Rutgers University New Brunswick

NSF Awards · FY 2025 · 2025-07

With the widespread use of 3D scanners, imaging systems, and sensors, digital and polyhedral surfaces are now being generated at an unprecedented rate. This explosion of geometric data creates an urgent demand for mathematical tools to organize, classify, and analyze these surfaces, much like how search engines categorize webpages. This project focuses on discrete conformal geometry, a powerful mathematical tool for addressing this challenge. Rooted in classical conformal geometry of surfaces, discrete conformal geometry aims to bridge theory and computation. For instance, the classical uniformization theorem, a cornerstone result in conformal geometry, implies that surfaces such as human faces or the surfaces of brains can be flattened conformally (i.e., angle-preserving) onto a disk. However, it does not provide a constructive method to compute such a flattening. Discrete conformal geometry seeks to fill this gap by developing mathematical structures and practical algorithms that achieve conformal flattening of surfaces. These algorithms will have wide-ranging applications in shape analysis and comparison, texture mapping and remeshing, surface flattening for visualization and analysis in medical imaging, and many others. Over the past two decades, the PI and collaborators have developed a discrete Riemann surface theory for polyhedral surfaces and successfully proved a discrete uniformization theorem for all compact polyhedral surfaces. Extending it to non-compact polyhedral surfaces, especially without any topological assumptions, remains a significant open problem. Recently, the PI and his collaborator, Dr. Yanwen Luo, formulated the discrete uniformization problem for all polyhedral surfaces without any topological constraints. The primary objective of this project is to solve the discrete uniformization problem in full generality. This work lies at the intersection of several mathematical domains, including classical convex geometry (e.g., the Cauchy–Alexandrov rigidity theorem and Alexandrov’s realization theorem), the Weyl problem in hyperbolic 3-space, and complex analysis (e.g., the Schwarz lemma and Liouville’s theorem). The award aims to integrate techniques from discrete and computational geometry, Riemann surface theory, convex geometry, and 3-dimensional hyperbolic geometry to develop a unified framework for addressing this fundamental problem. 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 focuses on two central combinatorial topics where recent breakthroughs, several by the PI and his students, offer hope of further progress on some fundamental old problems. The first topic, "balancing problems for partially ordered sets," is motivated by questions about sorting, among the most basic of algorithmic tasks; the second, "thresholds," has been central to the study of discrete random systems since its beginnings around 1960. Though much of the PI's work has ties to other disciplines---and some of it has had unanticipated applied consequences---the emphasis is usually on what seems most interesting from a mathematical standpoint. The PI has long been interested in working across mathematical boundaries. He has had success both in applying ideas from areas beyond combinatorics (e.g., algebra, geometry, topology, probability, Fourier analysis, information theory) to settle combinatorial problems, and in bringing combinatorial ideas to bear on problems from other areas (e.g., geometry, computer science, probability, statistical mechanics). As in the current work, he has usually focused on simple, basic questions with histories of resisting solution, motivated in part by the idea that success with such questions almost always forces one to go beyond existing methods. The project will involve graduate students. The project treats two combinatorial topics that are among the PI's main current interests. The first deals with a set of notorious old, algorithmically motivated "balancing" questions for partially ordered sets; here the PI and his student M. Aires have recently made striking progress, eclipsing all that was previously known in the way of general results, and introducing new methods together with an extensive list of previously unconsidered, though seemingly basic, questions. Part of the appeal of this area is the interplay of extra-combinatorial ideas (from geometry, probability, and information theory) that underlie some of the results. The second topic is "thresholds," roughly meaning the intensities at which various behaviors of interest appear in a (large) random system; these have been central to probabilistic combinatorics and related parts of statistical physics since at least Erdos and Renyi in 1960. Recent breakthroughs by the PI and others on the ``Kahn-Kalai Conjecture'' of 2008 have refocused research in this area, rendering previously formidable results easy, and offering hope for (and already a few resolutions of) problems previously considered completely out of reach. 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 includes a multi-faceted analysis of marine carbonyl sulfide emissions to be coordinated by a collaborative team from institutions in the US, Germany, and Israel. Carbonyl sulfide is a trace gas capable of providing insight into the global carbon cycle. Mass balance estimates from isotopes and atmospheric inversions both suggest the missing sources of carbonyl sulfide are tied to marine fluxes. This effort will significantly increase understanding of the marine source of carbonyl sulfide (OCS) by conducting a series of coordinated experiments that combine: (1) direct marine flux measurements of OCS; (2) dissolved measurements of OCS and its isotopologues and precursor gasses; and (3) data assimilation and modeling. The project includes an extended field campaign to continuously measure the direct fluxes of OCS. The team will collect data using an air-sea interaction tower on the US Atlantic seaboard (near Martha’s Vineyard, MA) and in Bolkins Eck (Bering Sea), as well as using shipboard measurements to quantify fluxes and resolve sources (via sulfur isotopes) of OCS from these coastal sites. The project includes training for early-career scientists and graduate students from a team of experienced scientists. This work is supported by the Atmospheric Chemistry and the Chemical Oceanography Programs. 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 research project seeks to uncover the fundamental principles governing how cells sense and respond to mechanical forces at their surface, a process critical to numerous biological functions and human diseases. By focusing on mechanosensitive membrane proteins, this work will provide new insights towards understanding cell migration, tissue homeostasis, and touch sensation. The project will also enrich interdisciplinary education through integrated teaching, mentoring, and outreach activities, benefiting students at multiple levels. The research will employ advanced microscopy and biophysical tools to systematically investigate the interplay between cell membrane properties (e.g., tension, curvature, composition, and dynamics) and the subcellular behavior (e.g., distribution and activity) of mechanosensitive membrane proteins. Using Piezo channels as a primary example, the study aims to establish a thermodynamic framework for understanding mechanosensitive membrane proteins behavior at the subcellular level and explore their dynamic behavior within the cell membrane. This multifaceted approach will generate a comprehensive understanding of mechanosensing at the subcellular level, which could lead to potential applications in therapeutic development for various mechanobiological diseases. 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 proposed research work focuses on open problems and developing programs arising from geometry and physics, including singularity analysis, canonical metrics, and geometric flows. The proposed research aims to develop new conceptual frameworks and technical tools that will provide profound insights and understanding of the geometric and analytic structures of the universe. The proposed project also aims to bring in research and teaching innovation both at Rutgers and in the regional mathematical community. The PI will continue to organize and participate in the integrated research/education programs and activities that will promote the education level of the nation. The PI aims to develop the theory of geometric analysis on complex spaces with singularities. In particular, he will study Riemannian geometric properties of singular Kahler metrics and related moduli problems for Einstein manifolds with applications to nonlinear partial differential equations, algebraic geometry and physics. The PI will continue to investigate and make progress in the analytic minimal model program with Ricci flow with a focus on formation of singularities as a global and local metric uniformization by solitons. The PI will also study analytic and algebraic criteria for solving global Hessian type equations and their applications to geometric analysis on singular spaces. The deep understanding of these problems will help make fundamental progress in the study of analytic and geometric singularities from fully nonlinear partial differential equations in geometry and physics. 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 project aims to design a Cyber-Physical System (CPS) comprising networked Autonomous Underwater Vehicles (AUVs) to perform Machine Learning (ML)-assisted identification of marine plumes via modeling, multi-AUV coordination and swarming, and dynamic collaboration between AUVs and a Land-based computational cluster. Although ground and air settings have seen the increased use of Internet-of-Things (IoTs) systems, robot swarms, and distributed sensor networks, CPSs remain extremely rare in the underwater domain. To address this and promote the progress of science, this project aims to use an adaptive and intelligent network of heterogeneous AUVs for plume search, classification, and mapping in scientific understanding and forecasting. The ability to sense in real time the ocean is highly beneficial for oceanography and environmental monitoring as well as for national defense/port surveillance and industry, such as aquaculture and oil & gas. This project will help answer the fundamental question of whether there is a correlation between plumes and the presence of gas/oil hydrocarbons. This project will perform sampling/mapping of ocean bottom plumes such as oil spills and chemical release from minerals, which is useful for exploration and extraction of ocean resources or for the monitoring of seabed mineral exploitation activities. This project will provide a better understanding of temporally and spatially limited ocean features, and could well be adapted for sensing other small-scale and dynamic phenomena, such as biological distributions in the water column. Last but not least, this project will develop a pipeline of computer-literate individuals who can solve research-related scientific problems trying to generalize to CPS as a science. This project includes three interconnected high-risk/high-reward innovative research tasks centered on science and engineering: (1) Developing multi-fidelity numerical models of multiphase plumes to understand their current state and enable real-time forecasting. (2) Designing the physical underwater sensor network, which will consist of heterogeneous AUVs acting as mobile sensor nodes, equipped with heterogeneous plume sensors and acoustic array communications and positioning systems, and a surface gateway acting both as an underwater and above-water communication relay as well as as a centralized node for AUV positioning and operator command and control of the network; and devising novel adaptive control schemes that allow the AUVs to autonomously explore the extent of the plume. (3) Proposing novel ML-based methods for collaboration between AUVs and the Land-based computational cluster to balance network resources while taking into account communication failures, constraints on data storage on each node, their processing resources, the limited acoustic communication bandwidth and high latency, and the sensing and control capabilities of each node. This project will verify and quantify the capabilities of this CPS using a combination of hardware-in-the-loop emulations, field tests, and experimentations. 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 · 2025-05

This award supports the Mid Atlantic Mathematical Logic Seminar (MAMLS), an ongoing program of grassroots meetings in mathematical logic around the northeastern United States, and the first meeting will be the Northeastern Model Theory Day, to be held on April 5, 2025 at Towson University. This region has a substantial population of experts in mathematical logic, ranking, as a group, on a par with any region in the world. The decentralized nature of the discipline in the northeastern US makes meetings all the more essential. The conferences in the MAMLS program will foster new collaborations, train students, support early career researchers, and broaden the base of logicians in the region. Other series sponsored by this grant include the biannual New England Recursion and Definability Seminar (NERDS), the annual meeting Groups, Logic, and Dynamics (GLaD), and the flagship MAMLS meeting traditionally held at Rutgers University every fall. Future meetings will be announced on the MAMLS webpage at https://nylogic.github.io/MAMLS.html. 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 · 2025-05

Nontechnical description This project aims to revolutionize computer memory by developing new materials and devices for high-density magnetic random-access memory. The project focuses on enabling these devices using thin layers of special materials, called van der Waals heterostructures, on a wafer scale. This wafer-scale approach is crucial for practical applications in the semiconductor industry, unlike current lab methods that rely on delicate, small flakes of two-dimensional materials prepared by exfoliation. Achieving highly efficient switching in these magnetic random-access memory devices is essential for future “memory-in-computing” technologies, which promise faster and more energy-efficient computing. This research will address critical challenges in material development and device fabrication, ultimately paving the way for transformative advancements in computing. Additionally, the project will provide invaluable training for students, equipping them with essential skills for the semiconductor industry and contributing to the development of a highly skilled workforce. Technical description This research will address the limitations of conventional spin-orbit torque switching in magnetic random access memory devices by developing wafer-scale epitaxial van der Waals spin heterostructures. Current spin-orbit torque efficiency in three-dimensional materials is restricted by intrinsic and extrinsic factors, such as low spin Hall angles and imperfect interface spin transparency. Recent studies on exfoliated two-dimensional materials suggest the potential to surpass these three-dimensional limitations. This project seeks to achieve material breakthroughs by growing epitaxial van der Waals heterostructures with exceptional structural, electronic, and magnetic properties, including atomically sharp interfaces, on a wafer scale. These heterostructures will consist of a two-dimensional ferromagnetic material with room-temperature Curie temperature and tunable perpendicular magnetic anisotropy, epitaxially grown via molecular beam epitaxy onto a topological quantum material with strong spin-orbit coupling. Topological insulators (e.g., Bi2Te3) and semimetals (e.g., WTe2) will provide the spin-momentum locking necessary for high intrinsic spin-orbit torque efficiency. The ferromagnetic component, Fe3GaTe2, a recently discovered two-dimensional ferromagnet, exhibits a Curie temperature of approximately 380 K, among the highest reported for two-dimensional magnets. Furthermore, like Fe3GeTe2, Fe3GaTe2 possesses strong perpendicular magnetic anisotropy, a crucial property for high-density memory devices. Successfully integrating these components into a wafer-scale heterostructure via molecular beam epitaxy represents a significant scientific advancement compared to devices made with exfoliated flakes. The combined expertise of the PI and Co-PI in molecular beam epitaxy, nanofabrication, and spintronics will ensure the project’s success. The resulting epitaxial heterostructures will have a profound impact on nanoscale magnetism, spintronics, and related fields. First-principles calculations will be employed to provide theoretical insights into the fundamental properties and behavior of these novel materials, guiding the optimization of material growth and device design. 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 · 2025-05

This project builds on the long-running Research Experiences for Undergraduates (REU) program at DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science at Rutgers, the State University of New Jersey. The program's primary goal is to support the first steps of undergraduate students in getting involved with original research in Computer Science. In this way, the DIMACS REU program prepares the next generation of computer scientists and offers the students the tools to kickstart their research careers. Every year, ten students from all over the country are selected to participate in our program. A special effort is made to recruit members of groups underrepresented in computer science, including women and ethnic minorities, as well as students from institutions with limited opportunity for undergraduate research, through contacts with minority-serving institutions and announcements to relevant mailing lists and websites. The students are immersed in a unique multidisciplinary environment as they work on the design, analysis, and use of algorithms. The students are mentored by faculty from several Rutgers departments and DIMACS industrial partners. A carefully structured program offers a comprehensive research experience that combines one-to-one mentoring with various activities, including seminars, workshops, and field trips to industrial partners. This program integrates with other Rutgers undergraduate research programs to add cultural diversity. To foster a global perspective, we collaborate with the Department of Applied Mathematics at Charles University in Prague through a student exchange program. This allows our students to connect with international researchers, gain a better understanding of the global scientific community, and develop intercultural skills and mindset. The DIMACS REU program fosters student growth through diverse research opportunities and provides a rich scientific and cultural experience. Building on the center’s strong foundation in theoretical computer science, in particular complexity and graph algorithms, some projects focus on theoretical results, while other projects challenge students on the use of algorithms to address a wide range of real-world issues, including social networks, genetic analysis, prediction markets, and cancer treatment. In addition to publishing scientific papers and talks, new collaborations are developed with mentors, graduate student assistant mentors, and program coordinators. Involving graduate student assistant mentors and postdoctoral researchers develops the mentoring skills of junior researchers, while offering the REU participants the ability to observe graduate student life directly. An important goal of this program is to build awareness of graduate school and the breadth of options for post-graduate careers and research. The program leverages DIMACS’s position as an interdisciplinary center to involve mentors from various academic departments and industrial partners to offer students varied perspectives and projects whose applications cross disciplines to illustrate the power of computer science in a variety of settings. In this way, the program informs the students on choices about further education and future careers and gives them the foundation and confidence to pursue these choices. 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 · 2025-03

This project develops a combined numerical-experimental approach to understand and model the synthesis of large populations of advanced materials that are a hundred thousand times smaller than human hair. A data science-based framework is developed to enable learning, predicting, and simulating hard-to-model nanoscale fabrication processes, which underpin a variety of emerging applications in electronics, energy storage, and biomedicine. The project resonates with the global quest towards realizing the potential of artificial intelligence and machine learning in boosting American competitiveness in advanced manufacturing. The scientific community benefits from this research by extending the approach to a broader set of nanoscale material systems including different oxide-supported metal nanoparticles. The team studies the evolution of alumina-supported iron nanoparticles which serve as nanocatalysts for the chemical vapor deposition (CVD) growth of vertically aligned carbon nanotubes (VACNTs) for next generation thermal interfaces and electrical interconnects. The educational impact includes upskilling STEM students and junior scientists on timely topics at the nexus of data and manufacturing sciences. Moreover, the project generates jargon-free outreach materials explaining topics in machine learning and advanced nanomanufacturing to the general audience. The collective behavior and interactions among substrate-bound nanoparticles during the coupled physicochemical processes of oxidation/reduction, dewetting, coarsening, and catalysis are not well understood. This severely constrains the ability to reliably manufacture dense populations (hundreds of billions per square centimeter) of functional nanoparticles or active nanocatalysts. This project combines probabilistic data science methods with in-situ environmental transmission electron microscopy (E-TEM) to elucidate the dynamics of spatial proximity effects among ensembles of adjacent nanoparticles. The research is to leverage spatio-temporal point process theory, a branch of probabilistic machine learning, for quantifying, predicting, and simulating the time evolution of location and size distributions and spatial dependencies during the formation and evolution of metal nanoparticles from thin films. In pursuit of this research, the following tasks are performed: (1) In-situ E-TEM measurements of population behavior of metal oxide reduction, nanoparticle formation by dewetting, coarsening by Ostwald ripening, and catalytic activation; (2) Automated image segmentation of in-situ E-TEM videos to extract salient information about the time evolution of locations, sizes, areal densities, shapes and activation of nanoparticles; (3) Learning from experimental observations: Spatio-temporal statistical modeling of segmentation data using point process theory to characterize, predict, and simulate the evolution of interaction potentials; (4) Learning beyond experimental constraints: Elucidating the physicochemical dynamics of metal/support interfacial phenomena for larger spatial domains, finer temporal resolutions, and unsampled conditions. 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 · 2025-02

The average size of human families has dropped dramatically over the last several decades. This affects families’ abilities to meet household needs and the welfare of family members adopting different subsistence roles. Given the implications of such demographic transitions for individual, family, and societal well-being, changes in family size have been intensively studied, and yet remain poorly resolved, particularly within contexts where relatively large family sizes remain normative. This research project uses theory from cultural and biological anthropology to understand the role that livelihood diversification may play in driving family size in mixed (subsistence and market) economies. It creates new collaborations among minority-serving institutions and research-intensive institutions, offers significant, fully remunerated training opportunities for undergraduate and graduate students, including in field, laboratory, and computational methods, and supports both early career and underrepresented scholars. It disseminates results broadly to stakeholder communities and to academic and non-academic audiences via scholarly presentations and publications and a variety of public-oriented media outlets. This project tests the hypothesis that the need to diversify livelihoods in contexts with mixed economies is associated with larger family sizes than are predicted under typical models of demographic transition. Its first objective is to test whether and how economic and cultural factors influence ideal and realized family sizes. Its second is to investigate how family size affects children’s daily activities, both productive and consumptive, and how activity profiles influence children’s nutrition, energetics, physical fitness, and well-being. To do so, investigators collect data from a large sample of children residing in market-integrated, mixed, and subsistence economies, including data drawn from socio-demographic questionnaires that characterize livelihoods and cultural norms surrounding family size, physical activity and dietary logs, and anthropometric and biomarker assessments of health. Together, these data provide a much more detailed assessment of the patterns, causes, and consequences of family size variation as affected by variation in economic and cultural factors. Results thus inform missing aspects of demographic transition theory by focusing on the proximate means by which families adjust subsistence strategies to accommodate variation in economic and cultural landscapes. 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 · 2025-02

This award provides support for Rutgers Symplectic Summer School 2025 (RSSS2025), which will take place at Rutgers University at New Brunswick, New Jersey, August 18-22, 2025. Symplectic geometry has been thriving in the past few decades and have very rich connections to other fields such as algebraic geometry, low-dimensional topology, and dynamical systems. The growing number of graduate students and young researchers calls for opportunities to learn beyond their regular curriculum, to be exposed to the cutting-edge developments, and to build professional connections. The RSSS2025 will strongly encourage graduates and postdocs to participate in this event and engage in formal and informal research collaboration. The RSSS2025 features three days of minicourses on topics of Fukaya category, Atiyah-Floer conjecture, and Floer theoretic applications in low-dimensional topology. They are followed by two days of research talks covering a variety of topics including recent developments on Viterbo conjecture, contact geometry, global Kuranishi chart theory, equivariant Floer theory, and works related to birational geometry. These topics cover many recent important developments in this field, providing valuable opportunities for learning and exchanging ideas. More information is available at the conference website: https://sites.google.com/view/rsss2025. 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 · 2025-02

Adoption and fosterage are life-altering events that arise in diverse contexts in the United States and across the globe. Whether and in what ways the welfare of adopted and fostered children improves outside their natal environment are subjects of much debate. Important factors affecting the welfare of adopted and fostered children include: the circumstances that lead to adoption and fosterage; differences between the activities and treatment of adopted and fostered children and of biological children; and societal acceptance versus stigma surrounding adoption and fosterage. Understanding how these factors interact to facilitate or inhibit successful outcomes for adopted and fostered children is critical for tailoring family programs to suit the individual needs of the many children who are reared primarily by non-natal parents in the United States. This research will be conducted in the Melanesian island nation of Vanuatu, where adoption and fosterage are commonplace and arise in contexts comparable to adoption and fosterage among minority populations in the United States. The research team, including a postdoctoral scholar, two ni-Vanuatu graduate students, and an international team of highly trained PIs, will investigate the causes and consequences of adoption and fosterage using an evolutionary framework that seeks to understand how adoption and fosterage contribute to familial success. The researchers will use a diverse set of methods that leverage the significant variation in these practices in Vanuatu to evaluate which specific factors or sets of factors best predict differences in child outcomes. Specifically, the investigators will use questionnaires, interviews, dietary and activity recall assessments, and anthropometric measurements of health to describe the circumstances surrounding adoption and fosterage and to tie such circumstances to differences in child health and well-being. Findings from the research will provide insight into tailoring support for adoptive and foster families by examining how social and demographic factors contribute to variation in the welfare of adopted and fostered children as compared to their biological counterparts. 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 · 2025-02

Modern computing systems face increasing challenges due to the rapid growth of data-intensive applications such as machine learning, data analytics, and graph processing. Traditional approaches relying on the scaling of central processing units (CPUs) and graphics processing units (GPUs) struggle to meet high demands for memory, storage, and network bandwidth without significantly increasing data movement, energy consumption, and operational costs. The project introduces a coordinated approach that leverages low-power computational hardware accelerators, located near key resources -- such as near-memory, near-storage, and network-based solutions—to provide a scalable, high-performance, reliable, and energy-efficient framework capable of meeting future data demands across edge, datacenter, and high performance computing (HPC) systems. To achieve these goals, the project first designs an end-to-end framework to manage different accelerators by developing operating system abstractions for memory and address space management, inter-process communication, and data sharing. Second, it develops compiler and runtime support to enhance parallelism and incorporate machine learning models focused on energy efficiency, enabling effective task distribution across heterogeneous accelerators. Finally, to scale the solution across disaggregated local and remote accelerators, the project applies distributed systems principles for task scheduling and system reliability. To assess the solution's effectiveness and validate performance improvements, the project studies a wide range of applications, including graph and machine learning applications, key-value stores, data analytics frameworks, and HPC simulations such as climate modeling. By focusing on energy-efficient near-hardware accelerators and advanced software management, this project aims to accelerate datacenter, science, and healthcare applications. The project aims to leverage industrial collaborations to enhance practical impact that aligns with real-world application demands. The innovations focus on cross-layered hardware and software system design, equipping students with sought-after skills in cutting-edge technologies and a comprehensive understanding of end-to-end system design—from application development through operating system design to hardware integration. 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 · 2025-02

This Faculty Early Career Development (CAREER) grant will fund research that contributes new knowledge related to multi-agent autonomous systems, thereby promoting the progress of science and advancing the national prosperity and welfare. The National Academies underscore the urgent need for autonomous systems to improve adaptability to novel environments, enhance efficiency, and boost resilience by enabling quick reactions to avoid critical situations. These capabilities are essential for transformative applications, including search and rescue missions, infrastructure maintenance, and large-scale environmental monitoring. This award supports fundamental research that enhances the capabilities of autonomous systems, aligning them closer to natural systems by drawing inspiration from collective fish behavior. Contrary to traditional solutions that typically rely on complex environmental models and continuous inter-agent communications, this bio-inspired approach will prioritize minimal computation and passive communication, reducing energy consumption at large scales and supporting sustainable practices. The interdisciplinary research in dynamic systems and controls, data-driven science, and experimental biology provides a platform to engage and empower STEM talent. Central to this endeavor is a comprehensive multi-tier education strategy to inspire K-12 students, expanding interest in STEM through outreach activities open to all, and develop new courses and revise core courses in systems and controls within the PI’s department. This research aims to make fundamental contributions to uncovering the feedback principles governing visuomotor control dynamics in fish schools and provide a blueprint for full autonomy. It intends to achieve this goal by establishing a data-driven framework for inference and modeling of complex stochastic multi-agent systems. The research work includes (1) gaining insights on visuomotor feedback principles from individual fish navigation principles using a new geometric framework on manifolds, aiming to uncover control policies mapping visual inputs to locomotor outputs and fundamental properties of motion estimation based on visual cues, (2) understanding the role of neighbor visual information on collective motion behaviors and establishing a data-driven framework using stochastic differential equations to elucidate how individual visuomotor control dynamics contribute to collective behaviors, and (3) examining visual feedback mechanisms for detection and response to threat and understanding how complex evasion maneuvers emerge from visual interactions through the modeling of vision-based feedback control strategies. This research effort will be supported by a series of rigorously structured, hypothesis-driven experiments with live fish schools to capture high-dimensional gaze and locomotor 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 · 2025-01

Clustering is the task of grouping objects so that those in the same group are more similar to each other than to those in other groups. It is widely used in unsupervised learning, data analysis, information retrieval, and community detection. This project contributes to the theory of clustering by aiming to understand the extent to which clustering can be efficiently approximated by leveraging tools and ideas from metric embedding, coding theory, extremal combinatorics, and analysis of Boolean functions. Seeking the optimal inapproximability of clustering not only sheds light on problems important to the theoretical computer science community but also enriches other fields such as machine learning, data science, and computer vision, equipping future researchers in all these fields to tackle other geometric optimization problems effectively. The educational activities of this project include integrating findings into graduate and undergraduate courses, mentoring students, and broadening participation in theoretical computer science. In technical terms, the primary goal of this project is to establish tight NP-hardness of approximation results for key clustering problems, such as minimizing k-means, k-median, and k-center objectives in L_p (i.e., L subscript p) metrics. A secondary goal is to study these objectives through the lenses of fine-grained complexity and the theory of stable instances. The three main research directions are: (i) resolving the Johnson Coverage Hypothesis, (ii) determining tight hardness of approximation factors for popular clustering objectives in the Euclidean metric, and (iii) proving conditional lower bounds for the Euclidean k-means problem with fixed k. 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 · 2025-01

There is a tension in neuroscience between the emergent phenomena of interested, such as learning and memory, and the level at which most data are acquired. For example, numerous experimental labs study how the strengths of synaptic connections and their dynamics affect cognition by establishing empirical correlations between in vitro electrophysiology measurements and data collected in animal behavioral experiments. However, these correlations fall short of causal explanations: to date, there exist no mechanisms connecting recordings in individual neurons and synapses with cognitive learning dynamics. The problem is not due to a lack of observations at either the neuronal or the systemic level; rather, it reflects a principal gap in our ability to link these two scales. Even if a full description of every neuron in the brain could be produced, there would still be a gap in our ability to transition from local data to making qualitative conclusions about how it combines to produce systemic cognitive outcomes. Addressing this problem requires a conceptual framework encompassing a computational model that would link the experimentally derived characteristics of individual cells with effects of those characteristics at the ensemble level. The proposed research aims to provide a way to establish such a connection: developing a data-driven, systemic model of hippocampal spatial learning based on the parameters of the hippocampal synaptic architecture, including the parameters of synaptic plasticity, using novel topological and geometric techniques. Recent developments in Algebraic Topology will be used to integrate the parameters of synaptic connectivity and synaptic plasticity (e.g., long- and short-term potentiation and depression), to study structure of this map, the mechanisms of its formation and deterioration, and to evaluate the time required to produce a spatial map of a given environment, etc. This project is a natural evolution of prior work done by the Dabaghian group on modeling the mechanisms of spatial learning, based on algebraic topology methods developed by the M?moli group. The theory-based insight into learning phenomena will produce a qualitatively better understanding of how to interpret data, how to design new experiments, what variables should be targeted in measurements, as well as how to minimize use of animals, and in general how to optimize use physical and intellectual resources. 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 · 2025-01

Inefficient methods for transformation and regeneration of recalcitrant plant species prevent widespread applications of genome editing technologies for both basic and applied research in established and emerging crop species. Overcoming these limitations is particularly relevant in monocotyledonous crops, such as maize, which alone provide most of the calories consumed by humans. In this project, maize lines expressing genes that promote regeneration, also known as morphogenic factors, will be used to provide a thorough understanding of the molecular events leading to the successful formation of new plants starting from differentiated tissue. This knowledge will be instrumental in developing new strategies for improving transformation of maize and other plant species, and will be integrated into course-based undergraduate research experiences (CUREs) as well as hands-on transformation workshops. The proposed research will exploit a morphogenic-based system called “GGB” to understand how certain morphogenic regulators reprogram somatic cells to develop into embryos and identify key regeneration genes that could be targeted to improve transformation efficiency in recalcitrant genetic backgrounds. This will be accomplished by the identification of direct targets of regulation of the GGB components via single-cell transcriptomic and DNA-binding approaches, and by the development of a diverse panel of maize inbred lines expressing the GGB morphogenic regulators. By exploiting the regenerative capacity of this system, protoplast regeneration, a challenging but advantageous system for the rapid generation of non-GMO edited plants, will also be revisited. This research will provide insights into the molecular basis of tissue- and genotype-dependent regeneration, helping to identify and eventually bypass roadblocks to regeneration, and will facilitate the development of high-throughput systems for genome-editing and transgenic line generation in diverse genetic backgrounds. This project is jointly funded by Genetic Mechanisms (BIO-MCB). Emerging Frontiers (BIO), and the Plant Genome Research Program (BIO-IOS). 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 · 2025-01

The US generates more than 100 million tons of wet organic waste each year. Directing this waste away from landfills can bring great environmental benefits. However, the conversion or re-use of waste-derived products may release contaminants such as microplastics into the environment. Microplastics are plastic particles smaller than 5 millimeters in size. Recent research has raised concerns about the risks of microplastics in the environment to human and ecosystem health. Hydrothermal conversion is a promising process to make renewable energy and valuable products from wet organic waste. However, limited knowledge is available on the fate of microplastics during hydrothermal conversion. The goal of this project is to understand the transformation of microplastics in hydrothermal systems in an integrated fashion to address gaps in our knowledge. Successful completion of this research will benefit society by facilitating the design of better waste management systems to reduce and/or eliminate microplastic contamination. Additional benefits to society result from public outreach and educational opportunities to increase scientific literacy and build sustainability skills for college students to improve the STEM workforce. Land application of waste-derived products is a significant source of microplastics into terrestrial ecosystems. Compared to landfilling, hydrothermal technologies have the potential to degrade/depolymerize microplastics in municipal solid waste while substantially reducing the environmental impacts at a competitive cost. The overarching goal of this research is to investigate the fate, conversion mechanisms, and resource recovery implications of microplastics during hydrothermal valorization of wet organic wastes. Two hypotheses form the basis of this project. First is that microplastics undergo resin-specific degradation reactions that can be enhanced by specific catalysts. The second hypothesis is that matrix effects from waste feedstock and reaction intermediates affect microplastic reactions. To test these hypotheses, the research team will undertake a series of experimental and modeling activities for the hydrothermal conversion of three widely used plastic resins: polyethylene terephthalate (PET), polyethylene (PE), and poly(vinyl) chloride (PVC). Specific tasks are designed to: i) establish conversion limits of plastic resins under autocatalytic conditions, ii) evaluate microplastic conversion and mechanisms for a variety of catalysts, and iii) investigate interactions between microplastics and waste matrices for hydrothermal waste valorization using techno-economic analysis and life cycle assessment. Educational activities focus on providing research opportunities for undergraduate students and course development on sustainable waste management. Public outreach events will be hosted during each year of the project and shared with the public to support the broader adoption of sustainability. 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 · 2025-01

Graphs are one of the most common types of data across various application fields in science and engineering. Graph analysis has been central for multiple communities, including the classical graph theory community, network analysis, graph optimization, as well as the modern day graph learning communities. Traditionally, graphs are regarded as purely combinatorial objects. However, as applications of graphs proliferate, they tend to be regarded as much richer structures. For example, a graph might be viewed as a noisy skeleton of a hidden geometric domain, and there could be rich, complex data associated with its nodes or edges. While this viewpoint is not new, existing algorithmic treatments of graphs have not yet fully leveraged this perspective. In this project, the investigators aim to further integrate various (geo)metric and topological perspectives into graph analysis in order to enrich graph analysis algorithms and broaden the range of methodologies one can use to tackle diverse graph related tasks. This project will integrate ideas and notions from metric geometry, applied topology, spectral geometry and also algorithms to develop new perspectives and effective methods to analyze complex graphs. It will inject new ideas to graph analysis and learning, while at the same time also advancing the field of geometric and topological data analysis. Given the ubiquity of graphs data, methods resulting from this project can potentially impact various application fields, from scientific domains such as molecular biology, materials science, neuroscience, to engineering domains such as chip design. Results from this project will be integrated into the data science curriculum, strengthening the workforce by training undergraduates and graduates in data science. More specifically, the investigators will consider a range of important problems related to the study of individual as well as of collections of graphs. A central theme of this project is to view graphs as objects enriched beyond their combinatorial structures. Two specific research thrusts that the investigators will focus on are: (1) various graph distances, trade-offs between their discriminating power and computational complexity, and potential applications in graph sparsification and in the study of graph neural networks; and (2) modeling, recovering and using (potentially higher order) structures in graphs. To tackle the challenges emerging from these two research thrusts, the investigators will use various metric and topological methods. Examples include viewing graphs as metric spaces and bringing in topological tools (e.g., the interleaving distance from applied topology) to compare them; viewing graphs as metric measure spaces so as to use optimal transport ideas; and bringing together topological persistence through the high dimensional Laplace operator to study spectral structures induced by graphs. 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 · 2025-01

The next era of spectrum is envisioned to have spatially and spectrally adjacent systems that are dynamic, resulting in frequent cross-system interference. Naturally, interference lies at the heart of spectrum sharing and involves a network of radio transceivers, distributed in space with varying behavior over time. Mechanisms used in the current and past eras of spectrum management, have all run up against limitations owing to the cost and potential lack of scalability of such solutions. Cost is relevant to hardware complexity of the radio front-end, which, for the case of higher frequencies in the-tens of gigahertz-regime, becomes even more critical. Computational complexity of the proposed algorithms, coordination among the network terminals required for the proposed solutions (and necessary power and bandwidth resources for such coordination), and finally, distributed channel estimation (and the necessary resources to acquire such information) all contribute to the complexity. This project enables affordable, accurate, near-real-time spectrum situational awareness, including simple spectrum sensing algorithms, distributed mechanisms, and relevant spectrum sensing hardware. In addition, this project targets mechanisms at the physical layer that provide some form of radio waveform protection against malicious or unwanted interference, without modifying the core of the existing radio infrastructure. This work puts forth both spectrum situational awareness and protection from interference, exploiting ultra-low complexity radio hardware and non-coherent techniques; the basic idea lies at the heart of backscatter radio, which enables a fabric of low-complexity backscatter tags for said objectives. These tags are controlled through the receiver/gateway, connected to the cloud, without however requiring channel state information (CSI) regarding any of the involved links. The proposed fabric offers an intelligent, yet low-cost solution with minimal hardware complexity (due to the adopted backscatter radio tags), limited channel state information (due to the proposed non-coherent algorithms), with the capacity to observe signal strength (power), frequencies and direction-of-arrival (DoA) for a set of in-band, simultaneously operating links. Such multidimensional spectrum situational awareness comes with a collateral dividend: interference protection, i.e., the ability to cancel interference at specific receiver locations. Techniques developed include both model-based, as well as data-driven machine learning (ML) approaches. In addition, this work targets demonstration of the proposed principles in the FR3 band, with a particular focus on the 12.2 − 12.7 GHz band, where next generation cellular, digital video broadcasting and low-earth orbit satellite (SAT) radio applications have the potential to coexist. The research will focus on three key thrusts: (1) Thrust 1 develops a framework for multidimensional spectrum situational awareness using a backscatter fabric. (2) Thrust 2 develops a framework for spectrum protection at the PHY layer using non-coherent, data-driven, DoA assisted protection algorithms against interference. (3) Thrust 3 focuses on experimental evaluation on the COSMOS Testbed using the illustrative example of 5G Terrestrial Network and SAT co-existence in FR3 spectrum. The project will also quantify the density and spatial coverage requirements of the backscatter fabric to enable spectrum situational awareness and spectrum protection across a variety of spectrum bands. The creation of the backscatter fabric will lead to the development of robust solutions for spectrum situational awareness and protection, contributing to the envisioned Spectrum Era 4 and the ever-expanding problem of meeting increasing wireless data demands. Furthermore, the project’s theme is well-suited for the development of STEM projects that will captivate students at various educational levels. 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 · 2025-01

Complex datasets arise in many disciplines of science and engineering and their interpretation requires Multiparameter Data Analysis, which broadly speaking, studies the dependency of a phenomenon or a space on multiple parameters. For instance, in climate simulations, scientists are interested in identifying, verifying, and evaluating trends in detecting, tracking, and characterizing weather patterns associated with high impact weather events such as thunderstorms and hurricanes. In recent years, topological data analysis (TDA) has evolved as an emerging area in data science. So far, most of its applications have been limited to the single parameter case, that is, to data expressing the behavior of a single variable. As its reach to applications expands, the task of extracting intelligent summaries out of diverse, complex data demands the study of multiparameter dependencies. This project will help address this demand by developing a sound mathematical theory supported by efficient algorithmic tools thus providing a powerful platform for data exploration and analysis in scientific and engineering applications. The educational impact will be accelerated by the synergy between mathematics and computer science and integrated applications. Graduate students supported by the project will be trained to develop skills in mathematics and theoretical computer science, most notably in algorithms and topology, and analyze some real-world data sets. The investigators will follow best practice to recruit and mentor students from underrepresented groups who will participate in the project. The investigators also plan to broaden research engagement via workshops or tutorials at computational topology and TDA venues. Although TDA involving a single parameter has been well researched and developed, the same is not yet true for the multiparameter case. At its current nascent stage, multiparameter TDA is yet to develop tools to practically handle complex, diverse, and high-dimensional data. To meet this challenge, this project will make both mathematical and algorithmic advances for multiparameter TDA. To scope effectively, focus will be mainly on three research thrusts to: (I) explore multiparameter persistence for generalized features and develop algorithms to compute them; (II) exploit the connections of zigzag persistence to multiparameter settings to support dynamic data analysis, and (III) generalize graphical topological descriptors. From a methodological point of view, the geometric and topological ideas behind the proposed work inject novel perspectives and directions to the important field of computational data analysis. In particular, the project team will investigate several novel mathematical concepts in conjunction with algorithms to address various challenges appearing in the aforementioned thrusts. The resulting TDA methodologies have the potential to complement and augment traditional data analysis approaches in fields such as machine learning and statistical data analysis. The investigators bring together expertise in theoretical computer science, algorithms design, mathematics, and in particular topological data analysis to conduct this research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Global mean sea level is rising today at approximately 3.4 mm/year and accelerating, though it is unclear how processes of sinking of the land and sediment input to the coasts influence the impact of sea-level rise on ancient, modern, and future shorelines. This research will improve knowledge of how local processes drive observed sea-level variations on continental margins in general, and at the US mid-Atlantic margin in particular. The investigators will combine information from sediment samples, high-resolution subsurface imaging, and modeling to evaluate global, regional, and local impacts on the stability of shorelines in a changing world. Broader impacts include support for two Early Career Researchers and a community workshop. The investigators will continue their long-standing sea-level outreach to students, the public, and stakeholders through research opportunities, teaching, public events, and press interviews. IODP Expedition 313 drilled, cored, and logged Neogene clinoforms on the New Jersey middle shelf to recover geologic evidence of the impact of changes in sea-level, sediment delivery, and tectonism on shallow water sedimentation. The RV Marcus G. Langseth collected 550 km2 of high-resolution 3D seismic reflection data surrounding these drillsites, placing the cores, logs and previous regional 2D seismic data in a spatial and temporal context. The current research will apply state-of-the-art imaging to the 3D volume and iteratively match it with 2D forward models of sequence evolution to improve knowledge of how coastlines and shallow shelves evolve in response to changes in sediment supply, global mean sea level and tectonics (including thermal subsidence, flexural response, and mantle dynamic topographic variations). The investigators have proposed three hypotheses: 1) a major increase in sediment supply to the NJ margin began at ca. 13 Ma, significantly widening the shallow continental shelf and sharply changing sequence geometries, facies distributions, and processes of seaward sediment transport; 2) gravity-driven debris flows played a major role in downslope sediment transport before the Middle Miocene Climate Transition (14.8-12.8 Ma), but during and after this interval canyon cutting coupled with channelized flows became the major agent moving sediment to the outer continental shelf and beyond; and 3) global mean sea-level change throughout the Neogene was the dominant process forming sequence boundaries on the Myr to 405 kyr scale, while variations in sediment supply were responsible for changes in sequence geometry and facies distribution. These hypotheses will be tested by applying 3D seismic imaging techniques in a sequence stratigraphic framework including analyses of seismic attributes to map stratal geometry and facies ground-truthed at the Exp 313 drill sites. The investigators have developed a 2D forward stratigraphic model that simulates deposition of marine siliciclastic sediments on a passive margin in response to parameterized variations in global mean sea level, sediment supply, and subsidence due to thermal cooling, flexure, and compaction. They will use optimization algorithms applied within a Bayesian framework to yield a probabilistic fit of the model output to the observed stratigraphic record and generate the most likely history of these drivers. This Bayesian inference framework will be used to derive model output values for metrics like shoreline position, which is challenging to measure independently from the underlying data, but can be constrained within the model output by core data and the proposed 3D seismic facies analysis. Integrating continuously cored and logged drillsites with high-resolution 3D seismic data and forward stratigraphic modeling poses a unique research opportunity. Results will advance understanding of the response of ancient shorelines to changes in sea-level, sediment supply and tectonics and contribute to informed predictions of how these factors will impact coastlines of the future. This project will support two Early Career Researchers and foster their development as leaders in seismic imaging and stratigraphic modeling used to evaluate the relative effects of change in global mean sea level, deformation of the Earth, and rates of sediment supply on shoreline position. The researchers will run a two-day community workshop on sequence stratigraphy and stratigraphic modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

One of the main tools for understanding the distribution of prime numbers is through properties of the Riemann zeta function. The zeta function is the most fundamental example of an L-function, which is a mathematical construction that combines arithmetical information about all the primes at once. More general L-functions, such as Dirichlet L-functions, are useful for understanding primes in arithmetic progressions. One of the main ways that L-functions are studied is by placing them into families, such as the family of all Dirichlet L-functions, and viewing their properties through this framework. Much of the proposed work in this proposal concerns the development of properties of new families of L-functions. One of the main goals is to better-understand the size of these L-functions, especially in certain ranges that have been inaccessible using previous methods. The PI will continue to mentor and collaborate with undergraduate students, particularly through the Texas A&M REU. Such opportunities are important for preparing students for graduate studies, particularly for undergraduate students from non-PhD granting institutions as well as from population groups underrepresented in STEM fields. The PI will also continue to advise PhD students to work on problems related to families of L-functions and their moments. The PI will study new families of automorphic forms and their associated L-functions, especially via moments of L-functions and large sieve inequalities. The PI plans to study high moments of L-functions in order to make progress on the challenging but important L-functions in conductor-dropping families. The proposer will also study narrower families of L-functions through the use of new versions of the relative trace formula that isolate small families based on their local behavior. In a related vein, the proposer will study large sieve inequalities for families of automorphic forms, with two main goals. One objective is to establish large sieve bounds in some of the new, narrow families. A second goal is to develop heuristics for conjecturing the size of a large sieve bound for more general families. The PI will mentor PhD students on problems on moments of L-functions for both narrow families and for higher degree L-functions. The proposer will study newform Dedekind sums with his undergraduate students. The methods employed will be techniques from analytic number theory such as functional equations, exponential sums and integrals, and the spectral theory of automorphic forms, including the Arthur-Selberg trace formula and the relative trace formula. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The Caribbean Sea has experienced significant warming and salinification over the last couple of decades. An important component of the Atlantic Ocean circulation that transports heat northward and that is part of the Atlantic Meridional Overturning Circulation (AMOC) flows through it. Yet, little is known about what happens to the AMOC waters within the Caribbean Sea that then continue onward through the Gulf of Mexico and the Florida Straits to form the Gulf Stream, carrying an increasing amount of heat from the tropics to the North Atlantic. This project addresses this issue by investigating how water masses are changing in the Caribbean Sea with the specific goal of identifying processes that contribute to spatial differences in mixing via eddies and to changes in transport. Tools that will be used include observations with autonomous vehicles (gliders) across key passages and a modeling effort with the Regional Ocean Modeling System (ROMS). The results will improve understanding of how heat is transported through the western Atlantic and provide information that can support improved prediction of hurricanes and other ocean–atmosphere processes, helping communities better prepare for potential hazards. This project will address critical gaps in understanding the water mass transformation processes within the Caribbean Sea, a through-flow region for North and South Atlantic waters that form both the upper ocean limb of the Atlantic Meridional Overturning Circulation (AMOC) and subtropical Atlantic recirculation. The Caribbean through-flow represents ~25% of the northern hemisphere’s northward atmospheric-ocean heat transport. In addition, the Caribbean is highly vulnerable to tropical cyclone impacts, ecosystem degradation, and climate impacts with steadily increasing upper ocean temperatures. The hypotheses focus on the processes that modify water masses along the Caribbean through-flow system. Thus, the project includes investigation of regional differences in water mass modification processes based on (1) the spatial heterogeneity in the mesoscale eddy field; (2) the influence of steep and complex bathymetry; and (3) the influence and variability of local wind stress curl gradients. To address these differences, the project will carry out high-resolution autonomous underwater vehicle observations, investigate long-term regional model reanalysis, and conduct process-oriented model experiments of the Caribbean Sea. These efforts will improve our understanding of the mechanisms driving water mass transformation and of implications for regional and global communities. The field campaigns and modeling efforts in this project represent a crucial step towards filling significant observational and conceptual gaps in our understanding of the Caribbean Sea's role in the broader oceanic circulation system. 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.