Indiana University

NSF Awards · FY 2025 · 2025-09

The orbits of planetary systems hold clues about their origins. Gas giants like Jupiter are thought to form on circular orbits far from stars. However, exoplanets with large eccentricity abound, and the “hot Jupiter” class have orbital periods of just a few days. In some cases, extremely eccentric planets may approach their stars, where tidal forces deform them, dissipate energy, and ultimately circularize their orbits—a hypothesis known as high-eccentricity migration (HEM), which is the likely origin of many hot Jupiters. This project focuses on the physics of “warm Jupiters,” which orbit far enough from their stars to avoid strong tidal forces yet are too close to have formed in place, also suggesting a migratory origin. Their orbital properties inspire new projects that this team, from Indiana University and Northern Arizona University (NAU), is pursuing. They will provide research opportunities and training of new astronomers at both institutions, making project software publicly available, and hosting a new Spanish-language public lecture series at NAU. This work tackles a recent puzzle: the origin of eccentric warm Jupiters. One of the strongest pieces of evidence of HEM is the misaligned orbital planes of many hot Jupiters relative to the equator plane of their host stars, but eccentric warm Jupiters are typically aligned. To potentially explain this trend, this study pursues in-depth modeling of planetary systems: (a) coplanar high-eccentricity migration with a dynamical tidal treatment, (b) the influence of inertial waves on obliquity damping, and (c) the role of disk gaps in resonant capture and escape. Through gravitational interactions between planets, between planets and the disk, tidal energy dissipation, and population synthesis – this project uses the puzzle of warm Jupiters as a springboard to elucidate many issues in planetary dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project will address the growing public health threat posed by disease-carrying mosquitoes in the United States. Mosquito species capable of transmitting debilitating viruses like dengue, Zika, and chikungunya are expanding their geographic range, putting millions of US residents at risk. Currently, public health efforts to control these mosquitoes are often reactive, responding only after a case has been identified. This project will shift the paradigm from reaction to prevention by developing an early-warning system that forecasts surges in mosquito populations, much like weather forecasts predict storms. By anticipating when and where mosquito numbers will be high, public health authorities can implement mosquito control measures more effectively, helping to prevent disease outbreaks before they start. Moreover, this project will provide valuable training opportunities for the next generation of scientists and public health professionals. The overarching goal of this project is to develop and validate a suite of modeling tools and ensembling approaches to generate 1- to 4-week ahead forecasts of the relative abundance of Aedes aegypti and Aedes albopictus. Forecasts will be produced at multiple spatial scales to align with the operational needs of public health and mosquito control agencies. The project will develop a multi-model framework that integrates different methodologies, including mechanistic compartmental models of the mosquito life cycle, a semi-mechanistic model based on the real-time estimation of the population reproduction number, and machine learning approaches. Furthermore, the outputs of individual models will be combined using several ensembling techniques and a signal decomposition method to improve forecast performance and reliability. All models will be trained and validated using a comprehensive mosquito surveillance dataset spanning from 2010-2024 across five locations in the United States. The project will develop an open-source package to run the forecasting tools proposed and validated within this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Representations such as diagrams, graphs, and charts are central to science and science education. However, learners often struggle with how to interpret science representations. The goal of this project is to develop, implement, and test a new AI assistant, the Representational Reasoning Assistant (RRA), to help middle school students interpret representations in their science classrooms. The AI assistant will draw on cutting edge Generative AI technologies to engage learners in conversations about the representations assigned by their teachers, ask the learners guiding questions, and offer suggestions about where to look in order to make sense of the representations. A key component of the design is to enable teachers to modify the AI assistant easily based on knowledge of their students and on the tasks which they set as priorities for their students. The project will help advance interdisciplinary research and practices in AI, computer science, learning sciences, and STEM learning. Throughout the three years of the project, teachers and students will be recruited from urban, suburban, and rural schools. The sequence of research and development activities reflects an integrated effort between the learning sciences and computer science teams. The project consists of iterative cycles of exploration, development, pilot and model refinements of the AI assistant, focusing on the types of representations teachers use in science activities and the types of feedback they give to students. Multimodal Large Language Models (MLLMs) will be adapted to be visually focused, supportive of pedagogical intent for young learners, and include innovations in rapid training to support a wide range of classroom topics and contexts. Early rounds of the piloting will gather teacher feedback on initial models and versions of the AI assistant. The AI assistant interface will then be fine-tuned based on teachers and students' feedback as well as measurements of students' engagement and learning. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award, under the Molecular Foundations for Biotechnology (MFB) program in the Division of Chemistry, funds Drs. Scott Aoki and Jonah Vilseck from the Indiana University School of Medicine to develop a computational strategy to accurately predict how unmodified and modified RNAs interact with proteins that control or regulate important biological functions. Chemical modifications to the bases in RNA affect their recognition by proteins and, consequently their function. With over 170 modifications identified thus far, new methods are required to elucidate how combinations of unmodified and modified RNAs interact with regulatory RNA-binding proteins. This project adapts a physics-based molecular modeling technique called λ-dynamics that, when paired with classic RNA biochemistry, determines the RNA sequence preferences of a host of RNA-binding proteins involved in gene expression. The research provides new insights into the biological function of RNA modifications in areas that impact biotechnology. In parallel, a summer program is being created that engages junior trainees in scientific literature about RNA modifications as an entrée into STEM careers. Current methods to study RNA-protein interactions are accurate but often expensive, time-consuming, and limited by available reagents. New methods are required to study how RNA and its modifications affect RNA-protein interactions and their subsequent roles in biology. λ-dynamics is an efficient computational method for simultaneously modeling the free energies of molecular interactions and modifications in biomolecular systems. The goal of this proposal is to advance the λ-dynamics method to accurately predict how chemical modifications to RNA bases will affect binding to a library of critical RNA-binding proteins. The two major contributions from these studies include presenting an effective means to predict RNA sequences that are preferred for protein binding and enabling the study of how relevant base modifications affect binding to proteins that are involved in RNA turnover. The computational predictions will be validated by classical biochemical assays for protein recognition and binding of unmodified and modified RNA oligonucleotides. These insights drive fundamental discoveries of the role of RNA modifications in biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Pathways to Enable Open-Source Ecosystems (POSE) project centers on the development of a self-sustaining open-source ecosystem (OSE) for security and privacy assurance in Internet of Things (IoT) standards and their implementations. Modern IoT systems are integral to daily life, supporting safety, health, energy, and convenience across residential, commercial, and infrastructure settings. However, inconsistent designs and development practices across IoT devices have led to significant gaps in security and compliance. This project seeks to address these challenges by creating shared tools and infrastructure for continuous security verification—making it easier for developers to build secure-by-design devices and comply with emerging standards. The resulting OSE will promote trust in connected technologies, reduce risks for consumers, and contribute to safer, more reliable systems. It will also support hands-on educational opportunities that prepare the next generation of security and software engineers with practical skills in verification, threat modeling, and secure software design and development. Consumers, developers, educators, and researchers will benefit from the ecosystem’s collaborative and transparent approach, which aims to improve technological understanding and long-term resilience in connected environments. This POSE project establishes the Formal and Human-Centered Security Verification in IoT Standards (FHS-IoT), a non-profit organization dedicated to providing formally verified security guarantees for IoT standards and to improving compliance assurance for consumer IoT products. The FHS-IoT will develop a Continuous Integration and Continuous Delivery/Deployment (CI/CD) infrastructure to support formal and human-centered verification tools and transition to an open-source ecosystem. This project focuses on three areas: 1) ecosystem justification and user engagement; 2) establishment of FHS-IoT governance models; and 3) content developer engagement and community building. The anticipated outcomes include a metric-based justification for the FHS-IoT OSE, creation of an open-source CI/CD pipeline for continuous security verification of IoT standards and standardization implementations, and the formation of a user and contributor community with resources and onboarding instructions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

One of the oldest and most important concepts in mathematics and science is the notion of symmetry, which describes how an object or system can be rearranged or permuted in interesting ways, such as in the rotations of a cube or the shuffling of a deck of cards. Mathematicians have studied symmetry for hundreds of years using the language of group theory, but in the past 40 years have investigated a more expansive notion of symmetry, known as quantum symmetry, which arises in several core areas of mathematical research, including operator algebras, quantum field theory, and representation theory. These fields are inherently vital research areas, and enjoy fruitful interplay with physics, computer science, and technology. For instance, mathematicians were inspired by physics to study abstract notions of quantum symmetry, and develop models that were later found to describe the physical properties of certain exotic materials. Within this broader context of the mathematics of quantum symmetry, this project aims to discover and study novel examples of quantum groups from analytical, algebraic, and topological points of view, including certain classes of examples of interest in condensed matter physics. The project will contribute to Indiana University’s broad emphasis on quantum science and create research opportunities for graduate students and postdocs. This project considers quantum symmetry in a variety of contexts, focusing on example-driven problems about von Neumann subfactors and tensor categories. The main emphasis is on problems that can be approached using skein theoretic or planar algebraic methods. The project uses techniques developed in the operator algebraic subfactor community applied to questions in other areas. More specifically, the proposed work uses module and bimodule categories to study subfactors and their generalizations, uses skein theory to understand families of tensor categories, and studies local topological field theories related to tensor categories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Forests are responsible for approximately 90% of all terrestrial carbon storage and are key regulators of the global carbon cycle. Moreover, strategies like forest conservation, reforestation, and improved forest management are widely viewed as promising avenues for natural carbon removal that confer a host of environmental and economic additional benefits. Yet, at scales ranging from individual sites to the entire globe, estimates of forest carbon uptake and storage vary by considerably. This uncertainty stymies efforts to confirm regional and global carbon budget estimates, and prevents robust evaluations of the potential of forest-based carbon removal strategies. Much of this uncertainty stems from a misalignment between our state-of-the-art understanding of forest carbon removal and the decades-old tools used to estimate it in practice. The overall goal of this project is to address these discrepancies using the best-available science, testing the central prediction that conventional monitoring approaches systematically underestimate how much carbon is removed from the atmosphere by undisturbed forests. This project will blend state-of-the-art field observations and synthesis of environmental network data to understand why measurement approaches give different results. The PI and her team will also develop novel techniques for more accurate forest carbon quantification that bridge field monitoring and regional to global-scale policy setting. To accelerate the transition of research findings into actionable information, the project will strengthen existing relationships with forest managers and policy-makers across the public and civic sectors, and emphasize the training of a workforce equipped to measure forest carbon removals using the best-available scientific tools. Most operational protocols for forest carbon monitoring rely on ‘stock-change’ approaches, which infer forest carbon uptake and storage from changes in woody biomass estimated from allometric equations and forest inventory data. While this approach is highly scalable, it has many limitations, including the omission or imprecise calculation of carbon changes in branches, roots, and the soil. Rapidly growing networks of eddy covariance flux towers have opened new opportunities to develop flux-based approaches for quantifying forest carbon uptake and storage. Although flux towers are the gold standard for measuring land-atmosphere carbon exchanges, they have not yet been leveraged for policy-relevant forest carbon quantification. This project integrates several independent but complementary activities to perform a robust comparison of stock versus flux-based monitoring approaches. These activities include: (1) a comprehensive synthesis of information from environmental observation networks including NSF’s National Ecological Observatory Network (NEON), the FLUXNET tower network, and the USDA Forest Inventory and Analysis (FIA) network; (2) an intensive, paired-site field study that will compare measurement approaches in undisturbed and commercially harvested stands, with tests of terrestrial laser scanning and tree-ring data for more representative quantification of aboveground biomass carbon stocks; and (3) the development of a novel, theoretically grounded approach to parameterizing allometric equations that surmounts key limitations of empirical methods. The research efforts will enable NEON, FLUXNET and the U.S. Forest Service to synthesize protocols amd relate overall inventories to individual flux tower sites. while providing training opportunities at the undergraduate, graduate student and postdoctoral levels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Shrublands have encroached on grasslands in the present-day Southwest US, threatening livelihoods and altering natural water and carbon cycles. Though shrubland expansion has been a focus of many present-day ecologic studies, the relative importance of rainfall, temperature, fire, and other environmental factors remains debated. This project will provide a long-term perspective on the causes and reversibility of natural variations in shrub-grass dominance by studying repeated fluctuations that occurred throughout the past 30,000 years. The project will use geochemical data recorded in ancient soils to investigate the relationship between heat (air temperature), dryness (evaporation), and shrubland expansion. The project will provide professional development to educators and students in the geosciences. The results will be disseminated to high school classrooms through a new workshop for K-12 teachers developed in collaboration with Educating for Environmental Change, an Indiana University School of Education program. This project will provide hands-on training in traditional and novel techniques to undergraduate and graduate students, contributing to training the next generation of leaders in laboratory-based geosciences. This project will investigate Quaternary grassland-shrubland transitions using pedogenic carbonate preserved in paleosols within the Jornada Long Term Ecological Research (LTER), New Mexico. The hydrologic factors that cause shifts between the dominance of shrubs vs grasses (detected via δ13C discrimination in the C3 vs. C4 photosynthetic pathways) has not been definitively addressed in the geologic record due to ambiguities inherent in δ18O of carbonate. The researchers will study two types of vegetation transitions that have been observed in the stable carbon isotopes of paleo-pedogenic carbonate: an abrupt shift from grass to shrubland in the mid-Holocene, and subsequent, episodic fluctuations in dominant vegetation in a mixed ecosystem in the late Holocene. Two emerging stable isotope techniques will be used to assess water stress in the paleo-rhizosphere: clumped isotope geochemistry (∆47) will be used to estimate temperature, and triple oxygen isotope geochemistry (∆'17O) will be used to assess evaporation. The geochemical techniques and interpretive framework developed in this work will have broad implications for understanding C3-C4 transitions at grass-shrub and grass-forest ecotones, and at timescales from the present to the Cenozoic. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The remarkable complexity of the mammalian brain arises from precisely regulated genetic programs that control neural cell number and diversity during embryonic development. Epigenetic mechanisms—heritable modifications that influence gene expression without changing the DNA sequence—are central to orchestrating these programs. However, how epigenetic regulation in neural stem cells determines neuronal number and diversity in the developing brain remains poorly understood. This proposal addresses this critical gap by leveraging sophisticated mouse genetic models and cutting-edge DNA sequencing technologies to investigate the role of epigenetic modifications in cerebral cortex development. The resulting insights will deepen our fundamental understanding of mammalian, including human, cortical development. Furthermore, this work will illuminate how disruptions in epigenetic regulation contribute to the cellular and molecular defects underlying some neurodevelopmental disorders, such as autism. Complementing the research, the proposal includes a comprehensive educational initiative to engage and train high school students, teachers, and both undergraduate and graduate students in the fields of genomics, epigenetics, and neurodevelopment. Central to understanding developmental cell commitment is elucidating how epigenetic mechanisms silence alternative cell fates. These mechanisms, mediated by chemical modifications in chromatin, play a crucial role in regulating gene expression programs. A defining feature of transcriptionally silent chromatin (heterochromatin) is the di- and tri-methylation of histone H3 at lysine 9 (H3K9me2 and H3K9me3). Previous studies indicate that H3K9-methylated heterochromatin acts as a barrier to cell fate conversions, underscoring its critical role in promoting cell commitment and preserving cell identity. However, how distinct H3K9 methylation states regulate neurogenesis and cell fate specification in the developing mammalian brain remains unknown. Here, we generated mouse genetic models to deplete H3K9me2 or H3K9me3 in the embryonic cerebral cortex. Our preliminary data show that H3K9me2 and H3K9me3 occupy distinct chromatin regions and control different aspects of cortical neurogenesis. In this proposal, we will dissect the molecular mechanisms of gene silencing by H3K9me2/3 in the cortical lineage. These insights will advance our understanding of neurodevelopmental disorders in which H3K9-methylated heterochromatin may play a central role. This project is jointly funded by the Genetic Mechanisms Program of the Division of Molecular and Cellular Biosciences (MCB) and by the Neural Systems Program of the Division of Integrative Organismal Systems (IOS) in the Directorate for Biological Sciences (BIO). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

In education, artificial intelligence (AI) enabled tools and applications are rapidly emerging, which show strong potential to improve student outcomes. However, the speed of innovation has outpaced our ability to evaluate these improvements. When implemented in a student learning environment, only some of the AI-enabled applications will be effective in improving student learning, performance, and success. Others may even harm learning. If the US education system is going to reap the benefits of emerging AI applications, the education research community needs novel infrastructure to evaluate these benefits and to inform education leaders and the educational technology marketplace about their effectiveness. Furthermore, AI-enabled learning technologies raise new concerns about privacy and safety that need to be addressed when conducting real-world evaluations with students. This project will lay the groundwork to create TOPSAIL (Testing Outcomes Privately and Securely for Artificial Intelligence in Learning), a testbed that enhances existing education research infrastructure and provides an expressway for safe and secure impact assessment of AI innovations in real classes. The TOPSAIL testbed will be situated in the Canvas learning management system, and will leverage Terracotta, an emerging open-source experimental research plugin, to support a range of ethical and privacy protections for students as research participants. Canvas, made by Instructure, is the most widely used learning management system in the US, and provides a well-established and versatile platform for testbed research. The current planning project will assemble a team including research scholars, industry leaders, and administrators at three school districts, who will co-create the design requirements for TOPSAIL. These requirements will address concerns about AI safety and privacy, in tandem with more general concerns about digital platform-based research with students. These concerns will be surfaced and documented during on-site focus groups with school stakeholders at the three districts. Focus group protocols will direct attention to known issues in education research (safety, privacy, data collection, experimental control, and collaborative involvement of teachers). Once these concerns are surfaced, solutions will be co-designed during a multi-day in-person workshop with the full planning team, and then synthesized into a detailed set of design requirements for TOPSAIL. The project will allow us to build a coalition of trust among involved stakeholders, resolve key concerns, and set a collaborative path forward for testbed 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 2025 · 2025-08

How do populations evolve in complex and changing environments? Why do some populations have the necessary genetic variation to adapt to environmental change, while others do not? This research proposes to answer these questions by combining ideas from genetics and behavioral biology. Instead of starting at the population level and working towards mechanisms, the investigators will instead start with the developmental processes that differ across individuals to produce variation that fuels evolutionary change. The focus will be on the relationship(s) between an individual’s choice of environment (e.g., where to live) and the developmental processes that are shaped by that environment (e.g., their later behavior and survival). The research will integrate theory, and experiments with fruit flies, to study the links between environment choice and development, and how these links differ between individuals, at the population, individual, and genomic scales, and across generations. This approach will develop and test new mathematical tools that will allow future researchers to predict the evolutionary consequences of environmental variation for any population. As part of this research, the investigators will mentor and train undergraduate students, graduate students, and postdoctoral researchers at multiple institutions for four years; and, they will run a summer research program for high school teachers to provide experience with hands-on research and guide them to develop lesson plans in mathematical theory and genetics for their classrooms. Therefore, this research will uncover fundamental principles of evolution necessary to predict population vulnerabilities to environmental change while training the next generation of leaders in science. The goal of this research is to develop a comprehensive framework that links functional genetic mechanisms of trait expression with organism-level environment preferences to predict GxE within and among generations. The research will combine experiments and theoretical models. At the organismal level, the Aims will interrogate links between preference for a particular environment, and experience in each environment—and how these processes result in expressed patterns of plasticity and fitness. This approach will provide understanding of which individuals will be plastic, and why. The next step is to identify underlying gene expression networks that produce variation in behavior and functional links between environment choice and plasticity. Simultaneously, the investigators will develop population level theoretical models that will examine how variation in environmental exposures influences genetic variation in responses to environments, and how these processes together control the expression of GxE and influence its evolution. By coordinating experimental work and population-genetic models of the evolutionary causes of GxE, this research will provide biologists with a rigorous conceptual toolkit from which to interpret or apply these ideas to any organism. Together, these efforts will “put the pieces together” to produce a priori, bottom-up predictions about GxE and its evolution, predictions which are currently lacking. At the same time, the investigators will run a Research Experience for Teachers (RET) program, using established best practices. The RET will impact hundreds of students from underrepresented groups by enhancing the expertise of their teachers with critical hands-on biology research experience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

How do you remember where you have been or how to get from one place to another? Although animals are very good at these forms of spatial memory, the mechanisms remain poorly understood. Understanding these mechanisms is important, because the skills they control are crucial for animal survival. Such skills also have practical applications for designing human devices. For example, a robot vacuum should remember where it has and has not cleaned, and it should find its way back to the charger. Yet, remarkable as they may seem, such smart tools are much less capable than animals at spatial memory-dependent tasks. This project seeks to better understand how animals perform such tasks and translate that understanding into advances in artificial intelligence. Importantly, this research focuses on a previously overlooked strategy that animals, rats in this case, often use when navigating: they take a break from doing the navigation task at hand and change posture, seemingly to absorb information from a wider or different angle. Preliminary studies showed that animals that engage in this additional posture shift perform better in navigation tasks. In this project, experiments will be used to determine what information animals absorb during the shift in strategy and how neural activity in the hippocampus--a brain region critical for spatial navigation--changes in association with the behavioral strategy. In combination with the experimental studies, computational models and machine learning will be used to understand the biology while advancing artificial intelligence systems for use in a wide range of devices to make daily lives of humans better. The project will also provide opportunities for students at all levels to gain training at the intersection of neurophysiology and machine learning/artificial intelligence. Spatial memory is well known to depend on intact hippocampal function. Yet, the hippocampus's role in spatial memory encoding remains unclear. The investigators recently found that blocking hippocampal activity during rearing (when rats stand on their hind legs) impairs spatial memory, identifying rearing as a key behavioral epoch for memory encoding. The discovery that rearing is key for hippocampal-dependent spatial memory encoding is significant, as it expands the very short list of behavioral markers linked to critical hippocampal functions. This project builds on this finding by determining: 1) what information rearing provides to update the cognitive map, 2) whether other behaviors elicit similar hippocampal dynamics and if blocking activity during these behaviors affects memory, and 3) whether rearing indicates curiosity-driven information foraging. Addressing these unknowns has implications for multiple fields of study. First, by examining how new information acquired during rearing is integrated into existing cognitive maps, the project uncovers principles of continual learning through active sampling. Second, by investigating what other behaviors elicit hippocampal dynamics like rearing, the project may identify additional exploratory behaviors that support spatial memory and connect them to underlying neurophysiological mechanisms. Third, by testing whether rearing reflects curiosity-driven information foraging, the project evaluates normative models explaining when and why rats choose to rear. The investigators address these questions using a combination of high-density electrophysiology, closed-loop optogenetics, behavioral analysis, machine learning and computational modeling. This project is supported by the Directorate of Biological Sciences Division of Integrative Organismal Systems Neural Systems Cluster Modulation Program and by the Division of Emerging Frontiers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Scientists study different atoms of an element called stable isotopes as a powerful tool to understand how, and where plants take up and use water. The use of this tool assumes isotopic consistency, which is the relative abundance of the isotopes in water, during plant water uptake. Stable isotopes of water come from hydrogen and oxygen. In recent studies, scientists found significant differences in the hydrogen isotope values (δ2H) between plant water and their source waters. These differences, known as δ2H offsets, challenge the reliability of the isotopic method for research. Hydrogen isotope offset also complicates how scientists interpret plant-water interactions in ecosystems. In this project, researchers will develop a new way to identify the appropriate water pools from where plant water is sourced. The new tool will help scientists develop a clear method to accurately separate water pools that connect plants and soils. Using this new method, the researchers will clarify the causes of δ2H offsets and develop well-validated and standardized approaches for isotope-based ecohydrology research. Standardizing will improve data reliability, enable better comparison of global isotope databases, and advance our understanding of water and carbon cycles. The research will further train and mentor early-career researchers and students and create new datasets for communities that study plant and soil interactions. This project will systematically assess the fidelity and causes of observed δ2H offsets using a global data-synthesis approach. A new framework of the “Three Soil Water Worlds” concept and the “Two Plant Water Worlds” concept will be developed to account for water flow and corresponding isotope heterogeneities within the soil-vegetation-atmosphere continuum. Researchers expect to demonstrate that δ2H offsets do not occur if correct water pools are used in isotopic studies. To explicitly test this, a global re-quantification of δ2H offset measurements between plant water and their possible source waters will be performed. Both the new framework and the re-evaluated global δ2H offset datasets will significantly advance the robustness and reliability of isotopic methods in ecohydrological investigations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project studies several nonlinear partial differential equations that are relevant to applications in science, economics, engineering, meteorology, and physics. They also have deep connections and applications in several areas of mathematics such as analysis, geometry, numerical methods, and the calculus of variations. For example, among the equations investigated in this project, the semigeostrophic equations are used in weather forecasting, while singular affine maximal surface equations and singular Abreu equations are used in the numerical simulations for the monopolist's problem in economics (where the monopolist needs to design the product line together with a price schedule so as to maximize the total profit) and for nonlinear diffusion and crowd-motion models. The project particularly covers various linearized Monge-Ampère equations with drifts where key structural quantities could be possibly extremely large (singular) or extremely small (degenerate), thus rendering them significantly challenging. Despite their important role in applications, these equations are still poorly understood. The project aims at discovering new underlying principles and developing innovative tools to systematically tackle fundamental problems in this area. Their solutions are expected to reveal the interconnectedness of analysis, partial differential equations, the calculus of variations, mathematical economics, and complex geometry, thereby stimulating interactions among these areas. The results of this project will be disseminated through publications of mathematical research papers and lecture notes and via presentations at national and international venues. An important educational component of this project includes the mentoring of graduate students and attracting undergraduate students to mathematical research. This project, in the field of analysis and partial differential equations (PDE), focuses on the solvability, regularity properties, and asymptotic behavior of singular higher-order linearized Monge-Ampère (LMA) type equations with drifts that arise naturally in complex geometry, meteorology, economics, elasticity, physics, and the calculus of variations with a convexity constraint. The project consists of three main themes. The first one investigates the solvability in higher dimensions of singular affine maximal surface equations and singular Abreu equations with drifts. These are fourth-order equations which can be rewritten as systems of a Monge-Ampère equation and a linearized Monge-Ampère equation. The second theme aims at establishing higher-order derivative estimates for singular LMA equations with certain twisted structures (which allow for changing the solution nature under suitable transformations). The third theme studies the solvability of singular Abreu equations with degenerate boundary data. The principal investigator and his collaborators have recently developed new PDE techniques in the LMA type equations with and without drifts, including new perspectives on perturbing away the potential singularities by combining techniques of both divergence form equations via energy functionals and nondivergence form equations. They are expected to be further developed to successfully attack the problems in this project, thereby bringing fresh insights into the study of nonlinear PDE and providing novel approaches to regularity theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award supports the Research Experience for Undergraduates (REU) site in the Department of Mathematics at Indiana University, Bloomington. The program will offer an intensive summer research experience for talented undergraduates working with internationally recognized faculty on unique projects. By providing a well-balanced and structured environment together with milestones for both oral exposition and written reports of results, the IU REU lays the groundwork for student success in their projects. Participant success is further encouraged by the fostering of an intense and collaborative work environment, and most importantly regular working sessions with mentors. In addition to the abstract problem-solving skills gained by the participants, their professional development is enhanced through regular research presentations by faculty members, LaTeX writing workshops, and other similar opportunities. The Indiana University REU program provides an immersive environment with a mentorship-oriented approach to research. Nine students will work one-on-one or in small groups with faculty members, spending eight weeks on IU campus tackling carefully selected research problems. These problems are unsolved, accessible, and mathematically significant. Topics in pure mathematics are chosen from a broad swath of fields including differential equations, quantum groups, combinatorial design theory, logic, quaternionic analysis, functional analysis, and several more. Topics in applied mathematics are drawn from areas in quantum mechanics (such as self-adjoint extensions of Dirac operators) and areas of statistics (such as evaluating statistical approximate methods). Housed together in a dormitory and sharing common office space, students benefit from being immersed in mathematics collectively and from the spontaneous collaboration that naturally follows. Students disseminate their findings by delivering presentations at a statewide undergraduate mathematics research conference, giving formal lectures to peers, faculty, and graduate students, and writing a formal self-contained research report detailing their findings. Some of these reports are expected to develop into peer-reviewed research papers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Chaotic systems exhibit the "butterfly effect": the future path depends sensitively on their starting points, so that a butterfly flapping its wings can set off a tornado on the other side of the world. How can we quantify this sensitivity? This project will develop new notions of how chaotic systems expand and stretch in different directions. Broader impacts of the project are through mentoring of student research, with the project's particular emphasis on concrete, physical models of the resulting fractal geometry, with its surprising and intriguing patterns. In more detail, the project will develop the theory of "topological Lyapunov exponents", a spectrum of rates of expansion for expanding topological dynamical systems. Unlike the older entropy, these new rates control how fast nearby points diverge, measuring distance growth rather than volume growth. Unlike the original Lyapunov exponents, the new rates are defined for topological systems without reference to a smooth structure or notion of differentiation. Nevertheless the topological Lyapunov exponents are interesting and new for smooth systems as well, for instance recovering core entropy for polynomials in the Mandelbrot set. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The investigator studies a selection of key open problems regarding the stability and behavior of shallow water waves arising in applications to hydraulic engineering and coastal flow in the scenario of large-amplitude waves that are sufficiently strong to develop shock-type discontinuities. The interest of such waves ranges from the large-scale understanding of destructive "rogue wave" type solutions, to the small, explaining the structure of the familiar "herringbone" or "crosshatched" flow seen in narrow fast-moving streams. The interest of the mathematical tools needed to understand them is also broad, extending to the general class of "relaxation systems" appearing in a variety of physical applications from plasma dynamics to many-particle systems, in which complex physical systems move toward much simpler equilibrium dynamics under the influence of physical mechanisms. Though these have been widely studied, the treatment of shock discontinuities is still largely undeveloped; the techniques developed in this project are hoped to remove this bottleneck in the general theory. The planned activities have both theoretical and physical components, and involve collaboration with domestic and foreign colleagues, undergraduates, current and former graduate students, and postdocs. This is expected to strengthen and extend existing networks of cooperation across fields and institutions, and to aid in the training of students and postdocs. It will also aid in the dissemination of results and techniques, which will be further accomplished by frequent presentations. The project seeks to resolve the outstanding problems of nonlinear time-asymptotic stability of discontinuous inviscid periodic waves and multi-dimensional hydraulic shocks. Objectives are the development of new theoretical tools in order to treat unresolved questions of practical interest in shallow-water flow, extended thermodynamics, and other models involving relaxation. Methods include a blend of finite- and infinite-dimensional dynamical systems tools with specialized techniques coming from the theory of shock waves and hyperbolic conservation laws. The problems investigated as part of the project involve interesting and nonstandard mathematical issues addressing puzzles from physical applications. For example, successful treatment of nonlinear stability of discontinuous periodic waves would represent a major theoretical advance in both shock and modulation theory while resolving long-standing questions on roll wave behavior in shallow water flow/hydraulic engineering. Likewise, the rigorous mathematical description of herringbone patterns and general hyperbolic bifurcation would mark important theoretical advances. The use of variable-coefficient Kreiss symmetrizers/pseudodifferential damping estimates for the treatment of turning points and regularity in multiple dimensions may be particularly consequential. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The PI will investigate collections of transformations of objects or spaces, known as transformation groups, which arise ubiquitously across mathematics, science, and engineering. The project will focus on understanding the fundamental dynamical, topological, and geometric properties of infinite transformation groups that act in a distance-preserving manner on spaces of nonpositive curvature, such as Euclidean and hyperbolic spaces. A key component of the project involves studying and computing topological invariants of these groups, including their ordinary and bounded cohomology. The PI and collaborators have previously established rigidity of such group actions in certain contexts, building on this foundation, the current project will tackle several challenging open conjectures using recent advancements in these techniques. The award will also support the PI to improve mathematics and STEM education at all levels. The PI will conduct three main projects. The first is to investigate the subtle interplay between the geometry of nonpositively curved closed Riemannian manifolds and the bounded cohomology of their fundamental groups. For instance, a conjecture of M. Gromov asks whether strictly negative Ricci curvature suffices to guarantee positivity of the simplicial volume in such manifolds. The second is to examine the implications of the "natural flow," a tool recently introduced by the PI and collaborators, for understanding the relationship between the (co)homological dimension of a discrete group acting on a Hadamard space and its critical exponent. The final project will deepen our understanding of local and global rigidity phenomena. In particular, the PI will extend recent work with collaborators on the local rigidity of higher rank cocompact lattices to the nonuniform setting. The PI also will continue advancing the hyperbolic rank rigidity program, building on foundational contributions by W. Ballmann, K. Burns, P. Eberlein, U. Hamenstädt, R. Spatzier, and others. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project aims to serve the national interest by modernizing the teaching of physics at the college level. Specifically, the project will enable college instructors to integrate specialized computational physics methods into their classes, and to rate their students' learning against a standard scale. Teaching computational physics is important because these methods are quickly becoming essential for physicists and other scientists around the world. This work will help colleges and universities keep up with these changes. Rating students against a standard scale will enable individual instructors and departments to determine how effective their teaching efforts are, and to make improvements that further benefit their students. Adding computational methods to college classes will enhance the effectiveness of the U.S. scientific workforce, one of the key goals of NSF and the IUSE program. This Engaged Student Learning: Level II project is the first effort to establish standards (and training materials for using the standards) designed specifically for evaluating students' achievement of seven essential learning goals in computational physics. The goals of this project are to expand and improve the teaching of computational physics at five universities in the midwestern U.S: Indiana University Indianapolis, Bradley University, Purdue University, University of Indianapolis, and University of Wisconsin - Stout. The project team will develop seven student learning objectives: 1) use generative AI effectively and ethically, 2) read, understand, and modify existing code, 3) apply common computational tools, 4) test code, 5) explore physics, 6) write clear code, and 7) communicate physics. Each partner institution will have a specific role and responsibility with respect to developing these learning objectives. The project will also develop, test, and improve rubrics related to these objectives that instructors can use to rate students' learning of these methods. Instructors are accustomed to evaluating students within a single class when they assign grades, but this type of rating does not give information about how a student has progressed over years. To measure progress, it is necessary to rate students against objective standards, therefore this project will also produce documents and procedures a department can use to help its members learn to use the rubrics to rate students objectively. This project will also study how students' development as rated by instructors compares to their own view of how much they have learned. Project work and findings will be disseminated through publications, presentations, and workshops for faculty. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project focuses on harmonic analysis, a part of mathematics that explores how different waves interact—combining or canceling each other out. A central problem in this field is the Fourier restriction conjecture, which seeks to understand the minimal amount of cancellation that occurs when a sum of waves is confined to a curved surface. Work on these problems has led to the development of powerful ideas and tools that have proven useful in other areas of mathematics, such as number theory and differential equations. A key objective of this project is to deepen the study of such problems and to develop new mathematical tools and insights to further our understanding of wave behavior. Additionally, this project provides research training opportunities for graduate students. The principal investigator (PI) will work on several projects related to the Fourier restriction conjecture. The first part of the project is concerned with weighted L2 estimates and their applications to Lp problems in harmonic analysis. Specific examples include the pointwise convergence problem for the planar Bochner-Riesz means and the Lp behavior of the maximal Schrödinger operator. The PI will explore a range of weight functions motivated by different applications. The second part centers on investigating the Fourier restriction conjecture itself, potentially leveraging the tools developed in the first part. The PI aims to create a new framework for analyzing this conjecture. The third part of the project focuses on identifying and understanding geometric obstructions related to the Fourier restriction conjecture. In particular, the Kakeya maximal conjecture is known to be a major obstruction. The PI plans to interpret the Kakeya maximal conjecture using algebraic methods, study specific examples, and ultimately explore new potential obstructions to the Fourier restriction conjecture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award provides support for the Bloomington Geometry Workshop in the years of 2025, 2026, and 2027, with the 2025 meeting taking place at Indiana University on April 4-6, 2025. It also supports the affiliated Geometry, Groups, and Dynamics (GGD) Day, a smaller-scale regional workshop, to be held on a Saturday each spring. The workshop will promote interaction and scientific cooperation among mathematicians engaged in cutting-edge research in geometry and related areas. The workshop emphasizes the training and professional development of graduate students and other early-career researchers. Such experiences are critical to the development of early-career mathematicians, new members of a workforce that will help the nation address the technological challenges of the 21st century. The workshop employs a format that strongly encourages both formal and informal interaction among students, postdoctoral associates, and more established mathematicians. The workshops begins with a Friday afternoon Colloquium by the plenary speaker. The 2025 plenary speaker will be Laura DeMarco. The workshop continues on Saturday with five talks, a curated grouping of junior participants with speakers for lunch, a poster session, and a banquet on Saturday. It concludes with two talks on Sunday. The GGD Day program includes 4 speakers on a single Saturday, held at a location which rotates among institutions in central Indiana and Illinois, including rural, primarily undergraduate-serving, and urban campuses. More information about the BGW can be found at https://bgw.sitehost.iu.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 2025 · 2025-06

This award supports the 22nd annual Graduate Student Topology and Geometry Conference to be held at Indiana University Bloomington from April 11-13, 2025. The conference is designed for graduate students and the majority of talks will be given by student participants. Such a gathering provides a chance for young researchers from many institutions and geographic regions to network and speak with experts in their fields. This conference exposes graduate students at all stages of study to the cutting edge of research in topology and geometry. This conference is designed for young researchers to benefit from in different ways: enabling them to present their own work, engage with research in the frontiers of mathematics, sow the seeds of future collaborations, and interact with experts in their various fields. Special attention will be given to a wide variety of topics so that the upcoming generation of mathematicians is aware of the current trends in topology and geometry. To this end, there will be plenary talks by Sarah Koch (University of Michigan), Mark Powell (University of Glasgow), and Inna Zakharevich (Cornell University). There will also be early-career talks by Agustina Czenky (University of Southern California), Beibei Liu (Ohio State University), Annibal M. Medina-Maradones (Western University), Maggie Miller (UT Austin), Carmen Rovi (Loyola University Chicago), and Roberta Shapiro (University of Michigan). The conference URL is https://topologyandgeometry.iu.edu/gstgc25/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This REU Site award to Indiana University, located in Bloomington, IN, will support the training of 10 students for 10 weeks during the summers of 2025- 2027. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program and contribute to development of the US STEM workforce. The theme of the site is behavioral biology and the influence of context on decision-making. Students will be engaged in research investigating how animals make social and non-social decisions based on external events, their internal state, and their past experiences. Students will explore these issues by conducting novel research projects in their host labs. Students will also participate in professional development workshops. All these experiences make students highly competitive for future career opportunities in research, biomedicine, and industry. Many students will present the results of their work at scientific conferences. The effectiveness of the REU site will be assessed using student feedback and tracking the career path and publication record of program participants. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The REU program is hosted by the Center for the Integrative Study of Animal Behavior, an interdisciplinary group at Indiana University. A view of context as behaviorally informative leads to new insight into the capabilities of animals for behavioral plasticity and lends itself to groundbreaking research in the mechanisms of behavioral decision-making. Students will pursue this theme in departments of Biology, Psychology, Anthropology, and Cognitive Science. They will use a wide range of approaches including AI- assisted behavioral analysis, genomic analysis, endocrine measurement and manipulation, and anatomical and physiological neuroscience. They will use animal models including insects, fish, frogs, birds, and rodents. They will work on topics including the mechanisms and generational trajectories of social behaviors like aggression and communication, or on environmentally cued switches in transcriptomes and behavior. In addition to research, interns will attend faculty research presentations, research ethics and other professional skills training, journal clubs, a GRE prep course, panels about graduate school, and tours focused on a range of career options. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Science education at the elementary level is crucial but often challenging for both young students and teachers. Traditional methods of teaching biology can be difficult for students to understand and engage with because they often involve a “one-way” approach that lacks meaningful learning contexts. Similarly, teaching computational thinking (CT) faces its own set of challenges: many teachers and schools lack affordable resources to integrate CT concepts into the existing curriculum. This project promotes science education using affordable, programmable, and easy-to-deploy embodied robotics as part of the culturally responsive curriculum to teach both biology and CT in elementary schools. The project aligns with four of NSF’s 10 Big Ideas, including Future of Work at the Human-Technology Frontier, Growing Convergence Research, NSF INCLUDES, and Understanding the Rules of Life. This project tackles multifaceted and interdisciplinary approaches across STEM education, computational thinking, embodied learning, robotics, and end-user programming, laying the foundation for future elementary education by integrating interactive and embodied learning into the curriculum. The project centers around three main research objectives. Firstly, it identifies the challenges and opportunities within the current curriculum and designs innovative learning strategies. Employing a co-design approach, the investigators engage local classroom teachers and students to integrate Biology and Computer Science (Bio+CS curriculum) through embodied robotics. Secondly, the project focuses on developing novel, low-cost, intelligent, and easy-to-deploy embodied robotics and software tailored for elementary classrooms. The embodied robot is designed to move across a student’s body, visualize different bio-signals in situ and on-body, provide direct visual and tangible feedback, and support embodied programming activities. This design and development process follows a human-centered iterative design approach, with early prototypes tested with teachers and students in small batches. Thirdly, through design-based implementation research, investigators implement the Bio+CS curriculum and examine how students from diverse backgrounds interact with it. The project identifies effective strategies for teaching complex scientific concepts using innovative technology, thereby bridging educational gaps and fostering a more inclusive approach. Integrating biology and computational thinking through embodied robotics thus broadens participation in STEM education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project aims to develop a simulation tool to model the information ecosystem, specifically how content spreads and is consumed by people in a network. By using agent-based modeling techniques, the project incorporates various features of information flow, network structures, and agent characteristics. The primary benefit of this research is to understand how information propagates and how that propagation may be influenced by various characteristics of agents or network properties. The research also evaluates the effectiveness of different interventions, making it a valuable investment to support a more informed ecosystem. The simulation infrastructure provides a scalable and empirically grounded model to predict the outcomes of individual actions at a larger scale. The research addresses critical issues such as the impact of network dynamics and the consequences of various dynamics. By balancing abstraction with ecological validity, the simulation infrastructure aims to produce realistic and interpretable predictions. The project also explores the trade-offs between accuracy, scalability, and robustness. This comprehensive approach informs better understanding of intervention strategies. 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.