Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2025 · 2025-08

Ground-based magnetic field observations play a fundamental role in geospace research, especially in understanding severe space weather events that drive geomagnetic-induced currents (GICs) that can impact navigation and satellite communication. The Magnetometer Array for Cusp and Cleft Studies (MACCS) array provides observations from the high Canadian Arctic, which is playing an increasing role in national security, and the observations are important for aviation and maritime navigation. Studying the Earth's space environment has become increasingly crucial to our technologically based society. The MACCS array fills a significant geographical gap at northern high latitudes between arrays in Greenland and Canada. MACCS data have been used by many space scientists worldwide for event studies and statistical studies and as input into empirical models. High-latitude geomagnetic observations during rapid changes in the Earth's geomagnetic field are critical, as they impact mission-critical navigation for the US, NATO, and our allies. MACCS data contributes to creating and validating geomagnetic models such as the World Magnetic Model. This project operates the MACCS network, disseminates its data, and conducts space weather relevant scientific studies. Scientific analysis of MACCS data is conducted in collaboration with other ground-based and space-based research teams and undergraduate and graduate students to support scientific workforce development. MACCS has contributed to the study of ULF waves, magnetosphere-ionosphere coupling, ionospheric convection, and magnetic storm and substorm processes for three solar cycles. The location of several MACCS stations under the field of view of the NSF-funded SuperDARN radar network, their proximity to the field of view of the Relocatable Atmospheric Observatory in Resolute Bay, Canada, the nominal "conjugacy" of four MACCS stations with U. S. Antarctic stations, and the co-location with dual high-sampling-rate GPS TEC receivers makes possible additional comparative studies and significantly leverages other NSF-supported infrastructure. Near-real-time digital data and on-demand plots via the MACCS website, as well as via THEMIS GMAG, CDAWEB, and SuperMAG, make it possible for all interested collaborators to freely access the MACCS data set. During the next five years, the team will focus on maintaining operations and conducting scientific analysis of MACCS 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 2025 · 2025-08

Graph Neural Networks (GNNs) are a powerful class of artificial intelligence models that help analyze complex relationships within data, from understanding how our brains function to predicting molecular interactions or identifying financial anomalies. While these models have shown remarkable promise, their widespread application in the real world faces significant hurdles: they often struggle to process extremely large datasets, adapt to unseen data, and maintain reliability when faced with intentional disruptions or faulty information. This project aims to overcome these limitations by focusing directly on the data itself, rather than solely on refining the GNN models. By making graph data more compact, cleaner, and better aligned with learning objectives, this project will enable more efficient, accurate, and robust AI systems across critical domains such as healthcare, finance, and national security. The project will address core challenges of GNNs related to data scale, distribution, and quality through three research tasks. The first task will tackle scalability by identifying key structural properties necessary for effective learning and developing graph condensation methods that significantly reduce data size while automatically preserving critical information. The second task will investigate how distribution shifts relate to graph properties and will introduce new data augmentation and test-time adaptation strategies to enhance generalization under out-of-distribution conditions. The third task will focus on data quality by creating unsupervised graph purification techniques to remove adversarial perturbations and by designing detection mechanisms to identify and mitigate various types of attacks. This project will include comprehensive evaluations using publicly available datasets and real-world applications, supported by collaborations with academic institutions and industry partners. The project outcomes will complement existing model-centric approaches and promote more efficient, robust, and generalizable GNN solutions across domains such as finance, neuroscience, and cybersecurity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Cyber-physical systems (CPS), for example, autonomous vehicles and delivery drones, consist of computational units tightly integrated with their physical environments. They are often built using small, constrained platforms and multiple control tasks shape the common platform. Thus the computation that is available to each control task is limited and may vary over time, which threatens to compromise the quality of control. This project will address this challenge through co-design of control algorithms and real-time scheduling techniques for control tasks. The new control algorithms developed in this project will be robust to early termination, with guarantees on the quality of control, and the scheduling frameworks will be capable of dynamically adapting scheduling decisions in response to changes in computational demand. The developed techniques will be applied to automated drone delivery in collaboration with industrial partners. The hand-on experimentation plan enables technology transfer to commercial delivery applications, as well as provides a valuable educational tool for engineering students studying robotics and autonomous systems. The approach will target optimization based control algorithms, such as model-predictive control and apply novel solvers based on state-of-the-art Robust to EArly termination oPtimization (REAP). The core idea of REAP is to construct a continuous-time dynamical system whose trajectory converges to the optimal solution, while a sub-optimal and feasible solution is guaranteed even in the event of early termination. Towards achieving this, the project will investigate i) closed-loop stability guarantees and discrete-time implementation; ii) proactive and safe real-time scheduling in CPSs; iii) cooperative computation-aware distributed model predictive control; and iv) control of systems subject to time-varying constraints. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The goal of this project is to turn two types of waste into two useful products at the same time. The first type of waste is nitrate, which comes from things like farm runoff. This nitrate will be changed into ammonia, which can be used as fertilizer. The second waste is glycerol, which is a leftover material from making biofuels. It will be changed into formate, which is another useful chemical. To do this, both waste streams will go through special chemical reactions using electricity. To make these reactions happen in a way that uses as little energy as possible, many parts of the system must work together. A novel idea in this project is to use a bipolar membrane. This membrane helps electric current flow between the two sides of the system, while also letting each side run under different, best-suited conditions. By letting each reaction happen under the best conditions, the whole system should work more efficiently. This project will also help scientists better understand how the parts of the system work, including the electrodes (cathode and anode) and the bipolar membrane. There are no reported studies that incorporate bipolar membranes (BPMs) in electrolyzers for the simultaneous reduction of nitrate under acidic conditions and organic oxidation under alkaline conditions. The project will begin by investigating the individual components in isolation. An important question is whether the ionomers used to bind electrocatalysts to a conductive support influence the electrocatalytic rate and mechanism. Understanding these interactions is crucial due to the close contact of the cation exchange layer (CEL) and anion exchange layer (AEL) polymers and electrocatalysts in a zero-gap BPM electrolyzer. This study aims to deepen the scientific community’s understanding of how ionomers affect electrocatalyst performance for these reactions. Additionally, investigating the transport of reactants, products, protons, and hydroxide through the CEL, AEL, BPM, and various interfaces is essential for creating efficient electrolyzer systems. Integration of BPMs into electrolyzers will enable simultaneous reduction of molecules under acidic conditions and oxidation of molecules under alkaline conditions, opening possibilities for a variety of chemical transformations. Furthermore, this project will help cultivate interdisciplinary skills among participating PhD students, fostering expertise in both experimental and computational aspects of electrochemistry and membranes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Pathways to Enable Open-Source Ecosystems (POSE) project is centered on strengthening the nation’s competitiveness in artificial intelligence (AI) for development and economic growth. This project supports the growth of an open-source ecosystem around a novel AI development stack that dramatically simplifies and accelerates the creation of scalable AI applications, with the potential to speed up AI development by 10 times. By significantly reducing the technical complexity and developer expertise required to launch production-grade AI systems, this project aims to increase participation in AI innovation as well as the pool of developers capable of building next-generation technologies. Early adopters, including major enterprises in finance and technology, have demonstrated the effectiveness of the platform in accelerating product development. By lowering the barriers to AI application development, this project will foster innovation across industries, enable faster commercialization of AI technologies, and strengthen the United States’ leadership in the global AI economy. The impact extends to sectors such as finance, healthcare, education, and public services, where more rapid AI innovation will create economic opportunities and societal benefits. This Pathways to Enable Open-Source Ecosystems (POSE) project establishes a foundation for a sustainable open-source ecosystem around a novel AI development stack designed to address critical barriers in creating scalable, production-ready AI applications. The platform introduces a new programming model that simplifies the design and development of distributed, cloud-based AI software, coupled with a runtime system that automates orchestration, microservice configuration, database management, and deployment optimization. The effort will focus on systematically discovering and growing the open-source ecosystem by identifying user needs, engaging early adopters, expanding dissemination channels, and creating high-quality educational materials. The project also lays the groundwork for transitioning to a sustainable governance model to support long-term community growth. Activities include developer interviews, workshops, courses, tutorials, and pilot events to refine onboarding processes and foster a collaborative contributor base. By building a strong foundation for adoption, governance, and community development, this project ensures the scalability and sustainability of the open-source ecosystem, positioning it as a key enabler for broader AI innovation and national technological leadership. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Wind and solar power are well-known to suffer from intermittency, i.e. periods when little or no power is generated. This project seeks to improve redox flow batteries (RFBs), a scalable and durable energy storage technology that can strengthen the electric grid and support U.S. energy independence. Reliable large-scale storage is critical for managing fluctuations in energy supply and demand, particularly as the power grid incorporates a broader mix of energy sources. Vanadium redox flow batteries (VRFBs) are promising for grid applications due to their long service life, safety, and flexible design, but further improvements in cost and performance are needed for widespread deployment. The research will investigate a new class of positively charged membranes with exceptionally high charge densities for application in VRFBs. These membranes have the potential to reduce system costs and increase the efficiency and durability of VRFBs. By identifying key relationships between membrane structure and performance, the project will generate fundamental knowledge that supports domestic innovation in electrochemical technologies. The knowledge generated in this project could also advance membranes used in water treatment, resource recovery, and energy generation. The project will train undergraduate and graduate students in advanced materials research, helping develop a skilled technical workforce. It will also include outreach activities to engage K–12 students and broaden participation in science and engineering. The proposed research investigates the transport properties and stability of ultrahigh charge density (UHCD) hydrocarbon-based anion exchange membranes (AEMs) in concentrated sulfuric acid and vanadium electrolyte solutions typical of VRFB operation. These UHCD AEMs, synthesized via copolymerization of custom charged cross-linkers and monomers, exhibit among the highest ionic conductivities and charge densities of reported AEMs. The study is organized into three tasks: (1) quantifying rates and mechanisms of hydrolytic and oxidative degradation of AEMs using Raman spectroscopy, XPS, FTIR, and chemometric analysis; (2) determining ion partitioning and speciation within AEMs under varying states of hydration using Raman, UV-Vis, and X-ray absorption spectroscopy; and (3) measuring both concentration- and electric field-driven transport of vanadium and charge-balancing ions through AEMs under simulated VRFB conditions. These efforts aim to establish structure-property relationships governing stability and selective ion transport in UHCD AEMs and to provide fundamental design principles for membranes used in VRFBs and other electrochemical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Discovering new molecules that have desired properties will address critical technological challenges ranging from energy storage to drug development. The traditional trial-and-error approach of creating and testing molecules is expensive, inefficient, and time-consuming. Likewise, it is too computationally expensive to use only quantum mechanical calculations to adequately screen the vast space of possible molecules for desired properties. In contrast, generative modeling based upon machine learning methods have the potential to efficiently navigate chemical space to discover molecules capable of addressing important problems. Generative modeling enables predicting the structure of molecules from the desired properties, known as inverse design. However, generative modeling approaches are hindered by data scarcity and struggle to accurately generate molecules when the desired properties lie outside the range of the training data. This issue is known in statistics as tail extrapolation. It is important to emphasize that when trying to generate new molecules, researchers typically look for molecules that have exceptional properties, thus making them rare and likely outside the range of the training data. This project will address this critical challenge via extrapolation-aware conditional molecule generation and experimental design methods. This project will develop methods that generate novel molecules with desired properties and will be demonstrated on organic molecules that are useful for reduction-oxidation (redox) flow battery applications for energy storage. This research connects to training and mentorship at the University of Michigan and also promotes education across data science, statistics, computer science, and engineering. Students from Washtenaw Community College will be mentored each summer of the project as part of an eight-week summer research internship. This project will develop extrapolation-aware conditional generative models. The key idea is to adapt pre-additive noise models, which can provably perform tail extrapolation (’tail-aware’) in classical regression tasks, to a variety of conditional generative models. Further, this project will advance extrapolation-aware experimental design for conditional generative modeling. This project will design efficient continual updates for experimental design in generative models. Continual updates, by design, do not require retraining; rather, it only requires updating with the newly acquired data points and thus are much faster. The methodologies developed will be validated on synthetic datasets of organic molecules and real-world datasets for organic molecule discovery for redox flow batteries using state-of-the-art equivariant generative models and large language models. This research focuses on developing novel and generalizable artificial intelligence techniques to accelerate scientific discovery. The proposed extrapolation-aware generative modeling and experimental design approaches are widely applicable to scientific problems involving the design of systems in small data regimes or with exceptional, rare desired properties. Thus, beyond making an impact on chemistry for molecule discovery, these proposed methods are useful for any generative modeling application that requires extrapolation. By developing a rigorous foundation for extrapolation-aware conditional generative modeling and experimental design with generative models, this project aims to make generative modeling far more reliable, enabling trustworthy predictions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This collaboration between researchers at the University of Michigan and the University of Pittsburgh will study the mechanisms of animal migration termination using a novel logger and analytics platform (mSAIL). Migratory species such as monarch butterflies are uniquely threatened, and understanding how and why they choose different habitats will be important for helping us mediate threats. This research will provide novel insight into how social and environmental information are integrated to guide decisions and shape migratory ecosystems. Broader impacts of this work include raising scientific literacy through public engagement and broad cross-disciplinary training opportunities. Community volunteers directly contribute data that enable machine learning algorithm development for mSAIL. This work will provide cross-disciplinary training opportunities for multiple student participants in biology and engineering. The project contributes to the bioeconomy and to biotechnology through the development and honing of a data logger small enough to be carried by an insect that will be of interest to other scientists and engineers outside of this project. Migratory animals often terminate their migrations in specific places. How and why specific wintering/estivating habitats are chosen is not well understood yet is important to know given the unique threat that migratory species face. This research will use mSAIL, a multi-modal integrated biologger and analytics platform, to monitor monarch butterflies as they terminate their iconic annual migrations at their overwintering sites. The recently developed mSAIL technology will be modified to capture multi-modal environmental data with higher spatial and temporal resolution, allowing behavioral inference. This work will contribute to our broader understanding of how and why migratory species are distributed as they are and how different sources of information (environment and social cues) are integrated to determine these patterns. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Programmability is fuel for network innovation. In today’s programmable networks, new features can be easily developed without having to rely on vendor support. However, deploying new features still requires fleet-wide maintenance to avoid disruption because device reprogramming incurs downtime. This severely constrains the speed of change, as maintenance operations require meticulous planning well ahead of time. This project proposes runtime programmable networks, where the end-to-end network infrastructure, vertically from the host kernels down to the network interface cards, and horizontally extending across switches to the other end of the network, can be reprogrammed on-the-fly without packet drops and with strong consistency guarantees. This represents a major leap from today’s programmable networks, which are reconfigurable at compile time but become fixed functions at runtime after deployment. According to this project's vision, FlexNet, the network infrastructure provides a collection of basic utilities and, on demand, extensions are partially reconfigured into the infrastructure by injecting, removing, or overriding specific functions. This accelerates the speed of delivering new features to end users, increases the manageability of large networks by lowering the barrier for change, and creates new possibilities unavailable in today’s programmable networks, such as powerful, dynamic security defenses. With FlexNet, this project can summon security defenses into the network precisely when needed. Defenses can migrate to the attack location or replicate across the network to maximize their effectiveness. They can even shapeshift in real time to mitigate changing attacks. When attacks subside, these defenses can be soon removed from the network to reduce overhead. This project aims to elevate network programming from a “one-shot” endeavor at compile time to “continuous” activities throughout the lifecycle of the network. In order to realize our vision, this project needs to innovate across the stack. Concretely, this project proposes a four-pronged approach to programing, compiling, verifying, and managing runtime programmable networks end-to-end. First, runtime network programming requires controlling disparate datapaths and their real-time changes as a whole, while ensuring runtime portability across devices; thus, this project will develop a new programming system. Compiling a whole-network program to a heterogeneous substrate, while continuously reoptimizing for runtime changes, requires a new compiler design. To ensure the safety of network changes, this project must simultaneously innovate on runtime verification and validation. Finally, FlexNet programs have dynamic footprints in the network—migrating, expanding, and shrinking across devices—so this project needs a new management system to control such unprecedented dynamics. This project will produce an integrated platform upon which the FlexNet techniques will be evaluated comprehensively at various scales and with diverse workloads. To achieve a wider community engagement, this project will release software and hardware prototypes and educational materials in open source, and by collaborating with industry partners, this project will transition the FlexNet technologies into practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Implant-associated infections are a significant challenge in healthcare, affecting millions of patients and often leading to severe complications such as implant failure, tissue damage, and even amputation. These infections cost healthcare systems billions of dollars annually in revision surgeries and related treatments. Current antimicrobial strategies focus primarily on killing bacteria but often overlook the role of the body’s immune system, which plays a crucial role in infection control. Upon implantation, biomaterials can disrupt the immune system, causing excessive inflammation and increasing infection risks. This CAREER project seeks to address these challenges by exploring how the physical properties of biomaterials, particularly surface topographies or patterns, affect the behavior of immune cells and their interactions with pathogens. Although surface nano- and micro-patterning is commonly used in medical devices such as implants (e.g., orthopedic, dental, breast) and catheters to improve performance, its effects on immune defenses and infection risk are largely unknown. By studying how topography affects host–pathogen interactions in both 2D and 3D settings, this research will provide deeper insights into the relationships between biomaterials, host cells, and bacteria. These findings will establish new design rules for biomaterials that can enhance immune defenses, fight infections, and improve implant success. Furthermore, the research program will be integrated with a collaborative education and outreach plan, including STEAM (Science, Technology, Engineering, Art, and Mathematics) initiatives and public outreach activities, to increase awareness about the importance of biomaterials and implant infections while promoting STEM education and interdisciplinary learning. This CAREER project will address critical knowledge gaps in understanding how surface topographies on biomaterials affect interactions between host immune cells and pathogens. Despite evidence suggesting that nanoscale and microscale surface patterns can modulate bacterial behavior and immune responses, their effects on host–pathogen interactions remain poorly understood. Using a bottom-up patterning approach, the research will generate a library of topographies with precisely tuned nano to microscale features to investigate how surface topography modulates host–pathogen dynamics. The central hypothesis is that precise spatial patterning of biomaterials can restore immune balance and enhance host defenses, thereby improving infection control. This hypothesis will be tested through two objectives: (1) elucidate how controlled 2D topographies affect the regulatory role of immune cells in controlling bacterial infections; and (2) analyze the impact of 3D topographical cues on cell responses to gain insights into the topography–host–pathogen interactions in extracellular matrix-like 3D environments. To achieve this, versatile 3D patterning tools will be developed by incorporating 3D elements into the fabrication process, enabling precise patterning across various scales and geometries. Fundamental insights gained on interactions between biomaterial topography, the host, and bacteria will facilitate the rational design of immune-instructive biomaterials, ultimately leading to effective strategies to combat implant infections and improve patient outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The I-Corps project focuses on the development of a generalizable diagnostic tool to tie together disconnected, disparate information sources at scale into a transparent, auditable system. The base technology for this diagnostic tool is platform-independent and can be applied to many areas, including maritime diagnostics. This solution addresses challenges presented by users and systems being inundated with vast quantities of disparate, disconnected, but relevant information sources. Such information sources include sensors, text reports, and existing models. The tool can provide detailed diagnoses of failures by facilitating the communication of relevant and contextualizing information between these sources. The private and public maritime sectors are undergoing rapidly increasing digitalization and maintenance costs. The diagnostic tool explored in this project leverages these new digital information sources, which are often disconnected from one another. It provides the maritime space with an all-encompassing diagnostic tool that can reduce maintenance costs by providing workers with detailed diagnostic information that may not be currently available without significant effort. Additionally, the tool will be fully transparent to the user in its diagnostic decision-making to enable users to fully trust the system and its diagnoses, as compared to current state-of-the-art methods relying on black box-based approaches. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a statistical physics-based diagnostic system utilizing a network of sensors to connect disconnected, disparate information sources. Entities within the system can ping one another and send concise, informational probability distributions describing their respective states. This state-based, lightweight communication protocol enables the system to scale to systems of any size and be applied to any state-based system. Its actions are fully transparent. Current state-of-the-art approaches typically leverage machine learning-based solutions, which are black boxes, and the reasoning behind their decisions is not readily apparent. Users benefit by receiving rapid, concise diagnostic information that leverages information from all over the system while maintaining traceability to enable user confidence and trust. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The rapidly evolving workplace landscape calls for the development of scalable upskilling and reskilling programs for maintaining a competent and competitive workforce. According to the World Economic Forum’s 2023 future of jobs report, six in ten workers will require training before 2027, but about half of the workforce does not have access to adequate training opportunities today. From what we know about human learning, it is clear that deliberate practice, appropriate scaffolding, and timely feedback are needed for learning to be effective. Practical implementation of such features requires substantial investment from domain experts or instructors, which makes it difficult to provide these opportunities at scale. The overarching goal of this project is to make expertise sharing more efficient through helping experts create example-based intelligent tutors. The research team will partner with other universities, local community colleges, and public schools to test the developed tools, which will then be made publicly available. Through this work, the project will make it easier to develop effective training programs that scale well to millions of workers, improving both their own opportunities and the U.S. economy as a whole. This project focuses on the teaching and learning of complex problem-solving tasks, e.g. “Identify a recent economic phenomenon that involves market failure, explain which type of market failure it is, and propose a solution.” To capture the expertise behind such tasks, the project introduces a “Checklist with Examples” approach, which outlines the key criteria a solution must meet. Four main activities guide this project. Thrust 1 involves creating example-based intelligent tutors for task domains that already have detailed checklists. This will produce human-AI collaborative techniques, and a platform (“Exemplify”) for instructors to gather examples that meet specific criteria. Instructors can find these examples from prior students’ homework submissions using retrieval-augmented generation (RAG)-based methods or generate them directly with Large Language Models (LLM). Exemplify also helps instructors create scaffolding exercises such as multiple-choice questions or example-annotation tasks with automated feedback. Thrust 2 involves performing randomized controlled classroom experiments to evaluate the resulting example-based intelligent tutors. Thrust 3 involves developing a feedback engine using the checklists and the examples collected earlier. The engine provides students with automated formative feedback as they work on open-ended problem-solving tasks. Thrust 4 develops a low-cost proxy method for cognitive task analysis by having LLM agents simulate novice learners and generate solutions. Experts review these solutions and their feedback is summarized into a checklist, making it easier to capture expertise in any domain. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project examines languages with a range of linguistic features to expand existing language databases and advance our understanding of language evolution, structure and function. Three languages with unusual grammatical structures are documented to facilitate comparative language analyses that can reveal how one language relates to or evolved from another and how speakers address grammatical challenges. Products include grammars, lexicons, and transcribed texts. The project leverages automatic speech recognition technologies and supports student training opportunities. This project documents three languages from a multilingual region. This work, combined with the results of previous projects, will result in the documentation of twenty languages from a multilingual region, allowing for historical and typological study and collaboration with archeologists and geneticists. Specifically, the data from this study will allow linguists to examine how unorthodox grammatical structures come into existence and how other features of language such as tone are utilized. How such unusual systems arise can only be clarified by historical-typological study at regional and language-family levels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This doctoral dissertation project investigates the role of craft specialization in shaping social roles and economic relations within village societies. Although the organization and socioeconomic role of craft specialists can vary depending on a society's level of complexity, craft production is integrated into both socioeconomic systems and relationships. Craft specialization can be detected archaeologically through careful examination of household spatial organization, house inventories, and the accumulation of goods. Previous Neolithic research has mainly focused on identifying craft specialization and exchange networks at the site level, whereas, this study focuses on both, as well as the production of items intended for exchange. The project provide scientific training opportunities for students in field methods and artifact analysis. The research team conducts expansive excavations of Neolithic houses, analyze artifacts, and documents house inventories to determine whether one or more households were involved in tool production, trace their exchange with other villages in the area, and look at the accumulation of goods to look if there are any differences related to craft production, house size, and quantity of goods. The team also examines faunal and botanical remains and investigates any differences in economic modes of subsistence, land ownership, and status within the community. The data collected helps to develop a model for understanding household craft specialization and its implications for village societies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The Industries of Ideas (IofI) expansion project seeks to develop new collaborations, data infrastructure, measures and information tools to assess the links between research investments in critical and emerging technologies and regional jobs and skills across the country. The project works with local businesses, chambers of commerce and state labor market, education and economic development agencies, and state governors, to collaboratively build data and actionable information tools that can proactively support the creation of high-wage, high-demand, high skill-jobs and help funding agencies direct resources more effectively to achieve intended workforce outcomes. The project focuses initially on investments in Artificial Intelligence (AI) to develop an approach and empirical methods that can be extended to any science and technology domain and economic sector. The results will provide a foundation that informs bottom-up approaches to strategic science and technology funding that both informs and accelerates the creation of new products, industries and jobs across the nation. This work expands upon an NSF pilot investment that developed a prototype collaborative network to measure how research investments in AI and electric vehicles are linked to regional firms and jobs in partnership with the State of Ohio. In expansion year 1 this project will extend the prototype’s scope to include higher education and skills data and begin to collaborate with other state participants through extensive outreach work anchored on the collaborative, stakeholder-driven product design process that was fostered in the initial prototype. Initial data extensions begin in year 1 in collaboration with the State of New Jersey. In year 2 the project will work with key communities to prototype community designed information products and facilitate the addition of eight to ten additional states. Year 3 will begin the process of scaling and measure validation by providing implementation tools to those additional states. Across all three years the collaborations will engage EPSCoR jurisdictions, federal science agencies, established and startup businesses, and institutions of higher education to collaboratively identify data, measures and tools capable of strengthening links between science investments, economic activity, and jobs. Analyses will be tuned to produce forward-looking insights into likely occupational and skills demand for new technologies as they go to full production. The results could significantly influence how regional science investments are made and optimized across the nation, driving increased economic competitiveness and productivity by deepening our understanding of the crucial link between research, technology driven workplace changes, jobs, skills and earnings. This new collaboratively developed empirical framework, data, and infrastructure will, for the first time, directly engage state institutions, data users, and producers to quantify relationships among research investments, regional job markets, local workforce dynamics, and other key economic outcomes of technology investment. A key IofI contribution stems from its integration of many constituencies and datasets including science and technology metadata, data from universities, state workforce and state higher education agencies, and private job postings information on emerging skills data. The IofI project, anchored on AI investments, will build new tools for an essential emerging sector of the economy that is of pressing interest at the regional, state, and national levels, but that can scale to other technologies. The tools will provide local, timely and actionable information built on comprehensive statistically valid data designed by and for relevant user communities. The insights and outcomes from this research project can help inform science policy and make significant intellectual contributions to multiple fields of research and practice. Investment in this project is both consistent and well aligned with the goals and broad strategic objectives of NSF TIP. This investment is consistent with agency’s goals and the scientific and technological community’s interest in understanding how investment to produce innovation in critical and emerging technologies drive economic outcomes of relevance to all Americans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Romantic relationships play a critical role in human happiness and well-being, yet predicting their success or failure has long puzzled scientists. This difficulty may come from not having enough detailed information about daily relationship experiences. With modern technology, like smartphones, people now track many parts of their daily lives, such as diet, exercise, and mood. This project uses similar technology to better understand romantic relationships by observing how they change day by day. It applies techniques like those used to forecast the weather or stock market trends to identify relationship patterns. These patterns help scientists learn about the different ways that relationships ebb and flow in daily life and predict which relationships are likely to last and which may not. This project seeks to transform the basic scientific understanding of romantic relationship quality. It uses innovative intensive longitudinal methods with large, heterogeneous samples of individuals and couples in romantic relationship to (1) examine dynamic patterns of change in relationship quality over time and (2) identify whether some relationship patterns are more adaptive than others. For instance, patterns involving quick recovery from negative experiences might be more adaptive than those involving slow recovery. Tracking day-to-day relationship changes over long periods allows for discovery of common patterns that emerge before a relationship ends, improving predictions of relationship outcomes. In addition, examining how partners change with, view, and affect each other adds an important dyadic component to prediction models, filling a key gap in our scientific knowledge. The development and sharing of innovative statistical methods for analyzing dynamic social processes provides valuable resources for the scientific community. Broad dissemination of study findings, including through an interactive website, ensures that the findings are shared broadly. Ultimately, this project expands our knowledge of relationship science by encouraging us to view relationships in a novel way - as potentially meaningful dynamic patterns of change which informs future research and interventions aimed at promoting healthy, lasting relationships. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Earth’s energy budget is balanced between incoming solar radiation and outgoing thermal energy. Clouds exert a significant impact in both directions, but there has been significantly more study of the impact of clouds on incoming energy from the sun. This project will focus on the opposite route, the emission of longwave, or infrared, radiation from the Earth’s surface and how that radiation interacts with ice clouds in the high latitudes. The impact of this study will be to provide better information to the scientific community on processes that are important for understanding the warming Arctic region. The project will also enhance collaboration between US and UK scientists and provide training for early career researchers. This project addresses three primary research questions: (1) Do current representations of surface properties capture the longwave emission spectrum of snow and ice surfaces correctly? (2) Is a new light-scattering model able to reconcile ice cloud microphysics (ice crystal sizes, shapes) with energetic (radiative) impact across the longwave spectrum? (3) Can our radiative transfer models successfully match simultaneous observations of the longwave energy spectrum at the surface, within the atmosphere and at the top of the atmosphere under a variety of different atmospheric and surface conditions? The research team will address these questions through a multi-faceted plan. First, the team will use measurements from a new instrument, called the Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) that has been deployed in Norway and Canada in the past few years. The deployment in Canada was matched with airborne observations of clouds, providing both radiative and cloud properties to the research team. This data will be used to evaluate a new ice cloud optical property model, assess the impact of snow and ice emissivity models in an Earth System Model, and combine the ground and airborne data with satellite observations to achieve radiative closure from the surface to space across the infrared spectrum for the first 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 2025 · 2025-07

This project will investigate the cycling of the nutrient element nitrogen in Lake Erie. This study will be the first to comprehensively study the nitrogen cycle throughout the year, including in winter when the lake is covered with ice. Recent work in other lakes suggest that during winter, under-ice processes are important in determining the forms of nitrogen available to phytoplankton in spring and summer. A healthy spring bloom of phytoplankton called diatoms, in turn, is important to the lake food web and in mitigating against harmful algal blooms that can affect water quality. However, very little is known about winter nitrogen cycling in any of the Great Lakes, including Lake Erie, arguably one of the most important resources in the country in terms of drinking water supply, recreation, and fisheries. The project will support undergraduate students at both the University of Michigan and Bowling Green State University, and a graduate student at BGSU. The team will create a curriculum module on the Great Lakes and water quality for high school students. The goal of this work is to understand how winter nitrogen cycling impacts nutrient balance and phytoplankton community structure in large, temperate waterbodies, using Lake Erie as a case study. The researchers will generate the first quantitative measurements of nitrification, nitrogen uptake, and ammonia regeneration during winter in Lake Erie, to assess the effects of ice phenology on nitrification rates and how changes in nitrogen availability and speciation may affect seasonal phytoplankton communities. By combining nitrogen cycling rate measurements with community composition data, they will also investigate how different nitrogen substrates may favor certain phytoplankton groups (e.g., diatoms, dinoflagellates, cyanobacteria) across seasons. Over three years the team will 1) Measure ice onset, duration, and thickness throughout winter sampling; 2) track water column nitrogen substrate pools (nitrate, ammonia, organic nitrogen) and rates of uptake/transformation; 3) examine seasonal phytoplankton abundance and gene expression; and 4) conduct stable isotope probing experiments to measure cell-specific nitrogen incorporation in winter phytoplankton communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Algebraic geometry studies solution sets of systems of polynomial equations. For instance, lines are solution sets of linear polynomial equations, while circles and hyperbolas are solution sets to quadratic polynomial equations, and their study goes back to the ancient Greeks. The solution sets of systems of many polynomial equations in many variables often have beautiful and complicated geometry. The PI will apply new and modern techniques to answer questions of classical interest in the field of algebraic geometry, and to address long standing open problems about the geometry of spaces defined by polynomial equations. He will also continue his energetic engagement with training future generations of mathematicians, including through mentorship of graduate students and postdocs. The PI will pursue three main research directions: cohomology of moduli spaces of stable curves, cohomology of moduli spaces of smooth curves, and the local monodromy conjectures for hypersur- face singularities. He will confirm predictions of the Langlands program and the Hodge conjecture for moduli spaces of stable curves, using new results on the Chow cohomology and cycle class maps for moduli spaces of smooth curves. He will apply new results on the cohomology of moduli spaces of stable curves to study the weight-graded cohomology of moduli spaces of open curves, proving new non-vanishing results for cohomology of mapping class groups and producing new generating functions for weight-graded Euler characteristics. And he will pursue a proof of the motivic, p-adic, and topological local monodromy conjectures for hypersurface singularities, along with related conjectures such as the monodromy and holomorphy conjectures for p-adic local zeta functions twisted by a character. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project concerns properties of solutions of nonlinear evolution equations and related ordinary differential equations that model the propagation of large-amplitude waves in physical media such as surface and internal water waves or electromagnetic waves in optical fibers. The equations studied are completely integrable systems, for which there are many more analytical techniques available than for more general equations. However, at the same time integrable equations are known to arise from more general equations in certain limiting cases. Each time that a new result is established for a completely integrable model that arises in this way, a version of that result immediately applies to all of the more general models. These properties make the study of integrable models both mathematically compelling and also of prime physical relevance. This project uncovers new solutions of integrable equations of recognized importance and studies their exact and asymptotic properties. Knowledge of the solutions obtained impacts several application areas such as marine engineering (prediction/properties of large-amplitude surface water waves or "freak waves" and deformations of the free surface between layers in a density-stratified ocean) and condensed matter physics (Bose-Einstein condensation). As part of the project, computer codes and a textbook are being produced for public consumption. Three PhD students are being partially supported to work on the project. Specifically, this project addresses open problems in the theory of internal waves in fluids and related evolution equations that are fundamentally nonlocal. Long-time asymptotics for the Benjamin-Ono equation are being established, and soliton gases are being constructed for this equation. A nonlocal model of nonlinear Schroedinger type is being studied. Pivoting to more classical nonlinear Schroedinger equations and their relatives, dynamical stability of slowly-decaying rogue waves on the zero background ("rogue waves of infinite order") is being analyzed, uniformity of asymptotic behavior of such solutions in the large is being determined, a mechanism for generating such waves from extreme self-focusing is being explored in several models, a family of solutions continuously interpolating between rogue waves and solitons is being analyzed, and a universality result is being proved for the first time in the setting of the defocusing nonlinear Schroedinger equation. The project also addresses open questions in the theory of Painleve equations, establishing an isomonodromic representation for general special-function solutions and studying asymptotic limits in the parameter space for which in some cases there are outstanding conjectures in the literature. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project focuses on the development of an innovative battery modeling software tool to meet various simulation scenarios and customer requirements. The increasing demand for enhanced electric vehicle performance places significant requirements on battery design and battery management systems, particularly in accurately and efficiently predicting battery states, capacity, and aging conditions. The solution helps to accelerate the design of battery cells and packs, facilitate their integration into electric vehicles, and improve their performance during operation. The technique being commercialized will facilitate the design of high-performance batteries that are essential for electric vehicles and other applications, addressing critical global energy and environmental challenges. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a robust, efficient, and versatile battery simulation software to foster innovation across a wide range of battery design and application needs, including battery cell design, battery pack design, life estimation, and battery management design. This software technology has several unique features, including building on advanced, validated physics-based battery degradation models; offering superior prediction accuracy and reliability across various conditions; providing a cost-effective solution to reduce development and deployment costs; and supporting a wide range of applications, including lithium-ion and emerging solid-state batteries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project focuses on developing new analytical tools for making informed decisions based on complex data types, such as text, images, and gene expressions. Traditional methods of analysis often rely on structured data, but modern challenges require innovative approaches to understand cause-and-effect relationships in high-dimensional datasets. The goal is to improve our ability to extract meaningful insights from diverse sources of information, enabling researchers and practitioners to draw reliable conclusions even when dealing with large amounts of unstructured data. By creating new causal inference methods and training the next generation of data scientists, this project aims to enhance our understanding of complex systems and promote inclusivity in the field of machine learning and data science. This project involves three primary research objectives to advance causal inference for high-dimensional unstructured data. Firstly, we will develop novel causal inference methods specifically designed to accommodate high-dimensional treatments or outcomes, enabling more accurate modeling of complex cause-and-effect relationships. Secondly, we will design probabilistic causal representation learning algorithms to uncover latent causal variables beneath high-dimensional observations. Finally, we will investigate new extrapolation and experimental design techniques for causal inference with unstructured data, allowing us to better design experiments that can effectively tease apart cause-and-effect relationships. Through these research activities, we aim to significantly enhance current methods in machine learning and causal inference, ultimately enabling more reliable and effective causal analysis across diverse scientific fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

People who are blind use assistive tools to read text, recognize objects, and get around. These tools work well for common use cases. However, this one-size-fits-all approach does not address many of the specific needs. As a result, people who are blind put forth a lot of effort to find workarounds to overcome these challenges. These solutions are often tedious, overwhelming, and do not work well. This project empowers people who are blind to create their own personal assistive technology to meet their specific needs. Personal computers have changed the way people create and innovate. Personal assistive technology may enable people who are blind to become more independent. This project provides them with a way to create their own solutions. This research program aims to transform the creation of assistive technology. A primary goal is to empower people who are blind to make their own solutions. This is accomplished using novel tools powered by artificial intelligence and end-user programming. This work helps to meet the persistent and specific needs of people who are blind instead of using one-size-fits-all solutions. The research will follow a human-centered design, development, and evaluation process. First, it will contribute to a deeper understanding of the more specific needs of people who are blind. The results and impacts will be recognized from a group to an individual level. Second, it will contribute a framework to support the creation of personal assistive technology using end-user programming. Third, it will identify and contribute methods to adapt user creations to be easy to share, discover, and reuse. This project will use developed tools to teach programming and computer science concepts to blind learners and users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project enhances the ability of the Research Data Ecosystem (RDE) to improve access to the Inter-university Consortium for Political and Social Research’s (ICPSR) Comprehensive Data Archive, the world’s largest social science data archive. In this project, ICPSR at the University of Michigan transitions its vast repository of over 20,000 studies and 6 million variables to RDE’s cutting-edge digital platform. This enhances the findability and usability of essential data resources. RDE’s modern technology gives scientists across the fields of science and engineering the ability to efficiently access and analyze different data types, increasing the reproducibility of their research. Such improvements are vital for advancing scientific breakthroughs that can lead to groundbreaking discoveries and for supporting data-driven policy decisions that can bolster national health, prosperity, and welfare and contribute to national security. Moreover, metadata mapping and enrichment analysis align existing data to FAIR standards, ensuring the discoverability and interoperability of various data types, including video and geospatial data, and increasing AI readiness. These invaluable data resources are made more accessible to all Americans, thereby enriching data literacy and strengthening the U.S. data workforce. This project migrates ICPSR's extensive data archives to the modernized Research Data Ecosystem (RDE) software platform, enabling improved data accessibility, usability, and security. Utilizing a robust ETL (Extract, Transform, Load) process, ICPSR's over 20,000 studies and 6 million variables are transitioned from outdated systems using a scalable and robust Data Migration Framework involving backend services and infrastructure for security and storage. Metadata mapping and enrichment analysis align existing data to FAIR standards, ensuring the discoverability and interoperability of various data types, including video and geospatial data. Workflow mapping analysis consolidates complex and disparate workflows from ICPSR’s legacy systems to a streamlined workflow in the modernized RDE platform, delivering a significantly better user experience for depositors, curators, project managers, reviewers, and administrative staff. Through three phases, the migration progressively integrates self-published, high-value public-use, and restricted datasets into the RDE software platform. This enables new and advanced functionalities, including for metadata enhancement, data exploration, and secure cloud-based analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Nanophotonics has become of critical importance in advancing the frontiers of modern science and technologies, including integrated photonics for information technologies, photonic quantum information systems, superresolution imaging, sensing etc. In nanophotonics, light is controlled by nanoscale structures engineered precisely for the desired photonic properties. Traditionally designs of specific nanophotonic devices are obtained through empirical, trial-and-error methods with very limited, high level guidance by physics models and intuition. The advancements in artificial intelligence (AI) techniques open up new opportunities to more efficiently design new and more optimal nanophotonic systems. Yet there are many fundamental challenges at present, such as requirement of large training data sets, domain adaptation issues, and limited generalization capabilities. A promising new approach that may mitigate these limitations is to use generative models, particularly score-based diffusion models. This project aims to develop an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models to overcome key challenges in scientific inverse design and accelerate scientific discovery. The research will advance the frontiers of artificial intelligence and nanophotonics. Furthermore, the developed methods are potentially generalizable to other scientific disciplines. Educational impacts include enhancing engineering and physics curricula for undergraduate and graduate students. Furthermore, the project will engage high school students in southeast Michigan through outreach initiatives and integrate undergraduate students into research activities. Collaborations with local organizations will further support academic research. The goal of the project is to establish the first model of generalizable, AI-assisted inverse design of complex nanophotonic systems through cross-disciplinary collaboration. Domain-specific generative diffusion models will be developed that efficiently capture photonic structure data priors with limited training data. A novel physics-informed machine learning approach will be integrated to ensure accurate predictions of physical properties and reliable conditional inverse design. A physics-guided posterior sampling method will enforce physical constraints during inference, enhancing the model’s reliability. The framework will be validated by inverse design of advanced topological photonic 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.