Brown University

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY Localization of mRNA and its on-site translation (localized translation) enables spatiotemporal control of protein synthesis in the cell. The mitotic spindle is a sub-cellular region where localized translation has been reported to occur, yet its mechanism of regulation is largely unknown in any model system. We previously reported a conserved DEAD-box RNA helicase, Vasa, as a promising factor that regulates localized translation on the spindle in the sea urchin embryo. At asymmetric cell division, Vasa asymmetrically localizes on one side of the spindle, which is critical for asymmetric translation and the formation of micromeres, a major signaling center in the embryo. The preliminary results suggest that the predicted Vasa's target mRNAs are maternally loaded. Further, transcription is scarce during early embryogenesis. Therefore, we hypothesize that Vasa recruits and unlocks the maternal mRNAs for translation on the spindle, providing spatiotemporal control in protein synthesis during embryogenesis. To test this hypothesis, Question 1 will identify Vasa's target mRNAs and their functional contributions to mitosis. As a preliminary study, we computationally predicted several of Vasa's target mRNAs, which all appear to be involved in mitosis. We will identify how Vasa is critical for these targets' localized translation on the spindle and how they contribute to mitosis by real-time imaging of translation in developing embryos. Further, we will perform APEX-seq to comprehensively identify Vasa's target mRNAs on the spindle, followed by experimental validations of the obtained data. Question 2 will reveal the significance of Vasa-mediated asymmetric translation to embryonic development. Using optogenetics, we will deplete Vasa specifically at the 16-cell stage when micromeres are formed. We will process the resultant embryos at the 16-cell, morula, and blastula stages for single- cell (sc)RNA-seq, followed by experimental validations of the obtained data. The resultant data will identify what mRNAs Vasa asymmetrically recruits and translates in micromeres at the 16-cell stage and how its depletion blocks micromeres' signaling function, casing developmental defects at later stages. Question 3 will identify how Vasa selects its target mRNAs for localized translation. Preliminary results suggest that Vasa may recruit mRNAs with Guanine quadruplex (G4) secondary structure. Using both endogenous and synthetic G4-mRNA constructs, we will determine the essentiality of the G4 motif for Vasa's recruitment by real-time imaging of the mRNA. We will also reveal how the G4 motif impacts the timing and location of Vasa's target mRNA translation by real-time imaging of translation in embryos. Future steps include identifying Vasa's partners responsible for its granule assembly and how that controls its target mRNA's localized translation. We will also integrate the obtained omics data into our current prediction to identify Vasa's conserved targeting mechanism and function across organisms.

NSF Awards · FY 2025 · 2025-08

Variability in winds and sea surface temperature in the tropical Pacific produces the cycle of El Niño and La Niña events. These events produce both powerful storms and droughts, but their cycling is irregular and difficult to predict. The isotopic composition of oxygen preserved in fossil corals is one of the best tools scientists have for understanding how El Niño and La Niña have changed in the past. The composition is influenced by temperature but also by the salinity of seawater. Their respective influences must be separated to understand the magnitude of past El Niño and La Niña events and their impacts on the global water cycle. The proposed research combines analysis of rain and seawater, climate models, and fossil coral data to address this scientific question. The researchers will create a detailed map, or “isoscape,” showing how oxygen isotope values in seawater and rainfall vary across the modern tropical Pacific during El Niño and La Niña events. This map will be coupled with simulations from an ocean model. As a result of this work, scientists will have a more comprehensive understanding of how the intensity of past El Niño and La Niña events are recorded in coral oxygen isotope values. The proposed research will advance understanding of how El Niño-Southern Oscillation (ENSO)-related hydrologic anomalies may be inferred from oxygen isotope (delta-18O) values in tropical Pacific seawater. The researchers will utilize samples from long-running seawater and precipitation collection sites across the tropical Pacific and create a new isotope-enabled ocean reanalysis product for the Pacific basin at high spatial resolution. This combination will allow, for the first time, a direct assessment of the simultaneous isotopic anomalies associated with ENSO phases across multiple sites. The results will enable researchers to quantify the contributions of ocean circulation, atmospheric moisture balance, and precipitation delta-18O to seawater delta-18O values during different phases of ENSO. Results will also quantify seawater delta-18O and temperature influences on coral delta-18O values and reveal whether coral records of seawater delta-18O indicate stronger ENSO-related hydroclimate variability in recent decades, which is critical to inform planning for the impacts of ENSO events. The temporal continuity of the dataset, capturing ENSO phases across the basin, will enhance the community’s ability to interpret paleoclimate information of past tropical Pacific climate change. The project will support training for a graduate student, a postdoctoral scientist, and high school students. A new educational module on stable isotopes and the water cycle will be developed for 6th grade students. This module will involve hands-on learning and stable isotope analysis of local water samples, with assessment based on pre- and post-tests to measure educational outcomes and student understanding. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary We request funds for a new high-performance computing (HPC) resource to serve as the cornerstone of GPU-based computing at Brown University. It will become a focal point for established and highly successful multidisciplinary educational programs and research with the Carney Institute for Brain Science. The Faculty and Senior Administration have taken steps to host this critically needed GPU resource. These include (1) hosting and management by Brown’s Center for Computation and Visualization (CCV); (2) partnership with Intel, NVidia, and IBM for training and support; and (3) development of a solid and viable computational science research community. This new resource will expand research capabilities within the multiple centers within the Carney Institute – enhancing the development of computational neuroscience models and broadening the application of machine learning and other modern data analysis pipelines to brain science, and allow unique instructional and training programs at all student levels. Brown has a proven record of collaboration between application scientists within CCV and researchers across scientific disciplines. This combination provides an opportunity to use a state-of-the-art multi-GPU system not simply as “yet another tool” but as a foundation for already planned advanced research in brain science, with examples provided in the main project description of this proposal. The acquisition of this HPC resource will directly impact the faculty listed in this proposal by increasing the speed, quality, and volume of scientific data processed and by enabling the development of computational models spanning all levels of analysis – from the level of biophysics and circuits to the level of systems and computation. Brown’s existing HPC cluster does not yet include large-memory GPU nodes. This has hindered brain science research and student training with state-of-the-art HPC technologies. The deeply interactive faculty from life sciences (neuroscience, cognitive and psychological sciences, biology, and medicine) and physical sciences (applied mathematics, engineering, and computer science) have created a unique scholarly environment of interdisciplinary activity; both students and faculty will benefit immensely from this technology. In response to our growing needs in computing, Brown has built a cluster of ~400 computing nodes, including over 300 GPUs (a mix of NVIDIA Ampere, Volta, and Turing architectures). However, the growth in the scale of the experimental data collected, the computational footprint of modern data analysis pipelines, and the scale of current computational models have severely strained our computing infrastructure. In particular, a common challenge is the high resolution, large dimension, and volume of scientific data that research groups must process, analyze, and model. The lack of large-memory GPU resources on campus and the projected growth in machine learning research needs across brain science, the demand for HPC-based research and education, and the need for on-campus access by outstanding undergraduate and graduate students, make acquiring additional GPU resources essential.

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

This project advances the understanding of nonlinear dispersive partial differential equations (PDE) - mathematical models that describe waves in contexts ranging from optics and fluid dynamics to quantum gases and plasma physics. It addresses fundamental questions about the formation, stability, and interaction of coherent structures such as solitons, vortices, and singularities in contexts leading to new and richer wave behaviors. Insights into these mechanisms promise to push forward mathematical theory in analysis and PDE, improve modeling in physics and engineering (e.g., ocean wave propagation, Bose-Einstein condensates, plasma dynamics), and serve the national interest by enhancing the theoretical foundation of applied scientific disciplines. The project also supports education through training of junior researchers and fosters broader impacts via cross-institutional student mentoring. This project explores fundamental questions in the analysis of nonlinear dispersive systems, with an emphasis on understanding the formation and long-time dynamics of coherent structures such as solitons, vortices, and singularities. The focus lies on situations where classical assumptions - such as locality, vanishing boundary conditions, or scalar structure - are relaxed, leading to new challenges in the mathematical analysis. These include the study of blow-up dynamics near minimal mass thresholds, the stability and asymptotics of localized waves in higher-dimensional or nonlocal settings, and the evolution of coherent structures in systems with nontrivial background states or multiple interacting components. The research develops and applies a range of techniques from nonlinear Fourier analysis, spectral theory, and dispersive PDE, including virial-type arguments, modulation methods, and iterative profile decompositions. Anticipated contributions include new mechanisms for stable singularity formation, improved understanding of multidimensional wave interactions, and advances in the long-time behavior of coupled or constrained dispersive flows. 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

This multidisciplinary research training program will improve research capacity in Ukraine, allowing American researchers and their Ukrainian partners to develop new health innovations that can improve the care of both Ukrainian and American civilians, service members, and veterans. Ukraine has among the highest burdens globally of both HIV and TB, including multidrug-resistant TB, making it an ideal place to study interventions to combat these related diseases. The current conflict in Ukraine has both damaged the healthcare system and led to significant rates of internal displacement, disrupting treatment and prevention programs for people with HIV and TB. It has also led to high numbers of veterans and service members with PTSD, substance use, and other mental health conditions. This provides a unique opportunity to train researchers to study new methods for implementing HIV, TB, substance use, and mental illness prevention and treatment programs that can be used in low resource settings with fragmented healthcare, similar to many parts of rural America, and among populations who suffer from high levels of psychological trauma and mental illness, such as American service members and veterans. For example, if found to be effective in Ukraine, new models for care being deployed there such as the use of mobile vans and artificial intelligence tools to diagnose and treat HIV and multidrug resistant TB among displaced people, can be used to similar effect in rural and unhoused populations in the US, which are also experiencing growing rates of multidrug resistant TB. New interventions developed by Ukrainian research trainees in partnership with American researchers on the integration of care for infectious diseases, substance use, and mental health conditions among the large and growing population of veterans in Ukraine, could also have important benefits for US veterans as well, who suffer from exposure to infectious diseases and psychological trauma as part of their military deployments. Expanding clinical innovations in HIV care (including care for people with HIV/TB coinfection and other comorbidities, such as SUD and mental health conditions) is critical to improve health outcomes for both individuals living in volatile settings such as Ukraine and for those working in volatile settings, such as US service members. Lessons learned about these new models for prevention, diagnosis, linkage, and maintaining care in settings of health system disruption and limited resources are of enormous importance both globally and in the US.

NSF Awards · FY 2025 · 2025-08

As the occurrence and severity of wildfires increases across the United States, post-wildfire debris flows and flooding represent an increasing threat to communities. This work focuses on using ground vibrations, produced by debris flows and floods and recorded by seismic instrumentation, to better understand the conditions that trigger flows from within recently burned areas. Using this approach will allow the investigators to monitor a burned area with higher spatial resolution than traditional monitoring equipment, allowing them to record and characterize small-scale changes in flows and the rainfall conditions that trigger them. By monitoring for years following the fire, this work will also allow them to assess how post-wildfire flow hazards evolve with time. This work will improve models of debris flow and flood triggering, which will allow for better assessment of post-wildfire risks to communities downstream from burned areas. Better understanding these hazards and triggering thresholds will lead to improved models of landscape evolution and more-accurate early warning for downstream communities. This project will lead to a better understanding of debris flow processes and will enhance tools to study them using seismic data. Leveraging recent advances in seismic instrumentation will allow the investigators to generate in-situ observations of post-wildfire debris flows using a network of nodal seismometers installed in a recently burned area. Specifically, the investigators will test the following hypotheses: a) debris flow initiation locations will migrate downstream over time as the landscape recovers, b) the timing and location of debris-flow initiation can be predicted using a slope-dependent dimensionless discharge threshold, and c) debris flow surge magnitude and frequency are influenced by drainage area, rainfall intensity, and sediment supply. Using data from ~100 seismic instruments, validated with additional instrumentation, the team will produce a comprehensive catalog of post-wildfire debris flows within the study area, including the location and timing of initiation, velocity, and changes in grain size as they move downslope. These data, which will provide a more spatially and temporally complete picture of the lifecycle of post-wildfire debris flows relative to traditional monitoring methods, will enable the investigators to better understand the behavior of these flows. Results will advance fundamental understandings of debris flow processes and the ability to extract information about environmental phenomena from seismic data. 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.

NSF Awards · FY 2025 · 2025-08

This project seeks to advance the mathematical analysis of plasma physics and rotating fluids. Plasmas are ubiquitous in astronomy and in geophysical fluids. They are also central to many challenges and opportunities facing the modern world, from neon lamps to fusion reactors. The many scales involved, and the very unstable nature of plasmas represent formidable challenges and require many different levels of description whose precise relationship (when to use one instead of the other, and which to believe if the results disagree) is still poorly understood. Since the Earth spins, all questions involving geofluids, such as weather predictions, necessitate delicate analysis of rotating fluids. One of the throughlines of this project is that, in some special cases, the more complicated settings may actually be better behaved or more stable than one would a priori expect; for example, a fluid in rotation may be more predictable than a fluid (almost) quiescent. Isolating such favorable situations in rotating fluids and plasma physics, and understanding how and why predictions are easier then, is one of the main goals of the project. Some of the problems addressed relate to the interaction between a charged, hot gas and a solid wall bounding it; how, in the absence of boundary, a hot plasma may be stabilized under a reversible phenomenon called ``Landau damping'' or whether a faster spinning Earth would have a simpler weather system. The project provides research training opportunities for graduate students and postdoctoral scholars. This project studies partial differential equations inspired by physics. The first part of the project concerns the description of the asymptotic behavior of solutions to non-collisional kinetic equations. The Principal Investigator (PI) studies the stability of homogeneous equilibria for the Vlasov-Poisson system, starting with the case of fat-tail equilibria, as well as the stability of vacuum for the same system in the presence of boundaries. Extensions to more involved models (e.g. Vlasov-Maxwell), will also be considered. The second part of the project addresses problems related to quasilinear dispersive equations. The PI studies the derivation of the Euler-Poisson system for ions from the two-fluid model in the case of confined domain and also investigates the stability of a constant background at rest for the compressible Euler equations in a rotating environment. These problems are addressed using tools from finite dimensional Hamiltonian dynamical systems, harmonic analysis, atomic spaces, functional analysis and more standard partial differential equations tools like the maximum principle, energy estimates, bilinear estimates and precise dispersion analysis. 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 · 2025-07

This project involves the study of probability flows to enhance understanding of complicated probability distributions, with applications ranging from the distribution of a cloud of electrons in a given molecule as building blocks of materials science, to the distribution of all possible coherent sentences in the English language which lies at the core of the advancements of large language models in artificial intelligence. In one direction, the project will propose a novel framework that facilitates the usage of probability flows to advance the theory and computation of multi-marginal optimal transport---a fundamental problem in fields ranging from quantum chemistry and economics to data science. In another direction, the project will investigate the structure of probability flows in broader settings, focusing in particular on lower-dimensional representations of the flows, in order to understand them better and increase their efficiency. The project will also contribute to workforce development by engaging graduate students and postdoctoral researchers in cutting-edge research and mentoring, while making all resulting software, publications, and teaching materials freely and publicly available. This project will center around two interrelated research directions. The first will develop a novel dynamical formulation of multi-marginal optimal transport and related optimization problems, such as versions of multi-marginal Schrödinger bridges. These formulations open the door to the applications of tools from convex optimization (e.g., proximal splitting methods), as well as methods from generative modeling such as flow matching, to finding quasi-Monge solutions of the multi-marginal optimal transport problem. The second direction will focus on the fundamentals of flow-based methods in generative modeling. In particular, the project will investigate the intrinsic dimensionality of probability flows via the new concept of the entropy matrix, which simultaneously generalizes the Fisher information matrix, as well as a matrix associated with optimal transport maps. 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 · 2025-07

The PI plans to continue his research in geometry and dynamical systems. One part of his proposed research, concerning optimal paper geometry, builds on his recent successful solution of the long-standing optimal paper Moebius band conjecture. Another part of the proposed research, concerning geometric dynamics, deals with simple processes which start with a shape like a polygon and apply some transformation to it over and over again. Such processes, like the pentagram map, often have deep connections to other areas in science like water waves and physics-based integrable systems. The PI also proposes to work on a number of more exploratory projects, such as the connection between the famous four color theorem and triangulations of the sphere based on fullerenes. This project will also support the PI's continued impacts on society, through public lectures, many colorful graphical user interfaces, and children's mathematics books. In more technical terms, the PI hopes to prove the knotted paper Moebius conjecture, which states that any sequence of aspect ratio minimizing embedded and knotted paper Moebius bands converges, in the Hausdorff topology, to a folded ribbon knot whose underlying shape is a regular pentagon. Relatedly, the PI hopes to prove the optimality of the newly discovered Hennessey-Neinhaus construction, which provides embeddings of paper Moebius bands with arbitrarily high twisting number and uniformly bounded aspect ratio. The conjecture is that if a rectangle can be folded in space so as to make Moebius bands of arbitrarily high twisting number, then the aspect ratio of the rectangle exceeds the square root of twenty seven. For both these results, the PI hopes to leverage additional topological constraints in a geometric way. For example, the high twisting number combined with the small aspect ratio ought to imply that the paper Moebius band must coil very tightly in certain regions, creating a kind of trap. The existence of this trap places constraints on the rest of the Moebius band, possibly leading to a lower bound on its aspect ratio. In the direction of geometric dynamics, the PI plans to continue his exploration of pentagram-like maps which act nicely on certain open subsets of the moduli space of polygons modulo projective transformations. The next step in the analysis is to show that the orbits in these subspaces are pre-compact modulo projective transformations and to try to understand their collapse point via analogues of Glick's collapse point formula for the pentagram map. 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 · 2025-07

Polynomials are equations that can be built out of the elementary operations, and are therefore ubiquitous in mathematics and science. In the first nontrivial cases, when their sets of solutions are one-dimensional, these sets are known as algebraic curves; familiar examples include circles and lines. One project that the PI will pursue is the interpolation of algebraic curves through special configurations of points. As an illustration, one can draw a circle through most configurations of three points in the plane --- but the circle "disappears" when the three points specialize onto a line. The goal here is a systematic understanding of what happens, in higher dimensions as the points specialize onto a hyperplane (a higher-dimensional analog of a line in the plane), or as the points collide in various ways. The PI will complement this, and other research projects described below, by educational activities for students at several educational stages, including teaching problem-solving skills to middle and high school students through math competitions, supervising undergraduate research projects, and running a summer school in algebraic geometry for graduate students. In addition to interpolation problems for curves passing through special configurations of points, the PI will also pursue several research projects concerning the geometry of moduli spaces of curves, which are geometric objects whose points classify algebraic curves. First, the PI will study the intersection theory of these moduli spaces, which describes how different conditions one might impose on algebraic curves interact. Second, the PI will use quadratic equations satisfied by realizations of curves in projective space to more deeply understand the birational geometry of these moduli spaces. Finally, the PI will develop higher-rank Brill-Noether theory, which describes how algebraic curves can be realized in Grassmannians. 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 · 2025-07

Composite materials, created by closely bonding two or more distinct substances, are common in both natural and engineered systems. Many of these materials display significant spatial variation in properties such as electrical conductivity and dielectric permeability, and are known as high-contrast composites. Examples include filled polymers, porous media, and biological tissues, with applications in bioengineering, medical imaging, and electronics. The Principal Investigator (PI) focuses on quantitatively analyzing field concentration -- a ubiquitous phenomenon in material science that arises when material properties change drastically over a very small length scale. Gaining a deeper understanding of this effect is crucial for accurately determining the effective properties of such materials and for designing them more efficiently to enhance performance. The PI will also perform undergraduate and graduate mentorship. Mathematically, high-contrast composites are modeled by partial differential equations (PDEs) with highly oscillatory coefficients. These equations are challenging to analyze, as classical analytical techniques often fail to apply. This project aims to develop new mathematical methods to address several open problems in this area. The first focus is on the buildup of the electric field between insulators, with particular attention to the asymptotic behavior of solutions and equations involving the p-Laplacian and the Lamé system. The second objective is to study composites composed of perfect or mixed conductors, emphasizing nonlinear governing equations and systems. Finally, the project will investigate models in which the conductors are imperfectly bonded. These involve more complicated transmission conditions and boundary conditions, including Robin-type boundary conditions. Such models are also of practical importance in biology, as they serve as approximations of membrane structures in biological 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.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Over the past 40 years, specialist physicians have supplanted primary care as the most frequently seen clinicians for older adults in the US. This shift towards specialty care is driven by advancing medical technology and increased “subspecialization,” whereby specialist physicians focus on narrower and narrower clinical areas. Subspecialization has grown markedly: in 1980, the American Board of Medical Specialties had 28 specialty boards, with an additional 28 certified subspecialties. By 2020, 40 specialty boards encompassed 147 separate subspecialties. While subspecialists bring greater clinical expertise, too much subspecialization could lead to inequitable access, overtreatment, overdiagnosis or fragmentation of care. There is little empirical evidence on the implications of growing subspecialization for the health of older Americans. A major obstacle to filling these evidence gaps is the lack of meaningful measures of subspecialization at the physician level. Existing physician directories, like the one used by Medicare, contain in-depth specialty data, but are also highly inaccurate. For example, Medicare data identify only 17% of board-certified advanced heart failure specialists in the US. Other specialties have similar data gaps. To understand how access to subspecialists influences access to specific advanced treatments and clinical outcomes, it is necessary to better define the hundreds of types of subspecialty care being provided to patients. We propose to characterize subspecialization in the US and assess its implications for the health and health care of older adults. Using comprehensive data from Medicare, we will develop novel methods to classify physician subspecialists by their observed practice patterns, focusing on 3 key specialties in the care of older adults (cardiology, medical oncology and general surgery) as “tracer” disciplines to fill evidence gaps in subspecialty care that can inform policy. Specifically, we will: 1) Use community detection algorithms, a common tool in network science, to identify subspecialists based on their practice patterns (as measured by services provided, drug treatments, and patient diagnoses). 2) Identify patient, health system and geographic factors associated with subspecialty supply and access. 3) Using quasi-experimental methods, measure the impact of access to subspecialist care on health outcomes and utilization in the three key specialties. These Aims will provide novel evidence to guide health policy, including improved methods to accurately measure subspecialist supply, guide health insurers and policymakers for applications such as determining adequacy of specialist coverage in insurance design (e.g., Medicare Advantage), identify populations with shortages in subspecialist access, and guide telemedicine development. Without this evidence, clinical advances may not reach older adults who could benefit the most.

NSF Awards · FY 2025 · 2025-07

The Algebraic Geometry Northeastern Series (AGNES) is a series of biannual conferences in the field of algebraic geometry. The conference is hosted on a rotating basis by an association of universities in the Northeast region. This award supports six AGNES conferences, which will be held at Dartmouth College on November 8-10, 2024, at Rutgers University in Spring 2025, at the University of Massachusetts, Amherst in Fall 2025, at Stony Brook University in Spring 2026, at Brown University in Fall 2026, and at the University of Pennsylvania in Spring 2027. Each AGNES conference has two goals. First, each conference promotes the dissemination of cutting-edge research in mathematics. The centerpiece of each conference is a series of research lectures by top mathematicians; there are also educational talks for graduate students and events which promote new collaborations or development of peer relationships. Algebraic geometry is a field in the mathematical sciences concerned with solution sets of polynomial equations. It has deep connections to many other areas of pure mathematics, such as topology, arithmetic, number theory, differential geometry, dynamical systems, and homological algebra. At the same time algebraic geometry has found important applications in many subdisciplines of applied mathematics, including cryptography, complexity theory, mathematical biology, and computer vision. The scientific scope of AGNES is greatly enriched by lectures from neighboring mathematical subjects, such as arithmetic geometry, dynamics, complex geometry, and computational geometry. Further information about conference events can be found at the website: http://www.agneshome.org/ 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 · 2025-07

Augmented Reality (AR) and Machine Learning (ML) are rapidly evolving fields that have potential to transform numerous industries by enabling immersive and intelligent applications. However, embedding advanced ML capabilities into AR devices is challenging because of limited hardware resources and the need to process large volumes of real-time sensor data. This award addresses these challenges by designing resource-efficient techniques that reduce computational load and energy consumption while maintaining high accuracy. The outcomes of this work have the potential to benefit a wide range of domains, including healthcare, education, and entertainment, by increasing the accessibility and reliability of AR technologies. In addition, the project includes a comprehensive education and outreach plan, which includes providing research experiences for undergraduate students, developing new computer engineering courses, engaging with high school students, and facilitating technology transfer to industry. This project focuses on a multi-task learning framework that integrates transformer- and convolution-based architectures with low-rank decomposition to enable efficient fine-tuning on resource-constrained AR devices. Task-aware dynamic feature sharing is employed to adaptively allocate computational resources, and quantization strategies are explored to balance performance and resilience against adversarial attacks. An adaptive policy network for inference is developed to accommodate real-time decision-making, and task scheduling algorithms are designed to optimize throughput across heterogeneous processing units. By evaluating these methods on real AR devices using diverse benchmarks, the project establishes foundational strategies for effectively deploying ML in AR systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The rapid advancement of artificial intelligence (AI) is reshaping various sectors, yet the field of design for manufacturing has not fully capitalized on these innovations. This project aims to bridge this gap by developing new design tools that combine AI with traditional design algorithms and principles. A central challenge for design tools in manufacturing is the need to simultaneously nurture the creative ability to conceive novel designs and the analytical prowess to critically evaluate and optimize functionality and production. The goal of this project is to address these challenges and develop novel tools that make it easier for both new and experienced designers to create a wide range of high-quality products. These tools are expected to not only improve product quality and variety, but also enhance sustainable manufacturing. Additionally, the project includes an educational component to make STEM fields more accessible and engaging through design and manufacturing. To unite design precision with creative exploration, this project will develop tools that combine neural abstractions with formal reasoning. Treating computer-aided design (CAD) models as programs that generate geometry, this initiative interprets design as program synthesis, benefiting from formal reasoning and advancements in AI-driven code generation. This approach ensures verifiability and constraint-adherent synthesis, essential for precise engineering analysis. The project will develop a new domain-specific language (DSL) to better support AI-driven design, create algorithms for generating DSL programs from diverse inputs using models like Generative Pre-trained Transformers (GPT) integrated with geometric knowledge, and devise methods to identify and explore design variations efficiently. Additionally, it will establish techniques for design decomposition and recomposition, enhancing the DSL's flexibility and users’ ability to innovate in design generation and iteration. 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 · 2025-07

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Prof. Ou Chen of Brown University is studying how one-dimensional (1D) perovskite nanowires form and grow, with a focus on precisely controlling their size and quality. These tiny wire-shaped materials, made from perovskite semiconductors, have remarkable optical, electrical, and mechanical properties that make them promising building blocks for future technologies in optics, photonics, and electronics. By studying how different conditions affect the formation of these nanowires, the project aims to uncover the fundamental processes behind their growth. This knowledge will help scientists create high-quality nanowires in a more predictable and efficient way. The findings will advance the broader fields of nanochemistry and materials science and have potential implications for next-generation devices. In addition to its scientific impact, this project will include varied educational and outreach components. It will provide hands-on research and training opportunities for graduate and undergraduate students, helping to prepare future scientists and engineers. The project’s discoveries will also be shared with high school students and the general public through outreach programs such as Brown STEM Day and engaging video content on platforms like YouTube, helping to spark interest in nanotechnology and science more broadly. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Prof. Ou Chen of Brown University is studying how to achieve atomic unit-cell-level control in the synthesis of ultrathin one-dimensional (1D) lead halide perovskite (LHP) nanowires (NWs), aiming to uncover the fundamental mechanisms underlying their growth. 1D semiconductor NWs exhibit unique properties—asymmetric quantum confinement, anisotropic optoelectronics, and mechanical flexibility—making them attractive for next-generation technologies. However, the ionic bonding nature of LHP nanomaterials causes rapid nucleation and growth, which limits mechanistic insights and synthesis precision of LHP NWs. The central hypothesis is that ultrathin perovskite NWs can be assembled from individual metal-halide octahedral units under mild and kinetically controlled conditions. By systematically investigating reaction parameters and employing a combination of ex situ and in situ characterization methods, this project will develop a reproducible synthetic strategy to fabricate ultrathin perovskite NWs with unit-cell-level thickness precision. The research will provide a generalizable framework for constructing environmentally friendly, lead-free double perovskite 1D nanostructures, and offer key insights into the anisotropic crystal growth of perovskite materials in general. 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 · 2025-07

The International Conference on Mathematical and Scientific Machine Learning 2025 (MSML2025) will be held in Naples, Italy at the University of Naples Federico II, August 4-8, 2025. This conference will be hosted in the “Aula Magna.” MSML2025 will be the fifth edition of a recently established international conference with an emphasis on promoting the study of mathematical theory and algorithms of machine learning, as well as applications of machine learning in scientific computing and engineering disciplines. This edition will be the second conference of the series to be held fully in person. This international conference aims to bring together the communities of machine learning, applied mathematics, and computational science and engineering, to exchange ideas and progress in this fast-growing field. This conference will help contribute to the training and growth of the workforce in this field by supporting the attendance of graduate students, postdoctoral researchers, and junior researchers. The objective of this conference series is to promote the study of theory and algorithms of machine learning and machine intelligence as well as applications in scientific and engineering disciplines such as physics, chemistry, material sciences, fluid and solid mechanics, etc. New themes of the conference will include transformers and state-space models, analysis of attention mechanisms, causal inference, optimization for surrogate training, and optimal transport, while more traditional themes such as graph neural networks, neural operators and general mathematics of machine learning will also be featured. More information about the conference is available at the conference website: https://sites.google.com/view/msml2025/home. 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 · 2025-07

Harmonic analysis is a major branch of mathematical analysis which focuses on studying the behavior of functions by breaking them down into simpler and easier-to-understand component parts. The developments in harmonic analysis have led to concrete advances in medical imaging, image compression algorithms, signal processing, and neuroscience. This project examines questions in harmonic analysis and related fields from a more theoretical or pure perspective of basic research. As part of this award, the PI also mentors undergraduate students in research projects, which increases the STEM pipeline and supports higher education and society at large. This project consists of two main streams: harmonic analysis in the special setting of non-doubling measures and applying harmonic analysis to problems in complex analysis which also connect to operator theory. In the context of non-homogeneous harmonic analysis, questions relating to a novel paradigm for the sparse domination of Calderon-Zygmund operators and commutators are considered. Within the second stream, the PI investigates two-weight and endpoint commutator estimates for the Bergman projection, Lp estimates for the Cauchy-Szego and Bergman projections on Lipschitz and other minimally smooth domains, and two-weight inequalities for the Bergman projection on the unit disk. The specific tools used to study these questions include a non-homogeneous Calderon-Zygmund decomposition, an approach to the study of holomorphic projection operators originated by Kerzman and Stein, and using weighted Haar decompositions and random dyadic grids to achieve two-weight estimates. 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 · 2025-06

This REU site will enable undergraduate students to participate in the production of original, interdisciplinary research on artificial intelligence for computational creativity. It will help individual students build critical computer science research capacity, communication and professional skills, to increase their confidence and gain a greater understanding of the research process. They will gain understanding of pathways to graduate degrees and prepare for the application process. More generally, this project will develop a multi-year pipeline of student researchers, helping to broaden participation in the fields of AI and visual computing. While students may be new to AI research, creativity applications in this field can help bridge the gap and get students excited about computer science by helping them realize the intersection of their personal creative visions and AI research. This site will bring together the well-established network of Leadership Alliance institutions with multiple AI research opportunities to produce cohorts of students highly-qualified for graduate degree programs. The research projects encompass the AI disciplines of machine learning, reinforcement learning, computer vision, natural language processing, and robotics, plus adjacent disciplines such as graphics and human-computer interaction. Areas of interest include creative generative models, evaluating generated content, and user experience design for creative AI. Students will be closely mentored by faculty and graduate students in their research labs, and supported by surrounding research groups and the resources provided by the Leadership Alliance. They will receive technical training in AI and machine learning fundamentals, including deep neural networks. They will be trained in reading, writing, and presenting their work and will put these skills into practice at research symposia and workshops. 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 · 2025-06

PROJECT SUMMARY Veterans are at significantly high risk for post-traumatic stress disorder (PTSD), depression, and suicide by firearm; all three are linked to lead exposure. Preliminary evidence from our team identified firearms as a significant source of lead exposure in civilians, likely through take-home dust from lead-based primers and ammunition. Disconcertingly, the mental and physical impact of lead exposure remains poorly understood due to a focus on acute (blood) rather than cumulative (bone) measures and despite the passage of the 2022 PACT Act encouraging research on Veterans Exposures. When considering cases of PTSD, depression, and suicidal ideation, one of the most successful treatments is Cognitive Processing Therapy. Cognitive Processing Therapy is rooted in cognitive behavioral theory; as such, individuals with diminished cognitive abilities and heightened anger experience worse treatment outcomes. Thus, the well-established effects of lead on lower cognitive abilities and elevated hostility may limit the efficacy of Cognitive Processing Therapy. To address the gaps in the literature and enhance the health of Veterans and active duty servicemembers, there is a vital need to: 1) understand how specific sources of lead exposure (e.g., firearm use) impacts overall lead levels within this vulnerable population; and 2) understand the impact cumulative lead exposure has on Cognitive Processing Therapy outcomes. To fill these critical gaps, we plan to leverage an ongoing study of 350 Veterans and active military servicemembers who provided comprehensive self-report measures of psychopathology, clinician interviews, firearm and lead exposure, and novel noninvasive in vivo 3-minute bone scans for cumulative lead exposure via portable x-ray fluorescence (pXRF). Next, we will examine the association of all-cause tibia lead levels with clinical outcomes (pre-and post-treatment, 3-,6-, and 12-months after treatment). This study will be the first to characterize firearm-related sources of cumulative lead exposure and all-cause leads effect on psychosocial treatment outcomes in Veterans and active duty servicemembers. The findings could improve mental health outcomes through actionable insights for participants and providers, eventually informing and enhancing psychosocial treatment options, of which there are none for adult lead exposure. This Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship (Parent F1) application aligns with my goals of becoming an independent and interdisciplinary researcher through the provision of valuable mentoring, protected research time, and coursework outside my graduate programming.

NSF Awards · FY 2025 · 2025-06

Island chains, like Hawaii, the Virgin Islands, U.S. Pacific Island territories, and others are frequently affected by extreme weather events that pose serious threat to public health systems and the infrastructure that supports them. Many inhabited islands in the Pacific Ocean and Caribbean are too small to be resolved in global climate models; thus, island decision makers often lack site-specific data needed to make informed decisions about current and future risks to their populations due to extreme weather events. This research explores the integration of high-resolution geographic and weather information to support planning for the protection and continuous operation of healthcare facilities for impacted populations. Using the island nation of Fiji as a pilot/testbed, this project combines state-of-the-art advances in machine learning and geospatial modeling and data on health facility access, infrastructure, and condition. The goal is better prediction of impacts on island chains of climate-driven hazards related to wind and rain/flooding. Key outcomes will be a decision-support platform to help health officials and practitioners assess and prepare for weather-related health infrastructure risks. It will also advance modeling capabilities in integrating health and weather data. Broader impacts will be improving the ability of island chains with far flung populations to respond to the health implication of weather-related disasters. The work also directly engages atmospheric scientists with island chain health officials and decision makers, stakeholders which rarely work together. This research develops cutting-edge, machine-learning-based, modeling tools for tropical cyclone weather events and integrates them with climate health vulnerability assessments. This will yield risk projections for health infrastructure for island chain populations. This project uses Fiji as a pilot/testbed, playing off already established relations between the science team and island health officials and decision makers. Research will involve development of computationally intensive generative tools that: (a) emulate tropical cyclone impacts, (b) downscale climate model data, and (c) statistically categorize extreme events. Researchers are part of a technical advisory group to the South Pacific Community which provides them with access to health facility information allowing the combination of that data with projections of cyclone frequency, intensity, and landfall. This will help decision makers better protect against damage and loss of health operations during tropical cyclone events. Projections will be embedded in a user-friend decision-support tool that allows visualization and analysis of high-risk areas and the distribution of climate risks to health infrastructure. The tools will be developed to ensure accessibility for health/medical practitioners and decision makers. They will combine machine-learning-enabled risk projections with empirical health infrastructure vulnerability data which can be used in other locations to develop similar contextually informed extreme weather-health data for island populations. 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 · 2025-06

Advanced data analytics involves performing computation on data sets to produce useful insights. Data analytics finds application across many fields and has enabled major breakthroughs, however the potential has been constrained by challenges in sharing sensitive, distributed data. Secure multi-party computation (MPC) provides a promising solution, allowing multiple parties who have relevant datasets to perform computations on their combined data while preserving privacy. As a special case of MPC, private set intersection (PSI), which securely computes the data elements in common in the private sets, has shown early success in practice. This project expands the initial success of PSI to a much broader range of applications of MPC. The project's novelties are identifying the fundamental challenges that currently limit the use cases of PSI, and developing new tools that not only enhance PSI but also address common challenges in many other MPC problems. The project's broader significance and importance are accelerating the industrial adoption of PSI and extending the frontiers of practical MPC, enabling large-scale, privacy-preserving data analytics on sensitive data. The project includes educational and outreach activities such as integrating research into curricula, organizing mentoring workshops, developing tutorial resources to guide researchers and developers in the field, and mentoring students at all levels, especially those from underrepresented groups in computing. The project focuses on three main thrusts. First, the project bridges the gap between standard PSI and PSI with enriched functionalities by developing unified frameworks for private join and compute that computes arbitrary functions on the intersection and for fuzzy-matching PSI that identifies fuzzy or noisy matches. Second, the investigator studies large-scale PSI for big data, designing efficient protocols for PSI with unbalanced sets and resources and for streaming data, which are better suited for many real-world scenarios. Finally, the project expands its scope beyond PSI, applying the new techniques to other important MPC problems, including privacy-preserving machine learning, genomic sequence matching, and private information retrieval. 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 · 2025-05

Abstract In complex natural environments, optimal behavior requires a precise understanding of body state. However, information transmission between body and brain is limited by the blood-brain barrier (BBB). Chiefly comprised of tightly coupled Endothelial Cells (ECs), this barrier is canonically considered a static blockade to most molecules during health. Preliminary Data from our lab shows that, in contrast to this canonical view, the BBB can be highly dynamic, with rapid, local moments of increased permeability. These ‘Plume Events’ can be driven by behavioral events and optogenetic activation of Ventral Tegmental Area (VTA) Axons. The circuit mechanisms underlying these newly-discovered Plume Events are unknown. Rapid EC Calcium Events (ECCE) are a strong candidate driver, as increased EC Calcium (Ca2+) enhances BBB permeability ex vivo, and recent studies show rapid, discrete EC Ca2+ events in anesthetized mouse Neocortex. My Preliminary Data support this Hypothesis: Optogenetic drive of VTA Axons evokes ECCE in Neocortical vessels within 1-2s. I have also found that chemogenetically driving Chloride (Cl-) channels, known to increase EC Ca2+, can ‘open’ the BBB. Here, I systematically test three closely-related Hypotheses. In Aim I, I test the prediction that VTA Axon activity, endogenous and optogenetically-driven, evoke ECCE; In Aim II, I test the prediction that ECCE predict Plume Events; and, in Aim III, I test the prediction that externally activating EC Cl- channels can drive both BBB permeability and ECCE. I will employ 2-Photon imaging in awake mice under multiple behavioral contexts, and cellular control tools co-developed in our lab. All studies will be conducted in the Primary Somatosensory ‘Barrel’ Neocortex (SI), a key neurovascular and behavioral model system. Even if my predictions are not supported, these data should provide unique insights and may have clinical relevance (e.g., to targeted opening of the BBB). My mentor Dr. Christopher Moore has extensive experience with all of these methods. Further, Dr. Moore’s track record of expertise in exactly my desired research domain, and history of excellent mentorship results, make me confident that I am well-positioned to thrive during graduate work. My co-Mentor Dr. Diane Lipscombe is an ideal complement, as she is a leading expert in Ca2+ channel structure and function, and in linking these variables to behavior. Their collective expertise, coupled with the Brown Neuroscience Department’s classes, technical and pedagogical resources, and supportive culture, will ensure I have the mentorship and support necessary to thrive. I will broaden my graduate learning experience through taking high-level classes, attending frequent talks from a wide range of leading neuroscience researchers, and engaging with a robust peer network. Most importantly, performing my Specific Aims within this training environment will provide ideal training for reaching my long-term research goals.