Indiana University

NSF Awards · FY 2026 · 2026-10

Cloud computing is essential to a growing number of science use cases, but configuring scientific environments for deployment in the cloud can be challenging given that it often requires specialized knowledge in system administration, networking, security, and involves numerous configuration settings. Furthermore, many scientific workloads depend on tightly coupled virtual clusters, specialized hardware, fast interconnects, accelerators, and custom drivers. Managing this complexity consumes valuable time and attention that researchers could otherwise devote to science. This project develops an AI-based conversational assistant for configuring scientific computing environments that lets researchers describe what they need in everyday language and then creates the environment and verifies its integrity on shared research cloud infrastructure. The benefits of this approach range from increasing scientific productivity and lowering the cost of using cloud computing to enabling practical reproducibility of computational experimentation. The project designs and deploys an AI-based agent framework that can plan, provision, and validate scientific computing environments on open research computing infrastructure, such as Chameleon and Jetstream2. The framework combines large language models running on open, high-performance academic hardware with a set of software tools exposed through standard interfaces that include cloud-based services for resource provisioning, hardware and software environment templates, correctness checks, as well as validation benchmark suite. Key components include planning modules with built-in checks on resource limits, timing, and hardware compatibility; state and error handling modules that track multi-step workflows and summarize system events; and search pipelines that organize information from a wide variety of sources, including documentation, logs, help desk tickets, and environment artifacts into a searchable knowledge base. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

The Great Salt Lake (GSL) is vital to Utah’s economy, contributing over $1 billion annually through mineral extraction, brine shrimp harvesting, and recreation. However, the lake has reached historically low water levels due to upstream water diversions for agriculture, industry, and municipalities. As the lake shrinks, the newly exposed lakebed is emitting wind-blown dust containing harmful heavy metals like arsenic and lead—byproducts of past industrial activity. This toxic dust threatens public health, agriculture, and ecosystems, with risks that extend far beyond the lake itself. This project will shed light on the role that dust plays in depositing heavy metals into ecosystems and onto important crops including corn and alfalfa. As metals accumulate in plants, they may ascend the food chain into livestock, predators, and ultimately humans, with a variety of negative health outcomes. Therefore, the results of this study will have direct implications for the health of ecosystems and communities both within the Great Basin and around the world. The results of this research be shared with communities that may be directly impacted by increased dust emission, by leveraging partnerships with local and state agencies and non-profit organizations in outreach through their regular programming such as fact sheets, newsletters, and community presentations. The research will be integrated with education activities by building and distributing soil test kits for students to use within their local communities, and by engaging local K-12 teachers in hands-on research through teacher internships. As the Great Salt Lake continues to shrink and emit more dust, native and agricultural plants may act as vectors of metal contamination, locally and regionally. As such, this study will utilize a combination of greenhouse and field-based sampling and atmospheric modeling to evaluate the risk to humans and ecosystems posed by GSL dust deposited on key native plants and agricultural crops through: 1) Assessing the extent to which plants take up these heavy metals through root and foliar (leaf) uptake, 2)Evaluating differing plant bioaccumulation among taxa key to the Utah economy, 3) Determining the impact of GSL-sourced dust on plants in the Great Basin region, and 4) Identifying potential source regions of dust that are impacting plants. The project will also analyze strontium, neodymium, and lead isotope ratios in plant tissues to determine the ability of these isotopic signals to determine soil and/or foliar dust compositions or “fingerprints”. Through geochemistry and atmospheric modeling, the research will assess the sources and transport pathways of dust from the GSL lakebed and other regional dust emissions areas and quantify the dust contribution to regional soils. The data generated by this project will contribute to environmental and health-related planning and serve as a new tool for understanding heavy metal (re)distribution during natural processes expedited by environmental change and human activity. These insights are essential for addressing the immediate consequences of the lake’s decline and for predicting more severe impacts in the future, as continued drought and future water management practices could expose more lakebed and increase the risk of toxic dust emissions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The 40th Summer Conference on Topology and its Applications will be held July 13-17, 2026, in Split, Croatia at the Faculty of Science of the University of Split. The goal of this project is to support the attendance of US-based researchers. Having completed four decades of annual meetings attracting scholars from across the globe, this conference series will continue its legacy of advancing the field of topology and its applications. Consequently, it is important to have as many US-based researchers attend as possible so that they may disseminate their work and stay current with the global research trends in topology. SUMTOPO was established in 1986 and attracts about 200 participants each year to discuss the latest advances in topology and its applications to other fields; it is one of the largest and longest-running conference series on topology and typically alternates meeting locations between the USA and Europe to sustain and promote an international community of researchers. The 40th annual meeting is expected to attract a broad spectrum of topologists to commemorate its 40th meeting, and the primary goal of this project is to support travel for early-career researchers, graduate students, and researchers from institutions throughout the country, including those with limited support for travel, as they would likely be unable to attend otherwise. For more details, visit https://events.pmfst.unist.hr/sumtopo/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Accurate and affordable detection of respiratory infections remains difficult outside centralized laboratories. Many respiratory pathogens circulate at the same time and produce similar symptoms, which makes rapid diagnosis challenging. This project will develop a compact electronic approach for multiplex nucleic acid detection that avoids the optics, fluorescent probes, and gel-based analysis used in many current tests. The work addresses a fundamental problem in molecular diagnostics: how to convert complex biochemical reactions into simple and reliable electronic signals. If successful, the project will advance portable molecular testing, support broader access to timely infectious disease detection, and strengthen the scientific foundation for next-generation diagnostic technologies. The project will also contribute to education and workforce development through curriculum modules, mentored student research, and community-facing activities that introduce biosensing, nanotechnology, and data-driven health technologies to broad audiences. This project will develop the electronic Multiplexed Amplicon Profiling (eMAP) platform, a fully electronic and probe-free framework that integrates single-pot multiplex recombinase polymerase amplification (RPA), solid-state nanopore sensing, and machine-learning-based signal classification for automated, gel-free molecular readout. Using a respiratory pathogen panel as a model system, the work has three aims. Aim 1 will develop and optimize a multi-target RPA assay with balanced amplification and minimal cross-reactivity. Aim 2 will establish a nanopore-machine-learning analytical engine that extracts multidimensional event-level current features and classifies amplicons across pores, voltages, and experimental variability. Aim 3 will integrate the assay and analytical engine into the complete eMAP workflow and evaluate performance through blinded zero-shot testing and benchmarking against conventional gel-based analysis for qualitative calls, sensitivity, and reproducibility. The expected outcome is a generalizable electronic sensing architecture that directly converts biochemical amplification into digital signals and enables scalable multiplex nucleic acid testing for respiratory pathogens and other diagnostic panels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This project provides funding for the Cybersecurity Program for the United States Academic Research Fleet (ARFSEC), which aims to enhance the cybersecurity infrastructure of the U.S. Academic Research Fleet (ARF). The ARF, operated by academic institutions within the University-National Oceanographic Laboratory System (UNOLS), supports critical scientific research in the oceans, Great Lakes, and polar regions. ARFSEC will help protect sensitive scientific data and support compliance with evolving cybersecurity regulations. By fostering collaboration among ship operators, providing training opportunities, and establishing standardized cybersecurity practices, the program will astrengthen the security and reliability of research operations that inform environmental stewardship, economic resilience, and national security. The ARFSEC program is designed to address the unique cybersecurity challenges faced by the U.S. Academic Research Fleet. It focuses on three core areas: Cybersecurity Operations & Technical Support, Cybersecurity Awareness and Organizational Resilience, and Cybersecurity Governance, Risk, and Compliance. The program will deploy advanced tools including vulnerability management systems, and fleet-wide log monitoring to detect and mitigate cybersecurity threats. It will also provide ship operators with training in cyber-incident response, tabletop exercises, and compliance with regulatory requirements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

NONTECHNICAL SUMMARY Quantum physics plays a determining role in the behavior of electrons in materials. Beyond the impact of the crystal lattice of atoms within which electrons move, the interactions among them help pick out particular collective electron states that determine a materials conduction, magnetic, and thermal properties. In principle, these states and their associated properties may be modified, manipulated, or probed by the coupling of the electrons to other degrees of freedom, such as a magnetic field. This project focuses on the properties of materials in which coupling to electromagnetic fields is important. This is particularly relevant due to the recent development of material structures that host electromagnetic modes –-essentially confined light/photons-- that can strongly couple to the electrons in a material. Both the photons and electrons have intrinsically quantum natures. Moreover, the mathematical descriptions of such systems involve geometric and topological properties of the quantum states themselves, which have profound implications for how the electronic states may evolve with time. This project will uncover novel physics that emerges when quantum cavity modes, electron-electron interactions, and quantum geometry and topology come together. The materials of focus are low-dimensional electron systems, which can be found in materials that can be thinned to single layers such as graphene, or may be created within semiconductor structures. The work will identify how strongly coupled photon fields modify the states and properties of the electrons, and, conversely, how the electrons impact the states and properties of the photons. Novel probes of the electron system via detection of the photons to which they are coupled will also be developed, with potential relevance for quantum information processing. In addition to elucidation of this novel physics, this project will have broader significance, by providing valuable training for young physicists in methods for analyzing quantum materials, helping them to participate in our nation's ever-evolving STEM economy. The PI will also mentor students in the IU Physics Department Bridge Program. Through the IU SynergySci program, K-12 outreach will be undertaken by partnering with high-school teachers, to develop lesson plans and activities that expose students to important themes in modern physics. TECHNICAL SUMMARY The specific systems to be investigated in this research will involve both van der Waals and semiconductor materials hosting two-dimensional electron gases, with and without magnetic fields. Part of the work will focus on semiconductor excitations known as excitons, in particular a quantum geometric dipole (QGD). The accompanying electric dipole moment changes with time when excitons are dynamical; the studies will focus on the implications of this for exciton modes with a band structure. Exciton transport models will be developed and used to understand how they may radiate into free space or couple to cavity modes. Situations where the excitons follow classical statistics or Bose-condense will be considered. Studies of transport properties, cavity photon statistics, and their correlations will be undertaken to learn what information they reveal about the internal structure and quantum geometry of low-lying excitations. An important focus of these studies will involve identifying situations where the QGD dynamics produces high harmonics of a fundamental frequency that can be detected in free radiation, or harvested when coupled to a cavity. A second set of studies will focus on quantum Hall systems coupled to a cavity, for which recent studies suggest the Hall conductivity may have its usual topological quantization weakened or even breached. The PI will consider edge state physics in such systems, using this to model coupling between electron reservoirs exterior to the cavity and current carrying states within the cavity, to develop a Buttiker-Landauer formulation for transport in these types of systems. Another thrust of the project will focus on collective modes when the system is in a waveguide or a cavity, to understand its thermal properties as well as transport activation gaps, which have shown surprising behaviors in experiment. The project will also consider the impact of a cavity on quantum Hall plateau transitions, and how cavities may modify coherent states such as those of a quantum Hall bilayer at or near integer filling factors. In addition to elucidation of this novel physics, this project will have broader significance, by providing valuable training for young physicists in methods for analyzing quantum materials, helping them to participate in our nation's ever-evolving STEM economy. The PI will also mentor students in the IU Physics Department Bridge Program. Through the IU SynergySci program, K-12 outreach will be undertaken by partnering with high-school teachers, to develop lesson plans and activities that expose students to important themes in modern physics. 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 · 2025-12

The broader impact of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in advancing battery-free, wireless, implantable sensors, with a particular focus on monitoring patients with early-stage heart failures. Battery-free implantable sensors have been growing rapidly in clinical uses because they have the advantage of near zero power consumption, potentially allowing for long-lifetime and maintenance-free operation. Several wireless biomedical implants have been developed for dynamic monitoring of physiological parameters in human bodies, enabling the management of chronic diseases (e.g. heart failure, eye disease, or brain injury) and improving patients' quality of life. Despite their potential benefits, implantable sensors face a significant challenge in accurately and reliably detecting physiological parameters using radio-frequency (RF) signals. This project will bring together commercial, research and education efforts across disciplines to develop a high-performance, energy-efficient, and reliable biotelemetric system. The purpose of the research is to enable real-time acquisition and wireless transmission of biological signals from wearable medical devices and bioimplants. This project will develop a parity-time (PT)-symmetric biotelemetry system that provides new ways to use RF interrogation of an implantable microsensor using an ultracompact, portable reader, enabling real-time, continuous wireless monitoring of physiological signs with high accuracy, unprecedented resolution, and good reliability. The COVID-19 pandemic accelerated the growth of wireless health technologies, and this growth is expected to continue far beyond the pandemic. This PFI project enables miniature and unobtrusive microsensors and bioimplants for future healthcare Internet-of-Things (IoT) systems, which require low-cost, energy-efficient, and ubiquitous operation. The team will translate research on PT-symmetric biotelemetry inspired by non-Hermitian quantum physics into in-vitro/clinical wireless biosensing, and, ultimately, commercialization. Advanced wireless sensors and systems will impact multiple medical electronics and surgical equipment industries, including pulmonary artery monitoring in heart failure, intraocular pressure monitoring for glaucoma management, and intracranial pressure monitoring in the intensive care unit. Additionally, the wearable and bio-implantable wireless sensors can also be connected to heterogeneous 5G and B5G cellular networks, enabling the cloud/edge-based intelligent system to cooperatively sense, collect, and process the information of the sensed data. This project is jointly funded by the Partnerships for Innovation (PFI) program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The project will develop tools to enable genetic manipulation of bacteria to facilitate an array of biotechnology applications. The tools build on established methods to influence the production of protein from individual bacterial genes, including scaling up to impact all the genes in a bacterium within a single test tube. Furthermore, advances to the technology will expand to enable more precise alterations to turn genes “down” or “up,” decreasing or increasing production of proteins encoded by those genes on a broad scale. Previously, such approaches were limited to a small number of targeted bacterial strains. This work seeks to expand the biotechnological reach of available bacteria with a generalized approach that will be tested in a group of marine bacteria called vibrios. Vibrios are important in our food supply, are prominent in causing animal and human disease, and are widely studied to understand animal-host interactions and basic microbiology. As such, they represent a valuable test bed in which to develop and deploy the new technology and assemble resources for laboratories to apply that technology in their own research and in classrooms worldwide. Sharing of the resources developed, including laboratory protocols, teaching materials, computer code, databases of DNA sequences to target bacterial strains, and the resulting bacterial strains themselves, will facilitate broadscale adoption and provide important tools toward growing our bioeconomy. The biotechnology tools developed will be made readily available to the research community, and the proof-of-concept experiments proposed here will be developed as part of a teaching curriculum that can be deployed in courses and workshops. Targeted gene perturbations using CRISPR technology have caused a paradigm shift in eukaryotic functional genomics, but comparable approaches for bacteria have lagged with a reliance on species and/or strain-specific genetic tools and a focus on laboratory strains. Thus, it is essential that bacteriologists extend their studies beyond lab strains and develop tools that can be readily deployed into environmental isolates. This project has two parallel goals: (1) to expand established tools for genome-wide gene knockdowns called CRISPR interference (CRISPRi), and (2) to develop novel bacterial CRISPR technologies for gene overexpression and gene interaction studies. This study focuses on the diverse bacterial group Vibrionaceae, which includes over 100 species that inhabit marine waters and can have significant effects on nutrient cycling and ecosystem health. Many bacterial species in the group cause vibriosis disease, leading to widespread mortality in marine organisms and impacting global aquaculture, disrupting the human food chain, and increasing risks to seafood consumption. The variety in this group is highlighted by the increasing application of Vibrio natriegens in biotechnology applications. Furthermore, Vibrio species serve as some of the most widely-studied model systems for understanding host-microbe interactions, in the context of both mutualism (e.g., V. fischeri) and pathogenesis (e.g., V. campbellii in marine animals, V. cholerae in humans). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This collaborative project aims to serve the national interest by exploring the use of augmented reality (AR) as an educational tool in undergraduate chemistry courses. Since learning chemistry involves abstract structures and spatial reasoning, AR tools offer opportunities to help students visualize and interact with chemical phenomena in new ways. However, instructors and curriculum designers currently lack clear, research-based guidance on how to use AR effectively in teaching. This project investigates how AR applications can enhance learning by supporting student engagement with complex spatial concepts, using molecular symmetry as a test case. The research will identify which features of AR tools contribute to student understanding, how students with different learning approaches use these tools, and how faculty incorporate them into classroom instruction. Outcomes will help shape future AR tool development and inform best practices using them in the classroom. The project will also support instructor development and prepare future researchers in the field of chemistry education. This collaborative project between Indiana University and the University of Wisconsin–Madison, investigates the affordances and limitations of augmented reality tools in undergraduate chemistry instruction. Anchored in theoretical frameworks from constructivism, embodied cognition, spatial thinking, representational competence, and technological pedagogical content knowledge, the research will explore how the leARnCHEM augmented reality application can support undergraduate students' learning of molecular symmetry. The project will examine (1) which AR features support student understanding of symmetry elements and operations, (2) how student characteristics such as prior knowledge and spatial reasoning influence AR engagement, and (3) how instructors plan for and implement AR-based instruction. The project employs a convergent mixed-methods design, including think-aloud interviews, pre/post assessments, classroom observations, and instructional workshops across multiple institutions. Comparative studies will assess student engagement with AR versus non-AR digital resources. Findings will inform instructional strategies and technology design in chemistry education. Dissemination efforts include peer-reviewed publications, conference presentations, and professional development workshops. The NSF IUSE: EDU program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project will study data management practices at user facilities with the goal of improving how research data is organized, stored, and shared. User facilities represent a major investment from NSF and each one has unique data formats and cyberinfrastructure. The project will target selected NSF Major and mid-scale facilities to examine current practices in order to create a roadmap aligned with FAIR principles that facilities can use for improvements in data management and to support open science and improve the national research infrastructure ecosystem. Current data management practices at selected user facilities will be assessed through surveys of the facility personnel and of the facilities' users. The assessment will include topics relevant to the FAIR principles, including data provenance, transfer, packaging, and storage, as well as how data is enriched and deposited for dissemination and citation. Best practices and data formats will be identified, and a roadmap will be created. The roadmap will be shared publicly for feedback through community-building workshops, and elements will be evaluated with hands-on pilot projects at the surveyed facilities. The findings are expected to be useful not only to the surveyed facilities, but to other user facilities and research facilities broadly for assessing and improving their data infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global environmental change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and environmental change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. The project intersects participatory action research, science co-construction, and transdisciplinarity. The RETRACE project will enable communities to identify and strengthen unique resilience levers, enhancing understanding and response to environmental risks through the integration of traditional knowledge and scientific insights. The approach will bridge the gap between theoretical resilience science and the actual experiences of community resilience, by synthesizing local narratives with scientific research, making resilience strategies more understandable and accessible to both communities and policymakers. A significant output of the project is the development of a spatial decision-support system, combining qualitative and quantitative data to aid decision-making. The project outcomes will offer a model for other vulnerable communities, providing a framework to understand and strengthen resilience in various settings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Safety, Security, and Privacy of Open-Source Ecosystems (Safe-OSE) project aims to make important research tools in the cloud safer from cyber threats by using artificial intelligence (AI) to detect and fix software vulnerabilities. The project focuses on two widely used open-source platforms: Jetstream Cloud, which helps researchers build secure cloud systems, and Exosphere, which provides an easy-to-use interface for accessing cloud resources. The project scans both platforms for security issues, uses AI to analyze and reduce risks, and builds tools to help users understand and address potential problems. By improving the safety and reliability of these tools, the project supports researchers, cloud operators, and broader scientific communities, helping the U.S. maintain leadership in secure cloud computing. The Safe-OSE project executes three inter-related activities to help national cloud services overcome the wide-range of software vulnerabilities that can threaten their security posture. First, extensive vulnerability scanning is conducted on Jetstream Cloud OSE, Exosphere OSE, and user-defined virtual machines, containers, infrastructure as code, and code repositories on Jetstream2. Second, reinforcement learning and large language model-based vulnerability management methods suggest alternatives for vulnerable software components and generate vulnerability-minimizing software. The AI-enabled vulnerability management information helps overcome challenges with existing practices by providing context-aware results considering user, time frame, and asset type, offering personalized recommendations, and learning user preferences over time. Finally, Exosphere’s user interface (UI) will be enriched with opt-in scan results and AI-enabled capabilities to help automatically address vulnerabilities in the Open-Source Software (OSS) products such as suggesting alternative libraries, generating potential re-implementations, and more. These enhancements in the OSS products will also help Jetstream2 users address OSS asset vulnerabilities when provisioning resources and could also be translated into commercial cloud offerings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Safety, Security, and Privacy of Open-Source Ecosystems (Safe-OSE) project focuses on OpenMRS, which serves as critical healthcare infrastructure for over 18 million patients across 40+ countries, functioning as the world's largest open-source electronic health records platform, particularly where healthcare access is most vulnerable. Across the healthcare sector, recent security vulnerabilities have exposed patient data to potential exploitation, with attacks on healthcare systems causing average losses exceeding $10 million per incident while disrupting life-saving medical services. This project addresses urgent security gaps in OpenMRS through comprehensive vulnerability management, developer security training, and community-driven security governance that will protect millions of patients' sensitive health information. The enhanced security framework will benefit populations who rely on safety-net healthcare providers using open-source systems, while strengthening U.S. emergency preparedness capabilities. By establishing security best practices for open-source healthcare software, this work creates a replicable model for protecting digital health infrastructure globally by ensuring that healthcare systems can access secure, sophisticated electronic health records without prohibitive costs. This Safe-OSE project focuses on advancing cybersecurity science through systematic integration of healthcare-specific Common Vulnerability Scoring System (CVSS) implementation with machine learning-powered vulnerability prediction for open-source healthcare software ecosystems. The project's intellectual contributions include: (1) development of healthcare-contextualized CVSS metrics that account for patient safety impact and clinical workflow disruption, extending beyond traditional information technology security frameworks; (2) implementation of proactive security architecture using threat modeling integration, predictive code pattern analysis, and continuous security pipeline automation that prevents vulnerability classes rather than reactively addressing discovered issues; (3) establishment of a community-driven security governance model that balances open-source collaboration with rigorous security controls through formal bug bounty programs, structured security training certification, and systematic penetration testing workflows. The methodology advances open-source software security research by demonstrating scalable security enhancement approaches for mission-critical systems serving vulnerable populations. Technical innovations include automated security testing pipelines with healthcare-specific threat detection, supply-chain security through cryptographic checksums, and behavioral analysis for anomaly detection in distributed healthcare deployments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. This Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region, and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among social systems, natural environment, and built environment in the following NNA focus areas: Arctic Residents, Education, and Resilient Infrastructure. The state of Alaska and many other areas of the Arctic are seismically active, experiencing thousands of earthquakes with different magnitudes every year. Given the vulnerability of the Artic to earthquakes and the accelerating environmental and social changes, it is critical to evaluate the resiliency of the region’s infrastructure to seismic events in the context of the natural environment, built environment, and social systems and understand how these changes interact and impact the region’s preparedness and response to earthquakes. The overarching goals of this NNA Collaborative Research project are to: 1) Improve the fundamental understanding of the impact of Arctic changes on the region’s preparedness and response to future earthquakes through seismic monitoring/modeling, community engagement, and targeted investigations of the interactions between the relevant components of the natural environment, built environment, and social systems; and 2) Enhance the seismic resilience of Arctic communities by providing them with the necessary training and tools to manage future earthquake-related disasters including planning, preparedness, mitigation, and recovery. The project research and education activities will include targeted research studies, workshops, and workforce development including the training and mentoring of three graduate students at the University of New Hampshire, one graduate student and an undergraduate student at the University of Georgia, one graduate student at Pennsylvania State University, one graduate student at the University of Alaska Fairbanks, and one graduate student at the University of Virginia. This NNA Collaborative Research project builds upon the results of a planning grant and a workshop with significant community input that identified six major community concerns and high priority research topics regarding the seismic challenges facing the new Arctic and the region’s preparedness and resilience to manage future earthquake-related disasters. To address these challenges, the Principal Investigators (PIs) of this NNA Collaborative Research project propose to carry out a convergent research and education program structured around four strategic pillars and objectives. The specific objectives of the research include 1) Infrastructure seismic response assessment under climate-driven changes of the Arctic ground through the monitoring of key infrastructure systems in Alaska in combination with centrifuge physical modeling to simulate and evaluate the impact of permafrost thawing on soils and building/infrastructure foundations, 2) Co-development of equitable seismic resilience capacities for local communities through surveys, interviews, and participatory mapping workshops, 3) Seismic resilience modeling and assessment through systems dynamics modeling, review of seismic design/planning strategies, and climate adaptation planning at community and state levels, and 4) Outreach and education through ACE youth training camps, STEM research experience for Indigenous youth, and an international workshop on Arctic seismic resilience and adaptation. The successful completion of this project has the potential for transformative impact by seeding and catalyzing research that could lead to breakthroughs in fundamental science and engineering, informed by community participation and Indigenous knowledge, to address seismic challenges facing the new Arctic and improve the region’s preparedness and response to future earthquake-related disasters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The rapid development of Artificial Intelligence (AI)-enabled technologies such as Large Language Models (LLMs) has brought significant benefits across various fields, including behavioral health. However, it is important to note that LLMs are typically trained on general-purpose data, do not explicitly adhere to the guidelines and practices followed by behavioral health professionals, and lack in-the-loop feedback, which can result in inaccurate or unhelpful responses. This project addresses the increasing demand for accountable and scalable behavioral health LLM software to help individuals manage personal challenges and enhance their overall well-being. Drawing on expertise in information systems, sociology, and public health, this research team introduces an Accountable LLM software system specifically designed for behavioral health applications based on Cognitive Behavioral Therapy (CBT). The proposed LLM aims to deliver consistent, high-quality guidance and therapy aligned with established practices. The impacts of this project include improved service delivery and the development of accountable designs for future LLM-based approaches in behavioral health applications. The project aims to develop and evaluate an Accountable LLM based on principles from behavioral science and decision theory, specifically for addressing behavioral health conditions such as depression or anxiety. The proposed Accountable LLM employs an innovative fine-tuning strategy that incorporates patient-therapist interaction pairs, supervised instruction fine-tuning, and a preference alignment tuning approach featuring a novel CBT-Kahneman-Tversky Optimization (CBT-KTO) method to ensure the model adheres to prevailing psychotherapeutic practices. The resulting LLM will be integrated into a user interface, enabling sociology and public health scholars to process and produce a wide range of qualitative data that is difficult to capture using conventional methods, such as direct observations. The proposed LLM will be evaluated through program evaluations, surveys, focus groups, and interviews with psychiatrists and end-users. Results from these evaluations will not only be used to improve the LLM iteratively but will also inform relevant health theories, policy development, user engagement, and service outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project focuses on enhancing program security by controlling how information flows within software, a concept known as Information-Flow Control (IFC). Imagine a program handling sensitive data, like medical records or financial transactions; IFC ensures that this sensitive information doesn't accidentally leak to unauthorized parts of the program or to external users. Traditionally, developers choose between two methods: static enforcement, which checks for security issues before the program runs, offering strong guarantees but requiring more upfront effort, or dynamic enforcement, which monitors information flow as the program runs, requiring less initial effort but potentially introducing runtime overheads and weaker guarantees. This project takes inspiration from "gradual typing," a technique that allows programmers to seamlessly blend these approaches, choosing the right balance of static and dynamic security checks for different parts of their code. The project’s novelties are in bridging the existing gap between theoretical concepts of gradual IFC and their practical implementation and application in real-world scenarios. The project's impacts are in enabling more flexible and efficient development of secure software, making it easier for developers to build applications that protect secure information without sacrificing performance or programmer productivity. The project addresses the theoretical and practical challenges of gradual IFC. On the theoretical front, the investigator mechanizes the noninterference proof for LambdaIFCStar, a gradual IFC language, ensuring its security properties are formally verified using tools like Agda. The project also explores a space-efficient semantics for LambdaIFCStar to optimize its memory usage. From a practical perspective, the investigator implements LambdaIFCStarPlus, an extended version of LambdaIFCStar, by developing a new compiler called GriftIFC. This compiler, based on the existing Grift framework, incorporates security types and coercions, and aims to provide an efficient implementation of gradual IFC. Through case studies in domains such as blockchain, e-voting, and mobile applications, the project evaluates the expressiveness of LambdaIFCStarPlus and analyzes the performance overheads of gradual IFC compared to purely static or dynamic IFC systems. The project's outcomes include open-source releases of mechanized proofs, the GriftIFC compiler, prototype applications, and a performance benchmark suite. 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

NONTECHNICAL SUMMARY This award supports theoretical and computational research and education to investigate a new and promising approach to dynamical control of quantum systems with potential applications to next-generation quantum technologies such as components of quantum computers and sensors. A major obstacle in this field is finding ways to manipulate quantum systems for desired functionalities without causing them to overheat or lose their fragile quantum properties. This research takes a novel path by exploring local drives—targeted, controlled external forces applied to specific parts of a system rather than the whole. By developing new theoretical tools to understand how local drives interact with the broader system, the project aims to unlock new ways to design and control quantum states across a range of experimental platforms, from solid-state materials to ultracold atomic gases. In addition to its scientific contributions, the project incorporates education, mentorship, and public engagement activities. Graduate and undergraduate students involved in the research will gain hands-on experience in advanced analytical and computational methods, preparing them for careers in the growing field of quantum science and technology, areas with strategic importance for national security and economic innovation. The PI also leads efforts to make cutting-edge physics more accessible through open-access publishing, online research forums, and public science events, such as Indiana University’s Science Fest, inspiring the next generation of scientists and promoting broader public understanding of science. TECHNICAL SUMMARY This award supports theoretical and computational research and education to investigate a promising approach to dynamical control of quantum systems. While periodic drives to control quantum properties, Floquet engineering, have shown promise in both theoretical and experimental settings for realizing nontrivial phases of many-body quantum systems, uncontrolled heating and decoherence pose significant challenges to their use. Most previous work has focused on global, uniform drives applied across the entire system. Motivated to limit heating, this project explores a different strategy by focusing on local drives. The overarching goal of this project is to develop the theoretical framework necessary to understand, characterize, and design such locally driven quantum systems. The research is organized around three core aims that explore different realizations of local driving: 1) Floquet boundary drives, in which the confining edges of the system are periodically modulated; 2) Floquet proximity effects, where the interface between two equilibrium systems is coherently driven; and 3) Local quench dynamics in Floquet topological phases, where a driven topological system is coupled at its boundary to a trivial equilibrium system. These investigations will employ both analytical and computational methods, including Floquet Green’s functions, Floquet perturbation and mean-field theories, and exact diagonalization methods. The results are expected to provide new insight into the interplay between topology and nonequilibrium dynamics of driven quantum systems, offer new perspectives on transient dynamics and potentially new thermodynamic principles of locally driven quantum systems, and establish design principles for their future applications in synthetic quantum matter and quantum technologies across a range of experimental platforms, such as solid-state materials, cold atoms, trapped ions, and optical cavities. 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 involves the development of methodologies to estimate unknown quantities from complex, sparse, and noisy data sets by incorporating first-principles physics into statistical modeling procedures. The approach pairs new inverse problem formulations with novel probabilistic high performance computing methods to resolve crucial statistical quantities (means, correlations, confidence bands). This framework has applications in medical imaging, weather modeling, robotics, geology, and geophysics, with mechanisms for improving methods to better formulate and quantify uncertainties for real world challenges in the above domains. Furthermore, the project will incorporate the comprehensive training and the promotion of excellence in applied mathematics and statistics for a new generation of scholars who will be involved in these research activities. This project leverages an emerging Bayesian inversion formalism to estimate non-parametric physical parameters from data modeled as sparse and noisy observations of solutions to partial differential equations (PDEs). The proposed strategy blends rigorous analysis, algorithm development, numerical case studies, and modeling to build a foundational understanding of these methodologies and to expand the scope of this statistical approach to PDE inference. The project is organized around three interconnected objectives. The first objective involves the development of fluid measurement problems featuring infinite-dimensional unknowns and nonlinear parameter dependence. This includes estimating a flow field from a passively advected solute subject to diffusive and reactive effects, determining domain geometry in a Stokes flow, appraising bottom boundary heating from bulk and top boundary observations in Rayleigh-Bénard convection, and specifying a flow underlying an observed mixture in a two-phase fluid. These concrete problems will have intrinsic interest for domain applications while serving as numerical and analytical test beds. The second objective involves the derivation and analysis of Markov Chain Monte Carlo (MCMC) algorithms. This project will formulate adjoint and approximate methods to resolve gradients in the newly developed PDE parameter-to-solution maps, study large proposal and high dimensional limits in multi-proposal algorithms, rigorously assess mixing rates in infinite dimensional methods, and develop diagnostics of bias in inexact MCMC procedures. This approach will be adapted to the fluid measurement problems but will also have wider significance for other areas of computational statistics. The third objective involves the study of concentration properties. This includes formulating general frameworks which isolate crucial properties of the PDE parameter-to-solution map, structure of observations, and the prior distribution to establish conditions for concentration in the large data limit, assessing large data behavior for posteriors when the non-invertibility of the PDE parameter to solution map prevents concentration in principle, and addressing experimental design for the situation when a fixed number of location for observations can be optimized. 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

As artificial intelligence (AI) continues to advance and transform society, it is essential that researchers work in direct partnership with teachers to prepare students to understand the world in which they are growing up. Advancing this goal across K-12 education requires a clear understanding of how to introduce AI concepts to elementary school students and how to effectively support teachers in doing so. The PrimaryAI scale-up project advances foundational knowledge in K-12 AI education that leverages immersive problem-based learning pedagogies for upper elementary learners in grades 3 to 5. The project will reach over 5,000 upper elementary students and more than 60 teachers while expanding the research and implementations across multiple states. The project team will partner with teachers from rural communities in Alabama, Indiana, and North Carolina to engage their students in authentic AI-infused problem solving. This approach aims to foster students' interest in science, technology, engineering, and mathematics (STEM) and equip them with fundamental AI knowledge they will need to thrive in the future. The project will investigate key factors that influence successful scaling of an AI education curriculum across multiple state contexts. It will examine the interplay among teacher professional development, localized classroom adaptation, collaborative design methods, and student learning and interest. These elements are central to understanding the conditions for implementation and mechanisms that sustain and expand the use of AI curricula on a large scale in rural upper elementary classrooms. The project will address three primary research questions: (1) What AI concepts serve as entry points for rural teachers to integrate AI into instruction, considering local contexts and individual pathways? (2) What are the impacts on student outcomes for learning, engagement, and STEM interest across rural contexts? and (3) How do local factors in each state's rural context influence the reception, implementation, and outcomes of PrimaryAI? Research questions will be addressed using multiple data sources as part of Design-Based Implementation Research (DBIR) (Fishman & Penuel, 2018). Pre-and post-tests will be used to assess impacts on student learning and interest. The research team has developed assessments for AI concepts, AI planning, computer vision, and machine learning (Chakraburty et al.,2023). To address the first question, the team will collaborate with teachers from rural communities in Alabama, Indiana, and North Carolina. The team will document ongoing collaborative discussions, professional learning processes, teacher designs, and plans for implementation. For the second question, the project will conduct comprehensive analyses of student outcomes using pre-post assessments of AI knowledge and skills, student engagement, STEM interests, observations of student interactions, and student interviews. Additionally, a cross-case analyses to explore commonalities and differences across various rural contexts and implementations will be conducted. To address the third question, a detailed case studies within each rural community to understand local factors such as pedagogical goals, student interests, community priorities, and educational policies is planned. Outcomes will include locally-contextualized versions of the PrimaryAI curriculum, comprehensive teacher professional development guides, case studies that detail successful strategies and challenges, and recommendations for scalability. Ultimately, the project will advance understanding of effective practices and approaches for integrating AI education into rural elementary classrooms. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

Nuclear matter is at the heart of all visible matter in our universe. One of the most profound scientific questions today concerns the understanding of how fundamental particles such as quarks and gluons make up protons, neutrons and ultimately various forms of nuclear matter. This project aims to address this outstanding challenge by investigating quantum transport effects in a form of matter called quark-gluon plasma that once filled the early universe and can now be created and studied at the Relativistic Heavy Ion Collider (RHIC) and at the Large Hadron Collider (LHC). The PI and his collaborators will perform state-of-the-art quantum transport simulations and will utilize advanced analysis tools like Bayesian inference and machine learning to extract key physics properties from comprehensive experimental data. Additionally, this project supports the mentoring and training of graduate and undergraduate students in scientific research. The PI will engage in outreach activities to disseminate fresh knowledge from current research to a broad public audience. This project has two key physics objectives on a unified theme of quantum transport in quark-gluon plasma. The first objective is to investigate chirality transport through the chiral magnetic effect (CME). This is done by performing anomalous viscous hydrodynamics simulations and advanced Bayesian analysis to quantitatively extract key physics ingredients of CME transport from comprehensive experimental data from heavy ion collisions over a broad range of collisional beam energies. The second objective is to explore angular momentum transport and its associated spin polarization/alignment effects. The PI seeks to establish the angular momentum initial conditions, develop the spin hydrodynamics theory framework, and phenomenological simulations to calculate observables, as well as utilizing machine learning (ML) tools to search for signatures of angular momentum transport. These objectives provide critical theoretical calculations for interpreting an abundance of experimental measurements from heavy ion collisions. The project outcomes contribute to the addressing the mission goals articulated in the 2023 Long Range Plan for Nuclear Science. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Skrabalak of Indiana University and her team will investigate how to precisely design and produce complex nanoparticles made from four or more elements. These materials, known as polyelemental nanoparticles, could offer unique properties that do not exist in simpler materials made of fewer elements. Such properties include improved strength, stability, and reactivity. This research could pave the way for new materials used in energy technologies, industrial catalysis, and next-generation electronics. Moreover, this project will provide immersive research experiences and professional development for graduate and undergraduate students, with a focus on scientific communication and mentoring. Public engagement activities—including interactive nanoscience exhibits and community events, will be used to enhance the visibility and accessibility of nanoscience while helping to prepare a well-equipped STEM workforce. This research will aim to establish general design principles for synthesizing high entropy alloy (HEA) nanoparticles, high entropy intermetallic (HEI) nanoparticles, and polyelemental heterostructures via thermal conversion of polyelemental core@shell nanoparticles. HEA nanoparticles—single-phase solid solutions composed of five or more elements in near-equal ratios—are expected to exhibit enhanced mechanical, chemical, and catalytic properties due to their high entropy of mixing. HEI nanoparticles, with their partially ordered multi-elemental structures, may offer distinct bonding environments that improve stability and performance in catalytic applications. The proposed strategy will involve the seed-mediated synthesis of core@shell nanoparticles followed by annealing to drive interdiffusion and phase transformation. Through three specific aims, Professor Skrabalak's team will investigate how nanoparticle composition, structure, and interactions with support materials influence the formation of HEAs, HEIs, or heterostructures. By integrating experimental studies with computational modeling, the research is expected to yield a broadly applicable framework for the rational design of compositionally complex nanoparticles. 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

Dr. Yager has been awarded a fellowship to conduct research and professional development activities under the mentorship of Dr. Samuel Nyarko at Indiana University, Indianapolis. This project advances research in geoscience education. The capacity to work in teams and having effective teamwork skills are essential in the STEM workforce, and especially in geosciences - a field highly reliant on collaborative fieldwork. Furthermore, the vision and change for the future of geoscience education is that all students should have the knowledge and skills to work in teams, and almost every STEM job advertisement lists teamwork skills as part of the position requirements. Thus, around 98% of geoscience field courses in the United States rely on teamwork as a core teaching and learning strategy, and 88% of geoscience faculty use teamwork strategy at least once in their courses. Most STEM job advertisements list teamwork skills as a position requirement, yet students rarely receive instruction on how to work in teams. This study will design and implement teamwork intervention lessons to connect teamwork skills and fieldwork to investigate how these teamwork lessons impact students learning in both science concepts and teamwork process skills. This project aims to (1) design and implement teamwork intervention lessons for geoscience students that makes explicit connection of teamwork skills to fieldwork, lab, and classroom activities and (2) evaluate the impact of the intervention on students' teamwork process skills learning outcomes. The project will conduct design-based implementation research (DBIR) to identify the design characteristics of teamwork interventions. Then, the intervention will be evaluated to identify compatibility of the lessons’ content, learning outcomes, and assessments. The interventions will be implemented in an inquiry-based workshop format at four field camp courses located at four institutions in the United States. Evaluation will be conducted using a mixed methods case study design to measure the impact of the interventions. It is predicted that incorporating explicit instruction on teamwork skills will help students understand teamwork process skills and aid them to effectively utilize these skills in collaborative team environments, as well as in workforce and career development. This project is funded by the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program aimed at enhancing the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. 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

Advancing the semiconductor industry is a national priority, but a major skilled workforce gap is projected in the coming decade. Closing this gap requires early, proactive engagement in STEM education. This project directly addresses this need by cultivating STEM interest and skills in younger learners to prepare them for careers in this critical industry. This project will advance scientific progress and workforce development by introducing middle school students to foundational semiconductor concepts and manufacturing processes. Through hands-on experiences in community-based makerspaces, the project will directly empower approximately 110 students, 20 educators, and 16 family members across two states to develop an interest in science, engineering, and technology. Using tangible toolkits and immersive mixed reality (MR) environments, students will learn about transistors, logic gates, and integrated circuits in ways that make abstract concepts more tangible and accessible. Partnerships with organizations such as the Rockville Science Center, WestGate Academy, and the KID Museum will further support outreach and implementation efforts, helping to expand STEM education opportunities for all Americans. Co-design with families, educators, and industry partners will ensure the curriculum is relevant and responsive to the needs of the community. Research findings and curriculum resources will be made publicly available to promote wide adoption and lasting impact. The project will follow a three-phase research and development model. In Phase I, the team will conduct co-design workshops with students, families, educators, and experts to identify learning needs and develop early prototypes. Phase II will focus on developing and piloting tangible kits and AR/VR applications through iterative workshops. In Phase III, the refined workshops will be implemented in makerspaces, and the research team will evaluate outcomes related to students’ understanding of semiconductors, development of STEM identity, and interest in related careers. Guided by a design-based implementation research framework, the study will investigate how hands-on, community-based learning supports students' conceptual understanding, fosters a sense of belonging, and strengthens STEM identity, particularly through family involvement. Data collection will include pre-/post-knowledge assessments, attitudinal surveys, learning artifacts, and interviews. Mixed methods analysis, including ANOVAs and thematic analysis, will be used to evaluate the impact. This research will contribute new knowledge on how embodied, informal learning can support broader participation in advanced STEM fields and help close the gap in the semiconductor workforce pipeline. This project is co-funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This project is also co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

The marine phytoplankton Synechococcus senses and responds to changes in the ratio of blue to green light in its environment. This response, called the Type 4 Chromatic Acclimation (CA4), allows these cells to efficiently adjust their photosynthetic machinery to optimally capture the most abundant of these two light colors. This is important because the amount of blue and green light varies tremendously throughout the ocean and light availability usually limits photosynthesis and growth. Synechococcus is the second most abundant photosynthetic organism in the oceans, where about 50% of the photosynthesis on Earth occurs. Therefore, studying CA4 will provide important insights into the regulation of photosynthesis on our planet. In addition, CA4 appears to be sensing light color through completely novel photoreceptors. This project characterizes these CA4 elements and the mechanisms through which they work, providing basic understanding for the application of important new components in Biotechnology, specifically in Synthetic Biology and Optogenetics. An understanding of the mechanisms of the CA4 elements could lead to improved process control by blue and green light during industrial processes. This project contains activities to encourage high school students to enter STEM disciplines and integrates education and research through the participation of high school teachers and students. This project's goal is to define the regulatory mechanisms controlling changes in the photosynthetic light harvesting antennae of the phytoplankton Synechococcus spp., the second most abundant photosynthetic marine organism. This process, called Type 4 Chromatic Acclimation (CA4), allows cells to sense the ratio of blue to green light and alter the transcriptional activity of specific genes. Two approaches will be used to define this regulatory system. First, light-sensing chromophores associated with the two putative, novel photoreceptors FciA and FciB will be identified by purifying epitope-tagged versions from CRISPR transformed Synechococcus cells and analyzing them using spectroscopy and mass spectrometry. Second, the CA4-responsive DNA binding sites of CA4-regulated genes will be identified by joining their upstream regions to reporter genes and replacing specific DNA sequences of these regions, then introducing these genes into Synechococcus to identify CA4 control elements. DNA binding by FciA and FciB, which are also putative transcription factors, will be examined in blue and green light using ChIP-seq and DNA footprinting. These AraC family proteins are likely the first of a new photoreceptor class. The CA4 blue-green light regulatory system will be potentially valuable for use in Systems Biology and Optogenetics. 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.