Dartmouth College

NSF Awards · FY 2026 · 2026-08

Nontechnical Description This CAREER project advances the understanding of atomic-scale defects that limit the performance of next-generation materials in quantum electronics. These defects, known as two-level systems, can absorb energy and create noise in materials at very low temperatures. The research uses Broadband Cryogenic Transient Dielectric Spectroscopy (BCTDS), a measurement technique developed by the PI. The BCTDS technique enables direct probes of defects in quantum materials such as two-level systems. By connecting defects to how the materials are made and processed, the project helps the research community identify which materials host harmful defects, their nature, and how to avoid them. The activity also produces open data sets, analysis tools, and teaching materials that make it easier for students, educators, and researchers at a wide range of institutions to explore real data from quantum materials. Outreach efforts include interactive online modules, a podcast that highlights the people and processes behind the research, and an illustrated children’s book that explains the hidden structure inside materials. These activities make advanced materials research more accessible to entering undergraduates and help prepare a new generation of students to work at the intersection of materials science and modern quantum engineering. Technical Description This project establishes Broadband Cryogenic Transient Dielectric Spectroscopy as a quantitative, modular platform for characterizing two-level systems and related point defects in materials that support semiconducting and superconducting technologies. Two-level system defects are dominant sources of microwave dielectric loss and noise in thin films, interfaces, and bulk substrates. Yet their atomistic structure and dependence on processing history remain poorly understood. Conventional probes based on resonators and quantum bits are narrow band, spatially localized, and typically infer defects only indirectly through device coherence. In contrast, BCTDS uses a three-dimensional waveguide to deliver strong, broadband microwave pulses at cryogenic temperatures and measures the transient dielectric response across gigahertz bandwidths, enabling direct extraction of effective Rabi frequencies, dipole moments, and interaction strengths. The research establishes this technique as an in-situ probe throughout material processing by incorporating tunable microwave polarization, static electric and magnetic field biasing, optical access, and cryogenic positioning to perform longitudinal studies through multiple processing steps and to establish causal relationships between processing and defect properties. The research team also builds an open database that links BCTDS spectra to detailed processing metadata and material stacks. In parallel, the team will model the materials using processing-aware density functional theory and effective spin models that are compared to experiment to identify likely defect species and to connect their spectroscopic fingerprints to specific structural motifs and processing conditions. Together, these efforts establish quantitative relationships between processing, defect properties, and dielectric loss, provide design rules for low-defect materials and interfaces, and enable the community to identify low-loss material stacks and processing conditions without extensive trial-and-error studies. 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 2026 · 2026-07

This project develops robust and efficient computational tools to predict how heat and fluids move through complex materials containing many small holes or pores. Such materials appear in important technologies, including water and air filtration systems, battery cooling devices, heat exchangers, and advanced manufactured materials. Accurately simulating these systems is challenging because their microscopic structures strongly influence large-scale behavior, often making conventional simulations prohibitively expensive. By enabling fast and reliable predictions without resolving every microscopic detail, the project aims to significantly reduce computational cost while maintaining accuracy. The resulting advances could improve the design of energy-efficient technologies, industrial processes, and engineered materials. The project also integrates undergraduate education by engaging students in hands-on research in scientific computing and data science, helping to train the next generation of scientists and engineers in modern computational and data-driven methods. The project focuses on developing a non-intrusive computational framework to approximate macroscopic solutions to multiscale heat transfer and fluid flow problems in perforated domains. Building on the Derivative-Free Loss Method, the approach combines a stochastic formulation based on particle trajectories with flexible function representations to capture large-scale behavior without resolving fine-scale geometry. The research extends this framework to time-dependent problems, where a central challenge lies in understanding the interaction between stochastic sampling, time discretization, and physical dynamics. The work will establish theoretical foundations for this coupling and design efficient sampling strategies that improve stability and accuracy across diverse geometries and boundary conditions. In addition, the project will develop a neural-network-free implementation to reduce computational overhead and improve scalability. Applications include the homogenization of transient heat transfer and large-scale fluid simulations in perforated materials, providing both methodological advances and validation benchmarks. 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 2026 · 2026-07

This project promotes the progress of science by drawing on principles of human cognition and social behavior to develop artificial intelligence systems that are more interpretable, generalizable, and collaborative. Humans organize knowledge through structured concepts and hierarchies, reason through relational and logical patterns, and coordinate through effective social interactions. In contrast, current artificial intelligence systems often struggle with tasks requiring such structured, human-like understanding. This project uses graph structures as a unifying framework to bridge human cognition and artificial intelligence. It uses artificial intelligence models to uncover how knowledge is structured and processed in the brain and in learner interactions, and in turn uses principles of human cognition to guide the development of artificial intelligence systems with improved reasoning, interpretability, and collaboration. The project has potential benefits for neuroscience by providing computational tools to study cognitive organization and brain activity, for education by enabling personalized learning tools that map students' knowledge organization and support individualized feedback, and for artificial intelligence by advancing systems that are better aligned with the structural and collaborative principles that support human intelligence. To pursue this goal, the project is organized around three interconnected research tasks. First, the project uses artificial intelligence models to generate concept graphs aligned with brain activity and learner interactions, to study how knowledge is organized and processed and to support applications in brain decoding and personalized learning. Second, the project investigates how human-like structural principles, such as logical equivariance and compositionality, can be recognized and acquired by large language models, and develops targeted training strategies to improve reasoning and generalization. Third, the project develops graph-based models for multi-agent collaboration that represent argument structure, roles, and interaction patterns inspired by human teamwork, using debate and joint decision-making as settings for studying coordination and adaptive collaboration. The project also integrates research with education and outreach through undergraduate research mentoring, a recurring hands-on tutorial series and regional summer camp for high school students, and cross-institutional graduate activities in graph learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Understanding human behavior in videos goes beyond recognizing objects or actions. It involves interpreting emotion, reasoning about context, and respecting privacy. Current artificial intelligence systems often rely on simple visual patterns, struggle to explain their decisions, and are trained on data that may include sensitive personal information. These limitations make it difficult to use them in real-world settings such as accessibility. This project addresses these challenges by developing methods for human-aligned video understanding that can interpret emotional and social context, provide clear explanations, and operate on privacy-preserving data. The outcomes will support socially responsible artificial intelligence systems, improve accessibility tools such as audio descriptions for blind and low-vision audiences, and provide open resources that advance research, education, and public understanding of artificial intelligence. This project introduces a unified, closed-loop learning framework in which tasks, evaluation, and data are jointly optimized to improve model performance. The approach integrates three components. Empathy-driven modeling captures social and emotional context. Reasoning-aware evaluation diagnoses and improves intermediate model behavior. Privacy-preserving data generation enables the creation of semantically meaningful data without exposing sensitive information. Within this framework, benchmark datasets and evaluation protocols are used to identify model failures and guide iterative improvements to both models and data. The project will produce benchmark datasets, evaluation tools, and privacy-preserving generative models. It will also integrate these resources into education and outreach activities that broaden participation in artificial intelligence. 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.

NIH Research Projects · FY 2026 · 2026-06

This project focuses on characterization of heme enzymes that function in electron-rich environments and proteins they interact with. Bacteria encounter low or no O2 in a variety of habitats and such environments are particularly common at sites of infection and in biofilms. At low O2, microbes have evolved to keep using this powerful electron acceptor to benefit their energy production. At no O2, nitrite is frequently employed and there are multiple pathways to process this electron acceptor, which optimizes electron flux. Heme enzymes having multiple redox sites are critical players in these multielectron transformations. Recent studies as well as analyses of rapidly rising genomic sequences suggest elaborate regulation of electron flow in these enzymes and features of the active sites that have not yet been characterized. Understanding intra- and interprotein regulation mechanisms and deciphering molecular details that define reactivity in bacterial redox enzymes are critical for guiding the design of new antibiotic therapies. Our recent efforts have focused on characterization of conformational dynamics and redox reactivity of cytochrome enzymes and proteins through interactions with membranes and on regulatory mechanisms to deal with the limited supply of O2 under microoxic conditions, to coordinate electron flow and substrate delivery. We have identified multiple metalloproteins critical for the microoxic function of cbb3 oxidases in Pseudomonas bacteria, elucidated interactions and redox cooperativity of their electron donors, and characterized the ability of these enzymes to reduce NO. We have initiated studies of nitrite reduction in a previously uncharacterized clade of NrfA enzymes and their mimics and also identified protein systems to enable spectroscopic studies of reaction intermediates during NO reduction. In the next five years, we will (1) develop understanding of the redox function of Pseudomonas cytochrome c5 and its regulation; (2) characterize catalytic activity and electron flow in Campylobacter jejuni and Helicobacter pullorum NrfA to establish reactivity differences between Lys- and His-ligated NrfA active sites; and (3) characterize enzymatic activity and reaction intermediates of heme enzymes that reduce NO. To probe the interplay of conformational dynamics and redox function in these systems, we will combine in vitro structural, spectroscopic, and electrochemistry studies with analyses of protein interactions and bacterial phenotypes in vivo. This multidisciplinary program will yield fundamental insights about mechanisms of redox enzymes relevant to bacterial fitness and pathogenesis.

NIH Research Projects · FY 2026 · 2026-06

Oogenesis is a remarkable and precisely regulated process that produces a developmentally competent egg, poised to give rise to an embryo upon fertilization. Central to oogenesis is meiosis, a specialized cell division that halves the chromosome number to generate a haploid egg. During meiosis, transcription is silenced, creating a critical reliance on post-transcriptional control of gene expression to drive cell cycle progression. RNA-binding proteins (RBPs) are important regulators in this context, modulating translation of maternal mRNAs to coordinate the complex events of polar body extrusion, chromosome segregation, and arrest of the egg at meiosis II. Following fertilization, the egg resumes meiosis and completes meiosis II, after which the two pronuclei fuse and undergo nuclear reprogramming essential for early embryonic development. Disruption of translational controls can lead to devastating consequences, including embryo inviability and pregnancy loss. Investigating how RBPs orchestrate translation during meiosis is crucial for understanding factors contributing to early embryo loss. This proposal investigates how the RBPs PATL2, PUM1, and PUM2 regulate translational control to coordinate meiotic and early embryonic cell cycle events. These RBPs have established roles in meiosis, with PATL2 variants linked to oocyte maturation deficiency and early embryonic loss. Using the mouse as a mammalian model, this study leverages cutting-edge technologies to address critical mechanistic questions. The rationale is to define the unique and shared mRNA targets of PATL2, PUM1, and PUM2 and determine how their spatial localization and target specificity shift during the cell cycle and in the absence of other RBPs. Given the conservation of these processes, findings are expected to reveal mechanisms of translational regulation relevant to human oogenesis. This research is driven by two aims: 1) Adapt the HyperTRIBE technique to identify direct mRNA targets of PATL2, PUM1, and PUM2 during oogenesis and early embryogenesis, and assess how loss of one RBP modifies the target prolile of the others; and, 2) Employ spatial transcriptomics to elucidate the spatiotemporal dynamics of translation in individual oocytes and early embryos and assess how these dynamics are disrupted by loss of PATL2, PUM1, or PUM2. By integrating advanced imaging, spatial single-cell RNA sequencing and spatial ribosome profiling with in vitro oocyte maturation and embryo culturing, we will map unique and overlapping targets, localize transcripts in space and time, and reveal changes in target specificity and localization upon RBP depletion. This innovative combination of cutting-edge technologies will enable new insights into translational control essential for producing developmentally competent eggs and embryos. The significance of this work lies in its potential to uncover fundamental principles underlying translational regulation in meiosis, advancing our understanding of infertility and early pregnancy loss and identifying potential therapeutic targets.

NSF Awards · FY 2026 · 2026-05

Early embryonic development is characterized by a dramatic transition from cellular dependence on RNAs that were in the egg prior to fertilization to RNAs produced by the embryo itself. This process is known as zygotic genome activation (ZGA). The timing of ZGA is linked to changes in cell size, specifically the ratio of nuclear content (DNA) to cytoplasmic volume (also known as the nuclear-to-cytoplasmic or N/C ratio), which increases as cells replicate their DNA without growth. How cells measure this ratio and use it to control gene expression is a fundamental biological question that remains poorly understood. This project will address it using fruit fly embryos as a model system, combining cutting-edge live imaging with genetic and genomic approaches to reveal the molecular logic by which cells sense their size and activate transcription accordingly. Understanding how cells coordinate gene expression with cell size has broad implications for developmental biology and human health, as defects in this process can lead to developmental disorders and are implicated in diseases such as cancer. This collaborative project will also provide interdisciplinary training opportunities for graduate students at the interface of genomics, quantitative imaging, and computational biology, addressing workforce development in biotechnology and biomedicine. The project will also include educational outreach activities that engage K-12 students with key concepts in basic biology and genetics. This project will use Drosophila embryos to uncover the molecular mechanisms by which the N/C ratio controls the timing of zygotic genome activation. In Aim 1, the investigators will use existing RNA-seq data sets from embryos arrested at low N/C ratios to systematically identify genes that directly respond to the N/C ratio to initiate zygotic transcription. A subset of the resulting candidate genes will be selected for quantitative live imaging using the MS2/MCP transcription reporter system to determine specific transcriptional parameters (e.g., probability and timing of transcriptional activation) that respond to changes in the N/C ratio. Mathematical modeling will be used to extract quantitative transcriptional parameters from the data and determine what aspects of transcription respond to the changing N/C ratio. In Aim 2, the investigators will leverage the genome-wide set of N/C ratio-sensitive genes to identify cis-regulatory sequences and trans-acting factors that confer N/C ratio sensitivity. Bioinformatic analysis will be used to identify DNA motifs enriched in the regulatory regions of N/C ratio-sensitive genes, and systematic enhancer dissection will define the minimal sequences sufficient for N/C ratio-dependent transcription. Candidate transcription factors identified through motif enrichment and enhancer analysis will be experimentally manipulated to test whether altering their concentrations shifts the N/C ratio threshold for gene activation, both at individual loci and on a genome-wide scale. The outcomes will provide new mechanistic understanding of how cells measure their N/C ratio and coordinate transcriptional activation during a critical developmental stage. 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.

NIH Research Projects · FY 2026 · 2026-05

Abstract Neurotransmitter sensing is important for both the fundamental understanding and clinical treatment of many diseases and conditions. However, currently, there are no FDA-cleared or -approved medical devices that can sense neurotransmitters within the living human brain. This application aims to fill this important gap by translating recent neurochemical sensor innovation through collaboration among academia (Dartmouth and VA Medical Center), neurosurgery (Beth Israel Deaconess Medical Center), and industry (NeuroOne). Recently, we discovered that mild annealing dramatically improved the electrochemical stability of electroplated carbon coating and transformed conventional microelectrodes into sensors similar to carbon fiber electrodes with excellent neuromodulator-sensing performance based on fast-scan cyclic voltammetry (FSCV). Based on this discovery, we created a prototype integrated probe that combines both electrical recording and FSCV capabilities and validated it in vivo in rodents, demonstrating parallel spike/local-field-potential -recording and sub-second dopamine sensing with synergistic multimodal results. In three parallel aims, we will develop designs to translate this novel technology to NeuroOne’s sEEG electrode, which is the first and only thin-film neural electrode FDA-cleared in clinics, with the goal of achieving the most efficient and effective translation towards first-in-human studies. Our long-term goal is to shift the current neurophysiological monitoring and recording paradigm from electrophysiology only to electrophysiology combined with chemical sensing by integrating interoperable neurotransmitter sensors. While we only focus on sEEG electrodes here for the sensor integration, we envision that the translational optimization of FSCV-stable carbon coating, the clinical- facing potentiostat and software design, and the regulator pathway developed are generalizable to standalone neurochemical sensors in humans or similar integrations on many emerging device platforms, such as thin-film DBS electrodes and intracortical arrays.

NIH Research Projects · FY 2026 · 2026-04

ABSTRACT Chronically-reactivating HSV is an underserved disease. Beyond lesions suffered by affected individuals, reactivation results in risk of transmission to others, especially as subclinical reactivation occurs with high frequency. Further, the isolation of ACV resistant strains, the inability of long-term ACV treatment to fully control reactivation, and the emerging evidence for HSV infection as a risk factor for neurodegeneration provide strong rationale for development of novel therapies to reduce rates of reactivation. Promisingly, epidemiological and preclinical evidence supports the ability of antibodies to modify rates of reactivation. However, because reactivation takes place in individuals with endogenous antibodies, there is reason to believe that monoclonal antibodies (mAbs) with attributes distinct from those commonly induced by natural infection will be needed to provide optimal benefit in this challenging clinical setting. To this end, diverse viral glycoproteins function to evade host defenses, and targeting these activities could both interfere with viral pathogenesis and drive immune-mediated clearance. Glycoproteins E and I form a complex (gE/gI) thought to function by binding to the IgG Fc domain and sweeping virus-specific Ab off the surface of virions and infected cells for degradation. Glycoprotein C (gC) inhibits complement activation by binding C3b. Both glycoproteins facilitate cell-to-cell spread. Blocking these host evasion and viral spreading mechanisms through binding of mAb Fab domains at the same time as driving innate immune clearance mechanisms through the effector functions of the Fc domain has the potential to turn these evasion mechanisms from assets into vulnerabilities. We hypothesize that mAbs targeting viral evasion factors gE/gI and gC will exhibit both direct and indirect antiviral effects. They will inhibit the functions of these evasion factors and contribute to viral and infected cell clearance through Fc-domain dependent effector functions. These activities can be further enhanced by Fc engineering, synergistically increasing the contribution of multiple mechanisms of action to the antiviral effects of mAbs in vivo. Our objective is to initially isolate and then systematically optimize mAbs with antiviral activity in vivo in reducing reactivation in mouse and guinea pig models. The rationale for this project is supported by the clinically apparent need to improve Ab potency to achieve robust reduction of reactivation, and preliminary data that Fc engineering can substantially improve mAb antiviral activity in vivo.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY Mammalian SWI/SNF (mSWI/SNF) is a multi-subunit, ATPase-dependent chromatin remodeling complex that regulates chromatin states by altering nucleosome occupancy. Numerous studies have provided compelling evidence that the SWI/SNF complex plays pivotal roles in development. Large-scale sequencing studies have found that members of the SWI/SNF complex are frequently mutated in various human diseases. Understanding the regulation and activity of SWI/SNF has profound implications for elucidating the roles of chromatin remodelers in both development and disease. A critical knowledge gap exists regarding how SWI/SNF precisely targets, establishes, and sustains lineage-specific enhancers, as well as how it cooperates with other regulators, such as pioneer factors. This obstacle is exacerbated by the structural diversity of SWI/SNF complexes, as there are three functionally distinct major subcomplexes. The goal of this proposal is to bridge this gap by investigating the distinct functional repertoires of the major SWI/SNF subcomplexes through a model of acute protein depletion. Our central hypothesis is that the cooperativity of SWI/SNF subcomplexes enables precise mosaic regulation of discrete chromatin states. We plan to test this hypothesis through the following specific aims: deconvoluting the cooperativity and competition between SWI/SNF subcomplexes (Aim 1), elucidating the role of SWI/SNF in 3D genome architecture (Aim 2), and exploring the role of SWI/SNF in potentiating cellular dynamics (Aim 3).

NIH Research Projects · FY 2026 · 2026-02

SUMMARY/ABSTRACT Dense microbial communities known as the microbiota are intimately associated with human health. Invading pathogens must overcome the microbiota to establish and successfully cause infections. In settings like the gastrointestinal tract, antagonistic interactions between microbes can strongly influence colonization outcomes. One highly prevalent pathway known to mediate these interactions the type VI secretion system (T6SS) which mediates contact-dependent killing via the translocation of toxic effector proteins into recipient cells. How the T6SS affects pathogen fitness and microbiota composition in other body sites, like the respiratory tract, is not well understood. A model disease for studying microbial ecology and interspecies interactions is Cystic Fibrosis, where polymicrobial communities of microorganisms colonize the respiratory tract due to defects in ion secretion and pathological mucus accumulation. Pseudomonas aeruginosa (Pa) causes chronic infections, dominates the airways of people with cystic fibrosis (pwCF) and is a major cause of morbidity and mortality. Pa harbors three T6SS with a variety of effectors delivered to host and bacterial competitors. We have recently reported that a T6SS effector, TseT, is delivered by the H2-T6SS and regulates microbial diversity in the upper respiratory tract of pwCF. In addition to TseT, the tseT operon encodes an immunity protein, TsiT, which our preliminary studies show also regulates Pa biofilm, in addition to its role as a bona fide immunity protein. TsiT has sequential and structural homology to general LysR-type transcriptional regulators (LTTR), which are known to regulate diverse genes including those involved in biofilm, metabolism, and quorum sensing. We have also identified diverse homologs for tsiT in a variety of Gram-negative organisms, including Burkholderiales. These results have led us to the overarching hypothesis that TsiT is a dual-function immunity protein that both regulates biofilm and neutralizes the TseT effector. In this proposal, we will define the mechanism by which TsiT regulates biofilm, investigate the evolution of tsiT homologs, and determine their impact on microbial ecology in polymicrobial communities in the respiratory tract. To this end, we propose three aims: 1) Test the hypothesis that TsiT is a transcriptional regulator that mediates Pa biofilm; 2) Test the hypothesis that tsiT diversity outside of P. aeruginosa confers protection against Pa TseT intoxication; 3) Test the hypothesis that tsiT genes modulate P. aeruginosa fitness during biofilm growth in a model CF respiratory microbiota.

NIH Research Projects · FY 2026 · 2026-02

Aspergillus fumigatus causes an array of respiratory diseases ranging from acute invasive aspergillus to allergic sensitization from chronic colonization. Allergic bronchopulmonary aspergillosis (ABPA) is one of the most severe and devasting chronic diseases caused by Aspergillus fumigatus. ABPA is particularly important in people with Cystic Fibrosis (pwCF) as it is associated with worsening lung function and increased frequency of exacerbations in those individuals. ABPA is associated with fungal persistence in the lungs, ultimately leading to fungal sensitization, characterized by high total serum IgE levels, as well as a large increase in airway Th2 cytokines that drive airway eosinophilia. Currently, there is a critical knowledge gap in our understanding of the host-pathogen interactions which enable certain Aspergillus fumigatus strains to persist long-term in the lungs thus driving fungal sensitization and ABPA disease progression. In collaboration with colleagues within the Dartmouth Cystic Fibrosis Research Center (DartCF) we have developed a novel ABPA murine model, which we will use to understand both the fungal and host factors enabling fungal persistence and ABPA disease initiation and progression. This proposal fills the aforementioned knowledge gap by examining both the role host lung-resident macrophages in establishing a protective, long-term survival niche for fungal persistence and alterations in fungal resistance to antifungal killing by those same lung-resident macrophages can alter ABPA disease initiation and progression. In SA1, we will determine the fungal- intrinsic pathways enabling Aspergillus fumigatus to persist in those lung-resident alveolar macrophages. Moreover, we will utilize a library of clinical Aspergillus fumigatus isolates from pwCF to determine if there is a selection of these pathways. In SA2, we examine the host pathways within the lung-resident alveolar macrophages which are responsible for long-term persistence of those cells after Aspergillus fumigatus engagement using classic immunological techniques (e.g. conditional knock-out mice and adoptive transfers) and a novel pooled genetic screen. Finally, we seek to provide proof-of- concept data that targeting these lung-resident macrophage populations could alter the disease course of ABPA in our mouse model. Together, these data will provide novel insights into mechanisms of fungal persistence which aids in the development and progression of ABPA in mammals. Overall, this research fills a critical gap by providing the field with a better understanding of how fungal traits beyond allergen expression may regulate chronic allergic fungal diseases, like APBA, while also identifying a unique host-targeted therapeutic approach to limit Aspergillus fumigatus persistence within the lungs of a mammalian host to ameliorate chronic fungal disease.

NSF Awards · FY 2026 · 2026-01

Global sea level rise poses significant threats to coastal communities, ecosystems and economies worldwide. A major driver of sea level rise is ice mass loss from the Greenland and Antarctic ice sheets, where complex and poorly understood processes operating beneath and at the edges of the ice sheets control how fast ice flows into the ocean. However, precise tools are still lacking for predicting how quickly these vast ice sheets will respond to a warming climate. This project aims to improve these predictions by combining existing physical understanding of ice dynamics with the power of artificial intelligence, applied to datasets derived from satellite imagery of Greenland glaciers. This work will contribute to reducing uncertainty in sea level rise projections, which are crucial for planning infrastructure, protecting coastal populations, and informing policy decisions. The project will also invest in training the next generation of scientists through the Glaciology and Machine Learning Summer School, providing students and early career researchers with skills to bridge glaciology and machine learning. Additionally, this project will contribute to the broader scientific community through further development of open-source software tools. This project seeks to develop innovative, physics-informed models of two critical processes in ice dynamics: basal sliding and ice front calving. This project will extend the open-source Physics Informed Neural Networks for Ice and CLimatE (PINNICLE) framework to handle time-dependent modeling and assimilation of satellite data, while ensuring consistency with fundamental physical laws. Focusing on the three glaciers with the largest ice discharge on the Greenland Ice Sheet, the team will train neural nets on historical data over the 1980-2010 period, then test the model on observations between 2010 and 2020. In the projection phase, the team will apply the trained model to estimate mass loss in future climate scenarios out to 2100. The uncertainty in the future evolution of each glacier will be quantified using an ensemble modeling approach based on a recently developed approximation of the Bayesian posterior distribution. Sensitivity to variables such as ocean thermal forcing, ice thickness and surface runoff will be explored to learn more about the physical processes governing the behavior of each glacier. Model outcomes and learned parameterizations will be openly available and integrated into broader community modeling efforts such as the Ice Sheet Model Intercomparison Project (ISMIP7) and the Coupled Model Intercomparison Project (CMIP). 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 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant professor and training for a graduate student at Dartmouth College. This work is conducted in collaboration with Michael Halbig and Mrityunjay Singh at the NASA Glenn Research Center. Through the fellowship, the PI will develop multifunctional piezocomposites to enable safe operation, reliable performance, and improved thermal management in the battery systems of electric vertical takeoff and landing (eVTOL) aircraft. This work contributes to the broader field of intelligent material systems by addressing key challenges in aerospace electrification and pushing the boundaries of materials-by-design approaches. The resulting models and tools will guide future material design decisions and provide onsite workforce training with industry-relevant engineering skills. This project will offer key insights into processing–structure–property relationships, which supports data-driven materials innovation and sustainable, nationally competitive manufacturing infrastructure. This fellowship focuses on the design, fabrication, and characterization of multifunctional lightweight piezocomposites tailored to enable mechanical energy harvesting for additional power generation, while providing impactful force sensing with structure integrity and thermal management solutions. The multifunctional nature of the materials directly addresses the size and weight constraints of traditional eVTOL designs, where one material only serves one functionality. The proposed methodology combines 3D printing and experimental characterization of piezocomposites with multiscale modeling to predict performance metrics, such as mechanical-to-electrical energy conversion efficiency, thermal hotspot formation, and deformation or failure mechanisms under various operating conditions. The work will strengthen Dartmouth’s research infrastructure by supporting the professional development and skill expansion of junior faculty in emerging engineering areas. It will also provide equipment and laboratory upgrades, while fostering strategic partnerships with federal research institutions to promote long-term workforce development and provide hands-on training opportunities for students in New Hampshire. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research 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.

NIH Research Projects · FY 2025 · 2025-12

PROJECT SUMMARY Light is a critical environmental cue that shapes daily health behaviors such as navigation, foraging, and sleep regulation across the animal kingdom. In humans, disruptions in light-sensing pathways can result in blindness, circadian dysfunction, and other health issues. Studying neurobiological mechanisms underlying light-sensing systems can improve our knowledge of both human health issues and fundamental principles of sensorimotor processing. The proposed research aims to elucidate how the relatively simple nervous system of the nematode roundworm Caenorhabditis elegans processes the color of environmental light to drive changes in foraging behavior. Notably, C. elegans lacks eyes and conventional molecular photoreceptors known as opsins but can discriminate colors of environmental light. The proposed studies seek to exploit the genetic tractability and experimental accessibility of C. elegans to analyze molecular and circuit mechanisms underlying color discrimination by an opsin-less animal and thereby yield generalizable insight into light-sensing systems and sensorimotor processing. Leveraging advanced molecular genetic, neurobiological, and behavioral approaches, the proposed studies will: (1) analyze the wavelength dependance of C. elegans color-sensitive behaviors; (2) define molecular substrates and cellular site-of-action for light processing by non-canonical phototransducers in C. elegans; and (3) identify and functionally analyze how integration across C. elegans photosensory circuits might drive color- sensitive behaviors. Taken together, these experiments will provide insight into how non-canonical phototransducers and integration across sensory circuits enable light responsivity in the absence of opsins. More generally, the proposed studies should increase understanding of elucidating the principles of light sensing and sensory integration systems of significant biomedical and fundamental biological import. In addition, the proposed studies are ideally suited to prepare the PI for an independent faculty position by equipping her with the advanced technical skills and scientific foundation required to pursue research in line with her interests. More specifically, the PI’s long-term goal is to establish an independent research program focused on uncovering how information is represented and communicated within and across neural circuits using C. elegans as a model system. Support from this fellowship – and pursuing these studies in the ideal training environment provided by the Ghosh Laboratory and Dartmouth more generally – will enable her to develop advanced technical expertise in C. elegans genetics and neuroimaging and to characterize a novel sensory integration circuit in C. elegans that will serve as the foundation for lines of inquiry pursued by her laboratory.

NSF Awards · FY 2025 · 2025-10

This research project advances the development of next-generation liquid nanosensors using surface-enhanced Raman spectroscopy (SERS), a powerful technique that enables highly sensitive detection of molecular signatures. These sensors have transformative potential for early detection of environmental pollutants in water supplies and non-invasive diagnosis of diseases such as cancer and neurodegenerative disorders. However, current SERS probes face major limitations in sensitivity and reproducibility due to the complexity of detecting trace biomolecules in liquid samples. This award supports a cost-effective, computation-guided approach to the design, synthesis, and application of high-performance SERS nanoprobes. By integrating multiscale simulations with experimental synthesis, the project reduces reliance on traditional trial-and-error methods. Computational models will guide the structural design of plasmonic nanostructures and predict optimal synthesis conditions, accelerating discovery while conserving resources. This interdisciplinary collaboration between engineering and the physical sciences supports NSF’s mission to promote the progress of science and advance national health and welfare. The project also fosters STEM education and expands the workforce by providing hands-on research opportunities for students, helping to train the next generation of scientists and engineers. This award supports the rational design and synthesis of metal-insulator-metal (MIM) nanoprobes for enhanced SERS-based liquid sensing. The project combines continuum-scale finite element analysis (FEA), colloidal theory, and atomistic molecular simulations to model field enhancement, nanoparticle assembly, and interfacial interactions. Simulation outputs will directly inform the experimental fabrication of MIM nanoprobes with tunable core shape, silica thickness, and surface ligand chemistry. The project includes three integrated tasks: (1) development of a multiscale modeling framework to design optimal MIM nanostructures and predict solution-phase assembly conditions; (2) synthesis of MIM nanoprobes with tailored morphology and surface functionality based on computational guidance; and (3) evaluation of SERS performance across a range of analytes, including in silico screening and experimental detection of biological and chemical targets in liquid media. This integrated approach aims to establish generalizable design principles for reproducible, high-sensitivity SERS nanoprobes and accelerate their application in diagnostic and environmental monitoring technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-10

PROJECT SUMMARY/ABSTRACT The US faces a mental health crisis; nearly 83 million Americans experience mental illness, but fewer than 50% of these individuals accessed past year mental health services. There is a critical need for highly disseminable, potent mental health interventions. Standalone digital interventions offer promise for improving clinical outcomes at scale; however, efficacy of these interventions to date is modest, likely due in part to insufficient personalization to patient heterogeneity and to momentary changes in mental health symptoms. The present study seeks to rapidly improve the efficacy of precision psychiatry digital health interventions by developing real-world, person-centered maintenance models for psychopathology using ecological (i.e., in patient’s daily lives) data and dynamic systems modeling. Dynamic systems modeling of repeated time series data yielded by ecological momentary assessment (EMA) and smartphone sensors will be used to evaluate the individual and interactive effects of empirical maintenance factors to understand each individual’s maintenance system for psychopathology in their daily life. Just-in-time adaptive interventions (JITAIs) are prompts delivered in-the-moment via smartphone at identified instances of risk for maladaptive behaviors. Although often evaluated in aggregate across months, JITAIs could be conceptualized as ecological quasi-experiments that induce use of specific therapeutic skills at observable times. Accordingly, this study will use micro-randomized (in which the content of the JITAI is randomly assigned in the moment) JITAIs delivered at times of elevated risk for maladaptive behavior based on the individual’s developed dynamic system to evaluate the effects and mechanisms of specific therapeutic skills on outcomes of interest, to provide granular insight into treatment mechanisms. Eating disorders are the ideal population in which to test proof-of-concept for these methods to improve efficacy of precision psychiatry digital interventions, as they are characterized by easily measurable maladaptive behaviors (i.e., dietary restriction, binge eating, compensatory behaviors), are maintained by a complex intersection of biological, psychology, and social factors, and their frontline treatment, enhanced cognitive behavioral therapy (CBT-E), is primarily comprised of behavioral skills. The aims are: 1) test the hypothesis that person-centered dynamic models will describe risk for dietary restriction, binge eating, add compensatory behaviors with ≥ 60% average goodness-of-fit, 2) test hypothesized effects and mechanisms of CBT-E skills on eating disorder behaviors and primary maintenance factors using JITAIs, and 3) characterize feasibility and acceptability of the standalone JITAIs. The study will enroll N = 170 adults with eating disorders who will complete four months of ecological data collection and receive JITAIs to use CBT-E skills at times of elevated risk for 10 weeks of the data collection period. The study will set the stage for future randomized controlled trail to evaluate the optimized, standalone JITAI system, which will have the potential to substantially improve access to evidence-based mental healthcare.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Macrophages are highly versatile innate immune cells that are essential for a range of functions, including coordination of inflammatory responses, regeneration, and tissue homeostasis. Heterogeneous subsets of macrophages arise from two primary hematopoietic waves. The primitive wave generates tissue-resident macrophages with specialized functions, whereas the definitive wave generates hematopoietic stem/progenitor cells (HSPCs) that differentiate into traditional circulatory macrophages. Imbalances in macrophage number or activation can lead to significant tissue dysfunction and disease. Unfortunately, the specialized roles of distinct macrophage subsets in immune and non-immune contexts remain poorly understood due to limitations in our ability to isolate and track subsets long-term in vivo. To circumvent these technical challenges, I will capitalize on the advantages of the zebrafish model, specifically their transparency throughout development, to track the dynamics of distinct macrophage subsets from their genesis to destination tissues. This proposal will leverage the transcription factor interferon regulatory factor 8 (Irf8) as a tool to separate macrophage populations based on their hematopoietic origin and will identify their autonomous functions in: (i) establishing tissue macrophage heterogeneity, (ii) mounting responses to challenge, and (iii) maintaining ratios of other blood lineages. Irf8 is required to generate primitive macrophages, but its role in regulating definitive macrophages remains poorly understood. Herein, I identify irf8 expression within a subset of emerging HSPCs, suggesting the presence of distinct irf8+ and irf8- definitive macrophage subsets. As such, I posit that macrophage heterogeneity can be classified by both irf8 expression and hematopoietic origin. I will investigate the functional and transcriptional differences between irf8+ primitive, irf8+ definitive, and irf8- definitive macrophages. In Aim 1, I will determine the heterogeneity of primitive and definitive macrophages across tissues from organogenesis to adulthood, the first longitudinal analysis of tissue macrophage dynamics. To achieve this, I designed a Cre/loxP- based lineage tracing system under the irf8 promoter to label macrophage subsets by their irf8 expression and hematopoietic origin, i.e., primitive or HSPC-derived. Through Aim 1, I will define the dynamics of macrophage subsets in their distribution across tissues, their differential responses to injury or systemic infection, and their unique transcriptional profiles. In Aim 2, I will determine the specialized functions of Irf8 within primitive and definitive macrophages as they relate to the production of macrophages versus other blood cells and colonization in tissues. I will achieve this by rescuing irf8 specifically within irf8+ primitive or irf8+ definitive macrophages in a global irf8 mutant background. By elucidating the ontogeny, heterogeneity, and functional contributions of distinct macrophage subsets, this research will provide new insights into macrophage biology and uncover novel therapeutic targets for manipulating macrophage functions in tissue repair and hematologic diseases.

NSF Awards · FY 2025 · 2025-09

This research focuses on understanding how bacteria respond to antibiotics and investigates non-genetic differences—such as variations in individual cells’ growth rates—that emerge under these conditions. Over the last century, it has become widely recognized that non-genetic variation is critical to understand, as it can influence the outcome of antimicrobial therapies and the evolution of antibiotic resistance. Moreover, it is deeply connected to fundamental questions about how cells grow and divide. The project combines cutting-edge experiments, where single-cells are imaged under varying conditions, with mathematical modeling to predict non-genetic differences in growth and biochemical composition of E. coli. These predictions will deepen our understanding of antibiotic resistance and microbial physiology while laying the foundation for broader efforts to combat drug resistance and improve treatment outcomes. The project is a collaboration between a mathematician and a microbiologist and will provide rich opportunities for undergraduate and graduate student training in quantitative biology. This project develops predictive models that link single-cell gene expression and growth dynamics to population-level behavior in bacterial systems under antibiotic stress. Focusing on the tetracycline resistance operon in E. coli, the research integrates stochastic modeling of gene expression, growth, and size regulation with single-cell data from microfluidic experiments. Aim 1 models steady-state distributions under constant drug by combining structured population models with stochastic differential equations for growth-expression coupling. Aim 2 examines dynamic gene regulation and resource allocation following abrupt antibiotic exposure, using mechanistic models incorporating proteome partitioning constraints and regulatory interactions. Aim 3 links single-cell dynamics to population behavior via a model-agnostic estimator based on a Feynman-Kac-type duality between lineage and population distributions. A key novelty is the ability to predict delayed recovery and growth curves in liquid cultures solely from mother-machine data, without additional population-level fitting. This multi-scale framework addresses how selection acts on phenotypic distributions and tests the assumption that single-cell experiments reflect bulk behavior. The project also develops analytical and numerical tools (large deviation theory, extremal statistics, and spectral analysis of population operators) that are broadly applicable for understanding non-genetic variability in single cells. The PI and co-PI will be engaged in mentoring students and postdoctoral fellows supported by this grant, as well as co-teach a course in quantitative biology. 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.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Suicide is a leading cause of death among young adults, and rates have been steadily increasing in recent decades, highlighting the urgent need for effective prevention strategies. The rise of social media use among this population has coincided with these increasing suicide rates, raising concerns about its potential role in the spread of suicidal thoughts and behaviors. While evidence suggests a link between social media use and suicide risk, the underlying mechanisms through which social media influences suicide transmission, particularly over the rapid timescales in which suicidal crises unfold, remain poorly understood. This research project aims to investigate the dynamic transmission of suicidal thoughts and behaviors among young adults (18-25 years) on two prominent social media platforms: Twitter/X and Reddit. The study will employ a novel, multi-method approach integrating computational social science, natural language processing (NLP), dynamic social network analysis, and ecological momentary assessment (EMA) to model suicide transmission as a real-time process and identify modifiable risk factors. In Phase 1, we will recruit 800 young adults (400 from Twitter/X and 400 from Reddit) who are already engaging with suicide-related content on these platforms, minimizing the risk of exposing participants to potentially harmful content. Data will be collected longitudinally over 90 days using both active and passive methods. Participants will use a smartphone app to complete EMAs three times daily, capturing real-time fluctuations in their suicidal thoughts and behaviors. At the end of the study period, they will download their complete history of posts, likes, replies, and reposts from both platforms, enabling us to reconstruct their dynamic social networks. We will also conduct a psychiatric interview with each participant to assess suicide risk and gather detailed clinical information. Advanced NLP and dynamic social network analysis will be used to analyze social media content and interaction patterns, while deep learning models will predict changes in suicidal ideation based on both individual and network-level factors. We will compare transmission dynamics between Twitter/X and Reddit to identify platform-specific characteristics that influence the spread of suicide risk. Phase 2 will employ a policy Delphi study with a diverse panel of 60 stakeholders, including young adults with lived experience, digital health researchers, ethicists, mental health clinicians, and representatives from relevant organizations. This study will explore the ethical and practical feasibility of using the insights gained from Phase 1 to inform real-world suicide prevention interventions on these platforms. Through multiple rounds feedback, we aim to achieve consensus on the ethical considerations of leveraging social media data and technology for suicide prevention. This research has the potential to significantly advance our understanding of suicide transmission on social media, paving the way for the development of data-driven, ethically grounded, and scalable interventions that could ultimately contribute to a reduction in suicide risk among young adults.

NSF Awards · FY 2025 · 2025-09

This NSF CAREER project aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs). The project will bring transformative changes to the use of measurements for dynamic state estimation, model deficiency diagnosis and calibration, and measurement configuration, thereby enhancing system reliability and security. This will be achieved through innovative dynamic estimation theories and algorithms that leverage the increasing diversity of sensors and communication infrastructure, as well as advancements in robust estimation, uncertainty quantification, optimization, and data analytics. The intellectual merits of the project include i) a generalized, computationally efficient derivative-free observability theory, with observability indices tailored for dynamic systems with black-box models, ii) integration of Bayesian inference with robust estimation to develop novel nonlinear dynamic estimation methods and iii) a scalable Bayesian framework for dynamic parameter estimation and uncertainty quantification. The broader impacts of the project include developing the next generation of robust dynamic estimation paradigms for IBR-dominated power systems, and industry-academia collaborative initiatives to promote industry-driven research, course renovation, and training to equip students (including K-12 students, and those from underrepresented groups across different disciplines and diverse backgrounds) with unique experiences in renewable energy technologies, data analytics and power engineering. The rapid deployment of IBRs, such as solar and wind farms, and battery energy storage is changing the dynamic landscape of electric power grids. Traditional steady-state-based static state estimation, used in current energy management systems, is insufficient for capturing these dynamics in real-time operations. This project addresses the critical need for improved dynamic observability and reliable models for system reliability analysis and decision-making. The research objectives include i) developing a generalized derivative-free state and parameter observability theory for black-box and hybrid dynamic systems, overcoming limitations of linearization-based and Lie-derivative-based theories for IBR-dominated systems, ii) fusing robust statistics with estimation and optimization to create nonlinear dynamic estimation methods capable of addressing black-box IBR models, control mode switches, current limiters, anti-windup constraints, unknown controls, and multi-timescale dynamics and iii) designing observability-informed, scalable parameter estimation and uncertainty quantification algorithms to continuously refine power system dynamic models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Generative artificial intelligence (GenAI) tools are often viewed as transformative for social science research, education, and real-world decision-making. Despite their capabilities, GenAI tools might produce results that are not accurate or impartial. This project aims to address this challenge by equipping researchers, educators, students, and others with the tools and knowledge needed to apply GenAI in their research activities responsibly and rigorously. The project seeks to enable research communities to capture the benefits of using GenAI. The project develops and tests new human-in-the-loop approaches, relying on human expertise, judgment, and critical thinking, to integrate GenAI tools into document analysis and synthesis (DAS). DAS is a widely used but labor-intensive method in the social sciences. The goal of the project is to ensure accuracy and impartiality while supporting usability and broader adoption. The project team will create a generalizable methodological framework for GenAI-enhanced DAS workflows, outlining use cases and guiding key decisions, including how to structure context, prompt models, and validate results. The framework is tested through classroom-based research in a set of undergraduate and graduate courses. Students will engage in structured problem-solving exercises and participate in focus groups. The project team will analyze interactions between the students and GenAI tools, along with student reflections, to examine decision-making, performance, and best practices. The project clarifies and demonstrates how GenAI can be responsibly and rigorously embedded within established social science research methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The early Paleogene (~66-48 million years ago) was an important time in Earth’s history: It immediately followed the mass extinction of all dinosaurs (except birds), many modern groups of mammals first appeared, and the Paleocene-Eocene Thermal Maximum (a significant climate event) occurred. Knowledge of these events is mostly based on a well-dated and characterized North American stratigraphic record; a global perspective on these events is missing. This project will apply modern, high-precision, age-dating techniques to the sedimentological and mammal fossil records of Mongolia. These methods will allow the building of a critical framework for comparing the North American and Asian fossil records across this important time interval. New physical and digital collections of fossils and a pop-up traveling exhibition on Paleogene mammal evolution and climate will be created. Developing a modern chronostratigraphy and paleoenvironment reconstruction for the highly fossiliferous Naran Bulak and Gashato Formations in Mongolia is the goal of this project. Four geochronological methods will be used, including magneto- and chemo-stratigraphy and Ar/Ar and U-Pb geochronology. Age and correlation data will be combined with careful sedimentological and paleoenvironmental analysis. These methods will be used to precisely constrain this important fauna and permit precise correlations with other parts of Asia and with the North America record. This geochronologic focus will be coupled with detailed sedimentologic analysis and stable isotope analysis of ancient soil and lake deposits to identify the PETM boundary by its signature global negative carbon isotope excursion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This doctoral dissertation award investigates human settlement and agricultural practices at a site that has largely characterized the region as dominated by hunter-gatherers with low population density, who moved seasonally to different environmental settings, and practiced limited, if any, domestic agriculture. New evidence suggests the possible presence of widespread maize cultivation and nucleated permanent villages. Resolution of questions surrounding past settlement and subsistence practices has broad implications for understanding of the relationships among sedentism, emergent social complexity, and agricultural intensification, as well as the anthropogenic impact on the natural environment prior to population expansion. The study’s use of phytolith analysis advances administrative priorities for investments in understanding the adoption of biotechnological innovations in scientific research. The researcher also makes use of remote sensing methods (LiDAR) and ground-penetrating radar systems (GSST), which enhance the integration of artificial intelligence in scientific research. The project also provides training for graduate students in these analytical and other archaeological methods. This project locates, documents, and analyzes archaeological remains through a multi-scalar research strategy. The region where the research is focused is ideal because of the minimal development that has occurred compared to other parts of New England, coupled with an environment well-suited for agriculture and a rich ethnohistoric tradition. The project integrates regional remote sensing, landscape-scale geophysical prospection using ground penetrating radar and other methods, as well as targeted excavations to secure dating, artifactual, and paleoethnobotanical samples. Soil samples are processed for phytoliths to understand the ecological composition of the region and recover evidence of domesticated crops. Collectively, the results reshape understanding of agriculture, the social organization of the communities who resided in these regions, and their potential impacts on the region’s ecology. Likewise, innovative methods developed by the project form a blueprint for archaeological investigations broadly. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The development of agricultural systems has been critical for the growth of human populations and complex societies. Such systems have been thought to emerge only under conditions of rigid centralized hierarchies, large populations, and favorable environmental settings. This project investigates the development of intensive agriculture in the absence of the usual social and environmental drivers. The investigators examine the impetus, technologies, and labor strategies behind intensive agricultural production in a northern environment. This research adds to a growing body of literature that reshapes conceptions of past societies and landscapes and encourages a search for agricultural intensification in new, unlikely spaces worldwide. The project fosters public science in the research process. This project establishes alternative conditions for intensive agriculture by investigating the settlement history, chronology, ancient technologies, agricultural products, and environment at an extensive agricultural site in the upper Midwest. The research team investigates the location and size of the settlement whose farmers built this landscape through ground penetrating radar surveys, shovel testing, and excavation. The site’s chronology and bed construction techniques are determined through biotechnological and other methods, including radiocarbon dating, stable isotope, and phytolith analyses. Macrobotanical analyses establish what crops were cultivated, all of which were grown near their northernmost extent. Pollen analysis determines the scale of land management and the timing of forest rebound. Together, these findings illuminate the scale and intensity of past agriculture and landscape modification and challenge foundational ideas of agricultural development. 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.