University Of Illinois At Urbana-Champaign

NSF Awards · FY 2024 · 2024-09

The Division of Atmospheric and Geospace Sciences (AGS) operates the Community Instruments and Facilities (CIF) program to enhance the scientific community’s access to instrumentation that is otherwise too costly or complicated to operate for most institutions. This award is for two Doppler on Wheels mobile radars and an array of surface weather stations, known as DOWNET. Mobile Doppler radars are used to study a variety of societally-impactful topics, from tornadoes to snowfall. The DOWNET instruments will also be used to teach younger generations through outreach events and hands-on training. The DOWNET facility consists of two mobile, dual-polarization, dual-frequency, Doppler X-band radars and an array of surface- and pole-based deployable weather stations. The DOWNET facility is available for studies of severe and high impact weather, convective initiation, storm transitions and upscale growth, winter storms, orographic precipitation, precipitation microphysics, hydrological processes, tornado and hurricane structure, weather modification validation/refutation, fire weather, land use impacts, and more. DOWNET can be used in mobile or fixed mode. DOWNET is also available for outreach and/or educational purposes. 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

Non-technical Abstract With support from the Solid-State and Materials Chemistry Program in the Division of Materials Research, Professor Joaquin Rodriguez Lopez's group at the University of Illinois Urbana-Champaign and Professor Jose Luis Mendoza's group at Michigan State University are developing new methods to enhance the stability and performance of dual-ion batteries (DIBs). These batteries, which utilize carbon electrodes to reversibly insert anions and cations, offer promising advantages due to their ability to achieve high voltages (up to 5V), their potential to eliminate the need for critical minerals, and their relative cost-effectiveness and sustainable sourcing. However, their performance is hindered by chemical and mechanical degradation, which can limit their competitiveness compared to current lithium-ion batteries. The research addresses these issues by modifying the surfaces of graphitic carbons to promote the formation of protective interfaces that facilitate ion insertion while suppressing harmful side reactions. By experimenting with the thickness and characteristics of graphite electrodes, the types of molecules used for modification, and through computational simulations to understand the insertion sites, the team gains a comprehensive understanding of the energy storage mechanisms in these DIBs. This project aligns with NSF's mission by advancing scientific knowledge and contributing to national prosperity through improved energy technologies. Additionally, it integrates concepts of battery science, surface science, spectroscopy, and computational simulations into an educational and outreach plan that provides learning opportunities in renewable energy science for graduate students, local undergraduates, and K-12 Hispanic students. Technical Abstract With this project, supported through the Solid State and Materials Chemistry Program in the Division of Materials Research, researchers at the University of Illinois Urbana-Champaign and at Michigan State University address interfacial dysfunction as a critical issue limiting the performance of DIB cathodes. They investigate the formation of passivated artificial graphene-electrolyte interphases (GEIs) as a promising strategy. Functional GEIs in this project are created through systematic surface modification of graphitic electrodes using heteroatom substitution, grafted molecules, and surface-modifying polymers. While extensive literature exists on anion intercalation in graphitic materials, this project's approach using ultra-thin electrodes (ranging from ~1 nm to ~100 nm) helps bring to light interfacial degradation processes, offering timely diagnostic capabilities. Utilizing techniques such as surface-enhanced infrared spectroscopy and scanning electrochemical microscopy, the research correlates real-time passivation processes of electrodes with interfacial molecular details to gain mechanistic insights. Advanced computational simulation methods, including hybrid-density functional theory, ab initio molecular dynamics, nudged elastic band method, and reactive molecular dynamics, provide detailed information on the thermodynamic and kinetic aspects of anion intercalation on graphene electrodes and their modified surfaces. The demand for high-performance energy storage to support rapidly growing renewable energy technologies requires durable, safe, and cost-effective batteries. Materials developed as part of this project can contribute to a transition beyond current lithium-ion battery technologies. 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 provide robots with new capabilities to predict how granular materials, like sand or soil, will respond to physical actions, and to improve those predictions through on-going physical interaction, observation, and learning. First-principles computational models built up from the forces between large numbers of individual grains are too cumbersome for robot control. In this project, the granular material is compactly characterized as a graph, consisting of nodes representing aggregated regions of material, which are connected by edges representing dominant inter-region interactions. The graph representation is integrated with an artificial neural network, creating a powerful computational object called a graph neural network (GNN). The GNN can be used, for example, to generate a series of robot operations to form the granular material into a desired shape, or to infer that unexpected resistance to digging may be due to a buried root or rock. The GNN is updated based on camera images and the forces sensed as the robot works. The results of this project will be demonstrated on excavator robots, with the eventual goal of enabling robots that can reliably form structures such as pits, channels, and level foundations in varied terrains, including sandy, rocky, or root-embedded soil. The datasets, models, and software libraries developed in this work will be made publicly available to the robotics and terrain mechanics communities. Because physics-based simulations of granular materials are too computationally expensive to be used on board a robot, this project explores how machine learning approaches can be applied to predict robot-terrain interaction efficiently. Specifically, the project will investigate how a state-of-the-art machine learning model -- graph neural networks (GNNs) -- can be applied to model large terrains such as those present in construction sites. The investigators' approach will identify portions of large geometric maps into localized GNNs to predict fine-grained dynamics for small portions of the terrain, allowing GNNs to apply to much larger environments than was possible in the past. Moreover, because terrain behavior changes depending on the material characteristics and the presence of hidden objects, the project will develop multi-material GNNs to predict a range of terrain behavior, and these models will be used to help robots map out a terrain by feel in addition to vision. The broader impacts of this project include the involvement of undergraduates in research activities, outreach to underrepresented groups to broaden participation in computing, as well as the release of open-source software for modeling and manipulating granular materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The resilience and robustness of autonomous robotic systems in dynamic, unpredictable, and ever-changing environments are central concerns of the robotics community. To address these challenges, this research project introduces a novel "introspective counterfactual reasoning" capability to empower robots with lifelong autonomy. While counterfactual thinking—considering the implications of changes in the world that could have happened, but didn’t—is a foundational cognitive function in human beings, its application in robotics remains largely underexplored. This project aims to bridge this knowledge gap by enabling robots to answer and learn from "what if" questions regarding both their surroundings and themselves, better preparing them for unforeseen events, potential hazards, and evolving contexts. This project introduces two different yet interleaved forms of counterfactual reasoning: Contextual Physical Rehearsal and Introspection Adaptation. Contextual Physical Rehearsal allows the robot to model the physical world and forecast the outcomes of actions without actual execution. Introspection Adaptation focuses on predicting and enhancing the robot's capacity to perform tasks in unfamiliar environments and unexpected situations. The strategy involves designing these capabilities, integrating them into diverse autonomy platforms as interconnected modules, and validating their efficacy in real-world tasks. The framework will be validated in a rigorous procedure from modular simulation testing to integration and deployment on real ground vehicles under challenging conditions. The project will create new interfaces that allow developing courses on field robotics and simulation and provide immersive and engaging programming activities for K-12 students. 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

In the era of advanced robotics and AI, there is a notable gap in leveraging these technologies for personalized human assistance, especially in settings that require nuanced understanding. Current robotic systems, despite their impressive locomotion capabilities, often lack the flexibility needed to adapt to the diverse needs of users, as their interactions are typically restricted to predefined functionalities. This limitation is particularly evident in scenarios where humans teach robots to perform highly personalized tasks within shared physical spaces, which may lead to discomfort and safety concerns among users. This EArly-concept Grant for Exploratory Research (EAGER) project will fund research that attempts to address the challenging task of a quadrotor to pick up/drop off a small package from/onto a human's outstretched hand following the human's instructions. The challenge of the task comes from the quadrotor's close proximity to a human, which can trigger stress due to noise and movement, potentially undermining task completion. The research effort seeks to address the above-mentioned task utilizing a framework that enables anyone to safely instruct robots in customized tasks. This research is crucial for the exploration of harnessing the intelligence enabled by machine learning to expand the capabilities of robots toward humans’ needs, especially in the industries that urge rapidly growing robot participation and coordination with humans, e.g., manufacturing, logistics, transportation, and national defense. This research aims to attain the objective of allowing quadrotors to safely interact with people for the chosen task of direct hand-to-hand package delivery, ultimately leading to robots that can genuinely adapt to and meet individual needs for more personalized and safe human-robot interactions. The researched effort incorporates the human's cognitive state into the quadrotor's decision-making and action processes, fostering a bidirectional sensorimotor interaction and allowing both the human and the quadrotor to sense and influence each other's decisions and actions while the quadrotor conducts the task in close proximity to the human. Two interconnected research thrusts will be pursued: (i) planning and control for safe human-robot interaction with models for human cognitive states and (ii) iterative learning for enhanced performance towards efficient and safe human-robot interactions. The research framework builds upon the advances made by the PIs in control theory/engineering and psychology and is expected to make important contributions to the society of the future, in which humans and robots behave and interact safely and effectively while occupying shared spaces. This EAGER award has been co-funded by the Dynamics, Controls, and System Diagnostics and the Mind, Machine, and Motor Nexus Programs. 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

During the past couple of years, Artificial Intelligence (AI) has had a dramatic impact in many areas. Many of the current AI systems are based on Large language models (LLMs), which are computer models that capture information across a variety of topic domains. LLMs have been increasingly deployed in many high-stakes applications including Web search and recommendation, healthcare and medicine, question-answering agents, and education. However, current LLMs are known to generate many kinds of unsafe system behaviors, such as providing false or inconsistent information, reporting unjustified confidence levels on rare events, or performing erroneous actions. These unsafe behaviors can lead to potentially catastrophic results in high-stakes domains, so ensuring LLM safety is a pressing question that we must address to protect against social harm. This project focuses on enhancing the safety of LLMs by proposing quantifiable safety measures and corresponding algorithms to detect unsafe behaviors and mitigate them. Furthermore, this project will support the development of a graduate-level course on trustworthy AI, which will be offered to students from underrepresented groups to promote diversity in AI research at the University of Illinois Urbana-Champaign. The technical aims of the project contains three key thrusts: (1) Robust-Confidence Safety (RCS), which ensures that LLMs recognize and appropriately respond to out-of-distribution scenarios and rare events; (2) Self-Consistency Safety (SCS), which enforces logical consistency in LLM outputs across similar contexts; and (3) Alignment Safety (AS), which aligns LLM responses with user objectives, particularly to avoid generating false or misleading information. The project will define these safety criteria, develop detection methods for unsafe scenarios, and create algorithms to enhance LLM safety. The proposed methods will be tested using the open-source LLM framework LMFlow, ensuring access for creating practical applications and community availability. The project promises significant benefits, including safer AI applications, advancements in the field, and contributions to education and diversity. 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 NSF project aims to create new techniques for fault detection in inverter-dominated power systems. The most typical faults are unintentional short circuits caused by, for example, lightning and tree contact, and can cause substantial damage if not quickly dealt with. Detecting and extinguishing short-circuit faults are thus critical aspects of reliable power system operation. In power systems with synchronous machine-based generation, large, unbalanced fault currents provide clear information about the existence and location of faults. Inverters prevent such currents even during faults. As a result, conventional fault detection schemes can fail in grids that are rich in inverter-based resources like wind and solar generators. One way to make it easier to distinguish normal operation from a fault is for the inverter to add a perturbation, or auxiliary signal, when there is suspicion of a fault. This project will design new, auxiliary signal-based fault detection schemes for inverter-dominated power systems. The intellectual merits of the project include characterization of when and what auxiliary signals are necessary, how many inverters in a grid must inject them, and minimal infrastructure investments necessary to guarantee fault detection. The broader impacts of the project include enhanced reliability of modern power systems, which will further facilitate the integration of inverter-based resources like renewables and energy storage; and curriculum development at the graduate and undergraduate levels, and energy-oriented programming for K-12 students. This project will develop the use of auxiliary signals in inverter-dominated power systems. Typical choices of auxiliary signal include negative sequence current, as in IEEE Standard 2800, and harmonics. The auxiliary signals will be optimized so as to minimize disruption while guaranteeing detection of all possible faults. The existing theory will be extended in scenarios not covered by existing tools, for example, networks with multiple inverters and relays. The mathematical formalization of this problem will constitute a streamlined, optimization-based procedure for designing new detection schemes, which is relevant today as grids and grid codes continue to rapidly evolve. All new detection schemes will be validated in electromagnetic-transient simulation and in controller hardware-in-the-loop testing. 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

Biotechnology has promised to solve many grand challenges of modern society. However, the traditional research and development process in biotechnology is very slow, expensive, and inconsistent. To overcome this key limitation, the goal of this project is to establish a national center for biofoundry applications (NCBA) at the University of Illinois at Urbana-Champaign. The center would serve as a hub for innovation, bringing together researchers, industry experts, and policymakers to foster collaboration and accelerate the development of new, efficient biomanufacturing processes. By centralizing resources and expertise, the center would streamline the creation of new bio-based products and technologies, ranging from renewable chemicals to advanced medical treatments. This not only boosts economic growth and competitiveness but also enhances public health and environmental sustainability. Additionally, the center would play a vital role in education and workforce development, preparing the next generation of scientists and engineers to lead future breakthroughs in the bioeconomy. The overall goal of the proposed project is to establish a national center for biofoundry applications (NCBA) to advance the bioeconomy. In the center, the biofoundry development will be enabled by a synergistic and fully integrated program consisting of an in-house research and technology development program, an external user program, and a knowledge sharing, education and outreach program. In the in-house program, existing workflows, tools, codes, parts, and organisms will be onboarded and new ones will developed. In the user program, a broad community of external users will be engaged to develop new capabilities (workflows, tools, codes, parts, organisms) for the biofoundry and solve important scientific problems through an externally peer-reviewed, competitive proposal process. Both the in-house and user programs are designed to synergistically develop a synthetic biology pipeline named AlphaSynBio that can be implemented on the biofoundry. The proposed national center is not simply an integrated collection of equipment for automated biological experiments and software. It will be an open ecosystem of disruptive thinking, education, and community engagement powered by state-of-the-art biofoundries. The success of the proposed national center will revolutionize the way biology is taught, capture the imagination of a new generation of biologists, and train next generation workforce which is well versed in biology, artificial intelligence, and robotics. This project is jointly supported by the Divisions of Emerging Frontiers (EF), Biological Infrastructure (DBI), and Molecular and Cellular Biosciences (MCB) in the Directorate for Biological Sciences (BIO), and the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences (MPS). 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 broader impact of this Broadening Participation for Engineering Track 2 (BPE- Track-2) project will be to enhance knowledge about how to increase the number of women in higher-power (tenure-track) engineering faculty and leadership positions. Though many stakeholders prioritize increasing gender equity for academic faculty, statistics show that women are over-represented in lower-power faculty positions (non-tenure track). These lower-power faculty roles are often not eligible for higher career advancement, including campus leadership positions. Without gender equity in higher positions of power, the benefits of a diverse workforce cannot be fully realized. We are researching why and how women are positioned in lower- or higher-powered faculty roles in academia. To do this, we will learn from people along this career path, including women graduate engineering students, women in lower-power engineering faculty positions, and women engineering faculty in higher-power positions. By interviewing these people and studying their experiences, we will be able to more deeply understand the underlying mechanisms that result in these power imbalances for women, including women of color. This understanding will then inform changes that institutions can make to support women faculty advancing toward leadership positions in academia. The proposed project leverages a qualitative approach to answer the unexplored research question: How can institutions support the advancement of women faculty in non-tenure track (NTT) ranks? Our study will explore women graduate student and faculty experiences through the lenses of intra-occupational gender segregation and social cognitive career theory to identify key mechanisms that influence women’s career decisions in academia and the factors that support women’s transitions from NTT to tenure-track (TT) roles. The project will leverage our prior research to determine: 1) how trainee perceptions of TT and NTT roles develop and influence career decisions; 2) how NTT women faculty experience their roles and seek career advancement; and 3) what are the pathways, including barriers and supports, of advancement of women from NTT to TT ranks. We will interview 40-50 participants, including (1) women engineering graduate students, (2) women NTT faculty in engineering, and (3) women TT faculty in engineering. Interview collection will include semi-structured, quantitative comparative, and critical incident techniques and analysis will include thematic analysis and corresponding quantitative comparative, and critical incident analysis. We will leverage our results to inform research-based practices to support career development and institutional policies that support gender equity. This project was partially supported by the NSF ADVANCE program which is designed to foster STEM faculty equity by identifying and eliminating organizational barriers to the full participation and advancement of diverse faculty in academic institutions. 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 will develop theory and methods to address network-level uncertainty in the control of large-scale networked systems. In such systems, the communication topology is described by a directed graph, whose nodes represent the agents in the system and the edges represent the communication links between them. To meet the modelling demands of realistic multi-agent settings, it is known that one needs to account for process and observation noise. The goal of this project is to explore what lies beyond these requirements and account for uncertainty at the level of the communication topology. To this end, the project will integrate the fields of random graph theory, and particularly graphon theory, with structural system theory to develop the needed theoretical tools to model and understand network-level uncertainty in control systems. Graphons are relatively new models in the landscape of random graph theory. They generalize many existing random graph models, such as the Erdös-Rényi model, by allowing for heterogeneous edge densities. A major research goal of this project is to characterize completely how a given structural system property behaves under network uncertainty. The main research problem of the project is the following: Given a desired system property (e.g., controllability and stability), what is the probability that a graph sampled from a graphon can sustain the property? The problem is by nature combinatorial and probabilistic. To tackle these challenges, this project will rely on tools from analysis and geometry to develop a new set of ideas geared toward the computation the aforementioned probabilities in the asymptotic regime, where the size of the random graph goes to infinity. The intellectual merits of this project lie in the use of methods from graphon theory, probability, and combinatorics to understand control system properties. More specifically, the project will (1) formulate new problems at the intersection of structural system theory and graphon theory, (2) develop a new toolbox for analyzing structural properties for network systems drawn from graphons, and (3) establish new theoretical results and algorithms that may have impacts on both areas and beyond. A major novelty of the proposed approach is that the project leverages tenets from graphon theory to circumvent the complexity of combinatorial problems, leading to the use of analytical tools and geometric approaches to provide complete solutions. 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

Precise measurements of the motions of stars on the celestial sphere are critical for understanding the formation and content of the Milky Way galaxy. Astronomers create computer models of the motions of stars to help understand assembly history of the Milky Way, the nature of dark matter, and look for planets around nearby stars. However, the motions of stars are extremely and difficult to measure. Moderately bright stars have been measured with exquisite precision by the European Space Agency’s Gaia spacecraft, but larger telescopes are required to measure fainter stars. The investigators will develop and apply new techniques to measure the motions of faint stars using some of the world’s most powerful ground-based survey telescopes. As part of this project, the investigators will provide scientific and technical training for graduate and undergraduate students. Furthermore, the investigators will engage and educate the general public with visualizations of the dynamic motion of stars in the Milky Way. The investigators will measure the astrometric positions of stars in hundreds of thousands of images collected by the Dark Energy Camera (DECam) on NSF’s 4-m Blanco Telescope. The investigators will use the DECam data to perform best-ever measurements of the proper motions of hundreds of millions of stars that are too faint to have been measured previously. The DECam data cover nearly the entire sky area of the Vera C. Rubin Observatory’s unprecedented Legacy Survey of Space and Time (LSST). The investigators will combine the DECam and LSST data to measure the positions and motions of stars much more precisely than would be possible with the first year of LSST alone. This research award is partially funded by a generous gift from Charles Simonyi to the NSF Astronomy division. The project includes significant contributions to Vera C. Rubin Observatory’s Legacy Survey of Space and Time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Mathematical literacy and statistical reasoning are critical for making day-to-day decisions in a world that is quickly moving online and increasingly data driven. For example, many lifestyle choices such as smoking, eating meat, or using a seatbelt while driving require individuals to think and reason about the probability of different outcomes rather than simple cause and effect. Unfortunately, a large percentage of students have misconceptions about probability and statistics that can lead to incorrect reasoning. More concerning is that these inaccurate ideas often persist even after taking courses in statistics. One promising line of research demonstrates that mathematics instruction that utilizes visual representations, authentic problem-solving, and physical manipulatives can help address these concerns when these components are connected through instructor and student gestures. However, less is known about how gestural strategies apply to statistics education, and even less is known about how to apply these strategies to online and remote learning environments. Therefore, this project aims to conduct a large-scale study using a diverse sample of high school and undergraduate students to examine how prompting learners to gesture when learning statistics content in video-based learning environments may improve their understanding of statistical concepts and procedural knowledge. The main objective of this project is to evaluate an approach to designing video learning environments (VLEs) for teaching statistics concepts that use gestures during instruction and cue learners to perform gestures. Using a mixed methods approach, researchers will employ design-based methods to create VLEs that incorporate instructional gestures, cue students to perform these gestures, and monitor and provide feedback about student gestures during statistics instruction using webcams that are standard on most computers. Researchers will also utilize randomized control trials to assess the impact of VLE design quantitatively on conceptual understanding of statistics concepts, procedural knowledge about how to solve statistics problems, transfer of understanding to future learning, and the impact on self-regulated learning. Case-study methods will examine which features of gesture effectively scaffold student learning and enhance transfer during online instruction. The project will contribute to our understanding of embodied learning, specifically the impact of metaphorical gesture on self-regulated learning and conceptual understanding of foundational math concepts. It will also contribute to an emerging body of literature situated at the junction between gesture and video-based, online learning. Researchers will make the resulting VLEs freely available to educators and researchers on the project’s website, where they will also post a design guide highlighting the gestures and other pedagogical features that were successful as well as those that were not. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This Level II project focusing on STEM Learning and Learning Environments is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

CI Pathways is an innovative project designed to help researchers effectively use advanced cyberinfrastructure (CI) tools in their scientific work. These tools are essential for modern research, aiding in critical tasks such as efficient and large-scale data collection, analysis, and sharing. However, many researchers struggle to integrate these resources due to a lack of training, an environment for practice, and continued support. CI Pathways addresses this by offering a structured, cohort-based training program where participants learn alongside peers and receive guidance from experienced mentors. By including participants from diverse backgrounds and disciplines, CI Pathways will promote inclusivity and broaden access to these critical resources. The project aligns with NSF's mission by promoting scientific progress and fostering education and diversity. It helps bridge the gap for underrepresented groups in science and engineering, advancing national prosperity and welfare. Additionally, by equipping researchers with the skills to use CI tools, the project contributes to national interests in scientific advancement and innovation. The program's success will be shared with the wider research community, enabling other institutions to adopt similar models and further amplify the project's impact. CI Pathways aims to empower researchers with the skills and knowledge to integrate advanced cyberinfrastructure (CI) resources effectively into their research workflows. The project addresses two primary challenges: a lack of awareness and training on the benefits and opportunities of CI resources and insufficient guidance and support in accessing and using these resources. By overcoming these challenges, CI Pathways seeks to enhance research productivity and innovation across diverse scientific disciplines. The project employs a cohort-based, mentor-supported training model. Participants will be selected through a diverse, equitable, and inclusive application process, emphasizing representation from underrepresented groups and non-traditional domains. The program offers a combination of self-paced and live training sessions with a focus on CI tools, data science, and machine learning. These sessions and their supporting content will be made available to the community at large through incorporation into the NSF ACCESS Knowledge Base. Each cohort will be supported by experienced mentors who will guide participants through their individualized learning paths. Participants will have access to CI resources to practice their newly acquired skills in real research contexts. A mentor training program will be offered to participants who complete their learning paths, preparing them to serve as mentors in future cohorts. By fostering a supportive and collaborative learning environment, CI Pathways will cultivate a community of researchers who can share knowledge and support one another in their use of CI resources, especially those provided by NSF’s ACCESS. 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

ALGECOM (algebra-geometry-combinatorics) is a series of biannual one day conferences. The central goal is to promote collaboration and regular interaction among Midwest students and faculty. Both (non-local) senior and early career mathematicians will be invited as speakers. ALGECOM is now a tradition in the Midwest going back to 2009. It has involved over fifty institutions. This grant will fund participant travel to six conferences, the first of which, ALGECOM XXIV is planned to be at the University of Minnesota, Twin Cities. A subsequent edition will occur at The Ohio State University. Both of these venues are new to the series. The conference website is at https://sites.google.com/view/algecom-main/algecom-main. Each session of ALGECOM consists of four talks as well as a student poster session. Dissemination of research at the border of algebra, geometry and combinatorics is the main intellectual merit of the series. There is a wealth of Midwest departments with individually small ALGECOM-related research groups. These one-day conferences provide the stimulus for research collaboration among these groups. An effort has been made to encourage the attendance of graduate students, recent graduates, and untenured faculty. In terms of broader impacts, there are ongoing efforts to recruit members of underrepresented groups in mathematics as participants and speakers. These efforts are enhanced by NSF support, as it allows for invitations to mathematicians from a geographically larger region. 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

Overcoming intrinsic fragility is a long-standing quest for developing light, strong, durable, and reliable ceramic materials needed for high-demand applications, such as electronics and aircraft components. This research explores introducing small quantities of nanofillers in 3D-printable polymer-derived ceramics to overcome this major barrier. Such a transformation in printable ceramic materials will provide remarkable advances in ceramic durability and reliability, ultimately paving the way toward their certification for practical industrial and aerospace applications. The scientific understanding of durable and 3D-printable ceramic composites will revolutionize ceramic materials design and technologies and preserve the leadership of the United States in advanced materials and manufacturing. The systematic scientific training through the research activities, which include nanotube/ceramics processing and characterization, 3D printing, nano- and micro-mechanical characterization, and multiscale computations, will provide the technical environment for nurturing the future scientific and engineering workforce. Research results will be integrated into mechanics of materials outreach courses for K-12 students, as well as incarcerated individuals as part of the Education Justice Project. Polymer-derived ceramics transform advanced ceramic technologies with superior manufacturability, and are compatible with 3D printing techniques, but their intrinsic porous microstructures are prone to fracture. This research exploits the multiscale reinforcement potential of small quantities of boron nitride nanotubes within the polymer-derived ceramic matrix to enhance the fracture toughness and reliability of these ceramic nanocomposites. The complementary multiscale experiments and computations will elucidate the synergistic role of the boron nitride nanotubes in reducing the statistical uncertainties in the bulk mechanical properties of polymer-derived ceramics, manufactured using 3D electrospinning writing techniques capable of achieving well-aligned nanotubes. At the nanoscale, scanning electron microscopy nanomechanical pull-out measurements of individual nanotubes embedded within the ceramic films, along with companion density functional theory calculations, will provide quantitative measurements of the nanotube-matrix interfacial strength properties. At the micro/macroscale, crack growth experiments with in situ Raman and field projection methods will inversely extract the experimental crack-tip cohesive zone law associated with the pull-out of multiple aligned nanotubes within the crack bridging zone. A two-scale micromechanical damage model, accounting for small-scale damage caused by nanotube/matrix failure and larger-scale damage due to microvoid growth, will provide the physical and statistical connection between microstructural parameters/uncertainties and the macroscopic cohesive crack growth response. 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

Quantum technologies have the potential to radically impact science and society in the information age. In particular, quantum networks may eventually enable distributed quantum computing and new forms of quantum sensing. However, a deeper theoretical understanding of quantum networking is needed in order to verify the functionality and guide the development of novel architectures and use-cases for quantum networks. This project will explore new theoretical tools to study how entangled states can be created, manipulated, certified, and characterized in realistic quantum networks. This project will make a strong connection with current experimental capabilities while developing methods and protocols that are of direct relevance to current and near-term quantum network implementations. This project is structured along two main research lines. In one direction the research team aims to characterize the structure of multipartite entanglement in networks. The key questions are: which entangled states can be generated in a network? How can one efficiently certify multipartite entanglement in networks? How can one quantify network entanglement in an operationally meaningful way? In a second direction this team will explore the properties of quantum measurements at each network node. The fundamental questions they will explore are: what type of local measurements are possible when multiple quantum signals are received at different times and quantum memory is limited? What classes of multipartite entangled states can be generated under constrained entanglement swapping measurements? How do restricted local measurements lead to novel notions of network quantum steering and data hiding? This research project is poised to initiate new experimental collaborations, such as with researchers at both the PIs home institutions, the University of Geneva and the University of Illinois Urbana-Champaign. The US-Swiss scientific collaboration supported by this project will therefore grow beyond just the two PIs. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Resiliency to stress is crucial to the health of individuals and of society. While the detrimental effects of prolonged stress are well-documented, there remains a critical knowledge gap regarding the mechanisms underlying individual resilience to adverse outcomes. The circuits underlying stress resiliency, and how resilient individuals tap into these circuits to maintain normal behavioral function, are not fully understood. Furthermore, resiliency occurs over both long and short timescales and likely emerges from interactions across neural subsystems that span molecules to whole-brain connectivity. To unravel these complex mechanisms, we will use a multidisciplinary approach that combines time course experiments, machine learning, signal processing, and statistical inference to integrate multidimensional data from the transcriptome to the whole-brain connectome to characterize how circuit activity gives rise to resiliency. We will collect behavioral, neuroimaging, electrophysiological, gene expression, and biomarker data from well-validated mouse models of resilience. We will also develop new computational tools to characterize the multiscale network dynamics of genes, molecules, and circuits that underlie the neurobiological substrates of resilience. We will validate these relationships in another mouse strain to determine the specificity of our model. This will be the first study to provide a comprehensive understanding of interactions between neural circuits and their molecular, genomic, and neuroanatomical contexts in animal models of resilience. If successful, our findings could yield insights into unifying principles of how neural mechanisms interact to generate behavioral phenotypes.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY Clinical disorders of fear and anxiety, including trauma- and stressor-related disorders, represent an enormous public health burden. Unfortunately, cognitive-behavioral therapies, such as exposure therapy, that are aimed at reducing pathological fear are vulnerable to relapse. This is particularly problematic for patients under high levels of stress, which undermines exposure-based therapies by impairing extinction learning and promoting fear relapse. Despite years of work elucidating the neural circuitry for extinction, the neural mechanisms responsible for stress-induced extinction impairments remain poorly understood. In previous work on this project, we established that stress recruits bottom-up neuromodulatory circuits that inhibit the medial prefrontal cortex (mPFC), a brain area that is critical for extinction learning. We have now shown that that noradrenergic neurons in the locus coeruleus (LC) are critical for stress-induced extinction impairments, such as the immediate shock deficit. Critically, chemogenetic activation of noradrenergic LC neurons induces basolateral amygdala (BLA) hyperexcitability, which in turn drives feed-forward inhibition of the mPFC. Preliminary data indicate that stress-sensitive corticotropin-releasing factor (CRF) neurons in the central nucleus of the amygdala (CEA) drive extinction learning deficits. Based on this, we propose a novel hypothesis that CEA-CRF+ neurons drive LC-NE projections to the BLA, which results in both BLA hyperexcitability and impaired extinction learning. We propose three specific aims to test this hypothesis using a combination of in vivo electrophysiology, calcium imaging, and intersectional opt0genetic manipulations in male and female rats. The first specific aim examines whether CEA-CRF+ projections to the LC are necessary and sufficient for stress- induced increases in BLA hyperexcitability and extinction learning deficits. The second specific aim examines explores whether noradrenergic modulation of parvalbumin (PV) interneurons in the BLA regulates stress- induced hyperexcitability and extinction deficits. Lastly, the third specific aim examines whether the BLA is critical for transducing stress-induced activation of CEA-CRF+ and LC➙BLA circuits to undermine mPFC activity and extinction learning. The outcomes of these aims will advance a novel circuit mechanism for stress- induced extinction impairments. Understanding this mechanism will facilitate the development of novel pharmacotherapeutic approaches that optimally engage mPFC circuits to promote extinction learning under stress.

NIH Research Projects · FY 2025 · 2024-08

Precise duplication of our genome and appropriate cellular response to genotoxic stress is critical to maintaining genome stability. Origin Recognition Complex (ORC, composed of six subunits) and ORC- Associated (ORCA) are required to initiate DNA replication and regulate heterochromatin organization. Multiple subcomplexes of ORC and/or individual ORC subunits regulate different aspects of cell cycle progression and thus play pivotal roles in the maintenance of genomic stability. In the last two decades, my (and my group’s) research effort has been instrumental in understanding how these multitalented ORC proteins govern origin- independent roles during the cell cycle. The long-term goal of my laboratory is to understand how ORC executes and coordinates various aspects of cell growth, proliferation and survival. With our expertise and experience in cell biological and biochemical characterization of ORC and strong preliminary data, we are ideally positioned to pursue the following two projects: 1) The role of the smallest subunit of ORC, Orc6, in replication progression and mismatch repair (MMR) 2) The role of pre RC factors in regulating DNA damage response (DDR) and chromatin organization during G1. The smallest and the most enigmatic ORC protein, human Orc6 is required for DNA replication and also coordinates cytokinesis. Our recent work has shown the dispensability of human Orc6 in DNA replication licensing and identified an unexpected role for human Orc6, which is to promote S-phase progression post pre-RC assembly and in MMR. The scientific premise of program projects is based on data from our lab that ORC is required for licensing-independent roles in DNA damage response. Our experiments are critical to establishing the new paradigm emerging ‘is human Orc6 a component of ORC?’ We propose that hOrc6 plays a fundamental role in genome surveillance during S-phase. The objective of project 1 is to answer fundamental questions on the biochemical roles of Orc6 in regulating S-phase and during MMR. Our unpublished work shows that the largest subunit of human ORC, hOrc1, shows robust accumulation at sites of laser-induced DNA damage. Orc1 is an AAA+ ATPase, possesses a BAH domain, and binds to chromatin by associating with various post-translationally modified histones. The objective of project 2 is to understand the fundamental molecular mechanisms of ORC in DDR and chromatin organization, using Orc1 as a tool, we probe into the coordination of these processes. This proposal is conceptually innovative because we will rigorously dissect novel regulatory mechanisms of ORC function in DDR and chromatin organization. This proposal is technologically innovative because it employs state-of-the-art cell biological techniques, including super-resolution imaging combined with biochemical and single-molecule biophysical approaches. Understanding how ORC governs multiple pathways, including DNA replication, mitosis, and DDR is expected to uncover novel pathways that would be useful to prevent tumorigenesis and key to allowing more effective therapeutic targeting to combat cancer.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Therapeutic interventions, such as exposure therapy, reduce pathological fear in patients with anxiety disorders. Extinction is a fundamental form of learning that underlies these therapies. A major challenge to extinction-based therapies is that the fear reduction is often transient and bound to the place or context in which therapy occurs. For example, when patients confront phobic objects or reminders of trauma outside of the clinic, their fear often relapses. This reveals that extinction learning does not erase fear memory, but yields a context-dependent “safety” memory that inhibits the expression of fear in the place where it is learned. Accordingly, the long-term goal of this project is to understand the neural substrates of fear extinction and relapse, particularly the specific brain circuits involved in the contextual control of extinction. Work in the current funding period of this project has focused on renewal, a relapse of extinguished fear outside the extinction context. Importantly, it was found that the hippocampus (HPC) mediates renewal by inhibiting retrieval of extinction memories encoded by the infralimbic (IL) cortex. In the extinction context, the suppression of conditioned fear is thought to involve IL inhibition of amygdala neurons encoding fear memory. However, recent data challenge this notion—silencing prefrontal-amygdala projections does not impair extinction retrieval. Hence, the precise mechanism for suppressing fear after extinction is still unknown. Recent work on this project suggests a novel alternative: the mPFC may suppress the reactivation of hippocampus-dependent fear memories to facilitate context-dependent extinction memory retrieval. The mPFC projects to the HPC via the thalamic nucleus reuniens (RE), and RE inactivation or chemogenetic silencing of mPFCgRE projections impairs the expression of extinction. Based on this work, it is hypothesized that the RE mediates mPFC-HPC interactions required for context-dependent retrieval of extinction memories. This hypothesis will be tested in three specific aims. The first aim explores whether the mPFC, particularly IL, suppresses the retrieval of extinguished fear memories via RE projections to the HPC. The second aim examines whether the activity of HPC ensembles representing fear and extinction memories are regulated by the RE. The third aim determines whether the RE coordinates oscillatory synchrony in HPC and mPFC during extinction retrieval. The proposed work will elucidate the specific neural circuits mediating the expression of extinction and has important clinical implications for developing therapeutic interventions that target these neural circuits to promote fear suppression and oppose relapse.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Traumatic brain injuries (TBI) comprising trauma, subarachnoid hemorrhage, and acute hydrocephalus are neurological emergencies that often require the intervention of a neurosurgeon. Annually, 5.48 million suffer from TBI globally, leading to an economic burden of $37.8 billion annually. Severe TBI occurs more often in low-to- middle income countries (LMIC), leading to increased TBI burden shouldered by these regions of the world. Life- saving treatment for TBI commonly begins with placement of an external ventricular drain (EVD), a catheter that drains fluid from the brain ventricles that has the potential to temporarily stabilize the patient by reducing pressure within the skull. Most LMIC lack critical resources and access to neurosurgeons, making severe TBI a leading cause of trauma-induced death. Developing an EVD placement approach that is accessible for emergency care providers has potential to reduce death from preventable causes, particularly in LMIC. This proposal aims to address a global disparity of access to neurosurgery by constructing a low-cost surgical instrument for real-time surgical navigation and EVD placement that is accessible to LMIC, rural hospitals, and military settings. The instrument will rigidly connect to an injured patient’s skull and act as a coordinate measuring system. The device will register to preoperative imaging and visually guide the operator through the steps of the EVD placement workflow. System accuracy will be tested using novel human ventricular simulators designed specifically for measuring EVD placement. Measurements will be taken with respect to a known target location inside the simulators, allowing for accuracy to be assessed. The research team will then test placement by a variety of care providers in the simulators, with measurements taken using magnetic resonance imaging. The device will be a linear four degrees-of-freedom arm operated with hand-guidance. It will be constructed from aluminum with a stainless steel self-tapping hollow bolt to connect the device to a 10mm skull burr hole. Four encoders will relay their position to a battery-powered Raspberry Pi, communicating wirelessly to a computer’s visualization software. An EVD will attach to the effector of the system with a known offset from the tip, and the operator will guide the drain through the burr hole under visualization into the target ventricle of the brain to achieve adequate drainage. This complete system will be designed to cost less than $5,000 USD at production scale and be capable of guiding an operator through the surgical workflow, allowing for improved access to neurosurgical stabilization treatment even in the absence of a neurosurgeon. The principles of this new type of navigation, named kinematic navigation, will be further explored in additional medical procedures to assess generalizability of its use. The same system used for intracranial pressure relief described above will be used to additionally guide tumor biopsy. Finally, a system with five degrees of freedom will be constructed using five axes to assess the feasibility of using kinematic navigation in image-guided spine surgery.

NSF Awards · FY 2024 · 2024-08

If a small piece is cut out of the surface of a beach ball or the surface of an inner tube, the resulting bit of rubber can be flattened out to look like a piece of the two-dimensional Euclidean plane. (Picture a sheet of paper, or the top of a table). Both surfaces are examples of two-dimensional manifolds; although they locally look like the plane, they are globally different from it and from each other. This project studies three-dimensional manifolds, which describe the possible global shapes of the universe, and four-manifolds, which describe the possible shape of space-time, using mathematical tools related to the quantum theory of fields. There are many such topological quantum field theories (TQFTs), but amazingly almost everything one knows about smooth four-manifolds comes from studying a single TQFT. The project aims to understand this theory more deeply in the hopes of understanding why this is and if there are other TQFTs like it. Project funds will support graduate students and undergraduate students mentored by the PI. Building on his extensive previous experience with undergraduate summer research and industrial placements, the PI will make special efforts to recruit and support members of groups under-represented in mathematics. From a more technical standpoint, the project will study Floer homology for 3-manifolds and its applications to topology in dimensions 3 and 4. The main focus will be on understanding the Floer homology of 3-manifolds with boundary and how it fits into an extended TQFT. Project research will focus on three related areas. The first involves extending invariants developed in the PI's previous work with Hanselman and Watson to manifolds with several torus boundary components. This will be used to study the invariants of satellite knots. The second involves showing that these invariants fit into the wider structure of a 2-3-4 TQFT. This work will draw on ideas from symplectic geometry and the theory of extended TQFT’s. The third area involves a large, but poorly studied, class of 3-manifolds known as Floer simple manifolds. The PI will study the topology of these manifolds and their relation to hyperbolic geometry and the L-space conjecture. 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 investigates the problem of information integrity, that is, identifying faulty or ungrounded information online. It focuses on a specific domain, that of information produced during the COVID-19 pandemic, and processes both text and image data. While significant efforts in artificial intelligence (AI) and machine learning (ML) have addressed information integrity in this type of multimodal setting, many solutions cannot be directly applied due to lack of domain specific knowledge and the expertise needed to provide meaningful, convincing explanations. Motivated by such limitations, this project develops a crowdworker-based interactive AI system that explores the collective strengths of the professional knowledge of domain expert crowd workers, the general logical reasoning ability of non-expert crowd workers, and the effective information retrieval capability of AI models. The resulting system will accurately assess information integrity in posts on COVID-19 and explicitly explain the detection results in natural language. This project complements two past research threads: (1) The prevailing AI solutions that primarily focus on extracting specific segments of input posts to serve as explanations, but fail to generate convincing explanations; and (2) Solutions that employ crowdworkers, but only recruit non-expert crowd workers and so fail to leverage the domain knowledge of experts. The results of the project will provide unprecedented accuracy by integrating diverse human and machine intelligence to address highly technical, domain-specific problems. While the focus is COVID-19, the framework and models developed in this project will address information integrity with explanations in other domains (such as those in healthcare and public safety). This project will also provide opportunities for students in STEM and underrepresented groups to study human-centered AI techniques. This project develops a human-centered AI framework that can be used to guide the design, development, and implementation of future explainable crowd-AI systems where the hybrid human intelligence from expert and non-expert crowd workers is integrated with AI models to make more accurate decisions and provide well-grounded, meaningful, and convincing explanations of those decisions. The research integrates AI, crowdsourcing, ML, and human-AI interactions. Specifically, the research includes: i) developing a deep text-visual alignment approach to construct a multimodal COVID-19 knowledge graph; ii) creating a logic-oriented crowdsourcing interface for non-expert crowd workers to validate the knowledge graph; iii) designing a topic-driven human-AI interaction scheme that will use expert crowd workers to construct a generalized multimodal COVID-19 knowledge graph; iv) developing a dynamic graph-attentive knowledge discriminator to address and explain issues in information integrity in COVID-19 information with natural language descriptions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Oropharyngeal swallowing disorders (a.k.a. dysphagia) are among the most common and dangerous manifestations of many neurologic conditions. Critically, dysphagia in neurologic diseases (i.e., neurogenic dysphagia) is often associated with highly negative health and quality of life outcomes. Despite this, optimal management for neurogenic dysphagia has yet to be established. In small-scale studies behavioral treatments in the form of head/neck exercises appear promising in improving swallowing but typically require the use of adjunctive biofeedback. Further, two significant clinical-research gaps remain: a) to date biofeedback as an adjunct to swallow therapy has not been systematically examined in the context of neurogenic dysphagia, and b) current swallowing biofeedback devices are large and/or expensive and thus not widely used. Hence, despite its promise, biofeedback-facilitated dysphagia treatment remains underused and understudied, limiting access to important swallowing rehabilitation services with potentially detrimental effects. To address these gaps, we developed and validated a cost-effective wearable surface electromyography (sEMG) biofeedback sensor technology (i-Phagia), optimized to record muscle activity from the head/neck and provide biofeedback to patients and adherence data to clinicians. In this proposal the objective is to conduct a large-scale randomized clinical trial (RCT) to determine the efficacy of sEMG biofeedback (using i-Phagia) as an adjunct to a standardized swallow therapy protocol delivered both in-person and remotely compared to a standard-of-care (SOC) approach on swallow outcomes in patients with chronic stroke or Parkinson’s disease (PD), i.e., two common neurogenic dysphagia populations. Our long-term goal is to improve neurogenic dysphagia management through the development of evidence-based and accessible treatments. The aims of this study are to: 1) compare the safety, satisfaction, and efficacy of sEMG biofeedback as an adjunct to in-person swallow treatment and a standard-of-care approach in improving swallow outcomes for patients with chronic stroke or PD and dysphagia; 2) compare the safety, satisfaction, and efficacy of sEMG biofeedback as an adjunct to in- person vs. remote swallow treatment in improving swallow outcomes for patients with chronic stroke or PD and dysphagia, and 3) examine the role of clinical data (such as diagnosis, disease severity, age, stimulability, and neuromuscular activity at baseline) on treatment responsiveness. We will achieve the aims by conducting an RCT of 120 people with stroke or PD and dysphagia randomly assigned to one of three treatment groups (in- person i-Phagia, remote i-Phagia, and SOC). Upon study completion, we will have established the efficacy of sEMG biofeedback-facilitated swallow therapy for both in-person and remote service delivery in two neurogenic dysphagia populations, and we will have started to identify variables determining response to treatment. Our findings will fill a critical gap in the rehabilitation of millions of patients with neurogenic dysphagia, while informing future work on the treatment of this debilitating condition.

NSF Awards · FY 2024 · 2024-08

Black holes are among the most exciting predictions of Einstein's General Relativity. Composed solely of spacetime fabric and described only by their mass, spin, and (electric) charge, they offer unique access to strong-field and nonlinear gravity. Black holes are now regularly observed via gravitational waves, radiation from accretion disks, the motion of stars around them, or black hole images. In this project, the PI's team will develop theoretical and numerical tools to advance our knowledge of gravity theory, black holes, and their interaction with fundamental fields, via gravitational wave observations. The project pursues three main research goals: 1) the study of the superradiant instability of black holes in a general class of cosmological spacetimes against massive scalar fields that represent axion-like particles or dark matter candidates, 2) the study of black holes in axi-dilaton gravity, in which two fundamental scalar fields are coupled to gravity and the simulation of the scalars' out-of-equilibrium dynamics around spinning black holes and determine their end-state. The team will simulate the scalars' evolution around a binary black hole, and determine their impact on the gravitational wave signal. Finally, 3) includes a simulation campaign of binary black holes in scalar Gauss-Bonnet gravity to create the first gravitational wave catalog in quadratic gravity. The PI's team will partner with the Grainger College of Engineering's Worldwide Youth in Science and Engineering (WYSE) program. The PI will offer six-week summer research experiences to Illinois high-school students from historically underrepresented groups in STEM. Students will be introduced to hands-on scientific research in gravitation and receive expert career counseling for college applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.