made of 3D-printed textile architectures and woven metamaterials. Quantitative mechanical measures of entanglement will be obtained both experimentally and numerically. This data will inform the development of network models in which filaments are converted into skeleton and contact networks with geometric and topological attributes. These models will then be used to optimize entanglement geometries for desired performance using graph neural networks and gradient-based refinements. The result will be new material prototypes with engineered entanglements and mechanical properties, along with a broadly applicable design methodology for entangled filament-based materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The exacerbating congestion and overcrowding of wireless spectrum strongly demand new spectrally efficient communication system architectures, e.g., full duplex and massive multi-input multi-output (mMIMO). These emerging systems necessitate fully-directional radiofrequency (RF) front-ends, which normally involve bulky and expensive magnetic devices for a critical function of signal circulation/isolation at the antenna interface. Although the non-magnetic counterparts promise chip-level integration with massive manufacturability, their very low power-handling capability remains as the bottleneck. This CAREER project aims to fundamentally unleash the high-power operation of non-magnetic non-reciprocal devices though a new paradigm of indirect signal circulation/isolation integrated into the prevailing load-modulation power amplifiers (PAs), named Non-Reciprocally-Coupled Load Modulation (NRC-LM). More broadly, this ‘indirect’ design paradigm can be generalized to other power-sensitive devices, e.g., tunable filters, acoustic-wave filters, and switches, which could enable high-power frequency-agile RF front-ends and impact the field of cognitive radios. Beyond the technological frontiers, this research will address the nation’s core interests in spectrum sustainability and ubiquitous coverage of high-speed connectivity, potentially leading to immense economic benefits. Moreover, by enhancing the efficiency of PAs (the most energy-consuming unit on all wireless platforms) with NRC-LM, the energy efficiency and environmental impacts of the entire wireless ecosystem can be improved. The impact of this research will be further expanded through several educational and outreach activities: (1) The RF/microwave curricula at the University of Central Florida will be enhanced with new class modules. (2) Mentoring and outreach programs will be designed to attract a broad range of students in STEM, thus preparing a new workforce for the RF industry. (3) The engagement of undergraduate students in RF/microwave research will be promoted through a comprehensive set of intriguing efforts. (4) To stimulate interests from K-12 students and general public, a series of “demystifying wireless communications” mini lectures will be designed, exhibited in outreach activities, and disseminated on social media platforms. The objective of this CAREER project is to establish the theoretical foundation and practical design methodologies for high-power magnetic-less fully-directional RF front-ends based on NRC-LM. By leveraging a unique characteristic of active load modulation, the circulator placement is transformed from the high-power node of PA output to an inner low-power node, while maintaining the critical signal circulation/isolation behavior. This transformation not only inherently eliminates the unaffordable power stress on non-magnetic circulator but also greatly mitigates the impact of its unforgiven loss and non-linearity on the overall transmitter. The proposed research comprehensively spans over theory, design practice, and system architecture: (1) As a foundation of practical designs, the new NRC-LM theory in terms of directional transmission and reception will be systematically established and generalized to all existing load-modulation modes. (2) Moreover, mixed digital-RF design in conjunction with advanced multi-input NRC-LM transmitter architecture will be investigated to synergize an optimal cooperation between non-magnetic circulator and active LM at arbitrary in-band frequencies, pushing extreme bandwidth, efficiency, linearity, dynamic range, etc. (3) Meanwhile, a novel quadrature-commutated circulator is proposed to offer ultra-wide bandwidth, watt-level power handling, and low loss. (4) Furthermore, innovative system-level designs will be studied to integrate the NRC-LM-based front-ends into mMIMO (antenna-array-based) and full-duplex systems, which can lead to unprecedented spectrum and energy efficiencies as well as multi-band and multi-standard capabilities. Overall, the success of this research will significantly enhance the next-generation spectrum- and energy-efficient communications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The Open Radio Access Network framework (O-RAN) is enabling open marketplaces where extensible radio-control applications (“xApps”) powered by artificial intelligence can be shared and deployed across multi-vendor cellular networks. Although this openness accelerates innovation, it also opens the door to security vulnerability. Indeed, malicious developers could hide software “backdoors” that silently degrade or manipulate the performance of competing networks. Currently, no testbed or certification pipeline verifies the security of these AI-driven applications before they enter the marketplace. The REPAIRT project addresses this pressing need by establishing the scientific and engineering foundations required to detect, neutralize, and ultimately prevent adversarial AI in O-RAN networks. REPAIRT couples the programmable wireless infrastructure of Saint Louis University and Northeastern University with new algorithms for systematic "red teaming" of AI algorithms in O-RAN networks. The project develops algorithms to flag adversarial AI behavior both before and after deployment, and introduces techniques that remove poisoned data and logic without costly retraining. A heterogeneous testbed supports reproducible experiments at scale and delivers an open framework that lets researchers and industry stress-test their own xApps under realistic traffic and threat conditions. These tools, data sets, and educational activities are enabling trustworthy xApp marketplaces, seeding advances in AI security applied to other dynamic cyber-physical systems, and prepare the future workforce in securing next-generation AI-driven wireless communication 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 2026 · 2026-01

Reliable communication, enabled by codes, is a primary workhorse of the information age. Successful code design (e.g., convolutional codes, Turbo codes, low-density parity-check codes, and polar codes) is sporadic and largely a product of individual human ingenuity, although the impact on humanity is enormous -- every cellphone designed uses one of these codes. In this project, we bring the tools of machine learning (ML) and deep learning (DL) to better decode existing codes and invent new code families, speeding up the code design process. The project also includes open-source code releases, graduate student mentoring, and outreach efforts to broaden participation in the field. This project investigates a family of codes called Plotkin Transform (PT) codes, which include Reed-Muller and polar codes as special cases (both are capacity achieving and polar codes are used in the 5G global cellular standard). Although Reed-Muller codes and polar codes were invented entirely independently (and six decades apart in time), PT codes provide a common framework design via the computation tree of the Kronecker Operation central to Reed-Muller and polar codes. This project exploits the PT code framework to explore the underlying design structures that enable good encoding and decoding properties via the tools of ML and DL to systematically generalize the family of PT codes by nonlinear parameterizations; data-driven methods allow us to explore the space of parameters via optimization techniques, such as gradient descent. The overall goal is to invent new codes within the generalized nonlinear PT code family as well as new decoder algorithms for both the standardized Reed-Muller and polar codes. 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 · 2025-12

PROJECT SUMMARY/ABSTRACT Motivation: Sensitive periods are temporal windows of elevated neuroplasticity, facilitating adaptation to and learning from early environments across a range of domains. In non-human animal models, excitation/inhibition (E/I) balance, or the expression of the primary excitatory (glutamate) to inhibitory (GABA) neurotransmitters, governs the degree of neuroplasticity throughout the sensitive period. Until recent methodological advances, measuring E/I balance minimally invasively in the human brain was infeasible, and thus our understanding of the role of E/I balance in human sensitive period neurobiology is extremely limited. Normative individual and clinical phenotypic differences exist in learning outcomes supported by sensitive periods. Research in non- human animal models suggests a range of clinical outcomes may result from differences in sensitive period neuroplasticity. Therefore, the proposed research will have a broader impact by first furthering understanding of normative sensitive period mechanisms, so future work can understand clinical deviations in these mechanisms. Here, we focus on an early visual cortical sensitive period because it is well-characterized and translatable across species. The proposed project investigates if E/I balance indexes sensitive period neuroplasticity in human infant visual cortical development as it does in non-human animal models. Methods: In this accelerated longitudinal study, infants will participate in visits at 2-to-4 months, 7-to-9 months, and 10- to-12 months. At all visits, infants will participate in a visual-evoked potential (VEP) electroencephalography (EEG) task as a readout of visual neurodevelopmental maturation throughout the sensitive period. Also at all visits, the MEGA-PRESS magnetic resonance spectroscopy (MRS) sequence will be collected from infants during natural sleep to extract concentrations of glutamate and GABA in a visual cortical seed. To measure visually-mediated cognitive outcomes, infants will participate in the cognitive scale of the Bayley Scales of Infant Development at the last visit. Aims: Aim 1 of the proposed project will characterize age-related changes in the MRS-derived E/I balance over the sensitive period using Generalized Additive Models for Location, Scale, and Shape. Aim 2 will relate individual differences in the MRS-derived E/I balance to the VEP readout of visual cortex function over the sensitive period using longitudinal multilevel modeling. Aim 3 will test if individual differences in the MRS- derived E/I balance from 2 to 9 months predicts emerging visually-mediated cognition at 10-to-12 months, at the end of the sensitive period, using longitudinal multilevel modeling. Training: To support the applicant’s goal of being an independent developmental neuroscientist using multimodal infant neuroimaging and to complete the proposed project, the applicant will develop expertise in 1) infant MRS, 2) advanced longitudinal modeling, and will 3) accelerate her professional development. The applicant is well-poised to complete this project and advance towards her career goals with a strong mentorship team, detailed training plan, and position at an R1 research institution.

NSF Awards · FY 2025 · 2025-10

This award seeks to fund US-based students to attend ACM CoNEXT 2025 conference, held in Hong Kong on December 1 - 4, 2025. ACM CoNEXT 2025 is a premier annual forum that attracts high-quality, forward-looking research contributions and provides a vibrant forum for technical and professional exchanges. CoNEXT will expose selected students to cutting-edge developments in the field and enable interactions with world-leading researchers. Students will gain feedback on their ongoing work, broaden their academic perspectives, and build lasting professional connections. This effort supports students from US universities to attend ACM CoNEXT 2025 conference in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the 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.

NSF Awards · FY 2025 · 2025-10

Wireless and mobile communications are one of the most prominent technological successes of the last few decades. They provide great economic and societal benefits. However, as very well-articulated in the National Science Foundation program solicitation for the Next Era of Wireless and Spectrum, existing approaches to spectrum access and management are increasingly showing inadequacy in addressing rising challenges for this emerging era of wireless systems. A plethora of emerging applications, such as Massive IoT (MIoT), autonomous cars, robotics, and augmented reality are driving the demand for spectrum to new heights. Spectrum scarcity is becoming a critical issue. Simultaneously, wireless systems, especially their physical layers, are increasingly implemented in software. Software Defined Radios (SDRs) are becoming more capable, featuring small form factors and low costs. This development is a double-edged sword: it facilitates the creation of innovative wireless communication techniques to tackle spectrum access and management challenges (such as flexibility, agility, and sensing). However, it also lowers the barrier for misbehaving devices and increases the potential for attacks on robustness, privacy, and security. Unfortunately, current methods for enforcing spectrum access policies are inadequate to handle the combined challenges of scarcity, rising demand, and the ease with which malicious behavior can occur. This project addresses the critical need for mechanisms that can prevent, detect, localize, and attribute misbehavior while preserving user privacy. Ensuring security and privacy during spectrum management enforcement is a significant challenge. This proposal focuses on developing RF-centric machine learning techniques for real-time situational awareness, including sensing, classification, detection, and localization of misbehaving devices. It aims at analyzing and mitigating a wide range of attacks, sharing open-source prototypes and datasets, and training the next generation of spectrum scientists. The research activities are organized in the following main tasks. - Threat Models and Attack Surface Analysis: This critical first task focusses on conducting a security analysis of spectrum management, specifically considering adversaries using advanced SDR platforms and machine learning to evade detection. This includes adversaries consisting of colluding emitters attempting to evade detection and attribution. - Real-Time Situational Awareness: The project aims at creating RF-centric machine learning models for real-time sensing and classification of RF emissions. These models are architected to handle misbehavior, collisions, and interference, leveraging multiple antenna sources in congested and contested environments. - Identification and Localization of Misbehaving Devices: To secure spectrum access and towards enforcing policing, this project targets the development of techniques to accurately locate and attribute misbehavior, overcoming evasion tactics like mimicry and collusion. At the same time, these techniques by-design embed user privacy guarantees to prevent unlawful tracking. - Mitigating Misbehaving Devices: In order to provide short-term mitigation solution to spectrum attacks, the project aims at developing algorithms to tolerate misbehavior, ensuring resilience and performance in contested environments. This work builds on the PI’s prior RF-centric machine learning models for beamforming and interference nulling. - Prototypes, Testbeds, and Datasets: An important activity within this project is to evaluate and demonstrate practicality and realism. It consists of prototyping the proposed techniques and comprehensively evaluating them on increasingly large-scale testbeds. To ensure reproducibility, the code for our prototypes and the ML models, will be open-sourced and designed to run on popular SDR platforms. The target evaluation environments include Northeastern University 60x60x30 ft. anechoic chamber, NSF-sponsored testbeds such as the DARPA/NSF Colosseum emulator, and the NSF POWDER City-Scale testbed. Building on his prior research, Principal Investigator Noubir, plans to systematically build and release curated datasets to support the spectrum science and education community in transfer learning, training, and evaluating new RFML 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 · 2025-10

Despite the advances in deep neural networks (DNNs) and edge computing, there exist substantial challenges to enabling end-to-end DNN inference on a full spectrum of edge devices, such as tiny wearables and low-cost Internet-of-things (IoT) devices. This problem has spurred the recent studies of brain-inspired vector symbolic representation (VSA) classifiers as an alternative framework for ubiquitous on-device inference. At a high level, VSA classifiers mimic the brain cognition process by representing each object as a vector (typically in a very high-dimensional space). While VSA classifiers offer advantages over DNNs in terms of inference efficiency due to parallel processing, the hyper-dimensionality in their design can still easily result in a prohibitively large VSA model size beyond the limit of many tiny devices with stringent resource constraints. If successful, this project will make it possible for more everyday devices to run advanced artificial intelligence (AI) on their own, without needing to send data to remote servers. This could improve privacy, save energy, and open the door to smarter wearables, medical devices, and home gadgets. Finally, the project will bring the latest discoveries into college courses to help train the next generation of engineers and computer scientists. To address the hyper-dimensionality challenge, this project moves away from hypervector-oriented VSA and proposes TinyVSA, which uses much smaller, compact vector representations. Specifically, this project focuses on three key directions: first, redesigning TinyVSA’s vectors to improve accuracy while the VSA dimensionality by orders of magnitude; second, making TinyVSA run continuously and efficiently on tiny, low-power chips; and third, developing an efficient, hardware-aware method to automatically find the best TinyVSA architecture for target devices. 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

Offshore wind energy is an emerging, safety-critical sector facing persistent workforce challenges in building and sustaining a skilled workforce. These include limited early exposure to real-world work environments, costly and risky site access, unclear career pathways, and rapidly evolving digital and automation technologies. These challenges reduce interest, slow skill development, and make it harder to retain workers. By coupling cohort-based mentoring, cross-sector partnerships, participatory co-designed modules, micro-credentials tied to industry skills, and shared digital assets, the project will broaden participation and build a prepared, resilient, and technologically agile workforce. The resources and training model developed through this work can also be applied to other maritime and emerging technology fields with analogous workforce challenges, helping to grow the U.S. workforce and increase access to science, technology, engineering, and mathematics careers. This project will pilot a multi-phase offshore wind experiential learning framework led by an interdisciplinary team with complementary expertise from Northeastern University, the University of Alabama, and a range of industry, educational, and public sector partners. It will support community college and engineering students through low-cost, immersive at-home training that includes personalized coaching powered by artificial intelligence, followed by a supplemental certificate program and co-op or internship opportunities for students who demonstrate strong interest. The program features a structured, modular curriculum aligned with industry needs; a cohort-based model that fosters peer support, reflection, and iterative improvement; and continuous mentorship from the project team, industry professionals, and previous cohort participants to support both learning and career exploration. The program will track participants’ readiness, learning outcomes, and retention to support an iterative co-design process that refines training modules and credentials. This project offers scalable early exposure to offshore wind careers, technical skills, and worksite challenges, preparing students for success in the field. The work aligns with ExLENT’s mission by creating scalable and data-driven experiential learning pathways for emerging technologies and by openly sharing its tools and outcomes for broader impact. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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.