University of California-Davis

NSF Awards · FY 2024 · 2024-10

This REU Site award to the University of California, Davis, located in Davis, CA, will support the training of nine students for eight weeks during the summers of 2025—2027. Research will be conducted on the UC Davis campus, surburban habitats, adjoining riparian and agricultural reserves in Yolo and Solano Counties, and natural areas in the Sierra Nevada and on the California coast. Twenty-seven students, primarily from schools with limited research opportunities, including recruiting partners Sacramento City College, California State University Sacramento, and Californa State University Fullerton will be trained in the program. The next generation of environmental scientists must be equipped to study phenomena in ecology and evolutionary biology, and communicate with the public, resource managers, and policymakers. The “Ecology, Evolution, and Working Landscapes” REU is designed to accomplish these goals. Many students in this REU site will present the results of their work at scientific conferences. Assessment of this program will be done through an online tool. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The “Ecology, Evolution, and Working Landscapes” REU focuses on the ecological and evolutionary effects of changing environmental regimes. Participants will conduct individual research projects co-mentored by UC Davis faculty, graduate students, and faculty at each student’s home institution. Programmatic activities include journal clubs and field trips, training in scientific communication (including filming individual research videos), training in the R scripting language, professional development focused on career exploration and preparation for graduate study. Research questions encompass challenges that organisms and ecosystems face due to rapid environmental change, especially in urban, agricultural, and natural systems. Faculty mentors are drawn from biology departments across UC Davis, with research approaches that incorporate field work, controlled experiments, greenhouse or laboratory observations, genetic/genomic tools, and modeling. Students will be selected using holistic review; prior research experience is not required. For more information, see https://ecoevoreu.ucdavis.edu or contact Principal Investigator Jeffrey Ross-Ibarra (rossibarra@ucdavis.edu) or co-Principal Investigator Anne Todgham (todgham@ucdavis.edu). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

To provide essential services, anchor institutions (which include public libraries, schools, and hospitals) increasingly rely on high quality broadband Internet connections that enable important services such as educational software, videoconferencing, and telehealth. However, little is currently known about whether the quality of such anchor institutional networks (AINs) meets their needs. This project will fill this gap by conducting rigorous measurements to identify locations, quality, and opportunities for improving AINs. Such information can help governmental and private efforts to improve the ability of anchor institutions to serve the technological needs of the general public. The intellectual merits of this project fall in three related categories on computer network availability, reliability, and performance. First, on network availability, the work will create an annotated map of AINs, including the providers that they use to connect to the Internet. Second, on network reliability, the project will collect evidence to assess how reliable AINs are, for example, how often they experience outages, using a mix of existing and novel methods in Internet measurement. Third, on network performance, this research will determine whether AINs meet the technology needs of users who rely on them, for instance, whether the connection speed at the library is sufficiently high to support videoconferencing for all patrons who need it. This category requires significant advances in network performance characterization, particularly on determining (and measuring) the adequate bandwidth needs for a varied mix of networked applications. This collaborative project, which brings together researchers from Northeastern University, University of California-Davis, and University of California-Santa Barbara, has the potential for substantial broader impacts beyond its scientific advances. AINs are often the last line of availability for many users from historically-marginalized communities, including school-age children in rural or tribal areas, who do not have reliable or adequate Internet service at home. Thus, adverse events affecting AINs (outages) or persistently inadequate connections (low performance) can lead to disproportionately negative impacts on these at-risk communities, including low-income neighborhoods in urban cores. By producing a comprehensive study that evaluates connectivity at anchor institutions, this project will facilitate broadband equity and access efforts from consumer advocates, Internet providers, and local, state, and federal governments. All code and non-sensitive datasets will be publicly released on this repository: https://github.com/anchor-institutions/anchor-institutional-networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Research funded by this award will promote the progress of data science in support of the nation’s smart transportation systems through establishing a theoretical foundation for quantifying the value of information from various data sources in a transportation network. Major transportation network operators, such as the California Department of Transportation, depend on good-quality data to operate and manage their systems. Acquiring and managing data can be expensive. A crucial question arises: What data should a network operator acquire to optimize planning and operations? Priorities in data acquisition plans must align with their value for the network operator’s decision-making. Currently, there's no established theoretical framework for developing these plans. This research looks to establish a unifying theoretical framework for evaluating and optimizing data acquisition in a transportation network. The research project will directly benefit society by facilitating effective utilization of information and leading to more sustainable and efficient transportation systems. Interdisciplinary curriculum development supported by the research findings, including modular course materials that can adapt to varying learning needs, will help better prepare and broaden participation of next-generation professionals in the smart transportation innovation ecosystem. The research project introduces novel concepts that quantify the value of data through the lens of robust estimation and decision processes and translates the impact on robustness to sensitivity analysis of optimal planning problems. The research centers on three tasks: Task 1 quantifies how changes in data affect estimates of network performance metrics, which will enable a network operator to identify what data is important and how the importance varies spatially and temporally. Task 2 concentrates on modeling of data acquisition and leads to stochastic optimization models that prescribe the best data acquisition plan in support of the subsequent estimation of performance metrics. Task 3 creates three case studies for the purpose of testing and validating the methods using both real-world and synthetic data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Despite recent clean-energy initiatives, the US currently ranks second in global yearly total CO2 emission, totaling 5.01 billion metric tons in 2022. As such, developing stable and low-cost solar cells is critical. An emerging class of material, named halide perovskites, have the potential to deliver high-performing and low-cost solar cells or be combined with silicon, which dominates ~90% of the market, to boost performance. However, device degradation under environmental stressors (e.g., humidity, oxygen, and temperature) still precludes their commercialization. Concomitantly, the implementation of low-cost polymer materials to help collecting the current produced by the devices while securing long lifetime is urgently needed. Therefore, a primary research goal of this project is to advance the state-of-knowledge of halide perovskite solar cells by developing high-performance devices with enhanced stability upon exposure to environmental stressors. The methodology to be implemented combines automated experiments with a machine learning driven analysis that will help identifying polymers that can enhance device stability and performance. Broadly, the research component of this project will impact the development of future solar cells by resolving the ideal combination of stressors. The outreach impacts will help female students securing leading positions in STEM by provide mentoring and training, including research experiences to graduate and undergraduate students. The Materials Science and Engineering at UC Davis curriculum will be enhanced by adding new lectures into a core course for undergraduates. All ML codes will be made available in GitHub and the scientific findings will be disseminated through peer-reviewed publications. This research aims to investigate three challenges related to HP solar cells: (1) determine the combined effects of environmental stressors on perovskites’ stability; (2) quantify hole transport material/halide perovskite (HTM/HP) interface stability using novel, state-of-the-art conducting polymers that enable matching the energetics of band alignment required for high-performing photovoltaics; and (3) demonstrate solar cells with >95% performance retention for >1,000 h. Because controlled stability tests conventionally require extremely time-consuming tasks, automated and high-throughput experiments will be used. The information acquired will, in turn, inform machine learning algorithms to forecast stable HTM/HP interfaces. First, in situ optical measurements will be performed under distinct environmental conditions to elucidate the individual and combined effects of humidity, oxygen, and temperature on HP degradation. Second, the HPs will be interrogated via in situ X-ray diffraction to resolve how structural changes correlate with and affect device performance. Third, the effects of the abovementioned stressors on the HTM/HP interfaces using six cutting-edge, novel HTMs will be quantified while gathering valuable data for training machine learning algorithms. Fourth, this information will identify the most stable HTM/HP interfaces through predictive, physics-informed models. The results generated will guide the future development of application-dependent encapsulation strategies for HP solar cells with prolonged lifetime. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Alkalinity refers to seawater’s ability to neutralize acids, such as those that form when atmospheric carbon dioxide reacts with seawater. Seawater alkalinity controls how much carbon dioxide the ocean absorbs from and releases to the atmosphere and is thus a key component of the global carbon cycle. Vegetated coastal ecosystems such as mangroves, seagrasses, and tidal marshes could be a significant source of alkalinity to the coastal ocean but currently we do not know how much alkalinity is produced in these environments. Seagrasses are vegetated coastal ecosystems that exist on the coastlines of every continent except for Antarctica. This research will measure how much alkalinity is being produced in seagrass ecosystems and will investigate the processes controlling alkalinity production. The project will evaluate the safety and efficacy of emerging marine carbon dioxide removal (mCDR) technologies that are designed to increase the amount of carbon dioxide the ocean can naturally absorb from the atmosphere by artificially increasing alkalinity in coastal environments. A stronger understanding of alkalinity cycling in the coastal ocean is not only critical to constraining oceanic and global carbon budgets, but also to predicting how these budgets could be perturbed by ongoing anthropogenic impacts and emerging mCDR technologies. This work will quantify sediment total alkalinity fluxes and investigate the biogeochemical processes controlling them across two contrasting seagrass ecosystems. A multi-analyte approach under both in situ and laboratory settings using microelectrode profiling, chamber incubations, and pore water analyses is planned. This combination of high-resolution measurements and characterization of sediment alkalinity cycling in seagrasses are designed to improve our understanding of alkalinity and carbon dynamics within seagrass ecosystems. Experiments will also be conducted to understand the impact of proposed ocean alkalinity enhancement strategies (enhanced silicate weathering) on sedimentary alkalinity production in seagrasses. The project includes training in interdisciplinary oceanographic measurements and techniques and a group mentoring network for graduate and undergraduate students as well as paid research opportunities for community college students through the Santa Rosa Junior College-Bodega Marine Laboratory Internship Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project tests theories of reinforcement to view high-quality balanced news and public affairs information. This project develops two robust and principled interventions that nudge social media algorithms to influence news and public affairs recommendations. This project is supported under the EAGER program to encourage high risk, high reward research. The project team is designing, developing, and testing a personalized reinforcement learning based intervention that considers a user’s past watch history and incorporates explicit user feedback to further adapt intervention to user preferences. The project team is comparing this tool with is a generic “one size fits all” intervention, which obfuscates the watch history. The system design includes a method for identifying quality news (using validated expert metrics). The effectiveness of each tool is being evaluated in systematic, controlled sock puppet-based experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Collaborative work is essential for solving complex problems and making critical decisions. This is evident in domains like national security, where intelligence agencies work together; scientific research, where different fields combine their knowledge; and public health, where coordinated strategies address health crises. Effective collaboration relies on clear communication, but this remains challenging due to the inherently complex and expressive nature of human interaction. Even with advances in cloud storage, telepresence, and virtual workspaces, achieving complete clarity is difficult. The integration of data and artificial intelligence further complicates matters, introducing issues such as uncertainty, trust deficits, and varying interpretations of actions. Ambiguity, where multiple interpretations of the same data exist, can significantly impede a team's ability to achieve a shared understanding and make informed decisions. Since collaboration often relies on visual mediums like displays, video conferences, and documents, there is substantial potential for visually conveying the nature and degree of ambiguity inherent in collaborative tasks. This project aims to develop advanced techniques and tools to improve the management and resolution of ambiguity within collaborative visual analytics, enhancing visual communication to ensure that all team members can comprehend and agree on the information presented. By addressing these challenges, the project seeks to improve collaborative efforts across various domains, leading to better-informed decisions and substantial societal benefits. This project aims to provide teams with methods to more effectively identify, visualize, and track ambiguity in collaborative analytics. The technical approach is divided into three main aspects. First, the project will develop techniques to identify the sources of ambiguity in collaborative analytics tasks, including data, AI/ML models, visualizations, and analytical narratives. This will involve pinpointing instances where data and results lead to multiple interpretations, influencing analytical inferences and decisions. Second, the project will design new domain-relevant visual metaphors to represent ambiguity for effective communication. These visual metaphors will be specifically tailored to convey ambiguity within domains involving critical decision-making, and the project aims to develop a general set of design guidelines for visually representing ambiguity. Metaphors should highlight multiple interpretations from different sources, but it is crucial to balance metaphorical and abstract data representation to ground the interpretation without overwhelming the abstract representation. Third, the project will develop techniques empowered by large language models to track, interpret, and visually articulate the downstream effects of ambiguity on collaborative decision-making processes. By referring to domain-specific knowledge bases (e.g., ontologies), the project will interpret verbal markers in natural language communication within the target domain and visually expose potential ambiguities in the interpretation. The project will employ graph representations and explore graph-based methods to capture interactions between the analyst and the analyzed information, the operations performed, and the visualizations used. This simplifies conflict resolution between multiple interpretations and makes it more computationally feasible. These three research aims will be complemented by a comprehensive evaluation plan involving both formative and summative studies, as well as longitudinal studies with domain experts from emergency management and clinical decision-making. The focused research efforts will introduce a new paradigm in the visualization field to convey and manage ambiguity. Such advancement will enhance the clarity, efficiency, and effectiveness of team efforts and decision-making in an increasingly complex, AI-powered world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

MicroRNAs are short RNA molecules (~20 to 24 nucleotides) that function to regulate levels of gene expression in animals and plants. The targets of microRNAs function in many key cellular processes, including development, biotic and abiotic stress responses. In crop plants, microRNAs have demonstrated roles supporting phenotypes important for fertility and yield, and thus contribute to food security and the agricultural economy. As with all features of genomes, cataloging and tracking microRNA genes consistently and across genomes using appropriate gene names is crucial. This requires uniform and rigorous assignment of identifiers, periodic reassessment and screening using the latest criteria, and community input. It is crucial that different publications and databases use the same name for a particular microRNA, and homologs in different species should ideally receive consistent names. A single, central registry for microRNA nomenclature and quality control has existed since 2004 to name and catalogue microRNAs: miRBase. miRBase faces challenges in the current era of 'big data'. In particular, registration of new microRNAs currently relies on time-consuming, manual curation. Inexpensive sequencing of genomes and small RNAs has massively increased the discovery rate of microRNAs, exceeding the capacity of miRBase for manual curation of new entries. Yet the need for a single trusted resource is more acute than ever, for rapid registration of new microRNAs, and to assess both existing annotations and new. This collaborative project between the Donald Danforth Plant Science Center and Pennsylvania State University in the US and the University of Manchester in the UK will develop an autonomous and automated microRNA registry system for miRbase. This will be integrated with miRBase naming protocols and adding additional quality controls. The project will resolve a critical problem for the continued growth of the small RNA field, particularly in plants. By automating submission, curation, and release of new annotations, this project will eliminate the current reliance on time-consuming manual annotations to maintain miRBase. This will allow miRBase to continue to catalyze fundamental discoveries in all areas of biology. The project will thus build on existing tools to annotate high-confidence microRNAs from plant small RNA deep sequencing datasets. The project will develop a pipeline to automatically assign consistent gene names to novel plant microRNA annotations generated by users from these deep datasets. And the project will incorporate new interfaces into the miRBase infrastructure to accept and display user submissions of novel microRNA data, and to expand options for users to visualize data. An output of the project is training researchers in the analysis of biological big data and full stack web development -- skill sets in high demand across the sciences and beyond. The project will also integrate research with undergraduate education, and it will build on programs to broaden participation by under-represented 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.

NSF Awards · FY 2024 · 2024-10

Globally, food systems are responsible for 34% of greenhouse gas emissions and over 85% of water consumption, fueling climate change and negative environmental impacts. In the US, approximately 30% of the food produced goes to waste, while over 10% of households experience some level of food insecurity, largely in underrepresented communities. Artificial intelligence (AI) is a powerful technology that can revolutionize the US food and agriculture sector across the food value chain, “from farm to fork,” to meet the growing needs for environmental sustainability and climate resilience, while ensuring equitable access to healthy food for all Americans. Before AI tools and applications are deployed widely in the food and agricultural sector, it is critical to understand needs, concerns, and potential impacts on stakeholders in the sector. The proposed convenings will assemble experts in AI and food systems and stakeholders in the food and agriculture sector to discuss how AI technology should be designed, developed, and deployed in the sector and how the sector’s workforce can be trained to support the responsible and ethical use of AI across the food value chain. The convenings will emphasize AI that helps food systems address three societal challenges: environmental sustainability, climate resilience, and equitable access to healthy food. Convening objectives are to: 1) raise awareness and identify approaches and needs for AI to address societal challenges in food systems; 2) create education and workforce training recommendations to support responsible and ethical AI use in food systems; 3) convene stakeholders across the food value chain to build cross-sector teams to achieve objectives 1 and 2; and 4) identify potential model projects and agendas for involving stakeholders in AI design, development, and deployment in food systems to minimize harm across the technology life cycle. The convenings will include five online Communities of Practice in January -February 2025 and an in-person Workshop in May 2025, with facilitated discussions to generate recommendations and develop partnerships that build towards the convenings’ objectives. Participants will include experts in AI and food systems and stakeholders from across the food value chain. Output from the Communities of Practice and Workshop will be presented to the public in an online Symposium in September 2025 for a final round of feedback. Final products of the convenings will include a Responsible Design, Development, Deployment of Technology (ReDDDoT) model for AI in food systems that can inform NSF’s ReDDDoT needs and priorities for other sectors and technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by disseminating NSF-sponsored research results to the engineering education community and to broaden awareness of these funding programs. The American Society for Engineering Education (ASEE) annual conference is the largest gathering of the year for engineering educators and researchers in the United States. The conference enables scholars to learn and exchange ideas and gain training in pedagogy. Since 1992, the NSF has funded a poster session to highlight the breadth of sponsored engineering education work on topics funded by Improving Undergraduate STEM Education (IUSE), Scholarships in Science, Technology, Engineering, and Mathematics Program (S-STEM), Research in the Formation of Engineers (RFE), and Broadening Participation in Engineering (BPE) etc. This poster session has historically showcased the work of 120 to 250 projects each year at the annual conference. This project will continue facilitating the poster session and expand its impact through a mentored reviewer program. Junior scholars, such as graduate students and postdoctoral fellows, will be trained to complete the peer reviews for the poster session and will be exposed to the breadth of sponsored projects. Each fall, researchers with engineering education projects will be identified and invited to participate in the NSF Grantees Poster Session at the ASEE annual conference in June. Authors will submit an abstract followed by conference paper, which undergo peer review, to be accepted to the poster session. Concurrently, a cohort of junior scholars will be solicited to participate in the mentored reviewer program. The selected scholars will be trained to effectively review conference abstracts and papers by following a given rubric. Each year, the poster session will result in hundreds of open-access proceedings papers published through ASEE. Additionally, the poster session will support networking between conference attendees and allow those without NSF funding to explore the breadth of sponsored programs. The participants in the mentored reviewer program will benefit from training and exposure to multiple NSF-funded projects. These complementary efforts will broaden the exposure of engineering education research sponsored by the NSF. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is also being co-funded by the Division of Engineering Education and Centers (EEC) in the NSF Directorate for Engineering, reflecting alignment with the overall goals of EEC. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project investigates the role of secreted RNA in the immune system of plants. The Innes and Meyers laboratories recently discovered that the leaves of plants accumulate RNA in the spaces between cells and on their surfaces. Although we usually think of RNA as a molecule that can direct cells to synthesize specific proteins (e.g., the mRNA in COVID vaccines directs our cells to make SARS-CoV2 spike protein), some RNAs serve other functions. Analysis of the base sequences of plant extracellular RNAs revealed that these RNAs are diverse in sequence, but do not appear to encode proteins. The discovery of extracellular non-coding RNA in plants raises two fundamental questions that this project will address: 1) how do plants secrete RNA? and 2) what is the function of this RNA? It takes a large amount of energy for cells to secrete RNA, thus this secreted RNA must benefit the plant in some way. This project will test the hypothesis that secreted RNA functions to protect plants from infection by fungi and bacteria. If this hypothesis is correct, the proposed research will enable generation of crop plants with improved immune systems that are more resistant to disease. Such crops are needed to feed a growing global population in a sustainable manner, while reducing the environmental impacts of agriculture. The Innes and Meyers laboratories recently discovered that the apoplast of Arabidopsis leaves contains abundant long non-coding RNAs, including circular RNAs, as well as small RNAs. These RNAs are bound to protein particles, which protects them against degradation. Notably, this extracellular RNA (exRNA) is highly enriched in the post-transcriptional modification N6-methlyadenine (m6A). These discoveries raise fundamental questions about plant biology: Are there specific exRNAs that are broadly conserved across plant species? How are exRNAs secreted, and are post-transcriptional modifications central to this process? And why do plants produce exRNAs? Do they play a fundamental role in plant-microbe interactions? To answer these questions, exRNA will be purified from the apoplast and leaf surfaces of seven diverse species: Arabidopsis, soybean, tomato, lettuce, pineapple, rice, and maize, which were chosen based on their phylogenetic diversity, genomic resources, importance as crops, and diversity in physiology. These exRNAs will be analyzed using both RNA-seq and sRNA-seq, which will allow identification of RNAs that are conserved between species. To assess whether m6A or other modifications are required for secretion, transgenic plants that express exRNAs that lack modification sites will be tested for their secretion efficiency. To investigate additional requirements for exRNA secretion, the exRNA content in Arabidopsis and rice plants with mutations in known RNA binding proteins and secretory pathway genes will be analyzed. Lastly, to assess whether exRNAs contribute to immunity, mutants compromised in exRNA secretion will be tested for resistance to fungal and bacterial pathogens. This award was co-funded by the Plant Genome Research Program and the Plant Biotic Interactions Program in the Division of Integrative Organismal 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 2024 · 2024-09

Engineering programs are known to be rigorous, with high expectations and workload. As a result, poor self-care habits might be perceived as part of the engineering identity, where rigor, stress, and suffering are considered norms of being an engineer. This perception has been studied by scientists, and literature data suggests that undergraduate engineering students are less likely to seek help when suffering from a mental illness when compared to non-engineering students. Consequently, a considerable number of higher education institutions have developed and offered a variety of wellness programs. However, engineering students are less likely to utilize such resources due to their high workload and the stigma associated with engineering identity. Prioritizing self-care activities over coursework can be seen as violating a cultural norm within the engineering discipline. Nevertheless, good mental and emotional health and self-care practices are essential not only for academic success, but also for a successful practice of the engineering profession. Most engineers’ time will be spent on interaction with a diverse range of people to define goals and make decisions where personal biases, cultural background, and emotions are involved. Not surprisingly, the starting point for effective engineering practice is to learn how to control one’s emotions and take care of one’s physical and overall well-being in order to be able to guide, give advice, and inspire others. This project has the objective of developing and delivering a wellness course for engineering students, where participants will allocate time with their peers and faculty to discuss and practice self-care activities and gain training in how to care about their overall wellness. Additionally, the project will measure the impact of the course on students’ emotional intelligence and psychological capital. This is aligned with NSF mission to expand knowledge in engineering education research. More specifically, it will equip students with skills required by 21st century engineers such as communication, resilience, emotional and stress control, among others. Each lecture/activity of the wellness course was designed to focus on a self-care domain (cognitive, emotional, interpersonal, physical, practical, and spiritual). It is hypothesized that the class will increase students’ resilience, optimism, hope, self-efficacy, emotionality, self-control, wellbeing, and sociability. Data will be collected to address the following research questions: 1) What is the level of emotional intelligence (EI) and psychological capital (PsyCap) among engineering students? 2) Does the wellness course significantly impact students’ emotional intelligence, and psychological capital? To answer the research questions, the project will survey undergraduate engineering students about their emotional intelligence and psychological capital by using the Trait Emotional Intelligence Questionnaire - Short Form (TEIQue-SF) and the Psychological Capital questionnaire, respectively. TEIQue-SF is divided into four sub-dimensions (emotional, self-control, well-being, and sociability) while Psychological Capital is divided into self-efficacy, optimism, hope, and resiliency to measure an individual’s strength towards each of these characteristics. Additionally, students will be surveyed to identify their personality profile by using the Big Five Inventory questionnaire. Students will be surveyed at the beginning and the end of each academic term. Furthermore, students will be asked to self-report whether they participated in the wellness course. The survey will be distributed to two different control groups: (i) students who did not participate in the course and (ii) students who participated in a short-term wellness assignment in a mandatory core chemical engineering class. The results of the quantitative surveys will be complemented with a qualitative interview. The project will provide useful insight for other engineering departments interested in improving student well-being through integration of training on mental health and self-care. 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

Reproduction in both plants and animals requires the fusion of egg and sperm cells – the maternal and paternal gametes – to form a zygote, a single cell that initiates the formation of an embryo. Seed production by plants requires embryo formation, and therefore the zygote is a critical stage in the plant life cycle. Zygote formation is accompanied by extensive reprogramming of gene expression, accomplished in part through modifications of DNA and associated proteins that are known as epigenetic processes. A comprehensive characterization of this reprogramming has not been performed in flowering plants due to technical difficulties in isolating egg cells and zygotes. This project will use improved methods developed in the model plant Arabidopsis to characterize genome-wide changes mediated by epigenetic mechanisms in plant zygotes, with an emphasis on rice, an important crop plant. The project will also provide training in plant biology and genomics to students and faculty. Knowledge of the mechanisms by which gametes give rise to zygotes has significant agricultural applications, including hybrid seed production through clonal propagation that substantially reduces the costs of hybrid seeds, production of haploids useful for accelerating plant breeding, and the regeneration of plants from tissue culture. In plants, rapid and extensive transcriptional activation of the zygotic genome occurs shortly after fertilization. Studies on rice zygotes show that transcription is primarily from the female genome, but the male genome provides key transcription factors for the initiation of embryogenesis after fertilization. This parent-of-origin-dependent gene expression must arise from genome-wide epigenetic modifications set during gametogenesis. However, there is a lack of understanding of the underlying mechanisms, and little information on the histone modifications in egg cells and zygotes, due to technical limitations imposed by inaccessibility and insufficient material. This project will employ recent technical advances that have made possible the analysis of histone marks from low numbers of cells, in combination with methods to isolate egg cells and zygotes in sufficient amounts for these new techniques. The methods will be optimized in Arabidopsis and implemented in rice, to elucidate details of the epigenetic reprogramming that accompanies gamete formation and identify epigenetic marks that establish the totipotent zygote. Integration of chromatin data with previously obtained mRNA, DNA methylation, and small RNA datasets from rice will generate a comprehensive genome-scale atlas of the gamete-to-zygote transition. The project will help fill a major gap in our understanding of a critical transition in the plant life cycle and provide fundamental knowledge that can be utilized in applications such as asexual reproduction, haploid production, and clonal propagation of crop plants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The project aims to develop innovative artificial intelligence (AI) tools to study polyhedra, which are fundamental in various fields such as combinatorics, discrete geometry, and optimization. The complexity of polytopes makes it challenging for researchers to gain insights and draw connections between their structures and properties. This project addresses this challenge by leveraging AI to enhance mathematicians' abilities to generate polyhedral samples, discover new conjectures, and conduct rigorous reasoning on polyhedral geometry. This research is significant as it not only advances the mathematical field, but these innovations are expected to significantly advance the understanding and application of polyhedral geometry in various scientific and engineering domains, as well as advance the potential of AI for mathematical reasoning. The project also supports education by creating tools that can be used in teaching. Additionally, the project promotes diversity and inclusivity in STEM by engaging underrepresented groups through workshops and mentoring programs, thereby inspiring a broader range of students to pursue careers in these fields. The technical scope of the project includes developing new methods for data generation, knowledge discovery, and formal reasoning in polyhedral geometry. The project will use AI techniques such as diffusion methods and reinforcement learning to create diverse, high-quality polyhedral samples. A key innovation is the development of Polyhedral-GPT, which integrates large language models to provide clear, interpretable outputs using a polyhedral transformer. The project also aims to enhance computational efficiency by combining fast, informal AI techniques with rigorous formal verification. Additionally, a black-box interpreter will automate the translation of polyhedral knowledge into natural language, minimizing human intervention and streamlining the process from conjecture generation to formal proof verification. The integration of optimization, algebraic geometry, and SAT solvers will further facilitate automatic proof processes, contributing to the project's overall efficiency and accuracy. 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 abundances of species fluctuate through time and differ across space. Variation in abundances is used to determine patterns of synchrony (i.e. correlation in abundances), which influence species extinction risk and the stability of ecosystems. Patterns of synchrony are driven by many factors, such as variability in temperature or precipitation, or the dispersal patterns of species themselves. To date, research on synchrony has primarily focused on the role of the environment as an external force driving synchrony, both across locations and across species. However, traits, which define characteristics of species, likely play major roles in regulating synchrony. Further, different traits might determine patterns of synchrony in different environmental conditions. This research combines plant composition data from a global grassland experiment with collections of species trait data to test how traits determine patterns of synchrony across environmental gradients. The researchers will additionally lead a virtual global seminar for grassland ecologists on integrating experimental data with ecological theory. The grant will support multiple students in attending and networking at an annual working group meeting that brings together grassland scientists. This research will enhance understanding of the processes that determine ecosystem variability, aiding our ability to manage ecosystems and predict responses to future environmental change. Synchrony is a ubiquitous phenomenon across ecological levels of organization and is driven by both biotic factors and abiotic environmental drivers. At the population level, correlations in temporal abundance fluctuations across distinct locations (i.e., population synchrony) increase species' extinction risk. In communities, correlated fluctuations among species in a given location (i.e., community synchrony) decreases ecosystem stability. Species traits (morphological, physiological, and phenological characteristics of species) and trait-by-environment interactions likely play major roles in regulating synchrony. The project integrates multi-site time series data from a global distributed experiment (the Nutrient Network) with novel trait collection, existing trait databases, and methods development to create and empirically test frameworks for linking functional traits to synchrony across scales of ecological organization under both natural conditions and experimental treatments that alter herbivory patterns and nutrient enrichment. The research will synthesize multiple trait databases and collect new data on traits hypothesized to mechanistically affect synchrony, characterizing how trait composition within ecological communities responds to increased fertilization and decreased herbivory pressure. Trait data, coupled with long-term time series data of plant composition, will be used to assess how trait variation and trait-by-environment interactions influence population and community synchrony across environmental gradients. 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

Bilevel optimization is a powerful paradigm used to solve modern problems in signal processing and machine learning, such as multi-task learning, sequential decision making, robust adversarial training, and hyperparameter fine-tuning. More recently, the online bilevel optimization framework has been proposed to handle practical applications where environments and datasets change over time. These online problems are challenging because streaming data require fast decisions on-the-fly, and only limited information about the objectives can be sampled due to their rapid variations. In general, online bilevel optimization is largely unexplored, calling for a systematic in-depth investigation. The primary goal of this project is to comprehensively study online bilevel optimization, aiming to (i) speed up online bilevel algorithms, improve their scalability, and ensure their performance, and (ii) explore two real-world applications to further leverage the advantages of online bilevel optimization in solving practical problems. The proposed program will focus on the following research innovations to significantly broaden timely applications of online bilevel optimization in real-world problems: (i) algorithmic and analytical foundation for online bilevel optimization; (ii) a novel approach for online robust adversarial training via the lens of online compositional bilevel optimization; (iii) accelerated online multi-agent meta-learning design via online nonconvex bilevel optimization; and (iv) extensive experiments to validate the proposed approaches and algorithms. This project will also provide exciting training and research opportunities through topic courses, tutorial presentations, undergraduate research programs, and K-12 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 2024 · 2024-09

Abandoned mines release very acidic water called acid mine drainage (AMD) that is rich in metals like iron, copper, aluminum, and arsenic. Bioremediation – using life, especially bacteria to remove metals and other toxins from AMD streams is more cost effective than other active treatment methods. In AMD bioremediation, bacteria take iron dissolved in water and turn it into rust. The rust then scrubs other heavy metals from the system. The success of bioremediation relies on how quickly and effectively these organisms can remove iron as rust. However, we don’t know what species remove iron the fastest or how or if iron is redissolved once it is buried. The overarching goal of this research is to unravel what controls the rate at which iron is removed, whether processes in the subsurface can undercut these processes by re-dissolving iron, and whether we can generate an environmental “probiotic” to increase iron removal in AMD sites. We do not know the species or the geochemical conditions that promote rapid Fe(II) oxidation. Therefore, the researchers will systematically link geochemistry and microbial metabolic potential to iron oxidation rate. Iron reduction in subsurface environments can undermine bioremediation efforts but little is known about biogeochemistry in the AMD subsurface. Therefore, the researchers will use porewater geochemistry and microbial communities to examine the biogeochemical processes occurring in the AMD subsurface. Seeding microbial communities is a promising strategy for enhancing bioremediation efficacy. However, these efforts can by stymied by complex interactions between geochemistry, ecology, and dispersal. Therefore, the researchers will perform a field scale AMD seeding experiment using constructed AMD ecosystems to determine if seeding is feasible. The researchers will also make significant contributions to undergraduate science education by developing a course-based undergraduate research experience, a data-rich module for undergraduate courses and offering a summer research opportunity for undergraduate 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 relative abundance of different stable isotopes of an element in natural materials and human-made products can provide valuable information about the material’s origin and history. As a result, stable isotope data plays a crucial role in many scientific disciplines from the Earth and Life Sciences to Archaeology, Nutrition, and Forensics. Unfortunately, the analysis of stable isotope data is complex and time-consuming, which limits the scope, utility, and reuse of isotopic data across scientific disciplines. This collaborative project thus aims to create open-source data tools, collectively known as the ISOVERSE, to enhance the processing of stable isotope data. The ISOVERSE project seeks to address the challenges of processing and sharing stable isotope data by providing efficient, transparent, and reproducible data analysis tools accessible to the broader scientific community. The significance of this project lies in its potential to foster new discoveries and advancements in stable isotope research through improved data analytics capabilities. By ensuring open access to data and promoting reproducible data processing, the ISOVERSE project can facilitate collaborations, methodological progress, and data sharing across disciplines. The ultimate goal is to create a common computational ecosystem that supports and trains researchers in overcoming obstacles in stable isotope analysis, data exchange, and reuse. The core of the ISOVERSE ecosystem consists of four modules: isoreader, isoprocessor, isoconnector, and isoexplorer. These modules provide core functionalities for stable isotope data input/output, computational tasks, data reporting, and graphical user interfaces, and will be built on a flexible framework for future extensions. The ISOVERSE will be implemented in R, a popular open-source programming language, and will be hosted on GitHub and distributed globally through the Comprehensive R Archive Network. The ISOVERSE will be developed using a user-centered design and team science approach, with continuous engagement from the stable isotope research community. This ensures that the ISOVERSE meets the needs of its users and that it is well documented, effective, and easy to use. Additionally, ISOVERSE will be integrated into education and research programs to train students and scientists in the use of stable isotope data. By creating a comprehensive and user-friendly platform, the project will empower researchers at all career stages and skill levels. This award by the Geoinformatics and Earth Sciences Instrumentation and Facilities programs within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Exploding stars, called supernovae, have an outsized impact on the Universe, seeding the next generations of stars and planets with newly-forged chemical elements, and shaping galaxies. Important clues about what exploded and how arrives only in the first hours and days after these explosions, so it is important to have telescopes that can promptly identify and study new supernovae. This research team will enhance its rapid survey for the nearest and brightest supernovae in the sky. They will incorporate new infrastructure to find even younger nearby. As part of this work, three graduate students will learn and contribute to supernova science and develop skills in technical software, data analysis, and scientific presentation and publication. The team will also communicate research results and technical expertise to the public, running a software bootcamp at Pima Community College, using supernova science examples, and contributing to planetarium shows at the Liberty Science Center in New Jersey. The team will search for supernova in galaxies within 40 Mpc of Earth, taking advantage of four small telescopes in the PROMPT network. The survey employs a real-time machine learning algorithm that can automatically trigger other telescopes once a strong supernova candidate is found; further improvements to this algorithm will be made, incorporating new telescopes and instruments. Combining this active search with the data streams of other supernova searches will yield 30 very young nearby supernovae over three years – the predict the discovery of 12 thermonuclear, type Ia supernovae; 12 core collapse supernovae and 6 stripped envelope supernovae. Early spectroscopy that displays narrow emission lines will probe the composition of the circumstellar medium and thus the final years of the progenitor star’s life. For all types of supernovae, densely time-sampled spectroscopy will measure the composition and distribution of the ejecta. 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 answers to real world problems, such as determining the behavior of particles in particle accelerators, are often quite complicated. Mathematics abstracts these complicated behaviors, and often reveals hidden structures; abstraction allows one to see the forest rather than the trees. For example, physicists Arkhani-Hamed and Trnka uncovered a high-dimensional mathematical object called the "amplituhedron" whose geometry should govern particle scattering. However, as abstraction increases, intuition decreases; it is easy to lose sight of the trees among the clouds. Algebraic combinatorics, as a mathematical discipline, is a tool to represent abstract mathematics in a more concrete way--similar to how a bar graph or scatter plot is a tool to represent a long list of numbers in a more intuitive way. In the case of the amplituhedron, combinatorics provides a way to break the amplituhedron up into smaller, simpler pieces. It also provides a way to visualize each piece, even though the pieces do not fit in three dimensions. It is through this combinatorics that the conjectural relationship between the amplituhedron and particle scattering is most apparent. In one project the PI will work to prove this conjectural relationship with collaborators Even-Zohar, Lakrec, Parisi, Tessler, and Williams. In general, the PI will seek to better understand the combinatorics of amplituhedra and related mathematical objects called cluster varieties. The PI will involve both undergraduate and graduate students in thisd research. The broader mathematical context for the proposed projects is the theory of total positivity. Classically, a matrix is totally positive if all minors are positive. Lusztig extended the notion of total positivity to partial flag varieties, while Postnikov independently defined the positive Grassmannian. The combinatorics of total positivity is incredibly rich, leading to the definition of cluster algebras by Fomin and Zelevinsky. The PI proposes to study two generalizations of total positivity through a combinatorial lens. The first project concerns amplituhedra, which generalize the positive Grassmannian and arise in particle physics. The PI will work to resolve conjectures on the relationship between tilings of m=4 amplituhedra and the computation of scattering amplitudes, as well as various conjectures on tilings of m=2 amplituhedra. The second project concerns cluster structures on braid varieties, which generalize positive partial flag varieties. The PI will further develop the combinatorics of this cluster structure, investigating 3D plabic graphs and their relationship to weaves, and explore 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 2024 · 2024-09

New technologies promise to augment the interactions of group members with their environment and with each other, enhancing the learning and effectiveness of work teams, community groups, classrooms, and other collectives. Already, many communities communicate via "smart" technological environments that are dynamically adapted to support shared goals. This project examines how to optimize such technological augmentations for collective intelligence and goes a step further, by investigating how to give groups, themselves, agency over the optimization process. Giving a community control over its own socio-technical learning environment could potentially allow a greater degree of effectiveness and legitimacy, but it invites new sets of challenges: How does a collective develop and learn the rules that will best structure their interactions? What are the unintended effects of granting such freedom on collective outcomes? The investigators address these questions through the development of large-scale online experiments and computational models aimed at understanding collective learning and its relation to self-governance. Data collected and analyzed will provide insight into technological mechanisms that support small-scale democratic decision-making. This research will advance science at the intersections of sociology, cognitive science, and computer science and will help prepare the workforce to work optimally and cooperatively, in future technological environments. The research aims of this project focus on the parameters that drive group-directed learning in three areas important to groups: cooperative behavior, collective intelligence, and collective creativity. The experiments bring together hundreds of participants in computationally designed environments to understand how their interactions can be tuned to optimize group outcomes along these three dimensions. This work increases the access of scientists to complex, large-scale experimental designs and accelerate the pace of scientific research on human group behavior. Research and planning tools that empower communities to incrementally explore the rule spaces that govern their interactions will be shared with the research and other relevant communities. 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 EArly-Concept Grants for Exploratory Research (EAGER) project will investigate the emergence, mechanisms, and applications of collective rationality (CR) among self-interested agents in the design of mixed autonomy networks and infrastructure systems. In many natural and engineering systems, various collective phenomena, such as spontaneous cooperation, spatial segregation, and behavior evolution and formation of social norms, can emerge at system level when the decisions and maneuvers of self-interested agents interlace with each other. Strategic agent behaviors play a key role in this process. This observation suggests that one may obtain a system with desired properties by carefully designing behaviors of its agents. We explore this idea and put forward the concept of “collective rationality” of mixed traffic towards explaining the formation of cooperation among self-interested driving agents in mixed autonomy transportation systems, to reduce travel cost, uncertainties, fuel emission, as well as to enhance equity among all road users. Broader applications include autonomous vehicle behavior design, emergency evacuation, and mitigation of pandemic spreading. The research will be further disseminated through curriculum design, K-12 education, and collaboration with practitioners, local government, and industry partners. This project will explore and rigorously define the concept of collective rationality in mixed traffic and explore its application in designing strategic behaviors of autonomous driving agents in mixed autonomy environments. Our core hypothesis is that collective rationality can emerge in broad scenarios even if the involved agents are self-interested. We will leverage game theory and reinforcement learning to verify this hypothesis theoretically and computationally. To establish theoretical models of collective rationality in mixed traffic, we will develop two classes of models with different levels of agent behavior details, respectively focusing on the one-shot interaction of n-class driving agents, and dynamic inter- and intra-class interactions and an analytical Fokker-Planck approximation to the corresponding evolution dynamics. To develop frameworks for collective rationality-informed autonomous vehicle behavior design, we consider two autonomous vehicle behavior design frameworks using reinforcement learning, which incorporate collective rationality in reward design and employ a bi-level pricing structure to equitably fine-tune the benefit of cooperation among agents. The research team will also expand and explore the CR concept in other application contexts such disaster evacuations. 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

Many real-world applications can be described by a list of entities and the relationships between them. For instance, a social network is a list of people and their friendships; a streaming media site might have a list of users and movies with a link between a user and a movie if the user has liked that movie; a genetic researcher might link specific genes with diseases if such a correlation has been established. Computer scientists express these relationships in a "graph", which represents entities as "vertices" and relationships as "edges" in the graph. Given such a graph, scientists can design methods -- "algorithms" -- to extract useful information from the graph, such as groupings of friends, suggestions for unseen movies, or potentially interesting gene-disease linkages. Insights from graph analysis are powerful and widely used today across science, engineering, and commerce, but designing and optimizing the algorithms that deliver these insights is a significant research and practical challenge that is rarely pursued in a systematic, automated, principled way. The researchers propose a new abstraction to characterize graph computation and, with that abstraction, a way to systematically explore the design space of a particular graph computation to identify efficient and novel implementations that can deliver best-in-class performance across different computer systems. The project will enable the combination of high-level programmability, automated exploration of design alternatives, and high and portable performance for graph analytics in a single open-source framework. It will also educate students on performance engineering topics critical to the efficiency of artificial intelligence (AI) applications. The investigators propose a framework to express, explore, and compile graph algorithms in a rigorous, succinct, expressive mathematical tensor framework suitable for both human and machine manipulation. The input to the new framework is an algorithmic description in Einstein summation notation, an "einsum", traditionally used in tensor-algebra and machine-learning applications, extended for graph computation. The new framework allows automated design explorations of graph and implementation choices that incorporate common algorithmic and implementation design patterns and transformations. The proposed design is separated into platform-independent and platform-dependent phases, enabling target-specific backends. The preliminary design exploration indicates that combining simple algebraic transformations within the proposed framework can produce meaningful results. The researchers will release the framework as open-source software, primarily focusing on an NVIDIA Computer Unified Device Architecture (CUDA) backend but also targeting AMD (Graphics Processing Unit, i.e., GPU) and C++ (Central Processing Unit, i.e., CPU) environments. Finally, the researchers will also work with the OpenCilk academic board and NVIDIA's education team to advance the field of GPU performance engineering and continue their strong record of mentoring and advising 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

Across the United States, wildfires are increasing in frequency, size, and duration. They blanket huge regions of the US with fire smoke, exposing millions of Americans across multiple states to dangerous air quality for days or even weeks. Currently, Americans have access to systems like the EPA’s AirNow to check current air pollution levels in their community, known as the AQI (Air Quality Index) score, to see if they should take any precautions before going outside. However, this score only considers certain types of pollution like ozone or particulate matter but does not consider a specific type of pollution: certain chemicals found in fire smoke known as volatile organic compounds (VOCs) that are known to cause cancer. Currently, there is no technology available to monitor our air for this type of VOC pollution. This project will develop devices that can monitor VOC pollution outdoors so that, long term, Americans can have a more complete picture of air quality in their region, and know when to take action to keep themselves safe and healthy. The team in this award will assemble a network of portable chemical sensors that can continuously monitor outdoor air, targeting specific carcinogenic VOCs such as benzene, toluene, and xylene. Devices can be set outside and will continuously measure VOCs, relaying data to a cloud for data processing to provide quantitative concentrations. Targeted VOC measurements will be accomplished using gas chromatography coupled with a differential mobility spectrometer chemical detector. First, the team will test different sensor packaging that ensures devices can survive harsh outdoor conditions long term such as heat, rain, or wind, while making accurate measurements. Also, the team will develop wireless communication capabilities for the chemical sensor network, which will relay data over long ranges using the LoRa radio communication technique. The team will test a prototype sensor network under controlled conditions using controlled releases of VOCs inside an airplane hangar, and outside using controlled burns. They will also test the sensor network in an actual wildfire event, to track the real time distribution of cancer causing VOCs within the afflicted community. 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

No longer a looming threat, climate change is an active and ongoing crisis, with desertification depleting access to arable land and sea-level rise threatening coastal communities around the world. Studies of living populations, although valuable, present a limited view of the range of possible human adaptations to environmental shifts. Archaeological research provides a means of investigating the diverse ways that human societies have responded to climatological changes through time. Thus, the driving research focus of this project will be to enhance understanding of the range of human responses to environmental and sociopolitical change. The majority of archaeological research on periods of major climate or sociopolitical change tend to fall in either one of two camps: scholars arguing for sociopolitical collapse as a form of failure to respond to an external or internal stressor, and scholars arguing against this position. Interestingly, scholars in both camps tend to focus on the time period immediately surrounding the event in question, and until recently almost exclusively on the before and during. More rarely do scholars ask what happened in the decades and centuries afterwards. In this doctoral dissertation project the student will use bioarchaeological methods to investigate the way people managed and adapted to changing environmental conditions in the past. Bioarchaeology, the study of human skeletal remains from archaeological sites, provides a means of reconstructing certain aspects about the lives of past-peoples. This research focuses on a desert based archaeological site. It focuses on a large cemetery site used from the 13th to 15th centuries. This coincides with the Medieval Climate Anomaly, a period of intense climate change and drought in the much of the world. Bioarchaeological and chemical analyses of human remains allow researchers to reconstruct the dietary patterns, disease load, and presence of violence within this population, providing a window into climate change adaptation. The results provide a foundation on which studies for multiple regions can be compared. The research enhances international collaboration and exchange of ideas between archaeologists of multiple countries. Laboratory work creates and enhances educational and training opportunities for university students. This project adds to scientific knowledge about how people adapt to and recover from environmental shifts, providing policy makers with a greater capacity to craft scientifically informed policy. 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.