Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2026 · 2026-09

Urban public spaces are filling with a growing number of micromobility vehicles: examples include electric scooters and bikes, personal vehicles, and delivery robots. Understanding how these vehicles can operate safely and considerately as part of larger transportation systems requires the novel use of techniques from many research disciplines, such as human-computer interactions, robotics, remote sensing, and artificial intelligence (AI). This project creates cyberinfrastructure software, data sets, and AI models that are needed to support this emerging field of research. This project enables its research community to better understand and improve vehicle interactions in complicated, rapidly changing, real-world settings. Direct outcomes include practical solutions for mitigating micromobility-related conflicts and accidents in public spaces. The development and use of this proposed cyberinfrastructure will prepare high school and college students for the nation's future workforce. This project serves the human-centric micromobility research community via three innovative AI-based service engines. First, a sensing and perception engine garners machine, environment, and human aspects from the project team’s established testbeds to strengthen the community's Micromobility-to-Everything Interaction (MEI) data preparation. This addresses the research needs in forming holistic, comprehensive understandings of diverse interaction data and augmenting them for AI model training. Second, an MEI model engine provides the research community with the AI models and tools to expand their sensing modality studies, with self-explainable graph model support. The community benefits from the expanded capabilities in performing extensive AI model studies over multiple datasets and gains interpretable AI model insights. Third, a coalition engine assistant interacts with researchers to help them navigate the cross-domain, cross-discipline research and methods needed to understand MEIs. The project includes a variety of co-designing and workshop training activities that assess research needs and engage participants in hands-on learning and practicing micromobility AI tools. All three key elements will be integrated into a service-oriented pipeline that is used to train a wide range of researchers, practitioners, and cyberinfrastructure and AI workforce in human-centric micromobility research. 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

Methane is a reactive gas that can degrade air quality. Facilities such as wastewater treatment plants routinely generate methane, and its release can affect nearby communities. Current management practices often burn or flare methane. This project will study an electrochemical methane oxidation reaction (eMOR). The reaction converts methane into methanol, a liquid chemical. The research will develop advanced materials to study methane reactions. It will also identify reaction pathways and operating conditions that favor formation of useful products. The results will guide the development of future methane conversion technologies. These eMOR technologies could be integrated with industrial biotechnologies. They may also support advanced manufacturing with more efficient use of resources. The project will also link research and education through undergraduate and graduate curriculum development, student research training, and outreach to K–12 learners. These activities will help prepare the future STEM workforce. This project will investigate mechanisms and kinetics of eMOR using heterostructured electrocatalyst materials that target a new reactive oxygen species (ROS) mediated C–H bond activation pathway. Electroanalytical measurements, including cyclic voltammetry, chronoamperometry, and controlled electrolysis, will be combined with quantitative product analysis to determine methane conversion, liquid product selectivity, and competing oxidation reactions as functions of overpotential, catalyst composition, and electrolyte hydraulic retention time. Inspired by semi-conducting materials, experiments will first evaluate methane activation using conductive diamond electrodes to assess the role of electro-generated hydroxyl radicals. The project will then examine composite conductive diamond-metal oxide catalysts to determine how transition-metal oxide co-catalysts influence oxidation pathways and suppress methane overoxidation. Complementary quantum-based density functional theory modeling will evaluate interfacial interactions and reaction energetics on the electrocatalyst interfaces to elucidate the underlying reaction mechanisms. Additionally, integration of experimental observations with microkinetic analysis will establish fundamental relationships among ROS formation, catalyst structure, C–H bond activation, and product distribution. The resulting mechanistic understanding will advance fundamental knowledge of selective methane oxidation using ROS-mediated reactions and provide new catalyst design principles and operating boundaries for eMOR. As a result, this interdisciplinary project will provide new generalizable knowledge of methane oxidation and C-H bond activation that can benefit related environmental chemistry, while opening the door to future methane valorization technologies that reduce pollution, recover value from waste streams, and improve the well-being of individuals in society. 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

The western United States has been the site of some of the largest and most hazardous volcanic events in the world. Two of these events occurred within the last million years: supervolcano eruptions at both Yellowstone, WY and Long Valley, CA. The magma that erupted is called high-silica rhyolite. To understand how high-silica rhyolite forms and then erupts in such large amounts, scientists need to study the structure of the underground magmatic systems that feed eruptions. Some minerals change their chemical make-up under different temperatures and pressures. That makes it hard to use their chemical composition to calculate at what temperatures and pressures those magmas formed. Experiments done in this study will help scientists more accurately find the temperatures and depths of magma beneath supervolcanoes. This will help improve our understanding of how these powerful eruptions occur, while training numerous students. During this project, a series of experiments (primarily at 200 and 500 MPa; 650-850°C) will be conducted on four natural high-silica (SiO2) rhyolites of varying titanium oxide (TiO2) content. The experiments will be focused on the temperature and pressure dependence to the partitioning of titanium (Ti) between (1) biotite and high-SiO2 rhyolite liquid and (2) quartz and high-SiO2 rhyolite liquid. Preliminary results indicate that Ti partitioning between biotite and melt has the potential to be a high-resolution thermometer in high-SiO2 rhyolite melts. The goal is to establish internal consistency among all three thermometers (and/or barometers) and to enable their application to high-SiO2 rhyolites without extrapolation. The updated biotite and quartz thermobarometers will be applied to natural samples. If successful, the updated thermobarometers will greatly improve our understanding of the magmatic conditions that precede supervolcano eruptions of high-SiO2 rhyolite. 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

The Conference on Game Theory and AI for Security (GameSec) Conference 2026 aims to bring together researchers interested in establishing a theoretical foundation for making resource allocation decisions that balance available capabilities and perceived security risks in a principled manner. The conference invites novel, high-quality theoretical and empirical contributions that apply game theory, AI, and related methodologies to security, privacy, trust, and fairness in emerging systems. This project provides funding to support the travel expense of graduate students to attend the GameSec Conference. Through this conference, students will advance the understanding and application of AI-driven strategies for securing critical infrastructures and emerging technologies. The event will also engage students through the activities among academia, industry, and government to explore interdisciplinary connections between game theory, reinforcement learning, adversarial machine learning, mechanism design, risk assessment, behavioral modeling, and cybersecurity. This project will strengthen the cybersecurity and AI workforce development. 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

Network data, which captures relationships and interactions among entities, is central to many modern AI and machine learning applications in areas such as neuroscience, social science, economics, and biomedicine. Examples include brain connectivity networks, social interaction graphs, and recommendation systems. This project develops new machine learning and statistical methods for analyzing complex network data, with a focus on prediction, representation learning, and comparing populations of networks. The project emphasizes interpretable and reliable AI methods that quantify uncertainty, provide theoretical performance guarantees, and adapt to heterogeneity across nodes, individuals, and networks. The project will also contribute to training graduate students in AI, machine learning, and network data science. Technically, the project focuses on developing AI-driven and statistically rigorous methods for heterogeneous network analysis. For single-network settings, it will develop tools to quantify the predictive contributions of both node covariates and network structure within flexible black-box machine learning models, including modern AI approaches. The project will also design methods to detect and test for latent subgroups in network-linked data that differ from the broader population. For multiple-network analysis, the project will develop methodologies to test differences between collections of networks and to estimate shared, group-specific, and individual-level network structure across populations. Applications include comparing brain connectivity networks between patient and control groups, both globally and within localized subnetworks such as specific brain regions. Across these problems, the project combines network machine learning, uncertainty quantification, and scalable algorithm design with rigorous theoretical guarantees. 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

One of the deepest mysteries in biology is how life has become so astonishingly complex, which is hard to ignore when looking around at the remarkable biodiversity on earth. Scientists know that evolution is ultimately responsible, but they don't fully understand what fuels the apparent drive for complexity. One hypothesis is that when species face an antagonistic push-and-pull interaction with one another, like between populations of hosts and their parasites, evolution speeds up and produces more novelty than when organisms are simply adapting to a changing environment. This project investigates whether such biological conflict is truly special in its ability to drive evolutionary innovation. To do this, the research team at the University of Michigan combines custom-built devices that control evolution in bacteria in real time, self-replicating computer programs that evolve following the same laws as life, and targeted experiments with bacteriophages—viruses that infect bacteria. Together, these tools let the research team watch and control evolution at a speed and scale far greater than nature alone allows. This work has direct relevance to biotechnology, particularly in understanding and countering antimicrobial resistance, informing potential phage therapy strategies when antibiotics are no longer an option, and improving directed evolution approaches for engineering more adaptable biological systems. Beyond the lab, this project trains Detroit-area teachers through a summer research program so they can bring real evolutionary expertise or experiments directly to their students. An interactive museum exhibit at the University of Michigan Museum of Natural History and online tools will make these ideas explorable to anyone curious about where life's creativity comes from. This award integrates computational modeling, microbial experiments, and theoretical analysis to understand fundamental questions in evolution. Starting with observations from a rather unusual system of self-replicating computer programs, the work investigates how host-parasite coevolution creates changing fitness landscapes and feedback between populations that promote continued innovation. Projects examine whether biotic interactions like parasitism drive fundamentally different kinds of evolutionary dynamics compared to abiotic environmental pressures, and investigates how evolution can move populations toward (or away from) more "evolvable" regions of their fitness landscapes. Three research aims systematically disentangle the effects of antagonistic coevolution by 1) using custom bioreactors to carefully control for the strength and dynamics of selection, by 2) developing mathematical and computational theory for understanding evolvability on increasingly more complex fluctuating fitness landscapes, and by 3) experimentally testing predictions about the evolution of evolvability with bacteriophage evolution experiments. Educational and outreach components are strategically integrated within these research themes. Interactive web-based tools will translate complex evolutionary concepts into accessible simulations for the classroom, while a year-long museum exhibit will showcase how “Unnatural History”, like the self-replicating computer programs described here, can illuminate fundamental biological principles. A Research Experience for Teachers program will engage Detroit-area educators in authentic bacteriophage/computational evolution experiments every summer that advance the scientific aims of this proposal, while also helping them create classroom-ready materials that demystify the practice of genuine scientific inquiry. 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

Bacteria must sense and respond to their changing environments. In particular, the coordinated expression of virulence genes, proteins that give the bacterium the ability to cause disease, is essential for bacterial fitness. Listeria monocytogenes is a foodborne pathogen that causes severe disease and results in significant public health concerns, economic burden, and societal costs. RNAs play a central regulatory role in gene expression and can sense and respond to the changing host environment, including temperature and metabolite concentrations. Interestingly, two L. monocytogenes RNAs work together to regulate virulence gene expression through an uncharacterized and unconventional mechanism. This project will investigate the RNA-RNA interactions that impact L. monocytogenes virulence gene expression. This work will develop new structural approaches to study RNA structure and conformational rearrangements, and inform RNA molecular recognition and RNA structure-function relationships. Further insights into RNA structure, stability, and intermolecular interactions will aid in engineering of new RNA-based biosensors for biotechnology and generate data for AI-enabled structural models. This project will integrate research and education to foster scientific training and education of high school, undergraduate, and graduate students. Outreach activities are aimed at engaging high school students in full-time research opportunities and preparing them for STEM careers. Furthermore, graduate students will receive training in science communication and develop inquiry-based activities that showcase research to the general public. The overarching scientific objective of this project is to elucidate the structural and molecular mechanisms by which distinct noncoding RNAs in the bacterial pathogen L. monocytogenes interact with each other to regulate virulence gene expression. The research will uncover the non-canonical role of a S-adenosylmethionine sensing riboswitch, SreA, and offer molecular-level insights into an unprecedented mechanism whereby a riboswitch impacts the expression of a non-adjacent gene. Objective 1 will reveal the mechanism by which the SreA riboswitch represses translation of the PrfA RNA thermosensor, using a suite of biological, biochemical, and biophysical approaches. Objective 2 will elucidate the structural basis for translation inhibition of prfA by the SreA riboswitch. A multidisciplinary approach combining chemical probing, mutational profiling, solution NMR spectroscopy, and small angle X-ray and neutron scattering will be employed. In addition to novel information about a non-canonical mechanism of translation regulation, the project will also generate methods that can be used to study other biologically relevant RNA-RNA interactions. 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

Scientific findings should come with error rates that mean what they say: among findings assigned a 5 percent chance of error, about 5 in 100 should turn out to be wrong. This standard, called calibration, underlies trusted probability claims from weather forecasting to machine learning, but it is not yet a routine part of the statistical tools used in many large-scale scientific studies. The issue arises whenever researchers must triage long lists of possible discoveries, anomalies, or published claims. In metascience, the question is which findings in the literature will replicate; in AI safety, which suspicious model inputs deserve greater scrutiny. Current methods control the average error rate across an entire list of discoveries, but they rarely provide individual findings with calibrated error probabilities. This award supports research on calibrated hypothesis testing, which will develop methods that distinguish strong evidence from borderline evidence with interpretable, rigorous guarantees. The work will support more reproducible science and safer data-driven systems, while training graduate researchers, developing new instructional materials, and releasing open-source software. This project will develop theory and methodology for calibrated, large-scale inference. The framework draws upon probabilistic forecasting but addresses a distinct challenge: unlike forecasting, where labels are eventually observed, in multiple testing the ground truth is never revealed, so calibration must be assessed stochastically and established indirectly. The investigators will combine empirical Bayes estimation with frequentist finite-sample guarantees, extending local and boundary false discovery rates beyond settings with independent p-values. Variable selection will serve as the first setting, using knockoff and sign-symmetric statistics to construct local error assessments for selected variables. Conformal outlier detection will extend these ideas to discrete and dependent p-values produced by a shared calibration dataset. Online testing will build on both directions by treating sequential threshold choice as an online learning problem under distribution drift. Together, these three settings will demonstrate that calibrated local error rates constitute a fully functional statistical concept with broad applicability. 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

The 2026 ASA Statistical Methods in Imaging (SMI) Conference will take place June 1-3, 2026 at the University of Michigan in Ann Arbor. This annual symposium serves as a vital hub for bringing together researchers to discuss the rapidly evolving role of statistics, mathematics, and AI in imaging science. From medical scans that detect disease to telescopes that capture distant galaxies, images are fundamental to modern discovery. However, extracting reliable and meaningful information from this visual data requires sophisticated mathematical, advanced statistical tools, and novel AI services. This conference fosters the collaboration needed to develop these tools, ensuring that scientific breakthroughs, from earlier disease detection to more accurate climate models, are built on a solid analytical foundation. By hosting this event, the University of Michigan will catalyze interdisciplinary dialogue, support the training of the next generation of data scientists through student awards and travel scholarships, and make advanced research accessible to a wider scientific community. The SMI-2026 conference will highlight the intersection of scientific innovation and cultural enrichment, and demonstrate how mathematical sciences contribute to a broader understanding of the world. The technical program for the SMI 2026 conference is designed to showcase cutting-edge methodological developments and their applications in imaging science. Over three days, the symposium will feature three keynote addresses from leading international experts, twenty special invited sessions, two short courses, student competitions, and networking events. A rigorous peer-review process will be implemented to select the best theoretical and applied papers, with a dedicated competition and award for the best student paper. The conference aims to highlight novel statistical approaches for complex imaging data, including high-dimensional inference, machine learning integration, and the analysis of multi-modal images. By leveraging the unique resources at the University of Michigan, such as the Statistics Online Computational Resource (SOCR), Biostatistics and Bioinformatics Centers, and the AI Institutes at Michigan, the symposium will facilitate hands-on demonstrations and deep dives into computational tools. The organizers will coordinate with the leadership of the ASA Journal "Statistics and Data Science in Imaging" to publish a special proceedings issue, ensuring that the presented research has a lasting impact on the mathematical sciences community. Workshop website: https://www.statsinimaging.org/SMI-2026/ 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

Cytokines are proteins that regulate cell identity and function. Repair of wounds repair requires coordinated interactions among multiple cytokines. This CAREER project will address a fundamental question in wound repair: which inflammatory cytokines determine whether a wound heals or remains chronic. The project will develop a smart, biomaterial-based wound dressing that will monitor in real time various cytokine patterns in the wound environment. The data will enable machine learning predictions to distinguish healing versus nonhealing paths. The project will advance STEM education through (i) a game-based learning module for K–8 students in Southeast Michigan that teaches problem solving skills; (ii) a hands-on research model for K–12 students; and (iii) an art fair “show-and-tell” experience to reinforce engineering concepts for college-level students. The outreach initiatives will improve engagement with biotechnology, artificial intelligence, and other STEM fields. Chronic wounds are immunologically heterogeneous but share a common failure mode: the inability to transition from protective early inflammation to pro-repair resolution. Although there has been progress in defining inflammatory signals in wounds, understanding is limited because most assays are endpoint and often destructive. Thus, they provide only indirect or retrospective insight into the wound microenvironment. This critical challenge has limited understanding of when and how productive inflammation tips into self-sustaining, tissue-damaging immune activation. This CAREER project addresses this gap by integrating multiplex cytokine sensing into a wound dressing, enabling time-resolved immune readouts that can (i) signal whether a wound is progressing along a healing trajectory and (ii) determine when inflammation deviates toward chronicity. The hypothesis is that the engineered biomaterial will reveal distinct spatiotemporal cytokine patterns that differentiate wounds on a healing trajectory from those progressing toward chronicity. These data will clarify the transition point at which acute, protective inflammation becomes maladaptive and obstructs repair, offering predictive insight into wound fate. This project will (1) define longitudinal cytokine and cellular signatures of resolving versus non-resolving inflammation, (2) engineer and validate a multiplexed biomaterial with spatially organized sensor arrays and quantitative calibration against standard assays, and (3) employ machine-learning prediction of cytokine patterns associated with wound trajectory, and (4) deploy the patches in splinted excisional wounds in diabetic and control models to generate spatiotemporal cytokine trajectories that are mechanistically linked to immune-cell phenotype. Collectively, this research will establish a new biomaterial-enabled approach to measure and model wound immune dynamics in real time, with the long-term goal of informing when and how to intervene to resolve chronic immune activation during wound healing. 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

Infections challenge the human immune system to balance two competing responses. The immediate response is acute inflammation. This targets the damaged or infected area with immune cells. The second response is to reduce inflammation as antibodies are produced and transported to the infection site. This is accomplished by producing T cells that defend against foreign entities and regulatory T cells (Tregs) that mitigate runaway inflammation. Tregs can also be effective in treating diseases associated with chronic inflammation such as diabetes, Alzheimer's, asthma, and heart disease. They are, however, difficult to produce outside the body. This CAREER project will employ an engineered three-dimensional culture system to investigate how Tregs form inside the body. An integrated education and outreach program emphasizing scientific communication skills will also be developed and delivered. The complex balance between inflammation and self-tolerance is fundamental to immune system function. Administration of therapeutic regulatory T cells (Tregs) to restore this balance holds tremendous potential to treat inflammatory conditions. Manufacturing these cells is a challenge, because how they are naturally generated is poorly understood. Although T cells can be reprogrammed to Tregs in vivo, a mechanistic understanding of this process is lacking. Current induced Tregs generated in vitro lack the stability and epigenetic signatures of naturally occurring peripheral Tregs, limiting their therapeutic potential. Assessing T cell reprogramming into Treg using engineered microenvironments that replicate natural cellular communication will be employed to address this knowledge gap. The primary project goal will be to understand how conventional T cells are reprogrammed into Treg through choreographed cell-cell communication. The central hypothesis is that in vitro T cell reprogramming on plastic surfaces fails due to missing microenvironmental signals, including molecular cues, mechanical forces, and metabolic factors. The project will focus on three integrated aims. First is optimizing hydrogel-based synthetic tertiary lymphoid structures (synTLS) that mimic natural lymphoid microenvironments. Second is assessing how factors including T cell receptor engagement, tissue stiffness, and metabolic cues affect T cell reprogramming stability within synTLS. Third is evaluating the effects of Thetis cells, immune checkpoint signaling, and co-stimulation blockade on induced Treg stability and suppressive function. 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 project aims to improve artificial intelligence (AI) systems that predict how three-dimensional environments change over time. Current AI models that generate videos or simulate future events often require massive computing power and routinely violate basic laws of physics, such as allowing solid objects to pass through one another. These flaws make them unsafe for critical real-world applications like medical surgery, robotics, or scientific simulation of future events. This award supports the development of a new type of artificial intelligence that creates fast and physically realistic simulations of moving environments. Building AI systems that respect physical laws will strengthen the United States' AI-powered scientific leadership and advance the national health by enabling safer medical treatments. Specifically, the project applies these new AI-based simulations to cancer radiation therapy, aiming to predict real-time tumor movements so that treatments can precisely target the cancer while minimizing harm to healthy tissue. The project also supports educational activities, including creating a new university course on computer graphics at the University of Michigan, mentoring undergraduate researchers, and hosting summer camps for high school students to learn about artificial intelligence. This award advances the fields of generative modeling and physical simulation by developing conditional sparse neural fields for four-dimensional scene generation. To overcome the computational bottlenecks of dense spatial sampling in current diffusion models, the investigator will design autoregressive networks that query only a sparse subset of points while adaptively maintaining full fidelity on regions of high physical importance. To ensure physical plausibility, the research integrates physical constraints typically described as partial differential equations (PDEs), such as momentum conservation, topology preservation, and incompressibility, directly into the generative models. Rather than relying on computationally expensive test-time optimization, these constraints are enforced through efficient residual conditioning during both training and inference. Finally, the project translates these advancements to medical imaging by developing a real-time predictive framework for organ motion. Using sparse clinical observations, such as magnetic resonance imaging slices, the system will forecast tumor dynamics to enable predictive, rather than reactive, adaptive radiation therapy for precise targeting. 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

Parsing untrusted inputs into formats usable by software systems is a ubiquitous, security-critical task. Bugs in parsing components are one of the most common sources of critical security vulnerabilities in practice. As more software is automatically generated by AI systems, this problem exacerbates as manual human auditing for correctness and security is reduced or eliminated. The project's impacts are to enable mathematical verification of correctness, safety and security of parsing components for a variety of parsing domains encountered in practice. Any code using this system, even AI generated, will be mathematically proven to be free of security vulnerabilities, without manual human intervention. The project's novelties are in the design of new domain-specific programming languages in which these specifications and parsers are to be written, extending prior formalisms to more realistic specifications. The key technical idea is to use dependent Lambek calculus, an ordered linear type theory, as a language for specifying formal grammars as types and intrinsically verified parsers as well-typed terms. The investigator will lead the design and implementation of embedded domain-specific languages based on dependent Lambek calculus in the Lean proof assistant. The project will develop libraries for specification of grammars and verification of parsers as well as verified parser generators within the domain-specific language. The project will target a variety of parsing domains: regular expressions, context-free grammars, data-dependent formats and type systems. These libraries will enable the implementation of modular verified parsing components that can be used to support larger formally verified software 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 · 2026-05

Earth’s largest reserves of iron ore derive from the giant iron formations, enigmatic rocks that formed in ancient seawater. These extensive iron-rich rocks signal a very different early Earth where life first emerged. However, exactly how these deposits formed is unknown and highly debated, including whether life was involved. This research project will use laboratory experiments to test which pathway(s) generate the most similar chemical fingerprints to the iron formation rocks. Thus, this research will not only help to understand how and where Earth’s largest iron ores formed but will also illuminate the chemistry and potential life of Earth’s ancient oceans—both topics that capture broad public interest. Graduate and undergraduate students will participate in the research, providing training and career advancement opportunities to the Earth science workforce. The debate about the origin of iron formations is at an impasse: iron isotopic data and other geochemical proxies such as manganese abundance seem to support primary iron oxides, but high-resolution microscopy identifies only iron silicate minerals as the original seawater mineral precipitates. However, the iron isotopic fractionation and manganese incorporation during iron silicate precipitation has never been directly measured. Recent microscale observations of the iron(II) silicate mineral greenalite has prompted new hypotheses for the origin of iron formations. This project will simulate three hypothesized formation scenarios with laboratory experiments that precipitate greenalite under different simulated ancient seawater conditions while monitoring the iron isotopic fractionation and iron-to-manganese ratios that are generated by each formation pathway. These laboratory experiments are designed to test each of three hypotheses: 1) greenalite formed as a high-pH precipitate; 2) greenalite formation was initiated by partial oxidation of Fe2+ to ferric iron (Fe3+); or 3) greenalite precipitated close to seafloor hydrothermal vents and was transported across the global oceans in plumes spreading from the seafloor. These possible pathways all have different implications for early life on Earth, the rise of oxygen in Earth’s atmosphere, and the availability of different nutrients in its ancient seawater. 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

The conference “Geometry and Dynamics of Discrete Actions” will be held at the Mathematics Institute of the National University of Mexico in Cuernavaca, during the week of June 15–20, 2026. This event is an official satellite conference of the International Congress of Mathematicians (ICM), the premier international mathematics gathering, hosted in the United States for the first time in 40 years. The study of discrete group actions remains at the heart of several key areas of modern mathematics and its applications, so participation in events of this caliber is vital for researchers to remain at the forefront of research. Funds provided by this grant will enable U.S.-based early career mathematicians to interact with senior experts in the field and learn about new techniques and results in this rapidly evolving area of mathematics. It is to be expected that this will increase interaction between the US, European and Mexican mathematical communities, and lead to new international collaborations. More information on the conference can be found at its official webpage: https://www.matcuer.unam.mx/SatelliteICM2026/ 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

This grant provides financial support for approximately 37 undergraduate students, graduate students, and recent graduates from institutions of higher education in the United States to participate in the 2026 Manufacturing Science and Engineering Conference (MSEC) and the 54th North American Manufacturing Research Conference (NAMRC), to be held at The Pennsylvania State University in June 2026. These co-located conferences constitute the leading research forum in advanced manufacturing in North America, bringing together researchers from universities, industry, and government laboratories. Participation in such conferences is often limited by financial constraints. By reducing financial barriers to attendance, this project expands participation in cutting-edge scientific exchange and strengthens the education and training of the future manufacturing workforce. Advanced manufacturing underpins critical sectors including energy, transportation, health care, and defense. Supporting student engagement in this field promotes the progress of science and engineering while contributing to national prosperity, workforce development, and technological leadership. The project will implement a structured and transparent participant selection process to award support covering conference registration and lodging expenses. Eligibility will require full-time enrollment at a United States institution or recent graduation, along with active participation in the conference through presentation of peer-reviewed research, poster contributions, or involvement in student-focused professional development activities. Applications will be evaluated using predefined criteria that emphasize variety of institutions and sectors, and encouragement of first-time conference participation. Awardees will attend the full conference program, including technical sessions and professional development forums. The conference program spans additive manufacturing, manufacturing processes, automation, manufacturing systems, quality and reliability, life cycle engineering, advanced materials processing, and related emerging areas. Through exposure to peer-reviewed research, plenary lectures, and cross-sector networking, supported participants will deepen technical expertise, strengthen professional skills, and contribute to the long-term advancement of advanced manufacturing research and innovation in the United States. 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

Recent advances in data science, statistics, and machine learning have opened new possibilities in precision medicine, enabling clinicians to tailor treatments based on individual patient characteristics. This project focuses on developing a unified and efficient statistical framework to improve treatment decisions by leveraging rich demographic, socio-economic, and biomedical data. By advancing personalized decision-making, this research contributes to better health outcomes, more efficient healthcare delivery, and overall national well-being. The project also offers broad societal impact through its commitment to education, collaboration, and open science. The investigators will mentor graduate students and develop new coursework at the intersection of machine learning, statistics, and personalized medicine. In addition, all software tools developed will be released as open-source, supporting accessibility and reproducibility in scientific research. The interdisciplinary nature of the project encourages collaboration across statistics, medicine, and computer science, and prepares a next-generation workforce to tackle complex health data problems. This project aims to develop an efficient learning framework for estimating optimal individualized treatment rules (ITRs) across a broad range of personalized medicine settings. The proposed methodology is based on semiparametric modeling and is designed to address complex relationships among covariates, treatments, and outcomes. Key challenges addressed include handling multiple treatment options with cross-treatment structures, modeling a variety of outcome types, and accommodating multi-stage decision-making with time-varying, history-dependent effects. The framework also supports incorporation of domain knowledge for interpretability and practical implementation. From a statistical perspective, the proposed methods achieve double robustness (consistency under two separate model specifications) and statistical efficiency (minimal asymptotic variance), even under model misspecification and in high-dimensional or limited-data scenarios. These contributions advance the state of the art in both semiparametric theory and algorithmic design for ITR estimation. The resulting models are interpretable, scientifically meaningful, and directly applicable to real-world medical problems, including drug development and treatment recommendation. This work not only contributes to foundational statistical theory but also facilitates translational research in healthcare. 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

This project aims to transform cancer radiotherapy by developing an innovative injectable transponder system that enables precise tumor targeting and enhances the effectiveness of radiation dose delivery during treatment. Radiotherapy is a cornerstone of cancer treatment, used in over half of all cases, but its effectiveness is often hindered by challenges in accurately monitoring tumor motion and radiation dose delivery in real time. Current methods rely on immobilization devices and computational models, which can lead to either under-treatment of tumors or damage to healthy tissues. The project addresses these challenges by creating a minimally invasive implantable device that can be powered by X-ray beams during radiotherapy treatment. This device will provide real-time feedback on tumor location and radiation dose, allowing for dynamic adjustments to improve treatment accuracy and patient safety. By enabling precise targeting and reducing damage to healthy tissues, this innovation has the potential to improve treatment outcomes for millions of cancer patients, reduce healthcare costs, and enhance the quality of life for those undergoing radiotherapy. Beyond healthcare, the project will contribute to education and workforce development by engaging students in hands-on research and fostering industry collaborations. The research of this project focuses on developing a photonuclear-powered, wirelessly localizable injectable transponder for precise radiotherapy tumor targeting and dosimetry during radiotherapy. The system combining novel hardware and signal-processing algorithms will address key challenges in power, localization accuracy, and miniaturization by leveraging high-energy photons and electrons generated during radiotherapy to power the implant. The transponder will harvest energy from X-ray or electron beams, eliminating the need for batteries and enabling operation during treatment sessions. The transponder features a miniaturized antenna and backscatter circuit capable of measuring and communicating radiation doses in real time. A custom-designed radar will be developed to achieve millimeter-level accurate localization of the implant, even within biological tissues. The research will involve designing and testing hardware, developing advanced algorithms including supervised AI/machine-learning algorithms, and conducting in-vitro evaluations using tissue phantoms and motion simulators. The project is expected to advance knowledge in wireless sensing, energy harvesting, and medical device design, paving the way for more effective and safer cancer treatments while contributing to interdisciplinary research and education. The outcomes of this project can potentially lead to significantly improved clinical effectiveness of cancer treatments and benefit millions of patients. 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

From June 15–26, 2026, the University of Michigan (U-M) will host a two-week summer school in random matrix theory, bringing together 55–60 graduate students and early-career researchers to learn from leading experts in the field. Random matrix theory is a mathematical discipline with far-reaching applications in a wide range of branches of science, engineering, and mathematics, from machine learning and biochemistry to number theory and theoretical physics. By training the next generation of researchers and emphasizing collaboration through daily group problem-solving sessions, the 2026 U-M Random Matrix Theory Summer School (RMTSS) will contribute to the important tasks of advancing and expanding the field and cultivating a healthy, supportive research culture for budding scholars. The 2026 U-M RMTSS will be the fifth summer school of its kind, following highly successful predecessors in 2016, 2018, 2022, and 2024. The 2026 U-M RMTSS will feature four one-week lecture series—two per week—delivered by leading random matrix theory researchers. Each lecture series will explore a different aspect of random matrix theory, enabling students to learn techniques outside their specialties and acquire the background necessary to understand how and when to apply these techniques to new problems. In addition to the lectures, participants will engage in intensive daily group problem-solving sessions designed to deepen understanding through active engagement and to foster collaboration among participants. The summer school's website is https://sites.google.com/umich.edu/rmtschool/home. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Non-convex and singularly perturbed optimization methods are ubiquitous in the mathematical modeling of complex mechanical systems, and the questions addressed in this project - on stress focusing in confined membranes, and shape change in mechanical metamaterials - are at the cutting edge of nonlinear mechanics and the calculus of variations. The work is interdisciplinary, and success will come from blending techniques from engineering and physics with pure mathematical analysis. Rigorous optimization questions are considered to identify the most extreme examples, with the aim of deriving a general theory for predicting the motifs of wrinkles and folds in packed elastic sheets, as well as general techniques for the design of load-bearing morphable materials. Outreach activities to high school students are planned, involving university students and researchers in science, technology, engineering, and mathematics. With the goal of training the next generation of effective mathematical researchers working at the intersection of variational analysis and the mechanics of materials, this project supports undergraduate research infrastructure, and provide support and mentoring opportunities for graduate and undergraduate students. The research concentrates on two sets of questions from mechanics: on stress focusing in confined elastic shells and related one-dimensional systems, and on the aggregate properties of many body interacting elastic systems known as mechanical metamaterials. On stress focusing: the aim is to develop a variational model of the wrinkle-fold state, which has recently been observed in confined shells but has yet to receive a systematic mathematical treatment. Based on prior successes with predicting the wrinkling patterns of shallow shells, the investigator seeks an asymptotic characterization of the more general wrinkle-fold state starting from fully nonlinear elasticity. On mechanical metamaterials: motivated by the question of predicting the overall behaviors of kirigami elastic systems in response to applied loads, the investigator aims to characterize the effective deformations and emergent stress-strain laws of these and other related many body elastic systems. The goal is to start from fully nonlinear elasticity and derive the relevant weak limits and stored energy in the limit of infinitely many bodies. Students are involved in the project at all levels, including through a high school outreach event in the Chicago area, as well as in a mathematical computing laboratory for undergraduate research co-directed by the investigator. 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 · 2026-02

The collection of active proteins in the body contains vast information that can improve disease understanding and diagnosis. However, many disease-related proteins exist at very low levels and are difficult to detect. Furthermore, many detection tools measure protein abundance but not protein function. This CAREER project will develop ultrasensitive tools to measure simultaneously low abundance proteins and their activities. The project will build on a recently developed single-molecule protein detection technology that enables protein measurements at concentrations far below the limits of conventional assays. The project will establish a design framework to detect many low abundance proteins and eliminate false signals. It will develop a complementary approach to measure both protein function and abundance. The platforms will be high-throughput, modular, and compatible with common laboratory equipment. These technologies will enable researchers and clinicians to maximize biological information from valuable samples. Results will help accelerate discovery of key proteins involved in disease processes. The project will help train the next generation of diagnostics innovators and promote diagnostics literacy. Activities will include mentored research experiences for high school and undergraduate students, hands-on K-12 diagnostics workshops with educational videos, and a new graduate course in biomolecular engineering. These efforts will promote scientific progress in protein measurement tools, strengthen the future biotechnology workforce, and enhance preparedness for public health challenges. Proteins play critical roles in biological processes. The extraordinary complexity of the human proteome presents a vast, untapped reservoir for understanding disease. Many current protein measurement tools lack the analytical sensitivity, multiplexing capacity, and broad accessibility required to probe low-abundance proteins and their activities in complex biofluids with high throughput and scalability. To overcome these challenges, this CAREER project will advance the multiplexing and dimensional frontiers of ultrasensitive single-molecule protein detection, integrating DNA barcoding and activity-based detection with a high-throughput digital immunoassay platform. The project will pursue two orthogonal research objectives: (1) establishing the foundational design principles for a cross-reactivity-free multiplex digital immunoassay platform; and (2) engineering a platform for multiparametric ultrasensitive protease activity profiling using single-molecule detection. These tools will not only maximize the biological insight from valuable clinical and research samples for biomarker discovery and disease understanding, but also advance knowledge in single-molecule assay design. Through integration with an education and outreach program focused on diagnostics literacy and early hands-on learning experiences, this project will also foster training of the next generation of biotechnology innovators in measurement science and diagnostics. 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 · 2026-02

Cavitation occurs when small vapor bubbles form in a liquid as the pressure drops. When the bubbles move into regions of higher pressure, they collapse and can create noise, vibration, and damage in engineering systems. Cavitation affects many technologies, including ship propellers, water turbines, medical ultrasound, and drug delivery devices. Yet it remains difficult to predict and control because it arises from complex links between fluid motion, pressure changes, and phase transitions. These gaps in understanding can reduce efficiency, shorten equipment life, and slow progress in healthcare and biotechnology. This award will develop a new method to study and control cavitation by combining advanced physical models with modern artificial intelligence. The goal is to improve cavitation prediction tools and support safer, more efficient, and more sustainable engineering designs. The award also advances the national interest by strengthening U.S. leadership in computational science and engineering and supporting economic competitiveness in marine, energy, and biomedical technologies. Educational and outreach efforts will help train the future science and engineering workforce. This award will develop a framework by integrating physics and artificial intelligence for predicting and suppressing cavitation in turbulent flows. High-fidelity simulations based on first-principles thermodynamics will be used to model the formation and evolution of vapor bubbles. These simulations will generate detailed data linking fluid motion to cavitation events. Explainable-deep-learning methods will then be applied to identify the flow patterns that most strongly influence cavitation. The award will establish causal links between turbulence structures and cavitation onset by systematically quantifying which features of the flow promote cavitation inception. Building on this understanding, the project will develop adaptive-control strategies using deep reinforcement learning. These controllers will act on the flow in real time, targeting the specific structures responsible for cavitation. The expected outcomes include improved predictive capability for cavitation; interpretable control strategies grounded in physics, and broadly applicable tools for managing complex fluid systems. These advances will contribute to more reliable engineering systems and deeper understanding of multiphase-flow phenomena. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Many complex systems in the real world can be modeled as networks. In fact, many networks vary over time. For example, contact networks change from one shape to another as people move around to meet different people. Friendship networks also vary over time on a longer timescale. Such temporal (i.e., time-varying) network data have been increasingly available, and mathematically founded methods that can efficiently summarize complex temporal network data to help enhance intuitive understanding of the data are desirable. The Principal Investigator (PI) will develop methods to map temporal network data to trajectories in a space. Specifically, the methods will enable representation of the network at a given time point succinctly as a point on the trajectory. This is a drastic reduction, but in this manner aims to capture gross properties of the data and potentially use them for data mining tasks such as visualization, anomaly detection, and discovery of hidden periodicity. The PI will then build mathematical foundations of the proposed methods and apply them to empirical data. The proposed methods are expected to find applications in online social network services, financial transactions, bibliographic citation data, neuroimaging data, and climate temporal networks, to name a few. Furthermore, the project outcomes are expected to encourage researchers in data science and engineering to work on various algorithms related to network embedding (e.g., use of deep learning architecture). In this manner, the project relates to multiple research communities and industries. The methods to be developed in this project are temporal network embedding (TNE) methods. In contrast with most TNE methods available to date, in which one embeds nodes into a latent space, the class of TNE methods the PI will pursue is a mapping from the space of networks to a low-dimensional latent space. A fundamental challenge to TNE is that empirical data usually come in the form of a set of time-stamped events between pairs of nodes, which would generate an extremely sparse network at any given time, hampering sensible network analyses. To overcome this situation, the PI will combine the modeling framework called tie-decay temporal networks with a Nystrom family of general-purpose dimension reduction methods to establish a family of TNE methods. This particular combination of techniques will allow the PI to narrow down the methodological choice, such as which dimension reduction methods, network distance measures, and tie-decay functions should be used, as well as to facilitate efficient computations and mathematical investigations. The PI will then develop mathematical foundations of the proposed methods such as continuity, responses to Markovian inputs, and sensitivity to perturbation in the input data. Finally, the PI will showcase the methods by applying them to social and financial empirical 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 · 2026-01

This award aims to revolutionize the design and manufacturing of advanced materials using artificial intelligence (AI), by improving the mechanical performance of nanocomposites (advanced materials made by combining different substances at extremely small scales). Research enabled by this award focuses on understanding and controlling a specific type of internal structure in these materials – called amorphous-crystalline interfaces – that can significantly enhance strength, durability, and reliability. If successful, the research findings could impact a wide range of critical applications in the areas of energy, defense, transportation, and others. By applying AI and advanced manufacturing techniques, the award seeks to uncover how processing methods can be used to tailor the structure and behavior of these interfaces for optimal performance. This award aims to develop a physics-based framework for the tunability of metastable amorphous-crystalline interfaces (ACIs) in nanocomposites through physical vapor deposition (PVD) processing. Research tasks focus on investigating how PVD parameters – such as chemical composition, deposition rate, temperature, and incident velocity – affect the local structural and chemical environments at ACIs, which in turn control deformation mechanisms like plasticity and shear banding. Self-propelling energy landscape sampling algorithms are employed to explore atomic-scale rearrangements without prior assumptions, combined with transition state theory to quantify the kinetics of transitions among various metastable micro-states. Machine learning models and Bayesian optimization will guide intelligent data acquisition and accelerate exploration of complex phase spaces. These computational approaches will be integrated with precision magnetron sputtering experiments, high-resolution electron microscopy, and nanomechanical testing to validate predictions. The resulting predictive, testable processing-structure-property loop could enable the design of high-performance, ACI-rich nanocomposites for advanced manufacturing applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Technology translation is the process of converting scientific research and technical innovations to practice. In order to move basic and use-inspired research into society most effectively, it is imperative that innovators and entrepreneurs have access to facilities that enable testing and validation of their new technologies under real world conditions. Such testing requires a safe and controlled environment to ensure the technology is robust, reliable, and ready for use. National “test beds” could include fabrication facilities and cyberinfrastructure to advance the development, operation, integration, testing, deployment, and, as appropriate, demonstration. This effort supports a workshop series facilitating conversations among critical test bed stakeholders from academia, industry, government, and non-profits. The stakeholders offer their unique perspectives on strategies and models for designing and using test beds to scale up technologies and accelerate the translation of innovations into the marketplace. This workshop series will provide opportunities for open dialogue about opportunities and challenges of bringing emergent technologies to practice using test bed facilities. Topics include lessons learned from existing test bed efforts, novel operational models, gaps in existing infrastructure, and how to expand access to physical and virtual resources, investment, and multisector collaboration. The workshop series brings together stakeholders to share community-wide perspectives in a large number of technology fields – from advanced communications to biotechnology and materials development. The workshop deliverables will include at least one white paper that will capture the conversations at the sessions. The workshop series includes virtual events and in-person sessions hosted by Iowa State University, the University of Alabama, and the University of Michigan in Spring 2026. 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.