Pennsylvania State Univ University Park

NSF Awards · FY 2026 · 2026-09

The United States faces growing challenges in maintaining and modernizing transportation infrastructure while addressing workforce shortages and rapidly evolving technologies. Rail transportation, one of the most energy-efficient and sustainable modes of freight and passenger movement, plays a critical role in supporting economic growth, national defense, and environmental sustainability. These challenges highlight the need for engineers equipped with interdisciplinary skills and knowledge of emerging technologies such as artificial intelligence, structural materials, smart sensing systems, and data-driven infrastructure management. This Research Experiences for Undergraduates (REU) Site at The Pennsylvania State University, Altoona will support 10 undergraduate students each year in a 10-week summer research program focused on smart and sustainable transportation infrastructure. Leveraging the nation’s only ABET-accredited bachelor’s degree program in Rail Transportation Engineering (RTE), the program uses rail systems as living laboratories to provide interdisciplinary research experiences spanning civil, mechanical, electrical, computer, and transportation engineering. Participants will work with faculty mentors, industry partners, and researchers affiliated with the Rail Center for Research Enhancing Shortline Transportation (Rail CREST), a federally funded initiative focused on strengthening shortline rail infrastructure and workforce development. Through mentored research, professional development, and industry engagement, this program will build a diverse talent pipeline and advance national priorities in infrastructure modernization and sustainable transportation. The REU Site will engage students in research organized around three thrust areas: intelligent infrastructure for safety and risk mitigation using smart sensing and AI-enabled monitoring; sustainable materials and structural performance for rail and multimodal transportation systems; and data-driven monitoring, digital twins, and predictive maintenance using machine learning and data analytics. Students will conduct research using laboratory experimentation, computational modeling, and data-driven analysis supported by Penn State Altoona’s rail transportation engineering facilities and partnerships with industry and the Altoona Railroaders Memorial Museum. Integration with Rail CREST activities will provide opportunities to investigate real-world shortline rail challenges and infrastructure performance. The program includes structured mentoring, research seminars, and professional development activities, culminating in technical presentations and written reports. This REU Site will prepare undergraduate students with interdisciplinary skills to advance smart, resilient, and sustainable transportation infrastructure 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 2026 · 2026-08

Cellular networks are the backbone of today's connected world, supporting real-time communication, commerce, transportation, emergency response, and daily life. As fifth-generation (5G) and future wireless systems become more advanced and programmable, they also introduce greater complexity and new attack surfaces. Control-plane signaling systems, responsible for connection setup, identity verification, authentication, key exchange, and mobility, are especially critical, as attacks on them can disrupt services, enable tracking, intercept communications, and expose sensitive data. Despite their importance, defenders often lack realistic, trustworthy data to study these threats because much of the device-side software is proprietary, and network infrastructure is difficult to access. This project addresses these challenges by creating privacy-preserving, verifiable datasets that capture real-world control-plane behavior and attacks, enabling stronger and more effective security research. The project develops new methods for collecting, validating, sanitizing, and analyzing control-plane data across user devices, radio access networks, and core systems. It introduces an in-device observability framework to capture security-relevant events and a hybrid replay environment that uses real-world traces to enable large-scale, realistic control-plane data collection of Open Radio Access Networks (Open RAN) and core networks. Privacy-preserving transformations and synthetic data generation techniques protect sensitive information while improving coverage of rare but critical scenarios. These efforts produce representative datasets that support the development and evaluation of critical security solutions, including intrusion detection, forensic analysis, and benchmarking tools. Fundamentally, the project strengthens the resilience of next-generation wireless networks, supports public safety and national defense, and expands research and educational opportunities in wireless cybersecurity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Arid and semi-arid lands cover a large fraction of Earth’s surface and support the livelihoods of hundreds of millions of people. Many of these landscapes are changing. A critical, but often overlooked, component of healthy drylands is biological soil crusts (“biocrusts”), thin, living soils formed by communities of microorganisms. Biocrusts stabilize soil, support nutrient cycling, and regulate dust emissions, thereby protecting soil fertility and human health. Biocrusts are sensitive to environmental stress, particularly exposure to strong daily and seasonal temperature variations that characterize dryland environments. Recent research suggests that abrupt or extreme events may be more disruptive to biological systems than average conditions alone. This project seeks to improve understanding of how biocrust microorganisms respond to temperature volatility, knowledge that is essential for predicting biocrust persistence and guiding efforts to maintain and restore dryland soil function. By advancing soil microbiology research, supporting education and training, and informing land management practices, this project advances science and contributes to national priorities in biotechnology, agriculture and workforce development. The project integrates laboratory experiments, global data analysis, and education-focused research to examine how biocrust microorganisms respond to temperature fluctuations. First, controlled mesocosm experiments will be used to define the thermal tolerance range of biocrust microbial communities and to identify thresholds at which community composition and metabolic function shift. Microbial activity and community structure will be characterized using genomic sequencing and functional assays. Results from these experiments will guide analyses of a global biocrust survey dataset spanning diverse dryland regions to evaluate whether similar microbial responses are observed across natural thermal gradients. Second, the project will focus on keystone cyanobacteria of the genus Microcoleus, which dominate biocrust biomass and drive carbon fixation and soil stabilization. Using microfluidic SoilChips that enable direct microscopic observation, the research will measure physiological, metabolic, and behavioral responses of these organisms to temperature variability, including tradeoffs between thermal tolerance and other cellular functions. Educational components are integrated throughout the project, including a hands-on SoilChip curriculum implemented in partnership with high schools in dryland regions, where student-generated data will contribute directly to research outcomes. An accompanying online gallery highlighting biocrust scientists will serve as a public-facing educational resource that increases awareness of biocrust research and careers in soil science. Together, these activities advance fundamental understanding of soil microbial resilience while strengthening education and research capacity in dryland 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 2026 · 2026-08

NON-TECHNICAL SUMMARY: When polymers crystallize, only about half of the material is crystalline, with amorphous material in between crystals that gets trapped and cannot crystallize. Applying a flow to the molten polymer stretches long chains and enables the polymer to nucleate many crystals rapidly, resulting in a finer scale structure with superior mechanical properties. With small enough crystals, each long polymer chain can span many crystals, with connections between crystals referred to as tie chains. Stronger flows stretch more polymer chains and create more tie chains for superior toughness. This study aims to understand the details regarding the control of structure and mechanical properties of semicrystalline polymers by applying flows of various strengths prior to crystallization. If successful, the fundamental knowledge generated from this research will result in the understanding needed to be able to design polymeric materials for a variety of applications, including injection molding. This new knowledge will be used by the US Plastics industry in advanced manufacturing. TECHNICAL SUMMARY: Brief intervals of shear flow can strongly accelerate nucleation of semicrystalline polymers, and this drastically changes the final morphology and mechanical properties. Above a critical shear rate needed to stretch the longest chains, shear thinning starts and smaller anisotropic crystals are formed by accelerating nucleation. A fundamental study of flow-induced crystallization (FIC) is proposed using binary blends of monodisperse poly(ethylene oxide)s, as these blends enable precise control of the number of chains the get stretched in shear flow (the fraction of long chains). There will in fact be a wide range of shear rates where all long chains stretch and none of the short chains stretch. We will verify this idea using flow birefringence and determine the consequences of stretching more chains (by increasing the long chain content in the blends) on final morphology using two-dimensional small-angle X-ray scattering. Since we apply the shear in a rotational rheometer, chains get stretched along circular streamlines and consequently have a net resultant force inwards. This “hoop stress” is responsible for measured normal stresses in shear that act to push the rheometer plates apart. We will test our hypothesis that this force pushing long chains inward will actually create a migration of long chains towards the center by applying the shear for various amounts of time and then measuring the molecular weight distribution at various radial positions. Since the stretched long chains might also shear degrade, we will pay close attention to whether the molecular weight distribution remains strictly bimodal at each radial position after extensive shear. 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 2026 · 2026-07

This project will develop artificial intelligence techniques to improve the operation of large-scale infrastructure systems, such as smart transportation networks and electric power grids, which are essential to modern life. These complex systems, known as societal-scale cyber-physical systems, integrate physical infrastructure with thousands of sensors, computing devices, and actuators. Due to their scale and distributed nature, managing these systems in real time poses a significant challenge. To address this challenge, the project will leverage deep reinforcement learning, a form of artificial intelligence that learns optimal decision-making strategies directly from data. By improving the efficiency and reliability of critical infrastructure systems, the research will further the national interest through reducing traffic congestion, improving emergency response times, and increasing the stability of the power grid. This project will also develop a highly skilled science, technology, engineering, and mathematics (STEM) talent pipeline. These development activities will include creating interactive, game-based learning modules for K-12 students, developing an interdisciplinary graduate course on AI and cyber-physical systems, and providing training materials to help community partners effectively adopt artificial intelligence technologies. This project will advance the foundations of sequential decision-making for societal-scale cyber-physical systems (CPS) by addressing the fundamental scalability and communication challenges faced by existing deep reinforcement learning (DRL) approaches. The scope of the research will encompass theoretical and algorithmic innovations to develop a hierarchical and communication-aware DRL framework, which will be tailored to the vast state-action spaces and distributed nature of societal-scale CPS. First, the project will introduce a cyber-physical feudal reinforcement learning framework that will provide automated, data-driven system decomposition. This approach will partition a complex cyber-physical system into tractable subsystems based on their underlying dynamic cyber-physical couplings. The framework will then train a hierarchy of DRL policies to control the system: a high-level coordination policy will generate abstract but physically achievable goals for ensembles of decentralized, low-level subsystem policies. Second, to address the communication constraints of distributed CPS, the project will extend the feudal DRL framework to be communication-aware. To minimize bandwidth overhead, the framework will integrate physics-informed causal abstraction to compress state representations and trigger communication only when necessary. To handle communication disruptions and missing data, the framework will employ adversarial curriculum learning to train robust policies that will rely on belief states and learned intrinsic motivation to ensure graceful degradation. The project will evaluate the proposed innovations using high-fidelity simulations, real-world datasets from transportation and power systems, and community partnerships, rigorously demonstrating the intellectual contributions required to deploy scalable, data-driven control policies across critical distributed infrastructure. 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 2026 · 2026-06

This NSF ERI project aims to enable self-powered smart tire systems by harvesting electricity from vibrations generated by tire rotation, road roughness, and vehicle loading to support onboard sensing and communication. Modern vehicles, particularly autonomous and connected systems, increasingly rely on tire-embedded sensors for monitoring pressure, traction, and driving conditions. However, conventional battery-powered monitoring faces challenges when batteries deplete, requiring periodic replacement. With millions of vehicles in the U.S., each having four tires, this leads to substantial electronic waste and environmental degradation. This project will transform current limitations by introducing a bio-inspired bistable energy harvester that converts tire vibrations into electrical power, extending battery life and potentially eliminating replacement. The approach leverages bistable nonlinear dynamics and rotational effects to continuously adjust operating conditions for efficient energy conversion. The intellectual merit includes advancing the fundamental understanding of bio-inspired design, nonlinear dynamics, stochastic resonance, and coupled electromechanical behavior in rotating systems. The interdisciplinary research integrates mechanical engineering, energy conversion, materials science, and dynamics to develop design principles for broadband energy harvesting. The broader impacts include improving the sustainability and reliability of intelligent transportation systems, reducing battery waste and environmental impact, and enabling future autonomous vehicles that require continuous sensing and communication. The outcomes also extend to self-powered sensing and monitoring in a broad range of rotational systems, including aerospace platforms, robotics, maritime systems, and smart manufacturing. The project incorporates education and outreach through hands-on research experiences, curriculum integration, and K-12 engagement to strengthen the future workforce in STEM. The overarching goal of this project is to investigate bio-inspired bistable nonlinear dynamics and hybrid energy conversion mechanisms to enable broadband vibration energy harvesters (VEHs) in rotational systems. Conventional VEHs are limited by narrow frequency bandwidths, whereas ambient vibrations are inherently wideband. Additionally, centrifugal forces in rotational systems introduce dynamic complexity that can suppress energy harvesting efficiency. To overcome these challenges, this project explores bio-inspired nonlinear designs and hybrid energy transduction, leveraging centrifugal forces to dynamically tune resonance and enhance power output. The research objectives include: (1) developing and optimizing a hybrid piezoelectric-electromagnetic bistable VEH; (2) constructing theoretical and computational models for nonlinear dynamics in rotating systems; and (3) achieving self-tuned resonance through centrifugal-force-induced adaptability to maintain broadband performance. The methodology combines bio-inspired design, modeling, numerical simulation, controlled experiments, and on-road validation. The expected contributions include new theoretical frameworks for nonlinear VEHs and practical technologies capable of enabling battery-free, high-reliability sensing and communication. 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 2026 · 2026-06

In nature, organisms often work together in mutually beneficial ways. However, while theory predicts these beneficial relationships reduce variation in both partners, real-world evidence often contradicts this, especially for bacteria that can thrive with or without plant hosts. This project investigates whether plant host presence over time and space helps maintain bacterial variation. The focus is on an ecologically, agriculturally, and economically important interaction between leguminous plants and their mutualistic bacteria, which convert atmospheric nitrogen into a usable form inside structures on plant roots called nodules. This proposal will benefit agricultural production and ecological conservation, prepare the future scientific and agricultural workforce, and connect students at multiple educational levels with ecological and evolutionary concepts, as well as artificial intelligence-enabled image, statistical, and modeling analysis tools. The nitrogen-fixing symbiosis studied here is one of the most ecologically, agriculturally, and economically important plant-bacteria symbioses. Understanding the host drivers of bacterial variation will inform better decision-making in agriculture and restoration projects, boosting the effectiveness of nitrogen-fixing bacteria, which are crucial for eco-friendly farming. Educational programs will develop curricula that foster project-based learning in Pennsylvania high schools through a “Community Symbiosis Curriculum” aligned with new science standards and in undergraduate plant science courses with plant distribution models to build quantitative and computational skills. This initiative supports workforce development by offering hands-on training with educational experts and creating near-peer mentors for high school students, inspiring more young people to pursue careers in agriculture and biotechnology. In nature, organisms interact in complex ways. This project examines mutualism, where both organisms benefit, a relationship that poses a paradox: evolutionary theory suggests mutualisms should reduce genetic diversity, yet empirical data often contradict this, especially for bacterial mutualists that can live with or without hosts. This CAREER project study tests the hypothesis that has received little attention: that variation in host presence and identity explains this diversity. The project has four main goals that leverage both traditional and artificial intelligence-enabled analysis of data from field sampling, greenhouse experiments, and computer-based simulations. First, identify patterns by analyzing characteristics of different plants to understand which ones support which bacteria. Second, conduct multigenerational experiments to determine whether cohabitation with certain hosts promotes bacterial diversity. Third, simulate evolution using AI models to explore how relationships with plants affect bacterial diversity over time. Fourth, collect field samples from legume plants and engage high school and college students in research activities, including an annual outreach event to promote agricultural education. This project focuses on a plant-microbe mutualism with significant implications for food production and nutrient cycling (biological nitrogen fixation). It identifies host traits that maintain mutualist diversity and employs machine learning to link ecological and evolutionary processes in bacteria—an approach rarely applied at these scales. By integrating research and teaching, the project offers unique insights into the evolution of bacterial mutualists, with practical benefits for engineering beneficial plant-microbe interactions. It also creates opportunities for scientists and students to explore ecology, evolution, biotechnology, and artificial intelligence through visible plant-microbe interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Einstein’s theory of general relativity is a highly successful and precise description of space and time. One of its most striking and central predictions is the existence of black holes: regions of spacetime where gravity is so strong that nothing, not even light, can escape from them. Another prediction is the existence of gravitational waves, ripples in the geometry of spacetime that propagate at the speed of light. The 2017 Nobel Prize in Physics was awarded for the successful effort to measure the gravitational waves emitted as two black holes as they spiral together and subsequently merge. The merger results in the formation of a single black hole, which rapidly settles down to its equilibrium state through a process called ringdown. The equations at the heart of Einstein’s theory describe these and other phenomena through a complicated system of nonlinear partial differential equations. They can be solved numerically, that is, with the help of computer simulations, but many outstanding foundational problems remain which call for theoretical work and rigorous mathematical analysis. The investigator studies two of these problems related to the aforementioned merger/ringdown scenario. The first problem concerns the construction and detailed description of solutions of Einstein’s field equations that describe large mass ratio mergers or the scattering of two black holes. The second problem asks whether small changes in the initial state of a black hole will compound or decay in time. A rigorous proof of decay will also provide a strong theoretical justification for the ubiquitous use of these black hole models in astrophysics. An important component of this project is the training of the next generation of researchers. Under the investigator’s guidance, they will contribute to this effort through the application and development of cutting-edge mathematical techniques for the study of wave propagation. The project studies the dynamics of spacetimes containing one or more black holes in the context of Einstein’s theory of general relativity. The overarching goal is a detailed description of the late-time dynamics of single or multiple black holes, possibly undergoing merger processes. The investigator plans to address three interdependent objectives: the proof of nonlinear stability of Kerr black holes in the full subextremal range; the stability properties of several classes of many-black-hole spacetimes; and the construction of black hole merger and black hole scattering spacetimes, including a precise description of their global geometry. Nonlinear stability problems have been a driving force behind the development of modern geometric approaches to the study of wave equations. While recent progress suggests that a full resolution of the Kerr stability problem is within reach, the geometric and analytic complexity of subextremal Kerr and its perturbations still calls for additional analytic insights. The second and third objectives provide the framework for a systematic and multi-faceted approach to the construction and analysis of many-black-hole spacetimes. The investigator expects many-black-hole spacetimes to become a topic of central importance in the mathematical theory of general relativity due to the intriguing and delicate dynamical properties they possess, and also due to their clear astrophysical significance. The resolution of the problems considered in this project require the development and sharpening of powerful and flexible tools in the theory of hyperbolic partial differential equations, spectral and scattering theory, microlocal analysis, and geometric singular analysis. The investigator expects these to be of broader applicability and interest in the field of hyperbolic (and related) partial differential equations. 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 2026 · 2026-06

Computer programming has evolved from low-level machine languages to modern high-level languages such as Python. Tools for coding programs in these high-level languages often use visual elements such as parentheses, spacing, and highlighting to help people understand and create the hierarchical structures that underlie most modern programming languages. Programming environments also use visual cues, such as highlighting changes in the program’s data as it runs, to help programmers find and fix errors. For blind and low-vision (BLV) individuals who rely on assistive technologies such as screen readers, much of this information is lost when visual representations of code and program behavior are converted into a linear stream of synthesized speech. This project’s goal is to create new ways to represent programs that allow BLV individuals to fully participate in programming while using their existing accessibility tools and practices. The project team will make the new insights, tools, and educational materials widely available, which will increase BLV individuals’ ability to participate in the many jobs and other activities where programming is an important skill. The project is structured around two main research aims, each of which develops a key new representation of programs and behavior. The first aim, Grid-Coding, transforms source code into an explicit two-dimensional grid in which rows represent code lines, columns represent scope, and padding becomes meaningful structure. This representation supports non-traditional navigation strategies, including right-to-left, bottom-up, and level-based traversal, so that learners can inspect hierarchy through multiple pathways, recognize recurring code shapes, identify code smells such as deep nesting or overly long methods, and turn padding cells into active learning surfaces for hints, annotations, and locally hosted AI assistance. The second aim, Grid-Time Volume, transforms debugging from a transient visual event into a persistent navigable space-time volume that stores program states over time. Learners will move backward and forward through execution, inspect memory layout and call structure, and monitor multiple variables through natural soundscapes inspired by birdsong in a forest, where changes in loudness, rhythm, and spatial position convey runtime behavior. Through participatory design, controlled experiments, and longitudinal studies with blind and low-vision learners, the project will examine how these representations affect code comprehension, code construction, debugging, mental model formation, cognitive load, and retention. The education plan will translate these advances into open-source programming environment extensions, two fully accessible video-based courses, modules for undergraduate curricula, and community-based summer programs. The work serves the national interest by widening access to computing education, supporting a larger technical workforce, and producing open resources that can strengthen instruction in mixed-ability classrooms and self-directed learning. 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 2026 · 2026-05

This project focuses on the security and efficiency of certain cryptosystems. Since currently used systems are vulnerable to attacks by quantum computers, it is essential to develop and analyze alternatives that are resistant to quantum computers, so that they remain secure in a post-quantum world. This project investigates both the classical and post-quantum security of cryptosystems based on supersingular isogenies, as well as lattice-based schemes proposed as replacements for currently used systems. The need for such work is increasingly urgent: advances in building quantum computers continue, while designing, implementing, and deploying quantum-resistant infrastructure requires substantial time. Another part of the project examines methods for improving the efficiency of cryptosystems. These are important practical questions. As part of its educational component, the investigator will engage middle and high school students in learning about cryptography and its mathematical foundations, helping to train the next generation of the cybersecurity workforce. One part of the project focuses on the classical and post-quantum security of cryptosystems based on supersingular isogenies. A variety of such schemes have been proposed, employing different techniques and frameworks. The investigator will study systems introduced in the last few years, in particular the ones which exploit the framework of higher-dimensional isogenies. In addition, the project examines the efficiency and security of recently developed protocols that conceal torsion point information, a vulnerability that compromised one of the original schemes. Another part studies the hardness of the endomorphism ring problem for supersingular elliptic curves, which underlies the security of many of these schemes. This is a fundamental problem in arithmetic geometry. The investigator will work to improve her existing algorithm for computing endomorphism rings and extend it to certain classes of abelian surfaces. The final part addresses the security of selected lattice-based systems. The lattice-based cryptosystem called "Soliloquy" was broken by a quantum computer. A key objective is to determine whether other lattice-based constructions, including those based on the Lattice Isomorphism Problem, are similarly susceptible to attacks by quantum computers. 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 2026 · 2026-05

The immune system is the primary defense animals have against infectious diseases. A key component of this defense, the adaptive immune system, generates antibodies that recognize and neutralize bacteria, viruses, and other pathogens. The repertoire of antibodies an animal can produce is determined by a set of genes that vary considerably across mammalian species, yet this variation is almost poorly describe outside of humans, mice, and cattle. This project will conduct the first systematic comparison of immune genes and antibodies across 60 species of mammals, using state-of-the-art DNA and RNA sequencing technologies. The research team will generate high-quality genome assemblies focused on immune-related genes and expressed antibody repertoires, develop new computational tools to analyze them, and identify patterns that explain why species differ in their capacity to respond to infection. All data, genome assemblies, and software produced by this project will be made freely and publicly available, providing a foundational resource for immunology and disease research for years to come with the potential to facilitate biotechnological advances. The project will also provide meaningful research training for graduate and undergraduate students in computational biology, genetics, and immunology. Graduate students will gain hands-on experience with both laboratory sequencing methods and advanced computational analysis, while undergraduate students will participate in paid summer research positions. The team will also host a working group to establish community standards for comparing immune gene data across species, laying the groundwork for a broader research consortium that will expand this work well beyond the lifetime of this grant and further facilitate biotechnology advances. This project will generate paired datasets of antibody repertoires and germline immunoglobulin (IG) loci across approximately 60 mammalian species. For each species, expressed antibody repertoires will be profiled using long-read bulk RNA sequencing (PacBio Iso-Seq, a technology enabling full-length transcript recovery) of whole blood samples, enabling unbiased identification of V(D)J recombinations, the combinatorial gene rearrangements that generate antibody diversity, across all antibody chains. In parallel, high-quality genome assemblies will be generated using long-read whole-genome sequencing (PacBio HiFi), and IG loci will be assembled using state-of-the-art assemblers followed by targeted quality evaluation. New computational methods will be developed to integrate repertoire and genomic data to: (i) improve detection and annotation of germline IG genes, including highly divergent and previously unrecognized gene families; (ii) identify non-canonical antibody features such as ultralong or structurally atypical antigen-binding sites; and (iii) characterize structural variation and gene organization within IG loci. Using these data, the project will quantify species-level variation in germline gene content, gene usage frequencies, and V(D)J recombination features. Phylogenetic comparative models will then be applied to test hypotheses linking variation within antibody repertoires to ecological and life-history variables (including lifespan, diet, and population dynamics) and to reconstruct the evolutionary history of IG gene families. Finally, the project will analyze relationships between germline IG gene copy number, genomic organization, and expression bias to assess how the molecular evolution of IG loci shapes antibody repertoires. 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 2026 · 2026-05

This project supports travel for students from US institutions to participate in the Doctoral Consortium (DC) program at the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026). AAMAS is the premier international conference in agents and multiagent systems. This consortium is oriented on research and career development for students who have identified their doctoral topics and are embarking on independent research. As Artificial Intelligence (AI) and multiagent systems become more prevalent, exposing students to the latest advances in intelligent agent technology will have a significant impact on their career trajectory on several domains, including infrastructure, logistics, manufacturing, transportation, the digital economy, and other sectors of national importance. The Doctoral Consortium program will provide significant opportunities for the students to strengthen their career and professional development. Students will present and discuss their research, receive mentoring from senior researchers, participate in a career panel to discuss career choices in industry and academia, and engage in discussions to build research collaborations. The Doctoral Consortium program also includes tutorials given by leading researchers on the latest developments and advances in AI and multiagent systems. Students will also benefit by attending the full AAMAS conference, including the demonstration track on AI and multiagent systems technology. These activities will play an important role in fostering the next generation of AI scientists and practitioners by exposing students to cutting-edge research in AI, building professional networks, and expanding research engagement. 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 2026 · 2026-04

This award supports the ICM Satellite Conference on “Conformal Geometry, Einstein Manifolds, and Minimal Submanifolds”, to be held at The Pennsylvania State University in University Park, PA, during July 13-17, 2026. This conference will feature talks by approximately 30 leading experts from around the world doing work in these closely-related fields. The meeting will also provide valuable opportunities for junior participants to present their work to and interact with senior mathematicians. Research in conformal geometry, Einstein manifolds, and minimal submanifolds has a long and intertwined history going back over 125 years, with many examples of ideas in one field enriching all fields. This conference will focus on cutting edge developments that are expected to have significant positive impacts in all these fields, including geometric regularity results for related geometric PDEs, existence and compactness results for distinguished (sub)manifolds, and the enumeration of local and global conformal invariants. The conference also includes many activities which will contribute to the professional development of junior participants, including a poster session and the broad distribution of research discussed during the conference. The conference webpage is https://sites.google.com/view/confeinmin-conference-2026/. 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 2026 · 2026-04

This Faculty Early Career Development Program (CAREER) award will support research to advance dexterous manipulation by aerial robots in real-world environments to perform tasks, such as close-contact inspection of an inaccessible structure, toggling a switch at an elevated location, or mounting a sensor on a tall radio tower. Existing aerial manipulators can perform inspection, grasping, and assembly at height; however, these systems have been primarily explored in controlled lab settings and rarely operated in the open, especially for tasks that require delicate contact or high precision. Achieving high precision is challenging due to robot floatation in air, where the supporting base and manipulator arm dynamics are coupled and significantly influenced by wind disturbances, degraded perception or awareness, and strict limits on payload and applied forces. This research aims to develop new methods to enable precise aerial manipulation under these adverse circumstances while maintaining safety. The outcome will unlock applications that are currently impractical or unsafe, including infrastructure contact inspection and maintenance, precision agriculture, and scientific exploration in remote or hazardous environment. This project will also integrate education and outreach with research to train a future robotics workforce through immersive research-integrated learning modules, capstone projects on enhanced autonomy, and STEM outreach to inspire K-12 students. This research will advance real-world dexterous aerial manipulation by developing a physics-infused intelligence framework that tightly integrates physical modeling, perception, planning, and control. The framework will enable aerial manipulators to achieve stable contact inspection under coupled contact–aerodynamic disturbances, precise manipulation despite sensing and state-estimation errors, and safe load-bearing manipulation that requires application of force and torque. The project has three research thrusts: (1) Developing a complex dynamics model that captures the strong contact–aerodynamic interactions through a unified representation to enable stable contact with desired force and motion during inspection; (2) Co-optimizing perception and control in an end-to-end differentiable framework with infused physics while accounting for sensing and estimation errors to improve precision; (3) Incorporating wrench space awareness into trajectory planning and control to ensure safe, accurate force application. These advances will be validated on real-world tasks with increasing complexity, including contact inspection, toggling a switch on a tall tower, and mounting sensors at height. 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 2026 · 2026-03

Contemporary research and education are driven by data-intensive science, AI, and advanced instrumentation that depend on robust cyberinfrastructure. While these tools enable rapid discovery, limited network capacity or connectivity is a bottleneck—especially for small institutions—restricting their ability to fully participate in modern scientific inquiry and educate the nation’s workforce. This project immediately transforms research and education capacity at three small institutions—Allegheny College, Harrisburg University of Science and Technology, and West Chester University—by addressing critical gaps in access to regional and national AI, high-performance computing (HPC), and storage resources. By deploying advanced cyberinfrastructure to connect these campuses to the PA Science network and key Pennsylvania-based (PA) resources, the project enables high-performance data transmission, sharing, and access to computational and storage services. Combined with other networking and storage investments, the national AI research cyberEcosystem, two state Research and Education Networks, and Pittsburgh Supercomputing Center’s ADAPT program, this effort advances research and education across AI, advanced manufacturing, astronomy/astrophysics, computer science, and cybersecurity. The project addresses these gaps to drive innovation, multiply intellectual impact, and create broader impacts through strategic activities: building relationships between researchers and campus cyberinfrastructure (CI) professionals, piloting innovative CI technologies, creating opportunities for research and education collaborations across PA, developing the CI workforce, and enhancing sustainability for CI technologies. The project provides a robust and efficient template for additional initiatives regionwide, fostering a more interconnected cyberEcosystem with an aim to create opportunities for all Americans everywhere. 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 2026 · 2026-03

Lightning produces highly reactive chemical compounds that influence the atmosphere’s ability to remove pollutants. This project uses laboratory experiments and model comparisons to improve understanding of lightning-driven atmospheric chemistry. The project will improve understanding of the atmosphere’s oxidative capacity and predictions of associated pollutant removal. Extreme heat, electrical currents, and radiation emitted by lightning initiates the direct production of large amounts of hydroxyl radical (OH) and larger amounts of nitric oxide (NO). A substantial amount of nitrous acid (HONO) may also be generated. This effort consists of 5 tasks: (1) measurement of the temporal evolution of the OH distribution in a generated spark core and corona sheath using a laboratory setup; (2) measurement of the dependence of spark-generated HONO on environmental conditions; (3) determining the composition of ultrafine particles generated by spark discharge; (4) modeling the potential for corona to form on aircraft inlets (such as in the NSF-supported DC3 campaign) and generate interferences; and (5) relate the measurements of corona UV radiation made during field studies to OH production. Undergraduate students will participate in the research and project findings will be shared through scientific presentations to middle and high school 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 2026 · 2026-01

This project aims to serve the national interest by strengthening critical thinking and independent learning among undergraduate students through the responsible use of artificial intelligence (AI) in computer science classes. As large language models (LLMs) become increasingly integrated into education, there is a pressing need to support students in using these tools thoughtfully, rather than relying on them passively. This Level 1 Engaged Student Learning project will implement and evaluate the Inquiry-based Learning Environment (SMILE), an innovative framework that guides students in developing deeper programming and critical thinking skills by combining structured questioning with metacognitive reflection. Broader impacts include publicly available instructional materials that develop analytical reasoning skills, faculty training, and alignment with workforce needs in an AI-augmented economy. The project plans to investigate the impact of unguided AI use on student critical thinking and compare it to SMILE-guided approaches. Using a mixed-methods design, which includes interviews and structured observational studies, the research examines the SMILE framework's effectiveness in promoting critical thinking and long-term metacognitive development in computer science, advancing the understanding of structured frameworks as an effective AI-driven pedagogy. Expected outcomes include validated rubrics, instructional exercises, and evidence-based guidelines for integrating AI into computing education. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 2026 · 2026-01

The U.S. pellet market is worth almost $9 billion. The majority of pellets are made from hardwood processing waste, and their applications are limited to heating and energy generation. Biomass-based pellets could replace coal in some industrial settings if they can achieve a higher energy density and strength than wood-based pellets. Converting low-cost, regionally available materials such as food processing waste and fast-growing crops into high-value solid fuels could enhance rural energy independence. However, designing biobased combustible fuels as drop-in substitutes for coal requires advances in fundamental energy science. This project will use experiments and process design to overcome limitations with single char-based solid fuels and design drop-in fuel mixtures that optimize properties between various biomasses. The project will bring together stakeholders from academia, industry, and government to explore implications of bioenergy. Project outcomes will help the U.S. become a leader in biobased fuels. Hydrothermal Carbonization (HTC) is an efficient way to produce solid biofuels from wet wastes. Yet hydrochars (HCs) do not combust like the coal they are thought to replace, owing to the presence of secondary char (SC), a tarry material that forms during HTC. This project will design true drop-in solid biofuels by aligning carbonization levels between hydrochars and torrefied biochars (BCs) to reduce fuel segregation and increase thermal stability and energy density of HC-only fuels. The SC present on HCs may serve as a pellet binder, improving hydrophobicity and tensile strength, but it will alter ignition and burning characteristics due to its higher reactivity and lower porosity. By integrating fuel and combustion science, the project team will develop rapid thermogravimetric analysis-based assessment method to predict key combustion behaviors (ignition delay, ignition mode, combustion mode, burning rate) and how SC impacts these behaviors. Through the development of a statistical model the team will determine the optimal SC composition and HC-BC blend ratios. The project will promote economic diversification in rural areas of the US by identifying key characteristics of feedstocks that optimize biomass-to-fuel conversion and utilization. Three key audiences are targeted in research translation activities to support a Resilient Rural Economy: students in the laboratory and in engineering Capstone Design courses; researchers across the fuel and combustion communities; regional stakeholders from industry, academia, and government. 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 2026 · 2026-01

Wildfires have become more intense and frequent globally, increasing risks to both natural and human environments. Africa has the highest incidence of fire on Earth today, yet further changes in patterns of fires, especially in places that historically have not burned, have led to complex hazards such as floods and destruction that make this region particularly vulnerable. Long-term information from the fossil record is necessary to understand and predict these changes, yet this data is currently not available in a centralized format that is usable by scientists or community-members to understand how fire changes over time. The overarching goal of this project is to support an open-science initiative that would create standards for past global fire data and mobilize data from Africa onto the Neotoma Paleoecology Database, a powerful platform for data access that integrates tools for detecting patterns in past fire data over time. This research helps improve scientists' ability to predict areas of greatest fire risk and the responses of environments to fire by building data resources supported by a connected, international network of experts. This project also provides opportunities for multiple graduate and undergraduate students to conduct interdisciplinary research. Additionally, this project develops educational tools to teach scientists of all levels to access and use past fire data to study impacts of fire to ecosystems. This project develops global standards for past fire data to launch a Global Paleofire Database on Neotoma. To do this, the project builds a Paleofire Network of global domain experts to mobilize data onto Neotoma and facilitate the use of new tools to address scientific questions about changes in fire over time and impacts to African ecosystems. This work features a series of in-person and virtual workshops and the development of educational materials to support the creation and use of this new resource within the community. The results are expected to contribute to the understanding of global and regional fire-ecosystem dynamics and the interactions between fire, ecosystems, and 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 2026 · 2026-01

Earth’s lower continental crust (LCC) plays a key role in shaping our planet’s structure, chemistry, and landscape, but much about how it forms remains uncertain. Although many scientists think it is mostly made of dense, dark-colored igneous and metamorphic rocks, the discovery of sedimentary rocks deep in the crust suggests that lighter, more silica-rich materials may also be present. This project will investigate how sediments get buried deep enough to become part of the lower crust by studying a unique exposure of ancient deep crust called the Serre Massif in southern Italy. By combining field mapping, lab-based analysis, and geochronology (rock dating), this research will test different models for how sediments are moved and transformed deep underground. This work will not only improve our understanding of Earth's evolution but also create opportunities for students to gain hands-on experience in geoscience research. A paid undergraduate research assistant will be recruited, mentored by the Principle Investigator, and supported in presenting their work at a scientific meeting. These efforts aim to train the next generation of Earth science researchers by addressing financial, physical, and mentoring barriers in the discipline. Scientifically, this project will evaluate three competing hypotheses for sediment incorporation into the LCC: burial, tectonic underplating, and relamination. Each predicts different patterns in the timing and conditions of metamorphism. The Serre Massif, a well-preserved Variscan granulite terrane, provides an ideal natural laboratory to investigate these processes, as it retains prograde mineral assemblages, chemical zoning, and metamorphic ages. Field-based structural analysis will identify shear zones and fabrics associated with sediment transport. High-grade metamorphic conditions will be constrained using thermobarometry, Raman spectroscopy, and pseudosection modeling. U-Th/Pb dating and trace element analysis of zircon and garnet will provide temporal and geochemical data to reconstruct prograde P-T-t histories and evaluate age-depth relationships across the crust. Results will improve understanding of lower crustal formation processes and inform models of crustal recycling, geodynamic evolution, and seismic interpretation. The project will contribute to both scientific knowledge of lower crustal evolution, processes, and properties and to building the geoscience community of 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 · 2025-11

Per- and polyfluoroalkyl substances (PFAS) are a class of chemicals that can contaminate the environment and affect public health. Because of the health concerns, there is a critical need to understand how humans are exposed to PFAS compounds transported through the environment. On August 19, 2024, there was a spill of PFAS-containing firefighting foam at the Brunswick Executive Airport in Brunswick, Maine. Investigators will measure PFAS concentrations in soil and water samples obtained from multiple sites around the spill over the course of one year. These measurements will be used to assess how these PFAS “forever chemicals” move through soil and water over time. Benefits to society from this project include data sharing with scientists and educators to advance knowledge, and results disseminated in the form of peer reviewed studies that regulators, policymakers, and other stakeholders can use to implement better strategies for emergency response to such spills. Research has demonstrated that PFAS contamination of the environment has significant effects on human and ecological health. Researchers typically have access only to studies of sites that have been contaminated in the past such as landfills, burn pits, or former manufacturing facilities. This results in a significant knowledge gap in our understanding of PFAS fate and transport from recent releases. A spill of firefighting foam containing high concentrations of PFAS at the Brunswick Executive Airport presents a once-in-a-lifetime opportunity to reveal mechanistic insights about PFAS contamination after a spill in a well-defined watershed. The goal of this project is to understand the distribution of PFAS within the soil horizon and the impacted watershed as a function of time and proximity to the spill site. The specific research objectives are to collect, archive, and analyze PFAS in soil and water samples at various locations over time to reveal mechanistic insight on PFAS fate and transport. Results from this study can help develop mitigation strategies for emergency response groups to prioritize containment and clean up. In addition, the work can help impacted municipalities implement effective water advisories for their residents, as well as benefiting a broader group of researchers, policymakers and other stakeholders who study and manage PFAS contamination. 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 · 2025-11

Per- and polyfluoroalkyl substances (PFAS) are fluorinated organic chemicals that have emerged as priority pollutants during the last two decades due to increasing concerns about their persistence, stability, and toxicity as they accumulate in the environment. The detection of PFAS in drinking water has raised significant concerns about their impact on human health. PFAS are difficult to degrade and destroy using conventional water treatment oxidants (e.g., chlorine, ozone, and hydrogen peroxide) due to their strong C-F covalent bonds and C-F bond polarization which causes steric hindrance to chemical attack. Sorption onto granular activated carbon (GAC) filter beds has emerged as the most efficient and cost-effective process from removing PFAS from contaminated drinking water sources. However, spent PFAS-laden GAC filter beds need to be disposed of or regenerated to enable their reuse. The overarching goal of this project is to investigate the viability of thermal regeneration as an efficient and cost-effective process to enable the reuse of PFAS-laden GAC filter beds while catalyzing the degradation and destruction of the sorbed PFAS contaminants. To advance this goal, the Principal Investigators (PIs) propose to test the hypothesis that the abundance of highly mobile electrons on the graphitic surface of activated carbon will catalyze the thermolysis and subsequent degradation of sorbed PFAS molecules from spent GAC filter beds. The successful completion of this research will benefit society through the generation of new fundamental knowledge to advance the utilization of GAC as an efficient and cost-effective sorbent for the treatment of PFAS contaminated drinking water sources. Additional benefits to society will be accomplished through education and training including the mentoring of one graduate and one undergraduate student at the University of Maine and one graduate and one undergraduate student at the University of Nevada, Reno. Granular activated carbon (GAC) has been demonstrated in the field and at scale to be the most efficient and cost-effective sorbent from removing PFAS contaminants from drinking water sources including pretreated surface water and groundwater. Thermal regeneration is an established process for the regeneration of spent PFAS-laden GAC beds. However, a fundamental understanding of the mechanisms of PFAS degradation, transformations, and destruction during the thermal regeneration of spent GAC filter beds has remained elusive. The goal of this project is to advance the fundamental understanding of PFAS degradation, transformations, and destruction during the thermal regeneration of PFAS-laden GAC beds under relevant process and field conditions. The specific objectives of the research are to: 1) Investigate the effect of thermal regeneration on the physicochemical properties of commercially available and well-characterized GAC PFAS sorbent candidates; 2) Evaluate the impacts of heating rate, regeneration temperature, gaseous atmosphere, and reactivation agents on PFAS degradation/transformations and GAC regeneration efficiency; 3) Assess the impact of GAC pore structure and surface chemistry on the extent and rate of PFAS thermolysis; and 4) Characterize and unravel the desorption, decomposition, and mineralization pathways of sorbed PFAS during the thermal regeneration of PFAS-laden GAC beds. The successful completion of this project has the potential for transformative impact through the generation of fundamental knowledge and performance data to advance the implementation of GAC sorption as an efficient, cost-effective, and sustainable process for the treatment of PFAS contaminated drinking water sources. To implement the educational and outreach goals of this project, the Principal Investigators (PIs) plan to integrate the findings from this research into existing undergraduate/graduate courses at the University of Maine and the University of Nevada, Reno. In addition, the PIs propose to leverage existing programs at their respective institutions to host a “Girls Scouts” outreach program to teach basic concepts of environmental engineering and water treatment to K-3 grade 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 2025 · 2025-11

Genetic manipulation is usually performed by injecting gene editing materials into embryos, which is difficult, expensive, and inefficient. We have identified a virus that infects the ovaries and testes of arthropods after injecting them or feeding it to them. When the virus is modified to carry gene editing materials, infected arthropods have genetically modified offspring, a technique that is much easier than traditional methods. We call this method “Germline Engineering by Viral Transduction”, or “GEValT“. GeValT is easy to use and will revolutionize gene editing for researchers working in any arthropod system. In this project, we will assess the range of arthropods that this virus infects, develop it into a gene editing delivery vehicle across multiple arthropods, develop new viral systems that act similarly for arthropods that our original virus can not infect, and disseminate information, technology and methodology to broad communities of scientists/public involved in research on animal behavior, animal physiology, insect-plant interactions, sustainable agriculture, and public health. We will host students and scientists to learn these techniques, and host two workshops to give interested scientists and students an opportunity to learn and apply these technologies to their systems of interest. All constructs/technology will be placed in public depositories to allow distribution and easy access for all interested scientists. Reagents and protocols will be shared prior to publication. The development of this biotechnology coupled with easy to use methods for arthropod genetic engineering will have broad impacts on scientific discovery, and will stimulate translational investments in the US bioeconomy industry. Genetic manipulation by embryonic microinjection is technically challenging and inefficient. We have identified a baculovirus that can infect multiple arthropod taxa where it infects diverse tissues and can deliver gene editing material to the germline, resulting in genetically modified offspring. We call this method “Germline Engineering by Viral Transduction”, or “GEValT“. GeValT is easy to use and will revolutionize gene editing methodology for researchers working in any arthropod system. Here, we will develop GeValT technologies that work across a wide range of arthropod taxa, opening the true power of this technique to all researchers interested in applying genetic engineering techniques to their questions of interest. This goal will be realized by the following four Specific Aims: 1) Characterize baculovirus infection tropism and develop compartment-specific gene transduction systems in multiple arthropods; 2) Develop GEValT methodology across multiple arthropod systems; 3) Develop novel viral infectious clone and delivery systems for arthropod taxa resistant to baculovirus infection; 4) Disseminate GEValT information, technology and methodology to broad communities of scientists involved in research on animal behavior, animal physiology, insect-plant interactions, sustainable agriculture, and public health. The proposal was funded by the Enabling Discovery through GEnomics (EDGE) program. 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 · 2025-11

Changing environmental conditions are dramatically transforming Earth’s drylands, which cover nearly half of the planet and support the livelihoods of 3 billion people through farming and food production. As these regions face increasing drought and more extreme weather, it is crucial to understand how the tiniest organisms in soil—microbes—cope with stress. These microbes are responsible for recycling nutrients like nitrogen, which plants need to grow. This project focuses on how microbial communities in desert soils continue performing these essential roles, even as their environment becomes more difficult to survive in. The research will examine soils in the Sonoran Desert over several years to explore how microbes adapt and reorganize in response to environmental stress. The team will use advanced genetic tools to study these tiny organisms—revealing who they are, what they do, and how they respond to environmental stress—alongside carefully designed laboratory experiments to detect early warning signs that soil ecosystems may be approaching collapse. In addition to advancing understanding of how life persists in extreme environments, the project will deliver practical tools for monitoring soil health. These tools aim to support decision-making for land managers, farmers, and environmental agencies working in dry regions. Educational resources will be developed, including an interactive public art installation that invites people to explore the hidden world of soil microbes through gameplay. The project will also provide research training opportunities for students and foster collaboration with agricultural extension professionals, ensuring the work benefits both scientific progress and real-world land stewardship. This project investigates molecular mechanisms underlying microbial community resilience in arid ecosystems using Thermoproteota as a model system for understanding organism-mediated stability. The research combines five years of temporal multi-omics analysis (2021-2025) with controlled mesocosm experiments to test three nested hypotheses spanning community, population, and molecular scales. The team will integrate metagenomics, metatranscriptomics, and metabolomics with Bio-orthogonal Non-Canonical Amino Acid Tagging (BONCAT) to distinguish active from dormant populations and track protein synthesis during stress responses. Controlled manipulation experiments will simulate intensified monsoon cycles to identify critical thresholds where adaptation mechanisms fail, particularly focusing on Thermoproteota’s unique stress tolerance strategies including manganese-based catalases and protein repair systems. Advanced statistical modeling using Structural Equation Models and Mixed Effects Models will connect molecular adaptations to ecosystem-level nitrogen cycling stability. The research will develop a hierarchical framework of resilience indicators spanning rapid molecular responses to slower community restructuring patterns. Key innovations include dual amino acid BONCAT protocols for soil systems, integration of activity measurements with genome-resolved multi-omics, and development of predictive models linking individual adaptations to community stability. The experimental design employs 272 archived samples and 243 controlled mesocosm samples to validate indicators across temporal scales and stress intensities, ultimately producing quantitative tools for monitoring ecosystem resilience and predicting critical transition points in arid 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 · 2025-11

When users interact with modern artificial intelligence (AI) services, they often transmit their data to the service provider. For example, individuals seeking to enhance their resumes using tools like ChatGPT must upload their resumes to the service. Those using AI-powered health monitoring devices send their biomedical signals to the provider's AI model. These interactions pose significant privacy risks, as the service provider gains access to potentially sensitive information, such as the user's address in the resume or the user's biomedical data. Existing solutions to mitigate these risks are either too slow and costly or insufficiently secure. This project will develop a hybrid solution that integrates the strengths of current approaches, realizing a secure and efficient AI experience. The outcome of this project will enable users to benefit from AI services without compromising privacy, with applications spanning from everyday tasks to critical sectors like healthcare and national defense. The project will explore a hybrid approach that combines two existing fields of research: multi-party computing (MPC), which offers strong cryptographic security but suffers from slow performance, and trusted hardware, which is much faster but weaker in security. The core idea is to design specialized hardware that accelerates only the subset of operations where MPC is slow, while maintaining a strong focus on security through a minimalist, security-first hardware design. The complementary use of the new hardware and MPC will significantly improve performance compared to standard MPC due to its selective hardware acceleration, while offering greater security than typical trusted hardware due to its security-oriented hardware design. The project will address three main challenges. First, the new hardware and the surrounding MPC system will be co-designed to balance security and performance, with various workload split strategies between the two being explored. Second, existing AI models will be adapted to better suit the unique properties of the hybrid system. Third, novel techniques will be developed to ensure the strong security of both the new hardware and the integrated MPC system. Both empirical methods and formal analysis will be employed to a degree not possible for traditional trusted hardware, thanks to the minimalist, security-oriented design. 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.