Brown University

NSF Awards · FY 2026 · 2026-01

Understanding how mixtures of solid particles and fluids, such as those found in avalanches, volcanic flows, and river sediments, initiate and how fast they propagate is vital for natural hazards and improving engineering models of Earth systems. These particle-laden flows are complex because the solids and fluids interact in diverse ways: sometimes the mixture acts like a solid, other times like a liquid, and often something in between. This project brings together geoscientists, computer scientists, and engineers to develop new models that better capture these behaviors by combining laboratory experiments, advanced numerical simulations, and artificial intelligence (AI). By using AI methods that are designed to be transparent and interpretable, this work not only enhances scientific understanding but also helps build public trust in AI-driven tools. The findings will support a broad range of geoscience applications and improve forecasts of events that can impact lives and infrastructure. Educationally, the project supports a vertically integrated training model, where postdocs mentor graduate and undergraduate students in a collaborative, hands-on research environment. The team will also create publicly accessible AI tools, YouTube tutorials, and organize quarterly seminars to disseminate their advances in AI for geosciences. These efforts will help prepare a new generation of researchers skilled in both scientific computing and Earth science. This project aims to discover and validate an elasto-viscoplastic (EVP) continuum rheology for dense granular suspensions under varying stress conditions, relevant to natural and engineered geophysical flows. Three central scientific questions guide the research: (1) how to represent stochastic force chains in a continuum framework, (2) how to define a rheology accounting for competing fluid–particle and particle–particle interactions, and (3) how to incorporate nonlocal and memory effects in stress evolution formulated using integro-differential equations. The approach integrates laboratory experiments, discrete element simulations, and interpretable machine learning. A novel by-design interpretable AI framework will be developed to discover analytical integro-differential equations for the EVP rheological model, while physics-informed operator learning with Kolmogorov–Arnold networks will enable its reduced-order surrogate modeling for GPU-based numerical solvers. The resulting models will be deployed in a large-scale application involving melt extraction from crystal-rich magmas. Open-source software and educational content will support broad dissemination. Collectively, this project advances both geoscientific understanding and AI methodologies for modeling multiscale, memory-driven systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

This project centers on problems in a recent new area of mathematics called arithmetic dynamics. This subject synthesizes problems and techniques from the previously disparate areas of number theory and dynamical systems. Motivations for further study of this subject include the power of dynamical techniques in approaching problems in arithmetic geometry and the richness of dynamics as a source of compelling problems in arithmetic. The funding for this project will support the training of graduate students and early career researchers in arithmetic dynamics through activities such as courses and workshops, as well as collaboration between the PI and researchers in adjacent fields. The project’s first area of focus is the setting of abelian varieties, where the PI plans to tackle various conjectures surrounding the fields of definition and S-integrality of points of small canonical height. One important component of this study is the development of quantitative lower bounds on average values of generalized Arakelov-Green’s functions, which extend prior results in the dimension one case. The PI intends to develop such results for arbitrary polarized dynamical systems, opening an avenue for a wide variety of arithmetic applications. A second area of focus concerns the relationship between Arakelov invariants on curves over number fields and one-dimensional function fields, and arithmetic on their Jacobian varieties. Here the project aims to relate the self-intersection of Zhang’s admissible relative dualizing sheaf to the arithmetic of small points on Jacobians, as well as to other salient Arakelov invariants such as the delta invariant. The third goal is to study canonical heights of subvarieties, especially in the case of divisors. A main focus here is the relationship between various measurements of the complexity of the dynamical system and the heights of certain subvarieties. The final component of the project aims to relate the aforementioned generalized Arakelov-Green’s functions to pluripotential theory, both complex and non-archimedean. 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 · 2025-11

Candida auris is an emerging fungal pathogen responsible for multi-drug-resistant infections and persistent outbreaks in hospitals worldwide. Azole drugs are a cornerstone in the treatment of invasive candidiasis because of their high oral bioavailability and ease of access even in low-income communities, but high rates of resistance (~90%) limit their use. Further, outbreaks tend to persist in healthcare settings despite strict sanitation protocols, and it can tolerate high salt and high temperature conditions that even sister species cannot endure. Together, these traits are hypothesized to contribute to the near-simultaneous emergence of multiple phylogenetically distinct lineages around the globe. Understanding how C. auris evolved widespread azole resistance and its ability to survive environmental stresses is fundamental to improving therapeutic outcomes and assessing disease threats. Interestingly, the evolutionary history of one transcription factor (TF) is linked to both traits. The TF regulating sodium pump expression in the related model yeast Saccharomyces cerevisiae, Hal9, evolved to primarily regulate azole drug pump expression in Candida. In the well-studied Candida albicans, its homolog Tac1 is the primary mediator of azole resistance via drug pump expression. But the closest Tac1 homolog in C. auris, Tac1a, is not reported to influence resistance. Instead, Tac1b is a primary mediator of drug resistance. Preliminary analysis of ~900 C. auris genomes has not identified any TAC1a variants that explain azole resistance. The proposed work investigates the evolutionary rewiring of this system to attribute these changes in phenotype to changes in regulatory elements, functions of target genes, or a combination. To determine changes in regulatory elements, I will map regulatory relationships in azole resistance circuits mediated by Tac1a and Tac1b using epigenomic and transcriptomic approaches. In C. auris, C. albicans, and S. cerevisiae, I will delineate the relative contributions of each homolog to drug and salt tolerance. To attribute changes in phenotypes to changes in target genes, I will characterize evolutionary histories of Candida and clinical C. auris populations in genes targeted by Tac1 homologs. To predict genes targeted in species without data on transcriptional regulation, I will train a machine learning model on C. auris gene promoter sequences bound by Tac1a or Tac1b. To test the hypothesis that the rewiring of circuits identified is an adaptive mechanism, I will detect accelerated birth-death rates of gene families containing homologs of predicted or known Tac1 targets in available Candida genomes, and selection on these genes in clinical C. auris populations. The research program will be performed at Brown University, with access to world-class facilities and interdisciplinary networks across computational biology, microbiology, and health sciences. Completion of the proposed research, paired with abundant mentorship, teaching, and outreach opportunities, will build my capacity to launch an independent program studying emerging fungal pathogens with integrative omics approaches and developing the next generation of scientists.

NSF Awards · FY 2025 · 2025-10

Non-Technical Summary This CAREER project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, advances fundamental insights into eventually designing higher energy, higher power, and more stable rechargeable batteries to meet the demand of the rapid-growing electric vehicle and energy storage market. Energy storage is a vital technology to enable the widespread adoption of renewable energy and to accelerate the technological advancement towards negative CO2 emission. The Li ion battery technology represents one of the most important energy storage technologies. Further development of Li ion batteries calls for more fundamental studies that can reveal reaction mechanisms and inform the design of new materials. Despite many years of materials development, most commercial Li-ion batteries still rely on several cathode materials that are derived from intercalation materials discovered in the 1980s. In these conventional materials, there are defined pathways for Li ions to transport. Recently, there have been exciting discoveries in new battery materials with disordered Li ion transport pathways. Unfortunately, these materials exhibit inferior battery performance compared to conventional materials, although theoretically they should provide much higher capacity. This project uses advanced experimental methods to develop fundamental understanding of electrochemical processes in these new disordered materials. The successful outcome of this project will establish a knowledge base for further improving these materials. This project also seamlessly integrates research with educating the future workforce for the United States. It provides learning opportunities for elementary students with dyslexia in Southwest Virginia. Dyslexic students, an underrepresented group in STEM fields, can be enormous intellectual assets as history, for example in the field of battery research, has taught us. Separately from this effort, the CAREER project also establishes a sustainable educational program between Virginia Tech and national labs, allowing undergraduate students to perform research in national labs. Overall, through this CAREER project Prof. Lin educates several underrepresented minority students, helping them to excel at performing scientific research and to become future leaders in the energy storage field. Technical Summary This CAREER project, supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, investigates the structure-property relationship for an emerging family of advanced battery materials. The hypothesis underlying the various research objectives of this project is that Li-rich disordered rocksalt oxides, with a globally disordered Li percolating network and combined cationic/anionic redox activities, can potentially increase battery energy density far beyond what is delivered by conventional layered cathodes. However, so far their irreversible chemical and structural transformations during electrochemical cycling have impeded their practical applications. Prof. Lin and his research group carry out holistic fundamental studies to understand how the chemical, structural, and redox properties transform at multiple length and time scales, during materials synthesis and under electrochemical operating conditions in order to resolve these daunting challenges. The project employs experimental methods, including synchrotron X-ray techniques and electrochemical diagnostics, to accomplish the following objectives: (1) probing and controlling the evolution of local coordination environment and global average phase during mechanosynthesis, (2) investigating the redox chemistry as a function of chemical composition, local coordination environment, global phase characteristics, and electrochemistry, and (3) quantifying the multiscale evolution of local coordination environment, global average phase, and redox chemistry upon prolonged electrochemical cycling. Taken together, results from these studies provide mechanistic insights into and advance the electrochemistry of disordered rocksalt oxide. 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 · 2025-10

The project in this award lies at the interface of algebraic dynamics and arithmetic geometry, motivated by an analogy between rational points on projective varieties and orbits of dynamical systems. Understanding the rational solutions to polynomial equations is a classical and fundamental problem, with advances such as Fermat's Last Theorem and the proof of the Mordell Conjecture being among the most celebrated examples of progress in modern mathematics. These questions find analogues as well as broader frameworks in the area of dynamical systems. The project funded through this proposal focuses on dynamical systems on projective varieties defined over number fields and function fields. A key aim is to extend knowledge on the arithmetic of points of small canonical height with respect to rational functions to more general polarized dynamical systems. The funding for this project will support the infrastructure of a growing group at UIC specializing in questions at the intersection of number theory, dynamics, and logic. It will also support collaborations between the PI and other researchers whose work applies non-archimedean analysis and Arakelov geometry to number-theoretic problems. The PI plans to organize a workshop at BIRS and to be a project leader for a future Women in Numbers research team. Three types of problems will be investigated. The first centers on the arithmetic of points of small canonical height for polarized dynamical systems in dimension larger than 1. Specific directions include the Torsion Conjecture for abelian varieties, along with its function field analogue, as well as a sparseness conjecture for torsion points on abelian varieties that are S-integral relative to a non-torsion ample divisor. Under this heading also falls a project studying certain distinguished local canonical heights on abelian varieties and their relation to the global Neron-Tate height. The second thread connects Arakelov invariants on higher genus curves to key Diophantine conjectures about their rational points, capitalizing on recent work linking these invariants to dynamics and analysis on Jacobian varieties. A central component of this thread focuses on the self-intersection of the admissible relative dualizing sheaf introduced by Zhang, and links this quantity to metric invariants of the underlying curve. The third project concerns pluripotential theory on Berkovich analytic spaces associated to projective varieties. Here a particular ultimate goal is the development of suitable quantitative equidistribution statements for small points in arbitrary dimension, yielding natural applications to the first aforementioned project. 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 · 2025-10

The Cosmic Microwave Background (CMB) is the relic radiation leftover from the explosive beginning of the Universe, known as the Big Bang. Analysis of the CMB suggests that the early Universe was smooth and homogeneous; whereas observations reveal that today it is mostly empty space filled with galaxies of different shapes and sizes. One of the most promising observational probes is 21 cm radiation from neutral hydrogen that was present in the early Universe. Researchers at Brown University will develop novel machine learning (ML) and artificial intelligence (AI) methods to model and interpret this cosmological 21 cm emission, providing insights into the nature of the first stars, galaxies, and the growth of cosmic structure. These ML/AI methods will have significant impact on any field where key information is encoded in image-wide patterns. The project will also provide training for a graduate student who will be deeply involved in the project and three undergraduate students as part of a continuing research experience program that provides flexible scheduling to accommodate familial, military, or other commitments. Measurements of highly redshifted cosmological 21 cm emission from neutral hydrogen during the epoch of reionization present the best way to study star and galaxy formation in the early Universe, because the cosmological 21 cm signal traces the neutral intergalactic medium and encodes the detailed properties of all the sources producing the ionizing radiation (i.e., stars and galaxies). The proposed ML/AI techniques will incorporate new interpretability and generalization methods that will make use of counterfactual data, which are inputs that produce significantly different outputs in the predictive models that are being explained. A framework to leverage the results to make predictive models more robust and generalize better across different physical simulations will then be developed. This work will provide an advanced tool to 21 cm cosmologists that is expected to improve robustness and trustworthiness of their predictive 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 · 2025-10

NON-TECHNICAL SUMMARY Sodium-ion chemistry provides a significant alternative to the current lithium-ion technology for rechargeable batteries with its foremost advantage of natural abundance, low cost, and much wider choices of material selection, therefore providing a critical strategy to reduce the risk of the low reserve of scarce elements in the US. However, compared to lithium reactions, sodium chemistry poses a considerable mechanical deformation to the electrodes and creates more stress and degradation that compromise battery performance. This project, supported by the Ceramics Program within the Division of Materials Research, seeks to create a fundamental understanding of battery degradation via a close integration of novel experiments, data analysis, and modeling approaches. Such knowledge is crucial to elucidating the aging mechanisms of sodium-based batteries, which synergistically contribute to the development of materials of enhanced reliability for the same applications. The multifaceted collaboration between Purdue and Virginia Tech provides unique training opportunities for developing workforce for STEM related careers, with particular relevance to meeting the demand of the energy industries, which is expected to grow significantly in the coming decades. The research also provides a platform to continue the recruitment and engagement of future scientists on convergent research skills and entrepreneurial training. TECHNICAL SUMMARY The project aims to achieve a holistic understanding of chemomechanics in phase-changing electroceramic electrodes through mechanistic studies of defects-charge coupling at the lattice scale, phase-stress coupling in single particles, and statistics of the particle network in the composite electrodes of sodium-ion batteries. The research is based on the hypothesis that: (i) the breakdown of the local structural symmetry not only induces lattice distortion and stress gradient at the nanoscale but also impacts the charge distribution in the lattice, (ii) the stochastic nature of material defects is coupled with the phase inhomogeneity in the single particles that gives rise to a stress/strain profile largely deviated from the conventional core-shell pattern; and (iii) in composite electrodes, the charge heterogeneity, mechanical damage, and electrochemical activities co-evolve, resulting in a dynamic ionic/electronic network in the cell. Following the hypothesis, the project includes the following research tasks. (i) Quantify the defect characteristics and map the defects-charging-composition at the nanoscale using controlled synthesis, synchrotron analytical techniques, and computational modeling. (ii) Understand the phase-stress coupling in the single electroceramic particles using the designs of grain engineering and surface coating. (iii) Identify the characteristic metrics of particle network in composite electrodes using machine learning, understand the dynamic evolution of particle network under operating conditions, and interpret the impact of mechanical degradation on battery performance. Overall, the research spans the basic understanding from the lattice scale up to the composite electrode and lays a foundation of mechanistic understanding of chemomechanical degradation in energy storage materia 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 · 2025-10

The demand for 3D assets is increasing across many fields, including synthetic data for computer vision and robotics, mixed reality, interior design, furniture retail, real estate, and games with user-created content. It would be valuable to make 3D asset creation accessible to users in these fields without 3D modeling expertise. Recently, there has been an explosion of development in machine learning models which can take as input a text description of any visual concept and output a 3D asset. While such models have great potential, they are limited by their reliance on text as their sole means of user control: text is ill-suited for precisely specifying many attributes of a 3D asset (e.g. lengths or sizes), these models do not perfectly respect attributes specified in text (e.g. counts of or spatial relations between objects), and users must resort to tedious and often-ineffective prompt tweaking to modify the outputs of such models. This research project seeks to address these issues by developing a new class of machine learning model for 3D asset creation. These new models will take text descriptions as input, but instead of generating 3D geometry directly, they will instead generate computer programs which produce 3D objects. The code of each program will be meaningfully-structured, exposing parameters which users can modify to produce desired changes in the output 3D object. These output objects will be decomposed into meaningful parts, where each part has detailed 3D geometry and texture. The researchers will leverage large language models (LLMs) to produce programs with meaningful structure and parameters. Since LLMs are error-prone, they will be used to propose procedural abstractions for modeling the part structure of objects; these abstractions will then be filtered and refined based on their ability to reconstruct 3D assets in a dataset. The researchers will then train neural networks to generate programs which use the final library of abstractions. Next, instead of learning programs from existing 3D asset datasets, the researchers will develop methods for synthesizing on-demand datasets of 3D assets from input text descriptions. This approach builds on recent advances in fast 3D generative models; it also includes a plan for automatically decomposing generated assets into parts. Finally, every structural abstraction in the learned library will be equipped with a module that generates detailed surface geometry and appearance for that structure. These modules will disentangle structure from surface details; the researchers will also explore techniques for enabling interpretable parametric control of these surface details. 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 · 2025-10

New battery materials and designs are needed to support fast battery charging - in an automobile, for example. Composites composed of silicon and graphite are a next-generation anode material that shows promise in lithium-ion batteries and in potassium-ion batteries. However, these materials suffer from issues with both mechanical and electrochemical degradation, which is exacerbated during fast charging, leading to poor battery life and safety concerns. Electrodes with engineered architectures have shown promise in mitigating these electrochemical issues during fast charging. This project combines these material and engineering approaches to develop safer, cheaper and efficient battery technologies. Using integrated experiments and computer simulation, this work will identify fundamental mechanisms in these complex chemical and physical systems. The approaches taken in the project will ultimately be broadly applicable to other materials and future engineering efforts. This project will also provide important educational opportunities, including (1) training a PhD student; (2) adapting models to make direct contributions to classroom teaching and lectures; and (3) providing research opportunities for undergraduates at both Brown and Wellesley College. High-capacity anodes capable of fast charging are necessary for more widespread adoption of electric vehicles. Si-based composites in lithium-ion batteries offer much higher capacity than commercial graphite, and mixtures with graphite and/or oxidized Si (SiOx) can help mitigate mechanical challenges from silicon’s enormous volume expansion during lithiation (>300%). More novel potassium ion batteries with graphite anodes show promise in competing with lithium chemistries but share electrochemical and mechanical concerns (~60% volume expansion). Additionally, architectured electrodes with channels and graded compositions can increase rate capability through improved charge/mass transport. The combination of silicon-based compositions and graphite with complex electrode architectures vastly expands the design space for new electrodes that can enable fast charging. This project is designed to enable the efficient creation of new electrodes with fast-charging capabilities in this space, by employing both experiments (including simultaneous operando visualization/electrochemical deformation measurements) and advanced modeling approaches. Specifically, in-situ (real-time) and ex-situ (postmortem) experiments will be used to track time-dependent and localized intercalation, plating, voltage, and mechanical deformation. Results will then inform theoretical and computational modeling that captures electrochemical/mechanical mechanisms of reaction inhomogeneity, plating onset, and mechanical failure with true predictive power. The combined research efforts will enable transformative impact through large improvements in battery fast-charging capability and energy density. 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 · 2025-10

Non-Technical Description: The development of solution-processable semiconductor materials has the potential to revolutionize emerging technologies in electronics and quantum engineering technologies by enabling scalable, cost-effective manufacturing methods. Among these materials, metal halide perovskite nanocrystals exhibit exceptional properties suited for advanced technological applications. However, their widespread adoption faces significant limitations due to presence of heavy metals, such as lead. This project aims to accelerate the discovery of high-performance, lead-free perovskite nanocrystals through the integration of high-throughput experimentation, artificial intelligence (AI), and advanced data-sharing strategies across multiple institutions. By establishing networked "self-driving laboratories" (SDLs) capable of autonomously exploring extensive materials synthesis parameter spaces, this research is expected to drastically shorten discovery timelines from years to weeks or months. The project's broader impacts include the development of new educational programs designed to train a skilled workforce proficient in AI-driven and autonomous scientific research methodologies, thereby promoting broad participation in innovative STEM careers. Technical Description: This research addresses the critical challenge of discovering lead-free metal halide perovskite nanocrystals by establishing distributed SDLs that integrate automated flow chemistry systems, colloidal nanoscience, and machine learning algorithms. The project aligns directly with NSF’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program and supports the objectives of the Materials Genome Initiative (MGI), aiming to create a robust, scalable framework for accelerated semiconductor materials discovery. A key technical innovation involves modular flow reactors with independently tunable reaction conditions, significantly expanding the accessible synthesis parameter space for semiconductor nanocrystals. The project will employ federated learning approaches to analyze and integrate experimental data from cloud-connected SDLs situated across multiple institutions, facilitating predictive modeling of synthesis parameters and resulting material properties. Outcomes of this project will include the establishment of a publicly accessible, AI-ready experimental database, serving as a valuable resource for the broader materials research community. Additionally, educational efforts will focus on developing innovative curricula and workshops to disseminate knowledge in autonomous experimentation and materials discovery, thus strengthening national expertise and capacity in AI-driven research and 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.

NSF Awards · FY 2025 · 2025-10

This project establishes the AI Research Institute on Interaction for AI Assistants (ARIA). ARIA will accelerate the development of next-generation AI assistants for mental and behavioral health—a field where trust, empathy and personalization are critical and where current AI systems fall short. ARIA embraces the integrated study of human cognition and machine cognition, treating them as inherently complementary scientific endeavors essential to achieving successful interaction with AI assistants. ARIA brings researchers in computer science, neuroscience, cognitive science, philosophy, law, and education together with mental health practitioners and civil society groups to forge new opportunities for synergistic scientific inquiry that advances technology and improves human well-being. The institute will grow a future-ready workforce through interdisciplinary education pathways from K-12 through postgraduate training, helping shape a generation that understands the technical and ethical dimensions of AI. ARIA’s research activities are centered around three pillars–Grounding, Instructability, and Alignment–that are interconnected and motivated by challenges of developing effective AI assistants for mental and behavioral health. In grounding, ARIA will develop new models for efficient learning and generalization, new learning algorithms leading to rich, causal models, computational theories for navigating the inherent trade-offs between learning algorithms or model architectures, and new evaluation metrics for tracking progress toward the goal of trustworthy AI assistants. In instructability, ARIA will design new paradigms centered on establishing trust in AI, new theories and models of how humans interact with AI, new methods for describing AI’s internal processing, and new models and training procedures for integration into a computational framework for the development of AI assistants. In alignment, ARIA will advance current best practices for human-centered design, establish precise definitions for what it means to be aligned, develop computational and experimental methods to operationalize these definitions, and develop cognitively and computationally sound metrics of alignment in complex ethical and social contexts. Across all activities, ARIA promotes integration between academia and industry, and between research and continuing education, enabling a holistic approach to AI conceptualization, development, and evaluation. 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 Falls are a major cause of injury and disability for older adults. In assisted living communities, 50% of residents fall each year and 25% will have a fall that requires transfer to the hospital. Falls are a common reason for residents to transfer from assisted living to nursing homes. Approximately 30% of falls can be avoided with proper screening and intervention. STEADI is an evidence-based falls prevention program developed by the CDC and tested in several community-based setting. STEADI involves universal fall screening, followed by personalized assessments and tailored interventions. Common, potentially modifiable causes of falls evaluated in STEADI include medication interactions or side effects, environmental hazards (e.g., floor rugs, thresholds), untreated visual impairment, low vitamin D, ill-fitting footwear, or low blood pressure. The goal of this research is to adapt STEADI to the assisted living setting. First, we will conduct structured observations of current processes for fall risk evaluation and interview staff about how best to adapt STEADI to the assisted living environment. Next, we will draft a modified version of STEADI, with technical expert and assisted living corporation input. Finally, we will test the new assisted living (AL)-STEADI in seven communities. New and existing residents will be randomly assigned to AL-STEADI (intervention) or a usual fall prevention control. We will evaluate the effect of the intervention on the number of falls per 12 months. Planned and unplanned adaptations to the AL-STEADI protocol will be systematically. The outcome of this work will be AL-STEADI, an Implementation Plan, Toolkit, and training materials adapted to the needs of the ALC environment, with preliminary evidence for its effectiveness. In addition to traditional dissemination routes, we will publish an implementation guide for ALCs, which highlights the technical support needs which would make AL-STEADI more scalable and help adhere to state-specific ALC regulations.

NIH Research Projects · FY 2025 · 2025-09

Older adults aged 65 or older, eligible for Medicare, must choose between traditional Fee-For-Service Medicare or Medicare Advantage (MA). MA has grown rapidly, covering 54% of Medicare beneficiaries in 2024. This growth has extended to nursing home (NH) residents, where about 23% of those turning 65 require NH care. In 2021, over 70% of NH residents were long-stay (LS), with the share of MA-enrolled LS residents rising from 13.3% in 2011 to 31.5% in 2021. To address the needs of LS residents, new care models such as Institutional Special Needs Plans (I-SNPs) have expanded. Despite the increase of MA and these new models in LTC, their impact on LS residents in NHs remains understudied. My F99 projects address this gap by describing MA trends and assessing MA’s effects on NH care quality and health outcomes among LS residents using rigorous causal methods. My K00 projects will expand the work using the unique NH electronic medical records (EMR) and newly developed AI algorithms to capture comprehensive health outcomes. While Medicare doesn’t pay for long-term custodial care in NHs, it covers all other healthcare, like hospital, post-acute, and hospice care. This incentivizes MA plans to manage care for LS residents. This may help reduce wasteful care, but it may also limit LS residents’ access to necessary care, making the impacts of MA on their welfare and health unclear. LS residents, with high levels of chronic illness and limited ability to advocate for care, are particularly vulnerable to delays or denials of care under MA plans. Aim 1 (F99) will study the growth of MA and I-SNPs among LS residents and identify determinants of this growth (Aim 1a), and examine MA’s impact on NH care quality and health outcomes using novel causal methods, including shift- share instrumental variable (SSIV) and generalized synthetic controls (GSC), which have not yet been used in aging research (Aim 1b). Traditional analyses use Claims or the Minimum Data Set (MDS) to assess health outcomes of NH residents, but the MDS may underreport the outcomes. Aim 2 (K00) will develop natural language processing (NLP) models to analyze NH EMR, which is the largest nationwide databases, to identify health outcomes like cognitive and physical functioning, and to uncover details of managed care process among MA-enrolled LS residents (Aim 2a). Using the most comprehensive outcomes identified by the full administrative data and the largest NH EMR, I will reevaluate the effects of MA on the health of LS residents, applying GSC (Aim 2b). My research aligns with the NIA’s goal to understand the factors affecting the health and wellbeing of older populations, while promoting inclusion of underrepresented groups in aging research. With extensive training under the mentorship from Dr. Mor (aging research), Dr. Meyers (MA; Health Economist), Dr. Rahman (Economist), and Dr. Mehrotra (NLP), I will gain the expertise needed to become an impactful faculty researcher focused on aging, advancing research that addresses critical gaps in the care of aging populations.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY: Alcohol acts on the brain’s natural reward circuits and has a multifaceted influence on food reward circuits. Consumption of alcohol primes the release of dopamine and can alter the physiology of the reward system, profoundly affecting circuit homeostasis, plasticity and function. This results in aberrant reward encoding, leading to aberrant value-based choices and maladaptive behavior. However, the heterogeneity of the mammalian reward system makes targeting distinct subpopulations of cells difficult, which has precluded a detailed understanding of how reward is encoded. The reduced numerical complexity of the Drosophila reward/dopaminergic system provides a tractable alternative to study cellular resolution mechanisms of reward memory and reward seeking. Like mammalian mesolimbic dopamine neurons, reward-encoding dopaminergic neurons that innervate the Drosophila mushroom body are diverse in molecular composition, neural connectivity and function. Moreover, we have shown that dopaminergic activation of the mushroom bodies underlies that rewarding effects of alcohol in flies. This provides an ideal functional framework for investigating the cellular and molecular mechanisms through which rewards with different values, such as sugar and alcohol, are encoded and how they interact. This work will reveal mechanisms through which relative reward value is encoded and how alcohol influences reward circuit function to elicit unconstrained reward seeking. Although we all have an intuitive understanding of what is very rewarding, and what is less rewarding, our memories of reward change with experience. Our first goal is to understand how absolute and relative sugar rewards are encoded in a dopaminergic memory circuit. Our second goal is to understand the mechanisms through which alcohol is weighted relative to sugar, and how prior experience with alcohol alters how sugar reward is encoded. Our third goal is to identify the molecular mechanisms through which alcohol can alter sugar motivation by addressing how alcohol-induced alternative splicing impacts sucrose response and encoding. Overall, this research will identify causal physiological and molecular mechanisms through which alcohol influences the formation and expression of reward memory.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Alcohol use disorders (AUDs) are the third most common mental health condition worldwide and the fourth leading cause of disability. Only about a third of those with AUD who receive treatment in the US remain abstinent a year later, suggesting that there is ample room to improve the efficacy of treatment. Helping patients learn coping skills is a key element of nearly all effective treatments for AUD and could represent a common factor of these treatments. Coping skills training is also a core component of cognitive behavior therapy (CBT) for AUD, an evidence-based approach to psychosocial AUD treatment that is commonly used in community settings. “Coping skills” refer to a broad range of cognitive and behavioral strategies to avoid drinking, including steps like controlling exposure to high-risk situations, employing various strategies to avoid drinking in high-risk moments, and assertively refusing social pressure to drink. One common way that therapists help patients learn these skills is by asking them to anticipate high-risk situations and identify how they might use specific coping skills in those situations. However, imparting these skills verbally, and in therapy rooms far removed from the real situations in which patients need to use them, has limitations. Virtual reality (VR) experiences can provide a way to immerse patients with AUD in realistic high-risk situations to help them identify important triggers, experiment with different coping strategies, and practice deploying them in realistic situations. VR has also been used as an adjunct to counseling for a variety of other mental health disorders, with notable success facilitating exposures for anxiety disorders and PTSD, supporting its feasibility as a tool to enhance psychotherapy. Meta-analyses suggest that these VR-assisted exposures are realistic and as effective as in vivo exposures. Several VR experiences that immerse users in situations that are high-risk for drinking have already been developed, but all have been used exclusively for research purposes. Using experiences like these to augment treatments that provide training in coping skills could help therapists provide more realistic contexts that help patients learn and practice these skills, ultimately improving the effectiveness of these interventions for AUD. In this project, we propose to use the Delphi method to develop consensus among experts about the most promising ways to use VR to enhance coping skills training and use these findings to develop a protocol to guide the use of VR within CBT, an evidence-based AUD treatment that involves coping skills training at its core. In a longitudinal, randomized controlled pilot trial, we will test whether a 12-week CBT treatment with VR-enhanced coping skills training is feasible, tolerable, and acceptable, relative to standard CBT in those with AUD. We will also explore preliminary efficacy across conditions and assess patient and therapist preferences relevant for future implementation. This work is the first step in a line of research that explores how VR can enhance treatments that are regularly used in community settings.

NIH Research Projects · FY 2025 · 2025-09

Alzheimer's disease and related dementias (AD/ADRD) present significant global public health challenges, with a projected majority of future cases occurring in low- and middle-income countries. Cross-national research is essential to understand the diverse factors influencing cognitive aging and to develop effective interventions. The International Health and Retirement Study (HRS) Family and its Harmonized Cognitive Assessment Protocol (HCAP) offer unique datasets across diverse populations providing unparalleled opportunities for such research. However, the complexity of these datasets and the advanced methodological skills required for their analysis are barriers to widespread utilization. We propose a research education program titled Best Practices for the Measurement and Analysis of Cognitive Data in the International Health and Retirement Study (HRS) Family and its Harmonized Cognitive Assessment Protocol (HCAP). The program consists of two complementary short courses: Course One: Designing and Leading Cross-National Studies with HRS and HCAP Cognitive Data introduces participants to the cognitive data available in the International HRS and HCAP studies, the concept of harmonization, and best practices for designing substantive research questions and analyses involving cross-national comparisons. It is tailored for principal investigators, lab leaders, and researchers planning studies utilizing international HRS or HCAP cognitive data. Course Two: Applied Measurement Modeling in Cross-National Cognitive Aging Research provides advanced instruction in statistical methods, including item response theory and structural equation modeling, with hands-on computing practice using the international HRS and HCAP data. It is intended for researchers who wish to develop practical data analysis skills necessary for conducting advanced psychometric analyses required for statistical harmonization. Our specific aims are to develop and deliver the two-part summer short course to equip researchers with the necessary skills to conduct rigorous cross-national AD/ADRD research using the International HRS and HCAP data (Aim 1); expand the reach and impact of the training program through online resources and outreach activities. Course materials and lectures publicly will be available on-demand, offering condensed versions as pre-conference workshops, and providing "flipped" classroom options for remote participants (Aim 2); and, develop a community of practice among researchers by facilitating communication and collaboration through online forums, networking opportunities, and continuous engagement (Aim 3). By providing targeted training and resources, our program will empower researchers to utilize the International HRS and HCAP data effectively to enhance the quality and impact of cross-national cognitive aging research. This initiative will contribute to significant scientific advancements in understanding the cognitive health of older adults worldwide and support efforts to mitigate the global impact of AD/ADRD.

NIH Research Projects · FY 2025 · 2025-09

SUMMARY The premise prompting this 12-week clinical trial paired to a human laboratory study is based on the direct evidence from our preliminary work with probenecid, first conducted in a preclinical model of alcohol use disorder (AUD) (R01AA028982, Alcohol Alcohol 2019) and then, in a human laboratory study testing probenecid when co-administered with alcohol (R21AA027614, ACER 2024). The scientific rationale for testing probenecid in AUD was derived by the well-known mechanism of action of probenecid as a pannexin 1 channel inhibitor, its role in alcohol-induced extracellular adenosine release, and that this process is promoted by a history of exposure to excessive alcohol. The preclinical work demonstrated that probenecid is able to reduce alcohol consumption in alcohol-dependent rats. The human alcohol-drug interaction study demonstrated that probenecid did not altered the pharmacokinetics parameters and the neuropsychopharmacological responses of alcohol. We also demonstrated that probenecid was able to reduce acute alcohol craving. The goal of this application is to replicate findings previously conducted in our rodent and human studies and to understand mechanistically the role of the pannexin 1 channels for reducing the inflammation process in AUD. To achieve this goal, this study proposes a 12 week, between-subject, double-blind, randomized controlled trial (RCT) with probenecid (2g/day) compared to placebo in 120 individuals with AUD. There are three aims in this study that test the hypothesis that probenecid compared to placebo, decreases: acute alcohol craving in an alcohol cue reactivity procedure (Aim 1), alcohol craving (Aim 2) and alcohol consumption (Aim 3) during the overall 12-week trial. We also included an Exploratory Aim to further shed light on potential mechanism of probenecid effect in reducing alcohol craving and consumption, that evaluates pro- and anti-inflammatory markers (cytokines, hormones and factors) pre- and post-treatment. The proposed research is significant because, holds the potential to evaluate the role of a novel pharmacological target (pannexin 1 channels) in a full-powered RCT and provide the possibility to discover a novel AUD therapeutic opportunity. This study also will be the first to assess the efficacy of probenecid for AUD in an integrated behavioral and clinical setting.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Per- and polyfluoroalkyl substances (PFAS) are toxic chemicals of significant public health concern due to their nearly indefinite persistence in the environment and widespread human exposures from contaminated drinking water, food, and consumer products. There is mounting evidence that PFAS exposures during gestation are associated with adverse health effects in childhood, including worse metabolic, vascular, and bone health. However, it is currently unknown whether these effects persist into adulthood. Further, as more communities discover historical PFAS exposures, there is an urgent need to identify interventions to mitigate adverse effects of PFAS exposures after they have already occurred. To address these critical knowledge gaps, the goals of this proposal are to estimate the impact of the effects of prenatal PFAS mixture burden on mid-adulthood health and to evaluate factors that may mitigate these effects. We will accomplish our aims using the New England Family Study (NEFS), a unique prospective cohort study of pregnancies in the 1960s with data collected from children annually from birth through age 7 years, and again in mid-adulthood (mean age=47 years, n=400). NEFS is an ideal cohort to address our aims, as it has amassed rich data from pregnancy through mid-adulthood including detailed adult health phenotyping. We will additionally measure concentrations of 44 PFAS in stored serum samples collected during pregnancy and adulthood. Using state-of- the-art analytical approaches to quantify PFAS mixture burden, we will determine whether prenatal PFAS burden is associated with worse metabolic function, vascular health, and bone mineral density in mid- adulthood, independent of concurrent PFAS burden (Aim 1) and assess whether adverse impacts are stronger among those with suboptimal diet quality and physical activity (Exploratory Aim). Our team of experts in exposure assessment, analytic chemistry, biostatistics, environmental epidemiology, and clinical medicine will be the first to examine long-term effects of PFAS mixture burden on multiple prevalent adult health outcomes, and to inform potential interventions to reduce the impact of prior exposures. Our findings will serve as the basis for a future follow-up of NEFS participants to elucidate the adult health impacts of early life PFAS exposures on metabolic, vascular, and bone health outcomes. Ultimately, this research will provide critical evidence on the long-term impacts of PFAS exposures to inform health screening guidance in PFAS-exposed communities, refine proposed PFAS drinking water regulations, and identify potential interventions to reduce the health impacts of historical PFAS exposures.

NIH Research Projects · FY 2025 · 2025-09

Sustainability of evidence-based mental health intervention (EBI) implementation is a challenge to increasing access to mental health services. Funding is a common barrier to sustainment. EBIs, while cost effective, are expensive to implement. The Fiscal Mapping Process is a strategic planning tool designed for behavioral health agencies to plan the financial sustainment of specific EBIs. Financial planning at an organizational level is required to enable mental health implementation and provider organizations to sustainably operate. Without careful consideration, mental health organizations’ business models could unintentionally decrease access to care for some individuals if profit is prioritized or if only certain patient populations are reached. Research is needed to help implementation and mental health organizations identify financing strategies to sustainably increase access to EBIs. The proposed K08 will adapt the Fiscal Mapping Process for implementation and mental health organizations to plan for financial sustainability of their organization (rather than a specific EBI), without sacrificing implementation of EBIs. The study uses human-centered design methods and has three aims: (1) Identify aspects of the Fiscal Mapping Process to adapt for use with implementation and mental health organizations; (2) Iteratively adapt the Fiscal Mapping Process for implementation and mental health organizations; and (3) Assess the preliminary impact and usability of the Fiscal Mapping Process for implementation and mental health organizations financial sustainability. To ensure its successful execution and to build the investigator’s capacity for independent research, the following training aims are proposed: (1) Acquire training in factors that affect the sustainability of EBI implementation, with an emphasis on financing strategies; (2) Develop knowledge of how implementation and mental health organizations function to increase access to EBIs; (3) Deepen expertise in D&I science by learning human-centered design methods; and (4) Deepen expertise of qualitative methods and learn mixed methods approaches. Training and research will be overseen by co-mentors Dr. Rani Elwy (Brown University), Dr. Alex Dopp (Rand Institute), Dr. Aaron Lyon (University of Washington), Dr. Bo Kim (Boston VA), Dr. David Mohr (Northwestern), Dr. William Aldridge (University of North Carolina Chapel Hill), and consultant Dr. Joshua Kemp (Brown University). The project will create a financial planning tool (the adapted Fiscal Mapping Process) to improve organizational financial sustainability while promoting access to evidence-based interventions.

NSF Awards · FY 2025 · 2025-09

The seafloor sediment provides an important archive of information about Earth’s past. Sediment accumulates nearly continuously for thousands to millions of years. Interpreting the geologic and environmental changes recorded by these sediments relies on knowing the age of each sediment layer. Researchers often use software to create “age models” that estimate sediment age and the uncertainty of that age. This project aims to improve the accuracy of sediment ages. It will compile radiometric ages in over 250 marine sediment cores. This new data will increase the constraints on the new modeling software, BIGMACS, by tenfold. This improvement will result in more accurate sedimentation rates, reduce age-model uncertainty, and broadly improve paleoclimate data compilations. This new software will be freely available to the scientific community. The project will advance the career of a postdoctoral researcher in applied math and geosciences, train graduate students in interdisciplinary paleoclimate studies, and expose an undergraduate student to research. The accuracy of paleoclimate reconstructions used to validate the climate models rely on age models when identifying cause-and-effect relationships, creating snapshots of the climate at a specific point in time, or characterizing the magnitude of natural variability on different timescales. Such information is crucial for testing the effectiveness of climate models and improving their ability to simulate potential future climate states. Several software packages exist that use statistical methods and different assumptions about variability in sediment accumulation rates to produce age models that allow for ages to be estimated at depths between directly dated sediments, for every depth in a sediment core. However, very few studies have measured variability in ocean sedimentation accumulation rates or tested the statistical models used by these software packages and how they affect reconstructions of Earth’s past. This study will employ two different techniques to measure sedimentation accumulation rate variability over the past 50,000 years using data from approximately 250 ocean sediment cores. These measurements will then be used to estimate parameter values that improve the statistical models used by age modeling software. The principal investigators will also develop improved statistical methods for a previously published software package to generate more accurate results. The improved model will also be made available as open-source, such as Python, for greater accessibility. The study also investigates how estimates of past climate change are impacted by different age modeling software packages and updated estimates of sedimentation rate variability. This project benefits the broader scientific community by providing improved age modeling tools for reconstructing past climate change and provides interdisciplinary training for the next generation of scientists, including graduate and undergraduate students in Earth Science and an interdisciplinary early career researcher in Applied Mathematics and Paleoclimate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Daniel Mittleman of Brown University and Professor Michael Ruggiero of the University of Rochester are investigating guest-host molecule interactions in porous materials using a combination of vibrational spectroscopies and computational methods. This project aims to uncover the atomic-level mechanisms that drive the adsorption of gases in porous materials such as metal-organic frameworks (MOFs) and clathrates. A key challenge is that the intermolecular forces are often weak, requiring probes in the terahertz range. The team will apply low-frequency infrared and Raman spectroscopies, exploiting a unique capability to obtain such measurements in a custom-designed pressure cell, to reveal how gas loading alters the vibrational dynamics in real time. Quantum mechanical simulations will help to interpret these spectral changes, linking them to structural information. The combination of computational and experimental results will clarify important open questions in the field, such as the impact of structural disorder on adsorption dynamics. These new insights will inform the rational design of materials optimized for particular applications such as hydrogen storage or toxic chemical remediation. These efforts are linked to a hands-on week-long summer course developed for high school students in Rochester and Providence, which will further the pedagogical training of the graduate students participating in the project. This project integrates state-of-the-art experimental and theoretical techniques to study the vibrational dynamics of porous media under gas-loading conditions. Vibrational spectroscopy, including terahertz time-domain and Raman measurements, will be used to monitor subtle structural changes, through changes in the low-frequency modes, which reflect shifts in the intermolecular forces during gas adsorption. A gas-dosing manifold with stoichiometric control will enable precise quantification of guest molecule uptake and its impact on vibrational spectra. These data will be compared to solid-state density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations to interpret experimental results and uncover structure–dynamics relationships. The results will reveal the role of host framework flexibility, host/guest molecule disorder, and cooperative phase transformations on the gas loading mechanisms and associated kinetics. The ultimate goal of this project is the development of predictive models that link spectroscopic signatures to molecular-scale mechanisms. This project will establish a new paradigm for characterizing and designing functional porous materials using laboratory-based spectroscopic 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 Indian Pacific Warm Pool located in the tropics is the heat engine of the globe. Previous work has proposed that past episodic freshwater fluxes known as Heinrich Events in the Northern Hemisphere abruptly modulate tropical hydroclimate in regions like the Indian Pacific Warm Pool. In this project, cave deposits from the Philippines are used to reconstruct past hydrologic change and investigate the hydrologic response over the Indian Pacific Warm Pool to Heinrich Events. The Philippines is ideally located for this work as it is situated within the Indian Pacific Warm Pool and is sensitive to seasonal variations in rainfall associated with the Asian monsoon systems. This work will accomplish three primary tasks: first, it will expand current cave monitoring efforts in the Philippines to encompass a multi-year, multi-cave monitoring network to better constrain seasonal variations in karst hydrology. Second, it will contribute new high temporal resolution cave geochemistry records spanning numerous Heinrich Events. Third, geochemical data-model comparisons will be conducted to evaluate the mechanisms driving geochemical variability in these records during Heinrich Events. Results from this work will help develop a foundational understanding of rainfall pattern changes during abrupt hydrologic variability. By combining multi-year surface and subsurface karst monitoring with new cave records, the influence of Northern Hemisphere freshwater fluxes on tropical hydroclimate will be assessed. The project will also fill in spatial gaps in the western tropical Pacific and improve model accuracy. This work will fortify connections with National Parks/Protected Landscapes, UNESCO World Heritage staff, and cavers. The team's findings will be incorporated in pamphlets/audio guides and guided tours for millions of domestic and international park visitors. Finally, the project includes training of multiple undergraduate students in the geosciences at Occidental College and a graduate student from Brown University. 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

Humans can perform complex actions even when based on degraded visual information, as when walking along a path at dusk or reaching for a water bottle that is partly hidden by other objects. A generally accepted explanation is that the brain creates the “best guess” given the available visual information and this best guess is usually good enough. For example, when you reach for a water bottle multiple times, your hand might fall sometimes short and sometimes overreach in an unpredictable way. On average, however, your hand will land in an appropriate location. Theoretically, when feedback is available —like touching the bottle—this situation is easier to correct than when errors are random and unpredictable. Understanding the computational strategies the brain uses to allow humans to interact with their environment despite degraded visual information could directly inform the design of more robust AI-robotic systems. This has vital implications for national security and public safety, where autonomous systems must operate reliably under poor visibility to detect potentially dangerous objects or agents, or find survivors in enclosed spaces after disasters such as flood or earthquakes. In addition, this research can translate into development of bio-inspired sensory technologies, such as advanced vision-based prosthetics and contribute to designing living environments for individuals with low vision. This project aims to challenge long-held theoretical assumptions about the role of perceptual uncertainty in behavioral errors and to redefine their theoretical and methodological foundations. The prevailing view, grounded in probabilistic inference, is instantiated in the Maximum a Posteriori (MAP) model of sensory processing. It suggests that both random variability and systematic bias in behavior are due to early-stage uncertainty in sensory estimation arising from inferential ambiguity and neural noise. Despite the presence of sensory uncertainty, the model assumes that sensory estimates are unbiased (on average accurate) and that biases only arise when uncertainty is high due to the default action of Bayesian priors (prior assumptions about visual structure) on early sensory estimates. It proposes that the overarching goal of sensory systems is the reduction of sensory uncertainty–which has the effect of reducing both perceptual variability and bias. The investigators propose an alternative model based on the Intrinsic Constraint (IC) theory, which offers a more parsimonious account of behavioral error. It claims that ambiguity and noise are attenuated early in sensory processing leading to more stable sensory estimates (low uncertainty) but that this attenuation leads to intrinsic systematic biases in the estimates. The project systematically tests the validity of these two theories for a range of visual domains. First, it builds on preliminary results supportive of the IC model in 3D vision by testing more robustly a range of 3D visual cues and refining the methodological approach. It then aims to replicate these findings more generally by applying these methods to other standard domains beyond 3D vision, such as orientation and speed perception. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Non-technical Abstract: Magnetic materials exhibit a wide array of phenomena that depend on the arrangement of and interactions between the constituent microscopic magnetic moments. In some materials, the magnetic moments cannot simultaneously satisfy all constraints imposed by the lattice geometry and the interactions between them. The competing interactions in these so called “frustrated magnets” often lead to the emergence of complex magnetic orders governed by quantum fluctuations. These phases may not exhibit a net magnetic field, but owing to their symmetries can still couple to externally applied ones. This property makes frustrated magnets compelling material candidates for energy efficient computing devices that encode information in magnetic degrees of freedom. In this project, the research team utilizes synchrotron x-ray scattering techniques at National labs to study the magnetic configurations and fluctuations of nanoscale frustrated magnets. These measurements will provide critical knowledge enabling the development of future low-power electronic devices. The educational component focuses on training future quantum material researchers through the development of a new cross-disciplinary quantum materials course at Brown University connecting the fundamental physics of quantum materials to quantum information. Technical Abstract: Frustrated magnetic materials can often realize non-collinear or non-coplanar magnetic configurations and textures that exhibit no uniform macroscopic magnetic field but nevertheless can couple to external electric or magnetic fields. Thus, they hold great promise for future fast and low dissipation spintronic devices. To realize such technologies, it is essential to obtain precise knowledge of the magnetic ordering, domain configurations, and excitations in frustrated magnets that have been prepared as device relevant thin film heterostructures and/or exfoliated nanoflakes. However, measuring the magnetic properties over broad length and energy scales in these nano-scale geometries remains a major challenge. This project is addressing this challenge by utilizing resonant x-ray scattering to study the static and dynamic response functions in model frustrated magnets across angstrom to micron length scales. The research team is using nano-focused resonant elastic x-ray scattering to map the spatial variations of magnetic order parameters over large areas in nanoscale samples and to study chiral domain configurations of frustrated magnets. Magnetic excitations in exfoliated two-dimensional frustrated magnets and thin-film geometries are studied over broad energy scales using resonant inelastic x-ray scattering. The microscopic material parameters that are being quantified through this work are essential to guide theoretical frameworks for predicting physical properties of model quantum magnets and provide essential input towards incorporating their novel functionalities into future antiferromagnetic spintronics. 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

PROPOSAL SUMMARY In the wake of the COVID-19 pandemic, overdose mortality and affordable housing shortages have reached unprecedented levels in Rhode Island and across the United States. Housing insecurity is a known risk factor for overdose, yet the effects of residential eviction—a structural and policy-sensitive cause of housing insecurity—on overdose risks are understudied. Eviction is a complex legal process composed of a series of stages, each of which may impact overdose risk and thus offers opportunity for public health intervention. The objective of this proposal is to pursue a thorough examination of the relationship between residential eviction proceedings (i.e., filing, hearing, and enforcement) and overdose at the address level in Rhode Island, to better inform eviction prevention policies that complement ongoing harm reduction and overdose prevention efforts. The proposed research will leverage the wealth of centralized, high-resolution data available in Rhode Island—and our team’s established relationship with the state health department—to construct a longitudinal dataset with data linked from several administrative sources. We will first characterize the prevalence and time course of eviction proceedings relative to non-fatal or fatal overdose events by conducting sequence and cluster analyses to operationalize variation in the order, duration, and timing of eviction proceedings before and after a given overdose event (AIM 1). We will then estimate the address-level causal effect of residential eviction proceedings on risk of non-fatal and fatal overdose, both overall and by race and ethnicity of overdose decedents, using a sequential target trial approach (AIM 2). The proposed research will be among the first of its kind to link eviction records with address-level health data to characterize heterogeneity in the duration and timing of eviction proceedings, to examine a causal link between eviction and overdose, and to explore differential impacts of eviction on overdose death by race and ethnicity. Because eviction is an intervenable mechanism of housing vulnerability that may be associated with overdose risk, findings from this work may elucidate novel avenues for addressing the intersecting housing and overdose crises in tandem. The proposed research will be completed by the principal investigator with support and mentorship from collaborators with substantial expertise in advanced quantitative methods for life course epidemiology and causal inference. The training activities detailed in this application, focused on advancing skills in epidemiologic study design and longitudinal data analysis and developing a deep contextual knowledge of the social and legal implications of eviction proceedings, will prepare the principal investigator for a career as an independent social epidemiologist and academic researcher.