Oregon State University

NSF Awards · FY 2025 · 2025-09

Remotely sensed spectral images, such as hyperspectral images (HSIs) and multispectral images (MSIs), are widely used across science and engineering fields, including agriculture, oceanography, forest monitoring, mineral discovery, and space exploration. These image modalities involve an inherent trade-off between spatial and spectral resolution: HSIs provide fine spectral detail but coarse spatial resolution, whereas MSIs offer the reverse. Spectral image fusion techniques seek to combine the strengths of both by integrating an HSI and MSI of the same region to produce fused images with high-resolution information in both domains, supporting various tasks such as pixel classification, target identification, and change detection. However, many existing fusion methods operate under the assumption that the spectral images are co-registered (i.e., covering the same region and sharing the same coordinates), whereas in practice the data are often spatially misaligned by pixel shifts, rotations, and other distortions (collectively referred to as “unregistered”), typically arising from differences in sensors or imaging platforms. Despite its fundamental practical importance and considerable interest, the fusion of unregistered spectral images still lacks rigorous theoretical underpinnings and reliable algorithms. This project addresses these gaps by developing new analytical and computational methods to establish a solid theoretical and algorithmic foundation for this long-standing and practically significant problem, enabling performance-guaranteed fusion of unregistered spectral data in real-world scenarios. Beyond remote sensing, the outcomes are expected to benefit areas such as cross-platform medical imaging and domain adaptation/transfer in machine learning. The project also offers undergraduate research opportunities, providing students with training in machine learning, optimization, and image/signal processing. This project develops a unified, unsupervised framework for fusion of unregistered spectral images with provable guarantees, tackling key challenges including spatial misalignment, lack of training data, and nonrigid deformation. Thrust 1 focuses on establishing theoretical foundations by integrating spectral unmixing with adversarial learning through diversified distribution matching in a latent spatial domain, enabling provable spatio-spectral super-resolution under practical, unregistered scenarios. Thrust 2 extends this framework to more complex real-world cases such as those involving unknown and potentially large nonrigid deformations. Thrust 3 develops stable and efficient optimization algorithms for the proposed fusion formulations, tailored to adversarial learning in latent domains and addressing the limitations of standard optimizers. Validation on semi-realistic and real-world datasets is used to assess the robustness and generalizability of the proposed methods. Expected outcomes include new theoretical insights, practical algorithms with convergence guarantees, and reproducible benchmarks to advance unregistered spectral image fusion and its applications in machine learning, signal processing, and scientific imaging. 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 ~40% coverage of the terrestrial biosphere, grasses represent one the major plant types on Earth; this percentage excludes additional coverage by all major grain crops, which are also grasses. Despite the global importance of grasses, significant gaps in understanding remain in how grasses respond to drought. The evolution and expansion of grassland biomes came at the expense of forests and was precipitated by an increase in aridity; therefore, grass evolution, physiology, and ecology are inextricably linked to the acquisition, use, and movement of water. The aim of this proposal is to provide a better understanding of grass physiological responses to drought from the cellular to ecosystem scales. The current understanding of plant responses to drought is dominated by data on woody plants, particularly trees, and this understanding does not translate readily to grasses. Elucidation of these drought response will enhance our understanding of wild grasses to drought, as well as discover relevant physiological responses for crop improvement. Additionally, the PIs will conduct the immersive data-collection and instrument training ecophysiology workshop for graduate students (Phys-Fest). This Phys-Fest will occur in the urban environment of Philadelphia. Urban environments can provide key ecosystem services, and when explicitly managed, these environments enhance overall human well-being. Participants are trained in four primary ecophysiological research areas and are provided with close interaction with faculty instructors, as well as evening activities designed to promote professional development and science communication. Several novel and previously unexplored aspects of grass physiology are developed within this proposal under the guiding question: How do grasses, individually and at the ecosystem scale, respond to changes in soil moisture and leaf-to-air vapor pressure deficit (VPDL)? This question is distilled into more specific questions that will be answered via the research plan: (i) What are the physiological and anatomical mechanisms by which grasses control stomatal sensitivity through changes in VPDL? (ii) How do grasses maintain leaf-level gas exchange at leaf-water potentials that are near or more negative than the turgor-loss point? (ii) How do physiological responses coupled with plant-atmosphere interactions affect grassland responses to soil and atmospheric drought? The proposed research will be comprised of lab, greenhouse, and field work at two N. American prairie sites. The field sites were chosen because of their ecological and phylogenetic relevance: the tall-grass prairie site is dominated by C4 Andropogoneae and the short-grass prairie site is dominated by C4 Chloridoideae and C3 Pooideae. These grasslands exist on opposite ends of the precipitation spectrum across the Great Plains and these grass lineages are globally dominant. This proposal was supported by the Integrative Ecological Physiology Program in the Division of Integrative Organismal System and the Ecosystem Science Cluster in the Division of Environmental Biology. 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

Hydrologic changes, such as shifting precipitation patterns and changing storage in glaciers, snowpacks, groundwater, and soils makes it increasingly complex to forecast water availability. These changes also make it challenging to accurately quantify water flow and storage. Water tracers are commonly used to determine sources of water based on field measurements. Current water tracers, such as ions and isotopes, are often limited in their ability to distinguish between water sources or pathways, especially when the underlying geologic materials are similar. This project will explore the potential for DNA-derived tracers to overcome these limitations in watersheds where snow and ice are major water sources. The main objective of this research is to establish DNA-derived tracers as a reliable tool for hydrologists to better understand how water flow and storage in catchments is influenced by snow and ice, providing new insights where traditional tracers fall short. By providing more precise tools to assess water flow and storage, this research advances knowledge in both hydrology and environmental DNA tracing, with cross-disciplinary applications in environmental monitoring and biodiversity analysis. The project will benefit society by improving water management strategies in climate-sensitive regions, supporting biodiversity conservation efforts, and contributing to climate change adaptation policies. This bi-lateral international project examines water sources and flow paths in snow and ice-dominated catchments of the Oregon Cascades (USA) and the Swiss Alps. Combining traditional tracers with naturally occurring environmental DNA (eDNA) data will allow a more detailed analysis of water dynamics during hydrologic transitions, such as seasonal shifts. This project will conduct eDNA sampling across four study streams as well across multiple water sources, including snowpacks, glaciers, groundwater, soil leachates, and tributaries. At each site, non-target metagenomics analysis will be conducted on samples of watershed discharge and water sources throughout the catchment to identify the individual genes present in water samples. Two traditional hydrologic tools, end member mixing analysis and concentration-discharge analysis will be applied using eDNA information from different sources as the end members and discharge-dependent target tracer. The combination of traditional and eDNA-based tracers offers a robust mechanism to assess the success of these methods in improving water flow and storage predictions. Success will be measured through data integration, model improvements, and the production of peer-reviewed publications and conference presentations. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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 convene a workforce development conference that brings together Pacific Northwest (PNW) Blue Economy industry representatives and academic faculty from Oregon’s universities and community colleges. The Blue Economy, defined as the sustainable use of ocean resources for economic growth, jobs, and ecosystem health, is a rapidly growing sector in the Pacific Northwest (PNW). In Oregon and Washington, it contributes billions of dollars annually to the regional economy and supports over one hundred thousand jobs across industries such as fisheries, shipbuilding, ocean research, aquaculture, maritime transport, and emerging sectors like offshore renewable energy and marine biotechnology. As this sector expands, there is an urgent need to align workforce training and academic curricula with the evolving demands of Blue Economy employers. By strengthening communication and information exchange between employers and educators, this effort will help ensure that future workers are equipped with the skills needed to succeed in ocean-related fields. Ultimately, this project will support economic growth, job creation, and sustainable use of ocean resources in the PNW by informing and modernizing training programs that prepare the next generation of ocean technicians, data scientists, engineers, and researchers. The conference will provide a forum to identify workforce needs, skill gaps, and educational opportunities to better prepare students for careers in ocean science, engineering, technology, and data-intensive fields. Complementing this event, the leaders of the event will conduct a comprehensive needs assessment of current and projected hiring needs within the PNW Blue Economy. The outcomes of this project will include collaborations between industry and academia, a workforce needs assessment based on employer input, and a publicly available report including actionable recommendations for updating or developing curricula. 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 goal of the project is to develop novel advanced materials integrated with real-time process feedback, assisted by a machine learning algorithm, to enable scalable, autonomous in-situ manufacturing of electronics. The technology will provide capabilities for on-demand fabrication, adaptive repair, and dynamic reconfiguration of circuits, functions that are particularly critical for long-duration space missions where resupply is difficult. These enhanced materials and manufacturing processes will support future space exploration initiatives. Beyond space applications, the methods developed here may also transform multiple technology sectors including flexible hybrid electronics for wearable devices, neuromorphic computing systems that mimic brain functions, and distributed manufacturing solutions for remote or resource-limited environments. The research incorporates workforce development initiatives to train students in cutting-edge techniques spanning materials science, artificial intelligence, and advanced manufacturing. Participants will gain hands-on experience in functional materials synthesis, intelligent process control systems, and semiconductor device fabrication, skills directly aligned with emerging needs in the advanced manufacturing sector. The project specifically addresses national workforce development priorities in critical technology areas including additive manufacturing, semiconductor processing, and autonomous production systems. This project develops a new method to manufacture electronics in space using 2D materials like molybdenum disulfide (MoS₂). These ultra-thin materials are ideal for space applications because they are lightweight, radiation-resistant, and energy-efficient. The key innovation combines three critical components: (1) specially designed chemical inks that transform into functional electronics at relatively low temperatures, (2) an artificial intelligence (AI)-controlled printing system that adjusts in real-time to produce perfectly aligned layers, and (3) precision laser processing that fine tune the material's properties after printing. First, new ink materials and formulations will be created, where the molecular structure determines how well the material performs in final functional semiconductor devices. Then AI systems will be implemented to monitor and optimize the printing process, catching and correcting any defects in real-time. Finally, laser sintering will be utilized to control and enhance the material's electrical properties, enabling complete electronic device processing onsite. This integrated approach solves a major challenge in space manufacturing by eliminating the need for complex equipment or high temperature processing. The methods could enable in space manufacturing of electronics during long missions without relying on Earth-based supplies. The same technology may also improve manufacturing of flexible electronics and advanced computing systems on Earth. 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

Oregon State University-Cascades, in collaboration with the University of Oregon and Portland State University, will address the challenge of building, expanding and sustaining high-quality computer science (CS) programs in K-12 schools throughout Oregon. The project team will investigate how teacher preparation, retention, and school-level administrative support create lasting CS educational opportunities for all students and investigate how this comprehensive approach creates lasting pathways for expanding and sustaining CS education across Oregon. The project outcomes will benefit society through having a more computationally literate pipeline of students for college, careers, creative endeavors, and civic participation. The CS for Oregon CSforAll High School Strand Research-Practice Partnership brings together expertise in Computer Science Research, Education Research, and High School teaching practice and works closely with key personnel from the Oregon Department of Education (ODE) such as the Oregon team participating in the NSF-sponsored Expanding Computing Education Pathways (ECEP) Alliance. The project staff will prepare existing teachers with any credential to teach Exploring Computer Science, an evidence-based course shown to engage all students. Alongside teacher preparation, the project researchers will support and study how experienced CS teachers deepen their practice through action research while building agency as educational leaders to expand and sustain CS. This bottom-up approach is complemented with top-down support for teacher leaders by preparing administrators to move from passive engagement to knowledgeable and active champions of rigorous and evidence-based CS. This project will contribute significant new knowledge about the educator workforce needed to establish and maintain evidence-based, rigorous, foundational CS education, with particular attention to how school systems can be developed and networked to support program sustainability, especially in rural and high-poverty 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 · 2025-09

The primary goal of this project is to develop new methods for identifying various types of turbulence and mixing regimes in the upper ocean that result from different forcings at the ocean surface. The forcings include heating/cooling, wind-driven shear, and wave-wind interactions. They produce upper ocean mixing that facilitates ocean-atmosphere exchange of properties and that is anisotropic, meaning non-uniform in the three spatial directions, in distinct ways. This project will employ novel statistical techniques to identify this anisotropy and thus the different mixing types, advancing previous methods not able to resolve this kind of detail. The approach involves testing the methods in a model that simulates upper ocean turbulence and applying them then to available ocean velocity data from ADCPs (Acoustic Doppler Current Profilers) that are commonly collected on research cruises. The outcomes of the project will help advance physical oceanography and other fields by shedding new light onto what type of mixing is occurring and by providing open-source software that will enable other researchers to employ the same advanced methods. The strength and depth of mixing in the upper ocean mediates the transfer of properties between the ocean and atmosphere, but direct measurements of this mixing are challenging to collect and only provide information about specific aspects of the dynamics. This project will develop novel methods that characterize and distinguish different 3-d mixing regimes in the upper ocean. These methods will also enable unique measurements of flow energetics, including the inter-scale transfers of energy and the directional variations (anisotropy) of these transfers and other flow properties. Together, the proposed work will provide a unique view of the structure and energetic mechanisms at play within upper ocean turbulence. The project goals will be enabled through the synthesis of three complementary and timely developments. First, recent advancements in 2-d turbulence analysis techniques, which demonstrate an exceptional ability to diagnose energetics without Fourier transforms, will be extended to diagnose important flow physics in 3-d turbulence. Second, cutting-edge computational hardware will be leveraged to enable both the computationally demanding proposed 3-d turbulence analyses and the numerical simulations that will provide synthetic data to validate methods and evaluate different flow regimes. Finally, ADCP instrumentation that measures upper ocean turbulence at high-resolution in time and space will enable application of these novel methods, identification of optimal methods, validation of upper ocean simulations, and diagnosis of distinct turbulent regimes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Summary Cells and cellular organelles are surrounded by membranes that are constantly undergoing lipid modification due to processes like cell growth, organelle biogenesis, exocytosis, autophagy, phagocytosis, and temperature adaptation. The recently discovered, evolutionarily conserved superfamily of bridge-like lipid transfer proteins (BLTPs) are essential for all these processes due to their role in lipid transport. BLTPs localize to membrane contact sites, where they fold into hydrophobic tunnels that are proposed to function like “lipid superhighways” that mediate the bulk transfer of lipids from donor to acceptor membranes. Despite the fundamental importance of BLTPs in cellular function and organismal survival, little is known about how they function, largely due to challenges associated with producing of BLTPs in heterologous cell lines. The overarching goal of my research is to overcome these challenges and answer fundamental questions surrounding BLTP function, including (i) how do BLTPs transfer lipids from the donor to the acceptor membrane, (ii) what is the role of BLTP-mediated lipid transfer in cellular physiology, and (iii) how does BLTP dysfunction lead to disease. As a foundation for this work, we have recently elucidated the structure of a native BLTP complex from C. elegans, revealing unexpected structural and functional features that provide mechanistic insight into the process of lipid transfer. Over the next five years, our research will focus on understanding the molecular mechanisms of lipid transport by BLTP1 and BLTP2, two of the five members of the BLTP superfamily, using single particle cryo-electron microscopy in combination with complementary genetic, biochemical, and computational methods. We will address key outstanding questions in the field of BTLP-mediated lipid transfer, including the selectivity for specific membrane lipids, the regulation of transport, and the mechanisms of lipid extraction and delivery. Moreover, as mutations in BLTPs are implicated in multiple neurological disorders, we will harness the C. elegans and D. melanogaster model systems to investigate the molecular underpinnings of BLTP dysfunction in these diseases. The knowledge gained from this research will provide a comprehensive understanding of the relationships between BLTP structure and function that will illuminate the mechanisms of lipid transfer at membrane contact sites. In addition, the methods for native protein isolation and characterization that we develop over the course of this work can also be applied to other challenging protein complexes, enabling the study of clinically relevant, previously intractable proteins.

NIH Research Projects · FY 2025 · 2025-08

Project Summary: Horizontal gene transfer plays a major role in the evolution of microbes and host-microbe interactions. The key drivers of horizontal gene transfer are mobile genetic elements (MGEs), diverse DNA elements that mobilize genes directly between and within cells in all branches of life. MGEs, such as plasmids and integrative and conjugative elements (ICEs) of bacteria, are implicated in the spread of genes important for pathogenicity, symbiosis, antimicrobial resistance (AMR), and other adaptive functions. While the role of MGEs in many host- microbe interactions is well known, the factors promoting or constraining MGE spread and microbial host compatibility are poorly understood. This proposal aims to characterize MGE compatibility at multiple scales, from interactions between individual MGEs and strains, among all strains and MGEs of a genus, to interactions in complex microbial communities. We will use plasmids of the well-studied agrobacteria/rhizobia complex as a model to uncover generalizable concepts on the evolution of plasmid-bacteria-host interactions. Several features make this system advantageous, including genetic tractability, the ability to control microbiome composition, and experiments with large sample size. First, we will characterize plasmid incompatibility by investigating the diversity of replication and transfer loci on all sequenced plasmids of a genus. We will characterize other barriers to MGE transfer by investigating variation in restriction-modification systems, DNA methylation, and defense/anti- defense systems across this genus-level group. We will then overlay this information and build a model to predict, for every plasmid, its compatibility with a given recipient strain. Second, we will investigate how MGEs and chromosomes co-evolve to maintain compatibility. We will focus on a family of plasmids restricted to polyphyletic host lineages and extend this model to predict other plasmid or chromosomal loci necessary for compatibility with these MGEs. We will use both genome reduction (Tn-Seq) and additive (cosmid library) screens to identify loci determining compatibility. Third, we will investigate factors determining the extent and frequency of MGE transfer in complex microbial communities. Long-read, methylation-aware metagenomics will inform on the diversity of MGEs in the disease environment. Hi-C sequencing and/or single-cell fusion PCR will be used to determine the association of MGEs with specific microbials hosts. The presence of restriction- modification systems, DNA methylation, and defense systems in members of these communities will inform on constraints of MGE transfer. Overall, this work will provide a holistic model of constraints on MGE spread and will shed light on how MGEs and their hosts co-evolve to maintain compatibility.

NSF Awards · FY 2025 · 2025-08

Current research on human and animal microbiomes is largely focused on monitoring and reshaping individual microbes or global microbial communities to diagnose, treat, and prevent diseases, as well as track and improve population health. These fine-scaled and coarse-scaled analyses likely miss an important intermediate ecological scale---functional groups of microbes, which may serve as potent biomarkers of host or ecosystem health as well as targets for medical therapies. This project aims to identify functional groups of microbes by learning their temporal dynamics through longitudinal microbiome studies. However, longitudinal microbiome data possess unique characteristics, such as compositionality, high dimensionality, sparsity, and temporal dependence, and their cluster analysis thus presents distinct challenges. The investigators will develop flexible and scalable functional cluster analysis methods to generate biologically meaningful microbial groups. The investigators will develop, distribute, document, and maintain R software packages for their developed methods, will provide tutorials with example datasets, and will test the software in real-world settings. The investigators will train high-school, undergraduate, and graduate students at the intersection of statistics, ecology, and genomics. The project aims to expand the traditional toolbox of functional cluster analysis by introducing broader similarity measures of functional curves, incorporating the effects of external factors on the curves to be clustered, and developing a general framework for clustering longitudinal profiles from multivariate non-normal data. Specifically, this project will (1) innovate functional cluster analysis to enable the identification of microbial functional groups defined by novel and flexible subgroups of microbes with similar dynamic patterns, such as scale-invariant and gradient-sign-invariant subgroups; (2) develop adaptable and scalable clustering tools for specific functional groups that react similarly to external factors, in order to link microbial functional group profiles to ecosystem or host health factors; and (3) adapt the clustering tools tailored to the specific characteristics of microbiome data, and identify functional groups of microbes that contribute to wildlife and human health by applying the proposed analytical methods to real ecological and biomedical datasets. 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

Ice cores provide detailed records of Earth’s recent geologic past, with the oldest records currently extending back in time six million years. Critically, ice cores preserve a direct sample of the atmosphere, allowing scientists to reconstruct its past composition. From such measurements we now understand past natural changes in greenhouse gas concentrations and their relationship to the ice age cycles. Ice core measurements of the major atmospheric gases (nitrogen, oxygen, argon) and their isotopic composition provide important information about past global photosynthesis, past conditions at the ice core site, and they can help determine the age of the ice samples. The US research community has lost the capability to perform these measurements over the last year due to retirements and recent instrument failure. The objective of the project is to retain these critical analytical capabilities for the US ice core community and to train the next generation researchers in their use. The work supports a graduate student, contributing to development of the STEM (science, technology, engineering and mathematics) workforce. The project centers around knowledge transfer from scientists at Scripps Institution of Oceanography (SIO), where these techniques were perfected, to scientists at Oregon State University (OSU). OSU has a dual-inlet isotope-ratio mass spectrometer with the correct cup configuration for the analysis, but no experience with the sample preparation and calibration. The project supports: travel between SIO and OSU for knowledge transfer focused on the sample preparation and analytical techniques; method development at OSU to implement the experience that was developed at SIO over the last decades; training of a graduate student at OSU to perform the measurements; and a measurement campaign to analyze ice core samples from two East Antarctic ice cores. The ice cores to be analyzed are the European-drilled Dronning Maud Land ice core, for the purpose of calibrating the ice core “water isotope thermometer”, and from a US-drilled core from Dome C to help constrain long-term changes to the galactic cosmic ray flux. The analytical capabilities retained under this proposal are critical to the success of several ongoing and future US ice coring projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

This proposal to develop a new PhD Training Program in Molecular Biophysics of Complex Systems (MBCS) at Oregon State University (OSU) connects mentors from six primary departments spanning three colleges at OSU. Disciplines represented in the MBCS center around biological complex systems are broadly grouped under three themes: 1) Data modeling and mining using computational methods, 2) Measuring and mapping using a broad range of biophysical techniques, and 3) Making and modifying using and developing innovative technology for better measurement and application tools. The MBCS mission with its focus on how biological complex systems work at the molecular level, using a wide range of experimental (in vitro and in vivo) and computational (in silico) techniques, will prepare Trainees for careers in molecular biomedical research at the interface of biophysics and cell biology; there is broad demand in academia, industry, or national labs. Our objective is to provide MBCS Program Trainees with a comprehensive and rigorous understanding of the fundamental underpinnings of transdisciplinary research in scientific disciplines. Such training will result in innovative scientific advances that benefit society. This Program will lower the barriers separating departments and colleges and facilitate integration of the diffuse biophysics-related research and educational programs under one umbrella, incorporating and coordinating the strength of each program. The 21 faculty Mentors provide extensive resources for research, student support, and a highly collaborative training environment. Students will be selected from a deep qualified pool of applicants using holistic admissions criteria, including dedication to science, research background, and perseverance, in addition to quantitative metrics. New programs such as a Summer Head Start for recruiting underrepresented minority, disabled or disadvantaged students will be supported as part of an active institutional diversity program. We request five slots per year (steady state of ten students) to be supported for the second and third year of graduate research. An additional slot per year will be funded by the institution. New course development is included along with innovative tailoring of existing courses ranging from cellular biophysics, molecular spectroscopy to mathematical modeling. Annual Summer workshops include theory and hands on application of methodologies from nuclear magnetic resonance (NMR) to genetic code expansion to workshops on new, cutting-edge techniques. Our intended outcomes are trained scientists, productive in their research during and as measured by first-author publications, presentations at meetings, fellowships funding and career outcomes which will be surveyed in our annual evaluations. Co-Program Director, Dr. Elisar Barbar and Dr. Chong Fang are strong proponents of collaborative science, diversity, mentoring/Mentor training, and innovative education; they have a successful history of funding and directing core facilities for the common good (NMR for Barbar and Ultrafast Spectroscopy for Fang). Both have a history of mentoring women and underrepresented groups in research. The faculty roster is highly collaborative and will model team science.

NSF Awards · FY 2025 · 2025-08

Coastal populations in the Pacific Northwest are increasingly threatened by impacts from natural hazards including flooding, erosion, and landslides. These hazards often overlap and intensify one another. This project will address the urgent need for coastal communities in the Pacific Northwest to identify and implement a range of possible solutions that are informed by local priorities and knowledge. Rapid advances in data sciences, Earth system science, and technologies have enabled assessment and the modeling of hazards, risks, and the effectiveness of reducing risks at a regional scale. By bringing together community members and professionals with researchers this project endeavors to collaboratively develop and test a suite of innovative solutions aimed at reducing risk and building long-term coastal community resilience. This incubator advances four types of solutions to coastal hazard risks. The incubator also tests different ways of reducing coastal hazard risks to enhance the resilience of all communities across the region. By focusing on both acute and chronic hazards, and by engaging with a wide range of partners across disciplines and sectors, this project advances scientific knowledge while promoting health and welfare by enhancing community preparedness and resilience. The knowledge and approaches developed through this project will have the potential to be applied broadly, offering a model for addressing coastal hazards in regions across the United States and beyond. In two pilot study use cases this project will explore assessment of and solutions to coastal hazards such as landslides, coastal erosion, and flooding. Broadly defined, the initial portfolio of four solution spaces address both chronic and acute coastal hazards, as well as their interactions; (a) natural and nature-based solutions (inclusive of field testing, simulations, modeling); (b) solutions for increasing community resilience grounded in assessing community assets, adaptive capacity, and alternatives; (c) community modeling solutions (hyper localized, assimilating existing data) that focus on the assessment and communication of risk and hazard science, and of knowledge across epistemological differences; and (d) solutions based on multi-hazard planning and decision support tools, in which the project will engage new technological platforms and partners. The incubator leverages years of regionally relevant Earth system science co-developed with communities to advance understanding of the four types of solutions explored. The project aims to 1) understand what solutions and engagement approaches to decide on solutions are most effective for reducing risk and enhancing community resilience to coastal hazards and 2) understand how these solutions/approaches can best be shared and how implementation can be promoted widely across the region and the nation. This effort leverages regional science and data integration across disciplines to identify hazard risks and model impact scenarios at a local level; a critical translational science lens to ensure practical utility of outputs and results, and a community-engaged and community-driven methodological approach that centers the experiences and priorities of local populations to drive solutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Oregon State University (OSU). OSU provides several student support services focused on student well-being, academic-progress, careers, civic engagement, and leadership. Over its 72-month duration, this Track 2 project will fund scholarships to 40 unique full-time students pursuing graduate degrees in in the science, engineering, management, and public policy of water resources. The overall goal is to prepare low-income students and their communities to address water resource challenges by developing a more inclusive graduate experience that addresses the funding, mentoring, early research, and peer connection challenges that currently limit entrance into graduate programs. Active mentoring, guided research experiences and peer cohort building, all evidence-based strategies that nurture student engagement, self-efficacy and growth will be leveraged to enhance student success. Broader impacts of the project include the acceleration of the careers of low-income students into leadership positions within the field of Water Resources and the dissemination of findings to professionals in education research and ecological engineering disciplines. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. A companion goal is to prepare low-income students to engage with water resource issues by providing a holistic graduate experience that focuses lowering barriers encountered in funding, mentoring, early research, and peer networking that these students currently face. While there is evidence that active mentoring, guided research experiences and peer cohort building are strategies that generally nurture student belonging, self-efficacy and growth mindset, much less is known about how financially insecure individuals navigate barriers to graduate education. The project will generate and disseminate knowledge that will potentially boost retention of low-income undergraduate students in their final year and increase their recruitment into graduate degree programs. Formative and summative evaluation of the project will be guided by several evaluation questions including "To what degree does the program accomplish recruitment, retention and post-graduation transition into employment of under-resourced students?" The summative evaluation will use a mixed methods design with triangulated data to test the evaluation questions while the formative evaluation will be designed as ongoing feedback and improvement informed by empirical evidence in which evaluators work with team members to answer decision-relevant questions in a timely and project-focused way. Findings will be disseminated through the project website, the PIs personal web pages, blogposts, the STEM Center's website, professional meetings and conferences such as the Annual Meeting of American Water Resources Association and publications such as the Journal of the American Water Resources Association. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income, academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Rivers are vital to life, providing drinking water and supporting agriculture, recreation, transportation, and fisheries. However, changes on land can alter runoff in ways that negatively impact river ecosystems and water quality. The goal of this research is to better understand and predict changes in river chemistry at the global scale. The investigators will (re)use publicly available water chemistry and flow data from more than 450 rivers across all seven continents. Novel machine learning and other data analysis techniques will be used to determine how and why substances in rivers, including nutrients, metals, salts, and trace minerals, vary throughout the year and across the landscape. The project includes training workshops for early career researchers and public outreach through the Science Museum of Minnesota. Project outcomes will provide insights into how we can better manage rivers and protect water resources for the future. This data-intensive project will examine the similarities and differences in the seasonal distribution of chemicals across rivers with varying flow regimes and watershed characteristics. Researchers will harmonize large datasets from Long-Term Ecological Research, National Ecological Observatory Network, and other federal science investments. They will use deep learning and statistical approaches to identify different drivers and seasonal regimes for a broad range of chemical solutes. The investigators hypothesize less synchrony amongst biologically active substances and across disturbed watersheds. The resulting dataset and analyses will enable prediction of water quality in unmonitored and remote river systems that are difficult and expensive to monitor. The project also supports workforce development in water resources and “big data” analysis. The outcome of this research will be new global frameworks for understanding and predicting how river chemistry responds to environmental changes over long timeframes and broad spatial scales. 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

NON-TECHNICAL DESCRIPTION: The goal of the 2025 Professional Development Workshop in Ceramics is to support the career and professional development of participants representing a wide cross-section of the ceramic materials community. The workshop is designed to provide recent CAREER awardees and early investigators in the Ceramics (CER) program within the NSF Division of Materials Research as well as young investigators seeking funding from the CER program with mentorship and feedback on their research and outreach activities. Participants include senior, mid-career, and early-career researchers (including assistant professors, post-doctoral researchers, and senior PhD students) in ceramics. The intensive, two-day workshop features presentations from CAREER awardees and early investigators with ample time provided for feedback and discussion. Panel discussions and breakout sessions cover topics such as emerging trends in ceramics research, data management, mentoring & advising, networking & professional development, as well as how to strengthen broader impacts. A poster session is providing all the early career attendees an opportunity to present their research and receive feedback from senior faculty and other workshop attendees. The workshop is also enriching early investigator’s perspective on research and education programming through community building, mentorship, and interpersonal interactions. In addition, researchers at all levels are benefitting from focused interactions and networking during the workshop. These outcomes provide a base of support that helps young investigators succeed in becoming outstanding researchers and educators while strengthening the broader ceramic materials research community. TECHNICAL DETAILS: The 2025 Professional Development Workshop in Ceramics focuses on three technical topics: 1) ion conduction through complex solid-state electrochemical interfaces, 2) design of ceramic microstructures by control of grain boundary motion, and 3) design of ferroelectric semiconductors. Together, these research areas are assembling a wide array of topics within the umbrella of ceramics science. Those participating in the workshop will encourage new idea generation at the intersection of these different disciplines and receive helpful guidance on how to advance the matters adjacent to successful research such as: data management, mentoring & advising, networking & professional development, as well as strengthening broader impacts. The invited early career faculty and technical experts are facilitating guided scientific and professional discussions for the purpose of critically evaluating the research plans, career development, and education efforts of presenters and poster session participants . The intellectual merit derives from the topics covered and the broader impacts flow from the professional development made available to a wide swath of participants from across the varied ceramics field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

The need for new antibiotic drug leads is pressing given the appearance of multidrug resistant (MDR) and extremely drug-resistant (XDR) strains of pathogenic bacteria. Kibdelomycin is a natural product produced by Kibdelosporangium sp. (MA7385) and Amycolatopsis sp. FERM BP-11411 and was previously identified to have a new binding motif to DNA gyrase, in part due to interesting structural features found in the molecule, and a low frequency of resistance making it an excellent lead compound for the development of Gram-negative antibacterial agents. Kibdelomycin represents an exciting platform that can be used for the discovery of lead compounds for the treatment of antibiotic resistant bacterial infections. In this R21 application, we propose a two-pronged approach to investigate the biosynthesis of kibdelomycin with the ultimate goal of creating kibdelomycin variants that have a greater activity against Acinetobacter baumannii and Pseudomonas aeruginosa, two bacterial pathogens that the Center for Disease Control classifies as urgent and serious threats, respectively. In Aim 1.1, we propose to develop a genetic system for Amycolatopsis sp. FERM BP-11411 in analogy to our team’s previous success in Amycolatopsis mediterranei S699. This will provide access to kibdelomycin variants that are difficult to access via synthesis. In Aim 1.2, we propose to investigate the early biosynthetic steps of how kibdelomycin is assembled by a bottom-up synthetic biology approach synthesizing constructs to produce early intermediates that can be used in analog synthesis. In Aim 2, we engage in a complementary approach to study the natural resistance of the producing strain Kibdelosporangium sp. (MA7385). These self- resistance studies will help dissect the amino acid substitutions present in the kibdelomycin-resistant DNA gyrase to preemptively understand how resistance might evolve in clinical bacterial strains and to guide future synthetic investigations into analogs that can avert this outcome.

NIH Research Projects · FY 2025 · 2025-07

ABSTRACT Mycobacterium abscessus (MAB) is emerging as a clinically important pathogen associated with infections in immunocompromised as well as healthy individuals. MAB incidents have been increasing worldwide leading to high morbidity and fatality rates. While clinically available antibiotics effectively kill MAB in vitro, despite of combinational and lengthy antimicrobial regimens, treatment outcomes in clinics are unpredictable and often ineffective, demanding the use of novel therapies and technologies for improved results. Phage therapy is one of the promising alternatives under development for Cystic Fibrosis patients that has been successfully used during difficult to manage cases such as multidrug-resistant and disseminated infections of MAB. Moreover, phage- antibiotic combination treatments have been shown to be more effective over the use of single agents. The current use of therapeutic phages in humans is limited to lytic phages that can efficiently lyse and inhibit the growth of the host bacteria. However, substantial research demonstrates that lytic phages can steer the evolution of phage resistance in bacteria at a cost of increased vulnerability to antibiotics and immune system, diminishing bacterial virulence in the mammalian host. The overall goal of this application is to deepen the fundamental understanding of phage biology by elucidating the interaction mechanisms with mycobacteria and assessing the fitness costs linked to the trade-offs between phage resistance and antibiotic resistance. This knowledge will be applied toward the translational goal of this proposal for designing phage formulations that can synergize antibiotic action, ultimately enhancing clinical outcomes. Therefore, in the Aim 1A, we will identify MAB surface targets involved in distinct phage resistance mechanisms. Our central hypothesis is that bacterial surface factors contributing to phage resistance can compromise the integrity of the mycomembrane and reduce the functionality of mycobacterial surface targets involved in antibiotic intrinsic resistance. By finding phages that can drive diverse pleiotropic effects, we aim to sensitize MAB to conventional antibiotics via phage-antibiotic combination therapy. In the Aim 1B, we will validate trade-offs between phage and antibiotic resistance and evaluate increased efficacy of antibiotics in drug-susceptible and drug-resistant MAB isolates through the use of phages. The new knowledge gained from this proposal will directly inform the rational design of phage cocktails and phage-antibiotic combinations, enhancing the clinical efficacy of current antimicrobials.

NSF Awards · FY 2025 · 2025-07

The cloud radiative effect (CRE) is a measure of the impact of clouds on the earth’s net radiation budget, defined as the clear-sky minus all-sky (including clouds) radiative flux at the top of the atmosphere measured by satellites or estimated from models. Cloud feedbacks are one of the largest uncertainties in future projections of global surface temperature and thus there is strong motivation for constraining these feedbacks. This project is focused on advancing understanding of the observed negative CRE in the East Pacific and Atlantic Intertropical Convergence Zones (ITCZs), addressing a gap in the current theory applicable mainly to the deep convective regions of the West Pacific and Indian Ocean where sea surface temperature gradients are weak and the CRE is close to neutral. The focus on oceanic ITCZs is motivated by the fact that the CRE for these regions contributes about 5% to the global CRE and has a larger magnitude than the estimated global energy imbalance. The negative CRE for oceanic ITCZ regions results from smaller anvil cloud areas relative to other deep convective regions of the Tropics, however little is known about what controls anvil cloud area in these regions. Thus, this project will also explore why a negative CRE is associated with small anvil cloud fraction in these regions. The study will use a combination of satellite observations and idealized modeling studies to address these issues. The project includes training undergraduate, graduate and postdoctoral scholars, and developing a learning module for elementary and high school students, meeting the mandate of the U.S. National Science Foundation to prepare the next generation of scientists in service to society. Beginning with the parameterization of the top of the atmosphere radiative flux from Hartmann et al. (2001), the project will investigate the hypotheses that the negative CRE in the oceanic ITCZ regions is due in part to differential energy export between the convective and non-convective regions associated with ocean circulation, as well as to the high albedo in the ITCZ subsidence regions resulting from the extensive stratocumulus clouds of the eastern subtropical oceans. The small convective cloud fraction relative to the subsidence cloud fraction is also suggested as a mechanism to amplify the CRE in these regions. The project will also investigate if the smaller anvil cloud fraction associated with oceanic ITCZs results from reduced cloud detrainment. These hypotheses will be tested using reanalyses and Community Earth System Model v2 (CESM2) non-rotating, aquaplanet simulations with a slab ocean. Causal relationships regarding what controls the anvil cloud fraction will be tested using mechanism denial and forcing experiments with CESM2. An expected outcome of this project is an update to the Hartmann et al. (2001) parameterization more applicable throughout the Tropics, by including both the original “shortwave” pathway through changes in albedo as well as a “longwave” pathway dependent on cloud anvil fraction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-06

ABSTRACT Dental caries poses a significant financial burden to the oral healthcare system in the US. Hence, early detection and preventive measures for primary caries are essential for reducing treatment costs. It is well-established that biofilm metabolic activity (lactic acid production) in the oral cavity, particularly close to the teeth, plays a pivotal role in the progression of dental caries, and hydrogen peroxide (H2O2) has been identified as a crucial factor in controlling lactic acid producing bacteria. We therefore propose to develop a quantitative and easy-to-read electrochemical biosensing platform that will use live dental plaque samples for potential point-of-care diagnostics to test the following hypothesis: Can the capacity of dental plaque to produce metabolites—such as H2O2, associated with oral health, and lactic acid, linked to caries development—be used to establish threshold values for the early detection of caries? AIM 1: Detect and quantify hydrogen peroxide and lactate produced by varying ratios of the bacterial species S. sanguinis (Ss) and S. mutans (Sm). Our innovative electrochemical sensors will be used along with microliter volume droplet hydrogel electrochemical cells, which can trap and analyze the picomoles of metabolites (H2O2 and lactate) produced by different ratios of beneficial bacteria (Ss) and pathogenic bacteria (Sm). AIM 2: Establish a correlation between oral caries conditions and untreated dental plaque metabolic activity. We will establish the correlation between the ratio of H2O2 to lactate produced by dental plaque from patients and determine the threshold value that distinguishes non-caries from early caries conditions. We will also investigate the time-dependent metabolic activity of dental plaque in the presence of various sugars such as glucose and sucrose to gain insights into the cariogenic potential of dental plaque samples. Our analytical platform will eliminate the need for sample pretreatment, separation, or preconcentration and thus will enable the rapid and accurate analysis of metabolites produced by dental plaque. This study will validate dental plaque as a noninvasive bio-sample for point-of-care diagnostics in oral health, paving the way for early detection of dental caries and personalized prevention plans. The need for invasive treatments will thereby be reduced, ultimately lowering healthcare costs.

NSF Awards · FY 2025 · 2025-06

Collaborative Research: X-ray tomography to characterize microstructure during stress tests constraining multiscale models of sea ice interaction Sea ice in the Arctic Ocean has thinned and become more fragmented over the past several decades, a trend that poses significant challenges for navigation, infrastructure, and climate-related research. Increased variability in sea ice conditions affects shipping routes, offshore platforms, and coastal regions, creating a need for advanced tools to predict its behavior and inform resilient design strategies. This research seeks to uncover how the microstructural features of sea ice, such as grain size, porosity, and void distribution, influence its ability to withstand forces, such as the pressure exerted by an icebreaker or the stability needed to support offshore platforms, under varying environmental and mechanical loads. By developing a multiscale framework that connects microscale processes to large-scale dynamics, this project will generate insights critical for Arctic navigation, infrastructure design, and climate adaptation. The outcomes of this work will address key challenges at the intersection of geophysical science and engineering. In addition, the knowledge generated has broader relevance to other fields, including rock mechanics and geotechnical engineering. Outreach and education efforts will focus on the theme of "North in the South," engaging students and the public through programs such as virtual reality experiences, and workshops on Arctic science. These initiatives aim to inspire the next generation of researchers and raise awareness of the critical role sea ice plays in the global climate system. The primary objective of this research is to develop and validate multiscale numerical models that link the micromechanics of sea ice to its macroscopic behavior under various environmental conditions. This goal will be achieved using a combination of advanced experimental and computational techniques, including: (i) high-resolution X-ray computed tomography (CT) imaging to analyze the internal structure of sea ice and identify characteristic patterns and scales that influence its behavior; (ii) discrete element modeling (DEM) to simulate microscale interactions and failure mechanisms; and (iii) hybrid FEM-DEM simulations to integrate micro- and macroscale behaviors for macroscopic stress and strain predictions. Laboratory experiments and numerical simulations will be used in conjunction to investigate key phenomena, such as sea ice deformation, cracking, and floe-scale interactions. The validated models will provide new tools for understanding sea ice dynamics, supporting Arctic engineering, and addressing challenges posed by evolving ice conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Microbiomes, the diverse communities of microorganisms found in environments like soil, water, and the human body, are fundamental to many natural processes, including carbon cycling, nutrient cycling, and ecosystem health. To better understand microbiomes, the scientific community has heavily invested in sequencing and multi-omics technologies, generating vast amounts of microbiome-derived data that offer valuable insights into microbial diversity and function. Despite these advances, studying soil microbiomes remains particularly challenging because soils host the most diverse microbial communities on Earth, leading to sparse and fragmented coverage. This coverage challenge is further exacerbated by small sample sizes and an inconsistent approach to collecting the different data types, making it difficult to develop comprehensive models that generalize across environments. This project aims to develop an Artificial Intelligence foundation model for soil microbiomes that leverages all existing public data sets in order to provide a more comprehensive framework representing soil microbiomes. This project aims to address the challenges of soil metagenomic data sparsity by developing the Multi-Modality Microbiome Foundation Model (M3FM), an artificial intelligence model that integrates microbiome data across various studies and of several data types. M3FM will use a self-supervised learning approach to leverage the large number of public data sets from diverse sources without requiring extensive manual annotations. To test and refine this model, we will apply it to two key case studies: (1) linking soil microbial profiles to soil organic carbon content across a large-scale global dataset, and (2) mapping microbial shifts in response to climate change in a controlled, long-term field experiment. These case studies will deepen our understanding of how soil microbes influence carbon storage and nutrient cycling, as well as how they respond to environmental changes, such as climate shifts. While the focus is on soil microbiomes, this research will provide a powerful tool for accelerating microbiome science, with potential applications in sustainable agriculture, ecosystem management, and environmental conservation, ultimately contributing to efforts aimed at sustaining ecosystems and enhancing resilience to environmental change. This award is co-funded by the Directorate for Computer and Information Science and Engineering and by the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project will design and test robotic nudging systems for older adult health. Nudging means gentle prodding toward some desired behavior. Human-centered design and working with performing artists will help this project to succeed. The design space of nudging is vast, but robots can immediately help in hydration support. (Future work could consider topics like supporting exercise, or even a healthy diet.) In the U.S., 55.8 million older adults could value this type of help. Computer-based reminder tools can support similar tasks, but their use does not last. Robots can help more because of their social appearance, unique interactions, and tirelessness. Yet, the study of robots in real-world environments over long time periods is rare. Thus, society has not yet benefitted from these promising robot nudges. New work is needed to harness unique robot strengths to support long-term nudging. This project has the potential to improve the quality of life for many through the use of robot nudges.   The project’s efforts will contribute to advances in health-related human-robot interaction (HRI). This benefit will come from robotic systems that can support healthy human behavior change in real-world and long-term use. The project has the following specific objectives. (1) The work will include human-centered design of robotic systems appropriate for end user needs. (2) The effort will design personalized and adaptive nudging that extends the lifetime of robot use. (3) The final evaluation will be a real-world user-study-based evaluation in settings seldom reached by HRI research. This project will make complementary educational impacts. It will fuel new course design, HRI researcher training, and creative dissemination of findings. These educational efforts will inform future research directions. Robot-supported healthy hydration is the initial underpinning example application for the proposed objectives. The research and education tasks under this domain will map to more nudging topics throughout the investigator’s career. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project will fund research that looks to develop techniques to help robots recover from grasping failures in various settings, including homes and underwater environments. Practical applications include picking up objects from cluttered tables, clearing disaster debris, and rescuing marine life trapped in abandoned fishing gear. These advancements will benefit multiple sectors of the US economy, from fisheries to household consumer goods. As industries increasingly rely on robotic systems for tasks in outdoor spaces, crowded areas, underwater environments, and other unpredictable settings, the ability to grasp moving or cluttered objects becomes essential. Failures are inevitable, so effective recovery strategies are crucial to improving the overall success rate of robotic grasping. However, current techniques often struggle to adapt after a failure occurs, limiting their reliability. Beyond research, the educational impact of this project includes several activities such as providing undergraduate students at Oregon State University with the opportunity to gain hands-on experience in robotic grasping and introducing K-12 students to search-based planning concepts by collaborating with local school robotics clubs in Oregon. Additionally, the project team will demonstrate grasping algorithms on a physical robot to enhance the daily experiences of residents with dementia at the Grace Center for Adult Day Services in Corvallis, Oregon. This grant aims to provide fundamental algorithmic contributions to heuristic search-based planning under uncertainty, specifically for addressing grasping failures caused by different factors, primarily: (1) unknown object parameters such as mass and friction coefficients, (2) unknown dynamics of a moving object, and (3) unknown scene segmentation and interaction dynamics in a cluttered environment. The goal is to improve the grasping success rate and reduce the number of attempts required to achieve a successful grasp while ensuring real-time planning speed and theoretical guarantees. The core premise of this work is to compute policies, rather than plans, for failure recovery. The key insight is to develop conditional policies that account for anticipated failure outcomes of a grasp, where each outcome explicitly minimizes the probability of failure for subsequent attempts. The PI will involve learning dynamic Bayesian networks, deep neural networks, and hybrid models to enhance generalizability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The Siberian Arctic ecosystem is experiencing significant changes due to increasing freshwater input, changes in water mass distribution, and varying anthropogenic pressures. This proposal integrates key microscale biogeochemical (MPs, trace metals) and food web (phytoplankton, zooplankton, and the microbial loop) components, to provide an understanding of how these environmental processes interact and impact ecosystem services (e.g. productivity) within the region. The observations will be conducted on the planned for the Nansen and Amundsen Basins Observational System (NABOS) cruise. Key advancements include determining the role of riverine inputs in delivering trace nutrients and MPs to Arctic seas, how sea ice processes dictate the distribution of these elements and compounds, and how these influences control net ecosystem productivity and lower trophic level species composition. This research will provide novel, full water column measurements of trace metals, MPs, and lower trophic level dynamics, including species composition, phytoplankton pigments, total and active cell abundances, net microbial productivity and respiration rates in the East Siberian and Laptev seas. Specifically, we will assess the sources and sinks of trace metals (Fe, Mn, Cu, Ni, Cd, Zn, Pb) and MPs in the coastal Arctic marine ecosystem; describe microbial (bacteria, phytoplankton) species composition and metabolism across varying biogeochemical and salinity regimes; assess phytoplankton taxonomic composition, physiological health, and productivity; and quantify MP microbial colonization and ingestion by zooplankton. 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.