Cornell University

NSF Awards · FY 2024 · 2024-12

Non-Technical Abstract The devices we use every day have enriched our lives immeasurably and are often cheaper and faster than the best available a generation ago. Many of these advances have relied on progress in condensed matter research that has ultimately contributed to the economic well-being of the Nation. This project will demonstrate a significant advance in the measurement of frequency – actualizing a precise clock, with an accuracy approaching that of the Global Positioning System (GPS). The research aims to identify exotic material behaviors or exotic magnetic behaviors that could, in the future, could form the basis of new types of devices for metrology and computation. The research team will accomplish this by designing and fabricating structures that will access new areas of quantum nano-fluidics and enable precision measurements on helium films down a few atoms thick. These films will be contained in specially fabricated silicon nanostructures, under conditions where new phases should emerge that have exotic behaviors such as chirality (where the fluid exhibits a handedness like the spin of a top), or exotic magnetic behaviors, quantized flow or highly conducting “edge states”. The conversion from one state to another (“phase transition”) should reveal whether models of the early universe (important for our understanding of the evolution of the universe) can be tested in the laboratory. Our past graduate students and undergraduates have gone on to productive careers in academia, high-technology industries and the financial sector, and the planned research will prepare a new generation of students for challenging careers. Throughout their tenure, our graduate and undergraduate students will present their findings to both technically advanced and lay audiences to encourage participation of junior and high school students in the STEM fields. Technical Abstract Superfluid 3He can inform research activity that extends across many fields in Physics. The project combines nanofluidics (examining fluid behavior confined within silicon cavities with sizes tuned to the scale of the superfluid’s coherence length), high-precision low-noise thermometry with low temperature physics, to expose new size effects. It will require the development of new measurement protocols to utilize low noise capabilities of SQUIDs and enable high precision measurement of frequency (and thus assay mass of ultra-thin films). The experimental activity will probe excitations in these thin films or bound near surfaces using transport (superfluid density and heat conductivity). It is predicted that entirely new superfluid states can be stabilized by such confinement. Confinement and new high precision techniques will also provide the means to study the surface/edge excitations. Furthermore, this research activity will provide a new example of "cosmology in the laboratory" where a transition from one superfluid state to another under confinement should proceed only by “intrinsic mechanisms”. The occurrence of such transitions will potentially provide a model of processes during the inflationary epoch of the early universe. New geometries will explore the physics of quantum transport across single and multiple interfaces. Eventually, the new superfluids that emerge under confinement might be of interest in quantum computation. By combining low temperatures, nano fabrication, and high precision measurements, graduate students and undergraduates will be exposed to the exciting training ground that has prepared scientists for leading roles in academia and high-technology industries. Personnel will also work to hone communication skills and enable participation into the STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Non-technical abstract “Limits” play an important role in physics, just like speed limits play an important role on our roads. For example, nothing can move faster than the speed of light. This limit was the basis of Einstein’s theory of relatively, and it governs everything from how we do astronomy to how we build nuclear reactors. Recently, a new type of limit has emerged called the “Planckian limit”. This proposed limit states that electrons can “run into” each other in a metal no faster than an amount set by the temperature of the metal and fundamental constants of nature – Planck and Boltzmann’s constants. This limit, if it holds true, has implications for physics problems as broad as finding room-temperature superconductors to how quickly quantum information can be transported. This project uses the world’s strongest magnetic fields and ultrasound to test whether the Planckian limit really is a limit and, if so, what underlying physical laws give rise to such a limit. This research will train the next generation of physicists in high-frequency electronics and quantum materials and will develop outreach activities for high school-student workshops held at Cornell. Technical abstract Strange metals have electrical resistivity that is linear in temperature (T-linear) all the way down to zero temperature. This distinctive behavior often appears in conjunction with unconventional superconductivity: high-temperature superconducting cuprates, iron pnictides, organics, heavy fermions, and magic angle twisted bilayer graphene all have T-linear resistivity in their phase diagrams near where the transition temperature is maximized. One captivating idea is that of a “Planckian bound” – a universal upper limit on electron scattering in metals. Such a bound, if it exists, could explain why such disparate systems all show universal T-linear resistivity. This proposal combines high magnetic field electrical transport experiments with state-of-the-art, high-frequency ultrasound to uncover what causes T-linear resistivity, why it appears to follow a fundamental “Planckian bound”, and how strange metals evolve into conventional metals as their carrier density is tuned. These experiments will broaden our understanding of how strange metals arise and whether they obey a new fundamental bound. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

There is a classic approach to solving large and complicated problems commonly employed in the field of algebraic topology. The idea is to break down a large problem into smaller ones, solve each of the smaller ones, and then reassemble the answers into a solution to the larger problem. This approach can be very fruitful, but it comes with one important caveat: it must be possible to reassemble the smaller solutions into a larger one, and to know when this reassembly is uniquely determined. A central focus of this project is to use the powerful tool of K-theory to address the question of which different objects can be reconstructed out of the same pieces. This will be done by modifying and extending techniques from algebraic and topological K-theory and applying them to the more recently emerging field of combinatorial K-theory. The outcomes of this project will have wide applications in geometry and combinatorics. In parallel with this research activity, the PI will continue their engagement with student mentoring, through enrichment activities at the K-12 level, and through mentioning at the college and post-grad level, with an overall focus on improving the accessibility of mathematics to a wide audience. The spectrum topological restriction homology (TR) has been useful in classical computations of algebraic K-theory and topological Hochschild Homology (THH). However, the construction of this spectrum relies on having a spectral enrichment, which combinatorial K-theory does not have. The goal of this project is to produce new constructions of TR that do not rely on this enrichment, and to use them to construct TR for examples of combinatorial K-theory, such as varieties. In recent work it has been shown that TR is the codomain of universal zeta-functions in many contexts, and this project hopes that a new construction will allow for a deeper understanding of the structure of zeta-functions and their relationship to combinatorial K-theory, especially in the examples of finite sets and varieties. In addition, the novel constructions of combinatorial K-theory (using categories with covering families or categories with squares) are far more general than previously-understood constructions. Another goal of the project is to study the behavior of TR in these examples and construct new types of zeta-functions for them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Climate change is increasing the amount of salt of coastal freshwater habitats, driven by sea-level rise and intensified storm surges. Anurans (frogs and toads) play a pivotal role in wetland ecosystems and are presumed to be intolerant of saltwater, but recent syntheses indicate more salt tolerance than previously thought. This project improves our understanding of the limits of salt tolerance in anuran species occupying coastal habitats on two sea level rise fronts (Gulf and Atlantic coasts). The project uses an experimental approach that investigates multiple salinities, species, life stages, and levels of biological organization, providing data that can guide coastal managers and practitioners in improving coastal resilience and preventing biodiversity loss. Specifically, the study will identify life stage-specific mortality from salt stress across species to predict vulnerable life stages and breeding times. This research will determine whether species relatedness and ecological traits can predict salinity vulnerability and characterize the extent that the effects of saltwater at one life stage cascade across life stages. Finally, this study aims to understand how saltwater affects amphibian physiology across species and life stages, focusing on membrane permeability, hormones, and cellular processes. In summary, this research will produce diverse physiological, life history, phylogenetic, and genomic datasets that span across life stages and species to determine the salinity tolerance of anuran species that occupy coastal habitats with the goal of informing conservation actions and predicting climate change impacts. The project will also educate students and the public on various topics such as conservation and physiology. Climate change-driven sea level rise is increasing salinization of coastal habitats. Sea levels along the United States coastlines are predicted to rise between 50 and 100 cm in the next 70 years, with the east and gulf coasts facing intensified impacts. Anurans, or frogs and toads, are expected to be severely affected by salinity increases, as they are considered largely saltwater intolerant. However, salinity tolerance among amphibians is more variable than commonly considered, leaving uncertainty in the predictions of seawater inundation effects on coastal communities. To address these gaps, this research will focus on 10 anuran species with known populations within five miles of coastlines near Houston, Texas and Sapelo Island, Georgia. These locations are dual sea level rise fronts with low-elevation coastlines and freshwater wetlands expected to be inundated by the year 2050 under moderate sea level rise scenarios. The project will use a comparative approach to investigate how life history traits and evolutionary history affect stage-specific survival in higher salinities, how chronic saltwater exposure affects long-term growth, development, and fitness, and the physiological responses to saltwater exposure. This research will inform questions on how complex life cycles affect persistence in novel environments, how physiological mechanisms facilitate saltwater tolerance, and how chronic versus acute exposure affect survival and persistence. Additionally, this research will provide baselines for predicting how different coastal species will fare as sea level rise pushes saltwater further upriver and inland, which can guide coastal managers and practitioners to improve coastal resiliency and prevent biodiversity losses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Climate change is pushing many organisms towards their limits, forcing species to move, evolve, or risk extinction. Frogs are among the most vulnerable species on the planet with roughly a third already under threat of extinction. Frogs, and other amphibians, can breathe across their skin. However, their permeable skin leaves frogs sensitive to changes in temperature and humidity, which is expected with climate change. Thus, understanding frog skin is key to understanding how frogs will react to climate change. Yet, knowledge of anuran skin evolution is surprisingly lacking. This project aims to measure variation in skin form and permeability and determine how skin variation affects key survival traits, like their ability to breathe and avoid dehydration. This project accomplishes a major goal in ecology by incorporating organismal physiology into predictions of climate vulnerability while simultaneously expanding our knowledge of a critically threatened animal group. Furthermore, frog “skin breathing” provides a framework to communicate complex topics ranging from evolution (e.g., convergence and adaptation) to physiology (e.g., oxygen transfer and water loss) to conservation (e.g., climate change). This project will: 1) mentor Native American students at USU and historically excluded students at ISU in research and 2) generate a low-cost, interactive, and publicly accessible frog skin activity focused on inquiry and discovery-based learning of evolutionary concepts. The work seeks to understand how a universal constraint underlying gas exchange dictates climate vulnerability and ecology in an imperiled vertebrate clade. Balancing the need for gas exchange with the risk of dehydration creates predictable evolutionary trade-offs across the tree of life and has selected for adaptations that decouple gas exchange from water loss (e.g. unique nasal morphologies in mammals and birds, stomata density and size in plants). Despite understanding the role of these clade-specific adaptations for promoting life in xeric environments,relatively little is known about the evolution of universal structures, such as skin. With their nearly worldwide distribution and reliance on their skin for gas exchange, anurans are an ideal system to investigate how skin has evolved to balance oxygen uptake and water loss in response to varying environmental selection pressures. The proposed project has three aims: 1) quantify how frog skin has evolved over the past 200 million years and in-response to what abiotic and biotic factors, 2) experimentally test anuran skin’s ability to decouple respiration and water loss, and 3) incorporate physiological data into activity budget models to improve an understanding of current anuran distributions and life-history evolution and predict species’ vulnerability to future climate change. Our integration of morphology, physiology, and modeling will tie skin form and physiology to anuran ecology and biogeography to improve our understanding of anuran distributions, life-history evolution, and species’ vulnerability to future climate change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to improve the cyberinfrastructure ecosystem for computer system research by expanding and improving the popular gem5 simulation infrastructure. The breakdown in Moore’s Law and Dennard Scaling is leading to a drastic increase in the scale of computing systems, making it increasingly difficult to make broad scientific progress in research to improve computer systems without considering the entire scale of the hardware-software stack from transistors to applications. This project will build on prior NSF investments and scale the gem5 simulator to more users, more types of computing systems, larger and higher fidelity computing systems, and more scientific communities beyond computer architecture. These improvements to the gem5 simulation infrastructure will enable researchers to design and understand the next generation of supercomputers, laptops, and mobile devices. It also supports education and diversity by creating teaching materials to broaden the participation in computer systems research for both graduate and undergraduate students. Working with a large and diverse team of experts from the computer architecture community, this project will develop a wide variety of improvements to the gem5 simulation infrastructure. This includes modeling future devices and phenomena such as reliability, security, and chiplets; creating scalable models for modern hardware by improving the accuracy of current models, adding support for emerging vector extensions, and improving support for common accelerators such as GPUs; scaling simulation performance by optimizing gem5’s performance and providing interoperability with other simulation infrastructure; improving modularity, reusability, and reproducibility by making baseline system easier to use and providing ready-to-use benchmarks for many domains; and sustaining the community with outreach and education by running workshops, tutorials, bootcamps and expanding gem5’s userbase beyond computer architects through developing asynchronous learning and teaching tools. Additionally, it will help ensure the long-term sustainability of the gem5 infrastructure by growing a sustainable ecosystem and increasing participation in computer system research. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Computing and Communications Foundations within the Directorate for Computer and Information Science and Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Learning-enabled systems have been rapidly increasing in size, acquiring new capabilities. These systems are typically deployed in complex operating environments, so their safety is extremely important. Ensuring safety requires that systems are robust to extreme events while we can monitor them for anomalous and unsafe behavior. While traditional machine learning systems are evaluated pointwise with respect to a fixed test set, such static coverage provides only limited assurance when exposed to unprecedented conditions in complex operating environments. One key question that still remains unanswered is: How can we design and deploy learning-enabled systems that can be robust to extreme events while monitoring them for anomalous and unsafe behavior by synthesizing model-free and model-based techniques? The overarching goal of the proposed research is to establish a framework that leads to the design and implementation of learning-enabled systems in which safety is ensured with high levels of confidence. The framework will leverage tools from control theory, multi-agent autonomy, and formal methods for rigorously probabilistic reasoning to yield safe learning-enabled systems. The expected outcome of this project will yield safe model-free, mode-based, and interacting model-free and model-based learning-enabled systems with sound design principles that practitioners could leverage to achieve safety specifications. The proposed research could effectively facilitate safe learning-enabled systems even within complex environments while monitoring them for anomalous and unsafe behavior. It will yield learning-enabled systems that could be deployed in complex operating environments while ensuring that the systems will be robust to extreme events and monitoring them for anomalous and unsafe behavior. The fundamental knowledge created in the proposed research will be the basis upon which future-safe autonomous systems with embodied intelligence can be built. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

State-ordered diversionary programs, such as treatment, reentry, and rehabilitation programs, provide alternatives to incarceration for defendants, probationers, and parolees. To take part in and complete these programs, various outlays and expenditures can be required. This project will examine the social, economic, and legal consequences of these costs on compliance with court-ordered treatment, rehabilitation, and reentry programs. This project will document the underlying structural mechanisms involved in participating in and completing state-ordered diversionary programs. Access to resources, including money, information, flexible work schedules, reliable transportation, and time, is necessary to avoid noncompliance with these programs. The research will include a randomized control trial (RCT) that will causally investigate whether and how investments in rehabilitation programming, as well as the alleviation of particular forms of disadvantage, can increase program participation and completion rates. The project will use full sample comparisons between treatment and control groups to estimate the effects of economic, socioeconomic, and informational variables on rates of program enrollment, dropout, re-enrollment, and completion. Findings from the RCT will be included as inputs to an agent-based simulation model that will evaluate influences on compliance with rehabilitation and reentry programs at the population level. The result will be an assessment of the causal relationships involved in compliance rates as the project tabulates shadow costs and measures willful noncompliance with court-ordered diversionary programs. This project is supported by the Law and Science Program and the SBE Science of Broadening Participation Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In this Science, Technology, Engineering, and Math (STEM) Education Organizational Postdoctoral Research Fellowship project, three postdocs will be recruited to join a vibrant Discipline-Based Education Research (DBER) community at Cornell University and develop as future leaders. This work will serve the national interest by promoting the progress of science through interdisciplinary and multi-institutional education research projects and by developing the Cornell Interdisciplinary Discipline-Based Education Research (CIDER) postdocs as future leaders in science education through a comprehensive professional development program that includes research mentoring and development, network building, leadership, teaching opportunities, and career planning. The CIDER program will nurture an inclusive, supportive, and diverse community of emerging scholars using a nested mentoring approach. By working together as a PI-team to engage in and refine CIDER's approach, there will be impacts on the postdoc mentoring system across Cornell DBER, which affects future scholars beyond the life of this grant. CIDER postdocs will also provide workshops to national audiences on developing DBER scholars and organize an Upstate New York DBER conference including faculty from community colleges, primarily undergraduate institutions, and PhD-granting institutions. The conference will provide a venue to share the range of research activities across the local community and include a collaborative session about designing and assessing the outcomes of DBER postdoc programs. These events strengthen the STEM education community more broadly and provide multiple ways to disseminate findings. The CIDER postdoc program relies on a nested mentoring approach to provide mentoring, guidance, and support to the CIDER postdocs. This approach avoids a hierarchical, top-down mentoring relationship and instead embraces mentoring as a partnership among a constellation of mentors, postdocs, and their networks and resources. To support interdisciplinary research, the CIDER postdocs will each work with Cornell DBER research mentors from multiple disciplines to design and conduct research aligned with their interests and career goals, and leverage insights across DBER fields. To support work at multiple institutions, the research mentors will help CIDER postdocs form networks to conduct their research in multiple contexts. This approach will involve engaging connections the PI team has previously established, attending conferences together, inviting outside speakers to give seminars in multiple departments throughout Cornell, and hosting a regional conference with instructors and researchers from multiple institution types. The application of the postdocs’ research results will contribute to broadening participation in STEM and improving STEM education for students from multiple institution types and disciplines, both directly through the research activities and indirectly through dissemination. The intellectual merits also lie in the long-term research activities of the CIDER postdocs as a result of the professional development opportunities afforded through this program. This project is funded by the Science, Technology, Engineering, and Mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the Improving Undergraduate STEM Education Program (IUSE:EDU) and the EDU Core Research: Building Capacity in STEM Education Research (ECR:BCSER) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. ECR: BCSER is designed to build the capacity of individuals to carry out high-quality, fundamental STEM education research in STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This RAISE project will develop new methods to connect multiple chips within computers using light instead of electrical wires. Using light to transfer data between chips can make data transfer faster and more energy efficient, which is crucial for working with large and complex data needed for societal applications like artificial intelligence, climate modeling, and biomedical research. The project will closely engage with industry partners to facilitate adoption of the proposed research into practice. The close collaboration with industry will help train a new generation of scientists and engineers with interdisciplinary expertise. The skills and insights gained through this project will prepare them to tackle future challenges that lie at the intersection of multiple scientific fields, aligning with the NSF's mission to advance the frontiers of knowledge and innovation. The project proposes to optically interconnect accelerators within compute servers using newly viable reconfigurable chip-to-chip optical interconnects. In contrast, today, commercial multi-accelerator compute servers that are workhorses of machine learning, use electrical interconnects to network accelerator chips in the server. However, recent trends show the prominence of an interconnect bandwidth wall caused by accelerator scaling at a magnitude faster rate than the bandwidth of the interconnect between accelerators in the same server. This has led to under-utilization and idling of Graphical Processing Units (GPUs) resources in cloud datacenters. Therefore, it is important to scale interconnect bandwidth in multi-accelerator servers to keep power-hungry and expensive accelerators adequately fed with data and parameters. This project will use novel silicon photonics to create optical interconnections between accelerators within a server to meet this need. This research will benefit the complementary efforts of hyper-scale cloud providers by unlocking customized multi-accelerator topologies that achieve bandwidth-optimal collective communication between accelerators during distributed machine learning and can minimize the blast radius of accelerator failures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The long-term goal of this project is to enable seamless integration of robots into people’s everyday environments. Robots can assist people in many ways, including guiding tours in museums, escorting travelers to their flight gates in airports, and stocking and delivering supplies for nurses in the hospital. These robots will inevitably commit social norm violations accidently. They might get closer to people than is comfortable, or move in ways that are surprising and unexpected. Such errors, if left unresolved, could have a lasting effect on how people perceive robots. Social understanding can be challenging; context plays an important role in social norms. There is limited prior work focused on the studies related to norms in human robot interaction. Norms, such as appropriate distances between human and robots, are hard-coded into the robot behavior. Behavioral strategies that enable robots to adapt to new circumstances are needed to help the robots adapt to contextually- and culturally-dependent norms. This makes it more possible to use robots in assisting people in everyday scenarios. This project enables robot systems to infer when they have committed a social norm violation by observing the social reactions of the people around them. Through field study deployments of physical cart and bin robots developed by the researchers, we collect naturalistic datasets of human-robot interactions in real-world settings, specifically hospitals and cafes. These interactions will be labeled as norm violations or not. This naturalistic dataset will support the development of data-driven approaches that allow robots to infer correct behavior based on bystander reactions. This project builds algorithms to enable robots to recognize when they commit social norm violations and develop strategies for recovering and repairing broken interactions using large language models. The research team will evaluate these algorithms on trashcan and cart robots in cafe and hospital environments by comparing how users respond to robots that attempt to repair social interactions after violations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project transforms engineering education by leveraging "meaningful failure" as a promising approach to learning and teaching. Failure is an inherent part of human life and learning processes, and early failure is often prerequisite step on the path to successful learning. However, typical engineering education currently punishes failure, which disincentivizes innovation, exploration, and risk-taking, ultimately resulting in engineers who are less prepared to tackle complex global challenges. By understanding students’ unique experiences during moments of academic failure, this project supports students taking risks and learning from setbacks, developing the skills and mindsets to embrace failure as a meaningful experience in their learning. Our research involves the use of biometric data, observations of classroom dynamics, and psychosocial assessments to better understand how each student experiences failure on a physiological, cognitive, and social level. We will use these data to develop new educational tools and strategies that will provide immediate, tailored interventions connected to individual student needs and experiences. This research will support the development of a workforce ready to persist past ubiquitous failure experiences in engineering to address tomorrow’s challenging engineering problems. Further, this research aligns with the goal of creating inclusive and equitable learning environments that can adapt to the diverse needs of all students. The project will explore meaningful failure in engineering education contexts by developing personalized learning strategies and pedagogical tools. The proposed research has three goals: identifying real-time failure profile signals, understanding how learners' responses to failure are individualized, and determining necessary changes in pedagogy and assessment to support personalized responses tolearning from failure. The research involves a multi-pronged data collection approach, including laboratory experiments using video and biosensing modalities (EEG, EDA, ECG), classroom observations, surveys, and interviews with educators and administrators. A convergent team from five institutions, with expertise in cognitive neuroscience, learning sciences, AI, and psychosocial theories of learning and development collaborate to create individualized failure profiles. These profiles will integrate multi-modal data sources to formally represent each learner’s unique cognitive, affective, and behavioral responses to failure. The project will culminate in the development of pedagogical tools and strategies to support personalized learning and resilience – increasing retention and success rates in engineering fields and pioneering a shift in engineering education towards valuing learning from failure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Testing is the primary means of validating software correctness in practice, and developers often write tests at different levels of granularity. Specifically, unit tests validate individual functions, integration tests validate interactions among functions, and system tests validate end-to-end system behavior. This decades-old categorization of tests is valuable, evidenced by widespread tool and framework support, but it hinders developers from testing at finer granularity levels, such as statements within functions. Yet, many software bugs occur at such finer granularity levels and those software bugs often escape the forms of tests that are used today. This project aims to enable developers to perform fine-grained testing, thereby increasing software quality. Developers will be able to test hard-to-reach and hard-to-comprehend code fragments, complementing existing testing methodologies. The resulting higher-quality software is expected to contribute positively to the US economy. The research will be integrated into curriculum and training. The project's underlying research objective is to increase the efficiency and efficacy of software testing by removing decades-old artificial boundaries that exist between tests and code. To achieve this objective, this project will (1) develop a language and a framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests, making it easier to retrofit them to existing code; (3) adapt fine-grained tests to software evolution and use fine-grained tests to improve current regression testing techniques; (4) use fine-grained tests to improve fuzzing and runtime verification; and (5) begin supporting fine-grained testing of non-functional properties, focused on specific security and performance bugs. Proposed techniques will be evaluated via experiments on open-source projects, to evaluate their ability to increase the coverage and bug-finding capability of existing test suites. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

There is a need to train skilled computer architects to design innovative computer hardware. Software-based simulation is the backbone of computer system design and development. Such tools are also widely used for teaching computer architecture concepts. Currently, the simulators used for educational purposes have a steep learning curve, are not interesting for beginners, and are error-prone. Therefore, these simulators are mostly used by experienced researchers. This project introduces a novel framework and technology called Scaffolded AI-driven Learning Simulation (SAILS). SAILS enables an interactive and supportive computer architecture learning platform and offers design exercises covering different learning modes and difficulty levels. In the development phase, SAILS will be used by instructors at the University of Kansas and Florida International University to teach introductory and advanced computer architecture courses to about 400 undergraduate and graduate computer science and engineering students every year. Once SAILS is fully developed, it will serve as a framework to teach computer architecture in several US institutions. SAILS implements a novel AI-driven paradigm for reducing the learning curve of computer architecture simulators in educational settings. SAILS implements a front end that reduces the complexity of simulating simple to advanced systems for students with various backgrounds. SAILS back-end seamlessly connects to a state-of-the-art computer architecture simulator and provides just-in-time personalized assistance to the users. The assistance is provided by a centralized AI model trained by individual users’ and team data and global users’ experience with the framework. SAILS integrates the faded scaffolding approach to provide appropriate levels of support to individual learners and teams to maximize their learning. SAILS’s easy-to-use graphical user interface, engaging learning activities, and personalized scaffolding support a broad and diverse student population, including female and underrepresented minority students, in the computer architecture and design field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Datacenters are the backbone of today’s digital world that power all daily cloud services. Over the past decade, the exponential increase in the data volumes resulted in computing specialization and over a 100x increase in datacenter network bandwidth utilization. Nevertheless, the network data delivery path to the compute nodes remained unchanged. As such, the current datacenter network suffers from the overhead of the multi-layered software stack, complex network protocol processing, frequent data movement, and network device management. The project novelty is to leverage hardware specialization and near-data processing to re-architect a datacenter’s network. The project's educational plans include organizing yearly outreach workshops to educate K-12 teachers on computing fundamentals, involving underrepresented, undergraduate, and low-income students in research, and integrating research into the higher-education curriculum. Such activities boost student enrollment in higher-educational institutions, train diverse students who deeply understand computer architecture and systems fundamentals, and can innovate across architecture, networking, and operating systems fields. The project’s impacts are to shape the future of datacenter networking and educate a high-quality workforce to supply the needs of the US IT industry and academia. The project aims to design, implement, and evaluate a network data plane that delivers data directly from top-of-rack switch ports to the server’s Central Processing Unit (CPU) caches and memory modules, leveraging an optical interconnection network. The investigator extends the CPU Instruction Set Architecture (ISA) with several network access instructions that enable a process to access the network with minimal software activity. The memory access instructions offload the notification between the CPU and the network interfaces to the hardware. The investigator develops libraries and transport protocols that utilize the proposed network data and control plane to provide robust connectivity between processes running on different servers. Lastly, the project explores the design space of domain-specific near-memory accelerators for network protocol acceleration. The potentially transformative network architecture will enable compute nodes to efficiently sustain tera-bit-per-second connectivity with bare metal network latency numbers. The investigator uses the research findings and evaluation infrastructure to boost the home institution's computer architecture and systems pedagogy. This project is jointly funded by the Software and Hardware Foundations (SHF) core research program in the Computing and Communication Foundations (CCF) Division 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 Awards · FY 2024 · 2024-10

Custom, domain-specific hardware accelerators are critical tools for advancing computationally intensive applications in the modern era of computing. While a new generation of accelerator design languages (ADLs) has raised the level of abstraction for designing specialized hardware, architects must also rely on analyses beyond the language itself to optimize performance and identify and fix correctness issues. This project will develop techniques for efficient debugging and profiling of accelerator designs, which will in turn reduce the time and cost of developing efficient hardware to support applications that require more performance than general-purpose hardware can offer. The project will also support a new cross-institutional community for industry and academic collaboration on open-source projects for ADLs and their associated tools. This project will focus on three main techniques for understanding the performance and correctness of accelerator designs. First, it will develop methods for collecting actionable data on three key efficiency metrics: resource consumption (i.e., area), critical timing paths (which determine the hardware's maximum clock frequency), and cycle-level timing. These techniques will be embodied in a tool that generates flame graph visualizations of these metrics, relating them to programmer-visible constructs. Second, the project will create a new method for combining the deep observability of software simulation with the sheer execution speed of field programmable gate array (FPGA) emulation. This involves collecting lightweight snapshots of executions running on a real FPGA and transferring this execution state to software emulation for detailed inspection. Finally, the project will design mechanisms to understand concurrency bugs by exposing issues arising from the interleaving of parallel events in accelerator executions. A new method will be developed to systematically manipulate an accelerator's execution schedule at runtime, searching for schedules that reveal buggy behaviors. This will lead to a deeper understanding of how concurrency affects the correctness of accelerator designs. This project is jointly funded by the Software and Hardware Foundation (SHF) core research program and the Advancing Informal STEM Learning (AISL) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Researchers at Cornell University are conducting a mixed methods study of the barriers and facilitators in the pathway from postsecondary STEM undergraduate education to STEM employment for Autistic undergraduate students. The pre-employment interview remains one of the most commonly used hiring practices, and yet it often poses a significant barrier for our subject population—many of whom face challenges with social interaction, communication, and behavior. Investigating how job interviews impact Autistic candidates is essential, as they continue to experience high rates of unemployment and underemployment. Improving the transition between college education and STEM careers will inform the development of solutions that create opportunities for students to pursue STEM careers. The STEM workforce is enriched by the inclusion of people of all abilities, including people with autism who bring unique perspectives to STEM problem-solving and discovery. Using a participatory action research approach, each step of the research is guided and informed by a community research team comprised of autistic college students, career counselors, and STEM employers. This mixed method study aims to: 1) explore and understand the experiences of students enrolled in the study as they relate to their pursuit of STEM interview preparation related to employment; 2) analyze the impact of various strategies and experiences on the preparedness of these students for STEM interviews and assess the prevalence of barriers they encounter; 3) evaluate the perceived readiness of college career counselors in assisting Autistic college students, in their STEM career pursuits; and 4) explore and understand the barriers observed by employers when engaging in pre-employment interviews with Autistic individuals. The results will be beneficial for identifying solutions and resources needed by STEM employers and higher education that will improve interview processes and systems for 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 Awards · FY 2024 · 2024-10

Forest regeneration is crucial for addressing climate change, but challenges such as scarce seed availability and time-intensive seedling cultivation hinder many projects. To reduce costs and increase the effectiveness of seeding, a convergent team comprised of experts from material science, engineering design, bio- and soil mechanics, soft robotics, plant science, and forest ecology will advance novel bio-inspired, biodegradable, self-burying seed carriers for aerial seeding. Autonomous seed carrier burial at optimal depths provides protection from herbivores and insulation against harsh environments, thereby enhancing germination rates and safeguarding seeds during critical early stages of growth. This research will expand forest restoration efforts, offering economic benefits and promoting ecological resilience. To advance forest regeneration practices through interdisciplinary efforts, this project: (1) addresses the challenge of low germination and seedling establishment rates in aerial seeding by developing self-burying seed carriers; (2) explores the rational design of seed carriers to accommodate the unique requirements of different tree species and environments, aiming to create a library of designs applicable across diverse ecosystems; (3) seeks to understand and optimize the self-burying process of seed carriers by establishing a new modeling framework to improve the efficiency and effectiveness of the proposed seeding techniques; and (4) emphasizes the importance of fostering biodiversity in forest ecosystems, resulting in new ecological field test protocols and an evaluation pipeline that is enabled by engineering and guided by forest ecology. 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.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Episodic memory involves learning and recalling associations between items and their spatio-temporal context. Those memories can be further used to flexibly support different behavioral demands. In this proposal we address the question of how the fine temporal coordination of neuronal activity across entorhinal and hippocampal areas support learning and memory. Oscillatory synchrony in the theta (~5-9 Hz) and gamma (~30- 100 Hz) frequency bands between hippocampus and entorhinal cortices has been implicated in these processes, although the precise mechanisms are not known. The medial (MEC) and lateral (LEC) entorhinal areas are the major source of inputs to the hippocampus. Previously, we found that gamma oscillations synchronize population activity in hippocampal-entorhinal circuits during navigation and learning. However, how the gamma-frequency coordination of hippocampal assemblies brought about by distinct entorhinal inputs supports the formation and reactivation of specialized memory representations in different CA1 subpopulations is not known. In this proposal, we will deploy a novel approach combining multi-region laminar recording and temporally selective optogenetic perturbations to elucidate the circuit mechanisms that support spatial and non-spatial learning in rats. Previous work suggested that different CA1 pyramidal cell subpopulations are specialized in encoding complementary memory representations, and they receive differential innervation from MEC and LEC. In Aim 1, we will perform simultaneous neural recordings across CA1-2, MEC and LEC while rats navigate mazes to examine how area and layer-specific gamma synchrony modulates neuronal firing dynamics. This will be enabled by a novel analytical method to isolate different pathway-specific gamma oscillations durign behavior. In Aim 2 we will investigate how functional interactions among neuronal assemblies across these structures are modulated by behavioral demands, by training rats in different types of learning tasks. We will investigate whether different hippocampal-entorhinal neuronal subpopulations form assemblies and sequences representing behavioral relevant locations during learning. We will also test the causal contribution of entorhinal gamma inputs to this process with selective optogenetic perturbations. The sequential activation of cell assemblies during behavior is recapitulated during pauses in exploration and sleep, coordinated by SWRs; a process that supports memory consolidation. In Aim 3 we will test if synchronous M/LEC inputs influence which assemblies are recruited into SWRs, therefore determining which aspects of experience are replayed and consolidated. To do so, we will perform closed-loop optogenetic silencing of CA1 condition on real-time detection of M/LEC inputs during sleep periods following different learning tasks. By combining technical innovations for recording, analyzing, and manipulating circuit dynamics, this proposal will reveal how entorhinal inputs support hippocampal representations, memory replay and predictive coding. These Aims will also expand our understating of fundamental circuit mechanisms of impaired cognition common to multiple neuropsychiatric diseases.

NIH Research Projects · FY 2025 · 2024-09

Project Abstract/Summary The project proposes to continue the work of the Produce Safety Alliance (PSA) to educate growers, packers, and other stakeholders involved in the U.S. fresh produce supply chain as the Food Safety Modernization Act (FSMA) Produce Safety Rule (PSR) continues to evolve. The PSA develops effective and accurate educational resources to enhance understanding and implementation of the FSMA PSR requirements and Good Agricultural Practices. The goal of the PSA since 2010 is to promote the safety of fresh fruits and vegetables by leveraging partnerships and collaborations to make required training (21 CFR §112.22(c)) available to covered farms and other stakeholders in the form of high-quality and engaging PSA Grower Training (GT) Courses. The PSA GT Course is available in three formats (online, remote, and in-person) and multiple languages. The PSA Train-the-Trainer Course, Lead Trainer Application review, and Trainer-of-Trainers development process ensures that a sufficient cadre of qualified PSA Trainers are available to provide covered farm personnel and others with access to the PSA GT Course domestically and internationally. The PSA supports the current cadre of 3,645 current PSA Trainers, Lead Trainers, and Trainers-of-Trainers in their efforts to stay current with FSMA PSR changes and scientific understanding. The PSA works to provide growers and packers with consistent information for implementing produce safety practices at their operations by making educational materials concise, readable, and accessible on the PSA websites in many formats (e.g., videos, factsheets, illustrations) to support different learning styles. As the FSMA PSR and guidance documents are revised, the existing PSA GT Curriculum also will need to be revised to incorporate regulatory changes, evolving policies, and guidance. The expanded budget in Years 2 and 3 will allow for the development of an updated PSA GT Curriculum and supplemental educational materials, in English and Spanish. The PSA will update past PSA GT Course participants, the PSA Trainer cadre, and other stakeholders through a coordinated outreach effort that includes update trainings in multiple formats. In addition, a proposed expansion of the PSA Team will reverse attrition and allow the PSA to increase efforts to mentor training teams, host continuing education efforts such as monthly Educators’ Calls, and support diversity and inclusion with enhanced outreach to traditionally underserved groups and others with limited access to the PSA GT Course and produce safety educational materials.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract Highly Pathogenic Avian Influenza (HPAI) H5N1 virus is an emerging pathogen in dairy cattle with zoonotic potential. Recently, HPAI H5N1 spilled over into dairy cattle and studies demonstrated the tropism of the virus for the mammary gland with the virus replicating in milk secreting cells in this tissue, leading to high levels of virus shedding in milk of affected cows. This property has caused major public health and consumer concerns, as milk and dairy products are consumed in large scale by the population in the US. While pasteurization is designed to reduce potential bacterial and viral pathogens in commercial shelf milk and have been shown to be effective by us and others to inactivate HPAI, raw milk which is handled in large quantities in farms represents a major risk factor for HPAI spread and transmission. Indeed, recent investigations by Dr. Diel’s group and others have shown that raw milk can serve as a route of transmission of the virus to cats. The practice of feeding unpasteurized raw milk to calves and other animals in dairy farms poses a risk for spread and dissemination of the virus. Given the high levels of HPAI virus present in milk from affected animals, disposal of raw non-saleable milk from those animals is a major problem, as the contaminated milk may serve as source of infection to other animals, birds and potentially humans. This needs to be urgently addressed to minimize environmental impact of HPAI and to prevent its spread between dairy farms and from affected dairy farms to other susceptible species. In the present project we will address this significant issue and will perform studies to improve our understanding of the risks posed by raw milk and raw milk cheeses, and to identify potential mitigation strategies to inactivate HPAI in raw milk and raw milk products. To accomplish this, we propose four specific aims: 1) to define efficacy of raw milk cheese aging on inactivation of HPAI; 2) to characterize the thermal inactivation kinetics of HPAI H5N1 in dairy products; 3) to identify effective mitigation strategies to treat raw waste milk prior to disposal or feeding to animals; and 4) to enhance capabilities and capacity for HPAI H5N1 testing in support of FDA’s research agenda. To achieve these goals and establish a long-term partnership with FDA we brought together a transdisciplinary team of investigators with complementary expertise in virology (Drs. Diel and Nooruzzaman), food safety, microbiology and dairy product processing (Drs. Martin and Alcaine), and on farm clinical and management practices (Dr. Mann). Successful completion of the study will provide a comprehensive understanding of the inactivation kinetics and efficiency of inactivation of HPAI in milk and several other dairy products, including high risk products such as raw milk cheeses. Thus, the project is directly aligned with the FDA goals and mission.

NIH Research Projects · FY 2025 · 2024-09

Some of the greatest human health impacts from infectious diseases are influenced by weather events and seasons, which can shape when and where outbreaks occur, influence the abundance of disease vectors, and affect the survival and transmission of pathogens. Billions are at risk annually from rainfall-dependent malaria, and viral pathogen spillover events and spread of vector-borne diseases (VBD) are increasing due to extreme weather events. In response to these urgent threats, Cornell University has created the Center for Transformative Infectious Disease Research (CTIDR). To have the greatest health impacts, we must change research and practice paradigms from reactive focus on response to outbreaks to proactive understanding of the complex social and environmental conditions that promote risk of outbreaks. We hypothesize that community-engaged research integrating human, reservoir and vector behavior, weather, land-use, human and animal health, and vector/pathogen genomic evolution datasets, will enable creation of predictive epidemiological models and future generation and rigorous testing of preventative interventions. Improved understanding of these relationships will also facilitate current preparation/response. Working toward these goals, we integrate dimensions of building research capacity and performing transdisciplinary research in every element of CTIDR. Administrative Core (Travis, PI): will facilitate routine meetings; administer a pilot grant competition with preference for Early-Stage Investigators (ESI) to generate preliminary data and test feasibility for future studies; and organize transdisciplinary training for ESIs, post-doctoral and graduate student trainees to broaden their skills and network for future predictive infectious disease research. Living Evidence Applied Data Modeling Core (Hayden-ESI; SmithESI; Bento-ESI, co-leads): will integrate seemingly disparate datastreams spanning different disciplines and leverage transdisciplinary modeling expertise to enable researchers to combine weather, land-use, animal and human health, and genomic data, thereby disentangling generalizable patterns from context-specific relationships in disease dynamics. Community Engagement Core (Meredith-ESI, lead): will engage with every project to enable/perform community-informed research that is relevant and can effect long-term positive change. Project 1 (Plowright, lead): hypothesizes that weather extremes and land use changes result in wildlife stress, increasing both viral shedding and interaction with humans, facilitating viral spillover events (paramyxo-, corona-, and filoviruses). Project 2 (Goodman-NI, lead): hypothesizes that an integrated framework for a community-based early warning system for weather-sensitive VBD can promote human health, building upon existing remote sensing with new focus on mosquito-borne viruses and tick-borne pathogens (Anaplasma, Rickettsia, Coxiella), including genomic analysis for hotspots of selection. Center function is supported by world-class transdisciplinary research environments and institutional commitment to community engagement and impact.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT The lymphatic vasculature is essential for organogenesis and dietary fat absorption and the intestinal villus plays the leading role in this process. However, the developmental programs governing the formation and organ-level role of villus lacteals, specialized lymphatic vessels responsible for lipid absorption, remain poorly understood. We recently discovered that the master left-right transcription factor Pitx2 governs lacteal function through a non-cell autonomous pathway involving the smooth muscle (SM). Pitx2-derived SM cells secrete growth factors to guide lymphatic development, forming the muscular-lacteal complex that is essential for lipid transport and villus maintenance. Pitx2 mutant mice develop abnormal SM and lacteals, and surprisingly shunt dietary lipids into villus blood capillaries of the portal circulation, the major blood supply to the liver. This causes fatty liver disease in Pitx2 mutants, the most common human liver disorder. Compared to the villus epithelium, research on the mesenchyme is scarce, and we lack a sufficient understanding of villus SM origin, assembly alongside lacteals, self-repair, and how villus SM dysfunction is linked to abnormal fat trafficking. Our research aims to address these critical gaps in understanding. In Aim 1, we will elucidate how Pitx2 patterns the villus SM program by studying a fibroblast-to-myofibroblast transition and its effectors as a potential mechanism. We will test the hypothesis that intestinal myofibroblasts are the major source of renewal of villus SM crucial to villus maintenance and repair. In Aim 2, we will define the role of Notch receptor-ligand signaling in SM development, assembly, and physiology. The focus is on understanding the expression and function of Notch3 and Jag1 in the villus vasculature and how they mediate cellular interactions between the endothelial and mesenchymal cells. In Aim 3, we will investigate the mechanism of gut-derived fatty liver disease in Pitx2 deficient mice. We aim to understand how lipids gain access to the portal vein in Pitx2 mutants. The potential role of Pitx2 in governing blood endothelial cell permeability and its contribution to fatty liver disease in mutant mice will also be explored. This research combines our demonstrated strengths in single-cell analysis, quantitative lineage tracing, functional assays, imaging, genetic manipulation, and targeted interventions. At the completion of these aims, our research will uncover how the muscular-lacteal complex is built and repaired through the complex intercellular interactions within the intestinal villus, opening new avenues for therapeutic interventions targeting lymphatic-related metabolic disorders.

NSF Awards · FY 2024 · 2024-09

Harnessing the value of data into applications with great societal value raises significant security and privacy concerns that are likely to stifle progress and decrease the return-on-investment of an AI-powered market economy. The goal of this GCR proposal is to accelerate the development of trusted, low-overhead tools that enable computation directly on encrypted data such that, for example, confidential data can be shared with an untrusted party who can extract insights from the data without having access to the unencrypted data. Such a capability, which is currently unavailable for application to large scale problems, would have the broader impact of greatly increasing the public's trust in modern AI tools and create new opportunities for data-powered, socially responsible innovation. The success of this project hinges critically on the interplay and synergy of ideas and state-of-the-art techniques and methodologies from physics, mathematics, and computer science. The paradigm shift and the powerful practical tools proposed can be realized and implemented only by the integration of expertise and the strong interdisciplinary interactions stimulated and supported by this project. Apart from their significant practical implications, the novel convergent ideas driving this project are also likely to raise new questions and stimulate new ways of thinking and new directions of research in each of the disciplines: physics, mathematics, and computer science. This project brings together state of the art tools from theoretical physics, mathematics, and computer science to: (a) explore a novel paradigm for circuit obfuscation, a fundamental tool in modern cryptography; and (b) to establish the security and efficiency of a recently proposed scheme for computation on encrypted data, referred to as Encrypted Operator Computing (EOC). The conceptual elements that drive both the new approach to circuit obfuscation and the EOC scheme are inspired by the project team's experience with the fundamental physics of complex quantum and classical systems. In particular, the obfuscation of circuits is related to ``local thermalization” of circuits implemented through ``gate collisions,” a novel concept in the context of gate-based computational circuits, which the proposal connects with relators of group presentations in geometric group theory. The connection with geometric group theory provides a natural mathematical framework for formalizing the notion of ``circuit thermodynamics.” Moreover, the critical design elements of the EOC emerge from an exact mapping of reversible classical computation (via circuits of universal reversible classical gates) into the dynamics of strings of Pauli matrices in the space of Pauli strings, a formulation which highlights many useful parallels (as well as differences) between classical and quantum computation. In this context, the quality of encryption by classical ciphers can be characterized by tools that quantify scrambling of information, entropy production, and irreversibility and chaos in the space of Pauli strings, tools commonly used in modern quantum information science. The goal of this project is to scrutinize these physics-inspired ways of thinking with state-of-the-art tools of modern cryptography, mathematics, and statistical mechanics; and to leverage the proposed collaboration and strong multi-disciplinary expertise to establish the proposed paradigm for circuit obfuscation and the EOC scheme for classical computation on encrypted data as trusted practical cryptographic 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.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY There is a fundamental gap in our ability to monitor antimicrobial use (AMU) at the individual animal level in dairy cattle nationally. The continued existence of this gap hinders the development of data-driven antimicrobial stewardship and understanding of the relationships between AMU in dairy cattle and antimicrobial resistance—one of the most pressing One Health challenges we face today. AMU monitoring requires an approach to collecting and quantifying data on AMU. Also, most herds must participate in data sharing for an AMU monitoring system to succeed. However, farmers lack the incentive to participate in monitoring, the labor involved in data collection is prohibitive for already busy farmers, and they have concerns about the loss of privacy and business advantage through sharing their AMU data via a monitoring system. These major bottlenecks are impeding the establishment of AMU monitoring in dairy cattle in the US. Thus, there is an urgent need for a system approach to animal-level AMU monitoring in dairy cattle that provides private value to the participating farmer, automates laborious data collection tasks, protects farmers’ privacy, and advances One Health goals. Our long-term goal is to deploy a functional and efficient system for monitoring AMU in food animals. Thus, the overall objective of this application is to develop a system for monitoring AMU in dairy cattle that provides farmers with actionable clinical and business insights, automates data collection, and protects their proprietary information. The rationale that underlines the proposed research is that such an AMU monitoring system will incentivize dairy farmer participation and enable One Health to benefit from the national- level AMU monitoring. This objective will be achieved by systematically building the three pillars of an effective AMU monitoring system: Data, Models, and People. Specifically, we will pursue the following specific aims: (1) Collect detailed, complete, and validated multi-year animal-level AMU data on dairy farms; (2) Develop a system approach to animal-level AMU monitoring in dairy cattle; and (3) Evaluate perceptions of farmers and veterinarians about AMU monitoring in dairy cattle. The AMU monitoring system developed in Aim 2 will have four innovative elements: (i) instant private clinical/business insights for the farmer to incentivize their participation in data collection and sharing, (ii) standardization and automation to ease the data collection burden on farmers, (iii) augmentation with synthetic data, and (iv) privatization techniques that give the farmer the governance over their AMU data while allowing peer learning, further incentivizing participation in AMU monitoring. The proposed research is significant because it is expected to enable scaling up monitoring animal-level AMU on dairy farms in the US with a system approach and technology that are tailored to the dairy farming industry and have translational value to other food animal sectors. In addition to technological innovations, the project will generate multi-year AMU data in dairy cattle and data about farmers' perceptions of AMU monitoring, which will be instrumental in developing a use-inspired AMU monitoring system.