Missouri State University

NSF Awards · FY 2026 · 2026-10

This project will provide undergraduate students with hands-on research experiences in the rapidly growing field of deep learning. Over three years, the program will engage 30 students from computer science, engineering, information technology, and related disciplines in meaningful research projects guided by faculty mentors. Participants will explore how deep learning techniques can be used to address real-world challenges in cybersecurity, robotics, hate speech in social media, and autonomous systems. Through these experiences, students will gain exposure to how deep learning technologies can be used to benefit society across a wide range of applications. In addition to research activities, the program will provide structured training to help students develop important professional skills, including scientific writing, oral communication, teamwork, and responsible research practices. By working in a collaborative and supportive environment, students will strengthen both their technical abilities and their confidence as emerging researchers. Overall, the program aims to inspire and prepare the next generation of scientists and engineers by giving them early, meaningful exposure to research in deep learning and its many beneficial uses. This project establishes a structured, research-intensive undergraduate training program aimed at increasing student engagement in state-of-the-art deep learning methodologies and applications. Research activities will focus on the design, analysis, and application of modern deep learning techniques to address contemporary challenges in areas such as multimedia security, robotic motion planning, autonomous multi-agent coordination, and malicious network activity detection. Faculty-mentored projects will emphasize both methodological advances and applied system development, with students contributing to end-to-end research pipelines including data preprocessing, model design, training, evaluation, and deployment-oriented analysis. Key objectives include: (i) developing students’ problem-solving and critical thinking skills in the context of deep learning research; (ii) building proficiency in contemporary deep learning frameworks and methodologies; (iii) training students in scholarly communication and research dissemination practices; and (iv) enhancing collaboration and teamwork. Collectively, this REU Site aims to cultivate a pipeline of well-prepared undergraduate researchers equipped for graduate study and careers in machine learning and artificial intelligence 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 · 2025-09

This project seeks to deepen our understanding of horizontal branch (HB) stars, a vital phase in stellar evolution that helps us learn about the life cycles of stars like our Sun. By studying these stars, specifically subdwarf B (sdB) stars, this work will improve our models of stellar physics, focusing on the structure and internal processes that govern their evolution. This project will provide significant educational opportunities, involving undergraduate and graduate students in hands-on research. The research team will also incorporate this work into their extensive outreach programs. The primary goal of this project is to apply observations and advanced modeling techniques to probe the internal structure of horizontal branch stars, focusing on sdB stars. These stars offer a unique opportunity to study the cores of stars that do not undergo helium fusion in the same way as other stars on the horizontal branch. Using data from the Gaia, TESS, and Kepler missions, the team will measure key properties --such as distances, radii, and masses -- for over 2,000 sdB stars. These measurements will be combined with spectral energy distribution fitting and asteroseismic data to refine our understanding of stellar models. 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 rapid growth of data-intensive applications from artificial intelligence to real-time analytics has pushed today’s computer systems to their memory limits. To keep up with this growing demand, many computing systems now use a two-tiered memory design. In this setup, a small but fast type of memory is used alongside a slower but larger and more energy-efficient memory. These systems try to improve performance by moving data between the two types of memory depending on how it is being used. However, current systems are not good at deciding where to place data when it is first created. Most use a one-size-fits-all rule that often puts the data in the wrong place to begin with, causing extra delays later when the system has to move it. This project addresses that problem by creating a more intelligent system that decides the best place for new data right from the start. This approach leads to faster and more efficient computing that helps accelerate scientific discoveries, reduces the energy consumption of cloud and edge data centers, and improves the performance of applications such as large language model training and medical imaging. In addition, the project supports the development of the nation’s research workforce by providing open-source tools, integrating research into education, and offering hands-on mentoring to graduate and undergraduate students in building advanced systems using cutting-edge memory technologies. This project develops, implements, and assesses a Dynamic Allocation Policy (DAP) for tiered memory systems. Working in coordination with existing tiering mechanisms, DAP enhances system performance by intelligently assigning each page to the most suitable memory tier at the time of allocation, reducing the need for expensive data migrations. The work proceeds in three integrated thrusts: (1) design of PAAT (Page Allocation and Access Tracing), a Linux kernel instrumentation framework that captures fine-grained page allocation and access patterns to produce detailed workload profiles; (2) development of an adaptive DAP engine that leverages PAAT’s insights to continuously analyze workload behavior and tiered system characteristics, selecting the optimal allocation policy in real time; and (3) systematic evaluation of DAP’s impact on performance, efficiency, and resource utilization, with empirical findings feeding back into policy refinement. Together, these contributions create a novel dynamic page allocation framework and deliver a practical, high-impact solution for emerging tiered memory architectures. 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 · 2025-02

Grapevines are among the most economically important berries in the world. As a long-lived (perennial) crop, grapevines are typically cultivated as a clonally propagated stem (the scion) which is mechanically grafted to a genetically distinct, clonally propagated root (the rootstock). Because grapevines are cultivated as clones, individual plants of the same variety are essentially genetic twins. Thousands of clonal stem varieties are planted across the globe and exhibit large variation in growth, berry chemistry, and wine volatiles based on vineyard environmental conditions and management. This variation in growth and performance is known as phenotypic plasticity and impacts both fruit and wine characteristics, a phenomenon known culturally and commercially as ‘terroir’, the signature of the local environment on the vine. Because of their clonal nature, one potential mechanism contributing to phenotypic plasticity in grapevines is changes to the epigenome, a collective term for non-genetic DNA modifications that can change how specific genes and gene pathways are activated or deactivated. The goal of this project is to understand which portions of the grapevine genome are impacted by epigenetic changes, how epigenetic change in the root and the stem interact in grafted plants, and how these changes contribute to optimal plant resilience in response to environmental stress. These results will be used to help plant breeders identify the next generation of elite grapevine varieties and grape growers improve grapevine production across diverse growing regions. Integrated education and outreach include providing research training for project personnel in collaboration with industry partners across six states. In addition, project participants will be involved in outreach and hands-on research training activities that leverage existing programs and partnerships to maximize STEM participation of high school and undergraduate students. How do long-lived plants (perennials) acclimate to different environments and what is the extent of phenotypic plasticity possible from a single genome? Grapevines are grown as a composite of a clonally propagated stem (the scion) mechanically grafted to a clonally propagated root (the rootstock). These unique combinations of shoot and root are planted across diverse geographic regions around the world; consequently, grapevines offer a powerful system for investigating the molecular basis of whole-plant, multi-year phenotypic plasticity and enables the experiment disentanglement of the shoot genotype x root genotype x environment interactions across diverse climatic conditions. The goal of this collaborative project is to develop an integrated understanding of how the genome of clonally propagated perennial plants produces “adapted” phenotypes, from roots to shoots, over time and under different environmental conditions, and to identify the molecular basis of this phenotypic plasticity. This study will use experimental vineyards planted with a single scion cultivar ‘Marquette’ grafted to three commercial rootstock cultivars, replicated in three different environments (New York, Missouri, South Dakota). The project will use an integrative systems biology approach combining measures of plant physiology, leaf ionomics and metabolomics, fruit ionomics and metabolomics, wine chemistry analysis, and connections between sRNA, mRNA, and cytosine methylation signatures in shoots and roots across sites and their interaction with the spatial and temporal changes that occur in the epigenome in clonal shoots and roots. All data will be made accessible to the public through long-term repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Missouri State University ADVANCE Adaptation project--Access, Climate, and Equity (ACE) Initiative--will induce positive, sustainable, systemic change for STEM faculty by implementing evidence-based practices that will foster a climate of equity in MSU’s STEM departments. Guided by rich institutional data, the project will increase access through equitable hiring, facilitate a more welcoming climate, and improve equity in tenure and promotion to associate professor for all faculty in STEM. The ACE Initiative seeks to adapt evidence-based strategies for (1) training search committees and tenure & promotion committees, (2) department chair professional development, and (3) early career management. Formative and summative assessment will be conducted by an internal evaluator, and additional guidance will be provided by internal and external advisory groups. The project can serve as a model for other public, regional institutions. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions. Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate. ADVANCE “Adaptation” awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education as well as non-academic, non-profit organizations. 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.