Tulane University

NSF Awards · FY 2026 · 2026-09

This project advances our ability to address a growing need in the field of behavioral ecology: how high-dimensional, correlated social and ecological variables interact to shape animal behaviors in the natural world. To meet this need, our team of AI experts and seasoned behavioral ecologists will pioneer the development of two complementary computational tools – AI interpretation of videos and neural network causal inference modeling – of broad relevance to field-based studies of behavioral ecology. In doing so, we offer two novel approaches to make sense of the many social and ecological factors that could influence mating behavior in birds, including where and when to display?; how do males optimize display phenotype and mating success?; and, how do display behaviors of frugivorous birds impact plant community succession in tropical forests? In addition to addressing these long-standing research questions, this project will result in shared research infrastructure – hundreds of thousands of hours of enriched videos and accompanying open-source software – to enhance integration of AI into field-based behavioral ecology studies. To amplify the societal impact and reach of this work, we will produce a broadcast-quality short film to engage public audiences with the natural history and ecological significance of mating systems and display behaviors in tropical birds manakins. We will also offer training opportunities to undergraduate and graduate students, a post-doctoral fellow, as well as high school teachers. This work advances the NSF priority area of use of Artificial Intelligence in research and in workforce training and development. This project investigates the eco-evolutionary dynamics of lekking behavior in the white-bearded manakin (Manacus manacus), a tropical frugivore found in Ecuador’s Chocó rainforest. Lekking is a mating system where males gather to perform elaborate displays without providing parental care, resulting in strong reproductive skew and some of the most extreme examples of sexually selected traits observed in nature. The proposed study concurrently evaluates how ecological and social environments interact to influence lek formation and dynamics, as well as how lekking birds may shape the environments they inhabit. First, we document, in real time, ecological and social conditions before, during, and after lek establishment in a replicated and rigorous manner. Second, we gather a rich, multifaceted dataset to evaluate the relative impact and chains of causality among multiple, inter-correlated variables on mate choice dynamics at active leks. Third, we model seed dispersal and demographic trajectories of favored plant species to better understand the degree to which frugivorous birds may act as ‘ecosystem engineers’ by shaping plant communities. Our multidisciplinary team combines deep knowledge of the natural system with expertise in contemporary computational methods, including AI-based video interpretation and neural network modeling approaches. Building from these strengths, we expect to deliver a comprehensive understanding of how ecological and social drivers interact to shape lek establishment and mate choice dynamics at leks, as well as the ways that lekking behavior in turn shapes the surrounding environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

The G-WAVE Initiative seeks to democratize access to complex environmental data, enabling researchers to advance science in water security, energy production, public health, and transportation systems regardless of their technical background. Furthermore, G-WAVE supports education and broadening participation by creating a workforce development pipeline designed to engage a wide range of participants from across the Gulf region. By equipping a new generation of researchers with essential skills in artificial intelligence and data science, the project provides a replicable national model for building coastal resilience through technical training and community engagement. The technical goal of this planning project is to define the architecture and requirements for a scalable, AI-driven data ecosystem capable of integrating heterogeneous water data from diverse sources. The project methodology centers on three core technological pillars: predictive and process-guided AI, which integrates hydrodynamic and social models with physics-informed machine learning to accelerate hazard forecasting at a lower computational cost; AI-driven agents and operational intelligence, utilizing LLMs and agentic AI to provide natural language interfaces for complex data orchestration; and Gulf Digital Twin, a virtual representation of the region’s water systems used for robust "what-if" scenario planning. By addressing critical gaps in data interoperability and standardization, the G-WAVE Initiative will establish a unified framework and registry that supports open science and reproducibility. This work will contribute a foundational roadmap for a cyberinfrastructure that enables data-intensive science and intelligent decision support for integrated water, health, and energy 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 2026 · 2026-07

Recent advances in tissue engineering have led to the emergence of microphysiological systems (MPS) -- bioengineered microtissues that capture the complexities of intact tissue while leveraging the strengths of simpler cell culture systems. MPS are a powerful advanced new technology for basic and translational science. However, barriers to widespread MPS acceptance and adoption are still present. These include a need for more rigorous application of MPS to explore basic tissue biology, and a need for more comprehensive education on MPS technology. The overall goal of this project is to remove these barriers and promote greater use of MPS for basic research and STEM education. This will be accomplished through research aims using MPS to study how blood vessel growth becomes abnormal in diseases such as cancer. In parallel, this project will also develop new educational programming on MPS technology, which will increase public awareness about MPS, promote MPS adoption in basic research, and excite students to remain in the STEM education pipeline. Overall, this project significantly advances biomanufacturing and biomaterials by i) enhancing the basic science, engineering, and applicability of MPS and their underlying biological components; ii) integrating MPS into translation of basic biological principles towards identification of unique biomarkers; and iii) preparing the STEM education and workforce for a future that includes integrated use of MPS technology. For its research aims, this project will use vessel- and tumor-on-a-chip MPS models to study how healthy and diseased blood vessel growth (i.e., angiogenesis) is regulated by partial endothelial-to-mesenchymal transition (pEndoMT), a complex systems-level tissue process. First, dense spatiotemporal responses to growth factor and fluid flow will be used to generate an ODE model of pEndoMT and angiogenesis, with subsequent model validation and parameter refinement in healthy and diseased MPS systems. Following model construction, the presence of bistable pEndoMT state(s) will be compared under healthy and diseased signaling and microenvironmental contexts using vessel- and tumor-on-a-chip MPS. Second, multiomic sequencing of vessel- and tumor-on-a-chip MPS platforms will identify novel pEndoMT markers (and their epigenetic status) conserved across healthy and diseased angiogenesis, with validation in intact mouse tissue. Together, these research aims will demonstrate how MPS can provide powerful insights into the biological processes that govern physiological and pathological angiogenesis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project will study how ocean oxygen content varies during the Holocene (last 11,700 years). A key research question is whether ocean oxygen levels vary with temperature. The study will use existing sediment cores from the California margin and the Arabian Sea. The investigators will carry out high-resolution analyses using a cutting-edge technique. The ultimate goal of the project is to measure whether global ocean oxygen content increased during the Holocene. The results are important as ocean oxygenation is critical for habitability of marine ecosystems. The project will support an early-career researcher and research and training opportunities for a post-doctoral scholar and an undergraduate student. Two workshops on oceanography will be designed for middle school students. Seawater thallium isotopic compositions have recently been proposed to track the global ocean oxygen. This study will construct high-resolution thallium isotope records over the Holocene for both the Arabian Sea and California margin. The results will be used to test if the global ocean oxygen content increased during the mid-late Holocene compared to early- to mid-Holocene. A second aspect of the project is to couple thallium isotopic compositions to a model to provide quantitative constraints on ocean oxygen. The results will have implications for understanding the ocean’s role in global biogeochemical cycles, especially carbon and nitrogen. New workshops on science topics related to the project research will be delivered through class visits to local middle schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Artificial intelligence (AI) is increasingly used to support decisions in areas such as healthcare, transportation, and environmental monitoring, yet many systems operate as black boxes, making it difficult for people to understand how decisions are made or to assess their reliability. This lack of transparency can lead to errors, reduce user confidence, and limit the safe and effective use of AI in situations where clear reasoning is essential. This project addresses these challenges by developing new AI methods that explain the decisions in straightforward, intuitive, and meaningful ways, enabling systems to identify and use recognizable patterns in data as building blocks for reasoning. By making AI more transparent and interpretable, the project will help professionals such as doctors and engineers make better-informed decisions, enhance safety in high-stakes applications, and strengthen public trust in AI technologies. In addition, the project includes education and outreach activities that train students in responsible AI development, helping to build a skilled workforce, broaden participation in technology innovation, and contribute to long-term economic growth and societal well-being. This project develops a novel framework for interpretable artificial intelligence (AI) that learns semantically meaningful data patterns and organizes them into transparent, compositional reasoning processes. The objective is to improve model interpretability by explicitly revealing how and why decisions are made, enabling systematic identification of errors, assessment of data quality, and development of more reliable models. The project focuses on designing architectures that capture representative, human-interpretable patterns while mitigating spurious correlations, and that generalize effectively to new tasks and evolving environments. The proposed framework will be integrated with modern deep learning paradigms, including convolutional neural networks (CNNs) and Vision Transformers, to ensure compatibility with state-of-the-art practice. It will be rigorously evaluated against established prototype-based interpretable methods using quantitative metrics of predictive accuracy, interpretability, robustness, and computational efficiency. Experimental validation will be conducted on standard benchmark datasets as well as real-world applications, including medical decision support and autonomous systems. The project also incorporates education and outreach by developing curriculum modules, open-source software, and interdisciplinary training opportunities, thereby advancing broader understanding and adoption of transparent and trustworthy AI 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 2026 · 2026-06

Submarine channel networks move large amounts of material from the continent to the seafloor. They are constructed by turbidity currents on continental margins. This project will conduct novel lab experiments that will accurately represent turbidity currents for the first time. The lab results will be used to develop sophisticated models of submarine channel formation. Understanding turbidity currents has important implications as these rapid flows through submarine channels can threaten infrastructure such as submarine data cables. The project is an international collaboration between scientists in the United States and the United Kingdom and includes research opportunities for high school students and workshops for teenagers. This project will integrate physical and numerical experiments to understand processes responsible for submarine channel formation. Physical experiments will use a novel sediment mixture that facilitates long-distance self-suspension of dilute sediment-laden flows. Numerical models will develop the framework capable of constructing submarine channels and strata. The project will test three hypotheses: 1) Bedload sediment transport critically contributes to the construction and maintenance of submarine channels. 2) The construction of submarine channels can proceed by levee growth alone. 3) Submarine channels constructed by short lived turbidity currents with prominent heads maintain open channel conduits for longer durations. Broader impacts include support for a team of scientists that spans high school, undergraduate and graduate students, postdoctoral research associates, and faculty. The project will support workshops for 13-year-olds that include physical experiments to convey marine geohazards. This award was made possible through the NSF/GEO-UKRI/NERC lead agency opportunity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

This project will study deep learning, a class of machine learning algorithms based on deep neural networks (DNNs) that are becoming increasingly popular due to their successful applications in many areas, such as healthcare, transportation, and entertainment. DNN programs, like any other software, may contain faults that might undermine their safety and reliability in mission-critical applications. Software engineering research has produced a rich body of software fault localization techniques; however, they are not immediately applicable to DNNs. This is mainly because traditional software and DNN models are based on fundamentally different computational models, and the definition of “bug” differs in the two kinds of software. The project will improve fault localization for DNNs with novel approaches for monitoring model behavior during the training of the neural networks. DNN models are also used by practitioners who may not be experts in DNN architecture, and fault localization techniques proposed by this project have the potential to make debugging DNN more accessible, improving the safety and quality of AI-based software. Training DNN models is known to be expensive. This project has the potential to reduce training costs by identifying errors early on that can be rectified. This project will explore three novel research directions. (1) Identify dynamic behavior of DNN models that need to be reified in traces. The preliminary work of the investigators has shown that reifying the dynamic behavior of fully connected neural networks (FCNN), such as changes in learnable parameters help with bug localization in FCNN; however, other model architectures like Convolutional Neural Networks (CNNs) have different kinds of learnable parameters. (2) Define novel abstractions of dynamic behaviors in DNN models that will enhance fault localization and repair. This research direction will explore the development of new abstractions that can represent the dynamic behavior of the neural networks succinctly. (3) Reduce the cost of re-training for fault localization by leveraging the abstraction of dynamic behavior. In this direction, the project will create abstractions that not only reduce the training dataset but also enhance the effectiveness of fault localization, thereby saving time and computational resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Proteins are essential to life and play central roles in medicine, biotechnology, and sustainable manufacturing. They enable critical chemical reactions that support human health, environmental processes, and industrial production. However, improving protein function remains a major challenge because current approaches often rely on trial-and-error methods or static structural information, which do not fully capture how proteins carry out complex chemical transformations. In particular, there is limited understanding of how energy flows from the solvent into proteins to enable and regulate their activity. This project seeks to uncover how energy flow governs protein function, providing a new foundation for designing more efficient and versatile enzymes. By advancing fundamental knowledge of protein energy distribution, this research will support innovation in areas such as therapeutic protein development, environmental remediation, and sustainable biomanufacturing, thereby contributing to national priorities in scientific progress, economic competitiveness, and public welfare. The project also integrates research and education through a comprehensive training and outreach plan. This includes hands-on K–12 workshops that connect basic science concepts to real-world applications, mentored research opportunities for community college students to broaden participation in science and engineering, and curriculum development that incorporates active research into undergraduate and graduate education. Together, these efforts will help build a diverse and highly skilled workforce while increasing public understanding of how molecular science contributes to societal challenges. This project will establish a quantitative biophysical framework that links intramolecular energy flow to enzyme function. The central hypothesis is that energy redistribution following ligand binding is not uniform but instead propagates through specific structural pathways that regulate catalytic activity. Using an engineered myoglobin scaffold that enables non-native catalysis as a model system, hydrogen–deuterium exchange mass spectrometry (HDX-MS) will be employed to map conformational ensembles and identify energetic hot spots under near-physiological conditions. These measurements will reveal networks of residues that mediate long-range communication within the protein scaffold. Guided by these insights, targeted mutagenesis will be performed to test how perturbations to these pathways influence catalytic efficiency and specificity. In parallel, active learning–based approaches will be used to iteratively prioritize mutations and efficiently explore combinatorial sequence space based on experimental feedback. By integrating experimental biophysics with data-driven protein design, this work will define general principles that connect sequence, structure, energy flow, and catalytic function. The resulting framework will enable more predictive and mechanism-informed strategies for enzyme engineering, advancing the broader field of molecular biophysics and expanding the capabilities of proteins for applications in biotechnology, medicine, and chemical synthesis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant professor and training for a graduate student at the Tulane University. This work is conducted in collaboration with experts at the Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS). Through the fellowship, the PI will investigate how artificial intelligence can analyze complex brain imaging data while providing explanations that are transparent, reliable, and consistent with expert reasoning. The project integrates computer science and neuroscience to design AI systems that analyze brain scans and explain their results. It addresses the challenge of making AI transparent and trustworthy for medical and research applications. The project will help build a skilled STEM workforce while promoting the development of trustworthy AI technologies that can be applied in healthcare and other high-stakes fields, including education, public policy, and industry. The project will develop interpretable artificial intelligence models for multi-modal brain imaging to improve understanding of brain function and support clinical decision-making. It will advance both Artificial Intelligence (AI) and neuroscience by creating models that generate human-understandable explanations from complex imaging data, allowing researchers and clinicians to interpret, validate, and trust predictions. The project will design inherently interpretable AI architectures for structural, functional, and diffusion MRI, and implement algorithms for transparent multi-modal data integration, providing a holistic view of brain structure and activity. Models will incorporate expert knowledge and neuroscientific principles, and outputs will be evaluated with domain experts to ensure scientific and clinical relevance. The project will support the PI’s professional development in brain imaging and provide PhD students with training in AI and neuroimaging. Activities will integrate curriculum development, student mentoring, and interdisciplinary research at the home institution to strengthen STEM capacity and workforce development. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows (ERF). The ERF program supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Non-technical description Every living cell is surrounded by a very thin membrane barrier that keeps most large and water-soluble molecules out of the cell. The membrane protects the cell, but it also makes it hard to deliver useful cargo molecules, such as drugs, into cells. Short molecules called cell penetrating peptides can sometimes carry cargo across this barrier, but most known examples work inefficiently and tend to trap their cargo inside internal compartments where the cargo cannot do its job. Recently, researchers discovered a new class of peptides that behave differently. These peptides can move directly across the cell membrane and deliver cargo molecules with much higher efficiency and without entrapment. The goal of this project is to understand how these unusual direct delivery peptides cross cell membranes and why they work better than earlier examples. The team studies how the peptides interact with the lipids that make up cell membranes and how the chemical complexity of real cell membranes affects the interactions. The team develops laboratory membrane systems that closely mimic natural cell membranes to study the interactions in detail. By uncovering the rules that allow peptides to cross membranes directly, this work helps scientists design new molecules to deliver useful cargos into cells. The project also supports the training of undergraduate and graduate students in interdisciplinary research and shares results through publications and outreach activities that introduce students to the science of cell membranes and biomaterials. Technical description The plasma membrane of a cell prevents most hydrophilic macromolecules from entering the cytosol. Classical cell penetrating peptides such as tat and penetratin can deliver these cargos, but they rely on endocytosis. This pathway is inefficient and often traps the cargo inside intracellular vesicles where they are degraded. Recently discovered direct delivery peptides perform much better and use a different mechanism. These peptides can deliver many kinds of cargo to the cytosol at low concentrations and with high efficiency. Current evidence suggests that they cross the plasma membrane by direct translocation, but the molecular basis of this process is still unclear. The goal of this project is to identify the peptide structural features and peptide–lipid interactions that allow efficient direct plasma membrane translocation. The central hypothesis of this work is that these peptides adopt flexible conformations that promote strong interactions between arginine side chains and lipid headgroups at the membrane surface, along with cooperative interactions involving aromatic residues. To test this idea, the team carries out systematic structure–activity and mechanistic studies that compare classical cell penetrating peptides with direct delivery peptides under the same experimental conditions. Plasma membrane lipids are isolated from plasma membrane–derived vesicles and used to build customizable lipid mixtures that reproduce the chemical complexity of biological membranes. The team measures peptide binding, translocation efficiency, structural dynamics, and peptide–lipid interactions in these systems and relate those properties to delivery activity. These studies define the molecular principles that allow efficient membrane translocation and guide the design of new peptide-based delivery materials that can transport a wide range of cargos into living cells. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Plant-based biomass is a potential source of fuels and valuable chemicals. Finding ways to convert biomass to fuels and chemicals could create value and improve U.S. energy security. Lignan is a major component of biomass. Lignan has molecular structures that are precursors to fuels, chemicals and materials used in advanced manufacturing. However, the catalysts that promote the reactions to final products are not selective. They promote both desirable and undesirable reactions. This lowers efficiency and reduces product value. This project will develop new catalyst designs that improve reaction selectivity by controlling the atomic-scale structure of sites where the catalytic reactions take place. The project outcomes will enable more efficient use of biomass and advance sustainable chemical manufacturing by minimizing unwanted side reactions. The project will support education and workforce development by training students in interdisciplinary catalysis research and by engaging K-12 students through STEM outreach activities. This collaborative project will develop a fundamental understanding of oxygen-removal reactions on a class of catalysts known as dilute alloys, which contain isolated early transition metal atoms embedded within copper-, silver-, or gold-based host materials. The project will investigate how the identity and atomic arrangement of these isolated metal sites influence chemical bonding, reaction pathways, and catalyst stability. To achieve this, the researchers will combine controlled surface experiments, catalytic performance measurements at near-ambient pressures, advanced spectroscopic techniques, and computational modeling of reaction mechanisms. By linking insights from well-defined model systems to more complex catalytic materials operating under realistic conditions, the project will establish broadly applicable design principles for catalysts that selectively convert biomass-derived molecules into valuable aromatic hydrocarbons. These principles may also inform the development of cost-effective alternatives to precious-metal catalysts for a wide range of chemical transformations. 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 · 2025-11

Global floods and extreme rainfall events have surged by more than 50% this decade and are now occurring at a rate four times higher than in 1980. However, the capability of physical models in predicting flood events remains limited across spatial scales, especially in intensively managed agricultural systems like the Midwestern U.S. The apparent disparity between observed seasonal patterns of extreme precipitation and high streamflow events presents a challenge when using precipitation alone to predict flood occurrence and severity. This project addresses a fundamental question in hydrologic science: how do watershed characteristics and in-land management practices regulate the precipitation-runoff relationship across agriculture-dominated watersheds? The modeling framework in this project will integrate the complex impacts of watershed characteristics, human land use, and management practices into hydrological prediction. An early warning system will be developed for projecting flood occurrence at a granular level in a managed system and will be shared for further evaluation of the flood forecasting performance and uncertainty assessment. The overarching goal of the research is to develop a data-driven, physics-informed early warning system to predict flood occurrence and support communities in agriculture-dominated watersheds across the Midwestern United States. This project will develop a graph-based transformer deep learning approach integrated with process-based hydro-ecological modeling to improve flood prediction accuracy and keep the interpretable structure. The results of the project will be tested, shared, and deployed as a real-time prediction tool on a web-based platform that integrates mapping capabilities, advanced visualizations, and mobile access. The early warning system will be accessible to multiple users, especially underrepresented communities, concerning the direct impacts of flooding on life and property and the indirect effects on the food security, economy, and livelihood of the communities. This project is jointly funded by Hydrologic Sciences, the Established Program to Stimulate Competitive Research (EPSCoR), and the Directorate for Geosciences to support AI/ML advancement in the geosciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

A fluid is any substance that can flow and that takes the shape of its container. Unpredictable changes can affect the way fluids mix and move and this work seeks to elucidate these effects by developing new mathematical tools to study how random fluctuations affect fluids. The principal investigator will also explore how unpredictable changes can lead to chaotic and unstable behavior in fluids, which is important for understanding phenomena like turbulence. This effort will train future scientists to continue this work. Findings will be shared with other researchers and the public. By combining different areas of mathematics, a deeper understanding of how fluids behave will be achieved. This endeavor brings together scientists from different fields to share ideas and work together, potentially leading to new discoveries. This project aims to develop a rigorous mathematical framework for analyzing the influence of random fluctuations on mixing properties in fluid models, including advection-diffusion and nonlinear partial differential equations (PDEs). The research focuses on the interplay between deterministic and stochastic processes in shaping fluid behavior, examining two interconnected themes: (1) Microlocal approach to mixing by random velocities - by employing the new techniques using pseudodifferential operators, this project aims to develop a rigorous mathematical framework for analyzing the influence of random fluctuations on mixing properties in various fluid models, including advection-diffusion and nonlinear PDEs; (2) Lyapunov exponents in Infinite-Dimensional Systems - this project aims to advance techniques for studying Lyapunov exponents in infinite dimensional stochastic systems, particularly developing methods for proving the existence of projective stationary measures and the finiteness of Lyapunov exponents. Expected outcomes include an enhanced understanding of mixing and transport in fluid systems and a characterization of chaos in infinite-dimensional systems. The project also seeks to achieve a proof of exponential lower bounds for advection-diffusion equations in compact domains with quantitative dependence on diffusivity. The results of this research will advance the theoretical understanding of fluid dynamics and potentially generate practical implications for fields such as climate modeling, oceanography, and engineering. This project is jointly funded by the Applied Mathematics and the Analysis programs in the Division of Mathematical Sciences as well as 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 · 2025-09

The primary aim of this project is to develop a process for modeling the hydrodynamic flow of coral reefs using observations obtained using Light Detection and Ranging (LiDAR) mounted on unmanned aerial vehicles, or drones. Current methods involve deploying instrumentation in the water column, which is expensive, time-consuming, and prone to lost instrumentation. If successful, this new approach would enable water-flow models to be readily developed and continually used following a single high-resolution LiDAR survey of a reef. The proposed objectives will advance physical oceanography on reef systems, via hydrodynamical modeling that will readily lead into broad-scale heat budget modeling of reefs. This project will develop hydrodynamic models of wave-driven flow on shallow coral reefs, with constraints for the models coming entirely from data collected by unmanned aerial vehicles (UAVs, or “drones”). Past work with in-water instrumentation has demonstrated the effectiveness of hydrodynamic models to capture water flow across shallow reef flats when the pressure gradient from wave setup is measured alongside bathymetry and an estimate of hydrodynamic roughness. This current approach, while effective, requires extensive in-water instrument deployments and the method needs to be “tuned” to each stretch of reef because the hydrodynamic roughness needs to be calibrated in lieu of a more general way to measure it. This project leverages technological advances that allow high-resolution bathymetry and water level to be measured from LiDAR payloads carried by relatively small drones. There are three scientific objectives: (1) test whether sea level gradients and bathymetry derived from LiDAR data effectively constrain hydrodynamic models, (2) develop an AI algorithm to derive hydrodynamic roughness from high-resolution bathymetric point clouds, and (3) evaluate the utility of modeling coral reef water flow from only LiDAR based bathymetry and roughness combined with remote sensing products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The objectives of the 3-year Tulane Use-Inspired Research and Entrepreneurship (TURE) REU site are to annually recruit a cohort of 8 students and to (1) provide students with comprehensive research training through a collaborative lab-based experience, (2) offer opportunities for student participants to publish and present their research results, (3) actively target student participants from institutions with limited opportunities to perform scientific research, (4) provide participants with information on the graduate experience, and (5) provide participants with entrepreneurship education. Research projects will span the fields of Health, Energy, and the Environment. Students will work collaboratively with their assigned faculty and graduate student mentors, in addition to continually receive guidance and education from members of the Tulane Innovation Institute team. TURE, comprised of 10 faculty mentors and in partnership with the Tulane Innovation Institute (TII), will engage undergraduate students in hands-on, use-inspired research at Tulane University applied to challenges in the fields of Health, Energy, and the Environment. Sample use-inspired projects include the use of machine learning to predict venous thromboembolism from venous pressure waveform data (Health), designing alloy catalysts for cleaner hydrogen production and utilization (Energy), and targeted destruction of harmful algae (Environment). TURE will leverage existing large scale NSF investments at Tulane including the NSF FUEL ENGINE and NSF Convergence Accelerator as platforms for students to work in use-inspired research. Participants will attend weekly research seminars and skills workshops to educate them on use-inspired research being performed at Tulane and teach them the necessary skills to be a successful scientist such as research ethics, designing publication quality figures, and preparing for the Graduate Record Examination. Participants will engage in interactive discussion panels, covering professional development topics. TURE’s long-term payoff is the promotion of STEM field study and entrepreneurship to a broad student base who support regional economic development across the focus areas of Health, Energy and the Environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Adrienn Ruzsinszky, John P. Perdew, and Jianwei Sun of Tulane University are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop density functionals on higher levels of a hierarchy of approximations. Density functional theory is very widely used in chemistry, physics, materials science and engineering, and even geology and pharmaceutical design. It enables practical computer calculations predicting the existence and properties of systems of many interacting electrons. The five rungs of the ladder of approximations to the exact density functional for the exchange-correlation energy of a many-electron system provide a range of options for electronic structure calculations for molecules and materials, from the more affordable first three rungs (local spin density approximation or LSDA, generalized gradient approximation or GGA, and meta-GGA) to the more expensive but often more accurate fourth and fifth rungs (hybrids and self-interaction corrections and random phase approximation RPA-like functionals). While the first two rungs of the ladder of approximations have been broadly explored, the third, fourth, and fifth rungs harbor numerous opportunities for improvement. The proposed plan is a comprehensive effort to make higher levels of the ladder of density functional approximations more accurate but still computationally feasible. The broader impacts of the proposed project will include more-accurate reaction paths (including those for catalysis) and more-accurate excited states, and will provide training and validation for machine learning approaches to chemistry. The proposed work will help to retain or expand a cohort of talented graduate students and postdoctoral fellows. It will also support the research of interested undergraduate physics majors. The PIs and some group members will participate in outreach to New Orleans public schools organized by Tulane University’s School of Science and Engineering. This award supports theoretical and computational research and education to advance density functionals on the higher rungs of a ladder of approximations. The approach is based on satisfying exact mathematical properties (constraints) of the exact but incomputable density functional for the exchange-correlation energy. The proposed research targets systems where self-interaction correction or spatial nonlocality is needed. Many systems of interest of chemistry are strongly affected by self-interaction error, which can be reduced by expensive hybrid density functionals. As an inexpensive third-rung alternative, the researchers will explore further degrees of freedom in the recently developed LAK meta-GGA that reduces self-interaction error and delivers good quality band gaps of semiconductors. They propose to exploit this degree of freedom by adding more ingredients so that the resulting meta-GGA can be simultaneously accurate for charge-transfer systems, molecules adsorbed on metal surfaces, polaronic effects and conical interactions and other scenarios of interest for chemistry. On the fourth rung, this team will design a constraint-satisfying hybrid functional that is exact for all one-electron and slowly-varying densities. They will also explore a local hybrid functional with the meta-GGA part constructed in the Hartree gauge. On the fifth rung, the researchers will develop a more accurate self-interaction correction to the random phase approximation (RPA). The broader impacts will include more-accurate reaction paths (including those for catalysis) and more-accurate excited states, and will provide training and validation for machine learning approaches to chemistry. The proposed work will also support the research of interested undergraduate physics majors, graduate students, and postdoctoral fellows. The PIs and group members will participate in outreach to New Orleans public schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

An award is made to Tulane University for development of an integrated data collection and management system designed to enhance U.S. scientific research, education, and training capacity. The project improves the coordination, consolidation, and sharing of field data collected from Chocó rainforest habitat in northwest Ecuador, a ‘biodiversity hotspot’. It also enhances the breadth and depth of data collected, by installing a network of automated sensors. In doing so, the project helps to ensure that ongoing and future data collection, curation, and sharing meets gold standards for quality and access. The project makes new, high-quality data available to US researchers; builds technical capacity via hands-on workshops and training; and helps to protect a threatened habitat type. The Chocó Biogeographic Zone is an exceptionally biodiverse but understudied region with few biological field stations. The FCAT Station in northwest Ecuador is an active research hub where longitudinal environmental data, including biotic inventories (e.g., botanical, faunal), ecological processes (e.g., phenology, frugivory), abiotic environmental conditions (e.g., rainfall, hydrology, temperature), and habitat change (e.g., aerial imagery) are all collected. Improvements to the quality and quantity of this data collection and its management and sharing will enable researchers from the U.S. and around the world to access this valuable information, which would not otherwise be feasible for them to collect or steward independently. The project will directly benefit in-progress and future research projects in the Chocó biogeographic zone and enable comparative research currently hindered by a lack of data from the project area. 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

Primates, including humans, have long lives and long times between generations, potentially limiting their ability to adapt to selective pressures that emerge quickly. Interactions between genetics and the environment that allow behavioral and physiological flexibility may therefore be particularly important in primates. This doctoral dissertation project characterizes individual behavioral and hormonal responses to acute environmental changes in a non-human primate species to examine how gene-environment interactions mediate health and reproductive fitness. The project advances fundamental understanding of adaptive processes and supports student training in biotechnological methods and public science engagement. Research linking individual variation in behavioral and hormonal responsivity to fitness-relevant measures requires rich, longitudinal datasets to support robust exploration. This study builds on an existing longitudinal (40 years) database, collecting new data on behavioral and ecological variables. Fecal samples will be collected to analyze glucocorticoids (stress hormones). Individual variability in stress response will be examined in relation to fitness (muscle mass and reproductive outcomes). GIS/remote sensing is used to assess ranging/resource use, and Bayesian linear mixed models are used to assess if variation in behavioral and hormonal responsivity is associated with individual characteristics—including age, sex, reproductive status, dominance, and early life experience—and longitudinal fitness-relevant markers of performance (i.e., relative muscle mass via urinary creatinine, lifetime reproductive success, reproductive output, female interbirth intervals). 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

In several areas of scientific investigation, such as genomics, astrophysics, and network traffic, traditional modeling tools are inadequate for handling the vast amounts of data generated by modern technology. This project will bring together a team of collaborators to tackle the challenge of developing new methodologies for analyzing complex and multiscale data. The group is both multidisciplinary (mathematics, statistics, and signal processing) and international (U.S. and France). The driving area of application is the modeling of brain dynamics and connectivity, as well as its repercussions for our understanding of neurological functions and disorders. By training both graduate and undergraduate students, this project will contribute to preparing the U.S. workforce for jobs that require knowledge of some of the latest trends in data science, including high-dimensional statistics, machine learning, and AI. The project will develop a mathematical framework for the fractal modeling of high-dimensional data through the lens of multiscale random matrices (MRMs). MRMs offer a general approach to large-scale, complex stochastic dynamics. They are particularly suited to the analysis of systems where the number of time series (namely, the dimension) is comparable to the number of observations. In the framework of MRMs, newly uncovered universality properties of random matrix statistics will guide the construction of robust asymptotic results. In addition, MRMs will underpin the development of graph-based methodology for high-dimensional, nonstationary systems. In the context of multi-sample problems, the project will further add to the recent stream of literature on random matrix theory applied to statistical learning by developing data-scientific methodologies that are intrinsically multiscale and robust. 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 Industry-University Cooperative Research Center (IUCRC) for Accessible Healthcare through AI-Augmented Decisions (AHeAD) will develop trustworthy and usable AI technologies, so quality care is accessible by all populations. AHeAD is a multi-university research partnership between UL Lafayette (lead), Tulane, University of Florida and Georgia Tech. The center’s research will create validated AI-enabled systems, quality assurance frameworks, and best practices that enable healthcare organizations to offer quality care for all, reducing healthcare gaps while saving costs. By training the next-generation AI workforce and releasing open-source AI models, the center will drive innovation, create new jobs, and grow the American economy. AHeaD's goal is to develop trustworthy AI technologies that improve healthcare access and outcomes for all populations. Research focuses on creating privacy-preserving, interoperable, explainable and resource-efficient AI models for healthcare. The center's multidisciplinary program includes AI/ML, data science, systems engineering, and health sciences, supported by computational infrastructure and real-world health data through industry partnerships. Research will advance trustworthy AI, privacy-aware data integration, behavioral context modeling, and human-AI integration. The center will foster workforce development through student training and industry collaborations, building a skilled talent pool to accelerate healthcare AI translation from research to practice. The Tulane site will focus on federated learning, personalized health visualizations, and human-centered retrieval-augmented generation models for enhanced patient-provider communication. During this planning period, Tulane will expand existing partnerships with the healthcare industry in New Orleans as well as local economic development offices in order to transfer AI research from academia into industry. AHeAD will address critical national healthcare challenges and advance U.S. competitiveness in AI-enabled healthcare. The Center creates a rich environment for training next-generation professionals through integrating industry-relevant AI applications into curriculum development and providing direct experience solving healthcare challenges with real-world data. The Center will create and maintain standardized healthcare datasets, publish open-source software and research outputs, and advance technologies with broad healthcare applications. This multifaceted approach promises to improve the health of millions of Americans while generating substantial cost savings for both government and industry. 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

Biological sex influences normal physiology and the manifestation of disease. Severe illnesses such as cardiovascular diseases and cancers can exhibit profound sex differences in their occurrence, progression, and response to treatment. Historically, the research models used in preclinical science to develop new drugs do not sufficiently account for sex differences. For example, lab experiments testing new drug compounds typically do not examine effects in both female (XX sex chromosomes) and male (XY sex chromosomes) cells. This is due in part to the absence of distinct methods for culturing female and male cells with the right combination of sex hormones such as estrogen and testosterone. This CAREER project will explore research and development of sex-based human cell culture methods and engineered tissue models. The research will be integrated with university education and K-12 outreach that emphasizes the roles of individual biological differences such as age, sex and genetic background in health and disease, thereby helping students connect with medical science research. Principles of sex-based biology and medicine will be integrated in undergraduate and graduate physiology curricula in biomedical engineering. Completion of the proposed research will deliver female and male culture medium formulations with defined sex hormone compositions and engineered tissue models for studying sex differences in the retina, skin, and other organs. Collectively, these broadly adoptable innovations may facilitate the development of sex-based medicines that can improve clinical outcomes. Completion of the proposed CAREER activities will deliver validated sex-specific human cell culture methods and microphysiological models of the microvasculature that serve as venues for investigating: (i) sex-specific cellular physiology, (ii) sexual dimorphisms in disease, and (iii) sex-specific pharmacology. Female (XX) and male (XY) media formulations with defined sex hormone compositions (mixtures of estradiol, dihydrotestosterone, and progesterone) will be validated by benchmarking transcriptomic profiles against published datasets from biopsied endothelial cells of both sexes (XX and XY). Focusing on effects in endothelial cells of multiple sources will elucidate the interplay between sex hormone effects and tissue-specific phenotypes. Benchmarked sex-specific states in culture will be correlated with functional outputs in microphysiological systems assays of angiogenesis and vascular permeability, processes that are central to homeostasis and pathophysiology of most organs. These systems and methods will be applied to define sex differences in VEGF-mediated endothelial activation and inhibition by glucocorticoids, as paradigms of physiological regulation and pharmacologic response, respectively. The foundational scientific knowledge and new methodologies for sex-based culture methods resulting from this research can increase the clinical impact of human cell-based models of numerous diseases. This project is jointly funded by the Engineering of Biomedical Systems (EBMS) 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 · 2025-06

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry, Professor Alex McSkimming of Tulane University is studying synthetic molecules relevant to the Earth’s nitrogen cycle. Conversion of atmospheric dinitrogen into bioavailable ammonia by microorganisms, a process known as nitrogen fixation, is essential for the sustenance of plant life on this planet. This reaction is mediated by the ‘iron-molybdenum cofactor’, or ‘FeMoco’, which resides within these organisms, and enables breaking of the remarkably strong dinitrogen triple bond. A paradigm shift came this century with the realization that nitrogen fixation at FeMoco occurs via intermediates containing iron-hydrogen bonds; i.e. iron hydrides. Through the study of model chemical systems, the proposed research will shed light on this important and unusual class of molecules. This, in turn, will further scientific understanding of biological systems critical to human health and development. As ammonia is a potential alternative fuel, an improved knowledge of FeMoco is also expected to lead to further advancements in renewable energy technology. This research project will be integrated into an education/outreach program aiming to improve the participation of high school students in STEM. A key distinction of hydride-bound FeMoco states is that the iron hydride sites are—in contrast to the vast majority of all other metal hydrides—projected to be locally high-spin. This spin state is expected to profoundly impact the bonding, spectroscopy and reactivity of such species. Nevertheless, high-spin hydride complexes, particularly those for which the hydride is terminally bound, remain rare, and their properties only poorly understood. This research project therefore aims to prepare and study new terminal hydride complexes of manganese and iron, particularly those with electron counts pertinent to FeMoco. Molecules with unusual oxidation states and/or unprecedented hydride binding modes with be particular targets. This proposal also seeks to explore the reaction chemistry of high-spin hydride complexes with unsaturated small molecule FeMoco substrates. 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

Plastics are everywhere in our daily lives, but disposing of them responsibly is a huge challenge. Making new plastics and fuels from petroleum uses large amounts of energy and generates greenhouse gases. A promising solution to both problems is to convert waste plastics or renewable materials into useful chemicals using a special kind of chemical reaction called olefin metathesis. Olefin metathesis is a catalytic reaction that takes two molecules, cuts them each in half, and stitches them back together so that the two new molecules are made from half of each of the starting molecules. For olefins such as ethylene and propylene, this is an important pathway to make precursor molecules that are incorporated in to polymers and plastics that everyone uses. Metathesis reactions are enabled solid catalysts containing W and Mo metals; however, while these materials work well, how they function during the reaction is not well understood. This collaborative project between Oregon State University and Tulane University aims to better understand and improve these catalysts. Efforts will focus on how the catalytic active sites, the place where the chemical reaction occurs, are created. The research team will use a combination of experiments and computer simulations to study how different chemicals, called “soft reductants,” can convert the catalysts from inactive to activate form by changing the oxidation state of metal atoms on their surface. Understanding this process could lead to more effective and reusable catalysts for turning plastic waste or renewable materials into valuable products. This project will discover specific ways to control the process of creating active sites through the lens of reductive chemistry. Preliminary work shows how the activity of a catalyst can be increased several thousand fold, with tunable populations based on the structure of a reducing agent. Techniques and tools like custom gas phase reactors, temperature programmed studies, in-situ and operando spectroscopy using DRIFTS, Raman, UV-Vis, ambient-pressure x-ray photoelectron spectroscopy (AP-XPS) will be closely integrated with computation. These tools will help identify the features of the reductant and the mechanistic steps needed to create and maintain the activity of these catalysts. The results of this research will not only improve olefin metathesis but also benefit other chemical processes that depend on similar catalysts, such as creating renewable plastics, fuels, and other materials. The project will support training of future scientists and engineers and develop innovative educational tools, including virtual reality experiences and methods to 3D-print educational models of catalysts, to make this complex science more accessible and engaging for students and researchers alike. 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

Social animals navigate a particularly complex environment in which their actions influence their social standing and reproductive success. Among their behavioral repertoire are poorly understood vocalizations that may relate to the complexity of social structures and the evolution of social organization. This doctoral dissertation research examines the vocalizations and hormonal levels of males from a highly competitive non-human primate species to determine whether vocalizations play a role in a male’s social rank and reproductive success. The study advances fundamental knowledge about animal communication, social evolution, and sexual selection. In addition, this research supports conservation efforts, provides scientific training, and promotes STEM education among K-12 and undergraduate students. This doctoral research project combines behavioral observations, acoustic analyses, and hormone assays to investigate if dominant males produce more frequent, diverse, and acoustically distinct calls linked to higher androgen levels. The research uses playback experiments by broadcasting recorded calls to other animals, assessing how individuals respond based on the caller’s rank and group membership. This experimental component reveals whether vocalizations alone communicate dominance and competitive ability in the study species. The project uses statistical models to assess relationships between vocalization features, hormone levels, and behavior while accounting for factors like group membership. This integrative approach advances the understanding of the ways in which sexual selection shapes communication strategies in primates. 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 the “Macaulay2 Workshop at Tulane University,” which will be held at Tulane University from April 14-18, 2025. This week-long intensive workshop will be focused on the development of the free and open-source computer algebra software system Macaulay2, which is widely used for research in commutative algebra and algebraic geometry. The workshop aims to develop research infrastructure through the creation and maintenance of Macaulay2 packages, to train early-career researchers in the use and development of the software system, and to facilitate collaborations. It will include a two-day mini-school providing training for participants new to Macaulay2. The workshop also encourages the formation of research communities among graduate students through scheduled lightning talks and a networking event. The specific software development goals are motivated by current needs in active mathematical research areas. Participants will contribute to Macaulay2 packages that implement algorithms and constructions from the invariant theory of tensors and auxiliary algebras, the computation of invariants of linearly reductive groups, injective resolutions in topological data analysis, Gaussian mixture models, and subvarieties under birational maps relevant to algebraic statistics. The conference website is https://sites.google.com/view/macaulay2tulane/. 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.