Auburn University

NSF Awards · FY 2025 · 2025-08

This project explores exciting new interactions between two central areas of mathematics - algebra and geometry - and their unexpected connections through physics. Algebra and geometry are foundational tools in mathematics, widely used in numerous scientific and engineering applications, such as computer science, data analysis, robotics, and theoretical physics. Historically, the interplay between algebraic equations and geometric shapes has led to powerful methods and profound insights, shaping much of modern mathematics and technology. In recent decades, researchers discovered surprising connections linking algebraic geometry, which studies shapes defined by polynomial equations, to symplectic geometry, an area crucial to physics and engineering. This project leverages these emerging connections to develop new mathematical tools that bridge algebra and geometry. Broader impacts of this research include significant training and mentoring activities. The project supports early-career researchers and graduate students, providing extensive professional development through workshops, virtual seminars, public lectures, and the creation of publicly available computational tools. On the technical side, the project aims to advance understanding in multigraded commutative algebra, toric geometry, and symplectic geometry. It addresses long-standing gaps and open questions in commutative algebra and toric geometry by introducing methods inspired by recent advances in homological mirror symmetry into purely algebraic contexts. The P.I.’s will explore new approaches to studying multigraded polynomial rings, aiming to uncover deeper structural properties that parallel classical results for standard graded polynomial rings. The project will develop algebraic analogues of effective symplectic geometry techniques, such as "stop manipulation," adapting these symplectic methods to algebraic settings. The project will also extend foundational results, including Orlov’s Theorem, to multigraded and toric settings, construct novel categorical structures that unify algebraic and geometric perspectives, explore applications to virtual resolutions and other questions involving shortest resolutions, and investigate extensions to broader classes of geometric objects through toric degenerations and natural generalizations from toric varieties. Furthermore, by establishing explicit links between algebraic constructions and Fukaya categories, the project will introduce new computational tools and theoretical approaches in symplectic 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 2025 · 2025-08

Topological insulators are a class of materials that are insulators in their bulk and that nevertheless allow wave propagation along their edge. Such materials are at the forefront of the rapidly developing industry of low-energy consumption electronics technologies as they yield a fundamentally new type of electronic transport with ultra-low resistance. Mathematically, such transport is modeled by wave functions corresponding to the eigenvalues appearing, after introduction of a boundary, in a gap of the spectrum of the material without boundary. The principal goal of the project is to develop mathematical methods for measuring the number of such states based on readily computable information about the physical models of the corresponding material. The principal investigators investigate discrete boundary value problems for the tight-binding Hamiltonians describing electronic transport in topological insulators. One of the main themes is the derivation of explicit formulas for the number of eigenvalues in the gaps of the essential spectrum, which correspond to edge states localizing along the boundary. The formulas are expressed through mathematical tools stemming from symplectic geometry, such as the Maslov index and its discretization, the Duistermaat index, with the latter being particularly amenable to numerical computation. In addition to eigenvalues in the gaps, the new formulas give access to the integrated density of states (in the bands of the continuous spectrum) and shed new light on the bulk-boundary correspondence by connecting the gap indices with the band indices. The discrete boundary value problems are described using the theory of boundary triplets of non-densely defined symmetric operators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This proposal seeks to fund US-based students to attend 2025 IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), held in Chicago, Illinois on October 6 - 8, 2025. IEEE MASS is a premier annual forum for sharing original, novel ideas in mobile ad-hoc and smart systems.  The conference brings together researchers, developers, and practitioners to address recent advances in mobile ad-hoc and smart systems, covering algorithms, theory, protocols, systems & applications, experimental evaluations and testbeds, security and privacy, as well as AI/ML-based smart design. This project supports students from US universities to attend IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS) 2025 in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant will target students who will substantially benefit from attending this conference but have limited travel funds. Priority will be given to first-time attendees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Accurately predicting the ecological effects of a warming planet is essential for lessening ongoing economic and societal harm. However, the potential impact of global temperature rise predicted for Earth by the end of the 21st century cannot be studied on the scale of human history alone. Fortunately, essential real-world data on the outcome of rapid and extreme warming is preserved in our planet’s deep-time rock record. Approximately 95 million years ago, Earth transitioned through an interval of global change known as the Cretaceous Thermal Maximum (KTM) with profound repercussions. Global temperature increase during the KTM matches predictions for Earth's near term, making the event a critical case study for our planet’s imminent future. Research demonstrates that during the KTM, 80% of marine life went extinct due to increased ocean temperatures and oxygen starvation. However, scientists do not yet understand the impact of warming on land. Our team of Earth and Life scientists will address fundamental questions about the KTM, producing results directly relevant to society's health and economic well-being. The project will generate freely accessible databases of temperature and precipitation records, species diets, migration and range patterns, plant community compositions, and landscape changes. A sustainable network of labs will use these databases to calculate the duration, rate, and magnitude of extinction and recovery and identify factors affecting ecosystem resilience, such as shifting habitats and destabilizing food webs. A cross-disciplinary postgraduate research exchange program will arm the next generation of scientists with the broad skill sets necessary to tackle some of humanity's forthcoming grand challenges. Finally, the project will increase STEM opportunities for youth via co-created teacher resources and a public science project that empowers secondary school students to contribute directly to scientific research. Approximately 95 million years ago, ecosystems transitioned through an understudied hyperthermal event, the Cretaceous Thermal Maximum (KTM), driven by increasing atmospheric CO2. Global temperature rise during the KTM was triple projections for Earth by the end of the 21st century—making the event a critical case study for predicting tipping points of functional ecosystem decline (economic risk) in as-of-yet unrealized planetary states. Previous studies have documented KTM's marine impacts, including global ocean deoxygenation and cascading extinctions; however, scientists currently lack essential data on terrestrial outcomes. This project will formulate comprehensive, open-access databases that enable cross-disciplinary study of the KTM aftermath. Research will focus on Mongolia's Gobi Basin and North America’s Western Interior Basin, which together preserve the world's richest records of Cretaceous terrestrial life. Data generated will include floral and faunal biodiversity and spatiotemporal records, as well as biofunctional traits such as niche guild, migration and range potential, habitat requirements derived from geochemical analyses, temperature and precipitation proxies, constrained by radioisotopic ages determined using C-isotope chemostratigraphy, eggshell and pedogenic carbonate, and zircon. By integrating across Earth-life systems, the project will tackle a series of hierarchical objectives, including establishing a refined chronology of ecosystem change, calculating the rate and duration of destabilization and recovery, assessing trends and drivers of habitat evolution, and exploring the impact of extreme warming on ecosystem resilience, functional biodiversity, and species threat. Beyond propelling comparative research on ancient hyperthermals, the collaboration will enable a cross-disciplinary postgraduate research exchange program to arm the next generation of scientists with the multifaceted skill sets necessary to tackle grand challenges. Finally, broader engagement objectives will increase scientific literacy and inspire youth to pursue STEM careers via a public science program that enables secondary school students to discover new biodiversity records, contributing directly to data collection and through co-created teacher resources. This project is funded by the BIO/DEB Biodiversity of a Changing Planet (BoCP) Program, the Division of Earth Sciences (EAR) and the GEO/EAR Life and Environments through time (LET) Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

For the last decade the Pan American Society for Evolutionary Developmental Biology (PASEDB) has held an international biennial meeting. Each of the previous meetings attracted a large community of scientists working in the field of Evolutionary Developmental Biology (EvoDevo) indicating the critical need in the community for this scientific and educational exchange. EvoDevo is a multidisciplinary field that sits at the intersection of Evolutionary Biology, Developmental Biology, Genetics, Genomics, and Physiology. The focus of this sixth meeting will be to highlight recent breakthroughs in comparing similarities and differences in the embryonic development of a diverse set of animals ranging from jellyfish to humans. The PASEDB meeting also provides an invaluable platform for young faculty, postdoctoral associates, graduate and undergraduate students to present their results, to learn about the latest research and technological advances in evolutionary developmental biology, and to expand their professional network. This award will support early career scientists (junior faculty members, postdoctoral scholars, and graduate students) from the United States to attend the meeting. In short, this award will help provide a forum that is vital for the continued growth of the EvoDevo community. A community that is essential to the progress and innovation in several fields, including those directly relevant to human development and health. The NSF funding for the PASEDB meeting provides the society with the ability to bring early career scientists (assistant professors), postdoctoral associates as well as graduate and undergraduate students from the United States to the biennial meeting. Scientific sessions include topics such as genomic basis of developmental evolution, the developmental origins of novelty, emerging research organisms, developmental biology in a changing world, cell biology in EvoDevo, neuro EvoDevo, new tools in EvoDevo, development and evolution, education in EvoDevo, external influences on development, genetic basis of trait variation, and comparative omics. The organizing committee has planned for a venue to conduct lively poster sessions that offer important opportunities for early career scientists to share their work in progress. Importantly, many students are attending their first conference or presenting preliminary results for the first time in this welcoming atmosphere and inspiring setting. Attendees will also be able to participate in workshops designed to address professional development, and successful strategies in academia such as “mentoring students and postdocs”, successful delivery of chalk talks and teaching demos during job interviews” and “how to succeed in teaching in your first year”. These are critical topics for increasing recruitment and retention of trainees and new faculty in the field. Finally, the meeting will feature an all-postdoc symposium to showcase future PIs in the field and offer an early career award for new EvoDevo faculty. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The research project focuses on stochastic partial differential equations (SPDEs), which play a significant role in explaining complex phenomena across diverse fields such as physics, biology, and chemistry; they can model real-world problems, including tumor growth, forest fire propagation, superconductivity, and the structure of the universe. The broader impacts of this project involve enhancing societal well-being through potential advancements in cancer treatment, fire management, environmental processes such as sediment transport, and improving manufacturing processes in pharmaceuticals and semiconductors. The project also promotes interdisciplinary applications of SPDEs in finance, numerical analysis, engineering, and machine learning, and provides research opportunities for graduate students. Additionally, the researchers will foster educational outreach through the continued development of open-access computational tools, comprehensive mathematical bibliographic databases, specialized analytical tools, and STEM education programs aimed at middle and high school students. This project addresses critical challenges in SPDE theory by investigating the stochastic heat equation (SHE), the parabolic Anderson model, and the Kardar-Parisi-Zhang equation under conditions of rough initial data and diverse noise structures. A significant innovation is demonstrating the existence of nontrivial invariant measures for SHE/PAM resulting from interactions between noise structures and rough initial data. The project also seeks to resolve prominent open problems related to the moment estimates of the Malliavin derivative, thus expanding the applicability of Malliavin calculus to SPDEs. By studying fractional SPDEs, the researchers aim to develop a unified theoretical framework interpolating among various properties, including the sample path regularities of SHE and the stochastic wave equation. Complementing theoretical developments, realistic simulations involving inhomogeneous particles and long-range correlations will be performed, aiming to produce novel conjectures and deeper insights into stochastic/disordered 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 · 2025-06

Collaborative Research: X-ray tomography to characterize microstructure during stress tests constraining multiscale models of sea ice interaction Sea ice in the Arctic Ocean has thinned and become more fragmented over the past several decades, a trend that poses significant challenges for navigation, infrastructure, and research. Increased variability in sea ice conditions affects shipping routes, offshore platforms, and coastal regions, creating a need for advanced tools to predict its behavior and inform resilient design strategies. This research seeks to uncover how the microstructural features of sea ice, such as grain size, porosity, and void distribution, influence its ability to withstand forces, such as the pressure exerted by an icebreaker or the stability needed to support offshore platforms, under varying environmental and mechanical loads. By developing a multiscale framework that connects microscale processes to large-scale dynamics, this project will generate insights critical for Arctic navigation, infrastructure design, and climate adaptation. The outcomes of this work will address key challenges at the intersection of geophysical science and engineering. In addition, the knowledge generated has broader relevance to other fields, including rock mechanics and geotechnical engineering. Outreach and education efforts will focus on the theme of "North in the South," engaging students and the public through programs such as virtual reality experiences, and workshops on Arctic science. These initiatives aim to inspire the next generation of researchers and raise awareness of the critical role sea ice plays in the global climate system. The primary objective of this research is to develop and validate multiscale numerical models that link the micromechanics of sea ice to its macroscopic behavior under various environmental conditions. This goal will be achieved using a combination of advanced experimental and computational techniques, including: (i) high-resolution X-ray computed tomography (CT) imaging to analyze the internal structure of sea ice and identify characteristic patterns and scales that influence its behavior; (ii) discrete element modeling (DEM) to simulate microscale interactions and failure mechanisms; and (iii) hybrid FEM-DEM simulations to integrate micro- and macroscale behaviors for macroscopic stress and strain predictions. Laboratory experiments and numerical simulations will be used in conjunction to investigate key phenomena, such as sea ice deformation, cracking, and floe-scale interactions. The validated models will provide new tools for understanding sea ice dynamics, supporting Arctic engineering, and addressing challenges posed by evolving ice conditions. 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 · 2025-06

This award supports participation by junior researchers, graduate students, and undergraduates in the 34th Cumberland Conference on Combinatorics, Graph Theory, and Computing at Auburn University on May 17-18, 2025. This annual conference fosters engagement among regional and international researchers in discrete mathematics, the area of mathematics studying finite structures, and computer scientists. The conference is expected to attract an attendance of 100 or more. The conference features four plenary speakers delivering 1-hour talks and approximately 40 contributed 25-minute talks. Focus areas include structural graph theory, matroid theory, applications of graph theory, and connections between graph theory and other mathematical areas. The conference provides a platform for mathematicians at all career stages to present and discuss their work, fostering communication and feedback opportunities for students and early-career researchers, thus contributing to the development of a globally competitive STEM workforce. The conference website can be found at: https://www.auburn.edu/cosam/departments/math/cumberland-conference/index.htm 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

Plants and pollinators share a fascinating relationship, with flowers attracting insects like bees using colors, scents, and shapes, while rewarding them with nectar. This interaction is essential for pollination and food production. Surprisingly, recent studies show that plants can "listen" to the sound of pollinators, such as the buzzing of bees, and respond by producing sweeter nectar. How plants achieve this remains a mystery. This award aims to uncover the physical and molecular mechanisms that allow plants to detect and respond to sound. The principal investigator (PI) proposes that the plant cell wall acts like a filter, vibrating at specific frequencies, while structures called Hechtian strands help transmit these vibrations to the cell's interior. This process could trigger changes inside the cell, leading to the production of sugars and other responses critical for pollination. By studying these processes in detail using cutting-edge tools and techniques, this award will provide new insights into how plants interact with their environment. Beyond advancing scientific knowledge, this project has practical applications in agriculture. Understanding how sound influences nectar production could lead to innovative methods for boosting crop yields and supporting pollinator populations. Additionally, the educational outreach program will bring this exciting science into classrooms, inspiring the next generation of scientists and making complex ideas about plant biology accessible and engaging for all. This project is jointly funded by the Physics of Living Systems 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 · 2025-05

Deep learning in Artificial Intelligence (AI) relies on deep neural networks (DNNs) to learn complex patterns from data. As DNNs become increasingly integrated into critical applications, ensuring their accuracy and robustness is essential. While testing is a widely used quality assurance method, strong performance on test data does not necessarily indicate that a DNN is robust or generalizable. In recent years, real-world failures--ranging from algorithmic bias to life-threatening errors--have highlighted the need for systematic test quality assessment. Mutation analysis, a technique originally developed for traditional software testing, has emerged as a promising approach for evaluating DNN test data quality. However, all forms of DNN mutation analysis remain computationally expensive, limiting their practical adoption. This project proposes novel techniques to increase mutation testing performance through approximation techniques based on Fast Fourier Transforms (FFTs). By making DNN mutation analysis more efficient, this research will enhance the reliability of deep learning systems while significantly reducing the computational burden of quality assurance. This project introduces several techniques for accelerating both model-level and source-level DNN mutation analysis, making it a more practical quality assurance method. Model-level mutation analysis involves creating small variations, or mutants, of a trained DNN by modifying its internal structure and observing how these changes affect its behavior. Source-level mutation analysis, on the other hand, mutates the code or training data used to build the DNN before retraining it from scratch. Both approaches require running a large number of mutants, making them computationally intensive. To address this challenge, the project will explore Fourier analysis, a mathematical technique that transforms data into frequency components, as a way to efficiently compare model behaviors and compress mutation-related computations. Additionally, methods such as mutant grouping--which clusters similar mutants to reduce redundant computations--and memorization--which stores and reuses previously computed results--will be adapted from traditional software testing to deep learning. For source-level mutation analysis, the project will investigate techniques based on training data selection (i.e., identifying the most informative data points), data distillation (i.e., compressing large datasets into smaller, high-quality subsets), and active learning (i.e., prioritizing the most uncertain or impactful data points for retraining). 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

Plants and pathogens interact in complex ways, with the outcome of this interaction being determined by various factors including their genetics, resident microbiome, and, most crucially, the environment. Variability in plant growing environments, including changes in temperature, have been linked to severe local outbreaks for various pathogens. The most effective way to control plant diseases is the use of disease-resistant cultivars. While current breeding programs evaluate disease resistant cultivars for their durability against concurrent pathogen populations, the sensitivity of plant resistance genes to growing temperatures is rarely considered while assessing their durability. The goal of this project is to develop crops with stable immunity that can tackle the simultaneous challenge of evolving pathogen pressure and suboptimal plant growth temperatures. This project led by a team of scientists from the United States, the United Kingdom, and Germany has the potential to develop crop varieties that are tolerant to changing growing temperatures and outcomes from this work has the potential to ensure future food security. This project will also support continued efforts by the team to provide STEM-based research experiences for high school students, and outreach events for the public in Alabama and Virginia. Graduate students and research associates that are a part of this project will be actively involved in these outreach events aimed to raise awareness of how plant growth temperatures impact plant health and disease pressure. Predicting plant disease outcomes in the face of projected suboptimal growing conditions is challenging for multiple reasons. Host responses to multiple simultaneous stresses are simply not additive. Multivariate selection pressures brought about by temperature fluctuations alter ecological, epidemiological, physiological and evolutionary processes in the host as well as pathogen populations and alter their responses due to trade-offs among traits under selection, thereby altering host-pathogen dynamics. The proposed work will utilize field and growth chamber experiments in parallel to understand how pathogen and plant carrying different resistance genes respond to temperature alteration. This project will investigate multi-generation host-pathoadaptation and assess fitness trade-offs that constrain the evolution in both plant and pathogen. The project will make explicit links between physiological and genetic processes in host and pathogen that inform potential candidates for breeding programs to make heat resilient and disease resistant crops. The project will evaluate temperature sensitivity of resistance genes in pepper and tomato in terms of their stability, function and alteration of immune pathways. This study has direct translational relevance for durable management of resistance genes in the context of variable environmental conditions. 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

Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon areas to sustain throughput and performance. However, manufacturing costs, yield limitations at advanced tech nodes, and die sizes reaching the reticle limit restrain us from achieving this on a monolithic die. With the recent innovations in advanced packaging technologies, disaggregated chiplet-based architectures have opened the next frontier of innovation in AI hardware and System on Chips (SoCs). However, the scope of stacking multiple chiplet tiers in 3D, various package interconnect options, and thermal challenges introduce significant design challenges. The project’s key novelties are developing AI/Machine Learning(ML)-assisted System and Package-level co-design methodologies that can optimize the power, performance, chip area, reliability, and cost of the next-generation AI hardware. The project's broader significance and importance include: (1) Generate knowledge and scientific methods to optimize the system-level architecture of multi-tier chiplet-based designs, and thus contribute to the advancement of the field of AI hardware and AI. (2) Develop educational resources and open-source tools for designing next-generation chiplet-based hardware in advanced packaging. (3) Cultivate a pipeline of skilled engineers and scientists with expertise in computer systems and AI/ML, and to promote participation from undergraduates, underrepresented groups, and K-12 students. In Thrust 1, using the first principles, the key performance metrics of chiplet-based AI hardware are analytically modeled. Heuristics and deep learning (e.g., Reinforcement Learning) based methods are explored to find the set of optimal parameters (e.g., chiplet area and count, number of tiers, packaging interconnects, etc.). Thrust 2 focuses on yield, reliability, and power integrity. Hardware and software-level comprehensive techniques are developed to detect and repair faulty Hybrid Bonds (HB) to achieve maximum yield at the package level. To ensure robust power integrity of the multi-tier chiplet system at optimum energy efficiency, the emerging technique of backside power delivery is explored. As heat dissipation is a major impediment to the widespread adoption of multi-tier chiplet stacking technology, Thrust 3 explores a proactive thermal management technique and workload scheduling algorithm to prevent simultaneous peak temperatures in adjacent chiplets in a stack. Overall, the collective advancement of these research thrusts will establish a comprehensive scientific methodology for the design of multi-tier 3D SoCs. 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

In the Alabama-Florida (AL-FL) Gulf Coast region, ecological disturbances such as hurricanes, wildfires, and harmful algal blooms can interact and have direct impacts on ecosystem health and services. Further, this region is the primary biodiversity hotspot of the continental United States, hosting the greatest total numbers of species and endemic species of mammals, birds, amphibians, reptiles, freshwater fish, and trees. Human impacts on landscapes and the climate system are also increasing ecological vulnerability to these disturbances, which can degrade ecosystem health, wildlife habitat, and water quality. This project focuses on the impacts of multiple interacting disturbances on ecosystems, as well as how disturbances interact across the boundaries between terrestrial and aquatic ecosystems, which have not been studied in detail before. Further, this project will provide new science to guide conservation of the biodiversity and endemic species of this region. In addition to funding new science, this project will also support the training of multiple undergraduate and graduate students, and broader stakeholder education through Auburn University’s Extension System. By using paleoenvironmental approaches, the project will resolve long-term environmental histories of disturbances (hurricanes, wildfires, and harmful algal blooms) and ecosystem diversity and state to resolve the complex relationships between disturbances and ecosystems of the AL-FL Gulf Coast. Project objectives are to: (1) Reconstruct hurricane and wildfire occurrence to identify relationships between these terrestrial disturbances; (2) Reconstruct harmful algal blooms (HABs) to characterize relationships between aquatic and terrestrial disturbances; and (3) Reconstruct terrestrial and aquatic community changes to characterize how the interactions of episodic disturbances (hurricanes, wildfire, HABs) impact community state. This multi-disturbance reconstruction approach is unique to the project and will enable novel insights into the long-term disturbance interactions and ecosystem impacts. The project will enable undergraduate and graduate students to obtain important research experience, and will facilitate public outreach and education. This project is jointly funded by the NSF Ecosystem Science Cluster 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 · 2025-02

Partial Differential Equations (PDE) of parabolic type are central to mathematical analysis, with extensive applications in physics, finance, biology, and other fields. The research in this project focuses on three types of PDEs: reaction-diffusion equations, Hele-Shaw type flows, and chemotaxis systems. These equations capture the evolution of diffusive processes, such as tumor growth, forest fire propagation, chemical diffusion, and crowd motion. The project aims to advance understanding of these equations and their underlying phenomena, with potential societal benefits such as enhanced strategies for managing forest fires and improving understanding of biological processes, including tumor cell behavior. The educational component is a cornerstone of this project, tightly integrated with its research objectives. In collaboration with Auburn University’s outreach programs, the Principal Investigator will engage K-12 students in interactive workshops and science fairs, inspiring curiosity and enthusiasm for science. A key focus of the educational efforts is increasing participation and retention of women and underrepresented groups in mathematics and science. This project will also provide interdisciplinary training opportunities for students and early-career researchers through workshops and summer schools directly tied to the research themes. These programs will offer hands-on experiences, bridging theoretical knowledge with practical applications to prepare the next generation of scientists and mathematicians. This project integrates innovative research with impactful education and outreach, strengthening the link between scientific discovery and learning for the benefit of society. Reaction-diffusion equations are central to modeling phenomena such as front propagation and interface motion in fields like chemical kinetics, combustion, and population genetics. In sufficiently random media, the long-term, large-scale dynamics of these equations are expected to converge to a deterministic propagation process (called stochastic homogenization). While previous research in the area in general dimensions is limited, this proposed research brings new methods and technical tools to study such problems. For example, it will extend several fundamental probability tools that were widely used in PDE problems, including Kingman's subadditive theorem and Azuma's lemma. Furthermore, the methodologies developed here will have applications to other equations. Hele-Shaw flows with source and advection terms are widely used to model tumor growth, where the region occupied by tumor cells corresponds to the positive set of the solution, and the boundary of this region forms the free boundary. The presence of drift and source terms brings significant difficulties and so there are few results in this direction. This project focuses on developing new PDE techniques to address the regularity and the homogenization of the free boundary. The outcomes are expected to provide a deeper understanding of free boundary dynamics and illuminate the effects of stochastic fluctuations on interface motion. Chemotaxis, the directed movement of organisms in response to chemical gradients, is a crucial phenomenon in biology. While chemotaxis models have been extensively studied, most work focuses on bounded domains. This project aims to first establish the global well-posedness of chemotaxis systems on unbounded domains and then investigate the asymptotic spreading properties of solutions, a key characteristic of these systems. These studies will employ tools from various areas of mathematics, including semigroup theory, viscosity solutions and parabolic regularity theory, to explore new aspects of chemotaxis models, with potential contributions to both mathematics and biological sciences. This project is jointly funded by the Analysis Program in the Division of Mathematical Sciences 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 · 2025-02

Multilayer coatings are important technologies for numerous industries, including the automotive, aerospace, marine, and consumer-products sectors. The coatings industry needs new capabilities to create multilayer coatings in one step. However, processes for making multilayer coatings are not fully understood, and existing models typically fail to predict coating composition at industrially relevant conditions. This project will develop a multiscale modeling framework that will empower scientists and engineers to (re)formulate coatings, shortening the research and development cycle in academic and industrial settings. New educational tools for teaching nanoscale engineering using virtual-reality technology will be created and disseminated to K–16 students and the public. The project will also broaden participation in STEM through undergraduate research opportunities, help train a more competitive U.S. workforce in computational science, and develop and disseminate open-source scientific software. This project will create a transformative multiscale modeling approach to predict the composition of colloidal-particle coatings made by solvent drying with unprecedented accuracy using: (1) a physics-based continuum model with realistic particle interactions and hydrodynamics, (2) a machine-learned model, trained from particle-based simulations, to refine the physics-based model, and (3) a surrogate model, constructed from (1) & (2), to relate particle properties and processing conditions to composition. This research will create new knowledge about how composition gradients form in colloidal-particle coatings under realistic conditions. It will enable the first systematic study of how surface chemistry and processing control the formation of self-stratified layers in coatings as well as the feasibility of forming other composition gradients. This project will also develop a virtual-reality (VR) platform for (1) visualizing nanomaterials in accessible technology such as cell phones and (2) making educational VR activities with less effort. The activities will incorporate products of the research and other nanoscale concepts in chemical engineering, and they will be delivered to K–12, undergraduate, and graduate students as well as the public through coursework and outreach. 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

Extended reality (XR) offers users immersive experience in virtual worlds, and enables a broad range of applications (i.e., training, gaming, and medical imaging). There has been an increasing interest on the study of the deployment of XR services over next era of wireless networks (nextE), so as to provide seamless wireless connectivity for XR users to eliminate the wired connection constraints thus enabling future wireless devices to use VR services. However, the few prior studies have two major limitations: 1) They are mainly focused on network optimization for XR data transmission and are lacking in novel user behavior sensing methods, 2) Their XR sensing methods mostly rely on statically installed sensors or cameras, which also restrict the operation range of users and suffer from user movement and blockage, 3) they are restricted to either a single XR system, or multiple XR systems where each XR system consists of only one user and hence cannot be applied for multi-user XR systems. To address the aforementioned challenges, a holistic wireless XR framework is developed, which utilizes mmWave for joint XR user movement detection and XR data transmission while satisfying the joint communication, computing, sensing, and XR service requirements. If successful, this project will enable highly efficient and robust wireless enabled XR networks and applications, with significantly enhanced accuracy, resilience, and user experience. The project integrates the research insights into new modules for communication and network related courses and hosts outreach activities with the vision of advancing the participation of underrepresented minorities in STEM fields. The untethered XR project presents a cutting-edge solution for eliminating XR wired connections and limitations of XR user activity space by utilizing mmWave, machine learning, edge computing, and joint sensing and communications technologies to truly unleashing the high potential of XR via: 1) developing novel mmWave-based sensing methods which exploit complex valued channel state information and radio map information to detect the full-body movements of multiple XR users; 2) designing a novel collaborative reinforcement learning (RL) framework to produce a low-complexity and reliable collaborative learning process that enables distributed XR access points (APs) to jointly optimize XR sensing and data transmission in order to improve the quality-of-experience of XR users; 3) building an open-source software platform and hardware testbed to validate the wireless XR solutions. This project provides a rich environment and virtualized platform that facilitate educating and training students at multiple levels. 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

Understanding what drives the evolution of new species is a central question in biology. Groups of species that have recently evolved provide a good system for trying to understand the genetic changes that led to establishment of new species. This research combines the fields of genomics, developmental biology, ecology, and physiology to examine a new lineage of flowering plants in Hawaiʻi in the genus Bidens (family Asteraceae). The project will generate new genome assemblies and experimentally identify the genetic and developmental changes responsible for leaf, fruit/seed, and flower evolution in this group of species. This project will also provide training in inter-disciplinary evolutionary concepts and approaches for undergraduates, graduate students, and postdoctoral researchers, including those from underrepresented groups; improving the scientific workforce in the United States by preparing them to strongly contribute to scientific research, education, and/or technological advancements. This project will use newly developed genome sequencing methods to infer the broader evolutionary history of Polynesian Island Bidens, along with continental relatives. The updated understanding of how Bidens reached remote Pacific islands and diversified will provide the backbone for comparative evolutionary genomics of our six target species (three Hawaiian endemics and three continental). Comparing these genome sequences and differences in gene expression will allow us to identify the genetic changes that contribute to the unique ecological and morphological diversity of the Bidens adaptive radiation. Concurrent with the other objectives of the project, undergraduate students at UH Mānoa (a Native Hawaiian serving institution) will receive year-long internships in Hawaiʻi and short-term exchanges at Auburn (AU) and Wisconsin (UWM) via AHi-WiRE; Auburn-Hawaiʻi-Wisconsin-Research Exchange to receive training in plant evolutionary genomics. 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 · 2024-12

This award supports a study of energetic electron acceleration and transport in magnetized plasmas in geospace and in a laboratory. Solar flares directed toward the Earth are known to cause massive radio outages, interference with GPS systems and aircraft communication, damage to spacecraft and to threaten astronauts’ safety. It has been shown that up to 50% of the energy released during solar flares can be carried by energetic electrons (EEs). However, the mechanisms for the observed electron acceleration and transport remain unclear. This project will investigate how EEs get trapped, accelerated and de-trapped in magnetized plasmas with magnetic islands and stochastic fields both in space and in laboratory experiments. The scientific goals of the project will draw together the fields of space physics, solar physics, and plasma physics, while also connecting to the development of models for space weather prediction and fusion energy reactor disruption mitigation. In addition, the project aims to establish the first plasma-focused professional development certification program in the state of Alabama, called “Gateway to Plasma”. The curriculum development includes writing an Introduction to Plasma textbook appropriate for students with little or no background in physics and mathematics. The content of the online lessons will be focused on plasma basics and teaching industry-relevant skills. Observations suggest that energetic electrons can be trapped and accelerated in regions of the Earth’s magnetosphere where the magnetic field forms islands, or twisted magnetic flux tubes. It is hypothesized that when such islands merge or break apart, the EEs are de-confined through the formation of chaotic and random, or stochastic, magnetic fields. This project will investigate the following specific questions: (i) What is the characteristic scale of islands needed to trap EEs? (ii) Can island contraction and expansion lead to diffusion regime switch? (iii) How do stochastic fields affect electron diffusion? (iv) How do changes in the disorder regime affect electron diffusion? (v) Which processes are universal and scalable from lab to space? These questions will be studied using a spectral approach where probability for transport in a given magnetic field topology is calculated from the energy spectrum of the corresponding Hamiltonian operator. The spectral model will be informed from and validated against laboratory experiments, spacecraft measurements, and tracer particle simulations. This work is expected to advance knowledge relevant to space weather forecasting, as well as the operation and safety of future fusion energy reactors. This project is jointly funded by the Division of Physics, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Atmospheric and Geospace Sciences. 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 · 2024-12

This project would provide a fellowship to an Associate Professor and training for a postdoctoral researcher at Auburn University. The brilliant colors of butterfly wings are a lead inspiration for understanding how naturally occurring structures manipulate light and the colors are perceived. Many butterflies have iridescent colors on their wings, which appear to change coloration as they flap their wings. Such iridescence appears to largely be a result of nanostructures on the surface of individual wing scales. However, how these nanostructures vary across butterfly wings and species remains relatively unexplored. This project leverages the Smithsonian Museum of Natural History collections to better understand how iridescent colors vary on butterfly wings. Using a combination of approaches involving high-resolution imaging and structural modeling, the project aims to identify specific scale structural variations associated with changes in ultraviolet and iridescent coloration. Data produced through the project will be made available through a joint database effort with the Smithsonian Institute and Auburn University Natural History Museum, and a workshop will be organized to train researchers to employ similar experimental approaches to study the diversity of structural coloration found in nature. The goal of this project is to develop a better understanding of how living structures modulate light to impact their coloration. This project synergizes phenotypic and modeling approaches with the Smithsonian Museum of Natural History collections to directly address gaps in our understanding of how butterfly scale architectures impact coloration through a series of species research aims. The first aim will fill a phylogenetic gap in the modeling of scale structure reflectance by identifying scale structures associated with UV and iridescent variation across Pierid butterflies. The second aim will produce digital imaging and reflectance spectra across Smithsonian collection specimens that span 100 million years of butterfly diversification. The third aim focuses on species pairs from each butterfly family that vary in UV or iridescence, to conduct high-resolution imaging and modeling in determining if homologous scale structures are involved in lineage-specific fine-tuning of structural coloration. This project will foster a new collaboration between the Smithsonian Institute and the Auburn University Natural History Museum and provide the research team with new modeling skills to better understand how structures throughout the scale impact color reflectance. To broaden the impact of the project, the research team will develop an educational module targeted at middle-school students that builds directly from the proposed scale structure research, promote data accessibility by developing a web-accessible database of butterfly structural coloration data, and lead a training workshop for the phenotyping and modeling approaches to empower a broader community of biologists interested in simulating reflectance spectra of living objects. 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 · 2024-11

This award provides funding support for students to attend the Second EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles (SmartSP 2024), to be held in New Orleans, Louisiana. Recent decades have witnessed an extraordinary growth in cyber-physical systems (CPS) such as self-driving vehicles, robotic devices, and drones. SmartSP is a global forum for researchers and developers from academia, industry, and government to present and discuss emerging ideas and trends in security and privacy issues in this exciting area with a particular focus on smart vehicles, smart transportation, and corresponding security challenges. The conference is particularly relevant for students and early-career researchers seeking to expand their knowledge, develop essential career skills, and engage with leading experts in the field. Together, the funding will help the intellectual and professional development of the students who attend and the SmartSP community as a whole. The funding will help cover travel expenses for up to 8 U.S.-based student attendees. Selection of student attendees will be based on a range of criteria, including academic achievement, research experience, and interest in CPS, smart vehicles, and security challenges. Preference will be given to students who have conducted research in these areas and who have demonstrated a commitment to advancing the field through their academic and professional activities. Students attending the EAI SmartSP 2024 conference will be involved in a range of activities, including attending technical sessions, presenting their research, and participating in panel discussions and networking events. These activities will provide opportunities for students to meet with leading experts in the field, engage in discussions about emerging trends and technologies, and explore potential career opportunities. 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

Physics graduate programs are designed to prepare students to be critical thinkers and independent scientists. Yet, most of the early parts of a physics graduate degree are focused on content-specific coursework in which students solve formulaic problems which bear little resemblance to the kind of ill-defined (“real-world”) problems they will encounter in the workplace. Indeed, most physics PhD recipients don’t necessarily do work directly related to physics. They need transferrable problem-solving skills that can be applied in a range of contexts so that they are able to solve problems that may not even be known at this point. This National Science Foundation Innovations in Graduate Education (IGE) Track 1 award to Auburn University will develop and validate assessments of real-world problem solving suitable for graduate physics coursework and use those assessments as a basis for program evaluation and teaching innovations. This work will modernize physics graduate education by bringing the canonical sets of physics content knowledge into the real-world. It will also contribute to literature on effective, research-based teaching in graduate STEM programs. This project will bring together graduate students and practicing physicists to collaboratively (1) define skills-focused learning outcomes for graduate physics programs, (2) develop and validate assessments of those problem-solving skills, and (3) redesign graduate coursework to improve students’ training in those problem-solving skills. The first step will be done through focus groups with physicists from a range of subdisciplines to better understand how fundamental physics knowledge is used in practice. These focus groups will then inform the design of research-based assessments which can be used to measure the application of this knowledge to novel problems in physics. These assessments will be validated with a nationwide sample of physics researchers and graduate students through think-aloud interviews. These results will then form the basis of a quasi-experimental pilot test of a novel problem-solving based curriculum at Auburn University. This will set the stage for broad-scale educational innovations across graduate STEM education. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project is jointly funded by the Environmental Chemical Sciences Program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR). Professor Ann Ojeda at Auburn University and Professor Natalia Malina at Florida Atlantic University and their students will study how dissolved organic matter impacts the degradation of chlorinated organic contaminants when exposed to sunlight. Chlorinated organic contaminants are widely used in industry as solvents or chemical reactants and enter the waterbodies with accidental spills or municipal wastewater effluents. Surface water in streams, rivers, and lakes contains a wide range of dissolved organic matter, which is a complex mixture of molecules derived from plant material and microorganisms. When exposed to sunlight, chlorinated contaminants interact with dissolved organic matter and sunlight to degrade into a mixture of different compounds, called daughter products. The goal of this project is to investigate how the chemical composition of dissolved organic matter relates to the degradation pathway of chlorinated compounds and produces the cocktail of parent and daughter compounds that are detected in the environment. The data generated in this project will allow us to better understand the reasons behind the formation of toxic chemicals in water systems and ultimately help protect human and ecosystem health. The proposed research will also cultivate the next generation of researchers at both the graduate and undergraduate levels, who can work productively in teams and whose expertise covers a broad spectrum of environmental sciences. This project will focus on the photodegradation pathways of 1,2-dichlorobenzene and triclosan in the presence of dissolved organic matter derived from two sources: algae and an isolate from Suwannee River. An array of mass spectrometric techniques will be combined to measure contaminant concentration, dissolved organic matter molecular composition, and degradation mechanisms of chlorinated organic contaminants. The quantum yield of the reactive oxygen species formed by dissolved organic matter under the light will be measured as well. Stable isotope enrichment factors will be first measured for 1,2-dichlorobenzene so that specific pathways of degradation can be established and then related to the chemical composition and the quantum yield of reactive oxygen species of each type of dissolved organic matter. This knowledge will be applied to study the photodegradation pathways of triclosan, a more complicated molecule with a wider range of potential degradation pathways. The focus of this project is to identify the molecular characteristics of dissolved organic matter that drive the formation of dioxins, a group of highly toxic daughter products produced from degradation of triclosan. 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

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) have emerged as a promising technology for 6G wireless networks, aiming to improve user experience and enhance people’s lives. By leveraging millimeter wave (mmWave) communications, UAV-enabled ISAC systems are expected to deliver high-throughput, ultra-reliable, and low-latency wireless communications, along with highly accurate wireless sensing and localization within 6G networks. Simultaneously, artificial intelligence (AI) and machine learning (ML) are anticipated to transform platform-based ecosystems, business models, and services in future 6G networks. The key challenge is integrating UAV localization, mmWave communications, wireless sensing, and security with AI/ML for future 6G systems. A multidisciplinary team of six investigators from Auburn University (AU), Florida International University (FIU), the Indian Institute of Technology Kanpur (IIT Kharagpur), and the International Institute of Information Technology, Naya Raipur (IIIT, Naya Raipur) collaborate closely on a project focused on learning-assisted integrated sensing, communication, and security for 6G UAV networks. The educational plan of this project includes developing joint course materials on AI/ML for UAV networks and IoT, enhancing undergraduate and graduate-level courses at the participating institutions. Simulation tools and testbeds developed through this project offer students hands-on experience with cutting-edge technology. The project outcomes are disseminated via technical publications, conference keynotes/tutorials, IEEE distinguished lectures and seminars, a project website, and open-source repositories. The investigators are committed to encouraging participation from underrepresented groups through outreach programs at their institutions and the NSFBPC/REU/RET programs throughout the project. The project aims to develop deep learning (DL)-based localization and sensing in UAV mmWave networks, location-aided UAV mmWave communications, and joint UAV mmWave communication and radar co-design to improve mmWave spectrum utilization, wireless sensing performance, and UAV device security. The research agenda consists of five well integrated thrusts: (i) Learning-based mmWave UAV localization and wireless sensing; (ii) Joint design of location-aided UAV mmWave communications and sensing; (iii) Multiple UAV communications and sensing co-design; (iv) Learning-based RF fingerprinting for UAV security; and (v) Integration and assessment: the proposed techniques are implemented with both ray-tracing software tools (e.g., DeepMIMO), mmWave devices (e.g., TP-link Talon AD7200) and TI mmWave radars, Parrot AR Drone2.0 UAV, programmable (e.g. USRP) devices, and the NSF PAWR AERPAW testbed, and validated with extensive experiments in real, representative outdoor and indoor environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Virtual reality (VR) can simulate immersive scenarios that are unsafe or even impossible in the physical world. Immersion is especially important for pilots, who rely on simulation to train for routine operations, but also for emergencies. While dedicated, high-fidelity, and approved flight simulators are already used as part of flight training, their cost and space requirements prohibit their wider use. VR headsets connected to a computer simulator setup can enhance training both at home and within a flight training program. Despite the popularity of air travel and the need for pilots in the aviation industry, becoming a pilot is a dream that most abandon after childhood, either in pursuit of other passions or because of its prohibitive initial investment in cost and time, resulting in a nationwide pilot shortage. This project will study skill transfer from VR-based simulation to the real world by evaluating the physical, functional, and cognitive characteristics of simulation which impact a trainee’s ability to learn efficiently and effectively, as applied to the flight training environment. This project will make flight more accessible as a career and field of study through the use of virtual reality flight simulators. Additionally, it will broaden participation in aviation-careers and interests through outreach activities at local airports and by providing future pilots with online modules to help them initiate their training. Flight simulation performance-based and emergency response competitions will allow those already in training to test their skills and educate them on using flight simulation for their own practice. This award will enhance flight training, resulting in reduced flight training accidents and decreased time to certification, thus improving our ability to respond to the pilot shortage. This project will study skill transfer from virtual reality to the physical world by evaluating the physical, functional, and cognitive fidelity characteristics of simulation that impact a trainee pilot’s ability to learn efficiently and effectively. Two experiments will measure skill transfer. The first will train novice pilots completely in virtual reality and evaluate their abilities in the aircraft. The second will follow a group of pilots who are pursuing an instrument certificate throughout their training and complement their training with simulation scenarios on both traditional simulators and a virtual reality flight simulator. Complementing flight training with simulation will investigate the impact of virtual reality interventions on skill acquisition and mental workload management. The data collected through both experiments will be used to evaluate and validate the use of virtual reality in both research and training as an alternative to physical simulators. This work will collect flight data through on-board flight data recorders and simulation software, physiological data through eye tracking and heart rate monitoring, and psychometric data through mobile surveys. Scoring algorithms will detect and evaluate human performance on a series of maneuvers. This project will advance our understanding of how humans learn in complex virtual reality environments such as flight training, validating virtual reality systems as a training device option, and therefore reducing the cost and time of highly advanced training in fields, which require the use of multiple skills simultaneously. The research will identify which types of skills transfer, use data-driven metrics to measure skill acquisition and transfer, validate the use of eye tracking and hear rate metrics in virtual reality, and develop a framework to assess modern simulators based on how they address learning outcomes, informing policy and regulation. The integration of education and research in this proposal is focused on a three-level approach: (i) creating interest through outreach activities; (ii) empowering actions through online training modules and the use of VR-based flight simulation; (iii) advancing training through self-practice and semi-annual competitive flight simulation events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This grant supports research aimed at advancing manufacturing processes by contributing new knowledge, promoting scientific progress, and enhancing national prosperity. Additive manufacturing, commonly known as three-dimensional printing or 3D printing, involves creating three-dimensional objects from digital models, presenting a significant leap forward from traditional manufacturing techniques. Although various additive manufacturing processes exist, most focus on macro-scale structural parts with little or no functionalities due to the limited control over designing compositions and interfaces among multiple materials at the micron and nanoscale. This award funds fundamental research to develop laser nanoparticle powder-bed fusion, an additive nanomanufacturing process, enabling layer-by-layer fabrication of micro- and nano-scale functional structures and devices with tunable chemical compositions, interface interactions, and physical architectures. Such micro and nanoscale architectures have increasing applications in energy, healthcare, biomedical, and aerospace industries. Therefore, the outcomes of this research benefit the U.S. economy and society. The project spans multiple disciplines, including manufacturing, materials science, and device engineering, and fosters broader participation of women and underrepresented minority groups in research and positively impacts engineering education. The fundamental science developed in this research significantly enhances the understanding of nanoparticle-based additive nanomanufacturing for 3D printing of advanced materials, nanocomposites, and heterostructures at the micro- and nanoscales, layer-by-layer. This research develops a process in which metastable nanoparticles formed by condensation of pulsed laser-ablated plumes serve as tunable nanoscale precursors for laser-based nanoparticle powder-bed fusion and elucidates the role of non-equilibrium processes in printing 3D materials and structures by design. Specifically, this research investigates the non-equilibrium laser synthesis of homogeneous and heterogeneous metastable nanoparticle assemblies that can serve as nanopowder coatings for 3D printing compositionally tunable microstructures from nanoscale building blocks. The research further explores the interface interactions as well as the laser sintering process of the nanoparticle assemblies at different energy and time scales to understand their sintering, phase evolution, intermixing, and alloying mechanisms. Additionally, the project examines the structural and morphological evolution of the 3D printed nanocomposites and heterostructures, elucidating their process-structure-property relationships. 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.