California State University-Long Beach Foundation

NSF Awards · FY 2026 · 2026-07

This project will examine how different substances mix and react in flowing mixtures of liquids and gases. When fluids mix and undergo chemical reactions, tiny random swirls of turbulence control how well molecules mix and react. Understanding these processes is very important because they affect the efficiently of energy production, how pollutants spread in the environment, and how chemicals, materials, and medicines are manufactured. However, predicting and controlling mixing and reactions in such chaotic flows is a big challenge because small changes at the microscopic level can lead to very different outcomes. To address this challenge, the project will develop new computer models that more accurately represent mixing and chemical reactions in complex flows at the molecular scale. Better simulation of these processes will help engineers design cleaner engines, more efficient chemical reactors, and processes to create materials with less waste and pollution. The project will also emphasize education and accessibility by training students in advanced simulation techniques, releasing new software tools as open source to the public, and engaging students through outreach activities. Through these efforts, the project will advance scientific knowledge while contributing to national prosperity, environmental protection, and public health. The research will develop a new bounded Langevin micromixing model to simulate molecular-scale mixing in turbulent multiphase flows. The stochastic model enforces physical bounds on scalar concentrations and provides improved predictions of how mixing fluctuations decay over time. Chemical reaction kinetics will be tightly coupled with the mixing process through Jacobian matrix analysis, allowing the model to adjust local mixing rates based on relevant reaction time scales. In addition, the model will adapt to local flow conditions using the Damköhler number, ensuring accurate treatment across regimes ranging from mixing-limited to reaction-limited behavior. The resulting models will be implemented within a computational framework and rigorously validated against high-fidelity data from direct numerical simulations and experimental measurements. The computational approach employs quadrature-based moment methods to efficiently represent the evolving distributions of scalars in mixing and reacting systems, capturing their inherent multiscale nature. The finalized model will be released as open-source software through the OpenQBMM library and integrated into OpenFOAM, enabling broad use and further development by the research and engineering communities. Finally, the project integrates research and education by actively involving graduate and undergraduate students in model development and validation, providing hands-on training in advanced simulation techniques and supporting workforce development in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

In the future, autonomous systems will increasingly rely on large networks of intelligent agents, such as drones, vehicles, robots, and smart infrastructures. These agents will need to work together and coordinate their actions. Achieving trustworthy and resilient coordination among collaborating agents is challenging, particularly when they may malfunction, act in opposition, or vary in capabilities and objectives. This project aims to revolutionize multi-agent systems (MAS) that exhibit resilient, adaptive, and trustworthy behavior by creating intelligent coordination mechanisms capable of maintaining performance and safety even in the presence of uncertainty, failure, or conflict. The expected outcomes of this research will lay the groundwork for building autonomous systems that can be confidently deployed in complex, real-world scenarios. The research will advance foundational knowledge through three key efforts: (1) designing algorithms to detect and mitigate abnormal behaviors in cooperating agents; (2) managing adversarial or non-cooperative agents using game-theoretic and adversarial machine learning methods; and (3) enabling resilient coordination among diverse agents through robust distributed control frameworks. The proposed work will support high-impact applications in transportation, disaster response, and smart infrastructure, where reliable MAS coordination is critical for public safety and operational efficiency. It will inform best practices and ethical guidelines for integrating AI and multi-agent systems into critical infrastructure, ensuring fairness, transparency, and reliability in their deployment, and ultimately foster public trust in AI technologies and contribute to sustainability, safety, and social good. This research will also strengthen research capacity by expanding interdisciplinary collaborations, developing new curricula and workshops, and offering hands-on research opportunities for students from a wide range of backgrounds. 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

Software performance is a critical quality attribute influencing system responsiveness, resource utilization, and user satisfaction. However, modern software systems face increasing performance challenges due to their complexity, making traditional performance testing and optimization approaches insufficient. This proposal introduces a unified, Large Language Model (LLM)-empowered framework to systematically address these challenges. The project will improve software performance engineering by advancing automation in performance testing, issue localization, and optimization. Additionally, the project’s research results will be integrated into computer science curriculum and provide online training modules to equip future software engineers with state-of-the-art performance engineering skills. The project will focus on automatic generation and prioritization of performance-centric test cases, leveraging Large Language Models (LLMs) to generate diverse test cases that specifically target performance issues. It will also introduce a multi-granularity monitoring framework, integrating dynamic profiling, static analysis, and Graph Neural Networks to accurately detect and localize performance bottlenecks across different system levels. Finally, the project will develop a dependency-aware, graph-based optimization recommendation system, employing LLMs with Retrieval-Augmented Generation to provide adaptive performance enhancement strategies, from fine-grained code improvements to architectural refactoring. 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 multi-disciplinary project aims to promote better data governance practices and bolster trust through the development of a Data Management Platform in Long Beach, Calif. The Platform is designed to support community access to information generated by smart city technologies. The project takes a user-centric participatory design approach to the development, evaluation and refinement of a Platform designed to enable people to readily discover and learn about the data civic technologies collect about them as they engage in routine activities, such as using the bike share or passing by a traffic camera. The new Platform will enable city residents to not only discover what data is collected about them but to also limit data practices with which they are not comfortable. The resulting Data Management Platform is expected to help close the gap between the discursive and material aspects of data privacy policy. Today, dozens of U.S. cities have adopted data privacy guidelines—without being able to take practical and effective next steps to implement or enforce them. At full scale, the Data Management Platform will make data practices associated with civic technologies more obtainable for Long Beach’s nearly 500,000 residents, potentially bolstering trust in local government. The project’s methodology and technical solutions are scalable and transferable to other cities. This project centers around the design, evaluation and refinement of an IoT mobile assistant that empowers people to exercise privacy choices. Part of this research will involve developing a simple taxonomy of privacy choices and data practices that enable city residents to specify ahead of time opt-in/opt-out choices, and develop support for authenticating users and functionality. The researchers will also develop a Privacy Dashboard that can help users review what data about them might have been collected over the past day or week. Formative evaluation and user testing will inform refinements to the mobile privacy assistant and other elements of the Data Management Platform. The project’s methodology and technical solutions are scalable and transferable to cities beyond Long Beach. 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

There is a growing shortage of cybersecurity professionals today, with the shortage estimated at 5.5 million in 2023. Cybersecurity professionals require education in data science to be able to properly collect, clean, correlate, store and analyze real system and network data, and make informed cybersecurity decisions. This project develops a large set of hands-on, virtual materials for teaching data science for cybersecurity. These materials will be hosted on the NSF-funded SPHERE research infrastructure and will be publicly and freely available to interested teachers and students. These materials will prepare new generations of cybersecurity professionals to tackle real-world problems collaboratively and at a large scale. This project builds learning materials that will enrich cybersecurity curricula at many schools and colleges, with practical, hands-on exercises that teach data science for cybersecurity. The materials will be useful as either homework assignments or class projects. The materials will include individual and group assignments. Individual materials will provide three difficulty levels - beginner, intermediate and advanced -- and they will support self-learning and self-assessment. Group assignments will engage groups of students on realistic, large scale cybersecurity tasks to promote teamwork, collaboration and communication. The materials will be publicly and freely available to all interested teachers and students, on the NSF-funded SPHERE research infrastructure. Teachers and students will also leverage SPHERE to collect, clean, store, manage and analyze cybersecurity data to gain practical data-science skills. This project is supported by the Data Science Corps program, which supports data science education and training to build a strong national data science infrastructure and workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Traditional archaeological training often includes participation in summer field schools. However, economic barriers can reduce opportunities for many promising students, limiting their participation in these fields. In addition, archaeology is incorporating more geochemical and geophysical techniques to open new lines of inquiry and these methods require specialized instrumentation. To expand the training opportunities for young archaeologists in these new approaches while removing obstacles that limit training participation, this project explores a hybrid model combining a classical field school with laboratory analyses focused on research questions exploring interactions between ancient peoples and their environments. In this project, students actively investigate how centuries of commerce, conflict, and landscape modification altered the ancient world by studying changes in human fecal biomarkers and paleoenvironmental signals. Specifically, they first participate in field work to learn traditional excavation methods as well as non-destructive geophysical imaging and paleoenvironmental techniques. Each student then focuses on a specific research question, engaging in field collection and contextual analysis, followed by laboratory work and data analysis. Student projects culminate in scholarly presentations at regional or national meetings, providing students with the opportunity to disseminate their work and findings. This training approach includes focused mentorship and the development of critical skills such as data analysis, scientific communication, and professional networking. The project also develops partnerships with universities to include students from underrepresented groups in archaeology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project examines the determinants of methane emissions from fires in regions that exhibit particularly high propensities for fires, including fires that arise from human activity. Methane, a potent gas, has been hypothesized to be a contributor to global greenhouse effects, but precisely measuring methane emissions from fires has been technologically challenging. For example, emissions from savanna fires are broadly regarded as a major source of atmospheric methane, but previous estimates have contained high levels of uncertainty. This research introduces new sensors, methods, and approaches for obtaining comparatively precise estimates of methane emissions from savanna fires. Emissions are predicted to vary in response to the vegetation type and its moisture levels, ambient air conditions, and properties of the fire. In turn, these measurements across a range of conditions and inputs can inform subsequent efforts to estimate the effects of fire management strategies on methane emissions, potentially revealing opportunities for savannas to serve as carbon sinks. The project also provides opportunities for students to gain training in the methods of environmental science. The general objective of this research is to determine the significance of each factor that affects methane emissions and model how changes in land use and fire practices dynamically influence the methane that is produced. A secondary goal is to use simulation methods to predict changes in future methane emissions from fires at regional scales. To obtain measurements of emissions, the researchers experimentally initiate burns using practices that are used by local human populations, and then they use sensors mounted on airborne drones to document the extent of methane associated with these fires. The measurements subsequently inform the simulations of burning practices and the resulting methane emissions across diverse contexts. Workshops with local partners provide opportunities for the study to shape local land use practices in ways that advance sustainable livelihood strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This partnership project builds on the success of a prior three-year Partnerships for Research and Education in Materials (PREM) Seed grant awarded in 2021 to California State University Long Beach (CSULB) and The Ohio State University’s Center for Emergent Materials (OSU CEM) Materials Research Science and Education Center (MRSEC). Leveraging CSULB’s status as an urban Hispanic Serving Institution and OSU’s commitment to workforce development in STEM, the partnership aims to support students in science and technology. The program offers diverse career pathways, including a research-based MSc program, post-baccalaureate Bridge programs, and the CEM Research Experience for Undergraduates (REU) program, focusing on inclusive recruitment and retention through engaging research and education activities. The goal is for students to complete their degrees and progress into STEM PhD programs or industry careers, addressing the “missing millions” identified by the National Science Board. Research projects integrate materials science, chemistry, mathematics, physics, and biophysics, exploring phenomena like topology and cooperative emergent behaviors with applications in magnetic storage, energy-efficient devices, and bio-inspired materials. The program fosters a dynamic and inclusive research culture at CSULB through collaborative research, professional development, and faculty mentoring. The goals of the program are supported by OSU CEM’s expertise in education research, workforce development in STEM and serving as a model for other disciplines. This PREM project focuses on fundamental materials research and is significant for its integration of materials science, chemistry, mathematics, physics, and biophysics, focusing on discovering and exploiting commonalities in topology and cooperative emergent phenomena in materials from layered two-dimensional crystals to metallic/molecular thin films and biopolymers. Research applications aim to advance ultra-fast and energy-efficient computing, fault-tolerant quantum information processing, and next-generation electronic devices. Specific projects focus on the interplay of topology, magnetism, and superconductivity in (Chromium/Platinum/Iridium) Telluride alloys; geometric magnetic frustration in sodium-based osmates; and exotic magnetic states on curved magnetic thin films. Other projects explore tunable metal-organic magnon-spin heterostructures, topological effects in biomolecules, and theoretical aspects of topological phases. Each project combines complementary expertise from CSULB and OSU, using advanced characterization techniques, sample synthesis methods, and theoretical approaches, further strengthening collaborative relationships and aligning with OSU CEM’s interdisciplinary research groups on metal/magnetic-insulator interfaces and topological phenomena in magnetic 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 2024 · 2024-09

The global aging population presents significant healthcare challenges, especially in low and middle-income countries with limited biomedical infrastructure and access to healthcare. This project aims to address these challenges by developing a portable and cost-effective biomedical diagnostic system minimizing human interaction in the form an integrated Lab-on-a-Chip diagnostic platform produced using additive manufacturing. This project aims not only to advance biomedical engineering by integrating cutting-edge technologies but also to directly address global health disparities. As proposed Lab-on-a-Chip platform is to be tailored for resource-limited environments, the project aims to enhance healthcare accessibility and affordability worldwide. Further broader-impact aspects of the work include an interdisciplinary research effort fostering collaboration between biomedical engineering, materials science, and computer science in order to contribute to broader educational and research initiatives in global health equity and technology innovation. Specifically, this project outlines the development of an integrated Lab-on-a-Chip diagnostic platform using Additive Manufacturing techniques. The technical activities comprise 1) advancing 3D manufacturing techniques, particularly in electrode printing, and establishing design rules to enhance fabrication precision and performance; 2) addressing the miniaturization of actuation, sensors, and readout systems using CMOS technology, aiming for seamless integration for bio-sample characterization without optical components; 3) defining design rules for implementing two independent tests with 3D-printed electrodes in the form of label-free cell characterization and dielectrophoresis-impedance methods for protein detection; and 4) leveraging artificial intelligence to optimize the fabrication process of 3D-printed conductive electrodes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Climate change and the warming of the ocean has raised concerns about the long-term welfare of many species. Warm temperatures can reduce survival, and for marine fishes such effects may be particularly acute during the larval phase. Given recent temperature trends, it is critical that we understand how population sizes are likely to change in response to warming over the coming decades. However, to do so requires that evolutionary processes be taken into account. This project uses a novel set of experiments and field studies on a coastal fish species to investigate mechanisms that may either constrain or accelerate evolution in a warming, but thermally variable ocean. A major goal is quantifying genetic variation in responses to temperature. For example, populations may contain some genotypes that are cool-water specialists and others that are warm-water specialists. Understanding such variation is critical to projecting responses to ocean warming. Another focus is determining whether the species-wide genetic variation is promoted by adaptation to local temperature conditions (e.g., along a North-South gradient). Based on the estimates of genetic and non-genetic inheritance, the project tests whether evolutionary changes can be quick enough to affect the dynamics of populations over timeframes of 10-100 years. This project strengthens research and teaching opportunities at a primarily undergraduate institution that is one of the most culturally diverse universities in the nation. The team is providing research training for undergraduate and graduate students from underrepresented groups, conducting a before-and-after assessment to evaluate the effects that a recent change in fishery regulations have had on populations of California Grunion, and sharing the results of the research through exhibits at the Cabrillo Marine Aquarium and public outreach activities. Ocean temperatures are warming at an alarming rate, and temperature affects the demographic rates of most species. However, responses to temperature are nuanced. Growth and survival are often maximal at intermediate temperatures, and individuals vary in their responses to temperature. Moreover, the effects of any long-term increase in temperature on populations need to be considered in the context of temperature fluctuations that occur at various temporal scales (e.g., annual, seasonal, weekly, etc.). This project tests the hypothesis that genetic variation in thermal specialization can both constrain evolutionary responses in the short term (via interaction with temperature fluctuations) and provide essential variation required for long-term evolutionary gain. Detailed breeding experiments to measure genetic variation are being combined with experimental manipulations of seawater temperature to measure thermal reaction norms. The project is also testing for local adaptation by making repeated collections of fish from northern and southern populations and rearing their offspring in a common laboratory environment and across a range of temperatures and 1) evaluating how the genetic covariance underlying thermal reaction norms for relative fitness can either constrain or accelerate evolution in light of observed trajectories of regional water temperatures; 2) comparing the relative magnitudes of genetic vs. non-genetic inheritance of variation in thermal reaction norms; and 3) developing an eco-evolutionary modeling framework tailored to studying the effects of ocean warming that is used for evaluating the effectiveness of fishery management strategies in a warming ocean. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project will build a partnership between the American Institute of Mathematics (AIM) and California State University Long Beach (CSULB) that will invigorate mathematics research at CSULB by creating innovative student research activities and expanding a research culture for students and faculty. CSULB is an urban comprehensive university and Hispanic Serving Institution with nearly 60% of students identifying as members of underrepresented groups and 60% identifying as female. Approximately 51% of CSULB students receive Pell grants. This project will improve representation in mathematics by providing high-quality research opportunities for students and scaling these opportunities up in size to reach a broad group of CSULB students. These projects will empower students at all levels to answer previously unsolved mathematical questions and engage in innovative research. The PI will leverage a leave at AIM to design and implement research projects in the areas of knot theory and low-dimensional topology for students at CSULB, establish a faculty learning community to support research mentors, and help create a sustainable model for ongoing student research engagement and mentorship. The PI will pursue two research directions, chosen based on their accessibility for student research projects. The first direction is knot theory, the mathematical study of loops in 3D space. The Meridional Rank Conjecture states that the meridional rank and bridge index of a knot are equal. The conjecture has been verified for an array of infinite classes of knots, but the general case is open. The PI will adopt a novel perspective to this conjecture which makes use quotients of the knot group and new definitions of bridge number. The PI has assembled a group of top researchers who are committed to attending an AIM SQuaRE led by the PI to investigate the Meridional Rank Conjecture. The second direction is the topology of self-replication, which seeks to make questions like ``Which shapes can self-replicate?'' mathematically rigorous. Topological models of self-replication include manifolds that can be decomposed along a surface into two homeomorphic copies of the original (these are known as idempotents in a topological category) and n-manifolds that embed in n-dimensional real space and can be decomposed in to a collection of isometric shapes each of which is a scaled version of the original (these are known as rep-tiles). The PI will investigate novel classification theorems for rep-tiles and idempotents, strengthen existing classification theorems for these objects, and pursue applications of these theories to the field of 4-manifold topology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project seeks to address a significant research gap at the intersection of computer science and mathematics education, focusing on the content area of discrete mathematics. Discrete mathematics encompasses topics such as logic, set theory, number theory, combinatorics, iteration, recursion, and graph theory. The objective of this project is to gain deeper insights into how undergraduate students in computer science and mathematics approach and comprehend these fundamental concepts, which are prominent in both domains. The project aims to identify productive strategies used by both groups of students and understand how and why students in each of the domains exhibit cognitively divergent or convergent approaches when solving these problems. Furthermore, by characterizing the approaches taken by mathematics and computer science students, and presenting these strategies to their respective counterparts, the project seeks to explore which strategies each group of students find to be the most beneficial for their understanding. By sharing this study's findings with both the computer science and mathematics education research fields, this project carries significant potential to benefit society by contributing to more effective mathematics education at the undergraduate level. Improved understanding and communication of concepts at the intersection of computer science and mathematics can lead to more skilled graduates who are well-equipped to tackle real-world challenges. This project aligns with broader societal goals of fostering interdisciplinary learning and preparing students for careers that demand proficiency in both computer science and mathematics. In summary, this work will inform fundamental research, and, eventually, evidence-based practices that can ultimately enhance the quality of mathematics education. The objective of this project is to gain deeper insights into how undergraduate students in computer science and mathematics approach and comprehend fundamental concepts which are prominent in both domains. The study will span one academic year and comprise of two phases, commencing with data collection and preliminary analysis in the fall semester of 2024 (Phase 1). The first phase will consist of research participants engaging in a think-aloud procedure, being asked to vocalize their thought processes as they work through the survey instrument containing questions related to logic, set theory, combinatorics, number theory, iteration, recursion, and graph theory. Analysis of this first round of interviews will focus on the cognitive activity of the two groups of students. To analyze the students' cognitive activity, the project will utilize a novel analytical approach which entails combining the analytical frameworks of process/object duality with the Instrumental Approach. By integrating both frameworks, the project adopts the theoretical stance that learning is theorized as the cognitive work the learner does at the individual level, as they move between process and object conceptualizations, in collaboration with any work they do to instrumentalize computational reasoning tools to solve a mathematical problem. In Phase 2, during the second round of interviews, the project will focus on the various approaches taken by computer science students with their mathematics counterparts, and vice versa. The interviews will serve as an opportunity to member-check with the research participants about their cognitive approaches, as well as to understand the cognitive approaches that they find to be most helpful for their own reasoning. Dissemination efforts of this study will include publishing asset-based narratives of the students' cognitive approaches, as well as the creation of in-class activities that may be used in Introduction to Proofs courses. By identifying, characterizing, and sharing strategies utilized by students in both domains, we can further the field's theoretical understanding of the relationship between computational and mathematical reasoning. Ultimately, this will lead to pedagogical improvements and reveal opportunities requiring further study. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Tectonic plates interact by moving towards each other (convergent), moving away from each other (divergent), and sliding side-by-side (transform). The tectonic plate boundary on the southwest side of the North American Plate has changed relatively recently, in geologic time (<50 million years), from a convergent boundary to a transform boundary. This plate boundary change caused new faults to form, and altered the surface topography (i.e., mountains and valleys). Documenting the types of faults and the timing and rates of fault motion across two different geologic regions (the Basin and Range and Walker Lane) will increase our understanding of how the surface changes in response to changes in tectonic plate interactions. This project develops a new approach that combines three different dating methods to provide information about fault activity on different but overlapping timescales. This research will be conducted by a multi-level cohort of high school, community college, 4-year undergraduates, and master’s students. The research and education components of this CAREER grant provide research and peer-mentoring opportunities designed to recruit, train, and prepare the future STEM workforce. This project will support an early career scientist, six master’s students, six undergraduate students, six community college students, and six high school students from institutions in southern California. The peer-cohort design is aimed at increasing recruitment and retention in the geosciences, and project participants will help provide guidance on how a research experience like this increases confidence in STEM. Additionally, the education plan includes creating teaching materials for high school and community college instructors to teach about faulting, tectonics, and geochronology. These activities will be designed to easily embed Earth science concepts into existing curricula and will be publicly published so that any instructors may use them, allowing for findings from this project to have a much wider reach. This CAREER grant combines geochronological methods to evaluate how the changing of a plate boundary, and therefore, the regional stress regime is expressed in the surface deformation. A novel combination of low-temperature thermochronology, cosmogenic radionuclide, and luminescence dating techniques is used to quantify spatial and temporal changes in the timing and rates of fault-slip and fault-growth patterns across the Basin and Range extensional province and the Walker Lane transtensional province from the Oligocene to Holocene. These techniques record information on different but overlapping timescales (e.g., Luminescence: 1 to ~300,000 years, cosmogenic: 10,000 years to 5Ma, low-temperature thermochronology: 1Ma to 3Ga) and from separate regions within the crust (i.e. <1m below the surface, 1-2 meters below the surface, 1-6 km depth). The complementary use of these geochronologic methods to address tectonic questions will fill the important temporal gap between shorter time-scale deformation, near-surface exhumation, and long-term geologic constraints on faulting. Fault initiation and growth as well as the development of these two provinces, as we know them today, documents the plate boundary transition from compressional regime with a subduction zone boundary between the North American Plate and Farallon Plate to a transform boundary between the North American Plate and Pacific Plate accommodated by the San Andreas strike-slip fault zone and the Walker Lane shear zone. Acquiring these new data and compiling it with existing data to address rates of deformation across the Basin and Range and Walker Lane provinces will advance our knowledge about the development of each of these structural regions and will contribute more generally to the understanding of how a complete shift in plate boundary relationships and stresses affect surface deformation over time and space. This is accomplished through the creation of a research cohort composed of undergraduate and master’s level students at California State University, Long Beach (CSULB), as well as community college (CC) and high school (HS) students from southern CA working together to create a community that provides support, feedback, and peer-mentoring about research and education. This CAREER grant supports field, lab, and computational work allowing students to be exposed to many different types of data collection and analyses to increase interest, access, and introduce the process behind scientific research. This project involves the creation of educational lessons for HS and CC instructors, to adopt for introduction of topics such as faulting, tectonics, and geochronology, in their current science classes. This project (1) exposes students to Earth science research experiences at an earlier stage in their education and (2) creates a peer-peer mentoring network between HS, CC, undergraduate and graduate students to facilitate a welcoming research environment. Evaluation of this peer-network research experience strategy will provide insights into the efficacy of such a model to recruit students to Earth science fields and retain students to help build the future 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.