Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2025 · 2025-09

To grow to their incredibly large sizes, galaxies need to accumulate material from their surrounding medium. It is the gas that surrounds galaxies that provides the raw material to create stars and to fuel the growth of black holes. This program will detect the gas that surrounds galaxies and advance our understanding of how galaxies grow. The investigators will analyze data obtained with a new generation of astronomical instruments. The program will support the training of a postdoctoral scholar. This program will also enter a partnership with the Wolverine Pathways program at the University of Michigan. Wolverine Pathways is a well-established program that offers college preparation and academic development opportunities made available to every student in middle and high school in the Metro Detroit area. On long timescales, gas flows from the intergalactic and circumgalactic medium (IGM/CGM) are thought to fuel galaxy and Supermassive Black Hole (SMBH) growth. Energy and momentum released by quasars and young stellar populations couple to the interstellar medium to regulate gas cooling while expelling metals to intergalactic space. The IGM and CGM serve as reservoirs to fuel galaxy evolution while also containing a record of past feedback. The advent of wide-field integral field spectrographs (IFS) available on large telescopes revolutionized our ability to directly image IGM/CGM flows in emission, providing unique 3D (2 spatial + velocity) information along with critical spectral diagnostics of density, temperature, and even metallicity. This program will leverage wide-field IFS observations of 10 to 100 kpc scale flows around 84 archival quasars and 30 new ones at z = 0.2 to 1.5 to study galaxy and SMBH feeding and feedback during the period of “cosmic dusk,” over which star formation and quasar activity declined dramatically from their peak at z = 2 to 3. All datacubes analyzed for the program and corresponding catalogs will be made publicly available. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Modern data centers rely on large-scale memory systems to support everything from search engines to scientific computing. However, the increasing demand for memory leads to significant resource and energy use, particularly when older hardware is discarded before its useful life ends. This project addresses that challenge by developing MemWise, an intelligent system that efficiently manages memory across a range of hardware types. The project’s novelties are a programmable memory controller that coordinates how data is moved and stored across different generations of memory; a technique for reusing older memory modules alongside newer ones to reduce cost; and new fault-tolerant methods to ensure reliability even as memory components age. The project's broader significance and importance are in demonstrating that thoughtful system design can extend hardware lifespans and reduce waste, without sacrificing performance. MemWise is tested in a range of real-world environments, including public cloud services, private infrastructures, and direct-to-hardware computing setups, highlighting its adaptability and impact across the data center ecosystem. Specifically, the project designs a tiered memory architecture connected via a flexible hardware interface called Compute Express Link (CXL). A programmable controller dynamically moves data among memory types based on usage patterns, age, and reliability. To guide decision-making, the research introduces a new metric that jointly considers system efficiency and application performance. This allows both software developers and system tools to make informed trade-offs in real time. The controller also supports automatic data migration and transparent fault handling across unreliable memory units, which is essential for mixed-hardware environments. By co-designing hardware and software and validating ideas with real prototypes, the project advances the state of memory system research and offers practical strategies for improving the long-term efficiency of computing infrastructure. Expected impacts include lower operational costs, reduced waste, and broader educational opportunities for students involved in the 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 2025 · 2025-09

Viruses experience frequent small random changes in their genetic material, their genomes. Because many of the changes in the viruses that infect one animal happen independently of those that happen in another animal, one can compare the genomes of sampled viruses to glean information about how far and fast an epidemic is spreading. This is known as phylodynamics. This project will develop new mathematical and computational tools to allow us to extract more information about how a virus is moving through a population of animals from virus genomes. Specifically, recent mathematical breakthroughs allow us to understand more precisely how aspects like virus transmission, severity of disease, and duration of immunity—and differences among animals in these aspects—leave their marks on virus genomes. The project will capitalize on these developments, along with recent advances in machine learning technology and the world’s premiere database of avian influenza virus genomes, to reduce some of the key uncertainties about how this virus spills over from wild birds into domestic animals, and potentially into humans. The project is expected to benefit public health by helping us better understand how avian influenza spreads and where the greatest risk-points are by increasing the usefulness of a very common kind of data. The mathematical and computational tools developed will also be useful in other scientific and medical fields, including cancer biology and microbiology. The project will develop a short-course for training epidemiologists and mathematical biologists in phylodynamic methods. Phylodynamics seeks to extract information from genomes of individuals to shed light on population-scale dynamic processes. Its development has largely been driven by applications in epidemiology, where pathogen genomes contain information concerning determinants of disease transmission. In this context, phylodynamics has become essential in guiding public-health response in epidemics at a variety of geographical and temporal scales. From the mathematical point of view, the aim of phylodynamics is to infer the structure and parameterization of mathematical models of demographic processes on the basis of accumulated differences among sampled genome sequences. Existing approaches rest on assumptions (large population sizes, small sample fractions, linearity of demographic processes) that are becoming increasingly dubious as the intensity and volume of genomic sampling grows and as phylodynamic methods are increasingly being applied at the leading edge of emerging outbreaks and in the face of strong nonlinearities. The project will develop accurate, scalable inference methods with minimal theoretical restrictions, based on recent mathematical advances by the project team. The first builds on mathematical breakthroughs that permit precise estimation of dynamic models from reconstructed phylogenies, while the second seeks to bypass the need for phylogenetic reconstruction altogether by applying new machine learning methods to structured genome-alignment data. Data from the world’s premiere database on avian influenza genomes will be used to resolve outstanding uncertainties regarding transmission within different host species, spillover rates, and seasonality in this system. The work will have applications beyond epidemiology in fields such as systematic biology, cancer biology, microbial ecology, and population genetics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The vision of this research project is to transform the field of adaptive structures and materials through pioneering the embodiment of intelligence in the mechanical domain of novel topological metamaterials. More specially, this research seeks to harness condensed-matter-physics-inspired topologically-protected wave networks and the computing power hidden in architected materials to achieve structures possessing the essential elements of intelligence, such as perceiving and learning information from sensory input, memorizing information, and making decision on actions. This effort looks to significantly elevate future machine autonomy with better energy efficiency, more direct mechanical interaction with the surroundings, and much higher resilience against harsh environment and cyberattack. The outcomes intend to address the emerging societal needs for highly effective, efficient, safe and secured autonomous systems, from human-centric robots, automated vehicles, and smart wearables, to self-monitoring infrastructures, widely benefiting many industries. In addition, this project will integrate its research outcomes into new teaching curricula and outreach activities, cultivating students’ interest in STEM pursuits under the inspirational theme of mechanical intelligence. The research goal is to advance the state of the art by pioneering topological wave dynamics and physical computing as the needed foundation to create and integrate the essential elements of intelligence in and through mechanical metamaterials as building blocks to achieve real-time, on-demand, and highly robust intelligent structural systems. Several research questions will be addressed: (1) How to create reconfigurable mechanical metamaterials and harness their topological states to compute and achieve mechano-intelligence? (2) How to realize topological elastic waveguide, wave networks, and higher-order topological physics in complex metamaterials? (3) How to best integrate physical reservoir computing and wave-based computing for mechano-intelligence? (4) What are the effects of the matter’s mechanical designs on its intelligent performance? (5) How should intelligence in the mechanical domain interconnect with electronics efficiently while maximizing performance? Four tasks with a combination of theoretical, computational, and experimental efforts are pursued. Task 1 aims to uncover the knowledge of creating higher-order topological physics in multidimensional mechanical metamaterials. Task 2 seeks to develop and embed physical computing in and through topological mechanical metamaterials to achieve the essential elements of intelligence. Task 3 looks to create integrated mechano-intelligence in metamaterials for autonomous engineering functionalities. Task 4 will experimentally investigate and validate the efficacy of the ideas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project maintains the ANES gold-standard tradition of a scientifically valid probability-based public opinion survey while adding cutting-edge innovations that link AI methodology to advances in survey methodology. The project uses AI and automation processes to improve the sample validation processes and to improve accessibility and usability of ANES’ online resources. The ANES project continues a strong working relationship with the Comparative Study of Electoral Systems (CSES), the General Social Survey (GSS), and commercial companies. Furthermore, the project has developed a substantial user community, and more than 45,000 individuals have utilized ANES data since 2018. The ANES is a powerful educational tool, as 80 percent of the 45,000 individual users are undergraduate or graduate students at universities around the country. This current ANES project administers both pre- and post-election interviews, produces several new data products and methodological innovations, and includes the first-ever ten-year panel in the ANES time series. The project makes methodological advances in the use of AI in survey research by employing AI and automation processes to improve the matching ANES respondents’ files with commercial voter files and using AI to automate programs that evaluate the website and improve the accessibility and usability of ANES’ online resources. The 2026 study adds a 2026 wave to the ANES panel and delivers a Social Media Study that will be the longest and largest panel study of social media information linked to individual survey data in existence. The project continues ongoing collaborations with a domestic survey project (the GSS) and 66 international survey projects (the CSES) that yield new datasets of interest to scholars in sociology, economics, communications, comparative public policy, and international relations. Methodological innovations include a non-response follow-up study, innovations that improve sample representativeness, improvements in video interviewing, and the use of a mixed-mode design to yield further insights about survey mode effects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Prototyping is a crucial activity in engineering solution design and development. Engineers regularly build multiple prototypes not only to test functionality, but also to explore ideas, communicate with teammates and stakeholders, learn from failures, and guide, decision-making throughout the design and development process. However, engineering undergraduate students often adopt a narrow view of prototyping, treating it primarily as a final step to verify that their design works. This limited perspective means that students may miss out on the deeper value of prototyping as a powerful tool for creative thinking, iterative problem-solving, and collaboration. In order to prepare engineering students to be effective and innovative engineers, we must better support them in understanding and using prototypes in the many ways they can support engineering work. Thus, this project will investigate how faculty teach prototyping, how students actually use prototypes in their coursework, and what factors support or hinder more expansive, reflective prototyping practices. This work will advance the national goal of improving engineering education by better aligning classroom experiences with the realities of professional practice. By studying both educator intentions and student experiences, the project will develop practical strategies that instructors can use to help students see prototyping not only as a technical skill, but also as a mindset and process for learning, communicating, and designing with purpose. This project will investigate the goals and practices of engineering educators as well as the behaviors and experiences of students with regard to prototyping in design-focused courses. Grounded in Social Cognitive Theory and the Prototyping for ‘X’ framework, the study will be guided by the following research questions: RQ1a) What goals do engineering faculty report having for how their students use prototyping? RQ1b) How do their pedagogy and assessment align (or not) with those goals as reported? RQ2a) How do students report using prototyping in their design projects? RQ2b) What factors do students report affecting how they use prototypes? Insights from this research have the potential to understand and address the current gaps in students’ use of prototypes and how engineering faculty goals and challenges and the classroom environment and experiences they create influence the ways that students prototype. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This research project aims to advance fundamental understanding of the deformation behavior of amorphous solids with many-body interactions among constituent particles. Unlike crystalline materials, amorphous solids such as metallic glasses and granular systems exhibit complex, non-crystalline structures and deformation mechanisms that are not captured by conventional models based on binary interactions. In many-body systems, the interaction between two particles can be influenced by the presence of a third — a phenomenon common in biological tissues and social networks but not yet well understood in the context of amorphous solids. This project seeks to extract governing physics from simulations and experiments of amorphous systems with many-body interactions and develop new solid mechanics models to predict their behavior. The outcomes seek to to impact a wide range of applications, including adaptive metamaterials, two-dimensional materials, and damage-tolerant structural materials. Educational activities include hands-on demonstrations, curriculum integration, and public outreach aimed at fostering future scientists and engineers. The technical objective is to quantify how many-body interactions influence deformation through the lens of energy landscape complexity. Atomistic simulations and machine learning–assisted analyses will be used to study emergent features in the energy landscape while systematically tuning many-body interactions. These findings look to be validated through two granular experimental systems with fluid-mediated interactions arising from air-fluidization and capillarity. Realistic interactions will be modeled using graph neural networks and integrated into atomistic simulations. A key focus is to uncover how many-body effects alter the synchrony between particle-level and system-level energy responses during deformation. The results will inform a meso-scale elastoplastic finite element model capable of capturing strain localization and shear band formation. Ultimately, the project aims to enable the design of advanced amorphous materials by engineering the morphology of their energy landscapes via tailored particle 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 2025 · 2025-09

The investigator advances artificial intelligence and optimization through the study of partial differential equations (PDEs) on Wasserstein space, addressing fundamental challenges in machine learning that bolster national prosperity and technological innovation. This project serves the national interest by developing mathematical tools to enhance the efficiency and robustness of artificial intelligence (AI) systems, which drive advancements in healthcare, technology, and economic competitiveness. By tackling critical problems in derivative-free optimization, stochastic filtering, and adversarial decision-making, the research strengthens the scientific foundation of AI, a priority area for NSF. Additionally, the investigator mentors graduate students and postdoctoral researchers through these problems, fostering a skilled STEM workforce and contributing to national scientific leadership through education and professional development. The investigator studies the well-posedness of second-order PDEs on Wasserstein space, applying weak propagation of chaos to achieve exponential convergence in high-dimensional optimization problems central to machine learning. The project employs rigorous theoretical analysis of master equations, extending results to particle systems on unbounded domains influenced by common and idiosyncratic noise. These methods address goals in consensus-based optimization, controlled stochastic filtering, and adversarial bandit problems, with applications to AI-driven technologies. The research contributes new theoretical insights into PDEs, delivers practical advancements for optimization and learning challenges, and supports NSF’s mission by aligning with national priorities in artificial intelligence and scientific progress. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Electromagnetic waves with frequencies in the ultra-low frequency (ULF) range are known to be among the leading causes of radial diffusion and transport of energetic electrons in Earth's radiation belts. The frequencies of ULF waves overlap with the range of drift frequencies of energetic electrons as they circle the Earth, leading to resonant interactions. Numerous expressions have been derived to quantitatively describe radial diffusion so that they can be incorporated into global models of radiation belt electrons. However, most expressions of the radial diffusion rates are derived only for equatorially mirroring electrons and are based on estimates of the power of ULF waves that are obtained either from spacecraft close to the equatorial plane or from the ground. Recent studies using the Van Allen Probes and Arase have shown that the wave power in magnetic fluctuations is significantly enhanced away from the magnetic equator, consistent with models simulating the natural modes of oscillation of magnetospheric field lines. This has significant implications for the estimation of radial diffusion rates, as higher pitch angle electrons will experience considerably higher ULF wave fluctuations than equatorial electrons. This project will derive the magnetic and electric field wave powers and incorporate them into the 3D test particle simulations to estimate the diffusion coefficient. The novel, pitch-angle-dependent diffusion rates will be introduced to a global model of radiation belt electrons to evaluate the effect of the pitch-angle dependence of the diffusion coefficient on radiation belt dynamics. The result could have significant implications for the radial diffusion rates as currently estimated. It will pave the way for incorporating pitch-angle-dependent radial diffusion coefficients in global models to predict the near-Earth radiation environment better. The main goal of this project is to quantify the role of off-equatorial Ultra-Low Frequency (ULF) waves on the radial transport and diffusion of relativistic electrons (100s keV to few MeV) in the outer radiation belt (L~4 to 7), investigating the effect of pitch-angle-dependent radial transport of energetic particles on global dynamics of the radiation belts. The following science questions will be answered: How are ULF electric and magnetic field fluctuations distributed in magnetic latitude and magnetic local time under varying solar and geomagnetic conditions? What is the role of off-equatorial ULF wave fluctuations on the radial diffusion and transport of relativistic electrons in the outer radiation belt (L~4 to 7)? How are off-equatorial ULF waves expected to impact current radiation belt models, and what is their contribution to the global dynamics of the radiation belts? The team will use multiple satellite datasets (THEMIS, Van Allen Probes, Cluster, and Arase), test particle tracing simulations, and a global radiation belt model to quantify the contribution of off-equatorial ULF waves on radial diffusion in the radiation belts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The prevalence of matter over antimatter is one of the most important unexplained observations in physics. As currently understood, the laws of physics predict that there should be an equal amount of matter and antimatter, which is at odds with our everyday experience and detailed astronomical observations. Such an inconsistency suggests that our current understanding of the laws of physics may be incomplete. ALPHA is an interdisciplinary antimatter experiment at the European Organization for Nuclear Research, known as CERN, that tests this notion by producing antihydrogen and sensitively measuring its properties in comparison with the hydrogen atom. These experiments are improved by the efficient conversion of collected antiprotons and positrons into antihydrogen. Trapping antimatter to produce antihydrogen is a plasma physics problem, consisting of collecting and manipulating large collections of charged particles using electric and magnetic fields. This award supports a joint effort between the University of Michigan and Marquette University, in collaboration with Brookhaven National Laboratory, that will advance the understanding of the novel plasma physics processes expected in antimatter traps and will conduct experiments using ALPHA to test the predictions. This project will also contribute to developing the next generation of the science and technology workforce by supporting the training of undergraduate and graduate students, and will contribute to local education and science engagement activities, including development of a plasma physics exhibit for the Discovery World Museum in Milwaukee, WI. Trapped antimatter is novel from a plasma physics perspective, as well as a particle physics perspective. These plasmas are so cold, and the magnetic field in the trap is so strong, that they exist in a state that is not well described by the usual models of plasma physics. Specifically, the low temperature causes the plasma to be strongly coupled, which means that it behaves more like a supercritical fluid or a liquid, than the more common dilute-gas-like behavior. The strong applied magnetic field, in combination with the low density, causes the plasma to be strongly magnetized in the sense that the circular gyromotion that charged particles make in response to the magnetic field is much smaller than the scale over which particles interact. Currently understood methods of plasma theory do not apply in either of these circumstances. This award will enable continued development of theoretical approaches that extend plasma theory into these domains, and then testing them by conducting two specific experiments on ALPHA that will measure (1) the rate of temperature relaxation between electrons and antiprotons, which is predicted to be delayed by strong magnetization of the electrons, and (2) the sympathetic cooling rate of positrons with other particles, such as Beryllium ions and protons. An expected outcome is that a better understanding of the underlying plasma physics may be used to improve ALPHA experimental operations by increasing production rate of antihydrogen atoms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project advances the mathematical foundations of mean field games, a powerful framework for modeling the collective behavior of large populations of strategic agents. While most existing research has focused on finite-horizon interactions, this project investigates the long-term dynamics of these systems, where questions of stability, equilibrium selection, and robustness are especially critical. Many real-world systems - such as communication networks, financial markets, and ecological populations - evolve over extended periods and require both coordination and long-run predictability. By analyzing how stable behavioral patterns emerge and persist in such settings, this research contributes to a deeper scientific understanding and supports the development of resilient technologies. The investigator aims to rigorously connect the long-horizon behavior of finite-agent stochastic games to their mean field counterparts. The project explores structural features of these games that remain stable as the number of agents grows, quantifying the long-run deviation from equilibrium. A learning framework is also developed to guide agents toward equilibrium behavior while adapting to unknown parameters, with particular attention to convergence rates and long-run regret. Furthermore, the project examines systems with multiple mean field equilibria, developing probabilistic tools and numerical methods based on large deviations and deep learning, to describe metastable behaviors and transitions between equilibria. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to advance artificial intelligence (AI)-driven complex system-of-systems research by designing an AI-ready testbed for scaled deployment of autonomous vehicles, building on the foundational work of Mcity 2.0. While current automotive testbeds primarily support individual vehicle testing and safety validation, the vision of this project expands to an ecosystem approach that enables real-world exploration of multi-vehicle interactions. This includes vehicle and vulnerable road user interactions, as well as interactions with the supporting infrastructure that is essential for autonomous mobility at scale. In addition, the Mcity testbed will explore other factors needed for the safe deployment of autonomous vehicles at scale including operational support systems, such as robotic charging, cleaning, and inspection processes that are critical to robotaxi deployment and fleet resilience. This planning grant prepares the work for the proposed testbed which will serve as a national resource for pioneering research on the operational dynamics of autonomous vehicle networks, encompassing fleet-level coordination, multi-modal traffic management, and seamless infrastructure connectivity. By enabling a holistic examination of autonomous vehicle deployment, the testbed will allow researchers to study the challenges of scaling from single-vehicle testing to comprehensive fleet management, ensuring safety, efficiency, and robustness in complex, real-world scenarios. This expanded test environment will bridge critical gaps in the field, supporting innovations that are key to realizing autonomous vehicle systems as a reliable, integrated part of urban transportation infrastructure. This planning grant enables the team to prepare for the full proposal by obtaining feedback from the community and identifying the concrete requirements to upgrade and improve the existing Mcity 2.0 testbed to realize the goals described above. 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 award supports research to develop inflatable soft robots that are portable, safe, and adaptable for a wide array of environments. Unlike traditional rigid-bodied robots, inflatable robots can be compactly stored and safely deployed due to their lightweight and compliant structure. This project introduces a new class of untethered shape-morphing inflatable robots that addresses the critical limitations of current inflatable robots, namely their dependence on external power sources and slow actuation speeds. These new robots will store energy in high-pressure fabric bodies, employ novel embedded high-flow valves for fast and efficient actuation, and achieve on-demand shape changes to adapt their morphology for specific tasks and varied environments. This research supports the national welfare by advancing robotic capabilities in domains where safety, deployability, and adaptability are crucial such as disaster response, exploration, and personal assistance. Beyond robotics, the underlying technological innovations in pneumatic actuation and inflatable structures have other potential applications as well, such as emergency shelters, wearables, and other systems that require lightweight, reconfigurable components. The research is integrated with a comprehensive education and outreach plan that includes graduate and undergraduate student mentoring, as well as hands-on workshops for K-12 students to foster broad participation in STEM and inspire the next generation of engineers and roboticists. The project introduces a new paradigm for soft robotic systems through three tightly integrated thrusts: (1) development of high-pressure fabric robot bodies that serve as distributed energy storage systems for pneumatic actuation; (2) design of compact, high-flow embedded valves that increase actuation bandwidth by reducing the fluid resistance between the pressure source and actuators; and (3) creation of shape-morphing structures capable of rapidly adapting robot morphology through selective pressurization. Together, these contributions enable inflatable robots to operate untethered for extended durations, execute dynamic movements, and transition between locomotion modes suited to different terrains or tasks. The research will also yield scalable fabrication techniques, geometric modeling frameworks, and experimental validation of robot prototypes. Ultimately, this work establishes a foundation for multifunctional, deployable, and adaptive soft robotic 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 · 2025-08

Advances in data processing and learning algorithms are paving a road that will transform our society through improvements in technologies such as artificial intelligence. In parallel, scientists are exploiting the far-reaching implications of quantum mechanics to dramatically improve the capabilities of computers and telecommunication networks. At the heart of quantum computing and quantum telecommunications networks is a purely quantum feature called entanglement, in which unique states of matter and radiation are created. The research team will explore new methods for efficiently and effectively creating such entanglement. To do so they will use optical fields to create arrays of atoms which interact with quantum radiation fields. By creating controllable environments of atom arrays using optical trapping fields, the research team will study the underlying mechanisms responsible for atom-atom and atom-field entanglement. The team will also employ different theoretical models to help explain the experimental observations. As a result this project will lead to a deeper understanding of the interaction between atoms and quantum fields that can serve as a springboard for the development of novel methods for achieving scalable generation and distribution of entanglement. During this project students will be trained in state-of-the-art techniques in experimental physics, optics, electronics, and computer-based data acquisition. The PIs will investigate two types of atomic systems aimed at quantum networking: the first one employing an array of single-atom qubits for processing, storage, and atomic qubit mapping into photons, and the second one using qubits encoded in ensembles of atoms. Both of these approaches allow for Rydberg-based generation of multi-qubit entangled states, second-timescale entanglement storage within ground hyperfine manifolds, and efficient generation of atom-light entanglement. Atoms will be confined using either optical tweezers or optical lattices that provide state-insensitive potentials for the atomic qubit states. This will allow for controlled preparation of strongly-interacting, many-atom quantum state superpositions, and their storage on timescales of seconds. Using these systems, the PIs will explore new approaches to generating and distributing atom-light entanglement. The research team will focus on elucidating connections between fundamental light-matter interactions and functionalities relevant to quantum networks; e.g., they will study the role of the dipole-dipole interaction processes in atom-field mapping, and spatial and temporal superradiance in atomic arrays. They will also study the validity and applicability of the rotating-wave approximation and the Weisskopf-Wigner approximation, while considering problems related to "mixed' superradiance where competition between unphased and phase-matched superradiance occurs when the atoms are excited to a phased, collective state containing more than a single excitation. 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 doctoral dissertation research project examines the factors that influence the adoption or non-adoption of biotechnological advances in agricultural production. Investigators specifically study how farmers and agricultural scientists manage enhanced food security and the preservation of biodiversity in the adoption of agricultural biotechnologies. Data collection includes interviews with and behavioral observation among research scientists and staff, agricultural workers, marketing experts, and farmers, with an emphasis on observations of scientist-farmer engagement. In addition to training a graduate student in anthropological science and expanding the US STEM workforce, broader impacts include collaborative, non-technical reports, outreach to farming stakeholders and other local food producers, and a digital archive of project findings for the public. The biotechnology focus on genetically modified agriculture contributes to environmental anthropology, the social science of agricultural technology, and NSF investments in understanding human adoption of biotechnology innovations. 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 fossil record has provided numerous insights about early hominin lineage. Through field research and laboratory analyses this study reconstructs the environmental conditions under which ape and hominin lineages diverged. To develop a refined perspective of the landscapes and environments that led up to and followed this divergence, this study collects fossil vertebrates, establishes a complete chronology, analyzes paleo-environmental proxies, as well as spatial information. AI-generated algorithms are used to analyze spatial data, and biotechnology methods are applied in the analyses of stable isotopes. The study’s approach significantly contributes to current scientific knowledge of ape and hominin evolution, provides field and laboratory training for students, and develops outreach activities that enhance public education. To reconstruct the conditions under which the ape and hominin lineages diverged, this study conducts systematic analyses of faunal collections, phytolith, pollen, and n-alkane. The study analyzes and interprets sedimentary rock formations and integrates spatial information using AI generated algorithms. The study also develops a detailed chronostratigraphy that refines current knowledge. Fossil herbivore dentition is examined through microwear and other biotechnology methods (stable isotopes). Together these approaches provide a detailed environmental reconstruction that advances understanding of the selective forces that led to the emergence of the hominin lineage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

In this era of extrasolar planet discovery and characterization, the leading theory for the formation of Jupiter-like planets is core accretion, in which a rocky core about 10 times the mass of Earth gradually gathers an extended gaseous envelope, which eventually collapses onto the core. One critical issue is the role played by angular momentum during the gathering phase, which limits the mass supply, alters the collapse structure, and generates a disk around the growing planet. This project will comprehensively model these effects, acting as an analytical guide to more complex modeling. It will also support an existing lecture series, Saturday Morning Physics, at the University of Michigan. The gathering phase, due to its long timescale, can be limited by the lifetime of the disk, so the protoplanet may fail to become a giant planet after all. This project models the interior structure of a growing, rotating protoplanet. Based on a heritage of rotating stellar models, a solution of hydrostatic equilibria will be found. How the equilibrium condition disappears depends on these rotational effects, leading to collapse, when runaway growth delivers most of the planet’s mass. The project additionally models how the disk generates and processes radiation, providing an important observational signature of forming giant planets. 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 purpose of this project is to use innovative tools to investigate open questions in the areas of analysis and geometry. These mathematical disciplines are crucial for a host of scientific fields, including engineering, biology, and physics. For example, geometry is the basis for numerous current industrial applications such as 3D printing. The PI will explore advanced methods in the aforementioned mathematical areas. The axiomatic development of geometry, which postulates a set of basic assumptions from which all other reasonable conclusions are logically deduced, dates back to ancient Greece. The major focus of this project is a detailed study of certain phenomena that occur when the Archimedean axiom, attributed to Archimedes of Syracuse, is no longer postulated. The resulting mathematics turns out to be useful even when the primary object of study is of the usual, Archimedean, kind. The project will also generate research opportunities at a variety of levels, suitable for work by graduate and undergraduate students. This project will employ non-Archimedean tools to study a range of problems in analysis and geometry. A through-line to the work is a deep understanding of Berkovich spaces, which are non-Archimedean analogues of real and complex manifolds. One component of the project involves an attack on the celebrated Yau--Tian--Donaldson conjecture, on an algebro-geometric criterion for the existence of constant scalar curvature metrics. In another project, the PI will use variational and non-Archimedean methods to study the existence of complete Calabi--Yau metrics on affine complex manifolds. and complex analytic geometry. A third project is centered around the Kontsevich--Soibelman conjecture arising in Mirror Symmetry, on the asymptotic geometry of degenerating families of compact complex Calabi--Yau manifolds. All these projects use Berkovich spaces in a crucial way. 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 90,000 different local governments in the USA provide essential services and protections for the American people, including running schools, libraries, and public parks, and delivering front-line services during emergencies. While governments strive for efficient use of time and resources, due to the variety of size, structure, and complexity of local governments, it is currently prohibitively difficult to collect data and study these functions comprehensively. As a result, much of the research into the effectiveness of service delivery focuses on institutions at the federal or state level. This project will improve the capacity for studying local governments service delivery by providing new datasets and tools that make it possible to do comprehensive, comparative research at scale. Doing so will empower local governments with new research at the intersection of public policy, civic engagement, and artificial intelligence. This proposal builds upon the team's current open-source testbed. The long-term vision for the testbed is to facilitate rigorous experimentation through the robust, secure, and safe deployments of AI. To achieve this vision the proposed planning grant will carry out four activities: 1. Prototype safely integrating AI in the current testbed; 2. Convene government staff and researchers across a variety of computational fields to build a research agenda; 3. Govern robust AI deployments to operate safely and manage risk; and, 4. Validate the ability of the prototypes to answer research questions. Collectively, these four activities will lay the foundation for a long-horizon project with the potential to catalyze the study of local government through robust AI. The existing testbed that this project builds on is an open-source software platform that provides federated search and retrieval of data about city councils in multiple major US cities. The project will build upon the current data retrieval and processing capabilities of this testbed by integrating novel AI prototypes - using techniques from machine learning to improve interaction with testbed data, and to produce valuable datasets for research and policymaking. 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

AI-assisted decision-making systems have revolutionized fields ranging from medical treatment to online marketing by learning from data to optimize sequential decisions. However, real-world deployment reveals fundamental challenges that compromise system reliability and effectiveness. For instance, human users may distrust or selectively follow algorithmic recommendations, creating implementation gaps; operating environments often differ frequently from the conditions under which systems were trained; and complete knowledge about the environment is often unavailable. Existing methods typically assume perfect implementation and stable environments, leading to substantial performance degradation when these assumptions fail. This project aims to address these limitations by developing new theories and principled algorithms to enable robust and trustworthy decision-making under realistic constraints. In addition, it will provide valuable opportunities for training students at all levels in the STEM field and introducing the general audience to advances in data science and AI. This project focuses on fundamental sequential decision-making problems: multi-armed bandit and reinforcement learning. The research pursues three complementary directions, aiming to characterize the fundamental statistical limits of learning and develop provably optimal algorithms while maintaining robustness to varied sources of uncertainties. The first thrust will devise trust-aware procedures that account for human behavioral factors when individuals deviate from algorithmic recommendations. The second thrust will tackle the challenge of distribution shifts between training data and deployment environments through robust transfer learning methods. The third thrust aims to design model-agnostic algorithms that function across diverse model types and automatically adapt to unknown environmental structures. The project will provide statistical insights that inform decision-making practice and develop efficient, robust procedures for real-world applications across various domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to improve understanding of the impact of cyanobacterial harmful algal blooms (cHABs) on the atmosphere. cHABs have been observed in freshwater lakes in all 50 US states and are increasing in frequency, severity, and spatial extent due to anthropogenic factors. This study addresses the knowledge gap regarding biogenic volatile organic compound (BVOC) emissions to the atmosphere from cHABs and the potential for cHABs to cause secondary organic aerosol (SOA) formation. A series of laboratory and field measurements, along with chemical modeling, will be performed. Advancing the understanding of SOA production due to cHABs addresses societally important issues of air quality and radiative forcing. The project includes education and research opportunities for high school teachers and students. To test the hypothesis that cHAB-SOA is a significant contributor to total SOA in regions with substantial cHABs, three scientific objectives will be targeted: (1) Determine SOA formation from hydroxyl radical oxidation of cHAB-BVOC in the absence and presence of pre-existing inorganic aerosols in the laboratory. (2) Measure cHAB-BVOC species and SOA production adjacent to a cHAB through field measurements. (3) Box model SOA production and species evolution using gas and particle data from lab and field measurements to evaluate potential contributions to atmospheric SOA. In the laboratory, a series of oxidation experiments investigating key BVOCs will be conducted using an oxidative flow reactor or chamber. In the field, using a variety of instrumentation, sampling will take place at Grand Lake St. Mary’s in Ohio, which has a predictable and intense cHAB each year. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will develop advanced computational tools to simulate and optimize the locomotion in a fluid medium of microscopic particles, such as bacteria or specially-designed micro-robots. Understanding how these microswimmers move is crucial for a wide range of applications, from improving our understanding of how biological systems function at the cellular level to creating innovative technologies. For instance, this research could lead to breakthroughs in targeted drug delivery, where microscopic robots deliver medicine precisely where it is needed in the body, or in developing new ways to transport materials within lab-on-a-chip or organ-on-a-chip devices. By making these simulations more efficient and accurate, this project aims to provide fundamental insights that will benefit fields such as biotechnology and materials science, ultimately contributing to scientific discovery and technological advancements that impact people's daily lives. Despite advancements in modeling and simulations of microswimmers, performing control and optimization in complex environments is still daunting, primarily owing to the lack of computational tools that scale to realistic problem sizes and work on arbitrary moving geometries. The primary goal of this project is to develop accurate, computationally efficient, and scalable numerical algorithms necessary for large-scale simulations, as well as shape and functional optimization of microswimmers. The research will address key computational difficulties, including the accurate evaluation of near-singular integrals and periodization schemes in three dimensions that support adaptivity. Adjoint formulations will be derived for various practical scenarios, such as microswimmers in confined flows or in the presence of other active or passive particles. Building on existing work with integral equation methods, the project will specifically develop adjoint-based shape optimization schemes, new high-order nearly-singular integration schemes to simulate flows through confined geometries, and spectrally-accurate, adaptive three-dimensional periodization schemes that can be accelerated by existing fast N-body algorithms. 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

Similar in some ways to the familiar surface waves, internal gravity waves exist in the interior of a stratified ocean and provide one pathway for the wind energy injected at the surface to reach deeper layers. This project will use theoretical and numerical methods to explore the turbulent mixing generated by internal waves, which is a crucial parameter for more reliable performance of global and regional ocean models. The results of this research will contribute to important practical applications such as the state of climate and securing the national defense. The project will also provide educational opportunities through a summer undergraduate research program and support outreach efforts through museum events and classroom workshops, fostering broader community engagement in ocean science. The goal of this project is to bridge theoretical understanding with realistic ocean scenarios to develop a process-based tool for estimating and parameterizing turbulent dissipation using wave kinetic theory. The research involves a comprehensive evaluation of turbulent dissipation with the inclusion of tidal peaks in the spectrum of internal gravity waves. Computational simulations of internal gravity waves, in both idealized and realistic conditions, will be used to investigate the local interactions, induced diffusion, and elastic scattering in shaping the wave spectrum and downscale energy flux. The expectation is that the work will improve parameterizations of turbulence in terms of measurable characteristics of internal waves. 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

Viruses are a major cause of waterborne illnesses. Water treatment plants use chlorine, one of the most popular disinfectants, to control viruses. However, bromide and chloride are often present at increasing concentrations in drinking water. How these chemicals impact virus disinfection is unclear. This project will investigate their impacts and to optimize viral disinfection in water. Successful completion of this project will benefit society by improving drinking water treatment practices and safety. Increasing levels of halides in water, especially chloride and bromide, are a growing concern for water utilities. When water utilities apply chlorine to control pathogens, reactions between chlorine and halides lead to the formation of halogenating agents (Cl2, Cl2O, BrCl, BrOCl, Br2O) that are orders of magnitude more reactive than chlorine, but are typically overlooked due to their low concentrations. Disinfection guidelines that account for these halogenating agents are needed to apply chlorine effectively. Underdosing chlorine may not sufficiently inactivate pathogens, but overdosing chlorine may exacerbate the formation of toxic byproducts. Simultaneously, there is an urgent need for methods that predict the susceptibility of viruses to disinfectants to be prepared for possible future pandemics caused by waterborne pathogens. Understanding the mechanisms of viral inactivation by disinfectants and how disinfectant species contribute to inactivation is essential for such predictions. This project will determine the impact of increasing chloride and bromide concentrations and the associated halogenating agents on viral inactivation mechanisms. Three tasks are included to accomplish this goal. In task 1, experiments will be conducted to determine the reaction kinetics between halogenating agents and viral biomolecular targets including nucleic acids and amino acids. In task 2, novel mass spectrometry and established molecular techniques will be used to assess the ability of halogenating agents to react with biomolecular targets in model bacteriophages and their impact on bacteriophage function. In task 3, the impact of chloride and bromide on inactivation of representative viruses, and by extension, the impact of chloride and bromide on drinking water disinfection requirements, will be assessed. The results of these tasks will be compared against inactivation rates and mechanisms established for chlorination in waters without halides to determine the relative contribution of overlooked halogenating agents for viral inactivation in halide-containing waters. The project will benefit society by developing guidelines for optimizing disinfection for treatment of halide-containing waters, which will improve water treatment practices and water safety. The project will provide critical information on viral inactivation mechanisms and biomolecule chlorination which will benefit the environmental engineering, chemistry, and biology fields. The results of the project can be extrapolated to other waters that contain halides and are chlorinated to remove pathogens, including wastewaters (for both discharge and reuse), desalination waters, swimming pool waters, and seawater aquaculture 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.