University Of New Mexico

NSF Awards · FY 2025 · 2025-04

This grant supports a workshop on the formalization of human factors into computational models. The workshop will convene 40 participants with expertise in human factors, behavioral economics, cognitive neuroscience, social psychology, human-centered design, control theory, robotics, formal methods, and autonomous systems. Through presentations and discussions, participants will articulate the state-of-the-art capabilities in these fields while identifying research gaps and challenges related to the development and implementation of theoretical and computational models of human factors. An interdisciplinary approach that facilitates a common language and objectives across disciplines is key to this endeavor. The results of this workshop will advance the fields of robotics, dynamics, and controls by identifying advanced capabilities and technologies that could arise from the use of models that methodologically capture non-trivial human behaviors. The results of this workshop will help advance the science of autonomous systems and could broadly impact research directions and capabilities in robotics and control. After the workshop, a report will be generated to summarize the findings, which will be shared with the general research community. Despite extensive work in the development of autonomous dynamical systems, system capabilities are fundamentally limited by the challenges associated with effective, accurate, and computationally tractable models of human factors. To move the field beyond simplistic assumptions of human behavior that limit system capabilities, new approaches are needed to more faithfully capture the complexities humans bring to autonomous systems. The activities of this workshop are designed to bring together systems and controls researchers who share the challenge of translating important concepts from behavioral economics, social psychology, human factors, and human-centered design into frameworks amenable to computation and control of dynamical systems. Workshop activities will help participants develop and employ a common language that facilitates a meaningful exchange of ideas across disciplines to articulate better a roadmap for addressing research gaps and challenges that exist in formalizing human factors into computational models. Identifying these challenges is essential for advancing the field of systems and control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The oxygen isotope chemistry (the 18O/16O ratio) of natural materials has been used for decades to understand the natural world, including topics as diverse as paleotemperature estimates, food chain feeding structures, meteorite classification and water resource availability. It has recently been shown that the rare 17O isotope provides additional information, but unfortunately, it is very difficult to measure with sufficient precision. A revolutionary laser spectroscopy system was developed for measuring 17O in the last several years. It is easy to use, costs less than traditional mass spectrometers and is more rapid than traditional mass spectrometry. It is truly a ‘game changer’ for isotope analyses. There are several limitations to this new method however. It is proposed here to develop a method where the traditional N2 carrier gas is substituted with N2O. This allows the sample to be frozen out of a vacuum line at liquid nitrogen temperatures, and therefore easily transferred in a vacuum line. If the system works as expected, sample size will be reduced by an order of magnitude, analytical precision should double and analysis time will be reduced. University of New Mexico personnel will work with the laser manufacturer, Aerodyne Research Inc. in an academic-industry collaboration to perfect the N2O system. The new instrument will be an integral part of the research of three Ph.D. students and serve as instrumentation for several undergraduate senior-honor’s theses. It is expected that this relatively simple instrument will become the standard method for analyzing triple oxygen isotope ratios. Laser-based spectroscopy (TILDAS) is a straightforward, rapid and accurate method for determining triple oxygen isotope ratios (d18O, Dˈ17O) of CO2 gas. Sample preparation and laser analysis are straightforward, however, the system requires a precisely diluted sample of ~450 ppm CO2 in an optically transparent carrier gas (N2 or CO2-free air) which involves a sophisticated interface. Gas mixing takes close to 30 minutes, and aliquots of the sample gas are pumped away after each measurement, limiting ultimate precision. Here it is proposed to substitute a liquid nitrogen condensable gas (N2O) in place of N2. Being able to freeze out the sample will lead to 1) rapid mixing times, doubling the throughput rate, 2) smaller sample requirements to as low as 14 micrograms calcite equivalent and 3) increased precision due to repeatable analysis of the same sample gas. It will significantly expand the reach of triple isotopes. Initial test results are very promising (d18O ±0.03‰; d17O ±0.05 ‰, Dˈ17O ±60 per meg, 1s), but further improvements require additional modifications to the TILDAS system. The inlet system will be redesigned and collaborations with Aerodyne Research Inc. will improve the analysis software and interface to develop an automated N2O based system that can be used for triple isotope analysis of CO2 gas, carbonates, water, soil water, leaves, blood, etc. This will be the primary research effort of a Ph.D. student working closely with the principle investigator. Results will be published in peer-reviewed scientific journals and work with the laser manufacturer will result in the system available to new users. Initial applications (3 Ph.D. students and one undergraduate student) will be for 1) soil water and associated carbonates, 2) lake carbonates for paleoclimate, 3) body water (measured as blood) of desert dwellers, 4) evaporation studies of equisetum, and 5) degassing kinetics of modern travertines. 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 · 2025-03

PROJECT SUMMARY The bioavailability of plastic particles less than 1000 nm in diameter makes nanoplastics the greatest biomedical concern. Therefore, the analysis and isolation of plastic nanoparticles from a variety of samples (e.g. bottled water, environmental water, animal tissue) is critical to many areas of biomedical and environmental research. Current nanoplastics isolation approaches are costly, time consuming, and do not adapt to real-time analysis. This limitation is a notable technology gap that broadly impacts studies on the biomedical effects of plastic nanoparticles. In this proposal, we are developing a high-throughput separations platform that uses differential application of bulk mode acoustic standing waves (BAWS) to simply and rapidly enrich plastic nanoparticles from diverse samples. To create a proof-of-concept prototype of our standing wave acoustics for nanoplastics separations (SWANS) platform for the detection and separation of plastic nanoparticles, we are constructing a multistage BAWS device that will first negatively select for nanoplastics via removal of microparticles into clearing streams and then enrich for nanoplastics via long exposure to high frequency standing waves. The SWANS system will be used to isolate nanoplastics from real samples and compared to existing nanoplastic isolation approaches. We expect that SWANS will dramatically simplify and shorten the process required to isolate nanoplastics for analysis, which is the rate limiting step in current research. SWANS will scale between 50 nm and 1000 m particles. Throughput will depend on particle size, but tens of mL per minute flow rates with efficient enrichment for nanoplastics will be achievable. As such, processing of mL samples in minutes and L scale samples in fractions of an hour will be possible. Further development will enable automated operation or in-line placement of SWANS for many applications, including monitoring and purifying potable water systems, controlling the effluent properties of industrial processing streams, and environmental analysis. Notably, this approach could be coupled with single particle fluorescence flow cytometry or stimulated Raman spectroscopy for real time monitoring of enriched particle streams. As such, SWANS represents the first step in a developmental pathway that will be of immense value to human health. Beyond the impact of the integrated technology platform, we also anticipate that this project will provide new insights in the fields of microfabrication and the micromanipulation of nanoparticles via acoustofluidics.

NSF Awards · FY 2025 · 2025-03

The acquisition of a laser diffraction particle size analyzer at the University of New Mexico (UNM) will transform how scientists and students investigate pressing environmental and societal challenges. By analyzing the size and shape of particles in soil, sediment, and airborne dust, this instrument will generate insights into Earth’s past and future, from understanding environmental change through sedimentary records to assessing air quality in vulnerable communities. One important application of this technology will be to address environmental health risks in communities where contaminated dust from abandoned mines poses a significant health risk. Additionally, this state-of-the-art tool will provide UNM students, with hands-on experience in cutting-edge scientific methods, preparing them for careers in the geologic and environmental sciences. The laser diffraction particle size analyzer supported by this project will be equipped with a dynamic imaging accessory, which enables high-precision measurement of particle size and shape across a wide range of scales, catalyzing research in geomorphology, carbon cycling, paleoclimatology, and geohealth. By providing a robust method for analyzing the full particle size and shape distributions of soil, sediment, and aerosol samples, the instrument will support investigations into sediment transport dynamics, microbial ecosystems in extreme environments, and the environmental impact of dust particles from abandoned uranium mines. The project includes an undergraduate research project focused on assessing the particle size distributions of airborne particles transported from an abandoned uranium mine, with implications for air quality in the Laguna Pueblo community. Overall, acquisition of this particle size analyzer will complement existing capabilities at UNM, facilitate interdisciplinary collaborations, and provide hands-on analytical training for students through research and teaching. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Solid-state batteries (SSBs) have been widely considered as the hope to eliminate the safety issues of flammable liquid electrolyte-based counterparts. Despite such a decisive advantage, realizing a functional and efficient SSB has been greatly hindered by limited understanding of unwanted physical changes and chemical reactions at the solid electrode-solid electrolyte interface. Such limited understanding is rooted in the lack of an analytical technique that can pinpoint the undesired physicochemical processes, disentangle the relationship of each process, and reveal the role of each process in battery performance. The project will realize a coupled multi-energy micro-spectroscopic analytical approach based on halide-based solid-state batteries, by incorporating different schemes of light-matter interactions and machine-learning-based analysis to reveal the nature of the interface of interest. The project will drive concerted research efforts along multiple cutting-edge directions at the University of New Mexico, a Hispanic-Serving Institution, and profoundly diversify the research profile of the university. In a broader scope, the method and results of the Fellowship project will contribute to the STEM education of various disciplines by connecting fundamental principles and data-driven problem-solving, thus promoting young generations to make more impactful innovations. This Research Infrastructure Improvement EPSCoR Research Fellows project will provide a fellowship to an Assistant Professor and training for a graduate student at the University of New Mexico. This work will be conducted in collaboration with researchers at the University of Texas at Austin. The objective of the Fellowship is to develop a coupled multi-energy-scale micro-spectroscopic analytical approach, to fundamentally understand the nature of entangled microstructural and chemical properties of Li|solid-state electrolyte (SSE) interface in halide-based SSBs. The fellowship will integrate the bright field microscopic imaging, micro-Raman mapping, photoluminescence imaging, and X-ray tomography to quantitatively survey the Li|halide-SSE interface. Moreover, the project will employ machine-learning-based data-driven analysis to extract the correlation between microstructural & chemical features and to unravel the contribution of each deconvoluted feature in electrochemical behavior of the SSB. The combination of the multi-modal micro-spectroscopic technique and the data-driven analysis will serve as a new multi-disciplinary experiment and data analysis platform that will make a strong impact, not only on the field of SSB development, but also on other materials systems that involve complex physicochemical processes. The project will make a valuable contribution in enriching textbook materials of important fundamental courses such as Analytical Chemistry, Instrumental Analysis, and Probability and Statistics, and forge more connection between these classroom knowledge and realistic research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Programming the biological world is a significant grand challenge for science and engineering, which the field of DNA nanotechnology addresses by implementing computational functions using designed molecules. A key goal of research in this area is to enable practical applications of molecular information processing, such as biomedical diagnostics within living cells. The specific research goal of this fellowship project is to develop and experimentally validate computational tools for simulation-guided design of DNA nanostructures and molecular systems. The long-term vision of this EPSCoR fellowship project is to foster deeper collaborative relationships between the University of New Mexico (UNM) and the hosting Biodesign Institute at Arizona State University (ASU) in the crucial research area of bioengineering. It aims to create new recruitment pathways for students graduating from UNM, including members of underrepresented groups, to pursue graduate work at ASU, a renowned institution for its research in DNA nanotechnology. Knowledge transfer from ASU to UNM will strengthen research and teaching capabilities at UNM in bioengineering, which is a strategic priority for the Albuquerque area. Thus, this EPSCoR fellowship project will enhance the ability of students to succeed in the biotechnology industry, thereby improving social and economic prospects. A range of computational tools exists to assist designers of DNA-based molecular devices, including low-level models of molecular dynamics and high-level models of the kinetics of interactions between molecules. However, there has been relatively little work on integrating high-level models, which are less detailed but easier to run, with low-level models, which are more detailed but require more resources to simulate. This EPSCoR Research Fellows project will combine these approaches to develop novel and powerful multiscale modeling frameworks for DNA nanotechnology. The goal of this work is to harness the predictive power of low-level molecular dynamics models while combining it with the ease of simulation provided by higher-level models. This project will also further develop existing coarse-grained molecular dynamics models of DNA by parameterizing them to model “heterochiral” DNA. This novel form of DNA is designed to resist degradation in the cellular environment by incorporating “left-handed” chiral mirror-image DNA that is not recognized by cellular defenses. Previous work has shown that heterochiral molecules hold significant promise for developing robust engineered molecular devices for applications in living cells and organisms. Model predictions will be validated experimentally. Simulation models developed in this project will be integrated with existing software developed by the PI, enabling multiscale modeling of molecular interactions. Therefore, this project will advance the state of the art in tool support for computational biodesign, simplifying the design tasks facing future researchers in DNA nanotechnology. 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

Nearly all plants and animals harbor microbes that live on or in them as “symbionts”. These include fungi that inhabit crop plants, bacteria that inhabit mosquitoes, and microbes that live inside the human gut. Harnessing these symbioses may advance the ability to solve problems in agriculture, wildlife disease, and human health. Before these applications can be fully developed, environmental biologists first require a better understanding of when and how microbial symbionts influence the health of their host. This project is testing the hypothesis that the effects of symbionts on their host fluctuate from year to year, being beneficial in bad years (when hosts need assistance) but neutral or costly in good years (when hosts are okay on their own). As a consequence, hosts with microbes may be more stable compared to hosts without microbes, and this may be an important but overlooked benefit of harboring microbes. The scientific team is exploring this idea with a unique long-term study of grasses and their fungal microbes in Texas and Indiana. Fungal endophytes are widespread microbial symbionts in grasses, including forage grasses that are important to ranchers and turf grasses used by landscapers, and this research will benefit these groups. The project will train undergraduate students and conduct outreach activities to high schools. The project’s core data derive from a unique symbiont-removal experiment in which populations of cool-season grasses were established either symbiotically with seed-transmitted Epichloë endophytes or with symbionts eliminated through heat treatment. Replicated across seven host species and now running for 15 years with thousands of individuals, the experiment’s longitudinal demographic data reveal the fitness impacts of fungal symbionts on their host plants and how these impacts fluctuate in response to fluctuations in the environment. The research team is using these data to build stochastic demographic models that address two novel hypotheses, rooted in population biology theory for fluctuating environments and testable only with long-term data. First, through context-dependent fitness effects (symbionts are more beneficial in more stressful years), microbial symbionts may reduce inter-annual variability in host demography. By buffering hosts against harsh conditions, symbionts may also limit genetic drift and promote higher genetic diversity in host populations. Second, unique responses of symbiotic and symbiont-free hosts to environmental fluctuations can generate niche opportunities in time via the storage effect, possibly promoting stable mixtures of symbiotic and symbiont-free hosts. By continuing this study for the next five years (providing a total of 20 years), the project will reveal for the first time how endophytes may help host plants cope with year-to-year fluctuations in climate. 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

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, more and more Cyber-Physical Systems (CPSs) are equipped with low-level regulators or high-level decision makers as AI/ML models. However, the safety verification of such systems is more challenging than that on the general dynamical systems due to the complex interactions among the various components. This project focuses on the development of new formal methods and tools which verify the safety of large-scale Learning-Enabled (LE) CPSs by computing size-reduced abstractions according to the safety properties. The methods also produce analytic verification results that can be used to diagnose the behavior of a system and generate solutions for improving its safety and robustness. The project will provide a fellowship to an assistant professor and training for a graduate student at the University of New Mexico (UNM). The research work will be conducted in collaboration with researchers at NASA Marshall Space Flight Center. The developed techniques will be used to prove and improve the safety of the AI-controlled systems built by NASA. Besides, the project is also going to strengthen the collaboration between UNM and NASA as well as broaden the participation of students/researchers from underrepresented groups. This project proposes to develop a series of formal methods for abstracting, verifying and correcting an LE CPS whose components may or may not be explicitly described by formal models. The research content has the following core thrusts: (1) Safety-directed model reduction: An approach will be developed to compute size-reduced formal abstractions for the AI/ML components in an LE CPS regarding to its safety specification. The obtained models are expected to be much less intricate than the original ones however the given safety property is preserved. (2) Safety verification via rigorous reachability analysis: We will develop a rigorous reachability analysis framework for verifying the safety of an abstracted LE CPS with uncertainties. We seek to extend the existing Taylor Model-based arithmetic by introducing more sophisticated simplification methods and more flexible remainder representations. The reason to do so is to achieve a better tradeoff between accuracy and efficiency than the state of the arts. (3) Counterexample interpretation and model correction: An approach for obtaining analytic counterexample interpretations will be developed. Such an interpretation is expected to cover all counterexamples along with their causes in a safety verification task. We will also investigate two ways (offline and online) to restrict the outputs of system components such that all counterexamples can be avoided. The developed approaches are expected to greatly improve the applicability of formal methods to analyze and improve large-scale autonomous systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The Sevilleta Long Term Ecological Research program (SEV LTER) studies the ecology of environmental variability, from individual organisms to ecosystems. Predicting the consequences of environmental change is one of the greatest challenges at the interface of science and society. Changing climate and resource use alter long-term trends in the environment, with results such as hotter weather. However, the environment has also become increasingly more variable, such as droughts followed by deluges. These dual changes to long-term trends and variability could have large biological impacts that are challenging to predict. Drylands are among the most variable places on Earth. Drylands are also facing large changes in vegetation, such as shrubs replacing grasslands and the loss of native forests. These transitions may be highly sensitive to dual changes in the environment. To understand the ecology of environmental variability, research activities include long-term monitoring and specimen archives for plants, animals, and microbes. Innovative field experiments make precipitation drier and more variable. Field sensors give high resolution data on changes in water, carbon, and phenology. Trait and genetic monitoring help to understand evolutionary change. Long-term data are used in models to forecast change in vegetation, animal populations, and ecosystem cycles. SEV LTER recruits and trains participants at all levels of learning. University courses connect students with community groups to solve environmental challenges. Our K-12 programs serve students in New Mexico, which is the most water-stressed state. Benefits to society include improved predictions on the future of dryland biodiversity and resources, training the next generation of scientists, and collaborating with land managers to sustain critical land and water resources. Prior ecological research has largely emphasized changes in mean trends (such as warming) or singular extreme events (such as a drought), yet theory predicts that temporal variability in the environment can have powerful ecological and evolutionary consequences. The consequences of temporal environmental variability arise from nonlinear biological responses to stochastic environmental drivers. Thus far, advances in theory on temporal environmental variability have largely developed independently in population biology, evolution, community ecology, and ecosystem science. Empirical understanding of the biological impacts of environmental variability has lagged behind theory because the effects manifest over long time scales, making long-term support critical to progress at this scientific frontier. The Sevilleta Long Term Ecological Research program (SEV LTER) is guided by the question: How do changes in the mean and variance of environmental conditions independently and interactively affect the dynamics of dryland ecosystems and transitions among them? Research activities will advance the frontiers of ecology by developing and testing theory on the ecological consequences of shifts in both the mean and variance of water resources, integrated over space, time, and levels of biological organization that span genotypes to landscapes. Activities include long-term observations, experiments, specimen archives, and models to confront theory with data from six major dryland ecosystems. A novel experiment factorially manipulates precipitation mean and variance to resolve their potential interactive effects. Trait and genetic monitoring build a mechanism-based understanding of biological responses to mean and variance that links evolution to ecosystem function. Continuous measurements of ecosystem fluxes, phenology, soil moisture, run-off, and groundwater provide exceptional resolution on water and carbon dynamics. Predictive models assimilate observational and experimental results to forecast change in (1) ecosystem transitions driven by the spatio-temporal trajectories of foundation plants, (2) ecosystem functions in carbon, water, and nitrogen cycles, (3) the eco-evolutionary dynamics of genotypic and phenotypic diversity, and (4) consumer dynamics in shifting resource landscapes. The diversity of ecotones against the backdrop of a drier and more variable climate advances long-term understanding of the causes and consequences of dryland ecosystem transitions. SEV LTER supports interdisciplinary graduate and professional training, undergraduate research programs, and a flagship schoolyard (K-12) program. Activities include a mentorship program, and embedded high school teachers that educate students in New Mexico. Community resources include innovative Resilience Solutions Incubator courses that bring undergraduates together with community partners, listening sessions with regional communities, and an outside-of-the-academy Data Science Bootcamp for the state workforce. Strong collaborations with regional land managers inform SEV LTER research and extend the broader impacts of the program’s substantial research infrastructure by increasing the use and application of high-quality public data products.   This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-01

Project Summary/Abstract Both positive emotional functioning and social connection are important to substance use disorder recovery, but scarce research has investigated how they are intertwined. “Positivity resonance” is what happens when two or more people share positive emotions, mutual care for each other, and biobehavioral synchrony (e.g., eye contact). This construct has gained traction in positive affective research as an indicator of positive emotional functioning and high-quality social interaction between persons, thereby functioning to have a lasting upward spiral effect on health and well-being in both individuals and communities across a range of contexts. Studying how positivity resonance might be related to recovery outcomes for people in early recovery could help shed light on the role of this supportive factor in the recovery process. Given the established link between certain disadvantaged social determinants of health (SDOH) and less-favorable social connectedness and positive emotional functioning, experiencing positivity resonance in recovery may buffer against the deleterious effects of certain SDOH. This study proposes to examine an understudied interpersonal affective construct, positivity resonance, across a diverse sample of people with varied recovery pathways. First (Aim 1), we will conduct secondary data analysis of two studies: (1) a randomized controlled trial of Zoom-delivered mindfulness-based relapse prevention compared to referral to online mutual help as aftercare among n=430 people in alcohol use disorder (AUD) recovery nationwide (“THRIVE Study”); and (2) a longitudinal naturalistic study of n=64 people receiving medication for opioid use disorder (MOUD) clinical care near recovery community centers (RCCs) in predominantly Black American communities (“RCC MOUD Study”). We will use latent growth curve and parallel process models to examine how positivity resonance changes alongside recovery capital (measured via the Brief Assessment of Recovery Capital) over 1 year. Aim 2 will examine the moderating role of positivity resonance in the relationship between certain SDOH (e.g., income, education, housing) and recovery capital across both studies, using a series of moderation analyses. Lastly, Aim 3 will involve conducting individual interviews, and subsequent qualitative analysis using a grounded theory approach, with a subsample of interested participants from each parent study (n=15 from each study) in order to contextualize quantitative findings and more holistically explore how positivity resonance occurs in recovery and its effects. The results of this study will increase understanding of how positivity resonance, a construct capturing interpersonal positive emotional functioning with links to adaptive individual- and community-level health outcomes, can facilitate substance use disorder recovery. Results might identify a treatment target and/or explicate mechanisms of peer recovery support, and how applicable positivity resonance is to those with more disadvantaged SDOH.

NSF Awards · FY 2024 · 2024-10

This project aims to develop innovative technology for environmental monitoring. The researchers are inspired by the natural design of dandelions to create a swarm of tiny, lightweight sensors that can be deployed in hard-to-reach areas by unmanned aerial vehicles (UAVs). These sensors will harness wind currents to spread across vast and difficult terrains, collecting crucial data on environmental conditions. This technology promises to improve our understanding of ecosystems and aid in disaster management by providing real-time, detailed information in places that are otherwise inaccessible. The project brings together expertise from three institutions and will enhance research capacity, support education, and promote diversity in STEM fields. The project focuses on developing a biomimetic swarm sensing system that emulates the dispersal method of dandelion seeds. The system includes sensors with pappus-like structures for flight and energy harvesting, and achene-like components for sensing and communication. Key research goals include designing and optimizing the sensor structures for aerodynamic efficiency, developing robust energy harvesting and communication circuits, and creating a transformer-based deep reinforcement learning architecture for autonomous UAV-assisted sensor deployment. The performance of the system will be validated through simulation and experimental testing. This interdisciplinary research integrates aerodynamic analysis, solid mechanics, microelectronics, signal processing, communication theory, and deep reinforcement learning, aiming to advance both basic science and the practical application of biomimetic swarm-based remote sensing technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract This Mentored Patient-Oriented Research Career Development application (K23) will provide protected time for Dr. Matison McCool to strengthen his trajectory as an independent researcher at the University of New Mexico, Center on Alcohol, Substance use, And Addictions (CASAA). His goal is to develop expertise in mindfulness- based interventions, heart rate variability (HRV), advanced quantitative methods, wearable sensors, and other mobile health approaches to help improve, adapt, and develop interventions for alcohol use disorder (AUD). To develop expertise in these areas, the candidate proposes an innovative, mentored research study using wearable sensors to collect psychophysiological HRV data before and after each mindfulness-based relapse prevention (MBRP) treatment session. The weekly HRV data is coupled with an ecological momentary assessment (EMA) protocol to examine the addiction cycle in daily life. This study builds on existing literature suggesting that mindfulness-based interventions induce changes in HRV by focusing on measuring HRV epochs weekly throughout treatment, instead of pre-post treatment only, and assessing the effect of HRV on the relationship between negative affect, craving, and alcohol use (consistent with NIAAA FY 2017-2021 Strategic Plan Goal 4). Combining multiple HRV measurement occasions with EMA data collection in a longitudinal structural equation modeling framework, this study provides training in basic science to understand HRV, and how the results may improve MBRP, and inform adaptations of MBRP for adaptive interventions (consistent with NIAAA Strategic Plan Goal 4 Objectives 4a and 4d). This mentored study will offer training to help the candidate develop expertise to establish his own independent research program examining mobile health adaptations of mindfulness-based interventions and their components while utilizing sensors to prompt treatment and assess treatment component effectiveness. With the guidance of his training team, Dr. McCool’s training plan and mentored study are integrated and selected to promote the development of a comprehensive skillset in the following areas: 1) mindfulness-based interventions for AUD (Dr. Witkiewitz; UNM) 2) basic psychophysiological science related to HRV and AUD (Dr. Eddie, Harvard; Dr. Buckman, Rutgers), 3) advanced quantitative skills (Dr. Pearson, UNM; Dr. Witkiewitz), 4) wearable sensors and other advanced mobile health technologies (Dr. Schwebel, UNM; Dr. Eddie), and 5) professional development, dissemination, grantsmanship, and the responsible conduct of research (Drs. Witkiewitz, Pearson, Schwebel, Eddie, and Buckman). Dr. McCool will visit Dr. Buckman’s lab at Rutgers University-New Brunswick and complete training across the country. Through this training, Dr. McCool will be prepared to develop an independent research career as a scientist who conducts patient-oriented research and will generate substantial preliminary data for subsequent grant applications as an independent investigator.

NSF Awards · FY 2024 · 2024-09

Program invariants, which describe properties that always hold at a program location, are essential for program understanding, debugging, and verification. Among existing modern invariant learning work, the DIG tool can discover rich numerical invariants in programs by integrating dynamic inference and symbolic checking. However, while DIG has inspired many research projects and applications, it needs better scalability to support industry settings, and like other invariant research tools, it is generally not accessible to software developers and engineers who may lack the familiarity or time to learn its usage. This project aims to develop DIG-I (DIG-Industry) to make DIG more practical and usable. The project's novelties are optimizations to improve DIG’s performance and scalability as well as integration with artificial intelligence (AI) to learn invariants more effectively. The project's impacts are that the open-source DIG-I tool will enhance the efficiency and usability of invariant learning, benefiting developers in industry and research labs, and will be used to introduce formal methods and invariant generation to students and professionals through courses at George Mason University. This proposal will develop DIG-I to make invariant research more practical and accessible. It focuses on (i) improving performance by transforming expensive matrix and constraint-solving operations in DIG to Compute Unified Device Architecture (CUDA) kernels to be run efficiently on Graphics Processing Units (GPUs), (ii) supporting additional useful invariants and their applications by integrating existing invariant work directly into DIG's base code, (iii) modernizing DIG by adopting large language models (LLMs) to learn invariants more effectively, and (iv) improving the usability and adoption of invariant analysis by developing a Language Server Protocol (LSP) that allows invariant tools to integrate with popular Integrated Development Environments (IDEs) and editors such as Visual Studio (VS) Code. The findings from this project will be used in the investigators’ courses, and mentoring and outreach activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project addresses the urgent problems caused by hazardous industrial waste and climate change, disproportionately affecting low-income and BIPOC (Black, Indigenous, People of Color) communities due to social and economic inequality. It aims to transform biomass and hazardous industrial wastes into inexpensive, green construction materials using Negative Emission Technology (NET). This approach promotes environmental justice and enhances resilience to climate-related issues. By incorporating CO2 into building materials from waste, the project reduces atmospheric carbon levels and provides a sustainable waste management solution. It also provides a long-term solution for handling biomass and industrial waste, which is presently disposed in landfills and causes health issues. The project also intends to create better building materials by combining pozzolanic reactivity, CO2 capture and mineralization, which will speed up scientific and technological advancement. It fosters diversity and education by involving researchers from one Historically Black College and University (HBCU) in Alabama and three public universities in New Mexico, Alabama, and Idaho. Comprehensive workforce development programs include early career faculty development, undergraduate and graduate training, K–12 education, and teacher training, supporting STEM jobs and education in underrepresented areas. Benefits include better public health from reduced hazardous waste, safer housing options, and improved well-being through economic opportunities and infrastructure enhancement in the targeted communities. The Fifth National Climate Assessment highlights the vulnerability of low-income and BIPOC communities in Alabama, Idaho, and New Mexico to climate change and land-filled hazardous industrial wastes. In response, a collaborative effort involving four institutions from these EPSCoR jurisdictions aims to develop a novel pathway to alleviate these effects. The project revolves around a Negative Emission Technology (NET) designed to convert alkaline industrial and biomass wastes into low-cost, low-carbon construction materials. This transformative solution provides an economically viable means of enhancing the climate resilience of these communities and sequestering CO2 into concrete. Using alkaline industrial wastes as feedstock, the process produces calcium-rich leachate for CO2 capture, creating CaCO3 precipitate. The filtered solid residual, with higher pozzolanic reactivity, can partially replace ordinary Portland cement in concrete. This method generates new revenues and addresses climate change through carbon capture and utilization in concrete. It is estimated that the NET method could sequester 2.9-8.5 billion tonnes of CO2 per year by 2100. The project is a consortium of researchers from The University of Alabama, University of New Mexico, University of Idaho, and Alabama A&M University, a Historically Black College and University. The diverse team includes nine assistant professors. Workforce development and outreach activities are extensive, covering K-12 education for teachers, undergraduate students, early career professors, industry partners, and communities. These initiatives promote STEM education in sustainable construction materials, circular economy practices, and the emerging interdisciplinary field of decarbonization of the built environment, directly benefiting the targeted low-income and BIPOC communities. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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 2024 · 2024-09

Project Summary We propose the creation of the Climate and Health Allied Network for Geospatial and Environmental Science (CHANGES) Exploratory Research Center at the University of New Mexico (UNM). The overall vision of our center is to synergize expertise in geographic information science, health data science, epidemiology, and biomedical research to identify – and ultimately predict – gaps in health care, inform policies, and create actionable strategies to optimally protect our vulnerable communities in New Mexico from climate change. Our team includes expertise in culturally-sensitive community engagement, environmental health and toxicology, cancer biology, disaster response and preparedness research, geospatial information systems, biostatistics, and environmental science and engineering. The following specific aims are proposed: 1. I Synthesize and integrate diverse datasets and leverage cutting-edge tools for geospatial analysis to geographically connect exposure and health outcomes data in order to discover previously unseen patterns; 2. Develop culturally responsive practices for community engagement in climate health that benefit diverse communities, underserved and underrepresented populations, and key stakeholders; and 3. Establish a sustainable transdisciplinary Climate Change and Human Health Center that serves a model for cross-campus team science and climate change interventions that maximize human health outcomes. Our center is dedicated to leveraging transdisciplinary sciences to develop innovative strategies, solutions, and policies that empower and protect vulnerable communities, not only in New Mexico but worldwide.

NSF Awards · FY 2024 · 2024-09

Increased aridity and weather instability due to climate change, along with biomass accumulation due to decades of effective suppression of naturally occurring fires, have resulted in much of the Western US being highly prone to catastrophic wildfire. The primary current tool for mitigation of forest-fires involves thinning followed by “controlled” burning of the resulting slash. Unfortunately, such activities have resulted in the ignition of catastrophic mega-wildfires, with significant loss of life and property and environmental and economic degradation. There is a significant need for new approaches for disposal of thinned slash, and at the same time, a growing need to replace petroleum-based products to lessen the greenhouse gases that imperil forest health through climate change. Coniferous forests contain significant stores of potential chemical feedstocks such as cellulose that can, in principle, be readily converted to valuable chemical products by bioprocessing methods such as fermentation. These feedstocks are present in wood in a closely associated matrix with lignin, a complex chemical compound which prohibits large scale usage of wood in green chemical processes. This TRAILBLAZER project seeks to develop a groundbreaking new approach toward lignin degradation that will unlock the potential of wood as a green chemical feedstock. The development of this new technology will require a research approach incorporating several fundamental advances to provide an efficient and transformative means for usage of woody biomass, which has the potential to change the economics of forest thinning as an effective and safe means of fire mitigation. This project’s relevance to forest sustainability will supercharge its Broadening Participation Plan by focusing recruitment, outreach and engagement efforts on communities that rely on forest health in North Central New Mexico, an area with some of the oldest communities in the nation and that is increasingly at high risk for devastation by catastrophic wildfires. This fundamental research aims to tackle the “lignin barrier” problem through revolutionary, eco-friendly approaches that stem from biochemical engineering, synthetic biology, and materials science. Enzymes such as oxidases and peroxidases that are known to break down lignin are found in numerous fungi and bacteria, nature’s primary forest product recyclers. The goal of this project is to exploit, engineer, and evolve enzyme-based methods to enable efficient, irreversible breakdown of lignin in model coniferous feedstocks that represent the typical makeup of most western thinning waste. The fundamental developments to be achieved will provide a platform technology for the pretreatment of lignin-containing biomass well beyond coniferous woody waste to several other biomass feedstocks. The key step of lignin degradation will enable the success of many promising yet still challenging processes for biomass utilization, including the production of biofuels, engineered lumber, bioplastics and textiles, pharmaceuticals, commodity chemicals, and other valuable natural products. While the focus of this project is on lignin degradation as a key limiting process for utilization of woody biomass, the fundamental approaches developed will find utility in a number of other critically important biochemical conversions, including biofuel cell catalysis, detoxification, bioremediation, disinfection, and enzymatic therapies. 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

Artificial intelligence (AI) and machine learning enabled technologies are rapidly growing areas of national priority and economic significance. Autonomous systems are engineered systems which use AI and machine learning to sense, communicate, react, and adapt in unpredictable environments. Autonomous systems are becoming vital in a wide range of systems, including smart infrastructure, transportation systems, internet-of-things technologies, and biomedical and health applications. However, advances in technology have outpaced the methods used to teach these skills . Most graduate training for autonomous systems engineering does not provide students with the necessary interdisciplinary foundation. Furthermore, training in ethical considerations is critical to embracing a human-centric approach. This National Science Foundation Research Traineeship (NRT) award to the University of New Mexico will 1) train students in state-of-the-art research in AI and autonomy, 2) provide opportunities for students to integrate their knowledge of learning and AI, as well as knowledge of the human context, into the engineering design process, and 3) broaden participation of under-represented and first-generation students. The project anticipates training 70-80 MS and PhD students, including 25 funded trainees, from Electrical and Computer Engineering, Computer Science, Mechanical Engineering, Civil Engineering, and Organizational and Informational Learning Sciences. Some of the most pressing challenges in autonomous systems are the need for resilience and robustness to errors, and the need for responsivity to the human context, including practitioner awareness of broader societal implications. This project will create novel theory, methods, tools, and implementations focused on addressing these challenges, along four interdisciplinary research thrusts: 1) space and defense autonomy, 2) algorithmic fairness, 3) robotics and intelligent swarms, and 4) distributed learning for extreme-scale systems. Two primary goals of the project are to develop student proficiency in interdisciplinary technical foundations, including machine learning, control theory, robotics and intelligent systems, and optimization, and to develop student awareness of the human context of AI and autonomous systems. The project will develop a new graduate certificate in AI and autonomous systems, whose cornerstone is a two-semester project-based design course that employs an ethics-as-design approach. The project will also feature research internships with industry and government labs to build exposure to non-academic pipelines, and to help motivate the students’ research. Additional project goals include fostering students’ professional development through activities that focus on communication, teamwork, and mentoring. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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

Many Indigenous communities in New Mexico (NM) and Oklahoma (OK) are at high risk for flood, drought, fires, dust, and their impacts. Sixty-one federally-recognized Tribes are headquartered in NM and OK. The climate projections indicate that these states are expected to be hotter and drier, with more extreme events. These changes affect and will continue to impact how Tribal communities live, work, and practice the cultural traditions that support their continued existence. This project aims to establish respectful, reciprocal, and sustainable research and education partnerships to advance Indigenous communities' resilience to climate change in NM and OK. In collaboration with Tribal and intertribal organizations, Indigenous researchers and educators and Western-science climate researchers and educators at the University of Oklahoma, University of New Mexico, Chickasaw Nation, and the South Central Climate Adaptation Science Center will braid Western and Indigenous climate sciences through a community-led process to identify information needs, conduct research and understanding, prepare for adaptation planning and actions, and engage in education. The project will first work with communities to identify information needs. Capacity will be built through the installation of air quality monitoring systems. Understanding the process of, and barriers to, incorporating climate science in the adaptation and resilience planning process in Tribal communities will support long-term resilience. A summer internship program will foster meaningful associations among Indigenous students’ lived experiences, new knowledge, and climate resilience, and empower them to pursue careers addressing the current climate crisis to benefit their communities. The project will further develop a land-based curriculum braiding Indigenous and Western climate sciences for formal and informal science educators. This project is focused on advancing Indigenous communities' resilience to climate change with a specific emphasis on improving the air quality and water quality in NM and OK with installation of air quality monitoring systems and interpretation of in-situ and satellite air quality data. American Indians/Alaska Natives comprise over 10% of the population of these two states. Indigenous communities have strongly expressed the need for their active participation in the development of scientific research questions, research design, analysis, and reporting. The project will utilize best practices in co-production to advance Indigenous communities' resilience to climate change by first working with communities to identify information needs in the land-water-air system. Research in water quantity will enable Indigenous partners to evaluate how water quantity change will affect their plans for food sovereignty or other community goals. Participating communities will learn about the range of climate information and infrastructure available to support their planning processes for enhanced resilience and adaptation. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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 project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges. The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs. 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 University of New Mexico’s School of Engineering will establish a Center for Equity in Engineering, building on the institution's 30+ years of experience as a Hispanic-Serving Institution (HSI) to enhance equity for Indigenous and Black students, faculty, and staff, while also deepening its commitments to increase equity for Hispanic, first-generation, and low-income students. By changing how we teach to better reflect students’ lived realities and local contexts, by enhancing connections between student support services and classroom experiences, by skilling up faculty and staff to better serve students of all backgrounds, and by incorporating equity into performance evaluations and reward systems, we will decrease inequity in student experiences, learning outcomes, time to degree, and graduation rates. As a School of Engineering with majority-minority enrollment, enhancing student success and closing equity gaps will diversify the engineering profession. As more institutions become Hispanic enrolling over time with national demographic shifts, this CEE will be positioned to demonstrate what equity work can look like at an HSI. The Center will (1) build infrastructure for equity through leadership accountability, resource management, data and metric development, partnership enhancement, and shifting reward structures and cultures in the School of Engineering and (2) deploy equity-centered pedagogies and (co)curricular strategies to enhance student success by motivating and equipping faculty and staff to focus on proven equity-enhancing practices that include and engage students, foster identity development, and provide equitable opportunities for technical and professional development. Our center planning will apply change theories and strategies refined within the engineering education community, including (1) shifting faculty reward structures (including tenure and promotion) to support and create accountability for equity and student success; (2) building a community of practice that facilitates transformational anti-racism and anti-classism work; (3) enhancing partnerships for smoother pathways into engineering from high school and two-year settings; and (4) strengthening our capacity for place-based engineering projects that demonstrate the value of engineering by, for, and with local communities. Sense of place is a core value in the state of New Mexico and at UNM; centering querencia (a Spanish word without direct English translation denoting deep connections among land, belonging, and identity) as a place-based pedagogy specific to New Mexico, we will advance equity knowledge and practice in engineering, forge links between cultural and engineering identities, and engage new conceptions of engineering informed by culture and place in both research and education. We will leverage our Engineering Student Success Center, campus cultural and diversity centers, UNM’s Division for Equity and Inclusion, ADVANCE program, and Center for Teaching and Learning, as well as deepen external partnerships with regional two-year colleges, pre-college organizations, and community groups. Student demographic diversity, persistence, and sense of belonging will increase, as well as course pass rates, student learning outcomes, career readiness, and time to graduation. While querencia is operationalized specifically in New Mexico as an avenue to advance belonging and success in engineering, our model of engaging place-based, asset-based, and culturally-sustaining approaches to connect engineering and cultural identities will readily propagate to other places and cultures. Partnerships with pre-college organizations and two-year colleges will expand, with improved pathways emerging for students, further diversifying our enrollment and the profession. This ecosystem of partners will extend well beyond our local community into national networks, facilitating the propagation of findings beyond our institution and region. The structural and cultural changes driven by the CEE will transform lives and communities by further increasing the realized social mobility of our graduates, and provide a replicable model for other institutions. 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

Extreme climate events are increasing in frequency and have had significant, negative impacts on crop production not only for industrial but also for smallholder farms. Food security, food nutritional quality, and the viability of disadvantaged communities are especially threatened by climate change. An educated application of Controlled Environment Agriculture (CEA) provides one avenue to mitigate climate pressure on tribal communities and meet future food demand in a sustainable manner. Advances in and widespread adoption of CEA are, however, limited by several factors. Significant knowledge gaps exist in the science underpinning CEA. Additionally, there has been limited investment in relevant research, community engagement, and workforce development. This project is a collaboration among community colleges, universities, tribal nations, and community organizations located in three NSF EPSCoR jurisdictions: University of New Mexico, New Mexico State University, and Santa Fe Community College (New Mexico); University of Wyoming (Wyoming); and University of South Dakota and Sicangu Community Development Corporation (South Dakota). The three collaborating jurisdictions are home to 36 federal reservations. The project team brings together community members and researchers from diverse fields (e.g., economists, engineers, biologists, and plant physiologists) and consists of early-career, mid-career, and established researchers. This project provides opportunities for learning among universities, community colleges, industry, and tribal communities to allow for the development of tailored CEA systems and a climate-smart and community-based workforce. The human and information infrastructure developed under this project is anticipated to attract and retain talent, stimulate economic development, and confer climate resilience in tribal communities within and across jurisdictions. The overarching goal of this project is to empirically determine best practices for secure CEA food production and to quantify the socio-economic impacts of CEA on tribal communities across the collaborating EPSCoR jurisdictions. The project proposes to take a convergence science approach to tackling food security under climate change, drawing upon diverse expertise with researchers ranging from basic (plant physiology and plant-microbe interactions) and applied biology (CEA and horticulture), environmental and natural resource economics, engineering, sustainability science, and applied knowledge of tribal community members. The research project leverages each institution's unique and complementary research expertise and resources to accomplish the following three objectives: (1) characterize how the environment, plants, and microbes interact in hydroponic systems and affect crop yield and nutritional quality; (2) identify environmental, nutritional, and socio-economic drivers and impacts of CEA on tribal communities to enhance climate resilience strategies via CEA; and (3) empower tribal communities through interdisciplinary training aimed at long-term retention of a highly-skilled climate-smart CEA workforce. The project includes training and mentoring activities for undergraduate and graduate students, post-doctoral scholars, early career faculty, and various tribal communities, including farmers, students K-12, and industry personnel. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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 relative abundance of different stable isotopes of an element in natural materials and human-made products can provide valuable information about the material’s origin and history. As a result, stable isotope data plays a crucial role in many scientific disciplines from the Earth and Life Sciences to Archaeology, Nutrition, and Forensics. Unfortunately, the analysis of stable isotope data is complex and time-consuming, which limits the scope, utility, and reuse of isotopic data across scientific disciplines. This collaborative project thus aims to create open-source data tools, collectively known as the ISOVERSE, to enhance the processing of stable isotope data. The ISOVERSE project seeks to address the challenges of processing and sharing stable isotope data by providing efficient, transparent, and reproducible data analysis tools accessible to the broader scientific community. The significance of this project lies in its potential to foster new discoveries and advancements in stable isotope research through improved data analytics capabilities. By ensuring open access to data and promoting reproducible data processing, the ISOVERSE project can facilitate collaborations, methodological progress, and data sharing across disciplines. The ultimate goal is to create a common computational ecosystem that supports and trains researchers in overcoming obstacles in stable isotope analysis, data exchange, and reuse. The core of the ISOVERSE ecosystem consists of four modules: isoreader, isoprocessor, isoconnector, and isoexplorer. These modules provide core functionalities for stable isotope data input/output, computational tasks, data reporting, and graphical user interfaces, and will be built on a flexible framework for future extensions. The ISOVERSE will be implemented in R, a popular open-source programming language, and will be hosted on GitHub and distributed globally through the Comprehensive R Archive Network. The ISOVERSE will be developed using a user-centered design and team science approach, with continuous engagement from the stable isotope research community. This ensures that the ISOVERSE meets the needs of its users and that it is well documented, effective, and easy to use. Additionally, ISOVERSE will be integrated into education and research programs to train students and scientists in the use of stable isotope data. By creating a comprehensive and user-friendly platform, the project will empower researchers at all career stages and skill levels. This award by the Geoinformatics and Earth Sciences Instrumentation and Facilities programs within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Detecting and measuring the stability of waves, be they sound waves, wave patterns of electrons or light, is a critical task in modern physics. Specifically, stable waves, bound to a position, are a central topic in the study of quantum materials. Low-power transistors and small lasers are some of the devices that can be designed from quantum materials. The critical mathematical tool for detecting these stable waves is called K-theory, a topic that cuts across many of the main subject areas of mathematics. Using tools from operator algebras, a subfield of mathematical analysis, a form of K-theory has recently been discovered to be useful in the development of mathematical probes of computer models of materials. Loring’s group will be developing the subject of K-theory for operator algebras, with particular emphasis on new mathematical techniques that can be implemented in software used by physicists. This project will also create opportunities for students at the University of New Mexico and Florida A&M University to do research and participate in internships at Sandia National Laboratories. The mathematics to be developed in this project will focus on multivariable spectrum for noncommuting operators and associated invariants in real and complex K-theory. These invariants can be applied to detect local topology in a variety of quantum materials. These invariants depend on a local gap in the Hamiltonian, a concept that can be made precise using the Clifford spectrum of the various position operators and the Hamiltonian. Local gaps can exist in topological metals, and in composite systems where a metal lead abuts a topological insulator. The fundamental challenge is to find invariants for matrix models of locally gapped free-fermion systems. Using unsuspended E-theory as a model for real and complex K-homology, Loring will develop simple formulas for these invariants and determine if these invariants are complete. 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 PIs propose the development of a generic sensor with enhanced resolution arising from measuring signal phase rather than amplitude, and enhanced sensitivity that comes from the implementation of the sensor in an integrated photonics platform. The team will demonstrate the enhanced resolution and sensitivity through sensing of magnetic field gradients. Gradient magnetic field detection is only one application, with a huge impact on magnetic imaging applied to the heart, brain, and magnetic nanoparticles for tracing cancer cells. The integrated optics devices being inherently lightweight and compact, this sensor will have the capability of leading to wearable and portable sensors. Additionally, this sensor technology can be applied to rotation sensing, measuring acceleration or displacement, and index of refraction measurements, which makes it useful for a large number of navigational and manufacturing processes. TECHNICAL DESCRIPTION Most sensors are based on measuring a phase by detecting the amplitude of interfering beams. The PIs propose instead a different approach to sensing, in which they exploit frequency comb techniques to make direct phase detection rather than signal amplitude. This project encompasses a theoretical study of quantum mechanics applied to sensing, (“exceptional points” and squeezing), verification of the theory with a discrete components laser, and application to a chip magnetometer. This work builds on successful tabletop experiments demonstrating sub-nanoradian resolution using frequency comb mode-locked lasers and Optical Parametric Oscillators. Use of dual-propagating frequency combs of the same repetition rate facilitates both large signal to noise beat and common mode cancellation of 1/f noise. By miniaturizing this setup using on-chip waveguide resonators, the investigators will be able to improve the sensitivity, which scales as the inverse of the cavity dimensions. Dispersion manipulation will be used to enhance the signal with no noise penalty. Once the noise has reached the classical limit, quantum mechanical squeezing will be devised to further enhance the signal to noise ratio. As an example of practical application, the team will concentrate their efforts on a demonstration of chip (gradient) magnetometry. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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.