Syracuse University

NSF Awards · FY 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) grant funds research that enables general purpose robotic systems that can change their forms to optimally achieve a range of functions. This research introduces a systematic framework for synthesizing form and function within a dynamical system, replacing manual motion design with a scalable, tractable, and data-efficient approach. Unlike traditional fixed-form systems, whose shapes are determined at design time and tailored to specific tasks, this research enables next-generation platforms that can continuously morph to select shapes for solving complex multi-stage tasks in an optimal manner, thereby promoting the progress of science, advancing national prosperity and welfare, and securing the national defense. Tightly integrated with the research activities, this grant also funds a comprehensive outreach strategy to engage participants across various educational levels, including K-12 students, schoolteachers, undergraduate students, and graduate students, and to establish a foundation for lasting contributions to robotics theory, system design, and STEM education through layered mentorship and interdisciplinary learning in the United States. Mobile robotic systems involve complex dynamics with high degrees of freedom, hybrid transitions, and sensitivity to contact and the environment. These challenges are magnified in morphable systems, where the configuration space is combinatorially large and time-varying. Overcoming them requires new representations, numerical methods, and control strategies that generalize across shapes and tasks. This research aims to develop a systematic framework for modeling, analyzing, and controlling hybrid dynamical systems with structured morphological variability and to provide theoretical and algorithmic tools that enable scalable co-design of physical form and control across diverse tasks. The research encompasses three thrusts: (1) constructing a unified framework for modeling morphology using symbolic representations of form and symmetry-aware model reduction; (2) characterizing the form-function relationship and constructing a task-based motion library through trajectory optimization, sensitivity-guided continuation, and bifurcation analysis; and (3) developing a novel data-driven hierarchical control strategy that enables rapid adaptation to changes in morphology and tasks, which will be validated on physical robot platforms with diverse morphologies. Beyond robotic systems, this research has potential applications in reconfigurable and automated manufacturing lines, space exploration missions, and senior and medical care centers, where multitasking capability and versatile operation are strongly required. 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 · 2026-09

Many problems in science and engineering require predicting how radiation, light, or particles move through and interact with complex media. These problems arise in areas such as atmospheric science, optical imaging, nuclear engineering, and astrophysics, where accurate predictions are essential to scientific discovery, engineering design, and decision-making. A central tool for making such predictions is numerical simulation based on partial differential equations, which provides a first-principles way to model the relevant physical processes. However, these simulations remain very expensive because of the high-dimensional and multiscale nature of the underlying problems. This project aims to address this barrier by developing a systematic computational framework that integrates efficient high-order adaptive numerical methods, substructure-based parallel computation, and localized machine-learning models. The substructure-based design makes the computation well-suited for modern parallel computing systems, including high-performance computing clusters and GPUs. It also allows machine-learning models to be trained locally and inexpensively at the substructure level, while keeping the overall solver grounded in reliable and theoretically justified classical numerical methods. This combination of low-cost machine learning and first-principles-based numerical computation is expected to broaden access to the interdisciplinary area of scientific machine learning and provide students with training at the intersection of applied mathematics, high-performance computing, and artificial intelligence. This project will develop and analyze a unified substructuring framework for the radiative transfer equation. The research has three main goals. First, it will establish hp-explicit a priori error estimates for discontinuous Galerkin discretizations of the equation, clarifying how high-order schemes affect accuracy in both advection- and scattering-dominated regimes. Second, it will derive spectral estimates for statically condensed Schur complement systems, providing the theoretical foundation for substructuring solvers that are robust across varying mesh sizes, polynomial degrees, and scattering regimes. Third, the substructuring framework will enable reliable and inexpensive training of localized machine-learning models to accelerate the numerical scheme, while maintaining rigorous error control through perturbation analysis grounded in the spectral properties of the condensed system. Together, these components will contribute to an efficient, scalable, and theoretically grounded computational approach for radiative transfer and related high-dimensional transport problems. 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

This is a project in commutative algebra, with connections to algebraic geometry, algebraic topology, and representation theory. Classically, commutative algebra is concerned with the solutions to systems of polynomial equations like those defining lines, circles, parabolas, planes, spheres, and other familiar geometric objects. As polynomial equations are ubiquitous in mathematics, commutative algebra is fundamental to many fields in pure math like algebraic geometry, number theory, and topology, and it also has applications in diverse areas such as cryptography, statistics, and physics. Depending on the number of equations, the number of variables, and the complexity of the equations defining a solution set, the corresponding geometric object can be difficult or often impossible to understand in its entirety. Instead, it is often effective to study the local behavior of these objects and “glue” this local information together to gain global insights. The research carried out will investigate the local algebraic behavior of solution sets arising in very general, abstract settings using a variety of homological and homotopical techniques. The project also includes the organization of masterclasses and immersive graduate summer schools aimed at developing in-depth, communal learning activities centered on cutting-edge mathematics that connects commutative algebra to other areas. The research program has two central long-term goals with the common thread of Koszul duality, a pervasive phenomenon in algebra, geometry, and topology. Techniques toward both goals include the application of differential graded and simplicial methods, and of A-infinity structures. The first research direction investigates several related constructions in local algebra that measure singularities. A focus here is on a problem of Avramov from the late 1980s that predicts a relative Koszul duality between deformations and the homotopy Lie algebra; the former connects to classical deformation theory, while the latter allows one to draw on a wealth of ideas from rational homotopy theory. Infinitesimal deformations, Koszul homology, and cohomological support varieties will also be investigated. The second overarching goal is to better understand the asymptotic nature of free resolutions over singular rings, exploiting a crucial feature of Koszul duality: it supplies explicit, computable constructions for understanding the relevant homological algebra. The PI aims to develop machinery—and in turn, an effective algorithm—for constructing minimal free resolutions over a large class of rings. This framework has the potential to recover known universal constructions, such as those over complete intersection and Golod rings, while also applying to many new examples, including generic Gorenstein rings and monomially-defined examples. The main strategy, building on previous work of the PI, applies homotopical deformations of Koszul algebras and Koszul modules. 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

Six million years ago (6 Ma), during the late Miocene epoch, tectonic activity closed the Mediterranean Sea. During this “Messinian Salinity Crisis (MSC),” the Mediterranean dried up, becoming a warm, windy desert around isolated brackish lakes. At the end of the Crisis (5.3 Ma), the Straits of Gibraltar opened, rapidly reflooding the Mediterranean, reconnecting it with the Atlantic and establishing present-day geography and circulation. The MSC’s effects on the hydrologic budget and plant species of Mediterranean Europe and Africa as well as Atlantic Ocean temperatures are still largely unknown. Using the MSC as a “natural laboratory,” this postdoctoral fellowship project will investigate what happens to the surrounding environment when 4 million cubic kilometers of water evaporates, then returns. This will improve our understanding of water resources in arid environments and regional-scale responses of plant communities to changes in rainfall and temperature, helping the U.S. predict and prepare for extreme weather in the 21st century and beyond. Broader impacts of this project will enhance the development of the American STEM workforce by training undergraduate and high school laboratory assistants in organic chemistry, time series analysis, and statistics. While the late Miocene is theorized to have been a close 21st century analogue (~450 ppm CO2), little is known about terrestrial Ibero-African conditions during the MSC because sedimentation inside the Mediterranean was limited to nonexistent. During the recent International Ocean Discovery Program Expedition 397 to the Iberian Margin, an unprecedented continuous 8-0 Ma section was recovered from the Atlantic side of the Straits of Gibraltar. Using organic geochemical proxy materials (alkenone-based sea surface temperatures, leaf wax compound-specific carbon and hydrogen isotope measurements) from a section of Expedition 397 spanning the MSC, this postdoctoral fellowship project will seek to answer several outstanding questions including 1) How does Mediterranean Outflow Water affect surface ocean circulation? 2) Did African and Iberian plant communities diverge with the reflooding of the Mediterranean? and 3) What was the Mediterranean water budget without a Mediterranean Sea? This project will yield the first high resolution (3,000-year) records of Iberian Margin sea surface temperatures and Ibero-African water isotopes and landscape ecology spanning the MSC, distinguishing baseline orbital-scale variability from large state changes caused by Mediterranean reflooding. Given the similarities between the Miocene and modern, this work will inform understanding of the past, present, and future Earth system and improve the ability to model Atlantic circulation and the Ibero-African hydrologic and carbon cycles in presently water-stressed regions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

SUMMARY After mating, sperm participate in diverse interactions with the female reproductive tract (FRT) that contribute to fertility. The molecular basis of these sperm-female interactions (SFIs) is not well understood, even though they have the potential to be critical in vivo determinants of sperm function and fate. Our long-term goal is to comprehensively characterize the network of molecular SFIs required for fertility and to elucidate fundamental SFI mechanisms. Our central hypothesis is that female-derived metabolic proteins participate in spatiotemporally regulated interactions with sperm and contribute to sperm motility and viability during storage and, in turn, fertilization competency. The rationale is that SFIs are challenging to study in vivo in mammals and we are poised to leverage the highly developed Drosophila research system outlined in this proposal to study processes mediated by SFIs. The central hypothesis will be tested through the following experimental objectives. First, fertility-related functions of FRT metabolic proteins that associate with sperm will be characterized through systematic assays of knockout or knockdown flies for parameters of sperm function and fertility. Second, in vivo spatiotemporal association of female proteins with sperm will be characterized using high-resolution microscopy, and the contribution of FRT-derived exosomes to these interactions will be determined. Third, in vivo metabolomic dynamics of sperm during transit and storage in the FRT will be characterized, for the first time, using quantitative targeted metabolomics. Fourth, mechanisms by which females influence sperm metabolism will be determined experimentally in females lacking FRT secretory tissues, exosome biogenesis or both. Our proposal is innovative because it leverages our unique and new knowledge of female molecules that participate in SFIs to test specific functional hypotheses relating to in vivo mechanisms that govern variation in fertility. Expected outcomes of the proposed research include systematic characterization of (1) the impact of female metabolic proteins on sperm function and fertility, and (2) the contribution of the FRT to in vivo sperm metabolome dynamics. The results will have a significant and immediate positive impact by advancing the mechanistic understanding of SFIs. In the long-term, they will provide the foundation for translational research into novel contraceptives and treatments to remediate infertility.

NIH Research Projects · FY 2026 · 2026-04

Project Summary Multiple sclerosis (MS) is an autoimmune disease that targets myelin, leading to neurological disability and chronic symptoms. Nearly 1 million people in the U.S. are affected. The total MS-related annual economic cost has reached $85.4 billion. Current treatments focus on symptom management and broadly suppress the immune system, which compromises protective immunity and fails to provide long-term relief. There is an urgent unmet clinical need for safe and effective treatment strategy for MS. MS pathology is driven by autoreactive CD4+ T cells. Th1 and Th17 cells can infiltrate the central nervous system (CNS) and cause inflammation after reactivation. Follicular T helper cells also contribute to MS pathology by promoting autoantibody production. The persistent inflammation impairs regulatory T cell function and hinders nerve tissue regeneration. Leveraging the cognate interaction between peptide major histocompatibility complex class II (pMHCII) and T-cell receptor (TCR) can selectively engage autoreactive T cells for suppression. However, the heterogeneity of MS and the diversity of T-cell clones and patient MHC haplotypes make targeted drug delivery challenging. The blood-brain barrier further complicates treatment delivery. Tolerogenic DCs (tolDCs) can suppress antigen-specific T-cell function through inhibitory surface proteins, including programmed death-ligand 1 and 2 (PD-L1/2) and Fas ligand (FasL). Mimicing the tolDC/T-cell interaction can potentially advance antigen-specific MS treatment. Our long-term goal is to develop a generalizable personalized treatment strategy that can selectively suppress autoantigen-specific T cells for MS treatment. The objective of this project is to develop protein-decorated rapamycin-encapsulated nanoparticles to selectively suppress CNS-infiltrated autoantigen-specific CD4+ T cells. We hypothesize that our proposed nanoparticle design can mediate selective suppression of antigen-specific CD4+ T cells. We also propose to promote nanoparticles brain accumulation to selectively suppress CNS-infiltrated autoantigen-specific CD4+ T cells. To test this hypothesis, we propose the following two specific aims: (1) developing protein-decorated nanoparticles for antigen-specific T-cell-targeted delivery and (2) determining their brain entry and therapeutic effects in models of MS. We expect to establish a proof-of-concept for a personalized antigen-specific T-cell- targeted drug delivery platform, leading to new CNS-targeted MS treatments and advancing understanding of T cell regulation in autoimmunity.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY This research program will elucidate the causes and consequences of evolution in multispecies microbiomes, leveraging experimental evolution in synthetic systems. The human microbiome modulates key components of health and its disruption and has been linked to diseases including inflammatory bowel disease, allergies, and inflammatory skin conditions. Research on microbiomes is shifting from describing diversity to developing a framework of how stable microbiomes are assembled, and what mechanisms underlie shifts to dysbiosis. However, such efforts have mostly focused on the ecological forces that operate within microbiomes, and much less is known about the consequences of evolutionary processes. The work proposed here will shed light on the evolutionary dynamics within multispecies consortia, by quantifying the consequences of evolutionary processes, such as intraspecies strain diversification following rapid adaptation and horizontal gene transfer, on microbiome assembly and function. Here we propose an innovative strategy to study evolution in complex communities using synthetic consortia from fermented foods, including sourdough starter that the PI has already established as a model microbiome. This proposal combines existing genetic variation from a global collection of fermented food microbiomes and experimental evolution based on manipulations of community members. We use an integrative toolkit, including long-read sequencing, comparative genomics and transcriptomics, genetic screens, and model testing to link evolutionary processes to assembly and function. Fermented food microbiomes are a powerful model system: they are easily manipulated in the lab and exhibit dynamics representative of natural microbiomes (e.g. succession, species interactions, adaptation). The synthetic consortia we have developed contain many microbial species that are directly relevant to human health, including Levilactobacillus brevis and Limosilactobacillus reuteri which are considered probiotic species and may aid in the maintenance of a stable microbiome. More broadly, we will determine generalizable principles that are applicable to human gut, skin, and oral microbiomes.

NSF Awards · FY 2026 · 2026-02

Swirling flow patterns known as vortices arise whenever air or water moves past structures such as bridges, aircraft wings, buildings, and underwater vehicles. While these motions can sometimes be useful, for example by improving mixing or ventilation, they often create serious challenges. Vortices can increase drag on vehicles, generate loud noise, and cause damaging vibrations that shorten the lifespan of infrastructure. Being able to anticipate and precisely control these swirling motions would allow engineers to design safer bridges, more efficient airplanes, quieter communities, and more reliable energy and cooling systems. Today, however, strategies for controlling these flows rely heavily on trial-and-error testing because it is difficult to determine which small disturbances in the flow meaningfully affect vortex behavior. This project develops a physics-based method that identifies the disturbances most responsible for strengthening or weakening vortices, enabling more targeted and energy-efficient control approaches. The project also integrates research with education by creating virtual-reality learning tools that let students explore complex airflow patterns in an immersive environment. These tools will enhance undergraduate and graduate instruction at Syracuse University and will be shared with local K–12 students through community partnerships, helping inspire students to pursue careers in science and engineering. This project supports new understanding for advanced manufacturing of many kinds of vehicles. This project advances a computational framework called Harmonic Resolvent Analysis to investigate how flow disturbances develop, interact, and exchange energy within unsteady, vortex-dominated flows. Unlike existing methods, which assume the background flow is steady, this new approach incorporates time-varying base states, allowing it to capture cross-frequency interactions and multi-scale disturbance dynamics that strongly influence vortex formation. The research includes three thrusts: (1) conducting high-fidelity simulations of flows with free and wall-bounded vortex shedding under both steady and unsteady incoming conditions; (2) applying the enhanced analysis framework to identify the dominant physical mechanisms that govern vortex growth, three-dimensional instabilities, and energy transfer across frequencies; and (3) using these insights to design and evaluate three-dimensional fluidic actuation strategies capable of suppressing or amplifying vortex strength using minimal energy input. The project will validate theoretical predictions against detailed simulations and establish guidelines for translating linear disturbance analysis into practical control designs. Educational outcomes include incorporating research findings into new and existing courses and creating virtual-reality content for classrooms and outreach programs, supporting the development of next generation of engineers and scientists. 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

Large whales face a number of threats to their survival, with vessel collisions (ship strikes) being one of the most serious. Species that are found close to shore or overlap with busy shipping lanes are particularly vulnerable. The endangered North Atlantic right whale (Eubalaena glacialis), with only ~350 individuals remaining in the entire species, and endangered sei whale (Balaenoptera borealis) feed in the heavily trafficked Massachusetts Bay and Stellwagen Bank National Marine Sanctuary. Both species often feed on the same prey near the surface, exposing them to possible injury or death from collisions with vessels. Despite decades of conservation efforts, including reducing the maximum speeds allowed during specific times of the year or when whales are detected on acoustic recorders, ship strikes are still impacting these animals. This project will collect whale behavioral and prey distributional data to better understand the conditions that lead to whale feeding activities and how whales move near the surface and underwater while they feed. The project will include a novel approach of using satellite imagery to identify areas where prey levels could lead to feeding right and sei whales and use that information to alert vessels and mariners of potential areas of feeding whales so that they are aware of possible overlap with whales, thus reducing the risk of collisions. Preventing ship strike injury and mortality is considered a priority conservation issue by the U.S. federal government. The endangered North Atlantic right whale (Eubalaena glacialis) and sei whale (Balaenoptera borealis) are both baleen whales that feed primarily on zooplankton and small schooling fishes and frequent habitats that are close to shore and overlap with busy shipping lanes. Massachusetts Bay, which includes the Stellwagen Bank National Marine Sanctuary, is an important feeding area for these whales and also a highly active shipping area. Their foraging strategy, which involves near-surface skim-feeding, places them at a high risk for vessel collisions due to their shallow depth, low visibility at the surface, and the typical speed of vessels. Although conservation strategies such as rerouting shipping lanes and restricting speeds for large vessels have been implemented, ship strike remains an issue that may limit population recovery for both species. This project will use suction-cup GPS biologging tags to collect fine-scale horizontal and underwater movement data that will provide information on the amount of time animals spend feeding near the surface. These data will be combined with data on prey distribution into movement models that will identify the foraging conditions that best predict the presence of feeding whales. Using remote-sensing technology, proxies for prey in the region will be monitored to identify foraging ‘hotspots.’ This information will then be pushed as alerts to vessels transiting the area that are using the vessel tracking global Automatic Identification System network and to the app WhaleAlert to mitigate the potential overlap between whales and vessels. 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

Rochester Institute of Technology, Syracuse University, and the University of Rochester aim to enhance high school computing education by providing them easy to adopt, experiential educational labs. This project will adapt and evaluate the Accessible Learning Labs (ALL)—a suite of experiential educational computing modules—for effective use in grades 9–12. The investigators, in collaboration with high school educators and Science and Technology Entry Program (STEP) programs across Upstate New York, will modify and implement several existing experiential educational labs to align with secondary education contexts. By including engaging, real-world computing topics such as artificial intelligence, cybersecurity, and software development into classrooms, the project empowers students with the skills and confidence needed for STEM careers. The work supports the national interest by promoting the progress of science and broadening participation in computing through experiential learning tools. Collaborations with high schools and NY STEP programs will ensure that the project benefits a future STEM workforce. In addition to directly supporting student learning, the project contributes to research on how experiential teaching methods can improve engagement in STEM fields. This small CSforAll High School Strand project will apply experiential learning principles to teach foundational computing concepts in artificial intelligence, cybersecurity, accessible software design, and machine learning. Through iterative co-design, classroom implementation, and rigorous formative and summative evaluation, the team will assess the educational impact of these labs on both students and instructors. The effort will contribute to pedagogical knowledge on experiential computing education at the high school level, address existing gaps in accessible STEM resources, and generate scalable, open-access materials to support nationwide adoption. 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 · 2025-10

Nontechnical summary The materials of tomorrow will have amazing properties. They should be strong, yet able to sense when they are breaking, move to heal themselves – similar to how your skin heals when it is cut. In Syracuse, NY, roads and bridges need constant repair when the asphalt and concrete materials break, especially in harsh winters and summers. The roads and bridges of the future could be made from materials that are active and able to sense when they are breaking and then move to fix themselves. These futuristic materials could be possible if we could understand how biological systems are able to do these same activities – sensing and healing. Unlike our current construction materials, biological systems and materials are active – using energy even at the smallest scales. These smallest scales are the nanoscale and they are about 1 billionth of a yard! At this level, proteins that use energy, called enzymes, are able to push and pull and organize everything. Enzymes could be a powerful source of understanding how biological materials can do the amazing things they do. Yet, we don’t know how they work. This research will give us new insights into how enzymes can move individually and collectively to change their organizations of themselves and other materials at the nanoscale. Further, we will be strengthening the local workforce through educating local students from the high school, undergraduate, graduate, and even post-doctoral levels with the funds from this grant. Together, this work will build our future – both our materials power and our human power. Technical summary The goal of this project is to understand how enzymes are able to use their energy at the nanoscale to perform productive work to organize themselves and other materials systems. We are currently at the beginnings of learning how such active systems work and can be harnessed to create materials. We use the biological enzymes as a starting place in the hopes that we can learn about how this works and ultimately reproduce the work in future, synthetic systems. We propose to main scientific objectives: (1) Can enzyme-powered active baths control condensed matter? (2) Can enzymes act as an active matter system? Both questions will be approached through careful experimental and theoretical means with informed skepticism. Question 1 will be explored using a protein condensate system to test the effects of a background of enzymes acting as a bath with higher energy than expected for a given temperature. Question 2 will examine if enzymes and groups of enzymes can collectively affect each other to cause self-organizations at the nanoscale, the way we know microscale active particles can. If we are to even understand the nanoscopic world, how it gains work from the seemingly chaotic melee of enzymatic activity, and harness it for our own purposes to make the active materials of the future, we need to explore, experiment, engineer, and design at the nanoscale. The proposed work does that to create new knowledge. 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 · 2025-10

The rapid growth of semiconductor manufacturing, quantum technologies, and optics offers a vital opportunity to strengthen regional economic development and workforce capacity in Central New York. Yet many adults face barriers to entering these high-demand fields due to limited exposure, financial constraints, and unclear pathways into education and training. This project expands access to these emerging technologies by providing flexible, hands-on learning opportunities that build technical skills, career navigation strategies, and industry connections. By opening pathways into these high-tech industries, the project advances STEM education, enhances economic resilience, and supports the progress of science by preparing individuals for meaningful careers. This initiative benefits society by helping local residents pursue new opportunities in advanced technology sectors of semiconductors and quantum, ensuring that the region can fully contribute to the nation’s scientific and economic growth. The project will develop and deliver a hybrid curriculum introducing adult learners to emerging fields in semiconductors, quantum technologies, and optics through experiential, problem-based learning. Instruction will incorporate foundational technical concepts, hands-on laboratory experiences, and exposure to industry-relevant processes and tools. Recruitment will focus on adult learners through partnerships with community organizations and workforce agencies, providing flexible, in-person and virtual engagement opportunities. Participants will receive financial support, personalized mentoring, and career navigation assistance to promote participation and completion. A multi-year evaluation plan will track growth in technical skills, confidence in STEM learning, and participant engagement with industry pathways. Active collaboration with industry partners will guide curriculum updates and ensure alignment with evolving workforce needs while offering mentorship and networking opportunities. Outcomes and instructional resources will be shared through an open-access platform to encourage broader adoption of effective, hands-on STEM learning models for non-traditional learners. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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 · 2025-10

As artificial intelligence (AI) expands into fields such as healthcare, robotics, and energy distribution, there is growing demand for faster, cost-effective ways to prototype AI hardware. Graphics processing units (GPUs) offer high throughput but often struggle to meet the low latency needed for real-time decisions. Field-programmable gate arrays (FPGAs) provide lower latency but can substantially sacrifice throughput. The key challenge is mapping AI algorithms to the right accelerator. The SPECTRUM project establishes a testbed that combines GPUs and FPGAs, both equipped with embedded tensor processing cores, to help reduce latency. Using the CHARM tool, developers can use the SPECTRUM testbed to quickly test hardware setups for real-time AI systems like autonomous vehicles. SPECTRUM advances the state of AI system prototyping, particularly for low latency designs, through the development of its reconfigurable testbed composed of FPGAs (e.g., AMD Versal ACAPs) and GPUs (e.g, Nvidia Hopper/Blackwell GPUs), which contain embedded tensor cores. The CHARM flow automatically partitions the AI computation between the traditional accelerator hardware and the embedded tensor cores. The work is organized into three thrusts: (1) acquisition and integration of hybrid accelerators supporting both edge and data-center configurations; (2) extension of the CHARM HW/SW framework to enable automated, domain-specific accelerator synthesis across heterogeneous platforms; and (3) creation of a user-facing SPECTRUM application interface for deployment and evaluation. The infrastructure enables scalable, low-latency (<10ms), end-to-end AI accelerator design, significantly lowering the barrier to entry for domain experts in real-time, safety-critical applications. SPECTRUM has so far developed an interested user base from research efforts across 26 groups at 16 institutions, delivering critical national infrastructure for real-time AI system design. Access will be provided directly from the lead site at Syracuse University and integration is planned into the FABRIC network. A key outreach strategy includes hands-on workshops and tutorials at major conferences in supercomputing, computer systems, design automation, and FPGAs to train a broad user base. Online courses will support workforce development across skill levels. The testbed will be integrated into undergraduate and graduate curricula at Syracuse, Brown University, and University of Pittsburgh, and will support interdisciplinary collaborations and K–12 outreach initiatives focused on AI hardware and real-time computing. To support long-term community engagement and reproducibility, the project team will develop and maintain a public-facing website at https://spectrum-ai.org, which will serve as the central hub for documentation, datasets, tutorials, benchmark results, and application deployment workflows. All software tools, including the CHARM framework, will be hosted on GitHub and linked from the main site. The project team will ensure that the website and repositories remain accessible and maintained for at least five years beyond the operational lifetime of the physical testbed, supporting continued use by researchers, educators, and students in real-time AI system 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.

NSF Awards · FY 2025 · 2025-10

This project addresses urgent safety, efficiency, and cost challenges in the roof inspection industry by developing a robotic platform that combines the mobility of drones with the terrain adaptability of legged robots. Roof inspection remains one of the most hazardous construction tasks in the United States, with a significant portion of injuries and fatalities attributed to falls and unstable surfaces. By equipping aerial robots with legged mobility and advanced perception capabilities, this project enables safe, detailed inspection on sloped roofs, reducing risk to human inspectors while improving access and inspection quality in challenging environments. The hybrid aerial-hexapod robot autonomously conducts detailed inspections by integrating visual, tactile, and light-detection and ranging (LiDAR) data to detect structural anomalies, surface degradation, and moisture intrusion. The resulting robot can seamlessly switch between flight mode and legged mode to navigate multi-layered and irregular roof structures, supporting scalable and task-specific operations. The project also offers impactful educational and outreach opportunities, including summer STEM workshops for K-12 students and teachers, as well as open-access datasets for robotics and artificial intelligence education. The research team collaborates with industry partners to ensure the system addresses real-world operational needs and facilitates technology transfer. This research addresses the scientific challenge of enabling detailed, autonomous roof inspection using a hybrid robotic platform capable of operating both in flight and on the ground. The project’s goals are threefold: (1) to develop an integrated robot with dual-mode mobility and multimodal perception capabilities; (2) to design algorithms that can interpret sensory data in real time for autonomous navigation and condition assessment; and (3) to validate the system’s performance through extensive experimental evaluation in both laboratory and real-world settings. To achieve these goals, the research team designs a lightweight legged mobility system that attaches to a quadrotor platform, enabling the robot to transition seamlessly between flight and stable ground locomotion. A modular sensor suite – including an RGB-D camera, a LiDAR scanner, and footpad-embedded tactile sensors – is developed to enable multimodal perception for the robot. Each sensing modality is selectively activated based on the complexity of the inspection task, enabling energy-efficient operation across diverse inspection scenarios. The team develops artificial intelligence (AI)-based algorithms to fuse data across modalities, build a unified representation of the inspection environment, and extract high-level semantic and geometric features for roof condition assessment. The research further explores intelligent control strategies to leverage these features for real-time decision-making and affordance-driven control to enable safe and efficient navigation and inspection on complex roof structures. Experimental evaluation follows a multi-phase strategy that includes high-fidelity simulations, controlled laboratory tests, and field deployments on residential and commercial roof structures. Finally, the project aims to advance foundational knowledge in robotics by pushing the state of the art in sensor fusion, multimodal perception, and robotic mobility. 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 · 2025-10

This project aims to support the development of an environmentally responsible infrastructure improvement solution by reducing the environmental impact of synthetic polymers commonly used in geotechnical construction and mining industries. The research team will investigate the use of biopolymers as alternatives for soil modification and treatment of mine waste. Biopolymers like xanthan gum and guar gum have been used in the food and pharmaceutical industries for many years due to their ability to bind small particles together and alter fluid viscosity. These same properties make biopolymers a promising material for a wide range of geotechnical engineering and mining applications. Biopolymers can help reduce soil erosion, improve soil strength, and enhance sedimentation without the environmental risks associated with widely used synthetic polymers. To fully understand and unlock the potential of biopolymers for use in geotechnical and mining applications, the research team will combine laboratory experiments with advanced computer simulations. The team will investigate how various biopolymers interact with different soil (mineral) types under a wide range of realistic environmental conditions, including changes in pH and salt concentration. These interactions will be analyzed at multiple scales, from individual molecules to bench scale tests designed to model typical field conditions. The findings will guide engineers in selecting the most effective biopolymers for specific geotechnical and mining applications, such as stabilizing loose soil or enhancing dewatering of mining slurry waste. In addition, the project will provide training opportunities for undergraduate and graduate students and develop hands-on educational modules for classrooms. These efforts aim to inspire and prepare the next generation of scientists and engineers to lead the transition to environmentally responsible materials and practices. By deepening our understanding of how biopolymers work at the molecular level, this research has the potential to drive science-based innovations across construction, mining, and beyond. This project integrates laboratory experiments and molecular dynamics simulations to identify and predict dominant biopolymer-mineral surface interaction mechanisms for typical soils and environmental conditions. Biopolymers—naturally derived polymers such as xanthan gum, guar gum, and chitosan—are increasingly being explored as alternatives to synthetic polymers like polyacrylamide for soil improvement in geotechnical and mining applications. Despite promising results in erosion control, flocculation, and soil stabilization, the use of biopolymers remains limited in the field due to a lack of understanding of how they interact with soil minerals at the molecular scale. This research focuses on understanding interactions between charged and neutral mineral surfaces and biopolymers with varying molecular weights and charge types under diverse environmental conditions (pH and ionic strength of salt). At the nanoscale, coarse-grained molecular dynamics simulations will be used to characterize polymer conformations and quantify interaction energies with mineral surfaces. Experimental techniques, such as Fourier Transform Infrared spectroscopy (FTIR), zeta potential, dynamic light scattering, and atomic force microscopy, will validate simulation results and quantify biopolymer adsorption mechanisms, including hydrogen bonding, electrostatic attraction, and polymer bridging. The interaction mechanisms of biopolymers with mineral surfaces will be compared to those of polyacrylamide, a widely used synthetic polymer. At the bench scale, sedimentation, rheological, and flowability tests will be performed on biopolymer-amended kaolinite to evaluate how nanoscale interactions influence macroscale soil behavior. The project will generate predictive models that correlate polymer structure and environmental conditions with soil performance. Project outcomes will provide a framework for optimizing biopolymer selection, thereby enabling environmentally friendly soil treatment techniques and reducing reliance on synthetic polymers, which pose environmental risks. Findings will inform engineering design in geotechnical engineering, mining, and waste management and contribute to a molecular-level framework for environmentally responsible technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY: Fei Pei, PhD, MSW, is a social scientist whose overarching career goal is to identify the complex effects of community resilient factors on child abuse and neglect and youth violence and to develop effective interventions/preventions that address these issues. This CDC career award will provide Dr. Pei with rigorous training and systematic mentored research experiences to support her transition to an independent investigator in youth violence prevention and intervention. A team of prominent mentors will guide her proposed research aims and training activities. The career development plan includes four hands-on training goals to strengthen her expertise in: understanding community resilience and interventions for vulnerable youth (Goal 1), exploring youth violence across racial groups (Goal 2), mastering ethnographic methods (Goal 3), and applying advanced longitudinal statistics (Goal 4). During the award period, Dr. Pei will apply these new skills and knowledge to investigate the complex associations among community resilience, child abuse, neglect, and youth violence across racial groups. Community resilience encompasses various domains, including violence, physical environments, social relationships, and economic status. A community may exhibit resilience in certain domains while lacking in others, and these domains can interact in complex ways that influence youth violence. Additionally, community environments can vary among different racial groups, yet empirical studies rarely explore how community resilience differs across youth from different racial groups. Therefore, it is crucial to understand how the profiles of community resilience influence youth violence across racial groups, which can further foster the achievement of the American ideals of a pluralist society in the next generation. This proposed study aims to: 1) identify how profiles of community resilience influence youth violence across racial groups; 2) estimate the mediating effects of child abuse and neglect on the relationship between community resilience profiles and youth violence; and 3) explore the challenges and gaps in current interventions for youth with violent behaviors from a community resilience perspective. This study will contribute empirical evidence on the relationship between community resilience and youth violence, enabling intervention scientists to consider community-level factors when working with adolescents at higher risk for violence.

NIH Research Projects · FY 2025 · 2025-09

Project Summary / Abstract Trauma and posttraumatic stress disorder (PTSD) commonly present among individuals seeking treatment for alcohol use, and trauma sequelae can considerably challenge alcohol treatment approaches. Following trauma, humans often turn to social connections to process their experiences. Yet, trauma and posttraumatic symptoms also can engender mistrust of others, interpersonal vulnerability, fear of rejection, and social avoidance that disrupt adaptive, protective social support. Unmet drives for social processing might lead individuals to seek out alcohol to facilitate interpersonal connection. Social motivations are the most common reasons for drinking broadly, and alcohol has been shown to increase reported social bonding, interpersonal disclosure, and perceived closeness. Despite this, current theoretical models attempting to explain PTSD- related drinking overwhelmingly suggest that drinkers turn to alcohol to avoid processing trauma-related affect. Such theoretical emphasis on negative reinforcement has shaped current treatment efforts for PTSD-related problem drinking, which often focus on processing and/or confronting trauma experiences to presumably reduce avoidance-based drinking. However, the research teams’ preliminary findings from focus groups of frequent drinking adults with provisional PTSD suggest that many drinkers also report drinking to approach processing trauma memories, thoughts, and emotions, particularly in social drinking contexts. Such perceived effects of alcohol on social trauma processing may play a key role in maintaining PTSD-related drinking yet, to date, have not been tested. The proposed study aims to characterize the extent to which drinkers with PTSD anticipate beneficial trauma processing effects before social drinking events (i.e., expected trauma processing; Aim 1) and whether drinkers experience social drinking events as having facilitated trauma processing (i.e., experienced trauma processing; Aim 2). Efforts also will characterize drinkers’ reflections of trauma processing in these specific drinking events, including aspects of the social context and relevance of trauma processing to future posttraumatic symptoms (Aim 3). Frequent drinking adults with PTSD (n = 100) will complete momentary surveys before and after drinking events in a 21-day ecological momentary assessment (EMA) design. Drinkers who report expected or experienced trauma processing (a subset, n = 20) will provide in- depth, idiographic information through a follow-up qualitative interview on the perceptual, sociocontextual, and successive factors at play in such drinking events. Findings will begin to characterize the extent to which perceived trauma processing effects encourage individuals to seek out social drinking in the aftermath of trauma and/or reinforce PTSD-related drinking over time, thus suggesting the need for larger investigations. Future research can explore psychosocial/physiological mechanisms underlying any such effects, and clinical research could work to modify extant interventions to encourage trauma processing in the absence of alcohol.

NIH Research Projects · FY 2025 · 2025-09

Summary Statement In vertebrates, motile cilia within the Left-Right Organizer (LRO) are pivotal for a developing organism’s left right axis formation, such as cardiac left-right development. Evidence from model organisms, like zebrafish LRO, highlights conserved cilia-driven leftward flow crucial for regulating target genes controlling asymmetric heart morphogenesis. LRO cells are made up of both motile and non-motile cilia with one population required for fluid flow generation and the other potentially for fluid sensing. Open questions that exist are: What are the mechanisms by which cells determine whether to develop motile or non-motile cilia within an LRO? What are the specific roles played by each of these cilia populations in development? However, answering these questions in mammalian LRO development is hindered by technical challenges, limiting real-time analysis of spatial cell arrangements, cytoskeletal characterization, cilia assembly, and transcriptional landscape exploration. We aim to address these challenges using Danio rerio (zebrafish) LRO to test our hypothesis: spatial and temporal cell division regulation guides intracellular and cellular remodeling essential for LRO maturation and function. Our R35 application encompasses two projects. Project 1: Assessing the impact of each LRO cell division event on LRO development. Here we will investigate cell redistribution mechanisms and the dominance of specific progenitor cells in LRO formation, addressing cell lineage and cell behavior questions. Additionally, comprehensive gene expression analysis across LRO developmental stages will identify key genes and pathways guiding LRO development. We will employ cell tracking, microtubule labeling with laser ablation, and transcriptomic profiling to understand mitotic events crucial for LRO development. Project 2: Characterizing mechanisms involved in microtubule pattern formation and reorganization during LRO development in relation to actin reorganization, tight and adherens junctions, and cilia formation. Utilizing live cell imaging, molecular and genetic manipulations, array tomography and AI, we will characterize LRO cilia localization and structure at rosette and lumen stages to identify models for LRO development events and fluid flow sensing. Success in addressing our outlined objectives in unraveling the temporal and spatial mechanisms coordinating cell division, intracellular remodeling, gene expression, polarity formation, junctional protein formation, and cilia ultrastructure during LRO development will position us to define how an LRO is assembled and provide novel models that can be tested for how other ciliated tissues develop.

NSF Awards · FY 2025 · 2025-09

This project seeks to explain how mechanical forces inside the body help shape developing tissues during early stages of life. By studying how cells move and interact with each other, and how mechanical forces guide their behaviors during tissue formation, this project will help explain how our organs and body structures take shape. To do this, the research team will combine knowledge and tools from several fields, including biology and engineering, which may also lead to new technologies useful in other areas of science and medicine. Beyond research, this project includes educational and outreach efforts to benefit students of all backgrounds. The research team will engage students at different educational levels and with all backgrounds. Hands-on activities and demonstrations will introduce these students to the importance of science and engineering, inspiring them to consider careers in STEM (science, technology, engineering, and math). Undergraduate students from all backgrounds will have opportunities to participate in this research, while new training programs and courses will prepare graduate students to work across scientific disciplines and solve complex biological problems. The formation of tissue boundaries during development is essential for generating the diverse body structures and functions observed across living organisms. Mechanical forces play a critical role in shaping these boundaries by coordinating the spatial organization, morphology, and differentiation of cells within developing tissues. Among developmental processes, somitogenesis serves as an ideal model for studying the biomechanics of tissue boundary formation. However, investigating the biomechanics of somitogenesis in mammalian embryos remains challenging. To overcome this limitation, this project employs an integrative approach that combines a stem cell-based model of somite development with live imaging, biomechanical measurements, perturbation experiments, and theoretical and computational modeling. This multidisciplinary strategy will advance three key areas. First, it will establish a human-relevant in vitro model to enhance our understanding of somite formation during early development. Second, it will elucidate how mechanical forces and cellular behaviors drive the formation of somite boundaries, potentially shedding light on the origins of musculoskeletal deformities and vertebral malformations. Third, by integrating experimental biomechanics with theoretical and computational models, the project will uncover fundamental mechanical principles governing tissue morphogenesis. This approach will enable more accurate predictions, reveal emergent behaviors, and offer a comprehensive view of the biomechanical processes shaping biological structures across scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This site is supported by the Department of Defense in partnership with the NSF REU program. We live in an information-rich society where information can be both opportunity and burden. Tomorrow’s researchers and professionals urgently need to improve competencies in handling and understanding the massive amount of one such type of information—text data—in an effective and scientific way. These competencies also include understanding how large language models such as ChatGPT, DeepSeek, and Claude work and utilizing them with awareness and accountability. This program will support 12 REU students per summer in a research setting designed to build knowledge and skills in interdisciplinary research areas. A key point of distinction for this REU site is that it will provide an opportunity for social science majors to build technical skills in the application of human language technologies for research. This REU program supports undergraduate students through a summer of science-, research-focused, hands-on, computational, and interdisciplinary learning experiences. Activities include social science text analysis research methods workshops, seminars, professional development, team-building opportunities, faculty research mentorship, and hands-on research project development and presentation. Students learn how to handle large text-based data, systematically conduct computational social science research, and be prepared for future academic or professional development. A key goal is to encourage students to sustain research interests in the context of subsequent educational activities and get exposure to research and text analysis that will benefit their future research and professional goals. This REU program is home to 35 undergraduate researchers from universities across the country. From the first two completed cohorts, at least four student researchers won conference awards and have papers published in conferences, and many have continued to graduate schools or PhD programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Blisters, tiny air pockets that form when adhesion fails between two surfaces, are a common but poorly understood phenomenon, appearing in contexts ranging from smartphone screen protectors to laminated aerospace composites. In soft adhesive systems, the removal of blistered films often leads to an unexpected transition from peeling to rolling, where one edge of the film detaches as the opposite edge spontaneously reattaches. This process is marked by discrete jumps in adhesion force and complex interfacial behavior that cannot be explained by traditional peeling mechanics. The goal of this project is to develop a comprehensive understanding of this peel-to-roll transition using a combination of experiments, analytical models, and numerical simulations. By revealing how geometric confinement and interfacial deformation can create spatially heterogeneous adhesion landscapes even between homogeneous materials, the research seeks to establish new strategies for controlling adhesion in soft and stretchable devices. Educational and outreach activities include a hands-on "Sticky Tapes" exhibit for children, a summer workshop for high school students, and research engagement opportunities for undergraduates. This project will investigate the mechanics of multi-step, heterogeneous adhesion through a detailed study of thin, blistered films partially bonded to soft substrates. Precision experiments will be used to identify the critical blister contact length that triggers the onset of rolling. Analytical modeling and finite element simulations will then be employed to determine how this rolling motion alters adhesion hysteresis and introduces discrete jumps in the force required for detachment. In parallel, the project will explore how blister geometry and spatial distribution can be engineered to program adhesion behavior across the interface. These results seek to yield fundamental insights into elasto-adhesive coupling in confined geometries and inform the design of novel soft interfaces in biomedical adhesives, flexible electronics, and soft robotics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

NONTECHNICAL SUMMARY This project develops key tools for designing new materials that can execute intelligent tasks – e.g. sensing input stimuli, reconfiguring their structure in response, and generating targeted mechanical behaviors such as global shape change. Such robot-material hybrids could quickly respond to protect humans and structures from damage, or even perform search and rescue tasks in dangerous environments. In order to transform on command, these materials must be active, harnessing energy. This project also focuses on disordered active materials -- where the constituent molecules or particles are jumbled up -- because, unlike crystals, these materials don’t need to change their volume or shape when transitioning from flowing to arrested states, simplifying their design and deployment. Currently, such materials cannot be self-assembled because scientists do not yet have a fundamental description of the design space -- i.e. how active forces, interactions between components, and local structure can be tuned to generate targeted large-scale responses. To address this gap, the PI will use computational and theoretical tools to predict how disordered materials deform and rearrange under active, self-generated patterns of forces. This will involve developing a new approach for finding defects – regions where the material is likely to flow -- in disordered active materials. The PI will then use this “defect field” as a key quantity in a set of large-scale differential equations that predict the shape and motion of materials as a function of geometry and active forces -- much like the Navier-Stokes equations predict the motion of water under different environmental conditions. Finally, the PI will study how to self-program the active forces -- much like the weights in a deep neural network that learns -- in order to generate specified patterns of deformation, ultimately allowing the rational design of shape-shifting disordered materials. This project will also support workforce development of graduate and undergraduate students via training in research readiness and computational materials science and engineering techniques. TECHNICAL SUMMARY The goal of this project is to develop a new framework to predict and program plasticity, flow, and arrest in dense amorphous active and non-reciprocal matter. Such materials exhibit glassy dynamics, and they cannot be described by a Hamiltonian, which makes theoretical approaches challenging. This project proposes to develop a continuum theory for the yielding of active solids, and then use it to program these materials to execute responsive tasks by changing shape and rheology. Past work has shown that there are large parameter regimes over which dense active matter rearranges at localized weak regions, or defects, that are defined with respect to a slowly evolving reference state. Therefore, this project will develop new nonlinear tools for identifying defects in dense active matter. A key innovation is developing a force-landscape approach that directly incorporates active forces on each particle into the definition of defects. A second innovation is that the first-principles nonlinear tools give direct access to information about the defect field (orientation, energy barrier heights, number density) that can be difficult to identify in, e.g., machine learning approaches. This is also precisely the information that will allow this project to develop coarse-grained constitutive models and physical learning frameworks to predict and control plasticity in dense active matter. One broader impact of this work will be the ability to rationally program deformation and arrest in dense active and non-reciprocal matter. These materials are already under development in the lab, and have the potential to be transformative because they can harness work to change their macroscopic morphology and respond to external stimuli. Amorphous active solids are not currently programmable, as they flow intermittently and uncontrollably due to plastic avalanches. To design an amorphous material that reconfigures on command, and then supports elastic stresses without flowing when needed, this project will use a coarse-grained defect field in a continuum constitutive law to predict and control yielding and arrest in dense active matter. The constitutive relations will also inform a physical-learning approach to program deformation and global shape changes in dense active matter. Taken together, this will allow rational design of dense active materials that alter their shape and rheology to execute tasks. Another set of broader impacts is in education and workforce development. This project will support participation of the PI and graduate student in a series of workforce-development activities to enhance retention and research participation in a cohort of undergraduate students at Syracuse University and in collaboration with Hampton University and North Carolina A&T. It will also directly support an undergraduate researcher on a project focused on materials design. The PI will additionally develop a formal set of typeset lecture notes for graduate students on the rheology of dense amorphous matter (both passive and active), give graduate school lectures based on those notes, and post them on preprint servers. STATEMENT OF MERIT REVIEW This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Primordial germ cells (PGCs), the precursors of eggs and sperm, are essential for human reproduction. A comprehensive understanding of the molecular mechanisms underlying specification, maturation and migration of human primordial germ cells (hPGCs) is critical for advancing infertility treatments, regenerative medicine, and potential therapies for genetic disorders. Most of our current knowledge on mammalian germline biology is derived from studies using laboratory mice. However, due to the unique transcriptional networks and developmental pathways of hPGCs, the knowledge from other species cannot be directly extrapolated. Moreover, PGCs emerge during the earliest stage of embryogenesis, undergoing complex morphogenesis and migration, which presents significant technical challenges for in vivo tracking and study. The overarching objective of my research is to develop stem cell-based modeling systems that closely recapitulate the landmarks of human embryonic developmental processes, and to apply these systems to elucidate the fundamental mechanisms governing human development. Notably, over the past few years, I developed a stem cell-based microfluidic human embryoid model that faithfully recapitulates the early development of human embryonic sac in a highly controllable and scalable fashion, wherein the emergence of hPGCs mirrors the molecular signatures and developmental trajectories observed in vivo. I also recently devised a novel method for deriving hPGC-like cells (hPGCLCs) using an embryonic-like culture system. This method significantly simplifies hPGCLC induction protocols and provides insights into how the native cellular microenvironment facilitates hPGC specification. The research objectives for this five-year project are to integrate approaches from developmental and stem cell biology, microengineering, genome editing, and bioinformatics to uncover the fundamental mechanisms driving early hPGC specification and migration. Specifically, 1) leveraging the microfluidic embryoid platform, we will generate a novel lineage reporter line to perform lineage tracing assays on hPGCLCs. Through single-cell RNA sequencing and functional genetic studies, we aim to elucidate the origin and lineage trajectory of hPGCs. 2) We will establish a PGC-hindgut co-development model to investigate the maturation and migration of hPGCs after specification. We anticipate this model will yield insights into the mechanisms governing hPGC migration and the cellular crosstalk between hPGCs and the hindgut. 3) Using the embryoid and PGC-hindgut co-development models, we will systematically dissect the roles of Wnt signaling in hPGC lineage commitment and migration. Successful completion of this project will deepen our knowledge of human germline biology and facilitate future research on hereditary diseases and reproductive medicine.

NSF Awards · FY 2025 · 2025-08

The National Science Foundation’s ground-breaking Advanced LIGO gravitational-wave observatories continue to explore the dark universe and have now measured the collision of over 250 black hole pairs. This award supports gravitational-wave detector technology research to improve the observational reach of the Advanced LIGO observatories. Their sensing is improved by controlling thermal distortion effects when operating at high laser power, and by lowering the thermal noise in their test mass mirror coatings. Specifically, this award explores technology to integrate solid-state laser cooling in optical mirror coatings to create radiation-balanced mirrors exhibiting no thermal deformations. It also supports investigating the suitability of much lower thermal noise crystalline optical mirror coatings. Both technologies can find much wider use in precision sensing and high-power laser applications, with examples including their use in laser fusion and defense. This award supports (i) investigating solid-state laser cooling to control thermal lensing in gravitational wave interferometers, to optimize ion-beam-sputtered materials for use in laser-cooled optical coatings. (ii) Characterizing the performance of low-noise crystalline coatings under high-power optical conditions found in gravitational-wave detectors. (iii) Preparing the LIGO Hanford detector for its O5 observation run, installing hardware to preheat optics so thermal transients during lock acquisition can be avoided. And (iv) designing test mass actuators to be used in the suspensions of the next-generation observatory Cosmic Explorer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Xiaoran Hu of the Department of Chemistry at Syracuse University is developing a new class of force-sensitive molecules (called mechanophores) that respond to mechanical forces by changing their stereochemical structures, leading to measurable or observable changes in material properties (e.g., color changes). The proposed research could establish “force-stereochemistry coupling” as a foundational mechanism for designing highly sensitive molecular force probes, with the most mechanosensitive structures having the potential to significantly enhance sensitivity of irreversible mechanophores and enable the study of previously unobservable nanoscale mechanical behaviors. These high-sensitivity force-sensing molecules could facilitate our understanding of nanoscale mechanical behaviors across scientific disciplines ranging from polymer physics to mechanobiology. Moreover, covalent doping of mechanosensitive structures in polymers holds promise for enhancing sustainability by enabling plastics to autonomously monitor and report mechanical damage, thereby enhancing safety and reducing the need for unnecessary preventive replacements. The proposed research is also integrated with innovative and interactive outreach and education activities. Outreach initiatives at the local museum and a local high school will educate a broad audience about smart responsive materials and their applications, enhancing scientific literacy and curiosity about cutting-edge science. Additionally, the proposed research will create research opportunities for graduate, undergraduate, and high-school students, directly contributing to training the next generation of scientists and promoting careers in STEM. This CAREER project could advance the fundamental understanding of mechanochemical reactivities by systematically investigating a mechanistically distinct type of noncovalent-yet-chemical force-matter interactions—specifically, force-triggered stereochemical conversion in atropisomeric mechanophores. It could establish “force-stereochemistry coupling” as a general strategy for designing highly sensitive mechanochemical transformations and could develop comprehensive structure-property relationships elucidating the effects of regiochemistry, “lever-arm” length, and steric factors on mechanically induced stereochemical conversions. Rational variation of mechanophore structures to exhibit diverse readout modes—including changes in color, circular dichroism, and fluorescence—could establish a framework for tailoring these structures for advanced signal analysis techniques, paving the way for applying the “force-stereochemistry coupling” mechanism in high-sensitivity force sensing across complex environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.