University of Connecticut

NSF Awards · FY 2025 · 2025-09

Intergenerational plasticity -- when environments experienced by one generation affect traits expressed by later generations -- is increasingly recognized for its ability to impact species’ responses to environmental stress. Predators are one of the most ubiquitous sources of stress in nature, and the effects of predation risk on prey can scale up to have broader impacts on communities and ecosystems. Intergenerational plasticity can also alter prey responses to predators in subtle but important ways, but these effects may vary across different environments, particularly across different resource landscapes. This project uses a rocky shore food chain to examine how parental exposure to predation risk under different resource regimes influences offspring prey fitness and how risk-induced intergenerational effects scale up to alter population dynamics, community structure, and ecosystem function. Because intergenerational plasticity often affects these same traits, it may elicit changes at larger biological scales that are currently unaccounted for. This project engages undergraduate and graduate students in authentic, discovery-driven research. Additional rocky shore exploration activities with K-12 students support discovery-based learning and ocean literacy for all. Organisms often respond to environmental change by altering their phenotype, with consequences that scale beyond individuals to impact populations, communities, and ecosystems. In many systems, prey respond to predator exposure via phenotypic plasticity, altering their traits and behaviors in adaptive ways that affect community and ecosystem dynamics, though specific responses to risk can be highly context dependent. Intergenerational plasticity is increasingly recognized for its ability to impact organismal responses to biotic and abiotic stressors, including predation risk. However, little is known about how intergenerational plasticity operates across different ecological contexts, particularly in the parent generation, limiting our ability to predict its relevance in natural systems. This research explores how resource identity -- a critical driver of prey responses to current predation risk -- impacts intergenerational effects of risk on rocky shores and the consequences of this risk-induced intergenerational plasticity for individuals, populations, communities, and ecosystems. On New England rocky shores, the snail Nucella lapillus responds to predation risk from the green crab Carcinus maenas both within and across generations by altering its foraging on barnacles and mussels, two species that play important but distinct roles in intertidal community dynamics. Through a series of manipulative field and lab experiments, the study tests how intergenerational effects of predation risk operate across different resource landscapes to influence prey demographic traits and foraging choices and the emergent impacts on community structure and ecosystem function. This work advances our understanding of intergenerational plasticity and how it operates in dynamic systems to affect population, community, and ecosystem dynamics. 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 set at the interface of two areas in the foundation of mathematics, computability theory and reverse mathematics. Computability theory, which traces its history to the pioneering work of Alan Turing in the 1930s on the formal definition of an algorithm, is broadly concerned with the question of which mathematical problems can be solved by a computer, and among those that cannot be, in measuring precisely how far away these problems are from being thus solvable. Reverse mathematics instead looks to understand how complicated mathematical theorems are, by determining the minimal axioms necessary to carry out the logical arguments in the proofs of these results. Although these areas are distinct, they are deeply related, with ideas and results in one often leading to ideas and results in the other. Together, they provide deep insight across all areas of mathematics, revealing connections between previously disparate areas, and often leading to novel and more computationally efficient methods. This project involves graduate students. A longstanding focus of research in computability theory and reverse mathematics has been combinatorics, especially Ramsey’s theorem and related combinatorial results. But more recent work has exposed fascinating new connections with other areas, including set theory and topology, which the PI is building on and exploring in this project. More precisely, the PI addresses a suite of problems concerning two important generalizations of Ramsey’s theorem: Milliken’s tree theorem, which comes from work in structural Ramsey theory, and the Ginsburg–Sands theorem, which is a purely topological result. Investigation of these theorems in computability theory and reverse mathematics over the past few years has already produced some significant developments, yet some of the most fundamental questions remain open. This project will seek to answer these questions, including through the development of new techniques and conceptual tools with which to approach problems in computable combinatorics more generally. In this way, the project will also further develop the interplay between computability theory, combinatorics, and topology. 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

Scientific breakthroughs in fields such as genomics, drug discovery, and materials science increasingly depend on large-scale computational workflows executed on high-performance computing (HPC) platforms. These workflows coordinate thousands of interdependent computational tasks, creating unprecedented collaboration opportunities but also introducing serious cybersecurity vulnerabilities. A single compromised step can lead to inaccurate scientific conclusions, disruption of critical research, or breaches of confidential data. Foreign adversaries actively target American research infrastructure to steal intellectual property and gain competitive advantages. By establishing a strong foundation of computational trust, this project protects scientific data and ensure research integrity, even when computations run on shared or potentially compromised computer systems. This enables confidence in scientific collaboration, protect sensitives information and helps preserve America's scientific leadership. The SafeSci-TEE award advances the state of the art in cyberinfrastructure by introducing novel techniques in runtime and distributed attestation tailored to scientific workflows. The project develops continuous runtime attestation mechanisms for confidential virtual machines, ensuring that HPC applications within trusted execution environments (TEEs) maintain integrity throughout execution. It also creates a distributed attestation framework that propagates trust across multiple HPC nodes and workflow stages, enabling end-to-end verification of scientific pipelines. In addition, SafeSci-TEE builds a hardware-agnostic TEE runtime and an integrity-aware scheduler that securely maps workflow tasks to trusted resources across heterogeneous computing platforms. These innovations empower scientists to run complex workflows securely and confidently, even in dynamic, multi-institutional environments. The technologies developed by SafeSci-TEE enhance the resilience and reliability of scientific computing, inform secure design practices for HPC systems, and have broad applications including fields such as healthcare, national security, and industrial R&D. Ultimately the SafeSci-TEE award accelerates scientific progress, bolsters U.S. competitiveness, and helps safeguard the nation's cyberinfrastructure by enabling seamless, secure collaboration among researchers and 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 Awards · FY 2025 · 2025-08

Seed production by trees plays an essential role in the ecological and economic stability of future forests because seeds directly contribute to the growth of new trees. This is especially critical in boreal forests, which cover about 30% of the Earth’s area. Previous studies of the effects of environmental variability on boreal tree species have focused on tree growth and species’ range shifts, however a key gap in knowledge is understanding how tree reproduction is affected by abiotic conditions, such as temperature or precipitation. Advancing our understanding of the North American boreal forest is challenging because it is very large, the environmental conditions vary by region, and boreal tree species differ in their habitats and traits. Also, current forest models either ignore tree reproduction entirely or simplify it to assume that seed availability is constant. This project will test how abiotic factors (CO2 levels, temperature, water availability, nitrogen deposition, wildfire) interact to affect seed quantity and quality (seed mass, seed chemistry, seed germination rates). This information will be used in models to predict the future of boreal forests. This research will inform federal and state agencies about drivers of seed production and viability, increase public scientific literacy about tree dynamics and boreal forests, and add cone specimens from North American boreal forests to the Missouri Botanical Garden herbarium for future use. The project will train three graduate students and six undergraduates in conducting scientific research, as well as support a youth training program. Boreal conifer species with widespread distributions, including balsam fir, black spruce, eastern tamarack, jack pine, and white spruce, are ideal for investigating how abiotic factors affect seed production. This project will combine historical collections of cones and seeds in herbaria dating back to the 1820s, present day cone and seed collections across the distribution of boreal conifer species, and cones from trees in an ecosystem-scale experiment. The information from these field collections will be used in spatial modeling of landscapes from interior Alaska to the eastern North American boreal forests, in order to forecast the future composition of boreal forests. This research provides more than a snapshot in time or space, as it leverages specimens going back 200 years and then forecasts until the end of this century, as well as sampling vast regions of the continental distribution of the North American boreal forest and forecasts across regions totaling 18 million hectares (44 million acres). This research has important implications for understanding the future of boreal forests across North America, and for forestry and future timber production. 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 will reimagine engineering education by creating innovative tools and strategies that support the success of all students, particularly in the critical first two years of the undergraduate experience. Grounded in the concept of neurodiversity as the natural variation in how individuals think, learn, and process information, the project embraces the idea that every student brings a unique cognitive profile to the classroom. The project will use artificial intelligence (AI) to build learning environments that flex to meet a wide range of student strengths, needs, and preferences. Through redesigned courses, professional development for faculty, and AI-powered tutoring and academic coaching, the project will help students develop essential academic behaviors such as time management, self-regulation, and metacognitive awareness. The interventions will be designed to be accessible to all students and will be embedded in existing instructional and advising structures. By improving student engagement and persistence in engineering pathways, this work will contribute to a more diverse, capable, and innovative engineering workforce. It will advance the national interest by expanding access to high-quality STEM education and supporting a broader spectrum of learners whose talents may not be fully realized in traditional academic settings. This project will implement a coordinated institutional change strategy at the University of Missouri and the University of Connecticut, focused on transforming gateway courses in engineering and mathematics using universal design for learning principles and inclusive pedagogy. Faculty will participate in a structured professional development sequence that includes the Neuroinclusive Teaching Institute and interdisciplinary I-teams to support course redesign and the integration of AI-powered tools. A virtual academic coach, built on large language models, will be deployed in tutoring, peer mentoring, and advising contexts to guide students through personalized learning strategies outside the classroom. All tools and redesigned instructional practices will be made openly available to all students. The project will advance fundamental knowledge in two distinct areas: first, in engineering education, it will examine how the deployment of AI tools in instructional and support environments affects engagement, self-regulated learning, and engineering identity formation across cognitively diverse learners. Second, it will provide insight into the policies and institutional practices that promote a culture that values the strengths of all students. Based on prior experience, these cultural shifts are essential for both the transformation and long-term sustainability of educational change. In addition to advancing research, the project will generate actionable tools and structures to support adoption of AI-enhanced, neuroadaptive practices across institutions. These will include a faculty teaching workshop with adaptable materials for engineering and math courses; AI-coaching tools with companion training for advisors and peer mentors; and a roadmap for onboarding faculty into neuroadaptive, technology-enabled teaching models. Research activities will assess how these innovations influence students, faculty, and departments, contributing new knowledge to the national conversation about AI in education and the professional formation of engineers. Together, these efforts will create a replicable model for building more responsive learning environments in undergraduate engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project is aimed to provide partial support for student participation in the 57th North American Power Symposium (NAPS 2025), to be held in Hartford, Connecticut, October 26–28, 2025. NAPS is a student-focused conference that brings together undergraduate and graduate students, faculty, and professionals from academia, industry, and national laboratories to share emerging research in power and energy systems. The symposium provides an important platform for students to present technical papers, receive constructive feedback, and engage with experts in the general field of energy and power systems. To remove financial barriers to participation, the project will fund travel-related expenses including lodging and discounted registration, along with access to technical tours and young professional networking sessions. The intellectual merit of this project lies primarily in its support for a national platform where students are introduced to advanced research topics and contribute to technical dissemination within the domain of power and energy systems. The broader impacts of this project include broadening access to meaningful educational opportunities, facilitating inter-disciplinary and inter-institutional collaborations, supporting the progress of science and contributing to the nation’s social and economic wellbeing by preparing participants to address complex infrastructure challenges and promoting innovation in energy technologies. NAPS 2025 will present a broad set of research and education topics, including coordination between transmission and distribution systems, artificial intelligence applications for grid operations, modeling of distributed energy resources, energy system cybersecurity, power electronics, transportation electrification, and optimization techniques for modern power systems. All accepted papers will be subject to peer review and will be published in the IEEE Xplore digital library to ensure both quality and visibility of student-authored work. In addition to technical sessions, the symposium will provide competitive student paper awards and guided mentoring activities. By enabling student attendance through financial support and creating direct engagement opportunities with leading researchers and practitioners, this project reinforces academic achievements across multiple disciplines and institutions. Through these coordinated efforts, NAPS 2025 will strengthen national expertise in power engineering and contribute to the advancement of scientific understanding in the power and energy field. 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 Electron Ion Collider (EIC), the next major nuclear physics facility, will explore the fundamental physics of Quantum Chromodynamics (QCD), the fundamental theory of strong interactions. One of its main goals is the experimental verification of the qualitatively new regime of QCD: parton saturation, which is predicted to occur in hadronic collisions at very high energy and/or large nuclei. In the extreme saturation regime, a hadron is similar to a "droplet" of fluid rather than a collection of free partons. However, the EIC will not achieve asymtotically high energies, and therefore to identify experimental manifestations of saturation one needs to bridge the asymptotic regime with the lower energy "partonic" picture. To this end, the PI and his collaborators will develop an approach to the hadronic evolution towards the saturated state which encompasses the effects important at asymptotic energies as well as those that dominate the physics at intermediate energies within one single unified framework. The team will study the relation between the entanglement properties of a hadronic wave function at high energy and the final states of a collision, with a special emphasis on understanding their collective behavior. The tantalizing question is the similarities and differences in behavior of soft (classical) modes and semi hard (intrinsically quantum) modes. The central topic of this project is to further develop and analyze the Born-Oppenheimer approach to quantum evolution, with emphasis on the interplay between low-x physics and intermediate-x physics, a crucial development for reliable applications of the theory saturation at EIC energies. The PI and his team will continue the study of particle production and correlations in the saturation framework, including correlated particle production in Deeply Inelastic Scattering (DIS) from high to low momentum transfer and searches for manifestation of quantum statistics of the gluon. The team will study theoretical questions that are fundamental to understanding and improving the high energy evolution, such as the further development of the Born-Oppenheimer approach for resumming large transverse logarithms at next to-leading order, thus unifying the approach to low-x and intermediate-x physics in a single framework. Furthermore, the team aims to understand the importance of quantum entanglement in the highly evolved hadronic wave function and to explore whether the properties of entanglement are reflected in the properties of the final state in collisions via the eigenstate-thermalization mechanism. These issues are crucial for reliable applications of the high-energy evolution approach to hadronic collisions of dense systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Forests provide substantial economic and ecological benefits to human society, including timber resources, wildlife habitat, and water quality regulation. However, an increasing frequency and severity of disturbances that kill trees (such as storms, pests, wildfires, and droughts) threatens the sustainability of these resources and services. This project combines existing field experiments and a recent ice storm in the forests of northern Michigan to evaluate how forest structure and productivity are affected by interacting disturbances. The project is focused specifically on how aspects of prior disturbances, such as timing and severity, might affect the response of the forest to subsequent disturbance. An improved understanding of the effect of interacting disturbances on forest structure and productivity will be highly beneficial to forest scientists and managers in predicting and managing for the effects of changing disturbance regimes. Openly available technical resources are being produced that focus on helping land managers and commercial foresters predict the outcomes of disturbances, such as ice storms, on the sustainability of our forest resources and develop management strategies to promote future forest resilience. Training is being provided to graduate and undergraduate students and a post-doctoral researcher with applicability to future careers in sustainable forest resource and land management, geospatial analytics, and data science. In addition, the data produced in the project and the field experiments at the University of Michigan Biological Station are an open training resource available to a large number of students, researchers, and educators. This project leverages a significant ice storm disturbance and multiple existing long-term ecosystem-scale disturbance experiments at the University of Michigan Biological Station to better understand the effect of prior disturbance severity, pattern, and timing on forest ecosystem structural and functional response to compounding disturbance. Mounting evidence indicates that changing frequency and scale of disturbances is producing more common and extensive instances of compounding disturbance, with uncertain consequences for core ecosystem functions. Based on prior work and preliminary data, forest ecosystem productivity is hypothesized to be more resistant to ice storm disturbance where prior experimental disturbance was: 1) less severe, 2) more focused on the lower canopy stratum, and 3) less recent. Study plots in the three existing disturbance experiments span gradients in prior disturbance timing (6-116 years prior), severity (0-85% basal area loss), and directionality (top-down vs. bottom-up) providing a novel template and extensive existing data resources on which to build an analysis of subsequent disturbance outcomes. In each experiment, the project is tracking change in forest NPP (relative to controls and pre-ice storm baselines) and shifts in structural and functional characteristics that are hypothesized to underlie variable resistance. To address these questions the project is utilizing existing long-term data resources, remote sensing-based analysis of forest canopy structural and functional change using terrestrial lidar and the National Ecological Observatory Network Aerial Observation Platform, and field plot-based assessments of tree damage, vegetation response, and wood production. The data and outcomes of the project are being used, in collaboration with regional and national forestry practitioner communities, to develop and deliver science-based management strategies focused on forest resilience to emerging and compounding disturbance regimes. 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

Filamentous fungi have a dramatic impact on the global economy (by one estimate, trillions of dollars annually) through both beneficial applications, such as pharmaceutical production and sustainable biomaterials, as well as harmful effects including crop destruction and human disease. In all these cases, fungi depend critically on their protective cell wall for success. Despite this importance, it is not fully understood how fungi respond to, and recover from, cell wall damage. This research investigates the fundamental biological question of how fungi detect wall stress, survive initial damage, and eventually restore normal growth. The research uses advanced microscopy, genetic tools, and computational modeling to uncover the molecular mechanisms that coordinate these responses in a model fungus. Understanding these processes will eventually enable "tuning" of fungal cell-wall properties for diverse applications, including: increasing productivity in bioprocess manufacturing, improving the physical properties of renewable mycelium-based materials that could replace petroleum-based products, and identifying new targets for antifungal drugs to protect crops and improve human health. The research also provides significant educational opportunities, training both undergraduate and graduate students in interdisciplinary approaches that combine biology, engineering, and computational sciences through collaborative teams across three universities. This project investigates how filamentous fungi respond to cell-wall stress, focusing on the model fungus Aspergillus nidulans. The molecular mechanisms involved in both immediate survival responses and subsequent recovery from wall damage are characterized using (i) advanced microscopy to visualize actin localization and dynamics during stress, (ii) genetic manipulation to identify key regulatory proteins, (iii) systems biology approaches to discover novel components, and (iv) mathematical modeling to integrate these findings into a cohesive network model. Specifically, the fungal response to inhibition of β-glucan biosynthesis is being characterized by testing the hypothesis that a two-phase response is involved. This includes an initial "survival phase," with rapid actin redistribution to form protective septa, which is followed by a "recovery phase" involving expression of specific proteins enabling growth resumption. In addition, a core set of stress regulators is being identified from proteomic analysis by comparing responses across multiple wall stressors, distinguishing universal responses from stressor-specific reactions. Finally, a hybrid modeling approach is being developed which integrates both mechanistic and machine-learning methods to infer the topology of regulatory pathways and their interconnections. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Internal bores generated by shoaling internal waves are known to be significant mechanisms of energy dissipation, cross-shelf exchange, and vertical mixing. Recent observational work has revealed that obliquely incident internal bores drive strong cross-shelf and along-shelf gradients in energy flux and water properties on the inner shelf. Robust theory suggests this energy flux divergence should drive a mean along-shelf current. However, mean along-shelf flows driven by dissipating internal waves have only been studied in the case of internal wave reflection on the continental margin, not for shoaling internal bores on the inner shelf. The project will examine the wave-mean flow interaction of dissipating shoaling internal bores for along-shelf currents on the inner shelf, including how the dissipation of shoaling internal bores can drive a time-averaged along-shelf current. Physical insights gained from idealized modeling will permit identification of the along-shelf flow in a detailed observational and high-resolution realistic modeling dataset. Along-shelf transport is relevant for biologically and physically important processes such as population connectivity and scalar transport. As such, this work may lead to reinterpretation or re-analysis of existing inner-shelf field observations and guide new experiments. The project will investigate internal bore-driven along-shelf currents, using idealized modeling, analyses of field observations, and realistic circulation model output. A process-motivated numerical experiment will be employed to characterize the along-shelf flow under simplified conditions and systematically varied forcing, while analyses of field observations and realistic model output will verify the presence, structure, and variability of the flow in nature. The relationship of internal bore dissipation to the magnitude and cross-shelf location of the along-shelf current is of particular interest. The numerical model will be used to determine how bore dissipation and the along-shelf current depend on ambient stratification, topographic slope, and planetary rotation. Field data and realistic modeling results will be used to quantify the time-averaged along-shelf circulation and its dynamics. A comparison between idealized modeling results and both observational and realistic modeling data will be vital in confidently attributing the measured along-shelf flow signal to internal bore dissipation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The aspects of the Earth’s climate that determine the character and extent of vegetation around the globe and the vegetation's subsequent impacts back on the regional and global climate are important issues for Earth system science. Diverse treatment of vegetation and its interaction with the global carbon cycle in Earth System Models (ESMs) is an important source of uncertainty in climate and vegetation projections. This study aims to construct an optimized machine learning model based on select climate state variables to predict vegetation parameters globally for both the historical period and the late 21st century. The project provides a first attempt at quantifying vegetation-climate feedbacks using machine learning on a global scale and has the potential to overcome deficiencies in process-based (physical) models of these feedbacks. The produced vegetation scenarios will be shared with the broader Earth system modeling community, which will facilitate an ongoing dialogue on the use of machine learning for vegetation modeling. The lead investigator is active in communicating scientific issues to local, state, and federal government stake holders, increasing the impact of this research beyond the scientific discipline. In addition, the principal investigators will incorporate the tools and results of this work into term projects and learning modules for their online courses and machine learning summer research and lecture series for high school students, increasing scientific literacy on artificial intelligence tools used to tackle important Earth system science questions. The overarching goal of this research is to quantify and understand the potential contribution of vegetation-climate interactions to projected climate changes, and to characterize and ultimately reduce uncertainties in climate projection related to vegetation feedback. Specific objectives include: 1) to innovate vegetation prediction via machine learning approaches, utilizing both observational datasets and output from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ESMs; 2) to project future vegetation changes and identify sources of uncertainties, employing the newly developed machine learning models in tandem with ESMs’ climate and vegetation output. The team hypothesizes that 1) machine learning models broadly trained on historical data are transferable (spatially and temporally) to future periods outside the training data sets, allowing the models to predict both gradual changes and abrupt shifts of vegetation caused by climate changes; 2) the process-based vegetation model dominates over the ESMs’ climate uncertainty as the primary source of uncertainties for the ESM-projected vegetation changes. The research will harness advances in machine learning and take advantage of high-resolution satellite remote sensing and reanalysis data, as well as existing output from CMIP6 ESMs. Combining machine learning with ESMs, the project will characterize and attribute uncertainties in vegetation and climate projections and examine the realism of the vegetation-climate relationship underlying each ESM vegetation model, and project vegetation using optimal machine learning models. The project will accelerate and improve vegetation prediction by innovating deep learning models, which uniquely enables the attribution of uncertainties to vegetation model structure and ESM climate. Results from this project will help strategize future model development efforts, pave the way for incorporating machine learning vegetation models into ESMs as an alternative to process-based models, and ultimately reduce uncertainties in Earth system projections. More broadly, the spatial hotspots of climate-induced vegetation changes produced in this project will guide the siting of future field experiments and long-term monitoring of ongoing changes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The Sub-national Nonstate Actor Governance (SNAG) project introduces a new measurement strategy and public dataset to measure territorial control at the local level within conflict zones, tracked over time. Understanding how groups gain or lose territorial control, and thus how conflicts begin, evolve, and end, is essential to national security and preparedness. Yet scholars, policymakers, and military strategists lack reliable and accessible techniques to measure and monitor territorial control within conflict zones. Existing empirical research is focused on a limited number of conflicts for which there happen to exist reliable measures of local-level territorial control over time. This limits ability to understand conflict more generally, and to apply knowledge to new threat environments. This research draws upon open-source information to ensure a transparent process that is easily replicated across contexts and adapted to new measurement challenges. The project uses machine learning and natural language processing (NLP) tools to automatically detect mentions of belligerent activity and control in a corpus of open-source texts, which are then used to produce spatially and temporally disaggregated estimates of rebel and government territorial control. The Subnational Nonstate Actor Governance (SNAG) project measures nonstate actors’ territorial control and governance at the local level, capturing temporal variation throughout conflict, comparable across contexts. This project makes both substantive and methodological contributions, generates new publicly available data capturing nonstate actors’ territorial control, uses an approach that translates across contexts to facilitate comparative analyses. The PIs annotate text from a corpus of news reports from conflict zones, identifying indicators of rebel and government territorial control with location and time information. These annotations are then used to train a new natural language processing pipeline, which is applied to the remainder of the corpus to automate the process of extracting relevant information from the full corpus. The information produced by this process is incorporated into a measurement model to produce fine-grained spatio-temporal data on conflict belligerents’ territorial control within conflict zones, facilitating systematic comparison of these phenomena within and across conflicts. The subnational territorial control data are used to investigate basic research questions related to the causes and consequences of territorial control and governance, fundamental to understanding the security risks in “differently governed” spaces, the efficacy of counterinsurgency aid, and the consequences for state-building after conflict. Methodologically, SNAG contributes new tools for generating geospatial data from text and for developing spatial latent variable models adaptable for additional social science applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project explores a range of computational problems that naturally emerge in the dimension theory of conformal dynamical systems. Conformal fractals are intricate geometric objects generated via iterated schemes of conformal (angle-preserving) transformations, and they have numerous interdisciplinary applications in mathematical physics, computer graphics, and data science. Measuring the size of conformal fractal attractors has been one of the central themes in the evolution of modern dynamical systems. One of the most well-known ways for measuring such complex geometric objects is the concept of Hausdorff dimension, which provides a robust way of determining the roughness of a set, extending the idea of dimension beyond integer values. The Hausdorff dimension of conformal fractals cannot be derived via simple analytic closed formulas, and obtaining effective and rigorous estimates becomes a challenging computational problem. The scope of this project is to introduce new methods from numerical partial differential equations with the scope of developing versatile, rigorous, and efficient methods for computing the Hausdorff dimensions of various conformal attractors. The project's topic is naturally interdisciplinary, encompassing a wide range of problems across Real and Complex Analysis, Dynamical Systems, Numerical Analysis, and Large-Scale Computations. The goal is to derive accurate and rigorous Hausdorff dimension estimates for a broad class of conformal fractals by integrating techniques from finite element methods, dynamical systems, and fractal geometry. Finite element analysis is a well-established approach for approximating solutions to a wide range of partial differential equations, with numerous refined methods developed over the years to ensure accurate and reliable numerical results. In contrast, the field of rigorous computation of Hausdorff dimensions for conformal limit sets is still in its infancy. The primary innovation of this project lies in adapting numerical methods typically used for solving PDEs to the estimation of Hausdorff dimensions. This new methodology demands deep analytical foundations and the development of novel theoretical results, presenting significant challenges, especially within the broader context of conformal graph-directed Markov systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award supports US-based participants in the conference "Perfectly Matched Perspectives on Statistical Mechanics, Combinatorics and Geometry" to be held June 16-20, 2025, at the Centre International de Rencontres Mathematiques (CIRM) in Marseille, France. The dimer model, a key system in two-dimensional statistical mechanics, has long served as a powerful tool to understand physical phenomena such as magnetism and crystal formation. In recent years, researchers have uncovered surprising connections between the dimer model and other fields, including geometry, combinatorics, and computer science. This conference will bring together leading experts and early-career researchers to share new discoveries and foster collaborations at the frontiers of this dynamic and interdisciplinary area. The dimer model is an exactly solvable model in 2D statistical mechanics, whose correlation functions can be expressed via determinantal point processes, making it a prototypical example of lattice-free fermions. Its conformally invariant scaling limits are often governed by the Gaussian free field. Beyond its role in statistical physics, the model is now known to possess rich algebraic and geometric structures, and it lies at the intersection of combinatorics, integrable systems, and discrete differential geometry. The conference will showcase cutting-edge results in these areas, bringing together experts and junior participants alike to promote cross-pollination between diverse yet interconnected mathematical disciplines. The conference website is at https://dimers.science/events/rick61/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Yao Lin of the University of Connecticut will develop new strategies to synthesize synthetic polypeptides, artificial protein-like polymers, under metal-free, auto-catalyzed conditions. These polypeptides hold potential for wide-ranging applications such as biocompatible polymers, advanced medical therapeutics, and self-assembling materials, yet existing methods often demand specialized catalysts or extensive purification, which limits their application. By refining a process called “helix-guided polymerization”, this project will enable more efficient and finely controlled synthesis, including robust beta-sheet-forming variants that expand the type of polypeptides that can be produced and broaden their applications. Graduate and undergraduate students will receive training in advanced polymer synthesis and computational modeling, gaining multidisciplinary expertise. To promote broader collaboration and accelerate discovery, the project team will provide an online platform for kinetic analysis and partner with high school enrichment programs, thereby engaging the next generation of learners in cutting-edge polymer research. Altogether, these efforts will lead to more sustainable and accessible methods for producing protein-like polymers, stimulating fresh ideas and solutions within macromolecular research and STEM education. Technically, this research addresses major limitations in amino acid N-carboxyanhydride (NCA) polymerization, particularly sensitivity to impurities and limited control over beta-sheet formation, through an auto-accelerated ring-opening mechanism using a “sergeants-and-soldiers” strategy. Here, alpha-helical macroinitiators act as “sergeants” to guide the rapid and precise polymerization of beta-sheet-prone (and other) NCA “soldier” monomers. The project will systematically expand helix-guided polymerization to a broader set of beta-sheet-forming NCAs, develop an enzyme-like kinetic model capable of predicting copolymerization outcomes (including potential off-pathway or inhibitory effects), and establish techniques for direct polymerization from non-purified NCAs. This comprehensive approach expands the scope of NCA-based syntheses, removing the need for rigorous purification and enabling large-scale, metal-free polypeptide production. Ultimately, advanced macromolecular/supramolecular design and robust kinetic analysis will deepen the fundamental understanding of auto-catalyzed NCA polymerization, facilitating the creation of functional biopolymers for diverse applications in macromolecular, supramolecular, and nanomaterials research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Copepods, tiny zooplankton, are the most abundant animals living in ocean waters. They play fundamental roles in Arctic food webs by consuming phytoplankton and protozoa and serving as food for fish, marine mammals, and birds. The Arctic is warming four times faster than the global rate, potentially putting many species at risk. The ability to predict the response of copepods to warming depends on robust knowledge of their upper thermal limits. This project will provide novel information on the critical thermal limit of key Arctic copepod species and whether or not this limit reflects their ability to succeed in their environment. The work will also provide insights into the success of a copepod species that is dramatically increasing its abundance in the southern Arctic, which may alter the transfer of energy in the ecosystem. The project’s findings, highlighting Arctic research, will reach broad audiences via social media and activities with K-12 students. The project will also train one Ph.D. student. Arctic species, which typically are cold temperature specialists, may be at risk of extinction because of the fast change the Arctic is experiencing. Thus, there is an urgent need to understand the physiological limits to survival in these species and whether or not these limits are a good measure of fitness. The project’s goals are: 1) Quantify critical thermal limits in three key copepod species of Disko Bay, Greenland, a widely studied Arctic ecosystem. 2) Evaluate the linkage between critical thermal limits and fitness proxies. The project will test the hypothesis that: a) Critical thermal limit is higher for the southernmost of the three species studied, which has dramatically increased its abundance in Disko Bay in the last 30 years. b) There is a positive correlation of the critical thermal limit, measured at an ecologically relevant temperature, and fitness proxies such as the balance of ingestion and respiration rate, egg production rate, egg hatching success, body size, and oil sac size. The educational and outreach activities of the project are proportional to the modest funding request and aimed at enhancing the public understanding of how marine species respond to change. A second objective is to communicate research in Arctic regions. The PI’s group will translate and disseminate results from the project via social media posts and in-person or hybrid presentations for K-12 audiences. The project will train one Ph.D. student and provide opportunities to network with other scientists working in the Arctic. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project is based on the development of a robot for use in inspecting complex machinery, such as jet engines. The inspection robot market is rapidly expanding, driven by a growing need for efficient, accurate, and cost-effective inspection solutions in sectors such as aviation, manufacturing, oil and gas, infrastructure, and energy. These robots play a crucial role by detecting faults, corrosion, or structural issues that could lead to costly failures if left unchecked. However, traditional inspection robots often face limitations when inspecting hard-to-reach or confined spaces, such as narrow pipelines, small machinery components, or tight structural cavities. This challenge has accelerated the demand for miniaturized robots designed to operate in these restricted environments. Conventional approaches use tendon-driven mechanisms, which are limited by friction that reduces range of motion along tortuous paths. This technology is an electrically driven system that bypasses the limitations of friction in tortuous environments. The solution addresses these challenges at a smaller scale using miniaturization to reach previously inaccessible points to detect failure points earlier in the process. This miniaturization may allow for faster and easier inspections of complex machinery. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of solid-state, miniaturized dielectric elastomer actuator (DEAs) inspection robots. This dielectric elastomer actuator navigation technology overcomes the challenge of tendon driven systems, which are limited by friction. Navigation using miniaturized complementary metal-oxide-semiconductor (CMOS) sensors allows access to narrow points, beyond the limit of conventional borescopes. In addition, the strategy for building solid state DEAs and connecting them with small metallic wires enables multi-degree-of-freedom end effectors. The result is a solid-state inspection tool that does not have friction limitations and can carry smaller cameras than existing borescopes. This technology may have application in inspection of complex industrial components such as jet engines, exploration of unstructured environments such as earthquake damaged buildings, or medical applications such as a steerable catheter for navigation inside the body. This technology may improve the inspection process by improving the speed of performing inspections as well as the ability to reach previously inaccessible points. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award will play a pivotal role in promoting advancing the field of statistics by supporting the participation of graduate students and early-career researchers in the International Chinese Statistical Association (ICSA) 2025 Applied Statistics Symposium, which will be held in Storrs, Connecticut from June 15 to June 18, 2025. With the theme of “Insights into Complexity: Empowering Policy through Statistical Learning and Data Analytics,” the symposium will highlight the role of statistics and data science in addressing modern policy challenges. The symposium aims to bring together a global community to explore how statistical learning and data analytics can address complex decision-making in various disciplines. The symposium will unite researchers, practitioners, and students passionate about advancing statistical methods, biostatistics, data science, artificial intelligence, and their applications in various fields and foster new directions for statistical inference, facilitating discoveries from seemingly messy data to inform real-world impactful decisions. The ICSA 2025 Applied Statistics Symposium will feature an engaging format, including plenary talks, invited sessions, and contributed posters. We prioritize broadening participation across all aspects of the conference, with a special focus on encouraging the active participation of early-career researchers (those who have earned their doctorates within the past five years) in invited sessions. For students, ICSA 2025 symposium offers numerous opportunities, including a student paper competition and the chance to present posters. The symposium's website, accessible at https://symposium2025.icsa.org/, serves as a hub for seamless communication and resource sharing among participants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Millions of people have undergone direct-to-consumer genetic and DNA testing to receive health and ancestry information. This project examines how customers of direct-to-consumer respond to and/or alter their decision-making about health and other risks when presented with their results. Specifically, the project explores whether unexpected results influence the decisions customers make about their health risks, family and kinship relationships, and other patterns of behavior. The research will produce a community-based network of people impacted by unexpected results to improve communication about the science and the lived experiences of genetic testing. The community-based research design and network of communication expands the participation of STEM learning for communities and the public. To investigate the impacts of unexpected DNA results, the researchers use a mixed methods approach that includes interviews, behavioral observations, and content analysis of online communication and social media. Using these methods, this research tests for the impacts of DNA results on family and other social relationships, and health decision-making. The research makes significant contributions to medical anthropology, the study of health decision-making, the social science of genetic testing, and to community-based 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 · 2025-05

Combustion devices that use a range of fuels are expected to partially satisfy global energy and propulsion needs for the foreseeable future. Emissions of soot, a byproduct of combustion, have detrimental effects on air quality and human health. Models that accurately describe soot formation could be helpful in designing and optimizing the next generation of combustion devices to reduce soot emission. However, experimental data for soot formation are needed to develop and validate soot formation models, and such data are scarce. This project focuses on the inception of soot formation. Despite many years of research on soot, inception is not well understood. This project involves a series of experiments designed to uncover the necessary information about soot formation for use in soot models. Although the focus of the project is on soot, the underlying inception process is similar to the formation of carbonaceous dust in the interstellar medium and the synthesis of nanocarbon material in industrial applications. Thus, improvements in modeling generated by this project may have broad applications. The proposed project integrates research and education to build toward mitigating the environmental impact of energy conversions and usages, by unraveling the soot inception kinetics. The research objective is to quantify the electrically neutral and charged flame products including small molecules resulting from fuel oxidative pyrolysis, Polycyclic Aromatic Hydrocarbons (PAHs), weakly bonded molecular clusters, and soot nuclei. Measurements will be performed in several carbon-rich laminar premix flames with different reactant compositions and maximum temperatures. The flame products will be measured directly or after making them collide with other neutral and charged species purposely seeded either in the sampled flow or directly in the flame. The results are expected to clarify the invariably neglected effect of the electric charge on the clustering of PAHs, by characterizing the kinetics of collision charging and condensation growth occurring in the flames and post-sampling. The research efforts are complemented by an educational plan with three objectives: 1) sensitizing the public on technical aspects affecting the sustainable satisfaction of the global energy demand; 2) inspiring K-12 students to undertake college education in STEM, and 3) equipping undergraduate and graduate students with engineering skills to study reactive multiphase flows relevant to the energy sector. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

The objective of this Faculty Early Career Development (CAREER) project is to support research on understanding snow and icing accretion on power and transportation infrastructures to improve forecasting of power outage and restoration during winter storms. The research intends to enable electric utilities to take proactive and appropriate actions to reduce disruption and its impact. Built upon an ongoing partnership with four major electric utilities serving more than 20 million customers, the project aims to yield substantial benefits to government agencies, electric utilities, and residents by enhancing their preparedness and response to winter storms. Additionally, this project contributes to classroom teaching and professional development as well as informing decision-making on severe weather. Every year, snow and ice storms knock out power to millions of people in the Northeastern United States. Loss of power in frigid conditions carries significant health and safety risks. This project represents one of the first attempts at developing a new correction model for snowfall density estimates based on weather forecast and field observation. A novel icing accretion model will drive the prediction of failure of power lines and travel speed on roads. With more than 80% distribution network being overhead, the results of this project intend to offer useful insights into impact of severe weather on critical infrastructures and measures for improving community resilience. The project is jointly funded by Humans, Disasters, and the Built Environment Program and Physical and Dynamic Meteorology 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 · 2025-05

This EArly-concept Grants for Exploratory Research (EAGER) project support research that will explore the untested, transformative potential of quantum computing to revolutionize transportation networks under uncertainty. The reliability of transportation networks is crucial for economic stability and public welfare, as disruptions and delays can lead to significant financial and societal costs. By leveraging quantum information science, this research project seeks to pioneer quantum algorithms that overcome these limitations, laying the groundwork for a new era of innovation in transportation. This project has the potential to create societal and economic benefits, such as reducing unreliability of transportation networks in the U.S., improving decision making under uncertainty, and enhancing resilience to disruptions caused by climate-driven weather events. Furthermore, this work looks to provide foundational insights that inform the development of practically applicable quantum algorithms and set the stage for quantum innovation in civil infrastructure systems and other critical engineering applications. The project looks to advance STEM education by integrating quantum computing into transportation courses, training graduate students to innovate at the nexus of transportation and quantum computing, and fostering interdisciplinary collaborations with academia and industry. This research project will undertake an ambitious exploration of the unique capabilities of quantum simulation and quantum optimization for solving key problems in stochastic transportation networks, pioneering quantum algorithms to tackle two core challenges: modeling stochastic transportation networks and solving reliability-based routing problems. Quantum simulation algorithms will be developed to encode spatial and temporal dependencies directly into quantum states, capturing complex stochastic dynamics at a level of realism unattainable by classical methods. These methods look to utilize quantum amplitude estimation and dimensionality reduction techniques to improve computational efficiency and achieve accuracy guarantees unattainable by classical sampling-based techniques for stochastic simulation. The project seeks to establish tailored quantum optimization algorithms and a hybrid quantum-classical approach for pathfinding and route choice problems in stochastic transportation networks, respectively. These approaches will be evaluated for resource cost and solution quality through computational experiments on toy and benchmark transportation networks using both noisy intermediate-scale quantum devices and fault-tolerant quantum simulators. The outcomes of this research look to demonstrate the potential of quantum algorithms to solve critical problems in transportation and seek to advance our understanding of the capabilities and limitations of quantum computing for complex engineering systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The rapid growth of smart cities necessitates advanced solutions to improve traffic mobility and public safety. This project proposes a novel multi-camera surveillance system, leveraging a network of distributed smart cameras to capture and analyze streaming video data in real time. By combining the computational power of edge devices and the cloud, this system intelligently processes video streams to address challenges in smart city applications. The project emphasizes privacy-preserving techniques to ensure sensitive information, such as images of pedestrians and vehicles, is protected while fostering scalable, efficient, and resilient real-time systems. It bridges research domains in systems and networking, machine learning, computer vision, and security and privacy, creating a unified framework for advancing smart city infrastructures. The project delivers transformative contributions across multiple domains. It introduces innovative unsupervised learning models for tasks such as human and object re-identification and tracking, enabling accurate and efficient analytics in distributed, real-time systems. A novel real-time and resilient cyberinfrastructure is designed with full-stack configurability, addressing system scalability and network performance challenges for large-scale deployments. Additionally, lightweight cryptographic systems combining advanced cryptographic primitives and Trusted Execution Environments (TEEs) enable privacy-preserving computation for sensitive video data. Beyond technological contributions, the project promotes societal benefits by improving urban services, fostering public trust in privacy-conscious surveillance, and training a new generation of students with skills critical to the systems, networking, data science, and cybersecurity industries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The project aims at bringing together researchers in partial differential equations (PDEs) and applied mathematics from the northeastern United States. The workshop features researchers at various career stages and will foster new research connections and collaborations among senior and junior participants. Special care will be taken to attract attendees from colleges and universities in the northeast region, which hosts a large population of students. The successful stories of the invited speakers will inspire younger generation of students to pursue careers in STEM fields. A panel discussion will offer graduate and undergraduate students valuable insights into potential career paths for individuals with a mathematics degree. Over the past two decades, significant progress and important breakthroughs have been achieved in several branches of nonlinear PDEs. An important development is that techniques developed in one area have played crucial roles in other areas. Moreover, there has been a remarkable growth in the use of computer-assisted proofs. The workshop will mainly focus on current research areas: i) recent progress in fluid dynamics; ii) new results in non-convex calculus of variations. Participants will be introduced to cutting-edge topics in nonlinear PDEs and learn about the latest techniques developed to address those problems. Whenever possible, lectures will be recorded, and a poster session featuring graduate students will be held. The event is open to anyone interested in the conference themes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Modern Artificial Intelligence (AI)-based applications, such autonomous systems, traffic forecasting, social media, drug discovery, and chip design handle increasingly large and evolving graph-based data. Efficient processing of graph-based problems is challenging because they involve complex mathematical operations that incur performance overhead on hardware processing units. Researchers have recently leveraged methods that reduce this computational complexity via pruning redundant computational tasks, but many challenges related to computer memory and task parallelism persist. This project devises novel mathematical operators that address the bottlenecks of graph-based AI applications to increase their performance. The project also develops computer science curriculum and provides student training through integration of research results and education. Moreover, through semiconductor industry collaborations, the project engages in disseminating its research outcomes, ensuring practical adoption and deployment of emerging AI applications, such as semiconductor chip design and autonomous systems, thus improving the U.S. AI infrastructure, with significant benefits to the economy and society. Efficient processing of graph models is challenging since the underlying computations require graph-proportional matrix operators. The strong input graph dependence has led to performance scaling and sustainability challenges for massively parallel hardware processing units. Although the research community has been attempting to reduce the computational complexity of graph processing operations by introducing sparsity in the model inputs, the resulting graph-proportional operators face underutilization of vector-level parallelism, data locality, and indirect memory access patterns, resulting in diminished hardware parallelism. The aggressively sparsified matrix operators exacerbate the computational structures and patterns in already unstructured and ultra-sparse inputs. This project devises novel matrix operators tailored to efficiently exploit extreme sparsity on highly vectorized and high-core-count processors to unlock sustainable and scalable performance. 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.