Brown University

NSF Awards · FY 2025 · 2025-09

Automated theorem provers, which combine logical rules with creative input from artificial intelligence (AI), are rapidly advancing. These tools are particularly effective and efficient in Euclidean geometry, while extending these tools to other complex mathematical domains remains a major challenge. This project builds on the successful framework developed for Euclidean geometry and aims to create a novel reasoning system for hyperbolic geometry, a natural but more intricate domain with applications in physics and computer science. Advancing automated reasoning in this domain is expected to lead to a better understanding of the underlying principles of effective reasoning systems and pave the way for broader applications across research-level mathematics. A complementary goal of the project is the development of an innovative undergraduate course that introduces students to both the theory and practice of automated reasoning, guiding them in building their own basic theorem provers. The course will equip students with essential skills at the intersection of AI and mathematics, advancing STEM education, and strengthening leadership in scientific innovation. Broader impacts of the project include open-access software, instructional materials, and a machine-generated “Hyperbolic Geometry Encyclopedia” to support educators, students, and the research community. The project's main goals are to develop a robust automated reasoning system for hyperbolic geometry and to create a new undergraduate course on AI in mathematics. The investigators strive to achieve these goals by building upon their pre-developed prototype for Euclidean geometry, which will also serve as a core example for the new AI in mathematics course. A critical first step involves adapting the rule-based components of the reasoning system to incorporate the axioms of hyperbolic geometry. Based on these rigorous deductions, the investigators will generate a comprehensive dataset of hyperbolic geometry statements and their corresponding proofs, which will then serve as crucial training data for the artificial intelligence component of the system. This work is expected to yield a neuro-symbolic engine capable of automated theorem proving in hyperbolic geometry, with a long-term vision of extending these systems to even more complex geometries and mathematical domains. Combining the theoretical and educational aspects of this project, the investigators aim to empower current and future researchers with the necessary skill set and tools to leverage AI in mathematics. 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/ABSTRACT This proposal addresses fundamental questions about Na+, K+ ATPase function both in wild-type animals and in a new C. elegans model for a rare disease called Alternating Hemiplegia of Childhood (AHC). AHC is one of many diseases caused by dominant mutations in ATP1A3, a Na+, K+ ATPase. It is unknown how mutations in ATP1A3 perturb cellular function, leading to disease. I propose a novel hypothesis to explain AHC pathophysiology: defective ATP1A3 upregulates other P-type ATPases that compete for β subunits; β is expressed at limited levels and essential for ATPase function. I rigorously test this hypothesis in vivo using C. elegans models of AHC that I have developed. I created the first C. elegans models of AHC by direct CRISPR/Cas9-based editing of patient missense mutations into eat-6, the C. elegans ortholog of ATP1A3. Heterozygous AHC model animals have dominant neuromuscular junction (NMJ) defects, unlike heterozygous eat-6 null animals. Therefore, AHC defects are caused by a mechanism other than simple loss of ATPase activity. In Aim 1, I use the C. elegans models of AHC to determine where wild-type and AHC model EAT-6 are required for normal and impaired NMJ function. In Aim 2, I determine if AHC patient mutations upregulate other P-type ATPases such as CATP-2 and if this contributes to NMJ defects in AHC model animals. In Aim 3, I determine which β subunits interact with EAT-6 and test if β is a limiting factor. This information is essential for future studies that delineate the cellular mechanisms perturbed in AHC and investigate targets for therapy development. I will complete this fellowship at Brown University in the Molecular Biology, Cell Biology, and Biochemistry Graduate Program. Institutional and departmental resources strongly support my training goals. I will learn new genetic and biochemical techniques such as determining where gene function is required in a live animal using inducible gene expression, analyzing gene expression at the RNA and protein level, and testing protein interactions based on pull down assays. Additionally, I will dedicate time and effort to professional development and networking, teaching, and mentoring to become a strong candidate for a career in academia.

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.

NSF Awards · FY 2025 · 2025-09

Harmonic analysis studies the behavior of various kinds of waves. This study is needed in a wide variety of applications including probability theory, physics, engineering, and medicine. Waves are complicated objects, and the mathematical language used to study them involves the so-called singular integral operators. The word singular shows that those operators are quite intricate. At the present moment singular integral operators are well researched in relatively smooth environments. However, nature is not smooth, and it's necessary to look at singular integrals in non-homogeneous environments. This is the principal aim of this award. Besides answering fundamental mathematical questions, the research will serve as a training ground for graduate students and young researchers. The PIs start their investigation with singular integrals with matrix weights, which arise in the study of the regularity of vector stationary stochastic processes. We continue with singular integrals on graphs that have cycles -- this is a completely new area developed for multi-parameter singular integrals. These, in turn, are needed for understanding the regularity of non-linear partial differential equations. The study of Banach space valued singular integrals on a hypercube (a graph with cycles) is related (very surprisingly) to open questions in theoretical computer science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Earth hosts millions of species, allowing energy and nutrients to flow through the ecosystem and creating a vital environment for life to thrive. How did this diversity of life develop on Earth? This project addresses this fundamental question in cell and developmental biology by examining changes in the developmental programs of echinoderms, marine organisms such as sea urchins and sea stars, as a model system. Echinoderms are ideal models because embryos from each species are accessible and show gradual changes in their developmental programs depending on their evolutionary distances. For example, sea urchins are considered more derived than sea stars within echinoderms, with an evolutionary divergence estimated around 500 million years ago. The research focuses on a cell type called micromeres, which is unique to sea urchins and absent in other echinoderms. Micromeres act as a signaling center, significantly altering the developmental program in the sea urchin embryo. The research aims to understand how these unique cells evolved and their potential role in species divergence, therefore, seeking to explore the essential mechanisms that allow Earth to host millions of living organisms, including humans. Additionally, the project emphasizes outreach: sharing findings through laboratory tours, mentoring local students and teachers, and engaging undergraduate students in research. These efforts aim to promote scientific interest at various levels throughout the year. Mosaic embryos typically contain pre-localized factors in zygotes that determine embryonic polarity and cell fates. In contrast, regulative embryos have limited pre-localized factors, suggesting that another mechanism may control the establishment of initial polarity, which remains largely unknown. This research aims to identify the mechanism behind regulative embryonic development and how different species acquire distinct developmental styles, using echinoderm embryos as a model system. Aim 1 will examine how the regulation of conserved polarity factors involved in asymmetric cell divisions contributes to the initial establishment of polarity in the embryo, influencing the regulative nature of embryogenesis in the sea urchin. The researchers will perform overexpression and knockdown of polarity factors and cytoskeletal components, combined with time-lapse imaging during and after fertilization. These experiments will reveal how polarity factors and cytoskeletal elements regulate each other to establish embryonic polarity without pre-localized maternal factors. Aim 2 will investigate how the molecular evolution of polarity factors affects the divergence of developmental programs across different echinoderm species. This will be done through introduction of these factors into other echinoderm species to analyze changes in downstream developmental pathways, using single-cell (sc)RNA-seq and real-time imaging or fluorescent in situ hybridization (FISH) of key markers for development. Echinoderm embryos are ideal for this study because of their optical transparency and the well-understood effects of asymmetric cell division, which can be experimentally tested. Additionally, some echinoderms naturally exhibit an evolutionary transition in their developmental processes, making them an excellent model for comparative developmental biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Abstract: We propose to establish and build the COBRE Center for RNA Biology in Health and Disease at Brown University in Providence, Rhode Island. The motivation for this effort is the promise that new developments in RNA biology will drive a better understanding and treatment of human disease. The recent global pandemic illustrated the importance of understanding RNA viruses(such as Aim 3.4) and the power of nanoparticle RNA delivery {like Aim 3.3). In addition, advances in structural mapping of RNAs and a new appreciation of modified RNAs promise are already driving new models for gene expression and human disease. This proposal seeks to create a research infrastructure and academic community that is positioned to build on these recent scientific discoveries. The Brown University biomedical community is an ideal environment to achieve this goal. Brown has recenUy committed over 12 million dollars to the creation of an RNA Center housed in the newly renovated CIC technology center. In addition to commitments of equipment for the Research Core, there will be 6 junior faculty hires offered over the next 5 years to replenish the cohort of junior investigators that graduate from the COBRE due to obtaining R01 funding. We will bring cutting edge RNA technologies to the state of Rhode Island and evolve a collaborative service culture where implementing new RNA methodologies will generate samples for downstream analysis by existing cores. The long-term goal of the Center is to build an RNA biology infrastructure for the greater Brown and hospital environments that will benefit the study of human disease across all of Rhode Island. The objective of this proposal is to establish and build the COBRE Center and support the research activities of Junior Investigators to ensure their transition to independent R01-funded scientists. There are three Specific Aims - two related to the establishment of the Center, and one encompassing five Research Projects spanning experimental and computational RNA research. The specific aims that will allow us to achieve these goals are: AIM 1. Build the Administrative Core that will establish and support the COBRE Center for RNA biology of human health. AIM 2. Build the RNome Core that will support the research of junior faculty, increase the ability of academic researchers to incorporate cutting edge RNA techniques into their research and educate the scientific community of Rhode Island. AIM 3. Build the initial cohort of RPLs with complementary backgrounds in biophysics, chemistry, computational biology and infectious disease.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Every day, there are more than 1 million new cases of sexually transmitted infections (STIs) worldwide that can have long-term health consequences, including infertility and pregnancy complications. For women, the vaginal microbiome has emerged as an important factor in STI acquisition and recurrence, but the mechanisms by which the microbiome influences this process is not well understood. Bacterial vaginosis (BV) occurs when the vaginal microbiome becomes dysregulated and imbalanced due to an overgrowth of anaerobes. Among women below the age of 50, this is one of the most common vaginal conditions that increases susceptibility to reproductive tract infections. Studies on how these anaerobes interact with the local immune system have mostly been focused on innate immune cells. BV-associated bacteria’s impact on CD8 T cell mediated responses and resident memory T cell differentiation and function remain unexplored. My long-term goal is to determine the role of commensal and dysbiotic bacteria on CD8 tissue resident memory T (TRM) cell immunity within the female reproductive tract (FRT). Previous studies on BV and innate immune cells have shown that bacterial metabolites from BV-associated bacteria lead to a state of chronic inflammation, partially mediated by their actions on local epithelium and the release of damage-associated cytokines. Based on this, I hypothesize that the inflammatory cytokines produced by these anaerobic bacteria will alter the differentiation of TRMs and enhance their pro- inflammatory function. To test this hypothesis, I will use a combination of in vitro and in vivo models mimicking human BV in mice to characterize the tripartite interaction between the microbiome, the vaginal epithelium, and CD8 TRMs. In aim-1, I will evaluate the impact of vaginal microbial metabolites on CD8 T cell function using a novel vaginal epithelial organoid (VEO) and T cell co-culture system. In aim-2, I will test the contribution of these vaginal microbial metabolites in vivo via either metabolite supplementation or direct engraftment of bacteria into the FRT of mice. Understanding CD8 TRM interactions with the vaginal microbiome will be instrumental in developing therapies to reduce STI transmission and pregnancy complications and improve overall reproductive health. In preparation for this proposed work, my training is being overseen by my mentor Dr. Beura, who takes an active role in developing my immunological techniques, critical thinking skills, and independent experimentation through regular one-on-one meetings. I am also supported by my co-sponsor, Dr. Laurent Brossay, and the oversight of the Brown University Pathobiology training program allowing me plentiful opportunities to engage the scientific community and broader public to improve my scientific communication skills. Completion of this proposal will equip me with the necessary attributes and key foundational knowledge to pursue a successful career as an independent researcher in the field of viral infection and immunology.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Safety net supports for families with children are a key component of the resources available to U.S. families with low and moderate incomes. Effective provision of social supports through the state may positively affect child health and development by augmenting access to resources both within and outside of the home, with benefits accruing from the prenatal period through early adulthood and beyond. Most research seeks to isolate the effects of a single program or public investment, providing critical evidence but not accounting for the fact that most households with children—particularly low-income households—access multiple public supports (Edelstein, Pergamit, and Ratcliffe 2014; Jackson and Fanelli 2023; Macartney and Ghertner 2023). Because federal, state, and local governments must determine spending levels and how to spend funds for the children and families who rely on it, it is critical to understand how different forms of public investment work independently and in combination to affect children. Drawing on an interdisciplinary team, we will use quasiexperimental methods to estimate the effects of early-life exposure to safety net generosity–spending and benefits levels–on a multidimensional set of outcomes. Specifically, we will: (1) Examine the causal effects of early-life exposure to safety net investments on health and development from birth through middle childhood; (2) Examine how early-life safety net environments differentially affect children by socioeconomic status (SES) and race/ethnicity. We will analyze two large-scale sources of data on short- and medium-term outcomes for children: birth outcomes from the universe of U.S. births, using data from the CDC National Vital Statistics System (NVSS); and age 8/9 health and skill development, using data from the National Survey of Children’s Health (NSCH). Our analyses of existing high-quality, publicly available data will enable additional research examining the effects of federal, state, and local investments on the health and development of U.S. children and families.

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

NONTECHNICAL SUMMARY This award supports research, education, and outreach activities with a goal to achieve a fundamental understanding of topological matter with emphasis on the existence and properties of unusual particles known as anyons. Topological matter is a new, important class of materials with uniquely robust properties insensitive to undesirable effects, such as material imperfections or interference from the material's environment. This research project focuses on the understanding of how heat is transported within such matter. This is of major importance to quantum information processing platforms, many of which depend on the use of anyons. Introductory physics textbooks teach us that electrons are truly fundamental particles with no constituent parts. The charge of an electron is understood as the smallest possible charge a particle could have. And yet, in topological matter, stable "quasi"-particles that carry a smaller charge than the charge of an electron can form. They are known as anyons. Anyons exhibit highly counterintuitive behavior when they run around each other, which is directly useful for quantum computing. Indeed, using anyons as building blocks of quantum information devices is expected to dramatically suppress error rates and preserve quantum information from undesirable influences. Presently, the properties and often even the existence of anyons are poorly understood and hotly debated. This proposal focuses on devising new ways to theoretically predict and experimentally probe the physics of anyons in some recently discovered promising classes of topological matter. This award also supports the educational and outreach activities, contributing to the development of US workforce in quantum science and technology and related fields through engaging students in quantum science research. Other planned activities include conference organizing, writing pedagogical review articles, and outreach at the K-12 level. TECHNICAL SUMMARY This award supports research, education, and outreach activities aimed at achieving a fundamental understanding of heat transport and neutral modes in topological states of matter. The last several years have seen dramatic progress in the field of topological matter. One major development was a direct proof of Abelian anyonic statistics via interferometry and of non-Abelian statistics via thermal transport. Another breakthrough resulted from the discovery of new topological states of matter in twisted bilayer molybdenum telluride and multilayer graphene. The proposal is motivated by these achievements and aims at furthering the understanding of the nature of topological orders in real materials, the development of new probes of topological order, and the explanation of surprising data from existing probes. The proposed research involves three directions. The first direction focuses on the nature of the fractional quantum spin Hall effect in molybdenum telluride. Experiments reveal broken time-reversal symmetry, which opens up the possibility of a large number of topological orders. These will be systematically classified and the ways to unambiguously determine the actual topological order will be identified. The second direction addresses interferometry in topological liquids with multiple edge channels. Interferometry in such liquids is poorly understood theoretically, and it is challenging to interpret the available data. Two types of interferometers will be considered: the type that was recently used to probe fractional statistics of the hierarchical state at the filling factor 2/5, as well as a new geometry, proposed by the PI, which inspired the recent observation of fractional statistics at the filling factor 3/7. The third direction focuses on neutral anyonic excitons recently discovered in bilayer graphene. The proposed research will advance the understanding of many-body quantum systems. Besides their importance for basic materials science, the results on non-Abelian topological orders is also relevant for the field of quantum information. This award also supports the educational and outreach activities, contributing to the development of US workforce in quantum science and technology and related fields through engaging students in quantum science research. Other planned activities include conference organizing, writing pedagogical review articles, and outreach at the K-12 level. 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

Unlike artificial intelligence systems, humans are capable of flexibly adapting to their environment, producing novel goal-directed behaviors that are appropriate for a situation. For example, with the goal of doing well on an exam, a student might enact some effective study strategies, while also avoiding distraction from things like social media. Psychologists and neuroscientists have called the set of processes that support this capacity “cognitive control.” This project aims to develop and test a novel computational theory of how cognitive control works in the human brain, built using recurrent neural networks that can learn about the demands of different tasks as well as how to allocate control to rapidly improve performance on a specific task. A deeper understanding of the neural computations that underlying cognitive control has several broader impacts. First, many disorders of the brain have been characterized by failures of cognitive control, and this work helps us understand these disorders better. Second, a computational understanding of how the human brain produces flexible, goal-directed behavior will help to design next generation artificial intelligence systems. Finally, this project provides extensive outreach about topics related to cognitive control, neuroscience, and computational modeling, from classroom visits to elementary schools to workshops open to a variety of stakeholders in the medical and education communities. Technically, the project relies on an existing large set of electroencephalography (EEG) and behavioral data collected from the flanker task, a classic test of cognitive control. Recurrent neural network (RNN) models are trained to perform the task, using different RNN architectures that allow for the discovery of latent units that can learn task demands and control processes. The RNNs and human participants are tested on untrained tasks to determine whether the model generalizes to unseen human performance, and both the model and the human brain are perturbed – the model by manipulating the latent units and the human by applying transcranial ultrasound stimulation to the posterior medial frontal cortex, to determine whether the two systems break down in similar ways with damage. The findings informs the development of next-generation AI systems that could incorporate these latent units, allowing future AI systems to show more flexibility and goal-directed behavior than current systems are capable of doing. 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 brain is the most complex organ in the body, composed of different types of cells connected by intricate networks essential for sensory perception, cognition, and behavior. Understanding how these cells are organized, connected, and functionally coordinated within the brain remains a significant scientific challenge. Recent technological advances have produced large amounts of structural and functional data of the brain, yet extracting meaningful biological insights from these complex datasets remains difficult. This research addresses this critical gap by creating computational tools integrating molecular data with structural imaging, offering a detailed view of brain organization. Specifically, the project leverages datasets from advanced imaging methods revealing fine anatomical details with spatial transcriptomics, a technique profiling gene expression within tissues. By integrating these complementary data types using novel Bayesian and deep learning modeling frameworks, the project aims to build the first comprehensive 3-dimensional molecular and structural map of brain areas for olfaction in mice, an important model system for understanding neural circuits. This map will provide fundamental insights into how molecular characteristics of neurons relate to their structural connections, enhancing our understanding of how brain circuits’ function. Broader impacts include training a new generation of interdisciplinary scientists skilled in the intersection of artificial intelligence (AI), data science, and brain science. The project further introduces students to exciting careers in science, technology, engineering, and mathematics (STEM), and fosters greater public awareness of brain science research and its potential benefits for health and society. Technically, this project develops a novel computational framework combining Bayesian statistical modeling and Graph Neural Networks (GNNs) to integrate spatial transcriptomics with high-resolution structural data from X-ray nano-holotomography. The Bayesian component quantitatively models the spatial distribution and molecular identities of neuronal structures (e.g., glomeruli) in the olfactory bulb, accounting for biological variability and measurement uncertainty. The GNN-based approach dynamically integrates multimodal spatial data—molecular, morphological, and connectivity—to capture complex neuronal relationships, by explicitly incorporating uncertainty-aware learning aligned with the Bayesian framework. The methods employ efficient graph-sampling algorithms and multi-view contrastive learning to achieve scalability for analyzing large-scale, high-resolution brain datasets. Experimental validation involves direct integration with detailed molecular and anatomical datasets from the mouse olfactory bulb, ensuring biological accuracy and interpretability. Expected outcomes include open-source computational tools that significantly advance our ability to quantify and interpret complex biological data. The developed methods promise broad applicability in multi-modal spatial omics datasets beyond brain science, potentially transforming data analysis across biological and biomedical research contexts. 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

Karthikeyani Chellappa PhD, Brown University Coordinated Control of Metabolism by Transporters and Enzymes The transport of molecules across the plasma membrane is a highly regulated process that impacts metabolism at subcellular to organismal scales. The physical association of enzymes in a pathway or the association of transporters and enzymes can increase the efficiency of metabolic pathways. Here, we address the broader implications of coordination between transporters and enzymes by investigating the transport mechanism of a precursor for nicotinamide adenine dinucleotide (NAD). NAD, a critical redox cofactor in over 400 biochemical reactions, also acts as a substrate for signaling enzymes like sirtuins, ADP-ribosyltransferases, and cyclic ADP-ribose synthases. The decrease in NAD levels is associated with a broad range of diseases, including obesity, diabetes, aging, and cardiovascular and neurodegenerative diseases. While boosting NAD levels by precursor supplementation has shown benefits in rodent disease models, its effectiveness in human clinical trials is limited. Understanding the biology of NAD metabolism has implications for basic science and translational research. Nicotinamide (NAM) is a primary precursor for NAD biosynthesis in most tissues in mammals. NAD-consuming enzymes cleave NAD molecules to release NAM, which is constantly exchanged between tissues and circulation. Our previous research has shown that NAM transport is a critical site of regulation in NAD homeostasis with potential implications in aging. We have further established that the transport of NAM is crucial for NAD synthesis in the gut microbiome and precursor supplementation. Our current work has discovered that NAM is transported into mammalian cells via a biphasic transport system. We also found that nicotinamide phosphoribosyl transferase (NAMPT), a rate-limiting enzyme that uses NAM as a substrate, regulates NAM transport. Hence, we hypothesize that a coordination of NAM transport and metabolism is essential to maintain cellular function. Therefore, it is important to understand how NAM, an essential intermediate in NAD metabolism, is transported across the plasma membrane in mammalian cells. Our aim is to address fundamental questions such as how NAM is transported in mammals, how NAMPT controls NAM transport, and what is the significance of NAM transport control by NAMPT. By unraveling the mechanisms of NAM transport, we can develop novel genetic and pharmacological tools to accelerate the next phase of discoveries in NAD biology, microbiome research, and nutrient supplementation. An over-arching long-term goal of this research program is to fill the gap in understanding the implications of coordination between transporter and enzymes to cellular metabolism and function at the genome level using a systems biology approach.

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

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

Bone shape across animal species is the result of evolution (passing down traits to offspring) and adaptation (change of shape during life). This research investigates how bones change shape in response to different behaviors and environments, using deer mice as a model system. Scientists will study two closely related mouse species—one that burrows underground and one that does not—to understand whether bone structure is primarily determined by genetics passed down through evolution or by physical activity during an animal's lifetime. By raising mice in environments that either allow or prevent digging, and using cutting-edge X-ray technology to film their movements in real-time, researchers will discover how specific behaviors reshape bones. This work addresses a fundamental question in biology about how form follows function in living organisms. The findings will have important implications for human health, particularly in understanding bone diseases, aging, and the role of exercise in maintaining strong bones throughout life. The project will also advance scientific infrastructure by pioneering new imaging technology that can capture detailed bone and muscle movements in very small animals, opening doors for future biomedical research. Additionally, the research will train high school, undergraduate, and graduate students in laboratory techniques and create educational materials for teachers to help students understand evolution, physiology, and engineering principles through hands-on bone studies. By revealing how animals naturally optimize their skeletons for their lifestyles, this research provides insights that could inform treatments for bone disorders and guide exercise recommendations for maintaining skeletal health. This study investigates how limb bone structure is shaped by genetic inheritance versus developmental responses to physical activity in deer mice. The project will test the hypothesis that bone changes during growth are primarily influenced by local mechanical forces, while overall bone shape and the capacity to respond to forces are determined by evolutionary adaptations to different environments. Two mice species will be compared: burrowing oldfield mice (Peromyscus polionotus) and non-burrowing cactus mice (P. eremicus). Mice will be raised in chambers that either allow or prevent digging behavior, and limb bone structure will be measured using high-resolution CT scanning to compare bone thickness, internal architecture, and overall geometry between groups. Bone function during movement will be studied using advanced X-ray video technology (microXROMM) to measure how bones move in three dimensions and calculate muscle forces during running and digging. To test how sensitive bones are to mechanical forces, controlled loads will be applied to leg bones while measuring structural, cellular, and genetic responses through tissue analysis and gene expression studies. Gene activity analysis will identify which genes respond differently to mechanical forces in each species, determining whether evolutionary differences in digging behavior correspond to altered bone sensitivity at the molecular level. This approach will establish how behavior, mechanical forces, and bone adaptation interact across evolutionary, developmental, and ecological scales, providing a framework for understanding how form follows function in mammalian skeletons. 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

Nontechnical description This project will explore a new class of materials for energy-efficient data processing and storage by harnessing the spin of electrons rather than their electric charge. Traditional microelectronics face increasing energy demands and shrinking size limits. Spin-based devices, on the other hand, can potentially run with lower power and higher performance. However, managing the direction of electron spins becomes essential at very small scales. To address this, the research team will design oxide crystals with carefully tuned symmetries, enabling spin currents to be generated in the exact orientation needed for switching tiny magnetic bits. By enlarging the pool of materials that can perform this function, the project aims to spark development of more compact, faster, and energy-conscious computing technologies. In addition, an integrated educational component will strengthen undergraduate instruction in materials science and offer lab-based experiences for students, cultivating broader skillsets in next-generation hardware design. Technical description This project will systematically investigate the generation of out-of-plane spin-polarized currents in anisotropic oxide thin films grown by pulsed laser deposition. The principal investigator will fabricate epitaxial bilayer heterostructures in which a crystalline oxide with strong spin-orbit coupling is interfaced with a magnetic layer capable of detecting and switching in response to spin signals. By controlling substrate orientation and strain conditions, the team will establish precise crystal symmetries that break conventional spin-current constraints, allowing a charge flow to produce a spin polarization component oriented perpendicular to the film plane. The research team will quantify spin-torque efficiency by combining spin-torque ferromagnetic resonance and harmonic Hall measurements under varied temperatures, film thicknesses, and anisotropic strain states. These data will clarify the role of crystal symmetry in determining the magnitude and direction of spin polarization. In addition, the research will integrate correlated electron states, such as polar or superconducting phases, to uncover new functionalities that arise when out-of-plane spin currents coexist with these effects. Overall, this project will provide a framework for expanding the materials palette used in advanced spin-based logic and memory technologies, with broad implications for future electronic and photonic materials research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This proposal seeks funds to acquire a state-of-the-art liquid chromatography mass spectrometer at Brown University for research and educational purposes. The instrument will be housed in an open facility and available for researchers and students at Brown from many departments including but not limited to Earth, Environmental and Planetary Sciences, Chemistry, Engineering, and Biomedical science. The instrument provides new capabilities that allow improved and more efficient analyses of important environmental and biological compounds relevant to environmental pollution and engineering, human health, chemistry and biochemistry, and environmental and climate studies. It will be used for teaching advanced analytical chemistry courses to both graduate and undergraduate students. Students will be trained by faculty members and experienced technicians on the operation of the instrument and data interpretation. The instrument will promote new research ideas, enable new collaborations among faculty members from different departments, and allow new research directions that are currently not possible using the existing instruments at Brown. This proposal seeks funds to acquire a Vanquish Neo UPLC System coupled to a ThermoFisher Scientific Orbitrap Exploris 240 mass spectrometer (MS) with an Isotope Solutions package for abundance, structural, and isotopic measurements of organic and inorganic species in geological, chemical, biological, and environmental samples. Development of the high mass resolution Orbitrap MS (e.g., the Exploris 240) represents one of the most important technological advances in the field of analytical chemistry and geochemistry in recent years. By raising the mass resolution to 240,000 at m/z 200, the instrument provides accurate mass for resolving near-mass isobaric species from complex mixtures of small to medium sized molecules, greatly increasing sensitivity and linear dynamic range for quantitation and confidence for molecule identification. Selective ion monitoring with accurate mass provides exceptional selectivity of molecular species, which minimizes baseline fluctuations and lowers detection limits. This new technology is now rapidly replacing lower resolution mass spectrometers for analyzing complex environmental pollutants, organic geochemical biomarkers and various biochemical compounds in the era of “omics”. The proposed system also includes an Isotope Solutions package, allowing direct analysis of individual isotopologues of oxyanions (nitrate, sulfate, phosphate and acetate) and position, compound-specific and clumped isotopic analyses of organic compounds. The proposed instrument will strongly enhance research capabilities at Brown University and allow exploration of many new directions previously not possible using the existing instrumentation. The new system will promote interdisciplinary collaboration across multiple departments at Brown. It will greatly elevate teaching and training of the next generation scientists including postdocs, graduate, and undergraduate students. The instrument will be placed in an open core facility and will be available to all researchers on campus, nearby institutions, and visitors to Brown. 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

Cellular senescence is a biological program where cells permanently cease to divide in response to molecular damage, activating the senescence-associated secretory phenotype (SASP). SASP recruits the immune system to clear these cells. Senescence plays a crucial role in processes like development, wound healing, and cancer resistance, but its accumulation with age can be detrimental, contributing to systemic inflammation. Recent single-cell technology studies have revealed significant heterogeneity in the senescent phenotype. Most research has focused on the variation in cell types and different forms of senescence induction. However, a less explored aspect is the age of a senescent cell, considering both the cell’s age at the onset of senescence and the duration it remains in this state. Our previous work and preliminary results indicate that the senescence phenotype evolves from early to deeper states, with significant changes in the transcriptome, particularly the SASP, and the epigenome. This project aims to study how the senescence phenotype changes between young and old cells and as a function of time in senescence (Aim 1). Additionally, we will advance recent technology based on a multi-symbol molecular recorder to track the duration of cells in the senescent state (Aim 2). Ultimately, our project will lay the foundation for studying the age of senescent cells in vivo.

NSF Awards · FY 2025 · 2025-08

This Pathways to Enable Open-Source Ecosystems (POSE) project accelerates the adoption of open-source tools to design energy-efficient artificial intelligence (AI) hardware systems. By enabling rapid prototyping and lowering the barriers to system-on-chip design, the project supports innovation across scientific domains that rely on specialized AI hardware. The solution aims to establish a sustainable ecosystem for modular, transparent, and reusable hardware design through collaboration with stakeholders and a shared vision for extensible design flows. These efforts advance semiconductor and computing innovation while expanding access to next-generation hardware design. This Pathways to Enable Open-Source Ecosystems (POSE) project scopes activities for an open-source ecosystem for customizable AI hardware generation and system-on-chip design. The project’s goals are to: (1) conduct a comprehensive needs assessment via structured surveys and interviews, (2) evaluate the ecosystem through domain-specific pilot deployments, (3) analyze barriers to educational adoption, (4) design community governance models, and (5) promote contributor growth through training and mentorship. The project aims to gather actionable insights to improve usability, documentation, and onboarding, ultimately supporting greater adoption and long-term sustainability. By enabling customizable, efficient hardware generation and fostering a scalable, community-driven governance structure, the project defines a roadmap for open-source hardware tools to serve a user base across academia, education, and industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Short-video systems deliver videos only a few seconds or at most a few minutes long, in a personalized way, to billions of users around the globe. In the last few years, they have become increasingly popular for delivering societally beneficial content, such as microlearning, news, citizen reporting, advertising, user-generated content, testimonials, sports feeds, and more. Unlike long-form multimedia which is well-studied, short-video systems are unique in terms of the associated user behaviors, video and audio content, recommender algorithms, and the delivery pipelines that transport videos from content providers via content distribution networks (CDNs) and eventually to user devices. This transformative project will help improve both the efficiency of, and our understanding of, the class of short-video systems. The project plan draws on a unique and innovative combination of systems and networking design philosophies, along with machine learning (ML) techniques, complemented by real human user studies; this combination is essential for short-video systems because of their user-facing nature. The expected project outcomes include new video delivery techniques that use less compute and network resources and reduce consumption of user devices’ energy; new analytical understandings of the behavior of short video systems; and browser plugins and open software. Educational content will include course modules, including ones for online courses, focused on short-video streaming systems. The project will broaden participation in computing for Americans from any and all backgrounds, including high-school students, undergraduate researchers, and graduate students. Technically, this project will build transformative new ways of building learning-based and adaptive techniques for short-video systems. Our project, called "LANDS - Learning-based Adaptive Networked systems for Delivery of Short videos," will build three systems: (A) MidLand, a system that uses novel video reordering to reduce content distribution network (CDN) costs of midgress and cache size investment, while maintaining high user engagement and QoE (Quality of Engagement); (B) HighLand, a system that leverages ML pipelines, such as large vision language models, to predict user behavior and capture recommender algorithm performance as well as improve system-level metrics like cache effectiveness and adaptive bit rate adaptation; and (C) LowLand, a system that executes the ML pipelines of HighLand in fast, resource-efficient, and scalable ways, with support for expressing many types of useful analytic pipelines. Overall, LANDS will consist of both "learning-independent" layers and rich ML-driven layers atop them to further improve performance. The project plan contains a carefully crafted mix of system design and implementation, along with ML techniques (e.g., Large Language Models) as well as human user studies (with IRB approval). The team is interdisciplinary, with expertise across distributed systems, networking, ML systems, and human-computer interaction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-08

Exposure to per- and polyfluoroalkyl substances (PFAS) is nearly universal in the United States (US) and has been associated with an array of adverse health outcomes in infants, children, and adults. Prior studies show that prenatal PFAS exposure may increase the risk of low birthweight, preterm birth, and altered childhood growth, which in turn are strong predictors of adult cardiometabolic disease. However, there are two critical gaps in our knowledge. First, no studies have determined if prenatal PFAS exposure increases the risk of adverse adult health outcomes, in large part because of the lack of prospective cohorts with the requisite biospecimens and long-term follow-up. Second, prior studies potentially underestimated the impacts of PFAS on neonatal, child, and adult health because they focused on a small number of PFAS and did not consider understudied PFAS or PFAS mixtures. We will address these gaps using a one-of-a-kind of resource, the New England Family Study (NEFS), a prospective cohort that recruited pregnant women in the 1960s and followed their children annually from birth through age 7 years, and again in mid-adulthood (mean age=47 years, n=751). We will conduct an unprecedented assessment of prenatal PFAS exposure, measuring 44 individual legacy and understudied PFAS, as well as total PFAS using extractable organic fluorine (EOF) in previously collected serum samples. Our team of experts in PFAS, analytic chemistry, exposure assessment, biostatistics, environmental epidemiology, and clinical medicine will apply sophisticated biostatistical methods to these biomarkers and previously collected health assessments to: 1) Estimate the impact of gestational PFAS mixtures and serum EOF on birth weight, gestational age, early adiposity rebound, and childhood BMI; 2) Estimate the impact of gestational and concurrent exposure to PFAS mixtures and EOF on risk of MetS and individual cardiometabolic components in mid-adulthood; and 3) Characterize the degree that birth weight, gestational age, postnatal growth, and childhood BMI mediates the association of prenatal PFAS with MetS and cardiometabolic components; and 4) Determine if mid-adulthood lifestyle factors mitigate the adverse effect of prenatal PFAS on risk of MetS and individual MetS components. Our study provides an unprecedented opportunity to be the first study to quantify the effects of prenatal PFAS exposure on early childhood and adult health. By conducted highly detailed measures of PFAS exposure during the susceptible prenatal period, we will inform risk assessments of PFAS mixtures and identify the most harmful components of this mixture. Moreover, our results will quantify the clinical impact of early-life PFAS exposure on infancy, early childhood, and adult cardiometabolic health, thus informing health screening guidance in exposed communities. Finally, using a solutions-oriented approach, we will identify behavioral or lifestyle factors that could ameliorate the effects of earlier life PFAS exposure, thus paving the way for targeted health interventions in historically exposed communities.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Trypanosomatids are important human pathogens that cause severe diseases around the world. These parasites feature free living life-cycle stages that have previously been intractable to rigorous study using long term live- cell imaging because most confinement strategies disrupt cellular processes or cause death. This has left significant gaps in knowledge about the rates of cellular events such as cell division and if states seen in fixed- cell imaging represent bona fide intermediates of cellular processes or are strictly endpoints or fixation artefacts. We recently devised a strategy using agarose microwells cast from PDMS stamps to confine trypanosomatids in nanoliter volumes of media without impacting their motility or viability. These volumes are sufficiently small that 10 to 25 microwells fit within the imaging volume of an inverted microscope, allowing us to observe multiple cells during one imaging run, which can last as long as 3 days using white light and fluorescence. The method is also compatible with small molecule and RNAi treatments, which allows novel investigation of loss-of-function experiments. We have used the agarose microwell method to study the cell division mechanism of Trypanosoma brucei and Trypanosoma cruzi, showing that the daughter cells produced by the parasites subsequently divide at different rates, which suggests that the inherent asymmetry in the daughter cells takes different amounts of time to resolve before they can divide again. We also showed that an intermediate that appears in fixed samples during RNAi that was proposed to function as an alternate cell division mechanism could be observed during live cell imaging, but that it did not lead to productive cell divisions, which emphasizes the value of our approach. We have updated our microscope to increase the number of microwells that we can observe simultaneously and decrease the amount of light we need to image cells. This update has now made it possible to observe rare or asynchronous events, such as life-cycle transitions, that also appear to be more sensitive to light exposure. We will use our updated imaging approach to study a variety of life-cycle transitions in both T. brucei and T. cruzi. In Aim 1a, we will be studying the mechanism of metacyclogenesis T. brucei, which produces parasites that are ready for transmission into the mammalian host, to determine how the parasite replaces cell surface proteins and how it reorganizes the cell body. In Aim 1b, we will study the T. brucei slender to stumpy transition, which preadapts bloodstream form parasites for transmission back into the insect host, using recent scRNA-seq data to identify intermediate states in the process. In Aim 1c, we will determine if the switch from the insect-resident epimastigote form to the metacyclic trypomastigote form in T. cruzi, which requires a significant repositioning of the flagellum and all its associated structures, requires a cell division to occur and how attachment to a surface affects the transition. Our work will provide unprecedented details about the mechanisms of trypanosomatid life- cycle transitions, which are poorly understood and could yield new avenues for blocking parasite transmission.

NSF Awards · FY 2025 · 2025-08

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Jerome Robinson of Brown University is investigating reactive oxygen species (ROS) of the rare earth elements (RE’s). Rare earth elements (group III + the lanthanides) are a group of critical materials found in a wide range of novel, emerging, and advanced technologies used in society, including applications in energy science, defense, and quantum materials. RE ROS have been proposed as key species in a range of processes; however, our fundamental understanding has been limited by synthetic access and systematic studies of well-defined materials. Through the proposed work, graduate, undergraduate, and high school students will gain specialized experimental and computational training working with critical materials with world-leading experts at academic and national labs. Furthermore, Professor Robinson will develop programs introducing high-school and undergraduate students to the chemistry of RE’s and their applications in technologies to further develop pipelines to a critical materials STEM workforce. RE ROS have been implicated as key species and/or intermediates with reactivity distinct from any other part of the periodic table, yet direct synthesis of many of these materials have yet to be achieved. This research project seeks to synthesize novel RE superoxide and (alkyl/acyl/hydro)peroxide species. Rigorous characterization in the solid- and solution-state and systematic reactivity studies will establish robust structure-function relationships, and elucidate differences from s- and d-block ROS. Additional collaborative efforts to evaluate the electronic structure of these novel compounds (magnetism, XAS/XAFS, computation) will help advance the field’s understanding of the structure and bonding of these materials, including potential applications in quantum information science. Information from this study will inform the identity of active oxidants at bulk and nanoscale materials and the design of novel and selective oxidation catalysts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Understanding and improving human health is a complex challenge because it involves multiple types of information, including medical records, images or scans, laboratory tests and genetic data. Each type of information provides a distinct piece of the puzzle. However, these pieces don’t always fit together easily because they come in different forms and different quantities and time scales. These differences make it challenging for doctors and researchers to obtain a comprehensive understanding of a person’s health and determine the most effective treatment. This project aims to develop new computational methods that can combine all these types of health information to better predict diseases and design effective treatments tailored to each individual. By improving how these diverse health data can be used, this research could lead to earlier diagnosis, more personalized care, and ultimately better health outcomes for patients. Additionally, the project will involve students in this work to teach them how to use these advanced tools, helping to build a future workforce capable of creating the technology that tackles complex health challenges. This project addresses two major challenges for developing integrative machine learning for health applications: effectively modeling the complex relationships within and between different data types and addressing the sample size imbalances commonly found in real-world datasets. The project approach involves building graph-based frameworks to integrate gene-gene interaction networks into counterfactual explanation methods, enabling precise identification of key genes for therapeutic targeting. Simultaneously, the investigator will embed knowledge of drug-drug interactions into large language models to enhance the prediction of adverse effects and guide treatment optimization. To address the heterogeneity and imbalance across modalities, such as imaging, clinical notes, and genetic screenings, the investigator will design novel joint representation learning techniques. The investigator will also evaluate explainability strategies tailored to multimodal models to improve the interpretability of predictions. These methods will be validated across diverse health datasets and tasks. This research will be closely linked with interdisciplinary educational initiatives, integrating novel multimodal approaches into student training and outreach programs, thereby fostering a synergy between research innovation and workforce 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.