University Of Southern California

NIH Research Projects · FY 2026 · 2026-02

Project Summary In the somatosensory system, the detection of external signals, such as mechanical, thermal, and chemical stimuli, is critical for survival. Cutaneous nerve endings sense these changes in the environment, conveying this information first to the spinal cord via specialized sensory afferents that can discriminate between innocuous and noxious stimuli, the latter by pain-sensing nociceptors. The sensations and the physiological effects of cold are unique among somatosensory modalities in that cold provides an innocuous analgesic sensation at mild temperatures but is also painfully noxious as temperatures decrease. The menthol receptor, TRPM8 is the principal cold sensor in mammalian sensory neurons. This and cells expressing the ion channel are required for the sensations of both innocuous cool and noxious cold, heightened cold sensitivity that results with injury or disease and, paradoxically, the ability of cooling to relieve chronic pain and itch. These findings suggest that TRPM8 can centrally differentiate and propagate the distinct percepts of pleasant and therapeutic cooling from painful and aggravating sensations of cold. Lastly, we have shown that TRPM8 is also critical for migraine-like pain in rodents, consistent with human genetic studies. But how does this lone channel and the cells expressing it mediate these diverse physiological effects? We found that the glial cell-line derived neurotrophic factor-like ligand artemin (ARTN) and its receptor GFRα3 are required for injury-induced, TRPM8-dependent cold pain as well as migraines, the first evidence of a molecule that directly sensitizes TRPM8 in vivo. While the cellular and molecular transduction mechanisms used by this signaling complex to induce sensitization have yet to be defined, these findings point to the cohort of TRPM8+ afferents that express GFRa3 as nociceptors. We propose a model whereby injury of any etiology leads to cold allodynia via peripheral release of ARTN that acts on GFRa3 receptors on TRPM8 cells to increase their sensitivity to cold, transmitting this centrally via distinct neurocircuits. A conserved pathway may also work in the meninges for migraine-like pain. We propose to test this first by determining the cellular basis for ARTN and GFRα3 mediated TRPM8 sensitization. Second, we have generated a novel mouse genetic strategy to target the GFRa3+ and GFRa3- populations of TRPM8 afferents, allowing the lab to differentially study these two cell types in vitro and determine their necessity for acute cold signaling, for pathological cold pain, for analgesic and anti-pruritic cooling, and migraine-like pain in vivo. Lastly, it is critical to unbiasedly identify TRPM8-tuned spinal cells to determine how the pleasant and painful aspects of cold are processed. We have adapted an innovative genetic approach to study TRPM8-tuned spinal neurons molecularly, functionally, and behaviorally, allowing us to determine if the activation of cephalic cutaneous TRPM8 can alleviate localized cutaneous allodynia associated with migraines. These studies will define the signal transduction pathways of cold and cold pain, providing not only insights into somatosensory signaling and the mechanisms that bring about pain associated with this modality, but also potential therapeutic interventions that use cold as a stimulus.

NIH Research Projects · FY 2026 · 2026-02

Project Summary The widespread prescription and illicit use of opioids over the past several decades has created a public health epidemic known as the "opioid crisis." Synthetic opioids acting as Mu Opioid receptor (µOR) agonists have demonstrated effectiveness as pain management therapeutics yet have high addiction and overdose potential in addition to fatal side effects which have driven this ongoing crisis. Towards mitigating this crisis, naloxone is the primary therapeutic used to reverse opioid overdose, yet its use can precipitate withdrawal symptoms. Kappa Opioid receptor (κOR) has emerged as a promising target within the opioid receptor family, as selective antagonists have shown to counter emotional states and behaviors associated with withdrawal in preclinical studies. However, the utilization of κOR-targeted therapeutics has been limited by gaps in our understanding of receptor signaling and inhibition, resulting in unintended effects and unsuccessful clinical trials. As such allosteric modulators have gained attention due to their potential to provide a more fine-tuned way to modulate κOR signaling. As allosteric modulators occupy binding pockets that are distinct from the highly conserved orthosteric site, negative allosteric modulators (NAMs) can be drastically more selective than conventional antagonists. Furthermore, NAMs, by definition, enable residual endogenous signaling, which significantly broadens their therapeutic window. Until recently, no κOR-selective NAMs have been characterized. This proposal seeks to address these gaps by investigating the molecular mechanisms of a recently discovered NAM at κOR. By elucidating the structural and functional basis of κOR modulation via negative allosteric modulation, this research will contribute to the framework towards enhancing the clinical utility of κOR antagonists. To explore this fundamental aspect of κOR inhibition, we will delve into the molecular aspects of negative allosteric modulators (NAMs) in the context of our recent findings. Our first ever cryoEM structures of a novel conformational state of an inhibitor bound receptor κOR: G protein complex with inverse agonists JDTic, GB18 and norBNI highlight that inhibition is driven by highly complex mechanisms including the allosteric modulation of receptor-G protein affinities or even G protein nucleotide exchange which can result from stabilizing different states along the receptor activation pathway. Similar to orthosteric antagonists, NAMs may also stabilize distinct receptor states along the receptor activation pathway, promoting the inhibitory effects of orthosteric ligands. Taken together, this highlights the importance of investigating novel conformational states to provide a more complete picture of opioid receptor signaling and pharmacology. Using cryo-electron microscopy (cryoEM), in vitro, and ex vivo techniques to investigate inhibition via a κOR-selective NAM we will test the hypothesis that signaling inhibition arises from structural alterations that modulate G-protein dynamics. Through an extensive 1) Pharmacological Characterization of a κOR-selective NAM and 2) Structural Analysis of NAM-Induced Conformational States and Their Impact on Downstream Signaling we will link NAM-induced conformational changes at the atomic level to distinct pharmacological profiles. The results of this study will support the development of more effective therapeutic strategies at κOR and across the opioid receptor family.

NIH Research Projects · FY 2026 · 2026-02

Identification of immune activation DNA inside cells Cyclic GMP-AMP synthase (cGAS) is a key sensor of cytosolic double-stranded DNA (dsDNA) that activates the STimulator of INterferon Genes (STING) pathway, triggering immune responses against infections and cellular damage. While cGAS plays a protective role in pathogen defense and tumor suppression, its aberrant activation by self-DNA is implicated in autoimmune diseases (e.g., Aicardi- Goutières Syndrome, systemic lupus erythematosus) and chronic inflammation associated with aging. Despite extensive studies on cGAS-STING signaling, the physiological dsDNA ligands that activate cGAS under various cellular conditions remain poorly characterized due to the transient and weak nature of cGAS-DNA interactions, which conventional crosslinking methods fail to capture. To address this challenge, we propose to develop and apply a novel photochemical crosslinking approach to covalently capture cGAS-DNA complexes in living cells. Aim 1 focuses on adapting and optimizing this crosslinking technology for cGAS-DNA interactions, ensuring high efficiency and specificity. Aim 2 applies this method to establish a first-in-class cGAS BFPX ChIP-seq protocol, enabling the genome-wide identification of endogenous DNA ligands that activate cGAS in Trex1 KO MEF cells—a cellular model of Aicardi- Goutières Syndrome. This innovative approach will provide a powerful tool for studying cGAS-STING pathway activation across diverse physiological and pathological contexts. By uncovering the sources and mechanisms of immune-activating DNA, our work will pave the way for novel diagnostic and therapeutic strategies targeting cGAS in autoimmune, inflammatory, and age-related diseases.

NSF Awards · FY 2026 · 2026-02

Wireless communications, which has become an essential tool both for American consumers and the US industry, requires constantly increasing data rates to accommodate growing consumer demands. The US frequency regulator (Federal Communications Commission FCC) is currently making available new spectrum in the frequency range 7-24 GHz, also known as ‘’upper mid-band”, to satisfy this demand. In order to efficiently use this spectrum, it is necessary to deploy adaptive antenna arrays at both base stations and user locations; such adaptive arrays can direct the transmissions into specific directions, thus reducing interference to other users, and extending the range over which wireless links can be sustained. The use of antenna arrays can reduce transmitted power as well. While adaptive antenna arrays have been widely used in the past, modifying them to communicate over the very wide bandwidth available in the upper mid-band presents a huge challenge. Conventional phased-array technology needlessly divides the upper mid-band into three or four sub-bands, each served by a dedicated narrowband antenna array. This project aims to develop a single wideband antenna array to communicate over the entire upper mid-band, in turn requiring a complete rethinking of both antenna structures and antenna adaptation algorithms, in addition to the development of new mathematical tools. A new generation of large ultra-wide-band (UWB) antenna arrays would offer powerful sensing capabilities on top of their communication functions. This research could materially improve the global competitiveness of the United States in wireless technologies. The standard phase-shift techniques that underpin antenna beamforming for narrowband operating conditions is not effective for ultra-wide-bandwidth (UWB) operation. To overcome this limitation, this project will develop “true time delay” (TTD) beamforming methods necessary for deployment of UWB adaptive antenna arrays. The project exploits the fact that TTD beamforming is mathematically equivalent to the Radon transform, which has been extensively applied to computer tomography as well as to wide-band geophysical signal processing. The traditional space/frequency characterization of narrowband beamforming is not valid in the extremely large bandwidth regime of UWB communications. In contrast, the Radon transform accurately describes the mathematics of beamforming in the UWB space/time domain. Aperiodic arrays, which have been explored for both communication and sensing, have received little attention in the context of UWB operation, but are expected to have significant benefits for this operating regime; their optimization will also be analyzed via the Radon transform. To address the problem of UWB impedance matching, the project investigates the radical idea of not doing impedance matching of the antenna to the channel: rather it is proposed to drive the antenna with high impedance current sources during the tramsmit stage and to measure open circuit voltages during the receive stage. The research combines the development of communications theory with the development of experimental prototypes to demonstrate this novel approach to UWB space-time communication systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The objective of this NSF Rapid Response Research (RAPID) grant is to document and quantify the impacts of an extraordinary landslide-generated mega-tsunami that occurred on August 10, 2025, when an estimated ~65 million m³ of a mountainside collapsed into Tracy Arm, a fjord in southeast Alaska. Preliminary satellite and Digital Elevation Model (DEM) analyses suggest near-field runup of ~470–500 m, potentially the second-largest tsunami ever observed, creating an urgent need for ground-truth measurements before winter snow, freeze–thaw processes, and vegetation obscure or destroy fragile high-water marks and other ephemeral evidence. A small team of 3–4 experienced tsunami researchers will conduct a rapid-response field survey in early-to-mid October 2025 (prior to winter closures) to capture perishable data in the near-source region. This project will produce the first high-precision, field-validated dataset for a mega-tsunami of this magnitude and directly address key scientific gaps in the near-field behavior of extreme, short-period landslide tsunamis, where wave heights, velocities, and forces are most intense and existing models are least validated. Field activities will integrate geotechnical, hydraulic, and remote sensing approaches, including: (1) centimeter-level surveys of runup elevations using Real-Time Kinematic (RTK) GPS and drone-derived DEMs to refine preliminary runup estimates; (2) documentation of indicators of extreme turbulence and inertia (e.g., intense scour, embedded debris, and erosional signatures) to constrain near-field hydrodynamics; and (3) mapping of boulder transport and impact signatures as proxies for the event’s high-energy flow regime. All survey points, imagery, and derived products will be archived with open access via the NHERI DesignSafe cyberinfrastructure, enabling broad community use for model validation, comparative event analysis, and education. Results will be communicated to agencies including the USGS and the National Tsunami Hazard Mitigation Program to support improved tsunami modeling and hazard planning, including implications for vessels and operations in Alaska’s glacial fjords. 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 · 2026-01

Project Summary/Abstract Neurodegeneration of the hippocampus is believed to be associated with cognitive decline in patients with Alzheimer’s disease (AD). Cognition is considered to be an emergent brain function resulting from neural network level computation rather than individual cellular processing, but exactly how AD cognitive impairment results from progressive disconnection of hippocampal networks due to cellular level neurodegeneration is unclear. Which neurons and how many neurons must be lost before cognitive symptoms become apparent? Multiscale computational models that can incorporate these brain features are critical but require spatially precise cell type-specific datasets across multiple progressive disease timepoints. Our previous work creating the Hippocampus Gene Expression Atlas (HGEA) integrated gene expression and connectivity data to define neuronal cell types in 20 spatio-molecular domains across the entire mouse hippocampus, delineated the hippocampal connectome wiring diagram, and revealed the hippocampus as a multiscale hierarchical network that contributes to brain-wide networks regulating cognition, social behaviors, and neuroendocrine function. Building on the HGEA dataset and our team’s unique expertise in the hippocampus and multiscale computational modeling, we propose to develop HGEA-NET, a new multiscale computational model of the hippocampus containing the HGEA-defined neuronal cell types and their cell type specific gene expression and connectivity. We hypothesize that the progressive degeneration of specific AD susceptible neuronal cell types leads to hippocampal network dysfunction associated with cognitive decline across longer disease timepoints. In this project, we will use Multiplexed Error Robust Fluorescent In Situ Hybridization (MERFISH) spatial transcriptomics and viral connectomics approaches to investigate neurodegenerative changes to hippocampal cell type gene expression and circuit connectivity in 5xFAD mice AD models and human AD post-mortem tissue samples with varying degrees of cognitive impairment. By incorporating these data into the HGEA-NET, we will determine which hippocampal neurons and their connections appear susceptible and simulate how the disconnection of these cell types in the multiscale model leads to network dysfunction related to cognitive impairment. Overall, the successful development of HGEA-NET will provide major impact as a new translational drug development virtual testbed for the treatment of cognitive dementia in AD.

NSF Awards · FY 2025 · 2025-11

Emissions from diesel engines negatively impact human health. Small diesel particulates (< 2.5 µm) have been linked to premature cardiovascular and respiratory deaths in metropolitan areas, as well as lung cancer. This project will investigate a new approach for electrostatic precipitation (ESP) technologies to reduce the emission of diesel particulates. The team will explore nanosecond high-voltage pulses to enhance ESP, also known as Plasma-enhanced Electrostatic Precipitation (PE-ESP). If successful, the new electrostatic precipitators will have a much smaller footprint. The new technology will open up new applications, such as in ships and trucks. By enabling cleaner transportation and shipping, the proposed work directly addresses urgent local and national air quality concerns, supports public health, and advances strategic efforts to meet stricter emissions standards and policy targets. This project will explore the application of nanosecond high-voltage pulse discharges as a novel approach in the context of electrostatic precipitation. The team’s preliminary results show that these nanosecond high-voltage pulses provide significant enhancement over conventional electrostatic precipitators (ESPs). However, the fundamental mechanism(s) underlying this enhancement are poorly understood. The fundamental understanding gained by this study will provide useful information about how to overcome current limitations and further improve PE-ESP. The studies include 1) investigating a reverse polarity two-terminal PE-ESP; 2) performing time-domain ESP simulations; and 3) evaluating a novel three-terminal PE-ESP configuration. The work is interdisciplinary, involving high voltage electronics, electrostatics, and fluid-dynamics, as well as combustion and aerosol science. The project will broaden its impact by expanding a workshop for high school science teachers, targeting schools in central Los Angeles and near the port area, to raise awareness about air pollution and engage students in scientific research. Undergraduate students will gain hands-on research experience to build confidence and interest in STEM careers. Additionally, a new course module on plasma-driven pollution remediation will integrate research findings directly into interdisciplinary curriculum development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Emergency Medical Services (EMS) are vital to public health and safety, providing life-saving care before patients reach a hospital. Yet EMS systems operate under intense pressure, making rapid decisions about ambulance deployment, dispatch, and transport in the face of constant uncertainty—from unpredictable emergency call volumes to traffic conditions. These decisions are often made using simple, rule-based approaches that can fall short under real-world complexity. This Smart and Connected Communities (SCC) project seeks to transform EMS operations using advanced data science, looking to create tools that allow EMS agencies to make more informed and adaptive decisions. By developing a digital shadow—a data-driven, virtual replica of EMS systems—this research seeks to enable agencies to test new policies in a safe virtual setting before deploying them in the field. The project builds on a unique partnership with the New York City Fire Department, ensuring that the outcomes directly inform real-world practice. Technically, the project intends to advance research in cyber-physical systems under uncertainty by developing a probabilistic digital shadow for EMS operations. This includes developing new machine learning models for ambulance travel time estimation and emergency call forecasting that incorporate granular uncertainty quantification and can be updated in real-time using multimodal data streams. The team will also look to develop novel methods for rare-event simulation and scalable, risk-averse optimization. These tools will be used to design and evaluate new EMS deployment and dispatch policies using simulation–optimization frameworks, with special attention to balancing computational efficiency and decision quality. By localizing simulation models and optimizing resource allocation strategies, the research looks to generate operationally meaningful policies tailored to urban environments like New York City. Continuous collaboration with FDNY will guide the research, ensuring that developed methods are actionable, scalable, and grounded in EMS system realities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Artificial Intelligence (AI) has enabled a plethora of applications today, ranging from the most recent chatbots that give you a human-like question/answer experience to autonomous driving cars. But, all these massive feats with AI incur huge costs in terms of energy, memory, and power consumption. In the past decade, Spiking Neural Networks (SNNs) have emerged as a low-power alternative to AI. SNN’s main attraction lies in the fact that they offer low-power architectural implementations, especially for arithmetic operations. Furthermore, unlike traditional neural networks, SNNs process information over time and the temporal dimension, if leveraged suitably, can help enable the next generation of AI applications at lower cost with better performance and robustness. However, training SNNs suitably for realistic tasks has been a long-standing challenge. This project innovates on fundamental optimization strategies, using the temporal features in SNNs to yield new architectures with diverse connectivity and sparsity that yield significant energy-efficiency benefits for distributed low-power edge computing applications. Furthermore, this research will support the interdisciplinary development of Ph.D. and undergraduate students and provides a unique education infrastructure to train the next generation of electrical and computer engineering researchers and practitioners. Today, deploying large-scale spiking neural networks (SNNs) for realistic computer vision and related tasks is a non-trivial challenge. This project targets two directions to build large-scale SNNs: 1) We innovate on Neural Architecture Search (NAS) to yield new SNN architectures with temporal feedback connections (that is in stark contrast to conventional feedforward deep learning networks). 2) We use the SNN-specific NAS optimization to perform distributed learning on multiple agents for vision tasks and demonstrate the benefits of using SNNs for low-power edge computing. Particularly, we develop a zero-shot approach that does not require training to search for the optimal network architecture while leveraging temporal and spatial sparsity with pruning and related techniques. This strategy is expected to shorten the design cycle of SNN architecture search by one to two orders of magnitude over existing work. The proposed NAS search will be integrated into a federated learning framework where multiple devices with different resources and data heterogeneity are learning together. Essentially, this project’s framework for discovering new SNN architectures can yield powerful and radical solutions for learning on multiple devices with extreme resource limitations to enable numerous distributed AI applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Boolean satisfiability (SAT) is a core problem in computing with broad, high-impact applications. A wide range of critical problems in industry and defense, e.g., in hardware and software design and verification, artificial intelligence, robotics, and drug discovery, use SAT solvers and often take weeks to complete on modern large-scale computing systems. This project will develop new types of accelerator chips custom-designed for SAT to reduce the time and energy required to solve all such important problems by more than two orders of magnitude compared to the best-known existing approaches. Completely new ways to combine logic circuits and memories will be developed, along with methods and tools to create these chips. The development of this hardware will dramatically benefit organizations across engineering, artificial intelligence, science, business, logistics, and defense. This project will also advance the art and science of custom computing, which will continue to increase in importance in the foreseeable future. Students will be trained in this new art and science. The models, methods, and tools developed will be shared with researchers as well as industry and defense experts to foster a vibrant community. The project will develop an algorithm-to-transistors co-optimization approach to accelerator design for SAT and an extensive set of combinatorial problems, demonstrating significantly higher efficiency than existing solutions. It will also provide new computer-aided design (CAD) tools for the realization of powerful SAT accelerators by mapping SAT algorithms and heuristics to optimized architectures, including combinations of memories, content-addressable memories, near-memory logic, and custom interconnects that enable maximal parallelization. The advantages of the new designs will be demonstrated via chip fabrication, silicon measurements, and the development of chiplet-based architectures enhancing the economics of accelerators for SAT and other problems. The technological advancements pursued by this project, and especially the new methods and tools for the design of hardware accelerators, will contribute to the state of the art in custom computing, which will become increasingly more important in the post-Moore era. The resulting designs will dramatically benefit organizations across engineering, artificial intelligence, science, business, logistics, and defense. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Subduction zones are the interface between Earth’s interior (crust and mantle) and exterior (atmosphere and oceans), where carbon and other volatile elements are actively moved between terrestrial reservoirs by plate tectonics. The efficiency of volatile transfer controls the chemical state of Earth’s interior and exterior, including the atmospheric composition. In turn, the distribution of carbon and other volatiles in Earth’s surface reservoirs has enabled conditions favorable for life on Earth. Despite the importance of carbon, its fluxes, sources, and sinks remain under-constrained in subduction-related fluids, with an almost complete absence of studies linking it to underground biological processes. The deep carbon mass balance has previously been estimated by comparing subducting slab inputs to arc volcano degassing outputs, with the difference representing the amount of carbon that is transported into Earth’s deep mantle. However, slab and mantle-derived carbon can also be sequestered in the form of hydrothermal minerals (e.g., calcite, aragonite) and by microbial uptake in the crust of the overriding plate, effectively masking an unknown portion of the carbon output from the subduction zone. The lack of constraints on these key processes, however, limits the understanding of the overall efficiency of the deep carbon cycle. This project involves substantive collaboration with colleagues in Chile, including an international workshop, and the work of several U.S. students will be supported. This project will characterize the extent of mineralogical and biological carbon sequestration along the geologically well-studied Andean Convergent Margin (ACM). The PIs hypothesize that calcite precipitation sequesters significant amounts of carbon in the ACM, particularly where the crust is thickest in the Central Volcanic Zone (CVZ). Furthermore, this project will test if subsurface microbial communities sequester carbon into biomass through chemosynthesis, forming an additional sink for carbon. Finally, the PIs will systematically assess how geochemical transformations and microbial communities vary as a function of subduction parameters (i.e., carbon input from the slab, upper plate thickness and lithology, as well as slab dip angle), which can then be used to compare results to a range of global convergent margins. To accomplish these goals, the PIs will conduct a field expedition to the CVZ in 2022 to collect fluid and gas samples from ~15 natural springs, seeps and fumaroles in the forearc and arc. They will measure helium and carbon isotopes, microbial chemosynthesis rates, and contributions of chemosynthesis to total microbial biomass in all samples. CVZ results will be interpreted alongside published and unpublished datasets from the ACM and other convergent margins globally (i.e., Central America). If subsurface geochemical and biological carbon sinks are found to be substantial in the ACM, it will add to growing evidence that carbon sequestration is widespread in the overlying crust of convergent margins. Such a finding could fundamentally alter the canonical understanding of deep carbon cycling between Earth’s surface and mantle. This project involves substantive collaboration with colleagues in Chile, including an international workshop, and the work of several U.S. students will be supported. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Extended reality (XR) technologies have shown significant promise in increasing user engagement and skill acquisition in a variety of domains. The goal of this project is to accelerate adoption of innovative XR applications for rehabilitation, for example, to enhance user experiences in treatments to improve motor function for diseases with motor disabilities. This project develops immersive XR exercise environments that enable users to move and interact in 3D space with each other and with virtual elements. A key goal is to make the experience of rehabilitation more enjoyable and effective by incorporating social interactions between remote users that can boost engagement and skill acquisition. The technology also allows clinicians to guide and interact with their patients remotely. To create a virtual environment that users experience as fast and seamless, this project develops novel approaches to the underlying networking infrastructure needed to run the application. Collaboration with industry partners will support technology adoption for XR-enabled rehabilitation technology and for the XR industry. Realizing multi-user, geo-distributed XR technology is challenging due to stringent motion-to-photon latency requirements for good user experience. Current wide-area Internet routing and cellular wireless management are one-size-fits-all across applications, hurting latency. A key insight of this project is that not all types of XR traffic require uniformly low latency; instead, the project enables prioritization of delivery of important XR traffic while intelligently managing lower-priority XR traffic. The project’s core technical contributions are an adaptive XR application with new delivery mechanisms, leveraging Internet path selection on the testbed, and programmable wireless resources using Open Radio Access Networks (Open RAN) and xApps. Performance of the XR environment will be assessed via user experience and key network performance indicators. The expected outcome is achieving key performance targets currently deemed impossible on today’s Internet by demonstrations of network-supported, multi-user XR technology for rehabilitation at different sites. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to use innovative materials called “2D materials” to enhance the capabilities of modern integrated circuits. These materials have unique electronic properties that make them very promising for compute, storage, and sensing technologies. However, integrating them with existing silicon-based technology has been a challenge due to temperature restrictions. Luckily, a new group of materials called “indium-based chalcogenides” offers a solution, as they can be synthesized at low temperatures compatible with current technology. The project team plans to create a range of devices using these materials to accelerate the performance of energy-efficient spiking neural networks (SNNs). These brain-inspired microchips will revolutionize how audio, visual, tactile, and olfactory information is processed, making devices smarter and more responsive. Moreover, these microchips could be used in autonomous vehicles, drones, and robots, helping them navigate and avoid obstacles. The project also focuses on training the next generation of scientists and engineers and promoting diversity and inclusivity in the field. This project aims to address the challenge of integrating novel 2D materials with the state-of-the-art silicon-based complementary metal oxide semiconductor (CMOS) technology at the back end of line (BEOL). The key innovation lies in leveraging indium-based chalcogenides, such as InSe and In2Se3, which can be synthesized at low temperatures, making them compatible with BEOL processes. The team plans to synthesize and characterize these materials to fabricate an array of sensing, encoding, computing, and memory devices for hardware acceleration of energy-efficient spiking neural networks (SNNs). The project will involve a cross-layer co-optimization approach that encompasses material discovery, synthesis and deposition techniques, process flow development, and device-circuit-architecture co-design. The goal is to develop brain-inspired SNN microchips through 2D/CMOS heterogeneous and monolithic integration, which will lead to substantial reductions in energy consumption and pave the way for sustainable computing paradigms. The broader impact of this work extends to applications on the Internet of Things (IoT) domain, where the brain-mimetic SNN microchips will enable advanced audio, visual, tactile, and olfactory information processing. Additionally, the project emphasizes education and training, promoting diversity and inclusiveness in the workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Abstract Title: Non-resonant Electromagnetic Vibration Energy Harvester with Magnetic Gear for Wearable Technology Abstract: The proposed research is to efficiently generate power from vibration energy associated with humans’ daily activities, such as walking. As the internet of things (IoT) is boldly predicted to connect trillion sensors, and now that smartphones and wearable devices (for fitness or health tracking) are getting ubiquitous, two key issues are how to power the devices and how to keep the devices working constantly and continuously without battery recharging or replacement. Battery recharging or replacement means down-time and human intervention. One approach to avoid frequent battery replacement or recharging is to use a battery with large energy capacity at the cost of large weight and volume, which makes wearable devices heavy and bulky. Indeed, current wearables are critically limited by battery capacity. Human motions offer ubiquitous vibration energy, which the proposed research will tap into to generate electrical power without loading or restricting the person, so that the power requirement of wearables may be substantially aided by a small and light vibration energy harvester (VEH). The proposed research will pave the foundational technology for generating power from human motion without loading the human who carries the power-generating device. The successful outcome of the research will ultimately mean a power-generating device smaller and lighter than 1 cc and 1 gram, respectively, that can generate up to 1 mW from a low-frequency and low-acceleration human motion, for wearable technology in order to replace or supplement battery. Various innovative approaches will be explored to efficiently generate power from vibration energy associated with human motion without loading or affecting the wearer of the power generator (with a total mass and volume of less than 4 gram and 2 cc, respectively. The proposed electromagnetic non-resonant vibrational energy harvester (VEH) incorporates multiple innovative ideas and approaches in order to address the following two key fundamental challenges in generating power from human motion: (1) extremely low vibrational frequency (sub-Hz – several Hz) and (2) inherently low level of vibration energy associated with the low frequency. The first challenge rules out vibrational energy harvesting based on a resonant mechanical spring, and thus, a non-resonant VEH with ferrofluid-based suspension and a non-mechanical-spring VEH with diamagnetic suspension are proposed for frictionless relative movement between an internal magnet array (suspended by a liquid bearing or through diamagnetic repulsion) and a coil array (mounted inside a frame over which an external magnetic array is arranged for linear magnetic gear). For the second challenge, a linear magnetic gear is proposed to increase the relative velocity between the internal magnet array and the frame by a factor of the gear ratio, which can be more than 10. In addition, the proposed research is to explore various ideas and approaches to make the proposed VEH be compelling for wearable technology through automatically winding micron-scale coils into a coil array along with studies on the surface treatment or structuring for reducing any contact friction for ferrofluidic bearing or internal magnet array, impact of the frame height on the gear effect, and ways to reduce the sidewall friction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

In today's rapidly advancing world of Artificial Intelligence (AI), energy efficiency has emerged as a crucial factor to facilitate the ubiquitous development of intelligent systems. The efficient deployment of AI holds the key to overcoming limitations posed by power-constrained devices and contributes to sustainable technological progress. Neuromorphic computing offers a brain-inspired paradigm of AI, called Spiking Neural Networks (SNNs), that represents a promising step forward in sustainable AI development. Inspired by the brain's neural architecture, SNNs process information in sparse, asynchronous, and event-driven patterns, resulting in reduced power consumption. This project aims to integrate SNNs with modern integrated circuits propelling energy efficiency across various AI domains, such as object detection, autonomous driving and image classification. The project team aims to devise novel algorithms and hardware design with prototype chips to accelerate the performance of SNNs in low-power and memory-efficient systems. These spiking neural chips will enable the practical and immediate application of neuromorphic systems in areas like drones, autonomous robots, portable medical devices, and wearable smart assistants. Furthermore, the project embraces an algorithm-to-system approach, providing opportunities for high school, undergraduate, and graduate students to explore research in the field of neuromorphic computing. An essential focus of this project also lies in training the next generation of scientists and engineers, fostering diversity, and promoting inclusivity within the AI and semiconductor fields. This project tackles the crucial task of enabling deep learning and AI algorithms on edge computing devices that have strict memory and power constraints. The key innovation lies in leveraging a brain-inspired spiking neural network (SNN) approach for edge computing. The team addresses the memory overhead issue of spiking neurons and takes a foundational approach, optimizing algorithms and hardware design for SNN deployment on edge devices. The project proposes algorithmic solutions, including novel architectures with shared computations and compression strategies, such as quantization and early exit. These optimizations aim to enhance the efficiency of SNNs on resource-constrained edge devices. On the hardware front, the project plans to demonstrate these ideas through prototype chip tapeouts with SNN-specific dataflow, event-addressable computations, and configurable support for proposed algorithm features. The goal is to develop a comprehensive understanding of the power, performance, and accuracy tradeoffs of SNNs for edge computing applications that will pave the way for sustainable AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Microbes that live deep in ocean sediments help drive Earth's elemental cycles, influencing key aspects of habitability on the planet, such as the amount of oxygen in the atmosphere. Microbes in the deep subsurface have a remarkable lifestyle. They turn over their biomass on timescales of decades, centuries, or even millennia, while microbes in most other environments do so every few hours or days. The driving hypothesis for this project is the unusually high stability of the deep subsurface microbes' enzymes is key to these microbes' survival. The scientific results of this project will help understand the function of a vast and mysterious ecosystem at the bottom of the ocean and may lead to new discoveries about enzyme structure and function that could have valuable applications in the food or pharmaceutical industries. The project supports the training of undergraduate and graduate students in research and teaching. In addition, K12 students participating in a summer school program called East Tennessee Freedom Schools help generate data and design experiments, giving them early input into the scientific process. The educational program allows a set of young scientists to gain experience with authentic scientific research. The central hypothesis that unusually stable enzymes help subsurface microbes to persist over long timescales is tested via a field program to collect subsurface sediments in the Gulf of Alaska, experimental manipulations of those sediments, laboratory experiments in simplified model systems, and bioinformatic analyses. Metagenomic sequencing of surface and subsurface sediments, followed by construction of metagenome-assembled genomes and computational analysis of enzyme stability, are employed to yield predictions on the stability of enzymes in subsurface sediments and the degree to which enzyme stability affects community assembly. Experimental manipulations of subsurface sediments are then used to test those predictions. The research team is also expressing, purifying, and experimentally determining the structure of a selected set of subsurface enzymes to allow independent testing of the bioinformatic predictions and simplified laboratory experiments. Together, these experiments will reveal the role of enzyme stability in driving microbial community assembly in subsurface sediments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Police traffic stops are common, complex interactions that can be a tool to improve public safety or escalate to violence. Effective communication in these situations is crucial to ensure officer and civilian safety, enforce the law, and build public trust. This project develops artificial intelligence (AI) tools that enable researchers to analyze footage from officers’ body-worn cameras, learn about officer-driver communication and refine best practices for traffic stop outcomes. To achieve this, the project draws on collaborative research capacity developed between academic researchers, the Los Angeles Police Department, and over a dozen community organizations. The research team’s multidisciplinary, community-engaged approach ensures that the AI tools being developed reflect a wide range of viewpoints and address the concerns of these different stakeholders. These AI tools are trained on assessments created by individuals from varied backgrounds, including retired police officers and Angelenos with a mix of past positive and negative experiences interacting with the police. After development, these tools can be used by police departments and local governments across the country to lower costs and enhance transparency, accountability and learning. This project advances computer, social, and engineering science by developing video language models that incorporate multiple stakeholder perspectives and building infrastructure to support collaborative development of AI tools for public safety. The project moves beyond text-only analysis by developing novel video language models for body-worn video footage and using them to generate accurate, explainable summaries. These models achieve a high level of performance across all stakeholder groups by directly incorporating varied stakeholder viewpoints via personalized reinforcement learning from human feedback and novel multi-task learning. The project improves researcher data access, fosters collaborative annotation and shared-task evaluation, and advances AI for public safety by building a secure, anonymized research corpus of publicly released body-worn camera footage and hosting a community platform for video annotation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The Southern California Symplectothon is an annual intensive learning multiday retreat for Southern California geometers which will take place in fall 2025 and fall 2026, with the upcoming workshop slated to take place at the University of Southern California Wrigley Marine Science Center in Avalon, CA. Participants will meet over a long weekend to learn about and disseminate important new developments in geometry and topology, with a special focus on symplectic geometry and adjacent areas. The Symplectothon will also make a concentrated effort to build the local regional network, by forging and strengthening in-person bonds and potential collaborations. Each Symplectothon workshop will focus on a specific emerging topic, and participants will typically consist of 20-30 graduate students, postdocs, junior faculty, and senior faculty from an array of institutions in the greater Southern California region. There will be one or two in-person invited experts and a similar number of virtual invited experts. Participants will stay together in a single dedicated location, and talks will be given by the participants, with plenary lectures by the invited experts. Each Symplectothon will also include several guided discussion and Q+A sessions, as well as a forward-looking talk on future directions by one of the invited experts. Outside of the intensive scientific programming schedule, participants will have ample time for open discussions, while building personal and professional ties through social activities such as collective cooking, hikes, and other outdoor activities. The chosen topics for Symplectothon 2025 include Lagrangian fillings and cluster algebras. Participants will learn about Lagrangian fillings of Legendrian knots, cluster algebras, and microlocal sheaves, culminating in recent deep connections between these, particularly the work of Casals–Gao which establishes the existence of Legendrian knots with infinitely many distinct Lagrangian fillings and separately shows that every cluster seed in the augmentation variety arises from a Lagrangian filling. This is an exciting and interdisciplinary area under active development, and Symplectothon 2025 will be designed to bring Southern California geometers up to the research forefront. It will create an opportunity for researchers from different scientific backgrounds (i.e. symplectic geometry and cluster algebras) to convene and learn from each other. The participants will also learn about and discuss many other potential applications, such as the recent construction of cluster structures on braid varieties using ideas from symplectic topology, as well as many possible future connections between symplectic topology and cluster algebra. The tentatively chosen topic for Symplectothon 2026 is "tropical curve counts beyond toric geometry". A dedicated Symplectothon website with application information, syllabi, and typed lecture notes will be maintained at https://kylersiegel.xyz/symplectothon.html. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Organizations across government, healthcare, finance, and research need to share and analyze data to solve important problems, but current methods for protecting personal privacy while sharing data are inadequate and difficult to evaluate. Researchers and practitioners struggle to determine which privacy protection methods work best for different situations, how much privacy they actually provide, and what trade-offs exist between privacy protection and data usefulness. This creates barriers to safe data sharing that could otherwise enable medical breakthroughs, improve government services, and advance scientific discovery. This project addresses this problem by building and operating a comprehensive testing facility that allows researchers and practitioners to evaluate, compare, and improve privacy protection technologies for data sharing and analysis. This work serves the national interest by strengthening data privacy protections across critical sectors, enabling secure collaboration for national security and public health initiatives, supporting American competitiveness in privacy-preserving artificial intelligence technologies, and accelerating the development of trustworthy data sharing systems that protect individual rights while advancing scientific progress. This project builds and operates the Testbed for Privacy-Preserving Technologies for Data Sharing and Analysis, a comprehensive evaluation infrastructure to support assessment, comparative analysis, vulnerability analysis, privacy risk assessments, privacy-utility trade-off analysis of privacy-preserving data sharing and analysis technologies and their applications. The project extends the existing mid-scale research infrastructure for security and privacy research with diverse evaluation scenarios, specialized software tools and user interfaces, and focused community building for privacy-preserving data analysis research. The research activities include developing a rich, modular, extensible and composable evaluation scenario framework with sample technologies and applications that allow researchers to reuse, combine, and extend evaluation workflows to explore specific research questions. The testbed will provide specialized hardware systems and software tools, including virtual and bare-metal machines with different trusted compute technologies, servers with graphics processing units, and resource-constrained embedded processors and Internet of Things devices, all connected by user-specified emulated networks. The project will grow the research community through workshops, tutorials, and meetings at community events, as well as through support for research artifact storage and reuse to promote sharing, collaboration, and reproducible research. The testbed will improve privacy technologies and applications, accelerate research maturation and transition to practice, support community building, and enhance workforce education in privacy-preserving data analysis methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Augmented Reality (AR) applications place virtual contents, such as markers, avatars, images or videos, in users’ views of their surroundings. Users of outdoor AR applications can use these while walking, riding a bike, or driving in a vehicle to, for example, navigate dense environments safely. Today’s AR frameworks lack comprehensive support for developing outdoor AR applications. This project will produce a suite of techniques that will enable advanced AR applications, especially those that permit multiple users to interact with virtual objects in the environment, and deliver continuously updated views in near real-time of moving objects in the environment. Every AR application involves three different entities: the environment, the user, and the virtual objects introduced by the AR application. The collaborative project, which brings together researchers from the University of Southern California and the University of Michigan, will systematically develop techniques to model the environment, determine human pose, and model virtual object interaction with the physical environment. It will explicitly focus on fast algorithms, resource-efficient realizations on mobile devices, and employ a judicious combination of latency hiding, offload, pre-computation and pre-fetch techniques to simplify the development of usable outdoor AR applications with rich functionality. Outdoor AR applications can potentially improve public safety, enhance education, promote public health outcomes, provide entertainment, and support business activity. In addition, this project will involve undergraduates in community-building, by developing outdoor AR applications to facilitate the work of local non-profits, and expose these undergraduates to research. Its collaboration with industry will result in the transfer of AR technology to improve vehicular safety. The project's products, including papers, data and software artifacts are available at https://nsl.usc.edu/projects/pervasive-outdoor-ar. This website shall be available for at least three years after the conclusion of the grant, but products such as published papers and software may be available in other repositories for longer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Many cells in the body, including muscle cells and immune cells, react to stretch. The myometrium is the smooth muscle layer of the uterus, the organ that supports the development of a fetus during a pregnancy. The goal of this research is to determine the impact of varying amounts of stretch on the contraction of muscle cells isolated from the human uterus, including identifying the parts of cells that respond to stretch and the influence of nearby immune cells. Project outcomes intend to include a deeper understanding of the impact of stretch on uterine contractions and the discovery of new pathways that could be modified in patients to help treat pregnancy-related complications. This project will also support the training of graduate and undergraduate students and on-site field trips to the lab for local high school students. This project looks to determine how mechanical stretch directly impacts the contractility of human smooth muscle cells isolated from the myometrium, the muscular layer of the uterus. The research team will engineer human myometrial smooth muscle cells into aligned microtissues, stretch them to various extents, and quantify changes to their physiology and transcriptome to identify potential mechanisms of mechanosensing. Further, the research project will test if mechanical stretch induces myometrial smooth muscle cells and/or macrophages to secrete pro-inflammatory factors that activate contractile responses. The research team will engineer a device to co-culture stretched and un-stretched cells and use it to evaluate the impact of crosstalk between (un)stretched macrophages and myometrial smooth muscle cells. Together, the project seeks to advance fundamental knowledge of how both stretch and inflammation regulate myometrial contractility that may inform new therapeutic approaches. The PI will also train graduate students who will enter the biomedical workforce and expose local high school students to the discipline of biomedical engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Infants improve their motor skills, such as sitting, crawling, and standing, through practicing these skills in everyday life. However, limited tools for measuring infants’ movements in the home are a barrier to understanding the factors that give rise to individual differences in opportunities for movement as well as individual differences in caregiving practices that can predict motor learning outcomes in infancy. This project uses new wearable sensor technologies combined with Artificial Intelligence to record infants’ behavior across a week-long period to understand how the patterns of infants’ movements unfold over time. Results of this research advance understanding of motor development and provide useful information for clinicians to help promote healthy motor development in infancy. This project aims to collect data from families across the United States by mailing wearable sensors for infants to wear over the course of a week. In contrast to current methods that rely on labor-intensive manual coding of infant movement data, this project leverages Artificial Intelligence to (1) measure the amount and types of movement that 7-month old infants engage in at home during a typical week, (2) examine caregiving practices that give rise to individual differences in opportunities for movement, and (3) predict individual differences in motor development outcomes, including sitting and standing proficiency, at 11 months of age. Collecting and sharing a large longitudinal dataset of wearable sensor data enables advances in understanding motor development trajectories as well as advances in Artificial Intelligence methods for robustly identifying human movement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project will study data management practices at user facilities with the goal of improving how research data is organized, stored, and shared. User facilities represent a major investment from NSF and each one has unique data formats and cyberinfrastructure. The project will target selected NSF Major and mid-scale facilities to examine current practices in order to create a roadmap aligned with FAIR principles that facilities can use for improvements in data management and to support open science and improve the national research infrastructure ecosystem. Current data management practices at selected user facilities will be assessed through surveys of the facility personnel and of the facilities' users. The assessment will include topics relevant to the FAIR principles, including data provenance, transfer, packaging, and storage, as well as how data is enriched and deposited for dissemination and citation. Best practices and data formats will be identified, and a roadmap will be created. The roadmap will be shared publicly for feedback through community-building workshops, and elements will be evaluated with hands-on pilot projects at the surveyed facilities. The findings are expected to be useful not only to the surveyed facilities, but to other user facilities and research facilities broadly for assessing and improving their data infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Today's Internet spans the globe, and Internet Protocol (IP) version 6 (IPv6) is important to meeting the needs of billions of people and tens of billions of computers. Unfortunately IPv6's huge number of addresses means techniques used to observe today's IP version 4 (IPv4) Internet cannot directly apply to IPv6. The goal of the CICI: TCR: Building a more Resilient IPv6 with Passive Outage Detection (BRIPOD) Project is to improve observability of the IPv6 Internet using passive observations of IPv6 data to understand how IPv6 is being used, how reliable it is, and how to improve it and encourage its use. The BRIPOD Project improves the IPv6 Internet in three ways: (1) developing new approaches to observe the IPv6 Internet, and creating IPv6 datasets that can be provided to researchers in ways that safeguard individual privacy; (2) developing new approaches to use such data to detect partial reachability and outages in the IPv6 Internet, and; (3) developing methods to use these approaches to advise network operators about potential network problems and improve IPv6 reliability. The outcomes of this project include: new IPv6 datasets for network research; better understanding of how the IPv6 Internet is similar to, and different from, the IPv4 Internet, including its reliability and how it is used; and encouragement for Research and Education networks to increase deployment of IPv6. This project is a collaboration between the University of Southern California/Information Sciences Institute and MERIT 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 2025 · 2025-10

Biological brains can swiftly perceive and learn from a single experience, invent creative solutions when faced with novel roadblocks, switch among diverse activities and cognitive tasks, and adapt to widely varied conditions and unstructured dynamic environments. Moreover, brains sustain life activities with minimal energy consumption (e.g., approximately 20 Watts of power for >80 billion neurons performing complex cognitive tasks with limited, if any, training). While humans can learn by observing something once, artificial intelligence (AI) systems must be trained thousands of times, can rapidly forget existing knowledge, and consume significant amounts of energy. For example, generative AI is approximately 4 to 10 orders of magnitude less energy-efficient than the human brain, consuming billions of Watts for training and executing just a single task. This project aims to better understand how biological neurons operate during learning and decision-making to replicate their energy-efficient computational feat into future brain-inspired AI architectures, known as NeuroAI. The overall goal is to lay the scientific and engineering foundations of NeuroAI to allow for future advancements in artificial intelligence, biotechnology, translational research, national security, and the science of public safety. This project aims to develop mathematical and computational science frameworks and tools for investigating and understanding how networks of neurons and non-neuronal cells interact to self-organize to perform complex learning and decision making on the fly. The research leverages multimodal and advanced neuroimaging and sensing and provides new computational techniques to (i) identify living neurons, non-neuronal cells, and their connections, (ii) measure and monitor the neuronal network growth, their intrinsic biophysical parameters (mass distribution alterations and membrane fluidity), and neuronal functional activity, and (iii) determine the learning mechanisms of living neuronal networks (LNNs) and glia networks. The knowledge learned in this project has the potential to guide the design of the next generation of distributed artificial neural networks (DistANNs) capable of multimodal representation, perception, learning and decision-making from limited observations. 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.