Brown University

NSF Awards · FY 2026 · 2026-10

Pre-trained AI models shared through open online repositories are becoming essential infrastructure for research, industry, and government. But this growing reliance also creates an important cybersecurity concern: just as traditional software can be attacked to include viruses or access backdoors, AI models can also be tampered with. This can lead to security breaches and errors in systems that rely on these pre-trained models. This project will develop methods and tools to help users verify whether a pre-trained AI model is trustworthy before it is incorporated into scientific workflows, operational systems, or other important computing environments. By improving the security of this emerging AI infrastructure, the project will help strengthen the U.S. research enterprise, support economic competitiveness, and improve the resilience of AI-enabled systems. The project will also advance education and workforce development by training students, providing research opportunities, and fostering collaboration among universities, industry, and other stakeholders. This project develops a novel approach to address three major security challenges in the machine learning (ML) model supply chain. The research integrates software engineering principles with machine learning techniques to systematically mitigate vulnerabilities during model selection, loading, and management. First, the team of researchers will tackle model spoofing, where adversaries upload malicious models using deceptive names. The project relies on novel anomaly detection schemes for naming conventions and architectural signatures to identify these threats. Second, the investigators will secure the model deserialization process. Because frameworks often use formats vulnerable to arbitrary code execution, the research will develop automated, least privilege deserialization mechanisms and define safe subsets for model loading runtimes. Third, the project will establish robust model lineage tracking to manage the risks of reusing models. The team will create a lineage graph data structure that combines static and dynamic analysis to trace model evolution and detect illicit modifications. By integrating these methods, the project provides a comprehensive defense system that enhances trust, integrity, and oversight in the open source model ecosystem. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Artificial intelligence (AI) systems increasingly influence both high stakes and everyday decisions across many sectors of the economy. These systems, however, are not developed in isolation. Instead, they depend on people to provide instructions that describe what the system should do and what outcomes it should avoid. These instructions can take many forms. They may be written explicitly by domain experts or learned from data such as human preferences over outcomes. However, providing clear and reliable instructions for intelligent systems is difficult even for relatively narrow applications. Instructions can be too rigid, too vague, or simply incorrect, and any of these problems can cause systems to behave in unintended ways. These failures occur because instructions are created by people, and human reasoning is shaped by limited information, context, and common cognitive mistakes. As AI becomes more widespread, improving how systems interpret human intent will be essential for safety and reliability. This project addresses that challenge by studying how people communicate goals to machines and by designing AI systems that can interpret imperfect instructions by reasoning about the intent behind them. The expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively. This project develops computational foundations for learning AI specifications from imperfect human input. The research integrates reinforcement learning, Bayesian inference, and computational cognitive modeling with empirical studies of human decision making to better characterize how people communicate goals and where specification errors arise. The work is organized around three research thrusts. The first thrust, Modeling and Inferring AI Specifications, develops probabilistic models of human reasoning that capture systematic specification errors and uses these models to enable AI systems to infer more accurate goals from flawed instructions. The second thrust, Richer Inputs and Representations, expands how AI systems learn from people by incorporating different forms of input such as preferences, demonstrations, explanations, gestures, and structured debate. New algorithms and elicitation interfaces will integrate these signals and resolve inconsistencies across modalities. The third thrust, Personalization and Governance, develops methods for learning multiple reward models that reflect differences in human preferences, enabling scalable personalization and avoiding one-size-fits-all objectives. In parallel, the project will develop educational programs that prepare students to design and govern AI systems. These activities include revising an undergraduate AI course to emphasize human decision-making in the design of AI systems, creating a graduate course on AI policy and governance, and expanding the AI Policy Summer School to help build a national workforce that is fluent in both AI technology and public policy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

This award supports the participation of early-career US-based researchers at the summer school on Kähler Geometry, Algebraic and Arithmetic Geometry, and Hyperbolicity, which will be held from August 24-28, 2026 at the Université du Québec à Montréal in Montreal, Canada. This intensive program provides research training for graduate student and postdoctoral participants, and will foster international collaboration. Kähler geometry is a branch of complex geometry, where functions given by convergent series are particularly well-behaved, in which one has a good notion of distance between points. A fascinating collection of Kähler geometries are hyperbolic—these are curved so much that they cannot contain infinite straight lines. A big source of such geometries comes from algebraic geometry, the study of shapes—called algebraic varieties—defined using only polynomial equations. There are tantalizing questions relating hyperbolicity of algebraic varieties and the arithmetic complexity of their points. The summer school is distinguished by its integrated focus on Kähler geometry, algebraic and arithmetic geometry, and hyperbolicity, bringing together analytic, topological, and algebraic perspectives within a single coherent program. The program will emphasize developments within the last two decades, including recent analytic methods, such as singular Kähler metrics and complex Monge-Ampère techniques, and their applications. The mini-courses will be delivered by leading scientific experts: Robert Lazarsfeld (Stony Brook University), Hélène Esnault (Freie Universität Berlin, University of Copenhagen), Dan Abramovich (Brown University), Benoît Claudon (Université de Rennes), and Philippe Eyssidieux (Université Grenoble-Alpes). For more information, please see the website for the program: https://ism.uqam.ca/Kahler/en/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

In this project, Professor Ming Xian of the Department of Chemistry at Brown University is investigating novel reactions of S-nitrosothiols (RSNO). The goal of this research is to develop useful synthetic methods and chemical tools based on RSNO compounds. Synthetic methods developed in this study could be used for the advanced manufacturing of challenging heteroatom containing compounds for medicinal applications. The project lies at the interface of organic chemistry and chemical biology. It provides excellent training opportunities for both undergraduate and graduate students. S-Nitrosothiols (RSNO) are known mediators in nitric oxide (NO)-based signaling transduction. As highly unstable molecules, the reactions of RSNO, particularly those that could be used for synthesis and chemical biology, are still limited. Their preliminary studies indicate that RSNO are useful synthons for the construction of sulfur-, nitrogen-, and/or oxygen-containing products. In this project, Professor Ming Xian and his team will test if RSNO and their reaction intermediates with different reagents could be used to prepare a variety of valuable products including heterocyclic compounds and peptides. These studies will advance the knowledge of RSNO and enable further understanding of their potential in chemistry and chemical 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.

NSF Awards · FY 2026 · 2026-07

Over the past two decades, efforts to reduce waste, reuse materials, and design products to last have become more important in how we approach business, design, and engineering. These ideas are foundational to a circular economy, wherein landfill waste is significantly reduced, and central to global efforts to fight climate change. As such, teaching engineering students the concepts of sustainability is now critical, yet many college programs focus mostly on theory and hypothetical scenarios. Hands-on experiences that help students connect classroom learning to real-world challenges are distinctly lacking. These endeavors can include working with local community partners, collaborating with businesses, or using creative spaces like makerspaces. This project proposes an approach that addresses these deficits: embedding sustainability into core courses using faculty expertise that already exists within the school, developing a new course that pairs undergraduate students with community partners and industry to tackle real-world challenges, and transforming how students learn about sustainability outside of the classroom, such as in makerspaces and independent research projects. Ultimately, helping students see themselves as engineers and sustainability problem solvers is critical to the future engineering workforce. As design and sustainability become increasingly integral to the engineering profession, we must support students in building a strong sense of identity in this field so they are prepared to solve sustainability challenges for and with society moving forward. This research will be aligned with the NSF-Lemelson Initiative on Environmental and Social Sustainability in Engineering Education by embedding critical Engineering for One Planet skills into the core curriculum for design engineering and applying the skills through collaboration with community partners and regional industry. The overarching goal of this research is to activate our teaching team’s collective expertise in differing facets of the Engineering for One Planet (EOP) framework to develop a co-teaching paradigm for the design engineering undergraduate curriculum at Brown University. The objectives are to (1) design and implement a mini-module framework that activates co-teaching to infuse sustainability across the design engineering curriculum, (2) investigate how students identify as sustainable design engineers through formal and informal educational experiences, and (3) characterize and identify opportunities to build a co-teaching community to enrich faculty teaching, community, and research experiences. We will accomplish these objectives through the integration of mini-modules into existing courses, building a new cornerstone course that partners engineering students with local industry and community partners, and by developing student-led independent research experiences with partnering faculty. In addition to these formal education strategies, we will work to transform our campus makerspace into an informal sustainable engineering facility, thus reaching beyond course instruction. We will utilize interviews, focus groups, surveys, and course evaluations to measure impact and guide iteration. This project will be led by faculty and senior personnel with expertise in design, engineering, mathematics, management, and the learning sciences. The expected outcomes are: (1) a framework that demonstrates that co-teaching through a mini-modules initiative is a scalable, cost-neutral model for embedding sustainability across the engineering curriculum while enhancing pedagogical collaboration and student engagement; and (2) a blueprint to transforming makerspaces into a sustainability ‘living lab’ that cultivates community-based innovation, strengthens cross-institutional networks, and positions these spaces as a regional hub for sustainable design education. This project is funded by the Division of Engineering Education & Centers with additional support provided by The Lemelson Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The field of graph algorithms aims to understand how to efficiently construct and use networks, as well as what properties make for a good network. This project aims to advance graph algorithms by way of a new theory of “length-constrained” graph algorithms. Length constraints are a way of encouraging communication to happen quickly in networks. However, most classic graph algorithms do not work in the presence of length constraints. This project aims to build new graph algorithms that work in the presence of length constraints and, in doing so, provide new algorithms for solving classic problems as fast as possible. Results from this project will be made broadly accessible through peer-reviewed publications, research tutorials, new survey papers and new graduate-level courses, with all material made available to the general public. The project aims to develop the proposed solutions by leveraging the theory of graph cuts and metric embeddings. Firstly, the project will develop a new approach to length-constrained cuts as the foundation of new techniques in iterative linear programming methods under length constraints. Second, it will develop a theory of metric embeddings for length-constrained distances using a new paradigm for embedding graphs into spanning trees. Additionally, the project will build close-to-linear-time algorithms for classic flow problems, such as min cost multi-commodity flow, by leveraging new techniques in length-constrained expander decompositions and flow emulators. Particular focus will be for established problem domains from non-length-constrained settings, such as directed Steiner tree, hop sets, and embeddings of graph distances into trees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The workshop on Arakelov geometry and Diophantine geometry is a one week workshop to be held August 3-7, 2026 at Brown University. Following immediately after the International Congress of Mathematicians (ICM) 2026 in Philadelphia, the goal of the workshop is to bring together experts in active and influential branches of arithmetic geometry to initiate conversation and collaboration and to make progress on central problems in number theory. This award will support the travel and accommodation for speakers and participants, with priority given to graduate students, postdoctoral researchers, and early career mathematicians. Information about the conference may be found at the website: https://ziyangjeremygao.github.io/Conference/Brown2026/Brown2026.html Diophantine geometry, that is, the study of rational solutions to polynomial equations has been a central topic of number theory. Many of its major problems, including the Mordell conjecture and the Birch and Swinnerton-Dyer conjecture, have shaped the foundation of arithmetic geometry. Arakelov geometry, the study of intersection theory in an arithmetic setting, has been a powerful tool behind many key breakthroughs in arithmetic geometry. In the past decade, these fields have witnessed significant growth and activity. In particular, tools from these two directions come together in many recent important works, such as the resolution of the Uniform Mordell–Lang Conjecture, the proof of the Unbounded Denominators Conjecture, the study of the Beilinson–Bloch height on algebraic cycles, and the progress towards the Uniform Boundedness Conjecture on rational torsion points of abelian varieties. The state of the art within the subject will be highlighted through 18 plenary talks and 5 shorter talks covering a broad array of topics in Arakelov geometry, Diophantine geometry, and related themes in number theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Pulmonary Arterial Hypertension (PAH) is a vasculopathy with no cure. Current medications for PAH delay disease progression by acting as pulmonary vasodilators; however, they are incapable of arresting or reversing the pulmonary vascular lesions that occur in this disease. Over the years, evidence of perivascular inflammation and metabolic dysfunction has been documented in PAH, but we have a limited understanding of how they contribute to vascular remodeling responses in this disease. Therefore, this research proposal seeks to elucidate the immune and metabolic dependent processes that occur in PAH specifically in relationship to the non-enzymatic protein chitinase 3-like-1 (CHI3L1). This moiety is the prototypic glycoprotein of the glycosyl hydrolase gene family and has been shown to mediate inflammation, injury, and tissue remodeling responses in various inflammatory conditions and vasculopathies. Our previous studies demonstrated that CHI3L1 levels are elevated in mouse models of Pulmonary Hypertension (PH) and that high levels of CHI3L1 are associated with worse hemodynamics in PH patients. Taking from the studies highlighting perivascular inflammation and metabolic dysfunction as hallmarks of PAH development, we sought to determine the role of CHI3L1 in these processes. Preliminary studies in our lab demonstrated that macrophages produce CHI3L1 abundantly and we also showed that CHI3L1 knockout and CHI3L1 transgenic mice have different metabolic profile signatures. At present, the question of what is CHI3L1’s function during vascular remodeling responses in PAH has not yet been investigated. As such, this research proposal will interrogate the role of CHI3L1 in macrophage metabolism and vascular remodeling. I hypothesize that CHI3L1 impairs metabolism in macrophages and facilitates macrophage pro-remodeling effects in PAH. In aim 1, I will explore the relationship between CHI3L1 and impaired metabolism in lung macrophages by a) identifying how CHI3L1 expression affects the bioenergetic profiles of bone-marrow-derived macrophages isolated from mice, and b) determining whether CHI3L1 influences dysregulated metabolism in primary lung alveolar and interstitial macrophages. In aim 2, I will a) determine the role of macrophage-specific CHI3L1 in endothelial-mesenchymal transition in vitro and how modulating CHI3L1 expression in macrophages affects vascular remodeling in vivo and b) evaluate the therapeutic effects of a CHI3L1 neutralizing antibody delivered to sugen/hypoxia-treated mice via a nanoparticle-based delivery system. The proposed research will be supported by Dr. Yang Zhou with the co-sponsorship of Dr. Alan Morrison. Additional support will be provided by Dr. Hongwei Yao, Dr. Olin Liang, and Dr. Jyothi Menon, for the execution of the metabolism, vascular remodeling, and therapeutics studies, respectively. The PI will acquire robust research skills in animal studies, immunology, metabolism, and therapeutics while gaining a fundamental understanding of how dysregulated immunity and metabolism lead to pathogenesis.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract The quiescence-senescence continuum hypothesis posits that cellular quiescence, a reversible state of cell cycle arrest, and senescence, an irreversible form of arrest, exist along a dynamic continuum rather than as distinct, mutually exclusive states. In this model, quiescent cells, which typically enter a dormant phase in response to environmental cues such as nutrient deprivation or growth factor withdrawal, can transition into senescence if exposed to chronic stress or DNA damage. This hypothesis suggests that cellular states are fluid and context-dependent, where the same molecular pathways involved in maintaining quiescence may be co-opted or altered to induce senescence. A key difference between the two is that while quiescent cells retain the ability to re-enter the cell cycle when conditions improve, senescent cells adopt a permanent arrest accompanied by the secretion of pro-inflammatory signals, contributing to aging and tissue dysfunction. Understanding this continuum has profound implications for aging research, as it highlights the potential reversibility of cellular fates and the fine molecular balances that determine a cell’s transition between growth, rest, and aging. In this project, we will investigate which hallmarks of senescence are shared with quiescent cells, explore the quiescent cell secretome, and examine various quiescent states that may be linked to stress-induced changes associated with aging and disease in tissue-resident quiescent cells.

NIH Research Projects · FY 2026 · 2026-06

Project Summary This revised proposal focuses on disease caused by dominant mutations in ATP1A3, a Na+/K+-ATPase pump that plays pivotal roles in maintaining excitable cell function. ATP1A3 missense patient alleles cause various ATP1A3 diseases; here we focus on Alternating Hemiplegia of Childhood (AHC). Critical questions in the AHC field include how do patient alleles alter protein function, what are the molecular and cellular consequences driving disease-associated dysfunction, and which proteins and pathways should be targeted for therapy development? The proposed studies are focused on understanding, at a molecular level, how ATP1A3 patient alleles cause disease and finding therapeutic targets. In Aim 1 of this R21 proposal, we use newly developed C. elegans AHC models and a functional genetic approach to identify conserved suppressor genes. In Aim 2, assess which cellular mechanisms are relevant to AHC pathophysiology. In Aim 3 we undertake a genetic screen to identify gene whose perturbation suppresses defects in a C. elegans AHC model. Combined these studies will 1) increase our understanding of how common AHC alleles perturb cellular function, leading to disease, and 2) swiftly identify genes and pathways that can be targeted for therapy development.

NSF Awards · FY 2026 · 2026-06

A rapid proliferation of data-intensive and autonomous applications is redefining the limits of modern computing systems, as the growth of computational scale and complexity outpaces the performance gains achievable through hardware scaling alone. This widening gap is driving a shift toward increasingly integrated and scalable heterogeneous architectures, in which diverse, specialized chips operate in coordinated and efficient ways. However, programming and designing these complex systems remain difficult, time-consuming, and prone to errors. This project addresses this critical challenge by developing a unified and easy-to-use framework that empowers domain experts to design and program these advanced computing systems efficiently. The resulting advances will support real-world applications such as autonomous physical systems, adaptive intelligent agents, and advanced healthcare technologies, enabling more efficient, responsive, and accessible computing capabilities. The project will create open-source tools, datasets, and benchmarks that will be shared through workshops, tutorials, and demonstrations. The research will be integrated into new curricular materials, support undergraduate mentorship and graduate training through hands-on, community-shared tools, and strengthen workforce preparation in microelectronics, computer science, and engineering. The project will establish a scalable high-level synthesis and programming framework that enables verified and efficient integration of heterogeneous computing systems across diverse hardware backends. This project consists of three tightly integrated research thrusts. First, it will construct a multi-level intermediate representation infrastructure that supports extensible compilation across field-programmable gate arrays, graphics processing units, tensor accelerators, processing-in-memory architectures, and application-specific integrated circuits, enabling reusable abstractions and flexible backend targeting. Second, it will embed formal verification throughout the compilation flow to ensure transformation correctness across abstraction levels and introduce microarchitecture-aware dataflow validation to enforce architectural constraints such as buffering and pipelining behavior. Third, it will develop a dataflow-aware system-level design space exploration framework that co-optimizes computation, data movement, and task partitioning across heterogeneous accelerators, enabling coordinated dataflow execution rather than isolated backend optimization. Together, these advances will enable scalable and verifiable system synthesis for next-generation heterogeneous computing platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

While natural intelligence often learns sophisticated skills through simple visual observation, current artificial intelligence (AI) systems largely lack this ability. Most modern AI systems require massive amounts of text or human-labeled data to understand the world, a dependency that limits the ability of machines to perform complex physical tasks that are difficult to describe in words. To address these limitations, this project creates a scientific framework that allows machines to learn directly from passive observations and active interactions with the physical environment. The project moves toward a new paradigm where video serves as the primary medium for machine intelligence, enabling autonomous systems to plan and act by observing videos of human and robotic behavior. By fostering the development of more capable and helpful autonomous agents, the project serves the national interest in AI leadership and technical workforce development. These efforts include specialized course design, cross-disciplinary collaborations, and mentorship programs that support students from high school through the doctoral level. This research establishes a new paradigm for machine intelligence centered on the concept of adaptable video blueprints. These blueprints function as a representation that allows an agent to translate a visual experience into a sequence of physical actions generalizable across diverse tasks, environments, and embodiments. Three integrated thrusts drive the technical approach. The first thrust develops visual planners that utilize video generation models to causally predict the future states necessary to reach a target goal. The second thrust focuses on inverse dynamics to map these predicted visual sequences into specific motor commands. The third thrust implements an automatic self-improvement loop, allowing the agent to refine its planning and execution through continuous experience and adaptation. This award advances the fields of computer vision and AI by grounding visual generation in physical interaction and providing a scalable method for machines to acquire sophisticated skills with minimal human intervention. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROPOSAL SUMMARY Malaria remains a major global health burden. One malaria species, Plasmodium falciparum, is responsible for the vast majority of malaria morbidity and mortality, particularly in Africa, where resistance to frontline antimalarials are emerging. Copy number variation (CNV) is a key but understudied source of genetic variation in P. falciparum, influencing gene expression, drug resistance, and parasite fitness. While some drug resistance-associated CN variants have been described in Southeast Asian (SEA) populations, including in key drug resistance genes multi-drug resistance 1 (mdr1) and plasmepsin II/III, there is an unmet need to understand the prevalence of P. falciparum CNV globally and particularly in Africa. This project aims to support the career development of an MD-PhD student as he applies computational approaches to identify CNVs in African P. falciparum populations across the range of sequencing methods applied in malaria molecular surveillance (MMS), from targeted to whole-genome sequencing (WGS). This proposal will improve a prototype statistical method for P. falciparum CNV detection in WGS data, and apply this method to thousands of publicly available samples to develop a global catalog of CNV across malaria populations (aim 1). This will be followed by development of novel Bayesian hierarchical methods for quantification of CNV in molecular inversion probe (MIP) sequencing, and application of this method to samples from across Africa to describe the spread of both novel and previously described CNV across a range of parasite population structures (aim 2). By elucidating the role of CNVs in malaria drug resistance evolution, this project aligns with broader scientific and public health efforts to combat resistance towards malaria elimination. This interdisciplinary network will allow for scientific gains in the understanding of copy number variants in P. falciparum and the selective forces acting on them. In addition, the student’s diverse set of scientific and clinical mentors in an academically rigorous computational biology department and medical school will support his training at the intersection of genomics, population genetics, and infectious disease research. This fellowship will prepare the trainee for a promising career as a physician-scientist pathologist exploring the population genetics of drug resistance in parasites and other diseases of their human hosts.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Tauopathies are a group of devastating and incurable age-related neurodegenerative disorders that are neuropathologically defined by toxic species of the protein tau. Therapies directly targeting tau have been largely ineffective, and it is therefore essential that we develop a better understanding of the downstream mechanisms of tau that may be directly driving neurotoxicity. A recently discovered causal mediator of toxicity in tauopathies is the activation of retrotransposable elements (RTEs). Tau causes RTEs to become aberrantly expressed in neurons and have been previously shown to drive neuroinflammation via activation of the innate immune system. Additionally, a new key mediator of neuroinflammation in tau pathology is the NLRP3 inflammasome. Studies have demonstrated that RTE-mediated neuroinflammation can be suppressed by repurposing NRTIs, a class of drugs used to treat retroviral infections such as HIV. The NLRP3 inflammasome is also inhibited by NRTIs. Despite this, the effects of NRTIs in settings of tauopathy remain insufficiently characterized. I will draw on years of molecular biology experience in my sponsor’s laboratory to further our understanding of RTE activation in the context of tauopathies. In other disease contexts, aberrantly expressed RTEs are released from cells. Given the neuroinflammatory sequela of RTEs, it is essential to determine if neurons also release RTEs, as this will likely unveil new therapeutic approaches for tauopathies. Additionally, inhibition of the NLPR3 inflammasome by NRTIs in the context of tauopathy has never been studied. Based on this, I hypothesize that NRTIs reduce tau-mediated neuroinflammation by inhibiting RTE expression-driven inflammasome activation. To test this hypothesis, I will examine the consequences of RTE activation by combining advanced modelling of human-derived iPSCs with molecular biology techniques. In Aim 1, I will identify which RTE products are elevated by pathogenic tau and determine if they are secreted by neurons by using confocal microscopy and western blotting. In Aim 2, I will characterize the effects of NRTIs on RTE-mediated activation of the innate immune system through cellular viability assays and culturing microglia and astrocytes in media derived from pathogenic tau-affected neurons. My long-term goal for this award is to transition into a career as a physician-scientist studying and treating age-related neurodegeneration. The sponsor team will share their expertise in molecular biology research, clinical evaluations, and career development. Further training will be acquired from workshops and conferences, both at and outside of Brown University, in addition to national and international conferences. Overall, this proposal will provide research and clinical training in addition to professional development.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY The United States continues to face a devastating and evolving overdose crisis, with more than 80,000 lives lost annually. While preliminary data from 2024 indicate a decline in national overdose mortality from pre-pandemic highs, the composition of unregulated drug supplies is growing more complex and complicates overdose mitigation efforts. Fentanyl continues to dominate supplies, while non-opioid sedatives and local anesthetics are increasingly detected nationally. In this context, many states and cities have implemented novel overdose prevention programs that include robust overdose risk reduction and wrap-around services, including real-time response to overdose events, naloxone provision, infectious disease testing, drop-in services such as food and laundry, and referrals to healthcare and social support services, among other wrap-around overdose risk reduction services. Global evidence indicates that these comprehensive programs reduce overdose mortality and improve access to care. However, prior research has primarily focused on individual-level predictors of program uptake and have failed to capture the influence of drug use networks as a key determinant of engagement. Drug use is often embedded in social networks, and these networks play a critical role in shaping overdose risk, risk reduction practices, and service utilization. Despite growing recognition of the influence of social networks on health behaviors, no studies have examined how characteristics of drug use networks impact overdose prevention program utilization. This pilot study addresses this critical knowledge gap by investigating how drug use networks influence program engagement among people who use unregulated drugs in Rhode Island. Nested within an ongoing evaluation of Rhode Island’s first overdose prevention program, this study will enroll 100 participants to complete a structured egocentric network survey. The assessment will collect detailed information on each participant’s drug use network, including overdose exposure, network members’ risk and protective behaviors (e.g., naloxone carriage, drug checking), and social tie attributes (e.g., emotional closeness, social support, contact frequency, demographic homophily). The Specific Aims of this study are to: 1) Assess whether greater overdose exposure within drug use networks is associated with greater overdose prevention program utilization; and (2) Examine whether the risk and protective behaviors of network members influence program utilization. We will also explore whether social tie attributes moderate these associations. This study is highly innovative because it represents the first investigation globally to examine the influence of drug use networks on overdose prevention program utilization. This pilot study will establish feasibility and proof of concept for capturing detailed egocentric network data among overdose prevention program clients—an essential step to inform strategies that leverage peer networks. Ultimately, this work is expected to identify scalable opportunities that harness peer influence to expand the reach, uptake, and effectiveness of overdose prevention programs, both in the US and globally

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY: “Balancing Iron and Manganese Homeostasis in Hereditary Hemochromatosis” Iron (Fe) and manganese (Mn) are essential for health yet toxic in excess. Fe is regulated largely by dietary absorption, while Mn is regulated largely by hepatobiliary excretion. Our understanding of Fe homeostasis has benefited greatly from studies of inherited defects in Fe transport. Our understanding of Mn homeostasis was limited in the past by a paucity of known inherited defects in Mn transport. This changed in 2012-16 with the first discoveries of inherited causes of Mn imbalance in patients with mutations in SLC30A10, SLC39A14, and SLC39A8. These membrane proteins dictate systemic Mn levels by mediating hepatobiliary and intestinal Mn transport. We previously reported that SLC30A10 deficiency results in Mn excess because SLC30A10 exports Mn from hepatocytes into bile and from enterocytes into the lumen of the gastrointestinal tract. More recently, we demonstrated that, despite impaired Mn excretion, Slc30a10-deficient mice develop increased Mn absorption. Intriguingly, this increased absorption is dependent upon divalent metal transporter 1 (DMT1) and ferroportin (FPN), two factors essential for dietary Fe absorption. While SLC30A10 deficiency is rare, upregulation of Fe absorption pathways and Mn excess are also observed in dietary Fe deficiency, the most common nutritional deficiency. In this condition, Mn excess is attributed to physiologic upregulation of DMT1 and FPN leading to increased Mn absorption, although this has yet to be tested. However, upregulation of Fe absorption does not always result in Mn excess. Mn excess is not observed in hereditary hemochromatosis, a common disease of Fe excess due to deficiency in hepcidin, an inhibitor of Fe absorption. Using a mouse model of hereditary hemochromatosis due to deficiency in hemojuvelin (Hjv), a signaling receptor essential for hepcidin expression, we recently demonstrated that hereditary hemochromatosis does not result in Mn excess because intestinal Slc30a10 normalizes Mn absorption. The goal of this application is to establish fundamental molecular mechanisms of mammalian Mn homeostasis by exploiting the observation that Fe deficiency results in Mn excess while hereditary hemochromatosis does not. We hypothesize that Mn absorption is not increased in hereditary hemochromatosis because intestinal SLC30A10 and SLC39A14 excrete absorbed Mn back into the gastrointestinal tract and because excess Fe outcompetes Mn for transport from enterocytes into blood. To test this hypothesis, we will establish the role of Dmt1, Fpn, Slc39a8, Slc39a14, and Slc30a10 in Mn homeostasis in Hjv-/- mice (aim 1), determine the impact of Fe deficiency on Mn absorption in Hjv-/- mice (aim 2), and determine the impact of Mn excess on disease severity in Hjv-/- mice (aim 3). This work will advance our understanding of the fundamental mechanisms by which the body simultaneously maintains homeostasis of Fe and Mn, two essential yet potentially toxic metals that can share transport pathways yet differ in abundance—Fe levels are much higher than Mn levels in the healthy human body. Our results will be applicable to rare conditions such as SLC30A10 deficiency and common conditions such as hereditary hemochromatosis and Fe deficiency.

NIH Research Projects · FY 2026 · 2026-05

Traumatic brain injury (TBI) affects an estimated 1.7 million civilians in the US each year at an estimated cost of $76.5 billion in the U.S. alone. Due to the complex nature of the pathophysiological events there remains an unmet medical need for therapeutics that prevent secondary neuronal damage. Brain-derived neurotrophic factor (BDNF) through activation of its high affinity receptor, TrkB, is a key player in promoting learning, and therapeutic strategies to enhance BDNF signaling after TBI facilitate recovery. PSD-95 is a TrkB associated synaptic scaffolding protein required for BDNF-induced signaling. We developed a high affinity and proteolytic stable macrocyclic compound, D-Syn3, targeting the PDZ3 domain of PSD-95. D- Syn3 increases the recruitment of PSD-95 to TrkB to augment pro-survival BDNF signaling. In a series of preclinical studies in TBI rodent models, we found that D-Syn3 rapidly penetrates the CNS to reduce post- injury neuronal death, cis P-tau, gliosis and demyelination, to improve long-term neurobehavioral outcomes. Taking advantage of the distinct pathology in the Controlled Cortical Impact (CCI) and repetitive moderate closed head injury (rmCHI) models, we will fully evaluate the efficacy of our novel BDNF-targeted therapy, which has demonstrated promise in both models. We will determine the efficacy of D-Syn3 to mitigate injury and behavioral deficits using two complementary TBI models: A contusive control cortical impact (CCI) model (Aim 1) and a concussive closed head injury (CHI) model (Aim 2). For each model, we will identify the optimal dosing regimen of D-Syn3 to mitigate pathology. Using this optimal regimen we will evaluate efficacy to prevent impairment in learning/memory, motor function, and depression-like behavior. Given the pilot efficacy of D-Syn3, we anticipate that successful completion of this work will lead to a new therapeutic for TBI.

NSF Awards · FY 2026 · 2026-05

This long-term program seeks to understand how food webs respond to environmental variability in semi-arid ecosystems, where rainfall pulses drive dramatic but short-lived bursts of plant growth. Drawing on more than 37 years of continuous experimental research, the team will investigate how small mammal populations respond to rainfall pulses and how their foraging behaviors shape the structure and function of the ecosystem. Better understanding how droughts, extreme rainfall events, and shifting resource availability shape populations and communities will enable informed responses to critical challenges related to desertification and ecosystem management. The project will generate broadly applicable insights into how pulsed resources shape consumer behavior and organismal interactions across arid ecosystems worldwide. In addition, the project supports education and workforce development through international collaborations between U.S. and Chilean institutions, hands-on training opportunities for students and early-career researchers, and outreach programs that engage audiences across career stages. These efforts will help prepare a skilled workforce equipped to excel across multiple career paths in STEM with the use of advanced data analysis using machine learning and hands-on experiences in metabarcoding and high-thoughtput analysis, thereby advancing the national interest in science, ecosystem management, and societal well-being. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence The project will integrate long-term field data with complementary analytical approaches, including stable isotope analysis and dietary DNA metabarcoding, to quantify how small mammals utilize distinct “fast” (forb-based) and “slow” (shrub-based) energy channels across both short-term seasonal cycles and longer-term interannual climate variability associated with El Niño-Southern Oscillation (ENSO). By combining isotopic and genetic methods, the research will generate detailed, individual-level dietary profiles that reveal patterns of trophic niche partitioning, dietary specialization, and temporal shifts in resource use across multiple trophic levels. These fine-grained empirical data will be used to parameterize and test dynamic, process-based models that link resource pulses, consumer population dynamics, and foraging behavior to emergent patterns of food-web stability and temporal variability. The project will also incorporate advanced data science approaches, including machine learning techniques, to identify nonlinear relationships and improve predictive capacity in this highly variable system. Methodological innovations in biotechnology, such as high-throughput sequencing and isotope-enabled analytics, coupled with open-access data libraries that are required to support both taxonomic and functional interpretations, will provide unprecedented resolution of the structure of food webs. Associated training programs will include hands-on course-based undergraduate research experiences in dietary metabarcoding, stable isotopes, bioinformatics, and reproducible practices in data science. Together, these approaches will allow the team to disentangle the relative roles of abiotic forcing and biotic interactions in structuring ecological communities. The resulting models and datasets will provide a robust framework for predicting how ecosystems respond to increasing climatic variability and will contribute broadly to ecological theory, long-term ecological research, and the development of transferable tools for analyzing complex, data-rich environmental 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 2026 · 2026-05

This award supports participation of US based mathematicians in the conference ``Geometry, Dynamics, and Computer-Assisted Proofs" at Heidelberg University June 10--12, 2026. The conference will bring together researchers and students from the fields geometry, dynamics, and geometric group theory with mathematicians who have been at the forefront of computer-assisted proofs in these areas. The conference will facilitate the exchange of ideas, the exploration of computer-assisted proofs and promote collaboration between experts in fields. The conference will also reinforce cooperation between the US and European mathematical communities. The fields of geometry, dynamics, and geometric group theory have become increasingly intertwined over the past few decades, with deep connections emerging between their fundamental structures and techniques. At the same time, computer aided proofs have grown into a powerful tool in mathematical research, beginning with Haken's groundbreaking resolution of the four-color problem. This conference seeks to bring together researchers and students from these fields to explore their rich interplay and the ways in which computational methods can advance our understanding of key problems. The conference will feature 15 research talks, lightening talks for junior researchers, and breakout sessions for discussing new research directions. This will promote the development of graduate students and postdoctoral researchers and strengthen the networks that connect mathematicians in a wide range of fields. It may also inspire the broader application of computer-assisted research and improve the infrastructure that this requires. The URL for the conference website is https://sites.google.com/view/rich-problems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

ABSTRACT The overall aim of this application is to develop a new class of antimalarials targeting PfGARP for use as parenteral therapy for severe falciparum malaria in direct response to MMV’s TPP-1-“For severe malaria, a parenteral formulation of a single fast-acting TCP-1 would be appropriate” (asexual stage agent) 1. Plasmodium falciparum is a leading cause of morbidity and mortality in developing countries, infecting hundreds of millions of individuals and killing almost one-half of a million children in sub-Saharan Africa each year. The spread of parasites resistant to the artemisinin family of compounds threatens recent progress achieved by antimalarial campaigns and underscores the urgent need to identify new anti-malarial drugs. In recent work 5, we discovered PfGARP, a vaccine candidate found only in P. falciparum. PfGARP is located on the exofacial surface of iRBCs and antibodies to the highly invariant carboxyl terminal of PfGARP kill parasite in culture in the absence of immune effector molecules (complement) or cells- thus the remarkable anti-parasite effect of anti-PfGARP results from antibody binding alone. This is further supported by the killing effect of recombinant mAb and its rec monovalent Fab that target aa 443-459 of PfGARP. The Scientific Premise of this application that PfGARP is a high value druggable target is based on: 1) its surface expression on iRBCs, 2) its absence of amino acid homology with host proteins, 3) its absence of significant sequence variation in over 3,000 field isolates sequenced, 4) the requirement for PfGARP for in vivo survival, and 5) the ability of antibody binding to PfGARP to kill essentially all parasites within 12-24 hours. In the current proposal, we will: 1) conduct a targeted, high-throughput drug screen to discover drugs which mimic the lethal activity of antibodies recognizing PfGARP, 2) optimize and down select these candidates, and 3) validate these new drug candidates in a humanized mouse model of P. falciparum.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract As the frequency, prevalence, and severity of extreme weather events and disasters increase, the long- term and life course consequences on the older population in the US of experiencing cyclones, heat waves, droughts, floods, and wildfires grows in terms of its significance for research, policy, and population well-being. Of particular urgency is developing a better understanding of the effects of disasters exposures and post-disas- ter experiences on disparities in well-being over the older life course years, on levels and differentials in health and mortality, and for policies aimed at mitigating long-term consequences of disasters. A predominant focus of the literature addressing disaster effects on health and well-being is the period in the immediate aftermath of the event. Much is known about short-term effects on mortality, mental health, displacement, living arrange- ments, and recovery. For subsequent post-disaster years, however, there is a dearth of data sources, and hence of population-level empirical research, on individual outcomes, especially among the elderly population. Key issues that remain uninvestigated include whether disaster effects are fleeting, whether they have long- term negative consequences, or whether individuals may actually be better off. This project will build on a ma- jor investment we have made to create a linked data source for addressing the medium- and long-run effects of Hurricane Katrina on the pre-disaster population of New Orleans. We will use these new linked data to exam- ine living arrangements, well-being, and mortality among the older population of New Orleans aged 60-plus years in the period following Hurricane Katrina. Using within-New Orleans comparisons along with an external counterfactual comparison group, we will address two aims. First, we will examine how exposure to Hurricane Katrina for the older New Orleans population affected long-term trajectories of living arrangements and resi- dential neighborhood attainment compared to the counterfactual comparison group. Second, we will examine post-Katrina mortality and disability disparities within the older New Orleans population by race, pre-Katrina social and economic characteristics, hurricane impact, and post-disaster experiences. This is a novel and ex- ploratory project to establish a general and broadly-applicable approach to studying the effects of disasters on the older population using linked census and administrative data. It will also make significant substantive con- tributions by combining stress and social vulnerability theories to generate and evaluate testable hypotheses about the effects of pre-disaster socioeconomic resources and disaster exposure on key health and well-being outcomes among the older New Orleans population. The project will set the stage for future replications for other disasters as well as for a variety of enhancements and extensions in order to answer pressing questions on the consequences of disasters on the health and well-being of the older US population.

NIH Research Projects · FY 2026 · 2026-04

Neural circuits allow animals to gather various types of sensory information from the complex environment and integrate this information to produce the appropriate behavioral responses. To decide whether to ingest potential food substances, animals must discriminate between nutrients and toxins. To this end, they integrate sensory information, such as taste, smell, texture, temperature, and visual cues, with internal states, such as hunger and satiety. It is well established that the integration of taste and smell, perceived as flavor in humans, is especially important for food discrimination. However, the precise points of integration between the taste and smell circuits remain unknown in humans due to the complexity of the nervous system. Studies monitoring feeding behavior upon smell stimulation in the fruit fly, Drosophila melanogaster, suggest that the taste and smell circuits also integrate in the fly. Since the neural circuits in fruit flies are simpler than those in humans, flies are an ideal organism for evaluating the anatomical and functional connections between taste and smell. Our laboratory has developed trans-Tango, a method for neural circuit mapping and manipulation in fruit flies. Using trans-Tango, we mapped the first and second-order neurons in the taste and smell circuits, showing that gustatory receptor neurons, which detect tastants, and olfactory receptor neurons, which detect odors, relay information to gustatory and olfactory projection neurons, respectively. Some of these projection neurons target the same higher-order brain areas, suggesting the possibility that shared neurons exist that integrate sensory inputs from both systems to influence feeding behavior. This proposal takes a two-pronged approach to investigate the integration of the gustatory and olfactory circuits. First, I will test how olfactory inputs affect feeding by activating, or silencing, olfactory projection neurons tuned to food-derived odors. In these studies, I will use the OptoPAD paradigm to measure feeding. I hypothesize that activating neurons tuned to attractive odors would enhance feeding, while activating those tuned to aversive odors would suppress it. Second, I will identify neurons in the lateral horn that receive inputs from both gustatory and olfactory projection neurons and integrate these inputs to produce the appropriate feeding responses. To this end, I have been developing trans-Tango(hub), a tool for identifying circuit nodes of integration. My experiments will determine whether these nodes maintain the valence of the stimuli. Further, since the gustatory and olfactory systems in insects and mammals are functionally homologous, identifying the mechanisms of this multisensory integration in fruit flies will provide insight into how the perception of flavor is formed in humans. This is crucial for understanding the pathologies associated with olfactory deficiencies, such as anosmia and hyposmia, and gustatory deficiencies, such as ageusia. Finally, this research program is at the core of a training plan that includes activities to develop professional skills for preparing Angel Okoro for a career in academic research.

NSF Awards · FY 2026 · 2026-03

Shell programming, the glue that holds modern computer systems together, is as prevalent as ever—steadily in the top 10 most popular programming languages in widespread use. It is also quite complex, due to the structure of shell programs, their use of opaque software components, and their complex interactions with the broader environment. As a result, even when exercising an abundance of care, shell developers discover devastating bugs in their programs during or after their execution—when it is too late to reverse any of their unintended effects. Bugs in these applications therefore affect—often with disastrous outcomes—engineers, scientists, and end-users alike: production bugs in industry platforms have resulted in the deletion of important user data. This project brings together a team of experts to develop fully automated, ahead-of-time program analysis techniques for checking the correctness of, and catching bugs in, shell programs before their execution. Drawing on techniques from programming languages, type systems, and program analysis, the project will benefit both developers and end-users to automatically catch and prevent undesirable or even catastrophic events. Of particular interest are the techniques proposed around the interaction of shell programs with the file system and the broader environment in which they execute. Beyond mere prevention, such techniques provide the foundations to precisely diagnose bugs and guide developers to implement effective fixes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-02

The HIV research agenda in Kenya and sub-Saharan Africa (SSA) is far-reaching. Much of it has been developed and carried out in partnership with institutions in the United States, Canada, and Europe, leading to gains in local capacity in terms infrastructure and professional development of scientists and physicians. One important domain where capacity building still lags behind is biostatistics and quantitative sciences: There is a shortage of African biostatisticians available to work on international research teams, and many HIV research programs in SSA continue to rely on their partners from the Global North for implementation of advanced methods for design and analysis. Although the past 5 years have seen substantial advances in research and educational capacity, many leading universities in SSA are only beginning to develop graduate-level training in statistics for health research. Statistics training in Kenya has typically been theoretical in nature, with applied work focusing primarily on biometry and agricultural statistics. Moi University, the second largest in Kenya, is typical in this regard. During Phase 1 of this training program, Moi began the process of implementing a graduate biostatistics curriculum. However there are only two faculty at Moi with PhD in biostatistics, highlighting the need for expansion of expertise among current faculty and development of potential new faculty members. At the College of Health Sciences, there continues to be high demand for masters-level statisticians to engage in externally funded research. Finally, the small number of African statisticians with advanced training working in HIV research centers in SSA tend to be geographically isolated from their peers, limiting opportunities for professional development and exchange of ideas. The proposed training program will respond to these needs by launching Phase 2 of a partnership between Moi and Brown Universities. The program will expand research and curricular capacity in HIV-related biostatistics and advanced quantitative methods at Moi and lay the foundation for sustainable growth in this field by accomplishing the following Specific Aims: (1) provide masters-level training at Brown (2 trainees, 2 years each), PhD training in a sandwich program at Moi and Brown (2 trainees, 4 years each), and postdoctoral training at Brown and Moi (2 trainees, 2 years each; (2) facilitate revision and expansion of the biostatistics curriculum at Moi through faculty development and faculty fellowships (4 trainees, 1 year each); (3) continue an annual workshop on advanced biostatistical methods at Moi that will offer short-term training and ongoing professional development activities for faculty members and professionals throughout SSA; and (4) lay the groundwork for a Center for Health Data Science on the campus of Moi University. This proposal builds directly upon the accomplishments and experiences from Phase 1 of the partnership, which provided training to 6 Kenyan statisticians; 4 masters students, 1 faculty fellow and 1 research fellow. Four of the six trainees are working at Moi or AMPATH in Eldoret, and one is pursuing a PhD in biostatistics.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY/ABSTRACT Cardiovascular disease is the leading cause of mortality worldwide, driven by factors such as hypertension, myocardial infarction, and genetic predispositions. Despite its prevalence, the molecular mechanisms underlying cardiac stress responses remain poorly understood. AIMP2, a core component of the multisynthetase complex (MSC), is implicated in protein synthesis regulation and cellular stress responses, but its role in maintaining cardiac homeostasis has not been elucidated. This project investigates the hypothesis that AIMP2 supports adaptive cardiac responses to stress by stabilizing MSC components and regulating protein synthesis. Aim 1 will assess the impact of AIMP2 on cardiac function through gain- and loss-of-function approaches, utilizing cardiomyocyte-specific AIMP2 knockout (AIMP2-cKO) mice and stress-induced rescue experiments. Aim 2 will explore the mechanistic role of AIMP2 in protein synthesis regulation, focusing on its influence on MSC stability, protein synthesis during cardiac stress, and functional rescue experiments in AIMP2-deficient cells. By integrating in vivo and ex vivo approaches, this research will provide critical insights into how AIMP2 modulates cardiac stress responses. The findings could uncover novel therapeutic strategies to mitigate heart disease progression by targeting the MSC and its regulatory components. The project will be conducted in the laboratory of Dr. Federica Accornero, a leader in post-transcriptional regulation of cardiac remodeling, providing a robust training environment in this emerging area of cardiac biology.