Stevens Institute of Technology

NSF Awards · FY 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) project supports research and education focused on trust in human-artificial intelligence (AI) collaboration during early-stage engineering design. In open-ended, creative design contexts, trust governs how designers engage with and rely on AI-generated suggestions, yet this reliance is often implicit, context-dependent, and difficult to capture by traditional acceptance-based or post-hoc survey measures. This project advances fundamental knowledge by modeling trust as a continuous, time-varying latent state variable grounded in designers’ affective and cognitive processes, creating a foundation for trust-aware human-AI interaction in early-stage design. This CAREER award advances theory and methods for trust in human-AI collaboration through two integrated research activities: (1) developing empirical datasets and computational inference models that estimate trust dynamics as a continuous, normalized quantity from synchronized behavioral, self-report, and psychophysiological indicators during real-time design interaction; and (2) formalizing trust-aware AI adaptation strategies that specify how AI feedback behavior should adjust to support calibrated reliance and effective collaboration. Education and outreach activities will deploy AI-assisted design tools in undergraduate engineering design courses and pre-college design bootcamps, and will integrate multimodal sensing to enable adaptive, personalized feedback that supports cognitively demanding learning. This research includes advancing the design and deployment of trust-aware AI systems that improve engineering decision quality and reliability, reduce design cycle time and downstream rework, and support effective human-AI collaboration across high-stakes industrial domains such as manufacturing, healthcare, and infrastructure. In parallel, the project will enhance STEM education and workforce development by integrating AI-assisted design and personalized learning tools into undergraduate and pre-college programs, broadening participation and preparing an AI-literate engineering workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Open-source software security vulnerabilities lead to severe impacts, e.g., stolen personal data, financial losses, and disrupted services. Due to the significant growth of vulnerabilities in recent years, it has become increasingly challenging for developers to efficiently manage security vulnerabilities, leading to supply-chain delays and prolonged security risks. While artificial intelligence tools are increasingly adopted in software development, it remains unclear whether they can reliably assist vulnerability management. This project builds tools that leverage AI to assist vulnerability management in open-source software and continuously monitors the security behaviors of AI agents in software development. The project's novelties are the combination of AI with program analysis on vulnerability localization, a new recommender system for prioritizing and discovering vulnerabilities, and novel testing methodologies for auditing the security behaviors of AI agents. The project's broader significance and importance are the strengthening of open-source software supply chain which supports modern software infrastructure, the training of next generation security researchers and software developers, including summer research camps in New Jersey, and the public release of benchmarks and tools that improve security research. The project's objectives are divided into three research thrusts: (1) using program analysis and graph neural networks to enhance patch localization and vulnerable code localization; (2) constructing a novel benchmark and recommender system for vulnerability discovery and prioritization based on a major bug bounty platform; (3) monitoring the security behaviors of AI agents for code review and generation by detecting security failures and inconsistency with user expectation. The project seamlessly integrates various methodologies and disciplines, including program analysis, large language models, natural language processing, software testing, and AI agents. Key deliverables include prototype tools, benchmarks, and monitoring workflows, which are easily adoptable through platforms such as GitHub. This research fosters a comprehensive approach for vulnerability management, contributing to a more secure and trustworthy software 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

This CAREER project establishes the scientific foundations of human-physical interaction and interaction-driven coadaptation within human-cyber-physical systems (HCPS). Humans and physical systems interact bidirectionally in HCPS, actively influencing and being influenced by each other. Under external disruptions, coordinated adaptations driven by bidirectional interactions – where adaptations in each domain both inform and respond to those in the other – enhance collective resilience beyond one-sided or independent adaptations. Biological systems underscore interaction-driven coadaptation as a fundamental organizing principle: interacting species coordinate adaptations based on interaction traits to thrive under changing conditions. To achieve this goal, the project formalizes interaction-driven coadaptation between humans and civil infrastructure systems (e.g., transportation, power, and water systems). This formalization strengthens collective resilience to increasingly frequent and severe disasters by enabling coordinated adaptations between humans and infrastructure systems, reducing disruption losses and accelerating recovery. The project will advance the U.S. national interests by improving life stability, infrastructure operability, economic continuity, and response to disaster-induced disruptions. It will also promote HCPS education through an attract-train-reward pipeline, research-education integration, and public engagement. The project advances the science of HCPS by moving beyond prevailing paradigms – such as human-in/on-the-loop and human-aware systems – that often model humans as passive or exogenous agents. Instead, it establishes bidirectional human-physical interaction as the foundation for synergistic coadaptation between human and physical agents under uncertainty in coupled human-physical dynamics. Drawing inspiration from biological models of coadaptation, including replicator dynamics and evolutionarily stable strategies, the project extends these principles to HCPS to enable robust modeling and control of interactive and coadaptive human-physical dynamics for greater system adaptability and resilience. To model and control these dynamics, the project introduces four key innovations: 1) a spatiotemporal, generative, and context-aware learning framework that enables reliable and scalable modeling of human and infrastructure states under sparse and uncertain observations; 2) an asynchronous, cross-domain, and adaptive learning framework that enables reliable and generalizable modeling of lagged, distributed, and context-dependent human-infrastructure interaction dynamics based on inferred human and infrastructure states; 3) a hybrid coadaptation modeling framework that enables integration of deterministic infrastructure adaptation with behaviorally grounded stochastic human adaptation to simulate coadaptation dynamics under uncertainty; and 4) a replicator-inspired, equilibrium-aware multi-agent reinforcement learning framework that enables stable and anticipatory interaction-driven coadaptation policy optimization under multi-agent competition and non-stationary dynamics. Collectively, these contributions transform how HCPS are modeled, simulated, and optimized – opening new scientific pathways for interactive, coadaptive, and resilient system design across CPS, infrastructure, and disaster resilience domains to advance HCPS theory and the resilience of real-world 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 · 2026-03

Toxic oxyanions such as nitrate (NO3−) and perchlorate (ClO4−) are persistent pollutants that have been detected in groundwater, surface water, and drinking water sources in the United States and worldwide. The consumption of drinking water containing toxic oxyanions can adversely impact human health. Ion exchange (IX) and reverse osmosis (RO) are the best commercially available technologies for removing toxic oxyanions from drinking water sources. However, IX and RO do not destroy contaminants. In addition, they generate residuals including concentrated waste brines that need to be treated and/or disposed of. Water treatment by catalytic hydrogenation has emerged as a promising technology that can rapidly and effectively destroy toxic oxyanions in contaminated aqueous solutions including concentrated waste brines. The most effective oxyanion hydrogenation catalysts (e.g., Pd) are in the form of nanoparticles. Anchoring catalytic nanoparticles on supports such as activated carbon can facilitate their use in water treatment. In this project, the Principal Investigators (PIs) propose to carry out a fundamental study of the activity and reactivity of Pd nanoparticles immobilized onto supports that contain nitrogen groups in aqueous solutions and brines contaminated by toxic oxyanions with the goal of improving their performance. The successful completion of this research will benefit society through the development of new fundamental knowledge to advance the design and development of more efficient and cost-effective oxyanion hydrogenation catalysts for water treatment. Additional benefits to society will be achieved through student education and training including the mentoring of one graduate student and one undergraduate student at the South Dakota School of Mines and Technology and one postdoctoral researcher at the University of Alabama. Palladium (Pd) nanoparticles have emerged as promising catalysts for reducing toxic oxyanions such as nitrate (NO3−) in aqueous solutions/brines and converting them to harmless by-products such as dinitrogen (N2) gas. Pd nanocatalysts are immobilized on support materials to 1) reduce nanoparticle aggregation and leaching and 2) facilitate catalyst handling and reuse. The presence of nitrogen-containing groups (e.g., amines) on the supports of Pd nanocatalysts have been found to significantly enhance catalyst performance (including activity, selectivity, and stability) during the hydrogenation of oxyanions in aqueous solutions. However, a fundamental understanding of the role of nitrogen-containing groups (NCGs) on the structure and performance of Pd hydrogenation nanocatalysts has remained elusive. To address these knowledge gaps, the Principal Investigators (PIs) of this project propose to carry out fundamental studies of the structure and performance of Pd nanocatalysts immobilized onto supports with NCGs. The specific objectives of the research are to 1) characterize and unravel the relationships between NCG support and catalyst structure and physicochemical properties; 2) investigate the impact of NCG support on the performance of Pd nanocatalysts for the reduction and conversion of oxyanions in model aqueous solutions and complex water matrices using hydrogen (H2) as reducing agent ; and 3) leverage the data collected in this project to develop machine learning (ML)-informed life cycle assessment (LCA) to guide catalyst design, synthesis, and optimization. The successful completion of this project has the potential to advance the practical implementation of Pd-based catalysts and reactors for the treatment of drinking water sources and concentrated waste brines contaminated with toxic oxyanions. To implement the education and training goals of the project, the PIs propose to leverage existing programs at the South Dakota School of Mines and Technology and the University of Alabama to 1) recruit and mentor graduate and undergraduate students from underrepresented groups to work on the project and 2) develop and implement outreach activities to advance diversity, equity, and inclusion in STEM education. 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

Shell programming, the glue that holds modern computer systems together, is as prevalent as ever—steadily in the top 10 most popular programming languages in widespread use. It is also quite complex, due to the structure of shell programs, their use of opaque software components, and their complex interactions with the broader environment. As a result, even when exercising an abundance of care, shell developers discover devastating bugs in their programs during or after their execution—when it is too late to reverse any of their unintended effects. Bugs in these applications therefore affect—often with disastrous outcomes—engineers, scientists, and end-users alike: production bugs in industry platforms have resulted in the deletion of important user data. This project brings together a team of experts to develop fully automated, ahead-of-time program analysis techniques for checking the correctness of, and catching bugs in, shell programs before their execution. Drawing on techniques from programming languages, type systems, and program analysis, the project will benefit both developers and end-users to automatically catch and prevent undesirable or even catastrophic events. Of particular interest are the techniques proposed around the interaction of shell programs with the file system and the broader environment in which they execute. Beyond mere prevention, such techniques provide the foundations to precisely diagnose bugs and guide developers to implement effective fixes. 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 · 2025-11

Drinking water is important for our health. Disinfection helps keep the water safe and clean. However, some disinfection byproducts (DBPs) can be harmful. These DBPs form when water treatment plants use chemicals like chlorine to kill germs. There are many different DBPs, and identifying all of them would be costly and difficult for water treatment plants. This project will use machine learning (ML) to identify the toxicity of DBPs in drinking water and develop strategies for reducing the presence of high-risk DBPs in drinking water treatment. The results from the project will improve drinking water safety and public health. The project will train students to use AI and data to solve problems of water quality. The project will mentor graduate and undergraduate students at South Dakota School of Mines and Technology, preparing them for future careers in science, engineering, and data science. Disinfection byproducts (DBPs) are a group of chemicals formed during the water disinfection process when disinfectants such as chlorine react with organic matter in water. These chemicals are often toxic and present in treated drinking water. The considerable number of DBPs, coupled with limited data on their occurrence and toxicity, complicates efforts to determine which DBPs should be prioritized for future studies and regulations. Current methods for assessing DBP risks rely on limited occurrence and toxicological data and thus face challenges in effectively identifying which DBPs need the most attention. This project will integrate machine learning (ML) and laboratory experiments to (1) create a databases for both regulated and unregulated DBPs, addressing critical gaps in available occurrence and toxicity data, (2) prioritize high-risk DBPs by analyzing their occurrence frequency, levels, and toxicity, based on their potential health impacts, and (3) develop predictive tools for high-risk DBPs, enabling more informed decision-making for future regulations and water treatment strategies. The results of the project will contribute to minimizing harmful DBPs in drinking water and improving public health. The educational activities from this project include creating student-led STEM summer camps for K-12 students, developing scaffolded educational modules to develop ML literacy in students, and conducting workshops for students on navigating graduate studies. The successful completion of this project will help advance the design and implementation of data-driven solutions in environmental engineering, contributing to improved water quality, public health, and the development of future scientific leaders. 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 · 2025-11

Chlorine is used to kill harmful germs in drinking water. However, chlorine can also react with other substances in the water, like dissolved organic matter (DOM), creating unwanted byproducts called disinfection byproducts (DBPs). Phenolic compounds are key parts of DOM that often lead to the formation of DBPs. Because DOM is a highly complex mixture of different compounds, researchers study individual components of DOM like phenol and lignin to understand how DBPs form. This research has been ongoing since the 1970s, resulting in a large body of available scientific data. Machine learning (ML) is a powerful tool that holds great potential to identify trends in these data. The first objective of this project is to collate data from past studies and use ML to identify key factors in DBP formation from phenolic compounds. The second objective is to study DBP formation from specific phenolic compounds chosen by ML to help elucidate underlying mechanisms and direct the gathering of more data to improve ML accuracy. The final objective is to develop treatment strategies to reduce DBP formation in drinking water guided by the results of this study. Successful completion of this project will benefit society by providing guidance on DBP control to water treatment facilities to better protect human health. Additional benefits to society will include student education and training, with one graduate student and one undergraduate student mentored at South Dakota School of Mines and Technology and one graduate student at South Dakota State University. Phenolic contents are major components of DOM that serve as a common precursor pool for a broad range of aliphatic DBPs. Because of the complexity and ambiguous structure of DOM, studying model compounds provides a more clear and distinctive profile of precursor chemistry and mechanisms responsible for DBP formation. Phenolic compounds (i.e., phenols) have been widely selected as representative structures within DOM to explore the kinetics and mechanisms of DBP formation. However, the chemistry involved in the process is not fully understood. Past studies of the oxidation of phenols were largely evaluated under relatively ideal experimental conditions (e.g., without natural organic matter and halides). Despite decades of effort, no accurate mechanistic model exists to predict DBP formation from phenols. The overall goal of this project is to identify critical characteristics of the transformation of phenols to aliphatic DBPs and minimize the production of intermediate halophenols in drinking water. This will be achieved using a multi-faceted approach to i) collect data from literature and our experiments, ii) conduct metadata analysis and develop ML models to reveal critical factors in the transformation of phenols to aliphatic DBPs, iii) investigate the formation of aliphatic DBPs and halophenols from phenols, and iv) develop effective treatment methods to minimize both aliphatic DBPs and halophenols. The outcomes of this project will help water utilities mitigate both HPs and aliphatic DBPs in drinking water treatment. 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 · 2025-11

Chemical oxidants such as chlorine are widely utilized as disinfectants to inactivate waterborne pathogens in conventional water treatment processes. However, chlorine can react with various background constituents in drinking water sources (e.g., natural organic matter, bromide, and iodide) to form undesirable and toxic disinfection byproducts (DBPs). Currently, the US EPA regulates the maximum contaminant levels (MCLs) of 11 DBPs in drinking water including 4 trihalomethanes (THMs), 5 haloacetic acids (HAAs), bromate (BrO-3), and chlorite (ClO2-). Unregulated iodinated DBPs (I-DBPs) are receiving increased attention as they are significantly more toxic than the regulated DBPs and can damage cells and DNA. I-DBPs are formed when chemical oxidants such as chlorine react with iodine and iodinated compounds (e.g., iodinated X-ray contrast media) during the disinfection of drinking water sources. Thus, the oxidation and conversion of iodine and iodinated compounds (ICs) to iodate (IO3–), a nontoxic source of iodine nutritional trace element, has emerged as a promising unit process to control and mitigate the formation of I-DBPs in water treatment systems. However, the ability of current water treatment processes to efficiently convert iodine and ICs to iodate suffer from several challenges including the concurrent oxidation of bromide to bromate and toxic brominated DBPs, and the incomplete transformation of iodine/ICs to iodate which can also lead to the formation of I-DBPs and other regulated DBPs in the final product water. The goal of this collaborative project is to explore the development of advanced oxidation processes (AOPs) and integrated treatment trains that can efficiently oxidize and convert iodine and ICs to iodate while minimizing and preventing the formation of toxic I-DBPs and regulated DBPs in the product drinking water. The successful completion of this project will benefit society through the development of new fundamental knowledge that could guide the design and deployment of more effective water treatment processes and systems for mitigating and eliminating and the formation of I-DBPs during water disinfection. Additional benefits to society will be achieved through student education and training including the mentoring of one graduate student and one undergraduate at the South Dakota School of Mines and Technology and one graduate student at South Dakota State University. Iodinated disinfection byproducts (I-DBPs) formed in drinking water treatment are highly toxic at low concentrations and have been found to be cytotoxic and genotoxic. Iodide (I–) and iodinated X-ray contrast media (ICM) are the two most common iodine sources that can react with disinfectants (e.g., chlorine and chloramines) to produce I-DBPs during drinking water treatment. The oxidation and conversion of iodine and iodinated compounds such as ICM to iodate (IO3–), a nontoxic source of iodine nutritional trace element, has emerged as promising unit process to control and mitigate the formation of I-DBPs in water treatment systems. The overarching goal of this project is to advance the fundamental science and engineering knowledge required to control emerging I-DBPs and regulated DBPs in drinking water treatment through careful selection and optimization of advanced oxidation processes (AOPs) and integration of the AOPs with conventional processes. The core guiding hypothesis of the proposed research is that the successful control of emerging I-DBPs and regulated DBPs in drinking water treatment systems would require the efficient oxidation and conversion of iodine species and iodinated compounds to iodate, the careful management of bromide formation, and the partial (decent) removal of NOM (Natural Organic Matter), a DBP precursor, prior to disinfection. The specific objectives of the research are to 1) to investigate the utilization of AOPs, including ferrate (Fe[VI]), ozone (O3), UV photolysis, and UV photolysis with O3, to optimize the oxidation of iodine and ICM to iodate; 2) investigate the integration of AOPs with conventional processes, including chlorination, and activated carbon sorption, to minimize the formation of both I-DBPs and regulated DBPs; and 3) develop analytical methods for measurement of iodine species and I-DBPs to unravel and quantify the transformation pathways of iodine and ICM to I-DBPs and total organic iodine. The successful completion of this project has the potential to advance the fundamental understanding of the reactivity and transformations of inorganic and organic iodine species/compounds by AOPs to guide the design and development of iodine source-specific treatment processes for effective mitigation of both I-DBPs and regulated DBPs in water treatment systems. To implement the education and training goals of this project, the Principal Investigators (PIs) propose to leverage existing programs at the South Dakota School of Mines and Technology (SDSMT) and South Dakota State University to recruit and mentor female students to work on the project. In addition, the PIs plan to interact and collaborate with drinking water treatment professionals to address water quality challenges in South Dakota, engage in local community outreach events, and collaborate with the SDSMT Ivanhoe International Center to engage and mentor international students from the African continent. 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

Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly in managing parameter variations in real time. However, ensuring both accuracy and stability in PINN training remains a major challenge, often requiring large datasets and exhibiting sensitivity to minor input changes. To address these limitations, researchers from Stevens Institute of Technology (SIT) and The Ohio State University (OSU) are developing an Open-Source AI-Driven Electronic Design Automation (EDA) Tool for Real-Time Synthesis of Short-Distance Wireless Interconnects on Silicon (OASIS), the first open-source, AI-powered EDA tool for real-time parametric EM simulation. OASIS will explore scalable strategies for training large-scale PINNs efficiently and robustly. This research will focus on the design of short-range (~10 mm) wireless interconnects on silicon for two cutting-edge applications: (1) contactless connectors that leverage spatial multiplexing to minimize interference and enhance data throughput, and (2) batteryless brain-machine interfaces (BMIs) that depend on real-time signal cancellation and sensitivity optimization. By replacing traditional slow solvers with a faster, AI-driven alternative, OASIS aims to transform next-generation EM design. To achieve the project’s objectives, the investigators will pursue six key research directions. First, the team of researchers will develop a graph-based importance sampling framework to accelerate the training and convergence of physics-informed neural networks (PINNs) on large-scale point clouds. Second, they will implement a stability-guided training approach to enable robust and efficient parametric EM simulations using PINNs. Third, the team will design a novel proximity communication method capable of multi-gigabit data transfer in dense, low-power environments where traditional EM solvers are ineffective. Fourth, they will investigate spatial multiplexing techniques to scale interconnect bandwidth. Fifth, the project will explore a new class of wireless, batteryless brain implants that utilize signal backscattering and AI-driven leakage cancellation to improve sensitivity. Sixth, the researchers will introduce real-time adaptive specifications for brain-machine interfaces (BMIs) to accommodate dynamic environmental conditions. To broaden the project’s impact, the investigators at SIT and OSU will also develop new courses that integrate advanced machine learning concepts into software-hardware co-design education. Collectively, this research aims to advance the frontiers of millimeter-wave and RF integrated circuit design, computer-aided design (CAD), machine learning, scientific computing, and biomedical 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 · 2025-10

This Capacity-Building Institutional and Community Transformation project aims to serve the national interest by creating an early career exploration community for biology and chemistry students ("ExploreBCC") at the Stevens Institute of Technology (Stevens) in collaboration with Hudson County Community College (HCCC). College students who are well educated in the fields of biology and chemistry are crucial for continued innovation and development in various sectors of the U.S. economy, including agriculture, biotechnology, energy, environment, medicine, pharmaceuticals, and manufacturing. ExploreBCC intends to empower first-year students to: (1) investigate the various career pathways that are possible via biology and chemistry; (2) assess their own interests through interactions with external career role models, faculty leaders, and academic advisors; and (3) connect their academic plan with career aspiration. Developing and propagating ExploreBCC, that guides students for thoughtful and strategic career choices, should serve as a meaningful model for STEM education. This project is significant for its potential to cultivate the nation's scientific workforce as well as to prepare it for rapidly evolving real-world roles that must respond to new societal challenges while adopting emerging technology such as artificial intelligence. The scope of this project is to start building ExploreBCC on the existing knowledge base, which indicates increasing self-efficacy around science careers and identity. The project should provide students with a guided opportunity to: (1) build self-confidence, (2) understand their abilities, and (3) align their actions with their expected career outcomes. ExploreBCC intends to bring a novel combination of synergistic and structured learning, networking, and planning experiences at the outset of the students' academic path. ExploreBCC aims to leverage the recruitment and cultivation of career role models, (1) who are professional volunteers working in various economic sectors and (2) who will engage first-year students about rapidly evolving real-world science careers and identity. This structured approach is anchored by a first-semester, one credit-hour course at Stevens. Preliminary data indicate that the course provides students with an important opportunity to question, realign, and/or confirm their chosen major at the earliest stage of their academic journey. In this Capacity-Building Project, the course will be updated and made available to first-year students at HCCC. The specific objectives of the project are to: (1) assess the learning experience, effectiveness, and outcomes of pilot ExploreBCC activities for two student cohorts at Stevens and HCCC; (2) recruit and cultivate career role models, faculty leaders, and academic advisors with emphasis on developing relationships with various campus units and leaders at Stevens and HCCC; and (3) plan and develop the multi-institutional framework for implementation and evaluation of ExploreBCC for its sustainable and scalable propagation. Results from this project are expected to provide new insight in rethinking and transforming the first-year student experience in four-year and two-year colleges. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Ethereum is a software platform used for decentralized payments (similar to bitcoin) and also for financial ("smart") contracts and applications. Since its inception, Ethereum has been increasingly adopted by various diverse applications such as finance, health, and gaming. Ethereum implements blockchain technology with a peer-to-peer (P2P) network where each participating node may run different clients and propagation models to enhance its security and efficiency. This effort addresses the following Ethereum P2P network challenges: how to understand whether all clients have compatible communication protocols and behave consistently; and how to prevent the leakage of neighbors' private information to a malicious node. To address these challenges, this effort will develop methods to systematically detect incompatible communication protocols and inconsistent behaviors in Ethereum clients, and design a new approach to preserve node privacy and improve efficiency. Two new courses will be developed based on this research, a course covering the foundations of cryptography and its applications in blockchains, and an elective graduate course on blockchain security foundations. This effort includes three research tasks. Task 1 is to develop an automated testing framework to discover incompatible communication protocols implemented in Ethereum clients that would cause communication failures. Task 2 is to develop a differential fuzzing framework to detect inconsistent propagation behaviors in Ethereum clients that could lead to Denial-of-Service attacks. Task 3 is to design a dynamic propagation model that can select the best propagation method based on the network conditions and neighbor status to mitigate the neighbor privacy leakage problem and improve propagation efficiency. 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

Modern software systems are entrusted with and expected to protect sensitive user data, making it critical that we have robust and rigorously tested techniques to ensure their security. This goal is complicated by the intricate process of compilation which transforms source code into directly executable machine code and includes optimizations crucial to the performance of software systems. Because of these transformations, a solely source-code level security analysis is not enough to ensure that the executable machine-code satisfies the same security guarantees. In particular, side-channel vulnerabilities allowing an adversary to leverage non-functional observable information such as a program’s execution time can arise during the compilation process even when the corresponding source code is deemed side-channel free. The project’s novelties are the development of a just-in-time compilation framework capable of balancing security and privacy concerns alongside performance optimization. The project’s broader significance and importance are facilitating developers’ ability to focus on writing functionally correct code without concerning themselves with the possible security risks posed by compiler optimizations and improving both security and performance for end users. The project addresses the challenge of developing a security-aware just-in-time compilation framework. The key components of this framework include: 1) a taint-analysis-backed approach to marking compiler optimizations that have the potential to introduce side-channel vulnerabilities, 2) an information-theoretic metric to quantify the strength of such vulnerabilities using profiling data already collected by the just-in-time compiler, 3) a heuristic framework to answer two core questions: “should we apply this optimization?” and “should we revert this optimization?” based on tradeoffs between performance gain and security risk, and 4) augmenting this framework with machine-learning powered strategies for leveraging historic data to better predict the security impact of optimizations. The project’s impact is a just-in-time compilation framework that can manage the complex tradeoffs between security and performance in a way that does not overburden the developer and delivers privacy assurances alongside strong performance to end users. 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 I-Corps project is based on the development of flexible and wearable electronics using adjustable materials that integrate both thermal regulation and electronic functionality into a single, lightweight platform. Currently, many next-generation devices, such as smart medical wearables, fitness trackers, and environmental sensors, face challenges related to overheating, inefficiency, and limited durability. These devices rely on passive cooling or bulky components that cannot meet the demands of compact and body-integrated applications. This technology addresses these problems by creating a new class of two-dimensional (2D) materials with engineered Moiré patterns, patterns created by mechanical interference of light caused by overlapping patterns of lines. These materials enable customizable heat flow while preserving excellent electrical performance and may be used to build flexible field-effect transistors (FETs) that dynamically control temperature and power in real time. Embedding heat management technology directly into the device material reduces the number of components, increases energy efficiency, and enhances long-term reliability. This solution may create safer and more effective health monitoring devices and longer-lasting consumer electronics, while lowering energy costs for users across medical, industrial, and home environments. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of Moiré-patterned two-dimensional (2D) field-effect transistors (FETs) with tunable thermal and electronic properties. This technology leverages recent scientific advances in bilayer and multilayer 2D materials, where precise interlayer twist angles create Moiré superlattices with highly controllable heat and charge transport. Unlike conventional materials that treat heat management and signal control as separate problems, these 2D structures offer integrated solutions by tuning both properties simultaneously. The core technical approach involves nanoscale fabrication and high-precision dual-laser Raman thermometry to characterize and optimize performance. Users may benefit from improved device performance, smaller form factors, and lower failure rates — critical factors for adoption in wearable electronics, smart textiles, and portable Internet of Things (IoT) systems. By embedding heat management directly into the device material, this technology may reduce component count, increase energy efficiency, and enhance long-term reliability. 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

Nontechnical Optics, the study of light, is centuries old. The control and manipulation of light has played a critical role in civilization from lenses in the ancient world to modern photonics. This project focuses on polarization, a property of light describing how electromagnetic fields oscillate as light travels through space. This project explores interactions between light and matter in pixels with nanoscale features and demonstrate how to control the polarization of light across a nanopatterned piece of glass or semiconductor. New phenomena arise when many nearby pixels act together. Notably, a phenomenon called resonance arises, wherein light is effectively trapped within the layer of pixels. Resonances offer control over the spectrum of light but conventionally comes at the expense of pixel-by-pixel control of polarization. This project overcomes previous limitations to develop resonant devices made with standard manufacturing techniques and offering pixelated control of the polarization of light. Investigators employ design principles based on symmetries, which simplifies the system and allows new avenues of control of light. Devices made from nanostructured materials are compact and lightweight and can be scaled to mass manufacturing. Understanding developed in this project thus has the potential for technological breakthroughs in holography, imaging, and sensing. In parallel, the PI explores the broadening of knowledge and participation of flat polarization optics in an undergraduate setting. While the concepts are accessible to students early on in higher education and are crucial for emerging technologies, they are not often covered. This project helps fill this gap by developing curriculum materials for an experimental lab class and by supporting undergraduate researchers in both theoretical and experimental study of these phenomena. Technical This project advances the design principles, capabilities, and device architectures of an emerging category of flat optical materials called “nonlocal metasurfaces”. In resonant flat optics, nonlocality arises due to the dispersion of waves guided along the structure thin film and resonantly coupled to external light. While these waves can be supported in a pixelated device with subwavelength pixels, the nonlocal resonant coupling to external light is conventionally destroyed when many neighboring pixels are not identical. Yet, pixelated control of polarization requires local control. This project incorporates local, pixel-wise control over the polarization response of nonlocal, guided waves by judicious use of symmetry-based design principles. This project implements two new categories of polarization control that are compatible with a single patterned layer. Chiral responses (in which the handedness of polarization state is differentiated) are particularly challenging to control, yet especially important in applications such as sensing of chiral molecules. This project explores chiral polarization capabilities within nanopatterned thin films of silicon placed upon glass and a metallic mirror layer. Symmetry-based design principles introduced in this project allow the decoupling of several degrees of freedom of the desired response, simplifying the design of pixels and enabling the production of millimeter scale flat optical devices whose geometry is defined by equations. The pixelated control of a resonance manifests in this project in a planarized architectures that achieve handedness-selective beam steering functionalities exclusively within the narrow bandwidth of the resonance. The approaches of this project increase the maximum theoretical efficiency of this functionality to nearly one (previously, one quarter), advancing the state-of-the-art towards high efficiency, multi-functional performance. This project is jointly funded by Office of Strategic Initiatives of the Directorate for Mathematical and Physical sciences and the Electronic and Photonic Materials program in the Division of Materials 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

Resolution is key to seeing fine details in imaging and sensing—whether it’s in biomedical imaging, observing distant stars, quantum measurements, or everyday optical systems. But all optical systems face a crucial barrier known as the diffraction limit, which sets a fundamental limit on how closely two points can be distinguished. Previous breakthroughs have achieved super-resolution by actively controlling or labeling the sample, which works well for certain biomedical systems. However, this approach isn’t possible for live biological samples that could be damaged by probes, for delicate quantum systems that can be disturbed by measurement, or for astronomical objects that we simply cannot manipulate. This project aims to overcome these challenges by developing new super-resolution methods that do not require controlling the source. The research team will combine advanced physical models of imaging with artificial intelligence (AI) to resolve details of passive, uncontrollable objects in real time. This research will promote the progress of imaging and sensing science by pushing the boundaries of what is possible in optical imaging, benefiting fields like medicine, astrophysics, and quantum sensing. In addition, by integrating artificial intelligence with optical engineering, the project will create unique educational opportunities in quantum and optical physics and AI for high school, undergraduate, and graduate students, helping to inspire and train the next generation of scientists and engineers. This project aims to achieve real-time quantum super-resolution imaging of practical passive point sources by developing a parameter-decoupled supper-resolution technique integrated with physics-informed robust deep learning. While prior super-resolution methods have overcome the Abbe-Rayleigh diffraction limit for controllable active sources, practical approaches for passive, incoherent, and unbalanced sources remain elusive due to real-world imperfections, multi-source complexities, and stability challenges. Building on the team’s previous theoretical advances and promising preliminary results (reaching 14 times better than the conventional resolution limit), the project proposes a three-step strategy: physics-informed machine-learning super-resolution, stability-driven real-time deep learning, and experimental validation. Scientifically, the primary objectives of this proposal are a high-precision, noise-tolerant technique that overcomes partial coherence, intensity imbalance, random phase and photon statistics limitations, with broader impacts across optical imaging in astrophysics, biomedical measurement, and quantum information science. Additionally, the objectives include comprehensive educational outreach and training efforts to promote STEM participation from high school through graduate education. 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

Rising sea levels, intensifying storms, and increasing flood risks are creating unprecedented challenges for coastal communities across the northeastern United States. In this region in particular, the magnitude of projected sea level rise is among the largest of any in the world threatening millions of Americans and billions of dollars in infrastructure. Small municipalities and neighborhoods often lack the expertise and tools needed to translate cutting-edge Earth system science into practical protection strategies for their residents. This project will build a collaborative network of scientists, engineers, policy experts, and community leaders to accelerate the development and implementation of adaptation solutions at the scale where they matter most, individual properties and neighborhoods. The project aims to reduce flood risks that currently cause over $32 billion in annual damages, protect vulnerable populations from extreme heat related illness, and preserve coastal infrastructure that supports regional economies. The collaborative approach of the project is driven by community needs in the Northeast region and will provide a replicable model for environmental adaptation nationwide. This project establishes a regionally coordinated adaptation network spanning Connecticut, Maine, New York, and surrounding northeastern states. The project employs a systematic 10-step coordination plan to engage collaborators from academia, private sector, and government agencies and promote knowledge sharing between established Technical and Policy (TAP) and Municipal, Agency, and Private sector (MAP) teams. Key methods include structured stakeholder meetings, working group development, and consensus-building processes to identify and prioritize environmental challenges including coastal erosion, flood prediction, localized heat risk assessment, and regulatory barriers. The research approach integrates advanced Earth systems science with community engagement methodologies, focusing on developing practical solutions for living shoreline design, high resolution wave and flooding modeling, machine learning-enable flood alerts, and policy innovation protocols. Phase 1 deliverables include: 1) a prioritized list of regional environmental challenges, 2) co-designed solution strategies, 3) a workforce needs assessment, 4) workforce training and development plans and, 5) a sustainable organizational framework for continued collaboration. The innovation and incubation component of the project aims to ensure long-term sustainability of academia-private sector partnerships beyond the grant period, creating lasting adaptation support capacity for municipalities across the region. 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 support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Pinar Akcora of the Stevens Institute of Technology and Jindal Shah of Oklahoma State University will combine experimental and computational techniques to study the motion of ions in mixtures of ionic liquids and polymers, called ionogels. Ionic liquids are solvents comprised solely of ions. When combined with polymers at high concentrations, the interactions between the ions and the polymer cause the structure of the mixture at a molecular level to be nonuniform, potentially giving rise to ionic conductivity gradients. Professors Akcora, Shah, and their students will combine neutron spectroscopy and dielectric spectroscopy with atomistic simulations to link polymer-ionic liquid interactions with ion distribution and ion transport in ionogels. Their discoveries could provide ways to regulate conductivity with potential applications in ionotronics, sensors and biomedical devices that require strength, conductivity and flexibility. The project will provide research opportunities for graduate students, as well as planned outreach activities for students of all ages, which will promote scientific curiosity and contribute to the development of a STEM workforce. This project will use neutron scattering and dielectric spectroscopy measurements coupled with atomistic simulations to explore the structure and dynamics of mixtures of uncharged and charged polymers with ionic liquids. The ion distribution and conformation of ionic gels will be explored to interrogate the role of dynamic heterogeneities in the system. These studies will allow us to identify molecular origins underlying nonuniform swelling, crosslinking and structure-dependent ion transport in ionogels. The specific objectives are to understand the role of ion-dipole interactions on chain conformations in polymers differing in chemistries; determine the relationship between ion correlations and ionic transport; analyze the field-induced ion distribution in gels; and measure and simulate the ion distributions within ionic liquid and polymer brush interphases. Understanding the structure and dynamics in gels will enable the design of novel structured ionogels that could have implications for applications in sensing and biomedicine. 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 opioid crisis is a national health emergency and has led to a six-fold increase in drug overdoses over the past 25 years. Opioid pain medications are very addictive; thus, the CDC recommends non-pharmacological approaches as the first course of action for pain management. Chronic musculoskeletal pain affects over 40 percent of US citizens. Musculoskeletal manipulation has proven effective at treating many types of pain but requires a human to perform costly, labor-intensive therapy. This Engineering Research Initiation (ERI) grant will support fundamental research in the development of soft robotic devices that can be pneumatically actuated to perform musculoskeletal manipulation for targeted pain relief. The human-robot interaction required for such therapy involves a large amount of physical contact; therefore, safety becomes a large design requirement. Current humanoid rigid robots can perform a variety of tasks. However, this ability to generalize gives them the freedom to be unsafe for physically therapeutic applications. This research aims to produce highly constrained, targeted soft devices with mechanical safety limits that are individually customized to each individual’s anatomy and pain pathologies which may range from back pain to arthritis to post-stroke hand spasticity. Besides the societal impact of pain reduction, this research will contribute to fundamental advances in soft robotic design, scalable customization, soft additive manufacturing, flexible electronics, sensor design, physics-based simulations, mechatronics, pneumatics, interactive control, and data-driven modeling. Student researchers will benefit from this applied, multidisciplinary problem space, rich with complex challenges, which can inspire a passion for future careers in various mechanical engineering, electrical engineering, robotics, and data science disciplines. This grant is about enabling autonomous delivery of musculoskeletal therapy through the development of customized soft robotic systems that can safely and effectively interact with the human body. The research effort consists of several tightly integrated components that span sensing, modeling, design, and control. At the core of the approach is the development of soft, stretchable e-skin sensors with dense pressure arrays, created using novel flexible electronics and soft additive manufacturing techniques. These sensors will be applied to pain-affected regions of participants to capture high-resolution force and motion data during therapist-administered manual manipulations. This data, along with detailed 3D scans of each participant, will be used to construct high-fidelity physics-based simulations that replicate therapist-patient interaction. These simulations serve as the foundation for optimizing soft robotic designs and pneumatic actuation profiles using evolutionary algorithms. Each design will be customized to the user’s specific anatomy and pain condition, whether it involves back pain, arthritis, or post-stroke spasticity. The optimized robotic structures will be fabricated through soft 3D printing processes and integrated with pneumatic control systems. During therapy, the same e-skin sensors will be reused to provide real-time force and position feedback. This feedback will drive closed-loop control strategies that look to combine physics-based and data-driven models of the human-robot system. The goal is to achieve safe and adaptive manipulation that can respond to the user’s changing physical state. Overall, this research contributes to fundamental advances in soft robotics, personalized therapeutic devices, human-robot interaction, and intelligent control 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 project will investigate how molecules behave when they are confined in very small pores. It is an important problem for a variety of applications including the design of catalysts, purification processes, and drug delivery systems. Controlling the size and geometry of small pores in an experimental system is challenging, especially for the case where the pores are hydrophobic (water-repelling). This project will synthesize polymeric nanospheres that contain gold nanoparticles to serve as templates for the pores. The gold nanoparticles will then be removed, leaving a porous nanosphere. The geometry and distribution of the pores will be adjusted by modifying the gold nanoparticle templates. The project will investigate how modifying these pores influences the physical and chemical properties of the nanospheres and their interactions with the environment. The project will provide opportunities for students at all academic levels to develop research skills, and the team will collaborate in outreach activities with Stevens’ High School Enrichment and ACES (Accessing Careers in Engineering and Science) Programs. This project will develop porous polymeric nanoparticles and investigate their porosity-dependent interactions with the surroundings. Control over the cavities will be achieved by incorporating sacrificial nanoparticles into the internal liquid crystalline phase of nanospheres and subsequently etching them away, leaving pores that resemble the geometry and distribution of the sacrificial nanoparticles. This project can determine principles for transferring directional properties of the liquid-crystalline phase by optimizing the incorporation process of sacrificial nanoparticles. Investigating the etching kinetics and thermodynamics of the embedded nanoparticles will inform non-hydrophobic interactions in confined environments, with implications for catalysis (e.g., nanozymes) and energy storage via controlled molecular transport. Additionally, porosity-dependent adsorption behaviors of molecules with varied properties and sizes will be determined, which can lead to improved strategies for impurity separation, energy storage, catalysis, and cargo delivery. In the long term, these activities will benefit the development of smart nanomaterials with advanced properties that can sense, report, and respond to environmental changes. 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

Porous polymeric membranes are used for a variety of applications including gas and ion separations, but there is a significant need for alternative strategies to design polymers with tunable porosity using scalable processes, to overcome limitations presented by the trade-off between permeability and selectivity. In a recently discovered processing method, commercial microporous high-density polyethylene (HDPE) pellets can be swollen with fatty acids or other amphiphiles to form a surface-functionalized, nanoporous structure. This Engineering Research Initiative (ERI) project will utilize comprehensive structural characterization to study HDPE processed with this method to establish design rules that lead to control of the membrane characteristics. The knowledge established in this research will aid in the development of a new scalable process for developing nanoporous membranes, which will serve to address the societal challenge of tuning permeability and selectivity in porous membranes. This project will involve the mentorship and training of one graduate and two undergraduate students, and outreach to local high school students, who will learn about materials research and advanced manufacturing. This project will identify how the process of swelling polyethylene (PE) with different amphiphiles can lead to control of the porosity and hierarchical nanostructure. This research has the potential to address a significant knowledge and technical gap in the creation of polyethylene-based membranes with controlled nanostructure, porosity, and surface functionalization, and a new fundamental understanding of how to attain this. This work will study in detail how amphiphile characteristics impact their co-crystallization with the HDPE during this new swelling process, and how this crystallization in turn affects the hierarchical nano- and microporosity of the resulting material. A wide range of amphiphiles will be used with varying head groups and PE-type tail with lengths. Complementary techniques will be used to study the hierarchical structure: differential scanning calorimetry to study crystallinity (<1 nm); X-ray scattering to study both the crystal structure and nanopores, for features between 0.1 nm – 100s of nm; SEM to image pores ranging from 10s of nm – 100s of μm; MicroCT for 3D imaging and pore distributions for features > 2 μm in size; broadband dielectric spectroscopy to study total porosity and corresponding changes with additional high temperature annealing. The primary outcome of this work will be the establishment of a set of design rules for predicting crystallinity and hierarchical porosity based on amphiphile head group and tail length in HDPE processed with this new method, potentially transforming the landscape of porous membranes. 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

Scientific progress relies on collaborative access to data, instruments, and computing across institutions. However, wireless infrastructure at research institutions often presents a tradeoff: researchers require flexibility to experiment, while campus IT teams must enforce strict security and compliance policies. This misalignment can slow innovation and introduce vulnerabilities. The Wireless Research Access Programmable (WRAP) project addresses this by creating a programmable wireless architecture that allows researchers to manage domain specific policies while providing IT teams with lightweight, formally verifiable mechanisms to ensure compliance. WRAP enables secure, cross campus collaboration in fields such as quantum computing and neuroscience, integrating seamlessly with existing infrastructure. Developed with domain researchers, it ensures alignment with scientific workflows and advances scalable, policy compliant wireless systems for national cyberinfrastructure. WRAP integrates programmable wireless networking, lightweight formal verification, and usability centered design into a unified architecture. Built on OpenWiFi and Open Radio Access Network (O-RAN), it introduces three key innovations: (1) programmable wireless enclaves using dynamic radio access slicing for cross-campus collaboration, (2) a policy assurance layer with formal verification for compliance and real-time monitoring via an IT dashboard, and (3) a declarative interface for researchers to express high-level intent with actionable feedback. Piloted at the principal investigators' institutions, WRAP is tested in real workflows for usability, policy enforcement, and legacy integration. The project provides open source modules, verified templates, and training resources to support broader adoption, enabling secure, adaptable wireless systems that accelerate scientific discovery while ensuring compliance and trust. 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

Many critical engineering systems, ranging from aircraft engines and power turbines to robotic devices and precision instruments, contain components that interact through friction and contact. These interactions are difficult to model, yet they play a vital role in determining the reliability, efficiency, and durability of such systems. Inaccurate vibration predictions can lead to fatigue, wear, or even failure, with significant safety and economic consequences. The research funded by this award aims to provide new mathematical tools and computational methods to more accurately simulate the complex dynamic behavior of structures with frictional contact. By reformulating how these interactions are represented, this research aims to eliminate longstanding simplifications and approximations that limit current analysis techniques. These advances will help engineers better understand and predict the performance of engineering systems, enabling safer and more efficient designs and longer-lasting technologies. The outcomes are expected to benefit multiple industries and support broader national goals related to economic prosperity, energy efficiency, and security. The project also supports the training of graduate and undergraduate students and will be integrated into engineering curricula. Outreach activities will introduce engineering topics to high school students through hands-on demonstrations and mentoring, thus fostering interest in STEM careers. The research objective of this project is to create a novel equality-based framework for modeling and analyzing the vibrations of large-scale structural systems with nonlinearities arising from friction and unilateral contact. Unlike traditional methods that handle non-smooth behavior by introducing penalization, regularization, massless interface, or time-domain approximations, the approach in this project reformulates friction and unilateral contact laws as exact non-smooth equalities. This allows the application of established frequency-domain techniques, such as weighted residual and harmonic balance methods, to compute periodic responses, assess stability, and perform nonlinear modal analysis. The methodology will be extended to handle a variety of contact and friction laws, two- and three-dimensional frictional contact, interfaces with multiple contact points, and finite element discretizations. In addition, reduced-order models for the vibration of large-scale structures will be constructed by defining invariant manifolds for systems with frictional contact constraints, enabling highly efficient simulations of complex dynamics. Non-smooth modal analysis will also be generalized to systems under periodic excitation, yielding compact models for forced response prediction. The new formulations will be benchmarked against conventional techniques and applied to large-scale engineering assemblies, such as bladed disks with shrouds, friction dampers, and blade root interfaces, demonstrating the effectiveness and versatility of the approach. 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

There is growing interest in wireless technologies that enable devices not only to communicate, but also to sense their surrounding environments using shared hardware and spectrum. Such technologies, known as integrated sensing and communication (ISAC), are key enablers for future applications like public safety, smart factories, and immersive wireless experiences. This project focuses on ISAC systems operating at high-frequency bands such as millimeter-wave and terahertz, which support high data rates and fine sensing resolution. However, the severe signal loss at these frequencies requires the use of extremely large (XL) antenna arrays to provide sufficient beamforming gain. This project addresses key challenges in such systems by developing technologies that allow wireless signals to focus not only on specific directions, but also at specific distances, improving both communication and sensing performance. In addition, the project explores new methods to protect transmitted information from eavesdroppers, enhancing the security of wireless communications and sensing in ISAC systems. By enabling shared use of spectrum and hardware, this project promotes more flexible wireless infrastructure, strengthens national competitiveness in next-generation wireless technologies, supports innovation-driven economic growth, and contributes to public safety through enhanced environmental awareness. This project establishes a unified framework for near-field integrated sensing and communication (NF-ISAC), focusing on extremely large (XL) antenna arrays operating in near-field conditions, where conventional far-field models break down. Key challenges addressed include spherical wavefronts, range-angle coupling, and nonlinear phase variations inherent in near-field propagation. Research activities span three thrusts. Thrust 1 develops computationally efficient and scalable NF-ISAC system design using advanced optimization tools. Thrust 2 focuses on fundamental signal processing algorithms for NF-ISAC systems, including multi-beam scanning based on random partitioning and compressive sensing to reduce scanning time, clustered channel modeling for efficient estimation, and structured methods for super-resolution localization using fragmented time-frequency resources in dense, dynamic near-field environments. Thrust 3 explores physical-layer security techniques using beamfocusing and symbol-level precoding to protect transmissions against passive eavesdroppers, even without knowledge of their positions. Together, these advances enable highly accurate, secure, and efficient joint communication and sensing, with broad applications in healthcare, robotics, and intelligent environments. The project’s outcomes are expected to support the development of next-generation wireless systems with enhanced performance, security, and adaptability. 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

It is an unfortunate truth in modern mathematics that theory often outpaces practice. More specifically, it is frequently the case that broad theoretical advancements in fields such as algebra are often made without immediate concern for how one might compute many of the objects under discussion. In this project, the PI suggests a plan for advancing a computational framework for an abstract field known as the representation theory of combinatorial categories. As part of the project the PI will also provide several concrete applications of this framework to explicitly computing quantities relevant to researchers in combinatorics, algebra, and topology. Graduate students will be trained as part of this project. More specifically, in seminal work Sam and Snowden developed what is known as the theory of Groebner categories. Roughly speaking, these are categories whose representation theory is well behaved enough to permit something akin to a Groebner basis theory. While Sam and Snowden note that because of the similarities to classical Groebner theory these categories should have a robust computational theory, this computational perspective has not yet materialized. In this project we will fully develop the computational theory of Groebner categories and their representations and provide a number of applications to explicit computations that can be completed in, for instance, the algebraic topology of configuration spaces. 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

Discovering how different features interact to influence outcomes is essential for understanding complex biological systems. Traditional statistical methods often fall short when applied to large-scale biological datasets. In contrast, newer artificial intelligence models show promise. These transformer-based foundation models, with their advanced capabilities and methods to focus attention on the most important features, are better suited to capture these interactions effectively. However, a gap remains between the computer science and biology communities. Many computer scientists are not fully aware of the importance of feature interaction discovery in biological research, while biologists are increasingly interested in using computational tools but may lack access to the latest developments in foundation model infrastructure. This project aims to bridge this gap by fostering collaboration between researchers in both fields. The goal is to build a scalable foundation model infrastructure specifically designed for identifying feature interactions in biological data. The main contribution of this project is to advance data-driven discovery of feature interactions through a shared foundation model infrastructure. The project will involve: (1) engaging computer science researchers through surveys, interviews, workshops, and working groups to explore feature interactions with foundation models; (2) developing scalable infrastructure for foundation models training and inference, along with creating datasets and benchmarks, for feature interaction discovery; and (3) applying the developed foundation model infrastructure to feature interaction discovery problems in biology. Together, this project will support both computer science and biology communities and fundamentally advance research in data-driven feature interaction discovery. 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.