Kansas State University

NSF Awards · FY 2026 · 2026-07

Analyzing single cells under a microscope is a powerful tool in healthcare, medicine, and homeland security. Typically, biological samples are spread onto glass slides for microscopic examination to detect pathogens or identify biowarfare agents. Current single-cell imaging systems use high-resolution microscopy, which captures only a small sample volume, resulting in limited sensitivity. This project will develop a gel electromicrofluidics (GEM) platform that enhances single-cell imaging by amplifying small, hard-to-detect cells into large, bright fluorescent spots. The technology will improve both sensitivity and throughput, enabling rapid and accurate disease diagnostics. The research will be integrated across several educational fronts, including student-designed projects, graduate program development, undergraduate research opportunities, and outreach programs. This research will establish a GEM-assisted platform, which incorporates sensing gel, electromicrofluidics, and electrokinetic manipulations, for rapid, sensitive, in situ single-cell analysis. The sensing gel will be engineered to amplify small, difficult-to-detect target cells into large, highly fluorescent DNA colonies, allowing visualization of single-cell signals using low-resolution microscopy. This will greatly enhance both sampling efficiency and detection sensitivity. Electromicrofluidics will be used to spread the gel evenly across smear samples via electrowetting, which will eliminate air trapping, a common issue in manual gel spreading, and ensure efficient cell recovery. Additionally, electro-aligned gel and electro-confined polymerase chain reaction (PCR), which leverage electric polarization and dipole-dipole interactions to align gel polymers and DNA molecules, will be studied to advance the mechanistic understanding of the electrokinetic processes involved in gel polymerization and PCR amplification. These electrokinetic manipulations will concentrate DNA and increase fluorescence intensity, thereby enhancing single-cell signals. Furthermore, the GEM platform will be adapted to assess the viability of individual cells following drug treatment, facilitating rapid drug susceptibility testing (DST). Successful completion of this project will lay the groundwork for the next generation of microscopy-based single-cell analysis, with broad applications in disease diagnostics and drug resistance detection. 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 · 2026-06

Clouds can consist of liquid water drops, ice particles, or very often a mixture of both. The proportion of water as ice versus water as liquid has a significant impact on how clouds form precipitation and modulate solar radiation. There have been many airborne field campaigns that have sampled cloud particles, but the instrumentation has not been sensitive enough to determine the difference between ice and liquid drops when the ice particle is in a sphere-like shape during the early stages of ice formation. This project will use a new methodology to observe particles on the micrometer scale to determine their phase. This work has implications for numerical weather models and forecasting precipitation-related hazards. The award will also provide opportunities for students in holography, optics, and aerosol sciences, giving them workforce-relevant skills. Digital In-line Holography (DIH) uses a laser beam and an image sensor to observe particles by measuring the interference pattern caused by the particle and using a process called reconstruction to derive an image of the particles. This work will use a new method called Holographic Ice-Droplet Aerosol Characterization (HI-DAC) to differentiate between small ice and liquid water droplets in a way that traditional DIH techniques are not able to. This method applies the idea that the morphological evolution of a droplet as it freezes will encode signatures of the phase change in the hologram’s interference pattern. Specifically, the research team will show how the structure of an optical phenomenon known as the photonic-jet caustic can provide a signature for the ice phase. They will then examine the symmetry of the hologram itself to show further sensitivity to the particle phase. The work includes simulations and laboratory tests. 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 · 2026-06

This award supports the Research Experience for Undergraduates (REU) site in Physics at Kansas State University. The site engages eleven students per year in research projects, which include the interaction of laser light with matter at atomic and molecular scales, condensed matter, biophysics, biomanufacturing, investigations of neutrinos and elementary particle collisions, and physics education research. A wide range of projects are available from faculty mentors that involve the undergraduates in state-of-the-art research. Previous students have reported being better informed for decisions about careers, graduate work and/or professional school. Whether they choose to continue in physics or a related technical field, or choose to move to a different area, bright students making good choices early in their careers is in the national interest. Students will do research on how atoms and molecules behave in intense and ultra-fast optical and x-ray pulses, how high-performance computation can be used to predict molecular properties and dynamics, the properties of nanoparticles in solution, and protein self-assembly with simplified models, and will help build next-generation hardware for high-energy particle detection, improve measurements of neutrino oscillations, and help faculty use research-based assessment tools. This experience will be supplemented by a series of lectures that provide the background these students need to carry out their projects. The participants will gain experience in explaining their research work to a variety of colleagues. Social activities, both organized and spontaneous, will enrich this undergraduate research experience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Interactions between proteins and lipid metabolites are highly dynamic in all living cells, yet their full scope and biological significance remain poorly understood. In plants, membrane lipids provide cellular storage of the vital chemical element phosphorus. Imbalances in lipid metabolism result in organismal abnormalities affecting growth outcomes. This project will investigate regulatory proteins that contain lipid sensors, addressing how metabolism and day-night signals integrate gene expression and growth control. By focusing on the molecular mechanisms decoding specific lipid and protein inputs, this research will uncover novel insights, potentially advancing our understanding of how lipid metabolites orchestrate growth decisions across organisms. In plants, the regulatory proteins play critical roles in the epidermis to protect against water loss and environmental stress, traits that are beneficial for optimization of crop productivity. The information gained from this research will facilitate improvements in renewable and sustainable plant-derived products, with applications in biotechnology and production of advanced materials in manufacturing. In addition, the project will train postdoctoral researchers and students across multiple levels in interdisciplinary research spanning biology, computational biochemistry and informatics, while promoting research and educational experiences through public outreach. In plant cells, specific lipid sensors maintain homeostasis by directly binding to phospholipid metabolites. The mechanistic details and causality relationships underlying this activity are not clear, presenting a gap in understanding how gene expression integrates diurnal cues with growth during development. This research will test the hypothesis that lipid metabolites act directly on plant transcription factors to modulate gene expression in response to developmental and environmental inputs. The goal of this project is to gain knowledge of the mechanisms governing this intricate regulation of gene expression. Focusing on transcriptional regulatory proteins required for cell-type differentiation of the epidermis in the plant model Arabidopsis, this project will: (1) elucidate the binding properties to lysophospholipids; and (2) characterize adaptor protein interactions with these transcriptional regulators. Within each aim, experimental and computational approaches will be applied in a complementary manner, merging genetics and biochemistry with molecular dynamics simulation and free-energy calculation techniques. This project will reveal novel insights in understanding the dynamic interplay between metabolic pathways and the regulation of gene expression. Gaining a mechanistic understanding of these key transcriptional regulators has the potential for significant impact in biotechnology applications and development of renewable plant-derived products for human nutrition, raw materials, and energy. 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 · 2026-06

Understanding the human brain is a vital area of research with broad scientific and societal importance. Revealing the underlying mechanisms of brain function has the potential to advance medical diagnosis and treatment planning for neurological and psychiatric disorders, e.g., Parkinson's disease, obsessive-compulsive disorder, and many others. In addition, a deeper understanding of brain processes and how the brain represents information can inspire the development of the next generation of Artificial Intelligence (AI) systems and significantly benefit a wide range of scientific and engineering fields. At the same time, quantum computing offers new ways to represent and process complex information, but its potential for understanding the human brain remains largely unexplored. This project aims to develop new quantum machine intelligence approaches for modeling human brain activity using functional magnetic resonance imaging data. The outcomes of this project could advance scientific understanding of neuroscience and quantum machine learning and inspire more efficient AI systems. The project will also support student training, interdisciplinary education, public workshops, webinars, open-source software, and collaborations that broaden access to research at the intersection of quantum computing, neuroscience, and artificial intelligence. Despite the urgent need to accurately model human brain activity, research on quantum machine intelligence has been limited, particularly for analyzing large-scale functional magnetic resonance imaging data to advance vision-brain understanding. Existing machine learning approaches often struggle to represent complex, high-dimensional, and brain-wide neural dynamics, while current quantum machine learning methods remain underdeveloped for human brain understanding. To address these limitations, this project will develop a new framework for vision-brain understanding by integrating quantum theory with machine learning models for functional magnetic resonance imaging. First, the project will introduce new quantum-inspired neural networks that leverage superposition and entanglement to model complex relationships between visual stimuli and brain activity. Second, a new quantum feature encoding will be developed to represent large-scale brain imaging signals in a quantum feature space, improving the ability to capture high-dimensional neural patterns with higher fidelity and better task performance. Third, the project will propose a novel hierarchical quantum circuit gate model that operates on quantum machines for scalable vision-brain modeling. All algorithms developed will be released as open-source software to support accessibility and reproducibility. This project is expected to advance human brain understanding, quantum machine learning, and brain-inspired vision systems by enabling richer, more scalable modeling of complex brain-wide dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The Heartland Mathematics Conference (HMC) is an annual graduate student mathematics conference to be held at Kansas State University (KSU), University of Nebraska-Lincoln (UNL), and University of Kansas (KU) in 2026, 2027, and 2028. The conferences are organized by graduate students at the three participating universities, under the guidance of Professors Dave Auckly (KSU), Alex Zupan (UNL), and Jeremy Martin (KU). The HMC provides a unique and valuable opportunity for graduate students to experience the benefits of presenting and taking part in a research conference and helps train organizing students in project administration. HMC is a continuation of the Kansas Mathematics Graduate Student Conference, which started in Fall 2021 and has run for four years as a joint project between graduate students at KU and KSU. With mostly graduate students and undergraduates in attendance, the conference provides a low-pressure environment in which graduate students can present their research and improve upon their presentation abilities. The conference introduces participants to current trends in multiple fields of research and provides an environment for interdisciplinary collaboration, with the hope of creating strong research groups moving forward. The networking possibilities and the experience gained from giving a research talk enable attendees to mature as mathematicians and as future faculty members or professionals outside of academia. The conference website is: https://sites.google.com/view/heartlandmathconference 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

The 38th Annual Workshop on Automorphic Forms and Related Topics (AFW) will take place May 18-22, 2026, at Kansas State University in Manhattan, KS. The AFW is an internationally recognized, well-respected conference on topics related to automorphic forms, which have played a key role in many recent breakthroughs in mathematics. The AFW will bring together a geographically diverse group of participants at a wide range of career stages, from graduate students to senior professors. This is the first time the AFW will meet in Kansas where many experts on automorphic forms and closely related topics are nearby. Thus, in addition to attracting speakers who participate annually, the workshop is likely to draw a mix of new attendees who will contribute new perspectives and energy and benefit from the workshop. The conference will provide a supportive setting for researchers to disseminate new results, learn from other researchers, and begin new collaborations. This grant will fund travel and lodging for junior participants and others without access to sufficient funds to attend the 38th AFW. Automorphic forms have played a key role in many breakthroughs in mathematics, including the proofs of Fermat’s Last Theorem (by Wiles and Taylor--Wiles, employing work of Frey and Ribet), Serre’s Conjecture (by Khare, Kisin, and Wintenberger), the Sato-Tate Conjecture (by Barnet-Lamb, Geraghty, Harris, and Taylor), the Monstrous Moonshine Conjecture (for which Borcherds was awarded the Fields Medal), and the Fundamental Lemma (for which Châu was awarded the Fields Medal). Automorphic forms are the subject of many important ongoing conjectures, among them the Langlands program, connections to random matrix theory, and the generalized Riemann hypothesis. They also appear in many areas of mathematics outside number theory, most notably in mathematical physics. Additional information can be found on the conference website: http://automorphicformsworkshop.org/ 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

Quantum effects play a central role in modern technology. This project supports the development of new tools that will allow scientists to observe and control the quantum behavior of electrons on attosecond timescales—billionths of a billionth of a second—which is the shortest window that researchers are able to control currently. By using an advanced laser system to produce exceptionally bright attosecond-duration pulses, the research team will create a new experimental capability that can take “snapshots” of electrons as they move and interact inside atoms and molecules. This ability will provide a clearer view of the quantum processes that underlie chemical reactions, light-driven materials changes and future quantum technologies. The project will involve graduate and undergraduate students in cutting-edge laboratory research, and will integrate new discoveries into coursework and hands-on training. Outreach activities, including engagement with local schools and community groups, will help broaden participation in STEM and improve public understanding of how ultrafast quantum science benefits society. This project aims to establish a high-flux, attosecond-pump attosecond-probe spectroscopy platform based on an industrial-grade, high-repetition-rate Yb laser system coupled to an advanced high-harmonic beamline and electron–ion coincidence detection. The PI and the research team will pursue three major objectives: (1) construct and characterize a compact, bright attosecond-pump attosecond-probe beamline capable of delivering isolated attosecond pulses with sufficient photon flux; (2) measure real-time electron–electron correlation dynamics in atoms; and (3) investigate coupled electronic–nuclear motion in molecular photodissociation to reveal the earliest steps of ultrafast charge migration. The platform developed through this work will enable time-resolved measurements of correlated electronic motion without perturbation from strong infrared fields, addressing long-standing challenges in attosecond science. The results are expected to advance AMO physics, benchmark many-body quantum theories, and provide new tools relevant to chemical physics, photonics, and ultrafast materials research. 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

Non-Technical Summary Materials at the nanoscale have evidenced a wide and remarkable set of properties that make them suitable for many applications. The behavior of nanomaterials under various external stimuli, such as temperature, mechanical forces, pH, etc., has been widely investigated. However, the interaction of nanomaterials with ionizing radiation, such as X-rays, remains largely unexplored. Investigating potential phenomena and understanding the interaction mechanisms of ionizing radiation with matter at the nanoscale is an unknown question in materials chemistry and will generate valuable information to allow the use of such structures in nuclear science and technology for applications in medicine, power transducers, energy storage, radiation sensors, and actuators. This collaborative research proposal will investigate a novel class of multicomponent nanomaterials responsive to both low and high energy X-rays. This research will have a tremendous educational impact on the Mechanical and Nuclear Engineering program at Virginia Commonwealth University (VCU). The new knowledge in materials and radiation chemistry, advanced nanomaterials synthesis, and manufacturing will be disseminated in the undergraduate and graduate courses. The research proposed here will also be a significant boon for the nuclear science at James Madison University (JMU) and will include undergraduates in the interdisciplinary-research projects. The diverse experience the students will gain while working on this interdisciplinary project will create a multitude of opportunities for those seeking careers in nuclear engineering, applied photon science, nanoscience, accelerator physics, or medical physics, as well as for those directly entering the workforce in nuclear industry or government. Technical Summary This research project will advance both the fundamental understanding of the underlying mechanism of radiation dose enhancement and the radioluminescence response upon the nanocomposites interacting with high-energy photons. The work will build upon the theory of radiation interaction with matter and expand on the surface and interfacial effects in aqueous media that lead to the radiation enhancement phenomenon. This project focuses on three key areas: 1) Expand on the controlled synthesis of multicomponent nanomaterials to explore their mechanisms of interaction with ionizing radiation; 2) Investigate their radioluminescence and radiation enhancing properties; 3) Implement computational models based on Monte Carlo simulations to assess the contribution of the physical enhancement to the radiosensitization properties of the nanomaterials based on their chemical compositions and morphologies. The experimental work will involve chemical, electrochemical, and spectroscopic techniques to quantify reactive species involved in the radiation enhancement and the materials' optical properties. Computational work will be carried out using GEANT4 particle transport code to model the interaction of the X-rays with the studied nanostructures. Ultimately, this research will establish correlations between the material structure and properties in the solid-state, specifically considering the effects of the X-ray parameters such as the energy spectrum of the X-ray beam and the rate at which the energy is delivered to the system have on the behavior of the materials systems. Overall, the proposed experimental and computational tools will lead to an understanding of the structure-property relationships of the nanomaterials and will advance the synthesis, evaluation, and simulation of radiation enhancing and radioluminescent nanomaterials to enable their implementation in various fields. Overall, both the undergraduate and graduate students involved in this work will have the opportunity to get hands-on experience in an accelerator-based environment at the JMU's Madison Accelerator Laboratory while participating in cutting-edge interdisciplinary research both at VCU and JMU. 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

Sintering is one of the most practical manufacturing processes in producing density-controlled materials from powder feedstock. However, producing industrially applicable powder alloy precursors is challenging. Although pre-mixing pure metal powders have allowed a wide range of compositions by conventional sintering, rapid heating and cooling in modern sintering techniques inevitably results in new but unwanted chemical gradients, as well as microstructural and property anisotropy. This Faculty Early Career Development (CAREER) award will investigate a high-power impulse magnetron sputtering process as an advanced technology to modify the surface of metallic powders. The deposition of thin films on the powders forming core/shell systems can provide solutions to make different alloys with more homogeneous and controlled microstructures when used as powder feedstock. This new class of powders will enable a reliable supply of raw materials for cost-effective sintering of reproducible components in the aerospace, automotive, energy, and healthcare industries. The integrated educational and outreach activities of this project will broadly concentrate on: 1) educating and training women and underrepresented minorities as the future generation of highly-skilled leaders in advanced manufacturing, 2) establishing a pipeline of diverse undergraduate students to pursue graduate studies in collaboration with James Madison University in Virginia through special summer programs, and 3) implementing an online set of virtual laboratories in advanced materials characterization of core/shell structures as well as materials processing. The overall research objective of this project is to establish the scientific underpinnings for the surface modification of micro- and nano-powders into core/shell systems that promote three-phase transformations in sintering-based processes. The core/shell of various combinations (e.g., aluminum-copper, titanium-copper, copper-chromium and nickel-chromium, etc.) will be the eutectic compositions for modeling because of their industrial relevance, an ability to form precipitation hardening alloys, and excellent high-temperature properties. It is hypothesized that high-power impulse magnetron sputtering combined with the vibrational motions of powder holders can achieve conformal thin films on powders, which are desired to produce favorable microstructures. To test the hypothesis, the project will study sputtering discharges to understand the nucleation and growth mechanism of thin films on complex geometries such as spherical powders when including vibrations. This research also aims to understand the role of powder-film interface in the two-phase core/shell diffusion couple and its effect on the final part microstructure from sintering. The combined strategy of the project will lead to a new method of manufacturing core/shell materials suitable for modern sintering processes. 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

Environmental effects on plant and animal populations can be mediated via both direct effects on individuals, such as water stress, and/or indirect effects, such as a reduction in the densities of competing species. Quantifying the strength of direct vs. indirect effects is critical to accurately predicting responses to a variable climate. If direct effects are strong, responses in one community will accurately predict responses in another community with different interacting species. If indirect effects are strong, effects in novel communities cannot be accurately predicted because the net effect depends strongly on the community of interacting species. In spite of their importance, a unifying framework for understanding the strengths of direct and indirect effects is lacking for primary producers, particularly for species interactions besides competition, or when multiple indirect effects operate simultaneously. This research will quantify the strengths of direct and indirect effects mediated through three species interactions (competition, and two herbivore guilds) for more than 120 plant species. In addition, this project will fund ~20 undergraduates to engage in a structured mentoring and training program designed to improve quantitative skills, provide training in modeling to up to 75 graduate students, and increase scientific literacy for 200 Kansas residents annually. The project addresses three questions that are central to ecological predictions: (1) What fraction of net precipitation effects are direct and how does this vary across species?, (2) How often are indirect effects of precipitation mediated through different species interactions simultaneously?, and (3) How general is the strength and direction of direct vs. simultaneous indirect effects across taxa? The project will unite data from three sources: 1) new experimental work manipulating indirect effects for three focal species at the Konza Prairie Long-Term Ecological Research site (KNZ); 2) an ongoing long-term (4 years to date) and multi-site observational demographic study conducted at KNZ on these three species; and 3) long-term (40 years to date) observational data on changes in percent cover collected at KNZ for 128 species. For the three focal species, the work will use both observational and experimental data to quantify the strength of direct vs. indirect effects on population growth rate. This approach will provide insights into the particular demographic rates that tend to drive direct vs. indirect effects. The less-detailed data on many more species will be analyzed using a common framework that allows fair comparison across disparate species, allowing generalities concerning when and where indirect effects are important. 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 tracheostomy procedure is frequently performed in children to bypass an obstructed airway due to blockage in the upper throat or for chronic lung conditions related to prematurity. Tracheostomy-dependent children living at home require continuous monitoring, and over 25% of them experience serious complications. The nationwide shortage of home care nurses makes continuous observation of these children impractical, hence the critical need for home-based monitoring systems. Such monitoring systems usually require training on images of tracheostomy-dependent infants. However, an important challenge is the scarcity of videos at any single healthcare provider, leading to the demand to share such videos between different healthcare providers. Nevertheless, healthcare privacy laws make such sharing dependent on the consent of privacy-aware caretakers. This project establishes a framework for sharing such sensitive data in a privacy-constrained manner, supported by formal privacy guarantees that can be explained and demonstrated to caretakers and healthcare providers. The project’s novelties stem from a comprehensive suite of privacy mechanisms for moving images, where the private information pertains to the identities of humans. The project's broader significance and importance are that the developed framework is readily applicable to videos of human subjects across all age groups, thus holding potential for use in any fields requiring people video supervision of , such as daycare centers, nursing homes, hospices, or prisons. This project brings together a set of advanced technologies—face identification and pixelation in video, adversarial generative privacy mechanisms, video component disentanglement, and AI-driven text-to-video generation—not as isolated tools, but as interdependent components within a unified system tailored to a real-world application governed by stringent safety, ethical, and regulatory requirements. The novelty lies in the integration of these technologies into a coherent framework that addresses complex, high-stakes challenges. By moving beyond controlled or idealized settings, the project enables practical evaluation and refinement of methods that have thus far seen limited deployment outside the lab. Due to the extremely limited access to real data via cooperation agreements, the first research focus is on generating a synthetic video dataset to train privacy mechanisms, identity disentanglement/recombination systems, and various other classifiers. The second focus is on evaluating multiple independent privacy designs by comparing their detection accuracy and privacy guarantees. The third focus is on establishing formal privacy guarantees, often lacking in some designs, by extending current theoretical notions of differential privacy and integrating them with empirical validation through specialized classifiers and feedback from medical personnel and patients' caretakers. 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

Soybean farmers across the world face a common problem—plants falling over before harvest, a condition known as lodging. This not only lowers yields but also makes harvesting harder and less efficient. One major cause is a hidden pest called the soybean stem borer, which is difficult to detect. Today, most farmers rely on time-consuming, manual methods to spot problems, which are often inaccurate and inconsistent. This project brings together partners from the Quad countries to create a new tool called Smart Scout. It uses advanced cameras and artificial intelligence (AI) to help farmers quickly and accurately detect damage, estimate yields, and make better decisions during the growing season. The system is flexible—it can be used by hand or attached to farm vehicles—and provides easy-to-understand, visual insights right in the field. While it currently focuses on soybeans, Smart Scout is designed to work with many other crops, offering a scalable solution for improving food production. By helping farmers make timely, data-driven choices, this tool can enhance productivity, reduce losses, and support economically prosperous agriculture around the world. Modern agriculture increasingly demands timely, accurate insights to manage crop health, yield potential, and environmental stresses—challenges made more urgent by climate variability, labor shortages, and sustainability goals. This project proposes Smart Scout, a user-inspired, AI-enabled computer vision system designed for real-time monitoring and decision support in soybean production, with adaptability to other major crops. The system will integrate visual data on pests, plant physical traits, and yield indicators to provide standardized, georeferenced insights at the field level. Its modular design allows deployment as a handheld tool, robotic platform, or machinery-integrated system, supporting scalable, flexible adoption. A core goal is to offer intuitive, actionable dashboards that both visualize AI reasoning and build user trust in the technology. The project will leverage collaborative data from QUAD partners to strengthen model accuracy and relevance across diverse production environments, while an independent testbed will validate technical performance and user experience. This unique AI-driven approach has broad potential to inform crop planning and management decisions, advancing precision agriculture through data-enabled, transparent, and adaptive tools that can be extended to a wide range of cropping systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

An award is made to Kansas State University to acquire a Waters SYNAPT XS quadrupole time-of-flight (QTOF) mass spectrometer (MS) with multiple options for sample introduction. The instrument will be housed at the Kansas Lipidomics Research Center (KLRC), an established analytical laboratory performing mass spectrometry-based lipid analysis at Kansas State University. Access to this state-of-the-art instrument will directly impact and advance the training of postdoctoral trainees, graduate students, and undergraduates. Through access to the SYNAPT XS and analysis of resulting data, trainee scientists will develop expertise in identifying and quantifying metabolites in complex mixtures, enabling them to pursue previously challenging research questions. Annual workshops on lipidomics and metabolomics will reach a broad audience and foster cross-disciplinary interactions involving metabolite analysis. By engaging with the KLRC and participating in these workshops, trainees will gain valuable technical skills in advanced mass spectrometry and metabolite profiling, positioning them for careers in industry and academia. The SYNAPT XS provides high-resolution, high-accuracy mass measurements that enable confident identification and quantification of lipid species and other small metabolites even in complex biological samples, offering insights into metabolic pathways and cellular functions. The high-accuracy mass measurements make it possible to detect and quantify stable isotope-labeled compounds to trace metabolite flow through pathways. The desorption electrospray ionization module (DESI) source will allow scientists to spatially map compounds to discover tissue-specific differences in metabolism. Planned projects will use the high mass accuracy provided by the SYNAPT XS to uncover the roles of lipids and metabolites in development, disease, host-pathogen interactions, stress resistance, and product quality. Key investigations focus on plant lipidomes, lipid dynamics in environmental stress responses, metabolic regulation in pathogen resistance, and improvements in biofuel feedstocks and agricultural products by manipulating lipid metabolism. Collectively, these studies will deepen understanding of metabolic pathways relevant to health, agriculture, and bioenergy. 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 examines land management strategies in the context of tradeoffs between mitigating wildfire risks and maintaining the infrastructural security of water systems. Insights are needed because different strategies entail heterogeneous impacts on community members. In this project, the researchers use ethnographic methods to examine how management decisions are made and the extent to which perceptions of these strategies are shared among urban and rural residents. The results are informative for management authorities and municipalities, and the researchers disseminate key findings to stakeholders. The project also contributes to the education and training of a graduate student. The project advances agency priorities in translational science by collecting data that provide paths to improve civic water engineering. This study contributes to an interdisciplinary literature on environmental governance by implementing a multifaceted research design to examine management decisions in arid regions that are prone to wildfires. To examine this question, the researchers use interviews, household surveys, stakeholder analysis, land use and systematic mapping. Perceptions of impacted residents are investigated geographically, and the holistic multi-sited design considers both urban and rural residents while contributing to geography. Results and recommendation are shared with state and county officials, community organizations, and managers. 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 genome is like a book containing all the information an organism needs for life on Earth. Just as books are organized into chapters, paragraphs, and sentences, and follow rules that help us interpret their meaning, genomes also have organization and rules that determine how and when genetic information is used. However, unlike books, many of the rules governing genome function remain unknown. This project will uncover those rules and generate new insight into how fungi live, adapt, and cause disease. To achieve these goals, scientists at the undergraduate, graduate, and postgraduate levels will receiving mentoring and training in advanced molecular biology and genomics, bolstering the nation’s scientific workforce. Additionally, elementary students and the public will be engaged through hands-on science activities to promoter science education and community engagement. This research will advance our understanding of the genome biology of filamentous fungal pathogens and lead to innovative strategies for protecting crops and animals from fungal infection, which is critical for food security, public safety, and the economy. Fungal pathogens of plants utilize a broad array of secreted proteins and small molecules, termed effectors, to alter host immunity or cellular physiology to promote infection. Transcriptional regulation of effectors and effector evolution play a critical role in pathogen success, but we lack mechanistic understanding of these two processes. Additionally, many effector coding regions have heterochromatic features, such as lower gene density, lower transcriptional activity, and specific epigenetic marks, but the reason for this association remains unknown. Do features of heterochromatin drive fungal pathogenesis and genome variation, or is heterochromatin a genomic bystander of fungal pathobiology? The proposed research will disentangle causality and correlation between effectors and heterochromatin using molecular genetics and bioinformatics to test falsifiable hypotheses. The research will determine the contribution of histone modifications to in planta transcriptional dynamics, characterize the causality between heterochromatin and DNA mutation, and identify how the epigenome impacts a newly discovered class of fungal mobile DNA, termed Starships. Findings from the research will inform our basic understanding of the genetics and genomics of filamentous fungal pathogens. This knowledge can impact critical topics related to genome evolution, protecting crops and livestock, and developing novel synthetic genomes. This project is jointly funded by Genetic Mechanisms program in the Molecular and Cellular Biosciences Division 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 · 2025-09

With ~40% coverage of the terrestrial biosphere, grasses represent one the major plant types on Earth; this percentage excludes additional coverage by all major grain crops, which are also grasses. Despite the global importance of grasses, significant gaps in understanding remain in how grasses respond to drought. The evolution and expansion of grassland biomes came at the expense of forests and was precipitated by an increase in aridity; therefore, grass evolution, physiology, and ecology are inextricably linked to the acquisition, use, and movement of water. The aim of this proposal is to provide a better understanding of grass physiological responses to drought from the cellular to ecosystem scales. The current understanding of plant responses to drought is dominated by data on woody plants, particularly trees, and this understanding does not translate readily to grasses. Elucidation of these drought response will enhance our understanding of wild grasses to drought, as well as discover relevant physiological responses for crop improvement. Additionally, the PIs will conduct the immersive data-collection and instrument training ecophysiology workshop for graduate students (Phys-Fest). This Phys-Fest will occur in the urban environment of Philadelphia. Urban environments can provide key ecosystem services, and when explicitly managed, these environments enhance overall human well-being. Participants are trained in four primary ecophysiological research areas and are provided with close interaction with faculty instructors, as well as evening activities designed to promote professional development and science communication. Several novel and previously unexplored aspects of grass physiology are developed within this proposal under the guiding question: How do grasses, individually and at the ecosystem scale, respond to changes in soil moisture and leaf-to-air vapor pressure deficit (VPDL)? This question is distilled into more specific questions that will be answered via the research plan: (i) What are the physiological and anatomical mechanisms by which grasses control stomatal sensitivity through changes in VPDL? (ii) How do grasses maintain leaf-level gas exchange at leaf-water potentials that are near or more negative than the turgor-loss point? (ii) How do physiological responses coupled with plant-atmosphere interactions affect grassland responses to soil and atmospheric drought? The proposed research will be comprised of lab, greenhouse, and field work at two N. American prairie sites. The field sites were chosen because of their ecological and phylogenetic relevance: the tall-grass prairie site is dominated by C4 Andropogoneae and the short-grass prairie site is dominated by C4 Chloridoideae and C3 Pooideae. These grasslands exist on opposite ends of the precipitation spectrum across the Great Plains and these grass lineages are globally dominant. This proposal was supported by the Integrative Ecological Physiology Program in the Division of Integrative Organismal System and the Ecosystem Science Cluster in the Division of Environmental Biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The PI will study distribution questions in L-functions and multiplicative functions, which are central subjects in multiplicative number theory. The first known example of L-functions is the Riemann zeta function. Classically, its zeros are connected to the distribution of prime numbers, which have applications to cryptography and modern security systems. There has been extensive research on the properties of L-functions, such as the distribution of their zeros and distribution of their values. However, many deep problems remain unsolved even though good conjectures have been formulated. The PI will explore various distributions in families of L-functions and provide insight into the structures of such families; the novel techniques developed will serve as powerful tools to shed light on other deep questions in the area. Another direction of the project is to explore ubiquitous statistical phenomenon known as Benford's law. This law first appeared as an observation about the first digits of the numbers in data sets. In particular, the leading digits do not exhibit uniform distribution as might be naively expected, but rather, the digit 1 appears the most, followed by 2, 3, and so on until 9. The goal is to give an answer in the context of multiplicative functions to the question "Is checking the first digit theoretically equivalent to checking many digits?" The award will provide opportunities for research training and collaboration for students and postdocs. The PI will also organize number theory seminars, AMS special sessions and continue engaging in outreach activities for middle school students and math circles. Many important conjectures on L-functions follow the Katz-Sarnak heuristic that the statistics of families of L-functions match the analogous statistics from classical compact groups of random matrices. In this direction, the PI will study the distribution of their values and zeros. In particular, the PI plans to study the nth centered moments of two families of GL(2) holomorphic L-functions. The aim is to provide the longest bandwidth in the literature as well as the application toward non-vanishing of higher order zeros of L-functions. Moreover, the PI will study various moments of L-functions (e.g. moments of twisted L-functions in a large orthogonal family, short moments of GL(4) x GL(2) Rankin Selberg L-functions) with applications toward critical line theorem, subconvexity and simultaneous non-vanishing of L-functions. These represent substantial progress in understanding open problems in the area, which are related to important conjectures such as the Generalized Riemann hypothesis (GRH) and the Katz-Sarnak philosophy. Moreover, the PI will investigate the Benford law phenomenon for multiplicative functions in connection with statistical universality and aim to deepen understanding of the structural interplay between harmonic analysis and multiplicative number theory. The methods employed in the proposal include harmonic analysis, development of new general Petersson's formula, random matrix theory, and Fourier analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Great Plains region faces an ever-increasing need to conserve dwindling water reserves from the Ogallala aquifer. The same region also annually generates more than 80% of the country’s total livestock wastes (dairy, beef, swine), raising concerns about the impact on water quality and living conditions. About 12– 35% of the water used annually for production-intensive agriculture in Kansas, Nebraska, and Oklahoma combined (8 M acre-ft/yr) can potentially be derived by recovering and treating water contained in livestock waste. A team of interdisciplinary researchers from Kansas State University (KSU), Seward County Community College (SCCC), Oklahoma State University (OSU), and the University of Nebraska-Lincoln (UNL), representing three contiguous EPSCoR jurisdictions, will synergize their complementary research capacities to enable adoption of circular waste resource recovery and water reuse technology platforms. This research will enhance economic resiliency, environmental sustainability, and quality of life in Great Plains micropolitan communities. The overall project objective is to build regional research capacity and develop an economically viable, socially accepted, and efficient circular resource recovery platform integrated with water reuse from livestock wastes that are copiously generated in the region. The proposed work would build capacity for use-inspired research to be demonstrated for adoption by livestock operations in southwest Kansas first (Liberal, KS), in collaboration with SCCC, and with regionwide adoption potential. The project will integrate a wide array of workforce development activities such as an early-career faculty development program and technical skills training through exchange site visits. Workforce development initiatives will be guided by an industry-government advisory council composed of livestock and agricultural producers, local associations and councils, and government/policy representatives. During this project, critically important and complex concepts such as resource recovery will be introduced to participating students and the public through science cafés, summer research field experiences, and interactions with public utilities to realize the research advances at scale. This will enable a holistic framework and encourage incorporation of the circular resource recovery and reuse systems into the rural communities and workforce. This collaborative research team seeks to achieve optimal circular waste resource recovery and water reuse technology platforms through three interconnected research thrusts. Research Thrust 1 aims to develop the Anaerobic Sequencing Batch Reactor (ASBR), Anaerobic Membrane Bioreactor (AnMBR), and Microbial Electrochemical Cell (MxC) platforms for holistic recovery of swine manure co-digested with fats, oils, and grease (FOG) to produce methane or organic acids, hydrogen peroxide, nutrients (N and P) as tunable-release inorganic fertilizers (Octacalcium phosphate and struvite), and treated water for reuse. Such groundbreaking advancements in membrane science will be guided by Artificial Intelligence/Machine Learning. Research Thrust 2 focuses on circular water reuse by combining advanced oxidation and membrane-based processes, including using waste-derived hydrogen peroxide to produce high-quality water. Specific focus will be placed on mitigating antimicrobial resistance, a prevalent and understudied issue in rural water supplies. Research Thrust 3 will integrate techno-economic and risk simulation with agribusiness decision node modeling for region-specific adoption of the circular systems. Human dimensions, including cultural perceptions, assessments of safety and security risks, and social-economic impacts of the proposed technologies, will be analyzed from representative communities. Collective research capacity from the contiguous jurisdictions will be synergized and verified through a field demonstration of the AnMBR + advanced oxidation unit at Liberal, KS, in Year 4 of the project. New avenues for cross-convergent research between applied and pure science-based researchers as well as potential manufacturing and industry partners will be achieved throughout this proposal. Synergistic research that co-addresses engineering grand challenges and society-based sustainable development goals, such as responsible consumption and production, clean water and sanitation, will also be demonstrated. This project is supported by the EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations Program (FEC). FEC supports interjurisdictional teams of EPSCoR investigators to perform research in topics that align with NSF priorities, with the goals of driving discovery and building sustainable STEM capacity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The field of dynamics studies how processes evolve over time, such as the motion of planets, population growth, or the behavior of digital networks. The mathematical theory of dynamical systems offers a powerful language to describe these changes, uncover patterns, predict future behavior, and identify when systems may become chaotic. One-dimensional holomorphic dynamics is a mature field of mathematics, rooted in the famous work of Fatou and Julia on fractal sets. In contrast, higher-dimensional holomorphic dynamics is a newer but rapidly developing area, marked by fundamentally different behavior and rich phenomena absent in the one-dimensional setting. The PIs will advance understanding of dynamical systems in several complex variables by bridging this gap between dimension one and higher dimensions. The project will also provide training opportunities for graduate students and postdoctoral researchers. This is a project funded jointly by the National Science Foundation's Division of Mathematical Sciences, in the Directorate for Mathematical and Physical Sciences, and the Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI), in accordance with the Memorandum of Understanding between the NSF and UEFISCDI. The PIs will investigate the dynamics of higher-dimensional germs of holomorphic diffeomorphisms, particularly those with neutral fixed points, which pose unique challenges. A key goal is to characterize the structure of the dynamical system near the fixed points and to extend concepts like hedgehogs—intricate invariant sets from one-dimensional dynamics—to higher dimensions, especially in the setting of conservative holomorphic germs. The PIs will also analyze the dynamics and bifurcations of polynomial automorphisms of two-dimensional complex space, with particular attention to the relationship between Julia sets and critical loci—sets of tangencies between dynamically defined foliations. The research activity conducted under this award will generate pioneering techniques in higher-dimensional dynamics, with impact in other areas of mathematics such as topology and geometry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Power systems are the backbone of a nation's infrastructure. As these systems undergo rapid transformation due to increasing integration of smart grid resources and technologies, they face unprecedented challenges in monitoring and control. This project aims to address these challenges by developing innovative solutions to enhance the safety, reliability, and efficiency of modern power distribution systems. Specifically, this project will develop advanced algorithms built on foundational mathematical and statistical principles to help utility operators make better use of the large volumes of data collected from the grid, leading to more informed decision-making and improved system performance. The ultimate goal is to advance national prosperity and welfare through reduced energy costs, minimum service disruptions, and transition to a smarter, more resilient energy future. This project tackles the complexity of modern power distribution systems by integrating and analyzing multi-time-scale, heterogenous data from advanced metering infrastructure, supervisory control and data acquisition systems, and micro-Phasor Measurement Units to enhance situational awareness and enable advanced control strategies. Existing data analysis methods, including statistical and machine learning approaches, often rely on overly simplified models that assume linearity, normality, and precisely known inputs. These limitations reduce their effectiveness in dealing with the stochastic, dynamic, and non-Euclidean nature of real-world power systems data. To overcome these challenges, the project proposes a novel framework for power distribution system analysis based on Optimal Transport Theory. This project is among the first to apply optimal transport in this domain, integrating it with semi-parametric Fréchet regression to develop new algorithms for state estimation and control that are robust to uncertainty and measurement errors. The research will advance the theoretical foundations of Fréchet regression in the presence of noisy, high-dimensional data and produce computationally efficient algorithms suitable for real-time grid operations. The framework will also support critical tasks such as anomaly detection by leveraging properties of correlation matrices and dynamic stability analysis through tracking of distributional barycenters. These contributions are designed to be flexible, allowing for deployment either as enhancements to current tools or as standalone, disruptive alternatives based on operator needs. The project draws on a multidisciplinary team with expertise in non-parametric statistics, power systems engineering, and control theory to deliver practical and theoretically grounded solutions that respond to the urgent needs of today’s evolving electric grid. 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 · 2025-08

PROJECT SUMMARY/ABSTRACT It is well established that the addition or removal of sarcomeres is central in determining muscle size and force output during normal development, but also in response to mechanical stimuli or pathological conditions. Overstretched muscle can initiate new sarcomere formation, while chronic understretching reduces sarcomere number and may shorten muscle lengths. Other than disease states or mechanical-load induced alterations, few studies have examined force-independent factors that influence skeletal muscle sarcomere remodeling. Moreover, a major knowledge gap is understanding how muscle-intrinsic factors contribute to sarcomere addition during muscle growth. To address these deficiencies, we have established genetic backgrounds that promote larval muscle growth to quantitatively study serial sarcomere addition at a single-cell resolution in the experimentally tractable Drosophila model. Completion of the following two aims will help us accomplish our long-term goal of understanding how muscle length increases at both the sarcomere and whole tissue levels during development and growth. Aim 1 will determine the identity of molecular targets that impact sarcomere addition using transcriptomic and candidate approaches. Aim 2 will use in vivo protein tagging methods to follow the fate of newly synthesized sarcomere proteins during muscle growth. We expect that this project will fundamentally advance our understanding of signaling pathways required for sarcomere addition and will establish new techniques for studying sarcomere dynamics.

NSF Awards · FY 2025 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EGFP awards provide support for a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at Kansas State University provides support to 9 EPSCoR Graduate Fellows whose research will align with the unique goals and programs supported by the Directorate for Mathematical and Physical Sciences (MPS). The program is hosted in the Department of Mathematics and is designed to meet career goals of each participant. Through mentorship and faculty engagement, the program has the potential to produce highly qualified graduates who will excel in professional fields, including top-tier postdoctoral positions, national laboratories, leading industries, and educational institutions at all levels. In this project, EPSCoR Graduate Fellows will participate in graduate studies in several fields of mathematics, including analysis, algebra, applied mathematics (including data science, materials science, and scientific computing), geometry/topology, mathematics education, and number theory. Fellows will be recruited nationally and mentored by a team of well qualified and dedicated faculty. Robust mechanisms are in place to support student success, ensuring personalized mentoring and encouraging professional development. Apart from intensive research experience, fellows will have the opportunity to pursue summer internships and engage in teaching and mentoring of undergraduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With support from the Environmental Chemical Sciences (ECS) program in the Division of Chemistry, Professor Daniel Higgins at Kansas State University and his students will study the accumulation of hazardous organic micropollutants on the surfaces of secondary microplastics. Microplastics are plastic fragments that form when larger objects are subjected to mechanical and chemical weathering. It is now well known that microplastics accumulate, concentrate, and transport organic pollutants into new environments, where they may pose risks to both aquatic and terrestrial life. Unfortunately, the mechanisms by which micropollutants interact with microplastics are not well understood. Prior studies have relied mostly on averaged measurements that neglect the possibility of nonuniform surface chemistry. Many have employed micropollutant concentrations that far exceed those found in the environment. These limitations will be overcome by employing super-resolved single-molecule detection methods. These methods afford nanometer-scale spatial resolution and allow for studies at environmentally relevant concentrations. The results will reveal the mechanisms by which organic micropollutants accumulate on microplastic surfaces, and how these evolve in space and time, as the plastics are weathered, pointing to micropollutant-microplastic interactions of greatest environmental concern. The team will incorporate related concepts into public science outreach activities hosted annually in western Kansas. Synthetic microplastics will be prepared from pristine polyethylene terephthalate sheets widely used in food and beverage containers commonly found in the environment. They will be artificially aged in a weathering chamber by exposure to UV light, water spray, and elevated temperatures. Changes in surface chemistry will be followed by water contact angle measurements, X-ray photoelectron spectroscopy, and vibrational spectroscopy. Surface morphology will be characterized by atomic force microscopy. Super-resolved single molecule detection methods will be used to follow the accumulation of fluorescent organic dyes on the plastic surfaces as models for organic micropollutants, revealing the strengths and mechanisms of relevant molecular level interactions on relevant nanometer length scales. The local isotherms associated with dye adsorption will be deduced and the mechanisms by which weakly associated molecules diffuse on the microplastics will be elucidated. An improved understanding of how microplastic surface chemistry evolves on nanometer length scales under weathering, and how these changes contribute to micropollutant adsorption, diffusion, and release will be important outcomes of this work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This planning project will build capacity for the Kansas State University Industrial and Manufacturing Systems Engineering Department and its collaborators to transform the industrial engineering undergraduate degree program into a career startup accelerator called Skill Xcelerator. In the transformed department, each student is empowered and supported to develop their unique industrial engineering identity, master in-demand technical and professional skills, and graduate ready to address society’s most complex challenges. In the Skill Xcelerator analogy, students are startups, and their graduation with an engineering degree represents the initial public offering. The valuation for each new startup is measured by the skills and experiences the student has accrued. The industrial engineering department, including the people, curriculum, co-curricular experiences, and environment, functions as an accelerator to position students for a successful launch. This planning project will examine the perspectives of invested parties, including students, faculty, administrators, alumni, and industry partners, to identify essential elements for transforming the educational experience using the Skill Xcelerator framework. By providing a platform for department faculty to partner with learning science experts to pilot evidence-based teaching innovations, the project will build a cohesive interdisciplinary team of partners in change. These activities will produce a data-driven roadmap for departmental change aligned with NSF’s Revolutionizing Engineering Departments (RED) program and its focus on the professional formation of engineers. The planning project will position the team to submit a full proposal that, consistent with the NSF RED program’s purpose, envisions transformative changes to the department’s culture, organization, structure, and pedagogy. This work serves the national interest by advancing research on engineering education while cultivating a globally competitive workforce. The central objective of this planning project is to identify specific needs for and attitudes toward change in the Industrial and Manufacturing Systems Engineering Department at Kansas State University. The project will combine qualitative and quantitative methods to gain insight into invested parties’ values and priorities regarding the industrial engineering undergraduate degree program and the Skill Xcelerator components. It also will examine faculty members’ perspectives on potential departmental change using semi-structured interviews with questions grounded in expectancy value theory. A monthly seminar series and mini-grants to support pilot course interventions will build partnerships between department faculty and experts in learning science and change management. Together, these activities will advance understanding about context-specific factors, which, when coupled with broader knowledge about professional formation and change management, will enable the team to articulate a plan for revolutionizing the department. The full RED proposal envisioned as a product of this planning project will study the Skill Xcelerator’s impact on students’ self-efficacy and engineering identity within a sociocultural context. The Skill Xcelerator incorporates evidence-based practices to facilitate professional formation of engineers. By explicitly engaging students’ values, developing self-efficacy, and focusing both on individual and cultural factors influencing student success, we expect to increase retention and graduation in industrial engineering and add to the growing body of knowledge about professional formation of engineers. 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.