University of California-Davis

NSF Awards · FY 2025 · 2025-06

This project will develop computational tools for designing new classes of high-performance mechanical metamaterials. Mechanical metamaterials are elastic materials engineered at the microstructure level to exhibit unique properties not found in nature. They have the potential to unlock new levels of performance in application domains like soft robotics, deployable structures, athletic gear, and prosthetics. However, designing microstructure geometries to create desired material properties poses significant challenges, especially for applications where the structures can undergo substantial deformations and self contact. In these settings, computationally intensive nonlinear simulation models must be used, and designing materials with controlled properties over their full range of possible deformations is prohibitively expensive with existing algorithms. The core aim of this research is to develop computational techniques to dramatically accelerate the simulation and design process for elastic metamaterials, making it practical to solve this challenging and important design problem. The project will also develop techniques for ensuring that the optimized metamaterials are as durable as possible and can be reliably manufactured on consumer-level 3D printers. The project will furthermore enhance STEM education by integrating these cutting-edge research topics into classroom lectures and facilitating outreach events where high school, undergraduate, and graduate students gain hands-on experience with computational design, numerical modeling and fabrication. The project will build a new computational framework for (1) fast periodic homogenization of nonlinear elasticity and (2) optimal design of elastic metamaterials to exhibit target properties over large deformations (finite regions of strain space). The central technical contribution is a suite of novel data-driven acceleration techniques based on adaptive high-dimensional interpolation, smart sampling, and machine learning that will enable solving metamaterial characterization problems to controlled accuracy at practical computational expense. This fast characterization method will be wrapped within an inverse design algorithm that is formulated as a shape optimization over a rich design space of smooth parametric lattice geometries. The design algorithm will incorporate physics-based manufacturability constraints and stress minimization objectives to ensure that optimized parts are robust to forces experienced during fabrication and use. The design tool will be evaluated on the task of creating metamaterials to emulate existing material models as well as producing exotic material behaviors like multistability and jamming. The performance of generated metamaterials will be confirmed with physical experiments, and the proposed acceleration schemes will be assessed on large-scale benchmark datasets that will be created as part of the project. Finally, a basic multiscale design tool for creating compliant mechanisms composed of spatially graded lattices will be developed using the generated metamaterials. The research will deliver powerful and accessible open-source computational design software, fast solvers for nonconvex optimization and sparse linear systems, and benchmark datasets to foster evaluation of future metamaterial design work. 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

Living organisms are versatile. They can produce many important chemicals and drugs. Most processes utilizing living organisms or enzymes are done at large scale in centralized facilities. Examples include brewing, pharmaceutical manufacturing, or high fructose corn syrup. The approach works fine in a resource rich environment. It does not work so well in low resource environments, where raw materials and energy must be imported to the site such as in underserved rural communities, battlefields, deserts, or space. The objective of this Future Manufacturing Research Grant (FMRG) project is to develop small scale, largely self-sufficient biomanufacturing. This effort will involve plant cells and fast-growing aquatic plants. They utilize an inexpensive energy source, sunlight. They consume an inexpensive raw material, carbon dioxide. The project could lead to the development of technology to replace energy-intensive manufacturing processes. The Engineered Plants in Culture (EPiC) project will also develop and implement educational and outreach activities that will attract and train students in this new field. The Engineered Plants in Culture (EPiC) project will use engineered plant cells, plant embryos, and fast-growing aquatic plants as bioproduction platforms. EPiC will incorporate plant synthetic biology and bioprocess engineering. Three research challenges to be addressed. The first is engineering plant cell lines for improved bioproduction and downstream processing efficiency. The second is recycling plant biomass waste for use in subsequent cultures to improve sustainability and lower production costs. The third is identifying stable genomic loci for targeted insertion of expression cassettes. Three types of plant production platforms will be investigated. These include rice cell suspension cultures, walnut embryo cultures, and small, fast-growing aquatic duckweed plants. Also under development will be bioreactors for the different production hosts that can be 3D printed at low cost and implemented locally. Techno-economic models will be developed for each of the platforms to evaluate commercial viability and identify priorities for process development efforts. To test the performance of one of these systems under a severely constrained, low resource environment, a bioreactor containing a plant cell suspension culture will be developed and tested in low Earth orbit on the International Space Station in the final year of the project. 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

Sex differences are a common feature of animal behavior. Yet, the mechanisms underlying these behavioral differences have proven difficult to understand because brains and genes are nearly identical between males and females, and behavior typical of one sex may occasionally be performed by the opposite sex. The goal of this project is to understand how hormones, neural activity, and gene expression interact to give rise to sex differences and reversals in behavior. Parental behavioral is a promising starting point, as many species exhibit sex differences in parental care, as well as the potential for sex-reversed care. The project capitalized on naturally occurring parental flexibility in a Neotropical poison frog, Dendrobates tinctorius. Males are the primary care givers in this system, with females only occasionally taking over, providing an important complement to existing work in mammals where parental care is female biased and limited by reliance on lactation. Outcomes of the project will provide new understanding of how behavioral sex differences are coded in the brain, how these differences lead to distinct behavioral patterns, and the conditions under which they can be altered or reversed. The project will train researchers at all stages, with a focus on recruitment of trainees from groups underrepresented in STEM. Additionally, poison frogs are fascinating, charismatic creatures that facilitate teaching of diverse audiences, including K-12 students and teachers through the “Froggers School Program” and the general public through community engagement events. A fundamental question at the intersection of evolution, behavior, and neurobiology is how novel behaviors arise and are incorporated into existing neural systems. Cross-sexual transfer, the emergence of traits initially exhibited only in one sex in the opposite sex, has been proposed as a major, underappreciated force in the evolution of behavior. Yet, the mechanisms that facilitate integration into opposite sex physiological, neural, and molecular systems are largely unknown. Solving this puzzle is intimately tied to understanding how behavior is coded across levels of organization – from genes, to gene networks, to cell types, to neural circuits, to physiology, to behavior. Leveraging naturally occurring parental care in Dendrobates tinctorius poison frogs, this project tests the central hypothesis that mechanistic interactions across levels of organization shape behavioral flexibility and evolution between sexes. Specifically, the project links hormone and neural activity patterns to individual variation and sex differences in behavior, asks whether shared versus distinct genes and cell types are associated with parental care in males and females, and causally tests these hypotheses by tracing the effects of hormone manipulations across levels of organization. Synthesis of data across multiple levels of organization is a key feature of this work. Additionally, the project will contribute to training of diverse researchers and community engagement. Project outcomes will provide inroads to understanding how behavioral sex differences are coded in the brain, the conditions under which they can be altered or reversed, and how underlying mechanisms are environmentally, developmentally, and evolutionarily tuned to coordinate complex behavior. 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.

CIHR Grants and Awards · FY 202526 · 2025-06

INJURY; LARHE ANIMAL MODEL; LOWER URINARY TRACT FUNCTION; NEUROMODULATION; PERIPHERAL NERVE

NSF Awards · FY 2025 · 2025-05

This project focuses on the changes in municipal waste management systems necessary to help local and regional governments increase the use of compost in agriculture and food production. Composting organic waste for agricultural fertilizer is key to optimizing environmental outcomes in landfill management while also improving soil productivity and promoting nationwide food security. This research analyzes both the spatial distribution of composting infrastructure and the socio-economic factors that influence regional waste management systems. Using a multi-scalar, mixed-methods approach, this research contributes to the emerging interdisciplinary field of political industrial ecology and promotes closer engagement of geographers with the Circular Economy (CE) concept. Additionally, the project provides graduate and undergraduate students with interdisciplinary research experience that will support their future transition into the scientific workforce. As an emerging production paradigm, CE aims to increase environmental and economic sustainability by “closing the loop” on linear production systems through recycling waste. The primary objective of this project is to empirically investigate these claims by examining production, circulation, and valuation in an emerging agricultural CE. Specifically, the project aims to (1) quantify the spatial scale of municipal organic waste management systems, (2) analyze the local factors that influence these systems, and (3) provide an understanding of the transformative potential of CE in the waste management and agriculture industries. These results generate new insights into the macro- and meso-level changes in governance mechanisms, economic arrangements, and institutional relations as part of the CE transition. This research informs local, regional, and state governments of actionable solutions that promote agricultural sustainability through organic waste recycling and develop a CE of food in the United States. 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 · 2025-05

Disruptions in the link between neurons and blood vessels within the brain can lead to severe neurological diseases. However, little is known about this link due to the complexity of the interaction. Current imaging techniques cannot capture this complexity at the resolution of a cell within the brain. This CAREER project aims to develop an innovative microscopy system capable of simultaneously imaging many aspects of the brain to better understand the link between neurons and blood vessels. The outcome of this project will pave the way to better understand conditions like Alzheimer’s disease and stroke. This project will also benefit society through STEM education activities aimed at strengthening the biophotonics workforce. These activities includes igniting high school students' interest in STEM, providing hands-on courses for undergraduate and graduate students, and engaging the public to raise awareness about biophotonics and STEM. The investigator’s long-term goal is to create a transformative field in fluorescence-phase multimodal imaging for simultaneous imaging of fluorescence-labeled and label-free cellular structures and functional activities in vivo. To realize this goal, the CAREER project will develop correlative, simultaneous microscopy of optical signatures (COSMOS) capable of noninvasively and concurrently recording fluorescence, second harmonic generation (SHG), and quantitative phase signals from a single dataset. Unlike current dual-contrast systems, COSMOS integrates fluorescence/SHG and phase imaging into a fully merged system. It collects two-photon excited diffracted fluorescence/SHG as multiplexed measurements, followed by computational decomposition. The reconstructed phase provides quantitative biophysical properties of neurovascular units, while the intensity of diffracted fluorescence indicates neural activity and blood dynamics, and the intensity of diffracted SHG reveals the spatial distribution of collagen in the extracellular matrix. COSMOS reconstructs multiple distinct optical signatures from the same dataset, enabling high-speed, high-throughput imaging of dynamic interactions in neurovascular units. Beyond neuroscience, COSMOS potentially has broad applications, such as studying cancer cell metastasis. 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 · 2025-05

This conference is the fourth annual program-wide meeting in the National AI Research Institutes Program. The Summit for AI Institutes Leadership (SAIL 2025) is an NSF-sponsored conference organized and executed by the program’s hub activity, the AI Institutes Virtual Organization (AIVO). The conference gathers the leaders and other key personnel from all AI Institutes to foster community building of those Institutes and other related activities into a network of collaborating organizations conducting knowledge exchange, growing their own competencies, and engaging with the broader public. The conference will take place October 21-23, 2025, in Reston, VA. This conference aims to maximize the value of the AI Institutes as a flagship national AI investment. The conference delivers on the intent of NSF and its funding partners to continue to nurture the AI Institutes into a fully cohered national program, resulting in synergy across the constituent institutes that is greater than the sum of its parts. This gathering builds upon the successes and lessons from the previous SAIL events (2022 through 2024) and continues a successful record of establishing SAIL as the cornerstone event for the National AI Research Institutes program. The conference program addresses the needs of AI Institutes in various stages of their lifecycle, from those in their fifth year to newly established AI Institutes. This greatly enhances knowledge transfer among all. The program includes knowledge exchange about education and outreach, project management, computing and research infrastructure, communications, workforce development, and ethics. The conference is comprised of a balance of community-moderated panels with plenary sessions and other program-wide community building. A day of workshops prior to the main conference allows the program’s special interest groups to hold smaller community workshops around topics of interest withing a specialized area, and across institute boundaries. The plan also calls for an embedded activity for Institutes to showcase their technical accomplishments to one another and to a broader audience of potential partners from the broader community of public and private sectors, followed by the opportunity for prospective partners to network at the event toward greater understanding of funded research in AI and the potential initiation of new collaborations. 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 · 2025-05

The large-scale use of hybrid seeds for growing crops over the past century has resulted in a revolution in agriculture. Hybrid seed is a result of cross-pollinating two plant varieties of the same species that differ in their genetic makeup and phenotype traits. These hybrids are selected for genetic configurations that provide favorable phenotype traits or characteristics like higher grain yield, disease tolerance, etc. However, the large-scale conventional production of hybrid seeds by crossing inbred parent lines/varieties is resource- and labor-intensive, making them expensive and placing limitations on their availability and benefits to farmers. This hurdle could be overcome if hybrid crops can be made to reproduce asexually as clones, which will maintain the favorable set of genes and combinations that result in high yields, for example. Recently the technology was developed to produce hybrid rice using the asexual method but it has not yet been successfully adapted to maize, a major crop plant in the U.S.A. In this project, new methods will be developed for maize to maintain the hybrid state of the parent chromosomes in the progeny of hybrid plants. The success of these methods will make possible the inexpensive production of high-yielding hybrid seeds from maize. With the importance of hybrids for maize cultivation, the project is expected to have a large impact on U.S. agriculture. Asexual reproduction occurs naturally in many flowering plants by a two-step process called diplosporous apomixis: first, the elimination of genetic segregation during meiosis to make clonal gametes, i.e. egg cells and sperm cells containing unreduced and unrecombined parental genomes, and second, the development of the unfertilized egg cells into embryos to produce clonal plants. Previously, synthetic apomixis has been successfully achieved in rice, through genetic manipulations that combined the substitution of mitosis for meiosis in gamete cells together with the induction of parthenogenesis, where a plant produces fruits and seeds from unfertilized eggs. In maize, efficient parthenogenesis has recently been demonstrated using the method developed for rice. However, the substitution of mitosis for meiosis has not been successful in maize, due to differences between the function of rice and maize genes involved in the meiosis. This project will develop a novel strategy to generate diploid gametes in maize, based on a hypothesis of conservation of cis-regulatory elements of cell division genes in meiosis and mitosis. The strategy involves editing multiple genes required for cell division and meiosis, as well as the utilization of selected cell division genes from rice. The methods developed in this project, if successful, could be extended to other major crop plants, and potentially adapted to study other cellular processes in plants. 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 · 2025-05

Plants require nitrogen for growth. Agriculture has greatly benefited from the external application of nitrogen containing fertilizer, which is in large part responsible for current crop yields. Nitrogen use efficiency is a critical agricultural trait that depends on the plant’s ability to efficiently uptake nutrients from the soil and to transport and utilize those nutrients for maximum yields. A plant with improved nitrogen use efficiency will require less fertilizer that will save costs and have less negative results on the ecosystem. The size, shape and responsiveness of root systems are important contributors to how a plant uses available nitrogen. For example, large root systems have been observed to be more efficient at utilizing applied nitrogen, and roots that can rapidly modify their growth and architecture to maximize nitrogen uptake and its associated metabolism can increase the chances of plant survival when nutrients are scarce. Our project will deliver programmed plants with predictable and novel approaches to control root growth in response to nitrogen and to make plants use nitrogen more efficiently. The novel and broadly useful tools developed to engineering plants will also be available for the community. Nitrogen (N) fertilizers are necessary to ensure high crop yields, but the production and application of fertilizers can disturb ecosystems. In addition, N-fertilizer production is highly energy demanding and cost intensive, representing a major challenge barrier to reliably ensure yield year after year. This project will develop tools to engineer desirable N-responsive traits and investigate their effects on nitrogen use efficiency. Engineered plants will be used to quantify how an enhanced root system contributes to improved uptake and use of externally applied nitrogen. Finally, synthetic optogenetic systems will be deployed to remotely control belowground development via the application of specific wavelengths of light to the plant canopy. All of these approaches will be employed in tomato to determine their effectiveness in a crop species. This award was funded as part of a lead agency opportunity between NSF, UKRI-BBSRC (UK Research and Innovation - Biotechnology and Biological Sciences Research Council; Lead) and DFG (Deutsche Forschungsgemeinschaft / German Research Foundation) where NSF funds the US investigator, UKRI-BBSRC funds the UK partner and DFG funds the German partner. 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 · 2025-04

This workshop will focus on the impacts of Low-Earth Orbit (LEO) satellites on Rubin Observatory images. Rubin is NSF's newest large telescope. It will image the whole sky above Chile every few nights. The telescope makes use of the largest camera ever built. The data will have a profound impact on all areas of astronomy. However, the data will also be negatively affected by LEOs passing through the foreground of the images. This workshop at University of California-Davis will bring together experts to discuss ways in which these impacts can be limited. Modifications to satellite design and operations will be discussed, as will methods for observing and reducing data. Rubin's Legacy Survey of Space and Time (LSST) will image the entire southern sky hundreds of times over a 10-year period, starting in 2025. Rubin is NSF’s flagship optical-infrared (OIR) observatory. The data from this survey are expected to have a profound impact on almost all areas of astronomy. However, the impact on the survey of the growing number of LEOs orbiting the Earth is yet to be fully appreciated. The goal of the meeting is to focus on specific deliverables - implementable engineering parameters and revised strategies for observing and analyzing Rubin data - that will allow satellite operators and observatories to mitigate these impacts. The workshop will concentrate on impacts to Rubin LSST data, using Rubin as the “bounding case” for astronomy in general. The rapid proliferation of LEO satellites could profoundly affect the future of astronomy by restricting research innovation, limiting funding opportunities, and discouraging future generations of scientists. The proposers will mitigate these effects by collaborating with the satellite industry to seek novel means of coexistence and building lasting relationships that can rapidly react to new challenges. 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 · 2025-04

Non-technical Abstract With Moore’s law beginning to push against the limits of quantum mechanics, new computing paradigms become more attractive, and these new approaches require novel materials and a trained workforce. One promising paradigm is to utilize the fact that electrons on the surface of a material often have behavior unachievable in the interior. The desired surface electronic behavior can be controlled by the types of atoms that are on the surface, and this surface composition can be inhomogeneous or variable. The project employs simultaneous measurements of surface atomic composition and surface electron motion to understand how to control desired surface electronic behavior via chemical composition. Broader impacts are achieved through outreach to the public about novel materials via social media and public lectures. Undergraduate and graduate students will be trained in materials physics and communication thereof via in-person and online tutorials and technical writing pedagogy. Technical Abstract This work assesses how characteristic surface states manifest amidst dynamic chemistry, electronic correlations, and real-space mesoscale phase separation. Bulk-boundary correspondence is a fundamental principle in topological materials including Weyl semimetals that establishes a connection between the bulk and surface electronic structure. The composition and morphology of the surface can markedly affect the surface electronic structure, and needs to be measured independently. In this project, the researchers are incorporating photoemissions measurements of local chemistry, periodic structure, and electronic structure into developing enhanced understanding and control of surface electronic states. This project uses bulk chemical substitution to push the Curie temperature to zero in magnetic Weyl semimetals. At these critical compositions, local chemical variation at the surface has an outsized role in driving topologically distinct surface electronic structures which can be tuned dynamically with ion migration. Multiple photoemission microscopies are coupled with automation, visualization, and correlation tools to connect surface chemistry, structure, and electronic structure in a closed loop. 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 · 2025-04

Computer models are widely used for predicting the performance of buildings and other civil infrastructure under earthquakes and other extreme hazard events. During such events, parts (or members) of steel structures often undergo fracture or breakage. To safely design structures, and to examine their resilience to such hazards, it is critical to determine whether such fractures will propagate through the entire structure, destabilizing it, or remain confined to one part of the structure. Current computer models cannot effectively simulate these aspects of structural behavior. This award will support fundamental scientific research to understand the physical phenomena that are responsible for such behavior and to develop computer models to simulate it. The result will be a novel method that enables greatly improved predictions of whether structures will collapse under various hazards and loads. The findings and software developed from this research will enhance the resilience of buildings and other civil infrastructure, benefiting the nation’s society and economy. The education of students with new knowledge and technology transfer to practitioners will result in broad impacts. This award will contribute to the US National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program (NEHRP). This research is a collaboration between researchers at the University of California, Davis, and the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland, and is part of the joint collaboration between the US National Science Foundation and the Swiss National Science Foundation (NSF-SNSF). Fracture is a common limit state in steel members and connections, often controlling structural collapse. However, effective approaches to predict fracture are almost entirely constrained to continuum-based finite element models that are infeasible to apply in frame-based structural simulation. The result is a knowledge gap, which hinders physics-based simulation of fracture in mainstream structural performance assessment, and precludes effective assessment of many structures, wherein coupling between member fracture and structural response controls safety and resilience. The main goal of this project is to develop a novel scientific formulation that integrates fracture mechanics with an enriched frame element formulation, resulting in a method that represents the initiation and propagation of fracture and fatigue in steel structures. The scientific approaches used will involve theoretical development, computational modeling, and experimentation for validation of the developed approach. The scientific formulation will be implemented in an open-source numerical platform, OpenSees, that is widely used by structural engineers and researchers. This approach (including its implementation and associated documentation) will be broadly disseminated to target audiences and user groups. Data generated from this project will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). 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 · 2025-03

Humans perform visual search tasks many times throughout the day. Examples include searching for the perfect snack in a supermarket, looking for a misplaced phone in a cluttered room, or finding a friend in a busy restaurant. The efficiency with which humans perform these tasks has a significant impact on the quality of daily life. The current project uses behavior, eye-tracking, virtual reality, brain activity, and computational modeling to test the idea that the efficiency of visual search depends on two stages of processing with two different computational goals: 1) a first stage with “good enough” attentional guidance to select potential targets for closer inspection, and 2) a second stage involving a decision that emphasizes accuracy over speed. The overall goal is to understand organizing principles that make visual search efficient. In addition to the scientific work, this project supports the training and professional development of undergraduate students with activities that build scientific literacy, analytical skills, and emphasize links between classroom learning and the workforce. The project tests hypotheses that guidance and decisions in visual search rely on different types of information because they have different computational goals, not because they work on different feature types. One set of experiments tests the prediction that during visual search, attentional guidance prioritizes speed over accuracy, but that decisions prioritize accuracy over speed. To do this, simple stimulus arrays in which the precision and feature-dimension used for guidance and decisions are separately measured using eye-tracking data, electroencephalography/event-related potentials (EEG/ERPs), and drift-diffusion modeling (DDM). Another set of experiments, also using behavior, EEG, and DDMs, test the prediction that attentional guidance relies on non-target information such as prior knowledge about typical scenes because they act as rapidly detectable spatial cues for the target. Additionally, the project uses immersive virtual reality and DDMs to examine search efficiency in naturalistic contexts and test for hierarchical processes in which guidance prioritizes information and leads to reductions in the search space. Overall, the project aims to address important limitations of existing models of attention and aims to understand how visual search is efficient. 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 · 2025-03

The wildfires in Los Angeles in January 2025 were the most destructive disaster in recent California history. While much is known about wildfire smoke, a significant knowledge gap remains about how far toxic chemicals spread from the active burn areas. To find out, Dr. Spada and his community partners set up an air monitoring network at locations across the Los Angeles area, downwind of the largest fires. These monitors continuously collected smoke, dust, and ash while the fires were still active and throughout cleanup activities. For millions of Los Angeles residents, these measurements will provide answers about how much toxic chemicals were released, how far they traveled, and when is safe to return to burned areas. Insights gained from this project will help bridge our knowledge gaps and allow us to be better prepared for the future. This RAPID grant will support field research to collect, archive, and characterize samples during and immediately after the Los Angeles (LA) fires that started on January 7th, 2025. The aim of the project is to quantify the concentration and determine the speciation of toxic elements in airborne particulate matter (PM) collected during and following the fires. Specific objectives are: 1) To collect and archive PM during and following the fires; 2) To quantify the concentrations of elements in all samples; 3) To disseminate validated findings appropriately in order for public health and government agencies to provide accurate recommendations for residents returning to their homes; and 4) To share collected materials with scientific collaborators to maximize the learning potential from this unique scenario. Ambient PM was collected using cascading impactors with rotating stages at four locations distributed across the LA area. Samples will be analyzed initially at Crocker Nuclear Lab’s 76-inch isochronous cyclotron using Proton-Induced X-ray Emission (PIXE), Proton Elastic Scattering Analysis (PESA), and Rutherford Backscattering Analysis (RBA). These combined measurements provide quantitative results for non-volatile H, C, and O alongside Na – Br and Pb. To the team's knowledge, this will be the first dataset of its kind. Subsequent measurements will include Synchrotron-induced X-ray Fluorescence (SXRF) at the Stanford Synchrotron Radiation Lightsource to measure higher atomic weight elements such as cadmium, cesium, and rare earth elements. Supporting measurements will include mass concentrations via soft-beta ray attenuation and broadband optical spectroscopy, which will enable factorization and source apportionment analyses by future collaborators. The sampling campaign captures PM during the fire, immediately after the fires were put out, while the burn areas were still smoldering, and during cleanup activities that may resuspend toxic substances. These results will provide information on the exposures to first and second responders and to residents downwind of the fire areas. 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 · 2025-03

The ACM SIGCOMM Internet Measurement Conference (IMC) is the premier venue for presenting innovative research on Internet measurement, fostering collaboration, and advancing knowledge in the field. The 24th IMC will take place in Madrid, Spain, from 4 November 2024 to 6 November 2024, bringing together researchers and practitioners to exchange ideas and showcase the latest developments. This travel grant program, with a proposed budget of $19,000, aims to support 10–15 undergraduate and graduate students from US institutions in attending the conference. By participating, students will have the opportunity to present their research, engage with peers and experts, and cultivate the skills necessary to become leaders in the field. These travel grants will serve to widen the audience attending the Internet Measurement Conference (IMC), raising the level of interaction and the potential for new collaboration, new investigations, and higher quality research. By facilitating broader attendance, the program aspires to foster a richer exchange of ideas, stimulate new partnerships, and strengthen the IMC research community. Supporting student involvement in IMC 2024 will not only expand the conference’s impact but also inspire the next generation of Internet measurement researchers. 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 · 2025-02

Plants synthesize a diversity of unique chemical compounds called specialized metabolites. These compounds contribute to flavors, aromas, and medicinal and nutritional properties, found in plants and plant products. In addition to the benefits to our society, these compounds also help the plant deter diseases, pests, and herbivores or build resilience to abiotic stress challenges in their growth environment. Though we know about the presence of such compounds, we lack information and research to determine the role of genetic diversity and biological processes that help various plant species synthesize these diverse sets of compounds. This proposal will study the genetics and chemistry of one such family of compounds called glucosinolates that contribute to flavor and health benefits for plants like broccoli, capers, and wasabi. The project will attempt to find one or more genes, their function, and how they differ in these plant species to create chemical diversity. The project will engage undergraduate students in research and training. Plants synthesize a diverse set of specialized secondary metabolic compounds characterized by a common core followed by ensuing chemical modifications and extensions. Plants use these modifications to contribute to different chemical and biological properties to ensure their fitness in a complex biotic environment, including deterring pests and pathogenic stressors and facilitating commensal or other beneficial interactions. This project will study how biosynthetic genes and chemical modifications create chemical diversity within the glucosinolate metabolites across the Brassicales genera. Not much is known about the genetic processes that facilitate glucosinolate-derived chemical diversity. Using the sequenced and annotated genomes from members of this taxonomic family, the key core structure enzyme coding genes will be phylogenetically mapped across the entire family, and the nodes representing genes with new activities will be empirically tested to assess their role in chemical and functional diversity. Broader impacts include the development of models that predict the role of genetics and gene function in creating chemical diversity and future applications on engineering plants for synthesizing novel chemicals that benefit society and contribute to the bioeconomy. 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 · 2025-01

The Earth’s faults can rupture abruptly causing hazardous earthquakes, but some can also slip slowly, in events that can last hours to years. In the Cascadia Subduction Zone, slow slip events (SSEs) have durations of days to weeks and occur at depths between 30 and 45 km, which are deeper than typical earthquakes. These events produce a weak seismic signal known as tectonic tremor. Despite their status as one of the most significant discoveries in geophysics, the physical mechanism(s) responsible for SSEs remain enigmatic, and the effects of deep slip on seismic hazards are unclear. This project explores processes that affect the strength of the fault through time, one of the factors thought to control SSEs. Areas in the Earth where SSEs occur are typically hot and under high pressure. These conditions facilitate chemical reactions, suggesting that rapidly growing minerals could act like a quick-setting glue, binding faults together through cementation, and promoting rapid fault strengthening between SSEs. To explore this possibility, the team will conduct experiments where small rock and mineral samples are subjected to the temperature and pressure conditions of SSEs. The team will then measure the material properties, such as their strength and permeability to fluids, of the experimental products as they vary with experiment duration. Using the experimental results, the team will develop a mathematical description of rapid fault healing for incorporation into numerical models exploring the influence of cementation on SSEs. Results of these simulations will be compared to real-world observations to determine whether this process plays a fundamental role in the generation of SSEs. This project will catalyze interdisciplinary research in subduction zone geoscience through the training and mentorship of undergraduate and graduate students, plus postdoctoral researchers in the fields of seismology, rock mechanics, and geochemistry. The team leaders will enable interaction between the Cascadia Region Earthquake Science Center (CRESCENT) and Subduction Zones in 4 Dimensions (SZ4D) to facilitate coordination between these two efforts to achieve common goals relevant to geohazards. This proposal explores the role of cohesion, which is normal stress independent fault strength, via cementation and pore fluid pressure evolution in the dynamics of SSEs. Cementation is commonly observed in exhumed faults zones and is thought to play a key role in fault healing during the interseismic period. The high temperatures (~500°C) and pressures (1 GPa) present in SSE environs should favor relatively rapid cementation. There is also abundant evidence for fluids in the SSE source region that appear to play a crucial role in the generation of SSEs. The team proposes that pore-fluid pressure evolution and cementation can explain several enigmatic features of slow slip events, including radiative phenomena like tremor and low-frequency earthquakes, the tendency for the same section of the megathrust to re-rupture on short timescales during an SSE in so-called secondary slip fronts, the lack of sensitivity to tidally induced normal stresses, and the existence of fault strength in environments inferred to have nearly lithostatic pore fluid pressure. This work leverages interdisciplinary expertise from the fields of petrology, geochemistry, rock mechanics, observational seismology, fault mechanics, and numerical methods to explore the role of cementation and resulting cohesion in SSEs. This team will constrain the mechanisms of cementation, mineralogy and petrology of the cement, and the resulting time-dependent strengthening by performing a suite of piston-cylinder experiments at pressure, temperature, fluid compositions, and other conditions relevant for Cascadia SSEs. The project will determine the resulting cohesion and permeability using deformation experiments and contact area using microscopy. The results will provide quantitative constraints on time-dependent fault strengthening and permeability evolution. Constraints from these laboratory experiments will be used to develop a mathematical framework for cohesion and fluid flow. This framework will be implemented in numerical simulations to determine the impact of rapid cementation and cohesion on SSEs. The models will be validated with observables including propagation speeds, spatial scales, and time scales representative of SSEs and secondary fronts. This project will catalyze interdisciplinary research in subduction zone geoscience through the training and mentorship of undergraduate and graduate students, plus postdoctoral researchers in the fields of seismology, rock mechanics, and geochemistry. The team leaders will enable interaction between the Cascadia Region Earthquake Science Center (CRESCENT) and Subduction Zones in 4 Dimensions (SZ4D) to facilitate coordination between these two efforts to achieve common goals relevant to geohazards. This project is funded by the Frontier Research in Earth Science (FRES) program as well as Education and Human Resources (ERF) in support of Research Experiences for Undergraduates and Postdoctoral Scholars. 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 · 2025-01

Wildfire can catastrophically affect natural resources, causing profound environmental, societal, and economic impacts. In 2024, the Park Fire burned 429,603 acres of land, making it the largest fire in California in 2024 and the fourth largest fire in California history. Fire changes the speciation of metal(loid)s in vegetation and soils through thermal alteration of native materials, yet many of these transformations remain unknown. Additionally, fires can lead to downstream water resource contamination, negatively impacting environmental and human health. The overall aim of this rapid grant is to collect and archive perishable samples and data that can be used in future research to determine the environmental impact of the Park Fire, including among others, the alteration of metal(loid) speciation in fire ash and soil collected within the Park Fire area and the concentration and speciation of metal(loid)s in surface waters receiving runoff from the Big Chico Creek watershed located within the Park Fire perimeter. Collecting and archiving perishable samples and from the Park Fire can be used to: 1) identify novel research questions to guide fundamental research in the area of fire-borne environmental contamination, 2) compare and validate results obtained from laboratory based simulation to those obtained on field-collected samples, 3) protect human health through a better understanding of the mobilization of redox-sensitive metal(loid)s to surface waters as a result of wildfires, 4) generate datasets useful to other fields, such as public health, to better understand diseases linked to exposure fire-emitted contaminants, and 5) provide open-source data sets that can be used by public utilities and water managers to ensure safe drinking water treatment and continued ecosystem services. The Park Fire ranks as the largest fire in California in 2024 and the fourth largest fire in California history, which consumed 429,603 acres of land with different vegetation and soil types and fire severities in Butte and Tehama counties. The concentrations of the redox sensitive metal(loid)s such as chromium and arsenic in soils within the Park Fire perimeter are among the highest in soils across the United States. Many redox sensitive metal(loid)s undergo transformations due to heating and interactions with carbon and reducing compounds such as carbon monoxide and hydrogen released by the combustion of organic matter. Fire induced redox transformations are likely to increase the mobility of redox sensitive metal(loid)s from fire ashes and soils to receiving surface water bodies. Therefore, the overall aim of this proposal is to collect and archive perishable samples and data that can be used in future research to determine the impact of the Park Fire on redox sensitive metal(loid) speciation in fire ash and soil collected within the Park Fire permitter and on their levels in surface waters receiving runoff from the Big Chico Creek watershed located in the burned area. The specific objectives are to: 1) Collect, process, and archive time sensitive samples, including fire ash and impacted soil cores from the Park Fire area, 2) Collect, isolate, and archive redox sensitive metal(loid)s such as As, and Cr from surface water samples from Big Chico Creek, and 3) Monitor water quality parameters in the Big Chico Creek, including pH, redox potential, ionic strength, and concentration of dissolved organic matter, nutrients, and suspended solids in surface waters draining burned areas and compare those to pre-fire levels. 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 · 2025-01

Chenchen Song of University of California, Davis is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new theoretical methods for simulating photoreactions in life. Since the early days of chemistry, it was well known that light energy can be harnessed to drive chemical reactions in ways that are not possible using other energy sources such as heat or electricity. Photoreactions are at the heart of essential life functions such as vision and vitamin D synthesis in humans, and photosynthesis and phototropism in plants. In these reactions, specialized proteins control the chemical environment around the light absorbing molecules and steer the reactions toward precise outcomes. Improving our understanding of photoreactions in biological environments will not only advance medical and agricultural applications, but will also assist in developing new technologies such as organic solar cells and photocatalysts. However, progress towards these outcomes has thus far been limited by a lack of understanding of how photoexcited molecules behave in protein environments. To address this need, Dr. Song and her research group will develop both novel excited state quantum chemistry methods as well as new classical embedding approaches. These combined approaches will make it possible to simulate the behavior of photoexcited molecules with spatial and temporal resolutions difficult to access experimentally. Simulation results from the research will be incorporated as course materials to introduce photochemistry in general chemistry, and the computational algorithms will be taught in a graduate level quantum chemistry course. Dr. Song will develop new theoretical methods to study photoreactions in life through a multi-scale QM/MM description in which the chromophore is treated quantum mechanically and surroundings are treated classically. This will involve developments in excited state quantum chemistry methods as well as in classical embedding approaches. The excited chromophore will be described with multi-state multi-reference second order perturbation theory (MSPT2), one of the gold standards for photochemistry. To make MSPT2 feasible for large systems, the computational prefactor will be reduced through parallelization and efficient implementation on graphical processing units (GPUs), and the computational scaling will be reduced with the supporting subspace factorization. This strategy will be extended to the evaluation of forces, nonadiabatic couplings, and properties (e.g. transition dipole moments and spin-orbit couplings). The biological environment will be treated with a polarizable all-atom force field (AMOEBA) that can capture a wide range of environmental effects important for photoreactions. To embed MSPT2 in the AMOBEA model, an extended dynamic weight scheme will be used to capture the nonequilibrium polarization effects from the protein environment. New developments will also be made to enable non-adiabatic molecular dynamics simulations on the MSPT2/AMOEBA potential energy surfaces using generalized ab initio multiple spawning. The significant reduction of computational cost will make it possible to perform these simulations on individual desktops and commodity-grade servers, which will be much more affordable and accessible to a broader range of research groups doing either theoretical or experimental studies. 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 · 2025-01

Subduction zone volcanoes occur where one tectonic plate goes beneath another. Many millions of people live near subduction volcanoes. This means that understanding subduction volcanoes and the hazards they present is important. Magmatic activity at a volcano is usually studied using methods from geophysics. One such method is monitoring how volcanoes change shape (volcano deformation) over time. Geologists can also study igneous rocks, which form from magmas, to learn about volcanoes and magmas. Extrusive igneous rocks form from magmas that erupt from a volcano. Intrusive igneous rocks form from magmas that crystallize beneath the Earth's surface. For this project, the research team will study an ancient subduction zone volcano in Washington where they find both types of igneous rocks. They will reconstruct the record of volcanic eruptions and subvolcanic intrusive activity. To do this, they will study the geochemistry, geochronology, and petrology of the rocks. They will use these data to understand three things. First, they will determine when the magmas formed and if the erupted magmas and intrusive magmas existed at the same time. Second, they will determine how the composition of the magmas changed through time. Third, they will determine how deep the magmas were beneath the Earth's surface. The research team will also make videos about the motivation, importance, and results of their research. They will work with Professor Nick Zentner (at Central Washington University) to make these videos. The research team will also work with the Indiana School of the Deaf to produce new lab exercises for their high school science courses. The ancient volcano that will be studied lies just to the north of Mount St. Helens and includes the upper crustal Oligocene Spirit Lake Pluton and surrounding volcanic rocks. It represents a deeply eroded portion of the ancestral Cascade Arc and was the focus of detailed 1:24,000-scale geologic mapping by the USGS in the 1980s and 1990s. Existing geo- and thermochronology constrains the duration of pluton emplacement to <1.5 Myr and demonstrates that eruptions of the surrounding volcanic rocks pre-dated, were coeval with, and post-dated the pluton. Existing whole rock geochemical data show a compositional range from quartz diorite to granite in the pluton, and a range from basalt to high-silica rhyolite in the volcanics. This research aims to produce a detailed chronological and geochemical record of pluton construction and associated volcanism to better understand when the intrusive rocks were emplaced, if intrusive activity affected the rate and/or style of volcanic eruptions, and if any of the erupted magmas were derived from the pluton. This project will produce a detailed timeline of events using high-precision U-Pb zircon geochronology for 15 samples each from the pluton and the associated volcanic section. These data, along with geochemical and textural observations, will allow the team to answer questions such as: 1) Was there a long-lived magmatic mush within the now solidified plutonic complex? and 2) Did emplacement of the pluton lead to changes in eruption style, composition, or rate? Geobarometric data will allow the team to directly test whether any volcanic eruptions were sourced from the same depth as the currently exposed pluton. Taken together these geochronologic, geochemical, and geobarometric datasets will offer a holistic record of how the magmatic system evolved over the lifespan of a single arc volcano. 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 2024 · 2024-12

Kelp are major components of coastal ecosystems and economies around the world. Their large contributions to carbon sequestration and the de-acidification of oceans have sparked interest in kelp-based strategies to address environmental fluctuations. However, our ability to develop sophisticated strategies is hindered by an almost complete lack of understanding of the biochemical processes that underpin kelp’s carbon metabolism: photosynthesis and respiration. This gap is mainly driven by our inability to purify kelp’s chloroplasts and mitochondria—the cellular compartments (“organelles”) in which photosynthesis and respiration take place. Being able to isolate these organelles would allow us to perform biochemical studies to understand how the details of kelp photosynthesis and respiration and how they are different from plants and other organisms. To close this gap, this project will develop methods to purify mitochondria from giant kelp (Macrocystis pyrifera). This will generate tools to better understand kelp’s carbon metabolism, laying the foundation to design better kelp-based strategies to address environmental fluctuations. The project’s Broader Impacts include: training the next generation of scientists, developing Macrocystis pyrifera as a new model organism for carbon metabolism, providing mentorship and research experiences for undergraduates and senior researchers, and propagating the differences between kelp and plants with the wider public. Brown algae are major primary producers and key contributors to global carbon sequestration. Understanding the molecular details of how brown algae convert energy via photosynthesis and respiration has strong biological, ecological and economic implications. Despite this, the details of brown algae respiration and photosynthesis remain unexplored at the atomic, protein, organelle and cellular levels. The long-term goal of the project is to uncover the molecular details of mitochondrial respiration of brown algae and understand its mechanistic differences with other photosynthetic groups. To achieve this, the first step is to isolate brown algae mitochondria and purify the respiratory complexes from them for biochemical and structural studies. However, the ability to isolate mitochondria from brown algae has remained a major technical roadblock. This project will address this gap by developing methods to isolate Macrocystis pyrifera (giant kelp) mitochondria at an appropriate scale and purity. The investigators will test chemical, enzymatic and physical treatments for increased mitochondrial recovery. They will also explore the purification of M. pyrifera’s respiratory complexes for biochemical and structural studies by testing approaches involving vesicles derived from native mitochondrial membrane. The methods will set the foundation to understand brown algae respiration at a level of detail (amino acid interactions) much higher than previously possible (ecology or whole-organism physiology). The work will generate mechanistic hypotheses to be tested biochemically, genetically and physiologically. 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 2024 · 2024-12

Water scarcity is a growing challenge for many regions, particularly in areas like California, where competition for water among cities, farms and the environment is intensifying amid increasing climate variability. Projects to intentionally infiltrate water into underground formations (aquifers), a strategy called managed aquifer recharge (MAR), are getting traction as an alternative to surface reservoirs, because they can provide long-term water storage and water resilience to groundwater-dependent communities. Properly implemented, MAR can enhance communities’ access to safe and reliable drinking water, lower costs, reduce flood risks, and provide ecosystem benefits. However, the development of infrastructure projects by highly fragmented water management institutions has often excluded historically marginalized communities from decision-making, resulting in inequitable and contested outcomes. This project’s goal is to analyze the role of decentralized investment partnerships in developing aquifer storage infrastructure that promotes economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The results will be presented to communities, water suppliers and state decision makers to evaluate and model political responses and improve the overall societal outcomes. The resulting decision-making framework for MAR development, accounting for political feedbacks, will provide useful direction to regions throughout the Western U.S. and beyond. The project will generate a better understanding of the socio-environmental systems surrounding water management in agriculture-intensive regions. The project will analyze the role of decentralized, multi-stakeholder, investment partnerships in developing aquifer storage infrastructure that promotes economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The research will clarify how decentralized investment partnerships can promote economic productivity and environmental benefits, while enhancing community access to safe and reliable drinking water under a changing climate. The research will integrate political and institutional feedbacks into simulation-based water resources models that can inform decision making in the challenging context of western U.S. water management. The project will use a co-production framework that involves collaborating with fourteen cooperating partners representing a diverse array of stakeholders, to advance theory and produce actionable results related to: water portfolio design, collaboration risk in large-scale infrastructure partnerships, multi-objective assessment of MAR benefits under uncertainty, and political advocacy by historically disadvantaged communities during periods of institutional development and change. Research dissemination will include policymaker briefings, stakeholder workshops and interactive web content. The resulting decision-making framework for MAR development accounting for political feedbacks will provide useful direction to regions throughout the Western U.S. and beyond. 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 2024 · 2024-12

Researchers are proposing an innovative project that combines cutting-edge artificial intelligence (AI) with vast ocean datasets to gain new insights into ocean dynamics and create datasets for the wider community. The team plans to develop a sophisticated AI technique to analyze satellite measurements and high-resolution ocean model outputs. This system will detect and measure ocean fronts—boundaries between water masses with different properties—using multiple types of satellite data. It will also estimate the heat content of the ocean's upper mixed layer by examining fronts and other remotely observable quantities over time. A key aspect of their approach is designing the AI system to be interpretable, allowing scientists to understand how it reaches its conclusions. The researchers will also develop methods to quantify the uncertainty in the AI's predictions, which is crucial for scientific applications. By applying this AI system to approximately 15 years of satellite data, the team hopes to track changes in ocean fronts and mixed-layer heat content over time. This could provide valuable insights into how various physical processes, from large ocean currents to smaller-scale phenomena, influence the ocean's heat storage. We will use output from COAS, a state-of-the-art global, high-resolution (4-km) coupled ocean-atmosphere model, to train nested physics-informed vision transformer (ViT) algorithms to (I) diagnose the incidence and strength of density fronts from “static” multi-field scenes of remote sensing measurements (e.g., wind, sea surface temperature and height); and (II) infer the heat inventory of the mixed layer from a time-series of the density fronts and remote sensing data. A key novelty is to design the ViT for interpretability and quantification of uncertainty, through a physics-guided pre-training procedure. With the ViT, we will assess changes in front incidence and intensity and the heat inventory in the mixed layer over the past ~15 years where sufficient satellite coverage is available, as assessed through ViT uncertainty estimates. A central scientific question we target is a better understanding of the interactions of various physical drivers of the mixed layer heat inventory, with contributions spanning mesoscale currents to sub-mesoscale processes. We aim to both predict the mixed layer heat inventory and occurrence and type of density-driven fronts but also to understand their physical drivers; this will require innovation within AI. We will focus on ViTs for ocean applications which offer unique challenges to the state-of-the-art, but transferable solutions. Challenges include uncertainty quantification to guide the scientific discovery and verification of the trustworthiness of ViT predictions. We will achieve this through a physics-guided pre-training based on latent-space manifold identification and a physics-guided approach to semantic segmentation and understanding. By assessing the sources of ViT predictive skill, we endeavor to verify existing theories for heat inventory and density-driven front variability determined using sparse and costly in-situ observations, and also if entirely new physical insight can be found using the ViT. From analysis of remote sensing datasets, we will estimate how identified drivers have changed from past to present, and assess the likelihood of future change. 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 2024 · 2024-12

Acid mine drainage is a major pollution problem in the United States and abroad. It often results from mining activities that expose reactive minerals to oxygen in the atmosphere or in groundwater. As these minerals react, they create acid and release heavy metals that are damaging to downstream ecosystems. Reservoirs can be constructed to hold back polluted sediments and water, preventing them from influencing downstream environments. However, these reservoirs can be difficult to manage long term. This project will examine an acid mine drainage reservoir near Redding, CA which contains contaminated sediments from the Iron Mountain Mining complex. When the lake level becomes too low, the reservoir becomes acidic and metal-laden so it is managed to keep the lake above this level. In September 2024, the lake was nearly completely drained for dam maintenance which provides a rare opportunity to examine the chemical and biological processes that generate pollution. This proposal will sample the lake water and sediment as the lake level remains low to determine what processes generate pollution and to inform future remediation. This research helps to improve the well-being of individuals in society by improving bioremediation in AMD-impacted reservoirs and will help to develop a diverse, globally competitive STEM workforce by training an undergraduate student. The team will collect and archive samples of surface and pore water and sediment cores from Spring Creek Reservoir. Because some analyses are time-sensitive, the team will also analyze water samples for the concentration of metals using ICPMS and sediment samples for acid volatile sulfide. The remaining samples will be preserved and archived for future analysis of organic carbon content and microbial community composition. Future work will also use the samples collected to perform an experiment that aims to determine if lake drawdowns can be a sustainable bioremediation technique for AMD reservoirs. This research helps to improve the well-being of individuals in society by improving bioremediation in AMD-impacted reservoirs and will help to develop a diverse, globally competitive STEM workforce by training an undergraduate student. 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 2024 · 2024-10

Artificial intelligence (AI) and machine learning (ML) has been widely and successfully used in many fields including transportation, autonomous driving, chip design, etc. Considering the profound impact of AI as a potent force of transformation across various societal domains, AI ethics has garnered significant scrutiny. AI systems trained on biased data can perpetuate or amplify negative biases, with profound implications for areas like criminal justice, hiring, and lending, where biased AI could lead to unfair or discriminatory outcomes. Designing an ethical AI system has significant social and political value. As AI models grow, the demand for cyberinfrastructure (CI) support becomes substantial. Much research has focused on designing high-performance computing (HPC) infrastructures to accelerate AI/ML. However, support from CI for ethical AI is lacking, primarily due to distinctive constraints introduced by ethical considerations. Notably, such ethical constraints or objectives integrated with AI algorithms can slow down the inference and training processes. Conversely, without consideration of ethical AI, traditional CI technologies such as quantization and approximation might compromise AI ethics, even if they expedite the computation. This project will establish interactive and integrated training for building high-performance ethical AI with three interdisciplinary training programs across philosophy, ethical AI, and HPC. These include nine training modules and activities for sustainability and fostering community. The goal is to fill the gap between CI and ethical AI and AI ethics and train both CI contributors and CI users to build high-performance ethical AI. The training programs include: 1) Philosophical AI ethics training for CI contributors and ethical AI designers; 2) Ethical AI training for CI contributors; 3) CI software and hardware technologies training for ethical AI designers. Moreover, several hands-on projects are proposed to deepen trainees’ understanding of those programs, including hardware acceleration for machine learning models, ethical AI implementation, etc. The long-term goal is to boost the adoption of new "Computing+AI+Ethics" to multidisciplinary students and researchers from different STEM domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.