Grants

NSF

Enormous health inequality persists in the United States. Even prior to the COVID-19 pandemic. In many areas of the country, people with a higher income live up to decade longer than those in the lowest income levels. Additionally, the pandemic itself has hit low income and under-served populations especially hard. Biased medical decision-making contributes to this health inequality. For example, previous work has shown that one of widely used health risk prediction algorithms assesses African-American patients as less sick than equivalently sick White patients. This research will make medical decision-making fairer by statistically analyzing the decisions made both by humans and by algorithms. The research will identify sources of bias (for example, when medical tests are given to patients with better access to healthcare rather than to patients most likely to have a disease), and propose solutions (for example, reallocating tests to patients who are predicted to have the highest disease risk). This will not only make healthcare fairer; it can also make it more efficient, by allocating medical resources where they will do the most good. The project will also create a publicly available class on how to design fair algorithms, and conduct a large-scale study of how engineers can be trained to design fairer algorithms, to improve the preparedness of the engineering workforce. Because important medical decisions are made both by humans and by algorithms, the research pursues three objectives: 1) detecting bias in human medical decision-making, focusing on three high-stakes medical settings: allocation of medical testing, healthcare quality assessment, and interpretation of medical images. Further, the project will also build algorithmic decision-aids to reduce human bias, by drawing clinicians’ attention to medically relevant features they may have overlooked. Finally, the project targets making algorithmic decision-making more equitable, by examining the features it is appropriate to include in a medical algorithm. The research will be conducted in collaboration with clinicians to maximize translational benefit to patients. The methods developed, which draw on techniques in Bayesian inference and deep learning to provide interpretable models of how bias arises, are more generally applicable to decision-making across a host of high-stakes domains—including lending and hiring—and thus can impact a wide range of fields concerned with equity in decision-making, including law and economics. 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 equivalence principle (EP) states that all massive objects fall at the same rate in a given gravitational field. This idea can be tested by measuring the difference in acceleration of two freely falling objects. Since the EP is the foundation of general relativity, our most precise theory of gravity, EP tests probe the relationship between gravity and the other fundamental forces. In addition, EP tests are sensitive to the existence of new particles that could comprise “dark matter,” the matter of unknown composition that has been observed in astronomical data but has not yet been detected in the laboratory. In this project, the PI and team will perform an EP test by measuring the difference in acceleration between two atomic isotopes in an ultracold atom cloud. The positions of the atoms as they fall will be read out with high precision. The experiment will use recent advances in quantum science to reach a higher accuracy than previous EP tests, providing the most stringent searches for new gravitational physics, new long-range forces, and leading dark-matter candidates. In addition, this program will provide opportunities for students to learn the experimental techniques of modern quantum science and will support a summer school on quantum sensing and precision measurement. To carry out this measurement, the research team will produce an ultracold atom cloud containing two isotopes of ytterbium, an alkaline-earth-like atom. The ytterbium atoms will be optically launched into an atomic fountain, where they will interact with a sequence of laser pulses to measure their accelerations as they freely fall. The laser system will be designed to enable hundreds of atom-light interactions, greatly increasing acceleration sensitivity, while avoiding atom loss and unwanted forces from the light pulses themselves. The team will characterize and control systematic effects from many sources, including gravity gradients, magnetic fields, black-body radiation, and the rotation of the Earth. After characterizing systematic effects, the results of the differential acceleration measurement will be analyzed to discover or set constraints on new gravitational physics and dark matter candidates. 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 explores questions in ergodic geometry, an interdisciplinary field connecting ergodic theory and geometry. Ergodic theory, a branch of dynamical systems, investigates systems that evolve over time, often exhibiting unpredictable and chaotic behaviors. Examples of such systems include planetary motion, weather patterns, and stock markets. Geometry, on the other hand, studies the shape and structure of objects. Ergodic geometry combines these perspectives, using tools from dynamical systems to address geometric problems. For instance, the shape of an object can influence the complexity of certain dynamical systems occurring on that object. This project aims to deepen our understanding of the relationship between an object’s shape and key dynamical quantities associated with it, such as entropic quantities, which serve as indices of complexity. In addition to advancing mathematical knowledge, the PI will establish the GW Experimental Mathematics Lab, creating a collaborative and vertically integrated research environment for students at George Washington University. This lab will foster hands-on learning and mentorship, preparing students to engage in cutting-edge mathematical research. In ergodic geometry, significant milestones have been achieved when the underlying dynamical systems are compact and uniformly hyperbolic. This project aims to extend these findings to geometric systems that are non-compact, nonuniformly hyperbolic, or both, using thermodynamic formalism. The research focuses on three objectives: studying correlations in higher-rank cusped geometric structures through orbital distribution analysis; examining the metric geometry of deformation spaces of these structures via Thurston’s asymmetric metric; and analyzing orbital correlations in nonuniformly hyperbolic geometric structures. Progress in these areas will involve developing new tools in thermodynamic formalism combined with geometric insights, potentially advancing our understanding of the interplay between geometry and dynamics in more general settings. 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

Hydrogen is used as transportation fuel, to produce ammonia for fertilizers, and to upgrade oil for numerous products. This project seeks to lower the cost of producing hydrogen by improving water electrolysis technology. Electrolyzers use ion-conducting membranes as separators. The project will identify relationships between membrane polymer structure of the membrane and its performance in an electrolyzer. These relationships are not well understood. The goal is to design efficient ion-conducting membranes that remain stable over time. The results of the project will help guide membrane design. The project will provide hands-on learning activities that connect to the research. It will create a training environment that prepares students for careers in polymer science and electrochemical engineering. Training activities will emphasize laboratory work, mentorship, teamwork, and clear scientific communication. The research will support manufacturing and the domestic energy industry. The goal of this research is to establish structure-property relationships in anionic ion-conducting membranes. This will be achieved through modular polymer synthesis and high-throughput characterizations. The research will develop design rules that link polymer chemistries and polymer structures to polymer performance. Polymers will be prepared using controlled synthesis methods that allow careful changes in molecular structure. High-throughput measurements will be used to study chemical stability, water uptake, ion transport, and swelling. A key innovation of this project is the development of a new block copolymer platform. The platform has the potential to decouple and optimize conflicting properties within a single material family. The knowledge and methodologies gained will advance polymer science and support the development of electrochemical energy technologies. 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). Most animals maintain certain types of bacteria in their gut that aid the animal by improving the nutrition of the diet and modulating the immune system. Microbial associations are particularly important in insects, which are critically important to agriculture, ecology, and human health, including pollinators such as bees, pests such as squash bugs and fruit flies, and disease vectors such as mosquitos and ticks. How insects recruit and maintain their preferred bacteria is largely unknown in terms of the specific genes and molecules the host uses to regulate colonization. The proposed research aims to close this knowledge gap by identifying these genes and molecules and how specific host cells regulate them. Due to critical roles of insects in ecology, agriculture, and public health, outcomes of the proposed research could have major impacts by developing uses of symbiotic bacteria to aid and control insects. Furthermore, the high conservation of gene function across mammals means discoveries in insects could apply to other animals, including vertebrates. Broader impacts of this grant also include development and implementation of hands-on and online microbiology curricula in public schools. Across animals, there is a knowledge gap around the precise developmental genetic mechanisms that construct and maintain the host tissues that house symbiotic organisms as well as whether a host can use certain bacteria to control others. Drosophila has long been a workhorse of animal developmental genetics, with unparalleled resources of publicly available genetic stocks with defined gene perturbations, and the wide genetic conservation across the animal kingdom. The PI’s discovery of symbiotic bacteria that form an adherent, multispecies community in a precise and understudied section of the Drosophila foregut presents a transformative opportunity to apply Drosophila genetics to the study of symbiosis between diverse gut bacteria and the host. This research will define the Drosophila symbiotic organ in terms of the fly genes that support the bacteria. Host genetic pathways, in secretion and immunity will be evaluated by quantitative cell biology, microbiology, and genomics approaches as well as by isotope ratio mass spectrometry, which will be used to measure nutrient exchange between host and bacteria. This will advance the field by providing candidate genes and developmental programs to discover symbiotic niches in other insects and animals, possibly even humans. 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

Earth’s continents have grown progressively throughout geologic time by aggregation of crustal blocks along major fault zones. As a result, continental interiors contain many dormant fault zones that represent locations where crustal blocks came together. These ancient fault zones are sites of weakness that can become reactivated to generate mountain ranges in the middle of the continents if the tectonic plate motions are appropriate and the ancient fault is sufficiently weak. However, it remains unclear which plate tectonic scenarios favor fault reactivation, leading to the unsatisfying observation that some ancient fault systems get reactivated by later tectonic events and others do not. This project will study uplift of the Black Hills, South Dakota, because it is an isolated mountain range near the middle of the North American continent that is focused on an ancient fault zone related to growth of the North American continent. To develop an understanding of ancient fault reactivation, this project will study the ancient faults exposed in the Black Hills to determine how those faults formed and evolved throughout the tectonic evolution of North America. The project will target specific sites in the Black Hills and use the chemistry of the rocks to determine their ages. This history of fault motion and rock ages from the Black Hills will be used together to determine: 1) when uplift of the Black Hills began and what tectonics processes were responsible; and 2) whether the faults that uplifted the Black Hills formed at the time of uplift, are older faults inherited from assembly of the North American continent, or both. The project will involve a high school from the Rapid City, SD region, and undergraduate and graduate students from the South Dakota School of Mines and Technology, who will receive training on how to perform geologic field work and help collect the data related to the project goals. The project PI will also generate and contribute field trip curriculum to high schools in the Black Hills region. Collectively, the project will contribute a better understanding of how plate tectonics works within continents, which will provide an explanation of why some continents have isolated mountain ranges hundreds of kilometers from the coastlines where active plate boundaries typically reside. The general view of “strong” cratons characterized by stable Precambrian basement is challenged by observations of Precambrian basement exhumed in the cores of geologically recent orogenic belts. Rather, intracontinental deformation that exposes Precambrian rocks provides a record of the evolving strength of continental interiors relative to the forces acting on continental margins and allows for an evaluation of how inherited deformation features in the Precambrian basement may influence, or not, the rejuvenated orogen. The Laramide orogeny of western North America is an example of a geologically recent orogenic belt that exhumed Precambrian basement but has an unclear relationship to the Precambrian structures that it spatially overlaps. This project will use the Black Hills of western South Dakota to test the hypothesis that the geologic evolution of inherited structures within a Precambrian suture zone facilitated strain partitioning of pure shear and simple shear components of Laramide deformation. Determining the relationship between Precambrian and Laramide structures in the Black Hills will require modern structural analysis of both Laramide and Precambrian structures, and low-temperature thermochronometry and U-Pb geochronology on exhumed Precambrian metamorphic rocks to place better time brackets on deformation phases. Outcomes from this research will reveal the prominent sites and style of structural reactivation in the Black Hills, leading to a mechanistic understanding of intracontinental deformation as recorded by the Black Hills. Outcomes of the project will contribute to modern research objectives of the Tectonics community, including constraining the factors that influence the mechanical behavior of the lithosphere, understanding the evolution of lithospheric scale fault systems, and developing a skilled geoscience workforce. The education plan associated with the research project will involve high school and university students in field work/education and develop field trip curriculum and training for dissemination to high school faculty. These activities will strengthen the relationship between local high schools and the South Dakota School of Mines and Technology (the host university), leading to increased student interest (and expectantly enrollment) in geoscience at the university level. 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

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Michael Eller and his students at California State University Northridge (CSUN) will work on developing new analytical approaches for performing 3D molecular analysis at scales approaching five nanometers. The research is expected to result in new instrumentation that is being designed to provide insights into the molecular organization of thin films used in the production of semiconductor devices. These insights may lead to new material designs, ensuring continued progress towards higher performing computational devices. Dr. Eller will also establish new recruitment and outreach programs to promote careers in science, technology, engineering, and mathematics (STEM) and provide opportunities for students to interact and network with chemists working in industry. The Eller group at Cal-State-Northridge will devise and validate an experimental methodology that tracks molecular associations in 3D nanometric space. Two complementary objectives will be pursued; (i) elucidating molecular organization laterally on a scale approaching 5 nm and (ii) also vertically 5 nm in depth. The analytical approach is based on a variant of secondary ion mass spectrometry (SIMS) termed nanoprojectile SIMS, where instead of using a focused ion beam, a surface is analyzed stochastically with a suite (10^6 – 10^7) of nanoprojectiles separated in space in time. Each of these projectiles samples a nanovolume (~10 nm in diameter) and the ionized ejecta are collected, mass analyzed, and stored as an individual mass spectrum. The overall hypothesis of the proposed research is that analyte-specific secondary ions carry information related to their original molecular organization. Recording the axial and radial energies of co-emitted secondary ions via spatially resolved detection will provide information on their lateral organization at a scale below the size of the impact crater. Combining this new capability with low energy argon cluster depth profiling will enable analysis of the molecular homogeneity in three nanometric dimensions. This new instrumentation will allow for the discovery of fundamental mechanisms in the SIMS process and provide enhanced insights into the uniformity of thin films used in the production of semiconductor devices. 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

Nitrogen (N) is an important part of cells in living beings, and it moves around different parts of the Earth like the air, land, oceans, and the deep interior. This movement helps shape the air we breathe and how suitable our planet is for living things over time. Places where the Earth's plates push against each other, called subduction zones, are excellent for studying how nitrogen moves between the surface and the interior. Recent studies show that these subduction zones send more nitrogen deep inside the Earth than volcanoes belch out. The continental crust might be a good place to store some of the nitrogen that is transferred to the Earth's interior in subduction zones. But scientists need to find out how much nitrogen is really in that crust to see if this idea is correct. In this project, the team will find out how much nitrogen is in the continental crust by taking samples from two places: one in Sierra Nevada, California, and another in the Transverse Ranges, California, and its equivalent lower crust in Chino Valley, Arizona. They will do experiments to see how nitrogen is shared between the lower crust materials and the melting rock. Some of their research will also be part of a summer camp for high school students from the Tucson Unified School District (TUSD). This camp will focus on the geology of Tucson and Arizona to help students connect with their community. The goal is to inspire students to learn more about geosciences and encourage them to study it at the University of Arizona. The uptake and exchange of the life-essential element nitrogen (N) between the Earth’s surface reservoirs (atmosphere, crust, oceans, biosphere) and the interior (lithosphere and deeper) via plate tectonics has regulated the atmospheric composition and habitability through geological history. The geologically recent subduction zones serve as an excellent natural laboratory to understand these uptakes and exchanges. Current estimates show that the output of nitrogen from subducted slabs is higher than the volcanically outgassed nitrogen. The continental arc crust could be an excellent contender for uptake of the ‘excess nitrogen’ but to test the hypothesis, the nitrogen abundance of continental arc crust needs to be determined. Also, the nitrogen abundance of primitive arc magmas is a key piece in understanding the fluxes of nitrogen between different subduction zone reservoirs and it is poorly constrained. Lower arc crust comprised of cumulates from differentiation of primitive arc magmas may serve as an excellent proxy to determine the nitrogen abundance of the primitive magmas. In this proposal, researchers will determine the nitrogen abundance of the arc continental crust. They will analyze samples from two sections of lower and mid to upper arc crust – one from Sierra Nevada, California and the other from Transverse Ranges, California (mid to upper crust) and its tectonically bulldozed lower crust in Chino Valley, Central Arizona. The team will perform nitrogen partitioning experiments between lower crustal cumulate minerals and arc melt. Finally, they will use those partition coefficients along with modeling the fractional crystallization of arc magmas to determine the nitrogen contents of primitive arc magmas that best explain the measured nitrogen abundances in the lower crust. Some aspects of the research plan will be integrated (along with other methodologies in geosciences) in a summer camp for high school students from Tucson Unified School District (TUSD). The summer camp will be themed around the geological history of Tucson and Arizona to provide the students a ‘sense of place’. The camp will serve as a feeder into a longer-term program to incorporate high school students in research-based education in geosciences. The objective of the summer camp is to motivate local high school students in geosciences, with the expectation that this would eventually result in enhanced enrollment of local high schoolers in geosciences at the University of Arizona. 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 is motivated by the increasing public concerns on privacy issues, new legislations and the high demand for privacy enhancing technologies such as differential privacy (DP) in applications from both private and public sectors. The overarching theme of the project is to address the pressing new challenges that arise as differential privacy transforms from a theoretical construct into a practical technology. The project advances the state-of-the-art of research in the area of DP, and contributes to privacy education. On the research front, the project develops new algorithms and analytical tools that enable more precise privacy accounting and higher utility in DP. On the education front, the project involves training future leaders in DP areas, creating educational materials and expanding an open-source software library called autodp that makes state-of-the-art differentially private computation more accessible. Collectively, the integrated research and educational activities contribute to ongoing collaborative efforts in building innovative applications of differential privacy. The project has three main components in use-inspired fundamental research. The first component unifies the recent breakthroughs in DP, such as, Renyi DP, moments accountant, f-DP and produce an intermediate functional representation that allows lossless conversions among these representations. The second component focuses on investigating the stronger privacy properties permitted by the structures of the actual data, and addressing the dilemma of interpreting worst-case privacy on average-case data. The third component focuses on using a public dataset to ``denoise'' the private data releases or to facilitate private machine learning. The outputs of the research will be broadly shared through integration in autodp library, and will be integrated in courses. 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

Examining Servingness: The Experiences of Latino Engineering Graduate Students in Hispanic- Serving Institutions Latinos are projected to soon become the largest demographic group in the U.S. Our representation in engineering higher education is critical for the country’s competitiveness and will broaden our engagement in the field. To that effect, Hispanic Serving Institutions (HSIs) play a crucial role in serving the Latino community— yet the concept of “serving” in doctoral education at HSIs remains largely unclear. Federal designation requires only that an institution’s enrollment consists of ≥ 25% Latino full-time students, so—as Latino population demographics increase—more institutions will become HSIs. HSIs’ high graduation rates of Latinos with engineering doctorates indicate a degree of success in how these institutions support their Latino students. However, how HSIs promote doctoral students’ success in achieving degree completion has not been studied. Therefore, to better understand the role of HSIs on Latino Ph.D. success, understanding how “serving”—that is, how to support Latino graduate students beyond enrollment—is crucial. This CAREER project will lay the foundation for HSIs to effectively empower Latino students to lead research and innovation through their doctoral engineering programs. Specifically, this project will explore how Engineering HSIs directly impact the experiences of Latino graduate students in engineering. This project addresses the research question: How do HSIs’ servingness support Latino engineering doctoral students? To do so, this project will employ a multi-case study methodology using institutions as the unit of analysis to longitudinally study the experiences of Latino engineering doctoral students at two different types of engineering HSIs: the University of Puerto Rico at Mayagüez, a research-emerging engineering HSI, and Arizona State University, an R1 recently designated as an HSI. This project will leverage trusted site coordinators in each institution’s College of Engineering to recruit Latino engineering students in the first year of their engineering Ph.D. and follow them over a four-year period via monthly prompts to understand their Ph.D. experiences. This project will also involve graduate education stakeholders at these institutions via periodic interviews to understand the institutional factors that impact doctoral students’ experiences at their institutions. The collected data will be analyzed alongside institutional data and documents relating to the servingness efforts at each university to contextualize the participants’ perspectives. By triangulating these data sources, this work will uncover the connection between doctoral student experiences as a function of the structures for “serving.” The methods will be guided via two frameworks: (1) Garcia, Núñez, and Sansone’s framework for servingness and (2) the Graduate Student Socialization framework. Combining these two frameworks will address two vantage points in the analyses—one from the students’ development in HSIs and a second from the HSIs’ impact on students. The education plan focuses on doctoral education leaders from both institutions. Using a community of practice model, graduate associate deans, program leads, and staff will learn how institutions can serve Latino doctoral students in engineering through collaborative workshops showcasing research findings. Outcomes include a list characterizing high-impact practices articulating insights across both institutions and an established network of HSI practice-sharing. Finally, this project will develop a scoresheet to be used as a rubric by HSI leaders at other institutions to evaluate institutional structures for servingness and their impact on doctoral students. As a result, this work will help identify and disseminate best practices that HSIs and other institutions can implement to better serve Latino doctoral students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The rapidly evolving workplace landscape calls for the development of scalable upskilling and reskilling programs for maintaining a competent and competitive workforce. According to the World Economic Forum’s 2023 future of jobs report, six in ten workers will require training before 2027, but about half of the workforce does not have access to adequate training opportunities today. From what we know about human learning, it is clear that deliberate practice, appropriate scaffolding, and timely feedback are needed for learning to be effective. Practical implementation of such features requires substantial investment from domain experts or instructors, which makes it difficult to provide these opportunities at scale. The overarching goal of this project is to make expertise sharing more efficient through helping experts create example-based intelligent tutors. The research team will partner with other universities, local community colleges, and public schools to test the developed tools, which will then be made publicly available. Through this work, the project will make it easier to develop effective training programs that scale well to millions of workers, improving both their own opportunities and the U.S. economy as a whole. This project focuses on the teaching and learning of complex problem-solving tasks, e.g. “Identify a recent economic phenomenon that involves market failure, explain which type of market failure it is, and propose a solution.” To capture the expertise behind such tasks, the project introduces a “Checklist with Examples” approach, which outlines the key criteria a solution must meet. Four main activities guide this project. Thrust 1 involves creating example-based intelligent tutors for task domains that already have detailed checklists. This will produce human-AI collaborative techniques, and a platform (“Exemplify”) for instructors to gather examples that meet specific criteria. Instructors can find these examples from prior students’ homework submissions using retrieval-augmented generation (RAG)-based methods or generate them directly with Large Language Models (LLM). Exemplify also helps instructors create scaffolding exercises such as multiple-choice questions or example-annotation tasks with automated feedback. Thrust 2 involves performing randomized controlled classroom experiments to evaluate the resulting example-based intelligent tutors. Thrust 3 involves developing a feedback engine using the checklists and the examples collected earlier. The engine provides students with automated formative feedback as they work on open-ended problem-solving tasks. Thrust 4 develops a low-cost proxy method for cognitive task analysis by having LLM agents simulate novice learners and generate solutions. Experts review these solutions and their feedback is summarized into a checklist, making it easier to capture expertise in any domain. 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

Large language models (LLMs) have reshaped artificial intelligence (AI) research and implementations. In the healthcare domain, extensive enthusiasm has been witnessed on the exploration of LLMs in answering medical questions, extracting clinical information, and assisting clinical decisions. Studies have also revealed the limitations of LLMs regarding their lack of knowledge, fuzzy inference, and hallucination. Knowledge graphs (KGs) have been widely studied due to their advantages in storing accurate, explicit and easily-modifiable knowledge. In the healthcare domain, various medical KGs have been used to support basic science research, pharmaceutical research, clinical decisions, and policy making. However, healthcare data are notoriously noisy and complex, where datasets about specific concepts and conditions come from various sources and include multiple data modalities. While pioneering studies have started to explore the combination of KGs and LLMs for healthcare, most of these studies have focused on applying existing methods and do not aim to fundamentally improve the KGs and LLMs towards solving various healthcare problems. In this project, the research team aims to comprehensively investigate and address the data, model and application challenges in healthcare, through Knowledge Graphs-Large Language Model Co-Learning (KG-LLM Co-learning), a systematic framework that will provide most (if not all) major functionality needed to build high-quality KGs that integrate complex healthcare data, enhance LLMs to obtain reliable healthcare models, and leverage multi-agent systems to account for data privacy, human values and broader factors in healthcare applications. The proposed KG-LLM Co-learning framework includes several transformative technical innovations. First, the investigator's team will develop novel LLM-based methods for constructing comprehensive healthcare KGs, where they will study: (1) ontology-infused prompt designs for unifying existing healthcare KGs collectable from different sources, (2) structure-oriented retrieval augmented generation to continuously improve the healthcare KG based on evolving biomedical literature, and (3) alignment-based instruction tuning to further enrich the healthcare KG based on multi-modality patient data. Second, the investigator's team will design novel usage of KGs to obtain reliable healthcare LLMs, where they will focus on (1) enhancing the planning capability of LLMs through providing biomedical knowledge from KGs, (2) enhancing the reasoning capability of LLMs through enabling biomedical neural symbolic rule learning over KGs, and (3) enhancing the grounding capability of LLMs through enforcing post-hoc biomedical error detection based on KGs. Third, the investigator's team will explore a new way of integrating data and models through the collaboration of LLMs in a flexible federated multi-agent system, which can rigorously facilitate (1) protection of data privacy, (2) alignment with human values, and (3) consideration of broader health factors. Finally, the investigator's team will conduct comprehensive evaluations regarding the important healthcare applications of risk prediction, treatment suggestion and disease subtyping based on de-identified patient data from publicly available databases, Emory Hospitals and the NIH Bridge2AI CHoRUS Consortium, through collaborations with healthcare professionals and clinical experts. If successful, the studies will fill critical gaps between AI advances and healthcare, informing methodology and technology designs in data management and AI/Machine Learning communities, while potentially generating new biomedical and disease knowledge for improving healthcare. 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

Infectious diseases are widespread and reduce the health of their hosts. Hosts thus experience strong selection to avoid contracting an infection. This research will advance our understanding of the evolution of host strategies to avoid contact with parasites. Researchers for this project propose that hosts avoid their parasites through dispersal, the permanent movement between sites. This idea is important because it asserts that dispersal, a widespread behavior, drives rates of contact with parasites and evolves as part of hosts’ defense against infection. The proposed work will use experiments and field surveys to test parasites as drivers of dispersal evolution. The project will also create educational programs that engage community college transfer students in scientific research. The main idea of this work is that dispersal strategies evolve to be sensitive to infection risk. The researchers predict that hosts evolve to disperse early in an epidemic, when infection risk is low. The work will conduct experiments with a free-living nematode and its natural parasites. The researchers will track the evolution of dispersal strategies under parasite selection and identify the genetics and mechanisms of adaptation. They will also test if dispersal limits selection for other defenses. Finally, they will assess whether dispersal reduces infection risk in the wild, by observing the natural distribution of parasites. Community college students will contribute to this work through a research course at the local community college and a summer research experience for incoming transfer students. A bridge course will connect transfer students with research and provide academic support. Community college transfer students are more likely to identify as low-income and first-generation, and this work will promote their success in STEM while advancing fundamental knowledge of parasites as selective agents. 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

Integrated Cyber-Physical Systems (i-CPS) have transformed industries like manufacturing, transportation, healthcare, and energy management. I-CPS is characterized by multiple CPS services functioning concurrently within the same environment, high involvement of domain experts and everyday users in decision-making processes, real-time interactions with other systems, and operation within uncertain and constantly changing physical environments. Examples of such systems include smart cities, smart agriculture, and intelligent transportation, among others. However, stakeholders often hesitate to trust these systems due to a lack of explainability, which raises concerns about reliability, especially in safety-critical areas. Additionally, while advancements in deep learning have enabled powerful CPS capabilities, understanding of these systems is declining. Despite the significance and urgency of enabling explainability for i-CPS, research in explainable CPS with AI-driven components is still in its infancy. This research aims to develop explainable and trustworthy i-CPS capable of justifying their decisions and incorporating field operators’ queries and domain knowledge to adapt to evolving environments. The technical contributions to CPS research and education encompass four thrusts. Thrust I develops a new explainable architecture with formal explainers that interpret individual components of i-CPS and their interactions. Innovations include the first explainable CPS architecture, novel formal methods-based approaches addressing five layers of i-CPS, and explainable online decision support for “what if” scenarios. Thrust II formalizes and incorporates user domain knowledge, specifications, and feedback to enhance the learning-enabled components of CPS. Key advances include developing new formal knowledge distillation and unlearning approaches with uncertainty quantification that quickly adapt the system to shifting distributions in a lifelong evolving deployment. Thrust III evaluates the proposed methods through a real-world emergency response deployment in Nashville, TN, involving multiple CPS-based city services and stakeholders from different departments. Innovations include a logic-based LLM-enabled system that connects our proposed explainers to field operators, real-world deployment principles, and qualitative and quantitative metrics to assess explainability and impact. Thrust IV develops an interdisciplinary education plan. The key innovation is to bring rigorousness and explainability to CPS classes in Computer Science and create a new course, CPS in the field, for multi-disciplinary students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Video surveillance systems, which are now common in cities and workplaces, present a valuable opportunity to predict and prevent harmful incidents before they occur. This project seeks to create advanced technologies capable of analyzing video streams in real time to anticipate potential anomalies with high accuracy. When such events are predicted, the system can initiate preventive measures, such as alerting users or triggering automated interventions—like activating a car's brakes or stopping an escalator. Importantly, the system will also provide detailed explanations, enabling users to understand why their actions or environment were adjusted. By leveraging cutting-edge techniques to identify patterns that precede abnormal events and generate visual or text-based explanations, this project has the potential to transform public safety and operational efficiency. For example, in intelligent traffic systems, it could help reduce pedestrian accidents and fatalities. In warehouses, it could prevent losses caused by equipment mishaps. In air traffic control, it could serve as a critical safeguard to ensure safe and orderly flight operations. By enhancing safety, reducing risk, and supporting human decision-making, this work addresses a critical need and aligns with national priorities to promote prosperity and welfare. While significant advancements have been made in video processing through artificial intelligence (AI), the task of anticipating video anomalies remains largely underexplored. Addressing this challenge involves overcoming three critical barriers: the absence of robust, annotated real-world datasets for anomaly anticipation, the lack of efficient real-time algorithms for accurate anomaly prediction, and the difficulty in explaining the reasoning behind AI-based decision-making in anomaly anticipation scenarios. This project will address each of these challenges. First, the research team will create a framework, based on the concept of digital twins, to semi-automatically capture and annotate anomaly anticipation datasets. This framework will focus on scenarios with static camera positions, enabling the dynamic extraction and recomposition of real-world objects and their motion patterns to create novel motion anomalies. Generative AI models conditioned on the designed anomalous patterns will be used to create realistic videos containing anomalies. Second, the team will design real-time AI models capable of accurately predicting motion-based anomalies prior to their occurrence by leveraging multimodal models trained to anticipate the trajectories and positions of objects as well as the probability of an upcoming anomaly. Third, the project will develop descriptive visual AI models that can provide stakeholders with clear, understandable explanations of anticipated anomalies. As a case study, the proposed methods will be applied to traffic anomaly anticipation, demonstrating the practical utility of the research. The project will also integrate research and education by incorporating in-class assignments and organizing annual student competitions. The success of this project will lead to a new paradigm for training explainable anomaly detection models and expand the conversation from anomaly detection to real-time anomaly anticipation and prevention. 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

With the joint support of the Macromolecular, Supramolecular and Nanochemistry program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Aaron Teator of the University of Kansas will develop new polymerization methods with enhanced control over molecular structure. While current synthetic approaches have led to incredible high-performance materials, the available structure space is limited to monomers designed to undergo chain growth by shifting electrons through existing carbon-carbon double bonds. In this research, a through-space transfer of electrons facilitated by functional group migration will be used to control the growth of polymer chains. This will allow for precise control over the number of carbons in each repeat unit based on monomer design. Careful optimization of this approach, coupled with a thorough kinetic and mechanistic exploration will provide access to a new class of synthetic polymers. This proposal has the potential to deliver fundamental knowledge in multiple areas of chemistry, including small molecule reactivity, mechanistic analysis, and polymer methods development. The associated education plan complements and enhances the project by integrating chemical research with education and using societally relevant plastics to generate excitement about organic synthesis. This project will focus on the development of chain-growth polymerizations centered on anionic silyl migration as a key propagation step. In the first objective, careful optimization of reaction conditions will establish through-space active site transfer as a viable approach for chain-growth polymerization. This will provide important fundamental scientific discoveries related to reactivity, reaction design, and structure that will serve as the foundation to expand the approach to a platform methodology. In the second objective, a thorough analysis of reaction kinetics will further mechanistic understanding of the polymerization and aid in the development of design principles to both improve the current approach and enhance future iterations. In the last objective, systematic characterization of thermal, mechanical, and physical properties will reveal important structure-property relationships. This research has the potential to establish a new platform method for the preparation of functional polymers with broad appeal to the organic and polymer communities. 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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Graphs, also known as networks, are used to represent many kinds of relationships between pairs of entities. For example, a graph may describe pairs of networking devices directly connected together or pairs of roadway intersections connected by a stretch of road. Many useful tasks, such as understanding the reliability of a computer network or finding the fastest route between two locations, can be performed by doing computations on these graphs. When a graph has certain properties such as being drawable on a piece of paper without crossings between its connections, it becomes possible to do these types of computations much more quickly than if the properties were not present. Many of these faster computations rely on important results from topology, the mathematical study of what properties geometric objects maintain after certain types of deformations. This project seeks to better understand the role topology can take both in performing fast computations on graphs and explaining what graph properties are necessary for these fast computations. The project aims to make substantial topology based additions to the toolkit used in graph computations. These additions should make new computational tasks possible and greatly simplify established tasks. The project involves a substantial education component as well that includes educating students on the known connections between topology and computer science and providing undergraduate minority students their first opportunities to participate in the research process. The research activities have three components. The first involves generalizing known planar graph algorithms for many fundamental problems in computer science to graphs embeddable in low complexity surfaces. The second component explores how topological intuitions and tools created during the first component can be used to solve difficult problems back in the setting of planar graphs. The third component involves pushing these tools to their limits in the creation of algorithms for more general families of graphs than those embeddable in low complexity surfaces with a focus on the so-called H-minor-free graphs. The types of problems studied for all three components include the computations of optimal network flows, minimum cuts, and shortest paths. Student education will take place through the development of a new course in computational topology at the awardee institution that focusses on graph algorithms and topological data analysis. Activities for undergraduate minority students will consist primarily of the implementation and experimental analysis of algorithms designed during the primary research activities. 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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A superlattice is a material that is formed in alternating layers to specific thicknesses. Ionic-conducting superlattices offer a novel and efficient approach to enhance the conduction of ions in devices that span a variety of technological applications, including renewable energy, sensors, and microelectronics. However, the complex interactions between structural defects, ions and heat within the superlattice present significant challenges to optimizing these materials. This project seeks to unlock the potential of ionic-conducting superlattices by decoding these intricate dynamics, thereby pushing the functional limits of various technologies. The new insights gained from this research not only will advance fundamental scientific understanding but also will significantly benefit society by improving energy storage and device efficiency. The research outcomes will be incorporated into educational programs ranging from university courses to K-12 initiatives, fostering a diverse STEM pipeline and inspiring the next generation of the science and engineering workforce. The goal of this project is to develop a comprehensive understanding of defect-facilitated phonon engineering in ionic-conducting superlattices, including localization/delocalization, and its impact on ion transport. Such understanding requires combining multiscale structural simulation with transport simulation, with spatial and temporal resolution, which has posed a long-standing challenge. In addition, the research outcome of this project seeks to bridge the gap between thermal transport and solid-state ionics—two scientific disciplines that are traditionally operated separately. This project will 1) Develop and validate a multiscale method for coupling phonon transport with ion conduction, capturing both long-range mesoscale structures and sublattice anharmonic vibrations; 2) Utilize a pre-selected list of geometric configurations to test the hypothesis of defect-induced phonon localization/delocalization; 3) Measure the relationship between mode-specific phonon excitation and ion diffusivity, testing the hypothesis of phonon localization-induced ion hop; 4) Integrate research into education through a variety of project-based activities to promote learning among peers and the general public. 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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NONTECHNICAL SUMMARY This award supports theoretical research and associated education on the dynamics of quantum systems. Recent experimental breakthroughs in a variety of quantum systems have made it possible to isolate and control many interacting quantum particles over extended periods of time. Due to interactions among the particles, these systems often appear to settle into equilibrium and remain static, a process known as thermalization. However, unlike in classical systems, the underlying quantum state continues to evolve in time, even when the system looks static on the surface. The quantum state contains complete information about the system. The time evolution of the quantum state can give rise to universal quantum phenomena with no classical counterpart. Building on recent developments in quantum many-body physics and quantum information science, this research explores the rich, hidden dynamics of quantum states that persist even after local equilibrium is reached, aiming to uncover universal behavior - properties that are independent of the details of a quantum many-body system, and to harness the complex quantum states for novel quantum technologies, such as new quantum error correction codes. This project incorporates educational activities aimed to train undergraduate and graduate students and disseminate quantum science to broader audiences. The research team will make a quantum booth showcasing interactive demos and games at public events and on social media to illustrate the core concepts and ideas of quantum mechanics. Additionally, the PI will work with local high school teachers through workshops focused on developing strategies to introduce quantum concepts into their curricula. TECHNICAL SUMMARY Integrating tools from quantum many-body physics and quantum information science, this research combines analytical and numerical approaches to study quantum many-body dynamics along three main thrusts: (i) revealing how unitary dynamics scramble local information into non-local entanglement and developing a general decoding protocol using the Petz recovery map; (ii) investigating how symmetries constrain and enable control over scrambling through the transport of conserved quantities; and (iii) characterizing the largely unexplored long-time behavior of many-body unitary dynamics using higher-order Green’s functions on multi-folded Keldysh contours to uncover new dynamical regimes. The overarching goal is to understand universal many-body dynamics beyond thermalization across different time scales and to harness the entanglement and complexity of the time-dependent quantum states for novel quantum technologies. For instance, insights into how unitary dynamics scramble information could lead to new quantum many-body teleportation protocol and new quantum error correction codes that store and protect quantum information beyond the stabilizer formalism; understanding the role of symmetries may enable manipulation of stored quantum information via conserved quantities; and exploring the post-scrambling regime after local information is fully scrambled may reveal new time scales of quantum many-body dynamics and new ways to exploit the complex structure of quantum states. This project incorporates educational activities aimed to train undergraduate and graduate students and disseminate quantum science to broader audiences. The research team will make a quantum booth showcasing interactive demos and games at public events and on social media to illustrate the core concepts and ideas of quantum mechanics. Additionally, the PI will work with local high school teachers through workshops focused on developing strategies to introduce quantum concepts into their curricula. 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 research aims to transform the future of genome editing, paving the way for new tools that could help address critical challenges in health, food security, and environmental sustainability. By exploring a recently discovered bacterial immune system, the project seeks to provide new genetic editing technologies that are simpler, more versatile, and easier to deliver to target cells. Beyond the scientific investigation, the research will support comprehensive educational and outreach activities designed to inspire learners ranging from early childhood students to advanced graduate trainees to engage with science, technology, engineering, and mathematics (STEM). Together, the project will foster a deeper understanding of gene-editing science among both specialists and the general public, ultimately broadening participation in STEM fields. This research will systematically investigate a newly identified DNA-guided, DNA-targeting bacterial system known as DdmDE, which can recognize and eliminate genetic material without relying on the distinctive genetic signatures (PAM sequences) required by CRISPR-based methods. To accomplish this, the project will conduct biochemical and structural analyses to reveal how DdmE identifies target DNA sequences and how DdmD, a partner enzyme, is recruited and activated to cut unwanted DNA. Additionally, the research will explore natural variations in the DdmE protein and develop methods to reprogram it for advanced genome editing applications. By integrating bioinformatics, biochemistry, and biophysics, the project will create a strong foundation for harnessing DdmDE as next-generation genome editing tools. 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

Bacterial protein Cas9, part of the CRISPR-Cas immunity system, has emerged as a transformative gene editing tool with applications in basic research, agriculture, and healthcare. However, despite its widespread adoption in biotechnology, many questions remain regarding how Cas9 functions in nature. Cas9 main biological function is to protect bacteria from viral invaders known as bacteriophages, or phages for short. These CRISPR-Cas9 systems acquire memories, in the form of DNA fragments, from prior phage infections, allowing bacterial cells to rapidly kill the same phage when it is encountered again in a later infection. This project will explore the hypothesis that a class of phages called “temperate phages” that are often neglected in CRISPR-Cas studies hold the key to understanding CRISPR-Cas memory acquisition. These results will inform new generations of Cas9 technologies, including molecular recording devices, and highlight how CRISPR-Cas systems and temperate phages together influence the evolution of bacterial communities. The project also details an educational plan that will provide undergraduate students an opportunity to learn more about the arms race between bacteria and their phages and careers in microbiological research. A central question in CRISPR-Cas memory creation is how cells survive a lytic phage infection long enough to create a new memory and utilize it for defense. Intriguingly, many CRISPR systems target temperate phages, which are capable of entering a program called ‘lysogeny’, in which their DNA is inserted into the bacterial chromosome, where it may remain in a dormant form. This proposal tests the hypothesis that CRISPR-Cas systems can exploit the temperate lifecycle by acquiring memories about those phages under non-lytic conditions. Specifically, the proposal examines whether new memories can be made during the initiation of lysogeny (aim 1) and/or after lysogeny has occurred and the phage has integrated into the chromosome as a prophage (aim 2). Finally, the proposal tests whether prophages can influence the ability of CRISPR-Cas systems to acquire memories from other infecting phages (aim 3). Temperate phages are understudied in CRISPR-Cas laboratory experiments, because the rates of lysogeny are significantly higher than spacer acquisition. This proposal will employ new genetic tools to thoroughly dissect the interactions between CRISPR-Cas systems and their natural temperate phage targets, revealing a new paradigm for how CRISPR-Cas immunity is established. 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

With increasing demand for high data-rate wireless communication systems, this project will explore new methods of radio-frequency transmission and reception using novel electromagnetic wavefronts with spin and angular momentum. These wavefronts provide a means to multiplex many different data streams at the same frequency in the air to significantly increase the data rate without increasing spectral bandwidth demand. However, practical demonstrations of such communication links in the microwave to millimeter-wave frequency band have been limited. This CAREER project explores new methods to generate and multiplex data channels using spin and angular momentum modes of electromagnetic wavefront. The research will develop enabling technologies based on electromagnetics solutions to bridge this gap through convergence of theory, design, fabrication methods, and measurement testbeds. Broadly, the proposed solutions tie into 5G wireless networks and the forthcoming 6G wireless systems. On the education part, this project will simultaneously develop industry-facing pedagogical methods, which are also inspired by the goal of increasing student engagement and integrating the research thrusts. Community outreach activities in combination with industry-academia collaborations will be pursued to support research and education goals of this project. The project will adopt a technical approach which moves away from energy-demanding signal-processing, MIMO-processing, or data-encoding techniques and introduce power-efficient passive electromagnetic guiding structures for multiplexing vortex wave modes simultaneously. Towards that goal, the project will introduce new antenna structures, underlying theory, design methods, and associated passive circuits for simultaneous generation and multiplexing of modes in free space. Specifically, the designs will enable coaxially aligned, multiplexed spin and angular momenta electromagnetic beams, which can be scaled to larger apertures and for longer distance wireless communication links. The research will also explore key features such as dynamic configuration of beam directions, focus distances, divergence angles, and suitable positioning of central-null angle to avoid link breakages. In addition, the research will bridge theory and practice by leveraging metal and dielectric 3D fabrication methods. Finaly, an experimental framework will be developed for characterization, analysis, and validation of components and for exploring novel properties of vector vortex waves. This project is jointly funded by the Communications, Circuits and Sensing Systems (CCSS) 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.

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The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will map cosmic structure across the sky to incredible depth. Such maps have immense statistical power to shed light on dark energy and dark matter, perhaps the most compelling mysteries in modern physics. To reach the full potential of LSST and other upcoming projects, a better understanding of astrophysical processes and observational uncertainties is required. This program focuses on two of the most important astrophysical effects, determining where galaxies form and what shapes they have. Prof. Blazek’s team will pursue several linked themes leading to robust, sophisticated cosmological analyses of LSST data. His tightly coupled education plan includes a mentoring and research program for a dynamic and comprehensive student cohort. Prof. Blazek will also develop a numerical methods course for graduate and advanced undergraduate students. This course will provide workforce training in cutting-edge techniques used in this research. Weak lensing and galaxy clustering are powerful probes of dark energy and dark matter. Intrinsic alignment (IA) and galaxy bias represent two of the most critical challenges to these measurements. This program combines Blazek’s leading expertise in IA with novel simulation and artificial intelligence / machine learning techniques. Applying a semi-analytic approach to IA and bias on gravity-only simulations will enable production of mock galaxy catalogs in large cosmological volumes with a wide range of IA properties. By developing sophisticated emulation with neural networks, the research will accelerate the process of creating and analyzing simulated galaxy data, providing direct simulation-based modeling. These innovations will enable Rubin-LSST data analyses not possible with existing methods and will broaden the discovery potential of future data through measurements of IA as a cosmic probe. Prof. Blazek’s advancement of research opportunities and targeted mentoring for students in physics and astronomy builds substantially on a successful pilot program he has developed. His simulated galaxy catalogs and modeling tools will be made public, benefiting the LSST Dark Energy Science Collaboration and the broader cosmology community, including through a cross-survey project that Blazek leads. This research award is partially funded by a generous gift from Charles Simonyi to the NSF Astronomy division. The project includes significant contributions to Vera C. Rubin Observatory’s Legacy Survey of Space and Time. 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 overarching goal of this research is to find a new and effective way to turn stem cells, cells that have the potential to be transformed/differentiated into any type of cell, into chondrocytes, a specific type of cell that is important for cartilage repair. The focus of this Faculty Early Career Development project is on exploring a technique called electrospraying, where electricity and mechanical forces (rather than special/usually expensive differentiating media) are used to guide the transformation of the stem cells into chondrocytes. This innovative method involves utilizing the patient's own stem cells, obtained from their fat tissue, to minimize the risk of immune rejection. By establishing a personalized approach to create cells for repairing damaged cartilage, successful development of this process will have significant implications for regenerative medicine focused on other tissues. Beyond the scientific objectives, the initiative aims to train science teachers in West Virginia. In-person sessions will focus on seamlessly integrating a cutting-edge tissue engineering module into their existing courses. Leveraging the field of tissue engineering, the project strives to infuse core scientific concepts into high school science classes, enriching the learning experience. The overarching goal of these activities is to ignite a passion for scientific exploration and nurture the next generation of innovators and problem solvers through this educational endeavor. In the pursuit of differentiating stem cells into a homogeneous population, diverse levels of success have been achieved, revealing the intricacies and challenges of this research field. Cell electrospinning and electrospraying offer a promising avenue to effectively differentiate stem cells into mature and functional cell types, potentially advancing tissue engineering significantly. The CAREER project’s primary objective is to explore electrospinning and electrospraying techniques, aiming to revolutionize the production of chondrocytes from human adipose-derived stem cells (hASCs). This study seeks to unravel the complex mechanisms underlying these processes, enhancing understanding and paving the way for groundbreaking advancements in regenerative medicine. Using this innovative methodology involving electrospinning and electrospraying of cells, the investigation explores cyclic adenosine monophosphate/protein kinase A signaling and ATP oscillation during the chondrogenesis of hASCs. This pioneering study holds the potential to provide profound insights into two critical domains: introducing electrospraying as a novel cellular differentiation method and establishing groundwork for a new source of chondrocytes in Autologous Chondrocyte Implantation (ACI). This project is jointly funded by the Engineering of Biomedical Systems 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.