Georgia Tech Research Corporation

NSF Awards · FY 2025 · 2025-10

Chemicals that are free of impurities are critical to everyday products such as electronics, medicine, and food. However, separating a chemical mixture into its pure constituents is energy intensive and expensive. This project will develop new membrane materials that can separate chemical mixtures at lower cost and use less energy. The research team will combine advanced data science with lab experiments to speed up materials discovery. The project will focus on separating a liquid mixture of small molecules called paraffins and olefins. This specific separation is especially important to the chemical industry because these molecules are used to make fuels and plastics. The results of this project will be new membrane materials, and better computer programs for finding these materials. Additional benefits to society will come from training science and engineering students in data science, undergraduate research and training, and public outreach at science festivals. This project combines researchers with expertise in polymer synthesis, materials science, chemical engineering, and data science. The goal is to discover new organic-inorganic (hybrid) membrane materials that can separate organic liquid mixtures. The research team will combine high-throughput physical experimentation with machine learning (ML) models to create new data-driven frameworks for membrane material discovery and optimization. This project will focus on using combinatorial chemistry to create structurally-tunable microporous polymers. These polymers will be combined with newly-developed inorganic vapor infiltration techniques to create a wide range of organic-inorganic hybrid membranes. These hybrid membranes will be designed for chemical stability and selectivity to achieve difficult organic liquid separations, including the separation of olefin and paraffin mixtures. Data-informed ML models will be developed to establish feasibility of the polymer synthesis, chemical stability, and permeation selectivity. The corresponding data-driven workflow will identify promising materials that can separate a given liquid mixture, and are also easy to manufacture. The most promising membrane material candidates will be tested to validate predictions. The experimental results will be fed back to improve simulation predictions. The project will also support a multi-disciplinary undergraduate research program that will train students in lab automation. Public outreach includes a demonstration module that visibly separates colored dyes using membranes. This will create awareness of how these “hidden” manufacturing processes are important to human well-being and economic 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 · 2025-10

This award is to support participants to attend the third United States Universities Council on Geotechnical Education and Research (USUCGER) Early Career Workshop that will be held in October 2025 in collaboration with the Geosystems Engineering faculty at the Georgia Institute of Technology in Atlanta, Georgia. The workshop will be a 1.5-day event focused on issues that are impactful for the initiation of an academic career in today’s rapidly changing research and teaching environment with an academic context significantly impacted by the COVID-19 pandemic and the advent and prevalence of artificial intelligence across university campuses. This workshop will bring together the newest generation of geotechnical engineering faculty to: 1) Facilitate research collaborations through formal presentations and informal discussions; 2) Identify best practices in research and teaching, with special attention to artificial intelligence in the laboratory and in the classroom; 3) Facilitate entrepreneurial thinking through sessions on research commercialization and technology transfer; 4) Facilitate mentoring, work-life balance, and human connection in a post COVID academic world; The format of the workshop is designed to broaden perspective and widen access to new ways of thinking for early-career faculty as they embark on an academic career path. The workshop will focus on practical best methods that researchers and teachers can take back to their home institutions to help build the most solid foundation for a productive and innovative career. Outcomes of the workshop will include a post-workshop report hosted on a dedicated website managed by USUCGER, as well as the creation of a monthly “Office Hour with the Program Director” which will allow junior faculty to sign up for small group mentoring sessions with the CMMI program director. It is anticipated there will be 10 one-hour sessions of 6-8 junior faculty who will be able to ask questions about a range of topics pertaining to researching with NSF and life as a junior faculty member. 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

Satellites serve as the backbone of our digital world, enabling global communications, navigation systems, weather forecasting, and internet connectivity in remote areas. They facilitate everything from GPS navigation in smartphones to international financial transactions and military operations. When satellite systems fail, the consequences are immediate and far-reaching: navigation systems become unreliable, affecting transportation and logistics; communication networks experience outages, particularly in remote areas dependent on satellite internet; weather forecasting accuracy diminishes, impacting disaster preparedness; and many financial and commercial systems relying on precise timing signals face major disruptions. The ability to mitigate failures and enable routine repairs and hardware upgrades of spacecraft in orbit hinges on advanced space robotics capabilities. However, the harsh environmental conditions of space—with challenging illumination, limited computational resources, and diverse motion regimes—make traditional computer vision and localization techniques developed for terrestrial applications inadequate for space applications. Space robotics demands perception capabilities that far exceed those of similar terrestrial systems. This research develops novel visual perception, localization, mapping, and planning algorithms to enable new capabilities for all future space missions such as failure mitigation, large flexible structure assembly, orbital debris removal, inspection, hardware upgrades, and many more. This research involves training of both graduate and undergraduate students. The results of this research are disseminated to the community by journal and conference publications, organization of invited workshops and seminar presentations, and by targeted exposure (press releases, interviews) to popular media. This research advances the state-of-the-art in computer vision and perception for space applications by addressing three critical limitations: first, existing feature detection methods struggle with the harsh illumination conditions of space; second current 3D reconstruction techniques fail to account for the dynamic orbital environment; and third, traditional simultaneous localization and mapping (SLAM) approaches cannot handle the diverse motion regimes of resident space objects (RSOs). To address these challenges, the research team develops a robust learning-based feature detection framework for space by using line features that exploit the inherent geometry of the target, along with multi-spectral imaging feature extraction. It also develops a novel dynamics-aware 3D Gaussian Splatting framework that incorporates relative orbital dynamics as physical constraints, enabling simultaneous state estimation and motion regime classification while maintaining physical consistency. The research team introduces a visibility-aware neural field representation that explicitly models observation uncertainty to drive information-theoretic view planning, therefore enabling autonomous space robots to systematically explore unknown objects through efficient observation sequences. Finally, the developed theory is experimentally tested and validated using Georgia Tech’s Autonomous Spacecraft Testing of Robotic Operations in Space (ASTROS) platform, a state-of-the-art spacecraft simulation platform, and also using a high-fidelity synthetic simulation environment. 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 past decade witnessed significant progress in quantum information science (QIS), an emerging discipline of modern scientific studies whose research interest is driven by saturation of downscaling and speeds of conventional information technologies. A grand strategy of the fast-advancing QIS is to harness intrinsic quantum mechanical properties of qubits to push the performance on information processing density, speeds, reliability, and energy-efficiency to the next level. Nitrogen-vacancy (NV) centers, optically active spin defects in diamonds, naturally stand out as a leading qubit candidate in this revolutionary quantum era and are finding increasing applications in QIS thanks to their excellent quantum properties under a broad range of experimental conditions. In this project, the principal investigator plans to integrate NV centers with on-chip magnetic nanodevices to develop hybrid quantum spintronic platforms to improve the scalability, electromagnetic tunability, and solid-state compatibility of NV centers for implementing transformative QIS innovations. In parallel with the proposed research topics, education, training, and outreach programs will also be included as an integral part of this proposal. A major effort will be dedicated to increasing society’s awareness of some of the most exciting developments and challenges in spintronics, quantum sensing, and novel computing technologies. It will promote participation of students, at both graduate and undergraduate levels, in the frontier of modern scientific research. Proposed outreach activities include lectures, workshops, learning and demo materials for local technical colleges, so that contemporary scientific knowledge can reach out to a significant amount of audience. Recently, NV centers, optically active spin defects in diamonds, have emerged as an appealing qubit platform for developing a range of cutting-edge quantum technological innovations. Taking advantage of the excellent quantum coherence, to date, this approach has been successfully applied to quantum metrology, sensing, and quantum-network research, showing remarkable field (spatial) sensitivity and extraordinary qubit operation merits. Despite the enormous progress made thus far, experimental demonstrations of NV-based quantum computing remain elusive in the current state-of-the-art. The major technical challenges center on how to locally address individual NV centers in a scalable, energy-efficient way, and precise control of NV-NV interaction at the nanoscale for large scale, high-density, and solid-state compatible quantum operations. This project aims to timely address these problems. Specifically, the principal investigator plans to introduce magnetic nanojunctions to achieve electrical voltage control of individual NV centers on a length scale down to ~50 nm. By synchronizing individual electron spin resonance frequencies and Rabi oscillations of two interacting NV centers (separated by 50 nm) by magnetic nanojunctions, this project proposes to realize electrical engineering of NV-NV dipole coupling and the overall two-qubit coherence performance for designing advanced quantum entanglement applications. The proposed research will promote the role of solid-state spin defects and magnetic nanodevices in advancing the forefront of quantum spintronic research and a broad range of emerging technological applications. The proposed quantum sensing study will further open a new perspective to investigate microscopic electromagnetic properties of nanoelectronics, which can be extended readily to many other device systems and benefit the community in the long run by impacting future quantum 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 2025 · 2025-10

The construction industry is not only one of the industries with the highest fatality rates, but also significantly consumes natural resources, with nearly $2.1 trillion worth of buildings constructed annually, resulting in a substantial ecological footprint. To address this, the construction industry is leveraging sensing technologies to achieve sustainable construction practices, making it necessary to upskill the future workforce in this area. However, the dynamic nature of construction sites and the ubiquitous safety challenges often impede hands-on experiences in practicing the use of sensing technologies to solve real-world problems and achieve sustainable construction practices. This project will investigate an embodied virtual reality and artificially intelligent environment (VRAPS) to facilitate the development of problem-solving skills of heterogeneous learners for sustainable construction. VRAPS will also be designed as an intelligent platform that can adapt to the diverse needs of different learners, making it a transferable learning environment with distinct features. This project will demonstrate how a personalized learning environment can close the competency gap between industry and construction education by developing a workforce capable of advancing sustainable practices in the construction industry. This planned research investigates a virtual reality and artificially intelligent environment for personalized learning in sustainable construction education (VRAPS). Through a mixed-methods approach, the project aims to answer the following questions: What competencies are required to advance sustainable construction using sensing technologies? What characteristics of an embodied virtual reality environment facilitate personalized learning of sustainable construction? And to what extent does a personalized and embodied virtual reality environment enhance problem-solving skills that are needed for advancing sustainable construction education? The first research question will be addressed through a Delphi study and a focus group discussion with industry practitioners to understand the competencies required to advance sustainable construction. The results will inform a formalized construction-domain learning content, which will drive the development of VRAPS. The proposed research will then combine interactive tools, technologies, and techniques, such as sensing technologies, virtual learning assistant, tangible objects, and artificial intelligence, to develop VRAPS. The development of VRAPS will provide engaging learning tools to complement traditional instruction, allowing teachers to tailor instruction to students’ needs. The second research question will be answered by leveraging behavioral data to investigate the characteristics of VRAPS that facilitate personalized learning. To answer the third research question, the research team will implement and assess VRAPS in two institutions to understand its effectiveness in enhancing the problem-solving skills required for advancing sustainable construction. VRAPS will contribute to the active learning theory through the use of multimodalities (such as virtual learning assistant and tangible interfaces) to support continuity during the acquisition of problem-solving skills for sustainable construction education. To reach a broader audience, VRAPS will be implemented for professional development, summer camps and outreach opportunities at Georgia Tech and CSU Fresno. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

Modern Algorithms, including those for artificial intelligence (AI) and Scientific Computing, rely on efficient optimization. This project addresses current challenges in optimization, with the goal of developing techniques that enable faster and more accurate algorithms than are currently known to be possible. The target problems are at the intersection of computer science with other theoretical disciplines, including convex geometry, analysis, statistics, and operations research. This project also includes research opportunities for undergraduate students and early exposure to computing concepts for K-12 students at local schools. The development of the theory of algorithms and complexity has gone hand-in-hand with the development of techniques for optimization. This project focuses on three related thrusts, all building on recent breakthroughs: (1) Understanding the complexity of the widely used interior-point method in terms of the number of iterations, in the worst case, on average and for sparse inputs; (2) developing continuous methods for solving discrete problems, particularly those at the frontier of discrepancy minimization, satisfiability and spectral optimization; and (3) improving approximation algorithms via better analysis of convex relaxations, as well as the analysis of practical cutting-plane methods for solving them. 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 develop methods to create individually tailored large language models (LLMs) in the context of systems for strengths-based coaching of job seekers. People with autism have a wide range of abilities and needs around the interactions needed to conduct a job search. Strengths-based approaches to job coaching, which identify and apply each person's unique strengths in preferred work environments, show promise in helping individuals secure competitive employment, but the high cost and labor involved make strengths-based coaching inaccessible to many young adults. Currently, individuals turn to tools like ChatGPT to help with cover letters, interview preparation, and other job-seeking tasks. However, these tools often produce generic responses that fail to reflect the unique strengths of job seekers and raise ethical concerns about handling sensitive, diagnosis-related information and the risk of over-reliance on the LLMs. Through developing new ways to build personalized LLM-based agents that discover and incorporate people's strengths and needs, this project will both advance the utility and safety of LLMs and increase the employment projects for individuals with autism and others, benefiting society. To address these challenges, the project will follow a three-part approach. First, the project team will analyze chatbot dialogue data and neurodivergent user profiles from the LLM job coach feature currently deployed on a neurodiversity-focused employment platform that connects neurodivergent job seekers, including autistic individuals, with employers. Insights from this analysis, along with input from individuals and professional job coaches, will guide the development of job coaching guidelines, identify opportunities for strengths-based support, and uncover ethical risks and safeguards to inform the design of a strengths-based job coaching LLM model. Second, the project team will create a two-part system based on the identified design guidelines: a fine-tuned LLM for job coaching and a control model that uses individual strengths to generate personalized, context-specific responses. This design addresses the limitations of generic, one-size-fits-all outputs. Third, the project will build a novel job coaching system leveraging these advances and test how effectively the developed strengths-based job coaching LLM can support job seekers with autism and others. Through co-design workshops and lab-based evaluations, the project will assess the system's ability to improve people's confidence, self-esteem, and perceived usefulness of the coaching process, while identifying areas where the model may need refinement to ensure ethical and practical implementation at scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Many types of disease can be treated with ablation, a medical procedure which applies energy to destroy small regions of tissue that do not behave normally. Ablation therapy can be used to treat conditions like arthritis, uterine fibroids, and cancer. It can also treat disruptions of the heart’s regular rhythm, such as atrial fibrillation. Ablation procedures can be difficult to perform, and sometimes multiple treatments may be necessary. A deeper understanding of exactly how the settings associated with the ablation procedure affect the biological tissue could lead to better results. This project aims to improve the understanding of radiofrequency ablation’s interactions with heart tissue through a combination of theory, multi-physics and machine-learning models, and experiments. To ensure the experiments reflect the differences in tissue structures and properties of real patients, tissue from human hearts no longer needed after being replaced by transplants will be used when possible. Medical doctors will help assess the practical significance of the project’s results. This study has the potential to lead to improved ablation treatments and patient outcomes, and the new methodology can be extended, with minor adaptations, to other types of diseases. Educational components include training of graduate and undergraduate students, contributions to undergraduate and graduate courses, and engagement of the general public with interactive programs available through a website. Radiofrequency ablation (RFA), used for a wide variety of physiological systems, faces limitations from an imprecise understanding of ablation and tissue interactions, along with challenges in optimizing the procedure given the many parameters associated with ablation and patient variability. This project aims to develop and validate a detailed multi-physics mathematical RFA model with an unprecedented level of accuracy and analysis. It will focus on cardiac tissue, but the tools can be adapted for other biological tissues and ablation therapies. First, the novel computational model will include advanced methods of domain decomposition and model reduction to address the multi-physics nature of the problem and will incorporate important physiological parameters of ablation-tissue interactions. Second, the model will be enhanced by rigorously integrating the sizes, thicknesses and thermal profiles of ablation lesions in cardiac tissue from varying thermal doses, contact angles, and pressures and by comparing with experiments. This project will be enhanced by using optical-mapping methods during ablation in live hearts, including live human explanted hearts from patients undergoing heart transplants, to simultaneously quantify the extent and sensitivity of the ablation at different tissue depths in real time as a function of ablation parameters. This information will enable continuous refinement of the computational model and accurate sensitivity analysis. Finally, simulations and experiments will be integrated to assess how ablation lesions will effectively terminate disorganized electrical wave propagation during fibrillation. The mechanistic RFA model will provide highly accurate predictions of ablation parameter effects on the success rate of terminating cardiac fibrillation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project investigates important problems in quantum topology and its deep connections with classical topology and hyperbolic geometry. Quantum topology focuses on the study and classification of invariants of three- and four-dimensional spaces and the knotted circles they contain. These structures naturally arise in various scientific contexts, including DNA modeling and theoretical physics, and have numerous applications. Techniques and methods from quantum topology may also contribute to the development of both theoretical and practical models for quantum computation. The project is inherently interdisciplinary, drawing on ideas and methods from topology, geometry, algebra, number theory, analysis, quantum field theory, and combinatorics. It also emphasizes the mentoring and training of students and postdoctoral researchers. The Principal Investigator (PI) will concentrate on three closely related research directions. The first is the AJ conjecture, which relates the colored Jones and HOMFLYPT polynomials to the fundamental group of knots. The second involves the development and exploration of hyperbolic topological quantum field theory, with the goal of advancing the volume conjecture and resolving the AJ conjecture. The third focuses on stated skein algebras of surfaces, which have wide-ranging applications, including a potential partial proof of the duality conjecture in higher Teichmüller theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This I-Corps project focuses on the commercialization of polymer beads for the fast, effective treatment of samples for wastewater testing. Using these small polymer beads, the shelf life of wastewater samples can be greatly extended, alleviating the requirement for cold-chain storage. Currently, the refrigeration process required for sample handling to ensure reliable and accurate results suffers from high costs, greatly limiting the implementation of large-scale wastewater testing. This technology offers promising applications for virus testing in wastewater. The beads provide opportunities to advance and facilitate sample preservation and monitoring of potential contaminants not only in developed areas but also in resource-limited environments where in-person testing is uncommon and unreliable. The beads can be produced in large quantities at low cost. Additionally, as more applications based on the technology are developed, the outcomes of this solution may also benefit disease monitoring and therapeutic interventions. 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. As wastewater is chemically and biologically complex, the handling, storage, and transportation of wastewater samples without refrigeration is extremely challenging. Additionally, due to the high costs of implementation, refrigerated storage and transportation may not be feasible or sufficient, especially in resource-limited settings. This solution is based on the development of a refrigeration-free microbial sampling and storage technology using porous superabsorbent polymer beads. The millimeter-sized beads are prepared by a dry-bath batch method via polymerization induced phase separation using low-cost starting materials. The analytical targets (e.g., pathogens or surrogates) can be captured and immediately preserved in the beads, then released from the beads for subsequent lab testing on demand. The beads use a well-controlled sieving structure to absorb and stabilize the targets in liquids like wastewater, while eliminating possible contaminants. The stabilized samples have an extended shelf life in this purified environment at room temperature. This technology could provide an effective and inexpensive method for wide-scale refrigeration-free microbial sampling and storage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Pathways to Enable Open-Source Ecosystems (POSE) project expands secure and reliable access to digital infrastructure that supports science, education, and innovation. As society increasingly depends on online platforms for research, collaboration, and data sharing, protecting access to these resources is vital for advancing discoveries and maintaining public trust. This project addresses national cybersecurity challenges by creating open and sustainable solutions for managing digital identities, access controls, and sensitive credentials. By simplifying secure participation in distributed computing, the project enhances access to advanced technologies for researchers, educators, students, and small businesses. These groups benefit from easier and safer ways to utilize shared digital resources. Additionally, the project provides training opportunities to prepare future professionals to work with modern cybersecurity practices. Overall, these efforts will strengthen U.S. leadership in open-source software, enhance national competitiveness, and increase confidence in the digital foundations that support science and technology. This project develops a community-driven open-source ecosystem focused on secure authentication, authorization, and credential management in distributed systems. Using open standards and proven tools, it offers a framework that integrates federated identity, group and role management, and secure credential handling. The project aims to enhance current practices by integrating authentication and authorization more closely, enabling flexible policy enforcement, and simplifying integration across various computing environments. Activities include developing deployment workflows that are easier to adopt, strengthening security practices led by the community, and encouraging contributor involvement through transparent governance. Expected results include better tools for resource sharing, improved credential management, and more efficient security operations. By evolving into a sustainable ecosystem, the project aims to provide reliable infrastructure for secure scientific platforms, including research gateways and data analysis environments, thereby supporting reproducible research, collaborative innovation, and long-term resilience in national cyberinfrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award provides partial support for three editions of the Tech Topology Conference, beginning with the meeting at Georgia Tech in Atlanta on December 5-7, 2025, the 15th conference in the series. The conference provides an opportunity for topologists from around the country to convene for a weekend of talks on the latest developments in the field. The speakers, selected for the quality of their results, their presentation skills, and their contributions to mentoring mathematicians, range from internationally renowned researchers to rising young stars. The conference features seven hour-long talks, complemented by three lightning talk sessions, which give junior researchers the opportunity to give a five minute presentation on their work. The schedule includes ample time for informal discussions amongst participants and speakers, providing opportunities for mentorship and collaboration. The grant will help support the travel expenses of graduate students, early career researchers, and speakers. This year's Tech Topology Conference will focus on contact/symplectic geometry and low-dimensional topology. These are both very active fields of research, both in the Southeast and beyond; four of this year's seven speakers are based in the Southeast. The conference serves as an opportunity to bring leading researchers to the Southeast and highlight the some of the recent results obtained by junior researchers in the region. The conference organizer will continue posting notes from the talks online in order to make materials from the conference publicly available. The conference website can be found at http://ttc.gatech.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 2025 · 2025-09

Neuroscience is the study of the brain and the nervous system. Human neuroscience is the specific study of how the brain controls our thoughts and behaviors. Human neuroscience research leverages artificial intelligence (AI) by seeking to improve our ability to analyze neural data and understand how neural data align with human behaviors in basic science and clinical populations. AI plays a transformative role in human brain imaging by enhancing the accuracy, speed, and depth of data analysis and is a critical part of the proposed REU program. REU students will learn about these techniques. The intersection of biotechnology and human brain imaging is driving significant advancements in understanding brain function. Biotechnology enables the development of novel imaging methods, genetic tools, and biological sensors that, when combined with advanced imaging techniques, provide deeper insights into brain structure and activity. This synergy allows researchers to understand developmental aspects of brain function, monitor therapeutic responses, and understand the biochemical underpinnings of brain disorders paving the way for clearer approaches to understand the human brain. This REU program engages laboratories that use advanced biotechnological approaches to enhance our understanding of brain activity patterns in health and disease. A principal goal of this undergraduate research program in human neuroscience is to enhance the intellectual understanding of human neuroscience methods and foster collaborative research. This program will engage students in hands-on research and bootcamp activities to teach them technical and research knowledge in electroencephalography and functional magnetic resonance imaging, while engaging in transformative laboratory research with a faculty member. The program will recruit students studying psychology, biology, neuroscience, or related fields from colleges and universities without access to magnetic resonance imaging or electroencephalography. We propose to harness the collaborative nature of human neuroscience research in the community to offer a unique summer research experience for undergraduates from universities without the human neuroscience environment that exists here. The program’s main goal is to develop expertise in students in the techniques and research procedures that neuroscientists use to study human brain processing. This REU Site will increase scientific and technical knowledge of students by providing a 10-week intensive summer program in experimental design, data collection, and data analysis using AI, biotech, and other techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Urban resilience is an interdisciplinary grand challenge that requires coordination of talents and resources to create transdisciplinary solutions that holistically consider how technology, people, and environment interact. Existing networks on urban resilience are often shaped by their regional, disciplinary and social contexts. The objectives this AccelNet Implementation (Phase 1) project are to (i) build strategic links among disciplinarily and geographically comprehensive U.S. and international networks, called Resilient-NET, to synergize complementary scientific expertise, and provide its members the critical access to data, platforms, pilot projects and research capabilities, and (ii) prepare the U.S. students and early-career researchers to solve future urban resilience problems, strengthen their global leadership skills through collaborations. Major networks to be connected include the Singapore-Switzerland-Netherlands network on future urban resilience, U.S. Extreme Events Research (EER) networks, International and U.S. networks in AI research, U.S. geospatial research network, U.S. logistics and supply chain resilience network, and Global South network. This project will generate an OPEN-NET platform for data sharing, focused research groups and projects, annual workshops, bi-annual symposiums, major workforce development activities including scholar/student visit. By consolidating a roadmap towards future resilient urban eco systems and nurturing future workforce in urban resilience, Resilient-NET will make long-lasting impacts on the livability and economic viability of future U.S. cities with a scope and magnitude beyond the capabilities of a single research community. Resilient-NET will take a holistic perspective with a special focus on the complex interactions among technology, people and environment. (i) The technology aspect (i.e., the New Dimension) will focus on the increasingly prevalent integration of AI-enabled systems into urban infrastructure and mobility systems. At the dawn of large-scale AI implementations in the next decade, the proposed activities will identify inherent vulnerabilities and key performance characteristics for AI-enabled urban systems, ensure resilience of emerging urban mobility systems, and identify a research agenda for effective and safe system-system interactions. (ii) The people aspect (i.e., the Missing Puzzle) will enhance our understanding on the increasingly complex interactions between humans and technology, as a singular focus on the technology aspect will not automatically build resilience into our societies. (iii) The environment aspect (i.e., the Uncertainty Accelerator) will focus on the increasingly frequent extreme natural events that simultaneously impose vulnerabilities and uncertainties on urban socio-technical systems, and will identify the solutions for data-driven disaster preparedness, urban digital twin, and the predictive capabilities for compound cascading extreme natural events. In summary, the integrated techno-socio-climate approach adopted by this project will enable our societies as well as research communities to better understand the complicated technology-human interactions, identify research gaps, priorities and solutions in trustworthy AI technologies and quantitative social resilience research for different social entities in relation to infrastructural 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 · 2025-09

Studies indicate that informal learning settings such as out-of-school learning (OSL) programs, including afterschool and summer learning programs, can support K-12 students' learning in science, technology, math, and science (STEM) as well as enhance youth career interests in STEM. This project seeks to model how two organizations, the Center for Education Integrating Science, Mathematics, and Computing (CEISMC) at Georgia Tech and Georgia Statewide Afterschool Network (GSAN), can partner together to understand the experiences of OSL educators and associated organizations related to STEM programming for youth. Over the course of the project, the activities will identify both the STEM professional development needs of OSL educators and create a plan of action to address them. This robust process will result in a plan for professional development that will ultimately support OSL educators as well as access to quality STEM education and learning for K-12 students who attend these OSL programs. This Partnership Development and Planning project is focused on determining the STEM professional development needs of the OSL educators in Georgia. Using Rogers et al.'s (n.d.) Strategic Partnering Conceptual Framework, which encourages intentionality and reflexivity of partners, the team will deepen mutual understandings, partnerships, and research and development capacity among stakeholders. A purposive sampling method will be used to gather a range of perspectives from educators from across the OSL field in Georgia. Stakeholder discussions will further the needs assessment as well as the development of an action plan, which will receive feedback from OSL educators. The results of this project will expand the partnerships of organizations dedicated to meeting the needs of informal educators in Georgia who work in OSL programs to provide STEM programing to youth. The insights and experiences from Georgia are likely to be relevant to the OSL field overall, so that other geographic areas and partnerships can build from this work in order to meet the specific needs of their communities. This Partnership Development and Planning project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports research that addresses critical vulnerabilities in remotely controlled robotic systems through a comprehensive study of a particularly sophisticated class of cyber attacks, thereby advancing the national health, promoting the progress of science, advancing prosperity and welfare, and securing the national defense. Modern robotic systems rely heavily on networked communication for coordination and control, creating opportunities for malicious actors to inject false data that can cause robots to perform unintended and potentially dangerous actions. Traditional cybersecurity approaches designed for computer networks are insufficient for robotic systems because robots operate in physical environments where security breaches can result in property damage, personal injury, or disruption of essential services. This project looks to address this critical gap by studying affine transformation-based perfectly undetectable attacks that exploit the geometric properties inherent in robotic systems to remain completely undetectable by conventional security measures. Understanding and defending against these attacks is crucial for maintaining public trust in robotic technologies and ensuring their safe deployment in critical applications. The project seeks to advance fundamental knowledge in robotic cybersecurity while training graduate students in interdisciplinary research combining robotics, cybersecurity, and mathematical theory, thereby strengthening the national workforce in critical technology areas. Additionally, the project will implement comprehensive outreach initiatives designed to engage students from various backgrounds, thereby strengthening the pipeline of future talent in cybersecurity and robotics. This research aims to make fundamental contributions to a comprehensive theoretical framework using Lie group theory and differential geometry to characterize geometric vulnerabilities in networked robotic systems and establishes novel countermeasures based on state monitoring signature functions. The project investigates affine transformation-based false data injection attacks that exploit coordinate transformations to maintain mathematical consistency in robotic dynamics while altering physical behaviors. This project looks to derive fundamental mathematical relationships between system symmetries and attack vulnerability, then plans to develop signature functions that create mathematical incompatibilities, making them irresolvable by potential attackers. The approach looks to be validated through theoretical analysis and experimental testing on three distinct robotic platforms: bilateral teleoperation systems, mobile robots, and robotic manipulators. The signature function countermeasures exploit the principle that while attackers can maintain consistency in plant dynamics through geometric transformations, they cannot simultaneously preserve consistency in carefully designed nonlinear monitoring functions. This research looks to establish new mathematical tools for analyzing robotic security, provide practical defense mechanisms for real-world systems, and create fundamental knowledge about the intersection of geometry, dynamics, and cybersecurity in robotic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project supports the continued operation and management of the Atmospheric Science and Chemistry mEasurement NeTwork (ASCENT). The ASCENT network was developed with support from the NSF Mid-Scale Research Infrastructure program and is providing the first, comprehensive, high time resolution, long-term aerosol data across the United States. It consists of 12 sites in urban, rural, and remote areas that are equipped with a suite of advanced aerosol instrumentation for real-time measurements of fine mode aerosol chemical composition and properties. The ASCENT network fulfills the critical need for long-term, high time-resolution atmospheric aerosol chemistry measurements in the US. for informing science-based policy decisions on air quality related human health and environmental issues. The specific objectives of this project are to: (1) Continue to acquire high time-resolution, high-quality, long-term state-of-the-art measurements of aerosol chemical and physical properties at the 12 established ASCENT sites that are essential to advancing research in air quality, atmospheric science, environmental change, and public health; (2) Develop a database infrastructure for automated quality assurance/control, data upload and download, data discovery and visualization, and long-term data preservation; (3) Enhance training of students and current professionals engaged in science and engineering related to atmospheric air quality research; (4) Conduct research to advance fundamental understanding of urban air quality, biomass burning aerosols, and biogenic aerosol sources and processing which leverages the uniform network of comparable observations; and (5) Catalyze and support future development of an integrated, world-leading, long-term, atmospheric observation research infrastructure for aerosols and trace gases in the U.S. and strengthen collaborations with international atmospheric observation networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Material fractures present a major obstacle to safety and economic efficiency across a wide range of industries, including construction, manufacturing, transportation, and aerospace. This highlights the urgent need for new material systems that offer fracture resistance far beyond the capabilities of traditional materials. This award supports fundamental research that seeks to enable the design and creation of a new class of fracture-resistant mechanical metamaterials (MMs). These materials exhibit extraordinary mechanical properties due to their structural geometry rather than chemical composition. Despite their promise, how these MMs break and how to design them to resist fracture remain underexplored. This project looks to address this gap by developing a new, hierarchical approach to analyze and optimize fracture resistance in MMs across global and local scales. The research is expected to generate new insights and design principles for fracture-resistant MMs, reduce economic losses resulting from material failure, and enable the development of safer and more durable technologies. In addition, the project seeks to contribute to national educational goals by developing interactive educational tools and training students in a multidisciplinary environment that spans mechanics, materials science, and computational design through university programs and courses. This project aims to develop a hierarchical framework for understanding and designing multi-scale MMs with enhanced fracture resistance. The key hypothesis is that local constitutive behavior can serve as an intermediary, linking local structural deformation to global fracture behavior. This conjecture decouples the complex problem into three manageable tasks: fracture of general networks, fracture of local structures, and their integration. At the global network level, the project investigates fracture behavior in homogeneous, defective, and heterogeneous networks under diverse loading conditions using theoretical and computational tools. At the local level, it explores and seeks to reveal geometric characteristics that achieve specific nonlinear constitutive behaviors for superior fracture properties using topology optimization and data-driven generative models. These components look to be integrated into a design pipeline for creating multi-scale MMs with unprecedented fracture properties. The resulting framework will be validated through simulation and experiment. The outcomes seek to contribute to the fundamental understanding of fracture mechanics in architected materials and enable the design and fabrication of MMs with unprecedented toughness for real-world applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Antarctica’s ice sheet holds enough water to raise global sea levels by over 200 feet, making accurate predictions of its future behavior critical for coastal communities, infrastructure planning, and climate adaptation worldwide. However, current ice sheet models struggle to predict how fast Antarctic ice will flow and contribute to sea level rise because they lack crucial information about conditions beneath the ice, particularly the temperature at the base of the ice sheet. When the base of the ice is warm enough, it can melt and lubricate the ice-rock interface, dramatically accelerating ice flow toward the ocean. This project will create the first comprehensive map of basal temperatures across the entire Antarctic continent by combining decades of radar data with cutting-edge artificial intelligence techniques, providing essential information to improve ice sheet models and sea level projections. The research will also develop innovative educational programs that teach high school students about polar science and artificial intelligence applications, potentially reaching thousands of students nationwide through the Science Olympiad competition and training the next generation of climate scientists. This project addresses a critical gap in Antarctic ice sheet modeling by developing a continent-wide map of basal temperatures using airborne radar sounding observations and generative AI methods. The research will compile radar data from multiple international polar programs spanning two decades, analyze englacial attenuation patterns to estimate depth-averaged ice temperatures, and employ conditional normalizing flow models to infer basal temperatures from these observations. These radar-derived basal temperatures will be integrated into the Ice Sheet and Sea-level System Model (ISSM) through a joint inversion framework to calibrate basal slipperiness parameters, replacing current approaches that rely solely on surface velocity observations. The improved parameterization will be used to revise Antarctic ice sheet projections from the recent Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6), providing more accurate assessments of future mass loss and identifying which Antarctic drainage basins are most vulnerable to basal temperature changes. The project will produce open-access datasets of standardized radar observations, artificial intelligence processing codes, and enhanced ice sheet model outputs that will benefit the broader polar science 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 2025 · 2025-09

This grant provides travel support for Principal Investigators (PIs) with current active awards from the NSF Mechanics of Materials and Structures (MoMS) program to attend the 2025 MoMS Grantee Meeting, an integral part of the Society of Engineering Science (SES) Annual Technical Meeting. Scheduled for 12-15 October 2025, in Atlanta, Georgia, the meeting will serve as a platform to bring together NSF MoMS grantees, offering opportunities to strengthen collaborations and facilitate the exchange of cutting-edge ideas and research findings. The meeting will feature a series of poster sessions where grantees can present their research, encouraging peer review, feedback, and discussion. Panel discussions, led by invited experts, will dive deep into current challenges and opportunities in the field of mechanics of materials and structures. Topics will include advances in structural and soft materials, new insights into metamaterials, and the use of data-driven approaches to tackle complex engineering problems. The meeting will also include various social activities designed to promote networking and foster potential collaborations across disciplines and institutions. SES has long been a catalyst for advancing research in mechanics, particularly in the areas of structural materials, soft materials, and metamaterials. By providing a forum for the exchange of ideas between seasoned researchers and emerging talent, the meeting will help drive forward-thinking discussions and innovative solutions to pressing challenges in the field. The event will also offer invaluable mentorship opportunities, where established PIs can share insights on how to navigate the research landscape, secure NSF funding, and develop competitive proposals. The 2025 MoMS Grantee Meeting aims to further solidify the mechanics of materials and structures community by strengthening its connections and providing the next generation of researchers with the tools and guidance needed to thrive in their academic and professional careers. This gathering will foster an environment of collaboration and innovation, propelling forward research that is critical to advancing the mechanics of materials and structures, as well as addressing emerging global challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Understanding what makes ecosystems stable over time is a fundamental question in ecology. While previous research has shown that biodiversity can help stabilize ecosystems, much less is known about how basic ecological processes, like competition between species, affect ecosystem stability. This project will investigate how competition influences the ability of ecosystems to maintain consistent functions over time (i.e., ecosystem temporal stability), both within individual ecological communities and across larger, interconnected systems. Using fast-growing, single-celled organisms called protists in controlled laboratory experiments, this project will test new ideas about how competitive interactions and the order in which species arrive influence ecosystem stability over time. In addition to advancing scientific knowledge, the project will provide meaningful educational opportunities. Undergraduate students will design and conduct experiments as part of their coursework, and high school students and K–12 teachers will participate in lab-based research through established programs at Georgia Tech. Outreach activities will include public engagement at science festivals and community events. The project will also provide training for a postdoctoral researcher, a graduate student, and multiple undergraduate researchers. This project will explore the role of interspecific competition in shaping ecosystem temporal stability at both local (community) and regional (metacommunity) scales, using laboratory microcosms containing assemblages of competing bacterivorous protists as the model system. At the local scale, the project will investigate how competition influences species stability and asynchrony, and, in turn, ecosystem temporal stability, using a series of complementary experiments. It will test the hypothesis that competition significantly increases species asynchrony and ecosystem temporal stability only in communities with sufficiently high response diversity. In addition, the project will incorporate modern species coexistence theory into stability research by examining how species niche differences and relative fitness differences influence patterns of species stability and asynchrony. At the regional scale, the project will test the hypothesis that priority effects, where early-arriving species influence community outcomes, are an important mechanism driving asynchronous dynamics among local communities, thereby contributing to metacommunity-level stability. Collectively, this project will provide rigorous, mechanistic insights into the roles of competition in ecosystem temporal stability across spatial scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award will study how to create packaging materials that can be easily recycled or biodegraded at the end of their use. Traditional packaging materials, often made from plastics, are difficult to recycle. Using cutting-edge artificial intelligence and physical lab experimentation, this project will seek to develop next-generation materials that not only meet the performance needs of food and goods packaging but also break down safely or can be reused as raw material—helping to reduce waste and reliance on new plastic. The research will train students in pioneering, interdisciplinary science while building tools and data that can benefit the broader scientific and industrial communities. The team will aim to turn their discoveries into real-world solutions that will support a circular economy. This project will study the design and development of high-performance polymers tailored for circular packaging applications, focusing on two synergistic recycling strategies: chemical recycling via ring-opening polymerization (ROP) and biodegradation through polyesters such as polyhydroxyalkanoates (PHAs). The project will integrate polymer informatics, synthetic chemistry, and materials testing to overcome the performance limitations that currently hinder recycling of plastics-based packaging materials. Machine learning-driven Virtual Forward Synthesis (VFS) and predictive artificial intelligence (AI) modeling will guide the design of novel polymers, drawing on extensive datasets to be produced in the program. Promising candidates will be synthesized, tested for mechanical, thermal, and barrier properties, and refined through iterative experimental-computational feedback. This integrated framework has the potential to yield recyclable and biodegradable polymers suitable for packaging applications, with broad implications for reducing plastic waste. This Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics (MFS-SPEED) award is co-funded by the NSF through the Division of Chemistry (CHE), the Directorate for Mathematical and Physical Sciences (MPS), and the Division of Innovation and Technology Ecosystems (ITE) in the Directorate for Technology, Innovation, and Partnerships (TIP). Additional MFS-SPEED funding is provided by Procter & Gamble, PepsiCo, Dow, BASF, and IBM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Partial differential equations (PDEs) play a fundamental role in modeling physical laws, chemical and biological processes, financial systems, and modern engineering designs. Despite their importance, most PDEs do not admit analytical solutions, necessitating the use of numerical simulations. While numerical methods have achieved considerable success over the past decades, solving high-dimensional PDEs and simulating PDE solution operators remain major challenges due to the curse of dimensionality and high computational demands. Recent breakthroughs in deep neural networks (DNNs) have opened new avenues in scientific computing. These developments provide promising tools for addressing difficult problems in applied mathematics. This project aims to develop novel mathematical theories and computation methods to efficiently solve high-dimensional PDEs and to learn solution operators using DNN-based approaches. The research will offer rich opportunities for training the new generation of applied and computational mathematicians and engineers. The project focuses on three interrelated objectives that leverage advanced nonlinear reduced-order models with recent developments in optimal transport theory and operator learning. First, it proposes a supervised learning method for solving high-dimensional Hamilton-Jacobi equations using a density coupling strategy. Second, it develops a parameter control framework to enable rapid simulations of high-dimensional evolution PDEs across varying initial and boundary conditions. Third, it introduces a deep tangent bundle method for efficient high-dimensional function approximation and PDE simulation. These contributions will be accompanied by a rigorous theoretical analysis covering model properties, computational complexity, and error bounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Groundwater contamination poses a serious threat to public health, ecosystems, and water security. Cleaning up contaminated aquifers often requires injecting chemical solutions underground to degrade pollutants. However, the efficiency of these treatments is limited by difficulties in mixing the treatment chemicals with the contaminants in complex groundwater aquifer systems. This research will investigate a novel way for improving mixing by leveraging natural fluid movement induced by density variations. The goal is to develop more efficient, cost-effective, and ecologically friendly methods for remediating polluted groundwater. In addition to advancing scientific understanding, the project will provide graduate and postdoctoral training at two institutions, encourage collaboration between modeling and experimental research teams, and create new open-source groundwater modeling tools that can be used by academic researchers and environmental professionals. Outreach initiatives include incorporating research findings into curriculum, organizing summer student exchanges across universities, and collaborating with an industry partner to transfer innovative remediation technologies into practice. The technical goal of this project is to create and verify new ways for delivering dense, reactive treatment fluids into contaminated aquifers in a way that facilitates spontaneous mixing via hydrodynamic instabilities. The dense fluids will be fed through surface infiltration galleries and injection wells to promote convective fingering and increase interaction between treatment chemicals and contaminants. The research will use laboratory visualization experiments, mathematical modeling, and high-performance numerical simulations to study the behavior of multi-species reactive transport in both homogeneous and heterogeneous systems. A new open-source modeling tool will be developed by incorporating density-driven reactive transport features into a popular MODFLOW family software tool. The study team will also conduct uncertainty studies to assess the reliability and limitations of these methods in real-world scenarios. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Pathways to Enable Open-Source Ecosystems (POSE) project focuses on making quantum chemistry (QC) easier to use with modern technology. QC helps scientists predict how molecules behave, which is important for things like designing new medicines or understanding chemical reactions. But most QC programs are old and use plain text files, which are difficult to integrate into today’s machine learning (ML) and artificial intelligence (AI) tools — especially when ML and AI need to run thousands or even millions of calculations. This project supports two tools, QCSchema and cclib, that help organize and read QC data in a way computers can easily use. By bringing the community together around shared data formats, the project will speed up the development of chemistry software and make it much easier for researchers to build fast ML- and AI-based models of molecules. In turn, this will help advance research in drug discovery, materials design, and many other fields. The POSE project brings together developers on the frontlines of connecting software QC tools. The team will develop automated onboarding and testing of QC programs with QCSchema and cclib so that upstream QC program developers and downstream QC data consumers can confidently connect with machine-readable formats. While expanding testing and documenting the interface are prerequisites for expanding QCSchema’s utility, a key component of this project is direct interaction with the community regarding next features, processes for data structure change, and governance. This outreach will include surveys and in-person workshops so that QC data may streamline research and be usable beyond its traditional spheres. 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.