Grants

NSF

NON-TECHNICAL SUMMARY: Nature is a constant source of inspiration for both art and science, particularly in the fields of materials design and innovation. Over the past 30 years, scientists have discovered renewable nanomaterials—tiny particles made from common, renewable resources like wood and crops. These materials combine the benefits of advanced technology with sustainability and safety, offering potential solutions to global challenges. Cellulose nanocrystals (CNCs), which are tiny, rod-shaped particles, are an example of renewable nanomaterials. CNCs can naturally form strong, color-producing structures, which have potential uses in products like coatings, textiles, and electronic devices. However, the exact factors that control how CNCs assemble and create colors are not yet well understood. This research focuses on studying how different surfaces and drying conditions affect the assembly of CNCs. By uncovering these details, this project seeks to create sustainable colors and materials, leading to innovations like smart windows, anti-corrosion coatings, and even semiconductors. This project also includes a significant educational component that focuses on engaging college students, middle-school teachers and the public in science, technology and sustainability. This project specifically fosters interdisciplinary educational opportunities, involving hands-on learning, research experience, peer-teaching and mentoring, and partnerships between academia, local schools and communities. TECHNICAL SUMMARY: The goal of this CAREER program is to develop a scientific foundation and an interdisciplinary, inclusive education platform to harness the self-assembly of renewable nanoparticles on solid surfaces for the controlled design of advanced sustainable materials and bio-inspired colors. Of particular interest are cellulose nanocrystals (CNCs), plant-based nanoparticles that spontaneously self-assemble in aqueous suspensions to form chiral nematic structures at critical concentrations. These helicoidal structures achieve an arrested state which may be characterized as strong, color-generating nanostructures as a result of drying the colloidal state into a coating or a film (a process referred to as EISA or evaporation-induced self-assembly). The substrates onto which the dispersions dry are hypothesized to critically influence the self-assembly of CNCs, which in turn controls the generated visible colors. This program, therefore, aims to (1) elucidate the role of mounting substrate effects on the assembly and anchoring of these CNCs and (2) investigate the influence of drying dynamics on EISA, known to primarily govern color generation. A series of mounting substrates of varying surface properties and topography is used to investigate the deposition of CNC mono- and multilayers and assess the influence of substrate properties and external drying conditions on the drying dynamics of CNC cholesteric structures and the formation of colors. The outcomes of aims (1) and (2) are to be integrated into a novel interdisciplinary, multi-generational educational platform that will train the next generations of leaders in bio-inspired and sustainable colors for materials design and innovation. This platform will also aim to (3) develop a globally competitive STEM workforce to increase the engagement of younger generations from all backgrounds and the broader public in science, technology and sustainability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

By effectively mimicking natural, energy-efficient, nano-structural designs through computer simulations, scientists could fabricate novel optical materials with applications in energy-efficient products and visual computing pipelines. Examples of such natural designs abound, including those found on insect wings, insect-eye lenses, bird feathers, and rock surfaces. However, challenges to this optical biomimicry are compounded by the scale, volume, diversity, and intricate interplay of underlying wave-optical phenomena. Subtle light-matter interactions significantly influence the observable properties of light perceived by human observers and/or man-made sensors. This project will develop an effective and efficient, data-driven computational framework that leverages mathematical simplifications to accurately and affordably model complex light-matter interactions. This work will empower researchers to design, prototype, and fabricate cutting-edge, energy-efficient products for capturing, sensing, and displaying visual information. This work will contribute to advancements in the automobile, military, biomedical, architectural, and art and entertainment industries. Furthermore, this project will create new learning materials, develop framework usage guidelines, and disseminate knowledge through K-12 engagement to foster STEM education among aspiring scientists and visual computing professionals. To achieve these goals, the investigator will develop a computational framework for replicating, designing, and rapidly prototyping nano-structural coloration architectures. The research team will: (a) organize the diverse phenomena of wave-matter interactions through visual characterization, phenomenological understanding, and structural features into a robust operational taxonomy; (b) refine recent advancements in computer graphics rendering techniques with rigorous mathematical derivations; and (c) integrate real-world, large-volume data into the computational process. Developing an operational, data-driven taxonomy will enable the investigator to streamline modeling workflows for future digital twinning efforts in structural coloration. The investigator will derive Wigner distribution-based formulations to model light propagation through heterogeneous media. This approach will unify, compact, and modularize the computational framework. Subsequently, the team will integrate the operational taxonomy parametrically into this unified framework. This integration will facilitate efficient and flexible general emulations of optical biomimicry. Finally, the team will develop efficient methods for adaptive data acquisition in conjunction with iterative and incremental model parameter estimation. The resulting computational framework will enable both forward modeling for emulating nanostructures and inverse modeling for determining the properties of structures based on their observed appearances. This work will significantly advance the field of optical biomimicry by facilitating data-intensive, physics-based modeling and simulations. 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

Recent advancements in Artificial Intelligence have produced general-purpose Large Language Models (as used in AI chatbots such as chatGPT) that millions of people use daily. These models not only produce text that is startlingly like human writing and spoken language, but have shown remarkable ability to understand people’s emotions and even offer empathy. But we are unable to objectively gauge their true understanding of emotions, because we do not have rigorous definitions and tests that measure social and emotional understanding in AI. Without objective measures, we cannot assess progress in developing these models, identify blind spots or potential harms, or understand how to improve the emotional understanding of AI. This project addresses this knowledge gap, by defining what such social and emotional understanding entails, and devising new ways to measure the emotional capacity of AI language models. In parallel to the technical work, this project will also produce policy guidelines for designing and implementing AI that relies on understanding of human emotions, and educational materials to provide teenagers and adults with better AI literacy, and enable them to navigate the AI landscape with an awareness of its dangers. This project will ultimately enable practitioners to build more capable and safer conversational AI conversational that can better understand their human users. The project combines theoretical frameworks from psychology with rigorous methods from computer science to define several types of reasoning over emotions. These include predicting emotions from situation contexts and mental states; reasoning about interventions to change emotions; and how to include modalities like facial expressions and vocal expressions to inform emotionally-aware AI. The research will systematically define reasoning tasks in a Question-Answer format and generate benchmark datasets that researchers can use to assess various aspects of social and affective cognition. The project will also assess the progress of modern Large Language Models and develop techniques to improve their performance on tasks that measure social and emotional capabilities. 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

Heavy-tailed probability distributions are routine across many scientific disciplines, from astronomy to ecology and finance and network modeling. Such distributions are often utilized in statistical modeling to incorporate non-linearity, robustness to large observations, and sparsity in high-dimensional data. The overarching goal of this project is to build new scalable statistical methods that incorporate heavy-tailed prior distributions in three disparate application areas: independent component estimation that recovers independent, latent sources from their observed mixtures, astronomical distance estimation from parallax measurements, and statistical modeling of compositional data. The research will result in powerful Bayesian tools with rigorous theoretical justification. This project will also narrow the critical gap between methodological advances in statistics and the tools used by the scientific community and promote increased usage and transparency of state-of-the-art Bayesian tools. The research findings will be incorporated into various educational activities to engage K-12 students. The project will provide research opportunities and training for graduate students and will enhance undergraduate and graduate curricula, accompanied by a monograph. This project develops Bayesian methodologies to address three significant statistical challenges: (1) unifying feature extraction techniques via novel latent space representations in independent component analysis, (2) improving astronomical distance estimation by incorporating measurement errors and non-linear relationships in parallax data, and (3) constructing prior distributions tailored for high-dimensional simplex-valued data that can adapt to arbitrary sparsity and dependence patterns. By leveraging heavy-tailed priors within hierarchical models, this work provides a new framework for controlling higher-order moments in blind source separation and as mixing densities for normal scale mixtures for handling non-linearity, robustness, and sparsity. The methods to be developed will be rigorously tested in applications spanning astronomy, blind source separation, community detection, and ecological modeling of species diversity and affinity, demonstrating their broad utility. The results will be disseminated through peer-reviewed publications in statistics, machine learning, and other scientific journals, and software implementations will be openly accessible as R packages that benefit the wider quantitative science community. 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 integration of mathematical models and ecological experiments presents a unique opportunity to understand, in simplified ways, complex ecological interactions. Parasitism is one of these complex interactions that can impact multiple types of hosts from humans to plants. The process of parasite transmission is complex, and parasites employ various mechanisms to ultimately exploit the host resources. Some parasites even use other organisms, like insects, as vectors to infect their hosts which adds, yet another layer of complexity. Mathematical models have been essential to allow us to study vector-borne parasite transmission. Yet, these simplified relationships ultimately may result in an incomplete picture of the ecological process. Moreover, these simplifications may result in biased understanding of key population-level outcomes such as the proportion of individuals infected or how likely is a parasite to persist. For instance, models assume that every individual host has the same probability of getting infected. However, we know that this may not be always true. This project considers individual differences, host movement, and spatial variations to study their impact on host infection and the transmission of disease in the system. This CAREER project integrates teaching and research to development a graduate course and will provide opportunities to graduate, undergraduate, and high school students in research. This study evaluates how much model complexity is needed to effectively describe and forecast vector-borne disease dynamics. To answer this question this project follows an interdisciplinary approach that integrates hypotheses derived from theoretical mathematical models of increasing complexity with field transmission experiments in a natural lizard-malaria system in Puerto Rico. The investigators will build models that describe vector-borne parasite transmission under a variety of heterogeneous scenarios for the hosts including (1) individual differences, (2) movement, and (3) spatial heterogeneity. Then, the hypotheses will be tested in enclosure experiments in a natural lizard-malaria system. In these experiments, the conditions of the mathematical models on populations of infected and uninfected lizards and mosquito vectors will be recreated to quantify parasite prevalence and persistence under different heterogeneity scenarios. The quantitative interdisciplinary approach that is the cornerstone of this project is also regarded as a key gap in undergraduate and graduate training in ecology. The educational component of this project addresses this issue by developing and testing a novel pedagogical approach providing novel tools to teach mathematical modeling concepts to non-math majors This award was supported by the Mathematical Biology Program in the Division of Mathematical Sciences and the Population and Community Ecology Cluster in the Division of Environmental Sciences. 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

Dr. Hong-Zhou Ye of the University of Maryland, College Park is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Section to develop new theoretical methods for accurately and efficiently simulating chemical reactions catalyzed by transition metal surfaces. Understanding surface reactions at the atomic level is essential for designing improved catalysts with higher energy efficiency, better chemical selectivity, and improved synthesizability, which have critical impacts on the energy, technology, and manufacturing sectors of the U.S. and global economies. However, existing computational tools often face a trade-off between accuracy and efficiency, limiting their ability to model the large and complex systems relevant to realistic catalytic processes. Dr. Ye and his research group will address this challenge by integrating advances in quantum chemistry theory and software developments to create a new generation of computational tools capable of reliably predicting catalytic reaction mechanisms and energetics. These methods will be implemented in the open-source software package PySCF to ensure broad access. In parallel with the research effort, Dr. Ye and his team will pursue educational and outreach activities aimed at strengthening quantitative and scientific computing skills across multiple educational levels, helping to cultivate the next generation of the STEM workforce. These activities will include hands-on instructional modules and summer research experiences for local high school students, as well as an annual computational chemistry workshop for undergraduate and graduate students at the University of Maryland. The project is directed toward developing a Gaussian-based local correlation framework for metallic solids to enable accurate and efficient simulations of transition-metal-based heterogeneous catalysis using correlated wavefunction theories such as the coupled-cluster theory, thereby complementing the prevailing plane-wave-based density functional theory paradigm in computational materials science. Dr. Ye and his team will address two key technical barriers limiting the scalability and accuracy of coupled-cluster methods for transition-metal surfaces by developing (i) a linear-scaling local coupled-cluster theory suitable for metallic systems and (ii) correlation-consistent Gaussian basis sets specifically tailored for transition-metal solids to improve accuracy and numerical stability. The resulting methods and basis sets will be rigorously benchmarked against experimental surface reference data and applied to investigate the reaction mechanisms underlying the industrial Fischer–Tropsch process for synthetic fuel production. Through open-source release of the developed tools and basis sets, this project will lower the barrier to high-level electronic-structure calculations for a broad range of materials systems beyond catalysis, thereby amplifying their impact by enabling the development of systematically improvable approximate theories and fostering synergy with modern data-driven and machine-learning approaches. 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-services rely on virtual machines due to their high efficiency and flexibility. Alongside regular services, current new offerings from cloud-service providers allow exploiting under-utilized virtual machines at a fraction of the original cost. Such computing resources, however, are highly heterogeneous and elastic. Heterogeneity means that virtual machines can have different computational speeds and storage constraints. Elasticity means that these virtual machines can be preempted under short notice (on the order of minutes) if a high-priority job appears; on the other hand, new virtual machines may be available over time to compensate for any shortage of computing resources. Such behavior can result in computational failure or significantly increase computing time. In response to the challenges of both heterogeneity and elasticity in cloud systems, this project will formulate new heterogeneous elastic computing frameworks, aiming for optimal and implementable solutions. The results of this project can lead to sizable economic benefits. The project's educational activities are designed to integrate research into teaching, and include mentoring both graduate and undergraduate students, alongside outreach programs to undergraduate and K-12 students with the goal of fostering interest in transformational computing technologies. Another highlight of this project is the newly developed YouTube channel by the investigator. Motivated by practical measurements and constraints, this project formulates new heterogeneous elastic computing frameworks with both coded and uncoded storage placements. Then, it develops novel methodologies using combinatorial and information-theoretic tools to establish fundamental tradeoffs for such systems. Using these theoretical tools, the project designs low-complexity algorithms for real applications and evaluates them on Amazon Elastic Compute Cloud (Amazon EC2) in order to show significant gains of the proposed approaches compared to the state-of-the-art solutions. The project is structured around research topics: (1) heterogeneous coded storage elastic computing; (2) heterogeneous uncoded storage elastic computing; (3) secure uncoded storage elastic computing from user’s perspective and (4) heterogeneous elastic computing: convert theory to practice. 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

Many viruses in the environment threaten human health. Enteric viruses that cause diarrhea in humans are commonly found in municipal wastewater. Treated wastewater is increasing being used in farming, in industrial processes, in recreation, and in other proposes. It is imperative to guarantee that disease-causing viruses are effectively removed from this water source. Treatment can be challenging because of the small size of viruses and their tendency to stick together with bacteria to infect humans more easily. This project will determine how these enteric viruses from wastewater cause human illnesses. This project will use cross-disciplinary tools in laboratory experiments and environmental surveys to address the question. The study outcomes will aid in the effective management of wastewater and will help protect public health. This project will also train students and help the public learn more about environmental viruses. Advancing knowledge about the association between viruses and bacteria in aquatic environments is necessary to develop effective pathogen control strategies. This project will fill research gaps on the transmission, occurrence, and health impacts of bacteria-associated enteric viruses in aquatic environments and provide insights into the control of viral pathogens and protection of public health. The objectives of this research are to i) elucidate the impact of bacterial association on infectivity, environmental persistence, and disinfection resistance of enteric viruses; ii) investigate the occurrence, abundance, and removal of emerging bacteria-associated viruses in wastewater treatment and natural environments; and iii) explore the health impacts of bacteria-associated viruses through examining host responses. A suite of microbiology, omics, surveillance, and immunology tools will be integrated to achieve these objectives. The research will also be integrated into education to i) develop new education modules to foster students’ understanding in environmental pathogens and water disinfection; ii) increase the number of students from diverse educational backgrounds pursuing careers related to water; and iii) raise the understanding of the public about environmental pathogens and their control. The outcomes of this project will provide a framework to evaluate the fate and transport of pathogens interacting with other substances in complex environments. In addition, this project will extend the boundaries of environmental engineering and environmental virology to include immunology, which will enhance the understanding of health risks posed by environmental pathogens through host-pathogen interactions beyond mere infection. 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). This Faculty Early Career Development Program (CAREER) grant supports research that will investigate theoretical and computational approaches to commit or defer problems with decision-making hierarchies. Problem settings in vaccine design, disaster response, and smuggling prevention, among others, involve decision-makers observing a system evolving over time who periodically decide whether to commit non-renewable resources, or defer their use, to optimize the system's overall performance. The evolution of the system is subject to randomness and its performance may depend on other decision makers, about whom there may be incomplete information, who seek to optimize their own performance. The research supported by this award seeks to determine what rules should guide commit or defer decisions in these settings, how and to what extent the decision-maker should use the information feedback observed, and how to computationally find the commit or defer decisions in specific problem settings. The educational activities include the creation of an online game to teach fundamentals of multistage decision-making to K-12 students. Standard commit or defer problems (CDPs) assume a single decision-maker and cannot model problems that involve multiple decision-makers, e.g., a Leader and a Follower, who interact in a hierarchical manner. This project will establish a mathematical and algorithmic framework to solve hierarchical CDPs. The framework will improve our understanding of real-life CDPs and their practical requirements. The project will simultaneously address a number of technical challenges. First, the Leader may face global resource constraints, such that the resources spent in one period, cannot be replenished in future periods; second, the Leader's performance depends on the optimal actions of the Follower; and third, the Leader learns about the uncertain parameters of the Follower's problem by observing their reaction to the Leader's actions. By using approaches at the interface of hierarchical and online optimization, the project will rigorously establish the manner by which commit or defer decisions should be made in hierarchical settings under uncertainty. Furthermore, the project will use tools from mathematical programming and probability to uncover how and to what extent the decision-maker should use the information that is learned, and then formulate and solve for optimal or near optimal policies in large instances of relevant 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

Next-generation cyber-physical systems (CPS) will increasingly rely on machine learning algorithms for situational awareness and decision-making, with the promise of enhancing human capabilities. Examples range from autonomous vehicles and robots to computer-controlled factory lines and wearable medical devices. However, learning-enabled systems have shown to be very sensitive to training data and have difficulty in ensuring functional safety and robustness. The undesired outcomes of recent deployments, such as the accidents involving semi-autonomous vehicles, raise questions about the design principles needed to build learning-enabled systems that are safe. This project aims to develop the foundations of a novel methodology for the design and verification of learning-enabled CPS. It will pursue a compositional framework and computational tools that can reason about the uncertainty and approximation introduced by learning components and enable system design via a hierarchical and modular approach. The proposed research can have a highly positive influence on the design and real-world deployment of safe and cost-effective autonomous systems for a variety of applications, including autonomous driving, robotics, and industrial automation. Moreover, it has the potential to offer a unifying framework for reasoning about a number of robust and fault-tolerant design approaches that are currently based mostly on ad hoc solutions. Collaborations with industry partners will be pursued to facilitate transitioning the research findings into practice. An educational plan including new undergraduate and graduate courses and a program for pre-college students will complement the research effort, aiming to educate the next generation of engineers and researchers on the concepts and the multidisciplinary attitude needed to realize "intelligent" systems that are safe, technologically and economically feasible, and seamlessly interacting with people. The project develops a compositional framework for reasoning about the probabilistic behaviors of CPS built out of unreliable components. The framework relies on stochastic models of the interfaces between the components and their environments, termed deep contracts, together with rigorous rules for composing and refining them. Rich, quantitative, logic-based stochastic specification formalisms and data-driven modeling techniques will be leveraged to express and propagate computationally tractable representations of uncertainty at different abstraction levels. The framework will be vertically-integrated and offer mapping mechanisms to bridge heterogeneous models and heterogeneous decomposition architectures in the design hierarchy. It will provide computational tools to efficiently solve verification and synthesis problems with stochastic contracts. Finally, it will offer mechanisms to monitor requirements throughout the entire system life-cycle and provide assurance both at design time and runtime. 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 will develop novel statistical theories and methods for handling large, heterogeneous datasets. Modern scientific applications often produce heterogeneous data of different types for the same problem. For instance, a single-cell biologist may observe multiple types of sequencing data from diverse instruments, all relevant for understanding the biological pathways of a single complex disease. The challenge lies in effectively combining these different data types to build statistical pipelines that outperform those developed using any one data type. Traditional statistical approaches struggle with this challenge. This project will establish a new statistical paradigm to address the complexities of such heterogeneous data while accounting for datasets with billions of variables. The project outcomes will facilitate principled prediction and inference in applications ranging from single-cell biology to precision health and neuroimaging. The project will involve graduate student participation and the development of new curricula at graduate and undergraduate levels that incorporate the project outcomes. Additionally, the research will engage medical professionals to facilitate the dissemination of the research products in current biomedical practice. This project will develop a modern statistical framework to address data heterogeneity in high dimensions, focusing on three key sub-themes: (i) creating principled and robust prediction strategies for multi-view learning, (ii) developing new inference pipelines and prediction analysis frameworks for meta-learning, and (iii) introducing novel inference methods for low-dimensional functionals under transfer learning. In multi-view learning, this project will quantify optimal strategies for cooperative learning, devise new adversarial learning techniques, and analyze the effects of interpolation learning. In meta-learning, this project will introduce new debiasing strategies to tackle inference questions that arise during fine-tuning following an initial phase of pre-training. In transfer learning, this project will develop general-purpose strategies for ranking source distributions and establish new inference schemes for low-dimensional functionals of scientific relevance. On the technical front, this project will introduce novel comparison inequalities, algorithmic proof methods, and leave-one-out techniques that effectively capture the interplay between high dimensionality and heterogeneity. 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) award supports fundamental studies to enable scalable manufacturing of high-resolution, liquid metal-based stretchable electronics. Gallium-based liquid metals uniquely combine metallic conductivity and fluidity, making them attractive for applications in wearable biomedical devices, soft robotics, and human-machine interfaces. However, their high surface tension and spontaneous oxide formation pose challenges in patterning liquid metals with high resolution, throughput, and stretchability on a broad range of substrates—a critical obstacle to their wide adoption in high-performance stretchable electronics. This project seeks to address these limitations by developing a novel manufacturing approach that incorporates colloidal self-assembly, surface functionalization, and micro-transfer printing to enable scalable, high-resolution patterning of liquid metals on substrates with high stretchability. The knowledge generated from this work will advance the synthesis, processing, and functionalization of liquid metals for a wide range of applications, including bioelectronics, thermal management, and soft robotics. This CAREER award also includes educational activities aimed at preparing future scientists and engineers in electronics manufacturing to meet the nation’s workforce needs. This project aims to address knowledge gaps in the physical properties of gallium-based liquid metals and realize high-resolution, high-throughput patterning. A new patterning approach will be developed, combining colloidal self-assembly of liquid metal particles with scalable transfer printing. High-resolution nanomechanical characterization of liquid metal particles, coupled with analytical modeling, will elucidate the processing physics. This self-assembly process will be combined with high-speed, roll-to-roll micro-transfer printing to fabricate liquid metal patterns on various substrates. Furthermore, this project will investigate process-structure-property relationships that govern the patterning resolution and the mechanical and electromechanical properties of multiscale liquid metals formed through this approach. By combining advanced micro/nano-fabrication, material characterization, and modeling, this project will advance the knowledge base of multiscale mechanical and electromechanical properties of liquid metals, enabling scalable production of next-generation liquid metal-based stretchable electronics. 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

Electric machines are critical to society, converting power between electricity and motion. However, innovations in electric machines are necessary to reduce costs, increase efficiency, and improve their capabilities for challenging applications, such as space exploration. Therefore, this project will develop and integrate fast, flexible, and accurate electromagnetic, structural, and thermal equivalent circuit models of electric machines. These models will be tailored for topology optimization (TO), which yields novel shapes that would not result from conventional optimization. Previously, TO has only been applied to portions of electric machines and has not considered electromagnetic, structural, and thermal performance simultaneously. However, the speed, flexibility, and accuracy of the developed models will enable TO of entire electric machines considering electromagnetic, structural, and thermal performance simultaneously. The resulting new designs will reduce costs and losses for existing applications and enable the use of electric machines in new applications. Other broader impacts of the project include research opportunities for high school, undergraduate, and graduate students and the development of assignment modules teaching students to combine coding and discipline-specific knowledge to develop models for solving engineering problems; these assignment modules will be designed to be incorporated into various engineering classes. This project will develop a new approach for multiphysics analysis of electric machines that is significantly faster than finite element analysis (FEA) with the accuracy and flexibility required for TO. TO, unlike conventional optimization, yields fundamentally new geometries that can take advantage of additive manufacturing. However, true TO of electric machines requires fast and extremely flexible multiphysics analysis. Thus, this project will develop high-resolution lumped parameter magnetic, electrical, structural, and thermal network models. These models will enable multiphysical TO of entire electric machines. Each network will divide the geometry into a grid of many small node cells, which consist of lumped sources (magnetomotive force, electromotive force, mechanical force, or heat) and impedances (reluctances, resistances, springs, or thermal resistances), and solve for the magnetic scalar potential, electrical current, mechanical deflection, or temperature of each node cell. Each network will share the same grid of node cells as the TO algorithm. To perform multiphysics analysis, each network will be solved individually and its results used to update the other networks iteratively until the analysis converges. To test the models’ accuracy, the networks will be applied with TO to design different electric machines, and the performance of the optimal designs will be verified with FEA and experimental testing. 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 rapid melting of snow can be hazardous, resulting in flooding and the transportation of excessive nutrients and pollutants into waterways. The potential for snowmelt is dependent on the availability of snow and the occurrence of specific weather patterns that trigger melting. By assessing the weather patterns suitable for snowmelt, further insights can be gained into how and why rapid snowmelt occurs and what impacts can follow. Particularly in the eastern and central United States, the snowpack typically completely melts and re-accumulates multiple times per season, where the variable release of water from the snowpack can pose challenges to predicting and preparing for large and rapid events. Using historical archives of snow and meteorological data, this research aims to evaluate the physical characteristics of weather patterns that result in rapid snowmelt and assess how variations in weather conditions have contributed to variability in snowmelt in the recent past. The investigator will then assess future projections of snowmelt conditions and associated weather patterns using a suite of numerical models under multiple future scenarios. Through this project’s research and educational objectives, over 20 students per year will be actively involved in research efforts. An improved understanding of the processes leading to snowmelt, the role of atmospheric circulation on regional energy balances, and the projected variability of future snowmelt conditions will better inform water resource and risk managers of potential outcomes. Such information allows for appropriate infrastructure and hazard mitigation plans to be determined for impactful snowmelt events. Synergies between research and educational objectives will further allow for the creation and dissemination of a variety of Open Educational Resources for university students that will advance STEM education beyond the investigator’s institution and facilitate the testing of additional underexplored hypotheses by students. The investigator will transform existing coursework in hydroclimatology into a service-learning pedagogical framework, where students, the investigator, and external partners collaborate to engage in developing novel, community-responsive research. Additionally, the dissemination of research and educational outcomes by the investigator and student team will enhance the climate services available to the public within Kentucky and the broader United States. This project is jointly funded by the Atmosphere Cluster within the Division of Atmospheric and Geospace Sciences (AGS) 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

Hodge theory is a branch of mathematics developed in the first half of the 20th century, which aims to study the non-linear geometry of shapes using linear invariants. More precisely, the shapes are complex projective manifolds and the linear invariants are Hodge structures. The famous “Hodge conjecture”, first presented in 1950 at the International Congress of Mathematicians, states that the essential geometry of a complex projective manifold is recovered by its Hodge structure. As one of the Millennium Prize Problems, this conjecture has attached to it a one million dollar prize for a solution. While complex projective manifolds involve a bit of abstraction to define, they are fundamental objects in mathematics and physics; for example, the “Calabi-Yau varieties” are a certain class of complex projective manifolds which can appear as the small dimensions of the universe in string theory. The main research goal of this project is to study the Hodge structures of Calabi-Yau varieties—to both gain insight into Hodge theory more generally for all complex projective manifolds, and to increase the depth of our understanding of Calabi-Yau varieties. Beyond pure research goals, the educational impact will be to develop an active community of young researchers and PhD students focusing on this circle of ideas. The project will continue lines of research of the PI on toroidal compactifications of Calabi-Yau moduli. Recent joint work of the PI with V. Alexeev constructs a modular toroidal compactification of the moduli space of degree 2d polarized K3 surfaces. One research goal is to extend this work to hyperkahler and Calabi-Yau varieties, with an eye towards understanding the combinatorial structure of degenerations. Additionally, the project aims to study and prove general results concerning variations of Hodge structure (VHS). Joint work of the PI with S. Tayou is advancing our understanding of finiteness properties of Z-polarized VHS, and it now seems within reach that conjectures of Deligne and Simpson on the algebraicity of the non-abelian Hodge locus can be resolved. The approach is novel, incorporating ideas from hyperbolic geometry. The PI will also pursue results about period mappings for Calabi-Yau variations of Hodge structure, such as questions of boundedness of moduli of Calabi-Yau varieties of various classes, and under what circumstances Torelli theorems hold. 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

Battery-less sensors that harvest energy from ambient sources are revolutionizing the possibilities for IoT applications, particularly in remote or challenging environments where sustainable, maintenance-free operation is crucial. When combined with tiny machine learning technologies, these devices are further enabled with in-situ data processing and real-time decision-making capabilities. However, integrating tiny machine learning algorithms into battery-less IoT devices presents significant challenges involving data acquisition, model training, and model deployment, all under stringent constraints of power, cost, memory, and computation. Operational intermittence due to frequent power failures, further complicates functionality. Once deployed, tiny machine learning models typically operate under fixed sensor sampling rates and are only able to make timely decisions when there is sufficient power. Moreover, the extended operational life of these systems introduces a novel challenge: the obsolescence of AI algorithms or programs as sensory inputs and/or environmental conditions evolve. There is a need for a holistic framework that offers energy efficient solutions capable of responding to sensory input, adapting to environmental changes, and maintaining code currency to ensure continued accuracy and responsiveness. This project seeks to address these challenges by developing innovative methods for adaptive tiny machine learning integration in battery-less IoT systems, ensuring long-term, reliable operation in dynamic, energy-variable environments. This research will advance battery-less systems through three coherent advancements: 1) fundamental redesign and optimization of tiny machine learning models for self-powered IoT devices to enable timely data analysis and decision-making; 2) development of a holistic framework that supports adaptive data collection, decision-making, and communication strategies attuned to the dynamics of varying ambient environments; and 3) design of reliable and efficient code update mechanism to maintain program currency under frequent power interruptions. The proposed co-design techniques will lay a foundation for designing intelligent self-powered IoT devices and applications. The outcomes of this research will include novel cross-layer co-design techniques, tiny machine learning model design methods, software packages, and end-to-end deployment solutions. In addition, this project will encourage the participation of all groups including underrepresented groups and K-12 students in STEM, enrich the current curriculum, and prepare future engineers in machine learning, embedded systems, and IoT applications. All designs and outcomes will be made publicly available. 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

Partial Differential Equations (PDE) of parabolic type are central to mathematical analysis, with extensive applications in physics, finance, biology, and other fields. The research in this project focuses on three types of PDEs: reaction-diffusion equations, Hele-Shaw type flows, and chemotaxis systems. These equations capture the evolution of diffusive processes, such as tumor growth, forest fire propagation, chemical diffusion, and crowd motion. The project aims to advance understanding of these equations and their underlying phenomena, with potential societal benefits such as enhanced strategies for managing forest fires and improving understanding of biological processes, including tumor cell behavior. The educational component is a cornerstone of this project, tightly integrated with its research objectives. In collaboration with Auburn University’s outreach programs, the Principal Investigator will engage K-12 students in interactive workshops and science fairs, inspiring curiosity and enthusiasm for science. A key focus of the educational efforts is increasing participation and retention of women and underrepresented groups in mathematics and science. This project will also provide interdisciplinary training opportunities for students and early-career researchers through workshops and summer schools directly tied to the research themes. These programs will offer hands-on experiences, bridging theoretical knowledge with practical applications to prepare the next generation of scientists and mathematicians. This project integrates innovative research with impactful education and outreach, strengthening the link between scientific discovery and learning for the benefit of society. Reaction-diffusion equations are central to modeling phenomena such as front propagation and interface motion in fields like chemical kinetics, combustion, and population genetics. In sufficiently random media, the long-term, large-scale dynamics of these equations are expected to converge to a deterministic propagation process (called stochastic homogenization). While previous research in the area in general dimensions is limited, this proposed research brings new methods and technical tools to study such problems. For example, it will extend several fundamental probability tools that were widely used in PDE problems, including Kingman's subadditive theorem and Azuma's lemma. Furthermore, the methodologies developed here will have applications to other equations. Hele-Shaw flows with source and advection terms are widely used to model tumor growth, where the region occupied by tumor cells corresponds to the positive set of the solution, and the boundary of this region forms the free boundary. The presence of drift and source terms brings significant difficulties and so there are few results in this direction. This project focuses on developing new PDE techniques to address the regularity and the homogenization of the free boundary. The outcomes are expected to provide a deeper understanding of free boundary dynamics and illuminate the effects of stochastic fluctuations on interface motion. Chemotaxis, the directed movement of organisms in response to chemical gradients, is a crucial phenomenon in biology. While chemotaxis models have been extensively studied, most work focuses on bounded domains. This project aims to first establish the global well-posedness of chemotaxis systems on unbounded domains and then investigate the asymptotic spreading properties of solutions, a key characteristic of these systems. These studies will employ tools from various areas of mathematics, including semigroup theory, viscosity solutions and parabolic regularity theory, to explore new aspects of chemotaxis models, with potential contributions to both mathematics and biological sciences. This project is jointly funded by the Analysis Program in the Division of Mathematical Sciences 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

Basic research into understanding how children learn physics concepts has been pursued relatively independently in several different disciplines (cognitive psychology, developmental science, and physics education research), each of which has come to very different conclusions. One research literature suggests that people's pre-educational intuitions about mechanical physics are largely veridical, another literature suggests that these intuitions are largely incorrect, and yet another suggests that they are incorrect in infancy but become veridical even before significant physics education. These seemingly contradictory findings complicate the development of educational interventions, since interventions suggested by one line of research are contraindicated by others. The overarching goal of this project is to start synthesizing and reconciling these different lines of work. The project is inherently interdisciplinary and will depend on new and emerging methods for robust and reproducible research such as automated online testing, citizen science, and advanced analytics. Such tools are increasingly important in human research but relatively little training in them for students is available. Thus, the educational component of this CAREER project will focus on enhancing such training and includes updating an innovative, interactive textbook on computational modeling and analysis, developing and running an intensive summer training program for citizen science; and providing undergraduates with direct, hands-on education. The project is funded as a CAREER award by the EDU Core Research (ECR) program, which supports work that advances the fundamental research literatures on STEM learning, broadening participation, and workforce development. Multiple research literatures have engaged questions about the nature of intuitive understandings of mechanical physics, but they have often supported discrepant conclusions, thereby making ambiguous what their implications are for the characterization of fundamental cognitive architecture as well as for the design of educational interventions. Moreover, because these conclusions arise from different disciplinary communities studying different populations using different methods, it is not clear whether these discrepancies reflect real differences in how people think about different physical processes in different situations or whether they are simply artifactual. Objective 1 of this project will apply the tools of psychophysics in order to test the reliability of a range of tasks developed in these literatures in an attempt to determine whether differential performance (if any) suggests multiple underlying cognitive systems. Objective 2 will use the tools of developmental psychology to study age-related differences in children on tasks adapted from the range of literatures to see whether the developmental patterns observed by developmental scientists are specific to their methods. Objective 3 will investigate whether the measures developed in these disciplines differentially predict student success in introductory university physics. Thus, the three objectives will systematically assess and compare the stimuli, tasks, and research methods common to the currently-disjoint literatures, allowing for direct comparison and synthesis, with subjects ranging in age from 6 months to adults. All three objectives will involve unusually large and diverse samples --- often by utilizing automated online testing and citizen science -- and advanced analytics. By attempting to reconcile these relatively disjoint literatures, the project will have the potential to contribute to the development of educational interventions in physics at a variety of ages. 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

To survive, animals must access food efficiently and, in some cases, they must fight for it. Adaptations that enhance food consumption or allow species to specialize on different resources—and therefore coexist—have been well-studied, improving our understanding of evolutionary and diversification processes. In comparison, there has been much less research regarding adaptations to defend food, especially in feeding structures. For instance, beak shape in birds has been linked to the demands of exploiting resources; hummingbirds, with their long, thin beaks suited for unique flowers, are a textbook example. Both across species, and even when comparing male to female beaks, the consensus is that the different beak shapes have evolved to better match the shapes of the flowers that the birds visit. Yet hummingbirds also fight over these resources, utilizing their beaks to stab or pluck feathers from opponents. This study investigates aggressive interactions (a previously underexplored evolutionary force shaping hummingbird beaks) and biomechanical performance in puncture, gripping, and fluid transfer, testing the hypothesis that beak shape is not only linked to matching flowers, but also determined by the use of beaks as weapons. In tandem, this project will foster new tools for public engagement and literacy in evolutionary topics of adaptive tradeoffs (e.g., a beak better fit for fighting might be worse at feeding), through a museum exhibit and development of curriculum for middle schoolers, centered around a game wherein students play as different hummingbirds and experience life with their adaptations. This research program provides lasting, transformative mentorship and interdisciplinary training to undergraduates, graduates, and postdoctoral professionals. Linking biomechanics to the ecological determinants of performance and species interactions is vital to grasp the processes shaping organismal evolution. This project challenges the status quo regarding bill-flower coevolution being the sole driver of hummingbird bill variation, to more fully consider the use of bills as weapons as a selective force shaping bill morphology and behavior. In opposition to the existing consensus that resource partitioning is the cause of dimorphism between male and female hummingbird bills, this study tests the hypothesis that differences in bill traits result from tradeoffs between nectar-feeding efficiency and performance in battles among males competing for territories and mates. This project leverages advances in high-speed videography, 3D reconstructions, geometric morphometrics, finite element modeling, and radio-based identification and tracking to assess multiple metrics of performance across organismal scales in both natural and laboratory settings. Finally, this research establishes a novel system to study the evolution of animal weaponry on feeding structures, with directly measurable tradeoffs, and constrained by extreme specialization. Detecting tradeoffs in hummingbird bill form and function will help reshape paradigms regarding hummingbird energetics, and guide research on other selective pressures involved in the evolution of these traits. Failing to detect tradeoffs would be equally compelling: if bill weaponization does not hinder nectar extraction, it would suggest alternative evolutionary workarounds and associated costs. This mechanistic approach to understanding the links between morphology, performance, behavior, and ecology, plus their evolutionary implications, will advance the emerging and interdisciplinary fields of ecophysics and mechanoethology. 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 There is an increasing incidence of bone fractures in the United States, which is caused in part by an aging population. The global market for fracture fixation devices (e.g., medical implants) is expected to reach $13.6 billion by 2027, growing at a compound annual growth rate of 6.1% over that time. This Faculty Early Career Development (CAREER) award addresses a significant healthcare challenge by enhancing the clinical feasibility of biodegradable magnesium-based metallic implants. The utilization of biodegradable implants in biomedical applications, including vascular stents and small bone fixation devices, presents an innovative alternative to the currently employed permanent metallic implants. These permanent metallic implants often entail significant complications and may necessitate surgical intervention. Although biodegradable magnesium-based implants hold promise, their rapid degradation undermines their efficacy before the completion of the healing process. This project introduces innovative hybrid coatings, combining different coating methods and materials, to mitigate degradation and allow the controlled release of bioactive agents. By investigating how bioactive agents interact with coated magnesium, the project aims to understand degradation inhibition mechanisms and modulate degradation rates. Additionally, a computational model will be developed to predict degradation and agent release, bridging theory and experiment. The significance of this research lies in its potential to revolutionize patient-specific biomedical implants by tailoring the degradation rates and hence the lifespan of an implant. Successful outcomes may lead to the improvement of a variety of implants including orthopedic, facial, oral, and more, benefiting patients with personalized needs. This project aligns with the NSF's mission by advancing science, promoting health, and contributing to national welfare. Recognizing the underrepresentation of certain groups, the project includes a community-based mentoring program that aims to expose underserved Hispanic children in Chattanooga and the Southeast Tennessee Area to STEM through hands-on activities, fostering engagement and civic involvement. The PI also plans to work with the Society of Women Engineers at the university to introduce female high school students to biomedical engineering research, encouraging their participation in STEM fields. Moreover, a graduate course on "Manufacturing of Biomaterials" will be developed, enriching education and nurturing future professionals. TECHNICAL SUMMARY This research project addresses the challenge of rapid degradation in magnesium-based biomedical implants, hindering their clinical impact. By employing hybrid coating systems, particularly plasma electrolytic oxidation (PEO) coupled with the sol-gel coating method, the project aims to control degradation rates and enhance bioactive agent release for smart biomaterials. This research project will address gaps in understanding the mechanisms behind enhanced corrosion resistance of coated magnesium implants and the effective regulation of bioactive agent release during prolonged implantation periods. Two primary objectives guide the investigation: Objective #1 focuses on understanding the interaction of bioactive agents with the porous surface of PEO-coated magnesium substrates, elucidating their role as corrosion inhibitors through microstructural evolution and electrochemical corrosion mechanisms. Objective #2 involves the development of a versatile numerical model based on a physical modeling approach to predict degradation rates and bioactive agent release from magnesium substrates coated with hybrid PEO-based and multiple layers of hydroxyapatite (HA) sol-gel coatings. One outcome of the project is the possibility of depositing thin (< 100 nm) subsequent coating layers. That enables the establishment of a clear relationship between the deposited coating layers and degradation rates. This understanding is crucial for ensuring a time-certain commencement of the implant’s degradation, a vital aspect for patient-specific implant applications. The interdisciplinary approach combines expertise in biomaterials processing, corrosion science, and numerical modeling. The anticipated outcomes hold the potential to integrate magnesium as a biodegradable metal technology in clinical settings, particularly for patient-specific devices in orthopedic and craniomaxillofacial applications. The educational objective of the project is to (1) develop a new community-based mentoring program for Hispanic children ages 11 to 18, (2) organize talks, presentations, and live demonstrations aiming at recruiting more female research students, and (3) develop a graduate interdisciplinary course on the topic of “Manufacturing of Biomaterials”. These educational activities will offer STEM exposure, social engagement, career development, and advising, particularly for Hispanic and female students in Chattanooga and the Southeast Tennessee Area. 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 aims to develop a new method to better understand how cells are organized in tissues and how cells interact with each other over time. Recent advances in a technology called spatial transcriptomics have allowed scientists to study gene activity in tissue samples and see how cells are distributed within their environment. However, this technology currently provides only a snapshot of what is happening in the cells at a single moment, which limits our understanding of how cells change and respond to different conditions over time. To address this gap, this project will focus on creating a system that can track molecular changes in live tissue over time. This work aims to develop a new technology using a nanostructure (e.g., nanowires) to extract important molecules from live cells with minimal disturbance, allowing for repeated sampling and enabling longitudinal measurements. This will allow for continuous monitoring of cell activity and interactions, which can help scientists learn how cells develop, change, and respond to diseases. By improving the understanding of cellular behavior, this research has the potential to advance medical treatments, particularly for diseases that involve complex interactions between cells. This project also supports education and workforce development by training students in cutting-edge research techniques. The ultimate goal is to use this technology to improve healthcare and contribute to advances in science that benefit society and national well-being. Recent advancements in spatial transcriptomics have transformed understanding of cellular heterogeneity, enabling the identification of new cellular interactions and functional dynamics between different cell types and their microenvironments that were previously elusive. This technology allows researchers to study gene expression in the context of tissue architecture, providing a more complete understanding of the cellular function and interactions between cells. Although this method preserves the spatial information, it only offers a snapshot of gene expression at a single point in time, providing a static view of the dynamic biological processes. To fully understand the complex and temporal nature of tissue function and disease progression, it is essential to monitor molecular changes over time. Integrating spatial transcriptomics with longitudinal studies will be crucial for unraveling the continuous and evolving nature of cellular behavior. To address this limitation, the overall objective of this CAREER project is to develop a new method for the spatiotemporal monitoring of live tissue gene expression. This work proposes to develop a nanowire-based molecular transfer technology that can extract molecules from live cells and transfer them to a hydrogel to achieve spatiotemporal transcriptomics. By monitoring molecular changes within specific regions of tissue over time, researchers can gain valuable insights into how different cell types interact and respond to changes in the tissue microenvironment. This platform represents a significant advancement in our understanding of cellular behavior, particularly regarding cell development, differentiation, and disease pathogenesis. 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 brain is composed of specific cells called neurons that connect to form circuits. These brain circuits allow the brain to process information, control bodily functions, and regulate behavior. However, these circuits can malfunction, which can lead to brain disorders. This research project aims to address challenges in damaged neuron circuits by "reprogramming" the brain. The long-term goal is to develop new ways to change and repair brain circuits, which will help understand how to treat brain disorders. The project will use zebrafish larvae as a model system to demonstrate that signals can be transferred from a healthy brain to a damaged one with the goal of rebuilding the malfunctioning circuits. The project will engage students and the public in science, technology, engineering, and math (STEM) activities. Local high school students will help build novel brain interfaces to demonstrate control over zebrafish movement. College students will create interfaces to demonstrate signal transfer between zebrafish and robots. These activities will be shared through educational videos and outreach events that will allow the community to learn more about neurons and brain. This research will improve our understanding of brain development and highlight the importance of early intervention for brain disorders. Within the brain, neurons interconnect to form neural circuits, which are essential for processing information, controlling bodily functions, and regulating behavior. However, in disordered brains, neural circuits can malfunction or become malformed, leading to a range of neurological and psychiatric disorders. This project aims to address these maladaptive neural circuits by reprogramming the brain that will ultimately pave the way to reconstruct maladaptive neural circuits by replicating those from a healthy brain. The project will develop a new method to manipulate neural circuit development by transferring and replicating neural signals from one brain to another using an optogenetic actuator and a neural activity indicator in zebrafish larvae and demonstrate a functionally and structurally replicated brain. By demonstrating the feasibility of replicating neural signals and mitigating malfunctioning circuits in zebrafish larvae, this research will lay the groundwork for future research in the field of neural circuit restoration. The overall educational goal is to engage and motivate students and the public in STEM through comprehensive and interactive activities. Toward this goal, the proposed research will be integrated into educational projects to engage student interns from local high schools and undergraduate students at the University of Illinois at Chicago in the development of zebrafish interfaces. In addition, the research will contribute to public education by creating instructional videos for an at-home science project, which will be disseminated via online platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Cooling has been used as a means of preservation for centuries. The ability to achieve very low temperatures has led to the storage of cells, tissues, and organs in a process called cryopreservation. Extremely low temperatures can rupture cells due to ice crystal formation inside the cell. Cryoprotective agents (CPAs) are chemical mixtures that suppress ice crystal formation. Unfortunately, many CPAs currently used are also toxic to cells. The goal of this project is to identify and design new CPAs that exhibit reduced toxicity. Disseminating educational videos and performing K-12 outreach will increase awareness and understanding of cryopreservation. Recruiting community college students to participate in laboratory research will expand the science and engineering workforce. CPA toxicity is the major limitation of cryopreservation technologies. This is especially true for vitrification. This is an ‘ice-free’ approach. High concentrations of CPAs are loaded into biospecimens followed by rapid cooling through the glass transition temperature. This causes amorphous solidification of water (i.e. glass formation). Vitrification is especially useful for multicellular systems where avoidance of ice preserves the tissue architecture. A major barrier to the development of vitrification protocols is CPA toxicity. Despite a vast chemical space of glass forming molecules, the field typically relies on a very small subset of fewer than10 penetrating CPAs. The central hypothesis of this proposal is that examination of a larger chemical space will yield new CPAs with reduced toxicity and ideal physicochemical properties for vitrification. High-throughput screening (HTS) assays will be used to examine a range of glass-forming molecules. The CPAs will be further examined as multicomponent cocktails to discover formulations that exhibit minimal toxicity. A range of chemical and biological assays will help identify the mechanisms driving CPA toxicity. Finally, small molecules will target pathways responsible for CPA damage as an approach to reverse or eliminate toxicity. This project is expected to lead to the development of minimal toxicity vitrification solutions, which are critically needed in the field of cryobiology. 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

It is widely believed by scientists that our universe follows certain symmetry patterns and principles, which lead to profound implications such as conservation laws. Artificial intelligence (AI) can and has already benefited tremendously from exploiting these symmetries. This project seeks to identify and exploit symmetries that are prevalent in cooperative AI tasks, where a group of multiple autonomous sequential decision makers, or agents, plan and learn to maximize their combined benefit. As an example, consider the application of adaptive traffic signal control, where each intersection can be modeled as an agent controlling its traffic signal in a way that adapts to real-time traffic conditions to reduce congestion. There exist certain symmetries when the topology of the road network is regular, e.g., as a 4-connected grid, and the road condition is uniform. When done properly, such multi-agent symmetries can be identified and exploited to greatly improve the efficiency and effectiveness of the current solutions to cooperative AI. This project also integrates the proposed research into an array of education initiatives, playing key roles in the curriculum development and undergraduate research experiences at the PI's university, as well as outreach activities that bridge academia with industry practitioners and community stakeholders. This research will establish a unified framework and develop a set of interdependent methods that formulate, identify, and exploit multi-agent symmetries for cooperative AI tasks. The research first adopts a mathematically rigorous language to formulate the notion of multi-agent symmetry into the framework of symmetric Markov game, revealing its core property which can be exploited by planning and learning methods. Then, the research plan concretizes how to exploit several most common types of multi-agent symmetries, including permutation symmetries, Euclidean symmetries, and hierarchies of multi-agent symmetries of mixed types. Next, the research plan discusses issues that are critical for practice, including identifying and exploiting approximate multi-agent symmetries and dealing with partial observability. Finally, the research features several real-world applications, including adaptive traffic signal control, automated circuit design, and material design, to evaluate and showcase the proposed methodology. This project is jointly funded by Robust Intelligence 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.