Temple University

NSF Awards · FY 2026 · 2026-07

Beneficial partnerships with bacteria are essential for the well-being of all of life on Earth, shaping health and fitness, ecosystem functioning, and even food security. Despite how dependent we are on these symbioitc associations; little is known regarding how hosts initially identify and select specific microbial partners from the complex communities in their surrounding environment. This is especially true at the finest scales, where hosts must distinguish between highly similar species or genetically distinct strains of the same species. This project addresses this challenge using the Siphamia- Photobacterium mandapamensis symbiosis as a highly tractable vertebrate-bacteria model system to investigate the molecular mechanisms that govern how animals recognize and initiate symbiosis with specific microbial partners and not others. Results will advance our understanding of how beneficial animal-microbe associations are established and maintained, with implications across a range of systems, including the vertebrate gut microbiome. This CAREER project will develop a new course-based undergraduate research experience in microbial genomics along with workshops and structured training of graduate students in cutting-edge genomic technologies, advancing national priorities in biotechnology and workforce development. The overall goal of this project is to determine the molecular mechanisms by which animal hosts recognize and selectively recruit specific microbial symbionts, with an emphasis on subspecies (strain)-level discrimination. This research uses the experimentally tractable Siphamia-Photobacterium mandapamensis symbiosis, a highly specific partnership between a coral reef fish and luminous bacterium, as a model for investigating the mechanisms underpinning host specificity. The bacterial symbiont is acquired from the seawater early in host development and housed in a specialized light organ attached to the fish’s intestine. Despite the strict species-level specificity of the association, individual fish associate with multiple genetically distinct strains of P. mandapamensis, and patterns of strain-level differences have been observed between host species, making this system ideal for investigating active host discrimination among closely related bacterial symbiont strains. Specific research aims include comparative genomics of bacterial strains isolated from co-occurring but divergent Siphamia species to determine whether different hosts selectively associate with distinct symbiont strains, controlled colonization and competition assays using fluorescently-labelled strains to determine whether hosts preferentially associate with native over non-native strains, and single-nucleus RNA sequencing throughout symbiont acquisition to identify host genes governing partner recognition at the cellular level, developing the first cell-type-resolved molecular atlas of this model association. By determining how hosts distinguish beneficial bacteria from closely related but incompatible bacterial strains, this project has direct implications for future advances in biotechnology, such as informed engineering of microbial symbionts for therapeutic applications, probiotic design, and the development of synthetic host-microbe 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 · 2026-06

This award supports a conference on Advances in Geometric and Quantum Topology that will take place at Temple University in Philadelphia during July 13-16, 2026. The conference location and timing are advantageous due to the upcoming International Congress of Mathematicians taking place in Philadelphia ten days later. As a result, the conference will provide an opportunity for US-based and international participants to meet and collaborate. Funding from the NSF will enable early-career US-based participants who do not have their own source of funds to travel to Philadelphia and attend the conference. The scientific goals of the conference are as follows. First, the conference will provide a venue for sharing new results in quantum topology, geometric topology, and hyperbolic geometry. Second, the conference will increase collaboration and communication among research experts globally. Third, to introduce researchers, especially students and early career researchers, to important problems and areas of current research, with an emphasis problems lying at the interface of quantum and classical topology in three dimensions. Quantum topology contributes to the theoretical framework underlying quantum computing and leads to various applications in quantum science, advancing our knowledge in an area of national priority. This conference has a designated public website at the following address: https://rabbine-web.github.io/ 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 investigates how environmental characteristics impact where, why, and for whom substance use treatment delivered via mobile technologies may be more effective in one place versus another. Substance use diagnosis represents a growing threat to public health in the U.S. and globally, but access to effective substance use treatment remains a challenge. Mobile technologies such as smartphones promise broad treatment access, but it is unclear how treatment effectiveness may vary across populations, neighborhoods, and environments. This research shows how different types of neighborhood and environmental characteristics can impact mobile technology-delivered substance use treatment, advancing individualized treatment customization, improving treatment effectiveness, and reducing the public health burden of substance use. This research leverages the results of a large randomized clinical trial of a mobile health substance use treatment to advance understanding of environmental impacts on substance use behavior change. Geospatial analysis of environmental and behavioral data provides empirical evidence to indicate how exposures to geographic factors in both the residential neighborhood and during daily travel impact the attitudinal and behavioral mechanisms and outcomes of treatment. This research advances the design and testing of technology-delivered substance use treatment and other behavioral health treatments by integrating geospatial data collection and analytical techniques into randomized clinical trial design and data analysis. 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

An award is made to Temple University to support transforming the Molecular Evolutionary Genetics Analysis (MEGA) software into a next-generation platform for analyzing large datasets in molecular evolution and phylogenetics. MEGA has long supported scientists in many areas of the life sciences, from biodiversity to biomedicine, by providing powerful tools for DNA and protein sequence analysis through user-friendly interfaces. The improved MEGA will enhance this support by significantly lowering the memory and processing requirements needed to analyze large, complex genomic datasets, making advanced computing more practical on personal desktops and accessible to a broader range of researchers and students. The project will also create MEGA-based educational modules for college and high school classrooms to boost participation in STEM and improve scientific literacy. The project will advance bioinformatics by developing new algorithms for big data analysis and enhancing the software’s architecture for faster parallel processing and improved memory utilization. These advancements will enable researchers to perform accurate evolutionary analyses of large biological datasets, which are becoming increasingly common due to lower DNA sequencing costs and the rapid growth of databases. The project’s success will be judged by improvements in computational efficiency, increased software adoption across diverse computing environments, a higher number of citations, and broader use in various biological fields. Ultimately, the upgraded MEGA will be a scalable, efficient, and user-friendly tool for phylogenomics, while also promoting environmentally sustainable computing practices. 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 Major Research Instrumentation Program (MRI) award supports the acquisition of a cutting-edge 3-beam multiscale 3D imaging and analytics platform at Temple University to advance multidisciplinary research, education, and training in energy, infrastructure, sustainability, and bioengineering. The system, the second of its kind in US academic institutions, integrates scanning electron microscopy (SEM), focused ion beam (FIB), and femtosecond laser technologies, enabling high-resolution imaging, nanofabrication, and tomographic analysis of materials previously inaccessible using conventional techniques. This platform will provide transformative capabilities to address critical challenges in battery materials, sustainable composites, the durability of cementitious materials, and biological structures. It will serve as a regional research hub, engaging academic partners, including Drexel University, the University of Pennsylvania, and a broad network of primarily undergraduate institutions (PUIs) across Pennsylvania, New Jersey, and beyond. The system will enable advanced structural and compositional analysis of complex materials, including polymer-based composites, solid-state battery electrodes, geological samples, and biological tissues, contributing to diverse areas of research from mechanics of materials to bioengineering. The platform will support collaborative, multidisciplinary research by providing high-resolution, multiscale analysis and enabling a better understanding of manufacturing-structure-property relationships and time-resolved degradation phenomena. It will support various applications, including Li-ion battery failure analysis, microstructure optimization in aerospace materials, and bio-interface imaging. The system will be housed in a shared core facility, offering research and training opportunities to students, postdoctoral scholars, and faculty members across Temple University, Drexel University, and the University of Pennsylvania. Additionally, the platform will support regional undergraduate institutions through shared governance, workshops, and virtual access modules. By integrating advanced instrumentation with collaborative education, the project will contribute to developing a skilled workforce in energy, infrastructure, and bioengineering. 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

Neonatal brachial plexus palsy (NBPP) is an injury that can occur during childbirth when a baby's head is stuck during delivery, which can cause pulling on the baby's neck and their nerves. How this stretching and pulling affects the nerves is unknown, which makes it difficult for a doctor to predict and prevent NBPP and delays treatment for injured newborns. This project will investigate how the nerves in the neck respond when subjected to various levels of stretch and for different durations. The team will use an animal model that has similarities to human newborns to understand NBPP. By stretching the nerves to different degrees and for varying lengths of time, the team will understand the response of these nerves and the limit when injury occurs. The project will also develop computer simulations that will mimic the conditions during complicated deliveries and allow researchers to estimate the stress the nerves experience during stretching. This study will enable the team to develop a practical tool that doctors can use to better predict NBPP and make informed decisions that reduce the risk of nerve injuries in newborns. The project will also help train the future science and engineering workforce by engaging students in research, fostering interest in STEM fields, and encouraging future contributions to healthcare advancements. NBPP untreated has serious long term impacts including muscle atrophy, impaired bone development, and osteoarthritis. There is a critical knowledge gap correlating the effects of over-extended neck, prolonged delivery, and mechanical forces imposed on the neonatal brachial plexus (NBP) during obstructed delivery. This limits understanding of the injury mechanism and ultimately delays prognosis and appropriate intervention in the affected newborn. This study will address this critical gap by investigating the stress relaxation behavior of the NBP when subjected to clinically relevant magnitude and durations of pre-stretch as observed during prolonged head-to-body delivery. The study will leverage a neonatal animal model to provide biomechanical failure data and structural changes in the NBP that are pre-stretched to a range of stretch magnitudes (10%, 15%, and 20% strains) and duration (90 and 300 seconds) as observed during prolonged delivery in the clinic. The study will also integrate experimental data with computational models of the maternal pelvis and neonate that will serve as a surrogate and help overcome the existing ethical limitations in accessing NBP biomechanical information. By using experimentally obtained viscoelastic properties of the NBP, the computations will evaluate NBP risk factors that include endogenous maternal forces, exogenous clinician-applied forces, and fetopelvic disproportion. This integrated approach will enable a highly translational predictive tool that can simulate prolonged delivery in an over-extended neck and predict NBP strains during birthing. The project will also enable hands-on experience for undergraduate students through design courses and summer research programs and for K-12 students through engineering summer camp 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

NSF Awards · FY 2025 · 2025-09

This project researches the development of a comprehensive framework that ensures the principled use of artificial intelligence (AI) technologies in data-intensive education research. The framework will benefit society by enabling more trustworthy education research, fostering public confidence in AI applications, and ensuring that technological advances serve all students. The project provides insights on responsible AI practices in education research and supports education by creating publicly available training materials for researchers and others with varying technical backgrounds. The project aims to bridge the gap between high-level principles and practical implementation by developing a responsible and principled framework for data-intensive education research in the AI era. The framework targets three key stakeholder groups: data administrators who manage access to education data; researchers who use education data to perform analysis; and individuals who are deciding whether to participate in research studies. The project uses a mixed method study with all three stakeholders to understand their current practices, concerns, and decision-making processes regarding education data usage. Based on these insights, the project team will develop and deploy a novel web-based assessment tool that leverages state-of-the-art responsible AI techniques to detect potential risks within datasets, helping stakeholders make informed decisions about data sharing and usage. Additionally, the project team will create a toolkit that identifies bottlenecks from the community and translates complex AI risks and benefits into accessible formats, utilizing interactive visualizations to facilitate understanding among non-technical stakeholders. Finally, the team will develop comprehensive educational support materials, including video tutorials, interactive modules, and real-world case studies that demonstrate principled AI practices in education research. 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

Gamma rays are the most energetic form of light in the universe. By studying them, this project seeks to understand some of the most energetic and mysterious cosmic phenomena, including exploding stars and black holes. One of the central questions in high-energy astrophysics is how subatomic particles traveling through space at nearly the speed of light, known as “cosmic rays,” reach extraordinarily high energies. This project supports efforts to identify and study the most powerful natural particle accelerators in the universe, known as “PeVatrons.” These astrophysical sources reach energies far beyond those of even the most advanced human-made accelerators and are believed to be responsible for the highest-energy cosmic rays that reach Earth. Insights from this work will help reveal the origin of cosmic rays, deepen our understanding of the extreme environments around supernovae and black holes, and may inspire future technologies that build on the physics of high-energy particles. This research has also opened a new window to study our own Sun. The unexpected detection of ultra-energetic gamma rays from the Sun challenges current understanding of solar physics and may shed light on how stars like our own behave under extreme conditions. This work promotes the progress of science by opening new observational windows into extreme energy environments, contributes to national scientific capacity by supporting student training and broadening participation in physics research, and ensures public benefit by maintaining open access to astrophysical data for the broader scientific community. The project involves operations and data analysis work with the High Altitude Water Cherenkov (HAWC) Observatory. HAWC is a wide-field, ground-based, continuously operating facility uniquely suited to detect very-high-energy gamma rays. The work focuses on improving HAWC’s sensitivity and extending its performance at both the lower and upper ends of its energy range. These enhancements are applied to the search for very-high-energy gamma rays from Galactic sources. A primary goal is the observation and characterization of sources with evidence of particle acceleration above 1 PeV, providing strong candidates for Galactic PeVatrons. The project also supports the development and use of novel data analysis techniques, cross-correlation with other observatories, and interpretation of results in the context of multi-wavelength and multi-messenger astrophysics. In particular, identifying positive correlations with neutrino observations would provide conclusive evidence of the hadronic nature of these accelerators. Secondary outcomes include the unexpected detection of TeV gamma-ray emission from the Sun, offering new insights into solar magnetic interactions and particle transport. Together, these advances underscore the capabilities of ground-based gamma-ray observatories and contribute to a deeper understanding of cosmic particle acceleration, multi-messenger phenomena, and the extreme universe. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

This project aims to produce new mathematics that encompasses several fields. The PI’s collaborative research will be conducted at the interface of geometry & topology, studying the interplay between rigid geometric phenomena and topological flexibility; algebraic geometry and number theory, seeking to understand geometric and arithmetic properties of solutions of polynomial equations; and dynamics, the study of evolution under continuous change. Many of the objects the PI studies were of central interest in classical mathematics as seen in the works of Klein, Picard, Poincaré, and E. Cartan. Several aspects of the research follow up on themes in their work. Furthermore, this project will broaden the mathematical literacy of mathematicians at all levels and bring them together to collaborate and learn from one another. The PI will disseminate new work to a wider audience through both writing and organization of conferences, workshops, and summer schools. This award will also support various aspects of training PhD students. Questions in low-dimensional geometry and topology dominating the field over the last forty years are closely related to classical problems about discrete subgroups of Lie groups. One of the PI’s primary goals is to explore the significant overlap between these two areas in order to answer important questions on either or both sides. Most of the PI’s work is on real and complex hyperbolic lattices, which are precisely the cases where many fundamental questions remain open, including even basic existence problems. An appealing feature of this program is that one can attack important questions using ideas from every one of the broad range of subject areas mentioned above. The PI is also particularly interested in profinite properties of discrete subgroups of Lie groups, whether or not hyperbolic 4-manifolds can admit symplectic structures, and finding a deeper understanding of the way in which complex hyperbolic lattices lie at the interface between flexibility phenomena of fundamental groups of hyperbolic manifolds and higher-rank rigidity à la Margulis. 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 · 2025-07

Movement of particles can be extraordinarily complicated. The theory of dynamical systems studies such movements by replacing the three-dimensional space with a finite two-dimensional slice. Though tremendously powerful, this technique has its limits. For instance, there is no finite, two-dimensional space that captures all of the original system’s dynamics. This project explores new ways of understanding low-dimensional dynamical systems that are inspired by the classical approach and seeks to unify major threads in the study of structures on three-dimensional spaces. In terms of broader impacts, the PI will continue to co-organize a regional seminar, mentor graduate students, and volunteer mathematics homework help and tutoring during the afterschool program at a local recreation center. The PI’s research divides into three interrelated themes. First, the PI will work on classifying all pseudo-Anosov flows that are transverse to a fixed taut foliation. His approach proceeds by first understanding the universal circles associated to such a foliation and builds on recent advances made in his joint work with Landry and Minsky. Second, the PI will connect the dynamics and topology of these flows and foliations with the hyperbolic geometry of the underlying three-manifold. In particular, the PI and his collaborators recently developed a new method to study endperiodic maps of infinite-type surfaces by producing a ‘pseudo-Anosov–like’ representative associated to the flow. The PI will exploit this new structure by studying the dynamics of the resulting maps and the hyperbolic geometry of their mapping tori. Third, the PI will extend these new techniques into the dynamical study of free-by-cyclic groups and toward the geometric study of mapping class groups. This includes answering long-standing algorithmic questions in mapping class groups and developing a new fixed-point theory of pseudo-Anosov maps, which itself has consequences for hyperbolic geometry. 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 · 2025-06

Remote health monitoring is highly promising for big data-driven precision medicine, through conveniently and obtrusively tracking health conditions of people. However, when the sensing device is placed off-body for remote monitoring, the captured human signal is usually very weak. This is because the signal quickly decays when it propagates from the human body to the device. Further, the signal of the target person may be interfered if there are more than one person in the environment. Targeting these crucial challenges, this project will advance the science of high-fidelity remote health monitoring, through efforts on innovating the remote signal sensing and decoding system architecture. This project will greatly advance the national health towards pervasive, high-fidelity, and long-term big data establishment. More specifically, this project will design a novel deep senor array decoding system, which leverages the data-driven deep learning algorithm to decode the noisy and weak signal, without needing a reference signal used for propagation-induced distortion estimation. Besides, the multi-sensor spatial information will be leveraged by deep learning to boost the signal fidelity and recover the signal-of-interest from noise and interferences. The project will further contribute to research-education integration through new course development, new pedagogy practices, curriculum enhancement, and broad student training. The PI will continue broadening the participation of undergraduate students as well as K-12 students thereby effectively training the next-generation engineers and researchers. This project will innovate a novel deep sensor array decoding system, which can decode the signal-of-interest from the noisy and weak signal remotely captured, towards promising remote health monitoring and precision medicine big data. The multi-sensor signal captured by a sensor array, will be analyzed by the deep learning algorithm to learn the noise patterns, suppress the noise, and decode the high-fidelity signal. This data-driven approach does not need the reference signal that is usually used for propagation-induced distortion estimation, thereby enabling intelligent and convenient signal decoding. The spatial dynamics captured by the sensor array encode complex information about the signal-of-interest, can be effectively learned with the deep learning-empowered signal decoding. Besides, the deep learning algorithm will learn to separate the signal-of-interest if there are more than one person in the environment. The specific signal patterns for the target user will be learned and used by the deep learning algorithm to mine the target-relevant patterns in the multi-sensor signal captured. The proposed system architecture will be further evaluated with real-world experiments, to demonstrate the generalizable innovation and the effectiveness of the system. The novel system architecture will broadly contribute to various remote health monitoring applications, advance national health with pervasive and convenient big health data establishment, and promote the science on deep sensor array decoding for high-fidelity remote health monitoring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

The era of big data is prompting a large-scale deployment of on-body monitors towards wearable massive-sensor computers. These massive sensors have promising and broad prospects to greatly advance big data-driven precision health, through comprehensively capturing behavioral, physiological or biological signals from the human body. Nevertheless, because of the big data volume brought by massive sensors, the system is very power-hungry and thus it is very pressing to innovate an ultra-low-power architecture. Targeting this crucial challenge, this project aims to develop new design methodologies, techniques and implementations to achieve a generalizable ultra-low-power architecture for wearable massive-sensor computers. Concretely, this project seeks to leverage novel deep learning approaches to minimize the power consumption of the system. Firstly, the data characteristics of sensor streams will be learned by deep learning to analyze, evaluate, and measure the redundancy in the data, which will then be used to activate just-enough sensors. The deep learning will learn the signal dynamics to intelligently determine the sensor activation schemes. Besides, the data on the activated sensors will be further analyzed and compressed to minimize the power consumption. The signal fluctuations and patterns will be learned by efficient deep learning models and then be encoded to sparsified representations. Real-world experiments will also be conducted to evaluate and validate the effectiveness of the proposed ultra-low-power architecture. This project will develop a new ultra-low-power architecture to enable energy-efficient wearable massive-sensor computers, and thus greatly advance their real-world deployment. This new architecture will dramatically boost the battery life, enhance the usability, and improve the long-term data capturing capability of the wearable sensors. This is essential for big data-driven precision health. The achieved human big data will effectively contribute to the study of time-varying, nonlinear, and unknown dynamics of the human body, and broadly benefit many areas like fitness and lifestyle management, medical decision support, disease model establishment, individualized treatment plan, and population-level big data mining. The research findings from this project will be broadly disseminated to the scientific communities, medical areas and other communities. The broad impact of this proposal also stems from the educational program for students from K-12 to undergraduate and graduate levels, through efforts like attracting undergraduate, women and underrepresented students to research, training students in real-world problem solving, and broader research training of high school students and outreach to K-12. This systematic plan of integrating education to research aims to train the next generation of professional STEM researchers and engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Non-technical Abstract This award provides funding for researchers, early career scientists, and graduate students to attend the Workshop on the Dual Nature of F Electrons, Idaho National Labs, Idaho Falls, May 19th-23rd 2025. The workshop aims to connect renowned experts with early career scientists and graduate students, offering an introduction to current topics in the field, sharing scientific advancements, exchanging ideas, and encouraging collaborative research to accelerate scientific discovery. The involvement of early career scientists and graduate students will enhance their scientific education, advance their careers, and fully prepare them to contribute to the U.S. scientific workforce. Technical Abstract Understanding strong correlations is crucial for discovering and designing functional materials with novel properties. These correlations and anomalous behaviors are particularly pronounced in materials containing f electrons, such as those based on lanthanide and actinide elements. Advances in the field of strongly correlated f materials will not only enhance our understanding of electron correlations but will also have significant implications for research on various families of functional materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This award provides partial support for US-based participants in activities related to the thematic program "Topological and Geometric Structures in Low Dimensions", which will occur July 2 through September 12, 2025 at Centre de Recherches Mathématiques, Montréal (CRM), with some activities held at Université du Québec à Montréal (UQAM). The primary purpose of the program is to disseminate current results in low-dimensional geometry and topology and to foster interaction between researchers in some of the most active and rapidly developing areas within the field. Participation of early-career researchers in these activities will provide them with opportunities to make new professional connections, collaborate with peers, establish access to experts in their field, and learn the details of distinct topics of contemporary interest through attending mini-courses, conferences, and other program activities. One component of this collaborative project is support through the lead organization (Temple University) for participation in the thematic program, including three focused activities, each for 40 to 50 participants, consisting of a week of mini-courses followed by a week-long conference: "Topological 4-manifolds" (CRM, July 2-11), "Low-dimensional topology and Floer theory" (UQAM, Aug. 18-29), and "Hyperbolic manifolds of dimension 4 (and more)" (CRM, Sept. 2-12). The second component, through non-lead organization the University of Texas at Austin, is support for participation in the conference "Knots, groups, and manifolds" (UQAM, Aug. 11-15) centered around current developments in low dimensional topology; this conference is attached to the thematic program but organized as a separate event of a significantly larger scale, with over 150 total participants expected. Broadly speaking, the scientific theme of the activities is contemporary classification problems in low-dimensional topology and the interplay between the algebraic, topological, geometric and analytic structures that arise in these classifications. Central focus will be on emerging areas, new progress in traditional areas, investigating conjectured connections between disparate structures on low-dimensional manifolds, and applications of these methods to the topology of manifolds of dimension 3 and 4.The workshop website is at: https://www.crmath.ca/en/activities/#/type/activity/id/3951 This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Microorganisms can turn organic waste into biogas, a renewable alternative to petroleum-derived natural gas. Biogas production is carried out by a microbial community consisting of organic-consuming bacteria and biogas-producing microorganisms known as methanogens. These microorganisms work together in a very complicated network of interactions, which makes the overall process of biogas production vulnerable to changes in the environment. The goal of this project is to improve biogas production by creating a special community of microorganisms that convert waste into biogas more efficiently by feeding on electricity. Research has shown that some organic-consuming and biogas-producing microorganisms can use electricity as their energy source to grow. Based on these studies, an innovative method is proposed for building the microbial community by continuously switching the direction of electricity. When the direction of electricity is switched, both the organic-consuming and biogas-producing microorganisms can gain energy to grow. As a result, the community can be made more resilient to changes in the environment, and biogas can be produced at a high rate. Successful completion of this research will improve our understanding of how these microorganisms work in nature and holds promise as a source of cleaner and cheaper renewable biogas energy. Additional benefits to society result from educational opportunities for high school and college students from underserved groups to diversify and enhance the Nation’s STEM workforce. Methanogenic microbial communities convert organic waste into methane biogas. Biogas production can be enhanced by building synthetic microbial communities capable of electro-methanogenesis. The goal of this project is to develop a novel approach to build electro-methanogenic communities as a model system to understand the mechanisms for microbial community assembly and extracellular electron uptake. The central hypothesis of this research is that electro-methanogenic communities can be readily assembled using alternating polarity. As the electrode potential is alternated, the electrode serves as an electron donor for electrotrophic methanogens as well as an electron acceptor for electroactive bacteria. Together, this process results in simultaneous selection of both populations. To test the central hypothesis, three interconnected research aims will be pursued to: i) build robust electro-methanogenic communities with alternating polarity, ii) quantify the contribution of different driving forces to community assembly, and iii) elucidate the metabolic pathways involved in extracellular electron uptake. Completion of this research will advance our understanding of the ecological role of electrotrophic microbial ecosystems, and potentially lead to new avenues for biogas production. Additional benefits of this project result from the training of high school, undergraduate, and graduate students from underserved groups by leveraging long-standing engagement between the research team and the Society of Women Engineers, the National Society of Black Engineers, and the Society of Hispanic Professional Engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Despite the importance of metabolism on whole body homeostasis, little is known about how adipose tissue, the major regulator of metabolism, functions in different environments, including in space, where mechanical forces are experienced differently, and radiation is stronger than on earth, leading to accelerated aging. Here, we plan to explore how true microgravity and radiation lead to altered cellular organization and thus metabolic (dys)function. This will be achieved by sending engineered adipose tissue aboard the International Space Station (ISS) for six months. Changes to cell and tissue remodeling and overall tissue function, looking specifically at adipocyte function and inflammatory/aging responses will be characterized upon return to earth. The broader impacts of this research include new targets for metabolic health in normal and aged populations. The US aging population is rapidly growing, where the 2020 Census found 1 in 6 people are 65 or older, which is nearly a 40 percent increase in 10 years (Census.gov statistics). A healthy metabolism is associated with longevity. However, 42 percent of US adults are classified as obese (CDC.gov statistics), where impaired quality of life, increased risk of many co-morbidities (cardiovascular disease, musculoskeletal disease, cancer, stroke, etc), decreased work productivity and increased healthcare costs become a growing problem. Therefore, development of models of adipose tissue function and aging are urgently needed to ensure not only a healthy and good quality of life, but also to ensure the workforce is strong and healthcare costs are limited. Adipocytes in engineered adipose tissue respond to simulated microgravity by remodeling their cortical actin, which improved insulin mediated glucose uptake and lipid metabolism. However, simulated gravity differs from true microgravity in shear fluid stresses and radiation. It is hypothesized that exposure to true microgravity would demonstrate differential responses in cellular mechanosensors by increased radiation and altered fluid shear stresses, thus changing the cytoskeletal response and ultimately the overall tissue response when compared to simulated microgravity. These hypotheses will be tested by comparing engineered adipose tissue constructs maintained on the ISS versus ground based simulated microgravity and static controls, to evaluate changes to adipocyte function (e.g. glucose uptake, lipid metabolism, adipokine release, key adipocyte gene/protein expression) and mechanosensitive pathways/receptors (e.g. actin, RhoA, Piezo1, TRPV) and overall tissue organization via extracellular matrix remodeling. Successful completion of the proposed work, would establish how true microgravity affects engineered adipose tissue function via mechano-signaling, potentially identifying new mechanosensitive targets for metabolic (dys)function on earth as a model of accelerated aging, and for metabolic (dys)function for those traveling in space. This would be the first time human adipose tissue models will be sent to ISS and would provide invaluable insight to this tissue function. The broader impacts of this work would have implications for society, national security, workforce, and economy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

This award will develop a multiscale experimental/modeling framework to obtain a fundamental understanding of the coupled ice/salt crystallization phenomena in low/zero clinker systems. Freeze-thaw causes billions of dollars of damage to concrete infrastructure and buildings in the US yearly. Impacts of climate change such as increased number and severity of freeze-thaw events are predicted to aggravate this damage. The socioeconomic consequences of these reductions in serviceability include impediments to economic activity and exacerbated inequities in access to quality infrastructure in marginalized communities. By exploring new materials with reduced environmental impact, this project will lead to the development of more resilient concrete formulations, significantly extending the lifespan of infrastructure. Project outcomes will open significant pathways for broad implementation of low/zero clinker systems in building and infrastructure applications. The project supports national interests by promoting scientific progress, improving infrastructure resilience, and reducing carbon emissions. It also enhances educational opportunities and STEM diversity through exposure of K-12 students and teachers to novel technologies and sustainability concepts. Furthermore, development of concrete sustainability seminars and advanced academic courses will lead to a more knowledgeable STEM workforce. The technical goals of this research are to elucidate the mechanisms of entrained air void formation/stabilization, saturation, and coupled ice/salt crystallization damage in low/zero clinker cementitious materials. Using a combination of multiscale experimental methods, molecular dynamics simulations, and advanced characterization techniques, the project seeks to understand the physico-mechano-chemical interactions at play. The project will develop a comprehensive multiscale experimental/modeling framework to study these interactions, linking microscopic characteristics to macroscopic performance. These findings will inform the creation of highly durable concrete mixtures suitable for cold environments. Ultimately, the project aims to produce a performance model to predict the longevity of low/zero clinker materials under freeze-thaw conditions, providing a pathway for their broader implementation in sustainable building and infrastructure applications. Microstructures of low/zero clinker systems could be engineered from the bottom-up to mitigate damage due to crystallization stresses. This award will advance the specific state-of-the-art in low/zero clinker systems and more broadly brittle porous materials from several scientific and technological perspectives. This research will also advance the knowledge base in material science, porous media mechanics, computational science, and advanced analytical and imaging techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

With this Award, the Chemical Synthesis Program of the NSF Division of Chemistry is supporting the research of Professor Christopher M. Beaudry of Oregon State University who is investigating new chemical reactions that are used to prepare pharmaceuticals, fine chemicals, and building-block molecules for the chemical industry. Aromatic rings are common features of many fine chemicals. Traditionally, aromatic rings are viewed as stable, less reactive chemical entities; however, the Beaudry group is developing new reactions to harness their underutilized reactivity. They will enable more efficient syntheses of clinical pharmaceuticals such as homoharringtonine (for leukemia), ergometrine (for postpartum bleeding), and novel lead compounds such as himgaline (potential applications in cardiovascular research). Educational benefits of this research include training PhD students and undergraduates for careers in the chemical industry. Additionally, outreach activities in Oregon schools involve the Corvallis public school system. Professor Beaudry and his research team develop new pericyclic reactions of aromatic rings that transform these molecules into new value-added molecules. Specifically, they are transforming benzenes, pyrones, pyridines, and related starting materials into other cyclic and polycyclic products with control over substitution. The research leverages the contiguous unsaturated carbons of the arene starting materials to give densely functionalized products. A key aspect is the recognition that polarized cycloaddition coupling partners offer increased reactivity and regioselectivity in these bond constructions. This work solves long-standing problems in chemical synthesis, such as the preparation of 4-substituted indoles, 2,3-disubstituted phenols, and nitrogen-containing bicyclic systems. Broader impacts of the work include training chemists in the practice and strategy of sophisticated organic synthesis; preparation that serves them well for careers in the agrochemical, pharmaceutical, and fine chemical industries. Longer term benefits of this research may obtain as the development of step-saving, convergent syntheses of high value-added targets could lead to more efficient processes for the manufacture of these targets. Moreover, the Beaudry group is actively engaged in reaching under-represented minorities and first-generation college students and in providing them with exposure to modern research in synthetic chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The award supports US-based graduate students and postdocs to attend and participate in the international conference and summer school “Arithmetic and p-adic geometry in Chile” in December 2024 (https://hdc-g.github.io/agchile2024/index.html). Arithmetic and number theory is the study of properties and patterns in the integers. These questions are deep, and geometric tools have emerged as a powerful structure that helps us understand them. Often, this geometry is not the usual Euclidean geometry, but a p-adic one in which distance is governed by divisibility properties of a prime number p such as 2, 3, 5. There have been fundamental recent advances in the theory of p-adic geometry, and the impact of those advances on questions in arithmetic is only just beginning to be understood. This conference will bring together experts in arithmetic and p-adic geometry to share their work and explore connections between these fields. The early career participants funded by this award will learn the newest developments from experts, enabling them to carry these ideas into their burgeoning research. Moreover, the location of the conference, in Santiago, Chile, will facilitate collaborations between mathematicians in the northern and southern hemispheres. Both the summer school and the conference will be organized around three themes: p-adic L-functions and Iwasawa theory, the p-adic Kudla program, and the p-adic Langlands programs. These are three active areas of number theory research where the recent advances in p-adic geometry are already making an important impact. These three areas also have important connections with each other. By bringing together experts in all of these fields, the conference will facilitate a sharing of knowledge and a flowering of new collaborations coming from different perspectives on the complicated objects studied in p-adic geometry. Participating in these conversations will be especially beneficial for the early career participants funded by this award. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to investigate the cognitive processes that support reasoning about complex atmospheric processes, including prediction of extreme weather events. There is currently no theoretical basis for understanding this kind of reasoning. Nor is there a complete understanding of how experts transfer their reasoning skills to students. The project is taking a novel approach that involves embedding researchers in 11-day long convective field studies. The field studies create opportunities for students to experience authentic atmospheric processes in a rich learning environment with mentoring by more experienced atmospheric scientists. The merger of expertise within the highly interactive learning environment affords a unique opportunity to observe student and expert practice, ask questions, and conduct interviews. This early-stage research is important for atmospheric science, cognitive science, and education because it seeks to understand how atmospheric scientists reason about real world atmospheric processes and how they share this expertise with students. By studying how experts convey deep aspects of their thinking to students and how students assimilate expert practice, the project will lay the groundwork for a program of future investigations into complex atmospheric processes that will inform both cognitive and natural sciences and guide evidence -informed atmospheric science education. The project seeks to build a foundation for future transformative work with goals to 1) Contribute intellectually to cognitive science with new theories of human reasoning about complex fluid phenomena; 2) Pursue an understanding of expert reasoning in atmospheric science and how experts transfer that reasoning to students; and 3) Connect current education practice with research based evidence of how students think and learn in authentic atmospheric science contexts. The project is taking an interdisciplinary perspective with a research team that includes a cognitive scientist, a geoscience education researcher, and two atmospheric scientists. The team is using methods associated with cognitive anthropology to observe student and expert practice, ask questions, and conduct interviews. How individuals think about fluid transformation is an unresearched area in cognitive science that exists at the frontier of our understanding of how the mind understands and makes predictions about complex processes. Through this project, researchers are investigating a whole domain of thinking that will motivate future work. For the burgeoning atmospheric science education research (ASER) effort, this is especially essential; research that seeks to develop theory is vital to providing a firm grounding for any studied phenomena. This project is supported by the IUSE Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The PI and her research team will theoretically model an extremely powerful new platform to study novel symmetry-violating forces beyond those in the Standard Model of particle physics. The study of these phenomena is needed to explain the observed cosmological asymmetry between matter and antimatter. Priority in the proposed research will be given to a platform based on milli-Kelvin, trapped 223FrAg, francium-silver molecules, which takes advantage of their enhanced sensitivity to the symmetry-violating effects. The shape or charge distribution of the unstable 223Fr nucleus is octupole deformed (pear-like) promoting increases in sensitivity to symmetry breakdowns. In addition, the electron bonding between Fr and Ag can lead to a strong, effective internal electric field along the axis connecting the two atoms at the positions of the nuclei magnifying the effects of externally applied fields. The research team’s primary goal is to provide guidance to experimental groups for “building” this novel molecular sensor with revolutionary sensitivity to symmetry-violations from its constituent atoms Fr and Ag. Both atoms have already been cooled with lasers to milli-Kelvin temperatures. Binding the two cold atoms together, also using lasers, however, has never been done before and, specifically, calculating the rate at which this can be achieved will help determine the success of the sensor. The precise determination of the rates with calculations spearheaded by the students are crucial to advance the field and will be performed in coordination with an experimental group building the apparatus led by Dr. D. DeMille at the University of Chicago. The technical research of the proposal has several components. Firstly, electronic states and radiative transition dipole moments of the FrAg diatomic molecule must be precisely determined. An important element of this proposal is the development of a state-of-the-art relativistic configuration-interaction valence-bond method for precise first-principle numerical calculations of di-atomic molecules. The valence-bond method is unique among molecular electronic structure methods as it is well suited for the larger internuclear separations, where the overlap of the electron wavefunction of the two atoms is relatively small. Secondly, the PI and her team will use numerical quantum scattering models to describe the relative motion of the two ultracold atoms in the presence of magnetic fields and laser radiation of multiple colors to assemble FrAg molecules. At cold temperatures molecular forces operating at large interatomic separations dominate the physics of the motion. Resonant scattering phenomena in the presence of a magnetic field can enhance the molecular formation rates. Finally, once FrAg has been formed in its energetically lowest rovibrational state, the research will characterize the light shifts on eigen states of FrAg due to the laser light that traps them. These forces, while essential for the experiment, will also be detrimental for precision measurements looking for small symmetry breaking effects. The light forces are classified as scalar, vector, and tensor light shifts in order of decrease size. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Education researchers have access to more extensive and heterogeneous data sources for their research and assessments, which requires skills in advanced cyber-infrastructures. Artificial intelligence (AI) can help improve the quality of educational research and assessment. This kind of research and assessment is invaluable in advancing national interest by enhancing the ability to answer research questions such as the effectiveness of education policies and pedagogy techniques and closing the achievement gaps. Utilizing AI in education requires additional skills beyond conventional statistics training education researchers, school administrators, and policymakers receive. This project addresses the fundamental issues of training users to use advanced cyber-infrastructure, such as cloud computing systems, to deal with the challenges of working with large quantities of education data. The training materials, software tools, and hands-on project assessments developed as part of this project help prepare future educational researchers in learning analytics to use advanced cyberinfrastructure systems in the cloud. The other potential benefits include expanding the utilization of cyberinfrastructure resources beyond the traditional natural science researchers to involve other social science researchers in education to serve national needs. This project, AI4EDU, aims to develop innovative training materials for education researchers to enable them to utilize AI in educational research and assessment using cloud infrastructures. AI4EDU consists of three integrated thrusts to address this challenge. The first thrust is the development of educational materials that introduce critical aspects of planning, configuring, and utilizing cloud computing resources and frameworks (e.g., Hadoop, federated learning) to support various educational analytical tasks. The second thrust is to develop tools in data quality, cloud monitoring, cloud planning, and configuration to support utilizing cloud services. The last thrust is to design sample projects with accompanying datasets for real-world, hands-on training. In addition, AI4EDU includes a public repository to collect and share machine learning programs and datasets tailored for various educational research tasks to help build up the community of users. The AI4EDU project helps support the AI for Education initiatives by bridging the gap between the analytical techniques taught in the classroom and the tools and skillsets needed to work with data in education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Rivers carry mineral particles eroded from inland soils and deposit them as sediments in estuarine wetlands on a large scale. Chemical reactions with the brackish water of the estuary cause these mineral particles to gradually transform. These mineralogical alterations affect the filtering of pollutants and the availability of nutrients and therefore have a great impact on estuarine ecosystems, but the underlying geochemical processes are poorly understood. This project will study the transformation of soil minerals in the New Jersey Meadowlands, a brackish tidal wetland area in the Lower Hackensack Estuary in northern New Jersey, focusing on how estuarine water chemistry controls the rate and end-products of this process. The project provides scientific training to two graduate students, who will participate in the NewGeo initiative at Rutgers-Newark to collaborate with community partners on environmental issues in the municipalities around the Lower Hackensack Estuary. The project further involves undergraduate student researchers and will engage high school students from the City of Newark participating in the Geoscience Summer Scholar program, who visit the Meadowlands Environment Center each summer to learn about environmental science and coastal wetlands. Studies will be conducted at low- and high-marsh field sites in the New Jersey Meadowlands with variable salinity and redox status. Common soil minerals (gibbsite, montmorillonite, kaolinite) will be incubated in dialysis tubes to allow chemical interaction with wetland porewaters, and shallow water wells and diffusion samplers will be installed to monitor water levels and chemistry. The incubated samples will be retrieved for compositional and structural analyses with various complementary techniques, including synchrotron X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), and analytical transmission electron microscopy (TEM). Incubation time intervals ranging from 8 weeks to 2 years will enable monitoring and quantification of the formation and transformation of the secondary mineral phases over time. Concurrent monitoring of solution chemistry will allow determination of the aqueous geochemical controls on mineral evolution, and assessment of the feasibility of thermodynamic models to predict mineral transformation products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award will fund US-based participants in the conference "Hyperbolic manifolds, their submanifolds fundamental groups", which will consist of a conference January 6-10, 2025 and a follow-up workshop aimed at early-career mathematicians January 13-17, 2025, both held at Instituto de Matematica Pura e Aplicada (IMPA) in Rio de Janeiro, Brazil. The conference will bring together around 100 participants from around the world to learn about the latest exciting developments in the fast-moving and very active area encompassed by the title of the conference. This will include 23 lectures by world-renowned experts of various career stages all at the forefront of the field, along with an extended lightning talk session to provide young participants an opportunity to communicate their work. The follow-up workshop is aimed at early-career participants and will be dedicated to four detailed mini-courses given by rising experts. The primary use of this grant will be to fund applicants that are early-career (graduate student or junior faculty), with a particular emphasis on those from underrepresented groups in the mathematical sciences and those from institutions with extremely limited access to resources and research opportunities. The primary purpose of the conference is the disseminate the most current results in and around the study of hyperbolic manifolds, which are fundamental objects in differential geometry and topology. Hyperbolic manifolds, i.e., manifolds of constant curvature -1, are among the most important and basic examples in differential geometry. Yet, they remain profoundly mysterious in comparison with their flat and positively-curved analogues. Forty years ago, Thurston laid out a grand vision for geometrization of 3-manifolds which, if validated, identified hyperbolic 3-manifolds as the essential case to unlock. Twenty years ago, Perelman substantiated this by proving Thurston's geometrization conjecture, and ten years ago Ian Agol made another profound breakthrough by proving Thurston's conjecture that all compact hyperbolic 3-manifolds virtually fiber over the circle. Despite this immense progress, much remains to be done before we have a satisfactory understanding of hyperbolic 3-manifolds, and the field continues to flourish and move forward at a rapid pace and find new deep connections with other areas of mathematics like profinite groups, geometric analysis, and number theory. In comparison, our understanding of higher-dimensional hyperbolic manifolds remains far more unsatisfactory. However - often guided by the significant progress in dimension three - the last decade has seen an explosion of fundamental new results leading to significant optimism that a much deeper understanding in higher dimensions is within reach. These new advances are coming from researchers with a wide range of perspectives and toolkits, but also from mathematicians from Europe and throughout the Americas, for example in the chosen location of Brazil. A primary aim of this conference and workshop is to provide a significant opportunity for both experts and young researchers to learn about the newest developments in an environment fostering international collaborations that will drive the next advances. The follow-up workshop will boost the intellectual development of early-career participants through opportunities to make new professional connections, collaborate with their peers, and learn the details of four distinct topics of contemporary interest from a rising expert. https://impa.br/en_US/eventos-do-impa/2025-2/hyperbolic-manifolds-their-submanifolds-and-fundamental-groups/ 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.