Purdue University

NSF Awards · FY 2025 · 2025-10

Catalyst materials that speed up chemical reactions play a critical role in the production of energy and chemicals. The catalyst can change during this process, as metal atoms rearrange on the nanoscale, forming new structures with distinct properties and performance. Manipulating such changes could lead to improved materials for industrial reactions, but research progress has been limited by a lack of general principles to understand and control catalyst dynamics. To address this challenge, researchers will integrate advanced computer modeling, accelerated by artificial intelligence and machine learning, with experimental tools to study how catalyst structures evolve during reactions. This workflow enables efficient screening of a wide range of materials to accelerate the discovery and design of more effective catalysts by controlling their dynamics. Specifically, the project will study ammonia fertilizer production, which supports global food supply but is highly energy-intensive (~2% of annual global energy consumption goes to this process), to guide the design of new energy-efficient catalysts. The project will also study how ammonia can be used as an energy carrier through cracking to hydrogen over earth-abundant catalysts. Interdisciplinary training of graduate students in state-of-the-art computer modeling and experimental methods, combined with educational outreach efforts to K-12 students, will prepare students to become leaders in catalytic materials design. This project will construct a unified, predictive model of the dynamic restructuring of metal nanoparticles on metal-oxide supports by elucidating the effects of materials properties and reaction environments on dynamic catalyst performance. In turn, these principles will enable the design of more active, stable, and ‘self-healing’ materials for industrially relevant ammonia synthesis and cracking reactions by tuning material properties to stabilize the most active nanostructures under reaction conditions, and enabling regeneration treatments that reverse the deleterious effects of catalyst sintering. The research team will develop a closed-loop workflow to integrate ab initio molecular modeling and artificial intelligence/machine learning (AI-ML) tools to efficiently screen materials composition space, combined with experimental synthesis of shape-controlled metal nanoparticles on metal-oxide supports, in situ characterization of dynamic behavior using high-resolution microscopy and spectroscopy, and high-throughput reactivity evaluation using steady-state and transient methods. Insights from this project will be used to develop more energy-efficient and stable non-precious metal catalysts for catalytic ammonia synthesis and ammonia cracking to hydrogen. The general principles developed here will have broad relevance to industrially important catalytic reactions involving catalyst restructuring. Databases and AI/ML workflows will be made publicly available to enable use of research products by the catalyst materials community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Research Experience for Teachers Renewal Site, hosted by Montana State University in collaboration with Purdue University, addresses a critical national need: preparing a skilled STEM workforce by starting with our youngest learners. In many rural and American Indian reservation communities, elementary teachers often lack the training and confidence to teach engineering concepts. This gap limits students’ early exposure to engineering and their ability to see themselves in STEM careers. To bridge this gap, this MSU RET Renewal Site offers a unique, hands-on summer research experience for elementary teachers from Montana’s rural and American Indian reservation communities. Over three years, at least two dozen teachers will participate in a six-week program where they will work alongside university researchers on real-world energy challenges - such as renewable energy systems, sustainable materials, and fluid dynamics. This RET Renewal Site will coordinate many of its activities with the Montana Nanotechnology Facility (MONT), one of sixteen National Nanotechnology Coordinated Infrastructure (NNCI) sites. All RET participants will engage with NNCI materials fabrication and characterization resources and experts during orientation, research tours, and instrumentation demonstrations. RET research mentors and their students will have unique and direct NNCI interactions, routinely collaborating with NNCI in their research, leveraging these experiences toward teaching in STEM. These experiences are paired with professional development in teaching practices, curriculum design, and relevant industry and community field trips. The project strengthens partnerships among schools, universities, industry, and Indigenous communities, creating a sustainable ecosystem of support for STEM education. In alignment with the NSF’s mission, this project promotes the progress of science and enhances national prosperity by investing in future innovators. It demonstrates how thoughtful, place-based education can inspire the next generation of engineers by starting in the elementary classroom. The Montana Engineering Education Research Center RET Site Renewal project aims to strengthen STEM education in rural and American Indian reservation communities by equipping elementary teachers with the skills and confidence to teach energy and engineering concepts. Over three years, the project will engage at least two dozen pre- and in-service K–5 teachers in a six-week summer research experience, followed by academic-year support. Teachers will participate in hands-on research in MSU engineering labs, exploring topics such as renewable energy systems, sustainable materials, and quantum applications. The project’s focus on energy, a topic of both regional and global importance, makes science and engineering relevant and accessible to students. These experiences are paired with professional development in curriculum design using the 5E model, Universal Design for Learning, and Montana’s Indian Education for All framework. Teachers, supported by ongoing mentoring and peer collaboration, will co-develop NGSS-aligned curricula for their classrooms which will be shared widely through open-access platforms, multiplying the project’s impact. The project will assess impacts on teacher self-efficacy and student STEM identity through mixed-methods evaluation. By fostering early STEM engagement and building place-based STEM pathways, the project contributes to national goals in education, workforce development, and energy innovation. This project is jointly funded by the Division of Engineering Education and Centers (EEC) and Division of Electrical, Communications, and Cyber Systems (ECCS). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by enhancing the preparation of the engineering workforce through the development of "world readiness"—the knowledge, skills, and behaviors necessary to collaborate effectively in interdisciplinary, multi-stakeholder environments and to create long-term technological solutions. Traditional engineering coursework often emphasizes technical learning alone. However, to address real-world challenges effectively, engineers must be equipped to analyze problems from multiple stakeholder perspectives, identify root causes, and assess the long-term societal and environmental impacts of potential technological solutions. This Improving Undergraduate STEM Education Level-1 Engaged Student Learning project seeks to cultivate world readiness among Engineering Technology students by integrating elements of Cultural Intelligence (CQ) and Humanity-Centered Design (HumD) into the curriculum through short, self-paced online modules- Portable Intercultural Modules (PIMs). The project will develop theoretically grounded educational materials on CQ and HumD that will be openly available to be used by STEM educators to train themselves and implement at their institutions. The project activities will help students reflect on societal challenges associated with engineering problems and understand their roles as design professionals in addressing them. The project will advance the understanding of potential correlations and interlinkages between an increase in CQ and an increase in understanding of and commitment to HumD, and their impact on world readiness. The goal of this project is to support undergraduate students in engineering technology courses to develop world readiness skills through CQ and HumD frameworks. The project will use the design-based research framework for implementation. PIMs will be developed involving case studies, reflection, and other learning activities designed to improve and foster motivation for improving CQ, teach HumD principles, and help students understand the link between CQ and HumD. To cultivate the HumD lens among students, specialized workshops on real-world design considerations and implementation challenges will be conducted by experienced design professionals. Furthermore, as a component of their coursework, students will be required to showcase the application of the HumD approach to their capstone project solutions. To evaluate the efficacy of these educational interventions, a sequential explanatory mixed-method research design will be used. The findings from the project will be disseminated in different forms including publications, educational material, and implementation guidelines. 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

Highly efficient and low-cost light emitting diodes (LEDs) are critical for the future energy landscape in the United States. They are widely used in displays and lighting. Current technologies use high temperature and high vacuum for materials processing and device fabrication, which are energy- and infrastructure-demanding. In this project, LED devices based on a new type of semiconductors—the so-called organic-inorganic hybrid perovskites—will be studied. These materials exhibit excellent optical and optoelectronic properties required for LED applications. In addition, they can be processed at low temperature under mild conditions, making the device fabrication and integration much easier. This research will enable future LED devices that are efficient, scalable and cost-effective. This project will also provide interdisciplinary training to undergraduate and graduate students, providing them with critical-thinking and problem-solving skills needed for future careers in areas of semiconductor technology. Halide perovskites are an exciting family of materials with excellent optical and electronic properties, particularly promising as next-generation light sources. However, a significant scientific gap exists in understanding the defect activities and degradation pathways associated with their reactive surfaces and mixed ionic-electronic transport. The goal of this project is to directly address these questions via design of novel materials and 2D/3D heterostructures that are less reactive and lead to reduced defect density, suppressed ion migration, and enhanced stability. Particularly, this project aims to demonstrate extremely bright and stable perovskite LEDs (PeLEDs) that can operate at application-relevant and high current density over 1000 mA/cm2 with over 200 hours lifetime. Based on such devices, the team will also explore the possibility of electrically driven lasing via nanosecond pulsed excitation exceeding 10 kA/cm2, unlocking a range of applications with a non-epitaxial laser diode. Three specific objectives are: 1) Materials design and interface engineering toward efficient, stable, and color-tunable halide perovskite LEDs; 2) Elucidation of materials and device degradation mechanisms via multi-modal characterization; 3) Evaluation of device behavior at extremely high injection current densities toward electrically driven lasing. The proposed approaches of interfacial engineering and fundamental understanding of degradation pathways systematically address instability issues caused by surface reactions which release ions that then trigger other issues. This will lead to a breakthrough toward long-term stability in perovskite-LEDs. Moreover, this new materials and device platform will allow researchers to explore the possibility of electrically driven lasing – a long-lasting challenge in non-epitaxial semiconductor devices. 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

Advanced long-range imaging systems are critical for defense missions involving tracking moving targets and identifying adversarial objects, and astronomical imaging applications for ground-to-sky observations. Today’s long-range imaging systems are often equipped with adaptive optics to compensate for the distortions caused by the atmosphere. At the core of adaptive optics is the wavefront estimation problem where one needs to recover the phase information from the measurements. However, existing hardware solutions are limited by the resolution and hence they cannot retrieve high-order aberrations, whereas computational techniques generally require multiple measurements to ensure uniqueness. These, in turn, put many restrictions on the problem where it is generally difficult to estimate wavefronts involving moving targets. This research aims to advance wavefront estimation techniques with the goal to ultimately allow wavefront estimation from a single image. Achieving this technical goal will transform today’s long-range imaging systems, hence supporting new defense and space applications. The educational activities of the project stress on workforce development by training scientists and engineers for critical missions in national security, space exploration, and scientific imaging. The technical approach taken by this project is to develop an optics-algorithm co-design framework through neural mappings. By simultaneously seeking the optimal geometry of the aperture and recovering the phase using a new set of neural representations, the project positions itself as a potential solution for ultrafast wavefront estimation. The research consists of three thrusts: (1) theoretical foundations which study the optimality, symmetry breaking, and neural representations; (2) dynamic imaging conditions which involve the development of new models, recoverability analysis, and the decomposition of motion trajectory and phase aberrations; (3) computational and optical applications involving optical neural networks and phase-based image deconvolution algorithms. On the education front, the project supports new course development in camera physics, optics, and machine learning. A summer outreach program on machine learning and computational imaging is planned to serve students in grades 9-12. 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 supports research that focuses on the development of Human-centric AI for Reconfigurable Manufacturing Operation and desigN sYnthesis (HARMONY) to harmonize human workers with increasingly autonomous resources. Emerging technologies, such as automated guided vehicles, mobile manipulators, collaborative and reconfigurable robots, offer new opportunities to address persistent challenges like machine breakdowns, material shortages, and demand fluctuations through dynamic reconfiguration of hardware, software, and logistics. However, fully realizing these benefits requires advanced analytical and programming skills to orchestrate multiple autonomous resources while meeting multi-facet performance targets and operational constraints. While generative artificial intelligence (GenAI) offers an intuitive interface for human-autonomy collaboration, its current application in manufacturing is limited by a lack of domain-specific knowledge. This project seeks to create preference-aligned decision options that humans can explore, select, and refine through low-barrier, multimodal interfaces. The project looks to also include an educational program featuring an innovative curriculum in AI and digital manufacturing, hands-on K–12 engagement and professional development opportunities delivered via workshops, webinars, industry partnerships, and Smart Learning Factories. Successful implementation of HARMONY has the potential to transform manufacturing systems to incorporate autonomous resources in rebuilding national manufacturing capacity and prepare future workforce with advanced technologies. The goal of this research is to innovate GenAI solutions along with digital twins, and progressive data cultivation strategies for translating the autonomy of individual resources into system-level performance gain, all while under human supervision. Existing GenAI models face significant limitations due to a lack of domain specificity, limited manufacturing decision data, and the risk of hallucination. To overcome these challenges, this project pursues three key objectives: (i) the development of a progressive data cultivation strategy leveraging digital twins and statistical surrogate models; (ii) the design of tailored GenAI architectures that embed manufacturing-specific constraints, objectives, and interdependencies; and (iii) the systematic evaluation through Smart Learning Factories and industry collaborations. This project aims to advance the next generation of GenAI technologies for human-centric decision-making in dynamic, reconfigurable manufacturing environments. 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 new three-year REU Site project will focus on Built Environment Decarbonization, which includes the manufacturing and transportation of building materials, construction, and facility operations. Eight REU students each year will address challenges requiring innovative solutions to reduce carbon emissions across the lifecycle of the built environment. Students will tackle real-world challenges in design, engineering, construction, and operations and explore ways to support the decarbonization of the built environment using advanced technologies. The program is designed to enhance participants’ understanding of how systems-level thinking and lifecycle assessments (LCA) can drive innovation in reducing carbon emissions in the built environment and related disciplines. Participants will engage in professional development activities about pursuing advanced degrees or innovation-focused careers through diverse research experiences, cutting-edge projects, and strong industry partnerships that provide real-world relevance. By offering a supportive mentorship framework, students will enhance their analytical reasoning, critical thinking, and problem-solving abilities. The project furthers the interests of the nation and supports the mission of the National Science Foundation by promoting research in the built environment and enhancing the capabilities and competitiveness of the STEM workforce. This REU Site will engage undergraduate students in conducting applied research in Built Environment Decarbonization. The primary goal of the program is to address a systems-level educational gap by creating a novel, integrative pathway for undergraduates to engage in research and development in built environment-related fields. Students will participate in state-of-the-art research collaborations making use of methods and tools that include artificial intelligence and digital twins, Internet of Things sensing platforms, immersive reality, and smart energy assistants. The experiences with these and comparable technologies will enhance the participants’ analytical reasoning in areas such as design optimization, material substitutions, on-site construction improvements, utilization of advanced digital technologies and facility operations based on a lifecycle assessment (LCA) framework. This systems-level perspective will equip students with the skills and knowledge to address complex decarbonization challenges in the built environment. The program emphasizes applied and use-inspired research that bridges academic theory and industry practice. Students will collaborate with faculty and industry mentors on projects spanning design, construction, and operational stages and gain hands-on experience and exposure to interdisciplinary approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This grant seeks to fund US-based students to attend ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc) 2025, held in Houston, Texas on October 27 - 30, 2025. ACM MobiHoc 2025 is a premier annual forum that attracts high-quality, forward-looking research contributions and provides a vibrant forum for technical and professional exchanges. ACM MobiHoc 2025 will expose selected students to cutting-edge developments in the field and enable interactions with world-leading researchers. Students will gain feedback on their ongoing work, broaden their academic perspectives, and build lasting professional connections. This project supports students from US universities to attend ACM MobiHoc 2025 conference in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant will target students who will substantially benefit from attending this conference but have limited travel funds. Priority will be given to first-time attendees. 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 address critical workforce development challenges at the intersection of emerging technology and natural resource management. As digital tools transform the forestry and natural resources industries, there is growing demand for professionals trained in data-driven technologies such as artificial intelligence (AI), uncrewed aerial systems (UAS), and environmental sensor networks. However, existing training programs have not kept pace with these advancements, leaving a widening skills gap that threatens economic resilience, ecological sustainability, and workforce preparedness across these sectors. This project, Driving Real-World Integration of AI into Digital Forestry and Natural Resources (DRIAD), will build an inclusive, scalable, and collaborative ecosystem for experiential learning. Through immersive, real-world training and cross-sector partnerships, DRIAD will prepare a new generation of professionals to lead in technology-enabled natural resource management. The program serves the national interest by strengthening the STEM workforce, expanding participation in high-growth fields, and supporting sustainable resource use across forestry and allied disciplines. The DRIAD program will support at least 60 participants through a year-long, cohort-based model designed to provide hands-on, industry-informed training. Core activities include a three-week residential summer short course, monthly virtual professional development sessions, and ongoing mentorship by faculty and cross-sector professionals. Participants will complete applied projects grounded in real-world data collection and analysis using AI, UAS, and Internet of Things (IoT) technologies. The curriculum will be developed in collaboration with partners from government agencies, NGOs, and industry to ensure relevance, scalability, and alignment with emerging workforce needs. In addition to developing technical fluency, the program emphasizes career-ready competencies such as data interpretation, systems thinking, and professional communication. Outcomes will include transferable models for experiential learning, new insights into effective technology adoption in workforce training, and contributions to the use of high-resolution digital tools in ecosystem monitoring and management. Findings will be disseminated through peer-reviewed publications, technical reports, and presentations at professional society conferences, supporting innovation across natural resource professions. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national need of preparing highly qualified K-12 STEM teacher leaders to address emerging workforce demands in microelectronics. The rapid expansion of the microelectronics industry in the United States reflects a critical need to strengthen national security and economic competitiveness. This expansion requires K-12 teachers to be equipped with microelectronics content knowledge and integrated STEM pedagogical practices to inspire and prepare students for future careers in this vital sector. Over six years, the project will recruit and prepare two cohorts of 12 practicing K-12 teachers (24 total) to become Microelectronics Master Teacher Fellows, supporting the integration of microelectronics-focused curricula and instruction in high-need school districts. These teacher leaders will have the opportunity to play a critical role in fostering student interest in STEM fields and preparing the next generation for careers in the growing microelectronics industry. By promoting workforce-relevant STEM teaching practices, the project has the potential to advance opportunities toward STEM career pathways for K-12 students. Additionally, the experiences and insights gained through this initiative will contribute new knowledge on strategies for equipping STEM teachers to integrate emerging workforce contexts, such as microelectronics, into K-12 classrooms. This project at Purdue University leverages partnerships with the Regional Opportunity Initiatives workforce development organization, the Naval Surface Warfare Center-Crane Division, and the Silicon Crossroads Microelectronics Commons Hub to serve four school districts: Washington Community Schools, Lafayette School Corporation, Loogootee Community Schools, and Purdue Polytechnic High Schools, spanning both rural and urban settings near microelectronics facilities. The project will provide advanced professional development through microelectronics-focused summer institutes, graduate coursework, and annual summits, while supporting teachers in leading district-wide vertical alignment plans for microelectronics education. The 24 master teacher fellows will be provided opportunities to develop expertise in microelectronics-related content/technologies, STEM pedagogy, and leadership to embed microelectronics-focused STEM initiatives into school districts. Project goals include supporting teacher retention in high-need schools, inspiring/preparing students for microelectronics careers, and creating national exemplars of microelectronics-integrated school districts. The project’s evaluation and research will assess the impact of its strategies, with findings disseminated through national conferences, microelectronics hubs, and open-access platforms for instructional materials. This Track 3: Master Teaching Fellowships project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This project is funded by the Robert Noyce Teacher Scholarship Program and is supported in part by funds from the Micron Technology, Inc. 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 is a smart-agriculture collaboration between Purdue University and Bayer Crop Science that will create an integrated network of small, plant-wearable sensors to improve early pest damage detection in agriculture. When insects attack a plant, the plant releases specific volatile organic compounds (VOCs) as a distress call. The proposed sensor system can identify these VOC signatures in real time. Each sensor is built with advanced nanomaterials and is flexible enough to attach to a corn plant’s leaves or stalk like a tiny sticker. The sensors are designed to be highly sensitive and selective to VOC indicators of pest damage, for the accurate detection of insect infestations of crops in the earliest stages. Development and testing of the proposed early pest detection technology will occur in stages to confirm reliable outcomes. In the first phase, the researchers will design and fabricate the sensor array devices by using high performance nanomaterials and device design and printing technologies. Next, they will test the plant-wearable VOC sensor array in the laboratory and in controlled greenhouse environments at Purdue and Bayer facilities by exposing corn plants to pests (e.g. European corn borer). The team will fine-tune the sensors’ responses (sensitivity, selectivity, robustness) across different plant varieties and growth conditions. Once validated in these settings, the sensors will be deployed on actual farms in Indiana for field trials. This project innovatively integrates sensors into a smart Internet-of-Things network with AI-driven data analysis. Each low-power sensor node will transmit its readings via a wireless link to a central hub for processing. There, data analytics and Artificial Intelligence (AI) algorithms will filter out background noise and recognize the chemical patterns that signify early pest. This automated analysis will trigger alerts to farmers and provide quick and actionable information far sooner than traditional crop scouting methods. The project will contribute to the workforce development by training a cohort of graduate, undergraduate and high-school students in hands-on skills transferable to agriculture and engineering careers in the U.S. economy. 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.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Millions of Americans engage in substance use (SU), which poses myriad adverse health effects including risk for overdose. Consistent with RFA-CE-25-149, there is a critical need to develop, implement, rigorously evaluate, and scale feasible and efficacious approaches to Screening, Brief Intervention, and Referral for Prevention for individuals engaging in at-risk SU (SBIRP). Emergency departments (EDs) provide a unique opportunity to access the general population including young, diverse, and disadvantaged individuals to provide SU SBIRP who otherwise cannot or do not access other care and services. This proposal seeks to leverage the ED setting for an SBIRP approach paired with a subsequent telehealth-delivered motivational interviewing (MI) prevention program to avert the development of substance use disorders (SUD) in populations engaged in at-risk SU. We aim to: 1) Evaluate the efficacy of Emergency Department- Substance use screening, Motivational interviewing and Active Referral Targeting substance use disorder and overdose prevention (ED-SMART) for patients with at-risk SU. Using an established, ED-based at-risk SU screening approach, we will enroll a sample of consenting participants with at-risk SU use via recommended measures (N=650) in a two-arm RCT comparing (i) a brief, single session, ED-delivered MI intervention plus referral for 5, monthly, brief telehealth MI sessions to (ii) an SU informational control condition. Assessments will occur at baseline (prior to randomization) and at 1- and 6-months post- baseline. We hypothesize that ED-SMART will have greater reductions in SU (primary outcome) relative to control at 6 months. We will secondarily evaluate: (i) incidence of positive screen for SUD, (ii) changes from baseline in proportion with positive biological toxicology assays for SU, (iii) self-reported healthcare utilization, and (iv) frequency of fatal and non-fatal overdose. 2) Characterize ED-SMART feasibility, acceptability, and processes in preparation for future clinical implementation. We will (i) measure intervention dose, (ii) conduct time and motion observations of resource requirements for intervention delivery (ED and telehealth), and (iii) engage (n=30) patients (both completing ED-SMART and declining AIM 1 study), and (n=50) ED nurses, physicians, and administrators in planning for a future effectiveness- implementation hybrid trial. Using mixed-methods guided by Consolidated Framework for Implementation Research constructs, we will evaluate perceived feasibility, acceptability, and organizational readiness after sharing AIM 1 efficacy results and develop an operational plan for clinical ED-SMART implementation. Should ED-SMART not demonstrate efficacy, participants will also engage in a rigorous assessment of possible alternative intervention targets. This high impact research promises to launch a new paradigm in emergency care and overdose and SUD prevention. At scale, innovations to optimize and implement ED- SMART in the ED could provide meaningful reductions in the prevalence of SUD and overdoses.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The molecular mechanisms driving physiological adaptations to exercise remain insufficiently understood, despite their critical role in promoting human health. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) has generated an extensive multi-tissue transcriptomic dataset from Fischer rats undergoing endurance training, offering an unprecedented opportunity to investigate systemic responses to physical activity (PA). However, current analytical methods fall short in capturing the complex regulatory programs that operate across tissues, timepoints, and species, limiting translational insights into human biology. This project introduces a transformative computational framework leveraging GeneCompass, a state-of-the-art foundational model pretrained on over 100 million human and mouse single-cell transcriptomes. GeneCompass integrates diverse biological knowledge, including promoter sequences, transcription factor binding sites, and gene regulatory networks, to create biologically informed embeddings that enable precise cross-species comparisons. We will extend and fine-tune GeneCompass to analyze MoTrPAC’s rat RNA sequencing data, bridging molecular adaptations in rats to human biology. By aligning orthologous genes across species, this approach will uncover conserved transcriptional regulators and pathways, revealing shared mechanisms of exercise-induced adaptations. Our project focuses on three key aims: adapting GeneCompass for multi-species transcriptomic analysis, performing high- resolution tissue- and timepoint-specific regulatory analyses of PA-induced adaptations, and translating findings from rat models to human health using cross-species comparisons and in silico perturbation experiments. These efforts will produce innovative computational tools, dynamic molecular models, and actionable regulatory maps that enhance the value of MoTrPAC data. By providing a systematic framework for understanding the molecular underpinnings of exercise, this research will inform precision medicine and support the development of evidence-based strategies to improve human health through physical activity.

NIH Research Projects · FY 2025 · 2025-09

Project Summary - Abstract The mission of the Office of Indiana State Chemist (OISC) is to protect public consumers, livestock, pets, and manufacturers from misrepresented or unsafe feed, fertilizer, seed, and pesticide products, and from misuse that may result in harm to animals, people, or the environment. This mission is fulfilled through science-based regulation, truth in labeling, education and training of users, and active enforcement of state and federal laws. OISC’s regulatory approach ensures safety in the marketplace, responsible use of agricultural products, and support for the goals of the Integrated Food Safety System (IFSS). This proposal supports Indiana’s continued implementation and advancement of the Animal Food Regulatory Program Standards (AFRPS). During Years 1 and 2 of this funding cycle, OISC will finalize documentation and processes to close remaining program gaps and continue improvements in procedures, training, inspectional consistency, sampling, and enforcement practices. In Year 3 of this funding cycle, OISC will transition to the AFRPS Maintenance Track, maintaining full implementation with an emphasis on continuous improvement, internal audits, and consistent compliance and enforcement. Under this proposal, OISC is requesting $300,000 annually in grant funds for the first two years (AFRPS Development Track), and $225,000 in the third year (AFRPS Maintenance Track). Grant funds will support one full-time equivalent (FTE) employee, training, travel and collaborative work with FDA and other state programs, and equipment and supplies to strengthen Indiana’s feed safety infrastructure. This cooperative agreement will develop and sustain a high-quality, standards-based regulatory feed program that reduces the risk of animal feed contamination and contributes meaningfully to national food safety efforts.

NSF Awards · FY 2025 · 2025-09

NON-TECHNICAL ABSTRACT: The next generation of advances in dependable energy and space exploration technology in the United States will require scientists and engineers to develop functional devices capable of operating in extreme temperatures, high radiation, and chemically reactive environments. In addition to operating in extreme conditions, these devices will remain under the increasingly intense technological demand for miniaturization. Existing materials cannot satisfy these performance requirements. For example, the reliability of current state-of-the-art electronics (logic, memory, contacts, sensors, packaging), based on silicon technology, significantly degrades above 150 °C. Even proposed material solutions (e.g., silicon carbide) rely on thicker metallic contacts that are at best limited to ~350 °C. Layered, few-atom-thick two-dimensional (2D) structures can meet the miniaturization and performance requirements for these next-generation devices for extreme conditions. However, while there are high-temperature-capable 2D semiconductor and insulator candidates, no 2D conductors satisfy the stringent requirements for conductivity, interfacial stability, and thermal stability. This project advances the feasibility of ultra-thin next-generation extreme environment nanoelectronics by advancing the atomic-level design of 2D conductive sheets of transition metal carbides, known as MXenes, for extreme conditions. This is achieved via a synergistic combination of experiments, simulations, and modeling. To accelerate US-based innovation, all experimental and theoretical results produced, and models developed will be made available online to enable education, workforce development, and to develop new machine learning models for research. Undergraduate students on this project are being prepared for the future workforce to develop critical materials and devices for the advancement of US knowledge and technological capabilities. Further, to inspire the next-generation of the US workforce, a science-as-art competition called NanoArtography will be integrated to expand public outreach and education. As a part of this NanoArtography outreach, hands-on nanoart workshops involve training young students to color black-and-white electron microscopy images of 2D carbides to learn more about how nanomaterials appear at the nanoscale. TECHNICAL ABSTRACT: This project discovers new two-dimensional (2D) transition metal carbide (MXene) electrical conductors for next-generation functional devices operating under extreme conditions (> 1000 °C). This is accomplished through structural and compositional modification of their interior transition metal carbide/nitride chemistry and surface functionalities. To meet the future technical demands for device miniaturization and functionality while operating in extreme conditions, assemblies of 2D materials must be scientifically advanced in order to fabricate stable ultrasmall nanoelectronics capable of operation at high temperatures. While insulating and semiconductor 2D materials that can perform at ~500 °C are available, the metallic contacts critical to device performance have been comparatively overlooked. Current metallic conductors are comparatively thick (tens of nanometers or more) and cannot perform above ~300 °C. To address this gap, this project aims to enhance the thermal stability limits of 2D MXenes beyond current capabilities by altering their interior transition metal carbide core structure. Additionally, it aims to modify the surface of MXenes with halogen and chalcogen terminations through surface ligand exchange reactions to enhance MXenes’ stability as a 2D flake in high-temperature conditions (> 1000 °C). By pairing computational modeling with experimental validation, this project identifies and synthesizes MXene core compositions and surface chemistries that can reach these stability limits and experimentally benchmark the resulting high-temperature stable MXenes’ electrical and thermal properties as compared to other state-of-the-art materials for metallic conductors in extreme environmental conditions. 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

Metabolic diseases, such as diabetes, affect millions of people worldwide, profoundly impacting health, quality of life, and healthcare costs. However, traditional approaches to dietary management rely on generalized guidelines and intermittent blood tests, which limit their effectiveness in capturing the real-time metabolic changes necessary for personalized care. To address this, researchers at Purdue University and Oregon Health & Science University (OHSU) are developing NOURISH, an innovative digital twin technology that continuously tracks multiple metabolic indicators using wearable biosensors. By pairing these real-time measurements with advanced computational models, NOURISH simulates whole-body metabolism in individual patients and provides tailored dietary recommendations. The initial validation of these sensors will focus on healthy volunteers with controlled metabolic challenges, providing foundational data necessary for future clinical applications. NOURISH aims to significantly improve personalized dietary interventions and metabolic health outcomes. The project will also provide interdisciplinary training opportunities in advanced technologies and prepare a diverse and skilled workforce to meet critical national needs for healthcare innovation. The NOURISH project directly aligns with the NSF’s Foundations for Digital Twins (FDT-BioTech) program by developing advanced biosensors and integrating them into a physics-informed whole-body metabolism digital twin (WBM-DT). The team will retrofit FDA-approved continuous glucose monitors (CGMs) with tellurene-based nanosensors, enabling real-time measurement of multiple clinically relevant biomarkers, including glucose, lactate, β-hydroxybutyrate, branched-chain amino acids, glutamate, glutamine, acetoacetate, and glycerol. Sensor validation will be conducted initially in healthy adult volunteers using mixed-meal tests and standardized metabolic challenges. Data will be assimilated using an ensemble Kalman filter and Bayesian uncertainty quantification (UQ) to calibrate mechanistic metabolic models that accurately simulate systemic body-level metabolic fluxes. This framework will be combined with probabilistic AI-based control algorithms to deliver precise nutritional guidance tailored to individual physiological states. Additionally, large-scale, bias-audited synthetic cohorts will validate the model's accuracy, reliability, and fairness across diverse populations. These foundational methodological advancements will provide essential regulatory science toolkits for precision nutrition, facilitating broader biomedical applications for managing metabolic and chronic diseases. 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

Recent advances in data science, statistics, and machine learning have opened new possibilities in precision medicine, enabling clinicians to tailor treatments based on individual patient characteristics. This project focuses on developing a unified and efficient statistical framework to improve treatment decisions by leveraging rich demographic, socio-economic, and biomedical data. By advancing personalized decision-making, this research contributes to better health outcomes, more efficient healthcare delivery, and overall national well-being. The project also offers broad societal impact through its commitment to education, collaboration, and open science. The investigators will mentor graduate students and develop new coursework at the intersection of machine learning, statistics, and personalized medicine. In addition, all software tools developed will be released as open-source, supporting accessibility and reproducibility in scientific research. The interdisciplinary nature of the project encourages collaboration across statistics, medicine, and computer science, and prepares a next-generation workforce to tackle complex health data problems. This project aims to develop an efficient learning framework for estimating optimal individualized treatment rules (ITRs) across a broad range of personalized medicine settings. The proposed methodology is based on semiparametric modeling and is designed to address complex relationships among covariates, treatments, and outcomes. Key challenges addressed include handling multiple treatment options with cross-treatment structures, modeling a variety of outcome types, and accommodating multi-stage decision-making with time-varying, history-dependent effects. The framework also supports incorporation of domain knowledge for interpretability and practical implementation. From a statistical perspective, the proposed methods achieve double robustness (consistency under two separate model specifications) and statistical efficiency (minimal asymptotic variance), even under model misspecification and in high-dimensional or limited-data scenarios. These contributions advance the state of the art in both semiparametric theory and algorithmic design for ITR estimation. The resulting models are interpretable, scientifically meaningful, and directly applicable to real-world medical problems, including drug development and treatment recommendation. This work not only contributes to foundational statistical theory but also facilitates translational research in healthcare. 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 support from the Division of Chemistry, Professors Jonathan Hood and Libai Huang of Purdue University, along with their collaborator from the University of Bristol in the United Kingdom, are developing molecular platforms for quantum memory applications based on cryogenically cooled conjugated organic molecules. While these molecules are exceptionally coherent emitters of photons, the ground and excited electronic states are singlets and hence lack the spin degree of freedom needed for memory. Triplet states could provide this functionality, as well as long coherence times, but the exceedingly small intersystem crossing rates and extremely weak absorption cross-sections make them nearly undetectable. Accessing this hidden state holds the key to transforming excellent light-emitting molecules into quantum memories. Professors Hood, Huang, and their collaborators will use advanced steady-state and time-resolved laser techniques capable of coherent quantum state manipulation to access and study the triplet states of large organic chromophores. Their discoveries could establish fundamental principles that connect chemical structure to quantum memory performance, opening new avenues for chemically tailored quantum technologies. The project would create research opportunities for postdoctoral scholars in cutting-edge quantum molecular science, thereby contributing to the development of a quantum-enabled STEM workforce. This award is made under the NSF-UKRI lead agency opportunity. The research addresses the fundamental challenge of accessing triplet states in dibenzoterrylene (DBT) molecules which are doped as crystal defects within an anthracene lattice. Intersystem crossing to the triplet state occurs in only one out of 10 million excitation events, and as a result has remained difficult to detect. The team will employ sensitizers to enhance triplet population through Dexter energy transfer, enabling phosphorescence detection near 1550 nm and circumventing the weak direct optical transition. Following triplet state discovery using time-resolved spectroscopy, the team will implement coherent two-photon Raman schemes for deterministic state preparation, while optically detected magnetic resonance will provide zero-field splitting between triplet sublevels. Critical to quantum memory functionality, the project integrates DBT with silicon nitride nanophotonic cavities targeting Purcell factors exceeding 20 and collection efficiencies above 50%. Coherence optimization employs three complementary strategies: dynamical decoupling sequences via integrated microwave control, isotopically purified host matrices to minimize nuclear spin interactions, and innovative sensor-emitter architectures where spatially separated molecules provide real-time environmental feedback for adaptive protection. These advances will demonstrate spin-photon entanglement protocols essential for quantum repeater applications, establishing DBT as a chemically tunable platform where molecular design principles enable control over quantum information storage and retrieval. 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

A group is a mathematical abstraction of symmetries of a physical object or a theoretical space. Groups are fundamental objects in mathematics that also emerge in various applications such as in computer science and physics. The algebraic notion of a group associates to a set a binary operation, like multiplication, which satisfies a list of axioms. Groups emerge naturally as symmetries of various types of concrete or abstract spaces in mathematics. There is an intricate relationship between the geometric properties of these spaces and the algebraic properties of their groups of symmetries. The PI will continue his investigation of the landscape of infinite groups that emerge as symmetries of the most natural spaces in mathematics, the circle and the real line. The PI will organize two research workshops aimed at graduate students, and two research experiences programs for undergraduates. These shall be aimed at training a diverse body of students to become future leaders in mathematics. These activities will incorporate computational methods into the students' mathematical exploration of the landscape of infinite groups. This project is jointly funded by Topology and the Established Program to Stimulate Competitive Research (EPSCoR). The PI will investigate the relationship between the algebraic structure of left orderable groups and the topological and dynamical properties of their actions on 1-manifolds and the cantor space. One goal is to investigate the class of finitely presented, infinite, simple groups, and exhibit new conceptual phenomena. This involves investigating notions such as uniform simplicity, and whether there is a finitely presented infinite simple group that acts on the real line by homeomorphisms. Finally, the PI will investigate a family of closely interconnected open problems emerging in combinatorial group theory. This includes a systematic study of normal generation in the class of finitely generated perfect groups, the conjectured existence of non-abelian free subgroups in non-indicable finitely generated left orderable groups, and fundamental groups of subcomplexes of aspherical 2-dimensional CW complexes. 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

From food production to processing to distribution, food systems are an integral part of the local, regional, and national economy. These systems also drive STEM innovation and require interdisciplinary actions from across STEM fields. The need for STEM-related food system jobs shows the importance of high school youth exploring related STEM careers and developing skills to solve complex food system problems. At the same time, technology in K-12 education often centers on the use of technology, rather than understanding the role of technology in complex STEM systems. The Internet of Things, such as smartphones, sensors, and remote-control systems, includes digital technologies that can help students learn how to use technology to collect and manage digital data and actively use data to make decisions. Building on the outputs and outcomes of previous USDA investments, this project will design and study professional development and curricula that (1) advance urban high school STEM teachers' enactment of learner centered teaching; (2) support urban high school students' design thinking using the Internet of Things to solve incubation design challenges; and (3) develop students' connections to local STEM workforce opportunities through community and scientist partnerships. This project will directly impact 11 urban high school teachers and 2,020 students attending 5 urban high schools by supporting students' STEM knowledge, problem-solving skills, design thinking with the Internet of Things, and interest in pursuing STEM careers. This project is based on and seeks to further inform frameworks such as learner-centered teaching and design thinking. These frameworks will be used to design and implement teachers' professional development and high school curricula that engage students in local food system incubation design challenges via the Internet of Things and community partnerships. Qualitative case studies using observations, interviews, reflections, and artifacts will address teacher impact questions: 1) What were teachers' dispositions, self-efficacy, and learner centered teaching enactment when connecting the food system problem to students, their families, and the community? 2) How did learner centered teaching develop community relationships? Additionally, longitudinal concurrent mixed methods case studies using journals, focus groups, and pre-post surveys will explore student impacts: 3) Did students have higher levels of design thinking mindset and situated STEM motivation upon completion of the incubation design challenges? 4) How did design thinking in solving local food system problems enhance students' community connections and STEM career interests? Resulting professional development and curricular materials will serve as an innovative model for 20 STEM Certified High Schools and Career Centers (serving 18,170 students from 54 high schools). Research results will be shared through community presentations, local conferences such as the Indiana Small Farm Conference, the Science Education Research Center, as well as national conferences and journals. 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary: As children enter their second year of life, they experience rapid growth in their language knowledge which includes substantial increase in vocabulary size and the speed of online word recognition. Yet, there is also substantial variability in language skill during toddlerhood; 2-year-old children range from knowing a handful to hundreds of words. Children that fall on the low end of this variability – frequently called Late-Talkers (LTs) – are at increased risk for developing persistent language disorders, affecting their academic, social, and professional lives. Recent evidence suggests that LTs experience not only delays in building a sizeable vocabulary, but also show differences in the structure of their vocabularies. This project builds on this finding by seeking to establish a mechanistic connection between building early vocabulary structure and subsequent language growth by building word learning studies that are focused on building the individual structure of children’s vocabulary by focusing on semantic linkages between words. We propose to recruit 18- to 27-month-old children with diverse language abilities (including those who are late-talking) and backgrounds who take part in a series of experiments that probe how different types of individually tailored word learning tasks lead to short-term growth in language processing skills and vocabulary growth over a period of three months. This project advances theoretical accounts of early language learning and has high potential to inform early intervention practices by establishing a causal link between toddlers’ existing semantic knowledge and their growing language skills.

NSF Awards · FY 2025 · 2025-09

Many solar energy projects are installed in agricultural lands, creating land competition with crops, orchards, vineyards, and pastures. However, relatively little is known about how these solar installations affect surrounding communities, landscapes, and agriculture. Moreover, careful design and siting of these installations can yield a variety of benefits, including increasing farm income, enhancing water resources, improving plant and animal habitat, and enriching soil. To address these issues, this project will bring together an interdisciplinary team of scientists and engineers with agricultural extension specialists, landscape designers, community members, and industry and nonprofit partners. The project will focus on two questions: 1) how is solar energy affecting the landscape and surrounding communities?; and 2) how can the U.S. build a stronger, more productive, and more resilient agroenergy landscape? The project explores practices that will improve outcomes of solar energy in agricultural landscapes. To do so, the project will collect novel data at existing solar facilities and launch a first-of-its-kind scientific research facility to collect data on how solar installations affect agricultural land and communities. Using these data, the research team will study how solar facilities change soil and habitat conditions, the water cycle, crop production, economic returns, and surrounding communities. Throughout the project, an advisory team of farmers, stakeholders, policymakers, and community members will help shape the research and focus the project’s efforts on the needs of farmers, utilities, and the public. This approach will bring together new forms of biogeophysical data collection, modeling, and life cycle assessment with community co-creation. The project’s findings will be used to create decision-support tools, design new solar installations, conduct workforce training, and develop educational workshops and programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Mergers of ultra-compact stellar remnants called neutron stars have now been observed. They provide new tools to study general relativity, compact objects, neutron star equation of state, plasma physics, astrophysical jet physics, and nucleosynthesis. A joint US-Israeli research team will develop numerical simulations of binary mergers through their full development. The US effort is jointly led by California State University, Sacramento, Purdue University, and Northwestern University. The team will aim to construct “meter-to-parsec” models of binary mergers, which follow through the entire journey of length and time scale, by directly connecting the pre-merger state of a neutron star - neutron star (NS-NS) or neutron star - black hole (NS-BH) binary to the regions where the observed photons are produced. This project has two main broader societal impacts : (i) Development of a globally competitive STEM workforce; and (ii) Improvement of STEM education of K-12 students. The models will allow interpretation of various observables of the system by connecting them with the conditions inside the ejecta and in the pre-merger phase: (i) Generating consistent models of the jet profile will allow the researchers to connect the observed off-axis emission to the conditions at the base of the jet and help construct reliable emission models for the multi-wavelength counterparts of gravitational wave events. (ii) To date, it is still not known if the compact merger remnants powering short gamma-ray bursts (sGRB) jets are BHs or NSs. By mapping the main differences between the physical properties and emission profiles of jets powered by these two types of engines, the research will provide a method to distinguish between them, independent of the kilonova emission, which may not be detectable in typical multi-messenger events. (iii) The question of energy composition in the jet at large scales is a long standing puzzle in sGRBs. The answer can shed light on the acceleration mechanisms and emission processes responsible for the prompt gamma-rays. In addition, the Sacramento State principal investigator (PI) will partner with the Sacramento State planetarium to develop a new curriculum for K-5 audiences that visit the planetarium as part of their free school field trips. The Purdue PI will partner with the Saturday Morning Astrophysics Purdue program for middle and high-school students to develop and lead an annual session on the detection of gravitational waves and the study of compact objects in the Universe. The Northwestern PI will partner with the REACH high school program to mentor Chicago-area high school students each summer. 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

Modern science and engineering increasingly rely on extracting meaningful information from large and noisy datasets, such as those arising in medical imaging, environmental monitoring, telecommunications, and numerous other disciplines. This project develops advanced statistical methods that improve signal recovery and noise reduction through innovative shrinkage and thresholding techniques applied in multiscale domains like wavelets. In addition to classical computational tools, the project explores emerging directions involving quantum computing simulators to prototype quantum-inspired shrinkage methods, aligning with growing national and institutional emphasis on quantum technologies. These approaches simplify complex data by selectively attenuating noise while preserving essential features, leading to more accurate and interpretable results. The project integrates education by mentoring students at multiple levels, incorporating findings into graduate and undergraduate courses, and creating open-source software tools that promote reproducible research and broad access to cutting-edge statistical techniques. This research advances the theory and application of shrinkage estimation in multiscale settings, with a particular emphasis on quantum-inspired methodologies that complement classical Bayesian and frequentist frameworks. It develops adaptive block-shrinkage procedures employing priors that capture dependence among wavelet coefficients and introduces absolutely continuous shrinkage priors that maintain computational tractability without relying on spike-and-slab or point-mass priors. The project also devises novel thresholding strategies informed by refined extreme-value approximations and Bayesian decision rules based on Bayes factors. Computational implementation includes efficient posterior simulation algorithms and exploratory shrinkage techniques using quantum computing simulators. These innovations will contribute to foundational methodology for nonparametric regression, signal processing, and scalable high-dimensional inference. 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 supports the 2025 Midwestern Workshop on Asymptotic Analysis, held October 10-12, 2025 at Purdue University Fort Wayne. The conference advances research in the field of mathematical analysis by offering a venue for dissemination of results and building cooperation among researchers in various disciplines, especially those represented in the Midwest. An emphasis is placed on introducing graduate students, early career faculty, and other young researchers to a wide range of current problems, techniques, and results. The conference features a poster presentation to foster networking among all attendees. More information can be found on the conference webpage http://mwaa.math.indianapolis.iu.edu/. Topics covered by the conference include complex analysis, several complex variables, potential theory, approximation theory, and applications. The workshop convenes researchers in these disciplines with the goal of stimulating new mathematical interactions among them. Exposure to current work across disciplines allows for new mathematical and professional connections to be made. Presentation of cutting-edge results and building collaboration are the goals, which contribute to the professional development of all attendees and is especially valuable for early-career researchers. 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.