Columbia University

NSF Awards · FY 2025 · 2025-09

This I-Corps project is based on the development of a technique for large area laser processing called spot beam annealing. This technology represents a fabrication method in which a focused laser beam melts or vaporizes material in a localized area, creating intricate cuts, patterns, or designs. Laser techniques offer a number of advantages for the processing of thin film semiconductors. The spot beam annealing technology may be used in thermal laser annealing applications and employs advanced lasers and optics to spread the light from a high-power laser system, allowing the treatment of a large area. Currently, there is a need to reduce the cost and complexity of the beam forming optics. The technology has been shown to simplify the optics that are used for beam spreading and control. In addition, the technology may reduce costs and permit the development of new laser processing approaches that may improve the performance of a range of electronic systems including displays, memories, and microcontroller devices. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of spot beam annealing and related techniques for large area laser processing. Spot beam annealing is an advanced laser crystallization and heat treatment technology that can be used in low-temperature polycrystalline silicon (LTPS) and low-temperature polycrystalline oxide (LTPO) processes for producing large-area display panels. This solution may be used for large area organic light-emitting diode (OLED) displays, x-ray imagers, touch sensors, or backplanes for micro-light emitting diodes (LEDs). In addition, spot beam annealing may allow for the use of lower cost optical elements than traditional line beam shaping techniques while delivering potentially superior electrical performance for annealing. Spot beam techniques also may be useful for other laser processing such as spike annealing and laser liftoff. Spot beam annealing rapidly scans a high-power pulsed laser spot forming a continuous melt in the amorphous silicon layer. This technique provides highly efficient laser power usage and the ability to shape the spot beam in a predetermined geometrical dimension, intensity profile, and temporal profile. These additional parameters allow for more control of the process to create an improved uniformity of crystallization, and transistor performance. This solution may offer reduced cost and better performance in applications including laser crystallization, laser liftoff of polymer and inorganic substrates, and other techniques used for laser thermal annealing. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program, Professor Jonathan Owen of Columbia University is studying a new strategy to synthesize nanometer scale semiconductor crystals that convert blue light-emitting diode (LED) light into red emission. A “redox conversion” process will use low-valent gallium and indium precursors together with phosphorus and arsenic reagents to grow nanoscale particles in more widely accessible surfactant solution. If successful, this research will produce cadmium-free materials for energy-efficient lighting and infrared detectors, while training high school, undergraduate, and graduate students in cutting-edge synthesis and spectroscopy. With the support of the Macromolecular, Supramolecular and Nanochemistry Program, Professor Jonathan Owen of Columbia University is studying a novel colloidal synthesis strategy to prepare III–V quantum dot heterostructures with controlled crystallinity, size, and composition. GaP, GaAs, InGaP alloys, and InP/GaP core/shell architectures will be synthesized by pairing Ga(I) and In(I) complexes with tailored phosphine, phosphite, and phosphonium reagents. The resulting nanocrystals will be rigorously characterized by synchrotron X-ray total scattering and pair distribution function analysis, Raman spectroscopy, solid-state ³¹P Nuclear Magnetic Resonance, ultraviolet photoelectron spectroscopy, electron microscopy, and photoluminescence measurements to assess defect density, phase purity, electronic structure, and biexciton quantum yields. This effort will establish design rules for covalent semiconductor nanomaterials and demonstrate heterostructures with enhanced flux stability and reduced Auger recombination for solid state lighting, micro-LEDs, and infrared optoelectronics. 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

With this award, the Molecular Foundations for Biotechnology (MFB) Program is funding Drs. Chaolin Zhang and Harris H. Wang from Columbia University to engineer modular proteins that can be programmed to precisely edit sequences of RNA messages or control the expression of genes encoded in these messenger RNAs in human cells. This project adapts a unique class of plant proteins, called pentatricopeptide repeat (PPR) proteins that naturally recognize RNA sequences through a modular code. Such proteins will overcome limitations of current RNA-targeting technologies, including imprecise binding and difficulties in introducing them into cells. PPR proteins will be custom designed to bind specific RNA targets with high accuracy, and their performance tested in human cells and compared to existing technologies. In parallel, the project will include community outreach and education through workshops hosted with a public biology lab in New York City, offering hands-on experiences in RNA biology and biotechnology to citizen scientists. Overall, this work could lead to powerful new RNA-based tools with broad applications in biotechnology, agriculture and medicine, while also promoting public understanding and engagement with cutting-edge biological science. The technical goal of this research is to establish a versatile and high-performance platform for programmable RNA targeting using engineered PPR proteins. The team will apply computational approaches to decipher the natural code that allows PPR proteins to bind specific RNA sequences, and then use synthetic biology and high-throughput screening to optimize designer PPR variants (dPPRs) for use in human cells. These tools will be evaluated in terms of binding specificity, efficiency, and functional versatility, and benchmarked against state-of-the-art RNA technologies such as CRISPR and RNA interference. The project integrates computational modeling, synthetic DNA assembly, and RNA functional assays to explore the full potential of PPR-based RNA engineering. By addressing key technical limitations of current platforms—such as off-target effects and delivery complexity—this research will significantly advance molecular tool development. It also offers new insight into how modular RNA-binding proteins function, which may inform future efforts to engineer RNA recognition in diverse biological systems. This project is supported by the Division of Chemistry in the Mathematical and Physical Sciences Directorate, by the Division of Molecular and Cellular Biosciences and the Genetic Mechanisms program in the Biological Sciences Directorate and by the Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Lyme disease and other diseases carried by ticks are generally linked to suburban and rural areas, but they are increasingly found in urban parks and other natural habitats in and around cities. The germs that cause these diseases are maintained by wildlife that ticks also depend on for survival, as they are their main source of blood meals. Therefore, areas with the greatest chance for humans to get sick occur where there are enough greenspaces to support wildlife and many human visitors. The project will identify these risky settings by collecting and combining wildlife, tick, and human movement information using advanced computational models. The findings of this investigation will be directly translated into disease prevention by empowering the community to improve their own health through the use of The Tick App smartphone app, a research and educational tool created by the research team. The App includes AI functions to identify ticks and provide information on risk factors and human movement; wildlife will be identified using AI identification of trail cam photos. Furthermore, the project’s outcomes equip city planners, park managers, and health officials with science-based information and practical tools to design urban green spaces that support wildlife while reducing tick encounters in urban areas. This work offers training for students and a skilled future workforce—from elementary students to postdoctoral researchers—in research methods with real-world applications to protect human health. This research project takes an integrated transdisciplinary approach to study how the risk of tick-borne diseases changes from rural to dense urban fabric in large metropolitan areas, such as New York and Boston. Wildlife and ticks will be sampled in 80+ green spaces with different levels of connectivity quantified as the total ‘current’ flowing through the landscape (Omniscape platform). Researchers use and improve AI platforms to automatically identify ticks using photos from The Tick App, wildlife species from trail camera photos, and bird species from audio recordings. Human movement patterns are assessed using GPS locations derived from The Tick App for people’s park use, in addition to self-reported tick encounters and wildlife sightings. Machine learning models, along with advanced network models, analyze and predict patterns of wildlife and tick distributions with people’s use of parks and other green spaces. The team interviews planners and land managers to understand if and how they try to reduce disease risk in park planning and management to fine-tune the Machine Learning models. The models will then simulate how changes in land use, such as adding or removing green spaces or reductions in deer populations, affect the animals that ticks feed on, and how human behavior and movement affect exposure to ticks. By building advanced computer models using real-world data and informed by manager interviews, the team aims to predict how changes in cities might increase or decrease the risk of disease, and translate the findings to improve management strategies for green spaces and wildlife to protect public health. 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 is to study the new partial differential equations arising from complex geometry and the search for a unified theory of all fundamental interactions. These equations present many novel challenging features and are interesting in their own right, so this is a particularly important area at the interface of mathematics and physics. The project also incorporates the training of graduate students and postdocs and organizing seminars and conferences. More specifically, in Kaehler geometry, equations on spaces with singularities will be studied. It is necessary to allow singularities in view of many applications to algebraic geometry and string theory. In non-Kaehler geometry, a key step will be to identify the underlying special geometry in the sense of holonomy, and weakly parabolic flows will play a major role. New tools will be developed, building in particular on the new methods for estimates introduced recently by the PI and his collaborators. 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

Weather and climate have substantial impacts on human activity and the economy, among them damage to life and property from extreme weather. To manage these impacts and mitigate their harms -- to do "adaptation", in other words -- it is helpful to assess the risks ahead of time, by estimating the probabilities of impactful events and how costly they might be. Such risk assessment is practiced in multiple sectors and fields including the insurance industry, infrastructure planning, agriculture, public health, and so on. Yet the methods, data, tools and models differ across sectors, particularly in the extent to which and ways in which they account for possible changes in risk driven by changes in climate. Historical data by itself does not adequately capture such changes; climate models can, in principle, do better, but they bring in additional uncertainties and possible errors. Researchers and actors in different sectors make different choices about how to handle the trade-offs involved in using model simulations versus historical observations, in part because of differences in the impacts that matter to them. But it is not always clear how they decide what trade-offs to make, or if the trade-offs they make are the best possible choices. This project seeks to develop a general framework for assessing the value added by using climate model output to inform decision-making for weather and climate risk. The work applies a theoretical framework from economics in which the value of information is assessed by quantifying how different types of information would lead to better or worse decisions according to pre-defined criteria which differ from one decision type to another. The researchers use synthetic weather and climate information, generated by both simple idealized models and much more realistic ones, to determine how the answers depend on the characteristics of the relevant weather and climate phenomena, as well as those of the economic sector and decision type. By putting the different sectors into a common intellectual framework, and examining how differences in the types of adaptation decisions might or might not imply different trade-offs in the use of observations and different types of models, the project seeks to build the basic science foundation of a broader field of climate risk. The foundational approach could unify the current patchwork of sector- and decision-specific methodologies, making it easier for actors in different sectors to learn from each other, and for climate scientists to engage with decision makers in all sectors. 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

Dark matter is a mysterious substance that makes up most of the matter in the Universe, but it has never been seen directly. To uncover this cosmic enigma, scientists are conducting the XENONnT experiment. This experiment uses a detector filled with nearly nine tons of ultra-pure liquid xenon to search for extremely rare interactions that could help us understand what dark matter is composed of. XENONnT is the last experiment in the international XENON Dark Matter project, which has received support from the National Science Foundation since it began. This project creates a rich environment for educating students and researchers in the U.S. and around the world, with more than twenty institutions collaborating globally. The scientists working on this project are trained in advanced science and technology that cover multiple disciplines. The specialized tools and techniques they use, along with advanced data analysis and statistical methods, are not only important for understanding dark matter but also have significant applications in fields like medicine, nuclear safety, and data science. Candidates for the dark matter which dominates the matter content of the Universe span decades in mass and interaction cross-section with normal matter. The class of Weakly Interacting Massive Particles (WIMPs) has been the most studied theoretically and experimentally with indirect and direct searches as well as at the Large Hadron Collider. The sensitivity for WIMPs direct detection has increased by many orders of magnitude in the past twenty years thanks to experiments using liquid xenon in dual-phase time projection chambers with increasing target mass and decreasing background. The phased XENON Dark Matter project has led the direct detection field with its XENON10, XENON100 and XENON1T experiments and has paved the way to the current generation of multi-tonne scale liquid xenon detectors, including the largest of the XENON detectors, XENONnT with 6 tonnes of active target. The unprecedented ultra-low background achieved by XENONnT, the lowest among all direct searches, has enabled a sensitive search not just for WIMPs but also other rare interactions, such as the recent first observation of coherent elastic neutrino-nucleus scattering from solar B-8 neutrinos. This award will enable the XENON US groups to continue to contribute to the operation of the experiment at the Italian Gran Sasso Underground Laboratory (LNGS) and to continue to lead several science analyses using the data acquired to-date with the XENONnT detector. 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

Non-technical summary: The development of new types of batteries that do not rely on lithium will enable cheaper and more readily available energy storage. Potassium is an especially promising alternative to lithium because it is two orders of magnitude more abundant, with processing facilities in place worldwide and significant reserves in the southwest United States (220 million metric tons). However, simply swapping lithium for potassium in batteries that use the same graphitic carbon anodes and analogous electrolytes does not lead to comparable electrochemical behavior. In contrast to lithium, potassium-ion batteries show severe performance degradation during cycling including poor capacity retention and high internal resistance. This project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research and the Electrochemical Systems Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems, both at NSF, uses advanced characterization tools such as magnetic resonance spectroscopies, diffraction, and imaging to understand how the crystal structure and the surface chemistry of different graphite anode materials influence electrochemical properties. The research creates a fundamental understanding of how graphite crystal structure, particle shape and size, and surface passivation alter the insertion and removal of potassium-ions during battery cycling. Insights from this research provide structure-function relationships that may inform graphite preparation and electrolyte design. Broader impacts of this project include science communication efforts where graduate students collaborate with New York City-based comedians to make easy-to-understand videos about their research for social media. Undergraduate students from Barnard College and Columbia University conduct hands-on battery research during a semester or in the summer, and local high school students visit Columbia University to learn about the history of carbon materials and see scientific instrument demonstrations. Technical summary: Elucidating the precise mechanisms that enable reversible potassium-ion intercalation into graphite anode materials is critical to expanding the number of materials that can potentially be used for various energy storage applications. This project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research and the Electrochemical Systems Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems, both at NSF, investigates the role of graphite crystallinity and surface chemistry on potassium-ion intercalation dynamics in graphite anodes for potassium-ion batteries (KIBs). In contrast to lithium systems, potassium-ion insertion is highly dependent on graphite microstructure, where structural disorder leads to increased defect concentrations and poor reversibility during cycling. The quantity and type of defects in a range of graphite materials and resulting K-graphite intercalation compounds are evaluated with state-of-the-art X-ray diffraction (XRD), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and electron paramagnetic resonance (EPR) spectroscopy. These techniques are used in conjunction with traditional electroanalytical methods to further assess the impact of graphite structure on ion transport and capacity retention. The relationship between graphite surface termination, electrolyte formulation, and the composition of the solid electrolyte interphase (SEI) is evaluated using a combination of solid-state NMR and advanced imaging metrologies. In particular, NMR spectroscopy has the ability to identify structural changes in metal fluoride components in the SEI that may enable ion conduction to further advance these formulations. In situ NMR spectroscopy is leveraged to determine how electrolyte oxidation at the cathode in full cells impacts the stability of electrolyte and electrode components in KIBs, including acid formation, transition metal etching, and crosstalk between the positive and negative electrode during cycling. Overall, the project generates molecular-level descriptors that connect structural disorder in bulk graphite anodes and at interfaces to the electrochemical properties observed in KIBs. This work is tightly integrated with educational initiatives including undergraduate training, high school outreach, and public-facing social media content constructed in a three-minute thesis format in collaboration with local comedians that communicate graduate-level research for general audiences. 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 human digestive tract contains complex communities of microbes that contribute to human health by facilitating nutrient metabolism and digestion, promoting immune function, and providing protection against pathogens. To provide these human health benefits, microbial communities must be able to colonize and persist in the human digestive tract. This project will analyze several factors that are important for assembly and stability of these communities, and will provide tools that the scientific community can use for future studies. This project will inform microbial engineering efforts and perhaps lay the groundwork for future biomedical applications. Beyond the research community, the project will engage the public through hands-on microbiome workshops where participants learn about and study microbial communities. Microbial communities are essential for human health, environmental processes, and biotechnology, yet the basic rules that govern their assembly, stability, and function remain unclear. This project will develop a defined synthetic human gut microbiome, called SynhCom, created from a curated set of common and ecologically important gut bacteria. Using gnotobiotic mice, the research will systematically examine how spatial organization and biofilm formation influence the persistence and resilience of microbial communities under changing conditions such as diet. High resolution spatial metagenomics, functional genomics, and biofilm characterization will be used to uncover the mechanisms that shape microbial interactions and host responses. Unlike studies of natural, complex microbiomes that make it difficult to identify cause and effect, SynhCom provides a reproducible and controlled model to directly test community-level principles. The findings will establish a framework for understanding microbial ecosystems that could be applied to different settings, from the human gut to soil and aquatic environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Extreme rainfall events in South Asia affect over a billion people, causing floods that devastate agriculture, infrastructure, and communities. While scientists have long known that El Nino events typically reduce Indian monsoon rainfall, this project reveals a surprising paradox: in India's wettest regions, extreme rainfall events actually become more frequent during El Nino years, even as total seasonal rainfall decreases. Such different responses of average and extreme rainfall challenge our understanding of the underlying dynamics that relate global climate variability to regional precipitation. The research thus contributes to basic science understanding of global climate variability and regional precipitation while also addressing practical issues of water resource management, agricultural planning, and disaster preparedness across South Asia, where rainfall extremes directly impact food security and economic stability. The project focuses specifically on the pathways through which El Nino events influence extreme rainfall through their effects on Low Pressure Systems (LPSs), the weather systems responsible for much of India's intense rainfall. The work addresses three main objectives: (1) documenting how LPSs change in response to climate variability using decades of satellite and ground-based observations; (2) developing a mechanistic understanding of how large-scale atmospheric conditions control the formation, movement, and intensity of LPSs; and (3) evaluating climate models' ability to simulate LPSs and their interactions with large-scale conditions. The project leverages recent advances including high-resolution rainfall datasets, sophisticated storm tracking algorithms, new theoretical frameworks for understanding rainfall distributions, and climate models capable of simulating regional weather systems. By connecting planetary-scale climate forcing to local extreme events through intermediate-scale weather systems, this research addresses a critical gap in our ability to predict and prepare for rainfall extremes in a changing climate. 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

Earthquakes are known to occur at plate boundaries, yet these natural hazards can also occur within stable continental regions beyond the plate boundary, such as in eastern North America. The hazard and risk posed by earthquakes in eastern North America is great because the region includes half of the top ten most populous metropolitan areas in the United States and generates 25% of its GDP. Though earthquakes are rare in the region, the recent April 2024 Mw4.8 earthquake in New Jersey highlights the importance of studying earthquake seismicity within stable continental regions. However, the earthquake rate in eastern North America is low, and sparse seismic networks have hampered progress in understanding the nature of faults and the earthquakes they produce. In this project, scientists will develop new machine-learning and cross-correlation methods to detect previously undetected earthquake events at a significantly lower magnitude detection threshold and higher location precision compared to existing catalogs, providing fundamental new data to study seismogenesis and seismotectonics in eastern North America at a broad range of spatial scales. This project aims to significantly improve on and expand currently available catalogs of earthquake parameters (including location, magnitude, focal mechanism) for eastern North America by applying advanced machine-learning and cross-correlation based earthquake detection and characterization methods to decades of continuous waveforms recorded in the region. The instrumentally recorded seismicity in eastern North America is sparse and typically only complete down to ~M2.5 and often higher in regions of sparse instrumentation. The new high-resolution, deep-magnitude earthquake catalog will include many previously undetected events that are expected to illuminate active faults at depths, providing new data and new insight into seismotectonics, fault mechanics, earthquake generation, and the stress conditions under which faults fail. The project harnesses the availability of long seismic archives, recent game-changing developments in event detection and characterization, and a recent Mw4.8 event in New Jersey that serves for ground-truthing both methods and new knowledge gained from the data. 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 builds the next-generation Python Simulations of Chemistry Framework (PySCF) software platform to make electronic structure simulations faster, more robust, and more accessible to computational scientists across many disciplines. The new cyberinfrastructure will enable researchers to better understand the behavior of complex molecules and materials, which plays a crucial role in advancing energy technologies, catalysis, drug discovery, and quantum materials. By harnessing modern computing architectures such as graphics processing units (GPUs) and developing advanced quantum chemistry algorithms, the project will significantly speed up large-scale quantum simulations while reducing computational cost. The project will also produce user-friendly interfaces, manuals, tutorials, and training materials to support education and workforce development in computational science. As an open-source and extensible platform, the PySCF software will catalyze innovation across a broad research community, including chemistry, physics, materials science, artificial intelligence (AI), and quantum information science. First-principles simulations play an essential role in chemistry and materials research, yet the user adoption of more robust electronic structure methods has been hindered by the lack of open-source, high-performance, and user-friendly software infrastructure. The sustained innovation of new quantum chemistry tools is also often hampered by high code complexity and limited extensibility of existing software implementations. This collaborative project addresses these fundamental challenges by advancing the PySCF framework to deliver high-efficiency electronic structure tools and an extensible method development platform. Specifically, this project will develop GPU-accelerated quantum chemistry infrastructure, a low-rank density fitting engine to exploit sparse tensor structures, and a quantum embedding library to enable simulation of complex systems. By incorporating automatic capabilities such as autodifferentiation and designing reusable and modular libraries, this project will substantially lower the barrier for developing quantum chemistry methods and incorporating electronic structure components into AI workflows. Furthermore, a wide selection of cutting-edge stochastic and multireference methods, such as auxiliary-field quantum Monte Carlo and complete active space perturbation theory, will be implemented and integrated with new acceleration techniques. Overall, this project will open new frontiers for accurate and scalable simulations of molecules and materials. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry in the Directorate for Mathematical and Physical Sciences. 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 project is at the nexus of Applied Mathematics and the Applications of the Physics of Waves to Novel Material Media. The principal investigator’s (PI) goal is to develop mathematical methodologies – analytical to computational - for the prediction of phenomena in new generations of quantum materials (such as graphene) and quantum physics inspired metamaterial variants. These questions are central to understanding of the physical wave effects which play a role in emergent applications of wave phenomena in communication and computing technologies. The PI will study a range of problems in fundamental and applied mathematics. This project focuses on wave propagation in quantum materials, and their synthetic analogues (metamaterials). A part also explores the nonlinear interaction dynamics between fluids across a deforming interface. Some specific topics to be investigated are: (A) energy propagation as waves in bulk two-dimensional (2D) materials, and as ``edge states'' along line defects, such as domain walls within or sharp terminations of the bulk in 2D materials. This is studied in the context of topological materials, for which edge transport properties are completely determined by spectral properties of the bulk ``boundaryless'' structure. Specific topics include: (i) novel effects in quantum materials due to strong magnetic fields (ii) the pseudo-magnetic effect arising due to non-uniform deformations of non-magnetic classical or quantum wave media, and (iii) edge states in non-commensurate structures, e.g. non-periodic / non-translation invariant line defects. (B) nonlinear dispersive wave propagation in discrete and continuous media with novel underlying lattice structures, building on the PI's research on linear wave propagation in novel bulk media with special symmetries, and bulk media with defects. This work is informed by what the PI has learned about effective models in quantum materials governed by linear wave equations in periodic media with novel symmetry. (C) the partial differential equations (PDE)/free surface problem describing nonlinear deformations of a gas bubble immersed in an incompressible liquid, with a view toward extending our results on the radial symmetric case to general deformations. A further direction is one where the liquid is slightly compressible. 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

Quickly determining the dynamic response of complex structures, such as buildings and bridges, when faced with uncertainties that can include, but are not limited to, actual member dimensions and loads they experience, is still a challenge for today's advanced computer modeling tools. Research funded by this award aims to create new methods for analyzing the behavior of complex engineering systems and structures in the presence of uncertainty, advancing scientific progress and improving civil infrastructure safety. Advances in experimental techniques and emerging technologies have led to increasingly complex mathematical models for large-scale systems. Current methods for solving these models exhibit either high accuracy or computational efficiency, but not both. This restriction limits the effectiveness of the methods for system analysis, design and optimization. This research will create a novel solution methodology that combines high accuracy with computational efficiency, enabling more reliable and efficient analysis of structural systems. The methodology will also impact emerging technologies like the use digital representations of actual structures, where accurate predictions of a system's future performance are critical for informed decision-making. Advancements from this research will enhance infrastructure resilience and support emerging industries. Additionally, the project includes innovative education, outreach, and community engagement activities to inspire the next generation of researchers, engineers and educators, helping create a diverse and skilled STEM workforce. The research objective of this project is to create a methodology for efficiently and accurately addressing uncertainty propagation in structural dynamics problems. The approach aims to revolutionize the field of stochastic structural dynamics by achieving drastic improvements (spanning several orders of magnitude) in computational efficiency. Central to this effort is the creation of a joint time-space extrapolation methodology for determining the non-stationary response joint probability density function (PDF) of high-dimensional structural systems. Unlike existing methods, which become computationally prohibitive for high-dimensional systems due to the exponential growth of computational cost with the number of degrees of freedom, this methodology intends to overcome the curse of dimensionality by striving to leverage information embedded in the time-history of the most probable Wiener path integral path. It also intends to introduce a variational formulation of the problem with mixed fixed/free boundary conditions that renders computational cost independent of the total number of degrees of freedom. 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 will provide significant new information about how single cells are able to squeeze through tight spaces, by understanding how the scaffolds that support cell shape are remodeled and coordinated. Cells come in all shapes and sizes and often need to move through constricted spaces to perform their functions. The mechanisms that allow small cells to move quickly and nimbly can be conserved between different organisms, from the smallest nematode worms to humans. Specifically, this project will focus on proteins named septins, which can act like a girder to protect cells as they crawl through constrained environments. The primary aim of this project is to understand how evolutionarily conserved septin proteins function similarly or differently under varied conditions, including in the human immune system and in migrating worm cells. Septin protein function will be studied, in part, by developing specialized microscopy techniques that to allow observing cells moving within dense tissues and tight environments. This project will also train undergraduate and graduate students, and is designed to provide specialized training in biology, optics, and computational analysis approaches. In order to share discoveries and inspire the broader community, modules on cell movement and microscopy will be made available to local high schools and incorporated into outreach activities. Cell migration is critical for organismal development and cellular function. Despite the overwhelming importance of cell migration, much remains to be understood in how cell-cell interactions in complex and 3-dimensional tissue environments shape cell migration and the cellular functions that are regulated by cell migration. The difficulty of obtaining this knowledge is the result of the inherent biological and technical complexity of models needed to visualize and measure these processes. An additional challenge in the field is the ongoing specialization of cell biology, microscopy, and image analysis fields, which can act as a barrier for learners who are motivated to contribute to areas of research found at the intersection of these fields but lack uniform training across all these highly specialized areas of research. The overall objective of this project is to define how septin-based cytoskeletal scaffolds regulate the migration of single cells in varied tissue environments. The research to be performed includes the study of novel cell biological mechanisms, the development of tools for imaging and analysis, and the design of best practices for educating learners. The specific objectives of this project are to: 1) Define conserved and unique functions and architecture of septin proteins in 3-dimensional cell migration in complex microenvironments; 2) Advance the use of light microscopy to visualize complex events within physiological systems; and 3) Generate best practices for the effective teaching of multidisciplinary approaches contained in this project to learners from a range of backgrounds, expertise, and levels of training. This project is funded by the NSF/BIO/MCB Cell Dynamics & Function Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This doctoral dissertation research investigates the societal impacts of forensic science, particularly of genetic and DNA analysis. As genetic identification and DNA analysis are becoming widely used at the community level, the data collected from this study expands the translational impact of forensic science. The data collection uses a longitudinal research design to track changes over time in how communities engage forensic science and DNA analysis. Using ethnographic and behavioral data collection with families, forensic scientists and community members, and the analyzing of archival sources, the investigators track ongoing forensic investigations. Broader impacts inform educational and citizen science initiatives through wide dissemination of data and findings and will improve public literacy on the relationships between forensic science, technology, and society. The research makes contributions to the science of forensic science and community responses to genetic analysis and DNA testing. The research will expand the participation and training in science and the scientific workforce by supporting the training of a graduate student in anthropological science. The research also provides insights into the science of forensic science. By expanding our understanding of the societal impacts of DNA testing and the new tools and technologies of forensic science, the project responds to NSF priorities in translational science. The project also advances NSF priorities in biotechnology by furthering understanding of the human adoption of biotechnological innovations. 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

Plate boundaries are locations where two tectonic plates meet. New plate boundaries form by rifting an existing plate at places that are weak. This project will focus on the Davie Fracture Zone in the Mozambique Channel. The study will integrate several datasets to study how inherited oceanic structures control the development of incipient tectonic plate boundaries. Methods include machine learning approaches. The project will produce new maps of high seismic hazard zones. Other broader impacts include international collaborations and training of a graduate student. The project will test two hypotheses: Hypothesis 1: The kinematics of extensional reactivation of oceanic fracture zones are variable along-strike of the fracture zone, driven by inherited bends and offsets along the fracture zone. Hypothesis 2: The kinematics of reactivation is uniform along-strike of the fracture zone, facilitated by local stress field rotations that deviate from the regional stress field but act to maintain a fault reactivation style that is consistent along-strike. Here, inherited bends only influence the incipient rift geometry, but do not influence the rift kinematics. This study will use temporary and permanent seismic stations, including historical data in Madagascar, to develop an enhanced earthquake catalog, earthquake source mechanism solutions, and the contemporary stress field. In addition, the PIs will generate an updated neotectonic structure map of the entire reactivating oceanic fracture zone. The project will help evaluate hazards in the region, as fault reactivation offshore may trigger tsunamis. The newly developed earthquake catalog and active fault database for the Davie fracture zone will be invaluable for refining seismic hazard models. 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 leverages a variety of large data sources to assess how network connectedness within and among neighborhoods affects rates of neighborhood entrepreneurship. Informed by recent advances in artificial intelligence (AI) and machine learning, researchers are developing new, network-based indicators of neighborhood connections. They are using these measures and AI tools to generate a nation-wide, neighborhood-level dataset that includes indicators of connectedness, entrepreneurship rates, and other variables for every U.S. census tract. A key scientific contribution of the project is its use of machine-learning/AI methods and new techniques in causal analysis to generate and test propositions about the effect of network connectedness on rates of entrepreneurship. An important broader impact of the project is the resulting publicly available nation-wide database that will inform scholars and practitioners and aid future research on how neighborhood factors affect entrepreneurship and other critical economic outcomes. This project creates measures of network connectedness metrics using data that capture acquaintance and contact ties within neighborhoods and data that measure the anonymous flows of people between two neighborhoods. The two measures are used to assess the extent to which people are acquainted with within and between their zip codes and are in direct contact with people (via visits) within and between zip codes. The resulting measures are computed for all neighborhoods in the continental United States. Supplementing the data are yearly entrepreneurship data and control on neighborhood characteristics from multiple sources including the American Community Survey and EPA’s Smart Location Database. This project is jointly funded by the Sociology Program and the Secure and Trustworthy Cyberspace (SaTC) Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Wave-particle interactions are a fundamental process underlying phenomena across the plasma universe, from laboratory plasmas to the magnetosphere. Understanding how energetic particles interact with waves in space and laboratory plasmas has the potential to improve our ability to protect satellites, design cleaner energy sources, and develop technologies that rely on controlling high-temperature plasmas. This award supports a collaboration between Columbia University, West Virginia University, and New York University to study how modulations of the background magnetic fields can impact the interactions between energetic particles and plasma waves. Machine learning techniques will be leveraged to discover simplified models that capture the relevant dynamics. In addition to advancing science, this project will support the training of students and early-career researchers, develop interactive classroom tools for K-12 and graduate education, and promote open, accessible science through videos, software, and tutorials. This project will bring together expertise from energetic particle dynamics in magnetic confinement fusion, radiation belt electron transport, and data-driven reduced models to address two fundamental questions: How are resonant wave-particle interactions (WPI) modified by three-dimensional (3D) structure of magnetic fields? and How do 3D magnetic fields modify wave-induced particle transport? These questions will be addressed using two model problems: resonant interaction of energetic particles with Alfvén waves and transport of radiation belt electrons by ultra low frequency (ULF) waves. The project will develop a reduced particle-based simulation framework to address these questions, taking advantage of the separation of timescales between the background evolution and resonant population evolution. This analysis will be complemented by data-based development of reduced-order models of WPI. An interpretable machine learning paradigm, sparse identification of nonlinear dynamics (SINDy), will be used to discover reduced models for particle transport due to WPI and 3D fields. These reduced transport models will fill the gap between quasilinear diffusion coefficients and particle tracing simulations, while also informing global magnetospheric modeling, where a neural network with an autoencoder architecture will be used to identify a nonlinear low-dimensional latent space where the nonlinear behavior of WPI can be mapped. 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

Some of the most important problems in mathematics and physics are related to the understanding of singularities. These are anomalies in the behavior of a physical quantity where the norm that is used to measure such quantities breaks up. It may be related to the understanding of turbulence, of black holes, the accumulation of cancer cells in human bodies, or the behavior of neurons in the brain. These phenomena are often described mathematically via a differential equation which involves time and space. Studying the qualitative behavior of the solutions of such equations becomes crucial for understanding the related physical problem and is also essential for computing. The main goal of this project is to study the singular behavior of partial differential equations that is related to physical problems, as discussed above, and also to see how the fundamental shapes of spheres, cones, and cylinders, appear in the singularity formation of these equations. The goal of the studies is to enhance our knowledge of the behavior of solutions near singularities. The project provides research training opportunities for graduate students and postdoctoral scholars. The Ricci flow is a geometric equation that describes the intrinsic change in shape according to its Ricci curvature, a notion of curved space that played a fundamental role in the theory of relativity. G. Perelman, in his seminal 2002 work on the Ricci flow and the resolution of the 100 years old Poincare Conjecture, showed that high curvature regions are modeled on ancient solutions to the flow, that is solutions that have existed for a very long time. He also formed a conjecture regarding the classification of such solutions in three dimensions, under certain natural geometric conditions. During the past several years, the Principal Investigator (PI) and her collaborators resolved this conjecture. However, the higher dimensional problem has remained open since it requires the advancement of new methods. The PI investigates this problem, and work on the related problem of Ricci flow singularities as part of this project. In addition, the PI intends to work on the classification of asymptotically conical ancient solutions and study the analytical behavior of solutions near cylindrical and conical singularities. The interplay between analytical and geometric techniques will be crucial for the resolution of these problems. The project lies at the intersection of several active areas of mathematics, in particular nonlinear partial differential equations, geometry, and classical analysis. Applications to topology, quantum field theory, and the theory of relativity are also explored. 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

High-precision light sources, such as lasers, are essential for many applications, including atomic clocks, GPS, quantum computing, and microwave generation. It’s becoming increasingly important to understand the fundamental limits of how stable and low noise these light sources can be. In this project, the noise properties of a special light source known as an optical parametric oscillator will be investigated using light cavities, known as microresonators, fabricated in photonic chips. The goal is to determine if the standard quantum noise limit in these light oscillators can be surpassed, which would allow for even greater precision and stability than was thought possible. Such sources will further enhance the performance of applications, such as quantum networking, in quantum information science and technology (QIST). Additionally, the project will provide for the training of undergraduate and graduate students in the important field of QIST. Members of the research team will also perform outreach to middle- and high-school students on topics in optics and quantum information processing. Coherent optical sources are a critical part of experimental atomic, molecular, and optical physics. These sources include lasers, optical parametric oscillators, and optical frequency combs. As researchers push the limits of experimental precision and complexity, understanding the ultimate quantum limits of these sources is essential. Furthermore, as experimental setups become increasingly complicated, the need for developing sources that can be made more compact, robust, and scalable in number is becoming acute. In this proposal, the quantum-noise properties of optical parametric oscillators and optical frequency combs generated in microresonators will be investigated. The goal will be to significantly improve the performance of such oscillators by manipulating the input vacuum noise reservoir to achieve phase noise and frequency linewidths that are well below the standard quantum limits (i.e., the Schawlow-Townes limit). Various designs and regimes will be explored, which will be enabled through the use of integrated photonics. The vacuum squeezing elements, filters, and optical oscillator devices will all be integrated on a photonic chip. This will reduce the potential losses, make the system robust to environmental perturbations, and enable noise performance that surpasses what can be achieved with conventional sources. Ultimately, these parametric oscillators could be used for a wide variety of measurements and control for atomic and molecular systems that include precision measurement, spectroscopy, time-and-frequency metrology, ultralow-noise microwave generation, and quantum information science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Sea-level rise is a major issue affecting coastal communities, infrastructure, and the environment. Flooding already costs the U.S. economy $200 - $400 billion annually, and sea-level rise will worsen flooding impacts for coastal communities, which support 58 million jobs and produce $9.5 trillion in goods and services. This project aims to better constrain the sensitivity of ice sheets to future melting by examining the Last Interglacial period (~ 129,000 to 116,000 years ago), when Earth was slightly warmer and global mean sea level was higher. The research focuses on fossil coral reefs and cave deposits in the Yucatán Peninsula, which preserve an exceptional record of sea-level change. By analyzing and dating these records, the project seeks to reduce uncertainty concerning how fast and how high sea level rose during this period. These insights will improve future projections and contribute to more informed coastal planning. The project fosters international collaboration with active involvement of local researchers, supports two graduate students and two early-career faculty, and includes broad public outreach in the U.S. and Mexico. The project combines fieldwork, laboratory analysis, and modeling to study local sea-level changes in Yucatán, as well as global mean sea level, during the Last Interglacial. Researchers will collect fossil corals from coastal sites, assess their preservation, and determine their ages using uranium-thorium dating. To estimate long-term uplift (or subsidence), they will study cave deposits once near the water table in coastal caves dated to different time periods over the past 300,000 years. The team will also provide new estimates for the effect of karst isostasy to better understand the drivers of long-term deformation. By correcting the local sea-level record for these factors and glacial isostatic adjustment, the researchers aim to produce a more accurate global sea-level reconstruction and explore the processes that caused ice sheets to melt. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Pacific Ocean is a key region for coupled atmosphere-ocean phenomena that influence global climate across different timescales. Currently, seasonal-to-interannual climate predictions predominantly rely on forecasting El Niño-Southern Oscillation (ENSO) while there are emerging efforts on Pacific-rim decadal predictions based on forecasting phases of Pacific Decadal Variability (PDV). In turn global climate change projections hinge on predicting forced changes in the tropical Pacific. On this, however, there is substantial disagreement between models which favor a reduced zonal sea surface temperature gradient and observations which show an increase. The discrepancy reduces confidence in models’ future projections; the differences are critical for associated regional climate change, tropical cyclone behavior and climate sensitivity. By integrating observational analyses, theoretical demonstrations, and model experiments, this research will significantly advance our fundamental understanding of key aspects of Pacific dynamics. The gained understanding is essential for reconciling why climate models struggle to simulate historical trends in the tropical Pacific Ocean and how the models can be improved so as to provide more reliable and accurate predictions and projections of regional climate change and the rate of global warming. The lead investigator is an early career researcher and this work will advance her career focused on the ocean’s role in climate variability and change. This project aims to explain why the patterns of thermocline depth and mixed layer temperature change are meridionally broader for PDV than interannual ENSO and why the climate change patterns are again more meridionally confined and generally distinct from those of PDV. In this context, the research aims to comprehensively investigate the dynamics of contrasting pattern formation of thermocline and mixed layer temperature in the Pacific Ocean across interannual, decadal, and climate change timescales by understanding the different ocean dynamics, surface heat flux and cloud-radiation-sea surface temperature feedbacks involved. Of particular interest is to determine why the climate change signal does not amplify via Bjerknes feedbacks into a pattern more akin to ENSO or PDV and, instead, remains stable. In addition, the study will quantify the connections across timescales of the tropical Pacific Ocean and the Southern Ocean and determine the role of the latter in influencing the climate change pattern in the tropics. 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 aims to make the Internet more reliable by giving operators more control over how data reaches their network from other parts of the Internet. Today, network operators rely on trial and error, and often settle on subpar outcomes, because they do not have direct control over or visibility into which geographic locations and other networks the data will traverse to reach their network. This project aims to change that by creating a system where operators can simply specify their goals, such as improving performance or avoiding particular countries, and then the system figures out how to make that happen. This collaborative project brings together investigators from Columbia University, Northeastern University, and Federal University of Minas Gerais (UFMG) in Brazil. The project envisions a system that allows a network operator to describe the policy for how to set preferences for possible ingress routes (which the project calls “intents”), then automatically configures routing announcements to achieve the most preferred (feasible) outcome. To realize this vision, the project will address the following research questions: 1) What intents are desired, and how can it be made easy for operators to express them? 2) How can a system predict the routes and traffic engineering metrics that will result from an announcement? 3) How can a system automatically learn which configurations are possible and what their semantics are? 4) What are efficient ways to search through large numbers of configurations spanning multiple networks to satisfy general intent? The project will enable new ways for Internet providers and cloud services to improve the reliability and performance of Internet services on which society increasingly relies. For example, our project can help Internet providers maintain service during and after natural disasters, as well as identify and block Internet attacks. It will help improve performance for a wide range of services that include online educational technologies, telemedicine, remote work, and/or various forms of e-commerce and entertainment. The research outcomes will also serve as a foundation for future academic and industrial innovation in Internet routing. Project updates and outcomes will be published at https://ingress-routing.ee.columbia.edu/, with the plan to maintain the site for at least three years beyond the award period. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The research in this project investigates how scientists’ decisions – ranging from the type of AI tools and models to use, to how to train models and label behaviors – can shape what neuroscientists come to know about animal minds. Drawing on philosophy of science, neuroscience, and science and technology studies, the project analyzes how decisions about data, software design, and interpretation affect scientific outcomes. The study’s overarching goal is to improve how behavioral tracking tools are used, thereby making research more thoughtful and effective. The findings of this project will be of interest to scientists, educators, designers and users of AI. This project conducts a comparative assessment of several widely used AI-based behavioral tracking tools to investigate how different algorithmic approaches shape the study of behavior. The tools are built on distinct algorithmic foundations, ranging from supervised learning techniques that track visible features, such as body pose, to unsupervised models that infer behavioral patterns over time. Because many of these tools rely on machine learning, the project contributes to a better understanding of how artificial intelligence is now shaping scientific inquiry. The research examines how differences in these technical methods influence how behavior is organized, categorized, and interpreted. It addresses how behavioral knowledge is developed and defined, in turn offering pathways for developing more transparent, flexible, and representative research across species and settings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.