Tennessee Technological University

NSF Awards · FY 2026 · 2026-07

Artificial intelligence (AI), large-scale data, connected devices, cloud and high-performance computing systems, that together form the nation's cyberinfrastructure, are central to national competitiveness, yet most computing students encounter them only in advanced elective courses. This project infuses aspects of AI, Big Data, and Parallel and Distributed Computing concepts and practices into three foundational computing courses. The first two form the usual introductory programming sequence, and third is the computer systems course that is often taken shortly after them. Thus, all computing majors, not only those pursuing upper-level elective courses, will develop critical skills for understanding and contributing to the modern computational ecosystem. The project addresses a persistent barrier to such curriculum modernization: many instructors need focused preparation, classroom-tested examples, and adaptable teaching materials before they can confidently introduce these topics in early courses. To overcome this bottleneck, the project develops courses and materials to train about 200 current and future instructors through three intensive in-person summer workshops, an additional six hybrid tutorials at major conferences, and complementary online workshops. Summer trainees adapt and implement the course exemplars at their own institutions and contribute evaluation data, classroom-tested refinement and local adaptation, enabling broader adoption. With the potential to impact about 250,000 students over 5-10 years, the project serves NSF's mission by strengthening computing education, expanding access to AI and advanced cyberinfrastructure skills, and building the nation's long-term technological and research workforce capacity. The project advances knowledge in computing education by producing rigorously classroom-tested exemplars infused with Artificial Intelligence (AI), Big Data (BD), and Parallel & Distributed Computing (PDC) for Computer Science 1 (CS1), Computer Science 2 (CS2), and Computer Systems courses. Implementation across 60 diverse institutions will generate evidence-based models that can be widely adopted, thereby transforming early computing education at scale. The project investigates how AI-enabled learning tools and pedagogy can modernize core curricula by enabling students to construct, explore, and reason about modern computing systems earlier and in more depth than was previously possible. Evaluation data from trainees' implementations - including student learning, retention, and institutional adoptability - will contribute to generalizable knowledge on the design and scaling of Cyberinfrastructure-centric curriculum innovations. The project incorporates aspects of AI, BD, and PDC concepts and practices into the foundational computing courses ensuring that all computing majors, not only those pursuing upper-level electives, develop critical Cyberinfrastructure-ready skills. The project's three core course exemplars will be nationally adoptable, with trainees providing local adaptations to build a community-driven ecosystem of shared materials. Two key innovations in this project are: (i) harnessing AI both for pedagogy and for enabling course modernization: AI tools will make it possible for introductory students to develop, experiment with, and understand software, data, and other artifacts that were previously too complex to explore meaningfully at scale; and (ii) explicit integration of how BD and PDC power AI, giving computing students insight into the Cyberinfrastructure ecosystems underlying AI-driven discovery and innovation. 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 contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Tennessee Technological University. A total of 30 scholars pursuing bachelor's degrees in Chemical Engineering, Civil Engineering, Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, and Engineering Technology will receive scholarships averaging $11,500 annually for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through supplemental instruction, internships, research opportunities, and career coaching. Additional activities for scholars include community-building experiences and engagement with industry partners. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in high-wage engineering fields within Tennessee and other key areas of need nationally. The project will be assessed by an experienced evaluator, who will help the project team gain knowledge about the specific needs of these high-achieving, low-income students. The evaluator will examine factors such as academic structures and experiences toward degree completion, professional development experiences, critical thinking learning gains and gaps, and engagement with industry and community partners. The data that is generated will contribute to the knowledge base regarding effective strategies to support students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income 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 · 2025-10

The project aims to serve the national need of recruiting and retaining highly qualified middle/high school STEM educators. The U.S. has a dire teacher shortage with many thousands of STEM teacher jobs unfilled at the high school and middle school levels. This Robert Noyce Teacher Scholarship Program Track 1 Project at Tennessee Technological University project aims to prepare 22 middle/high school STEM educators to teach in the rural Upper Cumberland region. This project has the potential to significantly increase the number of licensed STEM teachers produced by Tennessee Tech University and contribute to alleviating the national shortage. The project plans to leverage rigorous recruitment and retention strategies such as providing: (1) an early teaching experience internship; (2) the opportunity to take an introductory education course; (3) support and mentoring while Scholars obtain their STEM degree and licensure, and (4) help Scholars transition into the broader STEM education community during their first year of teaching and beyond. This project at Tennessee Tech University includes partnerships with four high-need local education agencies (LEAs) in the rural Upper Cumberland region: Jackson, Overton, Putnam, and White counties. Project goals include (1) recruiting 25 early teaching experience (ETE) interns (2) graduating 22 job-ready STEM educators with both a STEM degree and teaching licensure, (3) enhancing scholars' instructional skills so that they are better prepared to teach effectively, (4) supporting graduates in their induction year so that they receive formative feedback and integrate into relevant communities of practice and professional organizations, (5) establishing and maintaining a community to support scholars, and (6) implementing a plan to monitor compliance with teaching service commitment. The project will provide scholarship support for grades 6–12 licensure programs in mathematics, physics, chemistry, biology, and geology, and the grades 6–8 licensure programs in mathematics and science. The project will also investigate these questions: What are effective recruitment strategies? Does taking an introductory education course increase the likelihood of interns transitioning to scholars? What are the relative advantages/ disadvantages of obtaining licensure in this way, as compared to other programs? How does a carefully managed Noyce community affect persistence and retention of new teachers? This Track 1: Scholarships and Stipends 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 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 CSSI project is a multi-university collaboration between Tennessee Technological University, the University of Tennessee, Knoxville, Stony Brook University, and the Illinois Institute of Technology. This project improves how massively parallel computers run large-scale artificial intelligence (AI) applications by enhancing the Message Passing Interface (MPI), a widely used standard for coordinating work across many computers in parallel programs. Currently, the enabling data-transfer software used in AI, for communication between computers enhanced by Graphical Processing Units (GPUs), are often proprietary and/or limited in scope; they cannot be expanded or enhanced by an open community. That situation restricts innovation, making it harder for scientists to collaborate and enhance their science output on limited computer resources, while also creating dependency on a few vendors. By contrast, this project builds on and advances Open MPI, a major open-source implementation of MPI with a long history of broad impact, to make it more efficient, flexible, and better suited for modern AI tasks. In addition to improving the Open MPI implementation, MPI4AI aims at standardizing extensions to MPI so all implementations and users of MPI will benefit from this project's outcome. MPI4AI introduces key improvements to Open MPI, including native support for GPU communication, enhanced collective (group) communication operations including those that are AI-algorithm specific, compute stream integration, and optimized data movement. Specifically, these advances target performance bottlenecks in three AI patterns: neural architecture search with transfer learning, key-value prefix caching in large language model inference, and large-scale data-parallel training. The project improves resilience and malleability through fault-tolerant mechanisms, enabling AI applications to adapt dynamically to system changes and to use resources more efficiently. By forwarding these enhancements toward adoption in the upcoming MPI-5 and MPI-6 standards, the project ensures long-term impact across both academic research and industrial AI workflows. These contributions will lower the cost of running large AI workloads and broaden access to scalable AI infrastructure. MPI4AI's capabilities will enable researchers exploring new modalities of AI computation to express their algorithms and code efficiently and more effectively as compared to existing solutions that work within the confines of current MPI features and vendor-specific message-passing libraries. Underlying improvements devised for Open MPI will also be broadly beneficial to other use cases and users of this parallel programming system. Overall, key strengths of this effort are a strong commitment to standardization and emphasis on performance-portability across various hardware platforms with particular focus on AI-enablement. 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

In this project, funded by the MPS-LEAPS (Launching Early-Career Academic Pathways) Program and managed by the Division of Chemistry (CHE) at NSF, Professor Murphy and his students at Tennessee Technological University (TTU) will perform studies that aim to advance the frontiers of organic chemistry through the exploration of furfural as a starting material for novel compounds. A major need in the scientific community is to develop and contribute to new pathways in combating dangerous bacteria as well as microplastic contaminants in the environment. Toward this end, Professor Murphy and his students will synthesize N-acyl hydrazones (NAHs) from furfural, which will be a versatile platform for heterocycle formation, catalysis, and antimicrobial discovery. Their research could deepen the mechanistic understanding of oxidative cyclizations, microplastic degradation, and bioactivity of such NAH compounds. This project will provide students experience in experimental design, modern instrumentation, and problem-solving. Professor Murphy and his students will synthesize NAH compounds and analogs derived from furfural, a biomass-derived chemical feedstock. This project will investigate these NAH compounds for the following aims: i) developing high-yielding, synthetic methods to access NAH and NAH-like compounds, ii) exploring oxidative cyclization reactions to form valuable heterocycles such as oxadiazoles, iii) evaluating NAH derivatives as organocatalysts for depolymerizing polyethylene terephthalate (PET) microplastics, and iv) assessing the antimicrobial properties of these compounds against clinically relevant pathogens. Student researchers will be integral to this research, conducting hands-on synthesis, running analytical experiments, characterizing compounds, and evaluating biological and catalytic activity. This student-lead research could lead to the development of novel antibacterial agents, high-yielding oxidative cyclization strategies for pharmaceutically relevant moieties, and efficient organocatalysts for plastic waste depolymerization. This interdisciplinary approach bridges synthetic organic chemistry and catalysis as well as offers an innovative, flexible scaffold to address scientific challenges while providing students opportunities to gain experience working on impactful 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

Electric machines are essential to modern life, powering everything from transportation electrification, shipboard power systems for military applications, industrial equipment, robotics and wind energy systems. However, many high-performance machines that are required today for these highly demanding applications depend on rare-earth magnets - materials that are expensive, challenging to extract, and are vulnerable to global supply chain disruptions. This research explores a new class of electric machines called mixed-pole, multiphase synchronous reluctance machines. When multiple winding sets with different pole configurations are successfully combined to interact with each other and with the rotor of a different pole number, their associated magnetic fields can be manipulated to great advantage, resulting in improved energy conversion, high torque density, and fault tolerance - all without using magnets. Unlike conventional motors based on fixed number of poles, or harmonic excitations of additional windings, this concept leverages the interaction of magnetomotive forces at different fundamental frequencies, opening new possibilities for efficient, flexible and scalable motor control. This research has the potential to transform the future of electric motor systems across many sectors, particularly electrified transportation and aviation, shipboard systems, and wind energy, by reducing dependence on critical materials in the next generation of motor-drive systems. The outcome of this research will support a broader transition to sustainable, resilient and competitive electrified transportation, industrial high-power drives and wind energy infrastructure. The specific goals of this project are to advance the theory of electric machines with multiple m-phase windings modulated independently within the machine to enable each winding set to operate with unique and permanent pole configurations. Unlike harmonic frequency excitation recently exploited in some machine topologies, the proposed concept employs complex interaction of magnetomotive forces at different fundamental frequencies. The fundamental theory relating to the operation of mixed pole electric machines and the energy conversion principle will be developed to understand the energetic behavior and different mechanisms of pole modulation for this new class of machines. Analytical modeling and finite element design tools will be developed to assist in the computational modeling and design of this class of machines. An innovative power converter and control approach is proposed to harness the full capabilities of this motor-drive topology. The power converter, modulation techniques, fault-tolerant control criteria, and estimation methodologies will be developed to enable the application of mixed-pole synchronous reluctance machines in electrified transportation and energy systems applications. 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

Each generation of emerging supercomputers advances available compute power, enabling new scientific discoveries. However, parallel solvers and simulations often achieve only a small fraction of peak performance of the underlying supercomputer system because of inter-process communication overheads and complexities. In these applications, communication is handled by the Message Passing Interface (MPI) application programmer interface, provided through-standards-based implementations (via libraries and header files). The achieved performance associated with the numerous possible types of inter-process communication vary greatly, depending not only on the underlying architecture but also the version of MPI that has been hand-tuned for the given system. Typically, a proprietary vendor-dependent implementation is available on the largest systems, with open-source versions available on cluster systems. While multiple open-source versions of MPI exist, installing an appropriately tuned version requires significant expertise in both the MPI framework and the hardware of the system. The gap between both the performance and modern feature coverage of production MPI's on large-scale systems can present challenges for application developers seeking to reach higher achievable performance and manage the complexities of modern architectures. This project will therefore develop an open-source cyberinfrastructure, MPI Advance, which utilizes low-level communication routines, such as send and receive methods, from the hand-tuned system MPI installation to publish new and improved application programmer interfaces (APIs) to drive higher achievable performance without the need to supplant the built-in MPI implementations on large-scale systems. MPI Advance uses the MPI-eXtension framework to provide optimizations within existing MPI APIs, extensions to these standard APIs, and entirely new routines that do not conform to the current standard but can lead to significant performance optimizations within existing parallel applications. This cyberinfrastructure will be integrated within widely used solver frameworks, such as PETSc, hypre, and Trilinos, enabling straightforward integration into any application relying on these code bases. Further, MPI Advance will provide a framework for research within MPI, allowing researchers to test new extensions across a range of applications and achieve new community best practices before these new features and APIs are proposed adoption within future editions of the MPI standard. MPI Advance will be made broadly available under BSD-3 license and be published via GitHub, in addition to documentation and supporting infrastructure on web pages dedicated to its user base. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Total knee arthroplasty (TKA) is one of the most common surgical procedures in all of medicine and despite technical advances, about 10-20% of patients remain dissatisfied with their outcomes and about 9% of patients require revision. Leading causes of dissatisfaction and revision include postoperative pain, infection, aseptic loosening, and instability. Novel technologies, such as 3D surgical planning, patient-specific instrumentation, and sensor-guided and robotic-assisted arthroplasty, have changed the landscape of modern TKA, yet success rates have not improved over the past 20 years. Currently, there is no consensus in the arthroplasty community regarding ideal implant alignment, soft tissue balance, or implant design, and our understanding of aseptic failure mechanisms in knee arthroplasty is poor. Overall, there is a lack of technology to allow continuous postoperative in vivo measurement of joint motions and forces, thus precluding advances in knee arthroplasty. To address this problem, we propose to develop an innovative self-powered smart piezoelectric TKA implant platform that will enable continuous in vivo measurement of joint kinematics and compartmental joint kinetics, and allow expandability for future sensors. Our preliminary results demonstrate feasibility of piezoelectric transducers integrated into knee prosthesis to (a) sense compartmental forces with error <3%, (b) sense compartmental contact locations with error <1.6 mm, (c) generate around 300 μW of power, and (d) survive at least 10,000 simulated gait cycles. Our visionary design requires further optimization of the piezoelectric system, development of a magnetic joint angle sensing system, and pre-clinical evaluation, which is the focus of the proposed work. Aim 1: we will develop computational finite element models to predict the behavior of smart piezoelectric TKR force sensors during gait, and develop circuitry for energy harvesting, sensing, and wireless data transmission. We will experimentally validate the models and circuits via testing of prototypes under simulated in vitro 6 degree-of-freedom (DOF) joint motion. We will utilize the experimentally-validated models to optimize the design of the smart piezoelectric implant considering sensing, energy harvesting, and long-term survivability of the device. We will evaluate the accuracy and reproducibility of final optimized prototypes under various ADLs, and determine long-term performance over 1 million cycles. Aim 2: we will develop a magnetic joint flexion angle sensing system by creating computational models and testing prototypes. Magnetic flexion sensing will be fused with the aforementioned piezoelectric force sensing to resolve all six kinematic DOFs. Aim 3: we will implant our prototype smart knees into cadavers and determine performance over 100 cycles of various ADLs, and determine ability to sense abnormalities including cement loosening, ligamentous instability, condylar liftoff, and third-body contamination of the joint surface. The ultimate goal of this research is to fill the technological gap in postoperative monitoring of TKA to provide the community with valuable data to advance orthopedic surgical techniques and implant designs to ultimately improve patient satisfaction and human health.

NSF Awards · FY 2025 · 2025-07

Artificial Intelligence (AI) now permeates nearly every aspect of our daily lives—from personalized recommendations and virtual assistants to healthcare diagnostics, autonomous vehicles, and national defense systems. This widespread integration has placed a premium on individuals who can design, implement, test, and effectively use AI models and methodologies. However, despite the growing demand, there remains a significant shortage of skilled workers in the AI field. This talent gap poses a challenge to continued innovation and the ability of industries and governments to fully leverage AI’s transformative potential. This project will enable undergraduate and graduate students to attend the second oldest artificial intelligence conference in the United States, the Florida Artificial Intelligence Research Society (FLAIRS) conference. Since 1990, the conference has been open to all. The plan is to host approximately 15 students at the next FLAIRS conference in May 2026 to represent students from a range of US academic institutions, including those from institutions less likely to have the funding resources. Through the conference’s main and special track sessions, tutorials, and keynote speakers, the student participants will be exposed to a wide variety of research and development areas in AI. The new conference will serve the NSF interest in advancing science through increasing interest and workforce development in artificial intelligence. This year, a new component will be a special conference session entitled “Workforce Development in Artificial Intelligence,” where the students will be able to hear what academia and industry are doing in the field of AI. 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 I-Corps project focuses on the development of a technology to deliver wireless power and data through the ground. Present-day wireless power and communication techniques perform poorly underground, making the deployment of electronic devices at depth challenging. The technology supports underground sensing, which is essential for industries such as geothermal energy and mining, and plays a key role in expanding access to electricity. In addition, the technology could help bring electricity to more places and make the energy system more secure by lowering the cost of building and maintaining power equipment. This new approach connects ideas from both electrical engineering and geophysics. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an innovative approach to transmitting wireless power and data through the ground. The core innovation diverges from contemporary long-range wireless power solutions that rely on electromagnetic space-wave techniques through the air. This project is expected to advance the underground wireless transmission field, providing a robust solution for subsurface characterization and potential uses in a number of other applications. 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

Tennessee Technological University, the University of Tennessee Knoxville, Meharry Medical College, and Vanderbilt University have joined in this project to address health challenges via Artificial Intelligence (AI) and Machine Learning (ML) infused workshops and training. At present, Tennessee ranks 44th among the 50 states in national health outcomes. This project will advance the use of modern, AI/ML-enabled computer technology in medical research and healthcare delivery. At the heart of the project is a three-part workshop series, powered by National AI Research Resource (NAIRR) Pilot resources aimed at accelerating interdisciplinary research at the intersection of advanced cyberinfrastructure, AI/ML, and health outcomes. These workshops train participants in high-performance computing, cloud-based AI applications, and open data tools, while fostering sustained collaboration among medical professionals, engineers, scientists, and students who participate. Workshop course content and outcomes will be shared with the NSF NAIRR program and broadly with the public. This project brings together leaders in medical AI/ML research at Vanderbilt University and the University of Tennessee, along with emerging research cyberinfrastructure centers such as Meharry Medical College. It builds upon collaborative frameworks previously advanced by the AI Tennessee Initiative, a statewide initiative led by UT Knoxville and TN Tech's AI Center---structures that have demonstrated success in enabling cross-institutional efforts. The workshops are linked to the usage of NAIRR Pilot AI resources, and will train participants to use NAIRR resources through hands-on training. Significant training on NAIRR resources---both HPC and Cloud---for AI applications, methods, and practice is included in all three workshops. Relevant methods, applications, and techniques working on open data will provide participants with significant training and scaffolding to engage in further AI/ML use-inspired research and to use NSF NAIRR resources in the future. Overall, this workshop series will engage and train a significant group of medical professionals, scientists, engineers, and pre-professional students on NAIRR Pilot resources and AI/ML concepts, advancing the careers of medical professionals, scientists, and 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.

NSF Awards · FY 2025 · 2025-04

This I-Corps project is based on the development of autonomous robots that use virtual roads or paths to move. Currently, many real-world applications such as radiation detection, chemical and gas leak detection, and substation inspection, required inspections that put highly skilled people at risk for injury or death. The aim of this technology is to replace people with robots to perform these tasks. However, for a robot to function, a travel path must exist even though travel path infrastructure is not economical to develop. As an alternative, this technology replaces physical roads with virtual roads. The robot will use virtual roads created from an architectural drawing or satellite imagery that will be automatically generated to allow the robot to accomplish its mission. This technology may prevent injuries and save lives as well as allow for more frequent substation and other inspections to avoid equipment failure and premature equipment replacement. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of fully autonomous robots using virtual roads. Currently, the use of fully autonomous robots requires predefined roads or paths to operate. This technology eliminates the need for a pre-established travel path by using virtual roads that do not require landmarks and are independent of environmental changes. The fully autonomous robot using virtual roads navigates using a digital map that is extracted from an architectural drawing or satellite imagery. The virtual roads consist of interconnected nodes on the robot environment map. These nodes accommodate all the possible destinations required by the robot. Based on the selected destination, a virtual path is automatically generated out of the set of virtual roads allowing the robot to accomplish its mission. In addition, as this approach automatically generates a virtual travel path, it will not require training of the robot. 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 · 2025-03

This award will partially fund 12 students' attendance costs for the 15th ACM Conference on Data and Application Security and Privacy (CODASPY). CODASPY focuses on trust, privacy and security problems from various perspectives, particularly around artificial intelligence and machine learning contexts where data and privacy are core to both the utility and the risks of these systems. The conference invites participation from a wide range of communities including corporate and academic researchers, open-source projects, standardization bodies, government, system and security administrators, and software engineers and application domain experts. Bringing this wide range of perspectives is crucial to advancing both research and practice in this area. Attending CODASPY will be valuable to students' own research and professional development. Students will get an opportunity to present their research, receive feedback, and network with established researchers in these areas. Funding from this award will allow the conference to support early-stage student researchers who might not otherwise be able to attend. To that end, the conference organizers will widely advertise the availability for student travel support. Students will be selected based on the relevance of their work to the themes of CODASPY, their financial need, and their contribution toward increasing the range of institutions, topics, and student backgrounds represented at the conference. 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 · 2024-12

Despite being home to 83 institutions of higher education, including six research universities and a national lab, Tennessee lacks a dedicated research and education network. Such a network, common in other states, can be used to significantly enhance research and education capabilities and improve access to existing and future resources. This project, led by four under-resourced public universities in the state, is working to create a comprehensive strategy for the development of the Tennessee Research and Education Network (TREN), along with a regional cyberinfrastructure plan. By connecting these four universities (Tennessee Technological University, Middle Tennessee State University, Tennessee State University, and the University of Tennessee at Chattanooga) with each other and with larger research organizations in the region, TREN will catalyze and accelerate research for the under-resourced institutions while fostering statewide collaborations. The network connectivity that TREN will provide will elevate Tennessee’s capacity for cutting-edge research and facilitate education, thereby contributing to economic development. This project is organizing a series of workshops to organize the networking consortium and develop the regional cyberinfrastructure plan, while identifying science drivers that can benefit from TREN. In addition, the project is creating a prototype regional network over existing infrastructure, to better characterize and pinpoint technological and policy limitations of current institutional networks. This project is documenting crucial gaps in resources and expertise, which will help boost the immediate capabilities of the participating institutions while building a framework for advanced collaborations and educational methods, driving significant long-term societal benefits. 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 · 2024-09

An efficient, secure and resilient energy infrastructure is the critical driving force for our nation’s economy, health and industry sectors and the welfare of our people. The fast-paced transformation and complexity of science and engineering discoveries and future technological innovations provide an extraordinary opportunity for future STEM graduates to address grand challenges that require a convergence of complex ideas and an interdisciplinary assimilation of cutting-edge research and education in the national priority areas of energy, artificial intelligence (AI) and cybersecurity. The National Science Foundation Research Traineeship award to the Tennessee Tech University will address existing knowledge gaps plaguing the majority of engineers and scientists specializing in divergent areas of energy technologies, AI and cybersecurity, through a rigorous and immersive traineeship that will involve cutting-edge interdisciplinary research projects, classroom training followed by group discussions and hands-on experience in real world projects using physical infrastructure, professional development initiatives and workshops and certification programs. The traineeship anticipates providing a comprehensive training opportunity for around one hundred and fifty (150) students, including twenty-four (24) funded trainees at the doctoral and master’s levels by engaging faculty and students from departments of electrical and computer engineering, computer science, mechanical engineering and education. This National Science Foundation Research traineeship will bridge knowledge gaps existing in the national priority areas of energy, AI and cybersecurity by addressing the fundamental questions: a) Can engineers and scientists trained in cybersecurity and AI, yet lacking practical experience in physical energy infrastructure, effectively tackle energy security challenges? b) Are engineers and scientists specializing in energy generation, energy storage, and power transmission ideally positioned to identify and mitigate emerging security risks? The traineeship will equip and increase the number of qualified graduate students through an immersive and interconnected comprehensive training, based on new interdisciplinary courses and educational efforts, cutting-edge collaborative and interdisciplinary research, and through development of interpersonal and professional skills in the intersection theme of energy, AI and cybersecurity. Educational efforts will concentrate on jointly developing new, interdisciplinary courses to accommodate the research needs of the trainees. The courses will involve lectures and immersion through group discussions, an industry lecture series, project pitches, and hands-on class projects. These courses will also improve communication and teamwork skills through discussions and presentations. Research efforts will generate new knowledge that will be integrated into educational offerings for new cohorts of trainees. The interdisciplinary nature of research projects involving multiple students and faculty in the convergence theme will improve fundamental scientific understanding, critical thinking, teamwork and collaboration, and communication skills. Professional development trainings related to module development, career coaching, resume building, interview preparation, technology innovation and opportunities will improve skills and competencies. The traineeship will promote a cutting-edge research and education program that supports a sustainable pipeline of graduate students with high technical competency and transferrable professional skills to meet regional and national workforce needs in three very important areas of national priority. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 growing dependence of organizations on cloud cyberinfrastructure (CI), coupled with the intrinsic on-demand and elastic nature of the cloud CI, have widened the attack surface and made it an attractive target to rapidly evolving cyber threats. The development of fairness-aware Artificial Intelligence (AI) and machine learning (ML) based security solutions can make cloud CI more resilient and trustworthy. However, a key pillar of a successful secure cloud adoption necessitates scientific research workforce training. This project aims to train the future research workforce to develop and use AI-based cloud CI cybersecurity solutions that are fair, ethical, and unbiased. In addition, the project aims to instill the ability of the workforce to adapt and evolve these AI based cybersecurity solutions for cloud CI to improve their trustworthiness and resiliency, as new adversary models are discovered. The technical innovations of this project address the growing needs for a fairness-aware AI-skilled secure cloud CI research workforce in two-fold. First, the project will develop and integrate seven advanced experiential learning modules, referred to as AI4SecureCI, for secure cloud CI using fair and explainable AI concepts into undergraduate and graduate curriculum, training around 500 diverse participants including faculty and students directly. The developed AI4SecureCI modules will include the concepts of network security, authorization and automated access control, online malware detection, classifying malware threats, adversarial attacks and defenses, bias and fairness, and explainable AI, relevant to cloud CI. These modules will include the (1) lecture materials to provide conceptual knowledge for AI4SecureCI, and (2) hands-on lab exercises to provide practical experience. To support hands-on labs and enable wider adoption of the modules, the team will utilize ready-to-use datasets created from their own cloud CI security research and public security datasets, and free-tier cloud services such as AWS Educate. Second, the project will ensure broader adoption, via student boot-camps and series of faculty workshops of developed advanced AI4SecureCI and computational data-driven methods, into underrepresented groups of CI users and contributors to foster research advancements for evolving cloud CI security threat vectors. The advances made under this project, both in terms of research, modules developed, as well as training material will be made publicly available on a project website. The team will collaborate closely with the NSF ACCESS program to enhance the dissemination of knowledge and expertise within the CI community by incorporating the AI4SecureCI modules into the ACCESS Knowledge Base. "" This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.