University Of California, San Diego

NIH Research Projects · FY 2026 · 2026-02

Abstract Insulin resistance is a leading cause of type 2 diabetes (T2D), metabolic-associated steatotic hepatitis (MASH), and cardiovascular disease (CVD). Adipose tissue is a crucial regulator of insulin sensitivity and its dysfunction causes whole-body insulin resistance, in large part through adipokines–secreted proteins that influence the insulin responsiveness and metabolism of distal tissues. This project leverages advances in human proteogenomics to identify and credential novel adipokines impacting metabolic health. From human genetic and functional genomic work accomplished in the previous project period, we identified TNFAIP8 as a putative adipokine that promotes insulin resistance and T2D. Previously TNFAIP8 had only been implicated as a cell autonomous protein regulating autophagy. We hypothesize that TNFAIP8, and other yet-undiscovered adipokines, modulate insulin sensitivity and metabolic disease progression. We propose: 1) Identify and credential novel adipokines by integrating protein quantitative trait loci (pQTLs) for >3,000 serum proteins with Mendelian randomization to infer causality, direction of effect, and metabolic disease mediation. These analyses will be combined with human visceral and subcutaneous adipose tissue proteomic profiles. 2) Program visceral and subcutaneous adipocytes in vitro to evaluate adipokine dysregulation under metabolic stress. We will define transcriptional regulators of adipocyte depot fate and generate isogenic induced subcutaneous and visceral adipocyte cell models. These cells will be profiled for secreted proteins, using a novel intracellular protein biotinylation strategy to systematically identify canonically and non-canonically secreted proteins. 3) Perform in vivo adipose-specific biotinylation and genetic ablation studies to validate adipokines physiologically. We will generate a novel mouse model expressing an adipocyte-specific biotin ligase to enable proteomic identification of secreted adipokines under metabolic disease conditions. Adipocyte-specific Tnfaip8 knockout mice will be assessed for insulin sensitivity using hyperinsulinemic-euglycemic clamps. This research will elucidate the roles of novel adipokines in metabolic diseases, offering new therapeutic targets and advancing our understanding of adipose tissue biology.

NIH Research Projects · FY 2026 · 2026-02

SUMMARY Cellular senescence, a hallmark of aging, is an irreversible state of cell cycle arrest in otherwise proliferative cells. Senescence in immune organs is particularly significant in aging and disease, as immune cells circulate throughout the body and regulate the physiological functions of all major organs. Metabolic alterations play a crucial role in immunosenescence and are recognized as key mechanisms driving abnormal immune homeostasis. To better understand the distinct role of senescent cell metabolism – for example, in thymic aging and immune function – there is a pressing need for novel methods to map senescent cells and cell-type-specific metabolism within complex tissues to assess their impact on the local environment. Given the critical role of immune senescence in systemic, organism-level aging, we hypothesize that: (i) thymic epithelial cell (TEC) senescence plays a key role in driving thymic aging. (ii) abnormal metabolic dynamics, including lipid metabolism and accumulated adipocytes in aged thymi, contribute to reduced T-cell diversity, (iii) senescence and aging in the thymus lead to systemic immune function decline. To address these hypotheses, we propose to develop and deploy an integrated platform, Raman Enhanced Determination of Cell Atlas and Typing (REDCAT), for mapping single-cell metabolic activity in complex tissues and decoding the underlying transcriptional and epigenomic mechanisms. It will be applied to the study of thymic aging in wild-type, lineage-tracked, and FGF21 mouse models. Specifically, we will (1) develop REDCAT for single-cell resolution profiling of cellular senescence in complex lymphoid tissues, (2) examine senescence-associated transcriptomic mechanisms via integrating DBiT- Based spatial transcriptome sequencing, and (3) investigating dynamic phenotypic and metabolomic heterogeneity of senescent cells in thymic aging and the impact on tissue microenvironments and systemic immune function. The proposed techniques can be widely adopted by the cellular senescence and aging research community. This work will also generate a valuable data resource to unveil insights in thymic aging to advance fundamental understanding of immuno-senescence and propel translational developments in anti- senescence interventions aimed at improving immune function and overall healthspan.

NIH Research Projects · FY 2025 · 2026-01

Project Summary/Abstract Understanding the relationships between three-dimensional protein structure, metal reactivity, and catalytic function promises routes to new drugs and treatments, green chemical processes, and bespoke catalysts. However, the complexity of natural metalloenzymes makes the design of wholly new enzymes one of the greatest challenges in chemical biology. Thus, the de novo design of functional metalloproteins, in which both the protein scaffold and metal-containing active site are designed “from scratch”, presents the most stringent test of our understanding of protein folding and protein-metal structure-function relationships. Using new machine-learning tools for protein design and structure prediction, this work will investigate metalloenzyme structure-function relationships though the de novo design of vanadium- and heme-dependent haloperoxidase enzymes. These enzymes oxidize halides (X–) to X+ using H2O2 to promote halogen incorporation into diverse organic substrates. Halogen atoms often greatly increase the bioactivity of organic compounds, making these reactions important both for human health (e.g., pharmaceuticals, hormones, antibiotics) and the environment (e.g., toxic and ozone-depleting aerosols such as bromoform). The designed proteins will be expressed and characterized using a diverse array of structural, biophysical, and spectroscopic methods. Haloperoxidase activity will be measured, and the effects of primary and secondary sphere mutations will be studied. Potential alternative reactions catalyzed by the enzymes will be assayed, and the binding and activities of similar metal-oxo species (e.g., MoO42–, WO42–) will be investigated. By designing haloperoxidases using only minimal starting motifs, this work will provide valuable insights into the requisite structural and chemical features enabling efficient haloperoxidation catalysis.

NSF Awards · FY 2026 · 2026-01

The Experiential Semiconductor Training in Emerging Nano-Technologies (ExSTENT) project will deliver a nine-week, cohort-based summer program designed by academic and industry partners to provide community college students with foundational hands-on training in semiconductor manufacturing and immersive exposure to four emerging nanotechnology sectors: biotechnology, solar energy, photonic circuits, and quantum circuits. By combining a five-week core course in semiconductor fabrication—covering cleanroom operations, electronic materials, lithography, deposition, etching, and key equipment use—with four, one-week intensive modules co-designed with industry partners, the project will support experiential learning opportunities in nanotechnology for students from all professional and educational backgrounds to address critical workforce gaps in the U.S. emerging technology fields. Participants will gain real-world experience through laboratory activities in UCSD’s Nano3 cleanroom and site visits to local industry facilities, while weekly professional development workshops and individualized mentoring will foster career readiness. Participation in the program will help to create a robust nanotechnology workforce pipeline that advances national prosperity and competitiveness in alignment with NSF’s mission. The project will (1) design and deliver targeted, hands-on training modules that develop competencies in semiconductor engineering and adjacent fields; (2) implement a scaffolded curriculum integrating lectures, laboratory exercises, and industry site visits; (3) evaluate outcomes via pre and post-program assessments, employer feedback, enrollment and retention metrics, and summative annual reports; and (4) ensure sustainability by transferring the core curriculum to community college partners through annual workshops and piloting, and by releasing an open-source toolkit of activity guides, instructional videos, and assessment tools. An advisory board of industry and academic experts will guide curriculum refinement and program evaluation, ensuring long-term scalability beyond NSF support. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology 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 2026 · 2026-01

Large earthquakes occur infrequently, often separated from each other by decades or centuries. This makes it difficult for scientists to predict and understand large earthquakes because they have only a few historical examples to base their models on. To get around this limitation, scientists use computer simulations to create “synthetic” earthquakes that they can study. Unfortunately, these synthetic earthquakes currently require thousands of hours to compute. In this project, machine learning techniques will be developed to generate realistic, synthetic ground motions in minutes, enabling geologists to efficiently study how large earthquakes work and assess the hazards they may pose to the people of California and Nevada. The software developed will be publicly available for other researchers to use, and educational resources will be created to train future scientists and increase public awareness about large earthquakes. Large earthquakes occur on time scales of centuries or more. Earthquake prediction has proven elusive and recordings of large and damaging earthquakes are rare. Wave propagation simulations currently provide the only option for overcoming the lack of observational data, however, sufficiently realistic physics-based models are extremely computationally expensive. To address this, we will develop a physics-based machine learning model order reduction technique, Operator Inference (OpInf), to create time-dependent parametric surrogate models of seismic ground motions, fusing simulated ground motion wavefields with previously observed earthquake records. The OpInf model will be dramatically faster than traditional physics-based methods and more generalizable. The OpInf models will be applied to diverse faulting regimes in California and Nevada to quantify differences in the source and path influences on the ground motion. All software developed will be open-source and publicly available. We will train the future scientists in earthquake science and advanced model order reduction techniques. Our research will appear in events in California and Nevada to enhance public awareness of earthquake safety procedures. We will collaborate with both the U.S. Geological Survey and Statewide California Earthquake Center to assess how our research could enhance existing hazard assessment and earthquake early warning. 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 Objectives: Blood-Labyrinthine Barrier (BLB) dysfunction is implicated in a range of inner ear disorders (BLB- IEDs), including Meniere's disease, autoimmune inner ear disease, and sensorineural hearing loss. These conditions disrupt inner ear fluid homeostasis, leading to vertigo, hearing loss, tinnitus, and imbalance. The BLB comprises microvascular endothelial cells, pericytes, and perivascular-resident macrophage-like melanocytes (PVM/Ms), which are essential for auditory and vestibular function. This project aims to investigate the role of the BLB in inner ear homeostasis and disease pathophysiology while developing hiPSC-based models to identify therapeutic targets. Research Design: Using human induced pluripotent stem cell (hiPSC) technology, we will model BLB-specific microvascular interactions and explore novel therapeutic interventions. Our approach focuses on generating hiPSC-derived BLB pericyte spheroids by directing neural crest stem cells toward a pericyte fate using vestibular neuronal spheroid-conditioned medium (VNS-CM). Additionally, we will develop hiPSC-derived PVM/M spheroids by differentiating yolk sac macrophage-like cells into BLB-specific PVM/Ms. These models will be integrated into advanced microfluidic devices to create physiologically relevant 3D BLB spheroids, laying the groundwork for BLB assembloid development in future R01 studies. Methodology: We will characterize BLB-specific structural, molecular, and functional properties of hiPSC- derived pericytes and PVM/Ms using advanced imaging, molecular biology, and functional assays. Structural characterization will involve transmission electron microscopy (TEM) to examine ultrastructural features. Molecular profiling will be conducted through immunocytochemistry and RT-PCR to confirm BLB-specific gene and protein expression. Functional validation will include transepithelial electrical resistance (TEER) and dextran permeability assays to assess barrier integrity, as well as cytokine response assays to evaluate BLB-selective properties under inflammatory conditions. By integrating stem cell engineering and microfluidic technologies, we will construct 3D spheroids that replicate BLB molecular and functional characteristics, providing a robust platform for disease modeling, mechanistic studies, and therapeutic screening for BLB-IEDs. Clinical Relevance: By addressing a critical gap in BLB research, this project will advance our understanding of BLB dysfunction across multiple inner ear disorders. Our hiPSC-derived models will facilitate drug screening for patient-specific responses to treatments such as diuretics, histamine modulators, and corticosteroids, reducing the current trial-and-error approach. Additionally, these assembloids will enable disease modeling of BLB-IEDs, offering new insights into disease mechanisms and therapeutic development. This research aligns directly with the NIDCD's mission to support biomedical and behavioral research in hearing and balance disorders, ultimately improving public health and quality of life.

NSF Awards · FY 2025 · 2025-12

As the semiconductor industry transitioned from an in-house fabrication model to a third-party foundry model, it provided widespread access to cutting edge technology at affordable cost and accelerated development of advanced electronic circuits. At the same time, it introduced new concerns regarding protection of the intellectual property of these electronic circuits, whose blueprints must now be shared with globally distributed and potentially untrusted entities involved in the contemporary electronics supply chain. To address these concerns, this project is developing a methodology which enables redaction of proprietary or sensitive portions of an electronic circuit prior to fabrication and reinstatement upon receipt of the final product, thereby protecting the critical intellectual property from theft and/or malicious modification. By focusing on transitioning this technology to practice in collaboration with other academic institutions, industrial partners and federal laboratories, this project addresses the need for high technology-readiness level solutions as well as workforce development. Considering the pervasive development of electronics in every facet of modern life, including critical infrastructure, the objective of this project aligns with NSF's mission "to secure the national defense" and "to advance prosperity". At a technical level, this project builds upon a novel Transistor-Level Programmable (TRAP) fabric which challenges conventional practices in reconfigurable computing by pushing granularity of post-fabrication programmability down to the transistor-level. Thereby, TRAP achieves significant reduction in area, performance and power consumption overhead over conventional Look-Up Table (LUT) based solutions, while at the same time presenting harder obstacles for brute-force or intelligent search-based reverse engineering attacks to overcome. To transition this technology to practice, this project is developing an ecosystem of design tools, training material and proof-of-concept silicon fabrication opportunities, to enable the use of untrusted facilities for manufacturing trusted electronic components while preventing unauthorized entities from being able to understand, use or copy sensitive hardware Intellectual Property. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-12

PROJECT SUMMARY /ABSTRACT Alzheimer's disease (AD) is the sixth leading cause of death in the U.S. and the fifth for adults aged 65 and older. Projections suggest the number of affected individuals could reach 13.8 million by 2060 without advancements in diagnostics, prevention, or treatment. AD poses significant challenges in elderly healthcare due to high misdiagnosis rates and limited early intervention options. Diagnostic accuracy largely depends on symptomatic assessment, leading to misdiagnosis in up to 25% of cases. Unfortunately, these misdiagnoses often go unrecognized until postmortem examinations, resulting in inappropriate treatments in 18-67% of cases. These issues create substantial challenges for AD research, including A. Inaccurate diagnostic labels that undermine research reliability, B. The depletion of patient samples over time, which restricts research, and C. Timely intervention is crucial for AD, rather than postmortem discoveries. To address these challenges, reduce misdiagnosis, and facilitate early interventions in Alzheimer's disease (AD), this project introduces innovative technologies and methodologies with three specific aims: Aim 1: Construction of a Comprehensive Multidimensional Database. Utilizing SILVER-seq, a groundbreaking small input liquid volume extracellular RNA sequencing technique, the project will repurpose over 1,500 limited-volume samples that would otherwise be wasted due to their remaining volume being too small. This will generate a multidimensional database spanning 25 years of diverse dementia samples, providing sufficient samples confirmed by the gold standard for subsequent research aims. Aim 2: Identification and Validation of Crucial exRNA Markers for Precision Diagnosis of AD. By employing artificial intelligence (AI) technologies, the project will identify crucial extracellular RNA (exRNA) markers for precise AD diagnosis, significantly reducing misdiagnosis risks. These biomarkers will be integral for developing protocol and computational pipeline. The project aims to establish an AI-driven gene detection process to extend practical applications for early intervention diagnostics based on SILVER-seq technology. The significance of this project lies in its transformative technology and precision diagnosis methods, aiming to revolutionize AD research and healthcare efficiency. The introduction of SILVER-seq and AI-driven marker identification represents unique innovations. In conclusion, this project advances AD diagnosis and early intervention, potentially transforming AD research and healthcare practices. This training will enable me to complete the proposed studies within three years. It will deepen my understanding of AD-related diseases, facilitate data generation using novel techniques, and support the identification and validation of biomarkers. The F32 grant will advance my progress toward becoming an NIH funded independent investigator, focusing on studying multiple diseases through a genetic lens, with bioinformatics as the core skill.

NIH Research Projects · FY 2025 · 2025-12

PROJECT SUMMARY Spinal cord injury (SCI) is a neurological condition that induces a wide array of transcriptional and functional changes to a variety of cell types in the central nervous system (CNS), including fibroblasts. After SCI, fibroblasts undergo a wound healing response called fibrotic scarring, in which they activate, proliferate, and migrate to the lesion site to produce extracellular matrix proteins. While acute fibrotic scarring is critical in orchestrating tissue repair and preserving tissue integrity; aberrant, chronic fibrotic scarring, has been implicated to contribute to the CNS limited regenerative potential after SCI. Chronic fibrotic scarring remains poorly understudied in elucidating the cellular mechanisms that govern its development, formation, extracellular influence. Transforming growth factor beta (TGF-β) has been extensively characterized as the central regulator of fibrotic scarring in the liver, heart, lung, and kidney while also being implicated in the CNS through sc-RNAseq and pharmacological evidence. However. there is no genetic, cell-specific evidence establishing the role of TGF-β and chronic fibrotic scarring after SCI and how the fibrotic scar shapes the injury site. This proposal aims to determine the contribution of TGF-β signaling in chronic fibrotic scarring and its role on the extracellular microenvironment using mice transgenic models and spatial transcriptomics. My overall hypothesis is that fibrotic scarring is modulated by TGF-β signaling and contributes to a nonpermissive microenvironment for neural repair and recovery after SCI. To test this hypothesis, I will conditionally delete TGF-β receptor 2 (TGFBR2) specifically in fibroblasts using a collagen type 1 alpha 2 promoter and assess fibrotic scarring in mouse models of SCI. Aim 1 will be focused on determining if fibrotic scarring is driven by TGF-β signaling in fibroblasts following SCI and its impact on hindlimb functional recovery. Recovery will be assessed in three locomotor behavioral assays. Aim 2 will leverage spatial transcriptomics to examine the impact of fibrotic scarring attenuation on various spatially resolved injury site cell populations, structures, and the overall microenvironment. Impacts on the genetic signatures of astrocytes, macrophages, and microglia will be of specific interest due to their role in neuroinflammation and neurotoxicity after SCI. Aim 3 will focus on manipulating fibrotic scarring to promote corticospinal tract axon regeneration after a dorsal hemisection SCI. This work will provide better insight into the cellular and molecular processes involved in fibrotic scarring after SCI, which will lead to more effective therapeutic interventions for spinal cord injury in the future.

NSF Awards · FY 2025 · 2025-11

Determining the sequence of amino acids in a peptide—peptide sequencing—is a foundational technique in drug discovery, food safety, and environmental monitoring. This project aims to enable confident identification of challenging classes of peptides for which current state-of-the-art methods fail or produce unreliable results. Today’s dominant technology for peptide identification is mass spectrometry (MS), which performs well when peptides match sequences in a reference database. However, MS struggles in two important scenarios: (i) de novo sequencing, where no reference sequence is available and (ii) heavily modified peptides, which are common in post-translational modifications (PTMs) relevant to epigenetic regulation and drug development. This project addresses both challenges by developing new computational tools for a next-generation mass spectrometry approach: two-dimensional mass spectrometry (2D-MS). This technique introduces fragment-fragment correlation (FFC)—the ability to determine whether two fragments originated from the same peptide molecule. FFC dramatically improves the ability to reconstruct peptide sequences and pinpoint chemical modifications, even in complex or novel peptides. At the core of this approach is a concept adapted from coincidence/covariance spectroscopy (CCS), a Nobel Prize-winning technique traditionally applied to atoms and small molecules. Our team recently pioneered the application of CCS to proteomics through the development of two-dimensional partial covariance mass spectrometry (2D-PC-MS). While this breakthrough offers clear advantages over conventional MS/MS, fully realizing its potential requires the development of new algorithms and software for de novo sequencing and PTM analysis by 2D-PC-MS, which are being developed with this project. The project will target three long-standing challenges in proteomics. Sequencing long, multi-charged peptides is essential for antibody characterization. Identifying cyclopeptides is critical for antibiotic discovery as well as understanding in basic biology. Analyzing mixture spectra of co-fragmented peptides is crucial for understanding combinatorial post-translational modifications (PTMs) in epigenetics, which can then inform basic biological regulation. The novel tools being developed, which are enabled by two-dimensional partial covariance mass spectrometry (2D-PC-MS) and fragment-fragment correlation (FFC), will provide biologists with unprecedented capabilities in peptide sequencing. By unlocking these previously intractable problems, the project will catalyze a shift in proteomics, accelerating progress in understanding of basic biology as well as in various areas of biomedical and other research. 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 critical scientific challenges, from understanding complex diseases to designing innovative materials, rely on sophisticated computer simulations. However, scientists often encounter a "silicon ceiling," where current computational power restricts their ability to model these intricate real-world phenomena accurately enough to achieve major breakthroughs. The SINAPSE project directly addresses this issue by developing a powerful, open-source software toolkit that combines Artificial Intelligence (AI) with High-Performance Computing (HPC). This integration promises to enhance simulation capabilities, effectively offering significant orders-of-magnitude performance gains. SINAPSE will provide foundational software that benefits the broader AI-HPC research community, advancing the field itself. The project is also dedicated to supporting education and training for students in these cutting-edge computational methods, fostering the next generation of STEM professionals. By making advanced simulations more powerful and accessible, SINAPSE serves the national interest by driving innovation and enabling solutions to pressing scientific challenges. The project aims to overcome the "silicon ceiling" limiting complex simulations by developing a Scalable Infrastructure for AI-driven Predictive Simulation Enhancements (SINAPSE), delivering an open, sustainable Software Development Kit (SDK) that seamlessly couples Artificial Intelligence (AI) with High-Performance Computing (HPC) workflows. The project will provide functional capabilities through new and enhanced core software elements for AI-coupled HPC and integrated problem-solving frameworks for common scientific discovery patterns. The methodology begins by convening the SDK with a community focus. The SDK will then be populated by creating several novel core software elements and significantly enhancing existing tools like Colmena and RHAPSODY to support diverse AI-HPC coupling needs, including dynamic and asynchronous execution. These components will be assembled into problem-solving frameworks such as "Muse" for online surrogate model training, "Music" for model-directed sampling, and "Melody" for multi-scale campaigns. Finally, the entire SINAPSE SDK and its frameworks will be validated and strengthened through applications in biophysics, focusing on viral glycoprotein dynamics, and materials engineering, specifically for catalyst design. 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

Researchers have deployed 32-bit Internet address-based (IPv4) network telescopes and honeypots in academic networks and public cloud infrastructure to capture and react to unsolicited Internet traffic that often carries threat intelligence signals. However, the transition to 128-bit Internet addressing (IPv6) presents a fundamental challenge: its vast address space renders traditional (brute force) IPv4 scanning techniques ineffective, requiring new approaches for traffic collection and threat detection. This project introduces iVoyager, a cyberinfrastructure (CI) designed to empower researchers to effectively explore the evolving landscape of Internet threats by gathering cyber threat intelligence across both IPv4 and IPv6 network deployments. This project plans to design and implement iVoyager that provide three capabilities: (1) a flexible virtualized environment to facilitate development and deployment of distributed dual-stack (IPv4 and IPv6) telescopes and honeypots; (2) a proactive telescope that applies novel active techniques to attract malicious IPv6 traffic; (3) deployment of lightweight telescope and honeypot vantage points in public clouds and collaborating networks. The project plans to operationalize the reference design of iVoyager to collect longitudinal datasets that facilitate use of machine learning/artificial intelligence (ML/AI) for cyber threat hunting, anomaly detection, and malware analysis. iVoyager will complement existing CIs by providing additional datasets for more comprehensive cyber threat analysis. The data collected by IPv4 telescopes has enabled hundreds of network researchers to study Internet-wide cybersecurity incidents, such as denial-of-service attacks, malware propagation, and malicious scanning activities. The datasets produced by iVoyager will expand this capability to a dual-stack IPv4 and IPv6 world, with a focus on advanced applications of machine learning and artificial intelligence (ML/AI) for cyber threat hunting, anomaly detection, and malware analysis. The infrastructure will also support deployment of novel experiments and ML/AI-driven methodologies for characterizing IP spoofing, scanning strategies, and other malicious behaviors. These capabilities will inform and enhance cybersecurity practices and policy development. A link to the project website will be provided from https://www.caida.org/funding/circ-ivoyager/ 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

Nontechnical Description The rapid advancement of deep neural networks (DNNs) and large language models (LLMs) is transforming many facets of modern society. These AI models are trained and deployed in data centers powered by specialized hardware such as graphics processing units (GPUs), resulting in significant energy demands and raising critical concerns around sustainability and energy security. This project aims to explore the use of light for performing neural network computations, enabling the development of energy-efficient AI hardware. Specifically, the project will leverage the integration of thin-film lithium niobate (TFLN) — a high-performance electro-optic material — with silicon photonic chip platforms to fabricate analog optical modulators that offer significantly lower loss and higher speed compared to traditional silicon-based devices. In addition, the project will design new architectures and circuit techniques to achieve high-resolution AI computation using low-precision building blocks, optimizing both efficiency and accuracy. The educational component of this project will train students in both photonic and advanced electronic chip design, equipping them with the skills essential for next-generation AI hardware development. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and AI. The industry sponsor will be actively engaged as a strategic partner to help transition the technology from research prototypes to real-world deployment. Technical Description The heterogeneously-integrated electronic-photonic AI accelerator (HIEPAA) project features cross-layer innovations from device design to integrated circuits, to wafer-scale architecture to achieve significant improvements in throughput and energy efficiency of AI accelerators. By combining co-packaged electronic-photonic ICs (EPICs) with bonded TFLN modulators promising above 50 GHz bandwidth and extremely low loss, this architecture will enable space-time multiplexed computations, delivering over 2 Tera operations per second (TOPS) per tile with 2 TOPS/W energy-efficiency and scaling to 1 ExaOPS performance at the wafer scale with 200 TOPS/W energy-efficiency. Architectural innovations will solve the long-standing challenge associated with the precision and energy consumption tradeoff of data converters and devices used in the accelerator tile by investigating residue number system (RNS)-based photonic VMM architecture. The EPIC photonic core will support coherent vector-matrix multiplication (VMM) at up to 60 GS/s symbol rates. The space-time multiplexed architecture will enable flexible VMM operations with vector lengths ranging over 1000s to perform inference on transformer-based LLM models. Fabricated PICs and EICs will be independently verified, packaged, and integrated into a system, with a packaged printed circuit board (PCB) prototype with a field-programmable gate array (FPGA)-based digital backend to validate the HIEPAA tile's performance on the state-of-the-art LLM models, which will guide wafer-scale architectural performance benchmarking. A comprehensive education and workforce development plan will focus on building expertise in electro-optic AI accelerator architecture, photonic and electronic chip design, and AI and Machine Learning. A key emphasis is to fast-track the training of students on newer FinFET CMOS nodes through a complete revamp of analog IC design courses and developing structured training material with a focus on photonics IC design. New undergraduate research opportunities will be introduced to sustain the tradition of involving undergraduates in the PIs' labs through summer scholar programs and NSF-sponsored REU initiatives. 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

As global connectivity becomes central to several critical societal functions ranging from disaster relief and climate monitoring to defense and underwater exploration, today’s wireless infrastructure must evolve beyond isolated domains. Specifically, traditional wireless networks, confined to a specific terrestrial, aerial, or underwater environment, fall short in supporting coordinated and resilient operations across these heterogeneous domains. Addressing this challenge and motivated by the need for a unified high-performance communication architecture that spans land, air, and sea/ocean, this project aims to lay the foundation for an integrated network that leverages optical, radio-frequency (RF), and acoustic wireless technologies to connect aerial nodes (e.g., drones and high-altitude platforms), terrestrial wireless devices (e.g., mobile phones), and underwater nodes (e.g., autonomous underwater vehicles) into a cohesive system, referred to as Integrated Air-Ground-Underwater Network (IAGUN). The hybrid use of optical and RF communication modes (or optical and acoustic communication modes in underwater scenarios) offers complementary advantages, enabling faster, more secure, and more reliable communications than either alone can achieve. The project also features an educational and outreach component that supports interdisciplinary training at the intersection of optical communications, wireless networking, and machine learning. Students will gain hands-on experience in testbed development, simulation, and system-level optimization, contributing to the development of next-generation engineering workforce. Publicly released datasets and benchmarks acquired during the execution of the project will further empower the broader research community to explore and build on the project's results. Ultimately, by advancing the design of hybrid wireless systems and fostering applied research in real-world scenarios, this work aims to push the boundaries of networking technologies while training future engineers and innovators. This research pioneers in addressing the core technical challenge of developing high-performance and resilient communication infrastructures that merge RF, acoustic, and optical wireless technologies within hybrid RF/acoustic/optical IAGUNs. To this end, the project introduces six strategic pillars for enabling convergence in RF/acoustic/optical technologies: (1) modeling and optimizing communication across networks with multiple relays and source-destination pairs, (2) co-usage of RF and optical as well as acoustic communication modes, (3) incorporating link establishment overhead, (4) accounting for heterogeneity and mobility, (5) cross-layer network optimization, and (6) embedding and enabling intelligence across hybrid RF/acoustic/optical IAGUNs using distributed learning. The research is organized into three thrusts. Thrust 1 focuses on optimizing communication performance through novel system model and problem formulations that jointly improve underlying performance metrics of interest (e.g., minimizing end-to-end delay) and network establishment/configuration overhead in hybrid RF/acoustic/optical IAGUNs. These formulations are then solved using advanced optimization techniques, such as mixed-integer programming, non-convex optimization, and reinforcement learning, as well as graph-theoretic approaches that are rooted in network flow optimization. Thrust 2 addresses network resiliency of IAGUNs through redundant transmission strategies, robust optimization under adversarial attacks, and signal- and network-level defense mechanisms. Thrust 3 creates one of the first public datasets and benchmarks for hybrid IAGUNs. The interdisciplinary methods of this project, spanning wireless communications, optics, optimization, graph theory, and machine learning, address open gaps in the existing literature and promise impactful solutions for future intelligent, secure, and ubiquitous wireless 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

Considering the widespread deployment of machine-learning hardware in myriads of modern-life applications, ensuring its reliable and safe operation is crucial to advance the national prosperity and welfare and to secure the national defense. While software and/or digital hardware implementations of neural networks currently enjoy the lion’s share of the market, a number of emerging realities are necessitating the development and deployment of analog neural networks. Specifically, the exponential growth of sensory data from world-machine interfaces, known as the analog data deluge, along with the area, power consumption and response-time constraints of distributed edge-computing systems, necessitate autonomous sensing, perception, reasoning and rapid action. While analog neural-network implementations promise to deliver this ability, their robustness and reliability are susceptible to parametric differences introduced by manufacturing process variation, operational conditions variation, as well as silicon aging. Accordingly, this project seeks to enable robust and resilient operation of analog neural networks and the applications wherein they are deployed, as well as to educate the next generation of engineers on the risks and remedies of using analog machine-learning hardware. At a technical level, this project combines state-of-the-art methods for designing, testing, and calibrating analog integrating circuits, with advanced concepts from training machine-learning models, in an effort to comprehend the vulnerability of analog neural networks, develop error-mitigation solutions, and assess their effectiveness. To this end, the research activities undertaken by this project include (i) investigation and mitigation of the impact of parametric and operational differences on machine-learning models implemented as analog neural networks, (ii) development of methods for specifying and evaluating the learning capacity of such designs, and (iii) demonstration of the efficiency of the proposed methods through custom analog neural-network experimentation platforms. 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 ROOTBEER award ensures that Internet routing policies align with the security and integrity goals of the U.S. science ecosystem. Research and education (R&E) networks offer specialized capacity to their member institutions, enabling data-intensive collaborative research across scientific disciplines. To support seamless workflows for domain scientists, R&E networks typically prioritize R&E routes ahead of commodity Internet routes. Although optimal for scientific collaboration, this approach introduces vulnerabilities to the integrity of the underlying routing infrastructure for two related reasons. First, prioritizing R&E paths increases the importance of ensuring correctness in all R&E router configurations. Even minor misconfigurations have resulted in significant route leaks, inadvertently transmitting sensitive U.S. scientific traffic through unintended international routes. Second, academic networks operate under constrained budgets with limited staffing, and many of them have yet to implement routing security innovations recommended as best practices for over a decade. Furthermore, as with many security measures, routing innovations can introduce unforeseen vulnerabilities, creating additional barriers to adoption. ROOTBEER develops and deploys a security-focused routing observatory and operational support system designed to ensure that routing policies are both correct and effective. The observatory leverages recent innovations in network measurement and analytics, providing a rigorous assessment of routing infrastructuresecurity properties otherwise unobservable by external parties. The project is structured into three tasks: measurement and analysis; an operational dashboard; and community engagement. The effort is executed in close collaboration with R&E networks such as Internet2 to ensure maximum effectiveness. Overall, the project facilitates development of a routing security auditing framework that strengthens the ability of network operators to protect the integrity, availability, and performance of U.S. scientific cyberinfrastructure. 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

Non-technical Abstract: This project aims to revolutionize the discovery of new solid-state materials that can precisely control the mobility of ions and electrons, an essential step toward building the next generation of energy storage systems, neuromorphic computers, and smart sensors. By leveraging advanced artificial intelligence (AI), machine learning (ML), and automated synthesis tools, the team will develop a transformative approach to design solid-state ion conductors using multi-element doping, enabling materials tailored for next-generation energy and electronic systems. A central goal is to establish a new data-driven approach to achieve an optimal balance of ion and electron conductivities for targeted applications while ensuring material stability during operation, a task difficult to achieve using traditional trial-and-error techniques. The project will also provide hands-on research and training opportunities in AI-driven materials discovery, fostering collaboration among U.S. and Canadian universities, national laboratories, and industry partners. Technical Abstract: This research will develop and apply a closed-loop, data-driven framework to design and optimize multi-element co-doping strategies in alkali-ion conductors. By integrating AI/ML-accelerated property prediction, high-throughput computational modeling, autonomous synthesis, and in-situ characterization, this project will systematically investigate how co-doping influences ionic transport, electronic structure, and lattice stability across bulk phases, grain boundaries, and interfaces. A fast, iterative inner loop will enable the screening of thousands of dopant combinations, while a slower outer loop will focus on extracting mechanistic insights and ensuring scalability, feeding knowledge back into the predictive models. Target systems include sodium- and lithium-ion based oxides and halides, where varying the balance of ionic and electronic conduction is critical for applications ranging from batteries to neuromorphic computing. The project will generate foundational design rules for tuning transport properties through co-doping, creating new pathways for energy-efficient materials innovation. 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

Non-technical Abstract: This work focuses on experimental studies of fundamental optical and electronic properties of structures made of thin layers of semiconductors. A special design of the structures on a length scale of a billionth part of a meter allows the creation of new quasi-particles called indirect excitons. The indirect excitons are unique because they can be cooled down to ultralow temperatures. Furthermore, the indirect excitons are unique because they have long lifetimes and can travel over long distances. In particular, the project address excitonic Bose polarons, which are recently found new quasiparticles in Bose gases of indirect excitons. This research increases our understanding of the physics of electronic systems in semiconductors. The students involved with this project have the opportunity to perform exploratory research on the cutting edge of contemporary physics. The potential impact of the project is in development of knowledge in condensed matter physics and increase of fundamental understanding of the optical and electronic properties of materials. Technical Abstract: This individual investigator award supports a project directed towards experimental studies of indirect excitons, also known as interlayer excitons, formed by electrons and holes in separated layers in a semiconductor heterostructure. Due to their long lifetimes, indirect excitons can cool below the temperature of quantum degeneracy. This gives an opportunity to study quantum systems of excitons. Indirect excitons can mediate spin transport. This gives an opportunity to study spin transport. The goals of the research are to study spin transport mediated by indirect excitons and to study excitonic Bose polarons – quasi-particles formed by direct excitons immersed in Bose gases of indirect excitons. The methods and approaches: Indirect excitons are formed either in GaAs heterostructures, which form a low-disorder platform for indirect excitons, or in van der Waals heterostructures composed of atomically thin layers of transition metal dichalcogenides, which form a platform where binding energies of indirect excitons are high. The studies include optical measurements of excitonic Bose polarons and spin transport mediated by indirect excitons. The research is performed by students, and it is integrated with education. The potential impact of the project is in development of knowledge in condensed matter physics and increase of fundamental understanding of optical and electronic properties of materials. 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 goal of this research project is to build mathematical foundations for reasoning about the behavior of modern machine learning systems. Foundations are needed to direct future developments in Artificial Intelligence (AI) research, and also to diagnose and remedy problems that arise in existing AI systems. This project specifically focuses on how AI models represent data. For example, many language models are "trained" using vast collections of text from books, websites, and other sources, but these texts are not "stored" in the model in the same way that documents are stored on a computer. Rather, the texts are transformed in a way that seems to facilitate their use for a variety of tasks, ranging from generating code for a website to solving mathematical word problems.These representations, however, are not perfect, and they also seem to lead to embarrassing errors committed by AI models, such as miscounting the number of Rs in the word STRAWBERRY. Developing a mathematical theory of the representations used by AI models will help demystify how the models perform these tasks and reveal fundamental limitations that result in errors. The theory will also guide the development of next-generation models that go beyond the limitations of current models. Feature learning is a key ingredient in the success of modern machine learning systems, and thus its understanding is a essential in any theory of deep learning. The aims of this project are as follows. The first is to develop general principles of how features emerge by building on well-established mathematical structures, such as low rank structure and circulant structure. The second is to study these principles in analytically tractable and practical architectures--such as two-layer multilayer Perceptrons, kernel methods, and transformers--in the context of specific data frameworks/inference problems such as multi-index models and modular arithmetic. The third is to develop new architectures that are potentially viable alternatives to neural networks that are currently in widespread deployment, thereby mitigating risks of over-reliance on specific technologies while suggesting directions for improving or controlling current architectures. 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

Researchers rely on Internet Background Radiation (IBR) data to detect a wide range of malicious activities and cyber threats. However, as the volume and complexity of Internet activity grow, ensuring the integrity, authenticity, and provenance of this data becomes increasingly challenging, especially when deploying advanced machine learning and artificial intelligence (ML/AI) techniques that depend on high-quality input data. To overcome these challenges, the CANIS project is developing and deploying a new monitoring framework to safeguard the integrity of cybersecurity research workflows. The framework results in AI-ready datasets that accelerate the development of ML/AI techniques for cybersecurity and enable advances in anomaly detection, threat intelligence, and attack mitigation. The framework combines active internet measurement with data from the University of California San Diego's Network Telescope (UCSD-NT), a long-standing NSF-funded scientific cyberinfrastructure that supports the collection of unsolicited IPv4 traffic. The framework sends beacon packets from globally distributed vantage points, and combines this signal with traffic generated by known Internet scanning campaigns to continuously verify the fidelity of the data collected by the UCSD-NT. To support ML/AI-based cybersecurity applications, this project disseminates IBR data in AI-ready data formats that contain labels and rich metadata. These resources facilitate model training, benchmarking, and evaluation. The datasets also serve as valuable resources for cybersecurity and AI education, helping to train the next generation of experts. 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

Nontechnical Description Nonlinear optical wave mixing processes are a key component of modern optical diverse technologies in fields such as communication, health, and environmental sensing. However, wave mixing processes in nonlinear optics present significant challenges, among them the vanishingly small magnitude of the nonlinear susceptibility of most materials, the consequent high pump power densities required, and phase matching. The pursuit of phase matching has shaped the development of nonlinear optics as a field. Most strategies to achieve it rely on the natural birefringence of most materials; however, the certain materials and nonlinear processes cannot use birefringent phase matching schemes. In these cases, quasi-phase matching (QPM) is required. QPM, in particular as achieved through periodic poling (PP) of nonlinear media, is a crucial technique today for a broad swath of technologies relying on nonlinear optics. This is especially true in quantum optics, where PP is used to create sources of entangled photons for use in quantum computing, networking, and sensing. However, PP is an intensive, fickle, and extreme process, and moreover many nonlinear materials of interest are not ferroelectric and cannot undergo PP. Alternatives to PP are thus highly desirable, particularly schemes in which light could possibly facilitate its own QPM. This project will investigate such “self”-QPM schemes which are expected to offer enhanced nonlinear interactions for laser technology and quantum light sources. Technical Description The overarching goal of this EAGER project is to investigate a new means of QPM that we term “Polarization-Enabled Self-Quasi-Phase Matching” (PESQPM). In PESQPM, pump light enters a specially designed waveguide which causes periodic variation of light’s polarization state inside the waveguide. This periodic variation of the polarization state facilitates a QPM effect, one which requires no PP and is compatible with standard lithographic fabrication procedures. In this EAGER, we will 1) Develop a complete theory of PESQPM in arbitrary wave mixing processes governed by arbitrary susceptibility matrix elements, and 2) Implement a proof-of-concept demonstration of the PESQPM effect for phase matching second-harmonic generation in lithium niobate. The work of this EAGER, then, will facilitate a new way of phase matching for a broad class of nonlinear optical materials without requiring PP. This offers the potential to simplify any systems which rely on phase-matched wave mixing interactions, including technologies such as frequency doublers, OPOs, and OPAs. This work also offers significant potential for quantum computing, networking, and sensing applications which are poised to have significant societal and economic impact within the United States. Here, scalable, mass-producible integrated sources of entangled photons are desirable, a need which current techniques based on PP (with PP’s accordant difficulties) do not fully meet. Finally, this project will support the training of postdoctoral and graduate student researchers at UCSD. 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

Research shows that effective personalized feedback helps students feel more engaged and persist in computer science. Furthermore, having access to help as soon as a student is confused can help both student engagement and learning. Artificial Intelligence (AI) and Large Language Models (LLMs) show promise toward providing customized help in a way that is accessible to students in a timely fashion. However, more research is needed about how to foster effective use of these new technologies in educational settings. This project investigates how LLM-based technology can help build effective pedagogical tools within the context of college programming classes. This project explores novel approaches for using AI and LLLMs in a pedagogical setting to: (1) provide customized help to students that is tailored to the class they are taking; (2) support instructors in material preparation, monitoring student engagement, and training teaching assistants; (3) augment existing pedagogical approaches, such as peer instruction; and (4) facilitate the education of AI-assisted programming. Towards these goals, the research team will create a Smart Learning Hub that interfaces with students, instructors, and teaching assistants in an integrated coordinated way, within the context of programming courses. The research team plans to deploy this Smart Learning Hub in real classroom settings and measure its pedagogical impacts. The research team plans to share the products of this project in an open-source executable format to facilitate further advancements in research and computing education. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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 this project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Clifford P. Kubiak and his research group of the Department of Chemistry at UCSD is developing an understanding of the effects of electric fields on electron transfer (ET) at interfaces. The goal of this research is to control and utilize these effects to create switchable and tunable ET and catalytic systems. Electron transfer is the engine that powers living systems and is the basis of every electronic device. A deeper understanding of interfacial ET will lead to advances in semiconductor chip design and fabrication, and ultimately computation. When applied to catalytic processes, electric field effects of surface bound catalysts can provide a tunable dial of control for product activity and selectivity that can be deployed by changing a voltage, rather than the synthesis of numerous new catalysts in attempts to optimize them. Such a clean electric field-controlled approach to catalysis molecule can lead to reduction of industrial and pharmaceutical chemical waste. This interdisciplinary project incorporates elements of inorganic, materials, and physical chemistry and therefore provides a broad educational intersection for all scientists and trainees involved. Electro-inductive effects on electrode-bound molecules show promise for control over interfacial electron transfer and control of rates and selectivity of catalytic processes. Through specialized in-house spectroelectrochemical techniques (i.e. infrared and UV-Vis-NIR), electron transfer in mixed-valent complexes and interfacial electric field effects on electrode-bound complexes are being studied as electrical bias is applied. The proposed project includes the immobilization of well-understood mixed-valent ruthenium clusters and transition metal catalysts on planar Au electrodes and Au nanomaterials using N-heterocyclic carbene (NHC) based linkers, which extend the potential window of stability over traditional thiol- and isocyanide- based Self Assembled Monolayer (SAM) attachment strategies. Specific goals for the implementation of a specialized surface-sensitive Phase Modulated – Infrared Reflection Absorption (PM-IRRAS) spectroelectrochemical cell involves the study of NHC-bound systems on gold including: 1) electronically bi-stable mixed-valent ruthenium clusters, and 2) electric field influences on transition metal catalysts on Au surfaces in situ. They will also expand their library of NHC attachment motifs for these purposes. The proposed goals embark on answering the following questions: a) Can electric fields tune the charge distribution of mixed-valent NHC-bound ruthenium dimers as they step the potential through the mixed-valent state? Will this induce switchable intramolecular electronic behavior? b) How are the ET dynamics of ruthenium clusters affected by colloidal Au nanomaterials acting as bridging motifs? c) How does an interfacial electric field affect closely confined transition metal catalysts? Can these effects be used to target products and control catalytic reaction rates? Such understanding and control over charge distributions and catalytic activity could lead to broad advances in the life sciences, industrial chemical processes, and technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The overarching goal of this project is to evaluate the effectiveness of Safe Night Out (SNO), a community-level primary prevention program offered in drinking establishments in Sacramento County, California. Developed and implemented in 2019 by WEAVE, Inc., Sacramento County’s primary domestic violence (DV) provider, SNO aims to train nightlife staff on how to recognize warning signs of sexual violence (SV) and intimate partner violence (IPV), and how to respond through active bystander skills to keep patrons safe. SNO has been tailored for drinking establishments in predominantly LGBTQ+ neighborhoods given the disproportionate rates of SV and IPV among sexual and gender minorities. SNO, a single session education and bystander skills training, is to our knowledge, the only program implemented in LGBTQ+ drinking establishments. Guided by Social Cognitive Theory, Social Norms Theory, and the Bystander Education Model, and in collaboration with our Research Advisory Board, our community-academic team will conduct a quasi-experimental mixed methods evaluation study using a comparative time-interrupted series design to Aim 1: Determine the effectiveness of SNO on increasing bystander intervention and reducing SV and IPV at the individual-level (e.g., patrons, staff) and population-level (e.g., census block). Individual-level primary outcomes among patrons include past 6-month SV and past 6-month IPV victimization and perpetration; secondary outcomes among staff include past 6-month increase in bystander intervention and safety checks in drinking establishments. We will also determine the effectiveness of SNO on 2-year average rates of rape, domestic violence assault and domestic violence police calls. Aim 2: Examine moderators (e.g., age, sex, history of SV/IPV) and mediators (e.g., self-efficacy, social norms) impacting the effectiveness of SNO on reducing SV and IPV and increasing bystander intervention among patrons and staff, respectively. Aim 3: Assess implementation processes based on the RE-AIM framework, on SNO reach, integration, fidelity, and engagement as well as costs associated with SNO program implementation. We will enroll 25 drinking establishments in the queue at WEAVE to receive SNO training and identify 25 drinking establishments who will not receive the SNO training during the study period. This will allow for comparison between the two groups based on individual-level and staff-level outcomes, before (3 months prior) and after SNO program training (6, 12, and 18-month follow-up) using quantitative survey data (n=500 patrons, 10 per establishment and n=75 staff, 3 per establishment). We will also examine the annual rates of reported sexual assault and domestic violence calls before implementation of SNO training that was tailored to LGBTQ+ communities (2019-2021) and after SNO training implementation (2022-2025). Finally, we will use quantitative survey and qualitative data (n=9 patron focus groups [FG], and n=9 staff FG, 5 per FG) to evaluate implementation outcomes and conduct cost analysis. If effective, SNO will serve as an evidence-based program that can be scaled up in other LGBTQ+ drinking establishments in the U.S.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Parkinson’s disease (PD) is the second fastest growing aging-related neurodegenerative disease in the world. One of the most commonly mutated genes in PD is Leucine Rich Repeat Kinase 2 (LRRK2). LRRK2 mutations that are linked to PD result in hyperactive kinase activity, but unmutated LRRK2 can also be hyperactive in idiopathic forms of PD, making its kinase the most actionable target for PD therapeutics. Thus, it is critical to understand how LRRK2’s kinase activity is regulated, something that remains poorly understood. LRRK2 encodes a multi-domain protein, whose N-terminal half is comprised of three protein-protein interaction repeat domains that form an arm-like structure that drapes across its kinase and GTPase domains, to dock at the C- terminal WD40 domain. When these arm-like repeats are docked on the WD40 domain, LRRK2’s kinase is autoinhibited. Here, I will use LRRK2-specific Designed Ankyrin Repeat Proteins (DARPins), which I was involved in developing, to determine how LRRK2’s kinase activity is regulated. We identified three DARPins, E11, C12 and G10, which bind tightly to LRRK2 at partially overlapping sites on its WD40 domain, yet have distinct effects on LRRK2’s kinase activity. Our data suggest that DARPin C12 activates LRRK2’s kinase activity, while DARPin E11 and G10 inhibit it. Our preliminary cryo-EM data support the hypothesis that DARPin C12 relieves autoinhibition by blocking binding of LRRK2’s repeat domains to the WD40 domain. My preliminary data also supports the hypothesis that some PD alleles outside of the kinase active site effectively act like C12: they activate the kinase by relieving autoinhibition. Based on the binding sites of E11 and C12, we hypothesize that E11 may be blocking binding of an activating factor, while C12 maybe stabilizing the autoinhibited conformation of LRRK2. Thus, DARPin E11 is an excellent tool to identify novel activators of LRRK2, as well as to screen existing small molecule libraires to identify allosteric inhibitors of LRRK2’s kinase. I expect to determine how mutations in LRRK2 that are distal to the kinase domain increase kinase activity. In addition, my goal to identify novel activators of LRRK2’s kinase and allosteric inhibitors of its kinase will lead to new therapeutic strategies for targeting LRRK2 for the treatment of PD.