Texas A&M Engineering Experiment Station

NSF Awards · FY 2025 · 2025-08

This Faculty Early Career Development (CAREER) award supports research into how the movement of the uterus, called peristalsis, affects the cells in its inner lining. Many cells in the inner lining grow and shed in sync with this movement. The mechanics of the uterus may be key to this process because all cells in the body sense and respond to mechanical forces. Abnormal uterine movements may change growth rates and the immune environment, leading to problems in the uterus. Using a special device to apply uterine mechanics to different cells in the inner lining to investigate how these cells sense uterine movements and convert them into responses like growth, migration, and immune activation. The insights gained are likely to help better understand women's health issues like endometrial cancer, endometriosis, and adenomyosis. The project also includes educational programs. This combined research and education effort will help close gaps that exist in women's health research, engineering, and technology. This award will create new fundamental knowledge on how different cells in the uterine endometrium transduce the mechanics associated with uterine peristalsis using the peristalsis bioreactor, a device capable of applying mechanical patterns associated with uterine peristalsis to several types of cells in the uterine endometrium (endometrial cells, macrophages, etc.). The research team will isolate and experimentally interrogate if the mechanics of uterine peristalsis (or dysregulation thereof) results in endometrial cell motility or invasiveness, and the amplification of macrophage inflammation. The research approach integrates bioreactor methodology, gene expression and protein localization assays to investigate mechanobiological signal transduction pathways that contribute to aberrant cellular behavior in the uterine endometrium. The mechanobiological insights gained from these studies will advance the understanding of many conditions that impact women’s health outside of pregnancy like endometriosis, adenomyosis and endometrial cancer. In parallel, the research and integrated educational objectives plan to create programs across learning spheres in K-12, higher education, and community learning spaces to improve awareness of women’s health 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 · 2025-08

This project investigates how artificial intelligence can overcome current challenges in designing biosensors based on two-dimensional materials, moving beyond laborious trial-and-error methods, while simultaneously enabling human experts to acquire new knowledge in this domain. Rational design for these crucial devices faces four primary challenges: insufficient data due to expensive experimentation and inherent measurement uncertainties; the absence of accurate theoretical models to capture complex biochemical interactions; the constant demand for rapid responses to emerging needs (e.g., new pathogens); and the necessity for transparent, data-driven design approaches in high-stakes biomedical applications. To address these issues, we propose a “white-box” data-driven design and knowledge discovery framework that integrates interpretable machine learning, data fusion, and statistical inference. This methodology seeks to accelerate on-demand biosensor development through a “design-by-learning” paradigm and enhance expert capabilities to address new demands by providing interpretable insights derived from the design process (“learning-from-design”). This project looks to advance biosensor design, promote national healthcare readiness, and support STEM education through novel research and practical applications. The developed design methodologies are expected to generalize beyond biosensor development to broader areas, including advanced materials design. This research looks to spearhead studies of AI trustworthiness in data-driven design for high-stakes applications, shedding light on how model transparency impacts design cognition and performance. The core objective of this project is to establish a “white-box” data-driven design framework for 2D material-based biosensors, emphasizing model transparency and quantifying its influence on designers’ knowledge acquisition, perception, and overall performance. To achieve this, the research looks to develop and implement several key innovations: (i) the development of novel methods for discovering analytical relationships between biosensor properties and performance, even when confronted with highly uncertain experimental data; (ii) the creation of interpretable multi-fidelity modeling approaches designed to distinguish between generalizable and non-generalizable influences of design variables, thereby guiding reliable extrapolation to new design scenarios; and (iii) design studies to quantify how model transparency impacts designers’ cognitive processes, knowledge acquisition, and performance, thereby advancing our understanding of the practical importance of transparency in data-driven design. This research framework will undergo rigorous evaluation through its practical application in designing biosensors for various biomarkers. The developed design interfaces will be showcased in dedicated workshops aimed at disseminating findings, training new users, and gathering valuable feedback to quantify the impact of this research and further refine the 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 2025 · 2025-08

PART 1: NON-TECHNICAL SUMMARY Polymeric materials with ionic groups stand to make important impacts in applications ranging from therapeutics to water purification and reprocessable plastics. However, because they are extraordinarily sensitive to changes in pH, salt, and temperature, it remains challenging to control the formation, performance, and properties of these materials. This project examines polymeric materials that contain positive and negative charges, which assemble into larger structures called complexes. One of the defining features of these complexes is their ability to separate into solid and liquid phases during the assembly phases. There is an important knowledge gap in understanding how these solid-liquid boundaries respond to temperature, other types of molecular interactions (hydrogen bonding), and polymers with mixed charges. This project seeks to answer what molecular factors control the phase boundary and why does this boundary exist; how the molecular motions of the polymers in the material vary with assembly conditions; and how these materials can be translated to analogous coatings. Because complexation is influenced by a multitude of factors intrinsic to the polymers themselves, model systems are selected to isolate the effects of these factors. The technological broader impact will be the expanded knowledge of charged polymer materials’ characteristics for assembly, processing and stability. The project will include outreach to the general public through TAMU’s Chemistry Open House, 4-H youth development of the Brazos Valley, and other venues. PART 2: TECHNICAL SUMMARY This project will experimentally investigate and compare the phase behavior of polyelectrolyte complexes (PECs) and polyelectrolyte multilayers (PEMs) using different polyelectrolyte systems that have tunable hydrogen bonding or zwitterionic charge to systematically isolate the contributions of these features. Better understanding of the factors that influence the phase behavior of polyelectrolyte complexes and their similarities with polyelectrolyte multilayers is important for the future design of these assemblies for more widespread applications. Most experimental and theoretical investigations have focused on electrostatic factors with varied success, but there is a knowledge gap in exploring non-covalent interactions and temperature effects. To address this knowledge gap, the project is organized into three objectives, centering around complexes and multilayers made from synthetic polycations, polyanions, and polyzwitterions. The first objective is to probe the diffusion of polyelectrolyte chains in solid and liquid complexes using fluorescence recovery after photobleaching technique. The second objective is to observe solid-liquid-solution phase boundaries and their respective upper critical and lower critical solution temperatures using optical microscopy, turbidity measurements, and isothermal titration calorimetry. The third objective is to compare polyelectrolyte multilayers and complexes in solid and liquid regions of the phase map by monitoring multilayer growth and deconstruction at a variety of conditions including pH, ionic strength, and temperature. Swelling of the multilayer under different conditions post-assembly will also be examined. Altogether this work aims to reveal the nature of the liquid-solid phase boundary in complexes at varying temperatures and if that boundary corresponds to the nature of multilayer growth. . This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The objective of this project is to establish and operate an Interdisciplinary Network of Networks for Well-being in the Built Environment (IN2WIBE 2.0) to advance research where human health, building design, and performance intersect. To move beyond siloed efforts, a unifying platform is critical to connecting diverse perspectives and translate them into integrated, scalable solutions. Building on the success of IN2WIBE 1.0, which engaged engineers, architects, and designers, IN2WIBE 2.0 seeks to expand to include cognitive scientists, neuroscientists, environmental psychologists, ergonomists, healthcare professionals, and others whose joint efforts are essential to understanding and improving humans’ well-being in the built environment. By integrating 10 core networks and 21 supporting partners, this network of networks aims to catalyze cross-disciplinary collaborations, support evidence-based design, and inform future standards and policies for healthier, more resilient, and human-centric buildings and livable communities. It embarks on a suite of coordinated activities, including TEDx events, Pecha Kucha research exchanges, roundtables, and thematic workshops. The project looks to raise awareness of the importance of healthy built environments, educates building occupants and professionals, and supports long-term improvements to public health and quality of life. IN2WIBE 2.0 seeks to generate new scientific knowledge by advancing understanding of how individuals perceive and process spatial features, multisensory cues, and social affordances in the built environment, and how these shape mental models, risk awareness, and interpersonal interactions. More broadly, it looks to serve as a scientific accelerator that integrates diverse disciplinary insights, from cognitive science to engineering to public health, to develop holistic frameworks, methodologies, and tools. Focusing on health, comfort, and work performance, the network intends to drive innovation that improves productivity, cognition, convenience, and well-being across populations and contexts. 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

Recent major structural damage, economic losses, and societal disruptions due to hurricane, tornado and downburst wind hazards highlight the need for improved resiliency of low-rise structures in coastal communities. This research will look to estimate wind loads on a variety of low-rise structures by establishing a machine learning (ML) framework to complement physical wind tunnel testing. Due to high costs of physical experiments in laboratories, peak wind loading on buildings are difficult to quantify using wind tunnel tests alone. Uncertainties in the estimation of peak wind effects on cladding elements can render design provisions unrealistic and cause building envelope failure and water intrusion. The ML framework looks to generate the necessary information for advancing wind design for low-rise building communities. Contributions of the research will provide new knowledge on wind effects at the building level which can help to enhance community resilience and national welfare. These contributions are important for the natural hazards engineering community to develop improved designs for new buildings and innovative retrofitting approaches for existing buildings, thus benefiting society through future damage reduction during windstorms. This award will contribute to the NSF's statutory role in the National Windstorm Impact Reduction Program (NWIRP). The overall goal of this research project is to utilize ML methods informed by large-scale model wind tunnel testing to: (1) provide a quantitative, robust, and cost-effective ML methodology that will enable a deeper understanding of building aerodynamics; and (2) address a major challenge regarding scaling effects on bluff body aerodynamics by predicting wind loads on full-scale real buildings based on data obtained for scaled wind tunnel models via an advanced data-driven technique, known as few-shot learning. The scientific objective of this project is to create, verify, and validate a data-driven framework to accurately predict wind loads on low-rise structures and associated uncertainties, while considering inflow turbulence, geometry, and scaling errors due to Reynolds number violation. This data-driven framework seeks to enable professionals to predict full-scale pressures more reliably on buildings, leading to improved design. The central hypothesis is that with a data-driven ML approach, low-rise building aerodynamics and the associated sensitivity to critical parameters can be better understood, while the need for expensive physical testing or numerical simulations can be substantially reduced. The outcomes of this study will provide an extensive data repository for wind effects on low-rise structures. Wind tunnel testing will be conducted at the Natural Hazards Engineering Research Infrastructure (NHERI) Wall of Wind at Florida International University. Project data will be archived in the NHERI Data Depot (https://www.DesignSafe-ci.org). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award provides funds to support the Workshop on Prospects of 3D Bioprinting for Biophysics Design Platforms and Advanced Manufacturing to be held in the Washington DC area during the early fall of 2025. The workshop will consist of panel presentations and in-depth discussions by leading scholars and researchers. The output from this workshop will be the identification and recommendation of opportunities that push the boundaries of the technology, ensuring its readiness to address the most pressing challenges in biophysical science and biomedical engineering. Topics covered in the workshop are directly relevant to the US Air Force and the advanced manufacturing industry in the country. Further research in this area would generate fundamental, transformational knowledge that enables US Air Force operations to have enhanced sensing and maintain situational awareness. The advanced manufacturing section of the workshop will focus on achieving reproducibility, functionality, and integration with downstream processes for the research community, thereby accelerating advances in the control of complex biophysical systems, biosensors, and sensory systems. The field of 3D bioprinting has rapidly emerged as one of the most transformative technologies in tissue engineering and regenerative medicine. By enabling the precise arrangement of cells, biomaterials, and bioactive factors, 3D bioprinting provides unprecedented control over the creation of functional biological tissues and constructs. These advancements have significant implications for fields ranging from elucidating biophysical mechanisms to drug discovery and personalized medicine, offering new tools for replicating complex physiological systems in-vitro. The workshop will last for two and a half days and dive into two critical areas: platform design and advanced manufacturing. The overarching goals of this workshop are to assemble a group of diverse, world-class scientific leaders to: (1) review the progress to date in the field; (2) address challenges in hardware, software, and bioink development, exploring how innovations in platform design can enhance the precision, scalability, and biocompatibility of bioprinting systems, and (3) delve into strategies for scaling bioprinted constructs for the research community and industrial/clinical applications, with a focus on achieving reproducibility, functionality, and integration with downstream processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

PART 1: NON-TECHNICAL SUMMARY This research proposal will develop the knowledge required to create capsules with liquid core and polymers shells, in which the liquid core is pure, and the properties of the shell can be fine-tuned for a desired application. Capsules are used in a broad range of technologies including those for medicine, food science, energy discovery, textiles and the like. The fundamental issue that the proposed research will address is how to localize precursors to the polymer shell (monomers and initiators) such that the shell is grown around the liquid droplet, essentially shrink wrapping it. To this end, emulsions will be used as a platform for capsule formation, with droplets of one liquid in a continuous phase of the other (the droplet will become the core liquid). The ability to produce such capsules is important for creating new materials that meet societal needs; for example, capsules with a protective polymer shell and core of a salt hydrate solid-liquid phase change material can be used to passively manage heat, so that air conditioning does not have to be used as often. An important component to consider is that for these capsules to be used multiple times, the shell must be strong enough to prevent leakage but also impermeable so that the composition does not change. Notably, this research can be applied to different core liquids and polymer shells relevant to other applications, such as for carbon capture, pesticide delivery, or additive manufacturing, to name a few. Through this research, graduate and undergraduate students will be trained in how to design and implement research; how to collect, organize, and report data; and how to collaborate across disciplines. PART 2: TECHNICAL SUMMARY This proposal addresses the critical need to understand how confinement of initiators and monomers in emulsions impacts the production of capsules and their properties. Limitations to an interfacial polymerization approach are that the liquid core is contaminated and that only a few polymer chemistries can be used, thus restricting composition and performance-related properties such as permeability. To overcome this, the proposed research will (i) use modified particles as both surfactant and initiator for radical polymerization of monomers located in the emulsion continuous phase; and (ii) leverage double emulsions in which the interphase contains monomers to polymerize around the inner droplet. For this purpose, the interface is defined as the zone where two domains touch and the interphase as a self-contained region between two domains (thus flanked by two interfaces).These two distinct approaches are supported by initial results from the PI’s lab and will provide unprecedented control of capsule composition (both shell and core) such that structure-property-application relationships can be defined. The PI has access to all resources and infrastructure required to complete this work, and the research component is complemented by the training and professional development of undergraduate and graduate student researchers, as well as development of a hands-on module to be incorporated into the undergraduate laboratory curriculum. 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 · 2025-06

Emerging memory technologies, which facilitate both analog and digital in-memory computations, have captured the attention of commercial and defense sectors as promising replacements for traditional von Neumann computing architectures commonly used in edge sensors. Over the past five years, numerous publications have illustrated the pivotal role of these advanced memory arrays in supporting intelligent systems capable of real-time learning and swift adaptation to changing conditions. Nonetheless, the adoption of these technologies is hampered by the limited availability of mainstream fabrication processes. Currently, TSMC is the predominant provider, which imposes significant cost barriers for research groups eager to develop and test new circuit designs. This innovative digital twin (DT) is designed to validate new designs and propel research and development in this emerging field. It promises to revolutionize design processes across various technical specifications, including frequency bands, signal-to-noise ratios, and spectrum classifiers. The DT has a scalable architecture that facilitates the integration of extensive library models for memory devices and supports these devices across a broad spectrum of material systems and operational scenarios. The potential benefits of this DT, with sensor technology and computational model for advanced receivers, extends to enhancing national security by providing significant advancements in secure communications and threat detection by improving surveillance, data processing, and decision-making capabilities. This project also seeks to improve STEM education and foster a skilled workforce by deepening students' understanding of integrated sensor systems and digital twin technology. The project incorporates a broad educational effort that creates opportunities for students to engage in cutting-edge research. The technologies developed through these student projects are poised to be highly effective across diverse environments and designed to operate on minimal energy budgets, making them economically and environmentally sustainable. This "Emerging Ideas" research effort strategically focuses on leveraging advancements in digital twin technology to fundamentally transform Intelligent Electromagnetic Sensors’ design, development, and testing processes on a Chip (iEM-SoC). This initiative significantly streamlines and enhances the sensor development process by substituting physical sensor models with digital replicas. Chip-based sensors, rapidly advancing across sectors such as autonomous aerial vehicles, industrial robotics, and consumer electronics, demand high sensor resolution, real-time response capabilities, low power consumption, and extended operational times, typically ranging from 0.5 GHz to 1-2 THz. This interdisciplinary endeavor is set to explore the feasibility of developing a Digital Twin for iEM-SoC and aims to achieve multiple objectives: it will produce a digital twin for the design and performance testing of an Intelligent RF Receiver (EM Sensor) capable of real-time operations with extremely low energy consumption; create the first digital model of an analog memory array that accounts for high-frequency effects; provide the first demonstration of a memory array operating system that enables precise segmentation for storing, accessing, and processing analog data with high accuracy (11 bits); and ultimately showcase how a digital twin can facilitate the development of a new class of EM Edge Sensors that surpass the capabilities of current design methodologies. 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 · 2025-06

This I-Corps project focuses on developing a commanding insole, which utilizes intelligent foot motion recognition technology collected by onboard sensors to prevent freezing of gait and falls. Freezing of gait is the most perplexing and disabling symptom of Parkinson's disease and often leads to falls. As the fastest-growing neurodegenerative disease, Parkinson's disease affects nearly 1 million Americans and 10 million people worldwide. Eighty-five percent of patients suffer from freezing of gait, which increases fall risks and reduces the quality of life and the likelihood of independent living. Commanding insoles can be worn in various home settings to enable processing of large amounts of data in real time. The solution can be potentially expanded to include orthotics, diabetic pressure ulcer prevention, and sports performance monitoring. 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 developing three innovative components: an insole hardware; an onboard, tiny, machine learning algorithm; and a mobile app. The tiny machine learning algorithm and app visualization techniques including, but not limited to, heat maps and bar plots, make more accurate and reliable decisions. Existing monitoring devices have restricted interconnectivity and comfort and cannot identify new or unseen gait freezing events in living conditions beyond their original training context. The training observations constantly gathered by the same patient in the place of inference keeps the classifier accurate, even with the limited size of the dataset. The automatic score scheme correlates highly ranked features with a personalized rehabilitation plan through toe/heel-tapping exercises to mitigate future freezing of gait or fall risks. The new understanding of multi-model sensing and tiny machine learning will advance home monitoring and healthcare. The mobile application manages real-time performance for end-users transparently and in a straightforward manner. Parkinson's patients have validated the ability to monitor disease progression and evaluate fall risk. 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 · 2025-05

This project seeks to accelerate the execution of large graph problems on large, distributed machines, such as those found in datacenters. The graph computations considered appear in computational biology problems (for example, how species evolved), social network analysis problems, and verification of software systems (for example, how to prove that software is correct). These problems have many basic sub-computations in common, which this project will accelerate. The investigators will identify new ways to perform these sub-computations that are more efficient and will conceive new computing hardware that can execute them faster. Our society will benefit because this work will enable solving bigger versions of these problems faster and with less energy consumption. In addition, the project includes an education program that will teach computer science to high-school, college undergraduate and graduate students. The challenges of the graph problems considered stem from both the complexity of the algorithms used and the large compute and storage requirements of many graph problems. To address these challenges, this projects pursues an ambitious, cross-layer effort based on three interdependent main thrusts: new algorithms for graph problems, a core software framework for the efficient execution of these problems, and heterogeneous hardware to provide acceleration to these problems. The first thrust focuses on a few high-payoff algorithmic directions for the application domains considered: graph clustering in both static and dynamic settings; graph construction while preserving important information; and the application of machine learning (ML) techniques. In all these directions, the project uses approximations. In the second thrust, we develop a flexible programming layer that generates efficient code for a datacenter-scale platform. The project introduces a graph programming framework with a novel Domain-Specific Language (DSL) for graphs, high-performance numerical libraries for graph processing with scalable sparse methods, and a smart compiler with two intermediate representations that uses machine learning (ML) techniques. In the third thrust, the project speeds up the execution of graph applications in a large, distributed machine with a novel hardware accelerator. The accelerator features a high-level Instruction Set Architecture (ISA) with instructions that perform sparse matrix operations on tiles. A smart auto-tuner software helps generate and map code to various accelerators and general-purpose engines. The investigators are ten professors at the University of Illinois Urbana-Champaign, MIT, and Indiana University, with expertise in several distinct areas. The work will be done in close collaboration with industrial research groups. 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 · 2025-05

This I-Corps project focuses on the development of computer-assisted prediction tools for the real-time personalized therapy of patients suffering from acute respiratory distress, which are often associated with secondary complications (like ventilator-induced lung injury). To reduce the high mortality rates that are common among such patients, real-time decision-making tools are needed urgently. The solution helps to understand and predict the air flow variations in the respiratory systems of individual patients (e.g., during mechanical ventilation) and diagnosing the level of lung damage (e.g., ascertaining the level of disease progression). The computer model of the lung can be customized for personalized therapy of individual patients. Such assisted therapies include optimization of the mechanical ventilator settings, while simultaneously avoiding unnecessary stress and strain on lung tissue, thus avoiding or minimizing damage to the lungs of these patients. In addition, software prediction tools can be used for training medical students and trainees. 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 digital twins of lungs of patients with acute respiratory distress syndrome. The long-term goal involves the development and commercialization of a virtual lung model for personalized therapy of individual patients. Artificial Intelligence (AI) and machine learning techniques were explored for efficient model order reduction. By leveraging AI, equivalent network representations of lungs can be developed which consists of non-linear flow impedances. The flow regimes range from turbulent flows (in the trachea, which is several centimeters diameter) to laminar flows (in smaller bronchi/ bronchioles and alveoli, which are millimeters to microns in diameter). The model enables real-time personalized therapies of such patients and helps reduce the high patient mortality in acute respiratory distress, which is often caused by secondary complications. This technology produces network-simulation models for predicting patient outcomes and for optimizing ventilator settings. 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 · 2025-05

Machine learning (ML) techniques are demonstrating record-breaking performances on many tasks such as speech recognition, image recognition, composing new documents, and even solving mathematics Olympiad problems. Encouraged by these remarkable results, machine learning-based techniques are now being researched to design better communication systems. An important communication problem is the transmission of sources such as speech, images, and video over noisy communication networks. To efficiently accomplish this task, the source must be compressed and encoded prior to transmission. This project considers the use of machine learning models for joint compression and encoding of sources, i.e., joint source-channel coding (JSCC). One significant drawback of many existing designs of joint source-channel coding schemes based on machine learning techniques is that they use an end-to-end approach based on deep neural networks (DNNs) which hide the underlying operations, and in turn, provide little insight and are less interpretable. The objective of this research is to significantly improve the understanding of the underlying mechanism of machine learning-based solutions, such that they become computationally efficient, less storage-hungry, more adaptive, more robust, and they are easily generalized to complex communication settings. This collaborative project between Texas A&M University and Imperial College, London will offer opportunities for new curriculum development and study-abroad programs for undergraduate students. Techniques developed in this project can inform the development of compression techniques and channel coding techniques for 6G cellular communications. This project will investigate deep neural-network-based solutions to isolate their functional components, which will lead to lower-complexity, more robust, and more flexible designs for future communication networks. The proposed approach is based on comparative studies of the JSCC designs from two related but distinctive perspectives: an information-theoretic perspective and a machine-learning perspective. By studying a sequence of more and more complex vector Gaussian sources and channel scenarios, and by contrasting and comparing the schemes designed using these two different perspectives, the project aims to develop new insights and simplifications of the ML-based JSCC coding scheme designs. The research plan is organized under the following thrusts - 1) Study of signal representations and neural network interfaces in point-to-point communication settings, 2) understanding the sources of performance gains and the mechanism that results in graceful performance degradation in channels with unknown parameters; 3) Study of neural JSCC for feedback channels and multi-user communication settings; 4) Study of generative models in neural JSCC, particularly Gaussian diffusion models. Some of the JSCC schemes will be implemented on a software-radio testbed to demonstrate the effectiveness of the designs in computation-constrained environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

When a strong shockwave passes through the interface between a solid and a gas, the solid can melt and form liquid sheets that are released into the gas. Typically, the solid surface will have roughness features that can cause ripples on the interface, leading to the formation of the liquid sheets. This phenomenon is called ejecta and occurs in several engineering applications and natural phenomena such as semiconductor manufacturing, nuclear fusion and weapons, ballistic projectile impact on an armored vehicle, asteroid impacts and supernovae explosions. Once the liquid sheets form, they can undergo further destabilization and break up into ligaments and eventually droplets. The process leading to breakup of the sheets is poorly understood and will be the focus of this study. The research will address this problem through detailed numerical simulations and detonation tube experiments. The findings from the simulations and experiments will be used to develop a ‘lifecycle’ model that can describe all stages of shock-driven liquid sheet evolution from initial formation, to growth, to fragmentation into ligaments and droplets. The project will support training of students in areas vital to national security. The overall goal of this project is to improve our understanding of the hydrodynamic instability mechanisms that govern the breakup of ejecta sheets in a gas, and the effect of such mechanisms on the size and velocity distributions of ejecta particles. Thus, the proposed work will help us understand the complex physics governing the breakup of ejecta into droplets. This is accomplished by investigating the problem at lower strain rates, so that every stage of the flow can be resolved by our detonation tube experiments and numerical simulations. Insights from the experiments and simulations will feed into the development of a ‘lifecycle’ model that will tie breakup dynamics to early-stage, linear instability physics. Having such an end-to-end model will enable control of liquid sheets in several industrial applications, by controlling the surface corrugations on the solid, so that a desired droplet distribution may be achieved. The breakup model developed will find broad usage as a sub grid model in simulations of impulsively driven sheets and jets. The project will help support the defense and security industries and will provide students the training to fill a vital workforce need in areas of national security. 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

Modern agriculture relies on pesticides to protect crops and maintain stable yields. Developments in nanotechnology are changing the ways pesticides are formulated. Nano-based approaches offer targeted pest control, sustained release, and improved stability compared to conventional products. However, it is important to understand how pesticide-bearing nanoparticles behave once they are introduced into an agricultural setting. This project will investigate the behavior of cyclodextrin-based particles, which is a key class of nanopesticides. Using both experimental and numerical computational approaches, the project will examine how these particles aggregate and attach to relevant materials such as silica and cellulose. The resulting fundamental knowledge will guide agricultural and industrial sectors toward safer and more effective pesticide formulations that protect crops while minimizing risks to waterbodies. Integrated research and education activities will introduce learners to advanced nanomaterial studies, foster practical innovation in pesticide development, and promote informed decision-making that protects vital water resources. This approach will support workforce development and advance more sustainable farming methods. This research applies an integrated experimental and computational framework to investigate how the encapsulation of pesticide molecules within cyclodextrin alters colloidal self-assembly, transport, and adsorption in aqueous systems and solid-liquid interfaces. Systematic studies using dynamic light scattering, nuclear magnetic resonance, and cryogenic transmission electron microscopy will determine how active ingredient loading affects the formation, size distribution, and charge properties of cyclodextrin-based clusters. Parallel molecular dynamics simulations with free energy calculations will reveal atomic-level driving forces for self-assembly and the thermodynamics of cyclodextrin-nanopesticide adsorption onto relevant surfaces, including silica and cellulose. Quartz crystal microbalance with dissipation and atomic force microscopy will characterize interfacial binding mechanisms, adsorption kinetics, and thermodynamic parameters under systematic variations of pH, temperature, ionic composition, and natural organic matter. These findings will inform the rational design of safer and more effective pesticide formulations by connecting molecular-scale architecture and thermodynamics with macroscale metrics. The integrated research and education activities will train students in advanced nanoscale characterization, computational modeling, and data analysis, thereby promoting workforce development in sustainable nanotechnology and agricultural science. 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 three-year REU Site: 3D Printing of NextGen Multifunctional Batteries is hosted by Texas A&M University (TAMU) and partners with the University of Texas at El Paso (UTEP). Undergraduates will engage in 10-week summer research projects featuring additive manufacturing, testing, and modeling of multifunctional batteries (printable multifunctional batteries to power vehicles and autonomy for remote operations). Students will gain hands-on experience in multifunctional batteries, addressing a critical gap in the knowledge and skills needed by the emerging professional STEM workforce. The REU site offers students the opportunity to engage in professional networks, deepen relationships with industry, and leverage the unique resources and support of both universities. Participants will contribute to sustainable energy solutions and influence future technological development. The REU site at UTEP will focus on 3D printing and characterizing multifunctional batteries, and the site at TAMU on synthesizing new active material systems for 3D printing, as well as modeling and performing electromechanical durability tests. Research projects feature multifunctional battery components that are both conformable and structural, with dual-energy storage and load-bearing capabilities and innovative 3D printing techniques used to customize batteries to meet specific design requirements. At the end of the summer, REU students will present their work at a battery development facility in Houston, near the NASA Johnson Space Center. The REU students will develop world-class skills in energy storage for additive manufacturing and scientific research, preparing them for successful potential careers in industry, national laboratories, and academia. This site is supported by the Department of Defense in partnership with the NSF REU program. 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 grant provides funding for travel support for junior researchers wishing to attend a workshop on future trends in computational mechanics for design and manufacturing. The Workshop on Future Trends in Research and Education in Computational Mechanics for Design and Manufacturing, scheduled for 11-13 August 2025, in College Station, Texas, aims to broaden participation from regions and universities with limited access to such opportunities. The selection process will prioritize participants in terms of institutions, and engineering programs. By engaging these participants, the workshop will strive to create a more well-rounded ecosystem in the field of computational mechanics, ensuring that its impact extends beyond well-resourced institutions and that all stakeholders are included in discussions about the future of research and education in the field. Organized in collaboration with the Texas A&M Engineering Experiment Station, this workshop will focus on the rapid advancements in simulation technologies, particularly those driven by Artificial Intelligence (AI) and Machine Learning (ML). It will bring together experts from academia, industry, and national laboratories to explore the impact of AI/ML on computational mechanics and education. Key topics will include promising AI and ML techniques for developing new computational tools, fostering interdisciplinary collaborations to drive innovation, and addressing the data requirements for applying these technologies effectively. Additionally, the workshop will examine how to integrate AI/ML into curricula to better prepare students for future roles in research and industry. It will also discuss how these technologies can be used responsibly, taking into account ethical considerations and their potential societal impacts. The event will feature presentations by rising faculty, discussions at various career levels, and a writing session to create a widely disseminated roadmap for the future of the field, emphasizing the integration of research and education. 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

Space infrastructure plays a critical role in socio-economic development-enabling scientific discoveries and advancing communications, remote sensing, geophysical and astrophysical applications. The exponential growth in the launch of space objects in orbits around the Earth over the last few years has made space more congested and has contributed to increased space debris. Additionally, the increased government and commercial interest in lunar and Mars missions pose both potential benefits and risks to safe and sustainable space operations. The primary goal of the Center for Research in Emerging Sustainable Space Technologies (CRES2T), a partnership between the Pennsylvania State University, Texas A&M University, and Purdue University, is to research novel concepts and associated technologies that enable safe and responsible use of space for humanity. CRES2T researchers will investigate the intricate relationship between hardware and software design, autonomy, artificial intelligence, and modeling and simulation to enable safe in-space assembly, service and manufacturing (ISAM) while addressing the unique challenges posed by the harsh space environment. The secondary goal of this center is to stimulate and train the next-generation workforce in this critical area of national need. CRES2T activities have the potential to impact the new global space economy, create new jobs, and advance our nation’s economic, scientific, technological, and national security interests. The CRES2T activities will focus on an ecosystem where researchers not only conduct research and development of individual technologies related to space operations but also focus on integrating different technological advances in a seamless manner to accelerate transitioning of these advances to commercial entities. The research thrusts and the operation of CRES2T are formulated to address the complex and rapid commercial pulls involved in developing space technology. The Texas Space Commission has made significant investments in the state of Texas with the aim of catalyzing commercial investments and maintaining the state’s leadership in advancing the space economy. Developed using the state investments, the Texas A&M Space Institute will provide an invaluable test facility and emulation testbeds for human and robotic space missions and associated technologies of the future. The Texas A&M site will examine the role of robotic systems in enabling sustainable space operations. It will work closely with other sites (Penn State and Purdue) to test these technologies in a seamless manner and developing the US operational workforce through student internships, annual workshops, short courses, and virtual tutorials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Interactions among entities are fundamental to physical, social, and cyber-physical systems worldwide. In these complex networks, vertices symbolize entities, and edges depict their interactions. Large-scale networks are prevalent in scientific and business applications, such as protein similarity networks with billions of vertices and trillions of edges. As networks continue to grow, there is an increasing demand for algorithms and software capable of utilizing large-scale cyberinfrastructure for analyzing massive networks across scientific domains. This project addresses this need by developing a software infrastructure consisting of foundational algorithms for scalable, portable, and user-friendly graph analysis, ensuring scalability to trillions of edges, optimal performance on heterogeneous infrastructures, and accessibility for domain scientists. This software infrastructure directly enhances vital applications in extreme weather prediction, the discovery of novel proteins, and forecasting energy usage in industrial settings. The project extends the accessibility of these advanced technologies to students at various academic levels. Integration with university courses and initiatives for high school students and teachers in rural Indiana ensures widespread educational impact. A complex network, modeled as a graph in mathematics, reveals intricate topological features encompassing dynamic edges, vertices, and a mixture of static and dynamic ones. Due to such networks' unpredictable and dynamic nature, the independent development of scalable algorithms and software for each application has become prohibitively costly in terms of time, effort, and research funding. This project addresses these challenges by introducing a general-purpose software infrastructure tailored to analyze and learn from complex networks. Users can leverage this infrastructure to expedite a multitude of graph-based applications. Confronting the diversity of graphs and computing platforms, the project employs a flexible two-layer framework. This framework seamlessly maps dynamic graph and machine learning computations to a concise set of sparse matrix operations, followed by the development of parallel algorithms. This linear-algebraic mapping offers a transparent pathway from mathematical algorithm descriptions to sparse-matrix functions, ensuring multiple levels of parallelism, communication reduction, and extreme scalability. Usability, the second challenge in this undertaking, is addressed through a comprehensive set of novel unsupervised and supervised graph algorithms tailored for complex and dynamic networks. Integrating these innovative graph algorithms with massively parallel sparse matrix operations results in a versatile software framework that analyzes complex spatiotemporal systems such as streamflow, traffic flow, and energy 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 · 2025-01

The protection of coastal communities, economic hubs, and infrastructure networks is critical to our national security and long-term prosperity. Natural infrastructure consisting of barrier islands, dunes, wetlands, mangroves, and reefs has been promoted to mitigate coastal flooding and erosion. However, the collective effectiveness of natural elements and how they work together with hard infrastructure as a hybrid system to provide flood risk reduction remain unknown. This project harnesses the unique hydrodynamic, eco-geomorphic, and geotechnical data collected from rapid field campaigns of Hurricanes Laura and Delta (2020) on the Louisiana Chenier Plain to fill this key knowledge gap. The research integrates morphodynamic modeling of coastal wetlands with field-based hydrodynamic, eco-geomorphic, and geotechnical measurements to assess and predict the performance of natural and hybrid infrastructure subject to hurricane impacts. The project will leverage existing outreach programs and regional partnerships to disseminate research results. The research team will share the modeling tools and new understanding developed with the coastal and geotechnical research communities, as well as practicing engineers and resource managers. The project provides unique opportunities to cross-train undergraduate and graduate students in both coastal and geotechnical engineering areas at two institutions and develop both field observation and numerical modeling skills. Effective coastal hazard mitigation requires integrative field observations and numerical modeling to characterize dynamic coastal processes at appropriate space and time scales. This project integrates overland flow and morphodynamic modeling of coastal wetlands with hydrodynamic, geomorphic, ecological, and geotechnical measurements to transform our understanding of the response and recovery of natural and hybrid infrastructure impacted by hurricanes. The project harnesses the unique data collected during hurricanes to (i) advance the understanding of the spatiotemporal variation in overland flow, storm surge, and wave attenuation (flood protection) provided by hybrid infrastructure; (ii) evaluate the efficacy of numerical models to predict coastal erosion and sediment transport across a natural landscape; and (iii) explore the role of geotechnical properties, stratigraphy, and vegetation biomechanical properties in controlling the magnitude of shoreline retreat and vegetation uprooting by hurricanes. The comprehensive field, laboratory, and remote-sensing data collection, complemented by numerical modeling, will identify and evaluate root and soil properties and their roles in uprooting and erosion processes. The novel field testing will enable successful collection of observations that were previously challenging or impossible to quantify. It is anticipated that the integrative approach and modeling tools will be applicable to other coastlines for evaluating natural and hybrid infrastructure performance for coastal resilience. 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 · 2025-01

The Texas Power and Energy Conference (TPEC) is a student-run IEEE technical conference that started in 2017. TPEC 2025 will be held on the campus of Texas A&M University in College Station, Texas, in 2025. This two-day event will bring together participants from industry and academia to present and discuss the latest technological developments and challenges in the area of power and energy engineering. The conference will include paper sessions, keynote speakers, a student poster competition, and an industrial sponsor and job fair. There will also be tours of the Smart Grids Control Room Lab located at the Center for Infrastructure Renewal (CIR) at Texas A&M University. This grant will be used to support students traveling to and attending TPEC 2025. Students will benefit from hearing and discussing new research developments, hearing keynote speakers, networking with industry and academia, and having the opportunity to present their work in a paper or poster. The whole research community in power and energy engineering will benefit as well, by the value and perspectives these students will bring to the event. 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 · 2025-01

This research will study the fundamental relationships that control the assembly of solid materials from many small, shape changing ribbon-like particles using combined and integrated experiments and numerical modeling and simulation. Made of liquid crystal elastomers, these particles can reversibly change from flat, ribbon-like shapes to 3D curved shapes in response to changes in temperature. If many of these particles change shape in proximity to one another, the individual particles are linked through entanglement, thus creating a porous solid with controlled mechanical properties. This porous solid exists only until the temperature is reverted and the individual particles return to their flat state, breaking the entanglement. Typical porous materials are notoriously fragile and difficult to recycle. The use of reversible physical entanglement will enable new ways to extend the lifetime of porous materials through healing and new ways to recycle porous structures. The structural approach to material assembly is applicable to a wide range of other shape-changing materials, like hydrogels. The fundamental principles in this work could be used to design injectable biomaterials. These entangled materials will also serve as powerful tools to demonstrate basic scientific concepts to the next generation of scientists and engineers. This research will elucidate the fundamental stimulus-structure-property relationships that govern a new class of responsive materials, which is derived from the reversible physical entanglement of many shape-changing polymer ribbons. This work will enable porous synthetic materials that self-assemble via macroscopic entanglement on command and have widely tunable mechanical properties and porosity. In this class of materials, changes in temperature will induce a fluid dispersion to assemble into an open-celled porous material with controlled viscoelastic properties. Furthermore, these dynamic, transient solids will have self-healing capability without needing chemical reactions or diffusion. This research includes closely integrated experiments and computational models and will provide fundamental understanding of structure-property-assembly relationships of these materials as a function of the shape change and mechanical properties of the individual polymer ribbons. This research is comprised of three tasks: assemble dynamic aggregates from liquid crystal elastomer ribbons and characterize microstructure and thermomechanical properties of aggregates; construct a theoretical model to fundamentally understand the link between the network statistics and the emerging behavior of the material; and fabricate and characterize aggregates of ribbons that can combine bending and twist deformation. 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

Learning a structural model of dynamic decision-making helps us better understand and predict how agents, whether human or machine, make decisions over time in changing environments. Instead of just copying actions, this approach allows us to capture both the agent’s goals (preferences) and how it understands the world (environment dynamics). This provides a much deeper insight into behavior, enabling predictions about how the agent would act in new or unseen situations. Such models are valuable because they can help improve decision-making systems, allowing them to adapt and make reliable choices in complex real-world scenarios, such as personalized AI assistants, autonomous systems, or decision support tools. There is an urgent need for models and algorithms that can create such structural frameworks. The outcomes of this project will have broad applications, including areas like control systems, natural language processing, and autonomous driving. Moreover, these efforts offer valuable opportunities to enhance the optimization and reinforcement learning curriculum, engaging students from diverse backgrounds in cross-disciplinary research and K12 outreach initiatives. This project develops machine learning models of an agent’s dynamic decisions subject to structural constraints on observed behavior. Specifically, the agent’s observed behavior (data) is modeled as being consistent with the inter-temporal optimization of a reward function (preferences) given a representation of how the environment evolves pursuant to control actions (dynamics). Unlike behavioral cloning models, a structural model of observed control behavior is a solid basis to perform counterfactual analysis and/or transfer learning. However, developing structural models of control is computationally challenging and the statistical properties of structural estimators are not easy to characterize. This project aims to advance the state-of-the-art on methodologies for learning structural models of control, by considering a diverse set of data (including demonstration and preferences), and by considering both online and offline settings. Finally, extensive experiments will be conducted to evaluate and apply the proposed methodologies in aligning large language models (LLMs), and in autonomous driving. 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 · 2024-10

Modern embedded systems rely on hardware as the root of trust and are utilized across industries such as healthcare, transportation, and communication. However, the increase in use and complexity of these systems has led to a rise in security-critical hardware vulnerabilities that can be exploited by cross-layer attacks, disrupting traditional threat models that assume either hardware-only or software-only adversaries. These attacks do not only threaten the reputation of companies and cause monetary damage, but the attacks also undermine the safety, security, and resilience of critical infrastructure in the nation and society at large. Existing hardware validation and verification techniques neither scale to large designs nor achieve sufficient coverage. This project aims to improve scalability and effectiveness of hardware security verification. The results are disseminated through organizing a new generation of Hack@EVENT hardware security competitions and outreach activities at venues such as Grace Hopper Celebration of Computing. The team uses the developed techniques to empower validation and verification efforts both in educational and industrial contexts. Fuzzing, an automated input generation technique, has recently been adapted to hardware security validation. Although fuzzing achieves better scalability compared to traditional validation techniques, existing hardware fuzzing approaches do not achieve sufficient design coverage to provide high assurance. The team develops novel fuzzing techniques through the orchestration of formal verification, symbolic execution, and static analysis in providing guidance for effective input state space exploration. The team also models the patterns of various hardware vulnerability types such as information leakage, denial of service, and micro-architectural vulnerabilities to support fuzzing without relying on golden models or property specifications. This project further automates hardware bug injection. The team leverages their experience in organizing and participating in hardware security competitions and integrating large language model with fuzzing to facilitate bug injection. 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 · 2024-10

Reinforcement learning is a powerful machine learning approach for autonomous decision-making. It has found extensive and impressive recent applications in various domains ranging from core algorithms used in Google AlphaGo to its use in ChatGPT. However, most of these advancements require an abundance of computing resources, including both complex hardware and energy. These requirements are easily met in the context of datacenter computing, but they are often invalid for “edge” devices such as smartphones, embedded computing, and IoT (Internet of Things) systems, where resources are extremely limited. This project will develop a cross-level methodology that enables the computations of state-of-the-art reinforcement learning techniques to run on resource-constrained devices, including the inference and training of multiple neural networks. The proposed methodology and resulting computing platform have the potential to extend the success of reinforcement learning to a vast range of application domains, similar to the impact that GPUs (graphics processing units) have had on large language models. These domains include personal computational platforms, biomedical devices, health assistants for people with disabilities, precision agriculture, smart manufacturing, and many others. As a result, the benefits of this project to human society and quality of life could be significant. In addition, the research results will be incorporated into workforce development efforts. The proposed methodology involves innovation and co-design of techniques spanning different levels, including devices, circuits, system architectures and algorithms. The central idea is an innovative integration between a novel approximate computing circuit technique and flash device-based computing, each of which offers at least one order of magnitude improvement in computing efficiency compared to conventional circuit implementations. To address the limited write cycles of flash devices, the algorithmic level approach adopts the meta learning framework, which allows few-shot online adaptation training. Moreover, the use of the approximate computing technique provides fault tolerance and graceful precision degradation. At the architectural level, neural network models and their mapping on hardware will be co-designed for further resource efficiency improvement. Another essential co-design element in this methodology is the co-optimization of hardware parameters and algorithmic parameters. Both types of parameters impact the tradeoff between computational accuracy and cost, but they do so in different ways, requiring joint optimization. The proposed methodology will be used to design an overall system, which will then be validated through a silicon prototype and a robotic control system. The research outcome will significantly advance the knowledge of reinforcement learning computing on resource-constrained edge devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This grant will support research to generate new knowledge related to mechanical resonators, promoting the frontiers of technology and advancing national prosperity. Examples of mechanical vibrators include communication devices, vibration isolators and energy harvesting devices. Existing resonators suffer from intrinsic limitations due to leakage of energy into the surrounding structure and offer limited or no means to release energy on demand. This research will introduce and investigate a class of artificially engineered structures, known as metastructures, that can achieve zero energy leakage. It will also investigate the possibility of confining and releasing energy by applying a force. These novel mechanical resonantors will open new avenues for elastic wave-based computation and signal processing, with potential applications in robotics and Internet of Things devices. Therefore, results from this research will benefit the U.S. economy and society. Furthermore, the accompanying educational and outreach activities will help broaden participation of underrepresented groups in research and positively impact engineering education. This research will investigate the possibility of achieving mechanical resonators that have zero leakage, are defect-insensitive and can confine and release energy on demand using metastructures. Current designs suffer from the fact that they leak energy into the surrounding structure and are unable to release energy on demand. The research will overcome these limitations by drawing inspiration from two recent advances in wave physics: the demonstration of electromagnetic bound modes in the continuum (BICs) and topologically protected elastic waves that enable defect-insensitive energy transport. It will test the hypothesis that exploiting the symmetry and topology of the dispersion surfaces can lead to topologically protected BICs in elastic media. The research will use a combination of analytical calculations based on the plane wave expansion method, numerical simulations using finite element analyses and experimental measurements on fabricated samples by using laser Doppler vibrometry to measure the displacement field. Metastructures of increasing complexity, i.e., beams, plates, shells and three-dimensional architected solids, will be introduced and investigated. The results of this research are applicable across a wide spectrum of length scales and will translate across disciplines to acoustic, electromagnetic and plasmonic metastructures. 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.