Grant & Funding Directory
Browse active grants and funding opportunities from government agencies and foundations.
Collaborative Research: Novel neurocognitive assessment of engineering education interventions applied to systems thinking
The challenges facing society today and in the near future are inherently complex systems problems. For example, improving transportation in cities or managing the growth of an international company both involve complex systems. The ability to recognize interactions and optimize connections between components in a system is called systems thinking. This type of thinking is vital for innovation and necessary for humanity’s longer-term survival. However, systems thinking is not intuitive for many people, and it can require significant mental effort. Most engineers benefit from the formal instruction provided in their undergraduate program. There are numerous methods to teach students about systems thinking but, unfortunately, assessing the effectiveness of these methods is a challenge. Assessment typically focuses on what students are able to produce rather than the mental effort required to produce it. Measuring mental effort is important because if mental effort can be measured and minimized, systems thinking is more likely to be adopted by students when they experience a real-world context. This research will test an approach to help students--more quickly and with less mental effort--solve complex systems problems using systems thinking. The research tests the effectiveness of priming students to think about the connections and interactions between components in a system using concept maps, a type of conceptual diagram to depict relationships within a system. This project tests the effects of concept maps to help students solve complex problems in engineering. The project will not only evaluate student solutions but measure their mental effort using a brain imaging technique. The expectation is that concept mapping makes complex systems problems mentally easier to solve, and this is measured via patterns of activation in their brain. Priming students for systems thinking with concept maps holds the potential for adoption across many college programs because of the minimal adjustments needed in teaching and the possibility of widespread application of concept maps in engineering. The project will use concept maps to prime students to think about the complex and dynamic relationships in engineering problems. Measurement of students’ brain activation will provide new data about the effects of this approach to help aid engineering students to solve complex engineering problems. Three cohorts of undergraduate engineering students will receive either multiple, single, or no concept map priming intervention. Assessment of students’ solutions to subsequent engineering systems problems will be correlated with patterns of brain activation. Brain activation will be measured using a non-intrusive technique called functional near infrared spectroscopy. Students will repeat the experiment to measure the effects of priming over time. The proposed research will extend current knowledge by measuring how changes in brain function persist, and how repeated educational priming interventions affect students’ ability to solve complex engineering problems. The results will offer a new type of evidentiary support for cognitive load theory in engineering education by demonstrating how priming students in ways that use specific regions and patterns of activation in their brain reduces subsequent cognitive effort to solve complex engineering problems. The research findings will be translated into short research briefs for college instructors to implement in their classroom. The project will also offer annual training in the brain imaging technique used in this project at engineering education conference workshops and a summer program for faculty and 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 Award ID: 2625962 | Program: 01002526DB NSF RESEARCH & RELATED ACTIVIT,01002324DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: John Gero | Institution: Drexel University, PHILADELPHIA, PA | Award Amount: $44,096 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2625962 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2625962.html
ENG-EAM: Parallax Additive Manufacturing of Sustainable Electrical Interconnects
This project will improve the fabrication precision of volume additive manufacturing by controlling residual stress through improved CAD tools, mechanical modeling, and materials. Volume additive manufacturing is a relatively new polymer additive manufacturing method that projects hundreds of three-dimensional light fields into a container of photosensitive resin. These light fields overlap within the resin, solidifying the part by exceeding a total exposure threshold in the desired shape. This process is orders of magnitude faster and provides better material uniformity than traditional layer-by-layer processes. However, parts fabricated by volumetric exposure have lower stiffness before post processing and are thus subject to greater deformation, reducing shape accuracy. This project will improve part fidelity by studying residual stress development, focusing on one volumetric manufacturing architecture, parallax volume additive manufacturing. Improved dimensional accuracy and reduced stress will extend the applicability of volumetric additive manufacturing to meet the needs of high precision, high volume industrial production such as high bandwidth electronic connectors. The hundreds of images projected into the resin container are found by solving a very large inverse problem using the mathematics of computed tomography. To make this problem computationally tractable, current algorithms ignore the inevitable stresses that develop during polymerization and post-processing steps. This project will build a finite element model as a digital twin, comparing this to the Virtual Volumetric Additive Manufacturing model created at Lawrence Livermore National Laboratory. Simplified models of viscoelasticity will be implemented to find a minimal description of the fabrication process. This model will be implemented in a new image generation algorithm that provides greater computational efficiency by representing fields in basis sets developed for computer image generation. These algorithms solve an analogous problem of mapping three-dimensional radiance fields to two-dimensional images with orders of magnitude efficiency gains. Those gains will be exploited here to incorporate more complex materials models while maintaining tractable computational cost. To validate this process, custom resins will be formulated with distinctive stress development characteristics. These will compare step to chain growth monomers to manipulate polymerization shrinkage and covalent adaptable networks to relax stress during post processing. The expected outcome of this program is a computational tool that optimizes image sets for final shape after post processing, significantly improving shape fidelity relative to current tools which optimize only for monomer conversion during exposure. 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 Award ID: 2430936 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Robert McLeod | Institution: University of Colorado at Boulder, Boulder, CO | Award Amount: $400,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2430936 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2430936.html
Conference: 54th Barrett Memorial Lectures: Mathematical Foundations for Computational and Data-Driven Scientific Discovery
The project supports the 54th Barrett Memorial Lectures at the University of Tennessee, Knoxville, centered on the mathematical foundations of data-driven scientific discovery. Many problems in science and engineering depend on extracting reliable information from high-dimensional, noisy, or limited data, requiring advances in mathematics and statistics to ensure models are accurate and trustworthy. This project brings together researchers, students, and early-career scientists to exchange ideas on methods with applications spanning biology, materials science, and engineering. Through lectures, poster sessions, and collaborative discussions, the project promotes interdisciplinary training, broad participation, and workforce development. By strengthening the mathematical foundations underlying artificial intelligence and data-driven modeling, the project contributes to national priorities in AI and biotechnology, and advances the national interest through scientific innovation, education, and the development of a diverse and skilled workforce. The project convenes researchers to investigate core mathematical and computational challenges in data-driven scientific modeling, with emphasis on uncertainty quantification, Bayesian inference, geometric and topological data analysis, and multiscale modeling. The Lectures integrate advances in probabilistic computation, Monte Carlo methods, numerical analysis, and scientific computing to address high-dimensional and data-scarce regimes. Particular focus is placed on unifying physical principles with machine learning through mathematically grounded frameworks that improve generalization, interpretability, and robustness. Activities include plenary and invited talks, poster presentations by junior participants, and structured breakout sessions targeting key research directions such as optimization, topological learning, and stochastic modeling. These interactions are designed to catalyze new collaborations and identify open problems in computational mathematics and data science. Anticipated contributions include advancing foundational theory for data-enabled scientific discovery and strengthening connections between mathematics, artificial intelligence, and domain 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 Award ID: 2608376 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Duc Nguyen | Institution: University of Tennessee Knoxville, KNOXVILLE, TN | Award Amount: $30,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2608376 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2608376.html
RAPID: Ecosystem Response to a Major Sewage Spill in the Potomac River Estuary: Disturbance, Contaminant Legacies, and Biological Thresholds
Acute nutrient pollution, such as raw sewages spills into waterways, represent one of the most pervasive and ecologically damaging stressors to freshwater and estuarine systems in the U.S., with recovery timescales often spanning decades. Changes in water quality and biodiversity immediately and soon after a pollution event are rarely studied in real time and are proposed to have outsized effects on the long-term trajectory of water systems. In January 2026, the Potomac Interceptor sewer outside of Washington DC collapsed, causing one of the largest raw sewage spills in history until it was capped in March 2026. This discharge released into the river a mixture of nutrients, pathogens, heavy metals, pharmaceuticals, and per- and polyfluoroalkyl substances, including E. coli concentrations reaching up to 10,000 times above recreational water quality limits. This project documents an intensive investigation of ecosystem response, from tidal freshwater to brackish water regions of the Potomac River Estuary, following the sewage spill. Data will be collected monthly at eleven sites for six months. This data will be examined in the context of 3 months of rapid-response monitoring as well as baseline data since 1984, which allows it to contextualize impacts on biodiversity, community structure, and ecosystem function. These initial and near-term changes may later be used to understand longer term ecological trajectories and consequences. This project will generate policy-relevant, publicly accessible data to directly inform human health advisories, recreational use decisions, and adaptive management strategies for the Potomac River, while developing a framework useful for future sewage spill events nationally. It engages community members and students in data collection and labwork, as well as includes results in on-going education programs. The Potomac Interceptor spill introduced a massive and discrete nutrient and contaminant pulse into an ecologically important estuarine system, creating a natural experiment for testing foundational ecological theory, including the intermediate disturbance hypothesis and alternative stable state dynamics, in a real-world context. Through monthly data collection, in conjunction with historical data and immediate post-pollution data, this project will document early responses that represent a transient disturbance signature that cannot be reconstructed once the system begins to reorganize. The project will quantify biodiversity and functional composition of bacteria, phytoplankton and zooplankton, macroinvertebrates, and fish across eleven sites, using eDNA metabarcoding and whole organism taxonomic identification. By tracking contaminant redistribution across water column, sediment, and biological compartments simultaneously, this study will generate mechanistic understanding of recovery trajectories, ecological thresholds, and the cascading trophic consequences of large-scale disturbance that is broadly applicable to impaired river systems nationwide. 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 Award ID: 2623281 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jennifer Salerno | Institution: George Mason University, FAIRFAX, VA | Award Amount: $299,886 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2623281 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2623281.html
CAREER: Unraveling the Epigenetic Grammar Governing Chromatin Organization
It is well understood that genetic information encoded in DNA is passed on to future generations. However, genetic information can be modified without altering the DNA sequence, and these modifications can be passed on to future generations. Epigenetics is the study of this processes which shapes the three-dimensional organization of DNA and thus controls gene activity. Dysregulation of epigenetic processes has been implicated in numerous human diseases. Despite decades of research, the precise "epigenetic grammar" -- the rules by which specific combinations of epigenetic modifications collectively shape chromatin structure -- remains elusive. Understanding these molecular mechanisms is critical for advancing fundamental biology and improving the diagnosis and treatment of human diseases. This project will integrate artificial intelligence (AI) with physics-based computational simulations to uncover how epigenetic regulation modulates chromatin structure and function, including its role in the dynamic compartmentalization of DNA within the cell. This project aims to establish detailed molecular links between specific epigenetic modifications and genome function, guiding the rational design of therapeutic strategies targeting epigenetic dysregulation. The tools and methods developed through this project will enable predictive, mechanistic studies of biomolecular assemblies beyond chromatin. The educational activities will launch an AI-visualization suite to engage students from K-12 to graduate levels, both regionally and nationally, in data science, computational modeling, and biomolecular visualization. This project employs a predictive, sequence- and epigenetic-specific simulation model to investigate how epigenetic modifications and regulatory proteins modulate chromatin organization. A central computational challenge in studying epigenetic regulation is the need to simultaneously model large-scale chromatin organization and fine-grained chemical interactions at residue resolution. This project will address this gap by integrating physical modeling with data-driven approaches to build a predictive, residue-level simulation model that quantitatively captures sequence- and epigenetic-specific molecular interactions across hundreds of nucleosomes. Using this model, this project will: (1) Examine key epigenetic modifications -- including acetylation, ubiquitylation, and methylation -- and their interactions with regulatory proteins, focusing on their effects on higher-order chromatin structure and gene regulation; and (2) Elucidate how epigenetic regulation drives chromatin phase separation and governs the biophysical properties of chromatin condensates in the crowded in vivo nuclear environment, as well as how this environment, in turn, modulates biomolecular interactions. By linking chromatin phase separation, epigenetic profiles, and regulatory proteins, this project aims to reconcile different observations of epigenetic effects, decipher the epigenetic grammar underlying chromatin organization, and identify critical chromatin interactions that drive genome compartmentalization. Ultimately, our research will deepen our understanding of genome organization and enhance our ability to evaluate, forecast, and design preventive strategies to mitigate the adverse impacts of epigenetic dysregulation on the genome and epigenome. 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 Award ID: 2540505 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Xingcheng Lin | Institution: North Carolina State University, RALEIGH, NC | Award Amount: $801,725 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540505 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540505.html
Conference: Advancing Crop Genome Engineering through Innovative Transformation and Automation Technologies
The Society for In Vitro Biology (SIVB), founded in 1946 as the Tissue Culture Association, promotes the exchange of knowledge in plant and animal (including humans) in vitro biology. Its annual meetings serve as ideal platforms for presenting new research and concepts in cell biology and biotechnology. As part of the SIVB’s 2026 annual meeting, two workshops and a panel discussion on lab automation in the plant sciences will be organized to address key challenges and emerging solutions for critical bottlenecks in plant bioengineering. These activities will expand access to advanced plant transformation technologies for a diverse audience, including students, educators, and researchers from academia, government, and industry. In conjunction with PlantGENE, an NSF-funded, Research Coordination Network (RCN ) that is a community-driven initiative dedicated to advancing plant transformation and gene editing through knowledge sharing, online resources, and facilitation of collaboration and training opportunities, the first workshop will focus on morphogenic transcription factors and Agrobacterium strain engineering, offering practical insights for enhancing plant transformation. The second workshop will explore laboratory automation, featuring current technologies and future trends to help participants conceptualize automation projects in partnership with the Viscon Group (vicongroup.eu), an EU-based technology company specializing in automation, robotics, and software solutions for agriculture and food industries. Finally, a cross-sectional panel discussion will follow, bringing together experts from plant, animal, and invertebrate sciences to share best practices in automation implementation. These sessions will be recorded and made available on-demand to ensure broad accessibility. Presentations and materials will be shared with interested groups for educational and research use, supporting curriculum development and laboratory training across diverse institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2611965 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Michele Schultz | Institution: Society for In Vitro Biology, GLEN BURNIE, MD | Award Amount: $33,150 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2611965 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2611965.html
RUI: Regulon identification for metalloregulatory transcription factors in the extremophile, Thermus thermophilus HB8
In all organisms, gene regulation is primarily controlled by transcription factors, proteins that bind DNA and influence gene expression (i.e., which genes are turned “on” or “off”). The activity of many transcription factors is controlled by environmental cues like nutrient availability, toxic substances, and heavy metals. As such, transcription factors can act as biological sensors that detect external signals and transmit them to generate internal cellular responses. How transcription factors recognize these cues and what genes they regulate remain important biological questions. Answers to these questions provide valuable insights into how life senses environmental stressors and which genes are important for mediating a stress response. They also provide a foundation to engineer transcription factors as biosensors in environmental or industrial biotechnology. This project will uncover the gene regulatory properties of metal-sensing transcription factors in the heat-loving bacterium, Thermus thermophilus HB8. The research will be conducted at a primarily undergraduate institution in rural Georgia, and will provide undergraduates with hands-on experience in DNA-protein biology, microbiological techniques and bioinformatics, which are valuable skills for workforce development in biotechnology. In bacteria, metalloregulatory transcription factors (MTFs) play a critical role in sensing intracellular metal ion concentrations and eliciting a genomic response. While extensively studied in mesothermic organisms, there are limited studies exploring the biological functions of thermophilic MTF homologs. T. thermophilus HB8 contains a variety of transcription factors from different MTF families; however, the regulons of many of these MTFs remain unknown. Characterizing these regulons will reveal how thermophiles adapt to fluctuating metal ion concentrations and uncover novel gene products with biotechnological potential. The project will employ an iterative selection technique, restriction endonuclease protection, selection and amplification (REPSA), to identify the preferred DNA-binding motifs of eight uncharacterized T. thermophilus HB8 MTFs. These MTFs include members from the ferric uptake regulator (FUR), mercury resistance operon regulator (MerR) and arsenic resistance operon regulator (ArsR) superfamilies of bacterial transcription factors. The discovered motifs will be mapped to the T. thermophilus HB8 genome, and transcription regulatory networks will be validated using in vitro and in vivo analyses. The research will be conducted by undergraduate students in the PI’s laboratory and as part of a new course-based undergraduate research experience (CURE) that will serve as a laboratory component to a biochemistry course at the institution. Understanding DNA-protein interactions is a learning objective for the biochemistry course; thus, students will make strong connections between the concepts learned in lectures and applied in CURE experiments. 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 Award ID: 2540489 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: John Barrows | Institution: Reinhardt College, WALESKA, GA | Award Amount: $332,976 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540489 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540489.html
CAREER: Approximation-First Telemetry for Hyperscale Networked Systems
As cloud computing, artificial intelligence infrastructure, and internet services continue to grow, it becomes increasingly important to monitor the large networked systems that support communication, commerce, education, health, and science. These systems include massive collections of servers, network devices, storage services, and software components that must work together reliably and efficiently. However, the data generated about network traffic, resource usage, and failures can be too large to analyze in full, especially when operators need answers in real time. This project develops an approximation-first approach to telemetry for hyperscale networked systems, using compact, informative data summaries to answer important monitoring questions quickly while greatly reducing cost and overhead. The project establishes an end-to-end approximation-first telemetry architecture for hyperscale networks through four research thrusts. The first develops mergeable summaries that can be created on end hosts and networked devices while tracking uncertainty. The second develops low-latency aggregation and query methods that answer telemetry questions directly from these summaries. The third develops learning-guided compression for long-term telemetry storage using both lossy and lossless approaches. The fourth creates a management engine that maps user goals for accuracy, responsiveness, and cost into efficient telemetry configurations. Together, these thrusts advance telemetry systems, networked systems, and large-scale distributed computing. The project's cost-effective telemetry can help operators detect network anomalies, bottlenecks, failures, and attacks more quickly while lowering the compute, storage, and energy required for monitoring. The project will also create educational materials and hands-on learning opportunities in networking, cloud computing, and systems, and will release open-source software to support researchers, students, and practitioners building new analytics tools. Project software, documentation, and research artifacts will be released through a project repository (frootlab.cs.umd.edu/projectasap) in University of Maryland hosted web resources. These materials may include software, publications, experimental artifacts, and selected datasets or benchmarks. Public resources will be maintained for at least five years after the project ends or after data release to support reproducibility, reuse, and follow-on 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 Award ID: 2544434 | Program: 01002829DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Zaoxing Liu | Institution: University of Maryland, College Park, COLLEGE PARK, MD | Award Amount: $358,200 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544434 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544434.html
CAREER: Decoding active and passive mechanisms driving bacterial chromosome dynamics
Chromosomes carry genetic instructions that allow cells to grow, respond to the environment, and pass information to future generations. Just as the function of a protein is understood through its amino acid sequence, three-dimensional structure, and conformational dynamics, a complete understanding of chromosome function requires knowledge of DNA sequence, chromosome structure, and dynamic behavior. Modern sequencing and imaging methods have transformed the ability to read genomes and capture snapshots of chromosome architecture, but much less is known about how different regions of the chromosome move inside living cells. The project will address this gap in knowledge by creating a genome-scale view of chromosome dynamics in the bacterium Escherichia coli and establishing physical principles that explain why different chromosomal regions move in different ways. The outcomes have broad implications for the U.S. national interest by promoting fundamental discovery at the interface of physics and biology, strengthening quantitative approaches in biotechnology, and helping build a foundation for future efforts to predict and engineer genome function. Integrated education and outreach activities will train students across multiple levels in quantitative biophysics, broaden access to research experiences, disseminate protocols and analysis tools, and highlight the contributions of physicists to biology and medicine, including Nobel laureate and Illinois alumna Rosalyn Yalow. The project will combine high-resolution single-molecule tracking, MINFLUX super-resolution microscopy, quantitative polymer-physics modeling, and in vitro reconstitution to decode how active biological processes and passive physical constraints shape bacterial chromosome dynamics. In living cells, the project will measure fast-timescale motion of many genomic loci to build a high-resolution atlas of chromosome motion. These measurements will be combined with complementary genomic and cellular data to determine links among chromosome organization, cellular activity, and locus-specific dynamics. In vitro experiments will then test the contributions of specific biological and physical factors to chromosome dynamics in a controlled setting. The resulting data will be used to develop and validate predictive models that connect measurable chromosome dynamics to underlying molecular mechanisms and polymer properties. By linking dynamics, structure, and function in the bacterial genome, this project will provide new concepts and tools for understanding genome regulation and may inform future efforts to engineer synthetic gene expression systems and other biotechnology applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2542305 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Sangjin Kim | Institution: University of Illinois at Urbana-Champaign, URBANA, IL | Award Amount: $763,130 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542305 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542305.html
Nonparametric causal factor models for reliable generative AI
Modern artificial intelligence systems produce striking images, text, and other creative outputs, but it is often unclear what these systems have actually learned internally. This makes it difficult to ensure that these models are reliable, safe, and trustworthy when deployed in the real world. Although these models can imitate patterns in data, the process through which they do so does not necessarily correspond to meaningful causes, stable mechanisms, or interpretable concepts that stakeholders can decipher and diagnose. This project develops a new statistical framework for building AI models designed to uncover interpretable, generalizable structures hidden inside complex, high-dimensional data such as images, language, and scientific measurements. This research investigates the statistical foundations of generative AI. A key goal is to understand how and when generative models learn reusable, causal structure and what the tradeoffs are. Specifically, the project focuses on understanding how generative models learn complex, high-dimensional structures without suffering the curse of dimensionality and how they can learn interpretable causal factors from data. This will deliver a framework with practical models and algorithms for reliable generative AI that is both independently verifiable and reproducible. The work will combine ideas from causal inference, nonparametric statistics, latent variable modeling, and deep learning to develop methods with rigorous guarantees. The goal is to move beyond black-box imitation toward AI systems whose internal factors can be interpreted, tested, and used to understand how complex systems change. 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 Award ID: 2610618 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Nikhyl Aragam | Institution: University of Chicago, CHICAGO, IL | Award Amount: $200,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2610618 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2610618.html
CAREER Excited States and their Role in Protein-RNA Binding Dynamics
The recognition and binding of nucleic acids by proteins is a central phenomenon in biology, governing processes that range from the flow of genetic information to viral infection and propagation. Despite the importance, our current understanding fails to capture the conformational complexity and dynamic nature of the recognition and binding process. Molecules like RNA exist as an ensemble of accessible conformations, including rare conformations that are not observable using traditional measurements but can be biologically active. Furthermore, a huge span of timescales can be relevant in the process, from microsecond conformational motions to binding/unbinding dynamics occurring over hundreds of seconds. This study leverages advanced spectroscopy and computational modeling to quantitatively map protein-RNA binding dynamics with unprecedented sensitivity and resolution. It will provide a new perspective in protein-RNA interactions that will advance our fundamental understanding of basic biomolecular processes and bring to light the role of hidden states that mediate biomolecular recognition and binding. The study will also strengthen the training of young biological engineers in skills that are critical in research but rarely part of educational curriculum, such as the design and operation of home-built spectroscopic equipment. The central hypothesis of this study is that protein-RNA binding occurs over a range of time and energy-scales, with some interactions being mediated directly through thermally excited conformations of the target molecule. The recent development of new single-molecule spectroscopic methods, in conjunction with non-perturbative fluorescence labeling using non-canonical nucleotide analogues, has enabled the observation of biomolecular dynamics over a temporal range of microseconds to hundreds of seconds and with a sensitivity of < 0.1% of an ensemble population. The study will investigate the binding of the TAR RNA, a small 30 nucleotide hairpin RNA dominated by local secondary fluctuations, and the stem loop A RNA, a large 80 nucleotide RNA structure with secondary and tertiary fluctuations. Taken together, the two systems provide an elegant exploration of how conformational motions of different time and energy scales contribute to protein-RNA binding. The proposed interdisciplinary approach will provide a quantitative (thermodynamics and kinetics), nucleotide-resolved, and temporally resolved picture of protein-RNA binding. The information-rich experimental data, interpreted with the aid of computational modeling, seeks to build the foundational molecular scale knowledge required to understand and predict protein-RNA interactions. 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 Award ID: 2540408 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: John King | Institution: University of New Mexico, ALBUQUERQUE, NM | Award Amount: $600,620 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540408 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540408.html
STAR: Forecasting populations for conservation: The role of life history and model structure on forecast accuracy
Population forecasts are used to manage threatened and endangered wildlife populations. Natural resource managers use these predictions to anticipate future changes in species abundance, assess extinction risk, and prioritize management interventions. Inaccurate forecasts may lead to erroneous interventions or inefficient uses of limited resources including funding and personnel. Despite the widespread adoption of population forecasts over the last four decades, there have been few efforts to assess the historical performance of these predictions. This project will use the growing number of long-term monitoring datasets collected by research scientists and state and federal agencies to assess the forecast performance of population models. Findings will inform management strategies for threatened populations by identifying the types of data and models that generate accurate forecasts. Outcomes of this project include improved guidance for natural resource managers on effective monitoring strategies for threatened populations, and the development of a framework that can be applied to evaluate other historical ecological forecasts. The research will train the next generation of scientists with modeling and programing skills, handling and development of databases, and the development of AI-ready databases for the scientific community. Population ecologists have been making predictions on the risk of population decline and extinction for almost 40 years. While there has been some past work evaluating forecast ability in stable populations, most assessments of population viability forecasts have been through indirect methods, thus, there is little empirical evidence assessing the long-term accuracy of these forecasts. This project will apply a retrospective approach to assess the reliability of population predictions by developing a publicly available database of historical population viability forecasts linked to updated monitoring data of vertebrates, invertebrates, and plants. This database will be an asset for natural resource managers and scientists studying the properties of ecological forecasts, while the analysis will provide real-world measures of forecast accuracy and precision by comparing predicted trends to updated monitoring data. The project will determine how the life history of target species interacts with statistical survey methods and demographic model details to influence forecast skill. This project provides the first comprehensive evaluation of published population forecasts using monitoring 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 Award ID: 2531659 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jake Ferguson | Institution: University of Kentucky Research Foundation, LEXINGTON, KY | Award Amount: $400,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2531659 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2531659.html
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