Indiana University

NSF Awards · FY 2024 · 2024-10

Artificial Intelligence (AI) significantly influences various aspects of life, yet its development often prioritizes business and governmental applications over the needs of everyday citizens. To address this gap, our project facilitates International Research Experiences for Students (IRES) that enable 30 US undergraduate and graduate students to collaborate with experts in Citizen-Centered AI (CCAI). CCAI combines Community Informatics and Digital Civics approaches to ensure technological advancements are aligned with community needs and stakeholder perspectives. Students will participate in a six-week immersive research experience with our Northumbria University collaborators, whose backgrounds in human-computer interaction, computer science, design, and psychology will improve the breadth and depth of the students’ CCAI projects. This initiative supports NSF’s mission by advancing science, promoting national welfare, and fostering diverse educational opportunities, particularly for underrepresented groups in computing. This IRES project focuses on providing students with hands-on research experiences in Citizen-Centered AI (CCAI) across diverse contexts such as health, democracy, security, and co-creation. Students will collaborate with faculty and doctoral trainees at Northumbria University, working on interdisciplinary projects that integrate public participation in AI design and critically assess AI's societal impacts. The program encompasses four key themes: 1) Communities, Democracy, and Society, where projects will enhance civic engagement through AI-driven public service delivery and governance participation; 2) Health and Wellbeing, where research will focus on AI tools in medical decision-making, transparency in health recommendations, and ethical AI mental health chatbots; 3) Identity, Privacy, Security, and Mis(dis)information, where projects will address AI personalization, misinformation detection, and bias mitigation; and 4) AI, Design, and Co-Creation, where students will explore AI as a collaborative actor in design processes, policy co-creation, and speculative futures. In all of these key areas, students will develop skills in relevant research methods and technologies, engage with local community partners, and build professional networks, enhancing their expertise in CCAI and contributing to the broader field of human-centered AI. This program aims to produce publications, foster interdisciplinary collaboration, and create pathways into STEM careers, ultimately promoting responsible AI development that benefits society. 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

Private data federations (PDFs) are emerging systems designed to address the challenge of multiple parties collaborating on sensitive data. They enable secure analytics across isolated private data without requiring direct data sharing, and provide end-to-end privacy throughout the entire process. Despite significant efforts to develop efficient PDF systems, their adoption within the scientific community remains limited due to a substantial usability gap, as these systems often require expertise in both security and system fundamentals. SciPDF democratizes this complex PDF pipeline by making cutting-edge PDF features accessible to the general scientific research community without the need for specialized expertise. This work significantly lowers the barriers to research collaboration in critical domains, including healthcare, biomedicine, federal statistics, finance, and criminal investigations. Furthermore, the research findings are part of a comprehensive education, dissemination, and outreach plan that includes new graduate and undergraduate courses, mentoring of students especially underrepresented minorities, and open-source tutorials accessible to the public. To achieve these goals, the project encompasses four main research thrusts. First, the design of an innovative self-sustaining query optimizer that autonomously handles complex PDF optimization primitives across various workloads. Second, the design and implementation of a full-fledged compiler to automatically translate logical queries into various PDF secure protocols. Next, the construction of high-level interfaces for system tuning, enabling non-expert administrators to fine-tune a PDF system with digestible policies and reason about the trade-offs between domain-specific research goals and privacy concerns. Finally, the assembly of a complete prototype system, benchmarked with real-world scientific workloads and evaluated via usability studies. 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

The project aims to serve the national interest by infusing data and artificial intelligence (AI) literacy in liberal arts (LA) curricula. The curriculum interventions will specifically enhance LA students’ basic understanding of data analytics and AI methodologies. The interventions will also enable the LA students to understand the limitations and ethical implications of data analytics and AI methodologies when they are applied for data driven and autonomous decision-making in real-life environments. The significance of the project is the transformation of LA curricula, which will prepare non-STEM students for current and future jobs, for which the capability of data analysis and knowledge extraction, along with critical understanding of AI's ability and limitation is vital. Overall, the project will build human capital by infusing data and AI literacy in the non-STEM workforce, enabling them to be productive members of today’s knowledge-driven workforce, regardless of their specific disciplinary training. With an overarching goal of improving data and AI literacy (DAIL) among the students taking courses in LA disciplines, the research team will pursue the following four research activities. First, is to define data science and AI literacy for LA curricula. Second, is to investigate the alignment of AI and data science education across different liberal arts courses. Third, is to design and develop AI and data science course projects and software tools by considering: (a) the existing course materials, the learning objectives, and the duration of a given course; (b) instructors’ and students’ ability and facility with AI and data science technologies; and (c) the long-term impact and broader understanding of the key concepts of AI and data science by the students. Fourth, and finally, is to design methods and metrics for independent assessment of the short- and long-term progress towards transforming LA education by improving the learning of AI and data science concepts by LA students. The project’s deliverables will be learning materials, software, and publications, which will be disseminated through the project website and public access repositories. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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 2024 · 2024-10

This project aims to serve the national interest of improving science teaching at the elementary level by infusing a critical computational thinking perspective into the science methods courses and field placement experiences of elementary level preservice teachers. Computational thinking is a means to understand and solve complex problems using computer science concepts. Despite its increasing importance, as reflected in national standards and initiatives, no single preservice teacher prepared to teach computational thinking has graduated from the teacher education programs in West Virginia, and the situation nationally is similarly challenging. Most often, a sole focus on computer science provides no explicit connection to disciplinary classroom settings and student context, missing important opportunities for preservice teachers to recognize what computational thinking may mean in their future classrooms. Without systemic changes in teacher education, preservice teachers are likely to develop insufficient levels of understanding of and interest in the computational nature of science. Consequently, preservice teacheers will be limited in providing their students with important knowledge and skills necessary to participate in the workforce of the future. To address the issue, this project aims to develop and implement a computational thinking-infused curriculum in science methods courses. The project intends to co-design and pilot science lessons in schools where elementary preservice teachers are placed. The research agenda of the project is designed to investigate the impact of the efforts on the teaching of preservice teachers. By the end of the project, the participating preservice teachers are expected to know, practice, and teach critical computation thinking-infused science lessons. Project activities are anticipated to result in a model and a set of resources that can be tested and used in teacher education nationwide to cultivate preservice teachers’ computational thinking-enhanced STEM learning and teaching. The overarching goal of the project is to help preservice teachers develop interest, competence, and a positive perception of the utility value for incorporating critical computational thinking into their science instructional practices. Project research questions focus on measuring these constructs and identifying computational thinking practices and processes exemplified in preservice teachers’ teaching efforts over time. Approximately 100 preservice teachers, enrolled in science methods courses in the Bachelor of Arts in Elementary Education Program of West Virginia University are intended to participate over two years. Preservice teachers are intended to have the opportunity to practice, design, and teach contextualized science lesson units that integrate both computational thinking processes and practices with the 5E Instructional Model, a widely accepted constructivist, inquiry-based instructional model for teaching science. Project research aims to determine the impact of computational thinking-infused activities on preservice teachers’ motivations and teaching. Then the project intends to employ a research-practitioner partnership perspective during full-time preservice teacher placement experiences. Selected preservice teachers are to be involved in iteratively co-designing, with multiple stakeholders from the local education community, piloting, and testing computational thinking-infused science lesson units in placement classrooms. Through the dissemination of project activities, research outcomes regarding how to integrate a critical computational thinking lens with the 5E Model, and developed materials, this project intends not only to help develop preservice teachers’ understanding of and interest in the computational nature of science teaching but also likely enable teacher educators and in-service teachers nationwide to recognize its relevance to pedagogy, content, and context, contributing to students’ preparation toward future STEM workforce. The NSF IUSE: EHR 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. Partial funding is from the Robert Noyce Teacher Scholarship 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

More than 350 languages and many additional variants and dialects are spoken in the United States and yet, voice technology recognizes only a handful. This research will create crucial training datasets, predominantly optimized for speech recognition (speech-to-text), for three underrepresented, American sociolinguistic contexts — a sociolect, a code-switching language context, and an Indigenous language. The methodology for co-creating these datasets with communities prioritizes building the agency, skills, and knowledge required for people to use and apply their dataset to serve their own social and economic context. Inclusive speech-to-text technology that recognizes more American language dialects means that more Americans can access critical information across citizen services, finance, education, health, and justice. The project iterates a community-mobilizing, inherently capacity-building, applied methodology for creating crucial machine-learning datasets, predominantly optimized for speech recognition (speech-to-text). The data creation process (text and audio) for these datasets will be run, hosted, and released through an open-source platform and infrastructure to ensure public accessibility. Communities will co-create the datasets from design phase to quality assurance, with space to shape the governance framework, diversity criteria, and domain representation. This program will: (1) bridge critical gaps for innovative technological research on under-represented languages and variants; (2) evolve understanding of culturally-conscious, consent-centric modes of community participation in the building of artificial intelligence (AI); and (3) accelerate first-language language technology tooling in key economic domains such as health, education, justice, and agriculture, thereby accelerating pathways to societal and economic benefits. The project will also advance skills development in machine learning by actively involving individuals who speak these underrepresented language variants in the data collection process. The project methodology is applied pedagogy, through teaching communities about AI training datasets by involving them in their design and build. This skill-building approach can lead to improved community representation within STEM professions, as well as immediately mitigating dataset biases and potential harms. 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

The fields that study mind and cognition are flourishing. Cognitive science has blossomed. Our understanding of animal minds has radically expanded. AI research has blazed past longstanding milestones like playing Go or writing credible essays. These fields study the same phenomenon—intelligence—in diverse forms, both natural and artificial. But they remain siloed, and each lacks a coherent foundation. This is most glaring in AI, where our capacity to engineer powerful AI systems has vastly outpaced our capacity to understand them. By building dialogue between the major disciplines studying natural and artificial intelligences, our project will produce a roadmap for Accelerating Research Collaboration on Artificial and Natural Intelligences (ARCANI). To create this roadmap, ARCANI will link several international research networks, drawing on insights and conversations that cross disciplines, career stages, and countries. The integration of these research networks will also help diversify participation in the study of natural and artificial intelligences. It will train a cohort of early career researchers in new ways of doing science and international collaboration. Through the existing Diverse Intelligences Summer Institute, the ARCANI team has an excellent record of fostering collaboration and leadership amongst researchers from underrepresented groups and developing nations. ARCANI allows us to scale up our efforts, improving training, collaboration opportunities, and skill development for young scholars across many disciplines. The ARCANI roadmap will lead to the development of theoretical foundations spanning from animal behavior to AI. It will establish a vibrant trading zone for sharing novel methods, data, and computational frameworks. And it will translate foundational concepts and empirical insights into practical technologies. These interdisciplinary efforts are poised to transform the ways humans think about the environment, interact with computer systems, and build AI to support science, art, and human endeavors more broadly. Such outcomes will enhance US scientific leadership in many critical lines of research. Our Network of Networks brings together researchers from the biological, cognitive, social, and computer sciences to develop new approaches to Accelerating Research Collaboration on Artificial and Natural Intelligences (ARCANI). ARCANI aims to break down disciplinary silos and develop a comprehensive framework for exploring human, animal, and machine intelligences. We will link networks in ethology, complexity science, comparative cognition, neuroscience, robotics, and AI, with nodes in every habitable continent. We have identified three promising areas for accelerated advance through network-to-network collaboration: (1) theoretical foundations of intelligence (DEFINE); (2) leveraging the data revolution to redesign the study of cognition (DESIGN); and (3) naturalistic approaches to AI and robotics (DEPLOY). Each area will be explored by a cross-network working group, holding several virtual meetings and one in-person meeting. Early career researchers (ECRs, including grad students and postdocs) will play a prominent role; the Diverse Intelligences Summer Institute (DISI) will help recruit diverse and exceptionally gifted ECRs. Working groups will address three questions: WHAT are the gaps in existing knowledge? WHO in their networks is best positioned to come together to address them? HOW can AccelNet support sustainable international collaboration to close those gaps? These questions will drive surveys of existing research, definition of key concepts, and frank assessment of barriers. Working groups will summarize their findings in white papers, which we will publish as agenda-setting documents. Working group members will join the leadership team in crafting a 2024 Implementation proposal, providing a roadmap toward transformational cross-network collaboration on the foundations and applications of research on artificial and natural intelligences. 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

An important goal of education is to help children independently control their own learning. To be able to self-control their learning, children should have a good sense of what they know, and how well. When children identify a gap in their knowledge, they need to then know what steps to take to address that gap. Yet, it is sometimes challenging for students to accurately judge what they do, and don't know well. This is especially problematic in challenging, but critically important domains, like when learning about fractions. This research will test methods for improving fourth through sixth grader's fraction learning, transfer of learning from one context to another, and children's ability to assess what they know and do not know, which is known as metacognition. Strategies for improving student's metacognition will be evaluated as they are learning fraction content by emphasizing both why students should be aware of what they do and do not know and how they should go about self-assessing this understanding using reflective questions, such as (1) "What information is given?" (2) "What steps do I need to take to get the right answer?" (3) "How can I check that my answer is right?" and (4) "If I think my solution is wrong, what should I do next?" This research will reveal insights on how to successfully encourage the productive use of metacognition and self-regulated learning in the context of math (fractions) learning. Few studies in the domain of math learning investigate both self-awareness of what one knows and does not know (i.e., monitoring) and decisions on how to proceed in the same experiment. This research plan will focus on implementing and evaluating metacognition training to improve fraction performance accuracy, self-awareness of what is known and not known about fractions, and control decisions, as well as the extent to which our metacognition training transfers broadly to other related math tasks and persists over a two-week delay. Fourth through sixth graders will be randomly assigned to one of three conditions: (1) a fraction content instruction, (2) fraction content instruction + metacognitive monitoring training, or (3) fraction content + metacognitive monitoring + control training. All children will complete a battery of their fraction knowledge at a pretest, immediate posttest, and delayed posttest to assess the potential benefit of metacognitive training beyond fraction-specific training on directly trained and related tasks. Investigating the promise of metacognitive training by asking children to engage in general, self-reflective questions in a "low cost" online intervention in the context of math learning will build on math cognition and math education theory. The results will also contribute important information regarding the design and implementation of potential educational interventions and teaching strategies leveraging metacognitive training in younger children. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. 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 recent rapid advancement in quantum computing has created a complex Quantum Cyberinfrastructure (QCI) that includes quantum algorithms, hardware, and software. However, this infrastructure is vulnerable to new quantum security threats, and the development of a secure QCI workforce has not kept pace. This gap poses risks to the technology industry, economic stability, national security, and US competitiveness. This project addresses this critical issue by proposing a comprehensive QCI curriculum, training projects, and various educational programs, designed for both beginners and advanced learners, with a special focus on engaging underrepresented minority groups. By enhancing quantum security awareness and QCI knowledge, this project aims to expand the QCI workforce, elevate secure QCI use in Science and Engineering research, and promote diversity in QCI education, ultimately supporting national interests in advancing science, prosperity, and security. The long-term goal of this research team is to establish a strong foundation for advanced and secure QCI learning and workforce development. This project will move toward this goal by developing a two-level QCI curriculum covering Basics, Algorithms, Hardware, and Software, along with corresponding training projects and activities. The key contributions include: (1) a modularized QCI curriculum, (2) specialized training projects, and (3) broadening educational activities. The work leverages the extensive collaborative research and educational experience of the three Principal Investigators, who possess expertise in quantum computing, physics, security, and cyberinfrastructure. The success of this project will expand the secure QCI workforce, improve fundamental Science and Engineering research and education, and enhance community diversity, thereby benefiting the national technology industry, economy, security, and overall competitiveness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project’s key goal is to investigate how (re)emerging zoonotic disease outbreaks shape the risk of different types of conflict and violence, highlighting local and global security impacts. An original database of confirmed outbreak events and conflict incidents over healthcare issues across the globe involving 30 different pathogens (1996-2024) will be in a collected. These data will be used to develop an understanding of both how zoonotic outbreaks shape conflict risk, and how conflict can impact the risk of epidemic outbreaks by inducing constraints on detection and response. This information will directly inform national security practices and improve American preparedness for emerging security challenges in the 21st security. The project will also lay a path toward better integrating infectious disease’s impacts across the social sciences, and – by directly involving graduate students as coauthors and collaborators – training the next generation of scholars studying emerging threats to global security. This project will address the present lack of a comprehensive theoretical understanding and assessment of infectious zoonotic diseases’ causal impacts on political conflict, including civil war and violence against civilians, as well as the limited availability of datasets with deep temporal and spatial coverages for infectious disease outbreaks. It will analyze several relevant pathways for zoonotic diseases outbreaks’ impact on global and local security, e.g., by destroying state capacity, impacting food security, and facilitating repressive violence. To this end, the project will collect global data on zoonotic disease outbreak events involving 30 emerging pathogens, measured at the subnational (village/town/district level) and sub-annual (day/week/month) level across the globe over the last three decades. In the process, it will also generate fine-grained data on political conflict incidents specifically perpetrated over infectious-disease related issues. The theoretical and empirical findings will aid in developing more generalizable theories of disease and conflict, with implications for improving national and international security and preparedness, offering a crucial ability to generate timely and critical insights into zoonotic diseases’ systematic impacts on these and other conflict and international relations aspects. These scientific findings will be disseminated to academic audiences via conference presentations, peer reviewed publications, and briefs for decision makers. All resulting databases will be made openly available online to scholars, policymakers, and students, contributing to a wide number of future science-use cases, not only in the arena of security and preparedness, but also across the social, environmental, and health sciences. 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 is a collaborative project among the University of Miami, Indiana University and Nova Southeastern University. Chikungunya (CHIK) is a viral disease transmitted to humans through the bites of mosquitoes infected with the chikungunya virus (CHIKV). CHIKV is endemic in Central and South American countries, posing significant public health burdens. As the “gateway to Latin America”, Miami-Dade County, Florida, has seen annual importations of CHIKV cases over the last decade. Miami-Dade County has an abundant population of Aedes mosquitoes, a suitable climate that promotes the growth of these mosquito vector species, and the potential for local CHIKV circulation. Integrating Aedes mosquito data collected in Miami-Dade County and local CHIK outbreak data from Brazil into a hybrid machine learning and mathematical modeling framework, the investigative team will reconstruct CHIKV dynamics in Brazil and evaluate control efforts in Florida. The project will further assess the risk for importation and local transmission of CHIKV in Florida considering global environmental changes. This study will provide valuable insights into the transmission dynamics of CHIKV and assist in developing more effective preventive and control measures. Findings can increase preparedness to anticipate and respond to other reemerging arboviruses such as dengue virus and yellow fever virus, as well as similar arboviruses yet to emerge. The project includes various activities for interdisciplinary training of undergraduate students, graduate students, and postdoctoral fellows. Networking activities are planned to encourage collaboration between researchers, especially young researchers from historically underrepresented groups in mathematics. The project aims to develop a novel method that integrates differential equations and machine learning techniques to incorporate complex features into traditional ecological and epidemic models. This method aims to: (i) identify climate and environmental factors affecting Aedes mosquito population growth; (ii) provide accurate projections on vector abundance to design mosquito control measures; (iii) reconstruct local transmission of CHIKV during recent outbreaks in Brazil; (iv) model the importation of CHIKV into Florida and the transmission of CHIKV from imported cases to local mosquitoes; (v) investigate how global environmental change may affect the population dynamics of Aedes mosquitoes and the local spread of CHIKV in South Florida. The obtained results will improve preparedness and response also for other emerging and reemerging arboviruses, such as the dengue virus, Zika virus, and yellow fever virus. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to computationally design, then fabricate and test, an electronic nose for classifying complex gas mixtures. Broadly, the gas sensing technology developed in this project could enable the design and fabrication of electronic noses for indoor and outdoor air quality monitoring, crop monitoring in agriculture, food quality assessment, and health assessment via breath composition analysis. Specifically, the hardware of the electronic nose will constitute an array of porous materials, metal-organic polyhedra (MOPs), coated as thin films on microbalances. Each sensor in the array functions by virtue of gas adsorbing into the film of material, which is registered by the microbalance. The software of our electronic nose will constitute a machine learning algorithm that parses the response pattern of the sensor array and makes a prediction about the gas composition that produced the response. A key advantage of MOPs is their tunability: the pore size and shape and surface chemistry of the MOPs can be tuned to arrive at a highly diverse set of MOPs that interact differently with each species in the gas phase. As a consequence, the response pattern of the MOP-based sensor array will provide a lot of information about the composition of the gas. While first tested for its ability to discriminate between pure analytes, the electronic nose in this project will be tailored and tested for classification of plant oils and, as a lofty goal, grades of olive oil, to counter fraud. For outreach, the project includes development of YouTube videos to educate the public about MOPs and gas sensors and hands-on learning modules with a gas sensor and an Arduino microcontroller. This project will design, fabricate, and test an electronic nose, consisting of (1) a gas sensor array employing diverse metal-organic polyhedra (MOPs) as gravimetric sensing elements paired with (2) a supervised machine learning model, to discriminate complex mixtures (eg. plant oils). As nanoporous, stable, recyclable, and tunable materials, MOPs may serve as sensitive and selective sensing elements for the next generation of gas sensors. Coating a thin film of MOP on a quartz crystal microbalance (QCM) gives a gravimetric sensor whose response is the mass of gas adsorbed in the MOP film. Mimicking olfactory systems in living organisms, the response pattern of an array of multiple QCM-MOPs—chemically and structurally diverse MOPs—will be interpreted by a supervised machine learning model to discriminate complex mixtures. The computational design of the QCM-MOP sensor array constitutes: (i) construct a database of candidate MOP structural models, (ii) conduct molecular simulations of adsorption of a portfolio of volatile organic compounds in each MOP, (iii) employ dimension reduction algorithms to embed the MOPs into a latent space wherein MOPs with similar adsorption properties congregate, then (iv) use a diversity selection algorithm to curate the most diverse set of MOPs for the array. Next, the investigators will synthesize and characterize the computationally-curated MOPs and employ surface deposition techniques to attach them to QCMs. To test the efficacy of different strategies to inject diversity into MOPs, the investigators will construct three generations of QCM-MOP arrays, wherein the MOPs differ by: (1) metal only, (2) functional group only, and (3) topology, metal, bridging ligand, and functional group. The reversibility, cyclability, and stability of the MOPs will be tested. Finally, the QCM-MOP sensor array will be tested for discrimination of (1) pure compounds, (2) plant oils, and (3) grades of olive oil. Dimension reduction algorithms will aid in exploring the discriminatory capability of each QCM-MOP array, then supervised machine learning algorithms will map its response pattern to a predicted compound/mixture. The design of the 3rd-generation QCM-MOP array will be evolved by replacing the least-informative MOP, flagged by interpreting the machine learning model, with the next MOP in the computational design queue. Finally, to make for a robust QCM-MOP array, the temperature and humidity will be varied for a context-sensitive classifier. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award supports participants in the 2024 Midwestern Workshop on Asymptotic Analysis, scheduled to take place in October 2024 at Indiana University. The goal of the conference is to disseminate research advances and to foster cooperation among researchers working in various areas of analysis and partial differential equations, particularly from colleges and universities in the midwest. Special emphasis is placed on attracting graduate students, both as participants and as speakers, to encourage the professional development of early-career scholars in various areas of mathematical analysis with both academic and industry applications. Major research themes to be addressed during the meeting include approximation theory, functional analysis, harmonic analysis, mathematical physics, potential theory and several complex variables. The event starts with a Friday afternoon colloquium talk by a senior researcher, and continues on Saturday and Sunday with an additional ten talks by researchers working in the above fields. All of the Saturday and Sunday talks will be given by early career researchers, including junior faculty, postdocs, and graduate students. The conference will also feature a poster session where other beginning researchers in analysis can publicize their work. 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 3rd KOI Combinatorics Lectures will be held October 4-5, 2024 at Indiana University. This regional conference is organized by members of the combinatorics community from the Kentucky, Ohio and Indiana (KOI) area. It seeks to rebuild and initiate research connections among the KOI area graduate students, postdocs and faculty, including individuals from over thirty nearby small colleges, regional universities and ethnically diverse colleges. The conference program consists of four talks from emerging and established researchers in combinatorics broadly defined, a problem session, and a poster session that is open to all participants. In Japanese culture, koi symbolize strength, courage, patience and success through perseverance. All of the conference activities serve to strengthen these attributes among the participants, with a strong focus on increasing the numbers of underrepresented groups in the mathematical sciences, including women. The follow-up yearly conferences will continue at the University of Kentucky in 2025 and the Ohio State University in 2026. The KOI Combinatorics Lectures showcase national and internationally recognized researchers in combinatorics. New developments in combinatorics and its interactions with other mathematical fields including algebraic geometry, algebra, topology, and artificial intelligence, will be featured. Interactions among all of the participants and the speakers, as well as learning the latest progress and techniques in the field of combinatorics, have the potential to contribute to the growing connections between combinatorics and other scientific areas, including physics, computer science and biology. The vertical mentoring, inclusion of educational activities, and recruitment of speakers and participants from a broad range of institutions and backgrounds contribute to the engagement, retention and equity goals of the NSF. Further details about the conference may be found on the website https://sites.google.com/view/koicombinatorics/ 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 Ninth Biennial Conference on Social Dilemmas builds upon a rich tradition of interdisciplinary collaboration initiated at Indiana University by Nobel Laureate Elinor Ostrom. This conference series, initiated in 2003, aims to deepen the understanding of collective action in social dilemma settings such as public goods provision, natural resource extraction, and climate change. Social dilemmas are situations where individual incentives conflict with collective interests. Understanding their dynamics is essential for developing effective policies and strategies that promote cooperative behavior for the greater good. The conference promotes and disseminates state-of-the-art research on social dilemmas. Furthermore, the ninth conference seeks to identify scientific and political issues concerning social dilemmas, with emphasis on relatively under researched and highly relevant areas such as conflict resolution, environmental and climate change economics, revisited considering recent developments, and the increasing use of Artificial Intelligence in these contexts. The introduction of Artificial Intelligence's (AIs) role in solving social dilemmas to the workshop's agenda is a particularly timely and innovative addition. This event brings together leading scholars from diverse disciplines, to share their latest research and insights. Emphasizing mentoring, the conference includes dedicated sessions for young scholars, particularly those from underrepresented groups, to support their professional growth and inclusion in these crucial research fields. These efforts aim to facilitate the inclusion and exchange of diverse perspectives and enhance the effectiveness of self-organized collective action and policy initiatives addressing critical societal challenges. The Ninth Biennial Conference on Social Dilemmas convenes a multidisciplinary group of researchers to discuss advanced topics in social dilemmas. The conference will feature sessions on a broad range of topics, featuring research that spans various methodologies, including theory, experimental methods, survey methods, and field data analysis. Specific sessions will be dedicated to pressing contemporary issues such as conflict resolution, environmental economics, and AI applications to social dilemmas. A significant component of the conference is its mentoring program, designed to support graduate students and junior faculty by providing them with high-quality feedback and fostering long-term professional relationships. This initiative aims to diversify the research community by prioritizing the inclusion of women, minorities, and researchers facing financial barriers. Mentoring activities include structured interactions between senior and junior researchers, dedicated mentoring lunch meetings, and a breakfast session focused on demystifying the publication process. These mentoring activities provide young scholars with the guidance and support necessary to advance their research careers. The conference builds on the success of previous social dilemma workshops, which have consistently promoted state-of-the-art research and generated new collaborations. Research presented at the conference will be disseminated through a special issue in a peer-reviewed journal, continuing the tradition of past publications. The conference represents a unique opportunity to advance the understanding of collective action problems and develop practical solutions to some of society’s most pressing challenges. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry (MSN) Program in the Division of Chemistry of the National Science Foundation (NSF) and the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG) under the NSF-DFG Lead Agency Activity in Measurements of Interfacial Systems at Scale with In-situ and Operando aNalysis, Prof. Xingchen Ye of Indiana University and Prof. Michael Engel at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, will develop advanced electron microscopy techniques to visualize the growth of nanoparticles in solutions. These nanoparticles are pivotal in various applications, such as medicine, electronics, and clean energy technologies. The collaboration between Indiana University, USA and Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, aims to unveil atomic level processes underlying nucleation, growth, dissolution, and phase transitions. The broader impacts of this project highlight the synergy between experimentalists and computational scientists, enhancing undergraduate and graduate education through multidisciplinary research. Outreach activities at local science festivals will engage the public, showcasing the significance and excitement of scientific discovery. The objective of this project is to elucidate the atomic-level and nanoscale processes in the formation and transformation of nanocrystals under controlled chemical environments, using multimodal in-situ electron microscopy and multiscale computer simulations. Aim 1 focuses on symmetry breaking and morphology development during the kinetically-controlled synthesis of metal nanocrystals. Aim 2 investigates the structural dynamics and deactivation pathways of nanocrystal electrocatalysts under practical conditions. The developed methods, findings, and insights will facilitate the design of next-generation functional nanocrystals with enhanced properties and stability. 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 · 2024-08

The Indiana University Site of the Center for Bioanalytic Metrology (CBM) works in collaboration with partner Sites at the University of Notre Dame and Purdue University to address two objectives: (1) to deliver best-in-class analytic metrology tools and expertise enabling the development of powerful new precompetitive technologies across the pharmaceutical, biotechnology, food/nutrition/agriculture, and energy sectors; and (2) to test applications of new instrumentation to cutting-edge chemical and biochemical problems. These objectives contribute to the national welfare by supporting the development of advanced industrial technologies across all four sectors. In addition, CBM provides compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. CBM operates under a unified site model, in which projects, independent of location, are associated with any member company with a significant interest in them. In addition, CBM operates by identifying the most timely and important problems of interest to its members and devising projects to address them. This approach is accomplished in a yearly cycle that starts with member companies identifying their most pressing Gaps, Needs, and Opportunities (GNO). These GNOs are used to solicit proposals, and the most timely and responsive proposals are selected for funding. CBM’s research groups projects into five thematic areas - (1) overcoming performance limits, (2) point-of-use technologies, (3) ML/AI data science & automation, (4) chemical imaging, and (5) enabling research technologies. The grouping of project themes recognizes a natural organization of the research carried out within CBM that reflects the strengths of the individual sites, but each theme contains projects of interest to members in all four industry sectors. The Indiana University Site contributes most substantially to the themes of chemical imaging, ML/AI data science & automation, and overcoming performance limits. This mapping reflects the strengths of IU scientists in mass spectrometry, optical spectroscopy, super-resolution imaging, electron microscopy, micro- and nanofluidics, environmental chemistry, and ML/AI. CBM research provides longer-term, larger-scale, and more cost-effective solutions to precompetitive industry measurement science problems than those that can be achieved in-house or through contract research organizations. In addition, CBM provides industry members with compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. Broader impacts of the CBM include increasing US economic competitiveness, increasing the number of partnerships between academia and industry, and contributing directly to the development of a globally competitive STEM workforce. This award is co-funded by the following Programs: Industry University Cooperative Research Centers Program in the Division of Engineering Education and Centers - in the Directorate for Engineering, and the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Directorate for Mathematical and Physical Sciences. 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 · 2024-08

A long-standing challenge of quantum information processing has been to predict the observable properties of complex physical systems found in nature. For instance, in state-of-the-art materials and chemical design, many behaviors may be fundamentally governed by quantum mechanics and too difficult to calculate classically. For the past 15 years, quantum technologies have pushed forward on this frontier to encode systems of interest into quantum bits, which may then be programmed to emulate the desired system properties. However, most of the work to date has been limited to studying constrained classes of quantum materials or has been limited by the large number of required gates and demanding technical overhead of universal quantum computing approaches. Here, this project combines enhanced analog quantum emulation capabilities with interspersed single-qubit gates to greatly expand the types of physical systems which may be studied, while avoiding the steep experimental resources required for full quantum computation. In addition, this project serves as a rich training environment for experimental graduate students and theory collaborators in this area of national priority. Using a customized trapped-ion quantum apparatus, this research probes disordered and topological systems in two dimensions, geometric versus long-ranged frustration in synthetic quantum materials, and the molecular dynamics and wavepacket evolution of hydrogen-bonded systems in quantum chemistry. These studies are each enabled by engineering the frequency spectrum of globally addressed laser beams used to generate entanglement between trapped ion qubits. Using this technique, there is a plethora of new interaction graphs required for materials and quantum chemistry studies (such as pure nearest-neighbor interactions, ring topologies, infinite-range couplings, and higher-dimensionality spin lattices), that all become accessible to trapped-ion quantum emulation devices. It is anticipated that the engineered global gate protocols developed by the team, which are not available on general purpose cloud-based quantum computing platforms, will allow for higher-fidelity emulations of complex physical systems when compared with more generic gate-model approaches. 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 · 2024-08

Sanitation services bring health and economic benefits to communities. Yet, regions remain where sanitation infrastructure is inadequate. This may cause feces to spread into the environment. When people are exposed to feces that contains pathogens, they may become sick with diarrheal disease. Globally, diarrhea causes 450,000 deaths among children under five each year. The goal of this project is to develop new methods to identify hotspots of contamination using coprophagous flies (i.e., flies that eat feces). This goal will be achieved by tracking microbes and genes carried by coprophagous flies during experiments conducted in the lab and in the field. Successful completion of this research will potentially transform our ability to use flies as remote samplers of feces in the environment. The data generated may enable public health departments and other stakeholders to better allocate resources to communities with inadequate sanitation. Further benefits to society include the training of undergraduate and graduate students at the interface of engineering, public health, and biology to better address complex health issues. Quantifying environmental fecal contamination typically employs culture-dependent or culture-independent assays for fecal indicator bacteria. Government agencies and public health departments almost exclusively apply these methods to surface waters as required by regulations. However, analogous methods are needed to characterize fecal contamination in terrestrial environments. One potential solution is the use of coprophagous flies that feed on fecal waste in the environment acting as composite samplers. This research project is based on the hypothesis that coprophagous flies may act as composite samplers of localized environmental fecal contamination by repeatedly feeding on uncontained wastes. Specific objectives to test this hypothesis are to: (i) Determine ingestion and persistence of fecal indicators in coprophagous flies under a range of environmental conditions; (ii) Quantify fecal indicators in wild caught coprophagous flies from communities with and without adequate sanitation; and (iii) Identify drivers of localized terrestrial fecal contamination in flies. Laboratory feeding studies include the testing of fecal waste transport by flies under controlled conditions (i.e., time, temperature, humidity). Field-based studies will focus on fly collection and analysis of genetic and microbe targets, with modelling to elucidate underlying mechanisms of localized fecal loading to the environment. The new data generated will advance our understanding of coprophagous flies as composite samplers and develop a novel approach to identify terrestrial fecal contamination hotspots that could be prioritized for improved engineering controls. 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 · 2024-08

Erosion is an important process that removes Earth materials from a source area then transports and deposits them somewhere else. Understanding the timing, rate, and magnitude of erosion is fundamental to the work of nearly all fields of geosciences and is critical to predict and identify water, hydrocarbon, and mineral resources. It has been particularly difficult for geologists to quantify the effect that vegetation has on erosion because in modern landscapes vegetation is ubiquitous and difficult to prescribe for a controlled study especially at a regional or continental scale. This study will address this question using data from sedimentary rocks deposited in the Midwest United States throughout different stages of land plant evolution including before plants evolved, and during different stages of plant evolution including the developments of roots and expansion of seeded plants. These rocks serve as a natural experiment for studying how erosion efficiency changed at the continental scale in response to land plant evolution. The PI will trace minerals eroded from different landscapes and measure the cooling history of these minerals, which is a proxy for erosion that exposes rocks once buried at warm temperatures deep in the Earth to surface temperatures. This work contributes to important societal outcomes including developing new quantifications of Earth processes that can be used to predict and locate Earth resources. It also cultivates a pipeline for recruiting and training Geoscientists in the Midwest including 1) support of MS student training providing students with practical skills working in core that prepares them to enter the regional workforce, and 2) development of a one-week Earth Science curriculum for high school teachers linking Indiana subsurface geology to active research in this project as well as regional resources, infrastructure, and careers. Although it is widely recognized that plant macroevolution modified the composition and character of sedimentary rocks through weathering, this project will provide one of the first efforts to systematically quantify volumetric changes in rock denudation across landscapes and vegetated environments. This project defines an integrated research and education project centered on the inherently “hidden” geology of the North American craton (Midwest USA) interrogating how vegetation modifies erosion in tectonic and cratonic landscapes. Does vegetation increase, decrease, or not affect erosion in tectonically active and cratonic landscapes? This project leverages the detrital record preserved in Cambrian – Pennsylvanian core samples from the cratonic Michigan Basin. This study will double-date zircon and apatite from clastic units indexed by stage of plant macroevolution (first vascular plants, emergence of large roots, evolution and expansion of seeded plants). U-Pb dating will establish single grain provenance identifying grains from the tectonically stable craton or the tectonically active Appalachian orogen. Low-temperature thermochronology will be used to measure changes in rock exhumation and erosion from grains derived from cratonic and active orogen sources across stages of plant macroevolution. Combined, these contributions contextualize the relative contributions of vegetation and tectonic landscape (cratonic vs. orogenic) on erosion providing measurements of erosion rates and magnitudes that will be used to frame future studies of erosion and reframe or even revisit decades of past work that was unable to extract the relative contributions of vegetation and tectonics on erosion. These results have the potential to revolutionize geoscientists’ ability to predict and reconstruct erosion patterns and magnitudes, calculate sediment flux in modern and ancient environments, and understand erosional systems in extraterrestrial (unvegetated) landscapes like Mars. 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 · 2024-08

This study examines the impact of non-science isolation practices on scientific production and collaboration. The research deepens our understanding of how national scientific systems are interconnected with global economic and political dynamics, providing insights on the impact of scientific cooperation across countries and sectors. The study addresses fundamental issues at the intersection of science strategy, international relations, and research productivity including how restrictive measures and geopolitical shifts affect international scientific collaboration, researcher mobility, and resource exchange. The project serves the national interest by promoting the progress of science through a better understanding of the factors that influence global scientific cooperation. The results provide decision makers with empirical evidence to inform strategic decision-making in the governance of scientific activities. This knowledge is crucial for maintaining the United States as a leader in global scientific research and innovation. The findings from this study have broader impacts on national health, prosperity, and welfare by helping to navigate international scientific collaboration in an increasingly complex geopolitical landscape. The researchers employ quasi-experimental designs utilizing three recent global events. These events serve as cases to examine the causal impact of disengagement from the integrated global scientific community on scientific development. The study will use causal inference techniques, specifically the synthetic control method and difference-in-differences analysis. These methods are applied to investigate changes in scientific publication production and the exchange of scientific capital, including funding resources and researcher mobility. To address potential biases in international databases, the project constructs a comprehensive dataset incorporating publication records from both international and national sources. By using causal inference frameworks, the researchers offer nuanced insights into the consequences of withdrawing from the integrated scientific community while mitigating the influence of other factors. 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 · 2024-08

High abundance of metals in the natural environment is a bit of a double-edged sword – metals are in greater demand now more than ever as we transition to renewable energy sources, so locating resources for extraction is economically important, yet at certain levels many of these metals may be damaging to the environment or toxic to plants, animals, and humans. Many metals can easily move from soil into plant material, where they can accumulate to a much greater extent, sometimes resulting in incredibly high quantities in plants. Here, plants may act as an entry point for metals to enter the food chain, posing serious environmental and human health risks, or they may allow for cost-effective extraction, helping clean up areas that have been contaminated while providing some information about what may be happening geologically, below the surface. This research focuses on one very toxic element, thallium (Tl), that is often associated with much more valuable metals, such as gold, and that can be taken up by plants quite efficiently. The project aims to use greenhouse experiments with plants grown in Tl-spiked soils to understand how underlying geology may affect plant metal signatures and how that information can be used for both metal exploration and environmental remediation. Throughout the study, a podcast will be published to highlight the societal implications of this research, and to introduce the public to “the person behind the science.” Thallium can accumulate, and in some cases hyperaccumulate, in a wide variety of plants with measurable biologically-induced isotopic fractionation during bioaccumulation, which may be at least partially controlled by underlying geology. This work will use two greenhouse trials of Brassica juncea (B. juncea), or brown mustard, grown in substrates with differing Tl redox conditions and distinctive starting Tl geochemical compositions to evaluate how accurately biogeochemical signatures reflect below-ground geogenic sources. After growth, each B. juncea plant will be split into specific plant parts (roots, stem, leaves, flowers, seed pods), processed, and analyzed for both trace metal concentrations and Tl isotopic compositions. These data will allow for calculation of the contribution of Tl from underlying geologic sources to the above-ground plant parts and quantification of how well various plant parts reflect original metal sources. This research will contribute to environmental and health-related planning and serve as a new tool for understanding heavy metal (re)distribution during anthropogenic and natural processes while establishing applications and potential limitations of utilizing biogeochemical signatures for metal exploration and environmental remediation. 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 · 2024-08

Parents sharing information about their children online is commonplace in the United States and comes with several benefits such as showing affection toward children, documenting cherished moments, and maintaining social connections. However, typically well-intentioned parents can also expose children to known risks such as identity theft, bullying, misuse of photos, or threats from child predators. Despite these risks, many parents continue to think of sharing their child's information as being similar to sharing more general posts on social media. Across various parenting styles, parents may need to be informed about these risks if we are to protect children against them. Towards the broader goal of improving the welfare of children and protecting them against current and future cyber threats, this project is focused on a scientific method for designing educational materials to foster more responsible, and less risky, parental sharing of children’s information online. Students and the public will be participating in this research. The objective of this project is to establish a more comprehensive understanding of parental sharing in the context of diverse family interaction styles and provide empirically validated educational materials that support informed parental sharing and young children’s privacy. In doing so, the proposed work is investigating privacy education interventions that are preventative, can be easily disseminated, and impact broader social norms. This proposal focuses on parents and their preteen children. At this age, children display increased autonomy as they approach the minimum allowable age for most social media use. At the same time, they are susceptible to influence from their parents and have typically not established the agency to prevent unwanted parental sharing. The overarching objective will be addressed via a three-phased plan to: 1) Demonstrate how family interaction styles contribute to parental sharing practices and outcomes; 2) Identify strategies to inform safe and responsible parental sharing based on insights drawn from parents and their children; and 3) Empirically-validate educational interventions to inform safer parental sharing standards. 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 · 2024-08

Successful oral communication in a second language largely depends on listening skills in the second language. Recognizing words in conversations is particularly challenging for second language learners, because of the wide range of pronunciation variation that they might encounter: in everyday conversations, words are often less clearly pronounced compared to their "standard" form. This doctoral dissertation research project investigates how native and nonnative listeners comprehend reduced speech in real time. This common occurrence of "reduced speech" poses significant challenges for nonnative listeners. However, little is known about nonnative speech comprehension in real life situations although the answer to this question is relevant for both theories of nonnative speech processing and for language teaching. This project also benefits society by providing education in language sciences research and by making the collected data accessible to the global research community, supporting further studies into how listening skills in nonnative listeners develop alongside increasing experience with the language. While native listeners can often quickly reconstruct the full forms of words in their mind during the processing of reduced words, nonnative listeners are thought to rely heavily on the pronounced segments they hear (the acoustic signal) and reconstruct the full form less quickly (or not at all) from the reduced segments and syllables. This doctoral dissertation project uses eye-tracking technology to examine the real-time processing mechanisms at play. Tracking participants’ eye movements on a screen displaying various competing options while listening to reduced words allows for examination of lexical competition (which word candidates are selected), to indicate whether reduced words are reconstructed and when. This reveals how native and nonnative listeners reconstruct reduced words in real time, and which alternative candidate words learners activate. This research also analyzes the influence of additional background factors including perceptual processing, vocabulary size, familiarity with, as well as exposure to casual speech, and time spent in countries where the target language is spoken. The results are used to refine cognitive models of both native and nonnative speech processing and have practical implications for language instruction. 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 · 2024-08

Terrestrial ecologists have faced a long-standing challenge - how to separate the uptake of atmospheric CO2 by terrestrial ecosystems (net ecosystem exchange, NEE) into its two offsetting components: gross primary production (GPP) and ecosystem respiration (Reco). A similar challenge has been plaguing hydrologists - how to quantify the two components of water vapor flux from land: transpiration (T) through plant stomata (pores) and evaporation (E) from non-stomatal surfaces. Despite numerous efforts in the past decades to partition the “direct observables” of NEE and ET, considerable biases remain in our estimates of the components. Without credible in-situ observation of GPP, Reco, T, and E, it will be impossible to understand or predict the complex dynamics of coupled carbon and water cycles under changing climate with the needed precision. Biases in these component flux estimates can further distort our understanding of their relationships, e.g., T:ET ratio and water use efficiency (WUE = GPP/T), driving uncertainty in how they are affected by environmental change. Therefore, it is urgent to develop innovative approaches that accurately partition NEE and ET into their component fluxes. This will reduce uncertainties in current terrestrial biosphere models (TBMs), improve water resource management, and inform nature-based climate solutions. This project aims to harness new theoretical and data advances in the remote sensing of Solar-Induced chlorophyll Fluorescence (SIF) to jointly partition ecosystem ET and NEE fluxes. It has three technical aims: 1) developing a mechanistic approach to jointly partitioning ET and NEE into their component fluxes using concurrent canopy-scale SIF and flux measurements across diverse NEON ecoregions and hydroclimatic regimes; 2) disentangling interacting mechanisms that control the temporal and spatial dynamics of individual component fluxes, T:ET ratio, and WUE across biomes and hydroclimatic regimes, and 3) improving the NCAR Community Land Model (CLM5) by better representing the interacting mechanisms among component fluxes through a hybrid modeling approach that embeds mechanisms into a deep learning framework, i.e., Biology-Informed Neural Networks (BINN). The proposed SIF-based joint-partitioning framework, guided by new ecophysiological theories, will provide valuable datasets of individual component fluxes, T:ET ratio, and WUE across diverse biomes and hydroclimatic regimes. These datasets will enhance the fidelity and realism of TBMs in predicting ecological and hydrological dynamics at multiple scales under climate change. This project will support a diverse array of students via the annual training course “New Advances in Land Carbon Cycle Modeling,” engaging high-school students via the Project SEED program, and contributing to undergraduate/graduate education at Cornell University and Indiana University Indianapolis. 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 · 2024-07

Social media platforms see a surge of user-created polls, known as social polls, which gauge social media users’ opinions for various societal issues. These polls are not scientific and often exhibit biases favoring particular poll responses. Such polls can mislead the public into believing these biased outcomes reflect true public opinion. Every month, well over a million social polls are created on social media. However, these biased polls can give a misleading impression about what the public believes. Given the rising popularity of social media polls, it is crucial to address their potential to distort people’s perception of public opinion. This project aims to investigate and mitigate the harmful effects of biased social polls by identifying the biases, studying their prevalence and dissemination, examining potential harms, and developing corrective measures. These efforts will help maintain the integrity of public opinion perception. This project is pursuing three key goals. First, the project is identifying publicly visible social polls that misrepresent public opinion and evaluating the level of bias reflected in those polls by analyzing the demographic characteristics of social media users engaging with them. Second, the project is examining the prevalence and uses of such polls. Third, the project is developing a novel algorithmic method for correcting demographic biases in social polls via regression and post-stratification based on inferred attributes of users interacting with the polls. Finally, the project is experimentally assessing the effects of exposure to biased and bias-corrected poll outcomes on public opinion perception. To achieve these goals, the project is analyzing data from polls published publicly on social media, comparing the results of this analysis with the results of traditional polls, and conducting survey experiments to assess the impact of social polls on individuals. The project will significantly contribute to understanding and mitigating the impact of biased social polls on the public. 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.