University Of California Santa Barbara

NSF Awards · FY 2025 · 2025-05

Understanding how to predict changes in the Arctic environment, especially sea ice variations, is crucial because these changes have big impacts on economies and societies both locally and globally. This project focuses on developing new ways to forecast Arctic sea ice during summer when sea ice is melting and reaches its minimum, looking at time periods from a few weeks to an entire season. It also aims to determine how far into the future these predictions can be made. The amount of summer sea ice in the Arctic is influenced by many factors, ranging from daily weather changes to long-term shifts in global wind patterns, affected by slowly changing ocean temperatures around the world. However, the understanding of how these factors interact is still limited. This limitation comes from the short duration of reliable satellite data monitoring and the complexity of the connections between these elements, which are challenging for traditional climate models or simple statistics to interpret. This project will use advanced and sophisticated machine learning methods to potentially improve predictions of Arctic summer sea ice. Additionally, the project will provide college students with opportunities to learn across different polar and climate science topics, leveraging the resources and expertise of the participating institutions. Preconditioning of sea ice before the summer months has long been recognized as a vital predictor of September's Arctic sea ice extent. The dynamic interactions between ice, ocean, and atmosphere are also major contributors to the changes observed in summer sea ice. The researchers will examine the impacts from external climate components and how they interact with the persistent local conditions before the summer season, which has not been fully considered in previous studies. This project will develop models of regional Arctic sea ice coverage based on a diverse array of observational data at a global scale, integrated by an advanced machine learning method. This approach aims to capture the complex, non-linear variations in both local and remote influences across timescales in a global context. The investigators will conduct a series of meticulously designed reforecast experiments to isolate and quantify the influence of various physical drivers on summertime Arctic ice within the predictive framework. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

Project Summary Ingesting food with very high osmolality can perturb internal osmotic balance and cause several physiological disorders. The fruit fly, Drosophila melanogaster, consumes food with varying osmolality and must therefore expend significant energy to maintain osmotic homeostasis. Hence it is imperative for flies to avoid consuming foods with high osmolality. However, whether high osmolality deters feeding in flies and, if so, the neurons and receptors in the fly taste system responsible for detecting high osmolality are unknown. The proposed project seeks to define the role of osmolality in modulating the feeding behavior of fruit flies. The proposed work will employ sophisticated genetic, electrophysiological, and imaging techniques in fruit flies to investigate the neural and molecular basis for the detection of high osmolality by the gustatory system. Aim 1 will define the behavioral and cellular basis for sensing the osmolality of food in the fly gustatory system. The aim is supported by my preliminary experiments that demonstrate flies have a lower preference for a sugar solution with higher osmolality compared to lower osmolality solutions at the same sucrose concentration. I will perform gustatory behavior assays to determine if the effect of high osmolality on feeding depends on the food source. I will perform further assays to investigate if high osmolality affects food consumption as well. Additionally, by silencing individual neuron types present in a taste sensilla followed by behavior experiments I will probe the role of different neuron types in feeding aversion caused by high osmolality. Aim 2 will further investigate the gustatory neurons that regulate osmolality-induced feeding behavior. My preliminary data suggest that high osmolality can inhibit sugar- induced activation of gustatory receptor neurons (GRNs) responsive to sweet taste. I will perform extracellular tip recordings, which assay action potentials in GRNs, to characterize the effects of high osmolality on the activities of GRNs. I have also outlined calcium imaging experiments to examine whether high osmolality inhibits sweet taste neurons cell-autonomously or whether activation of bitter GRNs and mechanosensory neurons also underlies the suppression of sweet GRN activity by high osmolality. Aim 3 outlines experiments to determine if chloride channels are required for the suppression of appetitive GRNs by high osmolality. This aim is supported by my preliminary findings that demonstrate that a volume- activated chloride channel is required for the inhibition of sweet GRNs by high osmolality. I have outlined tip-recording experiments to test if it is required for the inhibition of water GRNs by high osmolality as well. Additionally, I have outlined experiments to test the role of other chloride channels in the suppression of neuronal activity by high osmolality. The findings of this project would establish osmolality as an essential component influencing insect feeding decisions. Additionally, this work will provide novel insights into how aversive stimuli can deter feeding by inhibiting the activation of attractive taste pathways.

NSF Awards · FY 2025 · 2025-04

NON-TECHNICAL SUMMARY: Chronic diseases such as organ failure are difficult to monitor and treat long-term. Unlike temporary injuries, where therapeutic progress is evaluated through a follow-up appointment, chronic diseases can change in severity unpredictably and over long periods, making periodic doctor’s visits unreliable for assessing a patient’s ongoing health. One way to address this challenge is by developing tools that allow continuous monitoring of biomarkers—molecules in the body that signal disease activity. Peroxynitrite is a biomarker closely linked to disease severity, and this marker holds significant potential for improving the way chronic diseases are diagnosed and managed. However, current tools for detecting peroxynitrite are limited by their short lifespan and inability to work effectively in deep tissues. This project develops Extended Lifetime Peroxynitrite-Responsive Probes (xL-PRPs), a new class of materials designed for long-term, non-invasive monitoring of peroxynitrite levels in the body. By combining innovative probe chemistries with strategies like linking small molecules together into polymers and incorporating reversible sensing mechanisms, xL-PRPs offer the potential for long-lasting and deep-tissue visualization. This will reduce the need for traditional invasive procedures, such as biopsies, to evaluate organ health. Broader impacts of this project include outreach programs to introduce high school students to hands-on demonstrations in polymer chemistry using both technical and non-technical concepts, the development of interactive biomaterials courses to bridge undergraduate and graduate education, and mentoring initiatives to promote equitable career preparation in the chemical sciences. TECHNICAL SUMMARY: The development of advanced biomaterials for long-term monitoring of oxidative stress is essential for addressing critical challenges in chronic disease diagnosis and treatment. Peroxynitrite (ONOO⁻), a highly reactive oxygen species strongly correlated with disease severity, presents an opportunity to revolutionize disease monitoring; however, current fluorogenic probes are hindered by single-use functionality, short in vivo activity, and inadequate imaging capabilities in deep tissues. This CAREER project focuses on the development of Extended Lifetime Peroxynitrite-Responsive Probes (xL-PRPs), a novel class of probes designed to address these limitations. The specific objectives for these xL-PRPs are to achieve: (1) reversible ONOO⁻ responsiveness, (2) macromolecular structure for delayed in vivo tissue clearance, and (3) near-infrared fluorescence properties for enhanced imaging depth. These materials are fabricated using NITEC click chemistry and controlled polymerization techniques, while nuclear magnetic resonance, light scattering, electron microscopy, and fluorescence microscopy are used to investigate ONOO⁻ interactions with these biomaterials. This work will establish the first reversible ONOO⁻ probes responsive to biochemical scavengers in vivo, enabling localized, long-term, and non-invasive monitoring of oxidative stress. Anticipated outcomes include simplified synthetic methods, enhanced stability of ONOO⁻ probes, and improved imaging capabilities for chronic disease monitoring. Broader impacts include outreach programs to introduce high school students to hands-on demonstrations in polymer chemistry using both technical and non-technical concepts, the development of interactive biomaterials courses to bridge undergraduate and graduate education, and mentoring initiatives to promote equitable career preparation in the chemical 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 · 2025-04

Dust changes our view of astronomical objects. It interacts with light from distant stars to make them more dim. Dust also radiates heat, which astronomers can see with millimeter and radio telescopes. Early galaxies contained more dust, and that impacts our understanding of how galaxies form and change with time. The investigator will use multiple telescopes to understand how dust in the most distant galaxies affects the cosmos. Undergraduate students from underrepresented backgrounds will be trained in related astronomy research. Dust makes up a negligible fraction of the mass budget of galaxies (<1%) and yet has transformative effect on their integrated spectra. Its efficient absorption of starlight in the ultraviolet and optical results in half of all extragalactic light in the Universe being thermally re-radiated in the far-infrared and millimeter (FIR/mm). Yet galaxies' thermal dust emission is poorly understood relative to stellar emission processes. The importance of dust to a wide range of astrophysical processes -- from its role in the condensation of cold molecular clouds to form stars, the coagulation of planetesimals, or the reprocessing of starlight at longer wavelengths -- makes it a crucial ingredient of the Universe requiring detailed observations. This project will use a diverse range of observational FIR/mm datasets to comprehensively characterize the dust emission of galaxies across cosmic time. Core concepts of this research will be used to bolster the retention of under-represented students in astrophysics careers through support of summer research and innovative equity-focused pedagogical activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

Project Summary Biocatalysis represents a powerful emerging technology for the biomanufacturing of biologically active molecular agents and clinically significant therapeutics. Previously, the practice of biocatalysis is largely limited to the utilization and engineering of native enzymology from nature. New-to-nature enzymatic chemistries which are synthetically useful yet never encountered in biology remain largely underexploited. Combining biocatalysis and photochemistry, we propose a novel and generalizable photobiocatalysis strategy to reprogram diverse pyridoxal phosphate (PLP)-dependent enzymes as radical enzymes to catalyze stereoselective intermolecular radical reactions. Notably, these newly developed enzymatic reactions are not previously known in either biochemistry or organic chemistry. By merging photoredox catalysis powered by visible light and pyridoxal biocatalysis fine-tuned by directed evolution, a range of non-canonical amino acids that are often incorporated into medicines and therapeutic protein targets will be produced with excellent stereocontrol. By leveraging novel activation mechanisms involving open-shell PLP covalent intermediates, we will design and develop stereoselective biocatalytic radical C–C bond forming reactions at the a, b, or g position of abundant amino acid building blocks, furnishing an array of not easily accessible non-canonical amino acids, including those possessing a well-defined stereochemical dyad or triad which have remained out of the reach of traditional chemical and biological synthesis. Radical pyridoxal biocatalysis eliminates the need of tedious protecting group manipulation often required by traditional chemical synthesis, providing a modular, convergent and highly stereoselective approach to manufacturing biomedically valuable non-canonical amino acids.

NSF Awards · FY 2025 · 2025-04

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Volcanic hotspots, like Hawai’i and Cape Verde, are areas where hot rock from deep inside the Earth rises to the surface. This hot rock melts in the shallow part of the Earth's mantle to create magma, which then travels through the ocean floor and erupts to form volcanoes. Scientists don't fully understand how this rising magma interacts with the ocean floor or how it might change the Earth's crust as it moves upwards. This is hard to study because these processes happen deep underground—inside the volcano—and can only be inferred using indirect methods. The Cape Verde Islands offer a unique opportunity to study these processes directly. The islands have been lifted up several kilometers above sea level and eroded, exposing the deep volcanic layers inside. This makes it possible for researchers to study the volcanic systems, including the oceanic crust and sediments upon which the volcanoes are built. The scientists funded in this proposal will map and sample the interior of a volcano on Maio Island in Cape Verde to see how magma from the hotspot interacts with the ocean crust and seafloor sediments on its way to the surface. The samples will be tested for their chemical makeup, isotopic compositions, and ages. This research will help answer an important question in geology that continues to puzzle geoscientists: whether or not the ascending magma is modified by the process of assimilating seafloor sediments. The study will also explore how the oceanic crust and sediments are pushed upward by magma as it intrudes pervasively into the crust, which may be a key part of how Cape Verde volcanoes form, inflate, and uplift by several kilometers. A team of researchers from the US and the UK will work together on this project, with a graduate student from the University of California, Santa Barbara, who will gain valuable experience in fieldwork, chemical analysis, and mass spectrometry. These skills are critical to US national defense, as some of the US research team’s former students now work in nuclear forensics at Los Alamos National Labs. The project will not only help geoscientists better understand how volcanoes work, which is an important benefit to society due to hazards posed by volcanoes, but also provide important insights into the formation of critical mineral resources. Additionally, the US and UK researchers will collaborate with local scientists from Cape Verde, and share their findings through local Cape Verdean media, helping to strengthen the relationship between the US, UK, and Cape Verde. Volcanic hotspots, such as those in Hawai’i and Cape Verde, are sourced by upwelling plumes originating from deep within the mantle. Before erupting as ocean island basalts (OIB), the mantle melts rise and interact with the oceanic lithosphere. Plume melting ceases when the rising plume reaches the base of the lithosphere. OIB melts then continue to ascend through the lithospheric mantle until they break through the mid-ocean ridge basalt (MORB) crust by diking. These melts eventually migrate upwards through the seafloor sediments that cover the MORB basement. Despite advances in understanding, our knowledge of melt-lithosphere interaction during this process remains limited, primarily due to the inaccessibility of the deep regions where it occurs. Thus, instead of direct observation, the community relies on indirect evidence and chemical proxies to infer processes operating deep inside the lithosphere. Fortunately, the Cape Verde Islands offer a unique opportunity to study the deep structure of an oceanic hotspot volcano that, together with remnants of the underlying Mesozoic MORB crust and a 1-km-thick sequence of marine sediments, have been uplifted several km and exposed at the surface. By conducting detailed fieldwork, collecting samples, and analyzing them with geochemical and 40Ar/39Ar methods, this project aims to test broad questions: First, as upwelling OIB dikes and sills transit through 1 km of seafloor sediment, do they assimilate it? Second, is the dramatic island uplift in Cape Verde caused by cumulative intrusions and is the abundance of OIB dikes crosscutting the exposed MORB basement consistent with this? Third, can the record low 187Os/188Os observed in the world’s ocean basins—previously identified by laser ablation ICP-MS (inductively coupled plasma mass spectrometry) in sulfides hosted in Cape Verdean peridotite xenoliths—be reproduced by Thermal Ionization Mass Spectrometry, thereby confirming subcontinental lithospheric mantle under Cape Verde? This project will involve international collaboration across different scientific disciplines, with two principal investigators (PIs) bringing complementary expertise: PI Jackson (UC Santa Barbara) specializes in global OIB geochemistry, while PI Ramalho (Cardiff University, UK) focuses on Cape Verde’s geology and volcanic evolution. The proposal will also fund a UCSB graduate student and a Cardiff postdoc, offering them a unique interdisciplinary experience in a cross-disciplinary environment. The PIs will collaborate with colleagues from Cape Verde's Instituto Nacional de Gestão do Território (INGT) to produce new geological maps for the study areas, and will support an INGT professional to participate in the fieldwork and mapping efforts and co-author a publication. Together, the PI team plans to submit a proposal to designate the studied outcrops as a protected Geoheritage site, recognizing their global significance. Finally, the PI team will share their findings with the Cape Verdean public through newspapers, radio, and social media channels. 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

Modern network applications require high performance and security, yet smaller community and enterprise networks often lack the infrastructure to meet these demands. Machine learning (ML) has shown promise in improving network management, but existing ML models often fail when deployed outside controlled lab environments because they learn patterns that do not generalize well to real-world settings. This project develops a closed-loop ML pipeline that iteratively refines training data collection to improve model generalizability. By analyzing the decision-making process of ML models and identifying statistical biases in training data, the project hopes to ensure that ML-based network solutions are robust, effective, and suitable for deployment in diverse network environments. Technically, the project introduces a new ML framework that continuously improves generalization by iteratively refining training datasets. The research efforts are divided into two main thrusts: (1) designing a programmable data-collection platform that enables flexible and scalable training data acquisition across different network environments, and (2) developing methodologies that use explainable ML techniques to detect and address underspecification issues in network models. The closed-loop approach ensures that ML models adapt over multiple iterations, mitigating learning shortcuts and spurious correlations that degrade performance in real-world settings. This project has significant broader impacts by enabling the development of generalizable ML models for networking, lowering the barrier to collecting high-quality training data, and fostering trust in ML-based network management solutions. By making it easier to curate datasets across diverse network environments, the project supports reproducible research and improves ML adoption in production networks, particularly for research and education (R&E) and last-mile community networks with constrained resources. All project outcomes, including datasets, software, and model implementations, will be made publicly available at https://clml.cs.ucsb.edu/. The repository will be maintained for at least five years, with regular updates based on research progress and community contributions. It will provide documentation, reproducible experiments, and open-source implementations to facilitate further research and practical adoption. This long-term commitment to transparency and accessibility ensures that the project’s contributions benefit researchers, network operators, and educators. 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

Using ubiquitous Internet connectivity, it is possible to monitor and control the physical environment – from urban settings to rural communities to uninhabited wild lands – continuously, using digital technologies. Critical unsolved problems in the areas of extreme weather, agriculture, medicine, energy efficiency, education, sustainability, and other high-impact disciplines can be addressed through the creation and implementation of an always-on Internet of Things (IoT) that extends human perception and the ability to act at a distance using pervasively deployed and interconnected digital assets. This project develops a new unifying computer science and engineering approach to building and deploying IoT systems. This project’s novelties are a software environment and ecosystem that are operational ready using current digital technologies. The project’s broader significance is twofold. First, the project makes publicly available a new extensible IoT software infrastructure, enabling new cross-disciplinary discoveries and advances. Second, the project makes IoT more broadly accessible by unifying the technologies that are necessary to implement it as a new software capability. Scientifically, the project postulates a new, multi-scale set of computer science abstractions that are implementable on all devices from small-scale microcontrollers to the largest supercomputers. It does so as a full stack that can be implemented natively or as a guest of existing software infrastructure. It also includes a programming language approach that is general, highly concurrent (for efficiency) at large resource scales, and event-driven for implementation at embedded-system microscale. This combination of advances is integrable with existing development technologies and languages, thereby extending the accessibility of the research results that are required to achieve the project’s goals. 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

Part I: Non-technical description: Methane is one of the more effective atmospheric gases at retaining heat in the lower atmosphere and the earth’s crust contains large quantities of methane. Research that identifies the factors that control methane’s release into the atmosphere is critical to understanding and mitigating climate change. One of the most effective natural processes that inhibits the release of methane from aquatic habitats is a community of bacteria and Archaea (microbes) that use the chemical energy stored in methane, transforming methane into less-climate-sensitive compounds. The amount of methane that may be released in Antarctica is unknown, and it is unclear which microbes consume the methane before it is released from the ocean in Antarctica. This project will study one of the few methane seeps known in Antarctica to advance our understanding of which microbes inhibit the release of methane in marine environments. The research will also identify if methane is a source of energy for other Antarctic organisms. The researchers will analyze the microbial species associated with methane consumption over several years of field and laboratory research based at an Antarctic US station, McMurdo. This project clearly expands the fundamental knowledge of Antarctic systems, biota, and processes outlined as a goal in the Antarctic solicitation. This research communicates and produces educational material for K-12, college, and graduate students to inspire and inform the public about the role Antarctic ecosystems play in the global environment. This project also provides a young professor an opportunity to establish himself as an expert in the field of Antarctic microbial ecology to help solidify his academic career. Part II: Technical description: Microbes act as filter to methane release from the ocean into the atmosphere, where microbial chemosynthetic production harvests the chemical energy stored in this greenhouse gas. In spite of methane reservoirs in Antarctica being as large as Arctic permafrost, we know only a little about the taxa or dominant processes involved in methane consumption in Antarctica. The principal investigator will undertake a genomic and transcriptomic study of microbial communities developed and still developing after initiation of methane seepage in McMurdo Sound. An Antarctic methane seep was discovered at this location in 2012 after it began seeping in 2011. Five years after it began releasing methane, the methane-oxidizing microbial community was underdeveloped and methane was still escaping from the seafloor. This project will be essential in elucidating the response of microbial communities to methane release and identify how methane oxidation occurs within the constraints of the low polar temperatures. This investigation is based on 4 years of field sampling and will establish a time series of the development of cold seep microbial communities in Antarctica. A genome-to-ecosystem approach will establish how the Southern Ocean microbial community is adapted to prevent methane release into the ocean. As methane is an organic carbon source, results from this study will have implications for the Southern Ocean carbon cycle. Two graduate students will be trained and supported with undergraduates participating in laboratory activities. The researcher aims to educate, inspire and communicate about Antarctic methane seeps to a broad community. A mixed-media approach, with videos, art and education in schools will be supported in collaboration with a filmmaker, teachers and a visual artist. Students will be trained in filmmaking and K-12 students from under-represented communities will be introduced to Antarctic science through visual arts. 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

Many animals use information from vision when they navigate the world, yet we know surprisingly little about even the basic visual features that contribute to spatial and directional senses. For example, fruit flies rely on cues such as vertical stripes and polarized light to determine which direction to move. Yet, these visual features may represent only a fraction of those necessary for navigation. The main goal of this project is to understand the detailed network dynamics underlying visual processing and identify important visual features for navigation by combining anatomical, physiological, and computational approaches. The study will represent a significant step toward mechanistically describing the dynamics of an entire neural circuit, from sensory processing to developing spatial and directional senses––abstract cognitive entities. In addition, the proposed studies can inspire broader fields in engineering, including designing computer vision algorithms that can analyze task-specific features from the environment and robots that autonomously navigate in hazardous environments where external help signals such as the Global Positioning System (GPS) are unavailable. Based on these advances, the team will develop a new computational neuroscience course focusing on mechanistic models of neural circuits that have been validated in biological brains. The researchers will also reach out to high school students and families to increase awareness in neuroscience with hands-on lab experiences demonstrating advanced neuroscience techniques, such as using optogenetics to alter the mating behaviors in flies. Finally, they will provide a career workshop to undergraduate students to help efficiently advance their careers in biological sciences. The team will investigate how information is transformed across multiple stages of the pathway from the optic lobe to the compass neurons (also called EPG neurons), which encode the fly's sense of direction and share similarities with mammalian head direction cells. This pathway, called the anterior visual pathway (AVP), has three major types of neurons. MeTu neurons, which receive input in the Medulla (optic lobe) and send axons to the Anterior Optic Tubercle (AOTU), respond to specific visual primitives such as certain wavelengths and light polarization. TuBu neurons integrate the output of MeTu neurons from a specific spatial area, such as vertical or circular areas. Finally, ER neurons, which are postsynaptic to TuBu neurons and serve as the last stage of visual scene processing before the compass neurons, interact with each other to preprocess information about multiple visual features before they are integrated by compass neurons. The team will identify the visual features that two ER types extract along the AVP. To this end, they will tether flies and place them in a custom-built virtual reality arena that spans 360o azimuth and projects full-color stimuli. Using two-photon calcium imaging, they will monitor neurons’ activity in these pathways in response to visual stimuli with various visual features. This project will deliver anatomically and experimentally supported computational models that provide a comprehensive understanding of the visual processing essential for navigation, spanning the entire circuits from the peripheral vision to the sense of direction. 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

Digital fabrication uses computer-controlled machines like 3D printers to make objects. These machines can improve how individuals and small companies make products by increasing the speed and precision of making products while reducing the costs and labor compared to typical ways of manufacturing. The current ability of digital fabrication to support many kinds of manufacturing is limited by how existing equipment performs when run in an automated mode. Designers and manufacturers rely on techniques for making products that are specific to domains and materials. Their techniques often require combining automatic and manual operations. However, current digital fabrication technologies enforce the same ways of operating regardless of domain or material. The machine control methods limit operators to setting machine instructions in advance and responding to machine errors. These methods prevent operators from applying their domain knowledge or manual skills in the fabrication process and keep them from using digital fabrication with their existing way of making things. This project aims to enable experts to use digital fabrication technologies in powerful domain-specific workflows. The project will investigate new machine control methods and software design techniques that blend precise automated ways of making products with an expert controlling the machine by hand. This research will create new business opportunities by allowing domain experts to adapt digital manufacturing processes to the making of their products. This project will also help users tailor digital fabrication machines to their specific needs, which will lead to new products and materials, as well as new types of digital fabrication. This project will establish domain-specific computer-numerical control (CNC) toolpath abstractions, computer-aided manufacturing (CAM) software interfaces, CNC machine architecture, and collaborative engineering methodologies for building cross-domain digital fabrication technologies for professional practice. Prior research has examined novice-oriented digital fabrication technologies focusing on high-level design specification and entirely automated fabrication practices or separate efforts in interactive digital fabrication interaction. To date, research in digital fabrication lacks a systematic investigation of the relationship between CNC machine mechanisms, control architecture, CAM toolpath design, and operation paradigms that blend automated and manual control. The investigators will conduct a comparative analysis of the practices of contemporary product designers and manufacturers who produce either manually or digitally fabricated products in low volume. The investigators will use insights on the barriers that practitioners from different domains face in the manufacturing workflow to inform the development of CNC control methods that enable creators to safely and efficiently reconfigure CNC machines to build new products. The investigators will pair the development of new CNC engineering techniques with the investigation of CAM design technologies that allow practitioners to precisely control machine behavior, coordinate machining operations with manual intervention, and understand and predict the fabrication workflow. The investigators will integrate their research with an education agenda that spans higher education and secondary public school education aimed at engaging students in computer science and entrepreneurial learning through applying digital fabrication to produce functional products. 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

DNA, which codes genetic information, can be damaged by both environmental insults and normal biological processes. Cells have numerous mechanisms to repair damaged DNA, some of which maintain the encoded information and some that alter it. This study leverages novel methods to understand how cells choose different mechanisms to repair one type of DNA damage, the double strand break. The work will address key questions, including how cells commit to recombination, a class of DNA repair mechanisms that copy damaged DNA using a template molecule and maintain encoded information. Results from this study will have broad implications for fields like gene editing, cancer treatment, and personalized medicine. Additionally, this project will provide training and research experiences to junior transfer college students with the goal of improving academic success and retention in STEM fields. Together these activities promise to improve both our understanding of DNA repair and the accessibility of science education. The research will define how human cells control the initiation and early steps of recombination at DNA double strand breaks (DSBs). This project leverages the recent discovery that covalent modification of DNA with interstrand crosslinks enhances recombination frequency. Crosslink-stimulated recombination will be used to boost recombination frequencies at DSBs, thereby enabling the analysis of key recombination activation mechanisms through advanced microscopy, molecular biology, and biochemical techniques. Objective 1 of this project will define which step of recombination is altered in crosslink-stimulated recombination. Objective 2 will explore how the DNA repair regulatory kinase ATR is activated during DSB repair. Objective 3 will develop a new and comprehensive understanding of DSB repair factors involved in recombination. All objectives will integrate a cohort of pre-biology transfer students into this project through mentorship and authentic research experiences, thereby supporting their retention and success in STEM fields. 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

The shift from hunting wild animal species to managing domesticated herds marked a significant transformation in human society by providing reliable food and resources, supporting permanent settlements, and reshaping social and economic structures. This doctoral dissertation research focuses on the domestication of camelids (llamas and alpacas). As the only large-bodied domesticated animals native to the Americas, camelids have been integral to multiple societies for millennia, providing transportation, wool, and meat. Despite their cultural and economic importance, the genetic diversity of modern camelid populations has drastically declined due to environmental pressures and human interventions, particularly from colonial-era impacts. This project investigates how ancient communities adapted their subsistence strategies amidst the shifting environmental conditions of the deep past. The work has significant implications for modern conservation efforts, offering potential strategies for preserving the genetic diversity of camelids in the face of contemporary challenges such as environmental change and habitat fragmentation. This interdisciplinary project integrates archaeogenomics, isotopic analysis, and environmental data to examine the processes of camelid domestication and management. Researchers combine carbon and nitrogen isotopic data from camelid bones with dental calculus analysis to reconstruct the diets of grazing ancient herds. Additionally, analysis of ancient mitochondrial DNA (aDNA) sheds light on the genetic changes that occurred during the domestication process, providing critical insights into how human and environmental pressures influenced the genetic diversity of camelid populations over time. The findings contribute to broader discussions on the evolution of pastoral economies and the role of human-environment interactions in shaping animal domestication. 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

Understanding the genetic underpinnings of how plant cells produce energy represents a central goal in plant biology, especially in the context of crop improvement efforts. However, the connection between genes and the energy-related traits those genes encode is complicated for two primary reasons: (1) energy-related genes are spread across three separate cellular compartments (the nucleus, the chloroplasts, and the mitochondria), and (2) all plants have experienced one or more whole genome duplication events, in which the nuclear genome has been doubled or more, during their evolutionary history. Indeed, many of our most important crop species have more than two copies of their nuclear genome inside their cells. How this “genomic redundancy” affects energy production is largely unknown; however, the balance between nuclear genome copy number and the mitochondria and chloroplasts appear to be critical to plant energy production. We will employ and train students and researchers to investigate how plant cells maintain this balance. Whole genome duplication events (WGDs), in which the nuclear genome is doubled or more as a result of allopolyploidization or autopolyploidization, are a major force for plant diversification. Because the cytoplasmic genomes are separately replicated (and inherited) from the nuclear genome, the stoichiometric balance between the three genomic compartments (i.e., cytonuclear stoichiometry) is expected to be perturbed following WGD. Recent work indicates that cytonuclear stoichiometry is maintained following WGD in both monocots and eudicots, suggesting that gene dosage balance between the nuclear and cytoplasmic genomes represents an important component of polyploid lineage formation and evolution. We therefore hypothesize that cytonuclear stoichiometry is critical for establishing the cell’s chloroplast and mitochondrial content, such that variation in cytonuclear stoichiometry leads to variation in photosynthetic and respiratory performance and that the genomic architecture of cytonuclear stoichiometry is responsive to changes in nuclear genome size and content. First, we will test these hypotheses in diploid, polyploid, and aneuploid contexts by quantifying and measuring organelles, evaluating photosynthetic performance, and comparing nuclear vs. cytoplasmic transcript pools of single cells. We will also perform association tests in the Arabidopsis thaliana genome and confirm those associations with molecular knockouts to disentangle the complex genomic architecture underlying cytonuclear stoichiometry. The findings are expected to set the stage for future applied efforts aimed at improving metabolic function in polyploid crops. 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

De Novo Design and Evolution of Enzymes for Biomass Upcycling to Surfactants and Fuels This project aims to transform abundant plant materials into high-value products like fuels, oils, and soaps. By using artificial intelligence (AI) and machine learning, the research focuses on enhancing the activity of key enzymes that are needed to convert waste biomass, such as plant materials, into commodity chemicals through protein design. This project introduces a novel strategy to meet supply chain goals through biomanufacturing. Collaborations with industry partners will promote real-world use of these technologies in the U.S. bioeconomy. Additionally, the project emphasizes outreach and workforce development, providing opportunities to train the next generation of synthetic biologists. The research employs advanced computational and experimental approaches to design and optimize enzymes for biomass upcycling. AI-guided tools, including machine learning models and molecular dynamics simulations, will be used to enhance enzyme activity, stability, and substrate specificity. The project targets enzyme classes to convert fatty acids into hydrocarbons for fuels, lubricants, and surfactants. Directed evolution will further refine these enzymes for industrial scalability. The research integrates enzymology, biocatalysis, and computational chemistry, leveraging molecular dynamics, density functional theory, and spectroscopic analysis to establish foundational principles of de novo enzyme design. Collaboration with industrial partners ensures that these AI-enabled biocatalysts are implemented in scalable production systems. This interdisciplinary effort not only advances biomanufacturing capabilities but also sets a precedent for applying computational enzyme design to other challenges in chemistry. 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

Global sea levels are rising at unprecedented rates and will continue to reshape the coastline of densely populated regions both in the US and globally with implications for housing, transportation, agriculture, wildlife habitability, and tourism. Over the next 50 years, mass loss from the Antarctic Ice Sheet will be a dominant contribution to global sea level, but it is also associated with the greatest uncertainty in sea level rise estimates. Much of this uncertainty results from incomplete understanding of processes that occur near the Antarctic coast where there are close interactions between the open ocean, near-coastal waters whose properties are influenced by interactions with sea-ice, and ocean water that is carrying glacier meltwater originating from the Antarctic ice sheet itself. These regions also happen to be among the most biologically productive of all waters in the Southern Ocean, and the impact of climate-related biogeochemical changes here remain a blind spot in our understanding of a changing global carbon cycle. Current understanding of changes occurring around Antarctica are largely derived from decades of work in the Amundsen Sea. Yet, the melting of ice shelves in the neighboring Bellingshausen Sea are comparably high and pre-condition the physical and biogeochemical properties of the water that enter the Amundsen. Thus, the role of the “upstream” Bellingshausen Sea in ice sheet mass loss and ocean carbon uptake remains unconstrained, although models suggest this region can broadly influence these processes throughout West Antarctica. The Bellingshausen Sea: A Carbon and Overturning Nexus (BEACON) project will collect a broad suite of physical and biogeochemical observations needed to assess the Bellingshausen Sea’s role in the large-scale distributions of heat, meltwater, dissolved iron and other nutrients, and biological productivity. The research team will combine standard and trace-metal shipboard measurements, towed underway observations, and a small fleet of remote autonomous underwater vehicles aimed at capturing key transport pathways associated with narrow boundary currents located along the coast. These observations will capture dynamical processes related to mixing of water properties by ocean turbulence from centimeter to kilometer scales. This information about mixing will then be applied to an inverse-modeling framework to assess how changes in near-coastal processes in the Bellingshausen Sea impact larger-scale ice-shelf melt rates, nutrient supply to the upper ocean, the timing and intensity of seasonal primary production, and the oceanic uptake of carbon dioxide throughout West Antarctica. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-12

Project Summary/Abstract Misregulated extracellular matrix (ECM) remodeling is associated with both hypertrophic and dilated cardiomyopathy (HCM, DCM). Cardiac fibrosis, characterized by the increased deposition and reorganization of ECM components, is a hallmark of cardiomyopathy. Fibrotic ECM remodeling results in changes to the mechanical properties of the myocardium, such as increased elasticity (stiffness) compared to healthy tissue, and these mechanical changes can significantly disrupt cardiomyocyte (CM) function and phenotype. Fibrotic ECM remodeling in other organs and tissues has also been shown to alter tissue viscoelasticity, or ability of the tissue to dissipate energy through viscous flow upon loading. Substrates of varying viscoelasticity can differentially regulate cell contractility and phenotype, including in muscle myoblasts. However, how myocardial viscoelasticity changes during HCM or DCM progression is not well established. Further, how CMs, the motor units of the heart, respond to viscoelastic mechanical environments that mimic healthy or diseased tissue is unknown. The objective of this proposal is to determine how ECM remodeling during cardiomyopathy alters tissue viscoelasticity and the extent to which those mechanical changes can drive disease progression. We will address the critical outstanding question of how myocardial viscoelasticity varies between health and disease and to what extent disease-associated mechanical changes accelerate disease progression through two Specific Aims. Aim 1: Does cardiomyopathy-driven ECM remodeling alter myocardial viscoelasticity? and Aim 2: Does matrix viscoelasticity regulate cardiomyocyte phenotype and function? We will mechanically characterize human and porcine myocardium from DCM and HCM patients or models respectively, as well as assessing the contribution of ECM remodeling in driving mechanical changes. Then, we will use wildtype and mutant hiPSC- CMs with HCM mutations and culture them on hydrogel substrates with tunable viscoelasticity and assess key indicators their phenotype. Our results are expected to have positive translational impact as they can inform a more comprehensive interpretation of diagnostic imaging modalities, such as ultrasound elastography, by assessing viscoelasticity as a marker of disease progression. Additionally, incorporating substrate viscoelasticity into in vitro human CMs culture models would more accurately represent the pathophysiological context CMs experience in diseased tissue. Especially when combined with other advantages of iPSC models, such as patient specificity, these advanced platforms could yield novel biological insights or enable identification of potential therapeutic targets.

NSF Awards · FY 2024 · 2024-12

Nanotechnology can provide important advances in many areas, including renewable energy, advanced water treatment, medicine, food protection, and others, to increase sustainability. However, nanotechnology must also be safe, and it is best done by incorporating sound practices into the design of nano-enabled products. A key element for the safe use of engineered nanoparticles (ENPs) is knowledge of the response of organisms when exposed to ENPs released from nano-enabled products, directly or indirectly (e.g., via treated wastewater treatment plant effluent). Traditionally, ecological risk assessment (ERA) has focused on the toxicity of ENPs, since that is clearly an important concern. However, preliminary evidence indicates that at low concentrations some ENPs may induce a stimulatory response, increasing growth rates, called hormesis. Research on predicted exposure concentrations indicates that most ENPs will be present in the environment at these low to moderate concentrations, below conventional toxicity thresholds. This proposed comprehensive approach, in collaboration with researchers from the Université de Genève in Switzerland, will provide a very strong underpinning for decision-making by (1) regulators that need to evaluate ENP-enabled products; (2) industrial product designers as they consider the different types of ENPs for their products; and (3) the public at large, by having information to decide on the use of nano-enabled products (e.g., cosmetics, sunscreens, and other personal care products). The overall goal is to address a major research gap in nanoecotoxicology concerning hormesis, as an underexplored but potentially important response of phytoplankton species, the base of the food chain, to ENPs and its implications for ERA and sustainable use of nanotechnology. The specific objectives of ENHANCER are: (1) to systematically evaluate the frequency and intensity of hormesis responses in different phytoplankton species, exposed to a large variety of ENPs; (2) to assess the effect of environmental variables on hormesis responses; (3) to improve understanding of key mechanisms driving stimulatory responses in phytoplankton species upon exposure to ENPs; (4) to explore the environmental implications of ENP-induced hormesis with novel artificial intelligence (AI) approaches; and (5) to evaluate potential ERA scenarios under various alternatives. 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 2024 · 2024-12

Sinking particles and dissolved organic matter produced in the sunlit surface waters of the ocean provide carbon to the deep ocean. However, carbon budgets for the deep (dark) ocean suggest that the energy required by microbes is greater than the supply of carbon from surface waters. This means that there may be additional and unexplored sources of carbon to the deep ocean. Recent work indicates that some nitrifiers, a group of marine microorganisms living in the deep ocean, may provide a source of fresh organic carbon below the sunlit ocean. However, the chemical composition of this material and its suitability as a source of food for other microbes living in the deep ocean are unknown. This project is amongst the first to study the release of dissolved organic matter from marine nitrifiers under different nutrient, temperature, and pressure conditions. The goal of this study is to help understand the role that marine nitrifiers play in ocean carbon cycling. In addition to the direct scientific impacts, this project includes mentoring opportunities for undergraduate students, educational outreach to middle and high school girls, and public outreach in collaboration with local artists and/or art students. This project aims to provide the most thorough characterization to date of dissolved organic matter (DOM) released from marine nitrifiers. Specifically, the investigators plan to determine the chemical composition and quantity of DOM released from nitrifiers, the mechanism by which nitrifiers release DOM, the bioavailability of nitrifier-derived DOM and which heterotrophs may respond most readily to it, and the metabolic cost of DOM release to individual nitrifying archaeal and bacterial cells. The science team plans to use laboratory culturing and incubation studies, but laboratory findings will also be applied to the natural environment through a targeted field campaign and experiments with natural microbial communities. Results from this study will inform biogeochemical models by providing rates of DOM release from marine nitrifiers under environmentally relevant conditions, contribute information about metabolites produced by marine nitrifiers, and identify heterotrophic taxa that depend on this energy and carbon source. This work is expected to enhance our understanding of carbon cycling in the mesopelagic ocean, which may help to improve biogeochemical models and is also critical to understanding the potential impacts of proposed marine carbon dioxide removal activities. In addition, this project provides research experiences for undergraduates, educational activities for middle and high school girls, and a science-art collaboration that aims to communicate findings from this project to the public. 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

The goods and services provided by coastal oceanic ecosystems greatly benefit society, but their sustainability is increasingly threatened by coastal development, pollution, fishing, and changing climate. Long-term ecological studies of these important ecosystems are necessary for understanding the consequences of such threats and how to mitigate them. Focusing on key "foundation species" that create habitat and affect environmental conditions around them improves our understanding of the ecosystem as a whole. The Santa Barbara Coastal Long-Term Ecological Research program (SBC LTER) demonstrates the value of long-term studies for understanding foundation species through its focus on kelp forest ecosystems. The giant kelp Macrocystis pyrifera, the world’s largest seaweed species, creates extremely productive ocean forests that harbor a myriad of other species and are highly valued in coastal temperate regions worldwide. Giant kelp forests are dynamic, characterized by frequent disturbance from storms, grazing, and other natural and human-induced phenomena that remove kelp, often followed by rapid regeneration and recovery. This makes kelp forests ideal for investigating the effects of environmental change and human actions on fundamental ecological processes that require centuries to address in other ecosystems, including forests on land. Understanding the nature of such processes that apply to all ecosystems is a key element of SBC LTER research. Broader impacts of the project are enhanced by integrating the research with a diverse array of education and outreach programs that target K-12 education, teacher professional development, undergraduate and graduate student training, and stakeholder engagement. SBC LTER's research builds upon its prior results to advance a predictive understanding of how natural disturbance, climate variation, and human actions (i.e., fishing and coastal development) alter the ecological structure and function of kelp forest ecosystems, and identify the mechanisms that underlie these processes. Kelp forests are connected to one another and to the surrounding coastal ocean and adjacent intertidal beaches via the exchange of living and non-living materials. Thus, predicting the causes and consequences of kelp forest responses to environmental change requires integrated studies of a wide range of physical, chemical and biological processes occurring on the seafloor and in the water column within and outside of the kelp forest to fully capture the dynamics of material exchange. Integration of these studies is accomplished by research that is organized spatially in a dynamic setting of changing climate and oceanography from the scale of a local kelp forest community and the ecological interactions and ecosystem processes occurring within it to a much larger landscape of interacting kelp forests and adjacent waters and beaches. Synthesis of the project's findings across different levels of biological organization and different spatial and temporal scales is achieved through statistical, analytical and numerical models that combine SBC and other long-term ecological and environmental time-series data with relationships, mechanisms and processes obtained from shorter-term, but more intensive 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 2024 · 2024-10

Over the last several years, blockchain technology and Web3 applications and protocols have experienced explosive growth and generated significant monetary value. In 2024, the total market cap of cryptocurrencies has exceeded two trillion US dollars, and there are millions of applications (smart contracts) deployed on blockchains, providing an ecosystem of financial and other services. Unfortunately, attackers have taken notice of vulnerabilities in the Web3 ecosystem and found opportunities to steal money and defraud victims. This project will develop novel techniques and better systems to identify vulnerable applications and detect malicious attacks, with the goal of protecting the millions of users who interact with and invest in Web3 applications, as well as thwarting the opportunities of adversaries to use illicitly gained funds for nefarious purposes. This research effort will also train graduate and undergraduate students in the techniques and approaches necessary to secure the financial infrastructure and introduce new educational tools, such as blockchain-focused courses and capture-the-flag security competitions. This project introduces a novel approach, called Bedrock, to identify high-level, semantic vulnerabilities in Web3 applications through a multi-layer framework. This framework starts from the low-level bytecode of smart contracts, captures and reasons about the behavior of multiple interacting smart contracts, and takes into account their financial context. The Bedrock framework will introduce novel ideas at each of its abstraction layers. First, it introduces a multi-chain program analysis platform that directly operates on low-level smart contract bytecode. This platform allows for the re-use of analyses in different blockchain environments as well as the unified modeling of multi-chain applications, such as bridges. Second, the framework introduces novel analyses and techniques that identify higher-level, logic flaws in smart contracts. Third, Bedrock automatically extracts high-level models of complex DeFi (decentralized finance) applications. These models provide the context for the evaluation of the impact of the logic flaws identified by the security analysis. This will allow for the accurate and scalable detection of flaws in smart contracts and the financial protocols that they implement. 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

The vast amount of data generated by individuals in various aspects of life, from media to healthcare and transportation, has been instrumental in solving complex problems across different domains. For example, many machine learning algorithms rely heavily on data that is provided directly by users. Moreover, in many societal scale engineered systems, collection of user data can enable a more safe and efficient system operation. For instance, fine-grained power consumption data communicated through smart meters has enabled many technological advances in smart power systems. This NSF project explores how platforms can fairly acquire users’ data given their privacy requirements and their contributions to the performance of the distributed system, and how to optimally utilize such private data markets for safe and efficient operation of distributed learning and control systems. This project’s intellectual merit lies in its innovative contributions, including the development of a novel framework for privacy-aware and fair data acquisition that integrates concepts from machine learning, optimization, and game theory. The broader impact of the project includes enhancing societal trust in AI-based technology through enabling fair and private data acquisition. Further, the project engages undergraduate students in research and has outreach activities involving pre-college students. The project’s goal is to design fair data acquisition mechanisms alongside control and learning algorithms that incentivize strategic agents to contribute the appropriate share of private data, ensuring efficient and safe operation of distributed systems with critical constraints, such as those found in autonomous driving and power systems. We propose a mathematical setting where users exchange their data with a platform for payment or services, considering varying privacy requirements, and focus on creating fair incentives for distributed learning and inference applications within this framework. Building on this static analysis, we focus on dynamical systems which involve continuous data collection and feedback loops, introducing temporal correlations that complicate information leakage control and incentive design for data acquisition. We formalize these challenges as research questions, focusing on designing adaptive incentive methods for such dynamic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

To provide essential services, anchor institutions (which include public libraries, schools, and hospitals) increasingly rely on high quality broadband Internet connections that enable important services such as educational software, videoconferencing, and telehealth. However, little is currently known about whether the quality of such anchor institutional networks (AINs) meets their needs. This project will fill this gap by conducting rigorous measurements to identify locations, quality, and opportunities for improving AINs. Such information can help governmental and private efforts to improve the ability of anchor institutions to serve the technological needs of the general public. The intellectual merits of this project fall in three related categories on computer network availability, reliability, and performance. First, on network availability, the work will create an annotated map of AINs, including the providers that they use to connect to the Internet. Second, on network reliability, the project will collect evidence to assess how reliable AINs are, for example, how often they experience outages, using a mix of existing and novel methods in Internet measurement. Third, on network performance, this research will determine whether AINs meet the technology needs of users who rely on them, for instance, whether the connection speed at the library is sufficiently high to support videoconferencing for all patrons who need it. This category requires significant advances in network performance characterization, particularly on determining (and measuring) the adequate bandwidth needs for a varied mix of networked applications. This collaborative project, which brings together researchers from Northeastern University, University of California-Davis, and University of California-Santa Barbara, has the potential for substantial broader impacts beyond its scientific advances. AINs are often the last line of availability for many users from historically-marginalized communities, including school-age children in rural or tribal areas, who do not have reliable or adequate Internet service at home. Thus, adverse events affecting AINs (outages) or persistently inadequate connections (low performance) can lead to disproportionately negative impacts on these at-risk communities, including low-income neighborhoods in urban cores. By producing a comprehensive study that evaluates connectivity at anchor institutions, this project will facilitate broadband equity and access efforts from consumer advocates, Internet providers, and local, state, and federal governments. All code and non-sensitive datasets will be publicly released on this repository: https://github.com/anchor-institutions/anchor-institutional-networks. 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

Partial differential equation (PDE) is one of the most popular mathematical tools used in science and engineering. Representative applications of PDEs include (but are not limited to) aircraft design, semiconductor chip design, medical imaging, autonomous vehicles, and weather prediction. Traditional PDE solvers discretize the spatial and temporal domains into many grid points, resulting in huge memory and computing costs. In recent years, neural networks have been incorporated with physical knowledge to solve PDE problems. The resulting technique, physics-informed neural networks (PINN), has shown superior performance than traditional discretization-based PDE solvers in solving high-dimensional PDEs and PDE-constrained control problems. However, training a PINN can still be very time-consuming in many realistic engineering applications even on powerful graphic processing units. This has prevented the application of PINN from resource-constrained scenarios with strict requirements on the computing platforms' size, weight, and power. This motivates the research team to develop, for the first time, a real-time and real-size PINN training accelerator using photonic chips. The research results can be used to solve vast science and engineering problems with PDE descriptions in real-time and with ultra-low energy costs. The collaboration between the University of California at Santa Barbara (UCSB) and Hewlett Packard Labs will enable effective technology transfer and train next-generation workforces in semiconductor chip design and artificial intelligence via graduate education and industrial research internship. Despite the ultra-high speed of photonic computing, training a PINN with a realistic network size (with around 1000 neurons) on a photonic chip is very challenging due to the poor scalability of photonic chips and the hardware-unfriendly nature of backward propagation. This project will leverage the collaboration between UCSB and Hewlett Packard Labs to develop the first real-time and real-size end-to-end PINN training accelerator on a 2.5-dimensional photonic chip. The research team will create a highly compressed and completely backward propagation-free method for training large-size PINN, which only requires forward propagation in the training process. The training algorithms can be easily implemented on a photonic chip without using any photonic memory. A theoretical understanding of the training method will be developed to provide performance insurance. The unfunded industrial co-investigator from Hewlett Packard Labs will develop resonator-based wavelength-parallel photonic tensor cores and charge-trap flash memory to achieve a highly energy-efficient and scalable tensor-compressed inference accelerator with photonic in-memory computing. The co-package of electronic and photonic integrated circuits will be realized via 2.5-dimensional heterogeneous integration. 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 · 2024-09

Cellular immunotherapies like Chimeric Antigen Receptor T cells (CAR-T) extend the lives of cancer patients who are otherwise out of options. These therapies represent a leap forward in cancer treatment but suffer from severe limitations because 1) the tumor microenvironment excludes and exhausts T cells including CAR-T cells, 2) some tumor cells escape by downregulating the target antigen, 3) the therapy incites cytokine storms and autoimmune reactions, and 4) cellular therapies are currently complex, time-consuming, and exorbitantly expensive. Here we propose to overcome these limitations and thereby revolutionize, generalize, and democratize cellular immunotherapies. A promising new cellular immunotherapy approach is CAR-M (macrophage). Two Phase 1 clinical trials are in progress and it is clear that the approach is safe though there is substantial room for improvement in efficacy. While CAR-M has many advantages over CAR-T, it is clear that it would be of great value to enhance the capacity of CAR macrophages to attack and kill cancer cells while sparing normal tissue. Based on fundamental cell and developmental biology we have been doing in fruit flies, we discovered how to greatly increase the ability of human macrophages to attack and kill specific whole target cells of our choosing. Inspired by our discovery in fruit flies, we have been able to engineer mouse and human macrophages to avidly and specifically engulf and kill cancer cells. Here we propose to test this approach against the most prevalent solid tumors like breast, lung, and colon, and against hard-to-treat cancers like ovarian, pancreatic, and glioblastoma. Another huge limitation of cellular immunotherapies is their cost and complexity. We propose to overcome this problem by greatly simplifying the production and delivery of CAR-M. We propose to transform CAR-M into an affordable, off-the-shelf therapy. This will enable generalization of the approach to hard-to-treat diseases beyond cancer, such as multidrug resistant bacteria, viral and fungal infections, autoimmune diseases, and more.