University of Alabama Tuscaloosa

NSF Awards · FY 2026 · 2026-08

Hydrogen is used as transportation fuel, to produce ammonia for fertilizers, and to upgrade oil for numerous products. This project seeks to lower the cost of producing hydrogen by improving water electrolysis technology. Electrolyzers use ion-conducting membranes as separators. The project will identify relationships between membrane polymer structure of the membrane and its performance in an electrolyzer. These relationships are not well understood. The goal is to design efficient ion-conducting membranes that remain stable over time. The results of the project will help guide membrane design. The project will provide hands-on learning activities that connect to the research. It will create a training environment that prepares students for careers in polymer science and electrochemical engineering. Training activities will emphasize laboratory work, mentorship, teamwork, and clear scientific communication. The research will support manufacturing and the domestic energy industry. The goal of this research is to establish structure-property relationships in anionic ion-conducting membranes. This will be achieved through modular polymer synthesis and high-throughput characterizations. The research will develop design rules that link polymer chemistries and polymer structures to polymer performance. Polymers will be prepared using controlled synthesis methods that allow careful changes in molecular structure. High-throughput measurements will be used to study chemical stability, water uptake, ion transport, and swelling. A key innovation of this project is the development of a new block copolymer platform. The platform has the potential to decouple and optimize conflicting properties within a single material family. The knowledge and methodologies gained will advance polymer science and support the development of electrochemical energy technologies. 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 · 2026-08

This Faculty Early Career Development Program (CAREER) grant funds research on autonomous systems that are poised to transform industries ranging from transportation and manufacturing to environmental monitoring and space exploration. Use of these systems in safety-critical applications faces significant challenges, such as ensuring safety and performance in unpredictable environments and adapting to large and sudden changes, and handling complex dynamics. This project seeks to overcome these barriers by developing advanced control methods that ensure reliable and high-performing operation of autonomous systems, even in dynamic and uncertain conditions. These advancements could improve the safety, reliability, and performance of technologies such as self-driving cars, autonomous air taxis, and spacecraft. In addition to its technical contributions, the project will cultivate the next generation of innovators in control and autonomous systems. Educational and outreach efforts will include developing new course materials, organizing workshops, and engaging K-12 students in Alabama through interactive activities such as drone race challenges and twisty flyer events, sparking interest in science, technology, engineering, and mathematics (STEM) fields. The goal of this project is to develop advanced control methodologies that enable the reliable deployment of autonomous systems in dynamic and uncertain environments, addressing key challenges such as safety, performance, adaptability, and the ability to manage complex, high-dimensional systems. To achieve this, the research focuses on three main thrusts: (i) designing a control architecture that integrates robust adaptive uncertainty compensation and constrained control to ensure safe and efficient operation of nonlinear systems under complex uncertainties, (ii) establishing an adaptive nonlinear parameter-varying control framework to handle large uncertainties, including those arising from control authority constraints and unmatched uncertainties, and (iii) leveraging machine learning techniques to enhance the scalability and performance of the projected robust adaptive control algorithms, enabling their application to high-dimensional systems with stringent performance demands. Together, these efforts aim to advance the state of the art in managing uncertainty, constraints, and nonlinear dynamics, setting a foundation for deploying safety-critical autonomous systems across a wide range of applications. The educational and outreach components will complement the research by equipping students across all levels with the skills and motivation to pursue careers in control and autonomy, thereby contributing to a stronger workforce and regional ecosystem in intelligent 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 2026 · 2026-07

Underwater communication is much harder than over-the-air communication. Radio waves do not travel well in water. Underwater systems usually rely on acoustic communication, which uses sound to carry information. However, sound travels much more slowly and can transmit far less information than radio waves, making it difficult for underwater robots, sensors, and vehicles to communicate effectively and reliably. This project studies a new technique called underwater acoustic reconfigurable intelligent surfaces (UA-RIS). These are special panels that can control how sound waves reflect in water, helping signals travel farther and improving the amount of information they can carry. The goal is to build the scientific foundation for smart underwater communication and sensing systems that could support ocean research, environmental monitoring, offshore infrastructure inspection, and maritime security. The project also benefits society by training students, creating open tools and datasets for researchers, and engaging the public through education and outreach activities that encourage interest in ocean technology and engineering. This project aims to develop the theoretical, algorithmic, and experimental foundation of UA-RIS-assisted underwater networks through three tightly integrated research thrusts. First, it designs and models innovative UA-RIS architectures using coupled electromechanical and acoustic methods, including reflector analysis, propagation modeling, and communication-capacity evaluation, together with optimization of reflector structures, quantization levels, and spatial control codebooks. Second, it establishes a localization framework that includes new passive localization methods, modeling of effects of hardware timing jitter on angle estimation errors, bounds on localization accuracy, and integrated passive-active co-localization algorithms. Third, it extends UA-RIS from link-level performance improvement to network-level operation through multi-user optimization, UA-RIS-aware medium access control (MAC) design, and an open-access calibrated simulator. The outcomes include new UA-RIS theory, novel underwater localization methods, analytical bounds on localization errors, optimized architectures and networking protocols, passive-localization hardware and algorithms, and open tools for the underwater networking research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Scientific research is changing quickly because advanced automation and Artificial Intelligence (AI) can now run laboratory experiments on their own. This technology helps scientists make new discoveries much faster than before. Building these automated laboratories requires combining ideas from different fields, including robotics, and laboratory science. Currently, researchers in the United States and Japan do not have an easy way to work together and combine their unique strengths in these areas. This project addresses this problem by organizing an online conference series. The virtual event will bring together scientists and engineers from both nations to build an international network. These partnerships will push scientific progress forward and help the economy grow through faster technological innovation, serving the national interest. In addition, the conference supports training by involving early-career researchers. The goal of this project is to create a partnership between researchers from the United States and Japan focused on combining automated laboratory equipment and AI. Research topics include flexible soft robotics, human-robot interaction, and programmable cloud laboratories for automating scientific testing. To achieve these goals, the investigators will host a two-day online workshop in late May 2026. The event will feature presentations and panel discussions that pair international experts from both countries. To help participants build partnerships, attendees will share research profiles and capability summaries to match interests between researchers based in the United States and Japan. The team will coordinate follow-up breakout sessions throughout June to design concrete joint research plans. The core activities and findings will be written into a comprehensive report summarizing the new collaborative framework. 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

This project offers a renewed Research Experiences for Undergraduates (REU) Site at The University of Alabama (UA) focused on trustworthy artificial intelligence (AI) and cybersecurity for intelligent vehicles and transportation systems. Intelligent transportation technologies are rapidly transforming mobility by improving safety, efficiency, and accessibility; however, they also introduce new vulnerabilities related to adversarial AI, insecure communication, and human interaction with automation. The project's novelties are the integration of AI, cybersecurity, and human factors research within a unified undergraduate research environment centered on real world transportation systems. The project's broader significance and importance are in advancing secure and trustworthy mobility systems while preparing a skilled workforce capable of addressing emerging challenges posed by automation and connectivity in intelligent vehicles and transportation systems. The project engages undergraduate students from multiple disciplines, such as computer science, transportation systems engineering, and psychology in multidisciplinary research on trustworthy AI and cybersecurity for intelligent vehicles and transportation systems. Students participate in hands-on research using advanced experimental platforms, including driving simulators, sensing systems, and AI tools, while receiving structured mentoring and professional development. All recruitment, outreach, and participation activities are open to all eligible U.S. students. The project introduces practical innovations and mechanisms to address security, resilience, and trust challenges in intelligent transportation systems, including secure and resilient navigation, robust AI-driven decision-making under adversarial conditions, and enhanced human-AI interaction for safer mobility. Expected outcomes include new algorithms, datasets, and prototype systems that enhance the resilience and safety of intelligent transportation systems, along with dissemination through publications, open educational resources, and outreach activities that broaden awareness and engagement in AI and cybersecurity. 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

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Alabama. This work is conducted in collaboration with Dr. Christopher Mundy at the Pacific Northwest National Laboratory. Through the fellowship, the PI will aim to better understand how charged particles move inside batteries. This movement, known as ion diffusion, is one of the main factors that limits how quickly batteries can charge. By using advanced computer simulations powered by artificial intelligence, the research will explore what happens to ions at the atomic level as they pass through different parts of battery materials. By uncovering these details, the research could lead to improvements in how batteries are made, allowing them to charge faster and last longer. In addition to advancing battery technology, the project will help train the next generation of scientists in a field that's critical to the nation's energy future. The scientific goal of the project is to understand the mechanism of ion diffusion through the cathode- (solid) electrolyte interfaces of batteries. The project will elucidate the effect of crystalline and amorphous layers at the interfaces on the diffusion of ions with/without presence of carbonates that can form during battery manufacturing. The project will leverage machine learning interatomic potentials that can accelerate first principles-based molecular dynamics simulations. The project will transfer knowledge about enhanced sampling molecular dynamics simulations, provide training opportunities to a graduate student, and allow collaborations for the PI in the field of atomistic simulations and battery materials research at Pacific Northwest National Laboratory. Alabama is currently experiencing a rapid growth in its battery industry. The new skills acquired will enable the PI to undertake future research projects at his home institution that are relevant to Alabama's economic development. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This REU Site award to The University of Alabama, located in Tuscaloosa, AL, will support the training of ten students for 10 weeks during the summers of 2026-2028. This activity provides students with the opportunity for hands-on lab and field research experiences that put their knowledge learned in the classroom into practice, further developing their critical thinking and problem-solving skills, and in doing so improve graduation rates and increase the likelihood of participants remaining in a scientific discipline. In addition, the professional development provided to participants assists them in establishing professional networks, building confidence, and gaining scientific proficiency that is transferable to their future endeavors. Students will learn how to conduct research and have the opportunity to contribute to peer-reviewed publications and present their results at scientific conferences. Assessment of this program will be done through an online tool developed by the Institute for Social Science Research at the University of Alabama. This tool will be used to assess both academic and scientific self-efficacy and self-confidence, as well as attitudes about future participation in graduate school. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with NSF priorities in Biotechnology. The focus of this REU is Integrative Biology and Ecology, an interdisciplinary field that investigates interrelationships between living organisms and their environments, and in doing so can address a wide variety of issues of relevance related to human health, conservation and biodiversity, and/or ecosystem services. Students will perform research in the Department of Biological Sciences and have the opportunity to participate in a variety of disciplines including molecular ecology, comparative anatomy, behavior, physiology, toxicology, drug-discovery, neurobiology, immunology, microbiology, bioinformatics, computational biology, and/or ecology. REU students will be encouraged to develop research projects that address multi-dimensional questions that cannot be addressed by any single biological discipline. Participants will explore all aspects of research, including literature review, research ethics training, experimental design, data analysis, and presentation of results. Other activities will include field trips to Alabama research centers, professional development activities to assist students with pursuing future graduate or professional training, and other activities providing information on science careers in academia, government, and industry. After submitting applications using the NSF ETAP website (https://etap.nsf.gov), top applicants will participate in Zoom conversations before the final selection of participants. More information about the program is available by visiting https://bsc.ua.edu/undergraduate/reu-program/, or by contacting the PI (Dr. Jenny at mjjenny@ua.edu) or the co-PI (Dr. Ciesla at lmciesla@ua.edu). 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 · 2026-02

Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive, resulting in concerns related to security, safety, accuracy, and trust. With growing dependency on physical robots that work closely with humans, security is becoming increasingly important because cyber-attacks could lead to privacy invasion, critical operations sabotages, and bodily harm. The current shortfall of professionals who can defend such systems demands development and integration of new curricula. This project is creating a new curriculum with the scaffolding necessary to engage students at the junior/senior university level who are majoring in robotics, AI, and security related fields. More than 500 students are being directly served by this educational initiative at Mississippi State University. The project also indirectly impacts students across the nation at institutions in which faculty choose to integrate the modules developed for this project. On a broader scale, this project intends to increase public scientific literacy and engagement, especially in rapidly advancing fields such as cybersecurity, robotics, and AI. This is a significant societal benefit as it will improve the preparedness and well-being of individuals in a technologically advanced society and contribute to development of a diverse, globally competitive STEM workforce. The project is developing seven self-contained and adaptive modules on “AI security threats to pervasive robotic systems”. Topics will include: 1) introduction, examples of attacks, and motivation; 2) robotic AI attack surfaces and penetration testing; 3) attack patterns and security strategies for input sensors; 4) training attacks and associated security strategies; 5) inference attacks and associated security strategies; 6) actuator attacks and associated security strategies; and 7) ethics of AI, robotics, and cybersecurity. Course modules are evaluated to determine their impact on students. Workshops and tutorial sessions at conferences are used to expand the project’s impact and provide students and enthusiasts with hands-on experience. A two-day training workshop for external faculty further enables dissemination of the modules. 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

Technology translation is the process of converting scientific research and technical innovations to practice. In order to move basic and use-inspired research into society most effectively, it is imperative that innovators and entrepreneurs have access to facilities that enable testing and validation of their new technologies under real world conditions. Such testing requires a safe and controlled environment to ensure the technology is robust, reliable, and ready for use. National “test beds” could include fabrication facilities and cyberinfrastructure to advance the development, operation, integration, testing, deployment, and, as appropriate, demonstration. This effort supports a workshop series facilitating conversations among critical test bed stakeholders from academia, industry, government, and non-profits. The stakeholders offer their unique perspectives on strategies and models for designing and using test beds to scale up technologies and accelerate the translation of innovations into the marketplace. This workshop series will provide opportunities for open dialogue about opportunities and challenges of bringing emergent technologies to practice using test bed facilities. Topics include lessons learned from existing test bed efforts, novel operational models, gaps in existing infrastructure, and how to expand access to physical and virtual resources, investment, and multisector collaboration. The workshop series brings together stakeholders to share community-wide perspectives in a large number of technology fields – from advanced communications to biotechnology and materials development. The workshop deliverables will include at least one white paper that will capture the conversations at the sessions. The workshop series includes virtual events and in-person sessions hosted by Iowa State University, the University of Alabama, and the University of Michigan in Spring 2026. 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

This project will provide a fellowship to an Assistant Professor and training for a graduate student at the University of Alabama. This work will be conducted in collaboration with Drs. Natalie Griffiths, Marie Kurz, and Saubhagya Rathore at the Department of Energy Oak Ridge National Laboratory (ORNL). Through the fellowship, the principal investigator will collaborate with ORNL’s Watershed Dynamics and Evolution Science Focus Area research team to represent streamflow using a state-of-the-art hydrologic model, the Advanced Terrestrial Simulator (ATS). Through this project, the principal investigator and graduate student will gain expertise in generating and interpreting ATS model outputs. Further, the integration of stream chemistry data and advanced hydrologic modeling techniques will result in improved understanding of how headwater stream networks expand and contract in response to intensifying hydrologic disturbance. By developing predictive modeling expertise, the project will advance the knowledge and capabilities that will be required to protect Alabama’s surface water quality, habitat sustainability, and drinking water security. Intensifying disturbance regimes, including amplified duration of droughts, have significant consequences for the functional integrity of freshwater ecosystems. However, we lack predictive understanding of how disturbance regimes will alter headwater stream networks and their resulting function. Such understanding will require both empirical observations of surface water and advanced modeling approaches to fully characterize hydro-biogeochemical processes. Through this project, the principal investigator and a graduate student will collaborate with researchers in the Watershed Dynamics and Evolution Science Focus Area at Oak Ridge National Laboratory. The project goal will be to assess how stream network function, as whole-stream metabolism, responds to dynamic network expansion and contraction. The research team will develop a distributed flow simulation model for a watershed in central Alabama using the Advanced Terrestrial Simulator (ATS). The team will use ATS outputs to test how watershed expansion and contraction drive network function. By gaining a deeper understanding of ATS, this project will advance predictive capabilities in an understudied region of the United States. The project will also strengthen research collaborations between the University of Alabama and Oak Ridge National Laboratory. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

Autonomous systems — such as self-driving cars, robotic assistants in factories, and AI-powered medical tools — are becoming increasingly common in everyday life. These systems are expected to operate safely and reliably, even as their internal components grow more complex. Ensuring this safety is a growing challenge, especially when these systems behave in ways that are hard to predict or model. Traditional approaches based on formal methods can mathematically prove that a system is safe, but these methods often struggle with highly complex or partially black-box systems. This project considers an alternative, practical approach based on monitoring, which checks if a system behaved safely by analyzing its recorded data (logs). Monitoring offers a flexible and scalable approach, especially when traditional analysis tools fall short. However, current monitoring tools still depend on simplified models of the system and often fail when faced with noise, incomplete data, or systems that incorporate machine learning. This project aims to address these limitations by developing new system representations as well as data collection and analysis techniques. The ultimate goal is to make monitoring more reliable, efficient, and applicable to the complex autonomous systems used in the real world. This work enhances the trustworthiness of emerging technologies while contributing foundational methods that can benefit other domains such as robotics, transportation, and healthcare. The project aims to advance both offline and online monitoring techniques to improve safety assurance in complex autonomous cyber-physical systems. Unlike traditional formal methods that require exact models, monitoring can work with approximate system knowledge. Yet, existing monitoring still depends heavily on complex over-approximated formal models, which struggle with scalability. This project proposes hybrid monitoring approaches that integrate formal and non-formal models such as learning-based and Simulink models. It also seeks to develop algorithms for constructing such hybrid models on-the-fly or through static analysis. Further, the project will handle real-world logging uncertainties, such as noisy or missing data, by treating them as core concerns. It will also pursue energy-efficient logging policies and explore dynamic, performance-aware online monitoring strategies. 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

Differential privacy (DP) has been widely accepted as the de facto technique for protecting data privacy. Despite the decade-long research efforts on DP, there still exists a critical research problem that has been largely overlooked, that is all existing DP studies are grounded on the hypothesis that software can easily and faithfully sample and add noises from a probability distribution. However, this hypothesis is being constantly challenged by recent findings about its privacy violation and by the growing demand of privacy protection in low-end devices that may lack high-level software libraries. Hence, this project's innovative research angle is to realize DP mechanisms directly on embedded memories, which are ubiquitous in modern electronic devices. On the technical front, the developed innovation has the following merits. (1) It frees host devices from dedicated software and accomplishes the vision of "privacy by design"; (2) It concurrently improves manifold system performance such as power efficiency, privacy, and chip overhead; (3) The developed technique is primitive, generic, and scalable to every electronic device. This project will also create profound impact on our society, economy, and workforce development. Specifically, the developed technique will be transformative to numerous sensitive applications (e.g., surveillance and sensing) and critical infrastructures (e.g., Internet of Things devices). It will potentially increase the U.S. chip vendors' revenue and competitiveness by adding privacy-preserving functionality to their chips, protect taxpayers' and enterprises' sensitive data, safeguard national security, and help the U.S. out-compete global competitors in cybersecurity. Moreover, this project will help PIs to update existing curricula, engage students for research training, and promote community outreach through existing successful programs such as the NSF-funded RET site in Mobile, Alabama. 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

Collaborative Research: Tectonic Influence on the Greenland Ice Sheet (TIGRIS) The evolution of the Greenland Ice Sheet (GrIS) strongly depends on its underlying geologic structure. Changes to the ice sheet can cause the Earth’s crust and mantle to deform, with the amount of deformation being controlled by variations in the stiffness and thickness of these geologic layers. How the solid-Earth responds can either hinder or enhance ice loss, and this ice-Earth feedback mechanism plays a critical role in determining GrIS behavior. This project aims to evaluate the stability of the GrIS under different environmental conditions by employing an advanced computer model that combines ice-sheet, atmospheric, and geologic constraints. Results from this work will inform estimates of both past and future global sea-level change. Subglacial solid-Earth parameters are largely based on geophysical observations; however, conflicting interpretations of the geologic structure beneath Greenland limit our understanding of GrIS stability. Key portions of Greenland have been under-sampled, and prior studies have often only utilized data from select seismic networks. This project will develop new, self-consistent models of the solid-Earth structure beneath Greenland by combining geophysical observations from multiple networks with those from a new seismic deployment in central-eastern Greenland. Those new solid-Earth constraints will then be incorporated into a state-of-the-art, fully coupled tectonic-atmospheric-cryospheric modeling framework to evaluate the critical thresholds for ice-sheet recovery under different environmental scenarios. Three fundamental hypotheses will be tested: (a) solid-Earth structure plays a first-order role in the long-term future evolution of the GrIS as well as its response to past warming and cooling episodes; (b) under certain projected future warming scenarios, the GrIS will not fully retreat given feedbacks that are controlled by the solid-Earth structure; and (c) the interplay between different feedback mechanisms will result in at least partial ice-sheet recovery, and the GrIS will be resilient in the long term (10-20 kyrs). 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

Carbohydrate Memristor Empowered Environmentally Sustainable Processor-in-Memory Nontechnical description: Artificial intelligence (AI) systems have profound influence on societal wellbeing of humans and fueling significant economic growth. However, operation of conventional computing architecture in AI systems as well as manufacturing and disposal of conventional computing devices lead to significant energy consumption, depletion of nonrenewable natural resources, and ecological deterioration. Therefore, a serious concern of environmental sustainability to such increasingly pervasive computing systems has been raised, and improvements in system performance, energy efficiency, and ecological friendliness require new devices and systems. In this project, a new environmentally-sustainable processor-in-memory system is proposed to benefit the entire computing community including mobile and wearable computing, cloud computing and data center, electronic sensing and controlling, communication and networking. This project will also contribute to the development of high-quality workforce skilled in design, fabrication, testing, and modeling of memory devices and processor-in-memory computing systems for the growing needs in the US. The students and postdoctoral scholar participating in this project will receive unique training in engineering problem solving and technology development, and their research and educational experience will be enhanced by complementary expertise and close collaboration between the two research groups. Technical description: Processor-in-memory systems implemented with memristors have great potential to perform complex AI computations faster and on a smaller footprint. The goal of this project is to address the environmental sustainability challenge in computing by developing a novel brain-inspired processor-in-memory system empowered by memristors made from carbohydrate materials for energy-efficient operation, renewable material resource, sustainable device manufacturing, and ecologically-friendly disposal. The carbohydrate materials will be naturally extracted from plants, vegetable, and fruits with low cost and waste generation. Innovative fabrication techniques for carbohydrate-based memristor and processor-in-memory system will be developed with reduced water use and chemical waste, greenhouse gas emission, and manufacturing related energy consumption. The processor-in-memory system will be constructed by implementing carbohydrate memristors and reconfigurable peripheral circuits to achieve sustainable computing, enable performance improvement, and ensure high operation efficiency, longevity, and reliability. 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

Generative AI, such as ChatGPT, along with AI image and video generation platforms, have recently taken the world by storm. However, recent studies have revealed that running AI engines consumes a staggering amount of energy. Neuromorphic systems, which utilize memristors and crossbar arrays, leverage Computing-in-Memory (CIM) technology for AI computation, demonstrating significant improvements in energy efficiency. The goal of this project is to investigate memristor-based CIM for neuromorphic systems from the perspectives of design, optimization, and fabrication. This project offers a unique opportunity for the principal investigator (PI) from the Department of Electrical and Computer Engineering (ECE) at the University of South Alabama (USA) to establish a long-term collaboration with the School of ECE at the Georgia Institute of Technology (Georgia Tech), thereby enhancing the PI’s research capabilities. This collaboration will not only broaden the PI’s research scope but also greatly benefit his career trajectory, ultimately contributing significantly to his home institution and jurisdiction during and beyond the two-year award period. This fellowship provides the PI with an excellent opportunity to explore novel AI computing systems from a hardware perspective, thus steering his research toward transformative new directions. It will also significantly contribute to the economic and technological development of this EPSCoR-eligible jurisdiction. The PI will share the knowledge and resources obtained from Georgia Tech with his colleagues and the broader community, thereby raising the overall research and educational capacity and competitiveness of his institution and jurisdiction. Based on the knowledge acquired at Georgia Tech, the PI will also organize workshops and seminars at his home institution and jurisdiction to foster broad collaborations and maximize the benefits of this fellowship. The enhanced knowledge gained through this fellowship will serve as a crucial link, connecting scientific research with practical applications in AI hardware design, and facilitating the development of future novel computing 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 · 2025-12

Current computing systems are facing significant challenges including extremely high energy demand, tremendous consumption of nonrenewable materials, environmental and health issues by electronic waste, and lack of technologies to improve system performance by integration of complementary metal oxide semiconductor (CMOS) microchips with emerging device technologies (denoted X) – “CMOS+X”. This project will address these challenges by exploring an innovative technology to integrate CMOS periphery circuits with a novel memory technology - natural organic honey based resistive switching random access memory (honey-ReRAM) for a new brain-inspired neuromorphic computing system. The prototyped “CMOS + honey-ReRAM” computing system is promising to promote high performance, energy efficient, and sustainable in-memory computing capability for many high impact domains such as engineering, social science, national health, and defense. In addition, the proposed education activities offer unique training opportunities for underrepresented researchers including female, African American, and Native American students. This project targets to explore innovations in device fabrication and system integration technologies to optimize honey-ReRAM devices, establish the feasibility of 3D integration of CMOS circuits with honey-ReRAM arrays, and prototype a CMOS + honey-ReRAM enabled neuromorphic accelerator, an essential neural network hardware component for data processing in a vast range of devices in computing and artificial intelligence. The honey-ReRAM will have highly reproducible memory characteristics, thermal stability, long-term reliability, low-cost, as well as being sustainable. The honey ReRAM will also utilize an eco-friendly synthesis process and device manufacture. A novel three-dimensional (3D) architecture and fabrication technology with a formal design flow will be developed to ensure the compatibility of combining CMOS circuitry with honey-ReRAM arrays by a heterogeneous integration on the microchip. Furthermore, this research will provide a solution to the incompatibility problem in the integration between CMOS and X and effectively accelerate the development and application of CMOS+X technologies for system-level improvements in computing. This project is jointly funded by the Software and Hardware Foundations (SHF) program in the Computing and Communication Foundations (CCF) 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 · 2025-11

Nitinol is a remarkable binary alloy composed of nearly equal parts of nickel and titanium, with unique properties, such as superelasticity and shape memory. The alloy's distinct characteristics make it invaluable in various fields, including biomedical devices, aerospace engineering, and consumer electronics. However, micromachining Nitinol presents challenges due to its high strength, rapid work hardening, and susceptibility to thermal damage, which can alter mechanical performance. This research aims to challenge the status quo that Nitinol laser cutting is a relatively slow process by seeking to formulate a data-driven quantification of the mechanics behind fluid flow dynamics and microstructural alterations during processing. With laser micromachining, the photon beam interacts with the material, inducing significant heat accumulation, material melting, and subsequent fluid flow, which significantly influences the quality and integrity of the final product. This research looks to unravel the dynamics of melt flow, heat generation, and temperature distribution in real time during Nitinol laser cutting, all of which are crucial aspects of realizing the microstructure-process-property relationship. Additionally, this research will be complemented by the following educational outreach activities, providing learning opportunities for students: “Introduction to Engineering for High Schoolers” and “Engineer for a Week”. In summary, the results provided by this research aim to reduce the cost of life-saving medical device treatments using Nitinol worldwide. This research targets the underlying fundamental science surrounding the relationship between laser cutting melt flow dynamics and the resultant microstructural evolution to answer the following research questions: (i) Is there a difference in the melt ejection velocity between a reactive assist gas and an inert assist gas that can demonstrate variances in melt flow dynamics, (ii) Will increased ejection velocities reduce thermal gradients, subsequently reducing microstructural alterations, (iii) With reduced microstructural alterations, is mechanical performance preserved? Overall, it is hypothesized that when a melt-dominant laser cutting process is combined with an exothermic reactive oxygen assist gas, the oxygen combustion reaction will aid in expelling the melt pool away from the proximity of the laser cut region, resulting in higher cutting rates and lower thermal gradients, reducing conditions conducive to microstructural alterations. As a result, the integrity of the grain structure will be preserved, enabling enhanced material quality, improved performance, and eliminating the need for additional post-processing, all while achieving faster cut rates. The overarching focus of this research will be in-situ analysis of thermal gradients during cutting, high-speed imaging of melt flow behavior, and advanced microstructural characterization post-cutting. 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

Offshore wind energy is an emerging, safety-critical sector facing persistent workforce challenges in building and sustaining a skilled workforce. These include limited early exposure to real-world work environments, costly and risky site access, unclear career pathways, and rapidly evolving digital and automation technologies. These challenges reduce interest, slow skill development, and make it harder to retain workers. By coupling cohort-based mentoring, cross-sector partnerships, participatory co-designed modules, micro-credentials tied to industry skills, and shared digital assets, the project will broaden participation and build a prepared, resilient, and technologically agile workforce. The resources and training model developed through this work can also be applied to other maritime and emerging technology fields with analogous workforce challenges, helping to grow the U.S. workforce and increase access to science, technology, engineering, and mathematics careers. This project will pilot a multi-phase offshore wind experiential learning framework led by an interdisciplinary team with complementary expertise from Northeastern University, the University of Alabama, and a range of industry, educational, and public sector partners. It will support community college and engineering students through low-cost, immersive at-home training that includes personalized coaching powered by artificial intelligence, followed by a supplemental certificate program and co-op or internship opportunities for students who demonstrate strong interest. The program features a structured, modular curriculum aligned with industry needs; a cohort-based model that fosters peer support, reflection, and iterative improvement; and continuous mentorship from the project team, industry professionals, and previous cohort participants to support both learning and career exploration. The program will track participants’ readiness, learning outcomes, and retention to support an iterative co-design process that refines training modules and credentials. This project offers scalable early exposure to offshore wind careers, technical skills, and worksite challenges, preparing students for success in the field. The work aligns with ExLENT’s mission by creating scalable and data-driven experiential learning pathways for emerging technologies and by openly sharing its tools and outcomes for broader impact. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This 3-year IRES Track I project recruits three cohorts of U.S. students to conduct research in China, with the major research goal as developing, fabricating, and testing an ultra-low power application-specific deep learning integrated circuit, and evaluating its performance through the integration with physical Internet-of-Things (IoT) edge computing devices. It brings together three research groups with unique expertise from University of Arkansas (ultra-low power asynchronous circuit design), University of South Alabama (context-aware memory design), and Peking University, China (deep learning algorithm development and optimization). The expected research outcomes will accelerate edge computing for a large variety of IoT applications such as advanced medical and elderly care systems, and self-driving vehicles. Each year six U.S. student participants work onsite at Peking University for eight weeks, leveraging the onsite research facilities. The multicultural, multidisciplinary nature of this project provides a unique training and career preparation opportunity for the participating students, including multidisciplinary discussion, teamwork, effective communication, and technical writing. The PIs continue their prior efforts in recruiting student participants from underrepresented and minority groups, leveraging their contacts and the existing mechanisms at each university. The research outcomes and the student experience will be disseminated nation-wide for benefiting the research community and encouraging more students to participate in similar programs. Deep learning is transforming many modern Artificial Intelligence (AI) applications, in many of which deep learning has begun to exceed human performance. However, the superior performance of deep learning comes at the cost of extremely high computational complexity associated with large datasets. Therefore, deep learning algorithms are traditionally implemented in software and executed on powerful general-purpose cloud computing platforms. In contrast to the prevailing research in general-purpose counterparts, the application-specific deep learning IC has much lower power consumption, thereby ideal for integration with power-constrained IoT devices. This IRES project is to develop, fabricate, and test an ultra-low power deep learning integrated circuit (IC), and evaluate its performance through the integration with physical IoT edge computing devices. Technical innovations to be developed by the student participants include: 1) optimization of application-specific deep learning algorithms for alleviating the requirements of hardware implementation; 2) delay-insensitive asynchronous circuit design for substantially improved energy efficiency; and 3) context-aware memory development for power savings and low implementation cost. This project uniquely connects deep learning algorithm optimization, asynchronous circuit design, and memory optimization together to achieve a highly optimized system, which will benefit the semiconductor and AI societies at large by the revolutions in hardware-tailored deep learning algorithms and specialized computing hardware. It is expected that this research will demonstrate the advantages of application-specific deep learning hardware and layout the foundation of a new and promising direction for both academic research and industrial development. This project is jointly funded by the Office of International Science and Engineering (OISE) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This renewal Research Experience for Teachers (RET) site at the University of South Alabama will support middle and high school educators in an interdisciplinary research experience in bio-inspired computing systems. Through cutting-edge research projects and the development of standards-aligned curricular materials, the site will enhance teachers’ knowledge of and pedagogical skills in bio-inspired computing systems. The highly structured research projects span emerging technology applications such as data imaging, Artificial Intelligence (AI) algorithms, and microelectronics hardware. The project also includes a “teachers mentoring teachers” component, in which prior participants will serve as mentors and teacher leaders in the research training, curricula development, and classroom implementation. In addition, the site builds upon successful partnerships with local school districts, research facilities, and AI industry leaders to provide a holistic experience of emerging technology pathways in Alabama. Through this RET renewal site at the University of South Alabama, 27 middle and high school teachers will participate in a six-week summer research program to enhance their knowledge of and pedagogical skills in teaching bio-inspired computing systems. Technical innovations to be developed by the RET participants, as well as the research team, will include: (i) novel imagery collection techniques and image processing algorithms for cancer detection; (ii) new AI algorithms to classify and visualize the collected imagery data; and (iii) new memristor device-based bio-inspired system design for enhanced efficiency. The summer program will also include immersive curriculum development activities. Extensive follow-up activities will be offered in the academic year through on-site visits, follow-up workshops, and support community to ensure the translation of RET experience into classroom instructional practice. This project will empower a workforce of teachers with knowledge in bio-inspired computing systems and transdisciplinary teaching methods, which will enable them to prepare their students for the fast-paced digital era. 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

Using Artificial Intelligence (AI) currently requires access to the internet and very large and complex remote computers for making decisions and predictions. This causes long delays and privacy and security concerns. The latest techniques in AI, known as “Edge AI”, avoid these problems by collecting and analyzing data directly on cameras, smart phones, and wearable devices. However, Edge AI is still in its infancy and there are several important technical problems that need to be solved. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award is a collaboration between six universities (including two minority-serving institutions) and several private-sector partners in Alabama, Arkansas, and North Dakota. As a test of the project's new technology, the project team will build a smart wearable device to predict the onset of diabetes by monitoring a patient's own breath without the need for a doctor to interpret the results. It will provide research training opportunities for advanced college students and will also train high-school teachers in lessons to educate their own students in the principles of Edge AI to seed the future US workforce in these essential concepts for tomorrow’s world. The goal of this RII Track-2 FEC award is to develop integrated research infrastructure and workforce in Edge AI. Fundamental contributions and technical innovations to be developed by the team include: (i) light-weight AI-empowered reasoning and machine learning algorithms for edge platforms; (ii) a new Application-Specific Integrated Circuits (ASIC) design methodology to enable AI ASICs with ultra-low power, reconfigurability, and short development cycles; (iii) a sensor device platform for Edge AI based on novel functionalized nano-scaled sensing materials with nano-3D printing techniques; and (iv) an Edge AI device platform exploiting the previous advances to meet the requirements of different use cases. Based on the developed infrastructure, targeting the use case of diabetes care, the team will design, prototype, and test a low-cost smart wearable device for personalized diabetes management. The developed wearable diabetes device will enable significant cost reduction and high power efficiency compared to existing techniques. The leading institution is the University of South Alabama; the collaborating institutions are North Dakota State University, the University of Arkansas, the University of North Dakota, Alabama A&M University, and Nueta Hidatsa Sahnish College. The team will work closely with multiple industry partners to adopt and adapt the developed Edge AI infrastructure in different use cases. Research outcomes of this project will accelerate the development of Edge AI and will increase the competitiveness of the United States in AI. Also, this project will integrate research, education, and workforce development in order to provide effective training at multiple levels. The project will develop an Education-to-Workforce Pipeline from high school to undergraduate, graduate, Post-Doctoral training, junior faculty, and industry practitioners. 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

In a collaboration between the NSF Eddie Bernice Johnson INCLUDES Initiative and the NSF Research Experience and Mentoring (REM) programs, EArly-concept Grants for Exploratory Research (EAGERs) are awarded under NSF Dear Colleague Letter 24-062 to broaden participation and develop the workforce in microelectronics through research experiences and structured mentoring. This project is developing a scalable, replicable model for microelectronics research and clean room training through Virtual Reality (VR)/Augmented Reality (AR) and experiential learning. Access to specialized facilities and equipment for microelectronics research is limited in many parts of the United States. The model that results from this project is expected to transform student training at universities that do not have access to clean rooms. An iterative AR/VR development process and summer research program, are being used to advance learning outcomes and training in microelectronics and semiconductor manufacturing. This project promotes research and “innovative approaches to developing, improving, and expanding evidence-based education and workforce development activities and learning experiences at all levels of education in fields and disciplines related to microelectronics,” in alignment with the CHIPS and Science Act of 2022. Funding for this project is provided by the NSF Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national need for improved mathematics teaching and learning in elementary schools. Cultivating Elementary Mathematics Specialists (CEMS) will achieve this goal by preparing and supporting a cadre of Elementary Mathematics Specialists (EMSs) who are highly effective mathematics teachers and teacher leaders. Participating elementary teachers will engage in sustained, intensive, and evidence-based professional learning, endeavoring to develop mathematical content knowledge, pedagogical knowledge and capabilities, productive beliefs, and leadership skills of an EMS. As EMSs, they will provide effective mathematics teaching grounded in research-based ambitious practices, use effective coaching practices, and engage in teacher leadership. The improved learning experiences offered to students should bolster their mathematics proficiency and confidence. The EMSs' leadership will take various forms, such as coaching novice teachers or teacher candidates, providing professional development for teachers, and sharing resources with family and community members. This varying teacher leadership with different audiences aims to foster wide-reaching change in mathematics education. The EMSs' coaching of novice teachers and teacher candidates has the potential to strengthen teacher induction and the hiring pipeline. This project at The University of Alabama includes partnerships with four school districts, Tuscaloosa City, Jefferson County, Montgomery County, and Tuscaloosa County, and two non-profits, the Association of Mathematics Teacher Educators and Ed Farm. CEMS has three goals: (1) increase the number of EMSs in high-need school districts; (2) prepare and support EMSs who are highly effective mathematics teachers and teacher leaders; and (3) support EMSs as they serve as teacher leaders, including effectively coaching novice teachers and teacher candidates. Over the next 6 years, 30 elementary teachers will be developed as EMSs in the districts, teaching and directly impacting ~19,500 students and coaching at least 150 teacher candidates or novice teachers. The EMSs' leadership will directly impact numerous educators, school leaders, and family and community members. Partnerships between multiple organizations are critical, aiming to develop collective responsibility for improving the professional development of teachers, the preparation of teacher candidates, and students' learning. Grounded in conceptual framings and research on the preparation and support of EMSs and mathematics teacher education and professional development, participants will complete university graduate courses and engage in professional learning communities and individual mentoring. Mixed methods will examine the influences of CEMS, investigating changes in participants' mathematical knowledge, beliefs, and instructional and coaching practices, as well as participants' actions and influences as teacher leaders. On-going dissemination of CEMS will target various audiences and outlets, advancing knowledge in the fields of mathematics teacher education and professional development, particularly of EMSs in high-need schools. This Track 3: Master Teaching Fellowships project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This 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 Designing Materials to Revolutionize and Engineer our Future (DMREF) joint NSF-Department of Science and Technology of India (NSF-DST) project aims to establish a transformative framework for the development of structural alloys that simultaneously achieve high strength at high temperatures and enhanced ductility at room temperature. The research focuses on a relatively new class of metallic materials known as refractory multi-principal element alloys (RMPEAs), which are recognized for their high-temperature strength but typically suffer from limited plasticity under ambient conditions. The team will develop the new alloy design paradigm through a concept called “metastability engineering,” which activates novel nano-scale deformation mechanisms by controlling dislocation dynamics and phase stability. The research integrates combinatorial synthesis, advanced in-situ experiments, atomistic and mesoscale simulations, and machine learning (ML)-guided discovery. The resulting framework will enable accelerated design of high-performance RMPEAs across broad temperature ranges. In parallel, the project will contribute to training a new generation of materials scientists in experimental, computational, and data-driven methods, while supporting outreach and international collaboration through partnerships with five US universities and Indian Institute of Technology Bombay. This project aims to establish a transformative framework for metastability engineering in refractory-type multi-principal element alloys (RMPEAs) that combines high-temperature strength with improved room-temperature ductility and strain hardenability. This project will address two key technical thrusts: (1) understanding dislocation dynamics for solid-solution strengthening at both room and high temperatures, and (2) enabling nano-scale transformation-induced plasticity (nano-TRIP) and twin-induced plasticity (nano-TWIP) mechanisms for enhancing ductility at room temperature. To navigate the vast composition and processing space, the team will integrate combinatorial synthesis, high-throughput and autonomous mechanical testing, and advanced machine learning techniques to accelerate the discovery of high performance RMPEAs. In the first thrust, the project will quantify the contributions of dislocations to high-temperature strength through autonomous nanoindentation creep testing, in situ neutron diffraction, and atomistic simulations. Advanced microscopy techniques will be used to reveal how local chemical ordering and lattice distortion affect dislocation motion. In the second thrust, the team will identify composition-processing pathways that promote metastable deformation modes using thermodynamic modeling, combinatorial deposition, and transformer-based machine learning models. These models will predict TWIP/TRIP propensity and guide multi-objective optimization across large alloy design spaces. Down-selected alloy systems will be validated through multiscale mechanical testing and simulations that span atomic to bulk scales. Collectively, the project will deliver a mechanistic foundation and data-driven design tools for metastability engineering in RMPEAs, aligning with the DMREF project’s mission to accelerate materials innovation through the integration of theory, experimentation, and data science with closed-loop design cycles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The rapid expansion of artificial intelligence (AI) infrastructure across the United States presents challenges for water resource management. AI infrastructure requires a large water supply for cooling. This project will develop three new AI models for water resources. The models will identify where water resources exist that can reliably support the growth of AI infrastructure. First, a large-scale AI model will provide insights for water availability to choose sites. Second, digital twins will be created to reveal hazards that may disrupt local water supply. Finally, an AI method will help predict impacts of wastewater and thermal pollution. The broader impacts include practical guidance on water management and sustainability. The project’s primary scientific goal is to develop a multi-scale hydrologic modeling framework that integrates physics-informed AI and hierarchical digital twin technologies to inform water management for AI infrastructure development. The project consists of three main technical components for analyzing water resource sustainability and identifying optimal sites for AI infrastructure. First, an AI-driven hydrologic model will analyze geospatial data across the contiguous United States to identify regions with adequate water resources. Second, digital twins will be created for selected sites, enabling scenario analysis to understand the potential impacts of natural hazards and water availability fluctuations. Third, a fractional-calculus-based modeling framework will be developed to assess environmental outputs associated with data centers. The outcomes include novel methods for large-scale water resource analysis, new AI algorithms tailored specifically to geoscience applications, and practical guidance on environmental management strategies. The research team includes experts in hydrology, AI, and applied mathematics, supported by collaborations with national labs and water management agencies. 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.