Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2024 · 2024-09

This doctoral dissertation project examines the mechanisms of sociopolitical formation among ancestrally diverse people in colonial contexts. Anthropologists have long been interested in the outcomes of group formation. Fundamentally, how do people negotiate differences to build new social and political communities? Recent research challenges how scholars delineate ethnic boundaries in the past and their political implications for Indigenous and diasporic communities today. Multidisciplinary scholarship has demonstrated that Indigenous societies frequently built their nations by expanding kin ties, thereby forming adaptable political institutions and societies; this framework contrasts with widely held expectations for race-based ancestry and bordered sovereignty. These dynamics also challenge archaeological studies of community coalescence, which frequently assume political economy is a key driver of social and material change. As such, this study uses archaeological evidence from households to investigate the role and material correlates of small-scale integrative strategies––specifically daily practice, kinship, and local interaction––in sociopolitical formation. The research focuses on Western frameworks of nationhood and citizenship that legally code Indigenous identity and political sovereignty today. By using multiple analytical methods, this project joins a community of practice framework (meaning people are connected through a shared way of doing) with a formal social network analysis. Social network analysis is a powerful tool for empirically evaluating and visualizing the social relationships that emerge from shared practice. This approach is applied to an investigation of the one Native American “nation” that coalesced from disparate communities in the early eighteenth century. To understand how individuals built a nation over a few decades, this dissertation project combines extant and new data from household contexts at six archaeologically identified towns. Analysis identifies patterns of social learning that underlaid ceramic production and foodways among women. From ceramics, researchers identify manufacture and decorative decisions through macro-, micro-, and chemical attribute techniques. From plant and animal remains, researchers identify species and processing techniques to understand how Y households managed food resources within a broader political-economic context. Household practices are then compared within and across towns through social network analysis to identify discrete communities of 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 2024 · 2024-09

This grant provides funding for a conference/workshop on Brain Frontiers: Bridging Biology, roBotics, Brains, and Behavior (B5) to be held at the Janelia Research Campus in Ashburn, Virginia, in the fall of 2024. The diversity of life on Earth provides a plethora of opportunities to explore vastly distinct forms of sensing, reasoning, and acting, which can inspire robotic systems with greater autonomy, agency, and applicability. Robotic systems provide modularity that facilitates the precise experimental testing of nuanced biological hypotheses, often revealing how whole-organism behaviors emerge from interactions between multiple hierarchical levels of biological complexity. However, these fields are generally organized into different departments, conferences, and publications, limiting their ability to collaboratively work towards common research goals. This workshop will foster conversations between these two fields on the topic of understanding the frontiers of intelligence, both biological and artificial. This will spark creative brainstorming and foster a collaborative, adventurous, and profound set of discussions that will have a significant impact on the present and future directions of the emerging field of embodied biological and robotic intelligence. Participants will co-author a commentary piece on the topic of understanding behaviors using symbiotic approaches in biology and robots, which will encourage readers to seek out interdisciplinary approaches and collaborations. During the workshop, participants will record podcast episodes that will be available to the general public, aimed at middle school through undergraduate listeners, to inspire the next generation of researchers at the intersection of biology and robotics. Building upon decades of collaboration and mutual inspiration between neuroscience and engineering, our ever-increasing ability to embed artificial intelligence in embodied robotic systems presents novel challenges, opportunities, and goals for research at this interface. This 2-day workshop will include 40 researchers who span neuroscience, biophysics, biomechanics, bioengineering, human-robot interactions, autonomy, computer vision, and complex systems to discuss strategies that bridge the gaps between these fields to explore the frontiers of our understanding regarding both biological and artificial intelligence. The workshop will be organized around the following guiding questions: 1) When does robotics need neuroethology? 2) When does neuroethology need robotics? 3) What can we learn from virtual vs. physical models? 4) What can we learn from field work vs. laboratory work? 5) What can we learn from comparative approaches vs. model systems? 6) What can we learn from social groups vs. individuals in isolation? 7) How do we “close the loop” between neuroethology and robotics? 8) What resources would you need to address the major challenges at this intersection? The workshop is supported jointly by the Neural Systems Cluster in the Directorate for Biological Sciences and the Mind, Machine and Motor Nexus program in the Directorate for Engineering. 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 2024 · 2024-09

This Engineering Emerging areas of Advanced Manufacturing (ENG-EAM) award supports research that will focus on establishing systemic and robust resilience to cyberphysical attacks on connected digital manufacturing systems. Digitization and connectivity are the cornerstones of modern manufacturing, but these very qualities allow cyberphysical attacks to negatively impact part performance by stealthily altering the digital representations of geometry, process plans, and/or in-situ sensing signals. This has the potential to pose a significant threat to societal well-being, economic stability, and national security by introducing defective parts into electronics, spacecraft, planes, automobiles, biomedical devices, and energy components. The state-of-the-art practice of dealing with such attacks by sacrificing productivity, yield, cost, and connectivity to ensure part performance critically limits pervasive and trustworthy adoption of Industry 4.0 and digital manufacturing. This research project will create and validate a novel computational paradigm called Smart-Recover that actively assures every part’s performance despite cyberphysical attacks and with minimal loss in productivity, yield, connectivity, or cost-effectiveness. The research will be complemented by developing a multi-institutional manufacturing cybersecurity education program for workforce development across high school, undergraduate, and graduate educational levels. The specific goal of the research is to establish the mathematical basis for the Smart-Recover paradigm, which combines pre-fabrication correction of attack-altered geometric models with stoppage-free in-process mitigation of defects created by attack-modified process plans and attack-distorted in-situ sensing signals. To this end, the research objectives include the creation of techniques for: (1) pre-fabrication computational reconstruction of only the attack-altered features of the digital geometric model; (2) in-process remodification of process plans to disrupt formation of local defects induced by atypical attack-driven alteration of exogenous process parameters; and (3) in-process restoration of defect prediction accuracy for attack-altered sensor signals at speeds necessary for local defect mitigation. The research team will further explore the generalizability and collective interaction of these elements of Smart-Recover with stealthy and system-spanning cyberphysical attacks via two manufacturing testbeds. These advances will be achieved via innovations at the convergence of geometric design, machine learning, in-situ sensing, and physics-based modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research project will establish the first spherical wind-powered rover capable of moving in all directions, thereby enabling an unprecedented level of persistence (defined as the ability to execute a mission without stopping to recharge) and autonomy (defined as the ability to operate without human intervention). Analogous to how modern sailboats leverage lifting sails and keels, centerboards, or other vertically oriented foils to enable upwind motion, Spherical Sailing Omnidirectional Rovers (SSailORs) will use lifting sails and a directionally constrained hull to achieve the same result. Specifically, the lifting sails will enable forward thrust even when moving in an upwind direction. The directionally constrained hull will enable significant lateral resistance when the hull heels (tilts) due to the lateral force from the wind, thereby resisting sideslip. While preliminary investigations of the SSailOR indeed confirm its ability to move in all directions under different slopes, the robust achievement of these capabilities across a wide variety of operating regimes (characterized by different wind and terrain, for example) requires a delicate balance between several features related to both the physical design and control system. Achievement of this balance through a formal physical system and control co-design process represents the centerpiece of the research plan. The resulting designs will be validated through a progressive experimental campaign, including wind tunnel testing, dynamic characterization in a controlled environment, and dynamic characterization in North Carolina’s Outer Banks. The research activities will be complemented by outreach activities with both the Engineering Place at NC State University and the University of Michigan Engineering On-Ramp. To achieve robust omnidirectional mobility and optimize the expected performance of SSailORs, the project will pursue a combined physical and control system co-design process that is centered around a unique characterization termed the stochastic velocity polar (SVP). This characterization statistically quantifies the achievable speed of the rover along any direction relative to the wind direction, over a specified set of terrain types and grades. The SVP will first be parametrically characterized based on longitudinal, lateral, and heel (rotational) characterizations of the SSailOR. After this, a sequential plant-controller co-design process will be used to maximize an objective function that statistically characterizes the scientific information that can be gathered from a candidate design and controller, subject to chance constraints. Following the sequential co-design process, a stochastic reachability-based co-design process will be used to refine the design over a reduced design space. The co-design work will be complemented with an experimental plan that begins with static wind tunnel tests aimed at validating and refining the SSailOR model. This will be followed by controlled dynamic testing, with the primary goal of demonstrating upwind mobility in a constant wind field, and will culminate with testing in the Outer Banks, to demonstrate that upwind mobility is preserved in realistic, time-varying wind conditions and inconsistent terrain. 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 2024 · 2024-09

Robots capable of navigating unstructured terrains in diverse environments, such as water and land, are crucial for many real-world applications. While soft robots can navigate challenging environments like narrow tunnels and rough surfaces due to their flexibility, most current designs are limited by slow speeds, reliance on ties to the base unit (i.e., tethered), and use in only one type of environment, such as land or water. Additionally, soft robots are time-consuming and expensive to create compared to rigid robots, which benefit from centuries of innovative generation. This project aims to create a new class of untethered, reconfigurable (i.e., able to change shape), and multimodal amphibious soft robots (URSoRo) assisted by a machine learning (ML) design tool to overcome these limitations. These robots will leverage a new class of soft electromagnetic (EM) actuators that can operate in more than one state, enabling them to swiftly adapt to challenging environments. This project will leverage the reconfigurability of soft robots for environmental adaptation and promote their practical applications, such as search and rescue operations, monitoring of animals and plants, and inspection of infrastructures in extreme environments. Additionally, the project will contribute to an annual inter-university soft robot competition across the United States and integrate findings into graduate-level courses on soft robotics at the University of Michigan, Ann Arbor, and the University of California, Los Angeles. This project addresses two primary challenges in soft robotics: designing shapes and achieving bistability in soft actuators while maintaining a simple, low-cost fabrication process, and tightly integrating and engineering untethered reconfigurable soft robots with fast multimodal locomotion. The research will develop a soft bistable EM actuator with high force output (∼0.4N), high activation frequency (>30 Hz), and the capability to be powered by miniaturized onboard electronics (<15 g). An ML-assisted physics-based simulation tool will be developed to guide the design, fabrication and robotic integration of these EM bistable actuators, enabling a fully planar rapid fabrication process. Liquid metal embedded elastomers will be used to enhance both thermal management and electromagnetic field generation, boosting the actuator's performance. Overall, this project will result in a new class of untethered soft robots driven by soft bistable EM actuators, alongside ML-assisted physics-based modeling and design tools, achieving an unprecedented combination of speed, size, mass, and reconfigurability. By addressing these technical challenges, it will contribute to the field of robotics with versatile, efficient, and cost-effective solutions for creating soft robots with rapid reconfiguration and advanced locomotion performance in unstructured and diverse real-world environments. 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 2024 · 2024-09

The project will provide state-of-the-art computational tools for the development of novel 2D materials and their potential application to ultra-fast electronic, opto-electronic, and magnetic devices; unconventional optical and photonic devices; communication devices; and quantum computing applications. The project will address interconnected challenges in emerging areas of quantum science, computational mathematics and computer science by effectively merging highly domain-specific techniques with general machine learning techniques, thus informing and motivating analogous research on model order reduction across the sciences and engineering. 2D materials research is an ideal platform to motivate new mathematics training and curricula in the analysis, modeling, and computation of electronic structure, mechanical and topological properties of materials, and analysis of experimental data. The project’s outreach to female and underrepresented student populations will broaden the diversity of the mathematical research community, and the project provides research training opportunities for graduate students. Many quantum phenomena of scientific and technological interest emerge naturally at the moiré length scales of layered 2D materials which makes those materials an exciting platform to explore quantum materials properties and to prototype quantum devices. For example, correlated electronic phases such as superconductivity have been recently observed in twisted bilayer graphene (tBLG). Such pioneering results have opened up a new era in the investigation and exploitation of quantum phenomena. Despite the continuing increase in computational resources, high-fidelity modeling and simulation of many quantum materials systems remains out of reach. The limitation is particularly serious in 2D heterostructures due to the large scales at which the quantum phenomena of interest emerge. The objective of this NSF-NSERC Alliance project is to develop an advanced computational modeling workflow, merging state-of-the-art quantum modeling and machine-learning methods to enable rapid, automated, high-fidelity exploration of mechanical and electronic properties of 2D quantum materials. This award is jointly supported by the Division of Mathematical Sciences, the Division of Materials Research and the Office of Advanced Cyberinfrastructure. 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 2024 · 2024-09

This project seeks to address how supermassive black holes (SMBH) form in the early universe and how they create relativistic jets. The investigators will study two families of SMBHs that are very rare to determine the number of binary SMBHs. They will determine whether the recently discovered gravitational wave background is due to these objects. One early result from the James Webb Space Telescope is that there are far more binary supermassive black holes in the early universe than expected. The existence of so many binary supermassive black holes poses challenges to cosmology. The PIs will use the 40 m telescope at the Owens Valley Radio Observatory and the Very Long Baseline Array to study the structure of these objects. The PIs will combine the radio results with optical, infrared, X-ray, Gamma-ray, and gravitational wave observations. The work will be carried out by five graduate students, so this will be important of their career development. In addition, the PIs have started an outreach program, in which they will be studying the jets in these objects, with schools that work with students from disadvantaged backgrounds. The radio jets from SMBHs will be studied in great detail through their varying radio brightness and structure on the scale of light years. One rare class the PIs will study is SMBH Binaries (SMBHBs). About 1 in 100 radio jets is a SMBHB. The PIs have detected two convincing SMBHB candidates through their light curves. Their focused search should at least double the number of strong candidates. Each strong candidate is of great importance to multi-messenger astrophysics, since the SMBHBs produce gravitational waves that will in future be detectable by pulsar timing arrays. They will also study high-luminosity compact symmetric objects (CSOs). The brightest CSOs die out after ~5000 years, whereas most radio jets last up to tens of millions of years, suggesting that a different fueling mechanism drives the brightest CSOs. Astronomers think this is the capture of single stars by the SMBH in a tidal disruption event (TDE). The PI's will test whether these powerful CSOs really do die out at ~5000 years. By studying these two rare classes, each of which probes details of the central engines in a unique way, they will gain crucial insights into the jet formation and launching mechanism as well as into the SMBHs themselves. Most importantly - if the initial results on CSOs are confirmed in this study, then the smallest CSOs will evolve on timescales of a few years. This will make possible the direct testing of the general relativistic magnetohydrodynamic theory and simulations of relativistic jets. 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 2024 · 2024-09

While randomized experiments remain the gold standard for elucidating cause and effect relations, countless societally important "what-if?" questions cannot be addressed through clinical trials for a litany of reasons, ranging from ethical concerns to logistical infeasibility. For this reason, observational studies, wherein the assignment of group status to individuals is outside the control of the researcher, often represent the only path forward for inferring causal effects. While observational data are often inexpensive to collect and plentiful, regrettably, they suffer from inescapable biases due to self-selection. In short, associations between group status and outcomes of interest need not reflect causal effects, as the groups being compared might have considerable differences on the basis of factors unavailable for adjustment. This project will develop new methods for sensitivity analysis in observational studies, which answer the question, "How much-unmeasured confounding would need to exist to overturn a study's finding of a causal effect?" Quantifying the robustness of observational findings to hidden bias will help frame the debate around the reliability of such studies, allowing researchers to highlight findings that are particularly resilient to lurking variables. This project provides both theoretical guidance on how to extract the most out of a sensitivity analysis and computationally tractable methods for making this guidance actionable. Moreover, when randomized experimentation is possible, the developed methods will help researchers use existing observational studies for hypothesis generation, enabling them to find sets of promising outcome variables whose causal effects may be verified through follow-up experimentation. This award includes support for work with graduate students. This project develops a new set of statistical methods for conducting sensitivity analyses after matching. These methods aim to overcome shortcomings of the existing approach, conferring computational, theoretical, and practical benefits. The project will provide a new approach to sensitivity analysis after matching called weighting-after-matching. The project will establish computational benefits, theoretical improvements in design sensitivity, and practical improvements in the power of a sensitivity analysis by using weighting-after-matching in lieu of the traditional unweighted approach. The project will also establish novel methods for sensitivity analysis with multiple outcome variables. These innovations will include a scalable multiple testing procedure for observational studies, facilitating exploratory analysis while providing control of the proportion of false discoveries, and methods for sensitivity analysis using weighting-after-matching for testing both sharp null hypotheses of no effect at all and hypotheses on average treatment effects. Finally, the project will establish previously unexplored benefits from using matching and weighting in combination, two modes of adjustment in observational studies commonly viewed as competitors. This will help bridge the divide between matching estimators and weighting estimators in the context of a sensitivity, in so doing providing a natural avenue for theoretical comparisons of these approaches. 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 2024 · 2024-09

This project supports the annual Formal and Experimental Advances in Sign Language Theory conference which is a premier venue for presenting linguistics research on signed languages. The conference brings together deaf and hearing sign language scholars from around the world to present cutting edge studies in all areas of linguistic structure, from how signs are formed with the hands and body to how the human mind views and processes sign language. The conference provides important peer review and feedback to sign language researchers in a single forum and provides an accessible venue to highlight the contributions of sign language research to linguistics at large. Because sign language research is underrepresented in the programs of other conferences, this conference is vital for developing a modality-inclusive theory of language. Moreover, unlike academic conferences, in linguistics and in other fields, this conference is designed to be fully accessible, with full interpreting between three conference languages as well as providing human-generated captions, and other accessibility infrastructure like designated sensory relief rooms. The conference program has also been curated to platform deaf scholars, and a panel on disability and (sign) language is included in the plenary schedule to encourage attendance from scholars in other fields and to community members outside academia. Community engagement has also been built into the conference by organizing an evening sign language arts performance. 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 2024 · 2024-09

High precision control systems are crucial for numerous modern engineering applications such as hard disk drives, satellites, and photolithography systems. Challenges in high precision control include: (a) stringent performance requirements, (b) imperfect design models, and (c) unknown environmental disturbances. Existing control methods generally make optimistic assumptions about the disturbances acting on the system (e.g. completely random) or pessimistic assumptions (e.g. completely antagonistic). In contrast, recent online convex optimization methods aim to learn the disturbance characteristics and control the plant at the same time. However, model errors can cause catastrophic failures in such approaches and thus must be explicitly considered in the design and analysis. This research will explore the use of novel, robust online convex optimization methods to improve performance by using imperfect models to adapt the controller for specific disturbance features. The researched methods can lead to significant increases in the memory density of hard disk drives used for cloud storage. It can also improve satellite pointing accuracy leading to improved imaging for space science missions and higher data rates via optical communication. These outcomes will enhance the economic competitiveness of industries that rely on high precision control. The researchers technical approach merges tools from online convex optimization with robust control. This framework has recently achieved great success in sequential decision-making tasks. These methods use regret as a metric to balance between exploration and exploitation in the face of uncertainty. However, there is still a gap to make these approaches suitable for industrial control problems. This research focuses on bridging this gap for an important class of engineering problems, namely, robust disturbance rejection for high precision control systems. The research is divided into three thrusts. In Thrust 1, the fundamental performance limits for robust disturbance rejection under general settings will be developed. This will provide insight into cases that can benefit from more advanced nonlinear, time-varying controllers. In Thrust 2, novel versions of recursive least squares for robust disturbance rejection will be created. Recursive least squares and its variants are well studied online optimization algorithms. However, they can be sensitive to model errors when applied for disturbance rejection in certain feedback architectures. To address this, the researchers will create a new class of recursive least squares algorithms that are robust to such model uncertainty. Finally, Thrust 3 will focus on more general regret-based online convex optimization methods for robust disturbance rejection. These methods will also be validated via application to satellite line-of-sight control. 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 2024 · 2024-09

Human activities have significantly increased atmospheric CO2 concentration. Marine carbon dioxide removal (mCDR) techniques have emerged as a promising solution to mitigate climate change caused by increased CO2 levels, aiming to enhance natural biological and chemical processes in the ocean to absorb and store more carbon from the atmosphere. Monitoring pH is fundamentally important for mCDR, playing a crucial role in assessing environmental impact, optimizing the processes, and ensuring the overall success and sustainability of mCDR efforts. However, current pH sensors face limitations such as low sensitivity and accuracy, drifting with pressure and temperature changes, and discontinuity due to limited power. In this proposal, researchers from three universities (UNT, UMich, and UCSD) are collaborating to develop a novel marine energy-powered multimodal MEMS (Micro-Electro-Mechanical System) sensor array that can simultaneously detect pH, pressure, and temperature. This innovative device aims to provide uninterrupted, highly sensitive, and accurate pH measurements across vast ocean areas with varying depths. The proposed self-powered sensing system can also be adapted for other applications such as wave and tide gauging, tsunami detection, ocean surveys, seabed subsidence monitoring, inverted echo sounders, towed arrays, and calibration of underwater mapping systems. Additionally, the researchers will engage industrial offshore instrument developers and governmental labs to accelerate the deployment of the proposed instrument, particularly in mCDR, sustainable ocean monitoring, and ocean renewable energy fields. Furthermore, this project will also significantly benefit the three participating universities by supporting curriculum development, professional non-technical skills training, and research mentoring for graduate, undergraduate, and K-12 students, with an emphasis on diversity, equity, and inclusion. In this project we propose a MEMS resonant pH sensor that can significantly enhance the sensitivity, accuracy, and energy-sustainability of current pH sensors, addressing the challenges confronting the mCDR research community. This research focuses on three key areas: (1) the design, fabrication, and testing of a multimodal pH, pressure, and temperature sensor based on a piezoelectric single crystal wafer for highly sensitive and accurate pH measurements while remaining unaffected by variations in pressure and temperature; (2) the design, fabrication, and testing of a novel ocean wave energy converter that can break the fundamental challenge of mismatch of vibration frequency of small buoys and the low ocean wave excitation frequency, thus enabling efficient energy harvesting to provide sustainable power for uninterrupted long-term operation of the sensor system; and (3) system integration and test in the wave tank at UMich, and field demonstration at the marine lab facility at the Scripps Institution of Oceanography at UCSD. Upon successful completion, this novel tool will be suitable for long-term deployment in marine environments for mCDR monitoring. 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 2024 · 2024-09

Nontechnical description Interfaces between dissimilar materials such as diamond and boron nitride offer exciting possibilities for realizing exceptional electronic, optical, magnetic, and chemical properties. In this project, the research team explores these possibilities by studying the interaction of charges at the interface between diamond and hexagonal boron nitride. This boundary can be finely tuned by altering the chemical composition at the surface of the diamond, the number of boron nitride layers, and the angle at which these materials meet, providing broad design flexibility. The team uses this tunability to investigate how bound pairs of excited electrons and their corresponding positively charged empty states, which are known as excitons, form, relax, and move across the interface. The researchers employ advanced optical techniques and theoretical calculations to understand the unknown behavior of these excitons, focusing on how their dynamics change with temperature and modifications to the structure. This research could pave the way for new applications in energy conversion, optoelectronics, and microelectronics. Additionally, the project provides valuable training for graduate and undergraduate students in cutting-edge experimental and computational methods. To address education at the grassroot level, the team in collaboration with Museum of Natural History at University of Michigan will engage middle school students in scientific research via curriculum development and summer programs. Technical description Van der Waals semiconductor heterointerfaces present an opportunity to develop new classes of material systems with superior electronic, optical, magnetic and chemical properties. In this project, the research team theoretically and experimentally investigates the charge transfer excitons at the diamond-hexagonal boron nitride heterointerface. This interface provides immense design flexibility of the heterointerface due to the band alignment tunability via surface passivation of diamond, number of boron nitride layers, and orientation twist angle between the two materials. The research team utilizes this tunability to explore the formation/relaxation dynamics and transport properties of charge transfer excitons. They employ advanced optical spectroscopy, along with guidance from first-principles calculations, to unravel the unknown physics associated with charge-transfer excitons, and specifically their thermalization dynamics and transport dynamics as a function of temperature to reveal the potential pathways to control it. The tightly weaved experiment-theory effort is imperative for the development of a potential excitonic material system to serve applications in energy conversion, optoelectronic, and micro-electronics. The energy transfer and transport associated with charge-transfer excitons combining diamond and boron nitride remains an unexplored territory of research and thus, presents a unique opportunity to develop a new class of material systems for opto-excitonics. By combining two quantum ready scalable systems, the research team aims to establish diamond/boron nitride interfaces as a platform for room temperature excitonic devices. The project also provides training for graduate and undergraduate students in state-of-the-art experimental and computational techniques. The topic of two-dimensional semiconductors is particularly well suited to introduce a wide range of nanotechnology-related themes. Thus, the research-team will engage in curriculum development and summer programs for middle school students in collaboration with the Museum of Natural History at University of Michigan. 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 2024 · 2024-09

This project supports foundational research to create transformative structures and materials with multi-faceted intelligence in the mechanical domain (aka. mechano-intelligence). Such mechano-intelligence will be embodied and synergized throughout the physical body of these structures and materials to execute highly autonomous engineering tasks, such as acquiring information from the surrounding environment, memorizing it, and deciding on an action plan. The novelty of this project is the use of multi-functional origami as a mechanical neural network so that one can harness its computing power as the core foundation for creating and integrating essential intelligent elements. The impact of this project will be the advancement of many intelligent engineering systems widely applicable in different industries, with less power requirements, more direct interaction, and more resilience against harsh environments and cyberattacks. In addition, this project will integrate its research outcomes into new teaching curricula, outreach activities, and lab demonstrations, cultivating diverse students’ interest in STEM pursuits under the inspirational theme of physical intelligence. The vision of this collaborative effort is to create structures and materials with multi-faceted intelligence embodied in the physical body. Although some studies have attempted to distribute (offload) intelligence to the mechanical domain, there is still a lack of a broad and systematic foundation for constructing and integrating the different elements of mechano-intelligence, such as information perception, memorization, and decision-making. To this end, the investigators will bridge this crucial gap by harnessing multi-functional origami as a mechanical neural network and leveraging its physical reservoir computing power as the needed foundation for creating, measuring, and designing the essential elements of intelligence. More specifically, the investigators will 1) explore the extent of complexity and sophistication that mechano-intelligence can attain on physical platforms, 2) formulate performance metrics to quantify and measure mechano-intelligence, 3) correlate the design and mechanical properties of the physical platform to the corresponding intelligence performance, and 4) create a systematic design method for integrating mechano-intelligence with engineering functions. 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 2024 · 2024-09

The use of differential equations is widespread in various fields of science and engineering. Differential equations are used to model and understand natural phenomena and to design engineered systems. However, differential equations can be quite challenging to solve numerically on a computer. Recently, there has been substantial interest in using statistical machine learning to develop fast approximations that can give answers with the accuracy of classical solvers and also run much faster. This emerging area leads to novel statistical questions about the limits and possibilities of statistical learning where the inputs and outputs are both functions. In mathematics, a mapping that takes functions as inputs and also produces functions as outputs is called an operator. This project aims to develop new theoretical tools and algorithm design techniques for this emerging area of operator learning. This project has several interrelated themes. First, it will develop a deep understanding of learning linear operators. Second, the project will consider specific classes of nonlinear operators that are most likely to lead to practical successes: classes defined by operator-valued kernels and neural operators. Third, this research project will consider natural families of estimators such as empirical risk minimization and its regularized versions. Fourth, the investigator will extend the results in previous themes to more general models of learning particularly adversarial online learning which views learning as a sequential game between Nature and Learner. The project will offer multiple training and mentoring opportunities for a future generation of statisticians. 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 2024 · 2024-09

Ensembles of Rydberg atoms in laser traps serve as platforms for quantum simulation and neutral-atom quantum computing. Rydberg-atom quantum simulators offer a configurable, controllable and scalable approach to model low-temperature and many-body quantum systems, which are of interest in science and technology across the disciplines, including physics, engineering, chemistry and nuclear physics. Such systems are often too complex to be modeled on a classical computer. By providing quantum modeling capability, Rydberg-atom quantum simulators/computers have the potential to drive societal change. Laser-trap arrays are a versatile platform to prepare and localize atoms in these devices. However, laser beams are subject to pointing and intensity noise. The resultant random forces acting on the atoms cause motional decoherence, which in turn reduces simulator performance. To address this problem, the present project deals with research on Rydberg-atom laser traps that minimize motional decoherence and allow for in-trap quantum simulation by uninterrupted pinning of the atoms at locations where the laser intensity is near-zero, and by ensuring that all atomic quantum states experience the same trapping potential. In this way, the detrimental effects of laser noise are minimized. The traps further enable site-selective quantum-state transitions in individual atoms at randomly accessed locations, as required in certain quantum simulations. The trapping methodology under investigation also promises progress in other fields of science, which include the measurement of atomic constants and the search for light axionic dark matter via microwave photon detection. The project further benefits society through education of graduate students, outreach to the public, and connections with industry. In the work, linear arrays of laser-cooled rubidium atoms are employed. The ponderomotive force that the trapping beams exert on the valence electron is used for both the trapping of the simulator atoms and for driving Rydberg-state transitions in them. The ac polarizability of Rb 5S1/2 and the Rydberg ponderomotive shift match at a “magic” wavelength of approximately 790.14 nm, which is accessed by narrow-band near-IR lasers. The laser traps are formed by a linear, hollow cylindrical beam for radial confinement and an array of transverse beams. The traps have identical potentials for ground- and Rydberg-levels. The magic character of the traps minimizes motional decoherence and allows in-trap quantum simulation over a substantial fraction of the Rydberg-atom lifetime. Ground- and Rydberg-state atoms are trapped at locations of minimal intensity, thus minimizing laser-noise-induced decoherence. The preparation of defect-free atom arrays follows, in part, established protocols. Photoionization only plays a minor role. Transitions between Rydberg states are driven by microwave-modulated ponderomotive tweezer beams that are directed at selected trapping sites. The dynamic variables of the in-trap simulator include Rabi frequency, detuning, phase, and interaction strength. Interactions across trapping sites are afforded by strong dipolar couplings between nS1/2 and n’P1/2 states, which are conducive to fast simulators with interactions that can reach over several sites. Readout methods involve Rydberg-atom ionization and state-selective fluorescence imaging. Optical Rydberg-atom circularization methods will be tested towards future applications, which may take advantage of long circular-state lifetimes and coherence times. The effort overall will lay foundations for ponderomotive Rydberg-atom quantum simulation and processing. 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 2024 · 2024-08

Combinatorics studies discrete structures such as finite sets, graphs, and permutations. Many continuous phenomena allow for discrete representation, lending themselves amenable to combinatorial approaches. In algebraic combinatorics, it is often the case that similar combinatorial structures turn out to underlie seemingly unrelated mathematical phenomena. As a result, hidden connections are revealed, allowing the transfer of insights and techniques from one discipline to another. This project aims to extend and deepen such connections. It investigates combinatorial structures arising in algebra and geometry and is motivated by several classical areas of mathematics. On the geometric side, it aims to develop new combinatorial techniques in classical incidence geometry. This is a classical subject that has its roots in antiquity. It studies configurations of geometric objects, such as points, curves, and surfaces, focusing exclusively on their relative position. The algebraic side of the project concerns further development of the theory and applications of cluster algebras and their underlying combinatorial structures that have found applications in many areas of mathematics and theoretical physics. The project will involve students at various levels. Incidence geometry is famous for a panoply of beautiful theorems discovered over the course of centuries. A recently proposed combinatorial approach utilizes tilings of oriented surfaces to place all these results under one roof. This approach has already been used to discover new incidence theorems and to generalize several known ones. It suggests multiple directions of further research, including those involving non-Euclidean geometries and/or varieties of higher degree, as well as new connections with discrete integrable systems and low-dimensional topology. Another part of the project is dedicated to further development of the theory of cluster algebras. These algebras, and the underlying combinatorics of quiver mutations, have found applications in many mathematical disciplines including representation theory, Teichmüller theory, mathematical physics, and symplectic geometry. One research direction concerns the structural theory of cluster algebras, more specifically the study of quiver mutations and associated invariants. Another direction aims to deepen our understanding of real plane algebraic curves and related concepts of singularity theory and low-dimensional topology, by revealing and investigating associated cluster-algebraic structures. 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 2024 · 2024-08

Biological membranes separate living cells from the environment and create intracellular compartments as a hydrophobic barrier. They are composed of diverse lipids and form dense environments with thousands of transmembrane and peripheral proteins. These proteins are engaged in essential cellular functions, including metabolism, energy generation, signal transduction, solute transport, vesicle trafficking, cellular motility, recognition, adhesion, differentiation, and proliferation. Advances in experimental methodologies and artificial intelligence algorithms have led to a surge in membrane protein three-dimensional structures and in silico models that are available to researchers. Nonetheless, a comprehensive understanding of membrane protein organization, including facilitating the dynamic interactions within and between cells remains elusive. This gap in our understanding impedes our ability to decipher membrane protein functional roles and regulatory mechanisms in the context of the cells, organs, or organisms in which they work. The proposed BioMembHub cyberinfrastructure (https://biomembhub.org) is designed to advance, expand, and unify databases and web servers for structural modeling and analysis of proteins, peptides, and small molecules in lipid membranes of varying molecular compositional complexity. BioMembHub is distinguished by its integration of physics-based methodologies, bioinformatics techniques, and the deep learning capabilities of the widely used AlphaFold system. BioMembHub will be easy-to-use and thus serve not only as a valuable research resource for scientists and educators, but as an educational platform for students and an instrument for public engagement with cutting-edge biomembrane research. The goal of the BioMembHub collaborative project is to create an integrated platform consisting of seven web servers and three large databases, which would enable exploration of the structural and dynamic aspects of biomolecules in membranes using both implicit and explicit membrane representations. The suite of web servers, namely TMPfold, FMAP, PPM, OPRLM, and TMDOCK, enables all-atom modeling and analysis of folding, stability, conformational positioning, and molecular interactions of proteins and peptides in membranes. PerMM and CellPM web servers calculate membrane permeability coefficients and translocation pathways across lipid bilayers of small molecules and peptides. The OPM database includes the massive set of experimental structures of membrane proteins and peptides from the RCSB Protein Data Bank (PDB) positioned in membranes by PPM. The Membranome 3.0 database serves as repository for thousands in silico models of single-pass transmembrane proteins from six proteomes. The PerMM database collects experimental and calculated permeabilities data for five hundred small molecules. With execution of the project, the OPM/OPRLM database will be significantly advanced by streamlining its update procedures and broadening the dataset of membrane proteins and peptides with known structures aligned in flat or curved membranes. The Membranome(X) database will be expanded by incorporating single-pass transmembrane proteins from 20 proteomes and a novel collection of protein complexes. These complexes will be modeled using the AlphaFold Multimer methodology and validated by employing TMDOCK. The sustainability and expandability of these resources will be improved to ensure their long-term utility and relevance to the scientific community. 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 2024 · 2024-08

The solar system beyond Neptune is home to thousands of small, icy bodies known as trans-Neptunian Objects (TNOs) that are believed to be largely unchanged since the giant planets coalesced from the proto-planetary disk and migrated to their present locations. TNOs constitute an archaeological window into the solar system’s formation and early evolution. Using a machine-learning-based shift-and-stack pipeline, the team has detected TNOs roughly two magnitudes fainter than any previous survey of comparable area. This project will extend this work and enhance its detection sensitivity to discover fainter objects. The team will produce source catalogs of transient and moving objects and release software tools for the benefit of the broader astronomical community. This project will directly fund the Ph.D. thesis of one graduate student and will involve several undergraduate students who will acquire skills in data analysis, astronomical observing, mentoring, and in presenting results through publications, posters, and talks. The DECam Ecliptic Exploration Project (DEEP) is a NOIRlab survey that was awarded 46.5 nights on the 4-meter Blanco telescope at CTIO to explore the faintest trans-Neptunian objects (TNOs) ever observed from Earth. The full DEEP data that will be analyzed in the proposed work will achieve single-night detections of nearly 7,000 TNOs, which will enable measurements of physical and dynamical properties to be broken down by dynamical subclass. The team will develop and implement a novel shift-and-stack formulation called HelioStack, an algorithm that efficiently links DEEP detections across multiple oppositions. Using HelioStack, the project will obtain multi-opposition orbits of roughly 400 TNOs, which will be the largest sample of ultra-faint (r ~ 26) TNOs to date. DEEP will also discover roughly 20,000 new asteroids and provide time-series photometry for these objects. 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 2024 · 2024-08

This project will lay a solid foundation for creating new transformative technologies for bioremediation of toxic chemicals arising from biomass combustion, a widespread means for renewable energy production but causing severe harm to the environment and human health. The goal of this project is to identify, investigate, and engineer efficient microbial communities for bioremediation of a set of representative toxic chemicals, using high-throughput microfluidic technologies. In particular, the focus is on volatile organic compounds (VOCs) and study of microbial consortia consisting of microalgae and multiple bacteria, as an effective mini ecosystem for bioremediation. Additionally, the team consisting of researchers from the University of Michigan in the US and the University of Sheffield in the UK are carrying out educational activities that impact cohorts of undergraduate and graduate students, including female and under-represented minority students. Outreach activities engage the public on topics related to bioremediation and engineering microbiomes, by contributing to K-12 programs, partnering with local organizations, and creating online materials suitable for global audiences. The goal of this project is to identify, investigate, and engineer efficient microbial communities for bioremediation of a set of representative toxic chemicals, using high-throughput microfluidic technologies. In particular, the focus is on volatile organic compounds (VOCs) and the study of microbial consortia consisting of microalgae and multiple bacteria, as an effective mini ecosystem for bioremediation. To achieve the goal, the bi-national research team is combining complimentary expertise and pursuing three specific objectives: i) enrichment and isolation of microbial strains from relevant environments for biodegradation of representative VOCs; ii) development and application of high-throughput microfluidic technology to screen for top-performing VOC-degrading consortia; and iii) validation and mechanistic investigation of top-performing consortia. The project is intended to generate a multitude of advances in fundamental knowledge and technology development. This collaborative US/UK project is supported by the US National Science Foundation (NSF) and the UK Biotechnology and Biological Sciences Research Council (BBSRC), where NSF funds the US investigator and BBSRC funds the partners in the UK. 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 2024 · 2024-08

Mental health and well-being are rising concerns nationally. University faculty are no exception, as academia is known as a high stress environment. This is a national study of the prevalence and severity of mental health problems among faculty working in STEM disciplines. The project will examine specific factors that positively and negatively impact STEM faculty mental health and well-being and how academic systems affect these factors for different demographic groups. In doing so, the project will ultimately contribute to improved support for faculty by informing practices and policies that can promote well-being and more inclusive academic environments. The project leverages an exploratory mixed methods design informed by the Job-Hindrance-Support-Control model, specifically the hindrance appraisal component of the model, and Collins and Bilge’s model of multiple strata interacting to create unique hindrances for some. Results will confirm, extend, or modify the Job Hindrance-Support-Control model, thereby expanding occupational well-being literature in academic contexts. Collins and Bilge describe a model of systems of power within an organization and highlights structural, disciplinary, cultural, and interpersonal domains of power. The qualitative phase of the project will include exploratory interviews with 60 STEM faculty and 20 administrators at U.S. institutions. These interviews will be leveraged to develop a novel survey validated through cognitive interview and pilot data collection phases. Once distributed nationally to an estimated 1,244 STEM faculty members through institutional partnerships, the project will become the largest study to date on faculty mental health and well-being. Through this large-scale data collection, the project will identify stressors for faculty strata that are often excluded (e.g. non-tenure track) and contribute to understanding how multiple faculty identities impacts their mental health and well-being. As part of the partnership agreement, the project will return customized reports to partner institutions in addition to workshops reviewing the institutional findings and research-based workshops on supporting faculty. The project results will be shared broadly with research communities and the public, including but not limited to scholarly publications, popular media, and a project website that hosts community resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Classical Extreme Value Theory studies the behavior of the largest values in large collections of numerical observations of some random phenomenon, for example, the intensities of earthquakes that occurred during a certain period at a certain region on the Richter scale. Existing results allow for understanding the likelihood of observing extremely large values in the future, larger than any past observation, and thus they can help in the prediction, for example, of extreme natural phenomena. This project aims to extend this theory to sets of interdependent numerical values, with a certain kind of interaction that is present in models which are widely used in Finance, Medicine, and other areas. Models of this type are used to describe: (a) quantities like the default likelihoods of competing financial institutions, where an extreme value analysis can help in the estimation of the probability of a credit event in the future; (b) the market capitalizations of all the companies in some large market, where an understanding of the extreme order statistics can be helpful in the improvement of existing models in stochastic portfolio theory; (c) The electrical potentials in human neurons, where extreme values might be associated with certain diseases. This award will also provide opportunities for students to be involved in this research. The aim of this project is to study the convergence of the appropriately normalized upper and intermediate order statistics of certain systems of SDEs as their size grows towards infinity. The equations interact through the dependence of the coefficients either directly on the systemic empirical measure, or on control processes that are picked to minimize some costs which are functions of the empirical measure. The first step is the reduction to the case where the SDEs are independent through the establishment of propagation of chaos, while the second step involves the treatment of this simple case using techniques from classical Extreme Value Theory and Malliavin Calculus. As statistical estimators for the parameters of the limiting distributions are functions of intermediate order statistics, properties like estimator consistency will also be extended to the case of interacting diffusions, allowing for the estimation of extreme value parameters associated with observed populations. Then, the probability of observing very large values in the future can be estimated by performing a time series analysis on the estimated parameters. 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 2024 · 2024-08

The field of Human-Robot Interaction covers the design, hardware fabrication, and algorithm development of robots that interact with people in personal and public spaces. Examples of these interactions are robot tutors in schools and robots that support older adults aging in their homes. This type of technology requires careful design of robot embodiment (i.e., the ‘look’ and ‘feel’ of a robot), their behaviors (e.g., what the robot says and does), and their interaction with people (e.g., the role it is created to embody). This is a highly complex problem that only recently has received attention in the field of Human-Robot Interaction. This project aims to create the future vision for the field of Design applied to Human-Robot Interaction with four different activities, organized within a series of design visioning meetings ("Design Retreat") that will be attended by academic and industry experts in Design Research and Human-Robot Interaction. The first activity will use metaphors to create new ideas of robot design that go beyond existing roles that robots have in today’s society; the second activity will create storyboards that describe in more detail ideas of robot interactions in society, such as their role, how they should interact with humans, and what humans should expect from robots; the third activity will identify existing resources in the scientific community that can support the consolidation of the ideas generated in the previous activities; the fourth and final activity will map out milestones about specific steps to turn the ideas derived from the activities into reality. The research team will disseminate the educational knowledge about this topic by making a documentary of the Design Retreat, giving lectures in traditionally underrepresented schools and populations, and sharing a website that supports communities in engaging in design visioning around novel and emergent technologies. “Design for Human-Robot Interaction” is the area of work within Human-Robot Interaction (HRI) that embodies design work. This area has the potential for high impact in robotics and HRI fields as it incorporates diverse methods to build robots, with most methods being human-centric and accounting for human values. However, the potential of Design for HRI has not been fully realized and outlined in the field. There are several reasons behind this challenge, such as the lack of consensus on what Design for HRI means, what methods should be used, and how design work should be evaluated. The research team believes that envisioning a future for Design in HRI can further scope promising new directions that not only provide a consensus on the meaning of Design for HRI, but also develop engagement with societal impact in the field. The research team will lead a Design Retreat to gather the HRI Design community, including academia and industry experts in design, and map out the future of Design for HRI through visioning activities. The Design Retreat will have the four Visioning Workshops to support participants in imagining and building the future of Design for HRI. The main outcomes of this project are scientific community building, the generation of the “Interdisciplinary Technology Visioning Toolkit” that will be released in open-access, a documentary with the highlights of the Design Retreat to proliferate education on this topic, and scientific publications in relevant areas including HRI and Human-Computer Interaction. 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 2024 · 2024-08

This interdisciplinary research project aims to develop new mathematical tools based on partial differential equations (PDEs) and apply them to address practical challenges in mathematical finance and game theory. PDEs serve as fundamental mathematical models for understanding complex phenomena in these fields, ranging from optimization strategies in uncertain markets to analyzing strategic interactions in competitive environments. The unique aspect of this research lies in its focus on PDEs formulated in infinite-dimensional spaces, which present both theoretical challenges and opportunities for developing innovative solutions tailored to real-world financial and strategic decision-making scenarios. This award will also provide opportunities for students to be involved in the research projects. In the first part of the project, infinite-dimensionality arises because one of the state variables is in the Wasserstein space of probability measures. Some of the equations to be studied are motivated by the large population limit of the equilibrium among N weakly interacting symmetric agents. The primary goal is to use these partial differential equations to demonstrate sharp convergence rates to the large population limit. A second class of equations in the Wasserstein space is inspired by causal optimal transport problems and the control of the solution of Kushner's equation for optimal filtering. While these transport problems were initially introduced to measure distances between the laws of stochastic processes, the project aims to establish a novel connection between these transport problems and issues of information asymmetry in finance. In the second part of the proposal, infinite-dimensionality arises from path dependence. The objective is to establish an Ishii's lemma applicable in this context and utilize this result to achieve well-posedness for two classes of second-order parabolic partial differential equations in the space of paths. The proposed research is expected to yield a comparison result for semicontinuous viscosity solutions of these PDEs, which directly impacts the convergence of numerical schemes for the hedging problem with rough volatility. In terms of applications, the infinite-dimensionality in this context stems from stochastic Volterra integral equations, time-inconsistent optimal control problems, and functional Itô calculus. 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 2024 · 2024-08

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational wave detections from colliding black holes and neutron stars have now become almost routine since the first epochal discoveries in 2015 and 2017, as the number of detections continues to grow. The next game-changing discovery may well come, not from such cataclysmic collisions, but rather from the smooth "hum" of a fast-spinning, slightly bumpy neutron star in our own galaxy, one emitting continuous gravitational waves. Such a source could emit detectable radiation for the foreseeable future, allowing follow-up investigations of exquisite precision. It would likely become one of the most studied objects in astronomical history, as gamma-ray, X-ray, ultraviolet, visible, infrared, and radio telescopes would all be trained on this nearby, ultimate multi-messenger target. Its study would provide critical insight into the still poorly understood structure of neutron stars, which contain matter in an extreme physical state inaccessible to terrestrial experiments. The research to be carried out will provide training to undergraduate and graduate students in state-of-the-art science at the frontier of knowledge. Research will focus on a number of specific areas related to the Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) Experiment. Searches for continuous gravitational waves from 1) unknown isolated neutron stars anywhere in our galaxy; 2) isolated neutron stars at the centers of young, nearby supernova remnants; and (as Nature permits) from relatively nearby post-merger remnants or very nearby newborn neutron stars in our own or neighboring galaxies. In addition, detector characterization of Advanced LIGO interferometers focused on spectral line identification and mitigation will be carried out, to improve searches for nearly monochromatic continuous gravitational 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 2024 · 2024-08

Concerns over energy security and global warming have driven a recent dramatic increase in installed wind capacity. Moreover, the need for optimal wind conditions and limited availability of suitable land has resulted in the aggregation of turbines into high-density wind farms. In such farms, aerodynamic coupling between turbines strongly influences the efficiency of the turbines. Because the coupling between turbines is mediated by wind, however, there is significant and variable delay in the interaction between turbines–the upstream turbines affect downstream turbines with a delay determined by velocity of the wind exiting the upstream turbines. Thus, our ability to efficiently control wind farms is fundamentally limited by our ability to control large-scale networks with uncertain, time-varying and even state-dependent delay. The project will develop methods to design and analyze controllers for such systems with networked dynamics and delays. This will enable a host of benefits for wind energy including improved power capture, reduced loading, and active power control for grid services. The work will also enable the design of safe and efficient networked controllers in other domains including fleets of autonomous vehicles and swarms of uninhabited aerial vehicles. The goal of the project is to develop new theory and algorithms which allow for robust analysis and control of nonlinear systems with uncertain and variable delay. To do this, we combine the Integral Quadratic Constraint (IQC) framework for robust analysis and control Integral with the Partial Integral Equation (PIE) framework for optimal control of fixed-delay linear systems. Unlike previous work which considered the entire delay to be a source of uncertainty, we partition the delay into nominal and uncertain/variable parts. We then use PIE’s for the known/fixed-delay linear part of the system and IQC’s for the uncertain/nonlinear part. This approach reduces the uncertainty in the system and thereby increases the accuracy/performance of the resulting analysis/controllers. The technical approach of the project is divided into 4 Technical Challenges. (T1) First, we consider uncertain/nonlinear dynamics and known, fixed delay and formulate a convex stability test. (T2) Next, we model static uncertainty in delay using a nominal PIE subsystem and an unstructured parametric uncertainty acting on infinite-dimensional channels. We generalize the PIE and IQC frameworks to handle infinite-dimensional channels including a parameterization of dynamic PIE multipliers. (T3) Third, we consider time-varying and state-dependent delay. These cases are modeled by a nominal PIE coupled with either linear parameter-varying uncertainty or a nonlinearity. (T4) Finally, for robust control design, we use an alternation between a synthesis step and an IQC analysis step. The results are applied to models of wind farm control. 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.