New Mexico State University

NSF Awards · FY 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) project supports integrated research and education to develop a novel class of adaptive modular circuit metamaterials (AMCMs) that will lay the foundation for secure computation and information-processing systems across multiple scales. Addressing critical challenges in thermal adaptability, computational scalability, and power supply has revealed new opportunities for transformative material systems and next-generation engineering design. This CAREER award advances the theory and methods of autonomous engineered materials by addressing four specific research objectives: (a) demonstrating a new generation of adaptive modular circuit metamaterials with integrated sensing, energy harvesting, and digital computation capabilities, enabled by a graph neural network (GNN) combined with a large language model (LLM) for accelerated AMCM design and optimization; (b) understanding the coupled thermal, mechanical, and electrical behaviors of AMCMs; (c) inventing new thermo-mechanical and thermo-mechano-electrical logic and responsive mechanisms for information processing and memory; and (d) integrating these novel AMCMs into practical systems for diverse real-world engineering applications. This research introduces a new paradigm of thermo-mechano-electrical circuitry, and the resulting knowledge will significantly advance the design of engineering systems based on autonomous materials. In addition, the modular design framework provides exceptional versatility for developing devices across a wide range of applications. By enabling adaptable and reconfigurable device architectures, this work opens new avenues for innovation in medical devices, robotics, human-machine interfaces, MEMS/NEMS, and flexible electronics, with broad impact across the electronic materials community. In parallel with advancing fundamental research, the program will provide comprehensive education and hands-on training in advanced materials, modular engineering system designs, and advanced manufacturing techniques while fostering STEM engagement and capacity building throughout New Mexico. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an assistant professor, and training for a graduate student, at New Mexico State University (NMSU). This work is conducted in collaboration with Prof. Darrell Schlom at Cornell University. Through the fellowship, the PI will study a special class of materials that can carry electricity with zero energy loss, a phenomenon known as superconductivity. The research focuses on a material called potassium tantalate, where unusual superconducting behavior has recently been observed when the material is engineered in extremely thin layers. The research combines materials science and condensed matter physics to understand how atomic structure and layer orientation influence superconductivity. Understanding these effects will help scientists design new quantum materials with improved electronic properties that will benefit emerging technologies. The fellowship will also strengthen research capabilities in quantum materials at NMSU, provide advanced training for graduate students, and expand collaboration with Cornell University. This project will investigate superconductivity in two-dimensional electron gases formed in 5d transition metal oxide heterostructures with strong spin–orbit coupling. The research will examine how quantum confinement and crystal orientation influence the electronic structure and angular momentum and ultimately their relationship to superconductivity. Superconducting KTaO3-based two-dimensional electron gases will be grown using molecular beam epitaxy, and their electronic properties will be characterized through low-temperature transport measurements in a helium-3 refrigerator. The fellowship will advance NMSU faculty expertise in condensed matter physics, provide training opportunities for NMSU students through exposure to Cornell’s advanced research environment, and upgrade the oxide thin-film growth facility at NMSU through the acquisition of a reflection high-energy electron diffraction system, strengthening the university's quantum materials research program. The research activities will also be integrated with curriculum development in modern materials and expanded collaboration between NMSU and Cornell University, supporting workforce development in quantum materials in New Mexico. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation's private, government or academic research 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 2026 · 2026-03

Solar flares are sudden releases of energy from the Sun’s magnetic field that can disrupt satellite operations, radio communication, GPS signals, and other space-based technologies. A major challenge is that scientists still lack reliable short-notice warnings for when a flare is about to begin. This CAREER project studies fast, small-scale activity in the Sun’s lower atmosphere, including chromospheric jets and compact brightenings that may appear shortly before flares. By identifying repeatable “magnetic fingerprints” of flare onset, the research will improve understanding of flare initiation and support the development of space-weather nowcasting tools that help anticipate solar activity affecting modern technological systems. The project, “Hunting Magnetic Fingerprints on the Sun: A Tale of Flare Jet Connections,” uses high-cadence observations from the Richard B. Dunn Solar Telescope and coordinated space-based context. It will analyze a curated archive of more than 50 flare events observed in chromospheric diagnostics. The analysis will quantify jet and brightening properties, including timing, occurrence rates, morphology, and basic kinematics. It will test whether specific precursor patterns appear in the 10 to 30 minute window before flare onset and whether they persist across many events. The work will apply reproducible detection and tracking methods to build event-level catalogs and standardized precursor metrics. It will also compare precursor regions with co-aligned coronal observations to assess how low-atmosphere activity relates to early coronal changes. Key deliverables will include a public archive of processed event cutouts, metadata, derived catalogs, and analysis scripts. These products will support community benchmarking and enable future real-time applications. The expected outcome is an observationally grounded assessment of flare jet connections and the reliability of precursor behavior for short-notice forecasting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This project to New Mexico State University strengthens regional cyberinfrastructure by expanding high-performance data storage to support data-intensive and AI-enabled research and education across New Mexico and West Texas. The project enhances the New Mexico State University's high-performance computing environment with scalable, high-throughput storage that serves a broad range of disciplines, including engineering, computer science, agriculture, biology, chemistry, and astronomy. The expanded infrastructure enables researchers and students to efficiently manage, analyze, and share large datasets while reducing reliance on external or commercial cloud resources. The project supports collaboration among multiple two- and four-year institutions by providing shared access to advanced research computing and data storage resources. Coordinated governance, training, and shared operational practices lower barriers to participation in high-performance computing and research data management, particularly for institutions with limited local infrastructure. Integration with national cyberinfrastructure ecosystems and federated data-sharing frameworks further extends data accessibility and enables broader scientific collaboration. Beyond infrastructure, the project advances workforce development through hands-on research experiences, training in data management and cybersecurity, and structured mentoring for undergraduate and graduate students. Together, these activities build sustainable regional research computing capacity while strengthening research productivity, educational opportunities, and workforce preparation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at New Mexico State University. This work is conducted in collaboration with the Ionic Liquid Science Lead at NASA Marshall Space Flight Center. Through the fellowship, the principal investigator will study how newly developed solid materials can selectively capture and recover critical materials from industrial, post-consumer, and environmental waste streams. Examples of critical materials include rare earth metals, precious metals, or metals from batteries or electronic waste. The project will explore how the structure of these materials influences their ability to bind specific metals. Drawing on approaches from chemistry, materials science, and chemical engineering, the research will aim to develop more efficient and reusable alternatives to traditional extraction methods. This work will support cleaner recycling processes, strengthen the supply of critical materials essential to modern technologies, and expand student training opportunities in New Mexico, preparing a more skilled workforce in science and engineering. This project will investigate the molecular-level interactions between immobilized ionic liquids and critical elements present in complex waste streams. The research will provide fundamental insights into how ion structure influences selectivity, extraction capacity, and material reusability. A suite of cations and anions will be synthesized and immobilized on tailored supports. These materials will be characterized using spectroscopic and surface analysis techniques, followed by evaluation in single- and multi-metal extraction studies. This project will advance the research infrastructure at New Mexico State University by enhancing faculty expertise in separation science and advanced materials development, providing hands-on research training for graduate students, and strengthening institutional collaboration with NASA. The project also supports broader goals in critical materials recovery, while integrating research with curriculum development and STEM workforce training in the state. Outcomes will inform the design of next-generation separation technologies for efficient chemical processing and enhanced resource security. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. The EPSCoR Research Fellows: @NASA track specifically supports the investigators' collaboration with researchers at the National Aeronautics and Space Administration (NASA) research centers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

As blockchains continue to grow, in terms of number of users and applications that rely on blockchains, current blockchains are unable to sustain the increasing demand and usage. The consequences are reflected in very low transaction throughput of cryptocurrencies and other applications that are built on the blockchain. This has accentuated the need for scaling solutions that can help increase transaction throughput, and support a large number of users and diverse applications built on blockchains. Layer 2 is a set of off-chain transaction processing mechanisms that help scale blockchains in a modular way. A prominent Layer 2 mechanism is a payment channel network (PCN), where several thousands of financial transactions can be processed with minimal blockchain read/writes, and with no changes required to the blockchain's underlying consensus mechanism. This project’s novelties are in solving fundamental research challenges in payment channel networks and designing novel cryptographic protocols of independent interest. The project’s broader significance and importance are in training students in critical cybersecurity topics such as applied cryptography and secure decentralized finance. This project’s objectives are: 1) Address fundamental research challenges in payment channel networks. 2) Build payment channel network protocols that are cross-chain compatible and blockchain-agnostic. 3) Build state channels that help execute arbitrary code off-chain. For achieving these objectives, the project uses cryptographic protocols and trusted execution environments. Specifically, this project contributes to advancing the state of the art in Layer 2 protocols in four areas: 1) It builds secure, decentralized, blockchain-agnostic authentication protocols in decentralized PCNs and investigates ways to prevent path inflation in decentralized off-chain PCNs, thus solving two foundational pervasive research challenges in PCNs. 2) It explores the design of secure, versatile, and efficient off-chain tumblers or off-chain coin mixers. 3) It builds upon payment channels to build secure and robust multi-user, decentralized state channels, which can enable users to execute multi-user smart contracts in a completely off-chain way. 4) It designs novel cryptographic protocols of independent interest. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Noyce Track 1 project aims to prepare highly qualified secondary STEM teachers in the state of New Mexico. The project will support 12 scholars in biology, chemistry, computer science, and mathematics; the recruitment of undergraduate STEM majors will take place at both New Mexico State University and Dona Ana Community College. The key components of the project, including near-peer mentoring and several clinical experiences within the public schools and the New Mexico State University STEM Center, will enable high-achieving prospective teachers to become secondary STEM teachers with relevant content and pedagogy expertise. This project will be iteratively evaluated. First, the recruitment and retention of Noyce scholars will be evaluated. Second, the effectiveness of key project components, including clinical experiences and near-peer mentoring, will be evaluated for the ways in which they contribute to teacher preparation. The results of this project will be disseminated to help enhance the field. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. 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

Internet search offers incredible learning opportunities by making information more accessible than ever before. This project examines whether internet search leads to shifts in thinking style and if it affects self-directed learning. The project also aims to investigate whether interventions which encourage people to use their memory as they conduct internet searches could influence search processes. This work seeks to grow understanding of how people can effectively use their mental abilities alongside digital tools. The project also includes STEM research opportunities for trainees and broad dissemination of project outcomes through online public forums. There are two major goals of the proposed research. First, this work aims to examine the extent to which using internet search might affect people’s ability to direct their own learning by changing users’ cognitive style. Prior work suggests that habitually using internet search can impart a low-effort approach to cognitive tasks, so the proposed work plans to investigate the extent to which such a shift in cognitive style influences learning on an unrelated task. Second, this work aims to investigate short-term and longer-term interventions to counteract potential effects of internet use on learning and cognitive style by encouraging users to practice using their cognitive abilities alongside internet search. 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

Recent molecular line observations of the young AB Aurigae star system have revealed intriguing velocity structures that may offer direct evidence for a long-hypothesized mode of planet formation known as gravitational instability, where planets form rapidly through direct gravitational collapse. In addition, optical, infrared, and millimeter images show a planet-sized clump at roughly twice the scale of the Solar System, roughly 90 astronomical units (AU, the average distance between the Earth and the Sun), surrounded by a ring of pebbles at ~150 AU and marked by striking spiral arms in the surrounding disk. This team of researchers will generate computer simulations that integrate multiple physical processes, including gas and dust dynamics, heating and cooling from starlight, and the system’s own gravity. They will train a graduate student and will also communicate with the public through regular astronomy columns and podcasts. The column reaches a readership of 30,000, and is contributed to by faculty and students, aiming to become a sustained effort by the New Mexico State University Department of Astronomy to promote public science literacy. A science outreach program and observatory tour for the public will be regularly scheduled at the University of Georgia. The team will determine whether the AB Aurigae system can be modeled self-consistently via gravitational instability in a protoplanetary disk. The team will employ dusty smoothed-particle hydrodynamics (SPH) using the Phantom code, with 25 million particles incorporating pebbles, self-gravity, and on-the-fly radiative transfer. The simulations aim to reproduce three major features seen in the observations: the spiral arms, the extended clump-like protoplanet, and the outer pebble ring. While spiral arms and clumps are relatively common outcomes of gravitational instability, the origin of the ring is more challenging. The working hypothesis is that the pebble ring forms near the boundary where the disk transitions between gravitationally unstable and stable states—specifically at ~150 AU. This transition is expected to result in a change in turbulent viscosity and create conditions ripe for the Rossby wave instability, a shear-driven instability analogous to the Kelvin-Helmholtz instability, which can trap dust and create ring-like structures. Post-processing will be carried out using the radiative transfer code RADMC-3D to produce synthetic images comparable to those observed by CHARIS in scattered light and ALMA in the midplane. The results will provide a stringent test of whether gravitational instability can account for the complex morphology of AB Aurigae, potentially validating or refuting a major planet formation mechanism. 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

Discovering planets orbiting other stars can teach us about how they form, and capabilities now exist to find gas giants orbiting at 5-30AU from their stars (an AU is the distance between the Earth and the Sun), like Jupiter and Saturn do in the Solar System. Unlike commonly employed indirect methods to sense a planet, the technique of infrared direct imaging requires that the planet still glows from its heat of formation. Hence stars that have newly formed, which may be accompanied by newly-formed planets, need to be identified and targeted. Imaged planets can be followed up with spectroscopy to characterize their atmospheres and determine their fundamental properties, and about a dozen have been found to date. Sometimes the youth of nearby stars can be inferred from how they move in groups with other stars, however a very limited number of these stars are currently known. In this work, isolated stars that are less than 1 billion years old will be identified, and their ages determined. Some of those stars will be targeted to search for planets, and the full catalog of stars will be published for follow-on imaging by others. Mentoring programs for undergraduate students in summer research will be done with combination of a traditional in-person mentoring at NMSU and an innovative online experience for a larger number of students at ASU. To produce an all-sky catalog of nearby (<50 parsec), young (<1 gigayear) stars, a combination of multiple spectroscopic and photometric age indicators across a range of spectral types will be interpreted with a robust statistical framework. The team will employ X-ray flux, Gaia-measured space velocity, and TESS-derived rotational periods, then confirm their youth with high resolution optical spectra for lithium abundance, H-alpha emission, and calcium emission. They will also screen the targets for binarity with snapshot Adaptive-Optics observations and radial-velocity monitoring. As the youngest nearby stars are identified, the team will begin a direct imaging planet search with available high contrast AO imagers, including Gemini-North/GPI, LBT/LMIRcam+SHARK, and Subaru/SCEx-AO. Undergraduate researchers will participate in observing runs (in-person or virtually), gain experience in data analysis, and present their results at conferences or in virtual reports/posters at team meetings. Graduate students at both institutions will participate in mentoring. 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 · 2025-05

This I-Corps project is based on the translation from lab to market of an assistive communication device that adapts to the context of the user in real-time. The innovation includes features such as incorporation of the user’s location, conversation partners, and current conversation topics, as well as biosensors to detect the user’s emotions so it sounds like natural human speech. This solution addresses issues related to current assistive communication devices, which can be tedious to learn and use, and sound like robots, resulting in the abandonment of 60% of devices in the first year. The commercialization of this innovation has the potential to benefit society by offering advanced communication options for users, simplifying the learning process, lowering device abandonment rates, and giving voice to millions of people of all ages. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an assistive communication device that that can be downloaded to a phone or tablet and utilizes artificial intelligence (AI) and biosensor data to easily adapt to the context of the user. The scientific advances that make this solution different from existing solutions include the use of AI, geolocation, identification of high frequency words in the conversation, and biosensor data. The benefits of this approach include providing the user with an easier and improved communication experience that could improve their quality of life. 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 impact of this I-Corps project is based on a water resistant, clear glass that is energy-efficient and low-maintenance and can be used by various industries, such as construction, real estate, hospitality, and healthcare. This solution seeks to address the high energy consumption and maintenance costs associated with traditional glass in buildings, which contributes to increased operational expenses. By reducing maintenance requirements, enhancing energy efficiency, and offering privacy through customizable translucency, this solution can improve both the functionality and aesthetic quality of residential and commercial architecture. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an advanced hydrophobic coating technology for glass surfaces. This solution consists of a high-performance coating process that imparts hydrophobic properties, high-temperature resistance, and customizable translucency to glass, providing a durable and cost-effective alternative to traditional glass coatings. The scientific advances behind this technology include the development of a long-lasting coating that overcomes the limitations of existing products, which often degrade quickly or fail under high temperatures. The benefits of this approach include lower cleaning costs, reduced energy consumption, and an extended lifespan for glass surfaces, providing users with a practical solution that improves maintenance efficiency, enhances privacy, and promotes long-term cost savings in both commercial and residential settings. 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 · 2025-03

The Jornada Basin (JRN) long-term research team will study why and how grasslands in low rainfall areas are shifting to shrub and bare ground in the southwestern United States and beyond. Losing grassland ecosystems can negatively affect human well-being by reducing livestock production and increasing soil erosion. It also changes how important elements, like carbon and nitrogen, flow through these ecosystems with consequences for carbon storage and water quality. This project aims to further advance understanding of this shift to shrublands by studying (1) how weather and land management affect the shift, (2) what happens to the flow of carbon and other elements, (3) how wind and water erosion could speed up the change in some cases, or even reverse it in others, and (4) how microbes, plants, animals and their interactions influence how fast the loss of grassland happens. Along with the planned research, the team will continue to expand successful K-12 education programs that reach thousands of students and teachers annually through field trips, classroom visits, public events, teacher trainings, and internships. The team will also continue to work with land managers, in collaborations with USDA and other partners, to provide information and data about land health and build capacity for evidence-based decision making in the face of environmental change. This renewal of the JRN LTER project will be organized around the site’s new Pulse-Interaction-Reserve-Feedback-Change framework for understanding state changes in dryland ecosystems. This framework integrates consideration of temporal and spatial variability and alternative stable states to explain and predict changes in grassland habitats. The research team will measure primary production, carbon and inorganic nutrient cycles, and plant-animal interactions in response to changing rainfall, temperature, and land disturbances through observations and experiments. Novel modeling activities will examine hydrologic connectivity and leverage machine learning and long-term data to advance understanding of ecosystem state change. Results from this site and others show drylands are critical components of Earth’s biogeochemical cycles and atmospheric gas concentrations. Long-term data and empirical insights derived from JRN research are expected to inform regional to global scale models and syntheses. 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

The digestion and absorption of food in the small intestine involve a complex array of processes. These processes encompass the transport of food particles along the intestinal tract, their mixing with digestive enzymes, and the movement of nutrients to the surface of the intestinal wall. These processes are facilitated by the rhythmic movements of the intestinal wall and microstructures on its inner surface. This suggests that the human body may regulate digestion and absorption in the small intestine by controlling these movements. This project will uncover the mechanisms by which the small intestine mixes and transports food particles, enzymes, and nutrients across different scales, ultimately regulating digestion and absorption. The study will employ advanced numerical simulations, theoretical analysis, and data analysis techniques to explore the intricate processes involved. This project comprises high-fidelity numerical simulations of multiscale mechanisms, advanced theoretical analysis grounded in physics, and data analysis techniques leveraging machine learning. The primary objective is to establish a mechanistic framework for understanding how human physiological systems manage digestion and absorption by regulating these mixing and transport processes in the small intestine. This will be achieved through two specific goals; 1) Uncover the mechanisms of mixing, transport, and breakdown of chyme particles, along with dissolved enzymes and nutrients in intestinal fluids and 2) Identify the impact of mixing and transport processes in the small intestine under various conditions on food digestion, nutrient absorption, and energy consumption. The goal is to clarify the regulatory mechanisms of the human physiological system through comparison with in vivo intestinal motility patterns. This award will also advance multidisciplinary research and education in multiphase flows and will enhance the education of students in New Mexico. This project is jointly funded by Particulate and Multiphase Program and the Established Program to Stimulate Competitive Research (EPSCoR). 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

Green hydrogen is produced from water using renewable energy sources, such as electricity or sunlight. Electrolysis uses electricity to split water into hydrogen and oxygen in an electrolyzer. Green hydrogen has many potential applications and can help reduce dependence on traditional fossil fuels and CO2 emissions. However, green hydrogen production through water splitting is expensive. There are several reasons, such as high energy consumption caused by high water splitting overpotential, low stability and durability of catalysts especially under harsh conditions, and higher water purification costs. Through this fellowship, the Principal Investigator (PI) will spend six months at the host institution acquiring fuel cell and electrolyzer device fabrication skills and access to advanced characterization facilities. The project will establish new collaborations between two Hispanic-Serving Institutions and benefit the PI’s career trajectory. Furthermore, the team will have opportunities to network and participate in the Hydrogen Hub to get connections with the industry. The PI will share knowledge and resources gained from this fellowship and help colleagues connect with the host institution to promote further collaborations. The PI will bring the skills back to the classroom and set up a proton conducting fuel cell and electrolyzer to train students. The fellowship and collaborations will provide students with more opportunities to work as summer interns in industries or pursue graduate programs. This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project will provide a fellowship to an Associate Professor and training for a postdoctoral researcher at New Mexico State University (NMSU). This work will be conducted in collaboration with researchers at the University of California at Irvine (UCI) with Dr. Plamen Atanassov as the main host. The PI will learn to use the well-established fuel cell and electrolyzer system in the host’s lab and obtain the related knowledge and skills. The objectives of this project are to achieve academic integration for hydrogen production through integration of knowledge and hands-on experimentation, integration of research lab and industry, and integration of undergraduate and graduate programs. The project aims to advance novel catalyst development, enable the PI to learn fuel cell and electrolyzer fabrication, and enable the PI to use an in-situ scanning electrochemical microscope for advanced electrocatalyst characterizations. The outcome from the fellowship project will be the advancement of direct seawater splitting and the scaling up of hydrogen production in the future. A long-term sustainable collaboration between the two institutions is expected to improve the electrochemical performance of water electrolysis for green hydrogen production. A fuel cell and electrolyzer will be set up at NMSU to bring new research, training, education, and outreach opportunities to graduate, undergraduate, community college, and high school students. 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

Only 3% of New Mexico high school students took a foundational computer science (CS) course in 2022-2023, in part due to a lack of access to such courses, especially those that integrate culturally responsive instruction. In 2024, the New Mexico State Legislature mandated that all high schools shall offer at least one computer science course. To support these efforts, this high school strand research-practice partnership between New Mexico State University, the New Mexico Public Education Department, the CS Alliance, the CS Teachers Association of New Mexico, and the Los Alamos National Laboratory Foundation, aims to understand how to prepare teachers to teach a culturally responsive, rigorous CS curricula that meet the needs of students in rural New Mexico communities. The project will provide pathways for high school teachers to obtain an endorsement in CS, by completing 60 hours of professional development that include community-centered, service-learning activities within the AP Computer Science Principles curriculum. This RPP will study how a professional development program will impact high school teachers’ perception and attitudes towards CS and CS teaching and learning, how the AP CS Principles course materials need to be modified to support rural students in New Mexico, and examine how external factors (e.g., school, community) impact teachers’ attitudes and perceptions about CS and CS teaching and learning. This project’s ability to impact teacher attitudes and beliefs is rooted in Social Learning Theory and Cultural Constructionism. Survey instruments will be employed and adapted to understand teacher dispositions and how they are tied to teacher motivation, curriculum adoption, culturally responsive instruction adaptation, and classroom climate and practice. Working closely with RPP partners, the project aims to create a professional development program that is adaptable to local needs while still meeting the CS AP standards using a Design-Based Implementation Research approach to collaboration. The RPP team will also study the efficacy of online and hybrid instruction to scale, retain, and sustain these practices in their respective school districts, communities, and 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 2025 · 2025-01

Multi-GPU (graphics processing unit) systems are the most common accelerator platform capable of handling large-scale problems in High Performance Computing (HPC), Machine Learning (ML), and Artificial Intelligence (AI). This project will develop a scalable and accurate multi-GPU simulation framework as part of the Structural Simulation Toolkit (SST) that will assist both computational scientists and the designers of the next generation of advanced computing systems. Unlike existing solutions, this framework will provide computational scientists and analysts with the ability to estimate the gains and overheads associated with accelerating their applications, codebases, and workflows on GPU systems. It will also enable system designers to efficiently explore system alternatives. The developed simulator will furthermore enable the comparison of the performance of GPUs from different vendors. The multi-GPU-SST will be publicized through a comprehensive strategy that includes in-person and online tutorials, training, and educational activities. The proposed framework will be shared through tutorials and workshops at conferences, ensuring that a wide range of computational scientists, analysts, and academics can benefit from and contribute to its development. Through three tasks, the project will develop a multi-fidelity, multi-GPU simulation framework in SST. First, it will create a single GPU model in SST. Second, the project will address a critical need in the field by developing a multi-GPU simulation model that supports state-of-the-art GPU networking, interconnects, and communication. The proposed research will not only study but also actively work to resolve the scalability issues in simulating large-scale GPU systems, developing techniques to improve the simulation scalability and ensuring the framework's applicability to real-world scenarios. Third, it will create an AI-based model to enhance the performance of SST, especially the proposed GPU interconnection network model. This AI-based model will leverage machine learning algorithms to optimize the performance of the GPU interconnection network, thereby improving the overall efficiency and speed of the simulation framework. The project will conduct a comprehensive performance analysis of emerging GPU workloads, including those from the Department of Energy's Exascale Computing Project, HPC applications, standard benchmarks, ML applications, and Large Language Models (LLMs). This analysis will provide valuable insights into the performance characteristics and potential areas for improvement of these workloads, thereby contributing to the advancement of GPU-accelerated computing in various 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 · 2025-01

The number of scientific publications is continually increasing, which poses challenges for researchers in digesting and discovering relevant insights from work within their discipline and across multiple disciplines. To help researchers overcome information overload and keep up with the latest research, the overall goal of this project is to support the scientific literature review process and facilitate creative scientific problem-solving by developing models and algorithms for the analysis and visualization of scientific texts. The project will provide a fellowship to an Assistant professor and training for a graduate student at the New Mexico State University. This work will be conducted in collaboration with researchers at the University of California, San Diego (UCSD). Through the fellowship, the principal investigator aims to create algorithms that model the intent, topics, and embeddings within scientific documents. Additionally, the PI, in partnership with UCSD, will develop an interactive visual tool that enables users to explore related articles based on the document's intent structure. This project will benefit the community by providing an efficient tool for researchers to browse, search, and manage research articles, enabling them to extract useful insights and relationships between articles, which will hopefully accelerate scientific progress. The project will contribute to text mining research by introducing new approaches for mining and visualizing scientific texts. More specifically, this project aims to (i) develop neural topic models for supervised document structure learning and intent structure comparison of scientific articles; (ii) create semantic visualization methods that jointly model textual content, rhetorical structure, and topic structure of documents for exploratory tasks such as browsing or finding relevant articles; and (iii) build an interactive visual tool for mining scientific texts based on the proposed models. This project has strong potential to advance the theory and practice of text mining, document modeling, and visualization The project's proposed methods and interactive visual tool can be integrated into computational systems that could help accelerate scientific progress by assisting researchers in managing information overload. 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 Computing Research Association (CRA) and New Mexico State University in collaboration with Association for the Advancement of Artificial Intelligence (AAAI), Association for Computing Machinery (ACM), and Institute of Electrical and Electronics Engineers Computer Society (IEEE-CS) will convene LEVEL UP AI to build consensus on strategies to increase capacity Artificial Intelligence (AI) education and to expand AI curriculum and infrastructure. With the increasing demand for AI professionals, faculty, and researchers, there is an imperative to develop a shared vision that includes expectations and plans for an expanded AI curriculum, the infrastructure needed to deliver a quality AI education experience, and the strategies, principles, and resources that are required to advance AI education. LEVEL UP AI adopts a 2-phase process: (1) a series of virtual roundtable discussions to gather multiple perspectives around issues of increased capacity in AI education, followed by (2) in-person workshops to develop community, consensus, and action. The outcomes of the 2-day in-person workshops are to create community and action for a common vision to mobilize stakeholders to increase capacity in AI education. The expected outcomes of the project include (1) reports that articulate the vision and its supporting arguments, (2) best practices for strategies to increase capacity in AI education, (3) infrastructure resource categories and types that ensure expansion of AI education, and (4) processes and metrics for assessing capacity and quality in AI education. 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

This project aims to develop advanced techniques for building structures on the Moon using 3D printing technologies, focusing on understanding and addressing the challenges posed by the Moon's unique environment. By investigating the materials and conditions necessary for constructing resilient lunar habitats, the research addresses fundamental issues related to space exploration and sustainability. This project is significant because it not only advances the field of space engineering but also has the potential to benefit society by fostering innovations that could be applied to building disaster-resistant structures on Earth. The study supports education by offering multidisciplinary training in engineering, material science, and artificial intelligence, and promotes diversity by involving underrepresented students in STEM from New Mexico State University. Furthermore, the project aligns with NSF's mission to promote scientific progress: By improving our ability to construct habitats in extreme environments, this research supports long-term human space exploration, contributing to future missions to Mars and beyond, and ensuring the safety and resilience of both extraterrestrial and terrestrial structures. The primary objectives of this research are (1) to quantify the uncertainties present in additively constructed lunar structures operating under microgravity and extreme lunar conditions and (2) to establish and enhance the predictability of structural behavior and the reliability of these structures. This project will undertake a comprehensive investigation into the various uncertainties affecting the system, including those in materials such as lunar regolith and potential metal alloys such as magnesium alloy, environmental loads such as thermal loads and moonquakes, and uncertainties inherent in the structural geometries and additive construction processes. Additionally, it will examine uncertainties arising from our existing knowledge, especially extraterrestrial conditions, and employ correlation modeling to understand the interdependencies among these factors. Advanced statistical methods, including Bayesian approaches, alongside cutting-edge artificial intelligence techniques, will be employed to model these uncertainties comprehensively. Once established, these models will be used to construct probabilistic models and analyze the fragility of proposed lunar structures under the specific challenges posed by microgravity and extreme lunar conditions. The intellectual merit of this proposal lies in its approach to systematically address the uncertainties associated with materials and environmental conditions critical to the sustainability of lunar structures. The integration of uncertainty quantification with advanced probabilistic modeling provides a novel methodological framework that will significantly enhance the prediction and management of risks in extraterrestrial construction. This research will utilize state-of-the-art analytical techniques, facilitated by collaborations with NASA's Marshall Space Flight Center, to push forward the boundaries of current knowledge in space habitat construction. 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

With recent advances in artificial intelligence (AI), the United States needs to develop a workforce with strong computational skills and the knowledge and capability to work with AI. Recent studies have raised questions about the extent to which youth are aware of AI and its application in industries of the future that may limit their interest in pursuing learning that lead toward careers in these industries. To address this challenge, Learning Trajectories (LTs) will be developed and researched for AI concepts that are challenging for middle and high school students. (LTs outline the learning goals, pathways, and learning experiences which would support related learning at grade levels.) The project will design and pilot test learning activities and assessments targeting these concepts based on the LTs, offer teacher professional development (PD) on the LTs and related activities, and research the effectiveness of the LT-based activities when implemented by teachers during the regular school day. The LTs and other educational resources have the potential to benefit society at large by preparing students for life and work in computationally intensive industries of the future and to have a significant impact on developing the workforce for AI-related industries. Project research and resources will be disseminated through an awareness campaign aimed at researchers, practitioners, school administrators, and teachers; a project website including lessons, activities, assessment tools, press releases, project reports, and journal articles; and presentations and journal articles reaching both educators and researchers. Research questions will focus on: (1) investigating the competencies and difficulties students demonstrate when making sense of AI concepts and processes, (2) how students' understanding of AI corresponds to the hypothetical LTs, and (3) to what extent the LT-based activities support student understanding of AI topics when taught in school. Teachers and researchers will be engaged co-designing, testing, and refining LT-based lessons in their classrooms. Initially, the research will engage 96 middle and high school students in cognitive interviews to build the LTs, 16 middle and high school teachers in PD and co-design, and 640 of their classroom students in the pilot testing. Additionally, 16 teachers and 640 students will participate in an effectiveness study of the curriculum with the LT-based learning activities inserted. Students involved in the project will be recruited from schools in urban, rural, and suburban school districts. The research will offer invaluable insights on student competencies and difficulties when learning AI and yield AI LTs for grades 7 - 10. The co-design team will include learning scientists and curriculum developers who have successfully developed AI literacy curricula and assessments; experts in AI and AI education including AI teacher PD; experts in cognitive science; and researchers who have developed LTs for Math and Computer science. The teacher-refined activities will reveal where and how teachers feel the AI topics can be connected to and fit within their existing curricula. The project is supported by the Discovery Research preK-12 program (DRK-12), which seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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 abundances of species fluctuate through time and differ across space. Variation in abundances is used to determine patterns of synchrony (i.e. correlation in abundances), which influence species extinction risk and the stability of ecosystems. Patterns of synchrony are driven by many factors, such as variability in temperature or precipitation, or the dispersal patterns of species themselves. To date, research on synchrony has primarily focused on the role of the environment as an external force driving synchrony, both across locations and across species. However, traits, which define characteristics of species, likely play major roles in regulating synchrony. Further, different traits might determine patterns of synchrony in different environmental conditions. This research combines plant composition data from a global grassland experiment with collections of species trait data to test how traits determine patterns of synchrony across environmental gradients. The researchers will additionally lead a virtual global seminar for grassland ecologists on integrating experimental data with ecological theory. The grant will support multiple students in attending and networking at an annual working group meeting that brings together grassland scientists. This research will enhance understanding of the processes that determine ecosystem variability, aiding our ability to manage ecosystems and predict responses to future environmental change. Synchrony is a ubiquitous phenomenon across ecological levels of organization and is driven by both biotic factors and abiotic environmental drivers. At the population level, correlations in temporal abundance fluctuations across distinct locations (i.e., population synchrony) increase species' extinction risk. In communities, correlated fluctuations among species in a given location (i.e., community synchrony) decreases ecosystem stability. Species traits (morphological, physiological, and phenological characteristics of species) and trait-by-environment interactions likely play major roles in regulating synchrony. The project integrates multi-site time series data from a global distributed experiment (the Nutrient Network) with novel trait collection, existing trait databases, and methods development to create and empirically test frameworks for linking functional traits to synchrony across scales of ecological organization under both natural conditions and experimental treatments that alter herbivory patterns and nutrient enrichment. The research will synthesize multiple trait databases and collect new data on traits hypothesized to mechanistically affect synchrony, characterizing how trait composition within ecological communities responds to increased fertilization and decreased herbivory pressure. Trait data, coupled with long-term time series data of plant composition, will be used to assess how trait variation and trait-by-environment interactions influence population and community synchrony across environmental gradients. 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 seeks to advance understanding of semigroups within the framework of operator algebras, an area of mathematical analysis. The mathematical field of operator algebras originated in the pioneering work of Murray and von Neumann in the 1930s to develop mathematical foundations for quantum mechanics and has grown into a vital subarea of modern analysis, with connections to many other mathematical fields, including geometry, topology, mathematical physics, and algebra. Operator algebras and the theory of semigroups have enjoyed a fruitful dialogue since the 1990s. Semigroups are algebraic structures in which (like the real numbers) elements can be combined using algebraic operations, but which (unlike the real numbers) generally lack inverse operations. Due to this algebraic structure, semigroups are often useful in modeling “irreversible” phenomena, such as the time evolution of some physical systems, and have proven to be an essential tool across the mathematical sciences. This project will develop new theories and methods to study semigroups and their associated operator algebras through their representations, boundaries and dynamics. The project will also foster international collaboration, enhance the research culture at New Mexico State University, and provide training of graduate and undergraduate students with an emphasis on including students from underrepresented groups. This is a project funded jointly by the National Science Foundation’s Division of Mathematical Sciences, in the Directorate for Mathematical and Physical Sciences (NSF-MPS-DMS), and the Israel Binational Science Foundation (BSF) in accordance with the Memorandum of Understanding between the NSF and the BSF. This project concerns the interrelationships among several classes of semigroup representations and associated operator algebras and dynamics. Unlike groups whose representations are always unitary, semigroups have richer classes of representations as operators on Hilbert spaces. The first goal of this project is to develop a general theory of dilation of isometric covariant representations to characterize boundary representations, and thereby calculate the C*-envelope of universal non-self-adjoint semigroup operator algebras. The focus is on the class of non-Nica-amenable semigroups, whose representations remain highly mysterious. The second goal of this project is to study semigroups from a dynamical perspective, seeking to understand the properties of semigroup operator algebras from the properties of the underlying dynamics. In particular, this project aims to build a general framework to study two-sided semigroup actions, which is motivated by mathematical physics. This project will also investigate the dynamics of self-similar actions, which exhibits new phenomena that generalize many group dynamics. Finally, this project will investigate the representation theory of Artin semigroups, which is a rich class of semigroups with deep connections to various areas of mathematics. In addition to providing concrete examples, the study of Artin semigroups will also contribute to multivariable operator theory and non-commutative geometry. 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

This project addresses the urgent challenge of climate change intertwined with aging energy infrastructure in the United States. This is a critical challenge most acute in underserved communities. The project aims to enhance local research infrastructure across four EPSCoR jurisdictions—New Mexico, Montana, Oklahoma, and Alabama. In turn, these regions will benefit from advancements in AI, digital twin technology, and renewable energy. By developing AI-driven digital twins tailored to the energy infrastructures and socio-economic needs of three representative underserved communities (Kit Carson, Mora-San Miguel, and the Navajo Nation), the DigiCARES project will optimize energy utilization, integrate renewable sources more effectively, and improve overall climate resilience. This project not only advances the field of energy system planning but also supports education and workforce development, promoting diversity in STEM fields through active collaboration with minority-serving institutions and initiatives such as New Mexico State University’s Pre-freshman Engineering Program, The University of Alabama in Huntsville Research and Engineering Apprenticeship Program, and Oklahoma State University’s NSF-sponsored Oklahoma Louis Stokes Alliance for Minority Participation. These efforts align with NSF’s mission to promote the progress of science, advance national health, prosperity, and welfare, and secure national defense by addressing energy equity and sustainability challenges. The project will include extensive outreach activities to engage underserved communities in co-developing these technologies, ensuring their specific needs and perspectives shape the outcomes. The approaches and outcomes of DigiCARES in AI and digital twins, especially in addressing climate resilience, will be applicable to various communities, scalable to other regions in the US, and potentially globally. DigiCARES embarks on a transformative approach in energy systems planning and operation, leveraging AI-driven digital twins to address the nexus of climate, energy, and community. The overarching goals of this project are to enhance energy efficiency, reduce energy burden, integrate renewable sources, and fortify climate resilience in underserved communities. The project integrates diverse scientific disciplines including data science, climate science, AI, sociology, and energy policy, to create interoperable datasets, interactive tools, and community-centric platforms. These digital twins will provide in-depth analysis and strategy testing for energy systems under various future scenarios. The research encompasses six key activities: multi-scale climate dynamics, sociodemographic energy mapping, community-centric planning and operation, AI-driven digital twin development, pilot studies in three communities, and translating energy insights into interactive visual narratives. The project will actively engage the communities in Kit Carson, Mora-San Miguel, and the Navajo Nation in the co-development process, ensuring that the technologies meet their specific needs and are effectively implemented. Through these activities, DigiCARES aims to generate advanced models, open datasets, and strategic decision frameworks, providing scalable solutions and setting a new benchmark for interdisciplinary research in climate resilience and sustainable energy systems. These efforts will significantly contribute to building a resilient and equitable energy future, supporting the nation’s net-zero emission goals. 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.