University of Georgia Research Foundation Inc

NSF Awards · FY 2026 · 2026-10

This REU Site award to the University of Georgia (UGA), located in Athens, GA, will support the training of 10 undergraduate students for 10 weeks during each summer in 2027 - 2029. It is anticipated that a total of 30 students will be trained in the program. Students will be immersed in an interdisciplinary environment and trained by mentors to conduct research at the frontiers of infectious disease ecology. Students will investigate research questions from a range of experimental and quantitative perspectives, including field-based, lab-based, mathematical, and computational approaches to better understand causes and consequences of infectious disease. The research projects will use different systems to analyze disease including genes and cells, whole organisms, host populations, and broader ecological communities. Program activities will include student-led lab tours, research presentations, paper discussions, computational workshops, and a final research symposium. Students will learn how research is conducted by actively engaging in all phases of project development, and many students will present their work externally at professional scientific conferences and/or earn co-authorship on peer-reviewed scientific publications. Assessment of this program will be completed through the Undergraduate Research Student Self-Assessment (URSSA), supplemented by specialized annual surveys administered by program directors. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with the NSF priorities in Biotechnology and AI. Infectious diseases are one of the greatest contemporary threats to the health of humans, livestock, wildlife, and crops. Critically, infectious disease dynamics occur across scales of organization ranging from genes and cells to populations and ecosystems. Addressing these complex dynamics often requires a combination of empirical and quantitative expertise. This program supports transformative research and student training at the intersections of the quantitative sciences (mathematics, computer science, statistics) and empirical disciplines of host-pathogen biology (ecology, epidemiology, genetics, immunology, microbiology). Demand for quantitatively-skilled health professionals, researchers and policymakers with expertise in infectious diseases will remain high in the decades to come. The students trained by this program will better understand the advantages of collaborative research and will be better prepared for a range of careers in infectious disease biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

The project aims to advance the field of statistical change-point detection by developing novel methods and their associated theory to handle complex data with irregular signals. Unlike the traditional setting in which signals before and after the change point are often assumed to differ by a constant shift, irregular signals refer to the situation when the post-change signal may vary in highly unpredictable ways without any pre-specifiable pattern or structure. This can pose a tremendous challenge on many existing change-point tests, often resulting in notable reductions in their statistical power and increasing their vulnerability to maliciously designed adversarial attacks. By allowing the post-change signals to be irregular and not necessarily follow the standard assumptions as in conventional change-point analyses, the research developed in this project is expected to lead to more robust and next-generation statistical and machine learning protocols and toolboxes with rigorous theoretical guarantees for change-point detection in a wide range of applications. For example, detecting abrupt changes in power grids, attacks in sensor networks, or emerging trends in social networks all require powerful methods for detecting irregular changes. As a result, the research will advance not only the field of statistics but also a range of other disciplines including machine learning and artificial intelligence where data with irregular signals may arise. The research will also be integrated into the undergraduate and graduate education at participated institutions to equip students with advanced yet accessible statistical and machine learning knowledge for analyzing data with irregular signals. The research involves the development of novel statistical methods and their associated theory for change-point detection and estimation in the presence of potentially irregular signals. To quantity the uncertainty in the estimated signals from dependent and noisy data, a causal representation framework is employed with a suitably constructed functional dependence measure to quantify the effect of dependence via the technique of perturbation and innovation coupling. This enables the use of deep probabilistic tools, such as the invariance principle and Gaussian approximation results, for a general class of dependent processes to guide the selection of a statistically appropriate alarm threshold for detecting change points in the presence of irregular signals. The project aims to address change-point detection under irregular signals both in the offline setting, where the analysis is performed after all the data are collected, and in the online setting, where sequential testing becomes desirable as data arrive. In addition, different asymptotic schemes are considered to address situations in which stable historical data are available and when such data are not available to practitioners. The research is also expected to promote scientific and technological advances in applications that require rapid anomaly detection with complex alternatives. 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 research investigates how biological diversity is maintained between closely related species in a changing environment. The work focuses on two species of yellow monkeyflowers—ecologically diverse wildflowers common across North America—that frequently meet and interbreed to create hybrids. In a unique, multi-year study of natural populations in the Columbia River Gorge, this research will track how fluctuating environmental conditions, such as drought, influence whether these plant species interbreed or remain separate. By combining detailed field observations with advanced genetic analyses, the project will identify specific genes that help plants adapt to their surroundings and maintain species barriers. Understanding the genetic basis of reproductive barriers like flowering time is critically important for modern plant breeding and biotechnology. For example, knowledge from this work could lead to developing crops that are better synchronized with specific growing seasons or more resilient to climate-driven stressors like drought. Additionally, these projects will offer rich training experiences for undergraduate and graduate students who will be involved in all aspects of the research. The projects will also provide significant outreach opportunities, including public lectures, experimental demonstrations at the field sites, greenhouse tours, and engagement with high school students. The origin of species is usually shown as a simple splitting of one lineage into two, but hybridization is pervasive across the tree of life and can complicate the speciation process. This long-term study of naturally hybridizing yellow monkeyflower (Mimulus) species will reveal how interspecific gene flow affects the probability of adaptation and tempo of speciation. The evolutionary impact of hybridization depends critically on the rate of interspecific mating (pre-zygotic barriers) and the fitness of hybrids (post-zygotic barriers), and these factors, in turn, are often highly contingent upon the environment. The specific scientific objectives of this research are as follows: 1) Identify the environmental determinants of Mimulus ancestry structure in secondary contact zones across space and time. 2) Determine how reproductive barriers respond to environmental variation. 3) Identify the genetic basis of divergent ecological adaptation and assess how mapped barrier loci respond to fluctuating natural environments. 4) Discover the ecological context and genomic impact of hybridization across a broad geographic region. Completion of the aims in this proposal will provide an unprecedented view of the ecological variables, phenotypes, and genetic loci that determine the fate and evolutionary impact of hybridization. Additionally, this project includes several public outreach initiatives and provides unique cross-training opportunities for students at a Research 1 institution (University of Georgia) and a predominantly undergraduate institution (Reed College). 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

Manufacturing contributes $2.9 trillion to U.S. gross domestic product, and improving its cyberinfrastructure resilience is essential for economic competitiveness and national security. Modern manufacturing has evolved into a data-intensive scientific cyberinfrastructure in which interconnected sensors, learning-enabled controllers, and human-in-the-loop decision systems drive production, yet the research community lacks open, FAIR-compliant security datasets that capture the tightly coupled cyber-physical-human interactions characteristic of realistic manufacturing environments. This project builds and operates a publicly accessible, annotated security dataset for manufacturing cyberinfrastructure by integrating a fully instrumented assembly testbed at the University of Georgia Innovation Factory with a high-fidelity digital twin simulator. The platform generates time-synchronized, multimodal traces organized by the Purdue Enterprise Reference Architecture, spanning network logs, physical process data such as torque curves and vibration signatures, and de-identified operator metadata collected under benign, fault, and adversarial conditions. Key innovations include a hybrid generation environment that uses a digital twin for deterministic replay and safe exploration of rare or safety-constrained events; a three-dimensional attack surface characterization encompassing cyber, physical, and human layers; and integrity-verified data lineage pipelines that ensure dataset authenticity and reproducibility. Datasets are released publicly through NSF-aligned repositories under an open-source license. This project will shorten the path from academic innovation to deployable solutions by providing defensible benchmarks for manufacturing security research, strengthen the workforce pipeline through open course modules integrated into university instruction, and sustain long-term community engagement through annual workshops, a public benchmarking leaderboard, and long-term institutional stewardship. 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

Wolbachia are bacteria that are widespread endosymbionts found in more than half of all arthropod species, like all endosymbionts they potentially affect many aspects of their host biology. In particular, many Wolbachia ensure their own transmission to the next generation by selfishly manipulating the reproduction of their host as Wolbachia are primarily transmitted from the host mother to her offspring. This research investigates both short and long-term interactions between hosts and Wolbachia. Specifically, it aims to understand how Wolbachia manipulate host reproduction and how hosts co-evolve to resist these manipulations. The results will be of broad significance to both basic and applied questions in biology. First, given their abundance and effects on their arthropod hosts, endosymbionts including Wolbachia likely contribute to species diversification and broader patterns of biodiversity. The findings will help understand how endosymbionts contribute to the evolutionary and ecological processes that generate and maintain biodiversity. Second, Wolbachia are currently used for biocontrol of insect vectors that transmit human pathogens as well as of agricultural pests that cause harm to plants. The evolutionary dynamics that occur in natural host-endosymbiont interactions likely also occur in engineered biocontrol systems. The findings of this study will inform the potential consequences of wide-scale release of Wolbachia-infected insects, including both how the host evolves to modulate Wolbachia’s effects, as well as how Wolbachia co-evolves to result in strain replacements. Critical to completing the scientific aims is training the next generation of the STEM workforce. This research will provide mentored research opportunities for trainees of all levels, who will receive hands-on training in scientific research methods and communication. This project specifically investigates the genetic interactions and co-evolutionary dynamics between two sister Drosophila fly species and two Wolbachia strains. In the wild, D. recens is infected with two closely related strains of Wolbachia, both of which cause moderate cytoplasmic incompatibility, or the death of offspring of uninfected females and infected males, in this host. When transferred to the host D. subquinaria, one of these Wolbachia strains causes strong cytoplasmic incompatibility, while the other causes strong male killing, where the sons of infected females die. This project will result in key insights into i) the mechanisms by which endosymbionts cause reproductive manipulations, ii) the mechanisms by which hosts counteract these reproductive manipulations, and iii) the dynamics of how endosymbionts invade and spread through host populations. To complete the aims, the research will integrate field assays, molecular genetics, genetic mapping, population genetics, and laboratory assays. 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

Bacteria can evolve and become resistant to antibiotics that are used to treat infections. Antibiotic resistance is a serious global health challenge and is a factor in millions of deaths each year. The environment plays an important role in how resistant bacteria survive and spread. Scientists have found antibiotic-resistant bacteria can spread in aerosols in air. This CAREER project focuses on how the formation of aerosols influences the transport and spread of antibiotic-resistant microbes. It uses a flow-through chamber to simulate bacterial transport in air and to measure the response of bacteria, including the development of antibiotic resistance, to environmental conditions. The experiments are complemented with field studies and computational models to predict transport, exposure and risk in high-risk areas. The project also provides hands-on research opportunities and international training programs that connect engineering, environmental science, and public health. Project outcomes provide data needed to develop better strategies to slow the spread of antibiotic resistance around the world. The proposed project introduces both technical and conceptual innovations that advance the understanding of airborne antibiotic resistance beyond detection and transport. While aerosols have been increasingly recognized as a pathway for antibiotic resistance dissemination, their role as dynamic environments that shape bacterial adaptation remains largely unexplored. The project deploys a modular, bench-scale aerosol chamber capable of systematically manipulating environmental variables such as humidity, particulate load, and source characteristics, to isolate mechanisms driving resistance dynamics of microbial communities during aerosolization, transport, and deposition. The research distinguishes between physical transport, stress-induced adaptation, and horizontal gene transfer as contributors to resistance spread using culture-based assays, digital PCR for absolute quantification of resistance genes, and metagenomic and transcriptomic sequencing to resolve community composition and functional responses. Iterative coupling of laboratory experiments with field sampling campaigns in built and natural environments enables validation of mechanistic findings under environmentally relevant conditions. Data generated through the project informs ecological and transport-based conceptual models that treat aerosols not only as vectors but also as selective environments capable of shaping bacterial evolution. The resulting framework advances fundamental understanding of aeromicrobiology while establishing a scalable biotechnology platform that integrates engineered aerosol systems with advanced molecular tools to investigate antimicrobial resistance and a broad range of environmentally transmitted pathogens. 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 CAREER project will develop artificial intelligence (AI) tools to better understand and monitor neurological diseases. The research will combine brain images, genetic information, and clinical information. These are complex data sets with uncertainties, which makes it difficult to analyze them separately. The AI tools developed in this project will integrate and interpret the combined data. The resulting analysis will identify patient specific disease mechanisms and predict how diseases progress. The outcomes of the project will advance personalized healthcare, disease monitoring, and pattern detection. The project will also support education and hands-on training in biomedical AI, statistical inference, and modeling. Outreach activities include summer coding camps and data science workshops for high school students, and tutorials at national conferences. Software and models will be released as open-source tools to promote reproducibility and wide adoption. These activities will increase participation in biomedical engineering, train a skilled workforce, and improve public understanding of AI-enabled healthcare technologies. A unified, AI-driven framework for multimodal feature engineering will be used to model complex neurological diseases. The framework will combine machine learning (ML) with statistical causal inference to unify multimodal data across biological domains and timescales, producing interpretable representations that identify patient-specific mechanisms, predict individualized disease trajectories, and support precise diagnosis, treatment stratification, and real-time disease monitoring. The project will introduce three core system-level innovations: (1) domain-aware causal inference that integrates probabilistic and generative modeling to uncover latent features linking imaging, genetic, and clinical data; (2) customized transformer-based architectures that fuse structured and unstructured biomedical data, such as imaging embeddings, genetic variants, and clinical narratives, using cross-modal attention and contrastive learning strategies; and (3) domain-specialized large language models (LLMs) trained on multimodal features and biomedical text to translate complex outputs into interpretable clinical summaries. Reusable, open-source tools and models will be developed for clinicians and researchers, enabling analysis of large, heterogeneous biomedical datasets. Education and training activities will include interdisciplinary coursework in AI and causal inference for biomedical systems, hands-on research experiences, and outreach programs for high school learners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The 2026 Georgia Topology Conference with the theme ”Flows, foliations and Floer homology” will be held at the University of Georgia in Athens, GA May 21-23. The Georgia Topology Conference is an annual conference that has been organized by generations of topologists at the University of Georgia over the past half-century. The conference traditionally serves as a place to showcase new and exciting results in the field of geometric topology, and as a meeting place for promising young mathematicians with established researchers in their field, fostering new collaborations and initiation of new research projects. While the mathematical communities studying pseudo-Anosov flows and foliations and those focused on Heegaard Floer homology and contact topology have largely evolved independently, recent breakthroughs have established significant connections between the two. This conference aims to bridge these fields, fostering shared perspectives that could lead to major advances in both. We are committed to making our conferences welcoming and productive for graduate students and early-career researchers. The different career stages of our invited participants offer unique mentoring opportunities, which we believe will encourage young mathematicians from all backgrounds to participate. Information about the conference is available at: https://topology.franklinresearch.uga.edu/2026GTC This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Vertebrate animals determine whether the gonads develop into male testes or female ovaries through a variety of ways, yet scientists still do not fully understand how these processes evolved or how they operate across different species. Reptiles, in particular, display an extraordinary range of gonad-determining systems, making them an important group for uncovering fundamental principles of biology. This project focuses on Anolis lizards, which have an ancient genetic system of gonad determination that is distinct from the genetic systems used by mammals and birds. By investigating how testis versus ovary formation is determined in these lizards, the research will provide new insights into the development of reproductive systems across vertebrates. This work will advance fundamental knowledge in genetics and developmental biology, while also supporting the training of undergraduate and graduate students, fostering hands-on research experiences, and expanding participation in science through collaborative training opportunities. These efforts contribute to the national interest by promoting scientific progress, strengthening the research workforce in gene editing biotechnology, and enhancing understanding of biological systems relevant to health and biodiversity. This project advances NSF’s priorities in Biotechnology. This project will investigate the molecular mechanisms of testis determination in the brown anole lizard, Anolis sagrei, with a focus on the Y-linked gene rpl6y and its X-linked counterpart rpl6. Preliminary data suggest that rpl6y is required for testis development and that downregulation of rpl6, potentially mediated by a gonad-specific antisense transcript (astra), is necessary for this process. To test these hypotheses, Aim 1 will use HCR RNA FISH to identify the cell types in which the rpl6y gene is expressed, and CRISPR/Cas9-mediated gene disruption will be performed to determine the functional requirement for rpl6y in testis determination. Aim 2 will manipulate astra expression in vivo and in cell culture to assess its role in regulating rpl6 and its contribution to gonadal differentiation. Aim 3 will evaluate whether RPL6Y and RPL6 proteins differentially influence protein translation using ribosome profiling and quantitative proteomics. Together, these studies will establish whether a translation-based mechanism underlies gonad determination in anoles, potentially revealing a previously unknown mode of vertebrate gonad determination and expanding understanding of how testis versus ovary development is controlled at the molecular level. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Cooperation has been a longstanding evolutionary problem because cooperators suffer an individual fitness loss to provide benefits to other individuals in the group. A notable solution to this problem involves the greenbeard effect, first conceived of by W.D. Hamilton in the early 1960s as a thought experiment to explain the evolution of altruism, and later popularized by R. Dawkins. The main tenet of the greenbeard effect is that there exists a hypothetical gene, or set of linked genes, that expresses a perceptible trait (e.g., green beard), enables the bearer to recognize this trait in others, and causes the bearer to behave differently towards others depending on whether or not they possess this trait (e.g., individuals with green beard help other “green beard” individuals but not ones without green beard). In recent years, these “greenbeard” genes have been discovered in diverse organisms. Yet, because most of these are unicellular microbes, such as amoebae, yeast, and bacteria, we still know little if and how the complex, intricately coordinated, consequential behaviors emerge based on the greenbeard status of group members in their environment. The fire ant, Solenopsis invicta, is one of a few social animals in which a greenbeard effect has been documented. In this project, therefore, we will investigate the relationship between a greenbeard signal and the individual and collective behaviors that it modulates, using the fire ant, as a model system. A diversity of educational and outreach programs is proposed, including participation in the EcoReach program that provides educational materials for K-12 teachers, outreach at the annual Insectival and to a lifelong learning program for adults, as well as research opportunities for graduate students and undergraduates, including some from nearby HBCUs. Social animals work together to the advantage of their group. This cooperative behavior poses an evolutionary problem, however, because cooperators typically suffer an individual fitness loss to provide benefits to other individuals in the group. Researchers have suggested a potential solution for this problem—cooperation can evolve if a gene expresses a perceptible trait (e.g., green beard), enables the bearer to recognize this trait in others, and causes the bearer to behave differently towards others depending on the possession of this trait. The main objective of the proposed project is to investigate the relationship between a greenbeard signal and the individual and collective behaviors that it modulates using the red imported fire ant, Solenopsis invicta, as a model system. The fire ant is one of only a few social animals in which a greenbeard effect has been documented and the underlying greenbeard factor, known as the Sb supergene, has been identified. Using this unique model system, we aim to (1) learn what distinctive cuticular chemicals constitute greenbeard signals in different classes of nestmates, (2) identify contexts in which workers use such signals during one-on-one social interactions, and (3) reveal how colonies composed of workers with a mix of greenbeard genotypes collectively determine the emergent colony organization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Developing advanced combustion technologies for transportation relies on understanding details of chemical reactions that take place during combustion. Engineers use computer models to simulate combustion processes to design and manage them efficiently. However, understanding the details of the chemistry and rates of reactions is required for accurate models. The reactions involve many short-lived, but important, chemical intermediates. When information about the intermediates is unavailable, the accuracy of computer models can suffer. This project will conduct experiments involving a group of intermediates that form during combustion of certain fuels. It will use spectroscopic methods to analyze how the molecular structures of the intermediates influence combustion, including ignition. The outcomes of the project will be data that can improve computer modeling for applications such as internal combustion engines and aviation fuel development. This project will conduct the first set of experiments on a group of six constitutional isomers and stereoisomers for 3-membered and 4-membered cyclic ethers, which are important intermediates in hydrocarbon combustion, yet are commercially unobtainable. Separate, high-pressure jet-stirred reactor experiments will be conducted on the group of cyclic ethers. Vacuum ultraviolet absorption spectroscopy and mass spectrometry detection schemes will produce quantitative, isomer-resolved measurements of species concentration. Effects of temperature, pressure, and oxygen concentration will be explored to probe regions of interest for next-generation combustion systems. The species measurements will serve as modeling targets for new combustion mechanisms produced in the project using a tandem approach combining Reaction Mechanism Generator and AutoMech codes. The results will identify connections between molecular structure and product formation from constitutional isomers and stereoisomers of cyclic ethers and their effects on the fidelity of combustion models. Such models show appreciable sensitivity to the narrow subset of reactions currently assigned to cyclic ethers. The influence of alkyl chain length in cyclic ethers on product formation and the influence of stereochemistry on product formation will be explored. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This grant will focus on developing an integrated computational modeling and experimental framework for simulating lithium-ion batteries (LIBs) under mechanical abuse conditions, such as impact loading. LIBs are the most used power source for electric vehicles, which leads to an ever-increasing need to improve the safety of LIBs so that they can be used in mechanical abuse conditions. To improve the safety design and ultimately reliability of advanced long life and high energy LIBs, a recent trend is to use numerical simulations as an alternative to expensive and time-consuming real-world testing for LIB response prediction under mechanical abuse conditions. However, due to the multiscale nature of LIBs and the nonlinear response of LIB components, it is computationally expensive to directly model the LIBs by accounting for the complex microstructures and nonlinear responses of different LIB components. To address this issue, the PIs plan to develop a multiscale modeling framework that better balances accuracy and efficiency for LIB modeling. The characterization and testing of LIB components at different loading conditions are also planned, which will facilitate the model development and eventually validate the computational framework. The research will also be complemented by establishing a responsive and flexible educational and outreach program based on curriculum development and summer research programs for undergraduate and high-school students with an engineering focus, as well as K-12 outreach through STEM education centers at both participating institutes. The objective of this project is to develop an integrated multiscale reduced-order modeling and experimental framework for LIBs under mechanical abuse conditions by integrating physics­-based constitutive models for LIB components with a multiscale reduced order modeling technique. To achieve this goal, the research encompasses the following three aims and plans: 1) Determine the constitutive models of battery components with full coverage of low, intermediate, and high strain rates; 2) Develop a multiscale reduced-order computational model to predict the response of LIB cells by advancing the eigendeformation-based reduced ­order homogenization model (EHM); 3) Conduct dynamic testing of battery cells to validate the developed multiscale models and exercise the validated model for LIB design and safety evaluation. The multiscale modeling framework will achieve reakthroughs in designing optimal LIB systems, which will expand the conventional boundaries of LIB performance. This project will allow the PIs to advance their current computational modeling and experimental testing expertise for LIB modeling and design, which could potentially accelerate the discovery, innovation, and certification of state-of-the-art battery technologies, and establish their long-term career in modeling and testing of complex material systems and structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Glycans consist of long branching chains of different sugars. They adorn proteins of all types. Some viruses use glycans to enhance the probability of infection. Some tumors hide from the immune system by using “stealth” glycoproteins on their surfaces. Most of the top drugs by revenue are glycoproteins, and their glycans affect drug safety and efficacy. This places a premium on making sure that their glycans are well-characterized and carefully managed. The complexity of glycan structures makes it difficult to control their structure on therapeutic drugs. Conventional glycan characterization methods have steadily advanced, but many challenges continue to hinder efforts to study and engineer these critical molecules. Here, an approach is being developed to rapidly and inexpensively sequence and quantify glycan structures. It will be first applied to accelerate the characterization and design of critical glycans required in biotherapeutics. A high-school outreach program on biological machine learning will introduce high school students to concepts underlying data science and its application to biological and biomedical questions. State-of-the-art technologies for glycan sequencing remain limited in their throughput and accessibility. They rely on methods with expensive, specialized equipment (e.g., mass spectrometry, NMR) or complex biochemistry (e.g., lectin arrays, exoglycosidase treatment). This research project aims to develop Glycosequencing, a technology that determines glycan structures using Next-Generation Sequencing (NGS) technologies. Using NGS to sequence and quantify DNA-barcoded lectins, Glycosequencing will measure a wide array of glycan features. The mapping of lectin binding patterns to glycan structures will be predicted using AI, trained on a large panel of recombinant glycoproteins with well-defined glycosylation patterns. This project will first identify the optimal set of lectins and biochemically characterize them. The lectin barcoding will be prepared, and lectin pooling will be optimized for NGS. To improve the AI accuracy, a training dataset will be built using 5 different recombinant proteins, transiently produced in a panel of >30 glycoengineered Chinese hamster ovary cell lines. To demonstrate the power of this technology, it will first be deployed to rapidly determine the structure of glycans on recombinant protein drugs. It will also be used to simultaneously profile glycans and the mRNA of the mammalian production host cells when cultured on 92 different media. This will allow the rapid characterization of the impact of culture conditions on protein glycosylation of monoclonal antibody drugs and a candidate hepatitis C vaccine. 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

Facial landmark tracking and 3D reconstruction are popular and well-studied fields in the intersection of computer vision, graphics, and machine learning. Despite their countless applications such as human-computer interaction, facial expressions analysis, and emotion recognition, existing camera-based solutions require users to be confined to a particular location and face a camera at all times without occlusions. This highly constrained setting prevents them from being deployed in many emerging application scenarios, in which users are likely to engage in three-dimensional body/head movements. This project aims to provide a new form of single-ear biosensing system that can unobtrusively, continuously, and reliably sense the entire facial and eye movements, track major facial landmarks, and further render 3D facial animations via cross-modal transfer learning. The research outcome of this project will push the limits of ear-worn biosensing to enable rich sensing capabilities that are currently infeasible, such as camera-free facial landmark tracking, and real-time 3D facial reconstruction, etc. Relying on the learning model studied in this project, the project team is building two representative applications, i.e., facial sensing for mobile virtual reality (VR)/augmented reality (AR), and speech enhancement using the reconstructed facial landmark dynamics. The project will substantially advance the wearable and biosensing techniques as well as transfer learning across multiple sensing modalities. The project is bridging the gap between the anatomical and muscular knowledge of the human face and electrical and computational modeling techniques to develop analytical models, hardware, and software libraries for sensing face-based physiological signals. In particular, the project team is building a low-power low-noise circuit to sense the entire facial muscle activities using single-ear biosensors. The team is also developing a compressing algorithm that activates the sensing and communication components only when facial changes are detected, which can significantly increase the battery lifetime and reduce the computational cost of the wearable system. Moreover, to enable camera-free 3D facial reconstruction, the team is developing a cross-modal learning model that consists of a visual facial landmark detection network and a biosignal network, in which knowledge embodied in the vision model can be transferred to the biosignal domain during training. To further enhance the model’s robustness, the team is integrating the third modality (i.e., inertial sensors) into the cross-modal learning model and exploring domain adaptation and continual learning techniques. Additionally, the team is exploring model compression and acceleration techniques to enable the on-device deployment on existing head-worn devices such as VR/AR headsets 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

Monitoring the health of living plants holds critical significance across various domains, such as precision agriculture, horticulture, and environmental conservation. Effective plant monitoring aids decision-making in agriculture related to irrigation, fertilization, pest control, and harvesting. In urban settings like parks and gardens, it improves residents’ quality of life as healthy plants improve air quality, provide shade, and contribute to aesthetics and well-being. In forestry, it allows early detection of tree stress or disease, helping prevent large-scale die-offs and promoting forest health. However, existing solutions are bulky and high maintenance and often fail to capture essential health signals like nutrient and water levels. The project’s novelties are the development of zero-maintenance, intelligent, and robust computer systems that use biocompatible sensor arrays implanted in the plant’s xylem to continuously monitor and wirelessly report water and nutrient uptake in real-time, enhancing water management and irrigation practices based on plant needs and environmental conditions. The project's broader significance and importance are demonstrated through its commitment to publicly sharing research materials online via open-source hardware and software libraries, tutorials, talks, publications, and datasets, along with the integration of sustainable computing into curriculum development, mentoring for graduate students, research experiences for undergraduates, and a summer event focused on a wind-based, battery-free coding competition. This project seeks to develop a swarm of ultra-long-lasting and zero-maintenance intelligent devices to monitor the full life cycle of a plant and provide insights into critical biological aspects such as timing and coordination of nutrient uptake and metabolism. The developed system provides real-time, highly synchronized data from which robust calibration learning models can be developed to predict water and nutrient levels to guide the water’s application, fertilizers, and chemicals. This project creates a biocompatible, ion-sensitive sensor array and installation method, develops energy-harvesting techniques for remote data transmission, and builds AI-powered calibration models to enhance sensor accuracy. The project involves designing, implementing, and testing these innovations through both in-lab and in-the-field experiments to improve plant health monitoring and inform practical applications in agriculture and environmental management. 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

Powered by the advancement of artificial intelligence (AI) techniques, the next-generation voice-controllable IoT and edge systems have substantially facilitated people’s daily lives. Such systems include voice assistant systems and voice authenticated mobile banking, among many others. However, the underlying machine learning approaches used in these systems, are inherently vulnerable to audio adversarial attacks, in which an adversary can mislead the machine learning models via injecting imperceptible perturbation to the original audio input. Given the widespread adoption of voice-controllable IoT and edge systems in many privacy-critical and safety-critical applications, e.g., personal banking and autonomous driving, the in-depth understanding and investigation of severity and consequences of audio-based adversarial attack as well as the corresponding defense solutions, are highly demanded. This project will perform a comprehensive study and analysis of the vulnerability and robustness of voice-controllable IoT and edge systems against audio-domain adversarial attacks in both temporal and spatial perspectives. The research outcome of this project will form solid foundations for building trustworthy voice-controllable IoT and edge systems. The developed defense techniques will improve the security of many intelligent audio systems, such as automatic speech recognition (ASR), keyword spotting, and speaker recognition. This project will involve underrepresented students, undergraduate and graduate students, and K-12 students through a variety of engaging programs. The objective of this project is to demonstrate the feasibility of audio adversarial attacks in the physical world, determine the attack severity and consequences, and further develop defending strategies in practical environments to build attack-resilient voice-controllable Internet-of-Things (IoT) devices and edge systems. To study the possibility and severity of audio adversarial attacks in a practical time-constraint setting, the project will develop low-cost audio-agnostic synchronization-free attack launching schemes, including audio-specific fast adversarial perturbation generator and universal adversarial perturbation generator. To investigate how the adversarial perturbations survive various propagation factors in realistic environments, the project will analyze the audio distortions caused by the over-the-air propagation using an advanced room impulse response simulator and physical environment measurements. The project will also develop several defense techniques, including defensive denoiser, model enhancement, and microphone-array-based liveness detection. The presented technique will help to secure the voice-controllable IoT and edge devices under audio adversarial attacks. The project will also contribute to a new computing paradigm in audio-based adversarial machine learning in both theoretic foundations as well as safety-critical audio-oriented emerging applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Artificial intelligence (AI) is evolving from answering our questions to acting on our behalf. However, we live in a complex world where individuals and groups must both cooperate and compete to achieve their goals, often without clear insight into others' behavior or intentions. For example, in business, teams work toward shared objectives while balancing their own priorities and competing for resources, frequently uncertain about how others will act. Similarly, AI agents acting on our behalf must navigate this uncertainty, learning when to collaborate and when to pursue their own goals. As another example, defender agents on a cybernetwork collaborate with other defenders while being adversarial against attackers. These scenarios raise important questions about how AI agents can learn to both cooperate and compete with one another, and how large multi-agent systems can be guided toward desirable outcomes. This project explores these challenges by studying how agents learn from experience, anticipate others' actions, and determine the amount of data needed to learn effectively. The project steps out of disciplinary boundaries to bring concepts from statistical mechanics, control theory, and management sciences to bear upon these challenges. The project will also educate students in the theory and practice of AI that is relevant to learning and will produce program libraries for public use. This project studies reinforcement learning (RL) for an agent sharing its environment with a large collection of other learning agents whose features may change. The approach seeks concurrency and Bayesian optimality of many-agent RL via full decentralization, and spans three research thrusts. The first thrust investigates techniques from statistical mechanics to let an RL agent effectively model a collective of other learning agents organized in various topologies, and studies the emergent behavior in the system. The second thrust investigates computational representations for mixed-motive settings and the stability of decentralized learning in such settings, specifically exploiting Lyapunov techniques from control theory. The third thrust investigates RL under agent type dynamism due to unknown events. The research results will be validated using existing benchmarks and in two use-inspired domains: one that models a business organization and another one that simulates a cybersecurity environment. The broader impact of this project is creating a foundation for the science of autonomous decentralized learning in systems with many agents with an emphasis on data efficiency. This will inform the management science related to future businesses and agentic organizations, as well as the science of successful human-AI teaming. 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 serve the national interest by preparing undergraduate students for careers in advanced manufacturing through the integration of data science and digital twin practices. As the manufacturing industry becomes increasingly data-driven and intelligent, there is a growing need to ensure students not only gain technical expertise but also develop the awareness required to navigate complex real-world challenges involving privacy, security, and responsible innovation. This Level 1 Engaged Student Learning project addresses the importance of decision-making in smart manufacturing systems by creating hands-on, interdisciplinary learning experiences. The project seeks to enhance student competencies, increase workforce readiness, and foster a culture of responsibility among future engineers. The project goals include the development, implementation, and iterative improvement of a comprehensive eight-week summer research and training program hosted at the University of Georgia and the University at Buffalo. The program will engage students in applied learning through four key components: theoretical instruction, hands-on modules, professional development, and guided reflection. Students will explore issues across the digital manufacturing lifecycle, while participating in collaborative learning and research activities across both institutions. The project will use a mixed-methods assessment strategy, including pre-, mid-, and post-program evaluations, to measure student growth in reasoning and data science skills. Long-term impacts will be tracked through student career outcomes, with ongoing input from an industry advisory board. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The National STEM Teacher Corps Pilot Program Regional Alliance project aims to recognize outstanding STEM educators in high-need schools that advance educational excellence in the Southern Region. This project will support 12 STEM Teacher Corps Members by recognizing and rewarding their accomplishments and effort to enhance STEM teaching and learning in K – 12 science education. The Regional Alliance aims to address the shortage of science teachers by identifying science teachers who demonstrate exceptional science instruction, building a mentor network to support and retain new science teachers, and articulating leadership pathways for experienced science teachers The project will also provide professional development, mentorship and support directly to more than 100 other STEM teachers within the region through the use of Hubs, which will build and strengthen existing connections between the different participating organizations to impact the science teaching community. The alliance will consist of three Hubs. Hub STEM Teacher Corps will identify and support 12 Georgia-based STEM Teacher Corps teachers who will contribute and work within the other two Hubs. Hub Capacity Building will organize established learning opportunities into new programming that targets newly hired science teachers and builds the mentoring expertise of experienced science teachers. Hub Empower will curate support and leadership programming for science teachers across Georgia. The Hubs will reach every K-12 science teacher in Georgia and easily involve science teachers from across the state. Strategically engaging more science teachers in various connected learning opportunities will enhance the work of Georgia's science teachers. Georgia's students will benefit from a strengthened science teaching network by experiencing science instruction that enhances their knowledge and prepares them to be informed citizens and for science or related careers.    This Regional Alliance at the University of Georgia includes partnerships with Georgia Department of Education – Science division (GADOE), Georgia Science Teachers Association (GSTA), Georgia Science Supervisors Association (GSSA), Georgia Youth Science and Technology Center (GYSTC), Regional Education Service Agencies (RESA) and several Georgia schools and School districts. This project has three goals: 1) identify and draw upon the expertise of Georgia STEM Teacher Corps members; 2) empower current and emerging science teacher leaders; and 3) build and sustain science teacher learning networks. To accomplish these goals and ensure impact of the project, there will be an Executive Committee consisting of Hub leaders and Project Directors and an Advisory Committee comprised of local and national experts, and an external evaluator will monitor the project's process and productivity. The Hub-based research questions will contribute to an understanding of how: 1) a distributed leadership approach enhances the science education community, 2) science teachers are supported through an alliance to build their expertise and resilience, and 3) alliances can contribute to elevating the science teaching profession. The ongoing analysis of data within each Hub will improve the products of the Hubs and provide insights into retaining teachers and strengthening teaching systems. This Regional Alliance project is supported through the National STEM Teacher Corps Pilot Program. The NSF National STEM Teacher Corps Pilot Program supports outstanding STEM educators in high-need schools that advance educational excellence in our Nation’s preK-12 classrooms. 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 serve the national interest by improving how student learning is measured and supported within introductory physics courses, key gateway courses for science, technology, engineering, and mathematics (STEM) majors. Traditional multiple-choice question formats in these courses often provide limited information to instructors, limiting their ability to adapt their instruction to the specific needs of their students. This Level 1 Engaged Student Learning project plans to explore the effectiveness of using multiple-choice questions presented in alternative formats to increase student learning and information available to instructors while preserving the efficiency and scalability of traditional multiple-choice questions. The project plans to investigate the effectiveness of three alternative multiple-choice formats in communicating students' partial understanding and improving conceptual learning in introductory physics. Specifically, this project aims to 1) characterize the problem-solving strategies introductory physics students use to answer alternative multiple-choice questions, 2) develop a series of pre-class videos that include embedded alternative-format multiple-choice questions for nationwide use in physics, 3) measure how students perform on alternative multiple-choice questions relative to traditional multiple-choice questions in high-stakes settings, and 4) produce evidence-based guidelines and best practices for physics instructors wishing to use these alternative multiple-choice formats in their courses. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Refractory metals such as Molybdenum (Mo) based alloys have a great potential for applications in harsh environments, because of their high melting point. However, Mo alloys have several inherent drawbacks such as low ductility and a tendency of oxidation and cracking, which are adversely augmented when they are produced by additive manufacturing, due to non-equilibrium processing phenomena. This results in several complex technical blockades that hinder a broader adoption of additive manufacturing of Mo alloys in the industry. This Faculty Early Career Development (CAREER) project aims at fundamental research of wire arc additive manufacturing (WAAM) for Mo alloy structures, and will explore and elucidate the root causes of processing defects and their effects on part thermomechanical performance. If successful, the research will directly impact the Nation’s economic welfare and energy security. For example, the energy efficiency of land-based power plants could be improved and their carbon footprint decreased by using Mo alloys for turbine blades. In addition, the project will enhance the existing manufacturing curricula with data analytics components, provide undergraduate/graduate student with internship opportunities at national laboratories, and run hands-on manufacturing experiences for K-12 students. The outreach activities will enhance the education and training of next-generation STEM leaders in advanced manufacturing and foster inclusions of underrepresented groups. The overarching research goal of this CAREER award is to understand the underlying mechanisms of process-induced residual stresses as well as pore generation and investigate thermomechanical performances of WAAM processed Mo-alloy structures, focusing on titanium-zirconium-molybdenum alloys. The core research challenges lie on the lack of data in the physicochemical properties and complex defects development from non-equilibrium thermal cycles in layer-by-layer stacking. A combination of computational and physics-informed, data-driven models will be pursued for process understanding with experimental verifications and validations, including multi-scale material characterizations, process imaging and fatigue testing, etc. The research is expected to gain fundamental knowledge of the residual stress development and pore formation while considering the ductile-to-brittle transition temperature, and elucidate the deformation behaviors and thermomechanical performances from room to elevated temperatures, as correlated with heterogeneous microstructures, pores and oxidation. In addition, the project intends to establish a quantitative relationship between the process, signature, microstructure, property and performance, called the “design rule,” for WAAM that will ensure satisfactory fabrications of refractory alloy structures. The linkage may serve as an effective tool to potentially tailor microstructures and properties of WAAM structures with controlled and improved thermomechanical performance of final 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 2025 · 2025-10

The NSF ECR Building Capacity in STEM Education Research (BCSER) program contributes to the NSF mission (42 U.S. Code Chapter 16) by building the capacity of the US STEM education research workforce to design, propose, and implement high quality STEM education research. The BCSER Institutes for Methods and Practices in STEM Education Research (IMP) track supports institutes that provide participants with training and support to advance the participants' knowledge, skills, and competencies in STEM education research including in the use of cutting-edge methodological techniques. Institute participants include investigators at any stage in their career development. This institute's focus is on building capacity in STEM education research by sustaining and expanding a novel, problem-led, and quality-focused approach to interpretive research design. This project extends the impact of the first ProQual institute by training 48 scholars and providing web-accessible case study examples of key elements of the ProQual approach. The ProQual approach reconceptualizes research design as a structured, design-based process, helping STEM scholars overcome epistemological and methodological barriers in educational research. This BCSER IMP project is providing training to approximately 48 STEM faculty interested in retooling to become STEM education researchers during the lifetime of the institute. The participants engage in a suite of activities to learn how to approach STEM education research as a design problem and to gain qualitative and mixed-methodology skills to undertake their own research project. Through an innovative 4-step program, participants develop research competence while engaging in a community of practice that fosters long-term knowledge exchange. The incorporation of "ProQual-in-a-Box" resources further extends these benefits beyond direct participants, enhancing dissemination and adoption. This expansion of the first ProQual Institute will strengthen STEM education by increasing the quality of interpretive STEM research that is designed and conducted by faculty and postdocs with technical backgrounds in STEM 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

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research project will contribute to the progress of science and the advancement of national prosperity by developing a framework for the inverse design and fabrication of multiphase composite materials with tailored mechanical properties. Despite recent advances in the deployment of machine learning techniques to materials science, the creation of materials with desired mechanical properties in multiple loading directions remains a significant challenge. This research plans a new data-driven framework to understand the relationship between material architecture and mechanical behavior, facilitating the design of nonlinear materials for a wide range of applications such as lightweight structures, shock absorbers, and aerospace components. This research will be integrated with educational and outreach programs aimed at attracting underrepresented groups to engineering and improving undergraduate and graduate learning in data-driven science and engineering. High school students and the public will be introduced to data-driven material design and applications in collaboration with a local museum and science center. This collaborative research will create and test a new physics-informed deep learning (PIDL) framework to tailor the multidirectional or multi-objective mechanical properties of exotic composite materials. It will utilize the principles of PIDL to build a data-efficient and physically interpretable surrogate model of structure-property relationships for multiphase composite materials. This research will formulate constitutive equations for constituents in voxel-based composite materials and incorporate them into a forward physics-informed convolutional neural network model. A novel multi-objective inverse physics-informed conditional diffusion model will be developed to reveal the property-structure correlation between a multiphase composite material’s bulk mechanical properties and its architecture, combining macroscopic and microscopic data to enhance model accuracy and robustness. Finally, the designed materials will be additively manufactured and tested, with validation through advanced additive manufacturing, X-ray imaging, and multiaxial testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Infants improve their motor skills, such as sitting, crawling, and standing, through practicing these skills in everyday life. However, limited tools for measuring infants’ movements in the home are a barrier to understanding the factors that give rise to individual differences in opportunities for movement as well as individual differences in caregiving practices that can predict motor learning outcomes in infancy. This project uses new wearable sensor technologies combined with Artificial Intelligence to record infants’ behavior across a week-long period to understand how the patterns of infants’ movements unfold over time. Results of this research advance understanding of motor development and provide useful information for clinicians to help promote healthy motor development in infancy. This project aims to collect data from families across the United States by mailing wearable sensors for infants to wear over the course of a week. In contrast to current methods that rely on labor-intensive manual coding of infant movement data, this project leverages Artificial Intelligence to (1) measure the amount and types of movement that 7-month old infants engage in at home during a typical week, (2) examine caregiving practices that give rise to individual differences in opportunities for movement, and (3) predict individual differences in motor development outcomes, including sitting and standing proficiency, at 11 months of age. Collecting and sharing a large longitudinal dataset of wearable sensor data enables advances in understanding motor development trajectories as well as advances in Artificial Intelligence methods for robustly identifying human movement. 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

Several important scientific and engineering research areas depend on the ability to accurately predict phenomena that occur in gas-phase, chemically reacting systems. For example, high-fidelity simulation of the combustion of new fuel mixtures enables the co-design of fuels and engines for cleaner and more efficient use of transportation energy. Such simulations play an important role in atmospheric sciences and astrochemistry as well. The accuracy of these simulations depends on what is known as a “kinetic model.” Kinetic models are data files prescribing the chemical network of a simulation: the possible chemical species formed and consumed, and the rates at which they undergo various reactions as a function of temperature and pressure. The process of collating the thousands of reaction rates and other properties needed for such a model has been recently facilitated by machine-learning tools that extrapolate from a database of known values. However, such technologies suffer from a shortage of data needed to produce reliable predictive models that are applicable to a broad range of engineering applications. To address this shortage, this project transforms and scales the AutoMech software suite to enable both the generation of new, high-accuracy kinetic models from scratch and the population of databases to enhance the predictions made by machine learning tools. This project leverages and extends existing open-source software for parallel workflow orchestration, standardized quantum chemistry program operation, and molecular geometry optimization and commits to the continued maintenance of these essential tools. It also supports the FAIR (findable, accessible, interoperable, and reusable) data publication through a JSON data exchange file with a publicly available schema. This file includes all electronic structure and master equation results from an AutoMech mechanism development workflow, with sufficient provenance information for reproducibility. The software is designed to be highly adaptable to new applications and technologies: for parallel execution, workflow orchestration back-ends are hidden behind a common interface with flexibility to add new ones; for electronic structure calculations, users have the option to connect their own methods and programs as plug-ins and call external codes for alternate implementations of high-level routines; for data management, the database schema adheres to an objective representation of potential energy surface data adaptable to any application or range of conditions. As quantum chemistry and high-performance computing continue to advance, these design choices enable AutoMech to rapidly incorporate new technologies emerging from these two fields, benefiting its users and addressing scientific challenges. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry in the Mathematical and Physical Sciences Directorate. 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.