Georgia Tech Research Corporation

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

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

Hyperbolic balance laws arise from the modeling of the nonlinear motion of fluid flows and are central to a wide range of natural and engineered systems – ranging from air conditioning efficiency to severe weather events like tornadoes and ocean tides. Despite their relevance, the complex mathematical structures of these systems continue to pose unresolved challenges. This project will address several fundamental problems in the theory of hyperbolic balance laws, aiming to develop new tools and advance our understanding of the dynamics of compressible fluids. In addition to its scientific goals, the project includes international collaboration and a strong education component focused on training graduate students and early-career researchers. There are three main themes in this research project. The first theme focuses on establishing sharp conditions for the global existence of smooth solutions to the one-dimensional compressible (non-isentropic) Euler equations with large initial data. This includes characterizing singularity developed from generic smooth data and continuing efforts to derive sharp lower bounds on density for generic large solutions. The second theme is to characterize the passage of singular limits in the isentropic approximation of both inviscid and viscous fluid models, with the goal of rigorously formulating the corresponding error estimates. The third theme aims to establish the mathematically rigorous validity of nonlinear dynamical Rayleigh-Taylor type instability in compressible fluids under the influence of gravity, especially for the compressible Navier-Stokes-Fourier system where heat transfer is essential. Collectively, these research efforts seek to advance the theoretical understanding of nonlinear hyperbolic balance laws, address longstanding open problems, and develop methodologies capable of tackling regimes beyond the reach of existing 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

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.

NSF Awards · FY 2025 · 2025-09

An award is made to Georgia Institute of Technology to enable research and educational infrastructure innovations in biophotonics and advanced microscopy. The research goal is to develop and implement parallel multifocal scanning microscopy (PMS), a super-resolution imaging technique for cells and tissues that involves minimal instrument complexity. PMS utilizes specimen movement, allowing multicolor, real-time, volumetric super-resolution microscopy through a standard epi-fluorescence platform. The project will develop the PMS platform (Aim 1), demonstrate its super-resolution imaging capabilities (Aim 2), and showcase its potential applications in pathology and in vivo cell biology (Aim 3). This technique will provide a timely and accessible infrastructure breakthrough for a wide range of cell biological research, fostering new insights into cell physiology and pathology. The project aims to advance imaging science and technology, transform cross-disciplinary research and education infrastructure, and increase participation from scientific communities and underrepresented groups in STEM. Success will establish and strengthen a leading biophotonics research and education infrastructure at the emerging intersection of imaging innovation and life sciences. These efforts will also impact the training of future imaging engineers and professionals, encouraging collaboration across diverse disciplines. The project will develop PMS technology for cell biological research. The innovation is driven by new microscopy concepts, engineering design, and system integration to enhance resolution without causing instrumental complexity. PMS will deliver sub-diffraction-limited resolution, confocal-like optical sectioning, and enhanced contrast in various cell and tissue samples, with minimal photodamage, making it ideal for subcellular and volumetric tissue studies. This approach will revolutionize existing microscopy platforms, including epifluorescence, scanning-based, and super-resolution microscopy. By advancing technology development and expanding infrastructural dissemination, this project aims to foster innovation in optical microscopy and enable new conceptual and methodological breakthroughs in both basic and translational biological research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The 2023 earthquake sequence in southeastern Türkiye, including the devastating magnitude 7.8 and 7.5 events, occurred along and around the East Anatolian Fault Zone. Studying this sequence provides a rare opportunity to improve understanding of how large continental earthquakes happen and how their source properties are controlled by mature active fault zones. The investigators will study the aftershock sequence in detail, with the aims to uncover the physical mechanisms that drive aftershocks, and potentially foreshocks, in this complex earthquake system. The dense seismic data previously collected will be used to create detailed images of fault zone structures, helping to identify the controlling factors that determine the rupture propagation directions and damage patterns away from the active faults. The results of this research will offer new insights into the behavior of earthquake sequences and the properties of fault zones, which are crucial for assessing seismic hazards in regions prone to large earthquakes. Thus, the findings will not only advance scientific knowledge but will also benefit society by contributing to improving earthquake risk assessments in Türkiye, a region that is highly susceptible to major earthquakes. The project will strengthen scientific collaboration between the U.S. and Türkiye and provide hands-on training opportunities for students and researchers in cutting-edge methods such as machine learning for seismic event detection, promoting a more skilled future generation of geoscientists in the U.S. while contributing to global efforts in earthquake preparedness and mitigation. The proposed project focuses on analyzing seismic data collected from an extensive deployment of ~200 nodal and 16 broadband/strong-motion seismic stations in 2023 and additional ~180 nodal deployment in 2024-2025 across the rupture zone of the 2023 Kahramanmaras earthquake sequence in southeastern Türkiye. The goal is to construct a comprehensive earthquake catalog that will provide a high-resolution understanding of the physical processes driving aftershocks and foreshocks, as well as the fault zone characteristics that influence earthquake rupture behavior. The research will focus on two primary objectives: (1) individual source parameters and collective behaviors of earthquake sequences, and (2) fault zone properties and their relationship with earthquake slip behaviors. On the first objective, by applying machine-learning and template-matching techniques, the project will relocate aftershocks and determine their focal mechanisms. This approach will shed light on the underlying mechanisms that govern aftershock sequences and foreshock triggering, and it will offer insights into rupture directivity and small earthquake behaviors. On the second objective, seismic data from ultra-dense fault zone arrays will be used to visualize internal structure of faults in the region, including its damage zone and connectivity at seismogenic depths. Three-dimensional models of seismic velocity, attenuation, and anisotropy will be inverted to identify correlations between fault zone properties and earthquake rupture velocities, specifically focusing on areas where subsidiary faults, such as the Narli Fault, where the M7.8 initiated before intersecting with the main Eastern Anatolian Fault. This detailed analysis will contribute to a deeper understanding of fault zone dynamics and offer critical data for seismic hazard assessments in a region that has experienced significant seismic activity. 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 Smart and Connected Communities (S&CC) development project supports research that aims to enhance access to fresh, nutritious food in urban areas by designing integrated supply-side, community-driven freight systems and urban agriculture models that scale into economically viable urban food logistics networks. This initiative aims to establish urban agriculture as a vital component of infrastructure, enhancing the availability of food for everyone. Despite the availability of food outlets in cities across the U.S., many neighborhoods still lack reliable access to healthy food due to persistent logistical, infrastructure, and economic barriers. Previous efforts have focused on consumer behavior and demand-side systems, while supply-side logistic systems—particularly those leveraging existing community assets—have remained underexplored. This research seeks to enable the U.S. to improve national health outcomes and urban economic vitality by supporting neighborhood-based food production and delivery, developing new planning tools for cities, and expanding opportunities for students and residents to actively participate in designing food distribution systems. The research project's vision is to establish a data collection system that will eventually enable the development of an Artificial Intelligence-Enabled Decision-Support System (AIEDSS), which models food distribution through the combined perspectives of infrastructure, logistics, and social networks. This project seeks to pioneer a new class of hybrid systems models that capture the interdependencies between spatial infrastructure, logistical performance, and social dynamics in urban food systems. The proposed AIEDSS looks to combine artificial intelligence, optimization, geospatial modeling, and social network theory to support context-aware, supply-focused, community-informed logistics planning and community-based logistics. The project also looks to integrate the Asset-Based Community Development (ABCD) framework into engineering and computational modeling, advancing the design of freight systems that reflect community-identified priorities, institutional relationships, and local food networks. The primary outcome of this effort is anticipated to be the definition of structured input-output relationships and validation-ready data templates that will directly inform system development during the next research phase. The datasets, analytical findings, and validation logic generated in this phase look to be immediately applicable to prototype development and calibration. These contributions seek to represent a conceptual shift in AI-enabled infrastructure planning from purely performance-driven models to adaptive, participatory systems grounded in local knowledge and social embeddedness. 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 award concerns research in number theory. The study of arithmetic sequences over prime numbers and the value of L-functions have been foundational topics of research in number theory over the past two centuries. Large sieve inequalities, which estimate the number of integers which remain after removing a set of residue classes modulo certain primes, are an important tool used to tackle such problems. Often, the strength of the relevant large sieve inequality dictates the progress one can make. For instance, Kummer in the 19th century (refined later Patterson in the 20th century) predicted that an important exponential sum that arises naturally in arithmetic geometry exhibits a subtle statistical bias over the primes. This problem was studied on some of the first super computers in the 1950s and 60s. The PI and his collaborators recently explained this bias with surprising new insights on the relevant large sieve ensemble. In this project, the PI will seek to push the boundaries on large sieve inequalities for families of harmonics that are perceived right now to be "stuck", or right on the edge of current technology. The PI and collaborators will explore the potential consequences of their methods for moments and zeros of L-functions, non-vanishing of central values of L-functions, and bounds for exponential sums over primes. Broader impact of this project includes the training of students and postdocs and organizing seminars. An influential conjecture of Chowla asserts that all primitive Dirichlet L-functions do not vanish at the central point. The PI plans to leverage the under utilised connection between metaplectic forms and fixed order characters over number fields to make progress toward Chowla's conjecture. The PI and his collaborators discovered that such a connection was a key ingredient in the resolution of Patterson's conjecture because it explained why a certain large sieve inequality was not optimal. The PI will extend these ideas to higher order characters, where much less is known, and will use techniques from analytic number theory to study the non-vanishing of central values of L-functions and moments of Hecke L-functions. The PI and collaborators will also use trace formulae to study large sieve inequalities for families that arise in moments of automorphic L-functions. 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 RAPID project will investigate the effects of ongoing wildfires burning across Canada that continue to degrade the air quality in many parts of the U.S., including the Atlanta area. Aerosols emitted by wildfires can travel thousands of miles away from their sources and continue to age along the way, blending in with aerosols emitted in downwind areas. The Atmospheric Science and Chemistry mEasurement NeTwork (ASCENT) site in Atlanta has recently observed increases in organics, black carbon, and potassium at the site that is attributed to Canadian wildfires. Aerosols from wildfires can adversely impact those with asthma and other respiratory diseases. Results from this work will facilitate a better understanding and representation of the aging of wildfire aerosols in models for improved prediction of their widespread impacts. The overall goal of the project is to deploy an advanced mass spectrometer at the ASCENT site in Atlanta to quantitatively constrain the impacts of Canadian wildfires on particulate levels and composition in the Atlanta area. This mass spectrometer is capable of real-time measurements of speciated gas- and particle-phase organics. With the timely deployment of the advanced mass spectrometer and co-located ASCENT measurements at a time with both wildfire smoke and biogenic emissions from local forests and plants, this work will provide for the first time a quantitative assessment of the full influence of Canadian wildfires on particulate levels and composition in Atlanta. The project will quantify not just fresh smoke aerosols, but also aged smoke aerosols, and how fresh and aged aerosols can affect gas/particle partitioning of biogenic organics. This project will provide training opportunities for a postdoctoral researcher and a graduate student on a timely and important research topic that has wide relevance and interest. 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 is to establish a field school and a network of scholars to study the way AI/ML algorithms shape, and are shaped by, conditions of space, place, and embodiment. An interdisciplinary group of scholars working together brings professional attention to the way information technologies are changing the places we live, and the way people interact with algorithms to orient themselves within spatial computing interfaces. This project is of interest to designers, decision makers, educators and computer scientists. Through an open call for participation, the field school documents mediated experiences of space and place, critique normative spatial representations, and using design as a mode of spatial inquiry. Spatial data training ML/AI algorithms occurs in a broad range of functions, such as real-time mapping, route-planning, and collision detection in co-present human-computer collaborations. The outcomes of this initiative include curriculum materials, and conference presentations and peer-reviewed publications addressing cultural and material conditions embodied in training data for shaping the outcomes of generalized AL/ML 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 · 2025-08

With the support from the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Younan Xia of the Georgia Institute of Technology and Professor Emmanouil Mavrikakis of the University of Wisconsin-Madison will develop a knowledge base for achieving robust, reproducible, and scalable production of colloidal nanocrystals. Colloidal nanocrystals with well-controlled properties are beneficial to the U.S. economy and society. For example, the nanocrystals have potential applications as advanced catalytic materials essential to energy conversion and environmental protection, as well as production of important chemicals and pharmaceuticals. The multi-disciplinary and collaborative nature of this project will offer a natural vehicle to enrich the education and training experiences of all participants. The results from this project will be adapted to enhance classroom teaching, including the development of demonstrations (both animations and experiments) related to the key concepts of chemistry and chemical engineering. Through an integration of experimental studies and computational modeling, three methods will be developed and validated for realizing nanocrystal synthesis under both steady-state kinetics and one-shot injection. In the first method, the dissociation equilibrium of a weak acid is leveraged to maintain its conjugate base (the actual reductant) at a constant and controllable level. Due to the dissociation equilibrium, the conjugate base will remain at a fixed concentration until all the added acid is consumed. In the second method, an insoluble salt precursor is used to ensure that the metal ion (the actual precursor) in the reaction solution will stay at a constant level. The third method borrows the concept of controlled release from drug delivery by loading the precursor or reducing agent in polymer beads. Under zero-order release, the precursor or reducing agent in the reaction mixture will exist at a low and constant concentration as it will undergo immediate consumption upon release from the beads. When the other reactant is used in large excess to stay at a constant concentration, these methods will enable the establishment of steady-state kinetics. Along with experimental inquiries, computational studies will be conducted to achieve a better understanding of the dissolution, dissolution, reduction, and growth mechanisms. 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

A basic principle in statistics and data science is that high-dimensional data can often be analyzed efficiently when it depends on only a few significant features. In analysis and geometric measure theory, an analogous question is whether an infinite set of points in space can be well-parameterized by a small number of variables—a property known as rectifiability. Determining whether a set is rectifiable is fundamental in analysis, and this project will develop new techniques to better understand rectifiability in non-Euclidean geometries relevant to physics and computer science. Another focus of the project is how to recover information from incomplete or noisy measurements—a challenge that arises in medical imaging, wireless communication, and the analysis of random systems. New forms of the uncertainty principle will be developed, with specific applications to these areas of science. Theoretical tools will be developed through a collaborative research environment that actively involves both undergraduate and graduate students. In doing so, the project will contribute to the advancement of mathematical understanding and the training of future scientists in disciplines that support developments in engineering, technology, and the natural sciences. A central question in geometric measure theory and harmonic analysis is whether the boundedness of a singular integral operator implies that the underlying measure must be rectifiable, or whether it can have purely fractal support. While this problem has been extensively studied in Euclidean spaces—where it connects to harmonic measure and free boundary regularity—much less is understood in non-Euclidean settings, such as the Heisenberg and parabolic groups. This project aims to extend the theory to these geometrically rich and analytically challenging contexts. A second major component addresses uncertainty principles and sampling theory. The focus is on characterizing the sets on which a function can be uniquely and stably reconstructed from partial information about its Fourier transform. These reconstruction results will be tailored for application to a range of problems, including mobile sampling, control theory for the wave and Schrödinger equations, and the long-term behavior of stationary Gaussian 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 · 2025-08

Despite the recent development of many non-invasive or minimally invasive diagnosis techniques, accurate and safe biopsy remains the cornerstone for ensuring precise diagnostic outcomes and facilitating effective treatment for pediatric patients with liver diseases. An ideal liver biopsy should be able to 1) precisely and safely sample the lesion in the liver subject to heart and respiration motion effects and 2) overcome the unique challenges in pediatric patients due to weight, size, and restrictions to exposure. This project supports fundamental research in tentacle-like continuum (soft) robotic needle and ultrasound-based perception to achieve the desired capabilities. In addition to advancing scientific understanding, the developed technology looks to be applicable to a wide range of needle-based procedures such as laser ablation, cryoablation, irreversible electroporation, and brachytherapy. Furthermore, the project will integrate the fields of continuum (soft) robotics, ultrasound, and medicine to create a STEM infrastructure for educating and training K-12, undergraduate, and graduate students, and promoting medical robotics. Continuum robots have emerged as one of the most promising techniques for diagnosis and treatment, but dynamically targeting the moving structures in pediatric patients remains a challenge. In this project, three complementary and interconnected thrusts will be developed to attempt to overcome this limitation. The first thrust looks to enhance dexterity and compactness through a novel continuum robot concept called concentric bevel-tip needle, along with the development of a comprehensive model incorporating friction components and an adaptive path planner for dynamic targeting and obstacle avoidance. The second thrust aims to achieve real-time robot perception by developing a pediatric-specific, deep-learning-based image processing algorithm for needle position tracking and low-contrast target detection using ultrasound image feedback. The third thrust looks to integrate the technical developments in thrusts 1 & 2 and systematically quantify the completed system’s accuracy, efficiency, and safety. Collectively, this project intends to advance the state of the art in interventional continuum medical robotics through innovations in design, modeling, planning, control, perception, and application. 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 considers a core computational problem, namely, how to draw samples from a high-dimensional probability distribution. Being a fundamental task in science and engineering, the sampling problem involves generating representative examples of a stochastic object, and the ability to do so enables the modeling, simulation, and inference that are essential for understanding complex systems with uncertainties. Because of its importance, sampling is a classical problem that has been considered in statistics, physical sciences, and social sciences for decades. Moreover, it recently received renewed interest due to the exploding number of applications in data sciences, machine learning, and artificial intelligence, which motivates the research. The project aims to provide a better understanding of the existing practice and guide the construction of new methods that scale better. To address such an urgent need, this project will develop innovative and versatile sampling algorithms, together with analytical tools that can facilitate their design and certify their performance. More precisely, the intellectual merit of this project is to propose, in a principled and mathematically provable way, samplers that scale well (with dimension, condition number, etc.) and work in versatile setups (e.g., sampling from Euclidean space, sampling from a constrained domain, and sampling difficult multimodal distribution). This goal will be enabled via a synergy of three strategies. The first is to view a class of sampling algorithms as appropriate time discretizations of certain underlying dynamics in continuous time. This perspective allows the algorithmic design and analysis to be modularized into those for the continuous dynamics and those for the discretization, thus not only enabling an exploitation of profound mathematical tools but also helping better identify and focus on the true performance bottleneck. The second strategy is to leverage rich tools in optimization and extrapolate them to the sampling setup. It also includes a modern perspective, which is to view sampling as optimization in the infinite-dimensional space of probability distributions. The third strategy is to utilize geometric ideas, which not only lead to a deeper understanding of various sampling dynamics but also facilitate sampling from constrained distributions. Educational activities will be closely integrated with the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Artificial Intelligence (AI) holds great promise for transforming critical industries across the Nation by driving efficiency and productivity. While computational power has increased, there remains a gap between fundamental science-driven advances in AI methods and their practical usage in real test cases. As part of NSF's commitment to advancing fundamental AI research, the national AI Institute for Optimization (AI4OPT) has pioneered new data-driven, AI-enabled methods that address unique challenges for power grids that must operate reliably and efficiently under highly variable conditions due to their diverse energy generation sources (e.g., natural gas prices, uncertain demand, and evolving capacity of battery and storage technologies). When historical power systems data is examined, the AI4OPT unit commitment optimization has shown significant advantages over state-of-the-art algorithms. This EArly-concept Grant for Exploratory Research (EAGER) project aims to translate these foundational advances into an AI-assisted platform deployed in practice and usable by power system operators, generators, load-serving entities, and energy traders to model, assess, and jointly optimize risk and costs in their planning and real-time operations. In conjunction with Southern Company, the platform looks to integrate fundamental innovations in trustworthy optimization learning methods via primal and dual optimization proxies, temporal fusion transformers, scenario generation, and stochastic optimization for high dimensional time series forecasting, on software and hardware infrastructures in daily use by planners and operators. The project seeks to produce commercially-viable tools for Unit Commitment and near optimal market clearing algorithms, as well as real-time risk management simulators that evaluate system-level, asset-level and financial risk for meeting real-time constraints. The platform is expected to offer orders of magnitude improvements over state of the art in market clearing algorithms that deliver both feasible solutions in milliseconds as well as certificates of quality. The project intends to produce significant economic benefits in terms of energy savings, emission reductions, and increases in equipment reliability for the southeastern United States and looks to serve as a demonstration of effective technology transition from proof of concept research to practical deployment. 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 program will create computer models of the very first galaxies and black holes to form in the Universe. New observations have discovered extremely distant galaxies, some of these galaxies appear more massive and evolved than expected by current models. Through computer simulations, this program will make an important contribution to establishing the new models needed to properly explain the recent observations of the most distant galaxies. To make the scientific results of this program available to everyone, the PIs will design and fabricate dozens of 3D-printed models from various simulated galaxies and any interesting structures within them. These 3D-printed models will be displayed in outreach events and art exhibits. This collaborative proposal will deliver theoretical and observational predictions for galaxy and Super-Massive Black Hole (SMBH) formation and growth during Cosmic Dawn with a particular focus on galaxies with overly massive black holes. Specifically, this proposal will investigate if galaxies in the early universe with and without a black hole seed can be distinguished observationally. Using the exascale-capable code Enzo-E, this program will carry out simulations of high-redshift galaxy formation, accounting for SMBH fueling, feedback models and their impact on the circumgalactic medium. The principal goal of this work is to determine the observational signatures of a galaxy that has hosted a massive black hole seed. This proposal will provide a basis for training astrophysical simulations enhanced by Artificial Intelligence. This proposed work will provide a breadth of research opportunities for the professional development of undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award supports research that enables precision manufacturing of materials with desired properties as well as coordination and control of large collection of autonomous agents such as robotic or biological swarm, thereby promoting national prosperity and welfare. Direct and precise control of large population of individuals has emerged as a new frontier across engineering disciplines. However, existing solutions fall short in practice as they fail to account for realistic nonlinear agent models, inter-agent interactions, statistical errors, and constraints affecting the uncertain dynamics of the population. This project will address this critical gap by designing theory and computational algorithms with performance guarantees. The research transcends the discipline of control engineering and will be impactful in machine learning where there remains a critical need for precise control of data distributions. The project will train the next generation of students and engineers working in the broad areas of control, machine learning and their intersection, via several educational and outreach activities. This project explores a new vision advancing the theory and algorithms for the control of distributions. The distributions may correspond to the stochastic states of a single controlled dynamical system giving rise to time-varying state probability distributions. Alternatively, the distributions may correspond to population ensembles wherein the dynamics of an individual agent in the population can be nonlinear in state, non-affine in control, and the agents may interact in a nonlocal manner. The project focuses on three main challenges that remain in this area involving nonlinearity in dynamics, interactions among agents and robustness of control policy. The project will deliver a suite of theory and algorithms for the control of distributions in either case, with optimality and robustness guarantees in the presence of hard deadline and controlled dynamical constraints, with or without process noise. The outcomes will enable scalable nonparametric gridless computation beyond second order statistics (i.e., covariance control), thereby unlocking the full potential of distribution control in practical applications. The broader impact of this project will result from fundamental advances in theory and computational methods to benefit the fields of stochastic optimal control, optimal mass transport, Schrödinger bridge -- all three are finding rapid adoption in generative diffusion models in machine 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 · 2025-08

Powered lower limb prostheses have made significant technological progress, yet they continue to face major challenges in navigating real-world environments. Individuals with lower limb amputation often struggle with activities involving transitions between surfaces such as grass, gravel, or slopes, where current prosthetic controllers are limited in providing the necessary stability and agility. This Smart and Connected Health (SCH) project seeks to address that challenge by advancing the control strategies used in robotic ankle-foot prostheses. By understanding how humans adapt their movement when walking on different terrains, the research will inform the development of next-generation prostheses that can proactively and reactively adjust to changing environments. The goal is to enhance the independence, safety, and quality of life of individuals who rely on powered prostheses. In doing so, this work contributes to the national interest by promoting the progress of science and engineering, improving public health and welfare, and inspiring future innovation in assistive technology. The project seeks top support education by engaging students in hands-on research experiences that foster interest in robotics, biomechanics, and assistive technologies. The project will investigate the sensorimotor mechanisms that enable humans to walk dynamically over uneven and unpredictable terrain. These insights will be used to develop and validate a novel, multi-level control architecture for powered prosthetic limbs. The approach integrates proactive strategies based on predictive terrain recognition with reactive feedback-based stabilization. The control framework will be tested in real-world scenarios with individuals with limb loss to assess performance and identify failure modes. The research seeks to improve understanding of how to merge human neuromechanical signals with environmental feedback in a unified, robust control system. Outcomes look to include new methods for real-time adaptation in wearable robotics, advancements in the field of human-in-the-loop control, and broader applications in rehabilitation robotics and mobility assistive devices. Through rigorous experimental evaluation and community engagement, the project seeks to redefine the future of lower limb prosthetic function. 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

Paper-based electronics offer a creative and accessible entry point for STEM education, grounded in the rich tradition of papercrafting. However, most K-12 education tools and activities remain focused on introductory concepts, leaving a gap in opportunities for advanced electronics learning. This project aims to address the gap by integrating emerging printed electronics techniques with craft-friendly conductive materials, such as conductive paint, ink, and spray, to support more complex, engaging experiences. For example, inkjet printing with metal-based inks enables circuit fabrication directly on paper, while origami-based techniques with thin-film materials can create self-powered interfaces. These innovations can transform paper into a versatile platform for both introductory and advanced electronics, connecting craft with essential skills and knowledge in microelectronics. Although techniques like inkjet and stencil printing with silver or carbon inks show promise in research, their application has been largely confined to laboratory environments. This project seeks to adapt these methods for educational contexts, advancing electronics education and inspiring youth to explore pathways into the microelectronics workforce. This project will investigate how to engage youth makers in electronics education by introducing paper-based electronics, ranging from basic components (e.g., switches, registers) to more advanced ones (e.g., transistors, capacitors, and integrated circuits). The project will focus on innovating paper-based electronics through emerging printed electronics technologies, connecting the hands-on, creative traditions of papercrafts with advanced electronics fabrication and exploration. Collaborating with Robert C. Williams Museum of Papermaking at Georgia Tech, which showcases paper-based art and science and provides hands-on workshops for youth and families, the project will examine the feasibility of designed electronics materials by conducting a series of youth co-design workshops at the museum and generate evaluative insights into how paper-based electronics can contribute to both approachable and advanced electronics education. The objectives of this project are twofold: (1) to establish a foundational understanding of how emerging fabrication technologies can be integrated into youth papercrafting for innovative electronics education, focusing on the impact on participants’ knowledge and interests in electronics, and (2) to identify scalable and educationally viable approaches to paper-based electronics for more advanced electronics education. The study outcomes will be featured in a special exhibition at the museum and youth participants from the studies will be invited for a summer internship at Georgia Tech’s WISH (Wearable Intelligent Systems and Healthcare) center. This project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding 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 · 2025-08

A graph is a mathematical object that models real-life networks such as road networks, communication networks, and supply networks. In structural graph theory, the goal is to understand the large-scale behavior of graphs whose local behavior is somehow restricted. This project focuses on developing structural techniques for dense graphs, that is, graphs with many connections. The PI will focus on two ways of restricting the local behavior: by forbidding a vertex-minor, and by asking for a dependent first-order theory. These restrictions have applications to other domains such as quantum computing, algorithms and complexity, discrete geometry, and extremal combinatorics. Graduate students will be trained as part of the project. This project will develop structural techniques for dense classes of graphs, focusing on classes with a forbidden vertex-minor or a dependent first-order theory. For classes with a forbidden vertex-minor, the goal is to obtain a complete understanding of the structure relative to a tour graph. This will be a key step towards proving a conjecture of Geelen, which would give a very precise structural description of graphs with a forbidden vertex-minor. For classes with a dependent first-order theory, the focus is on a well-known dichotomy conjecture. This conjecture roughly states that for classes that are closed under vertex deletion, having a dependent first-order theory is equivalent to admitting an efficient algorithm for checking the first-order properties of graphs in the class. 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

Topology is the study of shapes and spaces. One way to study spaces is to cut them up into simpler pieces. Depending on what space you start with, this decomposition might not always be possible. To study when it is possible, topologists use tools called invariants. This project will use algebraic invariants of three-dimensional spaces to answer the question of when a high-dimensional space can be cut into triangles, as well as to study knots and surfaces. The principal investigator (PI) will mentor graduate students and postdocs in topology, and will also organize conferences, workshops, and seminars in the field. The PI plans to use tools from Floer homology to answer questions about topology. One project uses algebraic tools to give a simpler characterization of when a high-dimensional topological manifold is triangulable. Another project deals with knots and ribbon concordances between them. Lastly, the PI studies properties of Heegaard Floer homology of 3-manifolds. These projects will contribute to the field’s understanding of smooth and topological manifolds in a range of dimensions. 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

The Industry-University Cooperative Research Center (IUCRC) for Accessible Healthcare through AI-Augmented Decisions (AHeAD) will develop trustworthy and usable AI technologies, so quality care is accessible by all populations. AHeAD is a multi-university research partnership between UL Lafayette (lead), Tulane, University of Florida and Georgia Tech. The center’s research will create validated AI-enabled systems, quality assurance frameworks, and best practices that enable healthcare organizations to offer quality care for all, reducing healthcare gaps while saving costs. By training the next-generation AI workforce and releasing open-source AI models, the center will drive innovation, create new jobs, and grow the American economy. AHeaD's goal is to develop trustworthy AI technologies that improve healthcare access and outcomes for all populations. Research focuses on creating privacy-preserving, interoperable, explainable and resource-efficient AI models for healthcare. The center's multidisciplinary program includes AI/ML, data science, systems engineering, and health sciences, supported by computational infrastructure and real-world health data through industry partnerships. Research will advance trustworthy AI, privacy-aware data integration, behavioral context modeling, and human-AI integration. The center will foster workforce development through student training. The center capitalizes on Georgia Tech’s leadership in AI, infrastructure (e.g., AI Makerspace), and proven contributions to the state’s economic development. The rapidly growing healthcare industry in Atlanta positions the site to successfully deliver on the objectives of the center. The Gerogia Tech site will focus on federated learning, Trustworthy AI, Explainable AI, and Human-AI interaction. AHeAD will address critical national healthcare challenges and advance U.S. competitiveness in AI-enabled healthcare. The Center creates a rich environment for training next-generation professionals through integrating industry-relevant AI applications into curriculum development and providing direct experience solving healthcare challenges with real-world data. The Center will create and maintain standardized healthcare datasets, publish open-source software and research outputs, and advance technologies with broad healthcare applications. This multifaceted approach promises to improve the health of millions of Americans while generating substantial cost savings for both government and industry. 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.