University Of California, Merced

NSF Awards · FY 2026 · 2026-07

The rapidly advancing quantum computers challenge the cryptographic systems that are protecting today's digital infrastructure, including banking, healthcare, government services, and cloud platforms. As the new standardized post-quantum cryptographic (PQC) algorithms are slower and more complex than current cryptographic methods, the deployment of PQC algorithms become challenging. This project improves both the performance and security of the new PQC algorithms, enabling their wide adoption in real systems. The project's novelties include a unified approach that jointly optimizes speed and security across common computing platforms, and a systematic evaluation of hardware-level vulnerabilities that may arise during acceleration. The project's broader significance and importance are enabling a smooth and secure transition to quantum-resistant infrastructure, protecting critical data and services, and strengthening national cybersecurity readiness. The outcome of this project supports broader adoption of quantum-resistant cryptography, promote open-source dissemination of secure implementations, and contribute to workforce development for the quantum era. The project studies the standardized PQC algorithms, including hash-based, lattice-based, and code-based schemes, and optimize them across versatile platforms, including Central Processing Units (CPUs), Graphics Processing Units (GPUs), and embedded systems such as microcontrollers and Field-Programmable Gate Arrays (FPGAs). It applies parallel processing, instruction-level tuning, and hardware-software co-design to accelerate key operations such as hashing and polynomial transforms while preserving constant-time behavior. This project also analyzes timing, cache, power, and fault-based side-channels and develops defense mechanism such as masking, randomized scheduling, and redundant computation. The optimized implementations are integrated into practical systems, including secure network protocols, vehicular communication platforms, secure boot processes, and confidential computing environments. 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 · 2026-06

PROJECT SUMMARY Transcription is one of the most vital processes in all living cells. To ensure that transcription works properly, the whole process is regulated at multiple levels. The complexity of the transcription regulations makes it difficult to reveal the complete mechanism of transcription. RNA polymerase II (Pol II) is the central enzyme for the transcription of all the coding genes in eukaryotes. Studies from different fields including structural biology, biochemistry, and genetics suggest that multiple proteins are involved in the transcription process and Pol II forms higher-order structures with other factors during different stages of transcription, which are initiation, elongation, and termination. Structural details of Pol II and basal elongation factors provide important insights about the elongation complexes; however, questions about the dynamics of the elongation processes and molecular mechanisms of regulation of these processes by elongation factors remain open. In this project, we aim to fill the gap between structures of elongation complexes and the molecular level of mechanisms of how these complexes form and regulate elongation by studying Pol II elongation complexes using molecular dynamics (MD) simulations. In close connection with experiments, we will computationally study the elongation stage of transcription to investigate 1) the molecular basis of the roles of elongation factors in facilitating transcription processivity, 2) the roles of elongation factors in gene-specific transcription and their relation to the neurodegenerative diseases, 3) the impacts of human disease mutations on the conformation and dynamics of the elongation complexes. MD simulations together with a variety of computational techniques including enhanced simulation methods, machine and deep learning algorithms and kinetic network models will allow us to obtain dynamics of the elongation complexes at microsecond time scales, propose mechanisms of action of the elongation factors, and investigate the defects in those mechanisms that could relate to human diseases. The successful completion of this project will have two important impacts: 1) an innovative perspective into studying large biological complexes as we will integrate advanced computational techniques to answer large-scale questions, 2) novel insights on the mechanism of transcription and its relation to human diseases as we will uncover the dynamics of these processes at the molecular level.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY/ABSTRACT Inflammasomes are large multiprotein oligomers that assemble in innate immune cells upon detecting danger signals from pathogens or cell dysfunction. Inflammasome formation results in the maturation of proinflammatory cytokines, leading to an inflammatory response as a defense mechanism. However, aberrant inflammasome activity is associated with the perpetuation of inflammation, which causes life-threatening diseases including autoimmune and autoinflammatory disorders, atherosclerosis, and cancer. Therefore, understanding the regulatory mechanisms of inflammasome assembly and activity is key to designing anti-inflammatory therapies. When detecting danger signals, cytosolic pattern recognition receptors prompt inflammasome formation by self- association. The protein AIM2 is a well-known cytosolic sensor that polymerizes upon binding to pathogenic DNA and recruits the inflammasome adaptor ASC, which massively self-associates, forming a filamentous speck. ASC carries two oligomerization domains, PYD and CARD, of the Death Domain family that interact with PYD- containing sensors and the CARD of procaspase-1 via homotypic binding. Thus, ASC acts as a molecular glue connecting the sensor and procaspase. ASC speck formation is required for procaspase activation resulting in the maturation of proinflammatory cytokines. ASC specks are formed several hours after infection, which provides a time window to interfere with inflammasome assembly. However, the factors regulating inflammasome formation and growth are poorly understood. Our laboratory has pioneered structural studies at atomic resolution of the adaptor ASC. We identified ASC-sensor binding interfaces leading to structural models of the assembly. Our biophysical studies on ASC isoforms have shown differential polymerization kinetics and interdomain dynamics which agree with their various capabilities to elicit inflammasome activity observed in cells. Using single-molecule techniques, we have provided mechanistic insight into the assembly of AIM2 on DNA such as oligomer size, shape and growth rates, movement of oligomers on DNA, and directions of oligomer growth. These studies are important for the mechanistic understanding of inflammasome growth. In addition, our recent structural studies on the ASC isoforms have led us to design peptides capable of decreasing inflammasome activity in cells. Overall, our laboratory is well-positioned to study inflammasome regulation at the molecular level. In this proposal, we will determine the effects of sensor and adaptor isoforms on inflammasome assembly, as well as regulatory factors connecting ASC with the effector procaspase-1. We will develop peptides with increased stability and enhanced capability to reduce inflammasome activity in cells, with the potential to serve as scaffolds for drug design. The combination of multiple biophysical and biochemical tools, including NMR spectroscopy, single-molecule techniques, computational methods, and cell-based assays will be key to the successful completion of this proposal.

NSF Awards · FY 2026 · 2026-01

Nanofluidics is the science and technology of fluid behavior when the fluid is confined to small spaces. The dimensions of these spaces range from roughly 1 nm to several tens of nanometers. At such small dimensions, unusual phenomena come into play. The Nanofluidics 2026 Conference is an international meeting to bring together leading researchers in this field. The conference aims to educate students, disseminate research on nanofluidics, and promote research collaborations. This award will support attendance of junior researchers from US institutions to attend the conference. Benefits to the US come from training the next generation of scientists and engineers, and from maintaining US competitiveness, in this technologically-important area. Nanofluidics examines the behavior of fluids when confined to spaces that are comparable to dimensions of the fluid molecules. Interfacial interactions dominate in such tight confinement which can strongly affect phase behavior, transport phenomena, and electrokinetic phenomena. The technological implications are relevant to a wide variety of areas including fossil fuel recovery from pore spaces, critical mineral separation using membranes, reverse osmosis desalination, and ionic computing. The Nanofluidics 2026 Conference scheduled in February 2026 in Tahoe City, CA aims to discuss the cutting edge research in this field. Ten students, post-doctoral fellows, or other researchers from US institutions will be supported by this travel grant. These researchers will benefit from close interactions with top researchers in this field, nucleate new research directions, and improve the impact of their own 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 2026 · 2026-01

Quantum emulation uses a controllable quantum system to mimic a more complex quantum system that is otherwise intractable to solve using classical computers. It allows the exploration of complicated problems in condensed matter physics, high energy theory, and quantum chemistry, and can lead to applications in broad areas such as pharmaceutical research and finance. Despite experimental demonstration in various physical systems such as neutral atoms, trapped ions and superconducting devices, quantum emulators are inherently sensitive to disturbances. In particular, intentional disruptions such as targeted noise or fault injection attacks pose new risks to the accuracy and trustworthiness of quantum emulation, especially when the emulation is performed remotely on cloud-based systems. This project addresses the urgent need for secure and reliable quantum emulation by developing tools and frameworks that enhance resilience against adversarial attacks. This work characterizes the effect of cyberattacks on Quantum Ising Models, a family of quantum emulation models widely used in material research. It then develops a general theoretical framework to describe such attacks and validate them by quantum emulation on commercial cloud-based quantum computing platforms. Based on the theory, this work develops end-to-end security validation and sign-off tools and establish a collaborative security operations center for scientists to adopt conveniently. Through cross-disciplinary collaboration between quantum physicists and cybersecurity experts, it builds critical trust in quantum technologies and lay the foundation for secure quantum computing in emerging scientific research domains. The project also creates graduate and undergraduate research opportunities as well as outreach efforts within both local and academic communities. This research develops a robust framework for securing quantum emulation against external malicious threats by combining quantum system characterization with advanced cybersecurity techniques. The approach first characterizes the effect of cyberattacks on Quantum Ising Models to study how induced noise and Trojan attacks impact the fidelity of the quantum emulation. The approach then develops a system-level theoretical model of how adversarial perturbations manifest in quantum emulation to distinguish them from natural noise or circuit errors. The project also investigates how adversaries can leverage the noise sensitivity profile of the quantum emulators to launch targeted attacks, focusing on Trojan attacks in the compilation stage and run time error and noise injection. In order to counteract these attacks, the project develops end-to-end security validation methods that involve the compile time static analysis and the run time detection methodology. A key innovation in this work is a user-friendly security sign-off software tool that automates the security validation methods to mitigate attacks, enabling secure use of cloud-based quantum emulators. Moreover, the project is working to establish a collaborative security operations center among quantum physicists who use the security sign-off tool, which improves the security validation efficiency and efficacy of the run time attack detection. This project strengthens the broader scientific cyberinfrastructure by improving the usability and resilience of quantum platforms through collaboration between quantum physicists and cybersecurity experts. The results enable secure discoveries in physics, materials science, and other 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

This project develops an innovative radio frequency (RF) signal propagation modeling framework to advance next-generation wireless technologies, including Wi-Fi 7 and sixth-generation cellular networks. These technologies enable critical applications in smart cities, precision agriculture, and smart healthcare by supporting efficient wireless communication and sensing tasks such as human activity recognition and environmental monitoring. A key challenge in data-driven wireless systems is the labor-intensive process of collecting large-scale RF signal datasets for training deep learning models. This project addresses that challenge by generating high-fidelity synthetic RF datasets using advanced propagation modeling. These synthetic datasets support improved network planning, resource allocation, and sensing accuracy, ultimately leading to more efficient and scalable wireless infrastructures. The outcomes of this research contribute to societal benefits such as economic development, cost-effective network deployment, and enhanced connectivity in dynamic and infrastructure-limited environments. The project also integrates educational activities at the University of California, Merced and the University of California, Los Angeles, incorporating wireless communications and generative artificial intelligence into undergraduate and graduate curricula. In addition, the research team trains graduate students and postdoctoral scholars to support workforce development in RF modeling and next-generation wireless systems. The research investigates the use of Neural Radiance Fields for RF signal propagation modeling, with the goal of synthesizing received signals at arbitrary transmitter and receiver positions in complex three-dimensional environments. The scientific problem centers on overcoming key limitations of existing models, including high data requirements, computational inefficiencies, and poor adaptability to dynamic scenes and spatial variations. To address these issues, the research team develops a scalable approach that combines Gaussian-distribution-based representations, Graph Neural Network-guided scene modeling, and accelerated neural ray tracing to reduce data needs, training duration, and inference latency. Temporal adaptability is introduced through the use of deformation fields that capture dynamic environmental changes, while spatial generalization allows for applications across varying receiver positions and new environments without extensive retraining. The approach integrates techniques such as multi-head deformation decoders, neural-driven ray tracing, and contextual scene embeddings. The resulting models are evaluated using both simulation and experimental testbeds at UC Merced and UCLA. Evaluation metrics include fidelity of signal reconstruction, computational efficiency, and task performance in key applications such as device localization, activity recognition, network design, and resource allocation for diverse wireless technologies. 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 Earth’s rock record preserves a substantial fraction of its history, attesting to our planet’s unique and complex systems from which tectonics, minerals, climate, and life have emerged. The Geological Time Scale (GTS) is the authoritative calendar of Earth’s history that has resulted from centuries of observation and quantification of this rock record, and which serves as a foundation for research and innovation in Earth systems science. However, the process of GTS construction and revision has yet to evolve into an open science structure that can harness the full energy and potential of the scientific community. This project will build robust connections among disciplinary scientists, data resources, and community organizations to create a new framework of open science GTS construction. The goals of this project are to bring together scientists to: a) discover and translate the existing data, metadata, and algorithms behind the GTS, in order to establish future community-sourced best practices for dynamic time scale construction; b) plan the design of a findable, accessible, interoperable, and reusable (FAIR) data system that leverages and adapts existing standard data formats and open source software into a community-accessible system; and c) explore how these data and algorithms can be best exposed through public-facing online tools that allow visualization and experimentation with the GTS. By engaging and coordinating an open community to lay the groundwork for production of the GTS within a public-facing system, the Open Science GTS will enable input and participation from all scientists, thus accelerating the amount and quality of data that can be compiled into the GTS and increasing its responsiveness to community needs across geoscience disciplines. 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

Electrical motors power countless systems in modern life, from electric vehicles and industrial robotics to air conditioners and infusion pumps, and drive the movement behind a wide range of emerging technologies and applications. Given their ubiquity and importance, motor monitoring is vital for ensuring safety, maintaining energy efficiency, and detecting potential mechanical failure early. Yet accurate motor monitoring, especially when motors are enclosed, embedded, or physically inaccessible, remains a significant challenge. Laser-based systems are precise and accessible, yet they require attaching reflective markers, requiring direct physical access and causing system downtime. Camera-based systems eliminate the need for markers but still depend on a clear line of sight to the rotating components. These methods fail when rotating components are obscured, as in washing machines, liquid pumps, or medical devices. Vibration-based tachometers, which infer rotational speed from mechanical oscillations, require rigid coupling and struggle in noisy environments. To overcome these barriers, this project introduces a new type of handheld, non-intrusive tachometers based on magnetic inductive sensing. The system detects the low-frequency magnetic fields naturally emitted by motors during operation that can penetrate walls, metal enclosures, and other obstacles, allowing rotation speed estimation without altering the device under test or requiring visual access. This innovation enables accurate diagnostics in environments where conventional tools fall short. Beyond its technical contributions, the project delivers broad societal benefits. It supports safer, more efficient maintenance practices in industry sectors such as energy, aviation, and healthcare, and contributes to sustainability by enabling early identification of failing equipment. Educationally, the project promotes STEM learning at multiple levels. The principal investigator is incorporating the research into a graduate course on low-frequency communications and creating interactive lab modules and outreach workshops for undergraduate and K-12 students. These efforts aim to inspire and train the next generation of engineers and scientists. This project benefits the nation by enabling more efficient and reliable monitoring of critical electric machinery while fostering STEM education and innovation in sensing technologies. The research of this project addresses three fundamental challenges in estimating the rotational speed of electric motors using the weak low-frequency magnetic fields they emit during operation. First, the magnetic signal strength decreases rapidly versus distance. Second, the emitted signals are complex and non-stationary, varying significantly across motor designs and operating conditions. Third, environmental noise and electromagnetic interference introduce uncertainty in signal detection and frequency estimation. To overcome these challenges, the research activity comprises three technical thrusts: signal enhancement, speed estimation, and embedded system integration. The research team designs a sensing platform that uses an array of custom-built inductive coils, incorporating both ferrite and air cores to capture radiated magnetic fields. A spatial filtering method aligns and sums time-delayed signals from multiple coils to enhance signal quality. A machine learning model based on a U-shaped encoder-decoder neural network detects harmonically related peaks in the power spectrum, while a fuzzy-logic inference engine estimates the motor’s fundamental frequency even when it is obscured by noise. To improve generalization to unseen devices and speeds, the team applies data augmentation by resampling real signals to generate synthetic training samples. All signal processing and estimation algorithms operate on an embedded microcontroller platform with high-resolution analog-to-digital conversion. The team conducts performance evaluations using controlled experiments with brushless motors, real-world appliances such as fans and pumps, and tests with various occlusions, distances, and interference sources. Evaluation metrics include speed estimation accuracy, latency, energy consumption, and robustness to environmental variability. This research integrates sensing physics, signal processing, and embedded machine learning to generate transferable algorithms, curated datasets, and system design principles that advance non-intrusive monitoring of electric machines across industrial and consumer 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

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI) Program, this Departmental/Division Transformation Track project at the University of California Merced aims to: 1) understand how challenges to engaging in field research may harm the success of undergraduates in the Department of Life and Environmental Sciences (LES) and 2) devise sustainable solutions to remove barriers and promote success for all students in the department. Many scientific disciplines rely heavily on field work, including ecology, biology, and the earth, atmospheric, and ocean sciences, yet broadly engaging students in field-based STEM fields remains a challenge. This project will develop high-impact research experiences for students that capture the breadth of LES disciplines, as part of a sustainable, student-centered plan to develop workforce skills and growth. These activities will improve student experiences and training and accelerate their trajectory towards environmental careers. This project aims to attract and retain more students in LES majors; complete a departmental self-assessment to strengthen curricula; highlight the impact of LES careers throughout the curriculum; and support high-impact research experiences for students. These aims will ensure that department practices designed to benefit all students will remain sustainable as LES continues to grow and transform. The project will use qualitative and quantitative data to assess the outcomes of different interventions to ensure success. Two key aspects of the department transformation efforts will be examined: 1) how the department transformation plan serves and engages students and are sustainable as the department grows and 2) the impact of the project activities on student persistence and scientific identity, particularly in the field sciences. This project will lead to higher student participation in research, so that students are better prepared for and more likely to seek out life and environmental science careers. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to implement innovations that improve STEM teaching and learning at HSIs. 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

Catalyst materials that speed up chemical reactions play a critical role in the production of energy and chemicals. The catalyst can change during this process, as metal atoms rearrange on the nanoscale, forming new structures with distinct properties and performance. Manipulating such changes could lead to improved materials for industrial reactions, but research progress has been limited by a lack of general principles to understand and control catalyst dynamics. To address this challenge, researchers will integrate advanced computer modeling, accelerated by artificial intelligence and machine learning, with experimental tools to study how catalyst structures evolve during reactions. This workflow enables efficient screening of a wide range of materials to accelerate the discovery and design of more effective catalysts by controlling their dynamics. Specifically, the project will study ammonia fertilizer production, which supports global food supply but is highly energy-intensive (~2% of annual global energy consumption goes to this process), to guide the design of new energy-efficient catalysts. The project will also study how ammonia can be used as an energy carrier through cracking to hydrogen over earth-abundant catalysts. Interdisciplinary training of graduate students in state-of-the-art computer modeling and experimental methods, combined with educational outreach efforts to K-12 students, will prepare students to become leaders in catalytic materials design. This project will construct a unified, predictive model of the dynamic restructuring of metal nanoparticles on metal-oxide supports by elucidating the effects of materials properties and reaction environments on dynamic catalyst performance. In turn, these principles will enable the design of more active, stable, and ‘self-healing’ materials for industrially relevant ammonia synthesis and cracking reactions by tuning material properties to stabilize the most active nanostructures under reaction conditions, and enabling regeneration treatments that reverse the deleterious effects of catalyst sintering. The research team will develop a closed-loop workflow to integrate ab initio molecular modeling and artificial intelligence/machine learning (AI-ML) tools to efficiently screen materials composition space, combined with experimental synthesis of shape-controlled metal nanoparticles on metal-oxide supports, in situ characterization of dynamic behavior using high-resolution microscopy and spectroscopy, and high-throughput reactivity evaluation using steady-state and transient methods. Insights from this project will be used to develop more energy-efficient and stable non-precious metal catalysts for catalytic ammonia synthesis and ammonia cracking to hydrogen. The general principles developed here will have broad relevance to industrially important catalytic reactions involving catalyst restructuring. Databases and AI/ML workflows will be made publicly available to enable use of research products by the catalyst materials community. 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 addresses a national need for a skilled workforce in semiconductor manufacturing by providing undergraduate participants with immersive learning experiences in plasma engineering and materials processing. As semiconductor applications expand across industries such as computing, telecommunications, automotive, and defense systems, workforce preparation in this area is critical to national innovation, economic competitiveness, and technological leadership. By introducing participants to applied engineering, materials science, and real-world problem solving, the project strengthens the STEM pipeline and connects learners to future-facing career opportunities. The project enhances the capacity of academic institutions to prepare students for pathways into technical fields such as semiconductor manufacturing, plasma engineering, and advanced materials processing. The project engages participants from three university campuses in structured, scaffolded learning that builds from foundational technical skills to advanced plasma system design, materials characterization, and process optimization. Participants gain hands-on experience in plasma reactor operation, materials analysis, computational modeling, and process control, and apply these skills to real-world challenges such as semiconductor device fabrication and quality validation. The program features a cohort-based model that fosters peer collaboration and continuity of learning. Faculty and graduate student mentors provide technical coaching and guidance on academic and career pathways. Industry partners support participant development through feedback, design reviews, and exposure to engineering standards used in semiconductor manufacturing. Workshops at industry facilities and research laboratories and certification opportunities further immerse participants in STEM environments and expand their understanding of professional opportunities. The project aligns with industry workforce needs and offers insight into multiple career paths, including direct workforce entry and advanced technical roles. Outcomes will be assessed through participant engagement, skill development, mentor involvement, and career placement tracking, creating a replicable model for expanding access to STEM careers and advancing national workforce priorities. 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 2025 · 2025-10

Recent advancements in large machine learning models have demonstrated that increasing the number of parameters enhances computational precision and unlocks capabilities once deemed unattainable. This trend is exemplified by the rapid growth in model sizes, for instance, GPT-3 contained 175 billion parameters, while GPT-4 reportedly utilizes up to 1.8 trillion. This trajectory is expected to continue in the foreseeable future. However, the explosive growth in model size presents two major challenges for computer architecture and systems research: prolonged simulation times, which can extend from several days to weeks for large-scale models, and infeasibility of deploying workloads on a single compute engine (e.g., a graphics processing unit (GPU)) due to limited on-device memory capacity. To address these challenges, this project proposes the development of scalable simulation techniques and advanced memory management strategies tailored for large-scale machine learning workloads on GPUs. Unlike existing application-agnostic approaches, this research will leverage the distinctive data access patterns and value distributions of modern machine learning models to enable more efficient memory compression and more accurate simulation acceleration. While the primary focus will be on emerging machine learning models, the broader objective is to advance GPU computing to better accommodate any big data workload constrained by memory limitations. This will facilitate faster and broader adoption of GPUs across diverse computing domains, driving continued innovation in computational science. The outcomes of this research will be integrated into both new and existing undergraduate and graduate curricula, as well as K-12 outreach initiatives, fostering a deeper understanding of cutting-edge computing technologies across educational levels. This project would answer two research questions: how to simulate large machine learning computing and how to utilize GPU local memory better when the memory is oversubscribed. While large-scale simulation and memory management have been widely studied, most existing approaches fail to capture the unique architectural characteristics of GPU computing and the specific behaviors of emerging machine learning workloads. Rather than relying on application-agnostic or user-dependent sampling techniques, this research will exploit the distinctive compute and memory access patterns inherent to machine learning models. The first thrust will research efficient simulator acceleration methodology by leveraging the fact that machine learning models are typically executed with highly optimized library functions. These library functions tend to have similar architectural behaviors depending on the operational and data size characteristics. The project will identify representative sample kernels whose performance can be extrapolated to other similar kernels, thereby significantly reducing simulation overhead. By leveraging characteristics of the library functions, the second thrust will explore efficient memory expansion and compression strategies such as dynamic memory prefetching and eviction policies to mitigate the effects of memory oversubscription. The second thrust will develop novel quantization techniques that take advantage of the unique value distributions of weights and gradients within individual tensors. Unlike tensor-oblivious methods, this targeted approach aims to reduce memory footprint more effectively while preserving model accuracy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award will enable the development of an integrated experimental and computational platform that accelerates the discovery of protein-protein interactions (PPIs). PPIs are important in governing numerous processes like development and the onset of pathological conditions such as cancer and neurodegenerative disease. PPIs form extensive networks that vary across time, conditions, and cell types, creating a complex and high-dimensional problem. There is a pressing need for universally applicable tools that can comprehensively and efficiently map these networks. The platform developed in the project will accomplish this by transforming the mapping problem into a mathematical one that is easier to solve. The award will also support three undergraduate students, one graduate student and two postdoctoral scholars to receive interdisciplinary training that combines proteomics and computational biology. This project will take a new approach to network mapping by transforming the problem into a compressed mathematical space that can be analyzed more efficiently. To implement this compression, the platform will combine an algorithmically optimized pooling scheme, immunopurification-mass spectrometry, and a novel sparse signal reconstruction algorithm. By increasing the throughput of PPI discovery, the platform will democratize the study of regulated protein interactions for researchers with varying resources. It will also empower scientists to construct medium- to large-scale PPI networks across multiple conditions, fostering transformative discoveries in the biological sciences. Additionally, it holds promise for industry applications, particularly in therapeutic antibody screens, making such research more accessible and impactful. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

NON TECHNICAL SUMMARY: From desalination membranes to sensors for detecting nerve agents to drug delivery carriers, porous materials represent a critical materials class that supports myriad applications. Over the past several decades, there have been significant advances in developing porous materials with tailorable physical and chemical properties from non-biological components (e.g., metal ions, organic molecules, etc.). However, there is a growing interest in fabricating functional porous materials from biological building blocks (e.g., proteins and peptides), which display unparalleled levels of structural, chemical, and functional diversity as evidenced by the complex and functional assemblies that are ubiquitous in biology. The goal of this project is to establish a new, synthetic route towards building porous materials from peptide-based building blocks. Peptides are ideal assembly building blocks, because they can be synthesized directly in the lab and thus their structural and chemical properties can be systematically tuned to create programmable materials that perform alongside their biological counterparts. Moreover, because they are synthesized chemically, one can easily integrate non-biological features to further diversify and enhance peptide-based materials with novel properties that extend beyond nature. This NSF project establishes a new family of porous materials that are derived from synthetically modified collagen-mimetic peptides. Results from this project aim to demonstrate that their chemical and physical features, i.e., pore chemistry and pore size, can be systematically engineered through altering the peptide sequence design. The outcomes from this project will be packaged into assembly design rules that will be used as a blueprint for creating future porous biomaterials that are suitable for biological and environmental applications. Broader impacts of this project involve providing hands-on research and professional experiences for area high school students to engage in cutting-edge scientific research, which is a critical step for maintaining and growing the pipeline of new American talent to enter the STEM workforce. TECHNICAL SUMMARY: This NSF project establishes a novel class of mesoporous frameworks that are self-assembled from amphiphilic collagen-mimetic peptides (aCMPs). These aCMPs comprise two domains: (1) a crystallizable collagen-mimetic peptide domain and (2) a hydrophobic domain comprising an alkyl chain that is appended to the N-termini of the CMP domain. Both domains, which contain electrostatic and hydrophobic driving forces, work synergistically to assemble into crystalline frameworks comprising hexagonally arranged, 1D mesoporous channels. This project entails three research aims that center on engineering the peptide sequence design to systematically adjust the pore size (Specific Aim 1) and pore chemistry (Specific Aim 2) of aCMP frameworks, and to demonstrate the potential utility of this materials class by encapsulating functional guest species, including proteins and inorganic nanoparticles via covalent and non-covalent interactions (Specific Aim 3). The knowledge obtained from this project serves as a guide for the development of future porous biomaterials with customizable physical and chemical properties, which is important for developing functionally relevant materials for applications in health, catalysis, separations, and sensing. The broader impacts of this project afford the education and training of area high school students to STEM research through supporting a 9-week summer research program at UC Merced and the dissemination of that research at an ACS Western Regional Meeting (WRM). These educational and professional activities bolster the training of the future American STEM 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.

NIH Research Projects · FY 2026 · 2025-09

Project Summary/Abstract Tissue renewal is essential for long-lived organisms such as humans. Billions of cells are replaced daily in the human body; paradoxically, this constant cellular turnover of aging and damaged cells provides recurrent opportunities for cancer development. Indeed, over 90% of human cancers originate in epithelial tissues that undergo frequent renewal throughout life. However, studying cellular transformation during tissue renewal is challenging as it involves asynchronously different turnover rates (e.g., skin and intestinal epithelia). Tissue turnover is also influenced by complex crosstalk across tissues that impact cellular decisions in the adult body. DNA damage represents one of the earliest manifestations of cellular transformation, and it is a poorly understood hallmark of cancer. In this proposal, we seek to gain fundamental knowledge about the mechanisms underlying the survival of stem cells with DNA damage and the gain of uncontrolled proliferation as they respond to demands of tissue renewal in the adult body. We analyze stem cell behavior in situ and consider the influence of surrounding tissues. We capitalize on an emerging experimental model system based on planarian flatworms. Planarians undergo the constant renewal of tissues fueled by stem cells called neoblasts. DNA double-strand breaks (DSB) are the most dangerous form of DNA damage, and we developed a strategy to induce a cancer phenotype in planarians that evolve in 12 days. This phenotype is based on the functional disruption of an evolutionarily conserved tumor suppressor gene PTEN, the second most inactivated gene in human cancers. Downregulation of PTEN with RNAi in planarians elicits a cancer phenotype resembling the mammalian counterpart. Cellular transformation becomes noticeable within a few hours after PTEN inhibition and evasion of apoptosis, cellular over-proliferation, tissue invasion, and the formation of lethal tumors appear in under two weeks. Strikingly, disrupting neural signals suppress the cancer phenotype offering unprecedented opportunities to learn how to control cancer. We propose using robust genetics and cell biology analyses and cutting-edge genomic studies to integrate information from body regions, neural input, and single cells to reveal multiscale information driving the survival and proliferation of cells with DNA damage during tissue renewal. The information obtained from these experiments is expected to uncover critical mediators connecting adult tissue maintenance and its relationship to cancer progression. Our project addresses fundamental tenets of health and disease by analyzing systemic tissue renewal containing cells that evade apoptosis and divide with genomic instability.

NSF Awards · FY 2025 · 2025-09

This project is a collaboration between the University of California - Merced and University of Alaska Fairbanks. Climate change and the overuse of natural resources are causing significant shifts in ecological communities, leading to novel states that lack modern equivalents. These changes often result in reduced species diversity and fewer interactions between species. To predict how future ecosystems will respond, it is essential to study the dynamics of past ecosystems, especially those from the distant past. Earth's history includes environmental disturbances of similar magnitude and direction to what we are experiencing today, and the imprints of these events are left on ancient ecological communities that are recorded in the fossil and historical record. This research aims to uncover how marine communities have responded to climate change and resource exploitation in the past, focusing on the evolution of large-bodied filter-feeding baleen whales and their apex predators near the Eocene-Oligocene transition, as well as the more recent anthropogenic impacts of industrialized fishing on these species. By studying these evolutionary and anthropogenic shifts, the aim is to reveal how past changes have shaped marine food web structures and their broader ecological function, providing insight into the potential future of marine ecosystems. The project will support the training of graduate student researchers at the University of California Merced and the University of Alaska Fairbanks. This project introduces a novel framework for reconstructing and analyzing the dynamics of historical and paleo-ecosystems, connecting physiological constraints of species to community structure. The aim is to address three primary questions: (1) Do species interactions predict specific body size constraints shaping marine communities throughout the Cenozoic? By integrating bioenergetic and generalized dynamic models, we will explore how energetic flows among small groups of interacting species (motifs) influence population persistence. (2) How do the dynamics of size-structured species interactions provide insight into structural constraints of food webs? The objective is to assess interaction feasibility based on body size and predict structural constraints in broader community contexts. (3) How do the dynamic limitations of size-structured species interactions impact the stability of Cenozoic marine food webs? This study will evaluate how ancient marine community changes resulted in unique ecosystem structures, offering insights into current and future marine ecosystems impacted by climate change and exploitation. The proposed integrative modeling approach aims to uncover new insights into the structuring forces of ecological communities and pave the way for reconstructing the dynamics of no-analog paleo and historical food webs. By improving our ability to predict and understand the complexities of past, present, and future ecosystems, the proposed approach, rooted in fundamental energetic trade-offs, can be broadly extended to communities across various timescales, from pre-Cenozoic to future climate scenarios yet to be experienced. This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences and the Office of Polar Programs Antarctic Organisms and Ecosystems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Star clusters are dense collections of hundreds to hundreds of thousands of stars. These star clusters are found around most galaxies in the Universe. This research team will study how these star clusters evolve and interact with their host galaxy. The investigators will analyze advanced computer simulations that trace the origin and evolution of galaxies like the Milky Way and their star clusters. They will create planetarium shows and other outreach programs accessible to everyone in the states of California and Texas. This program will provide research opportunities and training in coding and numerical simulations for undergraduate and graduate students in astrophysics. The research team will use high-resolution cosmological simulations to track the orbital dynamics of long-lasting open star clusters across time and space. They will examine how an evolving potential and galactic environment impact the orbital trajectory of star clusters. They will also assess the reliability of traditional orbit modeling techniques, specifically orbit integration using a fixed potential, to accurately trace the location and history of stellar systems older than 1 Gyr. This research will generate a comprehensive multi-scale perspective into the relationship between star cluster dynamics and the evolving galactic potential. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

To produce seeds and fruits, plants must be pollinated. While some plants can self-pollinate, approximately 90% of flowering plants rely on animals for this essential service. Bees are among the most important pollinators for maintaining healthy ecosystems and agricultural productivity. However, many pollinator populations are declining, posing a threat to food production, plant diversity, and natural habitats. This project aims to understand how the choices individual bees make—such as which flowers they visit and how much time they spend foraging—affect their reproduction and, as a result, their populations’ persistence. By studying a native solitary bee species, this research will reveal how these behaviors influence broader environmental patterns and contribute to sustaining bee populations. The unique approach of this study combines greenhouse experiments with camera technology and plant DNA analysis, offering unprecedented detail for uncovering how foraging behavior directly impacts offspring numbers. The findings will advance scientific understanding of pollination while informing conservation efforts and supporting both agricultural production and natural ecosystems. Finally, through explicit mentorship and experiential learning programs, this project will provide undergraduate students with valuable hands-on experience in both field and laboratory research, fostering the next generation of scientists and strengthening the future of America's workforce. Linking species interactions to individual and population-level fitness remains a significant challenge in ecology and evolution. Individuals and species are intricately connected through networks of interspecific interactions, affecting both individual and population fitness. These interactions occur between individual organisms and influence individual performance and survival. By combining greenhouse experiments, pollen metabarcoding, and an innovative camera system, this project will test the effect of interaction patterns on both individual and population fitness, specifically, (i) how variations in interaction structure affect offspring quantity and ratio of each sexual phenotype (fitness); (ii) whether observed interactions accurately represent the foraging and nest-building requirements for pollinator reproductive success (nectar and pollen); and (iii) how foraging behavior, measured as time spent outside the nest, affects dietary breath and pollinator fitness. Results will integrate individual variation into ecological networks, elucidating how higher levels of organization constrain plasticity in foraging behavior and cascade down to impact reproductive outcomes. Ultimately, this research will clarify how interaction networks scale down to pollinator fitness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Homogeneous transition metal catalysis is one of the most significant tools in drug discovery and biomedical research. The development of practical, environmentally friendly catalysts remains an important goal in medicinal and process chemistry, as the sustainable development of new medicines is dependent on the availability of a diverse set of synthetic methods. However, the pharmaceutical and chemical industry is over-reliant on precious metal elements which are scarce, toxic and, expensive. This proposal describes research into novel metal complexes based upon abundant 1st-row metalate species for application in catalytic transformations of biologically relevant small molecules. In Aim 1 of the proposal, we will develop and investigate the structural and electronic features of a new class of metal complexes in unusually low oxidation states and supported by N- heterocyclic phosphenium ions (NHPs). This knowledge will then be used to facilitate the development of functional catalysts. In Aim 2, the development of metalate-catalyzed transformations will be investigated. A major focus will be on the development of small molecule functionalization using careful mechanistic analysis of the novel pathways likely to emerge from the use of unprecedented metalate catalysts. The new catalysts and transformations developed in this proposal will allow medicinal and process chemists the ability to conserve valuable and non-sustainable resources (precious metals) which represent a key technology in drug development. These approaches will in turn be useful in the preparation of new materials and medicines to further biomedical research.

NSF Awards · FY 2025 · 2025-08

This program uses gravitationally-lensed quasars to study the nature of dark matter. By using high spatial-resolution spectroscopy, Nierenberg will measure positions and flux ratios for 200 multiply-lensed systems -- an order of magnitude increase in the existing number of similar measurements. These data will be used to determine the relative number of dark matter halos well below the mass threshold for galaxy formation, the presence of dark matter particles with high enough velocities to impact galaxy formation and galaxy clustering, and to confirm or exclude the existence of entirely dark halos. Research-integrated educational components include training and mentoring of a postdoctoral scholar and graduate students, a summer academy in STEM topics targeting 4th and 5th graders, course curriculum development in General Relativity, and public lectures series on Nierenberg’s research. Integral field spectroscopy with adaptive optics on multiple large-telescope facilities will be used by Nierenberg to constrain the distribution of dark-matter halos from the narrow emission-line flux ratios of quadruply lensed quasars, selected from Euclid and LSST imaging. Lens flux ratios are sensitive to the second derivative of the gravitational potential, offering unique sensitivity to the number and distribution of low-mass halos. A primary result from this work will be a definitive measurement of a possible turnover mass (at 106 solar masses) in the halo mass function, indicative of warm dark matter. The analysis approach is statistical, using forward modeling and Bayesian computing methods to evaluate the highly stochastic mapping of dark matter distribution parameters to predicted flux ratios. The educational components of this program are intertwined with the research themes, providing instruction and mentoring in the scientific method, scientific computing and dark matter research to students spanning elementary school through postdoctoral career phases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This CAREER project will provide new knowledge on the fate of peroxy radicals in the atmosphere. Experiments will be conducted in the NSF NCAR chamber facility to study the mechanisms and kinetics of peroxy radical reactions. As a result of worldwide reductions in nitric oxide emissions, the lifetimes of peroxy radicals is extended, creating a new regime for their fate. The new knowledge developed through this research will be critical for vast regions worldwide transitioning from polluted environments to a regime of limited nitric oxide influence. The overarching goal of the research is to gain a mechanistic understanding of the interconversion and ensuing isomerization of various peroxy radical (RO2) structural isomers at their extended lifetimes, and to evaluate the significance of this chemistry in driving the secondary organic aerosol (SOA) formation. The specific objectives of the project are to: (1) Characterize the range of RO2 lifetimes by creating a unique “intermediate- nitric oxide” regime in steady-state chamber experiments and box model simulations; (2) Estimate the rates of interconversion and ensuing isomerization of aromatic RO2 isomers by monitoring the characteristic product distributions across varying RO2 lifetimes; and (3) Pinpoint the dominant isomerization pathway of each aromatic RO2 isomer and identify the produced highly oxidized organic molecules (HOMs) that contribute to the SOA formation. The project is expected to support six undergraduate students as they prepare for graduate study in atmospheric science. Educational materials on air quality will also be developed for the local high schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Microbes existing within a host, whether permanent or transient, can influence the host’s health and fitness via shifts in microbe-host interactions, which can provide an opportunity for other microbes to invade. This research project seeks to understand how viruses alter the lung microenvironment to allow other organisms, specifically fungi, to establish and grow. The proposed research will not only expand our understanding of viral-host-fungal interactions, but will also benefit human health by providing information that may result in better interventions for and control of such infections. This project includes an education program that will expose undergraduate students early to STEM research through a first-year seminar course. The award also provides research opportunities for undergraduates recruited from this course, and training opportunities for both graduate and undergraduate students interested in careers in STEM fields at the University of California, Merced. Viruses, the most abundant biological entity on earth, impact nearly all organisms (e.g., microbes, plants, animals) through their capacity to alter the microenvironments of the organisms with which they interact. Studies of multiple viral-host systems have demonstrated that viruses alter the metabolic profile of the host microenvironment, thereby influencing other microbes within that system, including allowing invasion by organisms such as the fungus Aspergillus fumigatus. The investigators will use metabolomic and molecular approaches to determine the microenvironmental changes and molecular responses of viral-host-fungal interactions involving influenza virus and A. fumigatus in the murine lung. Specifically, the project’s research objectives are to: 1) identify the virus-host mediated microenvironment alterations that allow for A. fumigatus growth, 2) define the molecular adaptions that allow A. fumigatus to grow in the viral-host environment, and 3) determine whether the viral-host-fungal interaction changes over time. Results from this research will contribute to understanding the rapidly evolving field of viral-host-fungal interactions by deciphering viral- and fungal-mediated strategies that shape and allow these interactions to occur. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Immunological memory is the ability of our immune system to respond with greater strength and quickness upon re-encounter with the same pathogen (i.e. secondary infection). Immunological memory is the basis for vaccination which remains the most successful method for preventing infectious disease. Yet, a fully protective vaccine that prevents a single human parasitic disease has not been realized to date. Why is immunity to parasitic pathogens so difficulty to achieve? Our current work on secondary infections with the apicomplexan parasite, Toxoplasma gondii, suggests that protective immunity is genetically determined. In this grant submission, we highlight a published forward genetic approach using an AxB,BxA mouse panel that revealed the transcriptional regulator of NF-κB, IκBNS, encoded by Nfkbid, is required for humoral immunity to T. gondii. However, the mechanism by which IκBNS mediates protective immunity against T. gondii is not fully understood. We hypothesize that IκBNS determines the protective capacity of CD4 T follicular helper (Tfh) responses to Toxoplasma gondii. Experimental approaches from immunology and molecular biology will be utilized to test these hypotheses. In Aim 1, a series of mouse lines with cell-specific deletions of Nfkbid will be used to understand the nature of the humoral defect in Nfkbid null animals. We hypothesize that Nfkbid- deficient Tfh cells are unable to help B cells mount antibody responses to T. gondii. We propose to define the transcriptional regulation mediated by IκBNS, including its impact on NF-κB-gene interactions, that confers enhanced Tfh responses and immunity to T. gondii. In Aim 2, we will explore how T and B cell intrinsic expression of Nfkbid influences immunity to virulent challenge with T. gondii, and the role of IκBNS-dependent antibodies in mediating this protection. With the overarching goal of preventing human toxoplasmosis, insights gained from this R21 proposal will guide future projects on vaccine and therapeutic strategies that best prevent toxoplasmosis in susceptible mice.

NSF Awards · FY 2025 · 2025-07

Modern transportation systems have undergone a significant transformation, marked by increased design complexity, advanced networking capabilities, and an overwhelming surge in data. As a result, today's automotive system is a collection of interconnected embedded systems, some of which (such as the infotainment system) are also connected to the Internet. As the number of connected vehicles grows, traffic systems become networked and autonomous fleets emerge in the consumer space, the potential for cyberattacks on U.S. transportation infrastructure increases significantly. Given the criticality of the transportation cyberinfrastructure (CI), this project builds expertise in the automotive cyber domain through development of testbed and training curriculum material and summer training workshops for educators, students, and researchers. The project addresses critical issues in cyber workforce development in the transportation and automotive sectors through three initiatives. The first leverages faculty from different disciplines to develop a coherent open-source CI that provides a unified research platform for automotive and autonomous systems. Leveraging this CI, the second initiative delivers modular training materials on secure transportation system design. Finally, the third initiative arranges a series of workshops to directly train 80 participants, including faculty members, graduate students, and cyberinfrastructure professionals. The project also produces and disseminates royalty-free resources to support workforce development and education in transportation system security. 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 · 2025-07

This project will adapt and implement evidence-based practices from two prior NSF Revolutionizing Engineering Departments (RED) awards. The project will generate new knowledge about how successful strategies can be effectively translated to a new institutional context, with the goal of broadening their impact to benefit all students. Specifically, the project will implement a design-thinking approach among the electrical engineering (EE) faculty to empower a collaborative curriculum redesign process. Additionally, to enhance the professional development and formation of electrical engineers, this adaptation will extend training beyond EE standard technical skills to include cross-disciplinary expertise, thereby enhancing our students' unique appeal to potential employers. This project addresses critical national needs by preparing graduates who are professionally competent and adept in interdisciplinary collaboration. Such skills are necessary for maintaining U.S. leadership in rapidly evolving technological sectors such as high-speed communications, data centers, and modern power grids. Aligned with NSF’s RED initiative, this project addresses gaps in engineering education by ensuring continuity of professional competencies beyond the traditionally well-supported first and senior years. By collaboratively redesigning courses in sophomore and junior years, the project seeks to increase student retention and foster a stronger academic identity among EE students. Furthermore, the adaptation process itself will generate new knowledge on how educational innovations developed in prior RED-funded projects can effectively be scaled and contextualized to other institutional settings. At the regional and national level, the outcomes of this project will significantly enhance career preparedness for students, provide engineers with broader skill sets, and support industries critical to national economic prosperity and security. The project team will implement two core interventions: (1) a collaborative design-thinking-based course review process and (2) the development of socio-technical modules that contextualize electrical engineering practice in broader cross-disciplinary context. These interventions will be applied across the EE curriculum at UC Merced and aim to establish a coherent alignment between course content and the skills needed for professional success in engineering. Faculty from different disciplines (including outside of EE) will collaborate to build this curriculum and engage in training on evidence-based pedagogy, design thinking, and continuous improvement. Activities such as vision and values mapping will support the development of a shared cultural mindset among both senior and junior faculty and strengthen their roles as effective engineering mentors. The project’s research will generate new knowledge on: (1) the challenges of adapting organizational change strategies to new institutional settings, (2) the influence of collaborative faculty design processes on student self-efficacy, (3) the impact of our departmental interventions on student identity as they progress from first year to capstone, and (4) the relationship between student retention and motivation to contribute as electrical engineers. To address these questions, the team will collect longitudinal data on students and faculty throughout the life of the project, tracking professional identity formation, engagement, and motivation. The project aims to support pedagogical sustainability, improve student-centered teaching practices, and cultivate a departmental culture that embeds continuous, collaborative improvement as a central norm. 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.