Louisiana State University

NSF Awards · FY 2025 · 2025-08

The conference “Topology of Arrangements with an Eye to Applications” will be held at the University of Pisa, Italy, during September 1-5, 2025. This topic is of interest to a large international research community, spread across mathematical disciplines. This event aims to both disseminate recent research developments, as well as facilitate contact between emergent junior researchers and established senior members of the community. This award will increase the participation of US-based mathematicians, especially graduate students and early-career researchers. The central theme of the conference is the theory of hyperplane arrangements, with a special focus on the topological aspects of the field, the connection between arrangements and Artin groups (which witnessed striking recent developments) and applications (especially topological data analysis). A special feature of this field is its interaction with several areas of mathematics, and this blend of ideas and perspectives continues to drive significant progress in the area. As such, the conference will touch on connections to, for instance, algebraic topology, geometric group theory, algebraic and geometric combinatorics, matroid theory, algebraic geometry, and singularity theory. In addition to invited speakers, this event will include a contributed poster session and ample time for discussion to foster collaboration among participants. Details are available at the website: https://events.dm.unipi.it/e/arrangements2025 This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project evaluates the importance of ocean gateways, the connection between two important bodies of water. Specifically, the movement of tectonic plates has caused the Gibraltar and other straits to close multiple times in Earth’s history, separating the Mediterranean Sea from the Atlantic Ocean. Because ocean circulation is density-driven, isolating the salty, dense Mediterranean waters from the Atlantic can influence global circulation as well as regional climate. This project uses palynology, or the study of microscopic fossils of pollen, spores, and dinoflagellate cysts preserved in sediment. Sediment cores were collected during an expedition at four sites on either side of the Atlantic-Mediterranean gateway. The species of pollen, spores, and dinoflagellates present in a sample correspond to what the air temperature (pollen, spores) and sea surface temperature (dinoflagellates) would have been at the time. By examining changes in species abundance over time, this project will uncover how the opening and closing of ocean gateways influences climate. The project will train three future palynologists: one undergraduate student and two graduate students. Training the next generation of U.S. palynologists is important as this specialized branch of science serves a key role in many important economic and intelligence fields, including forensic science, agriculture, and oil and gas exploration. The lead researcher will also collaborate with artist Rebecca Kamen to create “The Secret Tales of Pollen” exhibit. This exhibit will showcase sculptures and photography of various types of pollen and provide details about what these fossils teach us. The exhibit will be on display in Louisiana, but content will also be shared online. Here, art and science combine to boost public engagement in science and foster understanding of how fossils can be used to study Earth through geological time. Marine exchange between the Mediterranean and Atlantic occurred through relatively narrow paleo-gateways. Previous studies have proposed that changes at the paleo-straits impacted the evolution of thermohaline circulation at a global scale. The International Ocean Discovery Program (IODP) Expedition 401 recently recovered key expanded and continuous upper Miocene sediments on the Atlantic and Alboran Sea margins, on each side of the Gibraltar Strait. This research will entail high resolution palynological analyses of these cores between 8 and 4 million years ago to reconstruct water-mass and local environmental changes leading up to, during, and after the Messinian Salinity Crisis at four IODP Expedition 401 sites. Results will be used as input for two different climate reconstruction methods. Finally, microcharcoal particles will be used to quantify regional aridity and fire history. Reconstructing marine and terrestrial conditions across this interval is important as the way Earth systems currently distribute heat and salt in the world’s oceans were established during this timeframe. This proposal is part of a larger international and multidisciplinary investigation of how these local changes affect global-scale changes. The palynological reconstruction will be carried out by Louisiana State University undergraduate and graduate students. Palynological results will be shared with the general audience through a new art exhibit featuring pollen sculptures and photography. 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

Coastal regions are vulnerable to flooding from rivers and rising seas, increasing storm strength, and destruction of ecologically-fragile areas. River deltas are especially impacted by the balance between increasing water levels from sea-level rise and tides, and land surface elevation changes. Bangladesh’s Ganges-Brahmaputra Delta (GBD), the world’s largest delta, is a particularly excellent place to investigate this problem. The land is sinking (subsiding), worsening the impact of sea-level rise, but the rivers supply ample sediment to elevate the land. However, there is a mismatch in the distribution of sediment and land subsidence; some areas are maintained by sediments, while others are at serious risk of land loss. This project will combine local, on-the-ground measurements of elevation change with broad satellite observations, and develop a comprehensive numerical model of elevation change. The numerical model will enable synthesis of all measurements and incorporate shallow processes that are missing from most models. Results will contribute to Bangladesh’s coastal planning through established collaboration with government agencies, academic institutions, and non-governmental organizations. This project will support 2 postdocs and 3 graduate students in the U.S. as well as build capacity for students and faculty in Bangladesh. U.S. undergraduate students will participate in the proposed research through internship programs and a capstone course that includes a Spring Break field trip to Bangladesh. The model will have great applicability for use in coastal areas prone to flood risk, especially lowland deltas worldwide including the Mississippi Delta. Unraveling the intersecting processes that contribute to vertical land-surface dynamics is critical for forecasting sustainability of lowland deltas into the future. This project will employ multidisciplinary research that integrates an existing delta-wide network of sediment cores and geospatial instruments with broad-scale, multi-sensor satellite remote-sensing analyses, producing novel high-resolution maps of decadal surface-elevation change, topography, and land-use across the coastal zone. A state-of-the-art poroelastic model will be developed, validated, and applied to coastal Bangladesh. The team hypothesizes that at any given site on the delta, surface-elevation change reflects the vertical integration of sedimentation, near-surface soil consolidation, subsurface compaction of Holocene sediment, and deep tectonic/isostatic response of the lithosphere. Across the delta, surface-elevation change reflects how modern land use restricts surface sedimentation and accelerates consolidation, and how ancient river dynamics constructed the alluvial architecture of compacting Holocene sediments. These hypotheses will be tested with a process-based, holistic understanding of vertical land-surface dynamics, and will guide coastal hazard mitigation and sustainability efforts on the GBD and other deltas that face similar environmental and anthropogenic stressors (e.g., Mississippi and Sacramento-San Joaquin river deltas). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project seeks to address a critical challenge in modern artificial intelligence (AI): efficiently analyzing large-scale graph data. Graphs are data structures used to represent interconnected information, such as social networks, molecular interactions, and recommendation systems. They are essential components in a diverse array of applications across various industries, including healthcare, cybersecurity, and financial services. However, as graph data continues to grow in size and complexity, it becomes increasingly difficult to analyze using existing AI models. This project aims to develop new techniques that make working with these massive datasets easier and more efficient, particularly when they are stored in modern data lakes, which are large, scalable storage systems used by organizations to handle vast amounts of data. By improving how we process and learn from graph data, this research holds the potential to benefit not only fields like AI and data management but also disciplines that rely on graph data for gaining valuable insights, such as biology, sociology, and cybersecurity. The project will also support education by providing students with opportunities to engage in cutting-edge research and contribute to the field. This project aims to design and develop a set of scalable and efficient techniques for graph representation learning (GRL), particularly tailored for graph data stored in modern data lakes. The project will focus on three primary objectives. First, it will create a partitioning-based framework that enhances the scalability of GRL by enabling various models to process large graphs without requiring significant code modifications. Second, it will develop methods to optimize the reading and partitioning of graph data from data lakes to improve the computational efficiency of GRL. Third, it will implement predictive optimization techniques to automatically select the most suitable GRL models, computational resources, and data lake configurations based on specific workloads. These advancements will be evaluated through practical case studies and benchmarks, which will help establish a methodology for running large-scale GRL pipelines considering compute and storage layers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project will focus on establishing a unified framework for adaptive sampling to enhance scientific machine learning algorithms. Scientific machine learning has proven to be a transformative force in advancing science and engineering. It blends the predictive capabilities of artificial intelligence (AI) with the precision of scientific models to address complex challenges beyond traditional numerical methods. It enables breakthroughs in fields as varied as healthcare and infrastructure development. A critical aspect of its importance lies in solving high-dimensional partial differential equations (PDEs), which are mathematical models central to describing phenomena like fluid dynamics, heat transfer, or electromagnetic fields. Traditional numerical methods struggle with the computational complexity of high-dimensional PDEs, but scientific machine learning dramatically reduces computation time while maintaining accuracy. As a result, this capability unlocks advancements in engineering designs and medical simulations, where such equations are prevalent. By enhancing the efficiency and affordability of research through improved adaptive sampling techniques, scientific machine learning can continue to drive innovation while also delivering practical solutions to pressing global issues like public health and energy, benefiting society at large. The project also includes a significant educational plan with three major components: (1) developing an introduction course on scientific machine learning; (2) training undergraduate and graduate students in research; (3) conducting outreach to educate high school students on basics regarding scientific computing and deep learning. The goal of this project is to establish a unified framework through adaptive sampling, aimed at simultaneously optimizing both the training set and the loss of deep learning-based techniques for solving high-dimensional (parametric) PDEs. While deep learning has achieved remarkable success in numerous AI applications as a data-driven approach, applying it to solve high-dimensional PDEs introduces an additional challenge: its performance significantly deteriorates if the chosen training set does not align well with PDE solution properties. This is similar to what occurs when one attempts to solve a low-regularity problem with a finite element method on a uniform mesh. We must optimize not only the loss function but also the selection of random samples in the training set. From a numerical perspective, we must balance the statistical error induced by random samples and the approximation error induced by the neural network model. Optimizing the selection of random samples requires a generic density model capable of approximating arbitrary distributions and generating samples efficiently. A good candidate for this is a deep generative model. In this project, we will further develop two normalizing flow models: KRnet, suitable for distributions with dimensions on the order of 10, and VAE-KRnet, designed for distributions with dimensions on the order of 1000. Using these deep generative models, we will develop adaptive sampling strategies that reduce statistical error when solving (parametric) PDEs with physics-informed neural networks (PINNs) or Deep Ritz method. In particular, we will address two important problems in physics and chemistry: the simulation of viscoelastic flow and the approximation of the committor function. Due to the curse of dimensionality, these problems are traditionally tackled using stochastic approaches. However, deep learning offers a promising alternative approach where the estimated physical quantities do not suffer from the stochastic fluctuation. Adaptive sampling, enabled by deep generative models, will play a critical role in the algorithms developed for these problems. The educational objectives will focus on training young scientists in tackling interdisciplinary problems across scientific computing and deep learning. 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 National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EGFP awards provide funding for a total of three years of stipend and an associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at Louisiana State University (LSU) will support 10 EPSCoR Graduate Fellows whose research will align with the unique goals and programs supported by the Directorate for Biological Sciences (BIO), Directorate for Engineering (ENG), Directorate for Geosciences (GEO), and the Directorate for Technology, Innovation and Partnerships (TIP). The Department of Oceanography and Coastal Sciences (DOCS) and Cain Department of Chemical Engineering (CDCE) graduate programs at LSU are strategically paired in this project to foster future academic, industry, and government leaders who understand the linkages between Louisiana’s most important industry and valuable coastal ecosystem. By designing intentional science co-production spaces into the graduate experience, Fellows will be well equipped to grow the State of Louisiana’s economic and research competitiveness well beyond the project’s conclusion. As a U.S. leader in the energy and chemical processing sectors, the fortification of these Louisiana industries with talented, highly trained personnel will advance national prosperity and security. Fellows will join impactful research programs confronting pressing coastal and energy challenges to improve resilience and achieve ambitious energy efficiency and independence goals. Fellows will be trained by highly qualified mentors with both emerging and long-running records of mentorship excellence with novel research projects across coastal sciences and chemical engineering, ranging from river delta connectivity and function, coastal hydroclimate drivers, and wetland soil-plant-microbe interactions to operando chemical reaction imaging, in silico design of high-entropy alloy catalysts, and microplastic dynamics in the environment. To facilitate synergy across projects and foster innovative partnerships, Fellows will participate in WIN-WIN Convergence Workshops focused on developing novel solutions to coastal and energy issues that will define the next generation of coastal scientists and chemical engineers. WIN-WIN will prepare Fellows for post-graduation advancement by emphasizing holistic student training through personalized mentorship plans that foster mental wellness, academic excellence, and career development. Accordingly, Fellows will be primed for success upon graduation, ensuring a true WIN-WIN for Fellows, LSU, the State of Louisiana, and national interests. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project is about optimizing geometric structures in physical and biological systems. The research will investigate optimal swimming motions of microorganisms, which can help explain their behavior, as well as yield new ways of actuating mechanical systems in fluids (e.g., underwater robotics). In general, the research will create new computational tools for shape optimization that can enhance the performance of physical systems; examples are structural optimization, minimizing fluid drag, and improving heat dissipation. Moreover, it will create new methods to control the self-assembly of geometric structures in fluids, such as liquid crystals. The outcomes of this research will open new avenues for material design, provide novel methods for optimizing geometric motion, and enhance the understanding of biological locomotion in fluids. Part of this project involves interacting with elementary and middle school students to show the importance of geometry in applications through the PI's "sit-with-a-scientist" program. The program provides an informal atmosphere with hands-on activities. The research objective of this project is to develop novel computational techniques for controlling geometry. It will advance the theoretical development of unfitted finite element methods (FEMs) to create robust and user-friendly numerical techniques for optimizing shape and time-dependent geometric motion. It will develop new theoretical and computational tools to optimize the swimming motions, or gaits, of microorganisms. And it will extend optimal control techniques to the self-assembly dynamics of geometric structures, with application to liquid crystals. The research will unite the "optimize-then-discretize" and "discretize-then-optimize" philosophies (which are usually contrary) for shape optimization in the context of unfitted FEMs and give new ways to compute minimizers. The research will yield new types of level set methods for simulating changing geometry. Moreover, it will extend unfitted FEMs to address time-dependent, tensor-valued, semi-linear partial differential equations with a focus on controlling the anisotropic Landau-de Gennes (LdG) model of liquid crystals. Other aspects of the research will create open-source software for the methods developed here, using both the PI's packages, FELICITY and AHF, and other open-source options (e.g., Firedrake and NGSolve). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project considers problems in harmonic analysis related to partial differential equations on curved spaces, with a focus on the Schrödinger equation in quantum mechanics. The PI will investigate the regularity properties of solutions to the linear Schrödinger equation and how these properties depend on the initial data. Central to the project is the question of how the geometry and shape of space influence the behavior of quantum waves governed by the equation. These problems are connected to modern developments in quantum physics, data science, and engineering. The PI also aims to build connections with other fields in mathematics such as number theory, dynamical systems and spectral theory. The project provides training opportunities for graduate students interested in mathematical analysis. More specifically, the PI will develop new mathematical tools to study wave behavior and derive space-time estimates for the linear Schrödinger equation on manifolds under varying geometric assumptions. Examples include hyperbolic manifolds with trapped geodesics, the flat tori, and settings involving constraints from interacting potentials, such as those arising in the many-body Schrödinger equation. The main questions focus on how the geometric and dynamical properties of the underlying space and equation influence the behavior of solutions. These estimates have broad applications in nonlinear partial differential equations arising from different physical contexts and are connected to other areas of mathematics, including number theory and geometry. The analysis employs local harmonic analysis and global analysis on manifolds. The local analysis involves adaptations of variable coefficient arguments developed in the Euclidean setting, along with tools from microlocal analysis, while the global analysis exploits tools from 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 · 2025-07

There are three topics in this proposal: elliptic optimal control problems, elliptic problems with rough coefficients and fully nonlinear elliptic partial differential equations. Ellipticity, optimization and finite elements are central to all of the proposed research projects. The results from the projects in optimal control are relevant for the optimal design processes in engineering. The results from the projects for problems with rough coefficients can be applied to multiscale problems that appear in materials science and geoscience. The results from the projects in fully nonlinear elliptic partial differential equations will provide reliable and useful computational tools for differential geometry and optimal transport. The proposed work will build bridges among the communities of numerical partial differential equations, optimization, elliptic optimal control, multiscale modeling and domain decomposition. The research in elliptic optimal control problems will extend the recent work of the PI and collaborators in distributed control with pointwise state constraints to general cost functions and general partial differential equation (PDE) constraints. It will also develop new error analyses for boundary control problems with control constraints that can be applied to multiscale finite element methods when the coefficients in the PDE constraint are rough. The research in elliptic problems with rough coefficients will develop multiscale finite methods that are based on the local orthogonal decomposition (LOD) methodology with a domain decomposition (DD) twist. It will extend the DD-LOD framework to problems with high contrast channels, to variational inequalities, to Neumann boundary value problems, to fourth order problems and to elliptic boundary control problems with control constraints. The research in fully nonlinear elliptic partial differential equations will focus on problems that involve Monge-Ampere equations: the Minkowski problem, the prescribed Gaussian curvature problem and the second boundary value problem for the Monge-Ampere equation. It is based on novel convexity enforcing finite elements discovered by the PI and collaborators in recent years and a nonlinear least-squares approach. The goal is to develop finite element methods that can capture smooth solutions and that come with a rigorous error analysis and convergence rates. 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

Computational science provides a foundation for modern scientific research. The very rapid development of high performance computing platforms, together with a similar emergence of highly accurate algorithms, allow the treatment and modeling of complex systems that were intractable just a few years ago. The use of Artificial Intelligence, including Large Language Models and Machine Learning techniques, has opened a new venue for computational approaches. In parallel, automated scientific instruments create massive amounts of measurements that demand modern scientific computing methods to process and understand. However, computational science will only fulfill its full potential if advances in undergraduate education accompany the advances in hardware and in numerical and data-based models. Frequently, students learn little, if any, computational science in the classroom and are not prepared for computational science or data science research. This project provides an evidence-based approach to address these issues and prepare the next generation of students. The 30 undergraduate students participating in this Research Experiences for Undergraduates (REU) site will be engaged in authentic computational science projects, learn how to use state-of-the-art cyberinfrastructure tools, manage large amounts of data, experience activities that characterize research careers, and work in interdisciplinary research teams. The faculty mentors will provide activities that help students understand the nature of multidisciplinary research and the value of working as a team. These skills have the potential to be transformative in both the students' education as well as in their future careers. The Louisiana State University's Center for Computation & Technology (CCT), with its research activities organized into interdisciplinary focus areas that span traditional academic departments, provides an ideal setting for the REU students to experience interdisciplinary research. With research groups working on problems like gravitational waves, complex emergent phenomena in material science, and computational arts, the participants will be working on cutting edge research in computational science. An extensive training component will remedy any weak preparation of students in computational science, providing knowledge on how to leverage state-of-the-art cyberinfrastructure tools. At the conclusion of the REU, students are encouraged to continue their research and present their work at their home institution, informing others about computational science. The combination of individual training with student immersion in a multidisciplinary research group has previously been successful in engaging students to explore computational science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project is a comprehensive, statewide initiative that will build strategic research and development capacity in Louisiana for the conversion of agricultural residues to liquid transportation fuels. The research will identify ways to use electricity for enhancing efficiency in converting low-value byproducts into high-value fuel components. Chemistry, engineering, and data science will guide process improvements through advanced modeling and analysis. An economic analysis will assess the financial viability of these processes, evaluating market potential, cost efficiency, and long-term benefits for farmers and industry stakeholders within Louisiana. Education and workforce development at universities and a partnering community college will target energy technology, and include STEM outreach to K-12 schools. The collaborative project will be led by Louisiana State University (LSU) and the LSU AgCenter. Partner institutions are Louisiana Tech University, River Parishes Community College, Southern University, and the University of Louisiana at Lafayette. This project will advance biomass-derived transportation fuels by integrating thermochemical conversion and electrocatalytic upgrading to improve overall efficiencies. Electricity will enhance conversion by transforming liquid and gaseous byproducts into viable fuel sources. Bench-scale experiments will enable rapid testing and data generation. Data-driven modeling, machine learning, and economic analyses will optimize production and assess cost-effectiveness. The initiative will expand Louisiana’s research capacity through faculty hires, research laboratory upgrades, and access to shared research facilities. It will integrate research with workforce training, preparing students and professionals for careers in energy technology and fuel production. Educational programs will provide hands-on training, STEM outreach, and technical instruction to build expertise in bio-electric fuels. Expanding research infrastructure and workforce development will create opportunities for industry collaborations and partnerships. By integrating research, education, and industry engagement, the project will position Louisiana as a leader in bio-electric fuel technology. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Incubators for STEM Excellence (E-RISE). E-RISE supports the development of sustainable research infrastructure and capacity in EPSCoR jurisdictions through collaborative, hypothesis-driven, or problem-driven research and workforce development to improve competitiveness in selected STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The tree of life is a powerful tool for understanding evolution that can also be used for research on medicine, wildlife management, and agriculture. However, recent studies indicate that genes transferred between species can make it difficult for scientists to build an accurate evolutionary tree or understand the relationships between species. This project will use parrots, one of the most endangered and illegally trafficked groups of animals, to better understand how often species transfer genes and how that impacts scientists’ ability to accurately build an evolutionary tree. The researchers will collect genetic data, including sequencing genomes, for all parrots. These data will be used to build an evolutionary tree of parrots and answer questions about gene transfer between parrot species. This data will also be used to aid conservation efforts by developing forensic DNA barcodes that will help law enforcement correctly identify illegal products made from endangered parrot species. The project will build collaborations between forensic scientists and natural history museums, provide training and mentoring for students through early-career researchers, and share findings with the public and scientific communities. To estimate accurate phylogenetic trees in the genomic era, the effect of genomic architecture (the structure, organization, and content of a genome) on phylogenetic signal must be understood. Large-scale phylogenetic methods often do not account for gene flow, nor do they address the interaction between genomic architecture and phylogenetic signal, which can lead to well-supported but inaccurate evolutionary relationships. Using the global radiation of parrots as a focal system, this project will reconstruct a nearly complete time-calibrated phylogeny of the clade to assess gene flow across an entire radiation and predict genomic regions prone to biasing phylogenetic estimates. The researchers will generate new genomic resources, including five chromosome-level reference genomes and genomic-scale markers for over 400 previously unsampled taxa. The project aims to test the following hypotheses to make general predictions about the factors causing gene tree discordance and to inform a species-level taxonomic revision: 1) non-monophyletic species are a common feature across the parrot evolutionary tree; 2) non-monophyletic species and weakly supported relationships are associated with a higher prevalence of gene flow that spans millions of years; and 3) genomic architecture is relatively conserved across the parrot radiation and can be used to mitigate the effects of gene flow in phylogenetic inference across temporal scales. This work will offer a high-resolution view of the interaction between phylogenetic signal, gene flow, and genomic architecture in parrots, and demonstrate how this framework can be used for improved taxonomic classification and wildlife management. 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

Bayou Arithmetic Research Days (BARDs) are one-day mini-conferences aimed at bringing together the vibrant community of students and researchers working in number theory in the Gulf Coast region. BARDs take place about two times a year at universities in Louisiana and neighboring states. The goal is to both bring high-profile speakers to the region and provide students and junior faculty within the region with speaking and networking opportunities. The conference series website is https://bardsmath.com. Each BARD event features two to three plenary lectures, six to ten lightning talks, and a pre-talk. The plenary lectures are given by visiting researchers and are thematically related; past themes have included arithmetic geometry, analytic theory of L-functions, explicit class field theory, and additive number theory. Lightning talks are short presentations usually given by graduate students (and sometimes by undergraduate students or postdoctoral researchers); lightning talks at BARDs provide many students with their first professional speaking opportunity. The pre-talk is given virtually by a local faculty member prior to the conference and provides background material aimed at 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 · 2025-06

As our planet's climate continues to change, finding new approaches to alleviate its impact is crucial. Microbiomes, which consist of smaller organisms making a living on larger species, can affect how their hosts respond to environmental change. This project evaluates whether and how we can assemble synthetic microbiomes to help host species cope with the impacts of environmental change. Due to the enormous diversity of microbial communities, there are numerous ways to engineer synthetic microbiomes, making it difficult to identify the principles that underpin their impacts on host species. The researchers will employ high-throughput techniques to simultaneously evaluate the impacts of different synthetic communities and environmental conditions on plant hosts. The results will expand our understanding of how environmental change alters the impacts of microbes on hosts and how engineered synthetic microbial communities may help hosts adapt to a changing climate. The researchers will actively mentor students from middle school to undergraduate level, create a new Course-Based Undergraduate Research Experience Lab course in the cutting-edge field of synthetic microbiology, and design a backyard research program for under-served middle and high school students to provide training to attract their interest to possible careers in STEM. This study will explore the intersection of different research areas to study the interactions between hosts and microbes, including symbiosis, evolutionary biology, and ecosystem functioning. It will assess how elevated temperatures affect three crucial components of host-microbe interactions: (1) microbe-microbe interactions and their impact on host fitness, (2) functional redundancy and its effect on biodiversity and ecosystem functions, and (3) evolvability during microbiome breeding. To achieve this goal, the researchers will collect duckweed, a widespread aquatic plant, across a temperature gradient, acquire bacteria from duckweeds and other sources, and use these collections in large multifactorial experiments on an automated, high-throughput platform. The collections, results, and experimental methods will contribute to the development of microbial consortia that can enhance organismal and ecosystem resilience in the Anthropocene. 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 focuses on the development of security solutions for the rapidly growing electric vehicle (EV) infrastructure. As the transportation sector transitions toward sustainable alternatives, the security of charging stations and their management systems has become increasingly critical. Recent research has indicated that charging station vendors are vulnerable to remote attacks, creating significant risks to users, infrastructure, and power grid stability. This technology addresses these vulnerabilities through advanced software and security measures, ensuring the safe operation of EV charging infrastructure. With the EV market growing, securing this critical infrastructure is essential to protect both individual users and the broader energy grid. The successful implementation of these security measures may accelerate the adoption of transportation solutions while maintaining the integrity and reliability of charging networks. The solution may also help prevent unauthorized access to charging systems, protect user data, and ensure the resilient operation of transportation infrastructure. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This technology is based on the development of intelligent security systems that leverage machine learning to analyze and protect vehicle charging infrastructure. The technology performs comprehensive semantic analysis of system components, enabling the identification and mitigation of vulnerabilities that traditional security methods often miss. This approach allows for deep inspection of software interactions and detection of potential risks unique to charging systems. The solution's ability to learn contextually correct vulnerability patterns from compiled code provides improved protection compared to conventional methods that rely solely on source code analysis. This technological advance enables proactive security measures that adapt to emerging threats while maintaining the operational efficiency of charging infrastructure. The system continuously monitors network traffic, analyzes system behavior patterns, and implements automated response mechanisms to prevent potential security breaches before they can impact critical infrastructure operations. 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

The award supports the Research Experience for Undergraduates (REU) site in physics and astronomy at Louisiana State University. This program is seeking to foster interest in careers in science by introducing students to research-oriented careers in physics and astronomy while further developing research-related skills and knowledge. REU students are recruited nationally and the students can be in any academic year. For the ten-week program, the participants are matched with faculty mentors based on student interests. Students join their mentors in active research projects, such as i) performing analysis on data from the long baseline neutrino oscillation experiment T2K, ii) instrumentation development and data simulations to support gravitational wave research, iii) designing quantum error correction codes and quantum memory for photonic qubits, and iv) analyzing astronomical data to search for exoplanets. There are weekly seminars and workshops that introduce the students to various topics such as faculty research, overall skills development, to common research resources, and to professional development topics such as ethics and professional communication. Students participate in field trips and other activities, some of which are coordinated with concurrent LSU summer science programs in computer science, biomedical science and other areas. Students from all programs are lodged in proximity at on-campus apartment-style housing, to foster group interactions and a sense of community. This site is supported by the Department of Defense in partnership with the NSF REU 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 · 2025-05

The field of computing is central to scientific pursuit and has touched almost every area of science, technology and humanity. Computing has ushered us into a whole new era of possibilities and discoveries as more and more data and computing resources are available. While throwing computational resources at a problem has been a successful initial approach, eventually the efficiency of resources becomes important. Theoretical computer science rigorously studies the difficulties of problems based on resource limitations like time, energy, space, compressibility, input/output, etc. For the text data, space resources and compressibility become important factors. In the field of compressed data structures, the goal is to develop data structures which take space nearly optimal with respect to the best compression while at the same time can answer the queries as fast as the best uncompressed data structure can answer. This project considers parameterized pattern matching problem and constructing compressed index for it. The problem has direct applicability in software plagiarism detection, cloning and versioning systems. Many of the results and components considered in this project are likely good inclusion for course projects and homework problems. The project will support a graduate student. The results of this work will be widely disseminated and published in conferences and journals. In parameterized pattern matching, the text consists of parameterized characters and static characters. Two strings are parameterized match if there is a bijection between parameterized alphabets of the two strings, which when applied to the characters of one of the strings it becomes equal to the other. A known encoding ‘prev’ can convert the text (or each suffix of the text) into a canonical form such that two parameterized strings are a match if and only if their ‘prev’ encoding is the same. In the investigator’s previous work, a Burrows-Wheeler transform (BWT) based approach as well as a Phi-function based approach have been shown to obtain a compact index for the problem. In this project. we aim to: (1) Define entropy-based compression measures for parameterized text and achieve an index with such space occupancies, (2) Explore the construction aspects of parameterized index – including implementation issues like memory bottleneck and external memory construction, and (3) Obtain best space-time tradeoff results based on combination of Phi function and BWT. Apart from these, the project will also focus on the longest common extension LCE problem and indexing for two-dimensional pattern matching. 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 project aims to develop a wearable device capable of continuously sensing a wide range of physiological signals for use in biometric authentication and health monitoring. As the society sees increasing use of digitally interconnected devices and systems, the need for secure and user-friendly authentication methods is more critical than ever. Existing methods, such as passwords, fingerprints, and facial recognition, are incompatible with wearable technologies. This project investigates a new approach using physiological signals to enable a continuous time-efficient authentication method that will facilitate a secure and seamless user experience. The broader impacts of this work are substantial: it will advance cybersecurity, protect user privacy, and support smart healthcare by greatly improving the reliability and accessibility of wearable technologies. Moreover, this project includes a plan of extensive education and workforce development activities. The investigators will create advanced interdisciplinary learning opportunities at both Michigan Technological University (MTU) and Louisiana State University (LSU) to engage undergraduate and graduate students in cutting-edge research across electrical engineering, biomedical engineering, signal processing, machine learning, intelligent systems, and data science. By training students with the interdisciplinary technical skills for high-tech industries, this project will contribute to the development of the nation's future STEM workforce. The research of this project will explore the use of multispectral photoplethysmography (PPG) signals for biometric authentication through custom-designed wearable devices. Unlike prevalent biometrics such as fingerprints and facial images which are not suitable for continuous data acquisition, PPG signals can be collected in real time through tiny skin-contact sensors. This feature enables the development of smart biometric systems for continuous and unobtrusive operations, making them well-suited for wearable applications. The goal of this project is to design novel, accurate, reliable, and secure authentication mechanisms using short-duration transient physiological signals collected from wearable devices. The research will investigate new signal processing and machine learning approaches to overcome key challenges, such as limited training data, time-varying signal quality, and the need for continuous classification with temporally evolving data streams. The research tasks include: (1) modeling and analysis of multispectral transient PPG signals; (2) design of novel algorithms for user identification and authentication in real time; (3) creation of adaptive learning models for continuous user-independent/user-dependent classification, and (4) integration of these models in a wearable prototype platform for evaluation in real-word scenarios. The investigators will also address fundamental challenges in signal processing, such as how to effectively identify, extract, and characterize short-duration transient biological signals and determine the signal qualities of sparse and/or noisy data and how to identify the motion artifacts and process them appropriately. The expected outcomes of this research project include new theoretical insights, algorithms, and system architectures that will advance the state of the arts in biometric authentication, signal analysis and processing, and wearable sensing. These outcomes will contribute to a wide range of applications including cybersecurity, health monitoring, and human-computer interaction. 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

There is a strong push to develop cleaner energy technologies that benefit the environment. One such technology involves storing hydrogen in ice-like structures called gas hydrates. However, the amount of gas that can be stored in these hydrates is too low to meet the economic goal, which is to store an amount of hydrogen that is at least 5.5% of the hydrate’s weight. This project will explore how to store more hydrogen by trapping the hydrogen gas as tiny bubbles inside the hydrates. The project will run computer simulations of hydrogen and water molecules to understand how the tiny bubbles are trapped. Experiments will be conducted to validate simulation results. The new approach proposed for increasing hydrogen storage could also be used to increase the storage, transportation, and capture of CO2 to help reduce the amount of CO2 in the atmosphere. This research will benefit the broader society by making it possible to store and use hydrogen in many applications, including transportation. Transitioning from traditional fuels to hydrogen will help reduce environmental pollution and global climate change. Middle and high school students will also see and interact with these ice-like structures using virtual reality (VR) headsets. This CAREER project seeks to significantly enhance hydrate-based hydrogen storage (HBHS) by trapping hydrogen gas nanobubbles within solid hydrate structures. Unlike HBHS approaches that use promoters, the proposed approach can significantly enhance hydrogen storage capacity and formation rate. Large-scale atomistic MD studies will examine how hydrogen nanobubbles become trapped within the growing solid hydrate. The results will be visualized using a Virtual Reality (VR) workflow that allows researchers to probe and visualize the internal structure of these hydrates with VR headsets and controllers. Experimental studies will also be conducted to confirm the game-changing potential of this approach. Hydrogen hydrates will be formed under conditions that favor nanobubble trapping, and analyses such as volumetric measurements, microscopy, and NMR imaging will be performed on hydrate samples. These tests will confirm the trapping of hydrogen gas nanobubbles in hydrates and quantify the potential to exceed the DOE hydrogen storage target. By combining advanced visualization of large-scale MD simulation results with experimental validation, this project aims to redefine the capabilities of HBHS, providing a clean energy storage method that could have broad applications in the fields of hydrogen storage, carbon sequestration, CO2 capture and transportation, and methane production, storage, and transportation. This project is jointly funded by the Process Systems, Reaction Engineering and Molecular Thermodynamics (PRM) 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 · 2025-04

NON-TECHNICAL SUMMARY Movements of atoms in solids govern many material properties and behaviors. For example, Li ions must move through solid-state batteries to release electricity, and then move back again to recharge. The known ‘highways’ of atomic motion are atomic-scale interfaces that exist between atomic building blocks called grains. Such interfaces are therefore called grain boundaries. While atoms are known to quickly move through grain boundaries, it is unclear how the rate of atomic motion changes if the atomic structures or chemistry of the grain boundaries change as well. It is possible that the rate of atomic motion may change by several orders of magnitude. This project studies how changes in grain boundaries directly affect long-range movement of atoms. Experiments examine atomic motion by studying uniquely fabricated ceramics that are made of bonded samples with different compositions, which causes certain atoms to move from one side to the other, and a variety of advanced electron microscopes are used to characterize materials on the atomic-scale. Computational models are also employed to complement experiments and understand how materials behave on the molecular level. The overall mission of this research has a wide-ranging impact on several industries related to the energy transition planned by the United States. For example, new knowledge gained from this research is likely to provide new insights about hydrogen degradation effects on steels while transporting or storing hydrogen gas, or metal dusting degradation effects on Ni alloys that are used in petrochemical plants. This research also focuses on boosting education outcomes of students at the University level as well as in grades K-12. Hands-on ceramic processing demonstrations are teaching K-12 students how ceramic materials are made, and virtual/mixed reality modules are used to accelerate training on electron microscopes and expose younger generations to how we characterize materials using state-of-the-art research facilities. TECHNICAL SUMMARY This CAREER project investigates effects of grain boundary complexion transitions on long-range mass transport in bulk ceramics and develops virtual and mixed reality applications to (i) reinvigorate materials-related curriculum at Louisiana State University and (ii) educate K-12 students, their parents, and their teachers. The research is based on prior knowledge that grain boundary complexion transformations discontinuously change bulk material behavior, yet atomic-scale mechanisms associated with diffusivity through various complexions remain unknown. This proposal evaluates grain boundary thermodynamics in ceramic diffusion couples, involves atomic-resolution microanalysis of interfaces, and uses Monte Carlo grain growth simulations of complexion-driven abnormal grain growth, while also employing in-situ electron microscopy experiments and Density Functional Theory calculations. The project is divided into 2 Research Objectives: (I) Identify Mechanisms of Discontinuities in Grain Boundary Diffusivity Related to Grain Boundary Complexion Transitions, and (II) Elucidate Effects of Interfacial Diffusion on Complexion Propagation and Microstructure Evolution. The Education Objective is to develop virtual and mixed reality training modules for electron microscopes, namely a scanning electron microscope training module for an undergraduate technical elective course, and a transmission electron microscope training module for advanced users. The Outreach Objective is to inspire K-12 students by using hands-on experiments related to ceramic processing and interactive virtual reality activities. Overall, this work provides a universal understanding of interfacial solid-state diffusion, thereby providing new insights useful to solving issues related to the energy transition, such as (i) hydrogen permeation and embrittlement in iron and nickel alloys, (ii) metal dusting degradation in steels and superalloys, and (iii) interfacial transport efficiency in next-generation solid-state ion batteries. 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 I-Corps project is focused on the commercialization of a software system for early identification of high-risk situations in the workplace, seeking to prevent accidents for industrial workers. This technology could help companies in the construction, environmental engineering, and industrial manufacturing industries where there are significant risks of worker injuries and worker compensation costs are high. With this technology, both management and workers benefit from real-time alerts, risk assessment, and accessible analytics. Improved systems to protect workers from safety hazards could result in fewer workplace injuries, illnesses, and fatalities, which have serious consequences for individuals, businesses, and society. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software platform designed for industrial safety. The technology utilizes a scalable cloud architecture and web application coupled with a mobile application, wearable and stationary Internet of Things (IoT) sensors, and cognitive assessments to monitor and alert worker risk in real time. By incorporating a high throughput data pipeline for processing biometric, environmental, and cognitive data, reportable safety incident identification and mitigation can occur when risk factors are high, potentially before a safety incident arises. Included in the system’s capabilities are configurable alerts for different safety conditions, historical analyses to identify safety trends, and the ability to incorporate different sensors and devices based upon users’ needs and preferences. Artificial intelligence capabilities trained on the repository of collected safety data is used to infer the presence of high-risk situations and predict critical intervention steps before situations escalate, preventing worker injury. 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 workshop "Around singularities in Poisson geometry" will take place August 4-8, 2025 and will be hosted by the Banff International Research Station (BIRS) at the Institute of Advanced Study in Mathematics (IASM) in Hangzhou, China. Its aim is to bring together researchers in Poisson geometry, foliation theory, and representation theory to study an array of questions related to singularity theory in the Poisson setting. The program will feature surveys on three of the most active topics in this area, followed by research talks, a series of open discussions, and several events designed to promote the engagement of early-career participants. The goal of these activities is to introduce the participants to new mathematical perspectives on Poisson singularity theory, and to create an opportunity for new interdisciplinary collaborations. The purpose of this award is to support the participation of US-based researchers in this international event. The rich geometric behavior of Poisson spaces around singular points, where the rank of the Poisson bivector drops, is governed by a subtle interplay of geometric and algebraic phenomena. The workshop will focus on three interrelated problems in Poisson singularity theory: normal form theorems, deformations, and desingularization. The surveys and talks will address these problems as they occur in an array of geometric settings, including differential geometry, foliation theory, holomorphic geometry, the theory of Lie algebroids and Lie groupoids, algebraic geometry, and geometric representation theory. It will give participants an opportunity to apply current methods in one discipline to open problems in another, fostering a new dialogue around singularities in Poisson geometry. More details can be found at https://www.birs.ca/events/2025/5-day-workshops/25w5442. 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

PART 1. NON-TECHNICAL SUMMARY The development of novel, efficient functional materials for energy applications is essential for addressing global energy challenges, promoting sustainability, and driving economic growth. With this project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, the Prof. Baranets and his research group at Louisiana State University design and create new semiconducting materials with potential for thermoelectric applications - specifically, converting waste heat into electricity. The researchers focus on a unique class of compounds known as charge-balanced heteroanionic oxypnictides. These materials incorporate multiple anionic (negatively charged) species, such as oxides and pnictides (e.g. phosphorus, arsenic, antimony, bismuth), with different ionic sizes and charges into their crystal structures. This approach enhances compositional and structural diversity, functionality, tunability of properties, such as thermoelectric performance. By integrating experimental techniques and computational tools, the research project uncovers the rational principles underpinning the targeted design of heteroanionic compounds, discovers novel semiconductors with unique atomic arrangements and functionality, and paves the way for advanced materials with enhanced thermoelectric efficiency. In addition, this research addresses the national need to improve STEM education. It offers mentorship and interdisciplinary training opportunities for graduate and undergraduate students, equipping them with skills critical to addressing future energy and technology challenges. This project actively engages students from diverse backgrounds in hands-on research, enhances learning via cutting-edge virtual reality tools, and fosters collaboration with local high schools to improve chemistry education through coaching sessions for the US National Chemistry Olympiad K-12 students and workshops for local high-school teachers. PART 2: TECHNICAL SUMMARY The primary objective of this project is to establish rational principles for the strategic synthesis and design of novel multinary Zintl-like heteroanionic oxypnictide semiconductors with narrow band gaps. These materials, featuring separated pnictide, Pn3− (Pn = P, As, Sb, Bi), and oxide, O2−, anions, exhibit significant potential for high- and mid-temperature range thermoelectric applications. Narrow-gap Zintl semiconductors are particularly promising due to their desirable combination of tunable transport properties (e.g., Seebeck coefficient, electrical and thermal conductivity, charge carrier concentration), making them ideal candidates for thermoelectric applications. Heteroanionic oxypnictides possess extraordinarily rich and complex structural chemistry due to the presence of multiple anions with varied ionic sizes, charges, and coordination environments. This complexity facilitates the tunability of transport properties through a balance of ionic and covalent bonding, characteristic of Zintl phases. By leveraging this structural versatility, the principal investigator and his research group investigate novel functional materials. The research integrates (i) a blend of traditional and modern approaches to solid-state synthesis; (ii) predictive methodologies, such as empirical Zintl counting, structural relationships, and comprehensive high-throughput computational discovery coupled with electronic structure analysis; (iii) a fundamental understanding of the impact of anion ordering effects on structure-property correlations and suitability of Zintl oxypnictides for narrow-gap semiconducting applications; and (iv) development of traditional (band engineering, doping) and non-traditional (high-pressure) tunability approaches to investigate thermoelectric properties. This project advances the landscape of novel compositions and structures, developes a broader understanding of heteroanionic materials chemistry, but also establishes foundational design principles for the potential development of high-performance thermoelectric materials. These efforts promote the discovery of unique and attractive chemical and physical properties and innovative applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Tropical mountains are among the most species-rich regions of our planet. Tropical montane habitats change quickly as one travels upslope due to rapid changes in temperature, precipitation, and plant life. Narrow bands of high-elevation habitats are sometimes called “sky islands” due to their patchy and isolated distributions. These “sky islands” can harbor species with similarly narrow distributions, creating specialized communities that are isolated across mountain ranges. This research examines the evolutionary and ecological dynamics of bird communities of Polylepis forests in the Central Andes Mountains of Peru and Bolivia. Polylepis forests are the highest-elevation forests in the world, occurring from 3500–5000 meters above sea level. These forests are highly fragmented, with differences in the connectivity and size of forest patches. Researchers will sequence DNA from populations of approximately 30 species of birds that live in Polylepis forests and the adjacent grasslands. The researchers will assess connectivity among populations and determine if certain characteristics, such as the ability to fly long distances, are associated with differences in population connectivity among the bird species. Current species’ classification will be evaluated in light of the population-level DNA data and data on bill shape, body size, and feather coloration. This project will provide experiential learning opportunities for undergraduate students by establishing the Museum Undergraduate Science and Exploration Opportunities (MUSEOs) program at the bird division of the Louisiana State University Museum of Natural Sciences. This new curriculum will use a near-peer network of mentors to expand entry points and progression routes for students in research and curation. The research will also foster international collaboration in the documentation of biodiversity in remote, understudied regions of South America. Finally, the project will build museum collections, advancing collections-based ornithology and biodiversity research. This research will characterize the diversification dynamics of Andean “sky island” avifauna through the comparative phylogeography and integrative taxonomy of birds in the Polylepis woodlands of the Central Andes. By leveraging unique strengths of the Louisiana State University Museum of Natural Sciences and an international team of collaborators, this project will generate integrative data sets including whole-genome sequence data and data for multiple phenotypes (morphology, color, song) to comprehensively study the origin and maintenance of avian biodiversity in an understudied and at-risk ecosystem. This project is jointly funded by the Systematics and Biodiversity Science 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 · 2025-03

This award supports travel for graduate students and researchers, mainly early career researchers, to attend the inaugural Southern Regional Harmonic Analysis Conference, scheduled for November 1 and 2, 2025, at Louisiana State University (LSU) in Baton Rouge, LA. Harmonic analysis is a foundational area of mathematics with applications spanning number theory, partial differential equations, and signal processing. This conference aims to build on the growing momentum of harmonic analysis research in the South. By bringing together mathematicians from across the region and at all career stages, the conference will promote research, education, and collaboration, thereby advancing the progress of science and enhancing educational opportunities in the Southern region. The conference will foster collaboration and communication among researchers in the Southern United States and to provide graduate students and early-career researchers with exposure to cutting-edge developments in harmonic analysis and related areas. The Southern Regional Harmonic Analysis Conference will focus on current research in harmonic analysis and its applications. Topics include discrete harmonic analysis, harmonic analysis on Lie groups and homogeneous spaces, improving and sparse bounds, oscillatory integrals, geometric measure theory, and related problems in operator theory and PDEs. The conference will include four plenary talks by leading mathematicians and nine contributed talks by junior researchers. Participants will have opportunities for discussions and networking during breaks and at a planned conference lunch. By facilitating engagement among researchers and students in the Southern region, this conference is expected to strengthen the regional harmonic analysis community, inspire new collaborations, and provide a platform for emerging researchers to present their work. For additional information, visit the conference website https://www.math.lsu.edu/~ha2025/ 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.