William Marsh Rice University

NSF Awards · FY 2025 · 2025-06

This award is funded through the NSF Directorate for Engineering - UKRI Engineering and Physical Sciences Research Council Lead Agency (ENG-EPSRC) Opportunity, a collaborative solicitation between NSF and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI). This project will design innovative device fingerprinting solutions for pervasive Internet of things (IoT) devices. IoT has become the new digital infrastructure by connecting everyone and everything together via billions of wireless devices. The majority of IoT devices are usually low cost, small size with limited computational capacity and energy resources, hence, cannot afford computational expensive cryptography. There is a trend to solicit non-cryptographic and lightweight solutions for IoT, as evidenced by MIT Technology Review in 2022 reporting the end of the password as the top 10 breakthrough technologies. Radio frequency fingerprint identification (RFFI) emerges as a non-cryptographic technique for secure device identification that exploits the unique and stable hardware impairments of radio devices as their identifiers. RFFI is promising for all wireless technologies, including WiFi, Bluetooth, and cellular. While RFFI has attracted active research interests in the last decade, there are still critical research challenges remaining for a more robust and reliable RFFI system, which will be the focus of this project. This project will bring together experts from Rice University and the University of Liverpool, UK. It will carry out a systematic and comprehensive investigation of deep learning-enhanced RFFI involving RFFI algorithm design and enhancement, adversarial attacks and countermeasures, as well as FPGA implementation. A synergistic research methodology will be adopted consisting of modelling, algorithm design, simulation and experimental evaluation as well as real implementation. A unique outcome of this project will be the creation of robust and secure RFFI systems, validated by both simulation and practical experiments & implementation. The immediate benefits of the project are: (i) well-designed channel elimination algorithms suitable to various channel conditions, (ii) hardware feature enhancement to further improve the classification performance, (iii) practical deep learning attacks against RFFI and countermeasures, (iv) real implementation based on FPGA platforms. The project's broader impact will be to study RFFI algorithms and feasible systems implementation for technology transfer in emerging IoT devices. The project's broader impact on education and outreach will include (i) training students as part of a collaborative, multi-institutional research team, (ii) integrating outcomes into undergraduate and graduate courses at Rice University, and (iii) making research outcomes broadly available through OpenStax courseware and the Rice RENEW and Houdini wireless testbed forums. This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI) where NSF funds the U.S. investigator and EPSRC funds the U.K partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This Partnerships for Innovation - Technology Translation (PFI-TT) project seeks to improve the chip packaging industry. Semiconductors are the foundation for top U.S. exports such as artificial intelligence, medical devices, and transportation vehicles. Optimizing thermal management via innovative packaging materials enhances the electronics' efficiency, reliability, and lifespan. In terms of societal impact, improved thermal management benefits high-computing applications like medical imaging and autonomous vehicles, improving quality of life. Economically, advanced packaging materials transform industries reliant on high-performance electronics, cutting costs and diminishing waste, benefiting businesses and consumers. Ultimately, advanced packaging materials’ progress reshapes the semiconductor industries by enhancing device performance and contributing to a more sustainable and efficient technological landscape. The project aims to develop advanced hexagonal boron nitride (hBN)-enabled molding compound materials with enhanced thermal conductivity, increased reliability, and a low coefficient of thermal expansion (CTE). The semiconductor industry's shift towards 3D packaging and increased digital logic scaling amplifies thermal challenges and research requirements. Escalating computing power magnifies energy density, leading to trapped heat issues like power loss and compromised reliability. However, current semiconductor epoxy molding compounds (EMDs) have not changed in over 60 years. These materials encounter limitations like low thermal conductivities, resulting in heightened thermal resistance and impeded heat transfer. Furthermore, EMDs can degrade due to thermal cycling, affecting reliability. hBN is a layered insulating material with excellent thermal and chemical stabilities, high thermal conductivity, and intrinsic strength. The large-scale hBN nanosheet production via chemical-assisted ball-milling may enable industrial-level hBN production. Introducing chemical functional groups onto hBN nanosheets broadens their applications, enhancing manufacturing flexibility and capabilities. To comprehensively assess the molding compound's technological viability, the team will optimize hBN combinations with various packaging matrices. This molding compound may enhance thermal conductivity, offer tailored attributes, and align with existing manufacturing processes, transforming thermal management for advanced electronic devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Selfish behavior can provide significant advantages in nature, allowing individuals to increase their survival and reproductive success at the expense of others. One of the most extreme forms of selfishness is cannibalism, where individuals consume members of their own species. While it seems highly beneficial, cannibalistic behavior varies within and across species. This project investigates this conundrum to reveal what factors restrict cannibalism and how it co-evolves with other social behaviors. Scientists predict that behaviors like kin recognition and movement patterns may evolve alongside cannibalism to reduce its negative effects. This study will test this idea using flour beetles, a well-established model organism in evolutionary biology. Researchers will first study natural beetle populations to measure differences in cannibalism. Then, they will conduct experiments in controlled environments to see how cannibalism, kin recognition, and movement behaviors evolve under different conditions, such as limited food availability and restricted movement. The results will provide new insights into how social behaviors evolve and why individuals within a species differ in their level of selfishness. This project is committed to expanding opportunities for students to engage in scientific research. Undergraduate students will take part in hands-on research experiences, developing essential skills for careers in science. Additionally, the project will create educational resources for K-12 teachers, enabling students to explore evolution and behavior through interactive experiments with flour beetles. These efforts will help introduce a wide range of students to scientific inquiry, spark interest in research, and encourage future careers in STEM fields. This project investigates how selfish behaviors, such as cannibalism, evolve alongside kin discrimination and dispersal. Theoretical models suggest that these traits should co-evolve to mitigate the costs of extreme selfishness, yet empirical tests are limited. This study will use Tribolium confusum, a widely distributed pest species where cannibalism strongly limits population size, to combine field surveys and experimental evolution in testing key predictions about the evolution of social behaviors. First, researchers will quantify natural variation in cannibalistic behavior within and among populations to identify the extend of natural behavioral diversity. Next, controlled experiments will manipulate dispersal constraints and genotype diversity to test how limited movement affects the co-evolution of cannibalism and kin discrimination. A second experiment will manipulate food availability to examine how environmental stress influences the evolution of selfishness and its interaction with kin recognition and dispersal. By initiating experiments with beetle populations that vary in their natural levels of cannibalism, this study will reveal the range of evolutionary trajectories that can emerge under different ecological conditions. This research will address major gaps in understanding how selfishness and cooperation evolve by providing a direct empirical test of theoretical models. Because the study examines fundamental evolutionary principles, its findings will be broadly relevant to other species, including those in agriculture and pest management. The results will contribute to longstanding debates in behavioral biology and significantly advance our knowledge of how complex social behaviors evolve across different environmental contexts. This project is jointly funded by the Behavioral Systems Cluster in the Division of Integrative and Organismal Systems and the Evolutionary Processes Cluster in the Division of Environmental Biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Advances in artificial intelligence, machine learning, and data science are driving innovation across key sectors such as healthcare, infrastructure, and environmental management. However, existing learning approaches struggle to handle the complexity and distributed nature of modern data systems. This project addresses this gap by advancing decentralized learning methods, where multiple agents interact locally over a network without needing a central authority. These methods improve data privacy, reduce communication bottlenecks, and enhance learning performance by leveraging localized interactions. The outcomes of this project will contribute to critical societal challenges, such as improving digital health monitoring and environmental data processing. In parallel, this research is integrated with a comprehensive educational plan to train a diverse group of future researchers, particularly from underrepresented communities. The project will strengthen the connection between cutting-edge research and inclusive STEM education through mentorship programs, graduate courses, and outreach initiatives. This project aims to enable next-generation performance in decentralized learning by addressing fundamental challenges related to communication efficiency, data heterogeneity, and algorithmic complexity. The research is structured around three thrusts. The first thrust focuses on designing finite-time aggregation networks to achieve faster and more efficient convergence in decentralized optimization algorithms, overcoming the limitations of traditional asymptotic approaches. The second thrust develops algorithms that handle non-classical aggregated costs, such as affine or convex compositions, enhancing the applicability of decentralized learning to a broader range of problems. The third thrust explores high-order optimization methods, including approximate cubic regularization and quasi-Newton techniques, to improve convergence rates and reduce communication costs in decentralized systems. The project’s contributions will advance the theoretical foundations of decentralized learning while offering practical solutions for scalable, efficient, distributed decision-making across fields such as machine learning, sensor networks, and autonomous systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Converting carbon dioxide (CO2) into fuels and chemicals is a promising way to make chemical manufacturing more sustainable and reduce greenhouse gas emissions. This project focuses on electrochemical CO2 reduction (CO2RR), a process that uses electricity to turn CO2 from industrial emissions from the air into valuable products. However, the buildup of salts inside the device used for CO2RR is a significant challenge. These salts clog the device's channels and prevent CO2 from reaching the reaction sites, which can cause the device to fail over time. To address this issue, the project team will study how salts form, move, and accumulate in CO2RR devices under different conditions. Using advanced equipment, investigators will track salt formation and movement in real time by measuring ions and observing the reactions. This will provide a better understanding of the process and help develop strategies to inhibit salt buildup, such as applying specialized coatings to improve the device's long-term performance. Improving CO2RR technology is important for several reasons: it helps reduce greenhouse gas emissions, strengthens economic competitiveness, and creates jobs. Additionally, the research will contribute to the advancement of knowledge in electrochemical systems, which could benefit other related technologies. The project will also provide valuable hands-on learning experiences for students, fostering critical thinking and innovation as they prepare to become the next generation of scientists and engineers. The practical deployment and long-term stability of CO2 reduction reaction (CO2RR) electrolyzers are hindered by salt precipitation within the cathode chamber, which obstructs CO2 diffusion channels, leading to performance degradation and eventual failure. The objective of this project is to develop a fundamental understanding of the mechanisms governing salt migration and formation in CO2RR electrolyzers, and to use this knowledge to devise effective strategies for salt removal, thereby enhancing long-term stability. This will be accomplished by integrating novel CO2RR reactor designs with in-operando spectroscopic techniques and cation mass balance analysis, conducted under various reaction conditions and interfacial microenvironments. The first step will involve establishing a comprehensive cation crossover monitoring platform, enabling detailed tracking of salt migration, crossover, and formation processes. This platform will also identify key factors influencing salt formation rates. Next, the project will investigate the primary driving forces behind salt migration from the catalyst-electrolyte interface toward the gas flow channels. Based on these findings, surface coating strategies will be developed for the cathode gas flow channels to enhance salt removal and improve the overall stability of the electrolyzer. Ultimately, this research will provide critical insights into mitigating stability issues in CO2RR systems and offer solutions that can be applied to other electrochemical processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

The way the DNA in the cells in our bodies is organized and interacts is essential for regulating gene expression. Proper gene regulation is required for the healthy functioning of cells, and impaired DNA organization and interactions have been implicated in many different diseases. Despite their importance, these processes are challenging to study at the required spatial and temporal resolution. This project aims to overcome these challenges by developing and applying advanced tools to directly visualize, quantify, and correlate DNA dynamics, organization, and gene activity in real-time with unprecedented resolution. Integrated with this research is a plan for educational and volunteer outreach activities targeting high school teachers and students from the Houston Independent School District. Together, these efforts will foster a synergistic program of research and educational enrichment that advances science and increases STEM opportunities for the next generation of scientists. This project will develop labeling and imaging tools and apply them to study the dynamics and interactions of key regulatory components of the genome, such as enhancers and promoters, and to correlate these interactions with gene expression in unperturbed human cells and during well-controlled perturbations of nuclear function. Using innovative labeling strategies for targeting specific genomic loci in the accessible genome, advanced 3D imaging at the nanoscale, and quantitative analysis of gene expression, this work will yield critical new insights into how the genomic structure, organization, and local epigenetic state regulate transcription. It will allow mapping of fundamental physical and molecular mechanisms underlying gene regulation, potentially revealing conserved predictive relationships. The methodologies developed in this project can be broadly employed to investigate the kinetics of interactions among various regulatory elements involved in chromatin reorganization and gene regulation, and they are generalizable for studies of a diverse range of biological systems and diseases in the future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Nontechnical Description Solar energy is crucial to continue to meet the energy demands of today’s society. One pathway toward increasing the performance of solar cells is to increase the portion of sunlight that can be used to generate electricity. Much of the sun’s light is wasted because it consists of low-energy photons which are not absorbed by the semiconductors used to make solar cells. Low energy photons can be made usable by transforming them into higher energy photons through a process called upconversion. For example, two low-energy infrared photons can be converted into a higher-energy visible photon. This project will use a combination of organic molecules known to enable the upconversion process and metal halide perovskites, which are currently being investigated for use in solar cells. The perovskite will be used to absorb the low energy light and excite the organic molecules. Upon successful upconversion, higher energy light can then be absorbed for use in a solar cell. A combination of optical techniques and microscopy will lead to a detailed understanding of the underlying processes. Beyond the scientific impact of this project, a goal is to guide students to further pursue a scientific career and inspire their independent research, critical thinking, and creative problem-solving capabilities by strong mentorship in undergraduate research and education. This activity aims to inspire a new generation of scientists. To close the growing rift between scientists and the non-scientific community, a strong foundation linking the PI’s institution with the local community will be built by outreach lectures and science communication (‘Kitchen Chemistry’ or ‘Kitchen Spectroscopy’) via local TV and social media outlets. Technical Description Efficient interconversion of solar energy to chemical or electrical energy is the key to meeting the future energy demands of our society. Improved photon utilization through an upconversion process involving triplet generation at the perovskite/organic semiconductor interface is a very promising approach to increase the photoexcited state lifetime and therefore, overall device efficiencies. The principal investigator will explore perovskite-sensitized solid-state upconversion via triplet-triplet annihilation to unravel the role of microscale and nanoscale molecular arrangement in OSCs. A combination of optical spectroscopy and scanning probe microscopy will be employed to elucidate the local optoelectronic properties originating from specific molecular arrangements of polyacenes on perovskite substrates. Control over orbital coupling by molecular placement will allow the involved steps to be systematically evaluated. The main goal is to understand why commonly utilized annihilators which exhibit high efficiencies in solution (e.g., diphenylanthracene) do not yield transferable results in the solid state, and how aggregation effects can be harnessed or avoided to improve solid-state upconversion yields. To achieve this goal, new perovskite/triplet acceptor pairs will be developed to investigate why promising solution-based triplet annihilators perform poorly in the solid state. The effect of local intermolecular interactions on the upconversion process will be studied on the microscale and the nanoscale using optical scanning probe microscopy and time-resolved optical spectroscopy. The combination of characterization methods spanning from the ensemble to the atomic scale will reveal the local structure-property relationships governing the bulk optoelectronic properties of hybrid perovskite/OSC-based TTA upconversion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This project will develop and implement a framework that leverages community knowledge and priorities to improve flood hazard assessments and mitigation efforts in rural Texas. Floods are the costliest natural hazard in the United States, causing loss of life, billions in damages per year, and widespread disruption to society. Rural communities face unique and persistent challenges in mitigating their flooding risk because flood hazards are not accurately delineated in rural regions and community priorities often differ from state or federal policies. Rural communities need new data as well as novel computer models and frameworks that integrate Earth system science with community-centered decision making. This project will create a framework to help rural communities better manage their repetitive flooding hazards and improve their resilience to evolving hazards. The framework will be implemented in two rural communities, with potential to replicate in other Texas counties. The proposed research brings together expertise across scientific fields to address the limitations hindering understanding of flood risk drivers and the design of effective mitigation pathways. This project creates a novel Earth systems flood risk framework based on principles of performance-based design. Within each component of the framework, the research will advance novel methodologies for revealing complex system dynamics and interactions. This research will establish a new model for integrating community knowledge into characterizing and evaluating floods and their impacts. The proposed work will also develop advanced statistical techniques for integrating hydroclimate variability in flood risk assessment. It will provide new workflows for integrating artificial intelligence and machine learning in Earth systems science research. The research will propose methods for combining multi-scale models and observations, and establish decision frameworks for generating robust flood solutions. This project is jointly funded by the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences, and the Office of Advanced Cyberinfrastructure through the National Discovery Cloud for Climate initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

A major environmental challenge of the 21st century is the spread of invasive plants - aggressive non-native species that disrupt native ecosystems. Invasive plants threaten biodiversity by outcompeting native species, disrupting plant-pollinator interactions, and degrading soil conditions, resulting in over $3 billion in annual economic damages in the United States. This project addresses a critical question in ecology: why do some non-native plants become invasive while others do not? Understanding the traits that promote invasiveness is essential for predicting and mitigating the impacts of invasive species on biodiversity and ecosystem functioning. The findings from this research will inform conservation strategies, enabling practitioners to identify and manage high-risk invasive species and protect vulnerable ecosystems. Beyond its scientific contributions, this project will foster the development of a skilled scientific workforce through the mentorship of high school, undergraduate, and graduate students, independent research opportunities, summer educational programs, and a community-oriented ornamental garden initiative. This research investigates the mechanisms driving plant invasions by examining how aboveground and belowground traits contribute to invasiveness and influence soil communities and ecosystem processes. Specifically, the study will explore the interplay between analogous traits - such as specific leaf area and specific root length, and leaf nitrogen and root nitrogen content - to understand their role in invasions. The project integrates field experiments and modeling to (1) identify plant traits that drive invasions under varying disturbance and resource conditions, (2) evaluate their effects on soil food webs, and (3) investigate how plant traits interact with soil communities to influence nutrient cycling. By comparing invasive plants with closely related native and non-invasive exotic species, this research will advance our understanding of plant-soil interactions in invasion ecology and generate actionable knowledge for managing invasive species. The outcomes will bridge gaps in ecological theory while offering practical tools to address the ecological and economic challenges posed by invasive plants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

The timing of seasonal life cycle events, such as when plants bloom or animals migrate, plays a vital role in shaping natural ecosystems. Changes in these seasonal patterns, whether due to natural variation or human influence, raise important questions about their effects on the relationships among species and the stability of natural communities. This project investigates how the timing of seasonal life cycles influences interactions among multiple species, especially those involving indirect effects, such as when one species impacts another through a shared predator or resource. Understanding these dynamics is crucial as ecosystems worldwide face rapid environmental changes. This project will also help predict the effects of shifting seasonal patterns on biodiversity, ecosystem stability, and services that benefit society. Members of the public will be engaged in the research through a citizen science initiative, and research training will be provided to undergraduate, graduate, and postdoctoral students. The proposed study will integrate experiments in amphibian pond communities with theoretical models to determine how variation in the sequence and spacing of phenologies of communities is linked to the dynamics of indirect interactions. First, the team will use a pair of complementary experiments that manipulate the sequence and spacing of phenologies of three amphibian species representing a classic one-predator two-prey interaction module. These experiments are designed to test competing hypotheses of how these critical aspects of temporal community structure are linked to the strength and direction of indirect interactions, and to identify the underlying mechanisms. Results from these experiments will guide the development of a temporally explicit community ecology model that incorporates seasonal variation in species’ presence, timing, density, and traits. This model will evaluate what factors can amplify or diminish the influence of indirect interactions and examine the short- and long-term consequences of these patterns across different systems. By linking empirical data with theoretical predictions, the research aims to provide a general framework for understanding how temporal structures shape the dynamics and stability of natural communities under changing environmental conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Optical photons are a key element for quantum communication because they are relatively free of decoherence and easy to manipulate and detect. Due to the ever-evolving quantum algorithms which require sophisticated gate operations, quantum light sources that can meet the many stringent requirements of these applications are needed. This project aims to develop a solid-state quantum photon source that is fully integratable with electronic and photonic devices and can generate high performance photon pairs on demand. This project will also provide training opportunities for our next-generation quantum workforce and promote the participation of underrepresented groups through a broad range of outreach programs. Optical qubits, where quantum information is encoded in the quantum states of photons using degrees of freedom such as polarization, are well suited for quantum information transfer and quantum metrology. This project will develop a new class of quantum emitters that can serve as polarization entangled photon pair sources. By controlling the atomic structures of the quantum emitters, their excitonic wavefunctions will be modified to support polarization entangled photon pair emission. To optimize the performance of the emitters, compact electro-photonic devices based on the host semiconductor will be assembled. The electrical modulation and Purcell effect afforded by the compact devices will allow emission optimization. This project will also shed light on fundamentals of quantum material design, as well as how environmental interactions affect optical coherence properties of quantum emitters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This award is co-funded by the Condensed Matter and Materials Theory, and the Condensed Matter Physics programs of the Division of Materials Research. It will support the attendance of early-career researchers and students to the 2025 instance of the Rice Spring School on Electron Correlation and Topology. The school will bring together leading theorist and experimentalist experts in cutting-edge areas of condensed matter physics to present pedagogical lectures, which will provide graduate students and postdoctoral researchers with a comprehensive understanding of exciting developments and foster new insights into the frontiers of research in quantum materials. The school will also provide a platform for students and early-career researchers to take on active roles as organizers, lecturers, presenters, and panel discussion leaders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Objects whose behavior is dominated by quantum mechanics, such as individual atoms, ions, or photons, can be harnessed for new approaches to computation, communication, and environmental sensing that have the potential to greatly advance applications in all of these areas. These new approaches take advantage of uniquely quantum phenomena known as ‘superposition’ and ‘entanglement’ to calculate the answers to certain types of problems much faster than a classical computer or perform amazingly precise measurements. In this project, the research team will develop new techniques to use the internal motions of the electrons in atoms, atom-atom interactions, and microwave electric fields to create a physical system that is dominated by quantum mechanics and highly controllable, which can be used for a form of quantum computing known as quantum simulation. This system can be tuned to simulate real materials or phenomenological models thought to display new phenomena that can stretch our understanding of how complex properties of materials emerge from simple building blocks of atoms and molecules. This will teach us about the behavior of systems that are far too complex for classical computers to describe. The ultimate goals of this work are to develop this new platform for quantum simulation and apply it to important problems related to the emergence of complex phenomena in many-body quantum systems, such as advanced material properties. The project will also provide research experiences for undergraduate students, improving retention in STEM fields, and train graduate students to produce the quantum workforce needed for these emerging applications. The specific platform that will be used for quantum simulation is a manifold of highly excited (Rydberg) atomic states coupled with resonant microwave fields. This creates a synthetic dimension that can mimic the Hamiltonian of particles moving through sites of a real-space lattice potential. The specific goals are to complete construction of an optical tweezer assembly that can be used to create interacting, multi-particle systems in synthetic space for the study of bound states, correlated tunneling, and scattering in few-body systems. Larger systems of ten or more tweezers each containing an individual atom will be used to study thermalization processes in synthetic space and search for phase transitions to correlated string and membrane states that are predicted to occur with increasing interactions. Phenomena arising from dipole-dipole interactions in a system constructed from ns and n’p states and van der Waals interactions in a synthetic space constructed solely of ns states will be studied. Both forms of interaction are localized in synthetic space, which is an important advance. The creation of two-dimensional configurations in synthetic space will give access to phenomena such as artificial gauge fields and higher-order topological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Nearly all plants and animals harbor microbes that live on or in them as “symbionts”. These include fungi that inhabit crop plants, bacteria that inhabit mosquitoes, and microbes that live inside the human gut. Harnessing these symbioses may advance the ability to solve problems in agriculture, wildlife disease, and human health. Before these applications can be fully developed, environmental biologists first require a better understanding of when and how microbial symbionts influence the health of their host. This project tests the hypothesis that the effects of symbionts on their host fluctuate from year to year, being beneficial in bad years (when hosts need assistance) but neutral or costly in good years (when hosts are okay on their own). In that case, hosts with microbes may be more stable compared to hosts without microbes. This may be an important but overlooked benefit of harboring microbes. The research team will explore this idea with a unique long-term study of grasses and their fungal microbes in Texas and Indiana. Fungal endophytes are widespread microbial symbionts in grasses, including forage grasses that are important to ranchers and turf grasses used by landscapers, and this research will benefit those groups. The project will train undergraduate students and conduct outreach activities to high schools. The project’s core data derive from a unique symbiont-removal experiment in which populations of cool-season grasses were established either symbiotically with seed-transmitted Epichloë endophytes or with symbionts eliminated through heat treatment. Replicated across seven host species and now running for 15 years with thousands of individuals, the experiment’s longitudinal demographic data reveal the fitness impacts of fungal symbionts on their host plants and how these impacts fluctuate in response to the environment. The research team will use these data to build stochastic demographic models that address two novel hypotheses, rooted in population biology theory for fluctuating environments and testable only with long-term data. First, through context-dependent fitness effects (symbionts are more beneficial in more stressful years), microbial symbionts may reduce inter-annual variability in host demography. By buffering hosts against harsh conditions, symbionts may also limit genetic drift and promote higher genetic diversity in host populations. Second, unique responses of symbiotic and symbiont-free hosts to environmental fluctuations can generate niche opportunities in time via the storage effect, possibly promoting stable mixtures of symbiotic and symbiont-free hosts. By continuing this study for the next five years (providing a total of 20 years), the project will reveal for the first time how endophytes may help host plants cope with year-to-year fluctuations in climate. 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 · 2024-12

To scale data rates in the face of rapidly growing application performance demands, next-generation wireless networks must provide efficient and low-latency access to the next spectrum frontier above 100 GHz. At such high frequencies, wider bandwidths will be available, enabling significantly higher data rates approaching terabits per second. Moreover, highly directive beams will be required to focus the transmission on the mobile user. This project provides two key building blocks for realizing such networks. First, this project will yield a rapid localization method so that highly directive transmissions can dynamically track mobile users. Second, this project will yield the first experimental network above 100 GHz in which an access point can simultaneously transmit to multiple mobile users. Such a multi-user capability is critical for realizing high data rates with low-latency access in dense user populations. One demonstration will be to simultaneously form signal spotlights to mobile users, adapting both the center and size of the spotlights according to the user mobility. This project will demonstrate a sub-THz multi-user data-plane and control-plane via three integrated research thrusts, each of which includes implementation and experimental validation. The first project thrust will realize multi-user sub-THz spectrum access with a radically simplified architecture requiring no RF chains and no antenna arrays. The key technique is to dynamically reconfigure a transmissive metasurface with high-entropy wavefronts that yield different data symbols in different directions. Namely, off-line pre-characterization of the metasurface’s angular response will enable the metasurface itself to simultaneously generate multiple independent directional data streams. Designs will realize beamforming gains, angular symbol diversity, and robustness to client mobility. The second project thrust will realize multi-user spectrum access by generating custom-sized and shaped “spotlights” for each user. Exploiting the fact that the sub-THz near field can extend to tens of meters or more, the key technique is to reconfigure the metasurface’s meta-atoms to dynamically shape each user’s electromagnetic energy in space according to their location, mobility and potential interference, thereby enabling both robust and high-rate access. Spotlight shapes can be custom tailored to a user’s needs, even including curved trajectories to better support mobility around corners and to reduce inter-user interference. The third project thrust will realize a low-latency sub-THz control plane that yields high accuracy location and channel information. The key technique is for the access point to generate beacons with pre-characterized high-entropy wavefronts such that a small change in receiver position will yield a distinct spectral signature. The method enables one-shot localization and promises to significantly advance the state-of-the-art in both spatial resolution and time required. Consequently, it provides a key building block for realizing highly directional sub-THz 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 · 2024-11

Infectious diseases are a major global health concern. Rapid and accurate detection of the presence of disease-causing RNA is crucial for effective diagnosis and control of these diseases. The CRISPR-Cas13 system is a cutting-edge technology for RNA detection. However, the currently used Cas13 enzymes can become unstable and lose their effectiveness during long-term storage and in field applications. This project aims to improve the stability and sensitivity of a heat-resistant version of the Cas13 enzyme, making it more reliable and sensitive for detecting RNA. This includes enhancing the enzyme's ability to recognize and cut RNA and combine it with advanced electrochemical devices to create a highly sensitive and stable detection method. The proposed scientific advancements are closely connected to educational outreach activities. The project will involve high school and community college students, particularly from underrepresented backgrounds, in biological and bioengineering research. Students will receive training in experimental techniques, data analysis, and scientific writing. Additionally, high school students will be introduced to CRISPR technology through a biotech academy and integrate the research findings into university courses. The goal of the project is to combine mechanism-based protein engineering and cutting edge electrochemical devices to generate next-generation RNA detection tools for infectious disease diagnosis. The project will leverage the CRISPR-Cas13 system, which has shown great promise as next-generation diagnostics for in vitro RNA detection owing to its high specificity, programmability, and fast reaction rate. The collaborative project aims to first investigate the structure and mechanism of the recently discovered thermostable Cas13. Leveraging the mechanistic understanding, rational engineering of the thermostable Cas13 will be performed to produce new variants with superior thermostability and protease resistance, as well as enhanced target sensitivity and reaction speed. The engineered Cas13 variants will be combined with innovative electrochemical devices to enable ultrasensitive and robust RNA detection of various pathogens derived from clinical samples. Successful completion will provide superior RNA detection tools for medical and research applications, alongside novel insights into the Cas13 nuclease mechanism. 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 · 2024-10

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. The United States is investing heavily in its electric vehicle infrastructure. Public charging stations represent an important part of that infrastructure. Achieving national goals will require the addition of 500,000 new public charging stations by 2030. This expansion is expected to yield significant economic benefits and to promote the adoption of electric vehicles. It will also reshape the way Americans travel and access opportunities. To fully leverage this investment requires attention to human and social considerations, and to anticipate unintended negative consequences. This SAI project develops a human-centered planning framework for public charging stations. It integrates human cognitive processes and social impacts into engineering models, with the goal of ensuring long-term community benefits. The research contributes to sustainable and fair nationwide public charging networks by gaining a better understanding of how people make choices about public charging station use and by considering the equitable distribution of those stations across the country. Bringing a convergence of expertise from psychology, sociology, and engineering, this project creates an integrated public charging station framework. Using survey data and laboratory experiments, a cognitive framework is developed to account for choices in the use of public charging stations. Equity and community impacts are investigated for both electric vehicle and non-electric vehicle users. Public charging station deployment decisions are modeled, and the outcomes are validated using an agent-based simulator with realistic social cognition dynamics and real-world data. By using an integrative and interdisciplinary research approach, this project establishes new theories and models of social decision-making for public charging station choices. It promotes new human-centered thinking of deploying public charging infrastructure, supporting the achievement of national electric vehicle goals. This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences, the Directorate for Engineering, and the Directorate for Mathematical and Physical Sciences. 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 · 2024-10

Wireless data traffic continues to grow at an exponential pace, demanding more and more bandwidth. Networks of the future will need to exploit frequencies above 100 gigahertz, which are much higher than what is typically employed today. These high frequencies need to propagate as narrow directional beams, rather than the wide- angle broadcasts currently used by base stations and cell towers. Using beams offers a number of important advantages, but also poses some significant challenges. One key challenge surrounds the question of how to adapt if the beam is blocked by an intervening obstruction between the transmitter and receiver, such as a person walking through the beam path. This research program explores a novel solution to this problem which relies on the generation of beams that follow a curved trajectory. Such beams can be generated in situations where the size of the transmitter is sufficiently large, with the appropriate engineering of the properties of the generated signal at all points across the emitting aperture. The use of such exotic beams in wireless communications is unprecedented, so many open questions must be explored in order to validate the feasibility. This work will open a new realm of possibilities for the implementation of local area networks operating at ultra-high frequencies. This project also includes a significant effort to broaden participation by under-represented groups, at the high school, undergraduate, and graduate levels. This research lays the foundations for the use of self-accelerating beams in mobile wireless local area networks (LANs) operating in the near-field regime. Since conventional link analysis cannot be applied in the near field of a transmitter, fundamental electromagnetic calculations are used to establish heuristic models for link budgets that can be employed to estimate the performance of such links, including a characterization of the effect of receiver aperture and of the near-field to far-field transition for various types of self-accelerating beams. Two different strategies are explored to create electrically reconfigurable metasurfaces that can be used to generate and manipulate such beams, which could be integrated into a transmitting base station for agile adaptation to transient blockage events. In addition, issues facing the control plane will also be explored, including the development of strategies for link discovery using beams with curved trajectories, and the implications of the asymmetry of the channel resulting from the fact that the receiver is in the near field of the transmitter but not vice versa. 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 · 2024-10

The Earth’s faults can rupture abruptly causing hazardous earthquakes, but some can also slip slowly, in events that can last hours to years. In the Cascadia Subduction Zone, slow slip events (SSEs) have durations of days to weeks and occur at depths between 30 and 45 km, which deeper than typical earthquakes. These events produce a weak seismic signal known as tectonic tremor. Despite their status as one of the most significant discoveries in geophysics, the physical mechanism(s) responsible for SSEs remain enigmatic, and the effects of deep slip on seismic hazards are unclear. This project explores processes that affect the strength of the fault through time, one of the factors through to control SSEs. Areas in the Earth where SSEs occur are typically hot and under high pressure. These conditions facilitate chemical reactions, suggesting that rapidly growing minerals could act like a quick-setting glue, binding faults together through cementation, and promoting rapid fault strengthening between SSEs. To explore this possibility, the team will conduct experiments where small rock and mineral samples are subjected to the temperature and pressure conditions of SSEs. The team will then measure the material properties, such as their strength and permeability to fluids, of the experimental products as they vary with experiment duration. Using the experimental results, the team will develop a mathematical description of rapid fault healing for incorporation into numerical models exploring the influence of cementation on SSEs. Results of these simulations will be compared to real-world observations to determine whether this process plays a fundamental role in the generation of SSEs. This project will catalyze interdisciplinary research in subduction zone geoscience through the training and mentorship of undergraduate and graduate students, plus postdoctoral researchers in the fields of seismology, rock mechanics, and geochemistry. The team leaders will enable interaction between the Cascadia Region Earthquake Science Center (CRESCENT) and Subduction Zones in 4 Dimensions (SZ4D) to facilitate coordination between these two efforts to achieve common goals relevant to geohazards. This proposal explores the role of cohesion, which is normal stress independent fault strength, via cementation and pore fluid pressure evolution in the dynamics of SSEs. Cementation is commonly observed in exhumed faults zones and is thought to play a key role in fault healing during the interseismic period. The high temperatures (~500°C) and pressures (1 GPa) present in SSE environs should favor relatively rapid cementation. There is also abundant evidence for fluids in the SSE source region that appear to play a crucial role in the generation of SSEs. The team proposes that pore-fluid pressure evolution and cementation can explain several enigmatic features of slow slip events, including radiative phenomena like tremor and low-frequency earthquakes, the tendency for the same section of the megathrust to re-rupture on short timescales during an SSE in so-called secondary slip fronts, the lack of sensitivity to tidally induced normal stresses, and the existence of fault strength in environments inferred to have nearly lithostatic pore fluid pressure. This work leverages interdisciplinary expertise from the fields of petrology, geochemistry, rock mechanics, observational seismology, fault mechanics, and numerical methods to explore the role of cementation and resulting cohesion in SSEs. This team will constrain the mechanisms of cementation, mineralogy and petrology of the cement, and the resulting time-dependent strengthening by performing a suite of piston-cylinder experiments at pressure, temperature, fluid compositions, and other conditions relevant for Cascadia SSEs. The project will determine the resulting cohesion and permeability using deformation experiments and contact area using microscopy. The results will provide quantitative constraints on time-dependent fault strengthening and permeability evolution. Constraints from these laboratory experiments will be used to develop a mathematical framework for cohesion and fluid flow. This framework will be implemented in numerical simulations to determine the impact of rapid cementation and cohesion on SSEs. The models will be validated with observables including propagation speeds, spatial scales, and time scales representative of SSEs and secondary fronts. This project will catalyze interdisciplinary research in subduction zone geoscience through the training and mentorship of undergraduate and graduate students, plus postdoctoral researchers in the fields of seismology, rock mechanics, and geochemistry. The team leaders will enable interaction between the Cascadia Region Earthquake Science Center (CRESCENT) and Subduction Zones in 4 Dimensions (SZ4D) to facilitate coordination between these two efforts to achieve common goals relevant to geohazards. This project is funded by the Frontier Research in Earth Science (FRES) program as well as Education and Human Resources (ERF) in support of Research Experiences for Undergraduates and Postdoctoral Scholars. 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 · 2024-10

Coastal storms and climate change, aging infrastructure, and rapid urbanization pose increasing risks to coastal communities. Institutions charged with supporting communities before, during, and following storm events require reliable and timely information on current and forecasted hydrometeorological conditions and infrastructure impacts, including roadway access and potential natural hazards-triggered technological incidents. Recent focus groups and structured interviews with emergency response organizations have revealed both the lack of integrated information sources and the lack of trusted, timely, and scientifically sound technology available to support situational awareness of compound hazard events and their anticipated impacts on infrastructure, a deficiency that hampers decision-making and response efforts. This project will design, develop, and deploy OpenSafe.AI, a framework that advances communities’ ability to reliably sense current conditions and forecast potential hazards and infrastructure impacts. This information is critical to inform response and recovery actions targeted at public health and safety and enhanced community resilience to coastal storm events. Working in concert with emergency response agencies in the Houston-Galveston area, we will not only iteratively design and tailor such a system, but probe transferability and scalability, design robustness, and data and model equity across diverse communities, including those that are under-resourced and under-served. This project combines expertise in hazard and infrastructure resilience modeling, user-centered design, and responsible AI to revolutionize intelligent systems for situational awareness and scenario exploration under multiple compound coastal hazards. This convergent research will address the technical, theoretical, and methodological gaps in responsibly designing and developing situational awareness tools to support emergency response actions and risk mitigation interventions during tropical cyclones and coastal storm events. With an overarching user-centered design approach, it will pioneer responsible design strategies to enable (1) equitable and fair, (2) reliable and safe, (3) human-centered applications of AI in the disaster resilience domain. Along the way, the team will develop multi-modal foundation models that gain insights from a combination of physics-based, data-driven, and human-in-the-loop sources, and will advance methods to detect and largely overcome systemic bias, paucity of real-time data, and equity issues in models and data to promote equitable and fair situational awareness. As a result, the OpenSafe.AI framework pursues estimates of near-real time conditions and short-range forecasts (e.g., hours to days in advance) of multi-hazards (e.g., wind, wave, compound flooding) and their impacts on the built environment (e.g., damage hampering access to critical facilities or yielding hazardous material spills), thereby affording practical, timely, and equitable situational awareness. 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 · 2024-10

Nontechnical description Quantum information science exploits the laws of quantum mechanics and has the potential to produce fascinating technologies that would impact many aspects of our lives. In analogy to the classical binary bits used in today’s information technology, the fundamental unit in quantum information science is the so-called quantum bits (or qubits), which are coherent superposition of quantum mechanical states. This research aims to deepen current understanding of electron spins in low-dimensional materials and develop material design principles that could lead to new types of qubits. The research project provides multidisciplinary training opportunities for graduate students and postdoctoral researchers in the areas of material science and quantum optics. It also serves as an exciting opportunity for the training of our next-generation workforce in quantum information science. Technical description The goal of the proposed research is to develop a material design principle for creating spin-active quantum defects in a one-dimensional host and explore their potential for spin qubits. In the first stage of the proposed work, the research team will focus on the design and fundamental studies of the quantum defects. In particular, the team will investigate the influences of the defects’ atomic structures and chemical compositions on their spin manifolds and coherence properties, with the goal of obtaining general design principles that govern spin properties. Quantum operations of the spin qubits will be carried out after the initial design and optimization of the defect 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 · 2024-10

Cellular networks are crucial for the national economy and security. With each generation, the networks are designed to support higher performance levels. The next-generation networks, namely sixth-generation (6G) networks, will target higher data rates and new features like city-scale perception. To achieve the desired performance in the next-generation networks, new spectrum in the 7-24GHz frequency range, also known as the FR3 band, is being considered. However, the FR3 spectrum band is highly fragmented, which means that the design of wireless radios is poised to be even more challenging than the existing systems. In particular, this project will address two important questions for the new FR 3 band. How to formalize a mixed-domain system design methodology that leverages programmable baseband and analog (i) to meet the need of next-generation communication systems in FR3, and (ii) to enable multi-function operation with communication and sensing over such diverse bands in a power-efficient manner? This project will develop MD2 (Mixed Domain Design), a novel theoretical framework and practical designs for next-generation networks using the FR3 wireless systems. The project thrusts will address foundations, algorithmic methods and prototype designs, using the platform of pixel antennas. The new designs will target communications-only, sensing-only, and joint communications and sensing wireless applications. A key innovation of MD2 framework will be to allow joint and systematic design across antenna, analog and digital domains, with practical designs using pixel antennas optimized for FR3 spectrum. The team consists of investigators from the USA and Finland who bring complementary expertise across multiple disciplines. This project will strengthen international collaboration between the US and Finland, with a significant impact on next-generation wireless communications systems.The project will involve undergraduate students to improve student retention in engineering programs and integrate new research modules into wireless communication courses. 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 · 2024-10

The project uses the experiences of a group in movement to examine that movement along three main lines of inquiry. First, the project investigates the motivations for movement: what factors motivate a larger grouping and this group in particular, to leave their home region to come to a new home? Second, it examines the process: How do home region ideals and destination region policies affect the movement process? Third, it investigates the consequences of movement: How does movement shape the life of those who have moved and the life of family and close relatives left behind? This project situates these experiences in the transnationalism theoretical framework to explore the global processes and conditions that are significantly shaping the lines of citizenship, culture, economic development, and the structure of society. The three lines of inquiry will be addressed with three different methods: using an archive of 2000 letters, oral histories of 100 families with two interviews per family, and ethnographic data collected over 14 months from three relevant communities. Coding of these data will be organized according to eight domains: education, economics, identity, housing, intimate life, health, and other important social domains. Students will assist in this research after being trained to conduct research across cultures. 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

Search is a universal problem-solving mechanism in Artificial Intelligence (AI), and it has enabled great leaps in the field. A variety of search methods have been used to solve diverse problems, such as playing games, planning the routes of autonomous vehicles, finding patterns in biological sequences, and many others. This project will develop ultra-fast algorithms and software cyberinfrastructure appropriate for searching high-dimensional spaces induced by problems in the physical world. Examples of such problems include manipulation planning, where robotic arms grasp objects and transfer them to new locations, potentially in the close presence of a human; autonomous underwater vehicle navigation; semi-automated laboratories for the synthesis of chemical compounds; infrastructure inspection with aerial vehicles; and analysis of protein shape changes. Distinguishing features of this work include (a) its generality that will be encapsulated in the design of the API of the cyberinfrastructure, (b) its implementation in commodity hardware, consumer CPUs, which will enable its widespread use, and (c) the training it will provide to postdoctoral, graduate and undergraduate students on a topic that is a building block for modern AI-driven research. The paradigm investigated is that of state space search. State space search is a widely used AI search method that finds a solution to a problem by searching through the set of possible states of the problem. A state space is a mathematical representation of a problem that defines all possible states in which the problem can be. A set of variables encodes each state in the state space. State spaces that arise from problems in the physical world typically have infinitely many states. This is because many, if not all, of the variables that characterize the underlying system, draw values from a continuous interval. Search in such spaces is demanding. For many problems, however, it is possible to find solutions without examining the whole state space. The paradigm that will be further investigated and implemented in this proposal is sampling of state spaces combined with local exploration. This paradigm, which has been widely successful in domains such as robotics, entails randomization schemes that exploit local geometric properties both for state selection and local exploration. Its performance has been characterized and linked to the properties of the underlying search spaces, establishing the relevance of these methods for search in spaces arising from problems in the physical world. This proposal will systematize and implement key insights that can drastically affect the performance of sampling-based search methods without affecting their generality. These performance enhancements include, among others, exploiting methods for constraint satisfaction, domain-specific compilers, appropriate data structures, and fine-grained parallelism for validity checking. It will deliver an integrated framework for sampling-based search, complete with a well-designed API for use in different applications. This proposal will train postdoctoral, graduate, and undergraduate students on the topic of search in high-dimensional spaces and diverse applications. Efforts will be undertaken to broadly disseminate the work and engage researchers from different disciplines. Outreach activities will include a workshop at a major conference. 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

Revealing the interiors, and constraining the equations of state (describing how materials behave under realistic pressure and temperature conditions), of gas giant planets in the solar system have been important objectives in planetary science, even more so since the detection of many gaseous exoplanets. These exoplanets are being examined to learn more about how the solar system came to be, and to compare the formation of our solar system to those planetary systems. Seismology has been playing a role in obtaining (instantaneous) models of gas giant planets, including their layering and equations of state, while planetary magnetic fields have been informing one further about their interior properties and thermal evolution. This project involves a novel mathematical framework to facilitate gaining new insights in the (new class of) inverse problems associated with seismology and magnetohydrodynamcs describing the generation of magnetic fields through dynamos. The project offers, via collaborations, a unique interdisciplinary educational experience for the students giving them a much broader appreciation of the importance of novel techniques and implications in space exploration. The principal investigator will study inverse problems for revealing the interiors of gas giant planets, that is, Saturn and Jupiter, in the solar system, pertaining to seismology and magnetohydrodynamics. Both are mathematically fundamentally distinct from their treatments on Earth and raise intriguing challenges in their analyses. These inverse problems are defined through systems of linear(ized) partial differential equations describing acoustic-gravitational oscillations and nonlinear partial differential equations describing magnetohydrodynamics (in the Boussinesq approximation) as well as edge operators. The project is foundational, but its significance extends to the data that have and will become available from NASA's Cassini and Juno missions; the investigator collaborates with members of the Science Team of the second mission. The results will contribute to discerning limits and possibilities, including guarantees of reliability or lack thereof. 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.