Grants

NSF

Sex differences in health and disease have often been attributed to sex hormones, but there is a growing appreciation that sex chromosomes directly impact sex differences in disease progression. The goal of this CAREER project is to improve the understanding of how two X chromosomes in female cells contribute to female-specific aortic valve stenosis progression, the most common aortic valve disorder that can lead to sudden heart failure. To accomplish the research objectives, sex-specific cell culture platforms will be engineered using hydrogels that mimic the soft tissue microenvironment of the aortic valve. These platforms will be used to investigate how X chromosome genes contribute to female-specific valve cell phenotypes. Using various biomaterial tools, female-specific genes on the X chromosome will be linked to increased aortic valve tissue stiffening in females. In parallel, the educational objectives focus on clarifying and communicating the importance of sex as a biological variable in biomedical research to undergraduate students, the UC San Diego community, and the public. Specifically, the importance of sex as a biological variable in biomedical research will be promoted though undergraduate senior design projects, campus-wide symposia, and public science communication events. Biological sex influences the progression of aortic valve stenosis (AVS), with males exhibiting increased valve calcification and females experiencing severe valve fibrosis prior to eventual calcification. The biological mechanisms underlying sex-dependent fibro-calcification of aortic valves remain poorly defined, partly due to significant challenges in disentangling the effects of sex hormones from those of sex chromosomes on sexually dimorphic VIC phenotypes in vivo. Existing in vitro strategies rely on cell culture substrates that fail to replicate the time-dependent dynamic changes in extracellular matrix (ECM) structures observed in health and disease. This CAREER project aims to engineer dynamic in vitro systems to investigate how X chromosome-linked genes modulate female-specific fibrosis during AVS, while isolating these effects from hormone biology. Valvular interstitial cells (VICs), the primary fibroblast population within aortic valve tissue, are believed to drive sex-specific differences in aortic valve fibro-calcification. In XX female VICs, X-inactive specific transcript (XIST), a long non-coding RNA that silences one of the X chromosomes during X chromosome inactivation (XCI), is expressed. The central hypothesis of this work is that reduced XIST expression in VICs regulates dynamic ECM remodeling and fibrosis during aortic valve stenosis. To test this hypothesis, the research objectives will focus on engineering hydrogels as cell culture platforms using biorthogonal chemical conjugations to create culture systems that mimic the dynamic ECM. These hydrogels will allow for spatial and temporal evaluation of XIST expression dynamics independently of sex hormones. Collectively, this proposal seeks to establish fundamental knowledge regarding how XIST expression regulates ECM alterations during valve fibro-calcification, independent of sex hormone effects. Future efforts will explore the independent and synergistic roles of sex hormones and sex chromosomes to fully elucidate the mechanisms defining AVS. 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

Scientists across various fields face the challenge of answering numerous related questions with limited or noisy data. For example, genomicists may need to assess thousands of genes using data from only a few subjects, while survey statisticians might analyze average incomes in many towns based on limited surveys. To address these challenges, researchers often borrow information from related questions, a process that requires sophisticated statistical reasoning due to varying underlying characteristics. Empirical Bayes offers a method to enhance individual question inference by sharing information, potentially improving statistical accuracy. However, it relies on strong modeling assumptions, limiting its applicability. This research aims to make empirical Bayes more powerful and accessible by integrating it with modern machine learning and causal inference, demonstrating its effectiveness under fewer assumptions, and developing methods to assess uncertainty more effectively. These advancements will help practitioners utilize empirical Bayes in various scientific and industry contexts without extensive statistical modeling. Additionally, the project will produce a monograph and provide training for students and preceptors on modern data science challenges using empirical Bayes. This research aims to advance empirical Bayes by developing new methodologies and statistical theories to address four key limitations: the assumption of known likelihoods, the treatment of nuisance parameter heterogeneity, the development of nonparametric inference methods, and the integration with machine learning. The project will create new inference methods for primary parameters by using empirical partially Bayes methods and Bayesian nonparametrics, enhancing frequentist Bayes multiple testing theory with guarantees like false discovery rate control. Additionally, the research seeks to learn the unknown Bayes rule through neural networks with a specialized loss function, incorporate James-Stein shrinkage for combining unbiased estimates with semisupervised machine learning, and integrate empirical partially Bayes inference with doubly robust double machine learning for large-scale causal inference. 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

This Faculty Early Career Development (CAREER) project intends to develop new legged robots that combine agility over rough terrain with speed and efficiency, enabling robots to go wherever human workers can. Robots with legs can traverse rougher terrain than wheeled vehicles and carry heavier loads for longer periods than flying drones. However, current legged robots are significantly slower and less efficient than their wheeled and flying counterparts. The hypothesis is that robot legs driven by a small number of powerful motors — similar to a car’s wheels or a boat’s propellers — can achieve higher speeds and efficiency compared to conventional robot legs powered by many smaller, less powerful motors. Achieving this goal requires new knowledge in controlling balance and steering with fewer motors, as well as investigations into efficiency, robustness, and payload capacity. The resulting robots aim to match human speed, even over long distances, to assist with tasks such as package delivery, emergency response (e.g., wildfire fighting), and scientific exploration. This project will encourage K-12 students' interest in engineering through hands-on activities with walking linkages and by providing training to expand access to the FIRST LEGO League. This CAREER project will attempt to combine a single-degree-of-freedom, linkage-based legs that recirculate in a stepping motion through continuous crank rotation with underactuated balance control algorithms based on reduced-order models to enable high-speed walking, low cost of transport, and flexible navigation over rough terrain. Investigations into passive dynamic walking and actuator coordination will focus on improving efficiency and payload capacity. Bipedal and multi-legged robot designs, along with control architectures developed around shared reduced-order models of walking locomotion, will be evaluated through simulation, physical prototyping, and hardware experiments. These efforts aim to set new records for walking robot speed and endurance. This work's improvements to the foundations of legged robot performance could enable future robot locomotion over uneven terrain for service in the home, transportation outdoors, and planetary exploration beyond. 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

This Faculty Early Career Development (CAREER) grant focuses on supporting research that intends to advance sustainable textile manufacturing through innovative investigations of hemp fibers. Hemp is emerging as a promising alternative to traditional textile fibers, offering an eco-friendly option to reduce dependence on cotton and synthetic materials for textiles. However, processing hemp into high-quality textiles presents challenges due to their stiffness, lack of crimp, and limitations in existing spinning technologies. This research project aims to overcome these barriers by introducing a modified spinning process that leverages wetting to improve fiber cohesion and adjustments to the spinning triangle for enhanced yarn structure. These advancements contribute to the broader goals of sustainable textile production by minimizing environmental impacts and reducing reliance on synthetic fibers. The overarching goal is to advance the knowledge base in sustainable textile manufacturing and integrate this knowledge with STEM education programs, spanning middle school to doctoral levels, through curriculum development, outreach events, and hands-on research experiences. This project serves the national interest by contributing to the progress of science, advancing environmental sustainability, and preparing a technically skilled workforce. The technical objectives of this project involve developing a comprehensive framework to understand the relationships between the structural, mechanical, and environmental properties of hemp yarns and textiles. Specific aims include: (1) investigating the interplay between hemp fiber surface topography, water capillary adhesion, and fiber cohesion to enhance yarn quality; (2) implementing automated methods for analysis of the spinning triangle using transparent rollers and deep learning algorithms; (3) exploring fiber dynamics in hemp yarns through AI-assisted micro-CT segmentation for detailed structural insights; and (4) performing a cradle-to-gate life cycle assessment to evaluate the environmental impact of hemp yarns and textiles. These efforts address fundamental questions in fiber science, such as how surface characteristics and processing conditions affect fiber cohesion and yarn performance. 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

This project's goal is to help communities better support the facilitation of audience engagement and information sharing in real-time social media. The real-time and ephemeral nature of livestreaming, social virtual reality, and other real-time platforms raises new concerns around community management relative to existing science on supporting online communities. Drawing parallels between real-time social media and best practices in conversational facilitation and conflict resolution, the project team will develop methods and tools to help community leaders identify behaviors that diverge from community norms and provide guidance (and when needed, warnings) interventions to encourage participants to engage in ways that serve the community's goals. Conducting this research will advance scientific knowledge around building strong online communities while providing many educational and outreach opportunities in computer science, interaction design, and real-time social media. The research plan is structured around two main aims. The first aim is to develop tools that support hosts of real-time social media in facilitating audience participation. This includes modeling audience behaviors and developing tools that allow community members to guide participants toward adhering to community norms. The second aim is to develop auditing mechanisms to identify potential biases in the facilitation decisions made by both community leaders and the tools they use, and to provide mechanisms and information that help community members to contest those decisions. Together the research will develop a deeper understanding of community behavior in real-time social media, along with practices and tools that operate transparently for both community leaders and participants. 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

Three-body recombination reactions, requiring three reactants to form the reaction product, are key to a multitude of relevant physical processes, spanning more than four orders of magnitude in temperature, from cryogenic environments to plasmas. However, despite their relevance, there is no general theoretical framework to deal with them. This project aims to develop a comprehensive theoretical framework for three-body recombination reactions, including atoms, molecules, and ions as reactants, with applications in cold chemistry, atmospheric chemistry (ozone formation), and plasma physics. Additionally, this project will focus on a new course designed to orient new students across departments on what a Physics degree can offer in the professional world. This project has three main objectives: (1) to develop a theoretical framework for three-body recombination reactions involving partners with internal degrees of freedom; (2) to apply semi-classical theory to three-body recombination reactions: hybrid ion-atom traps and plasma physics; and (3) to understand ozone formation in the stratosphere. To achieve these objectives, a novel semi-classical theory for three-body recombination reactions will be developed, based on a classical trajectory approach in hyperspherical coordinates. The results will be benchmarked against existing (but unexplained) experimental data in several systems of interest. Furthermore, the underlying potential energy surfaces required for evaluating the reaction dynamics of each system under consideration will be determined using a hybrid machine learning-ab initio approach, thus obtaining a more reliable and efficient description of the interatomic energy at a lower computational cost. 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

This Faculty Early Career Development (CAREER) grant will investigate the elastic behavior of structures that can unfold and deploy from compact configurations into useful 3D geometries. Deployable structures are used in several areas pertaining to robotics (e.g., soft robots used in minimally invasive surgeries and in search-and-rescue), to aeronautics and astronautics (e.g., smart airfoils, deployable mirrors and solar panels and sails in space missions) and to smart structures (e.g., deployable shelters and rooftops). The relationships that exist between the global behavior of a deployable structure and the local behavior of its elementary constituents (bars, plates and membranes) are governed by a mixture of geometrical and mechanical laws. The research aims to establish a fundamental understanding of such local-to-global relationships and of the way they dictate how a deployable structure changes shape under external loads so as to improve current modeling, design and control paradigms. The theme of geometry provides aesthetic and appealing connections with several areas of artistic and fashion design and will be leveraged to increase public engagement with science and technology through outreach activities which, with the help of an institutional center, will particularly target K-12 and underrepresented minorities. The main purpose of the project is to initiate a theory of the finite, geometrically non-linear, elastic deformations of deployable structures that are tailored on small space scales as in architected materials and metamaterials. The technical objectives of the research efforts are to (i) characterize the macroscopic deformation paths compatible with the small-scale kinematics of a deployable structure composed of rigid or inextensible elements; to (ii) compute the generalized, strain-gradient or enriched, elasticity functionals that govern the equilibrium geometries of a deployable structure and particularly so in cases where standard Strength of Materials theories break down; (iii) formulate conceptual control problems and solve them for actuation parameters understood as the boundary data that deform the deployable structure into a target shape; and (iv) establish inverse design paradigms that allow us to find deployable structures with pre-programmed deformation paths. To do so, the project will develop analysis methods marrying differential geometry, asymptotics and homogenization theory. This project will allow the PI to advance the knowledge base in solid mechanics and to support his long-term career within that field. 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

Plastic pollution is a global threat to human and environmental health. During everyday use and weathering, plastics breakdown and release highly toxic plastic additives. There is an urgent need for safe plastic additives. In this project, flavones, a class of natural chemicals found in plants and food, will be explored as sustainable plastic additives. Flavones are excellent candidates for light stabilizer additives and can kickstart plastic breakdown. This project will address the end-of-life management of plastics. Research outcomes will create: 1) safe alternatives to the toxic plastic additives that are currently in use; and 2) solutions to enhance the breakdown of biodegradable plastics to minimize plastic pollution. The project will provide educational opportunity to train the next generation of environmental engineers. The overall goal of this CAREER project is to advance flavones as sustainable plastic additives to enable the tuning of biodegradable plastic stability and persistence. A major focus is to understand how flavone chemical structure impacts fundamental photophysical and photochemical properties. The project will first evaluate flavones as light stabilizers in biodegradable plastics. Flavone functional groups that control light absorber properties will be identified by exploring the fundamental photophysical pathways of flavones with spectroscopy and reactive chemical probes. Key stabilization performance metrics will be evaluated with an array of materials characterization, chemical analysis, and aquatic toxicity testing to understand flavones’ 1) ability to stabilize biodegradable plastics; 2) photostability within biodegradable plastics; and 3) ecological effects. Finally, the project will evaluate flavones as pro-oxidants to increase biodegradable plastic deterioration. Overall, this CAREER project will advance flavones as sustainable plastic additives, opening the door to a new class of non-toxic additives derived from renewable resources. More broadly, it will advance light-triggered, pro-oxidant additives as an avenue to enhance biodegradable plastic deterioration in slow degrading environments such as surface waters. This project will enhance student training in sustainable chemistry with a focus on circularity, the end-of-life management of materials, and toxicity reduction and hazard prevention through 1) enhancing environmental engineering coursework; 2) connecting students to industry perspectives on sustainability; and 3) introducing sustainable chemistry to high school STEM students through outreach. 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

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Modern question answering systems, embedded in search engines and digital assistants, have improved dramatically with the development of large neural network models. When a user asks a simple question, these systems can typically return an answer directly rather than just linking to a webpage. However, these systems still fail on more complex questions, and when they fail, they may mislead their users. They lack an important capability that humans have: the ability to reason about and synthesize the information they see, retrieve and integrate additional information, and arrive at a justified conclusion. This CAREER project aims to address this shortcoming by developing systems that "think through" textual evidence, leading to more reliable answers that can be explained to a user. Such advances fit into a broader thread of building trustable artificial intelligence (AI) systems that explicitly show their work and are auditable before and during their deployment. This project specifically addresses the problems of question answering and fact-checking by developing a learning-based system that reasons in natural language. The system takes text as input, then applies pre-trained neural network models to reformulate that text, derive conclusions from it, and eventually check a claim or verify an answer. This process produces a series of logically connected statements understandable by a human. This outcome is enabled by two modules. First, a deduction module repeatedly combines two statements and generates a third that follows from the inputs, encapsulating common logical rules. Second, a verifier determines whether the final deduced evidence validates the original claim. Both systems are built from pre-trained models like T5 that have demonstrated strong generalization capabilities. Collecting training data for these models constitutes a core challenge; the project's approach blends multiple strategies including synthetic data generation and human-in-the-loop annotation. These techniques are applied to the domains of question answering and fact checking, problems where providing additional explanation and justification instead of just giving a best-effort answer are essential to make usable systems. This system paves the way for NLP tools that know what they don't know, provide interpretability for end users, and enable system developers to better understand and improve their models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Ramon Alain Miranda Quintana of the University of Florida is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop, implement, and test general methods capable of describing strongly correlated systems. A precise understanding of the behavior of electrons in molecules and materials is needed to reliably predict their properties. Such understanding provides an enticing way to explore new compounds in lieu of performing many delicate and expensive experiments. Unfortunately, current theoretical approaches fail catastrophically when applied to systems with a particular kind of complicated electronic structure: strongly correlated electron systems. Examples of strong correlation can be found in compounds with several metallic centers with unpaired electrons, such as rare-earth metals, lanthanides, and actinides. In these situations, the trade-off between the reliability and tractability of traditional computational techniques is unacceptable, so they can only be used in the simplest cases. Solving this problem will facilitate the design of new organometallic catalysts, and more efficient energy-conversion and high-density data-storage devices. Dr. Miranda’s goal is to develop accurate, computationally efficient, and universal methods capable of treating strongly correlated systems. Dr. Miranda will use the software packages developed in his group to boost the undergraduate quantum chemistry curriculum at UF, allowing the students to tackle more realistic problems in their classes. Moreover, he will create videos and other types of online content to introduce topics in algebra, coding, and computational chemistry to a broader audience. This project has three key aims: (1) design, implement, and test new wavefunction ansätze capable of describing multi-reference problems; (2) develop a new perturbation theory framework as a robust way to optimize flexible wavefunctions and to help calculate various molecular properties; and (3) extend the domain of applicability of our methods using novel embedding techniques and apply them to realistic chemical compounds and processes. Dr. Miranda’s team will develop and exploit a framework (the Flexible Ansatz for N-body Configuration Interaction, FANCI) that greatly expedites the development and testing new wavefunction ansätze. In particular, the focus will be on new families of wavefunction methods, combining traditional ansätze (CI, CC) with novel ideas (quasi-particles, symmetry breaking, seniority) in non-traditional ways. The second goal will be centered on a flexible Perturbation Theory (PT) framework that could suit any possible multi-determinant wavefunction. This approach will be leveraged to facilitate calculation of Reduced Density Matrices and response functions. The final focus on general wavefunction-in-wavefunction embedding protocols will help to model changes in the spin state, redox behavior, etc. of strongly correlated systems. Dr. Miranda’s team will exploit the Fanpy/FANCI/FANPT framework as a way to enhance the preparation in computational chemistry at the undergraduate and high school level. With the help of UF’s Center for Precollegiate Education and Training (CPET), they will interact with teachers to develop hands-on tools for their classrooms, while also hosting high school students. They will also work to engage the community through materials (mostly, videos/lectures) aimed to introduce coding, mathematic tools, and computational chemistry concepts and applications. 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

Driven by global energy challenges and advancements in renewable energy, modern grids increasingly integrate variable power sources and electrical loads. The increasing use of batteries, solar and fuel cells necessitate efficient and dense power conversion over a wide regulation range. This CAREER project aims to improve the size and efficiency of buck-boost power converters by leveraging advancements in wide-bandgap semiconductor technology. Moreover, instead of conventional inductive-based energy processing, we will leverage the increased energy density of capacitors to decrease passive component size and switch stress. These approaches result in a framework of condensed buck-boost converters capable of dramatically increased power density and efficiency across a wide range of operating conditions, enabling greater integration of renewable energy systems and improving drivetrains for future electrification of transportation. This work will also bring advancements in power conversion into the classroom with a curriculum focused on next-generation power conversion and lab-based learning opportunities. This project brings reaches out to the broader community through collaboration with the proposing institution's museum, in an effort that involves development of a demonstration on a vision for the future grid and integration of renewable energy, providing children and adults with hands-on experiences related to power and energy systems. In this work, we leverage novel monolithic bidirectional switches, which can replace two back-to-back devices with one device capable of bidirectional voltage blocking. We plan to perform careful calorimetric characterization to better model and design with these devices. Moreover, this work focuses on capacitor-based topologies, due to their increased energy density; however, capacitor loss modeling under realistic operating conditions is also necessary to take advantage of the improved performance. When combined, these models will enable better design and optimization of the proposed family of buck-boost converter topologies. We will also develop improved figures-of-merit related to reliability and volume to more accurately compare these potential topologies with conventional approaches. By designing several hardware prototypes to validate our models and comparisons, we will also tackle practical implementation challenges related to using monolithic bidirectional switches in multilevel topologies, including commutation loop design, gate drive, thermal design, and control. 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

This Faculty Early Career Development (CAREER) grant will advance scientific knowledge of the flow physics of rooftop vortices from hurricane-type strong winds on low-rise buildings and develop bio-inspired flow control strategies to attenuate the damaging effects of roof suctions on building resilience. Dramatic damage repeatedly occurs on low-rise building roofs during windstorms, as observed in recent hurricanes Matthew (2016), Maria (2017), and Michael (2018). Building roof failure often starts at the windward roof edges and corners, where extreme peak suctions are induced by flow separation and unsteady vortices. Improved understanding of vortex dynamics governing the worst roof suction and smart flow control strategies by learning from nature will contribute towards more accurate wind load prediction, enhanced wind design provisions, and reduction of wind-induced damage and economic and life losses, and thus advance post-windstorm national welfare and prosperity. The bio-inspiration approach will not only produce cost-effective, high-performance mitigation strategies for low-rise buildings, but will also motivate new thinking in broader engineering fields. This research will use the Natural Hazards Engineering Research Infrastructure (NHERI) Wall of Wind (WOW) facility at Florida International University (FIU). Experimental datasets will be archived in the NHERI Data Depot (https://www.DesignSafe-ci.org) and be made publicly available for validation of computational fluid dynamics (CFD) models. To strengthen the persistence of engineering students, first-year undergraduate students will be engaged in a new learning community by integrating scientific questions into team-based, early research experiences, as well as weekly open workshops and invited seminars. The learning community program will improve the STEM infrastructure, broaden underrepresented groups’ participation in engineering, and build a pipeline for the engineering workforce. This project will support the investigator's long-term career vision focused on fundamental research on wind-structure interaction and bio-inspired flow control to increase the wind resilience of civil infrastructure that contributes to community resilience and sustainability. This award contributes to the National Science Foundation's role in the National Windstorm Impact Reduction Program (NWIRP). The specific research objectives are the following: (1) quantify three-dimensional, transient rooftop vortices from hurricane-type high winds of high Reynolds numbers, (2) correlate the unsteady vortices with roof peak pressures, and (3) utilize bio-inspiration as an innovation tool to create cost-effective wind mitigation devices, ultimately enhancing the wind resiliency of low-rise buildings. A series of well-controlled wind tunnel experiments with unsteady flow and pressure over a scaled low-rise building model will be conducted at Cleveland State University and the FIU WOW facility. Systematic measurements of the unsteady three-dimensional vortical flow at high Reynolds numbers will also be beneficial to the broader fluid mechanics community to advance understanding, modeling, and control of a wide class of vortex flow phenomena. The research will result in (1) vortex flow mechanisms governing the peak roof suctions at high Reynolds numbers, and (2) bio-inspired, cost-effective mitigation strategies (porous fractal parapets) to manipulate vortex formation, applicable to new and retrofit of existing low-rise buildings. 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

Interactive dynamics, where individuals or particles influence each other and the overall system, are important in fields like biology, physics, materials science, and social sciences. These dynamics often involve rare events that can have large impacts, such as changes in protein structures or genetic evolution. Inaccurate predictions of these events can hinder our understanding of biological processes, drug design, and our ability to prepare for extreme events. This project seeks to improve our ability to predict and control these rare but critical events by identifying key patterns in energy landscapes and developing new methods for estimating fluctuations and controlling extreme events in complex spaces. The insights gained will not only help in predicting extreme events but also improve our understanding of various interactive behaviors, such as material design and social opinions. Educational initiatives will promote interdisciplinary learning and advance the growth of applied mathematics and related fields. This project aims to predict and control rare, significant events in interactive dynamics on complex configuration spaces. The goal is to advance the theoretical understanding of Hamilton-Jacobi equations (HJEs) and control theory for multiscale interactions by addressing key challenges, including non-uniqueness of stationary solutions, fluctuation estimates in multiscale interactions, and singular optimal control in probability spaces. Theoretical developments will be applied to practical problems, such as rare event simulations, to enable more predictable dynamics in complex systems. First, the investigator will develop a systematic method to select stationary solutions to HJEs, providing a uniformly converging vanishing viscosity approximation and a global energy landscape. Second, a novel decomposition for species concentration and reaction fluxes in non-equilibrium multiscale reactions will be introduced, along with a singular limit framework for fluctuation estimates. Third, rigorous justifications for the singular limit of variational solutions for HJEs with state constraints will be achieved via the equivalence between optimal control and optimal path measures for transition problems in infinite dimensional spaces, particularly in the presence of boundary singularities. The research outcomes will be disseminated through conferences, publications, and online platforms. The approaches developed will also be integrated into undergraduate and graduate teaching, offering research opportunities for graduate students. 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

Some of the ocean’s most efficient swimmers, including jellyfish, salps, and siphonophores, move by expelling water in the form of vortex rings. These vortex rings are created by the systematic contraction and relaxation of their bodies. Colonial siphonophores have many small, flexible, jetting units along their body. These units can coordinate to produce multiple pulsed vortex rings at once, enabling efficient swimming and dynamic and complex directional control. Inspired by these systems, this project will explore how body flexibility and the coordination of multiple vortex rings lead to improvements in swimming efficiency and maneuverability. Findings from this research will inform new design principles for next-generation bio-inspired underwater vehicles, and support advances in ocean exploration, environmental monitoring, and underwater soft robotics. The project will also provide opportunities for students to develop skills in engineering communication and support science education through teacher partnerships and classroom-ready learning materials. This project supports improved advanced manufacturing of robotic swimming vehicles. The goal of this research is to understand how fluid-flexible structure interactions and vortex-vortex interactions influence vortex formation, thrust, and maneuverability in multi-jet systems. This will be achieved by a comprehensive laboratory approach using simultaneous flow and material deformation measurements. Full-cycle analysis of vortex rings generated by flexible materials will reveal how flow and material timescales govern vortex strength and propulsive performance. Three-dimensional constructive and destructive interactions between neighboring vortex rings in multi-jet systems will be assessed to identify flow and geometric configurations that maximize efficiency and control. Understanding these mechanisms will lay the foundations for designing efficient, controllable multi-jet propulsion systems, and expand scientific knowledge of fluid–structure interactions in biologically inspired systems. This project will also be synergistically connected to educational activities that include: (i) establishing a multi-disciplinary module for undergraduate and graduate classrooms using flow visualization as a means of scientific communication; (ii) enhancing Montana K-12 education via teacher research experiences; and (iii) developing classroom-ready fluid dynamics education kits and outreach activities for K-12 classrooms. 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

This Faculty Early Career Development (CAREER) grant will support research that attempts to expand the understanding of how robots can make safe decisions. Ensuring robot safety is a complex challenge, as robots must understand how their actions could lead to harmful outcomes. Most robot safety efforts focus primarily on preventing collisions. However, safety concerns in real-world environments, such as homes, city streets, and hospitals, are far more nuanced. For example, robots should avoid entering areas marked with caution tape, slow down when transporting hot cups of coffee to prevent spills, and seek clarification when uncertain about a task. This research aims to develop new methods that enable robots to better understand and respond to these nuanced safety challenges. The project intends to advance scientific knowledge and contribute to national well-being by making robots more trustworthy and effective in a wide range of settings, from personal homes to public and professional spaces. Additionally, the project will support robotics education through the following initiatives: i) Developing a “Robotics Red-Teaming Challenge,” where college students will design and stress-test robot safety algorithms; ii) Engaging K-12 students in hands-on experiences with robot programming and troubleshooting; iii) Collaborating with government and industry stakeholders to establish safety benchmarks and promote the broader adoption of safe robotics practices. This project will develop an algorithmic framework to align a robot’s understanding of safety with that of human stakeholders. The approach builds on foundational methods in safe control, which characterize safety constraints as arbitrary sets in state space. While this mathematical model is theoretically highly expressive, its practical application has been limited to controlled, collision-avoidance scenarios. This award supports fundamental research to generalize this framework to more nuanced safety specifications by embedding learned patterns about the real world, inferred from robot and human data. Research conducted under this grant will strive to enable robots to: i) Automatically assess the trustworthiness of their learned models of human behavior and react accordingly; ii) Update their internal safety representations using online human feedback, such as language; iii) Generalize their safety policies to account for uncertainty and learned latent state spaces. These advancements will be evaluated through hardware experiments, where robot arms and mobile manipulators will be deployed in unstructured environments, such as kitchen and atrium settings. In addition to these research objectives, the project includes a robust education and outreach plan to teach the next generation a nuanced yet practical perspective on robot safety. This will emphasize the often subtle and unanticipated consequences of robot interactions in real-world contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Image-based computational models of the cardiovascular system play an increasingly important role in advancing the fundamental understanding of cardiovascular physiology and supporting clinical diagnosis and treatment planning. However, traditional models are primarily based on well-posed physics that are solved numerically, and their reliability is limited because of unknown or uncertain modeling conditions. On the other hand, sparse and noisy data have become increasingly available thanks to the rapid development of medical imaging techniques (e.g., flow MR images), which can be utilized for model inference and uncertainty reduction. Hence, forward uncertainty quantification and inverse data assimilation in cardiovascular simulations are of paramount importance to enhancing predictive confidence and prompting clinical translation efforts. This project will develop computational cyberinfrastructure for data-enabled forward and inverse stochastic cardiovascular modeling by leveraging recent advances in scientific machine learning. The project aims to establish a novel paradigm of data-augmented cardiovascular fluid-structure simulations, which could help transform personalized cardiovascular diagnostics/therapeutics, leading to higher quality of life. Moreover, this research program will also try to address long-standing challenges in effectively engaging students in STEM education across K-12, undergraduate, and graduate education by promoting an interactive and inclusive learning strategy. In particular, the PI will (1) design pedagogical software using physics-informed transfer learning for rapid interactive fluid simulation based on hand-drawn sketches; (2) develop new modules on Artificial Intelligence & Mechanics for U.S. Department of Education TRiO programs to engage K-12 students from low-income families in emerging interdisciplinary STEM fields. The overarching goal of this CAREER program is to pioneer a scalable and transformative computational cyberinfrastructure for forward and inverse uncertainty quantification (UQ) of cardiovascular modeling based on physics-informed Bayesian geometric deep learning, leveraging physics/physiological knowledge to enable efficient probabilistic learning with sparse and noisy data. This project tackles the fundamental challenges faced by the traditional paradigm of modeling cardiovascular fluid-structure interaction (FSI) dynamics. In the proposed framework, geometric deep learning models will be constructed based on both (partially) known physics and sparse measurement data in a Bayesian manner, enabling efficient forward and inverse FSI simulations with quantified uncertainties. Specifically, the PI will (1) formulate a variational PDE-informed, discretization-based learning framework using graph convolutional networks and use a reduced basis to constrain the dimension of the solution space, facilitating network training; (2) enable high-dimensional UQ capability of the proposed learning framework based on scalable variational Bayesian inference; (3) establish a multi-fidelity meta-learning strategy to parameterize solutions in the physical parameter space for rapid surrogate modeling, on the path to real-time cardiovascular simulations. The fast inference speed, strong expressibility, and GPU parallelization of deep learning models will be exploited to enable large-scale stochastic FSI simulations with patient-specific geometries. This project will build a solid foundation for developing the next-generation computational cyberinfrastructure of cardiovascular FSI 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

Understanding human behavior from video is a challenging and transformative area of research, with applications in robotics, assistive technologies, neuroscience, and beyond. Humans do not act in isolation; their actions are shaped by their surroundings, interactions with others, and the objects they use. This project aims to develop a new foundational paradigm for understanding humans in 4D — their 3D state over time — from any type of video. Unlike current methods, this approach integrates people with their physical and social context, enabling a deeper understanding of human activities. By creating a computational framework that can analyze both exo-centric (third-person) and ego-centric (first-person) videos, the project addresses the limitations of existing methods and supports downstream applications such as assistive technologies, wearable AI, and data analysis for neuroscience and practical everyday tasks. The resulting advancements will enable robots to learn from observing humans, assistive technologies to better support users, and wearable devices to provide richer context for human activity, contributing to safer, more effective, and accessible technologies with far-reaching impacts across science, industry, and society. This project will design a scalable, transformer-based model to capture the 4D state of humans and their situational context, including surrounding environments, social interactions, and object use. By leveraging recent advancements in 3D pose estimation, scene reconstruction, and large-scale multimodal models, the research will unify these aspects into a single flexible framework. The approach accommodates various types of video inputs, whether single or multiple views, and will include comprehensive evaluations of its performance. The resulting open-source code, models, and data will provide tools for researchers to advance 4D human understanding and related fields. This project also integrates research with education by developing curricula that combine vision, geometry, and machine learning, and by creating summer research opportunities for a wide range of students, along with accessible online tools to engage a broader audience. 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

The Internet of things (IoT) cloud is one of the key pillars of the foundation upon which modern IoT systems rest (Smart Home, Industrial, Smart City, Retail, and Health applications, etc.). Newer IoT devices are taking advantage of the managed Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) IoT cloud services (e.g., AWS IoT Core, Azure IoT Hub), which offload much of the security responsibilities and deployment burden from device manufacturers to the public cloud providers. IoT clouds must manage trust for hundreds of millions of IoT devices and users, and provide device manufacturers reliable and usable tools for secure IoT deployments. In the IoT cloud systems, compromised security or improper deployments can cause hazardous and deadly consequences. The outcomes of the proposed work will (1) establish the foundational scientific theory, security principles, and practices that define the field of IoT cloud security and (2) protect PaaS and IaaS IoT clouds that underlie the wide array of Smart Home, Health, Industrial, Smart City, Retail, and critical infrastructure from cyberattacks. Techniques and tools to be developed in this project will be used by IoT developers, security analysts in industry, academic researchers, and a wide range of students (system security, formal methods, and engineering). IoT cloud systems have specific challenges imposed by their requirements of large-scale distributed trust management and secure support of emerging IoT computing paradigms such as IoT interoperability, which preclude direct application of solutions devised for general-purpose systems. The project will characterize these challenges while addressing three key, novel research thrusts. The first thrust is to formalize the threats concerning the emerging paradigms of IoT interoperability to conduct novel attacks, and formally verify their security in IoT cloud systems and protocols. The second thrust is to explore and understand emerging cyberattacks leveraging misconfiguration of cloud IoT policies by device manufacturers, and develop innovative formal modeling and verification approaches to elevate security assurance of policy specification and cloud-based IoT deployments. The third thrust is informed by the first two thrusts, which identify the threats and challenges, and is to fundamentally address the threats by developing a systematized IoT-cloud security framework with a set of innovative techniques, including secure clean-slate design of IoT interoperability protocols, a novel in-device channel control framework, and hardened supply chain for IoT brokers (a core component of IoT clouds). Through these thrusts, this project will produce new foundational understanding and methods to safeguard modern and the next generation of IoT cloud 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

Distinct from conventional learning paradigms, interactive machine learning captures many real-world settings where learning agents adaptively acquire information and learn to make decisions or gain insights. This paradigm is beneficial in domains where experiments are expensive (e.g., biological imaging and material discovery), as well as in human-safeguarded artificial intelligence (AI) systems (e.g., autonomous driving and chatbots). Despite impressive successes, many critical challenges remain in deploying responsible and resource-efficient interactive learning agents, including a lack of data efficiency, safety, and reusability. The overarching goal of this project is to design principled and practical interactive machine learning algorithms with various feedback modalities that address these challenges, focusing on both single-step and sequential decision-making settings. To this end, the research team will establish theoretical guarantees for the algorithms and release their practical implementations. Integrated with this research project is an education plan that includes an intramural lecture series with the University of Arizona DataLab, a middle school outreach event series in collaboration with the University of Arizona Computer Science Ambassadors Program, as well as undergraduate research projects and curriculum development. The project consists of three interrelated research thrusts. The first thrust studies interactive learning for supervised learning, a single-step decision-making setting. Specifically, the project will focus on designing label-efficient active learning algorithms for nonlinear model classes, including moderately parameterized classes and overparameterized neural networks. The second thrust explores interactive learning for sequential decision-making. It focuses on safe imitation learning from interactive expert demonstrations and interventions, as well as robust reward inference from expert demonstrations. The third thrust aims to address the data reusability challenge in current interactive learning systems by bridging interactive learning and conventional offline learning. Specifically, this thrust will tackle two research questions: first, how to warm-start interactive learners using offline data, and second, how to design interactive learners that collect data amenable to future reuse. 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

This Faculty Early Career Development Program (CAREER) grant will advance national energy security and economic welfare by developing improved tools for optimizing complex energy and industrial systems. Critical infrastructures, such as electric power grids and chemical refineries, depend on solving large optimization problems to determine safe and efficient operating conditions. Current optimization tools are inadequate for large-scale planning and operational needs, limiting the ability to operate these energy systems efficiently, modernize them, and maintain resilient operations. This project will create a new generation of optimization algorithms that use machine learning to leverage shared structure in real-world applications, significantly accelerating solution times while preserving mathematical guarantees. It will develop new machine learning techniques to guide key algorithmic decisions in optimization algorithms while ensuring scalability, generalizability, and data efficiency. These advances have the potential to transform how energy systems are designed and operated, enabling more efficient operations, improved reliability, and lower operational costs and environmental impact. The educational plan will introduce optimization and machine learning concepts into high-school classrooms through an interactive web-based tool, teacher workshops, and partnerships with regional schools. Undergraduate research, new graduate modules, and interdisciplinary workshops will prepare the next-generation workforce at the interface of artificial intelligence, optimization, and engineering. This research will build a unified, theory-driven framework that leverages machine learning to enhance branch-and-bound algorithms for the guaranteed global optimization of mixed-integer nonlinear programs. It will (1) formulate new expert branching policies and develop supervised graph-based machine learning methods to imitate them; (2) create semi-supervised learning methods to generate high-quality feasible solutions and warm-starts; (3) use machine learning to accelerate decomposition algorithms; and (4) design generative machine learning models that construct realistic and varied families of mixed-integer nonlinear programs. These contributions will advance the understanding of effective branching strategies, reduce reliance on expensive labeled data, enable generalization to large-scale problems, and aid in benchmarking and sustained innovation. The project will produce open-source algorithms and benchmark libraries for power systems and refinery optimization, enabling rigorous evaluation of machine learning-guided global optimization methods. These contributions will significantly improve the efficiency, robustness, and scalability of global solvers, advancing the scientific foundations of global optimization and supporting high-impact applications across energy and large-scale industrial 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

Real-time systems underpin applications such as autonomous vehicles and industrial automation require timely responses to sensor data related to emerging scenarios with a precise coordination. In addition, these real-time applications face significant safety risks due to faulty or even compromised components, or to the interruption of inter-system communication. The project’s novelties center on developing foundational principles that enable multiple real-time systems to carry out coordinated operations correctly and safely with timeliness guarantees. The project's significance and importance lie in the establishment of real-time coordination principles and the creation of new security mechanisms that can be extended to security-critical applications. This project also strengthens the nation’s workforce in network and computer system security through integrated educational activities catered to students in higher education and the public. This project aims to develop a novel architecture called RESONET (REsilient and Secure Operation of NETworked real-time systems) that provides strong fault tolerance and real-time guarantees for real-time multi-systems to carry out coordinated operations. The research is organized into three complementary thrusts. The first thrust focuses on foundational cross-system fault tolerance principles, ensuring that coordinated operations meet timeliness requirements even in the face of component failures. It adopts a layered consensus-based approach enhanced by reinforcement learning that dynamically adapts the consensus parameters to network conditions. The second thrust provides a secure communication layer necessary for the above consensus-based approach by adopting lightweight group authentication and key establishment protocols to secure both intra- and inter-system communications among different components. The third thrust provides the last line of defense by developing an intrusion detection mechanism that can forecast imminent timing violations and detect intrusions and failures. The project uses a drone fleet and an automotive communication network to carry out the validation of developed prototypes and to serve as platforms for hands-on educational activities. All research outcomes and educational materials, including tutorials, presentations, publications, and open-source software, will be made publicly available online. 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

Contemporary machine learning techniques tend to be resource-intensive, often requiring good quality datasets, expensive hardware, or significant computing power. In a wide array of application domains, ranging from healthcare to mobile computing, these critical resources are lacking. Novel methodologies that enable the optimal utilization of resources can help unlock the full potential of the data science revolution for these domains. Towards this aim, this project will develop theoretically-grounded algorithms to facilitate the design of machine learning models under application-specific resource constraints. The outcomes of the project will help enable machine learning methods to operate with less human-annotated data, less computing power, and on a wider range of hardware platforms. To demonstrate interdisciplinary impact, the resulting algorithms will be employed in the design of efficient hydrological models which aid in predicting and managing water resources. The research will also be strongly coupled with education through the mentoring of undergraduate students, new undergraduate and graduate course development, and live broadcasts of the lectures over publicly accessible online platforms. This project aims to develop the foundational theories and algorithms to guide the efficient use of statistical and computational resources. The research on the statistical front focuses on the data and will uncover the fundamental tradeoffs between the data amount, label quality, and the model accuracy. Understanding these tradeoffs will lead to the design of improved loss functions and regularization techniques. On the computational front, theory-inspired model compression schemes will be developed by exploring the interplay between the model size and accuracy. Secondly, the model performance will be enhanced by identifying the optimal model architecture via computationally-efficient algorithms that co-design the architecture, compression scheme, and the loss function. These theoretical and algorithmic investigations will utilize tools from statistical learning, optimization, deep learning theory, and high-dimensional probability. The proposed research is expected to provide much-needed theoretical basis for poorly-understood heuristics in fields spanning semi-supervised learning, model compression, neural architecture search, and will guide the design of next-generation algorithms achieving the optimal resource tradeoffs. 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

Advances in artificial intelligence and machine learning provide the opportunity for using autonomous multi-agent systems to solve important social and economic problems, such as the application of multiple robots in wildfire monitoring, search-and-rescue, manufacturing, etc. In these systems, agents autonomously cooperate to make decisions in real-time to perform complex tasks. Reinforcement learning, a data-driven control method that enables agents to autonomously learn desired tasks by interacting directly with the environment, has emerged as one of the predominant frameworks for this kind of real-time decision making. While reinforcement learning provides a powerful and flexible framework, it suffers fundamental challenges in its scalability and resilience. Specifically, existing methods require a vast amount of data and computational power and can be unstable in the presence of various types of errors and adversaries. These challenges are the main barriers to the wide applicability of reinforcement learning for real-world problems. This CAREER project will develop new foundations of scalable and resilient distributed reinforcement learning for real-time autonomous cooperation in open multi-agent systems. The overarching goal is to design new learning and control methods that enable agents to interact effectively in open systems, adapt gracefully in time-varying environments, and be resilient to unexpected failures and adversaries. The project will also contribute to education and workforce development by integrating the research findings with rigorous educational and outreach activities, course development, student training, and public partnerships. The central idea of this project is to establish new fundamentals of two-time-scale stochastic approximation for non-monotone systems. The key approach is to leverage extrapolation techniques in optimization and singular perturbation theories in control to address the instability issues of stochastic approximation under non-monotone settings. New theoretical principles will be studied to characterize the finite-time complexity of the proposed methods. By leveraging these new results of two-time-scale stochastic approximation, this project will advance several foundational aspects of distributed learning and control in open multi-agent systems. The focus is to develop new scalable and resilient distributed multi-time-scale reinforcement learning methods that allow agents to cooperate efficiently in real-time under diverse practical considerations, including time-varying numbers of agents, unexpected failures, communication constraints, and adversaries. During the course of this project, the proposed research activities will be evaluated systematically through a series of simulations and field experiments of multi-robot navigation. 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

Manifold optimization is instrumental in control and engineering applications, ranging from trajectory optimization in robotics and safe reinforcement learning to subspace estimation, geometric deep learning, and adaptive fine-tuning of large language models (LLMs). Despite recent advances, most of the research on manifold optimization is limited to centralized approaches not implementable in multi-agent systems. This CAREER project takes a substantial step towards the development and adoption of decentralized manifold optimization (DMO) in large-scale, multi-agent optimization by overcoming three fundamental challenges: scalability, efficiency, and adaptation to dynamic environments. The PI proposes three integrated research thrusts to address these challenges. (i) Thrust 1 will fundamentally advance the scalability of DMO by innovating decentralized retraction-free methods that are computationally efficient, and it will also extend the theory of retraction-free methods to bilevel DMO. (ii) Thrust 2 will provide a systematic understanding of acceleration to improve the iteration complexity of DMO algorithms. This research will establish provably fast convergence guarantees for accelerated DMO and further expand this theory to the zero-order setting using smoothing techniques to generate high-fidelity gradient estimators. (iii) Thrust 3 will pioneer online DMO to contextualize multi-agent Riemannian optimization in dynamic, unpredictable environments. It will investigate projection-free methods to replace costly projection operations with efficient oracles and further extend the study to functional constrained online DMO by developing decentralized Riemannian augmented Lagrangian methods. The proposed research in this CAREER project will produce and draw upon novel scientific tools in Riemannian optimization, multi-agent systems and control, high-dimensional statistics and concentration inequalities. The research agenda also includes a concrete evaluation plan of the proposed algorithms on fine-tuning of LLMs and active mapping/planning with robot teams. The PI has an integrated education plan to engage high school and undergraduate students in research and organize STEM field trips for middle school students. 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.