Northeastern University

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

The Center on Responsible AI and Governance (CRAIG) brings together leading academics, businesses, and government agencies to develop the knowledge and workforce required to pursue artificial intelligence that is safe, accurate, impartial, and accountable. Expert faculty from four universities (Ohio State, Baylor, Northeastern, and Rutgers) and disciplines ranging from computer science to law, business, and the social sciences, work with industry and government partners to identify the most pressing responsible AI challenges and develop scalable solutions and the workforce required to implement them. By advancing trustworthy and responsible AI, CRAIG increases the acceptance and adoption of AI, thereby promoting competitiveness and national security. CRAIG will generate practice-informed research and forward-looking workforce development. CRAIG project teams investigate core questions such as how to design more accurate and safe generative AI systems; which AI governance technologies and strategies best achieve social and business goals; how to audit deployed AI models; how to provide privacy and transparency in smart, sensed environments; and how to engage constructively with ethical challenges pertaining to AI. The research teams’ multiple disciplinary lenses enable them to perceive how technology, law, ethics, and business management interact with and impact one another, and so to generate fresh and practical insights and translational solutions. Northeastern University leverages its depth of normative ethics expertise and existing interdisciplinary collaborations to strengthen CRAIGs research-based solutions. In combination with its research efforts, CRAIG educates and trains the next generation of responsible AI professionals. The Center provides students with research experiences, internships, “real world” mentors, and curricular innovations, and current employees with continuing and executive education, in the new and growing field of responsible AI. Northeastern leverages its strength in AI ethics curriculum development, training, and experiential education to help realize these educational aims. CRAIG’s research and educational pillars provide businesses and governments with the knowledge and workforce development approaches required to achieve safer and more trustworthy AI. Thus, the project contributes to the building of a more sustainable, prosperous, and secure AI economy. 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

Earth’s ionized outer atmosphere has a significant impact on radio-frequency (RF) signals used for communication, radar, and Global Navigation Satellite Systems (GNSS) such as GPS. These effects intensify and become less predictable during elevated solar activity, particularly at higher latitudes where auroral phenomena produce complex and transient ionospheric structures that cause GNSS signal scintillations and tracking failures. At high magnetic latitudes, Earth’s convergent magnetic field acts as a lens, channeling electromagnetic energy derived from the solar wind into a narrow latitudinal region. Most of this energy is dissipated in the lower ionosphere (<200km) through a complex interplay of particle precipitation, plasma heating, and turbulent transport. This multi-scale dynamic is poorly understood and challenging to observe. This project develops innovative AI-driven methodologies to enhance our understanding of these structuring processes and their implications for technologies we rely on for convenience, safety, and national security. The research will facilitate new approaches for monitoring and mitigating ionospheric effects on RF signals and will guide the creation of next-generation "smart sensors" that incorporate a hybrid suite of sensors alongside on-sensor generation of ionospheric models. This project will support graduate and undergraduate student training. This project addresses this challenge through a methodology that leverages the complementary nature of GNSS and optical data. Wide-field imaging of select emissions in the aurora and airglow spectrum provide quantitative information about plasma production and loss rates, while dual-frequency GNSS receivers offer precise measurements of path-integrated plasma density, referred to as Total Electron Content (TEC). These measurements are connected through established physics-based models. The objective is to identify ionospheric parameters—such as density, temperature, and ion composition—that are consistent with both observational data and established physical principles. Science questions to be addressed are (1) What are the important multi-scale plasma density patterns that define the electrical load seen by the magnetosphere? and (2) How do variations in latent parameters impact the ionospheric response? This work is ideally suited to a specialized form of AI known as Physics Informed Machine Learning (PIML), which incorporates known physics to uncover hidden states of the ionosphere, providing both detailed ionospheric representations and insights into unobserved parameters. The accuracy of PIML models improves with the amount of data applied. Thus, citizen scientists can play a significant role in this research, as GNSS and optical sensors embedded in consumer smartphones continue to improve. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This award provides funding to about eight (8) U.S.-based doctoral students to attend the doctoral consortium at the 27th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2025). ASSETS provides a leading forum for academic and industrial participants to disseminate research that addresses the use of computing and information technologies to support a wide range of people, including persons with disabilities, people with speech, motor, hearing, and vision impairments, cognitive disabilities, emotional and learning disabilities, and aging. The doctoral consortium is designed to provide student researchers a friendly, open forum to present their research ideas, listen to ongoing work from peer students, and receive constructive feedback while developing a supportive community of scholars and a spirit of collaborative research. Student participants will present their work during the Consortium and will receive feedback from both faculty panelists and other participants to help them position and scope their work relative to related research as well as to develop the ideas and methods they employ. The doctoral consortium also provides professionalization and career mentoring, along with opportunities to connect with other students and faculty, to support both their own career goals and to grow the SIGACCESS community. Through these networks, the consortium also works to encourage interdisciplinary collaborations that bring both technical and behavioral expertise to bear on the hard problems of assistive technologies and universal access. Calls for applications for funding support will be widely disseminated to attract highly qualified, early-stage doctoral students who are most likely to benefit from the mentoring and most likely to need additional funding support to attend. Student applications will include a summary of their research and its contribution to the accessibility of field and include statements from their advisors about the benefit the student will receive; students will be selected based on the potential of their research to impact the community and the value they will both receive from and contribute to the consortium. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Abstract Evaluation of the school-based Healthy Relationships Project for primary prevention of child sexual abuse and problematic sexual behaviors among youth in 7th and 8th grades Child sexual abuse (CSA) is a major issue worldwide, with rigorous evidence of its magnitude and impacts on social, emotional and physical health. Problematic sexual behavior (PSB) among youth, or youth-perpetrated CSA, accounts for more than 70% of CSA experiences (Gewirtz-Meydan A et al. 2019). While interventions for CSA primary prevention exist, few have been rigorously evaluated and even fewer focus on PSB. A review of school-based CSA prevention programs (Walsh et al., 2015) found 24 trials elementary and high schools across 7 countries that showed evidence of improvements in protective behaviors and knowledge, but no changes in CSA incidence/prevalence. The Healthy Relationships Project (HRP) created, modified, and run by Prevent Child Abuse Vermont, has been delivering CSA primary prevention curricula, with PSB prevention as a major tenet, since 1990 with implementation across 30 U.S. States, including statewide in Vermont. Substantiated cases per year in Vermont dropped 61% and importantly for this proposed evaluation, the number of child perpetrators per year dropped 69%. Utilizing a stepped wedge randomized trial design, we will evaluate the 10- lesson Sexual Abuse Free Environment for Teens (SAFE-T) curriculum of the HRP, and TECHNICOOL, an internet safety workshops for caregivers, in 16 schools with 7th and 8th grade students. SAFE-T uses an upstream universal public health approach for primary prevention of youth problematic sexual behaviors with developmentally appropriate intervention elements for youth, faculty/staff, and caregivers. Longitudinal data will be collected on key outcomes, including improvements in protective behaviors, self-efficacy, and knowledge among students and adults, and CSA-related incident school reports. This program will meet criteria for the 2018 School Safety Omnibus Amendment Act that obligates DC schools to address sexual abuse prevention. Our implementation partner, MCSR, has outstanding relationships with Washington, DC schools. MCSR has a long history of implementing evidence-based, gender-based violence prevention programs including the Men of Strength (MOST) Club and Women Inspiring Strength and Empowerment (WISE) Club in over 120 locations across 34 states, and Washington, DC. Our evaluation team has demonstrated success in a similar trial of HRP curricula for pre-K through 5th graders in 12 public/public charter schools in DC, with an additional 4 schools scheduled. Qualitative research will assess strengths/weaknesses of intervention rollout, fidelity monitoring, lessons learned, and sustainability. This innovative, novel mixed methods evaluation study will move the science of CSA/PSB prevention research forward with a community-based participatory research partnership between prevention research scientists, community-based organizations, and public schools in high need, diverse urban areas. In summary, a rigorous experimental evaluation will be conducted of an existing universal primary prevention program for child sexual abuse and problematic sexual behavior among 7th/8th graders, a program with 30+ years of implementation history in 30 U.S. states and robust pilot data.

NIH Research Projects · FY 2025 · 2025-09

The long-term objective of this project is to advance vaccine efficacy (VE) studies by developing novel statistical and machine learning methods that characterize heterogeneity in vaccine responses. Current VE studies primarily focus on population-level averages, obscuring variability across individuals and study contexts. To overcome these limitations, this project will develop robust prediction intervals for individual vaccine efficacy (IVE) using conformal inference, integrate information from multiple VE trials via privacy-preserving federated learning, and develop time-to-event methods to quantify VE that remain valid across study sites, regions, and calendar periods. These methods will accommodate surrogate markers, such as immune correlates of protection, to improve efficiency in treatment effect estimation. The research will leverage data from six harmonized COVID-19 VE trials to develop assumption-lean methods that allow for individual-level predictions while ensuring privacy, thereby enhancing the robustness of VE estimates and improving their applicability across different populations. The candidate will receive comprehensive training in virology, immunology, genomic sieve analysis, immune correlates of protection, and advanced statistical and machine learning techniques. This training will equip the candidate with the skills needed to develop innovative approaches for understanding variability in vaccine responses and to lead future VE research. An interdisciplinary mentorship team spanning biostatistics, infectious disease epidemiology, network science, immunology/virology, and VE trials will support the candidate in achieving these goals. The project will also involve collaborations with leading public health institutions to ensure that the methods developed are directly applicable to real-world challenges. By creating a flexible and scalable framework, this research has the potential to influence the design and analysis of future vaccine trials. Ultimately, the project aims to create a methodological framework that provides more precise VE estimates, informs personalized vaccine strategies, and has a significant impact on public health policy decisions, leading to more effective vaccine deployment and optimization of strategies to combat infectious diseases, to the benefit of all Americans. Modified

NSF Awards · FY 2025 · 2025-09

The project aims to unify the combustion research community's fragmented datasets by creating open-source, standardized databases for gas-phase chemical kinetics experiments and models. The project will develop a machine-readable, web-based, and API-accessible repository that initially compiles public data and later incorporates researcher-contributed datasets, all adhering to FAIR principles to promote accessibility and reuse. The project develops robust cyberinfrastructure to enable, facilitate, and encourage Public Access to and the widespread reuse of experimental and modeling data, stewarding Open Science in the field of gas-phase chemical kinetics, primarily combustion. This capability building project will: 1) devise open data formats for gas-phase chemical kinetics experiments and grow an open database of such measurements; 2) create a database for kinetic models and parameters with standardized formats; 3) build an innovative data repository model based on distributed version control to eliminate barriers to use, reuse, and contribution; 4) collect publicly available experimental and modeling data to bootstrap the databases; and 5) propose workflows and “apps” that use the database infrastructure, which will be useful in their own right, and serve as examples for others to follow in creating their own applications and workflows. This award by the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering is jointly supported by the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Michael Kienzler of the Department of Chemistry at the University of Connecticut and Professor Steven Lopez of the Department of Chemistry at Northeastern University are collaborating across synthetic and theoretical chemistry to study the reactivity of molecular photoswitches. Molecular photoswitches use light to trigger controllable changes in molecular function. In this project, the PIs are using ring strain as a second means of controlling and enhancing the photoswitching functions. Using ring-strain to increase molecule reactivity is well-established in chemistry. This proposal combines these two ideas to develop cyclized photoswitches to generate ring-strain reversibly and study the activation of functional groups in the rings for wavelength-dependent spatiotemporal control of target reactions. Results from this research project will significantly impact organic chemistry, energy storage, biophysics, and chemical biology. Furthermore, this interdisciplinary project includes a substantial educational framework for supporting STEM students from high school through graduate level chemistry. The long-term goal of this collaboration is to understand the photophysical effects of ring-strain on photoswitches and to demonstrate that the reversible generation of ring-strain can accelerate otherwise unfavorable photochemical reactions. Molecular photoswitches reversibly interconvert between isomers when irradiated with different wavelengths of light and have been a subject of fascination in the chemical community for over a century. Photoswitches like azobenzenes, fulgides, diarylethenes, and hydrazones have numerous applications in widely disparate scientific fields, from nonlinear optics to pharmacology. The broader scientific impacts include light-patterned polymerization to install photoswitches directly into the polymer backbone and to use different wavelengths of light to tune the reactivity of strain-promoted cycloadditions for bioorthogonal labeling. High throughput computations will create datasets of ground- and excited-state properties to guide experimental efforts. This collaborative work will produce structure-reactivity relationships needed to inform the discovery of new photoswitches and their reactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The aim of this project is to analyze how centers influence scientific collaboration across U.S. research universities, identifying the structural and organizational factors that predict their success. University research centers and institutes play a pivotal role in advancing interdisciplinary collaboration and knowledge production. These entities are often designed to break down disciplinary boundaries and promote innovation. This research fills a gap in knowledge of their long-term impact on researcher networks, institutional structures, and scientific outcomes. The project serves the national interest by improving the effectiveness of research investments—guiding universities, funders, and decision makers in designing more effective research environments—and by developing research methods using artificial intelligence. Public-facing tools, including an open database, make these insights broadly accessible to decision-makers. This work also strengthens U.S. research infrastructure by equipping institutions with evidence-based strategies to support interdisciplinary science, translational research, and the next generation of cutting-edge research centers. This study builds a comprehensive, longitudinal dataset of research centers and institutes at over 300 U.S. universities linked to affiliated faculty, funding sources, and collaborative research outputs. Using this dataset, the project (1) maps center affiliations and associated collaboration networks, (2) classifies centers by their founding collaboration structures and analyzes how those relate to long-term performance, interdisciplinarity, and scientific impact, and (3) uses natural experiments to estimate the causal effects of center affiliation on individual collaboration practices and interdisciplinary research production. These aims are accomplished through advanced computational and AI techniques, including affiliation disambiguation algorithms, network analysis, large language models, and quasi-experimental methods. The resulting empirical and methodological contributions inform theories of collaboration, organizational science, and the science of science, while also generating practical tools and guidance to support the future of interdisciplinary research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

An award is made to Northeastern University to enable the development of advanced cyberinfrastructure that will support the FAIR (Findable, Accessible, Interoperable, Reusable) use of single-cell proteomics (SCP) data. SCP is a rapidly emerging field that allows scientists to directly measure proteins in individual cells, offering insights into biology that cannot be obtained by measuring RNA alone. The project will develop open data standards, bioinformatics tools, and data-sharing platforms that ensure SCP data can be easily reused by researchers worldwide. This work will also provide educational opportunities for students through hands-on training in data science and computational biology, promote open science practices, and deliver outreach programs aimed at improving public scientific literacy. By fostering data sharing and reproducibility, the project contributes to a robust scientific workforce and broader societal goals such as improving human health, protecting the environment, and advancing biological understanding through collaborative research. The research addresses urgent needs in the scientific community by enabling rigorous and transparent analysis of single-cell proteomics data. As this technology matures, large volumes of complex data are being generated, yet the current infrastructure limits researchers' ability to standardize, share, and interpret these datasets effectively. The project will formalize open metadata standards, extend open-source pipelines for quality control and analysis, and integrate reanalyzed public datasets into widely used resources such as the PRIDE database, the Single Cell Expression Atlas, and the Chan Zuckerberg Initiative’s CellxGene portal. By supporting reproducible reanalysis and downstream meta-analysis, the project will unlock new biological insights, such as identifying protein covariation patterns that may reveal mechanisms of cellular regulation. This work will empower the scientific community to advance discovery through more transparent, interoperable, and accessible use of proteomics data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Unlike traditional pharmaceuticals, biopharmaceuticals use living organisms, e.g., cells, as factories to provide essential life-saving treatments for severe and chronic diseases (including cancers, metabolic diseases, and infectious diseases such as COVID-19) often with advantages such as increased efficacy and reduced side effects. However, current manufacturing systems lack the flexibility to produce existing and new biopharmaceuticals on demand. This is mainly because biomanufacturing processes are highly complex and variable, with hundreds of biological, physical, and chemical factors dynamically interacting at molecular, cellular, and macroscopic scales. Further, bioprocessing mechanisms are not systematically understood, and data are often very limited, sparse, and heterogeneous. To address these challenges, this Faculty Early Career Development (CAREER) project aims to optimize biomanufacturing processes via a bioprocess-specific AI that integrates uncertainty, intelligence, and science (i.e., systems and synthetic biology). Leveraging emerging sensing technologies that can monitor bioprocesses at molecular and cellular scales, this AI can also efficiently decode fundamental mechanisms. Moreover, by transferring this AI to industry practice, it is hoped this research will help make life-saving biopharmaceuticals rapidly available by accelerating biomanufacturing systems integration and automation with dramatically improved capabilities. The project will in parallel create a world-leading workforce pipeline from training the current workforce to educating (under)graduate and K-12 students. This project will create a mechanism-informed AI platform on Biological Systems-of-Systems to enable the quick assembly of flexible and robust biomanufacturing systems. To support biomanufacturing systems integration and accelerate the development of flexible optimal robust manufacturing systems, this research will answer two fundamental questions: (1) how to create a unified knowledge representation that enables integration of heterogeneous data collected at molecular, cellular, and macroscopic scales in different production processes; and (2) how to enable sample-efficient and interpretable learning for fundamental mechanisms and optimal control strategies within and across different scales. These questions will be addressed through three integrated research efforts: (i) creating a multi-scale probabilistic knowledge graph (pKG) hybrid (mechanistic + statistical) model with a modular design capable of representing spatial-temporal causal interdependencies from molecular- to cellular- to macroscopic scales for different biomanufacturing processes; (ii) developing interpretable federated learning to quickly fuse sparse and heterogeneous data collected from different production processes to advance scientific understanding and track critical latent states through sequential Bayesian inference on the pKG; and (iii) constructing new provably efficient model-based reinforcement learning schemes on Bayesian pKG, accounting for model uncertainty, informing design of experiments for digital twin calibration, and streamlining the policy search on optimal robust biomanufacturing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This research will advance community priorities in the areas of safety, security, and human health and wellness pertaining to existing and future neural implant devices. The team includes computer scientists, electrical engineers, MDs, neuroscientists, neural implant community groups, and manufacturers. The partner community groups include patients and their supporting families and caregivers from whom the team will understand the personal impact of neural implant technologies. Expanding engagement with partnered companies who manufacture and medical doctors who use neural implants, the team will address these community challenges and collaboratively deliver a neural implant hardware/software co-design solution that empowers the use and facilitates the safe adoption of emerging neural implant technologies. The research techniques developed for chip security will have applicability to other security related chip-based devices. This project will advance the scientific and technical security and chip hardware design by modeling the operations from a secure and dependable control perspective and developing innovative defense mechanisms that can be applied to emerging smart healthcare devices. Finally, by adding low-resource chip design, the team will advance manufacturing techniques for chip-based devices adding additional security features without impact to overall performance or lifetime. Researchers have demonstrated the impact of “brain-jacking” in mouse models but have not provided solutions. This team’s multipronged project resolves these challenges by leveraging expertise in computer security, sensing, microelectronic design, and strong affiliations with clinical settings. The approaches include the design of (1) verifiably resilient control system and simulations and design upgrades that build upon models of neural sensing and stimulation and explainable AI techniques, (2) automated cybersecurity and resilience testbeds that host physical neural implant devices for fault-injection, side-channel information leakage, and remote connection security analysis, and (3) low-resource (i.e., computational time, power, footprint) intrusion detection with on-chip sensing to continuously monitor anomalies and deter adversarial manipulations. The team will improve the resilience and security of neural implants by protecting system hardware and AI-in-the-loop control software from malicious information injection, disruption in operation, and privacy leakage. A test bed will be developed to validate and test physical devices, see the results from modeling and microelectronic design changes, with real world data collected in lab and translated back to the model for updating and control of realistic interactions. Testing the insertion of malicious intent or unintended interference will allow the team to discern impact and identify potential design changes on the device hardware to prevent future incidents. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This research project aims to prototype and evaluate an intelligent garment featuring an adaptive splint that clinicians can dynamically control to address the treatment goals of individuals with neurodevelopmental disabilities who engage in self-injurious behavior. This novel device will facilitate sensorimotor interaction between the adaptive splint and the individual. Additionally, the garment will communicate with therapists to enhance their understanding of the individual's responses to treatment decisions and to predict and analyze attempts at self-injurious behavior. Self-injurious behaviors in minimally verbal individuals with neurodevelopmental disorders can lead to significant physical, emotional, social, and economic challenges. Behavioral interventions currently represent the most established approach to managing self-injurious behaviors. Presently, interventions for severe cases involve individuals wearing rigid splints on their arms, which help mitigate injury risk while still allowing the behavior to occur. If successful, this research is anticipated to improve the quality of life for both patients and clinicians alike. The adaptive garment prototype will feature an active split that can adjust its mechanical stiffness in real time with a clinician-in-the-loop feedback mechanism. This research initiative has several key interdisciplinary objectives: 1) to assess the effectiveness of the first active splint designed for behavioral interventions, drawing on design and actuation principles from soft robotics, which is capable of withstanding self-injurious behaviors; 2) to address the challenge of accurately and automatically measuring the frequency, intensity, and duration of self-injurious behaviors in therapeutic settings, utilizing models developed from retrospective data gathered through ambulatory accelerometry, electrodermal activity, and photoplethysmography via a wearable biosensor, in conjunction with video data; 3) to analyze and evaluate the interactions between individuals and therapists during behavioral intervention sessions that incorporate the garment; and 4) to examine how the garment impacts therapists' decision-making processes. If successful, this research program will lay the foundation for healthcare garments capable of reasoning and modeling wearer behaviors to improve therapeutic outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will map cosmic structure across the sky to incredible depth. Such maps have immense statistical power to shed light on dark energy and dark matter, perhaps the most compelling mysteries in modern physics. To reach the full potential of LSST and other upcoming projects, a better understanding of astrophysical processes and observational uncertainties is required. This program focuses on two of the most important astrophysical effects, determining where galaxies form and what shapes they have. Prof. Blazek’s team will pursue several linked themes leading to robust, sophisticated cosmological analyses of LSST data. His tightly coupled education plan includes a mentoring and research program for a dynamic and comprehensive student cohort. Prof. Blazek will also develop a numerical methods course for graduate and advanced undergraduate students. This course will provide workforce training in cutting-edge techniques used in this research. Weak lensing and galaxy clustering are powerful probes of dark energy and dark matter. Intrinsic alignment (IA) and galaxy bias represent two of the most critical challenges to these measurements. This program combines Blazek’s leading expertise in IA with novel simulation and artificial intelligence / machine learning techniques. Applying a semi-analytic approach to IA and bias on gravity-only simulations will enable production of mock galaxy catalogs in large cosmological volumes with a wide range of IA properties. By developing sophisticated emulation with neural networks, the research will accelerate the process of creating and analyzing simulated galaxy data, providing direct simulation-based modeling. These innovations will enable Rubin-LSST data analyses not possible with existing methods and will broaden the discovery potential of future data through measurements of IA as a cosmic probe. Prof. Blazek’s advancement of research opportunities and targeted mentoring for students in physics and astronomy builds substantially on a successful pilot program he has developed. His simulated galaxy catalogs and modeling tools will be made public, benefiting the LSST Dark Energy Science Collaboration and the broader cosmology community, including through a cross-survey project that Blazek leads. This research award is partially funded by a generous gift from Charles Simonyi to the NSF Astronomy division. The project includes significant contributions to Vera C. Rubin Observatory’s Legacy Survey of Space and Time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project concerns the mathematics of multiple occupancy. Collecting the possible positions of a fixed number of agents in a given environment, and omitting those in which two or more agents collide, one obtains a space of inexhaustible theoretical and, in a present-future of automated factories and autonomous vehicles, practical interest. This project approaches configuration spaces from the vantage of algebraic topology, the mathematical inquiry into the global character of space, enriched and deepened by contemporary techniques and ideas from topological robotics, mathematical physics, representation stability, homotopical algebra, and higher category theory. In addition, the project funds will bring in seminar speakers, enriching the mathematical environment of local graduate students and contributing to the advancement of early career researchers. The project has two main components. In the first, the environment or background space is a graph. In this arena, the aim is to understand asymptotic phenomena in Betti numbers and multiplicities of irreducible representations, identify universal generators and relations, investigate torsion phenomena, and calculate topological complexity in the unstable regime. In the second, the background space is manifold, and the aim is to compute positive characteristic homology by leveraging a connection to spectral Lie algebras, to study the invariance properties of configuration spaces, and to calculate the homology of the ordered configuration spaces of the torus using the representation theory of combinatorial categories. 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 · 2025-09

Abstract This project seeks to develop an open XR platform that can make accessible basic vision science assessments and interventions to people living with visual impairment. In the US alone, over 6 million people live with low- vision, which negatively impacts well-being, economic independence and societal participation. Despite significant advances in healthcare, many researchers, clinicians and patients remain without ready access to the specialized, lab-based instrumentation and software required for the accurate assessment and treatment of vision loss. Our team seeks to leverage vision science insights, software development know-how, and extended- reality (XR) headsets to develop a software/hardware platform that can render basic vision science assessments and interventions widely accessible for all stakeholders. We take advantage of modern, commoditized, XR systems to “bring the lab” to participants. Our proposal builds upon decades of research from the vision science community, whose insights have long been used to benefit people with low vision. For example, automated gaze- contingent procedures that measure basic perimetry, as well as contrast sensitivity, acuity and motion discrimination in specific areas of the visual field have been transformative for understanding vision in those with either peripheral or central visual field loss. Likewise, eye-tracking based interventions that target training to the edges of functional visual fields in those with cortical vision loss, or to the preferred retinal loci of people with macular degeneration have been transformative in helping patients regain visual function that is often not accessible using other means. In this project, we will combine easy-to-use, high-resolution displays, with eye- tracking and optional non-invasive electrical brain stimulation into a single device. This will revolutionize ability to study low vision, and to deploy home-based but well-controlled visual “treatments”. In the R61, we will build and test feasibility of research grade assessments and interventions addressing retinal blindness (macular degeneration) and adult cortical blindness (stroke-induced). The R33 will support stage-1 feasibility trials targeting these two low-vision populations as well as new projects to address pediatric cortical visual impairments and amblyopia, and an open call for seed grants to support additional groups, extending the use cases of this accessible XR platform. At all stages of design, development and implementation, teams of researchers, clinicians, engineers and patients will work together to ensure that developed procedures address the needs and goals of all stakeholders. This principled, trans-disciplinary, team-based approach is designed to maximize access and thus, ultimately serve the lived realities of a great number of people with low-vision.

NIH Research Projects · FY 2025 · 2025-09

Alzheimer's disease is a debilitating condition that affects millions of people worldwide. Understanding the progression of memory decline in Alzheimer's and other memory-related disorders is critical for developing effective treatments and interventions. However, large-scale longitudinal studies of autobiographical memory, which would provide insight into the progression of memory decline, have been difficult to conduct due to the time and effort required to manually score the Autobiographical Interview, the gold-standard method for assessing autobiographical memory. To reduce the scoring burden and enable larger studies on autobiographical memory, PI Dr. van Genugten and OSC Dr. Schacter developed software to automatically score interview responses with natural language processing. Preliminary validation of this AI tool with five datasets shows strong agreement between software-generated and manually generated memory scores. This grant proposes to further develop and rigorously evaluate this AI tool for automated memory scoring. In Aim 1, we will use advanced machine learning methods to improve automated scoring accuracy. Specifically, we will fine-tune state-of-the-art large language models using over 200,000 annotated memory details from existing datasets to enhance performance. To address model limitations, we will use active learning to identify the types of content that the model misclassifies, then augment the training data with additional annotated examples of those types to improve accuracy. In Aim 2, we will develop software for more granular memory scoring. This software will score memories for nine categories of episodic and non-episodic information, including spatial and perceptual details. In Aim 3, we will conduct extensive psychometric analyses to evaluate the validity and reliability of this software, leveraging an existing psychometrics dataset that includes more than 1,300 memories. Finally, we will evaluate automated scoring accuracy on data from across the lifespan and in individuals with dementia, including individuals with Alzheimer's Disease, Semantic Dementia, and Fronto- Temporal Dementia, using eleven existing datasets. Overall, the purpose of this grant is to develop and rigorously evaluate software for automated memory scoring, enabling large-scale studies of autobiographical memory and advancing the ability to study the progression of memory decline in Alzheimer's and other memory-related disorders.

NSF Awards · FY 2025 · 2025-09

The Sequence Read Archive (SRA) is a vast but underutilized repository of genomic and related data, containing the majority of publicly available sequencing experiments in raw, unassembled format. Scientists could leverage this resource to search for newly discovered genes across the entire collection of existing public experiments, enabling rapid functional characterization and enhanced biological insights that would otherwise require extensive individual dataset analysis. However, building a sustainable and scalable index over the SRA presents significant challenges due to its massive size, continuous daily growth, and diverse data types. This project addresses these issues by developing new indexing tools that will enable scientists to search the entire SRA in real time. To further enable broad usage, the project provides a hosted, web-accessible version of the calculated indices. This project develops a real-time, scalable sequence search index for SRA using innovative data structures, compression algorithms, and distributed indexing approaches designed for cost-effective deployment on commodity infrastructure. This project has three main thrusts. First, it extends the previously developed Mantis index to efficiently index abundance, positions and experiment metadata while maintaining the original performance and scalability. Second, it develops a dynamic and distributed version of Mantis to scale out and incrementally index newly deposited experiments and support real-time queries. Finally, it develops an easy-to-use Application Programming Interface (API) for both command-line and web usage to enable scientists to perform rapid and advanced biological analyses over SRA. This project seeks to (1) empower researchers to conduct large-scale analyses with flexible, easy-to-use tools; (2) improve biological analysis algorithms and tools to keep pace with the scale and growth of modern datasets; and (3) rapidly identify and quantify novel transcripts, genes, and viruses among raw sequencing datasets. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Biological Infrastructure in the Directorate of Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The credit-loss problem is a persistent barrier for students transferring from community colleges (CCs) to four-year universities in STEM fields. Credits often do not transfer cleanly or apply toward major requirements, particularly in STEM, where rigid course sequences and prerequisite structures are common. As a result, students must repeat coursework, delay graduation, and face increased costs—all of which contribute to high attrition rates and student debt. These challenges are compounded by inconsistent articulation agreements, under-resourced advising, and institutional misalignment. Despite efforts to address this issue – such as statewide course-numbering systems and advising initiatives –substantial credit loss remains. The project will conduct a three-university pilot of a competency-based approach. The driving hypothesis is that transfer students often, but unnecessarily, lose all credits associated with a class because they are missing some but not all of the content in the equivalent class at the four-year institution. In this pilot transfer students will complete a placement assessment to identify the appropriate entry point in the Computer Science major at the four-year university. Students who demonstrate partial mastery of a required class will complete a one-credit supplemental course, while those with significant gaps will repeat the course — regardless of course numbering or existing articulation agreements. The goal is to help transfer students identify the appropriate starting point in the computing degree and to do this in a scalable and easy-to-update way. Although this pilot focuses on transfer in computing majors, the credit-loss problem spans all of STEM; thus a successful pilot will open the opportunity to use this approach in other disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Bone shape across animal species is the result of evolution (passing down traits to offspring) and adaptation (change of shape during life). This research investigates how bones change shape in response to different behaviors and environments, using deer mice as a model system. Scientists will study two closely related mouse species—one that burrows underground and one that does not—to understand whether bone structure is primarily determined by genetics passed down through evolution or by physical activity during an animal's lifetime. By raising mice in environments that either allow or prevent digging, and using cutting-edge X-ray technology to film their movements in real-time, researchers will discover how specific behaviors reshape bones. This work addresses a fundamental question in biology about how form follows function in living organisms. The findings will have important implications for human health, particularly in understanding bone diseases, aging, and the role of exercise in maintaining strong bones throughout life. The project will also advance scientific infrastructure by pioneering new imaging technology that can capture detailed bone and muscle movements in very small animals, opening doors for future biomedical research. Additionally, the research will train high school, undergraduate, and graduate students in laboratory techniques and create educational materials for teachers to help students understand evolution, physiology, and engineering principles through hands-on bone studies. By revealing how animals naturally optimize their skeletons for their lifestyles, this research provides insights that could inform treatments for bone disorders and guide exercise recommendations for maintaining skeletal health. This study investigates how limb bone structure is shaped by genetic inheritance versus developmental responses to physical activity in deer mice. The project will test the hypothesis that bone changes during growth are primarily influenced by local mechanical forces, while overall bone shape and the capacity to respond to forces are determined by evolutionary adaptations to different environments. Two mice species will be compared: burrowing oldfield mice (Peromyscus polionotus) and non-burrowing cactus mice (P. eremicus). Mice will be raised in chambers that either allow or prevent digging behavior, and limb bone structure will be measured using high-resolution CT scanning to compare bone thickness, internal architecture, and overall geometry between groups. Bone function during movement will be studied using advanced X-ray video technology (microXROMM) to measure how bones move in three dimensions and calculate muscle forces during running and digging. To test how sensitive bones are to mechanical forces, controlled loads will be applied to leg bones while measuring structural, cellular, and genetic responses through tissue analysis and gene expression studies. Gene activity analysis will identify which genes respond differently to mechanical forces in each species, determining whether evolutionary differences in digging behavior correspond to altered bone sensitivity at the molecular level. This approach will establish how behavior, mechanical forces, and bone adaptation interact across evolutionary, developmental, and ecological scales, providing a framework for understanding how form follows function in mammalian skeletons. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary: Magnetic resonance experiments are an indispensable tool in biophysics, providing detailed chemical insight and accessing unique degrees of freedom. However, these methods have poor sensitivity compared to fluorescence techniques, requiring low temperatures and/or concentrated sample conditions, which presents a roadblock to their wider utilization. The long-term goal of this project is to develop novel quantum sensor- enhanced magnetic resonance techniques which can reach the single-molecule regime, with the specific applications to electron transport in metalloproteins and nanoscale motion in membranes. The objective of this proposal is to develop novel strategies for integrating these quantum sensors (the NV center in diamond) with proteins and biomembranes. Our innovation is a suite of experimental strategies to improve the sensor compatibility with biological systems, ranging from minimizing photodamage using a total internal reflection geometry, to localizing targets at the sensor surface with supported lipid bilayer formation. Our rationale for this overall approach is that NV centers have found extensive applications in condensed matter physics and materials science because of their outstanding performance as magnetic resonance sensors, yet progress in biophysical contexts has been much slower because of the more demanding nature of the biological samples. Addressing these challenges would allow us to leverage the uniquely-powerful properties of the NV center – picotesla sensitivity, fluorescence readout, single-spin detection – to enhance the capabilities of magnetic resonance (NMR, ESR) techniques. The first research direction this new tool will enable is probing electron transfer in metalloproteins; we will be able to, at room temperature, probe the Fe-heme spin in cyctochrome c (and related proteins) to access spin and conformational information, while also probing time-dependent behavior to extract electron transfer rates. Our hypothesis here is that the complex spin-state behavior of Fe ions (e.g., admixture states first identified in the 1970s) is a functionally-important factor in electron transport, and which will be required to explain a range of novel behaviors in heme protein superstructures, such as metallic conduction and spin-dependent transport. Our second research direction focuses on probing diffusion on the <10nm length scale, beyond the limits of even most super-resolution techniques. Our hypothesis here is that nanoscopic domain formation (which we will induce with multicomponent lipid compositions) will introduce significant deviations from Brownian diffusion, and that experimentally measuring this will provide new insight into motion in confined environments in cell membranes. This research is significant because improved sensitivity in magnetic resonance experiments would enable transformative new research across multiple fields, and the specific areas we identify here will address long-standing, fundamental biophysical questions. This research will have a positive impact because the new tool we will develop can be broadly applied by other groups, and the new insight we develop will enable broadly applicable mechanistic principles to be articulated.

NSF Awards · FY 2025 · 2025-08

This planning project will integrate several existing wireless testbeds that encompass a wide range of radio frequency bands and communication techniques into a cohesive, software-defined virtual platform, offering novel capabilities. The project will create a strategic plan for testbed design, integration and workforce development. The base design of existing testbeds, once integrated, will form preliminary results for a highly scalable community infrastructure. By adapting existing testbeds as a prototype for incorporating AI-driven capabilities, the team expects new competencies to be spawned for US-based academic and industrial partners that build toward the national interests of economic development in emerging 6G and next-generation networking markets. The proposed testbed will integrate wireless testbeds at collaborating institutions into a single ultra-broadband, multifunctional, and uniquely capable platform for emerging experiments under AI control. The overarching goal is enabling cross-level autonomy and cognition in wireless systems. During this project, the team will develop a plan that helps with long-term goals of a unified wireless testbed infrastructure, including creating software and hardware tools necessary to provide a single-stop solution for researchers to deploy and run multi-site experiments, and developing the tools necessary to run AI solutions both within and across individual network layers, and multi-site experiments with real-time data streaming and visualization capabilities. The team will engage in joint development of high-quality workforce development material, mentoring, education, and scientific outreach activities in a manner that benefits everyone interested in science, technology, engineering and mathematics fields with particular focus on AI and wireless systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

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 / ABSTRACT People with serious illness near the end of life may be subjected to aggressive or uncomfortable medical procedures, and die in the hospital, even if this is against their wishes. Advance Care Planning (ACP) seeks to ensure that people have opportunities to reflect on their personal values and make decisions about the possible types of future care they want to receive – before they are too impaired to express themselves. Unfortunately, too few healthcare providers in the US are trained in palliative care principles, and current delivery of ACP tends to occur in hospital settings and later than optimal in the disease course. Increasingly, chronic illness care is being delivered in community and home settings. There is great need for high-quality, more flexible models of ACP that can reach more patients where they are and that are sensitive to the diverse array of cultural backgrounds and views on quality of life that exist. Our proposed R21 study will explore secondary data produced in an NIH-funded trial of a video-based ACP intervention for the home setting. The trial’s participants are racially and ethnically diverse homebound patients with advanced disease. In the R21, we will transcribe audio-recordings made of the conversations around the short educational videos, then conduct comprehensive qualitative analyses of these transcripts to better understand patients’ perspectives on their illness, intensive care services, hospice services, and ACP. Additional anticipated themes in these conversations include faith and cultural traditions, familial obligations, conception of death, and personal empowerment through ACP. Our analyses will also explore unexpected themes, and we will identify contrasts among patient groups defined by, e.g., demographic characteristics and diagnoses. The home setting and lower-income population in this study will be unprecedented within the body of research on serious illness communication. Emphasizing these particular patient voices will yield new insights that will inform future designs of community-based ACP. Finally, we will quantify selected themes from and other unexamined aspects of the video home visits and merge these data with downstream outcomes from the parent trial (e.g., patient-reported knowledge of and satisfaction with ACP, ACP documentation in the medical record, and use of intensive services and hospice near the end of life). Our explorations of patient-clinician communication around ACP during the video home visit will help explain variations in patient outcomes within the intervention arm of the parent trial. Ultimately, this R21 study will point toward ways that ACP can be made more effective and lead to care that is well- aligned with patient preferences.