Middlebury College

NSF Awards · FY 2026 · 2026-09

Islands are cradles of biodiversity – home to many species found nowhere else on our planet. This project will document the biodiversity of islands located within the iconic yet understudied coastal lands and waters of the Gulf of Maine. Through surveys carried out by academic and community scientists alike, this research will explore the environmental and human factors that shape North America’s island mammal populations, spanning the ancient retreat of glaciers through present-day sea level rise, habitat change, and the arrival of invasive species. These unique ecosystems sustain the Gulf of Maine’s “blue economy”, contribute to globally relevant fisheries, and attract thousands of nature-based tourists annually. This project will generate multiple types of data that can guide conservation decision-making and will strengthen pathways for knowledge sharing between rural communities and wildlife managers. By building STEM identities at high schools and a primarily undergraduate institution, this project enhances our national STEM workforce and promotes scientific progress in a region where conserving biodiversity is closely tied to vibrant local livelihoods. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence. Identifying the biogeographic factors that produce variation across scales— from genes and species to clades and ecosystems— is vital not only for understanding the past but is increasingly relevant to predicting future conservation challenges. Island systems serve as natural laboratories for studying the processes that govern the evolution and distribution of biodiversity on our planet, yet the long-term legacies of human activities are not traditionally integrated into biogeographic assessments. This project will systematically catalogue the diversity of mammals over the past ~12,000 years in three island meta-archipelagoes of the Gulf of Maine, using a combination of museum collections, historical archives, biological surveys, and camera trapping using artificial intelligence to help identify the images. These data will be integrated with an updated assessment of island characteristics and glacial history for each region to test expectations of island biogeographic theory and disentangle how humans may have “bent” the rules. Whole genomes will be used to assess phylogeographic and divergence patterns across lineages of varying dispersal abilities, potentially revealing previously unrecognized endemism or translocations. A combination of modern and paleo-ecological research techniques will be used to evaluate island “syndromes” on the Isles of Shoals and uncover shifted ecological baselines due to the arrival of non-native mammals. As both undergraduate and high school students are involved in generating data and sharing it with their communities via novel STEM engagement spaces (e.g., “pop-up” museums), this project represents a seamless integration of research and education goals inherent to a CAREER award. 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 undergraduate students at Middlebury College. This work is conducted in collaboration with Veronica Ciocanel at Duke University. Through the fellowship, the principal investigator (PI) will develop mathematical models to describe the accumulation of both healthy and pathological tau protein in brain cells, as seen in neurodegenerative diseases such as Alzheimer’s Disease. The project will unite experts in intracellular transport and mathematical modeling of neuronal behavior to propose mechanisms underlying changes in neuron activity observed at different stages of the disease. This work will give the PI and undergraduate students hands-on experience with cutting-edge mathematical and computational tools, and the results will be shared through student mentorship and public presentations to broaden understanding of brain health and disease. In certain neurodegenerative diseases known as tauopathies, communication across brain regions breaks down due to the accumulation of a pathological form of the protein tau inside neurons, disrupting normal activity and ultimately leading to cell death. Experimental studies suggest a two-way relationship: pathological tau affects neuron activity, and changes in neuron activity can, in turn, influence the accumulation of both pathological and healthy tau. However, the mechanisms underlying this interdependence remain poorly understood. This project will develop a mathematical modeling framework that integrates the intracellular dynamics of tau accumulation with neuronal voltage behavior. The goal is to create experimentally informed models capable of predicting how pathological tau buildup, neuronal electrical activity, and intracellular transport interact. This fellowship will serve as a critical foundation for future development of large-scale models that simulate the spread of pathological tau through neuronal networks. It will also provide undergraduate students with training in advanced mathematical and computational techniques, equipping them to make meaningful contributions to this research. 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.

NSF Awards · FY 2025 · 2025-09

A general framework describing how stars form has existed for decades. However, the details of how mass flows from larger-scale structures onto stars remains unclear. There is some evidence that the growth of stars is episodic in nature. Despite there being some evidence to support this picture, the true growth history of stars remains unknown. The investigator will perform theoretical work to reveal how stars gain their mass. This research will also provide training and professional development opportunities to undergraduate students and support the expansion of public astronomy outreach offerings in rural Vermont. Mass accretion onto at least some stars exhibits strong temporal variability, with direct detection of occasional large-amplitude bursts. In the episodic accretion scenario, large accretion luminosity changes will modify the temperature of the surrounding material, moving the boundaries between gas and solid phases for various species and driving irreversible chemical changes that will persist into the planet formation epoch. The investigator will assess the viability of stochastic and secular accretion scenarios by coupling existing (magneto)hydrodynamical simulations with evolutionary radiative transfer models, generating synthetic observations of protostars, and performing an apples-to-apples comparison with published observations. This work will ultimately investigate the physics governing accretion onto young stars and the role played by accretion variability in the formation of stars. 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 · 2025-08

Project Summary Approximately 20% of Americans live with chronic pain, the underlying causes of which are often unknown. Spinal cord stimulation (SCS) is a therapy that successfully alleviates chronic pain symptoms in many patients; however, the efficacy of SCS across individuals can vary widely. Recently, clinicians have begun exploring a larger range of SCS frequencies, from more conventional values of 50 Hz (cSCS) up to values as high as 10 kHz (HF-SCS). While some patients receive significant reductions in pain with HF-SCS, others have optimal pain relief with cSCS or other forms of SCS. The mechanisms by which these protocols lead to pain relief in patients with chronic pain remain unclear, especially as concerns the variability across individuals with different chronic pain states. Potential sources of interpatient variability in the efficacy of SCS include variability in the activation of dorsal column (DC) fibers and the corresponding sensory processing of DC input by neural circuitry in the dorsal horn (DH) of the spinal cord. This proposal aims to use biologically-motivated mathematical models at these two scales: a biophysical three-dimensional hybrid field-cable model describing the activity of DC axons due to the electrical potential fields generated from current impulses emitted by an SCS electrode array, and a firing-rate network model of the neural circuitry responsible for sensory processing in the DH. The proposed projects will be in majority be conducted by undergraduate students at Middlebury College, with mentorship by and collaboration with faculty and graduate student trainees in biomedical engineering and applied mathematics at the University of Michigan. This proposal will strengthen the institutional pain research environment at Middlebury College by providing unique opportunities for undergraduate students to not only actively engage in pain multidisciplinary pain research, but also access a cross-organization, vertically-integrated mentorship program. Together, completion of these computational studies will: 1) identify key anatomical features that most robustly contribute to changes in DC axon firing, and 2) predict the mechanism(s) underlying preferential pain-relief responses of projection neurons within dysregulated DH circuitries (i.e., those contributing to chronic pain) to a range of SCS frequencies.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY: Structural variants (SVs) play a causal role in numerous diseases. However, our ability to detect and analyze disease-causing SVs, particularly de novo SVs, in short read genome sequencing data is limited by inaccurate genotyping (determining zygosity). There exists a substantial gap between the genotyping accuracy for small variants, e.g., single nucleotide variants, and SVs. Improving the accuracy of SV genotyping will increase the rates of molecular diagnosis, improve our understanding of multiple diseases, and expand our knowledge of human genetic variation. Our aim is to develop more accurate tools for genotyping SVs in short read genome sequencing data by incorporating the specific genomic context, sequencing instrument, and analysis pipeline into the genotyping model. Instead of attempting to develop a parametric model for those complex and interconnected processes, we generate estimates of the expected evidence using simulation. Our goals are to: 1. Develop a deep learning-based SV genotyper that automatically learns informative features shared by the real and simulated data in an image-based representation of the SV. Treating SV genotyping as an image similarity problem will enable us to more accurately genotype the many different SVs that might exist, not just those observed previously. 2. Deploy our new method to generate accurate genotypes for an ensemble of short and long-read derived SV call sets in thousands of human genomes. The resulting dataset will increase our understanding of the spectrum of structural variation across diverse populations. 3. Leverage our similarity model to automatically correct otherwise imprecise or incorrect SV descriptions; doing so will increase genotyping accuracy, improve the integration of different SV call sets, and enable more sensitive SV discovery in the future.

NSF Awards · FY 2024 · 2024-09

The Earth’s Critical Zone (CZ) is the near-surface environment spanning from the top of the vegetation canopy to the bedrock beneath -- where geology, hydrology, ecology, and humans interact, and where Earth’s life-supporting systems converge. From a human perspective, it is where nutrients are released through the weathering of minerals, where agriculture produces food, where natural processes filter and purify water, and the crucible where life evolved. Understanding the CZ is crucial for addressing environmental challenges and ensuring the sustainable management of Earth’s resources. With a growing human population there is an urgent need to understand the processes characterizing the CZ today, how the CZ is likely to change in the future, and how humans and the CZ intersect. The United States and several other countries have established networks of field sites dedicated to the interdisciplinary study of the CZ. This project will bring these separate national networks together to catalyze global CZ research. In the United States, considerable investment from the NSF has supported research on observatory design, development of a nationwide system of CZ observatories, and, more recently, a network of collaborating projects focused on CZ themes. Similar CZ observatories and networks have been established in other countries around the world. While each network serves as a valuable research entity, they often operate independently and lack a unifying framework; thus the full potential of what these networks could achieve by working together in a coordinated manner remains unrealized. This project will establish CZInt, an international network-of-networks to guide the future of global CZ research. CZInt will bring the US CZ community together with the established OZCAR network in France, the SITES network in Sweden, and the TERENO network in Germany; similar networks in other countries will be added during the project. Programming components include collaborative workshops and theme sessions at international scientific conferences, synthesis activities to guide future CZ research, and outreach activities toward broadening participation and sharing outcomes and implications of CZ science. CZInt aims to develop a roadmap to guide future international CZ research collaboration, innovation, and discovery. This project is jointly funded by AccelNet and the Established Program to Stimulate Competitive Research (EPSCoR). 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 2024 · 2024-09

As the global oceans experience increasing stress from anthropogenic climate change, improving our knowledge of the responses of primary production and the concentrations of dissolved oxygen and carbon in the ocean is crucial for predicting impacts for ecosystems and human populations. Critical for refining our understanding and improving model representations of biogeochemical processes, are quantitative reconstructions of how these variables have responded to different climate states in the past. This research investigates a new proxy for bottom water oxygen concentrations and provides new records of organic carbon flux. Data generated by the project will help constrain the response of these key environmental variables to changes in global climate over the last two glacial-interglacial cycles (150 ka). This proposal broadens participation in the Earth Sciences by supporting an early career Principal Investigator from an undergraduate-only institution, providing research and professional development opportunities for an undergraduate from an underrepresented background, and supporting K-12 students via a University of Colorado at Boulder Museum of Natural History program: Girls At the Museum Enjoying Science (GAMES). This project quantitatively investigates the variables that control alkenone biomarker preservation in open ocean marine sediments. Previous work has used sedimentary alkenone biomarker concentrations as a proxy for surface ocean export production, neglecting the well-documented influence of changes in bottom water oxygen conditions on biomarker preservation. The investigator will employ a latitudinal transect of existing sediment cores from the equatorial Pacific’s Line Islands region (cruise MGL1208) that have experienced synchronous changes in BWO to evaluate several hypotheses, including: 1) alkenone biomarkers experience quantifiable preservation changes driven by variations in sedimentary bottom water concentrations, and 2) time series of preserved biomarker fluxes, measured at sites with a range of export carbon fluxes and conditions of sedimentary preservation, can be used to quantitatively constrain the multivariate equation that relates alkenone preservation, bottom water oxygen, surface ocean carbon export, and oxygen exposure time. To evaluate these hypotheses the investigator will use cores from five sites to constrain A) alkenone biomarker flux (C37:total), B) sedimentation rate (14C and δ18O-derived age models), C) oxygen exposure time (multisensor track data), and D) organic carbon fluxes (230Th-normalized Baxs fluxes). The investigator will also generate records of E) sedimentary redox state (aU), and F) alkenone-based sea surface temperatures (UK’37). New data, in combination with published results, will refine estimates of oxygen and respired carbon storage during the Last Glacial Maximum (~20 ka), and provide the first estimates for the penultimate glacial period (~140 ka). Results will also improve our understanding of C37:total-based paleoproductivity estimates by quantifying the extent to which reconstructions may be altered by changes in bottom water oxygen. These findings will permit better reconstructions of photic zone carbon export – a key determinant of carbon cycle changes, and a more quantitative understanding of variations in bottom water oxygen and their global drivers. This project is jointly funded by Marine Geology and Geophysics (MGG) and the Established Program to Stimulate Competitive Research (EPSCoR). 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 2024 · 2024-09

PROJECT SUMMARY / ABSTRACT Cognitive decline is a common component of aging, and spatial memory is especially affected by old age. Testosterone levels among men decline steadily with aging, paralleling the age-related decline in cognitive ability, which suggests that there may be a causal link between these two processes. Past studies have produced mixed results regarding the cognitive benefits of androgen therapies for hypogonadal aged men, highlighting the need for an animal model to experimentally test the therapeutic value of testosterone treatment. With increasing age, there is also a transition from the use of a striatum-dependent response strategy to the use of a hippocampus-dependent place strategy for solving navigational tasks, and declining testosterone levels may be the cause of this shift. The proposed experiments will assess the physiological mechanisms underlying testosterone-induced changes in spatial memory and strategies, using castrated, aged male rats as a model for hypogonadal aged men. The specific aims of the proposed experiments are to determine: 1) the relative effects of testosterone and aging on place and response learning in males, 2) the role of brain-derived neurotrophic factor (BDNF) in regulating testosterone-induced changes in spatial memory, and 3) the role of neural synchrony in regulating testosterone-induced changes in spatial memory. Each experiment will involve testosterone injections given to castrated male rats from young, middle-aged, and old groups. In Experiment 1, rats will be injected with three different physiological doses of testosterone and tested on plus-maze tasks that require the use of either a place or response strategy. This will test whether testosterone can shift older rats to increase their use of a place strategy, more typical of younger males. Such behavioral results would suggest improved hippocampal function. To test this further, BDNF and related markers of neuroplasticity (TrkB, PSD-95) will be assayed from hippocampal and striatal tissue collected from all subjects. Experiment 2 will test whether BDNF is necessary for the memory-enhancing effects of testosterone by injecting some subjects intra-cranially (hippocampus or striatum) with a TrkB antagonist (ANA-12) in combination with testosterone dosing. Experiment 3 will explore the effects of testosterone on neural connectivity of the hippocampus and striatum using electrophysiological recordings on active rats. Past work suggests that reduced in-phase theta waves between the hippocampus and striatum facilitate place learning. Testosterone treatment is, therefore, expected to reduce in-phase activity, possibly restoring neural synchrony in older rats to that which is typical of a younger brain. In combination, these experiments will provide a critical step in determining the therapeutic value of testosterone for treating age-related memory impairment.

NSF Awards · FY 2024 · 2024-08

Many social networks evolve through mechanisms that are only partially recorded in data. For example, the observed formation of a link between two new friends in a social network might depend on an unobserved third person who introduced them. In this project, the investigators will develop new mathematical models of social networks which evolve through unobserved events and use these models to analyze real-world data. The research team will focus on two broad phenomena to model. First, they will study how networks of multiway interactions become separated by agent attributes over time. Second, the team will study how social hierarchies shape and are shaped by networks of cooperative endeavor. This work will take place in collaboration with practicing anthropologists and theoretical biologists. The results of both workstreams will highlight the strengths and limitations of simple theories of human social behavior and will also generate novel analysis algorithms for several types of network data. Undergraduate students will be recruited via a summer work-study program to pursue these workstreams. These students will collaborate on interdisciplinary teams, learning best practices for collaborative research alongside technical skills. For each of the systems under study the team will pursue three primary technical tasks. The first task will be to perform data analysis and use this analysis to formulate a stochastic latent-variable model of the system. The second task will be to analyze the long-run behavior of each modeled system, with an eye towards detecting phase transitions: qualitative shifts in macroscopic behavior as system parameters are smoothly varied. The team will determine parameter regimes in which models of growing hypergraphs exhibit self-reinforcing separation or in which models of cooperation exhibit stable social hierarchies. These phase transitions will be determined using compartmental equations and associated analysis. The third task will be to develop efficient algorithms for inference: learning model parameters from observed data. The team will approach the inference problem through the classical lens of maximum-likelihood estimation. To perform optimization efficiently in the latent-variable setting, the team will develop and implement expectation-maximization algorithms for these models. The team will also develop online stochastic variants specialized for the case of very large data. In the case of hypergraph separation models, the inference framework will lead to novel algorithms for model-based hypergraph clustering, while in the case of cooperative hierarchies inference will lead to novel dynamic embedding algorithms for time-stamped undirected graphs. The team will validate the proposed models through parameter recovery experiments on synthetic data. The team will then use these models to analyze real-world network data sets across several social domains. 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 2024 · 2024-08

This project examines factors that lead to the regeneration of forests in developing regions that have been deforested by people. In particular, the research examines the extent to which forest regrowth arises from the purchases of rural property by urban landowners, who may use the properties to cultivate tree crops. Using a combination of satellite imagery and ethnographic methods, the researchers can discern the extent to which forest regrowth is attributable to these rural-to-urban land sales. An ethnographic analysis of landowner priorities further elucidates the determinants of tree cover gain across multiple spatial and temporal scales. While contributing to geographical debates about forest transition theory, this research contributes to the management and stewardship of forests in regions that are the focus of conservation efforts. The project also contributes to the training of graduate and undergraduate students in the methods of environmental science. This Human-Environment research project will expand geographic theory on tree cover gain. Forest transition theory is attentive to the kinds of tree gain, including natural regeneration and anthropogenic plantings, but needs further empirical and theoretical considerations particularly on the role of geographically distant factors, known as tele-coupling. The project uses remote sensing analysis and place-based rural geographic study to substantiate the association between mapped tree gain and rural-to-urban land sales. The theoretical and practical outputs are timely given the confluence of global funding for land-based climate change mitigation with profound shifts in land markets. Many developing governments state in their climate change mitigation pledges that they intend to use funding to benefit rural citizens, but shifts in land markets may result in reduced land ownership by rural residents. Linkages between land markets and planted tree cover gain need to be studied to mitigate such unintended policy consequences, which may in turn lead to migration and food insecurity. 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.