Grants

NSF

NON-TECHNICAL SUMMARY Enhanced electrical and electromechanical responses in ferroelectric materials often arise at chemically induced phase boundaries where multiple structures coexist—for example, the well-known morphotropic phase boundary in lead zirconate titanate. At these phase boundaries, external stimuli, such as electric fields, can trigger interconversion between different structures, leading to colossal physical responses that can be utilized for various functional applications. However, these chemically induced phase boundaries usually involve complex chemical compositions that introduce disorder and heterogeneity. Additionally, these materials often contain toxic lead, raising significant environmental and health concerns. To address these challenges, the research team aims to develop strain-engineering pathways to create phase boundaries in lead-free ferroelectric oxide heterostructures and membranes beyond the traditional chemical method. These new strain modalities open opportunities to explore, manipulate, and harness novel functionalities in oxide materials for next-generation applications. Aligned with the research, this program supports the launch of a teacher-training workshop for the special education teachers in K-12 public schools and offers research internships for high school students. These efforts aim to break barriers to STEM careers for students to promote a stronger workforce. Additionally, this program develops hands-on and online course modules to foster greater interest and access in functional thin-film materials research for undergraduates and graduate students. TECHNICAL SUMMARY This program aims to advance strain engineering beyond traditional heteroepitaxy to create strain-induced phase boundaries in lead-free ferroelectric thin-film heterostructures and membranes. By leveraging anisotropic epitaxy and dynamic strain tuning, the program seeks to generate coexisting and bridging phases with enhanced dielectric, piezoelectric, and ferroelectric properties in lead-free sodium niobate heterostructures and membranes. This program employs atomic-scale epitaxy and chemical lift-off techniques to synthesize epitaxial heterostructures and membranes while applying mechanical tuning to control the competition between nearly degenerate polymorphs near phase boundaries. A broad set of structural and property characterization tools are utilized to establish strain-structure-property relationships, enabling the rational design and precise control of strain to unlock new phases with superior electrical and electromechanical performance. In parallel, the program integrates educational initiatives to foster a stronger STEM workforce, with a particular focus on engaging students. These initiatives include teacher-training workshops for K-12 special education teachers, research internships for high school students and undergraduate students, as well as hands-on and online course modules focused on functional thin-film materials. 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

Modern cloud applications, such as those involving artificial intelligence, have become increasingly memory intensive. These applications often require large amounts of memory to achieve high performance. Due to its poor scaling properties, traditional dynamic random-access memory (DRAM) has become a bottleneck and a major infrastructure cost in clouds, where DRAM is virtualized to serve applications running in virtual machines (VMs). To address the DRAM scalability issue, emerging and future memory (EFM) such as Compute Express Link (CXL)-based memory has demonstrated high potential. EFM will encompass heterogeneous memory with multiple memory tiers and distinct characteristics such as cost and volatility. Traditional memory virtualization was primarily designed for virtualizing homogeneous volatile DRAM. It will incur high overhead, lack mechanisms for reducing cloud memory costs, and offer limited usability when used for virtualizing EFM. This CAREER project will redefine memory virtualization for EFM, aiming to significantly reduce cloud memory costs, while offering high performance and usability for modern cloud applications. This project incorporates innovative techniques to minimize virtualized EFM address translation overhead, virtualize slow memory as fast memory in EFM virtualization, and improve VM live migration performance. The success of this CAREER project is expected to enable data centers utilizing current and future cloud systems to achieve high performance, low cost, and high usability, fundamentally changing virtualization management for increasingly large-scale memory systems. Although this project is designed for virtualizing EFM, its concepts, ideas, and techniques can be widely applied to improve various system infrastructures. This project also involves extensive education plans to teach emerging cloud computing technologies to both college and K-12 students by collaborating with local schools. Ultimately, this research will advance next-generation cloud computing by enhancing efficiency, reducing costs, boosting performance, and improving usability, benefiting a broad spectrum of cloud workloads, such as artificial intelligence, databases, and data analytics. 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 NSF CAREER project aims to provide the theoretical foundation of aggregating and disaggregating nodal generation capacity, flexibility, and information, which will allow exploiting the full potential of distributed energy resources and electrified transportation in future decarbonized power grids. The project will transform the conventional energy and ancillary service co-optimization-based power grid operation scheme into a novel capacity-flexibility dispatch and redispatch control framework. This will be achieved by converting existing resource planning problems with system-wide requirements into granular control problems with multi-scale, multi-domain nodal requirements. The intellectual merits of the project include developing nodal demand, capacity, flexibility composite models and computationally efficient aggregation algorithms with guaranteed characteristics under uncertainty. The broader impacts of the project include promoting the integration of research and education for students with varied backgrounds. Pre-college and undergraduate students will benefit from resulted summer research programs, workshops, capstone projects, and open-access curriculum materials. If successful, this project will also provide power system operators with technology advancements to integrating large-scale renewable energy and enhancing grid resilience. Uncertainties by fast-growing penetration of distributed energy resources, proliferation of electrified transportation, and climate change intertwine and amplify challenges posed on power system reliability. Consequently, widespread and prolonged power outages have been occurring increasingly more frequently and severely in recent years, which illustrates the inadequacy of existing ancillary services provided by reserving unloaded capacity on generation resources. The proposed project will redesign conventional resource planning-based ancillary services into a novel capacity-flexibility dispatch/redispatch control problem through three major technical innovations: (1) Mathematical models and effective aggregation of nodal flexibility provided by both distributed energy resources and electrified transportation through novel cost-aware, multi-period optimization techniques. (2) Computationally effective, granular control policies for the proposed nodal level co-dispatch are established as theoretical co-optimization problems converted through transformations and information aggregation, which will be solved both precisely with guaranteed performance and approximately as a data-driven problem. (3) Aggregating nodal information to determine nodal ancillary service requirements by extended Minkowski sum of polytopes, which will be further integrated into a unified theoretical framework by Difference of Convex programming. 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

Graphics Processing Units (GPUs) are the go-to choice for deep learning due to their exceptional computational power and massive parallelism. However, maximizing GPU performance for model development and inference remains notoriously challenging as models grow increasingly complex, spanning multiple abstraction layers: the upstream Python layer, the midstream C/C++ layer, and the downstream GPU kernel layer. While this layered complexity meets diverse application needs, it also embeds inefficiencies that are difficult to detect due to intricate cross-layer interactions. The project addresses these inefficiencies through a comprehensive, cross-layer performance analysis of deep learning models. The project’s novelties are advancing state-of-the-art profiling techniques to enable systemic performance tuning across all layers. The project's broader significance and importance are deepening the understanding of systemic performance issues in deep learning, thus strengthening foundations in code analysis and advancing progress in fields increasingly reliant on deep learning, such as image processing. With interest from industry leaders like Meta, the project shows strong potential for translating academic insights into practical applications. Additionally, the project contributes to educational and outreach goals by integrating its findings into computer science curricula and K-12 programs to cultivate a workforce skilled in performance analysis and optimization. Three innovative analysis techniques structure the project. (1) Unified binary code analysis: It consolidates all layers of deep learning models into a shared binary abstraction, enabling the identification of cross-layer inefficiencies in code segments and data objects. (2) Incremental analysis: It incrementally narrows the scope of monitored performance metrics to pinpoint the root causes of inefficient code segments identified in the unified binary analysis. (3) Data object analysis: It addresses inefficient data objects identified in the unified binary analysis to diagnose their root causes. Together, these techniques form a comprehensive approach to performance tuning, addressing inefficiencies from a systemic perspective and maximizing GPU capabilities in deep learning. 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 CAREER project investigates how fat tissues and reproductive organs communicate and coordinate fat storage and utilization in relationship to oocyte production and nutritional status. A complex network of inter-organ communication underlies whole organism physiological responses to dietary input dynamics. There is a clear association between dietary input and reproduction, wherein suboptimal nutritional conditions, such as malnutrition and obesity, negatively impact fertility. Specifically, fat cell dysfunction associated with diet-induced obesity leads to aberrant egg or oocyte production in organisms ranging from insects to humans. However, the fundamental principles linking nutritional physiology with oocyte production are not completely understood. Using the fruit fly Drosophila melanogaster as a model organism, results from this work will uncover how ovarian and fat tissues sense, respond to, and communicate about dietary input. Overall, this study will advance understanding of the role that multi-organ nutrient sensing plays in the organismal response to dietary changes. In addition to the research goals, a Columbia, South Carolina, community outreach program centered around the nutrient sensing research theme will be developed for K-12-aged children and their caregivers. The program will consist of hands-on activities that provide practical experience with science and engineering practices, as well as exposure to pathways to careers in biological research. The leaders of the research activities will receive valuable training in effective scientific communication. In Drosophila melanogaster, direct nutrient sensing by the ovary and remote nutrient sensing by the adipose tissue regulate oogenesis; however, the different modes of amino acid sensing used are not fully understood. The overall goal of this work is to uncover the cellular and molecular mechanisms that mediate dietary protein sensing and communication. This work leverages the power of D. melanogaster genetics to perturb the amino acid response pathway, a branch of the integrated stress response that senses limitations in amino acids, in specific ovarian cell types, to assess its currently unknown role in oogenesis. Additionally, a targeted candidate approach will be taken to identify adipocyte-derived factors that function downstream of the amino acid response pathway and mechanistic Target of Rapamycin (TOR) signaling to regulate ovarian germline stem cell maintenance and ovulation, respectively. Taken together, this project will define the molecular mechanisms that govern amino acid sensing in and communication between ovarian and fat tissues. A post-doctoral fellow and both graduate and undergraduate students will participate in the research. This project is jointly funded by the BIO-IOS-Physiological Mechanisms and Biomechanics Program, the BIO-IOS-Animal Developmental Mechanisms Program, and the Established Program to Stimulate Competitive Research (EPSCoR). 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 cells that make up the brain called neurons contain internal structures that have to be positioned at precise locations within the cell. To ensure that these structures are transported to the right site, cells use molecular machines called motor proteins to move them to the right location. Such transport is essential for the formation and function of the nervous system. Defects in motor protein-based movement of specific structures in neurons have been linked to neurodegenerative and neurodevelopmental disorders. Despite the importance of this process, we still do not understand how these motor machines bind the right structures at the right time and how they “know” where to deliver them inside the cell. The proposed work will use in imaging, genetic manipulations, and biochemistry to determine how one of these motors, called dynein, is activated at the right time and place to bind specific structures to initiate their transport. A focus of this research is on the transport of the cellular structure called the autophagosome which is essential for the breakdown of cellular building blocks, as well as pathological aggregates in diseases such as Alzheimer’s, Huntington’s and Parkinson’s. Graduate and undergraduate students will participate in the proposed research and will be mentored by post-graduate trainees. This research will define fundamental aspects of dynein function, and form the foundation for future studies to target dynein within a therapeutic context. Dynein-dependent cargo transport is essential to move organelles, proteins, cytoskeletal elements, and signaling molecules from the axon terminal to the cell body in neurons. This process is highly regulated. Dynein accumulates in the axon terminal in an autoinhibited conformation prior to activation, cargo engagement, and transport initiation. How dynein is conditionally activated and cargo transport initiated is unknown. Given the ubiquitous localization of the motor, its activators, cargos, and cargo adaptors, conditional mechanisms must exist to trigger dynein-mediated transport for discreet cargos. This project will use in vivo imaging, biochemistry, and genetics to define the mechanisms of dynein motor activation and cargo transport initiation in the axon terminal of neurons. The results of the proposed work will delineate regulatory mechanisms for these essential steps to trigger dynein-dependent cargo transport initiation in the axon terminal. 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

Reliable access to water is crucial to the health and economic prosperity of the nation. However, water scarcity in some regions is creating a need for new methods to produce clean water from a variety of sources including wastewater and seawater. This project will investigate an innovative distillation process for water purification that can produce safe, high-quality water using less energy than other conventional processes use. The key innovation in the project is to use pressure instead of heat to distill water, which has the potential to reduce energy use by a factor of ten. Pressure-driven distillation will require a new class of water treatment membranes that can remove salt and contaminants from challenging water sources. The research team will build on its preliminary results to develop high-performance membranes suitable for pressure-driven distillation. The deployment of innovative water systems will require a skilled workforce. The project team will conduct project-based learning modules on water scarcity for high school students and will involve undergraduate students in the research. Overall, the project will foster both the technological innovation and skilled workforce needed to secure a sustainable water future. Addressing water scarcity requires efficiently treating impaired water sources, such as wastewater and saline water, to remove nearly all dissolved constituents. The goal of this CAREER project is to reinvent distillation as an efficient, pressure-driven process for advanced water treatment. Conventional distillation processes are driven by heat and have poor energy efficiency in water treatment. The central hypothesis of this work is that membranes with tailored surface structures and an ultrathin air layer will facilitate pressure-driven distillation in a more efficient, selective, and robust way than conventional processes. The objectives of the project are to (1) explore distillation membranes that resist wetting under applied pressure, (2) evaluate the maximum water permeability for distillation membranes, and (3) conduct application-relevant testing with high performance composite membranes. These goals will be carried out using advanced material fabrication methods, experimental performance characterization, and new modeling techniques. The project will contribute to the scientific foundation for a new class of water treatment membranes that produce higher-quality water with less energy than conventional systems. The project will also develop fundamental knowledge on wetting physics and evaporation phenomena. Education and outreach efforts will support the training of the next generation of water engineers by developing hands-on water treatment modules for high schools in communities impacted by water scarcity. A mentorship program will engage undergraduate students in advanced water treatment 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

This award supports research that studies how tissues modify their structure over time and how these changes are influenced by mechanical loading. The research focuses on the aortic valve leaflet, a vital part of the heart that opens and closes to control blood flow. The cells in the leaflet are constantly remodeling: building, breaking down, and rearranging the supportive material around them—based on various stimuli, including mechanical loading. However, these structural changes also affect the mechanical loading on the leaflet, creating a feedback loop that is not yet fully understood. In calcific valve disease, this process leads to the thickening and hardening of the leaflet, preventing it from opening properly. This increases the workload on the heart, ultimately leading to heart failure. To investigate this feedback loop, this project will: 1) Develop a new method to measure how the material properties of the leaflet vary across its surface and over time and 2) Use advanced imaging techniques to study the leaflet's structure at different scales, particularly around small calcium deposits (microcalcifications) that stiffen the valve and reduce its function. The developed technology and research findings will also enable future studies aimed at slowing, stopping, or even reversing the formation and growth of microcalcifications. This work will help to elucidate the role of mechanics during aortic valve leaflet remodeling. Remodeling is a continuous process: as cells synthesize, degrade, and rearrange their local extracellular matrix, its load-bearing properties change, which in turn alters local mechanical stimuli. This study will be the first to quantify the temporal changes in the regional mechanics and structure of individual aortic valve leaflets during culture. To do this the research team will develop a novel, structure-informed nonlinear anisotropic inverse mechanics method that quantifies spatially variable material properties from biaxial testing data. The team will also determine the multiscale structure of the leaflets at multiple time points during culture using quantitative polarized light imaging, a mesoscale imaging technique, to guide the application of microscale imaging. This will provide a detailed characterization of the extracellular matrix structure at and around microcalcifications as they form and grow. The spatial and temporal information collected in this study will enable to better understand the feedback mechanisms between leaflet mechanics and remodeling. Additionally, because this technology enables both spatial and temporal studies, results will be useful for future testing hypothesis concerning the underlying mechanisms of microcalcification formation and evaluating potential treatments. 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 world's infrastructure now critically depends upon cryptography for almost every task. While recent years have seen a blossoming of novel cryptographic techniques and applications, we still do not know if even our elementary cryptosystems are secure. Unlike most scientific domains, including much of computer science, it is impossible to empirically verify that a cryptosystem is secure. Instead, cryptographic security is founded on the conjectured existence of strong computational intractability: the inability of efficient algorithms to solve particular problems. The primary goal of this project is to strengthen the foundations of computational intractability needed for robust cryptography, which will yield a more robust cryptographic infrastructure. Another goal of this project is educational outreach, which will be presented through talks, tutorials, and lectures to encourage collaboration with diverse communities and, in turn, equip individuals with the ability to reason effectively about security. We propose exploring novel resource-constrained adversarial models to improve our cryptographic foundations. Traditionally, cryptographic attackers were modeled as probabilistic polynomial time algorithms. To circumvent barriers in this traditional setting and strengthen cryptographic security, we consider adversarial models that are alternately more powerful (e.g., algorithms with limited nondeterminism) and less powerful (e.g., algorithms with fixed polynomial time/space). We highlight two concrete aims: (1) improved cryptographic hardness amplification, yielding extremely hard problems from hard problems, and (2) understanding the feasibility of cryptographic security if it turns out that traditional cryptographic guarantees are impossible, planting our security on a firmer footing. 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

In this CAREER project, jointly funded by the Chemical Mechanism, Function, and Properties (CMFP) Program of the Chemistry Division and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Sebastian Stoian of the Department of Chemistry at the University of Idaho is developing new iron-based, multifunctional magnetic materials with tailored properties. The goal of this research is to map out subtle interactions between the building blocks of these materials. This project seeks to advance our knowledge of complex materials encountered in magnetism and catalysis, addressing important challenges in quantum computing and energy production. The project provides advanced research training to graduate and undergraduate students and includes a curriculum development plan for enhancing the computational chemistry competencies of undergraduate students. Finally, research experiences for teachers will also be part of the funded project as outreach activities aimed at boosting the STEM motivation of high-school students throughout Northern Idaho. Chemical species featuring metal ions supported by redox active ligands play critical roles in the catalytic cycles of many enzymes and synthetic systems. Yet, the oxidation and spin states of the metal sites and their supporting ligands are often difficult to assess. In this project, Professor Stoian’s team will use a spectroscopy-guided approach to synthesize and investigate a series of iron compounds with selected redox-active ligands. To elucidate the intricate interplay between spin and charge degrees of freedom in such species, the planned research relies on the unique capabilities of field-dependent Mössbauer spectroscopy, complemented by electron paramagnetic resonance and computational studies, to probe the electronic structure of iron sites in complex chemical environments. The target materials could exhibit a number of desirable properties including magnetic bistability and oxygen activation, as well as quantum entanglement of local metal spin states induced by their interaction with open-shell, redox-active ligands. 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 focuses on designing new methods to facilitate the evaluation of artificial intelligence (AI) agents. It the era where AI agents are rapidly proliferating, with new systems of increasingly capable AI technologies, it is crucial to thoroughly understand their performance capabilities and limitations—a prerequisite for both safe deployment and continuous improvement. Traditional evaluation methods require running AI agents in live environments to collect performance data, but this approach can be resource-intensive and pose significant safety risks. This project addresses these challenges by developing innovative evaluation methods that dramatically reduce the need for expensive and potentially hazardous live testing, thereby accelerating the safe deployment of current AI systems and enabling the development of next-generation AI agents. Additionally, the project will train future AI researchers, helping to expand access to AI research opportunities across the United States. This project pioneers three research thrusts to fulfill different evaluation needs. First, the project delivers methods to efficiently evaluate an AI agent in a holistic manner with a scalar performance metric by reimagining Monte Carlo methods. The key innovation involves repurposing offline data to inform the online sampling process of Monte Carlo methods, thereby reducing the required sample size for accurate performance estimation. Second, the project develops methods to efficiently evaluate an AI agent in a fine-grained manner across different initial conditions by reinvigorating value function learning. The approach identifies statistical metrics that are most indicative and influential to the performance of the learned value function, then optimizes those metrics during online data collection to maximize evaluation efficiency. Third, the project delivers methods to efficiently evaluate an AI agent according to human feedback by developing transformative techniques that substantially improve reward model quality while minimizing human annotation requirements. Through these research activities, the project aims to significantly enhance current methodologies for evaluating AI agents, ultimately accelerating the development pipeline of AI 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

Surfaces governed by their mean curvature model many physical phenomena, such as soap films, black hole horizons, capillary surfaces, and other interface behaviors. These topics, particularly minimal surfaces and mean curvature flow (MCF), are pivotal in geometric analysis and contribute to advances across mathematics, physics, and materials science. Under this project, the investigator will conduct a number of research projects concerning the theoretical construction and variational properties of minimal surfaces and mean curvature flow, including the emerging and physically relevant study of capillary action on the boundary. Additionally, the project will promote the development of a diverse and collaborative community through educational and outreach initiatives including undergraduate clubs, curriculum development, research workshops and mentorship programs to support early career researchers. This project includes three primary research directions. In the first subject, the investigator will study the singularity analysis of MCF near cylindrical and conical singularity models, in the presence of interior and boundary curvature. This subject revolves around uniqueness for blow-up analysis of MCF. In the second subjects, the investigator will continue their development of min-max constructions of hypersurfaces with prescribed mean curvature and boundary contact angle. The principle investigator will also discover the regularity and long-time behavior of MCF with capillary boundary action. In the third subject, the investigator will explore the complexity of submanifolds, quantified by area and entropy, and connections to the stability of minimal submanifolds. 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

As learning components based on artificial intelligence are becoming commonplace in cyber-physical systems (CPS), our engineering methods are lagging behind. Typically, engineers rely on their knowledge to make assumptions under which the system can guarantee a certain level of performance and safety. However, with black-box learning and increasingly large high-dimensional data, human engineers struggle to understand and formalize these assumptions, which severely limits the effectiveness of validation and monitoring. As a result, CPS systems end up susceptible to rare events, distribution shifts, and other unexpected circumstances — and are not equipped to respond to these intelligently and safely. This project aims to transform the management of assumptions in learning-enabled CPS by making novel fundamental connections between formal methods, machine learning, and decision-making. Improving our society’s ability to construct higher-performing and safer CPS for unforeseen situations, this project will deliver techniques, tools, and a catalog of typical assumptions that are expected to generalize across many CPS application domains. It will also train the workforce for future CPS in rigorous methods. This project represents a major step towards building assumption-aware CPS — ones that behave with an understanding of their own assumptions and limitations. To make these assumptions explicit and actionable, this project will build the mathematical and algorithmic foundation for assumption awareness via specifying, validating, and responding to assumptions behind the closed-loop CPS guarantees. To this end, this project will create an engineering methodology in three sequential thrusts: (1) discovering and representing relevant assumptions of learning-enabled CPS, (2) performing end-to-end validation of these assumptions across offline and online settings, and (3) enhancing decision-making and control to recover from online violations of these assumptions. The developed methodology will be evaluated on small-scale autonomous racing, underwater vehicles, and modeling autonomous street traffic. 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

In recent years, preference feedback—comparative inputs such as “A is better than B”—has emerged as a vital resource for guiding decision-making systems. Unlike explicit labels, preference feedback is often easier to collect and can be particularly valuable in subjective tasks where defining ideal outcomes is difficult. However, real-world preference data are often noisy, sparse, and heterogeneous, posing significant challenges to existing statistical methods. For example, recommendation systems may encounter incomplete feedback from users who abandon tasks due to fatigue or provide inconsistent inputs due to individual biases. This project aims to address the challenges of learning from preference feedback by developing robust statistical methods and advancing the theoretical foundations of preference-based learning. Additionally, it seeks to prepare students to tackle these challenges by integrating the research findings into innovative teaching platforms and educational curricula. The project will focus on three key areas of learning from preference feedback: ranking from pairwise comparisons, user-item rating systems, and reinforcement learning from human feedback. To advance the field, the project will (1) develop robust algorithms for ranking that account for ill-conditioned sampling mechanisms and relax parametric modeling assumptions; (2) propose new estimation and uncertainty quantification methods for user-item ratings that work effectively in sparse and heterogeneous settings; and (3) introduce novel frameworks for reinforcement learning that incorporate “out-of-list” preference feedback while addressing the issue of distribution shifts. Through these contributions, the project will bridge the gap between statistical theory and practical applications, creating tools to enhance decision-making systems across diverse domains. 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

Machine learning (ML) techniques play a pivotal role in modern artificial intelligence (AI) systems, but they remain notably vulnerable to disruptions caused by security attacks. These vulnerabilities can severely compromise AI system performance or be exploited maliciously, posing significant economic, ethical, and societal risks. For example, placing a small sticker on a stop sign could cause a self-driving car's perception system to misinterpret it as a speed limit sign, leading to potentially catastrophic consequences. As the reliance on AI grows, ensuring the secure, robust, and resilient operation of ML systems becomes increasingly essential. However, most robust ML research has focused on static, closed-world scenarios that fail to address the complexities of dynamic, real-world environments. This award aims to develop transformative methods to enhance the resilience and reliability of ML systems in these challenging settings. The outcome of this project promises broad societal benefits, including safer and more dependable AI applications in diverse fields such as biology, healthcare, cybersecurity, and manufacturing. Additionally, the project will transform AI education by integrating ML robustness as a foundational theme, preparing future workforce to tackle emerging challenges in trustworthy AI, and fostering public awareness of AI risks and mitigation strategies through extensive outreach. This award seeks to advance AI research by addressing three key challenges in open-world environments. First, it will develop novel techniques to enhance robustness generalization across data distributions, which mitigates robustness degradation under distribution shifts. Second, it will introduce new learning algorithms to ensure robustness against multiple attacks simultaneously. Third, this project will devise certification approaches to evaluate robustness in dynamic environments. Comprehensive evaluations will be conducted using public datasets and real-world applications, supported by collaborations with academic, governmental, and industrial partnerships. The success of this CAREER project will pioneer new frontiers of robust ML and establish solid foundations for building next-generation robust and trustworthy AI in the real world. 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

Machine learning (ML) systems have achieved impressive capabilities across domains such as vision, language, and planning. However, even state-of-the-art models can fail dramatically in unpredictable ways, including under minor shifts in data distribution. These failures are exacerbated in the presence of adversaries (i.e., other actors or systems that attempt to undermine or attack the ML system) These vulnerabilities present serious concerns for deploying ML in high-stakes settings. This project aims to develop principled defenses that make robustness a core design property of ML systems rather than an afterthought. The approach is to bridge rigorous analysis with practical experimentation in order to understand, predict, and ultimately fix brittleness in modern models. Through new algorithmic tools and conceptual insights, this work will lead to the development of reliable and trustworthy AI systems that can operate safely and reliably in complex, real-world environments. This project will establish a framework of robustness via analysis for building machine learning systems. This approach interleaves simplified theoretical models with real-world experiments to derive actionable insights that improve robustness in practice. The project proceeds along three technical thrusts. First, it develops robust finetuning techniques for large pretrained models (foundation models) by minimizing forgetting and preserving generalization across domains. Second, it introduces defenses against adversarial attacks on language models, including novel finetuning paradigms that enable models to robustly self-correct and consistently enforce safety guidelines, even under complex and varied jailbreak scenarios (malicious attempts to trick the system into generating undesirable output). Third, the project studies robustness in autonomous AI agents composed of multiple subsystems, identifying weakest links, developing methods to enforce trust hierarchies (i.e. what can be trusted and when), and introducing mechanisms to provide formal safety guarantees across the system. Together, these efforts aim to shift the foundations of robust ML from heuristic patchwork to theoretically informed, systematically validated 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

This project aims to advance machine learning techniques by developing new mathematical tools to better analyze and visualize complex, high-dimensional data. Many real-world data sets, such as genetic information or molecular images, contain hidden structures that can be uncovered using geometric methods. Leveraging these hidden structures can decrease the computational resources needed to analyze large data sets and can also provide scientific insight regarding various biological and physical processes. The goal of this project is to harness the full power of geometric methods for modern machine learning by the design of innovative solutions to the critical challenges facing the field. The project will focus on developing methods that can handle noisy data and tools for data visualization that preserve important geometric details. In addition to its scientific goals, the project will have a broad impact by offering new educational programs to support student mental health, increase diversity in data science, and encourage underrepresented groups to engage in this field. By addressing both technical and social challenges, the project aims to create more reliable, scalable tools for data analysis while promoting inclusion and innovation in science and education. In many applications, real data often concentrates around low-dimensional structures or manifolds, and manifold learning algorithms are a powerful tool for uncovering this low-dimensional structure. The project is on manifold learning algorithms, tackling the key challenges facing the field, including how noise affects algorithm performance, how to preserve both local and global geometric details, and how to effectively handle complex collections of manifolds. This research will investigate best practices for denoising, analyze how noise impacts spectral manifold learning algorithms, and clarify the regime in which algorithms can reliably recover manifold structure. The project will develop novel algorithms that utilize diffusion to faithfully encode geometric information at various scales and also hybrid methods for geometric regularization of manifold learning algorithms. Since real data often fails to concentrate around a single manifold, this work will contribute a robust and scalable solution for clustering collections of manifolds via a novel angle-based path metric on simplices, so that manifold learning algorithms can be applied under less restrictive assumptions. In addition, since data often contains numerous irrelevant features and features measured on very different scales, the project aims to develop a probabilistic framework for learning and adjusting features according to their actual relevance. Overall, the goal is to design a comprehensive data analysis pipeline that is noise-robust and capable of balancing local and global geometric information to represent data in just a few dimensions. 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

NON-TECHNICAL SUMMARY Movements of atoms in solids govern many material properties and behaviors. For example, Li ions must move through solid-state batteries to release electricity, and then move back again to recharge. The known ‘highways’ of atomic motion are atomic-scale interfaces that exist between atomic building blocks called grains. Such interfaces are therefore called grain boundaries. While atoms are known to quickly move through grain boundaries, it is unclear how the rate of atomic motion changes if the atomic structures or chemistry of the grain boundaries change as well. It is possible that the rate of atomic motion may change by several orders of magnitude. This project studies how changes in grain boundaries directly affect long-range movement of atoms. Experiments examine atomic motion by studying uniquely fabricated ceramics that are made of bonded samples with different compositions, which causes certain atoms to move from one side to the other, and a variety of advanced electron microscopes are used to characterize materials on the atomic-scale. Computational models are also employed to complement experiments and understand how materials behave on the molecular level. The overall mission of this research has a wide-ranging impact on several industries related to the energy transition planned by the United States. For example, new knowledge gained from this research is likely to provide new insights about hydrogen degradation effects on steels while transporting or storing hydrogen gas, or metal dusting degradation effects on Ni alloys that are used in petrochemical plants. This research also focuses on boosting education outcomes of students at the University level as well as in grades K-12. Hands-on ceramic processing demonstrations are teaching K-12 students how ceramic materials are made, and virtual/mixed reality modules are used to accelerate training on electron microscopes and expose younger generations to how we characterize materials using state-of-the-art research facilities. TECHNICAL SUMMARY This CAREER project investigates effects of grain boundary complexion transitions on long-range mass transport in bulk ceramics and develops virtual and mixed reality applications to (i) reinvigorate materials-related curriculum at Louisiana State University and (ii) educate K-12 students, their parents, and their teachers. The research is based on prior knowledge that grain boundary complexion transformations discontinuously change bulk material behavior, yet atomic-scale mechanisms associated with diffusivity through various complexions remain unknown. This proposal evaluates grain boundary thermodynamics in ceramic diffusion couples, involves atomic-resolution microanalysis of interfaces, and uses Monte Carlo grain growth simulations of complexion-driven abnormal grain growth, while also employing in-situ electron microscopy experiments and Density Functional Theory calculations. The project is divided into 2 Research Objectives: (I) Identify Mechanisms of Discontinuities in Grain Boundary Diffusivity Related to Grain Boundary Complexion Transitions, and (II) Elucidate Effects of Interfacial Diffusion on Complexion Propagation and Microstructure Evolution. The Education Objective is to develop virtual and mixed reality training modules for electron microscopes, namely a scanning electron microscope training module for an undergraduate technical elective course, and a transmission electron microscope training module for advanced users. The Outreach Objective is to inspire K-12 students by using hands-on experiments related to ceramic processing and interactive virtual reality activities. Overall, this work provides a universal understanding of interfacial solid-state diffusion, thereby providing new insights useful to solving issues related to the energy transition, such as (i) hydrogen permeation and embrittlement in iron and nickel alloys, (ii) metal dusting degradation in steels and superalloys, and (iii) interfacial transport efficiency in next-generation solid-state ion batteries. 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.

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Becoming a parent marks a critical life transition that introduces unique stressors and challenges. Stress during pregnancy can affect both parents’ health and their baby’s development. Many families across the United States turn to grandparents for emotional support and practical help during pregnancy and after the baby arrives, but the impact of these extended family supports is not well understood. This project examines the impact of grandparental support on prenatal stress and infant socioemotional outcomes. Broader impacts of this project include wide dissemination of the research findings to improve the public’s understanding of science and the scientific method. This project uses a longitudinal, multi-method design to examine how grandparental involvement during the transition to parenthood impacts prenatal stress mechanisms and subsequent infant socioemotional development. This project aims to (1) characterize patterns of grandparental involvement during pregnancy and postpartum through interviews and surveys; (2) investigate how grandparental involvement relates to psychological distress, physiological stress recovery, and inflammation during pregnancy; and (3) assess downstream effects on infant emotion and stress regulation at 3, 6, and 12 months of age. The project integrates psychological, biological, and observational methods (including heart rate variability during stress tasks, inflammatory biomarkers, and behavioral coding of mother-infant interactions) to advance understanding how extended family support systems can buffer prenatal stress and promote healthy infant development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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This project aims to uncover the intricate ways neurons communicate and how this communication influences brain development and maturation. The goal of this investigation is to discover how chemical signals between neurons, such as neurotransmitters and neuropeptides, contribute to the growth and function of the nervous system. By studying the nervous system of a simple and well-understood model organism, like the worm Caenorhabditis elegans, this study will contribute with insights that could benefit a wide range of species, including humans. This research could lead to a better understanding of brain health and disease, and the findings could have broad implications for improving educational and outreach programs. Additionally, the project will foster the development of future scientists, by providing hands-on research experiences and enhancing communication skills. This proposal seeks to investigate the role of neuron-neuron communication, particularly through neurotransmitter-based and neuropeptidergic chemical neurotransmission, in the post-mitotic maturation of neurons during postnatal development. Utilizing the model organism Caenorhabditis elegans, this investigation will leverage advanced genetics, behavioral assays, microscopy, and genomics tools to examine the molecular and functional maturation of neurons. The research will address key questions about the impact of various modes of neurotransmission on behavior, synaptic connectivity, and gene expression. By systematically manipulating neuronal transmission in a temporally and spatially specific manner, this project aims to delineate the interplay between extrinsic environmental stimuli and intrinsic genetic timer mechanisms in regulating neuronal maturation. This study will provide critical insights into the mechanisms of neuronal development, with potential implications for understanding brain function and disease across species. 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.

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This CAREER project focuses on assessing the role of atmospheric new particle formation in influencing the pre-industrial climate. A new parameterization of new particle formation that accounts for the role of temperature and atmospheric ions will be developed and its effectiveness will be evaluated through climate models. This research will help to improve the understanding of aerosol-cloud interactions and reduce the uncertainties in the role of aerosol in climate evolution. Currently, NPF and aerosol growth is poorly represented in most global climate models. The newly developed parameterizations of new particle formation mechanisms will improve model predictions of cloud condensation nuclei (CCN) and cloud droplet concentrations (Nd). The chemical production of organic material from biogenic volatile organic compounds (BVOCs) that plays a role in NPF will be investigated with and without sulfuric acid, ammonia and atmospheric ions. A land surface model including prognostic fires will be used to determine the role of NPF and BVOCs in buffering aerosol variability due to biomass burning. The combination of empirical data from the CERN CLOUD experiment and extensive field measurements will enhance the accuracy and relevance of the climate modeling. The models will be used to improve constraints on pre-industrial cloud droplet concentrations and aerosol radiative forcing, by considering aerosol sources and sinks in different meteorological regimes and simulated changes from 1850 to 1950. The models also will be tested on the current-day atmosphere under a variety of conditions that enable an assessment of the ability to recreate the preindustrial atmosphere. Working with The Citizen Science Lab, an after-school pipeline program will be developed for high-school students in the region to gain experience in field measurements and scientific data analysis through demonstrations and hands-on activities. For example, students will make and interpret smartphone photographs of clouds using NASA’s Globe Clouds app and will be introduced to environmental history over the industrial period via case studies in the Pittsburgh region. 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

Bacteriophages are viruses that infect bacteria. They are the most numerous life-forms on the planet. In general, bacteriophages consist of a genome made either of DNA or RNA that is packaged into a protective protein shell called a capsid. While much is known about bacteriophages with DNA genomes, far less is understood about those with RNA genomes. This project will investigate how these RNA genomes are packaged into their protein capsids. In particular, the project will describe the shapes into which the RNA genomes are folded, and how these shapes are recognized by the proteins and selected for packaging. By identifying these structures, the project will inform the design of synthetic capsids capable of packaging synthetic RNA molecules for use in RNA-based technologies. Furthermore, these RNA structures will facilitate the discovery of new RNA viruses by learning how to recognize viral RNA in the sequence libraries obtained directly from the environment. These discoveries will advance the understanding of RNA bacteriophage diversity and enable the production of novel nanoscale materials derived from their coat proteins. Throughout, the project will train graduate and undergraduate students in challenging fields of physical chemistry, biochemistry, and computational biology. Bacteriophage MS2 is the canonical model system for studying viral RNA packaging, yet the mechanisms by which MS2 coat proteins selectively package their RNA genomes while excluding abundant cellular RNA remain unclear. While RNA of a model phage MS2 is believed to contain structural signals that are recognized by its coat proteins during packaging, the precise nature of these signals and their role in selective packaging is not fully understood. This project will use quantitative packaging experiments inside the cells to identify key RNA structures involved in selectivity. Plasmid-encoded RNA molecules with defined structures will compete with the cellular transcriptome for packaging by MS2 coat proteins. Single-particle imaging and high-throughput sequencing will quantify the packaged RNA composition, providing a direct measure of selectivity. These measurements will be used to critically evaluate current hypotheses of selective packaging and identify the key RNA structures involved. The project will then use these structures to screen metagenomic datasets for previously undescribed RNA bacteriophage genomes. Once discovered, the coat proteins from newly identified bacteriophages will be expressed in cells to produce protein capsids. In addition to verifying the discovered bacteriophages, these capsids will provide the research community with new protein nanoparticles for applications in biotechnology and nanoscience. 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.

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Non-Technical Summary: Ordered intermetallic compounds (OICs) are metallic alloys with a periodic atomic arrangement of two (or more) metal elements. These OICs play an important role in technologies such as catalysis, batteries, and shape-memory alloys. Their application space is limited, however, because these materials can only be prepared at high temperatures, often eroding control over important material parameters. Making the low-temperature synthesis of OICs possible requires a precise understanding of how atoms move within solid materials. This Faculty Early Career Award (CAREER) will support research in the laboratory of Dr. Anthony Shoji Hall at the Johns Hopkins University to examine pathways that allow for the control of atom movement at low temperatures and thereby enable the preparation of ordered intermetallic nanomaterials at room temperature and atmospheric pressure. By enabling the synthesis of these materials at low temperatures, this work will substantially broaden the application space of OICs because it now allows for more fine control over important materials parameters. Dr. Hall’s laboratory will actively share their scientific passion and discoveries with the broader community by engaging in outreach at inner-city Baltimore high schools and universities. The first activity will leverage an established program, STEM achievement in Baltimore elementary schools (SABES), to encourage elementary students to pursue a degree in STEM. This project will also create a new program to encourage URM high school students to pursue degrees in STEM and to improve the retainment of URM (under)graduate students in STEM careers. Technical Summary: Despite decades of intense research, OIC nanoparticles have failed to replace conventional nanomaterials due to (1) lack of low-temperature synthetic methods that can overcome slow solid-state diffusion rates which inhibits atomic ordering, (2) inability to tune composition and phase to optimize the desired application, and (3) lack of fundamental understanding needed for progress on these issues. The purpose of this CAREER proposal is to examine the phase transformations of low melting point alloys to higher melting point OICs richer in the nobler and more active metal at ambient temperature and pressure by removal of the less noble component (e.g., transforming PdBi2 to Pd3Bi, or CuZn4 to Cu5Zn8) via a process known as dealloying. Fundamental insights from this project will enable the rational development of OIC nanostructures for applications of technological relevance and improve our understanding of material stability under electrocatalytic conditions. To understand the origin of the electrochemical dealloying-mediated phase conversion process, the PI will investigate the following objectives: (1) Elucidate the role of melting temperature on bulk diffusion and lattice reorganization. (2) Develop synthesis methods for controlled compositions of de-alloyed OICs. (3) Elucidate dealloying via in-situ spectroscopic methods. Materials made by this electrochemically mediated phase conversion process will be evaluated as anodes for Li-metal batteries to demonstrate the utility of the synthetic method. The broader impacts of this proposal will encourage underrepresented minority (URM) K-12 students and (under)graduate students to pursue careers in STEM through engagement in outreach programs. URM students lack access to relatable role models in STEM fields because of underrepresentation. To address this issue, Dr. Hall will make himself available for informal “coffee hour discussions” to serve as a mentor and role model for URM students (high school-aged, undergraduate, and graduate students) in the Baltimore area. The Hall group will also work with K-12 aged URM students on inquiry-based scientific projects by participating in the STEM Achievement in Baltimore Elementary Schools (SABES) program. 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

Plants obtain all water and nutrients through roots. Across grasslands, plants store most of the carbon that they consume from the atmosphere in their roots and the substances that roots secrete. Despite their critical role, plant roots are understudied. This NSF-CAREER grant will explore the effect of extreme precipitation and nutrient deposition on root growth, and the consequences of root expansion in the context of a changing environment. The project will involve an interdisciplinary team that includes graduate students and postdoctoral researchers, in addition to the principal investigator and his collaborators. The team will test the novel hypothesis that the response of root productivity to precipitation extremes depends on the nature of the response of root productivity to water availability. Mechanisms to be tested include root-system expansion through partnership with fungi, and root-system contraction when eaten by soil nematode worms. The team will use multiple approaches including greenhouse and lab experiments, as well as large-scale field experiments. This research will be of broad societal value because it will enable a better understanding of grassland ecosystems and their role in the carbon cycle. This CAREER grant builds upon previous work on aboveground responses to precipitation and nutrient availability manipulations by turning previous concepts, hypotheses, and mechanisms upside down and centering empirical tests on belowground processes in three hierarchical steps. First, it explores mechanisms behind the effect of precipitation variability on root productivity related to the shape of the relationship between root growth and water availability as stated by the Jensen inequality. Second, it incorporates nitrogen availability and tests for interactions that change the shape of such relationships altering the net root productivity response to precipitation variability. And third, it considers the effect of symbiotic associations between root and soil fungi that may enhance root fitness and nutrient uptake in contrast to root herbivory by nematodes that reduces the capacity of roots to uptake water and nutrients. In addition, the project considers structural differences among grassland ecosystems by testing the applicability of experimental findings across grassland types globally. This work will integrate a comprehensive education plan using multiple approaches from undergraduate courses to student mentoring at all levels (high school through graduate students) and dissemination of results to land managers and scientists. 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.