University of Hawaii

NSF Awards · FY 2026 · 2026-10

Artificial Intelligence (AI) technologies are transforming daily life, but their rapid adoption has also introduced serious security and privacy challenges. Addressing these risks requires a workforce that can both advance AI innovation and safeguard its deployment. This project will help meet that need by strengthening undergraduate computing education through a curriculum-based research experience program that connects classroom learning with real world research experiences. The effort will integrate the latest AI security and privacy topics into existing computing courses while helping students build professional skills such as communication, teamwork, and leadership. By creating flexible learning modules that can be used across a range of undergraduate computing courses and institutions, the project will support workforce development and contribute to the secure, reliable, and responsible use of AI in society. The project will establish a curriculum-based undergraduate research experience program focused on AI security and privacy across partner institutions. The research team will design, implement, and evaluate flexible educational modules including labs, tutorials, assignments, and research activities in computer vision, speech and audio, and network systems. These modules will address vulnerabilities across the AI lifecycle and will be designed for seamless integration into undergraduate computing courses. The instructional materials will also be aligned with the NICE (National Initiative for Cybersecurity Education) Cybersecurity Workforce Framework to strengthen career-relevant competencies. In parallel, the research team will study educational approaches that embed research into coursework, including project-based and competition-based learning, and evaluate their effects on student engagement, success, technical growth, and professional skill development. The project will generate transferable resources and evidence-based practices that can be adopted more broadly in computing education and shared with academic and community audiences. 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

The productivity of ocean regions near Hawai’i and US territories in the Pacific Ocean is controlled by the amount of essential nutrients in seawater. Nutrients are consumed by phytoplankton that form the base of the food web, including fisheries that deliver economic and nutritional benefit to the United States. The nitrogen-containing molecule urea is an example of a nutrient that is present at very low concentrations and cannot be measured confidently in open ocean waters. This project improves methods used to measure the amount and turnover of urea within surface waters. Advancing understanding of this potentially large pool of bioavailable nitrogen will provide an important step toward predicting the health and productivity of open ocean ecosystems. The project will also provide training in laboratory and oceanographic methods to a postdoctoral researcher and multiple undergraduate students. The science team will participate in research cruises funded by the NSF-supported Hawaii Ocean Timeseries (HOT) program. Numerous lines of evidence collected over several decades have implicated the urea molecule as a source of recycled nitrogen to oligotrophic gyre ecosystems, but this importance has escaped direct quantification at open ocean observatories like the Hawaii Ocean Timeseries site Station ALOHA. This project will rigorously investigate the accuracy of low-level urea analyses by optimizing and comparing several alternative methods and then conduct a series of measurements and experiments onboard Hawaii Ocean Timeseries cruises to quantify phytoplankton utilization of urea. Focusing on Prochlorococcus, the dominant phytoplankton in oligotrophic gyres, this project will determine rates of urea uptake by Prochlorococcus and estimate the extent of Prochlorococcus nitrogen deficiency by comparing growth rates and biomarkers in nitrogen-fertilized and control incubations. Completion of these aims will reconcile paradoxically large reservoirs of urea reported within nitrogen-limited oligotrophic gyres, which, if confirmed by improved analytical methods, may represent an unrecognized limit to the efficiency of the biological pump in oligotrophic gyre ecosystems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Professor Ralf I. Kaiser at the University of Hawaii is investigating experimentally the reactions of the simplest silicon-containing radicals silylidyne with key classes of cyclic hydrocarbons along with their nitrogen-substituted counterparts to unravel the chemical dynamics involved in the formation of bicyclic silicon-bearing molecules in the gas phase. These experiments are exceptionally challenging considering the sensitivity of organosilicon species to air, short lifetimes, and the tendency to form dimers thus classifying (bi)cyclic organosilicon species as one of the least explored classes of organosilicon molecules. Professor Kaiser and his students will unravel the energy-dependent chemical dynamics of bimolecular reactions under single collision conditions utilizing an ultra-clean crossed molecular beam setup equipped with an angular resolved time-of-flight mass spectrometer detector, hard and soft electron impact ionization, and laser induced fluorescence detection. The experimental results will be integrated with ab initio and quasi classical trajectory calculations to provide basic insights on the chemical dynamics of reactive systems forming silicon heterocyclic organic molecules. These studies will advance the fundamental understanding of chemical bonding, reactivity, and electronic structure of silicon-bearing molecules, and their role in the chemistry of the interstellar medium. This project will integrate research along with training and outreach activities through organization of an interdisciplinary symposium “200 Years Silicon–Celebrating New Directions of Silicon Chemistry” at the American Chemical Society (ACS) Meeting, broadening the participation of undergraduate students in hands-on research on reaction dynamics, and expanding public awareness by relaying the latest breakthroughs from the research to high school students. This study will systematically conduct state-of-the-art crossed molecular beam experiments of the simplest open-shell silicon radical (silylidyne) with prototype cyclic, unsaturated hydrocarbons carrying main group elements across the second row. By integrating these experiments with ab initio and quasi-classical trajectory (QCT) calculations in collaboration with Prof. Rui Sun, the project will provide a comprehensive understanding of the chemical dynamics of silicon-containing systems. These investigations will deliver basic insights on: i) the previously elusive reaction pathways and chemical dynamics of reactive systems forming silicon heterocyclic organic molecules, many of which have been only theoretically predicted, ii) predictive concepts for nonadiabatic and excited-state dynamics in silicon-containing systems, including energy transfer, isomerization, intersystem crossing, spin-orbit coupling, and heavy-atom effects, iii) fundamental principles of chemical bonding in silicon-based molecules, iv) the unimolecular decomposition of chemically activated silicon-bearing reactive intermediates and the factors controlling their reactivity (internal energies, electronic states), and v) novel reaction mechanisms unique to silicon-bearing species that have no analog in isovalent carbon molecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Quantum technologies have the potential to transform sensing, communication, and information processing by enabling measurements beyond the limits of classical systems. However, building practical networks of quantum sensors remains challenging because fragile quantum correlations are difficult to maintain, coordinating many distributed sensors is complex, and large volumes of noisy data can limit performance. This project investigates a new framework for Quantum Intelligent Sensor Networks that integrates quantum sensing with advanced data-driven methods to improve how weak signals are measured, shared, and interpreted across distributed systems. The goal is to develop sensing networks that can adapt to changing environments, reduce unnecessary data collection, and extract useful information more efficiently than existing approaches. Outcomes from this work may impact applications such as medical imaging, environmental monitoring, electromagnetic sensing, and next-generation communication systems. The project also contributes to education and workforce development through new courses, open-source tools, and outreach activities that broaden participation in quantum science and engineering. The project develops an end-to-end framework for distributed quantum sensing that co-designs network architecture, physical signal processing, and learning-based inference. One thrust establishes the architectural and information-theoretic foundations of quantum sensor networks, including scalable photonic implementations for entanglement distribution, models for non-local signal extraction, and performance limits that quantify sensitivity and robustness under realistic noise and loss. A complementary thrust formulates sensing as a machine learning–driven optimization problem, combining physics-based simulation with graph-based models and reinforcement learning to adapt network topology, entanglement routing, and sensor parameters to task-specific objectives. The framework incorporates selective quantum error correction strategies that protect task-relevant information while minimizing resource overhead, enabling scalable operation in noisy environments. Together, these efforts produce new algorithms, simulation tools, and design principles for adaptive, robust quantum sensor networks, and establish a general methodology for integrating quantum hardware and learning-based optimization in distributed sensing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The Hawaiian islands are part of a volcanic island chain that formed as the Pacific plate moved over a deep mantle hotspot. This project will apply new techniques to an existing dataset of geophysical profiles that span the Hawaiian-Emperor Seamount Chain. A key focus of the study will be to constrain the amount of melt that solidifies in the crust and upper mantle instead of erupting at the surface. The results will be important for understanding the formation and expansion of the Hawaiian Islands. The project will include training of graduate students and mentorship of undergraduate students. This study will gain new insight into the internal structure and formation processes of the Hawaiian–Emperor Seamount Chain and place key constraints on the properties of the oceanic lithosphere. The project will apply seismic tomography, full-waveform modeling, and complementary geophysical techniques to data from ocean bottom seismographs deployed along four seismic profiles. Research objectives are to 1) Characterize magmatic underplating, its contribution to the magma budget, and its control on flexural response, and 2) Identify zones of tensile cracking and fluid alteration in the volcanic moat and arch and determine how these zones affect lithospheric bending. The results will have implications for plate deformation and lithospheric rheology. Within the interior of tectonic plates, these are important factors for understanding what controls new volcanism. Graduate students will gain advanced training in marine seismic analyses techniques. The project will provide research opportunities to undergraduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Hawaiʻi at Mānoa. This work is conducted with collaborators at the NASA Goddard Space Flight Center. Through the fellowship, the PI and primary collaborator will develop a program to conduct multi-disciplinary earth sciences research while providing graduate and undergraduate training in team-based remote-work best practices. The resulting research will robustly quantify uncertainty in future glacial runoff, motivated by the critical role that glacial runoff plays in buffering downstream communities against drought. The results of this award can be used to inform efforts to mitigate the socioeconomic effects of earth processes while expanding the STEM workforce. Quantifying future water availability in glaciated river basins requires a multi-disciplinary “model chain” that includes earth system models and large-scale glacier evolution models. This proposal connects an earth system model and drought-focused faculty member with a large-scale glacier evolution model and drought-focused NASA primary research collaborator to improve these model chains. Together they will pursue a research program with two primary objectives: 1) to produce basin scale glacial runoff projections through the 21st century with explicitly quantified uncertainties; and 2) to train a next generation of STEM leaders that can secure employment in Hawaii. Faculty professional advancement will include critical knowledge transfer of glaciology and associated modeling, as well as in innovative undergraduate education approaches for training and retaining future leaders in government, academia, and industry. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows (ERF). The ERF program supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research 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.

NSF Awards · FY 2026 · 2026-05

Heavy rainstorms in Hawaii can quickly raise groundwater levels. This makes it easier for wastewater from cesspools to leak into groundwater. Since groundwater is Hawaii’s main source of drinking water, it’s very important to understand how storms can affect water safety. This project will examine how heavy storms move harmful bacteria and viruses from wastewater systems into underground water supplies. At a groundwater monitoring site in Honolulu, scientists will collect water samples over time to track how pollutants move after major storms. The results will help communities and agencies better understand and respond to contamination risks following heavy precipitation. This information will be important not only to Hawaii but also to rural regions across the United States that rely on onsite wastewater systems. This research examines the mechanisms controlling time-dependent transport of fecal contamination in basalt aquifers under episodic hydrologic forcing. The project will quantify contamination mobilization associated with storm-induced groundwater-table rise, identify distinct temporal transport pathways including dissolved transport, particle-associated transport, and delayed release from attached phases, and develop an integrated predictive framework combining process-based groundwater modeling with artificial intelligence-assisted forecasting. The numerical framework will simulate transient groundwater flow and multi-phase microbial transport under changing hydrologic conditions, while surrogate predictive models will enable rapid forecasting of contamination timing and persistence. The resulting mechanistic understanding and predictive tools will advance groundwater contamination science and support risk assessment and mitigation in vulnerable coastal and rural island groundwater systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The University of Hawaii Institute for Astronomy (IfA) REU program offers individually mentored research experiences at the forefront of astronomical exploration to foster and enhance the careers of a cohort of aspiring undergraduates and teaching professionals in science, technology, engineering, and math (STEM). The experience extends beyond the students’ research to include activities that promote their professional development as researchers, educators, and communicators. Students will be inspired by visits to observatories on Maunakea, complete research projects based on data from these and other facilities, participate in observations with scientists of the Institute for Astronomy, and help host public events. Students are funded to present and publish their research in professional conferences and journals. With over 40 faculty and staff mentors pursuing a broad range of astrophysical research participants will be immersed in the IfA's world-class research environment. Faculty at the IfA develop state-of-the-art detectors and instrumentation for optical and infrared telescopes. This program will support training in critical and emerging technologies and help develop the next-generation STEM workforce. Students will be trained in a large set of skills including coding, Python programming, version control, and workflow resources. Given the nature of the projects offered by the University of Hawaiʻi, students will be trained in advanced computational techniques for analysis of large datasets, statistics, and inference algorithms that form the basis of machine learning and artificial intelligence. Examples of these projects are the analysis of datasets collected by TESS and GALEX on stellar flares and black hole variability. Participants develop their presentation skills via instruction on preparing poster and oral presentations and then giving them to an audience of professional scientists. Their research projects culminate in a poster presentation at a professional meeting, and some will conclude with published papers in the refereed literature. Past students of this NSF-funded program reported that the experiences they had through this project were a key factor in their decision to attend graduate school or pursue a career in STEM. 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 Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Hawaii at Manoa. This work is conducted in collaboration with researchers at Mayo Clinic. Through the fellowship, the PI will develop a class of miniature, eco-biocompatible soft robots with multimodal deformation and locomotion capabilities, achieving both safety and practicality beyond soft interactions. By exploring and establishing a comprehensive framework for design, material selection, fabrication, modeling, and control, this project will drive the practical development of miniature robots for safe use in hard-to-reach yet commonly encountered environments across healthcare, industry, and natural settings. This project will support the growth of STEM workforces specializing in robotics, automation, materials, and biomedical engineering. It will also promote sustainable robotics to protect Hawaii's economic and ecological values and inspire other islands, lakeside, and coastal communities. This project will develop a design methodology for miniature soft robots that enable multimodal operations while maintaining eco-biocompatibility. It will use environmentally and biologically compatible materials to create heterogeneous responses in miniature robots, overcoming the limitations of robots with multimodal capabilities that lack eco-biocompatibility, and those based on eco-biocompatible materials that lack operational versatility. In collaboration with Mayo Clinic, the project will systematically assess, optimize, and establish fabrication strategies for robots’ operation safety. With the design methodology significantly enhancing practicality and safety, miniature robots can be broadly integrated into existing robotic ecosystems to address access and functionality challenges in hard-to-reach environments. This project will enhance the University of Hawaii's research infrastructure by expanding the PI’s expertise and fostering growth in robotics, materials, and biomedical engineering programs. The research will also integrate with curriculum development, multi-level project expansion, and outreach activities involving partner organizations, amplifying the project’s regional and national impacts. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research 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.

NSF Awards · FY 2026 · 2026-05

This Faculty Early Career Development Program (CAREER) award will support research to create tiny soft robots capable of demonstrating agile navigation and dexterous object handling in fluids. Mesoscale (i.e., millimeter-to-centimeter-scale) soft robots can provide unique advantages in hard-to-reach biological and natural fluidic environments. However, they currently lack locomotion, agility, and object manipulation capabilities comparable to those of mesoscale marine organisms, limiting their effectiveness in dynamic flows. This project will incorporate versatile fluid manipulation strategies, such as based on changing mechanical properties, into the design and control of mesoscale robots to address this gap. The resulting miniature actuators, physically complex robots, and data-driven control strategies will advance mesoscale robotics for broad applications in conditions that are challenging for humans or larger robots. Examples include exploring, monitoring, and sampling intricate coral reefs, mangroves, and densely stocked aquaculture, as well as improving access to hard-to-reach parts of the human body for timely medical diagnosis and treatment. Besides research, this award will support widespread engagement in miniature robotics through a knowledge-sharing website, project- and theory-integrated college courses, undergraduate research internships, and exhibitions at local museums. Achieving locomotion, agility, and object manipulation capabilities that millimeter-to-centimeter-scale marine organisms demonstrate remains a major challenge for mesoscale robots, due to limited understanding, realization, and utilization of interaction modes between robots and surrounding fluids in intermediate flow regimes. To overcome this challenge, this project will develop and systematically investigate versatile fluid manipulation strategies. Specifically, this project focuses on four thrusts: (1) enable active modulation of multimodal motion modes and mechanical properties of robots using hybrid miniature soft electric-magnetic-thermal actuation, (2) demonstrate swift motion (>30 cm/s), agile spatial maneuverability (>45 deg/s), and object collection, filtering, grasping, cutting/penetration, passing, and delivery through coordination among fluid manipulation modes, (3) demonstrate spatial position and object manipulation control using data-driven dynamic model and controller, and (4) incorporate electronics and batteries onto robots (<10 cm) to demonstrate locomotion and functions in laboratory and field tests. Thus, this award will advance design, fabrication, actuation, modeling, and control of high-performance miniature robotic operations in fluids. 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 · 2026-04

This grant provides funds to support students for travel to the International Conference on Software Engineering (ICSE 2026), which will take place in Rio de Janeiro, Brazil in April, 2026. ICSE is the flagship conference in the field of Software Engineering. A large part of the technical program is devoted to research on using artificial intelligence (AI) techniques to support software development, as well as using software engineering techniques to support AI-based software systems. The grant will provide travel and registration support for US-based students. The ICSE 2026 conference will have a doctoral symposium, student mentoring workshop, and a new faculty symposium. In addition, the conference features a workshop on Quantum Software Engineering. Conference attendance is important for the technical exchange of information and research conversations/collaborations made possible by the conference, as well as advances in the field made possible by these interactions. The conference provides opportunities for education, training and mentoring to build the next generation of researchers and practitioners in the field of software engineering. The international nature of this conference helps develop a globally-aware workforce of research and educators within the US and helps build the community of researchers in the field of Software 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 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Hawaii at Mānoa. This work is conducted in collaboration with researchers at the SLAC National Accelerator Laboratory. Through the fellowship, the principal investigator (PI) will develop artificial intelligence (AI) tools to interpret vast volumes of data generated by SLAC’s powerful X-ray light source. These tools will help researchers better understand the dynamics of matter at atomic spatial and temporal scales by identifying hidden patterns in ultrafast X-ray scattering data and linking these patterns to the physical processes governing matter’s properties. The outcomes will impact real-world applications, such as improving solar panel and battery efficiency, biomedical imaging, radiation therapies, and the design of new materials and precision drugs. More broadly, this work addresses growing challenges in data-intensive science and engineering and will strengthen research capacity in AI and imaging in Hawaii while supporting local STEM workforce development. This project will investigate the use of foundation models and physics-informed machine learning to interpret and simulate large-scale X-ray coherent diffraction data. It will develop a scalable, generalizable framework that integrates vision-language models (VLMs), physical constraints, and both simulation and experimental data to extract dynamic signatures from high-throughput X-ray speckle patterns. In collaboration with SLAC’s Linac Coherent Light Source (LCLS), the PI will access the state-of-the-art ultrafast imaging facilities and data critical to building the proposed framework. The project will enhance the PI’s expertise and support the development of an AI-driven computational imaging program at the University of Hawaii. Broader impacts include graduate student training, workforce development in AI and imaging science, and strengthened research infrastructure. This effort will combine technical innovation with institutional capacity building, positioning Hawaii as a future leader in data-enabled science and engineering. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows (ERF), which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research 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.

NSF Awards · FY 2026 · 2026-01

Communities in Hawaii and similar remote, rural, and austere regions often face dynamic local conditions that can impact public infrastructure and well-being. Events such as sudden changes in water quality or air particulates after a disaster can present significant challenges. Effectively monitoring and responding to these events requires timely, localized data, which is often difficult to obtain with existing infrastructure. This Smart and Connected Communities (SCC) project seeks to address this critical gap by enabling neighborhoods to become active participants in data collection and analysis for their immediate surroundings. The project will collaborate with participants across several sites in Hawaii to co-design and build novel, low-cost systems for localized data acquisition. This system will feature customizable sensors that are fabricated via advanced manufacturing processes (3D-printing) and connected to a powerful, portable data analysis platform. By making these tools more readily available to local communities, this project will enable rapid, localized responses to unforeseen events thereby improving community preparedness and providing valuable data for disaster planning and response. This work directly supports the NSF’s mission by integrating research and education to advance national health, prosperity, and welfare. The project’s technical goal is to develop and integrate three core innovations: a low-cost, open-source electronics printer; a scalable, AI-enabled edge computing platform; and a robust framework for participant-based co-design. The research will first establish a novel printing system fabricated from 3D-printed and commercial off-the-shelf parts, dramatically reducing the cost and expertise required to produce high-performance sensors for specific chemical and physical analytes (e.g., heavy metals, pH, organic targets). Secondly, a modular, durable, and low-power AI edge device that interfaces with these printed sensors to autonomously collect, process, and analyze data in real-time, even in remote locations with limited connectivity will be developed. The final thrust of the project validates a community participatory co-design process, ensuring the technology is directly responsive to local needs and that the data generated is accessible and actionable. This integrative approach will produce a complete ecosystem spanning sensor fabrication to data interpretation that can be adapted and deployed to address a wide range of localized data collection needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This project addresses a long‑standing challenge in fire science: understanding how flames spread across solid materials, a process that influences everything from everyday fire safety to spacecraft design. Current tools cannot reliably measure the rapid heating that occurs just before a material ignites, which limits the ability to predict and prevent dangerous flame‑spread scenarios. This project introduces a new optical technique that uses temperature‑sensitive paints made from the same material as the burning sample, which will allow researchers to capture fast, precise temperature changes without disturbing the flame. By providing the first proof‑of‑concept for this approach, the project could open the door to safer materials, improved fire‑resistant designs, and better models for extreme environments such as microgravity. The work will strengthen fire‑safety research, will train students in advanced diagnostics, and will expand scientific capability at the University of Hawaii. The technical goal is to establish whether temperature‑sensitive paints can serve as a fast, non‑intrusive diagnostic for quantifying the thermal processes that control flame spread in solid fuels under opposed forced flow. The research will develop and characterize a new class of chemically compatible coatings capable of resolving rapid temperature changes, heat‑flux variations, and the spatial extent of the preheated zone immediately ahead of the flame front. Controlled combustion experiments will be conducted in a flow‑regulated chamber using plastic slabs coated with paint made with the same material across multiple orientations, enabling systematic evaluation of flame‑spread behavior. High‑speed optical measurements will be integrated with analytical modeling and Computational Fluid Dynamics simulations to validate fuel pyrolysis submodels, assess radiative and convective heat‑transfer contributions, and reconstruct quantities that cannot be directly measured. The resulting dataset may lead to discovery of new pathways for predictive modeling of flame spread across materials, configurations, and environments, thereby strengthening the scientific basis for fire‑safety engineering. Broader impacts include enabling safer material design, expanding diagnostic capability for combustion research, and training students in advanced optical measurement techniques that support both academic and industry needs. 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 Blue Economy consists of research focused on the sustainable use of ocean resources to drive economic growth. It covers areas such as marine resource management, ocean energy, sustainable fisheries and aquaculture, and maritime transportation. Artificial Intelligence (AI) holds transformative potential to advance Blue Economy research by enabling data analysis and predictive modeling. The targeted areas include the Mid-Atlantic region, Gulf coasts, and Hawaii. This conference proposal aims to tackle these regional issues through coordinated efforts that enhance Blue Economy research, expand funding opportunities for innovative projects, and strengthen cross-sector partnerships. The conference will bring students, faculty, and researchers from four academic institutions, along with participants from various fields such as geoscience, computer science, biology, and civil engineering. A core objective is to promote Artificial Intelligence (AI) education and workforce development to support the Blue 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

The web-based application Blast helps astronomers understand the connection between transient events, such as supernovae, and the galaxies in which they live. This program will expand the capabilities of Blast to match the challenges created by the vast amount of data that will be generated by the new generation of telescopes, such as Rubin (NSF-DOE) and Roman (NASA). This program will also work with Zooniverse, a citizen science platform with more than 2.7 million registered users. With Zooniverse this program will engage the public in a variety of research projects related to transients and their host galaxies. Over the last several years, the wealth of transient data has increased dramatically and with it, the discovery potential. This program focuses on the ways in which the physics of astrophysical transients are fundamentally linked with the properties of the host galaxies in which their progenitor stars form and evolve. Understanding the stellar populations that give rise to these transients plays a key role in our understanding of the transients themselves, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients with galaxy data. This program will support a major upgrade to Blast, a web application for host-galaxy inference, which provides real-time spectral energy distribution fitting from ultraviolet to infrared wavelengths for every astrophysical transient using the Prospector Bayesian inference framework. Among several outreach initiatives in Hawaii and Illinois, the PIs will support the Institute for Astronomy’s Hawaii Student/Teacher Astronomy Research program, which has trained astronomy-enthusiastic high school students in research skills for over a decade. 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 investigates the commercial potential of a needle insertion system for interventional radiology. Each year, hundreds of thousands of image-guided needle procedures are performed in the United States, and this number is steadily increasing. Mistakes or delays in these procedures can lead to higher costs and poor outcomes for patients. Several pain points have been identified within the current field of interventional radiology; these include: high cost, lack of robotic solutions, and concerns with radiation exposure. These problems are especially important because many hospitals perform image-guided procedures every day, and as the number of these procedures continues to grow, the needle insertion system targets a large and growing need in healthcare. By enabling faster and more precise needle placement, the system can reduce overall procedure time, which contributes to lower operational costs. In addition, shorter procedures mean reduced radiation exposure for staff, supporting both economic efficiency and a safer clinical environment. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a piezoelectric-actuated robotic system for image-guided, minimally invasive procedures. The system is designed to operate safely within magnetic resonance imaging (MRI) environments and features seven independent axes of motion powered by non-magnetic piezoelectric motors. The technology achieves precise needle placement by using closed-loop control algorithms that respond in real time to imaging feedback, adjusting the needle trajectory during the medical procedure with millimeter-level accuracy. This accuracy represents a significant advance over conventional manual techniques and existing robotic systems that rely on open-loop control, that do not adapt to real-time changes in needle position or tissue interaction. The primary goal of the system is to improve the safety, consistency, and efficiency of interventional radiology procedures. By minimizing needle misplacement and reducing the need for repeat imaging, the system lowers procedure time, decreases radiation exposure for clinical staff, and enhances patient outcomes — ultimately contributing to more effective care. 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

Digital Twin technology aims to align the physical behavior of complex systems with online computational models, enabling real-time monitoring, prediction, and informed decision-making. Raw data gathered by sensors is often challenging to integrate into a model due to the sparsity of sensors. The sparse sensing problem is the central focus of this project. Along with developing theory and computational methods, the project focuses on applications to components of nuclear reactors. Open-source community software will be developed within the frameworks RAVEN, PySensors, and the Nuclear Data Research System. The project will involve traineeships, software carpentry, and open-source educational curricula. Curricula will be published using the University of Washington's Lightboard filming studio. The project aligns with the Presidential priorities in artificial intelligence and nuclear energy, and will enhance national leadership in these areas. Sparse sensors establish the critical bidirectional flow of information between virtual models and safety-critical decision-making in physical nuclear energy subsystems. These sensors are essential for estimating high-dimensional temperature fields, pressure gradients, and accident scenarios. However, in nuclear applications, sensor design, placement, and budgets are extremely constrained, making strategic design and budgeting of sensors crucial. This project develops fundamental theory, algorithms, and guarantees for sparse sensing optimization in nuclear subsystems. Control and information theory, statistical mechanics, and uncertainty quantification will be leveraged to develop robust, high-dimensional estimation methods with guaranteed performance. To achieve this, there are three major thrusts: 1) dynamical models and information theory of sparse sensing, 2) optimal regularization and uncertainty quantification using statistical mechanics, and 3) multi-objective decision-making in nuclear digital twins. Validation and verification of the methods and models will focus on high-dimensional estimation, anomaly detection and prediction within the transient water irradiation system at Idaho National Laboratory (INL). 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 the University of Hawaiʻi at Mānoa to enable the purchase and commissioning of a new Transmission Electron Microscope equipped with an energy dispersive X-ray spectroscopy system to be housed in the Biological Electron Microscopy Facility located on the main campus of the University. This advanced instrument will significantly enhance research and training capabilities across biology, materials science, and engineering disciplines while providing crucial educational opportunities for students annually through graduate research, undergraduate honors projects, and comprehensive training programs. The facility engages in numerous educational and outreach activities, utilizing the microscope for demonstrations in kindergarten through grade 12 science classes at local public and private schools, laboratory sessions for undergraduate and graduate courses, and specialized workshops for community college faculty and students. As the only fully equipped electron microscopy facility serving the state of Hawaiʻi, this instrument will strengthen existing partnerships across the University of Hawaiʻi system, government organizations, and industry collaborators, while supporting workforce development in advanced microscopy techniques essential for the research infrastructure and economic development of Hawai’i. The enhanced capabilities of the new TEM will advance frontier-pushing research programs across diverse scientific domains. Research activities enabled by the instrument's exceptional magnification, 0.20 nanometer resolution, and simultaneous elemental analysis capabilities include investigating physiological mechanisms enabling larval zooplankton survival in extreme sub-arctic environments, characterizing marine species dispersal and connectivity across vast Pacific oceanic distances, documenting fine-scale morphological adaptations in cave-dwelling arthropods, illuminating cellular characteristics of abundant marine bacteria, and understanding how giant viruses thrive in nutrient-poor ocean conditions. The system's integrated analytical platform will provide transformative insights into host-microbe interactions, protein topology, cellular ultrastructure, and viral-host dynamics, fundamentally advancing scientific knowledge across marine biology, evolutionary ecology, and molecular 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

This award supports a project exploring the connections between algebra and dynamics through the study of mathematical structures known as operator algebras. Operator algebras are a fundamental object of study in analysis and mathematical physics, with connections and applications to many other mathematical fields. In this context, they arise from dynamical systems called shifts of finite type, and from groups that exhibit self-repeating patterns, like the patterns found in fractals. These systems appear in a wide range of mathematical areas, including data encryption, statistical physics, neural biology, and fractal geometry. The research supported by this award will lead to new insights into how symmetry and complexity interact in mathematical systems, expanding foundational knowledge in pure mathematics. In addition to advancing theory, the project will support undergraduate and graduate students as well as earlier career researchers. This is a project funded jointly by the National Science Foundation's Division of Mathematical Sciences, in the Directorate for Mathematical and Physical Sciences (NSF-MPS-DMS), and the Israel Binational Science Foundation (BSF) in accordance with the Memorandum of Understanding between the NSF and the BSF. A major goal in the field of operator algebra is to produce new invariants for dynamical systems by studying algebras associated to them. This goal has been continuously advanced over the years, especially in Elliott's classification program for simple C*-algebras. The project aims to strengthen such connections between dynamics, group theory, operator algebra theory, and ring theory. Through (operator) algebras, a bridgehead will be created for studying some of the most subtle anomalies related to subshifts of finite type (SFTs) and self-similar groups. This will be achieved by studying specific interactions between graph algebras, as well as new invariants for SFTs. Inspired by several prominent interactions between graph algebras and symbolic dynamics, the project also aims to characterize structure-preserving isomorphisms between algebras associated to self-similar groups with the goal of uncovering new dynamical phenomena. This will shed light on some of the most important problems in symbolic dynamics, graph algebras, as well as on self-similar groups and their associated algebras. In particular, by working in the more general framework of self-similar groupoids, it will be possible to generalize shift equivalence and strong shift equivalence to self-similar groups in order to understand these structure-preserving isomorphisms and Morita equivalences in the context of self-similar groups. 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 addresses urgent challenges facing food security and community resilience in Hawaiʻi, where over 90 percent of food is imported. With increasing exposure to stressors such as changing sea levels, extreme weather, and ocean acidification, traditional food systems and ecological stability are at risk. Reviving and enhancing centuries-old aquaculture systems known as fishponds provides a sustainable pathway to improve local food production and reduce environmental vulnerabilities. These fishponds, once key sources of nutrition and ecosystem balance, are now being revitalized through community leadership and science-based innovations. By integrating place-based knowledge with microbiological tools, this project aims to restore ecosystem function, support regional self-sufficiency, and contribute scalable solutions for environmental adaptation. The broader societal impacts include empowering communities through education and workforce development and positioning Hawaiʻi as a leader in sustainable aquaculture innovation. This project advances scientific understanding of how microbial communities influence and reflect ecosystem health under stressors such as temperature shifts, salinity changes, and sedimentation. It will assess microbial dynamics in aquaculture environments, identifying key indicators of resilience and testing targeted interventions, including endogenous probiotics and bioremediation strategies. Using a combination of field-based environmental sampling, genomic sequencing, and mesocosm experiments, the project will generate actionable data linking microbial composition and function to ecosystem productivity. By co-developing monitoring tools and restoration strategies with community members, this work bridges microbial ecology and place-based management systems to create replicable models for ecosystem restoration. The project lays the foundation for adaptive frameworks that integrate Earth systems science and place-based practices to support long-term food security and community resilience throughout the Pacific 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

This project proposes to operationalize ethical principles in cybersecurity through the development of a novel ecosystem. It will support researchers, students, institutional review board members, and professionals in navigating context-sensitive cybersecurity challenges. The project team aims to develop educational resources that foster a culture of professional integrity in the realm of cybersecurity. The project is organized around four integrated research thrusts. First, the project team will conduct empirical studies to assess the ethical challenges experienced by cybersecurity researchers, culminating in a repository of real-world cases in the field. Second, through participatory action research and grounded theory analysis, the project team will develop an evidence-based taxonomy of protocols tailored to recurring cybersecurity research scenarios. Third, this taxonomy will be translated into an interactive simulation platform that enables users to explore cybersecurity challenges in a safe, dynamic environment. Users will receive real-time feedback and guided decision support. Fourth, the project will integrate these tools into cybersecurity education, developing a set of mini-modules for integration into technical curricula and launching pilot programs at partner 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.

NSF Awards · FY 2025 · 2025-09

The majority of rocky planets that have both size and mass measured are much bigger than Earth, and studies suggest that an Earth-like composition may be common among them. However, the interior composition of truly Earth-sized planets remains largely unexplored. This proposal makes use of the newly-commissioned Keck Planet Finder (KPF) spectrograph to precisely measure the masses of 12 Earth-sized exoplanets whose size was already determined by transit measurements. These precise mass measurements are necessary in advance of more detailed characterization efforts. The project personnel will support outreach and educational activities: mentoring opportunities in Hawaii and lecture series in both English and Spanish for the general public in Southern California. Giant impact simulations predict that Earth-sized planets may exhibit greater diversity in their interior composition than super-Earths: they likely experience only a few giant impact collisions, whereas super-Earths undergo dozens. Hit-and-run collisions could make the remnant planet denser. If mass growth is mainly by pebble accretion, interior compositions could be size-independent but depend on the stellar composition. This program will test these hypotheses using one of the most advanced echelle spectrometers ever built: KPF is designed to achieve radial velocity precision of 30 cm/s. Coupled with the 10m Keck telescope, it is the most powerful and efficient system in the Northern Hemisphere for this work. Subsequent to this work, the planets may be suitable for follow-up measurements to characterize their atmospheres or the mineralogy of their bare rock surfaces. 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

Wildfires are increasing globally in both frequency and severity, including on tropical islands adjacent to coral reefs. Burned materials from urban and rural fires, such as ash, smoke, and toxic compounds, are delivered to the atmosphere and waterways during and after wildfires. The impacts of wildfires, therefore, extend to terrestrial and aquatic ecosystems adjacent to burn zones, as well as those thousands of miles away. Within the last decade, research has shown how wildfires can impact nutrient cycling and production in the ocean. Yet the impacts of wildfires on ecologically sensitive coral reefs are largely unknown. Coral reefs foster the highest biodiversity per unit area of any aquatic ecosystem, providing $3.4B per year to the US economy alone through fisheries, tourism, and storm protection. This project expands our understanding of wildfire disturbances in tropical ecosystems by characterizing the effects of ash deposition on coral reefs and their microbial communities. This work contributes to our understanding of how wildfire and nutrient pollution affect coral reefs and will serve to inform coastal communities of fire risks to reef ecosystems. This project evaluates the functional impact of ash deposition from terrestrial wildfires on tropical coral reef ecosystems. Until the last decade, fire events in tropical latitudes were largely limited to agricultural controlled burns. Unfortunately, large scale tropical wildfire disturbances are becoming increasingly prevalent as the result of drought and more frequent and intense heatwaves. Fire acts as a nutrient transformer and ecosystem subsidy, changing the chemical composition of parent material and releasing large quantities of particles to the atmosphere (aerosols) and waterways (burned detritus), including nearshore and open-ocean ecosystems. Wildfires and the deposition of burned materials are known to have acute and long-lasting effects on nutrient dynamics and biodiversity in freshwater ecosystems. Yet little is known about the impacts of wildfires on coral reef ecosystems, alone or in combination with covarying environmental stressors, such as thermal stress. Through a series of bottle remineralization and controlled mesocosm experiments in Hawaiʻi, this research: 1) characterizes how microbial community composition and carbon metabolism is impacted by pyrogenic ash deposition; 2) evaluates the functioning of the coral holobiont in response to pyrogenic ash and increased temperature, both separately and in tandem; and 3) develops a mechanistic model to predict how scenarios of wildfire ash deposition and warming affect coral reef community composition. 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

Coastal beaches are vital for recreation, tourism, and ecosystem health. However, they are increasingly threatened by fecal contaminants as indicated by high concentrations of fecal indicator bacteria (FIB). These bacteria can enter beach environments through human wastewater, stormwater runoff, and animal waste, accumulating in the sand and shallow groundwater. Natural coastal processes such as tides and waves can then mobilize and transport these contaminants across the land-sea boundary, posing risks to public health and marine ecosystems. This project will investigate how coastal hydrologic forces, including tidal fluctuations and wave action, influence the accumulation, movement, and discharge of FIB in beach aquifers. The research team will conduct fieldwork and groundwater sampling at two beaches in Hawaiʻi where FIB contamination is known to occur, and will develop advanced computer models to simulate how bacteria move through beach sediments. The findings will improve prediction and management of water quality risks in coastal zones. Broader benefits of the project include training two graduate students and engaging undergraduate students through the University of Hawaiʻi at Mānoa’s Undergraduate Research Opportunities Program. The team also plans to involve local communities through educational outreach and citizen science activities to support long-term coastal water stewardship in Hawaiʻi. Fecal contamination is an increasing concern in coastal beach environments, posing serious risks to public health and ecosystem integrity. Fecal indicator bacteria (FIB) are commonly used to detect and assess the extent of such contamination. These bacteria can enter the subsurface through various sources such as treated and untreated wastewater, stormwater runoff, and animal waste, and persist in beach aquifers where they interact with dynamic coastal hydrologic forces. However, a mechanistic understanding of how tide- and wave-driven seawater-groundwater interactions influence the fate and transport of FIB in coastal beach aquifers remains lacking. The project aims to fill that knowledge gap through a comprehensive approach that integrates state-of-the-art surface water, groundwater, and reactive transport modeling, along with field measurements. There are three objectives: (1) develop a state-of-the-art modeling framework that captures both surface and subsurface flow dynamics and simulates the accumulation, transport, and discharge of FIB within and through coastal beach aquifers; (2) conduct field studies at two fecal-contaminated Hawaiian beaches subjected to high and low wave energy, respectively, to quantify FIB distribution and transport under varying hydrodynamic conditions; and (3) analyze site-specific data and extend modeling efforts across a range of hydrogeological settings to identify the key controls on FIB exchange across the beach-sea interface. The successful completion of this project will advance understanding of coastal hydrogeological processes that govern bacterial transport, not only for FIB but also for other microbial groups such as pathogenic, sulfate-reducing, and nitrifying bacteria. The knowledge generated will support development of more effective coastal water quality management strategies and inform stakeholders and communities about the resilience and vulnerability of beach aquifer systems to fecal contamination in the face of changing coastal conditions. This project is jointly funded by Water, Landscape, and Critical Zone Processes (WaLCZ), the Established Program to Stimulate Competitive Research (EPSCoR), and the Earth Sciences Division (EAR). 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.