University Of Wisconsin-Madison

NSF Awards · FY 2026 · 2026-09

Software updates are a fact of life, yet they are difficult for developers to get right because the new version must correctly interact with the previous version. Incorrect updates remain a surprisingly common source of catastrophic failures in practice. Formal verification, a technique where software is mathematically proven to behave as intended, is a promising approach to make software more reliable; however, with existing verification approaches, we don't know how to state (much less prove) compatibility. This project's novelties are to develop a definition of software update correctness and create the proof techniques to show that updates are correct before deploying them. The project's impacts are new verification tools that can be used to prove an update is compatible, and ultimately an understanding of update correctness that leads to more reliable systems. Verifying updates is especially important with increased use of AI-based coding agents, which will produce more reliable changes if they have feedback on whether an update is compatible with the already deployed code or not. Update correctness is also important for updates to machine learning (ML) infrastructure itself, which is also rapidly changing. We identify three fundamental update issues to focus on in this project: data-format compatibility, specifying the effect of data migration on a system's behavior, and verifying distributed-system rolling upgrades. The approach we take is to develop specifications for what a compatible update is in each of these cases: the specification is a desired property of the new code that considers any data that might be produced by the old code. Next, we develop a proof technique for proving these new specifications in Perennial, a program logic for the Go programming language that uses machine-checked proofs. Finally, we will apply the techniques to several examples of updates that are representative of real-world changes. The goal is to lay the formal foundations for an important aspect of software correctness. 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 · 2026-09

Horizontal gene transfer (HGT) is the transfer of genetic material between organisms that are not parent and offspring, and may even be from different species. Unlike traits passed from parent to offspring, HGT lets organisms gain new traits through contact or proximity with another individual. Many examples exist in nature, but the role of HGT in eukaryotes is still debated. In many cases, there is clear evidence that HGT has occurred, but few known mechanisms to explain how or why it happens. This project builds on a recent discovery of a clear HGT mechanism in fungi, a major group of eukaryotes, to answer key questions about how this process works. The work will improve understanding of fungi that affect human, plant, and environmental health. It may also support new advances in evolution and biotechnology, since gene transfer between eukaryotic species could help solve problems in plant breeding, medicine, and drug discovery. In addition, this project will help build the workforce by training early-career researchers, supporting team-based student learning, and expanding mentoring programs. Understanding how species exchange genetic information through HGT is critical for understanding the genetic bases of adaptation. Yet despite its importance for generating adaptive variation, HGT in eukaryotes has been near-impossible to study because the field lacks a system for hypothesis-driven experimentation. This project addresses this knowledge gap by capitalizing on the PI’s ongoing work, which reveals that HGT within fungal eukaryotes is mediated by giant transposons called Starships capable of transferring between species under lab conditions and in nature. The overarching goal of this project is to develop Starships into a model system to experimentally determine the ecological and evolutionary factors affecting fungal HGT. In doing so, the PI will address outstanding questions about the mode and tempo of eukaryotic adaptation with a long-term goal to leverage knowledge of fungal HGT to develop custom transformation-based biotechnologies for eukaryotic systems. This project addresses three independent and complementary Aims using a novel HGT assay: 1) to determine how rates of HGT change in response to diverse environmental cues; 2) to investigate within-species variation in HGT transmitter and receiver abilities and determine the genetic bases of these traits; 3) to test if closer relatives within a model genus transfer genes more often and whether more phylogenetically variable communities facilitate more distant transfers. A reproducible model for studying HGT will benefit systems beyond fungi because it will establish a paradigm for investigating transposon-mediated HGT in other eukaryotes, including plants and animals, and will improve our ability to predict how fast organisms adapt in nature. 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 · 2026-09

This project aims to significantly advance subsurface imaging by developing advanced, open-source, AI-powered ground-penetrating radar (GPR) technologies. GPR is a non-invasive sensing tool that uses electromagnetic waves to detect and visualize objects beneath the surface, making it invaluable for applications such as precision agriculture, defense, archaeology, civil engineering, and planetary exploration. Despite its broad utility, current GPR technologies face significant limitations, including the lack of standardized datasets and the inability of existing AI models to generalize across diverse systems and environments. This project addresses these challenges by creating innovative solutions that will enable real-time, high-resolution subsurface imaging. The outcome of this project is expected to transform how humans and autonomous systems perceive and interact with the subsurface world. The societal benefits of this work are profound: it will support sustainable water management, improve precision agriculture for global food security, enhance humanitarian demining efforts to save lives, and enable safer infrastructure monitoring. Additionally, the project includes a robust education and outreach component, engaging K-12, undergraduate, and graduate students in electromagnetics and AI through hands-on learning experiences. By fostering interdisciplinary collaboration and launching an open-source online platform, the project will create a global hub for sharing GPR datasets, algorithms, and educational resources, driving innovation and expanding access to cutting-edge subsurface sensing technologies. The research of this project will advance GPR technology through four integrated objectives. First, it will develop far-field and near-field GPR domain transfer frameworks to standardize data collected across different systems, addressing the critical bottleneck of data scarcity and incompatibility. Second, it will create a modular, physics-informed deep-learning AI framework to process GPR data for real-time, high-resolution subsurface permittivity mapping. This AI framework will include models for clutter removal, subsurface medium permittivity estimation, and 2D and 3D imaging, enabling rapid adaptation to diverse GPR systems and environments. Third, the project will validate these AI frameworks in real-world applications, including soil moisture mapping, buried explosive ordnance detection, and underground crop imaging, demonstrating their effectiveness and practical impact. Finally, the project will launch the OpenGPRxAI initiative, an open-source platform for sharing GPR datasets, pre-trained models, system designs, and educational resources. The research employs cutting-edge methods such as deep-learning AI, domain transfer techniques, and simulation-based dataset generation to overcome existing limitations in GPR technology. By addressing challenges in data standardization and model generalizability, the project will significantly enhance the speed, accuracy, and accessibility of subsurface imaging. These advancements will not only benefit diverse applications but also foster interdisciplinary collaboration and innovation, establishing a sustainable research and education community dedicated to intelligent subsurface imaging. 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 · 2026-09

High-quality genome data are being produced at a pace that is changing evolutionary biology. These data can clarify how species are related, but they also bring a hard problem: evolution is not uniform across the genome. This project develops new mathematics and algorithms for estimating species histories from whole-genome data while allowing a separate history for each region. The approach builds on a recently proposed, fast, and accurate method that uses simple scores computed from patterns in the data but is not yet well understood theoretically. By explaining why its scores work, finding better scores, and extending the method to additional settings, the project will make genome-scale evolutionary analysis more accurate, scalable, and robust. The resulting tools will be distributed as open software and taught through software schools. The project will also train students at the interface of mathematics, computer science, and biology. Its long-term benefits include stronger tools for biological discovery, including work relevant to biotechnology, invasive species, and disease outbreaks, and new ideas for artificial intelligence and machine learning methods for analyzing large heterogeneous datasets. This project will develop a mathematical framework for quartet-based linear scores for species tree estimation from whole-genome alignments. The starting point is CASTER, a site-based method whose empirical accuracy and scalability come from scoring site patterns over quartets and aggregating those scores without enumerating all quartets. The theory of these scores is currently incomplete. The project will characterize valid linear scoring schemes under hierarchical sequence-evolution and gene-tree-evolution models, including the multispecies coalescent, models with multi-copy genes, and substitution-rate heterogeneity. It will analyze the algebraic structure of the score space, connections to phylogenetic invariants, and extensions to site pairs and multi-site patterns. The project will also study the probabilistic properties of score gaps, including their signal-to-noise behavior, in order to design more accurate scores and to develop site-based estimators of branch length, branch support, local histories, and genomic outliers. The resulting algorithms will be implemented by extending CASTER and tested on simulations and large empirical datasets. The work combines applied probability, statistical theory, graph-based algorithms, and algebra, while connecting to machine learning through scalable statistical inference from large heterogeneous genomic data. 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 · 2026-09

Insects provide a vital role in pollinating crops, recycling nutrients, supporting food webs, and through their influence on human and animal health. Many beneficial insect groups are declining, while some harmful ones that damage crops, spread disease, or affect property are expanding into new areas. Extreme climate and weather events, such as heat waves, droughts, cold snaps, and heavy rainfall are becoming more frequent and severe. These extreme events can rapidly impact insect populations, but we still do not know which insects are most vulnerable, which may benefit, how those effects play out across regions and over time, and what the impact with be on the U.S. human population. This project will develop a new framework to understand how extreme climate and weather events affect adult insect abundances across the contiguous United States, with a focus on three groups: mosquitoes, butterflies and moths, and ground beetles. These groups are especially important from ecological and societal perspectives, and as targets for biotechnology development. By integrating multiple dimensions of extreme events with species traits, this framework moves beyond single-event studies, with the use of artificial intelligence (AI) tools, to obtain new insight and a more realistic and generalizable understanding of how environmental extremes shape biological systems. Improving the ability to anticipate these responses is critical to developing resilience strategies for conservation, food security, and One Health. The effort will combine more than 25 million insect records from monitoring networks, community science programs, and surveillance programs with 45 years of high-resolution climate data. It will also create a publicly accessible Extreme Climate and Weather Event Atlas for the contiguous U.S. that characterizes events by their frequency, intensity, duration, timing, and sequence. Using ecological theory, life history traits, advanced statistical modeling, and machine learning and AI approaches, the project will characterize and test how both single and compound extreme events influence insect abundances across latitude and climate context. The research will also examine whether prior conditions can amplify or reduce impacts, including cases such as false springs, ecological traps, and ecological bonanzas. By identifying likely insect winners and losers under increasing climate extremes, this project will improve ecological forecasting and provide tools that can be applied to other taxa and regions. The project also includes training for students and early-career scientists in data science, ecological modeling and AI, engagement with the public through webinars and events with monitoring networks and mosquito control programs, and development of freely available data and software tools for the broader research community and resource managers. 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

Modern applications of AI and machine learning in fields such as genomics, neuroscience, healthcare, and social sciences depend on the analysis of vast high-dimensional datasets that often include highly sensitive personal information. As AI systems rely more on data, achieving high predictive accuracy is no longer enough. Machine learning algorithms must also ensure privacy and remain computationally efficient at scale. This project investigates the fundamental trade-offs between accuracy, privacy, and computational efficiency, aiming to establish a mathematically sound foundation for trustworthy, scalable, and privacy-conscious AI systems. The project aims to develop new theories for machine learning algorithms that prioritize differential privacy and computational efficiency in high-dimensional settings. On the privacy front, it seeks to provide precise characterizations of privacy loss for commonly used techniques, such as differentially private principal component analysis. This is intended to enhance existing analyses that tend to be overly conservative, often introducing excessive noise that adversely impacts model utility. On the computational side, the project examines the limitations of efficient algorithms for low-rank matrix estimation and denoising. This includes investigating iterative and low-degree polynomial methods under realistic models of data dependency. The overarching goal is to identify algorithms that optimally balance statistical accuracy, privacy guarantees, and computational scalability. 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

Data-driven applications increasingly shape how people interact with digital services, physical environments, and intelligent systems. Many of these applications rely on artificial intelligence (AI) to support real-time decision making, interactive experiences, and continuous sensing. These applications must operate alongside traditional Internet communication, including web access, cloud services, and background data transfer. To meet growing performance and responsiveness demands, both computation and networking are moving closer to users through edge networks. As a result, new AI-driven data flows coexist and compete with established network traffic on shared devices with limited resources. Current network management approaches struggle with this combination of heterogeneous workloads and multitasking devices. They also fail to account for changing user context and rely largely on reactive monitoring rather than anticipation. This project addresses these challenges by developing methods that enable edge networks to adapt proactively. The goal is to improve performance, efficiency, and reliability for AI-enabled services that support education, healthcare, transportation, immersive media, and other societal needs. Meanwhile, the project supports the development of Ph.D. and undergraduate students. Research outcomes are also integrated into academic courses and outreach activities. The technical aim of the project is to develop and optimize an AI-native approach to edge network management. The research advances an intent-centric control framework in which the network learns context-aware representations of application behavior. These behaviors include inference tasks, collaborative model updates, sensing pipelines, and interactive workloads. Leveraging predictive and multi-timescale learning techniques, this project derives new methods to anticipate how application performance depends on time-varying interactions between communication and computation resources at the edge. This predictive capability enables adaptive orchestration across devices, edge servers, and cloud infrastructure. Building on general abstractions for coordinating heterogeneous AI workloads through learning-driven control and resource optimization, the research further develops methods that support hybrid task execution and collaborative learning while ensuring coexistence with traditional Internet traffic. All proposed methods are evaluated under realistic user activity conditions using representative workloads, including immersive media, digital twins, embodied systems, hybrid generative processing, and collaborative learning. Collectively, the results from this project establish a foundation for predictive, learning-based edge 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 2026 · 2026-07

Artificial intelligence (AI) systems, including modern machine learning models and large language models, are now routinely used to generate predicted labels and synthetic data across science, medicine, and policy. Researchers increasingly rely on AI-generated datasets to replace expensive or scarce human-labeled data, but the quality of such outputs is often unknown and can vary widely across AI systems. Treating AI predictions as ground truth can lead to incorrect scientific conclusions, misleading policy recommendations, and overconfident uncertainty quantification—risks that grow as AI is deployed in higher-stakes settings, such as clinical research and public health. This project develops the statistical foundations needed to use AI-generated data in scientific analysis. By turning AI-generated data from a potential liability into a rigorously calibrated scientific resource, the project advances national priorities in science, health, and the responsible use of AI. The project also supports U.S. workforce development through training of graduate and undergraduate students, integration of research outcomes into university curricula, and the public release of open-source software that makes the methodology broadly accessible to scientists, agencies, and industry. This project develops a unified statistical theory for the safe and adaptive integration of multiple, heterogeneous, and potentially low-quality AI-generated synthetic datasets into estimation, prediction, and causal inference. The framework treats synthetic data as auxiliary information whose contribution must be calibrated to ensure no loss relative to labeled-data baselines, while automatically exploiting informative sources when available. The research is organized around three connected aims. Aim 1 advances semiparametric estimation and inference by constructing unbiased, efficiency-improving procedures that optimally combine multiple synthetic datasets. Aim 2 targets prediction accuracy rather than estimation efficiency, developing non-asymptotic excess-risk guarantees that ensure prediction performance is never worse than labeled-only baselines and can be substantially better when informative synthetic labels exist. Aim 3 introduces a new framework for causal inference in multi-site randomized controlled trials augmented with synthetic data, addressing site heterogeneity and privacy constraints by integrating causal estimands, semiparametric efficiency bounds, and privacy-preserving computation. Together, the three aims provide rigorous statistical foundations for AI-augmented evidence generation, and their methodological contributions will be disseminated through peer-reviewed publications, open-source software, conference presentations, and graduate-level course materials. 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 CAREER project will help unravel the contributions of tissues that stabilize joints. Injuries and diseases, such as osteoarthritis, can lead to joint instability that limits function and accelerates degeneration. The root cause of joint instability is not well understood because the contributions of different stabilizing tissues are intertwined. This project will advance an algorithm to understand how different tissues stabilize joints during common movements. Robotic technologies will be developed to evaluate joint and tissue properties. Educational activities will include undergraduate student design of next generation exoskeletons for use by healthcare providers and student training. These activities will enhance student’s preparation for product design in industry and translation. This integrated effort combining biomechanics, robotics, and workforce training will pave the way for future treatments to mitigate the impacts of knee instability. This CAREER project will focus on exploring the key contributions of ligaments and muscles to knee stability. Ligaments are stiff tissues that passively guide and restrain joint motion. Muscles are active tissues that generate forces to drive joint motion. The research team will develop an algorithm to identify differences in the contributions of stabilizing tissues between patients with and without knee osteoarthritis. The algorithm will be built on direct muscle tension measurements and dynamics bone motion measured with an active knee exoskeleton. The algorithm will be validated in human cadaver knees using a state-of-the-art robotic testing system. Findings from these research projects will guide student teams as they design the next generation active knee exoskeletons for translation into healthcare settings. Together, the novel algorithm in conjunction with the next generation active knee exoskeleton will enable the research and clinical communities to identify the root causes of instability and open new horizons in personalized treatment planning for conditions like osteoarthritis. 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

Maintaining safe drinking water requires detecting trace-level amounts of harmful contaminants quickly. However, many water systems use laboratory tests that are slow and expensive. This CAREER project will create a faster, low-cost way to check drinking water for harmful chemicals by combining a chemical sensor with artificial intelligence (AI). The project will use a method that measures how light is scattered from chemicals attached to a surface. Each chemical generates its own pattern, like a fingerprint, that can be read. Drinking water may contain a collection of chemicals. The project will use AI to help read and understand the test results. The project will study how real-world conditions such as acidity and salts in the water affect how well the method works. The detection system will quickly flag possible contamination, so water utilities can do follow-up testing. The project will also use computer models to better understand how chemicals interact with the sensor, which will help improve the sensor design. Project outcomes will help protect public health and lower the cost of water testing. The project will also provide research opportunities to college students and high school teachers, create teaching materials about water sensors and AI, and offer hands-on outreach activities about drinking water quality problems. This CAREER project will develop a deep learning-enabled surface-enhanced Raman spectroscopy (SERS) platform for rapid, low-cost, and quantitative detection of organic contaminants regulated under the U.S. Environmental Protection Agency National Primary Drinking Water Regulations. The project will integrate plasmon-enabled spectroscopy, aquatic chemistry, and AI to improve sensitivity, selectivity, and matrix robustness in drinking water analysis. Objective 1 will establish predictive relationships among analyte physicochemical properties, adsorption thermodynamics, aquatic chemistry variables, and SERS sensitivity by combining adsorption isotherm measurements, controlled matrix experiments, and density functional theory modeling of analyte–sensor interactions. Objective 2 will develop multivariate statistical and computational methods for contaminant identification and quantification by leveraging orientation-dependent vibrational signatures as reproducible analytical features across plasmonic substrates. Objective 3 will build matrix-transferable deep learning models for spectral deconvolution, contaminant quantification, and interference mitigation in complex drinking water matrices, including mixed-contaminant conditions and variable water chemistries, with the goal of achieving sub-part-per-billion detection for most regulated organic contaminants and establishing a pathway toward part-per-trillion performance for priority analytes. The project will contribute new mechanistic insight into SERS signal generation in environmental systems, generalizable AI-enabled methods for quantitative spectral interpretation, and a scalable framework for intelligent water quality sensing. It will provide training for undergraduate and graduate students and high school teachers, curriculum modules in sensing and data analytics, and outreach activities that connect water quality challenges with hands-on learning in spectroscopy, AI, and molecular modeling. 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 addresses a fundamental challenge in understanding how genetics influences human health and behavior: distinguishing true biological genetic effects from influences that arise through family environment. Human genetic studies often ignore environmental factors, yet growing evidence suggests that parental characteristics can shape the family environment and confound genetic associations. As a result, common analytic approaches may overstate genetic contributions or misidentify biological mechanisms. This project develops new statistical tools to separate direct genetic effects from environmentally mediated influences, enabling more accurate interpretation of genetic association findings. The results will improve the reliability of genetic risk prediction through biotechnology, inform precision medicine, and support evidence-based public health and policy decisions. The project will also produce open-source software and provide interdisciplinary training opportunities at the interface of statistics, biostatistics, genetics, and data science. The research develops a unified statistical framework grounded in causal inference and high-dimensional data analysis. First, it introduces a new definition of heritability based on counterfactual comparisons between individuals with identical environments, allowing direct genetic contributions to be isolated and bounded under realistic assumptions. Second, it develops methods to estimate direct genetic effects by combining large population-based studies with smaller family-based studies using summary-level data. This approach leverages the strengths of both study designs to produce unbiased and statistically efficient estimates without requiring extensive family data. The methods will be supported by theoretical guarantees, scalable algorithms, and applications to large genomic datasets. By providing principled tools for disentangling genetic and environmental effects, the project advances both statistical methodology and the scientific understanding of complex traits. 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

In our universe, galaxies typically have neighboring galaxies and close companions. A galaxy’s local environment plays a key role in shaping both its history and its future evolution. However, measuring a galaxy’s environment is often challenging and requires advanced astronomical observations. This project is part of a new phase of research based on large-scale observational efforts, such as the NSF/DOE Rubin Legacy Survey of Space and Time (LSST). By combining large amounts of data from different telescopes, this project will identify key relationships between galaxies and their environments. For example, whether galaxies are located in dense clusters, filaments, or empty regions of space. Given the large volume of data involved, this project will also develop a new set of machine learning tools to analyze these datasets. In particular, it will develop a deep learning model using a three-dimensional (3D) convolutional neural network (CNN), a method commonly used in biomedical imaging, to analyze large datasets obtained from the Rubin/LSST survey and other telescopes. In addition, this project will provide foundational research training, with activities open to all undergraduate astronomy majors at the University of Wisconsin - Madison. These training opportunities will include, among other offerings, an introduction to scientific programming with Python and data visualization techniques. By providing both research experience and foundational skill development, the project will help prepare students for future careers both within and beyond academia. This program will integrate data obtained with the 4MOST spectrograph (4HS Survey), Rubin/LSST, and Euclid. In particular, the 4MOST/4HS Survey will observe a nearly complete sample of 5.8 million nearby galaxies in the southern hemisphere. By combining a sophisticated machine learning model, trained on state-of-the-art cosmological hydrodynamical simulations and verified using weak gravitational lensing, with novel 4HS measurements of stellar mass, metallicity, and star formation rate, this program will determine fundamental galaxy scaling relations as a function of local environment (nodes, filaments, sheets, and voids) with higher precision than ever before. This research award is partially funded by a generous gift from Charles Simonyi to the NSF Astronomy division. The project includes significant contributions to Vera C. Rubin Observatory’s Legacy Survey of Space and Time. 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

Counting rational points (fractions) near geometric objects has wide ranging applications across mathematics and its interdisciplinary domains. In geometry, it provides insights into the arithmetic properties of surfaces. In number theory, it contributes to understanding the distribution of rational solutions to Diophantine inequalities. The study of rational points near smooth surfaces has seen rapid development in the recent years. To make progress on some of the long-standing questions in this area, a deep understanding of the microlocal Fourier analytic behavior of these manifolds is needed. This specific type of interplay between harmonic analysis and number theory is very recent and not widely understood. The Principal Investigator (PI)'s long term research objective is to apply techniques from harmonic analysis and homogeneous dynamics (geometry of numbers) to make substantial progress on a variety of counting problems. The project provides research training opportunities for graduate students. Consider a smooth bounded manifold; for example, a compact piece of a sphere with non-vanishing Gaussian curvature; or a space curve like the helix which is not contained in any plane. The PI is interested in counting the number of rational points, with denominators of bounded size, in close proximity to such manifolds. This project aims to advance the existing knowledge in several directions: 1. Establish an asymptotic for the number of such rational points in a sharp range of distance from a convex hypersurface or a non-degenerate curve by using a precise understanding of the Fourier transform of their surface measure. Very little is known at the moment; 2. Use a combination of Fourier analytic techniques and methods from the geometry of numbers to count rational points near general smooth manifolds of arbitrary dimension under mild geometric conditions; 3. Establish how far the above methods can go in terms of the range of proximity to the manifold before local algebraic considerations become dominant; and 4. Use the above estimates to answer questions on Multiplicative Diophantine Approximation on analytic manifolds, which are currently wide open. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY The overall objective of this study is to understand and exploit therapeutically the role of neuropeptide receptor PAC1 in melanoma-infiltrating pericytes during melanoma progression. Melanoma is the most lethal form of skin cancer due to its tendency to rapidly metastasize, and current therapies often do not achieve durable responses. Novel molecular targets are urgently needed to develop more effective treatments. Obtaining a better understanding of the interplay between various cells and systems within the tumor microenvironment will identify potential new targets for therapeutic intervention. Recent reports from my laboratory and others show that cancer and the nervous system bear a close, entangled relationship. Melanoma-infiltrating neurons are an integral component of the tumor microenvironment, and manipulation of these innervations affect cancer progression, but the mechanisms underlying these findings remain poorly understood. Neurons signal to other cell types via neuropeptides binding to neuropeptide receptors. We identified ADCYAP1 and ADCYAP1R1, which encode the pituitary adenylate cyclase-activating polypeptide (PACAP) and its receptor PAC1, as the genes most significantly associated with increased risk of death in patients with melanoma. We found that PACAP+ neurons infiltrate within the melanoma microenvironment and increase in density with disease progression in vivo. Our preliminary data showed that chemogenetic silencing of PACAP+ neurons decreased tumor growth and pulmonary melanoma metastases, while intra-tumoral PACAP injection accelerated melanoma advancement. Single-cell RNA-seq data combined with immunohistochemistry and flow cytometry indicate that PAC1 expression is specific to pericytes, suggesting a potential role in pericyte function during melanoma progression. Our pilot data show that targeting PAC1 in pericytes, either through genetic deletion or a selective blockade, slows melanoma development and pulmonary metastatic outgrowth. This highlights the importance of understanding the mechanisms by which PACAP-PAC1 signaling in melanoma-infiltrating pericytes impacts melanoma growth and metastasis, in hopes of laying the foundation for the development of new therapeutic strategies. We hypothesize that PAC1 expression in tumor-infiltrating pericytes accelerates melanoma progression by inducing a pro-tumorigenic microenvironment. We will test this hypothesis using two Specific Aims: 1) Identify the role of PAC1 signaling in melanoma-infiltrating pericytes that drives melanoma progression in vivo and 2) Exploit therapeutically the neuropeptide receptor PAC1 in melanoma and determine PAC1 protein expression clinical significance. To our knowledge, the proposed work is the first to investigate the hypothesis that cancer-infiltrating pericytes are regulated by the nervous system. Overall, successful completion of our planned pre-clinical studies will reveal the mechanistic significance of the expression of the neuropeptide receptor PAC1 in melanoma. This can ultimately provide a rationale for clinically testing drugs that will modulate PAC1 signaling to obtain superior and lasting anti-melanoma responses.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Dysphagia (difficulty swallowing) commonly occurs after stroke and is an independent predictor of poor outcomes, need for long term care, and high healthcare costs. The recovery of oral-motor function after stroke is critical for both health and quality of life, yet the majority of post-stroke swallowing problems persist beyond 6 months and clinical care is often limited to compensatory strategies versus active treatment of underlying mechanisms. Neural and muscular plasticity allow for substantial recovery of motor function after stroke and optimization of plasticity is a primary target of most rehabilitation strategies. However, most of what we know about neuromuscular plasticity after stroke is specific to the corticospinal tract – plasticity of the corticobulbar tract and cranial muscles that support swallowing function are understudied. A better understanding of the unique mechanisms of corticobulbar neuromuscular plasticity after stroke is needed as this knowledge gap is a limiting factor to improving active treatments for dysphagia. Further, stroke most commonly occurs in aged individuals and thus understanding interactions between age and swallowing recovery is critical. Our long- term goal is to improve swallowing rehabilitation by quantifying the multilevel plastic changes that occur after unihemispheric stroke, and how these processes are impacted by age and therapeutic interventions. Many existing clinical treatments target the tongue as lingual weakness is commonly associated with post stroke dysphagia. Tongue exercise has the potential to improve lingual strength and swallowing outcomes after stroke, yet there is no consensus on efficacy or optimal methods. This work proposes examination of swallowing behaviors as well as putative muscular, brainstem, and cortical mechanisms of exercise-based plasticity using a translational rat model of post-stroke dysphagia. Based on our previous findings that demonstrate post-exercise changes in lingual and cortical measures, our hypothesis is that tongue exercise will significantly improve lingual muscle activation and function through both neural and muscular plasticity across the age range, but older age will significantly limit these improvements. Aim 1 will quantify the impact of tongue exercise on lingual activation, muscle strength, and swallowing function after stroke. Aim 2 will establish mechanisms of brainstem and cortical plasticity associated with lingual rehabilitation exercises. Together, these aims will determine the impact of age on neural and muscular plasticity induced by tongue exercise after stroke. This innovative research will generate foundational knowledge on the neuromuscular effects of post-stroke tongue exercise that will inform future studies (clinical and pre-clinical) seeking to improve the rehabilitation of post-stroke dysphagia by identifying specific biological targets and leading to more optimized, efficient, and targeted treatment approaches.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Muscle stem cells (MuSCs) provide myofibers with a robust tool for regeneration after injury. The efficiency of this muscle regenerating capacity depends upon the ability of MuSCs to integrate signals emanating from complementary cell types within the damaged muscle. Unfortunately, integration of these signals is often disrupted in disease, which results in a functional depletion of the MuSC population. MuSCs are still present, but they are not able to respond to regenerative cues from the environment. Although substantial evidence supports a role for epigenetic enzymes in orchestrating the transcriptional programs necessary for MuSCs to respond to niche-derived cues, how these enzymes enable MuSCs to actively shape the regenerative environment— particularly under inflammatory conditions—remains poorly understood. Our preliminary studies indicate that the H3K27 demethylase JMJD3 is rapidly induced in MuSCs after injury and regulates key genes that facilitate communication with immune cells, allowing MuSCs to exit quiescence and support tissue repair. Loss of JMJD3 in MuSCs leads to aberrant cytokine expression and excessive accumulation of inflammatory macrophages, suggesting that JMJD3 enables MuSCs not only to respond to signals but also to broadcast critical cues that help calibrate the immune response. Despite this emerging evidence, the underlying mechanisms through which JMJD3 governs MuSC–immune cell communication remain unknown. The overall objective of this project is to define the JMJD3-dependent transcriptional and epigenetic programs that allow MuSCs to modulate the inflammatory niche and initiate regeneration. We hypothesize that JMJD3 integrates signals from the regenerative environment by removing repressive H3K27me3 marks at immunomodulatory genes, permitting their expression to shape immune cell behavior and ensure efficient repair. We will address this through two aims: Aim 1: Use TEA-seq, a trimodal single-cell approach, to uncover the signaling pathways by which MuSCs regulate the magnitude and duration of the immune response to muscle injury. Aim 2: Determine how JMJD3-mediated H3K27 demethylation integrates niche-derived signals to control inflammatory resolution, testing whether modulation of H3K27me3 levels governs expression of MuSC immunomodulatory genes. Successful completion of these studies will elucidate how JMJD3 enables MuSCs to coordinate the inflammatory landscape necessary for regeneration, fundamentally advancing our understanding of how stem cells navigate and shape complex tissue environments. By revealing the extent to which epigenetic control of MuSC–immune communication influences regeneration, this work will provide a critical foundation for future efforts to fine-tune inflammation in muscle-wasting diseases.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Since the identification of the fungal pathogen clinically known as Candida auris in 2009, the incidence of infections caused by C. auris has increased around the world. (This pathogen has recently been reclassified by phylogenomic analysis as Candidozyma auris. Throughout this proposal, I refer to this pathogen as C. auris or Candida auris, the name used clinically.) In fact, between 2019 and 2022, the incidence of clinical C. auris infections reported in the United States increased five-fold. C. auris spreads rapidly within healthcare facilities, particularly critical care units. In addition, C. auris is often drug resistant. Nonetheless, with targeted antifungal therapy, invasive C. auris infection has a crude mortality rate of about 35%. Neutrophils are a crucial defense against many invasive fungal infections, including candidiasis. However, primary human neutrophils exhibit minimal activity against Candida auris in comparison to Candida albicans. There is a critical need to understand immune evasion and, particularly, neutrophil evasion mechanisms for this fungal pathogen with the goal of developing new treatment modalities for invasive infection. This project is focused on identifying neutrophil evasion properties of the C. auris mannans, the polysaccharide chains making up the outermost component of the cell wall. C. auris mannans have been shown to mask β- glucan and chitin, known immunostimulatory motifs in the fungal cell wall. Our lab is interested in defining evasion mechanisms of mannans beyond this masking. We have identified C. auris mannan mutants that are more likely to be engulfed by human neutrophils but do not have increased β-glucan or chitin exposure on the fungal cell surface. I will use these mannosylation mutants of C. auris to define how specific genes impact neutrophil receptor recognition of this pathogen. I will also generate fungal-like mannan-coated particles by binding isolated mannans to functionalized beads and examine mannan-receptor interactions independent of other cell wall components. In addition, I will develop a zebrafish model of invasive C. auris infection following skin disruption. This model will mimic the route of infection seen in hospital settings and allow for in vivo imaging of fungal-neutrophil interactions. The rich academic environment at the University of Wisconsin-Madison offers the medical, scientific, and graduate training resources necessary to actualize the proposed work. The proposal combines the fungal and neutrophil biology expertise of the sponsor and co-sponsor as well as their commitment to training the next generation of physician scientists. Overall, this predoctoral fellowship provides training to achieve the research, clinical, mentorship, leadership, and communication skills necessary to develop as an independent physician scientist investigator.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT Coronaviruses are important human and animal pathogens. In addition to common colds, coronaviruses have been responsible for several major outbreaks, including the SARS-CoV epidemic in 2002, the MERS-CoV epidemic in 2012, and the SARS-CoV-2 pandemic in 2020. Although accelerated research on coronavirus pathogenicity and biology during the 2020 pandemic has led to the design of several successful vaccines and antivirals, coronaviruses are constantly evolving. Continued research is therefore imperative for the identification of additional therapeutic targets. Coronaviruses contain positive-sense, single-stranded RNA genomes. Once a viral particle has infected a cell, the genome must be effectively copied and packaged prior to viral egress. Key molecular processes of the viral life cycle, including genome replication and genome encapsidation during viral egress, necessarily rely upon interactions between genomic RNA and viral proteins. However, the specific RNA- protein interactions driving these processes are poorly understood on a biochemical and structural level. Using a combined approach of virology, biochemistry, and structural biology, the current proposal seeks to understand the relevance of viral RNA-protein biomolecular interactions in the coronavirus cellular infection cycle. Replication of the coronavirus RNA genome is mediated by the viral polymerase and its essential cofactors. Previous work in the field has suggested that there are RNA structures within the coronavirus genomic termini that are critical for genome replication. However, the precise genomic RNA structures that are required for effective initiation of coronavirus genome replication remain unknown. Specific Aim 1 of this proposal seeks to elucidate which genomic RNA structures interact with the polymerase complex to initiate genome replication. Another common antiviral target is viral egress. For coronaviruses, viral egress occurs following encapsidation of the genome with a viral structural protein termed Nucleocapsid (N). In addition to it necessary structural function in viral egress, N has been proposed to play manifold roles throughout the viral life cycle. A major limitation of these studies is that the structure of full-length coronavirus N has yet to be solved. Additionally, how N is organized on genomic RNA within a virion remains unknown. Specific Aim 2 of this proposal seeks to examine the structure of N-bound genomic RNA in the context of the packaged genome. The research described in this proposal will take place under the mentorship of Dr. Robert Kirchdoerfer (fellowship sponsor), who studies coronavirus protein biochemistry and structure, and Dr. Samuel Butcher (co- sponsor), who studies the RNA-mediated regulation of gene expression. Supported by facilities including the Cryo-Electron Microscopy Research Center and the Biophysics Instrument Facility, the current proposal aims to explore critical coronavirus RNA-protein interactions on a structural and biochemical level. This work is anticipated to illuminate key viral biomolecular interactions that could serve as targets for antivirals.

NSF Awards · FY 2026 · 2026-06

Surrounding Earth is the exosphere, a vast cloud of hydrogen atoms that forms the outermost edge of our atmosphere and extends tens of thousands of kilometers into space. This region plays important yet poorly understood roles in atmosphere near-space interactions, including how Earth recovers from geomagnetic storms — solar-driven disturbances that can disrupt satellite communications, GPS navigation, and power grids. The project serves the national interest by improving space weather prediction and resilience, helping to protect satellites, critical infrastructure and astronauts. It advances fundamental understanding of how Earth’s atmosphere interacts with the space environment, with broader implications for atmospheric escape and planetary habitability. The work aligns with national priorities for distributed ground-based observing systems and supports student training through a multi-institutional collaboration with openly accessible data products. This project leverages a rare, time-sensitive opportunity created by recent start of science operations for NASA’s Carruthers Geocorona Observatory, coinciding with the decline from the peak of solar cycle 25. During the period of elevated solar activity, overlapping space- and ground-based observations of Earth’s extended hydrogen atmosphere are planned. Because spacecraft cannot fully observe the nightside of Earth, a distributed network of ground-based observatories across North and South America would be used to fill these gaps. Together, these measurements will produce a coordinated, three-dimensional view of the exosphere not achievable by existing observations alone and reveal its storm-time response. This project investigates the structure and dynamics of Earth’s hydrogen exosphere, where charge exchange with magnetospheric hydrogen and oxygen ions plays a central role in geomagnetic storm recovery. The project enables new constraints on exospheric hydrogen density by combining Lyman-alpha observations from the Carruthers Geocorona Observatory with near coincident ground-based measurements of Balmer-alpha and Balmer-Beta emission obtained using Fabry–Perot interferometers and narrow-band photometers. This effort represents a unique opportunity to obtain complementary measurements that are not achievable by the space-based mission alone. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract 100 million American adults experience prediabetes, or Metabolic Syndrome (MetSyn), a form of insulin resistance (IR) which greatly increases the risk of cardiovascular disease, stroke, and neurologic diseases, and costs the US nearly $500 Billion every year. The number of women with MetSyn is growing, and women with IR/MetSyn fare worse on many clinical fronts including cancer, heart attacks, and cognition. Interestingly MetSyn is linked to reduced Cerebral Blood Flow (CBF), although many factors remain unknown in this relationship, as most studies focus on older adults with multiple comorbidities, worsening MetSyn or even Diabetes, and none have explicitly studied sex differences on cerebrovascular function. Alarmingly, lower CBF is linked to larger declines in cognition in older adults-many of whom exhibit several IR characteristics suggesting MetSyn/IR may play a key role in Vascular contributions in cognition and dementia (VCID) in old age. Under this premise, identifying and treating CBF changes in earlier stages of MetSyn may hold promise to prevent or delay cognitive diseases and cerebrovascular diseases including strokes. Given this background, the primary goal of the current project is determine subclinical changes in cerebrovascular function in the absence of any over brain disease(s), and specifically identify sex differences in CBF in MetSyn. Additionally, we aim to test a potential vascular mechanism for altered CBF regulation. Cyclooxygenase (COX) is a potentially potent vascular signal whose expression changes in age/disease and can alter vascular function Our central hypothesis is that MetSyn decreases CBF more in females than males due in part to a greater loss of COX vasodilation in females compared to males. To test this concept, equal groups of MetSyn adults and Healthy Controls will undertake cutting edge MRI imaging using double-blind placebo-controlled COX inhibition (mechanistic aim). Structural MR imaging (T1, T2) will test for stability of brain structures in MetSyn, whereas exploratory imaging will assess brain connectivity changes. Finally, NIH Toolbox will test whether neurocognition remains intact. This integrative approach brings a level of rigor that few clinical trials achieve. We present substantial preliminary findings supporting our hypotheses. Using cutting edge MRI will yield previously unattainable insight into how MetSyn reduces basal CBF in a regionally-specific, sex-specific pattern, along with mechanistic testing. Novel data will serve as a knowledge platform for designing sex- specific treatments to optimize long-term brain health in other relevant high risk disease populations which exhibit strong sex-specific etiology (e.g. diabetes, hypertension, Alzheimer’s).

NSF Awards · FY 2026 · 2026-06

This CAREER project focuses on making food supply systems, especially those for fresh foods, more reliable. Disruptions in food supply systems lead to food waste, struggling businesses, and higher prices for consumers. This project will create math-based tools to help farmers, processors, distributors, and stores make better decisions, even during uncertain situations. These tools will consider how quickly food spoils, how people in the supply chain behave, and how the system can adapt to problems. The Wisconsin dairy industry will be used as an example because it is important to the state and involves perishable products. Overall, the project aims to improve scientific understanding, strengthen the economy, reduce food waste, and support education by developing courses, training teachers, and creating an interactive board game to help students learn about food systems. This project develops an integrated computational framework for modeling, optimizing, and analyzing resilience in food supply chains, with a particular focus on perishable products. It addresses a fundamental gap in supply chain science: the absence of unified models that simultaneously capture perishability dynamics, uncertainty, and decentralized stakeholder behavior. The first research thrust introduces a graph-based recourse-task-network representation that embeds spoilage dynamics and process constraints across all stages of the supply chain. Building on this foundation, the second thrust advances robust optimization under uncertainty through contextual uncertainty sets, enabling two-stage formulations and solution methods that connect long-term planning decisions with real-time operational adjustments. The third thrust develops multi-agent models and associated solution techniques to represent heterogeneous stakeholder objectives and interactions, supporting the analysis of cooperation, competition, and policy interventions. The Wisconsin dairy supply chain serves as a complex, data-rich testbed for model development and validation. The resulting framework is designed to improve supply chain design, enhance resilience to disruptions, reduce food waste, and provide interpretable decision support for small and mid-sized actors. By integrating mathematical optimization, simulation, and behavioral modeling, this work contributes generalizable methodologies applicable to other perishable goods systems, including pharmaceuticals and blood supply chains. Moreover, the project aligns with national priority areas such as artificial intelligence and advanced manufacturing by advancing data-driven decision-making, scalable optimization, and intelligent system design for complex, distributed production and logistics networks. 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 award provides support for U.S.-based junior researchers to attend the 2026 World Meeting of the International Society for Bayesian Analysis (ISBA), which will be held in Nagoya, Japan, from 28 June to 3 July 2026. ISBA World Meetings are the largest conferences in Bayesian statistics, whose key benefit, beyond showcasing the latest research advances and highlighting emerging directions, is the exposure of participants (especially junior researchers) to ideas and colleagues with whom they may not ordinarily interact. The 2026 World Meeting will bring together the world's leading Bayesian researchers and promising junior researchers, build ties between them, and foster new collaborative and mentoring relationships. By supporting travel awards that partially offset the cost of traveling to Nagoya from the U.S., this grant will improve junior researchers' chances of career success, lead to new collaborations, ensure the continued high quality of Bayesian research, and strengthen the U.S.’s leading position in Bayesian statistics. The Bayesian statistical paradigm is particularly well-suited to the task of deriving actionable insight from the vast amount of noisy, complex, and highly structure data collected in the modern world: it provides a coherent framework for (i) integrating information from different sources; (ii) communicating findings and conclusions using probabilities; (iii) easily incorporating relevant prior knowledge; and (iv) propagating all uncertainties into the final inference. But the increased complexity and scale of modern data and applications push the limits of existing methods, computational implementations, and theory, necessitating continued research in Bayesian statistics. The 2026 ISBA World Meeting will feature plenary talks, invited talks, contributed talks, poster sessions, and a short course selected to showcase cutting-edge research in Bayesian statistics. Attendance at the meeting will offer all attendees --- and especially junior researchers --- opportunities to learn about emerging challenges in Bayesian statistics, share their ongoing research with a truly international audience, and foster new collaborative relationships. The meeting website is: https://isba2026.github.io This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY The overall goal of this application is to develop next-generation, confounder-corrected magnetic resonance imaging (MRI)-based intra-voxel incoherent motion (IVIM) methods for simultaneous quantification of blood perfusion and tissue microstructure. IVIM is a diffusion MRI-based technique that probes various types of intra-voxel molecular motion without the need for ionizing radiation or exogenous contrast agents and with enormous potential in diffuse and focal disease. Despite this potential, current IVIM methods are limited due to instability and poor precision (repeatability and reproducibility). These limitations, which have so far precluded the widespread application of IVIM in the clinic or in clinical research, are caused by a set of technical challenges and confounding factors. To address these challenges, our group has recently developed multi-dimensional acquisitions that dramatically improve the stability and precision of IVIM. By expanding IVIM acquisitions into an optimized multi-dimensional sampling approach and relying on biophysical signal models for quantification, we have demonstrated IVIM with superior noise performance, test-retest repeatability, inter-reader reproducibility, and reproducibility across acquisition parameters, MR systems, field strengths, and liver lobes. This application will complete development of confounder-corrected IVIM and evaluate it in highly controlled ex vivo settings and in a clinically relevant patient population with chronic liver disease. Specifically, we will: develop and optimize confounder-corrected IVIM to enable precise quantification over the entire liver (Aim 1); validate the technical performance of confounder-corrected IVIM ex vivo using phantoms and perfused human organs (Aim 2); validate the technical and clinical performance of IVIM in vivo in patients with chronic liver disease (Aim 3), and evaluate the ability of IVIM to detect increased intrahepatic vascular resistance in patients with portal hypertension based on a meal challenge exam (Aim 4). Upon successful completion, this application will provide novel IVIM methods for stable and precise (repeatable and reproducible) mapping of perfusion and microstructure parameters. Further, we will have validated this method in a highly relevant application for the evaluation of chronic liver disease, a disease that affects 100 million Americans. Importantly, the methods developed in this work have broad application in other organs and diseases, by enabling quantitative mapping of blood perfusion and tissue microstructure.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT The use of conventional and novel therapeutics to treat children with advanced cancer leads to burdensome physical and psychological symptoms. Advanced cancer is metastatic upon initial diagnosis, relapsed, progressed, or refractory to treatment, resulting in an uncertain and prolonged disease course. Symptom burden and associated suffering are amplified for these children due to disease burden, intense therapy, and often lengthy treatment. Symptom patterns (trajectories) have been identified in children diagnosed with early-stage cancer, but research characterizing symptom patterns over time in the understudied patient population of children with advanced cancer is sparse. Associations with the biopsychosocial context, including demographic, social, and physiologic risk factors, and their contribution to the variability in symptom burden among children with advanced cancer is also limited, hindering clinicians’ abilities to anticipate and intervene. Consequences of high symptom burden include diminished quality of life, worse physical function, and increased psychological stress. Further, there is a risk that children with advanced cancer will normalize their symptom suffering. This perception of symptom suffering as unavoidable threatens the efficacy of future symptom management interventions. Thus, there is a critical need to understand the symptom experiences of children living with advanced cancer to shift the clinical symptom management paradigm to proactive, risk-based approaches that anticipate symptom burden and improve clinical outcomes. The aims of this application are to (1) define symptom trajectories over time among children aged 8-18 with advanced cancer, (2) identify biopsychosocial characteristics that distinguish patient subgroups, and (3) examine the association between patient subgroups and health outcomes. To achieve these aims, we will prospectively enroll 228 children aged 8 to18 living with advanced cancer across five pediatric cancer centers. We will collect reports of symptoms, quality of life, psychological stress, and physical mobility reports every two weeks for 4 months. We will also collect measures of the biopsychosocial context, including demographic, social, and physiologic (disease and treatment) characteristics. The expected outcome of this research is the discovery of distinct symptom trajectories, with children who belong to a high symptom burden trajectory experiencing poorer health outcomes than children in lower symptom burden trajectories. The proposed research will also identify biopsychosocial characteristics that distinguish children who experience different symptom trajectories. Together, these outcomes hold promise for the development of risk-stratified approaches to symptom management in children with advanced cancer, providing researchers and clinicians with biopsychosocial targets for monitoring and intervention. These novel approaches are required to advance symptom management research and improve health outcomes for children with advanced cancer.

NSF Awards · FY 2026 · 2026-05

Phytochemicals are chemical compounds produced by plants to provide defense against diseases, color for leaves, flowers, and fruits, and aroma or smell, to name a few. While plants use them to benefit themselves, such as attracting pollinators and avoiding harmful germs and insects that cause diseases and damage, the same phytochemicals are the source of nutrition and medicines that benefit human health. To advance phytochemical research, create new knowledge on plant natural products, how plants synthesize them, and their effects on organisms, the Phytochemical Society of North America (PSNA) organizes an annual meeting of researchers to learn about new developments, share ideas, and build collaborations. These activities and support for the conference are essential for developing biotechnology innovations to sustain food and energy security, human health, and provide an opportunity to train researchers and students. This NSF award will support the 65th annual meeting of PSNA, to be held at the University of Wisconsin-Madison on June 15-19, 2026. In particular, the award will support the attendance of pre-doctoral and early-career scientists working at U.S.-based institutions by providing an opportunity to network and foster interdisciplinary collaborations to learn about discoveries, innovations, and major challenges in phytochemical research. The 2026 meeting will showcase and discuss critical scientific priorities and new innovative approaches, including biotechnology and artificial intelligence (AI), to advance US leadership in these key areas. Beyond the scientific program, the meeting will host participation-based workshops focused on integrating AI into phytochemical research, discovery, and translation for commercial applications. A professional development workshop will target early-career scientists to explore various career paths in academic, non-academic, and private sectors, contributing to the development of the future U.S. workforce in plant chemistry and related biotech industries. The conference proposal aligns with the current NSF priorities in biotechnology, AI, and translational 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.