Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2025 · 2025-01

Today's cloud data centers struggle to keep up with the computing demands of big-data applications (e.g., social media, video streaming, and artificial intelligence) that form the backbone of our modern technological society. The current status-quo of executing compute at the end-host servers while using networks for routing packets only, restricts these data centers from scaling to ever-growing distributed and high-performance computing (HPC) applications. In-network computing scales and accelerate modern applications by performing parts of the high-level computation directly inside the network switches. However, today’s switches implement simple, stateless packet protocols (e.g., routing and forwarding) and cannot perform complicated stateful operations needed to run distributed applications. To address this challenge, the proposal seeks to develop (1) a stateful switch architecture for flexible management, computation, and sharing of applications' state in the network, beyond just traditional packet processing; (2) a network-wide abstraction and runtime to program and operate the proposed stateful switches collectively within a data center; and, lastly, (3) a suite of representative applications and test cases to guide the design and implementation of these stateful switches by exploring their architectural trade-offs. This work has the potential to significantly enhance large scale computation now used widely by researchers, the government and businesses in every sector of the economy. 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 2025 · 2025-01

Modern cyberinfrastructure (CI) powers many aspects of our day-to-day life (such as healthcare, finance, electricity, and communication). And, moving forward, our reliance on this infrastructure will grow even more as new use cases (e.g., augmented/virtual reality, autonomous transportation, and remote surgery) and users (e.g., self-driving cars and IoT devices) enter the technological landscape. To meet the strict security and performance requirements of this evolving landscape, the cloud datacenter networks and systems, which form the backbone of CI, must adapt and allocate their (heterogeneous) resources quickly and efficiently. Doing so, however, demands that (compute-intensive) network management and control decisions are applied per packet at line rate in a fast-and-intelligent way. Unfortunately, the dominant solutions available today are neither fast nor intelligent enough to meet these requirements. This proposal aims to bridge this gap between speed and intelligence by developing a holistic platform that allows datacenter operators to execute per-packet AI-driven decisions directly within the network at line rate. The proposal lays out the research across three progressively connected thrusts: (1) designing novel data-plane architectures for per-packet AI, (2) implementing high-level, declarative frameworks for expressing AI objectives (and models), and, finally, (3) developing a suite of per-packet AI applications to build confidence in the utility of the proposed platform. It is a radically new paradigm that converges multiple disciplines (machine learning, networking, and architecture), thus opening pathways for machine-learning researchers and architects—with their cross-disciplinary knowledge—to work alongside network designers to realize the full potential of per-packet AI. 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

This NSF-funded workshop is dedicated to understanding the complex systems of particles found in soil, mud, sand, and clay. These particles play a crucial role in addressing significant challenges such as climate change, agricultural productivity, and soil erosion. The workshop will take place from December 14-15, 2024, at the Carnegie Mellon University campus in Kigali, Rwanda, and will bring together researchers, policymakers, and students from both the United States and Africa. The conference aims to allow the currently disconnected research communities from geosciences, materials science, nanoscale chemistry, and complex systems to build common knowledge, grow relationships and exchanges for further understanding and collaborations. By tackling fundamental scientific challenges and practical applications of geoparticle systems, the workshop will contribute to global sustainability, economic growth, and poverty alleviation in regions heavily affected by soil degradation and climate-related issues. The workshop will address three main scientific challenges: developing comprehensive theoretical models for diverse particle systems, a better understanding of the formation and disintegration of fibrous and other metastructures and a unified approach to evaluate the impact of these factors on large-scale behaviors such as landslides and soil stability. Experts from various disciplines will engage in discussions on themes such as particle interactions at the nanoscale, the physics of granular flows, and the stress transfer mechanisms in soils. Experts will discuss particle interactions, granular flows, and stress transfer in soils through keynote presentations, panel discussions, and brainstorming sessions. A key outcome will be a detailed perspective article outlining new research directions, including the creation of theoretical frameworks and methodologies for monitoring and controlling metastructures. This document will serve as a roadmap for future collaborations and innovations, guiding efforts to develop technologies and methods for better environmental and agricultural practices. By fostering cross-continental partnerships, the workshop will advance scientific knowledge, support education and broadening participation, and contribute to societal welfare through improved soil and environmental health. 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

Spectroscopic quantum metrologies—the science of using quantum light–matter interactions for measurements—stand at the cutting edge of advancements in high-precision standards, techniques, and applications. Despite representing some of the highest achievements in spectroscopy to date, quantum metrologies have largely remained confined to laboratory settings, leaving their greatest potential––applications outside laboratories––mostly untapped. Following the NSF’s mission to realize practical quantum advantages, the QuPID team, led by the University of Michigan with members from Stanford University, Harvard University, Michigan State University, Ohio State University, University of Arizona, University of Southern California, industry leaders, and national laboratories, is working to remove these barriers. This team is developing groundbreaking chips designed to bring spectroscopic quantum metrologies out of laboratory confines and into the real world while surpassing traditional limits on precision, speed, and efficiency. The resulting scalable technology promises to revolutionize critical industries, ranging from microelectronics and space exploration to healthcare and beyond. These user-friendly, chip-based instruments also streamline education and ignite the imagination of future STEM enthusiasts. By making high-precision quantum measurements accessible and practical, this team is laying the groundwork for a technological renaissance, driving substantial societal progress. The QuPID team harnesses the collaborative power of the National Quantum Virtual Laboratory to develop the first ultrabroad-band Quantum Photonic Integrated Circuits (Q-PICs) for field-deployable spectroscopic quantum metrologies. By transforming existing quantum-photonic components and innovating new elements, the team creates Quantum Process Design Kits (Q-PDKs) that integrate ultrawide bandgap III–nitride ferroelectric quantum materials with silicon photonics, surpassing traditional limits on precision, speed, and resource usage. These efforts seamlessly combine coherent, attosecond, and quantum spectroscopies to a novel quantum-sensing tool for imaging quantum dynamics within interacting multi-particle systems. In eight years, the team engineers Q-PDKs that tailor quantum-sensing capabilities for diverse industries, making high-precision quantum measurements accessible and practical. Furthermore, the development of these Q-PICs facilitate the creation of compact, robust, and scalable quantum devices, enabling more extensive integration into everyday technologies. The QuPID team is set to transform cumbersome laboratory equipment into streamlined, chip-based solutions, heralding the widespread adoption of quantum advantages. This project advances the objectives of Quantum Information Science and Engineering at NSF in response to the National Quantum Initiative Act for the continued leadership of the United States in QIS and its technology applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Traditional control theorists are concerned with high-level control algorithms and their high-level properties such as convergence, robustness and performance. Notably, they typically assume all calculations are done with real numbers, and do not pay as much attention to the concrete implementation of their control algorithms, or to errors introduced by machine arithmetic. The project's novelties are to bridge the gap between control theory and low-level implementations, and to provide typical control theory guarantees on the implementation rather than on a high-level algorithm. The project's impacts are a comprehensive framework for end-to-end verification of control systems, and applications to the automotive and aerospace domains. This framework encompasses high-level hybrid models down to the verification of embedded C code. The investigators are working closely with several government agencies and industrial partners for technology transfer. The project is first exploring simple linear discrete-time systems, by designing an end-to-end process achieving verification at all stages, with discrete-time plant semantics. The project is then extending this process to focus on hybrid systems consisting of a continuous-time plant and a discrete-time controller. While typical control engineers work either with pure continuous-time or pure discrete-time models for verification purposes, the actual system combines both paradigms. The project also considers control algorithms that rely on optimization routines, such as model predictive control (MPC). Throughout these tasks, the investigators focus on numerical accuracy using machine arithmetic. One outcome of the project is to provide modular, reusable, open-source formal proofs of end-to-end correctness of common controllers, namely the Proportional–integral–derivative (PID) and MPC controllers. Finally, the project is applying these techniques on three different applications: car collision avoidance, aircraft collision avoidance, and spacecraft docking. The project strives to give research opportunities to students from groups underrepresented in the computing field, through different programs at the University of Michigan and at Ecole Nationale de l'Aviation Civile (ENAC). The project also incorporates findings into control theory and embedded systems college classes, giving students an understanding of the challenges faced when implementing a controller. 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

The molecular weights of medicine, biologically-derived drugs, and markers of different malignant diseases are getting larger and larger, from antibodies to packaged nucleic acids, to exosomes and immune cells. The larger size makes these biological structures difficult to analyze with conventional spectroscopic (light interaction-based imaging) and biomedical labeling techniques. However, the complex organization also creates new opportunities to create previously untapped techniques to identify them using recently developed biologically inspired chiral nanostructures of similar complexity, i.e., structures with mirror images of each other but yet cannot be perfectly matched, e.g., one's hands. The scientists from the University of Michigan and the University of Bath will develop rapid and selective identification of antibodies, exosomes, and malignant cells taking advantage of the chiral nanostructures with giant optical activity responsible for high sensitivity. Uniquely strong effects when red light shone on chiral nanostructures generates green light will provide high selectivity. Based on the prior extensive experience of the PIs with middle school outreach, the team will develop an inclusive science curriculum focusing on light and its interactions with matter. The curriculum will also include introduction of simple programming of Arduino chips for light control. The program’s effectiveness will be quantitatively evaluated measuring the engagement and inspiration of middle school children to pursue science and engineering. The comparative outreach results for UK and US middle school children will be analyzed and disseminated. A team of scientists and engineers from the University of Bath and the University of Michigan will develop a new spectroscopic tool – CHIROptical second-harmonic light scattering on Nanostructured particles (CHIRON). This spectroscopic tool will spur transformative advances in the data-rich assessment of large biological structures, such as biologically-derived drugs, and markers of different diseases exemplified by antibodies, packaged nucleic acids, liposomes exosomes and immune cells. The formation of biocomplexes between chiral particles and biological structures with multiscale chirality and the drastic enhancement of the second-harmonic effects by non-central symmetry of chiral scatterers enables high-throughput CHIRON analytics based on using single-particle multiplexing and nonlinear parameter analysis. Importantly, the second harmonic effects have ‘giant’ intensity for chiral nanostructures due to their asymmetry, size, and polarizability, which enables single particle modality. Using tensorial description of nonlinearity parameters, the team will deconvolute the signal and will thereby provide new biorecognition capabilities. Integration of theory and experiment will enable CHIRON to become a high-throughput, single-particle tool with wide applicability for the detection and analysis of large biomolecules, liposomes, exosomes, etc. To dramatically increase the applicability of the technique across different disciplines, the team will implement a quantum light source with entangled photons. Label-free methodology of CHIRON analytics will also increase its practicality and accessibility. 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

Distant quasars observed less than one billion years after the Big Bang are ideal tracers for studying the birth of the earliest supermassive black holes, the assembly of the first massive galaxies, and the buildup of the large-scale structures seen in the early Universe. The PIs will perform a detailed characterization of 25 such quasars by analyzing a legacy multi-wavelength dataset collected from the world's largest telescopes, covering a wavelength range from X-ray to radio. This program will advance our understanding of the growth of the first supermassive black holes, their interactions with quasar host galaxies, and the large-scale structures surrounding them. Additionally, they will collaborate with the Flandrau Science Center & Planetarium at the University of Arizona to develop a "fusion-camp" summer program that will have broad impacts on outreach and education. Theoretical models predict that the earliest billion supermassive black holes (SMBHs) form in massive galaxies and inhabit massive dark matter halos. The PIs will analyze a suite of multi-wavelength datasets collected from ALMA, VLA, and ground-based optical and infrared telescopes for a sample of 25 quasars in the epoch of reionization. These data, combined with data that is being collected from JWST, Chandra, and HST, will enable the team to resolve the long-standing question of whether the earliest SMBHs reside in the most massive dark matter halos and inhabit emerging large-scale galaxy overdensities. This program will also allow them to study the assembly of quasar host galaxies, understand the connection between AGN feedback and early massive galaxy formation, and constrain cosmic reionization and metal enrichment in the early Universe. 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

Oceans cover over 70% of the Earth's surface and their ceaseless movement from tides, waves, and currents creates a potentially important energy source that could be an important component of the energy transition for coastal and island communities. Similarly, the constant cycling of onshore and offshore winds over the course of a 24-hour period creates an additional marine related source of green, renewable energy. The Industry-University Cooperative Research Center (IUCRC), the Center for Growing Ocean Energy Technologies and the Blue Economy (GO Blue) engages in faculty-driven/industry-relevant, basic, use-inspired research focused on creating new knowledge and innovations of value to industries and start-ups in the marine energy ecosystem. Created by three universities: the University of Michigan, Ann Arbor; the Stevens Institute of Technology; and Texas A&M University at Corpus Christie, this national Center has the potential to address critical problems and issues that are holding the economy back from realizing economically viable electricity coming from marine and coastal marine-related energy sources that generate electricity to feed the national power grid. Broader impacts of the Center include the creation of new knowledge and solving of technical problems and socio-economic issues associated with marine electrical energy generation, close collaboration between university faculty and students and industry, training students in innovation; entrepreneurship; and workforce and workplace safety, and developing new educational programs to build the marine energy workforce of the future. Other impacts include public outreach and community engagement around marine energy issues. The Go Blue Industry-University Cooperative Research Center will harness the expertise of over 30 faculty and researchers from three universities and engage their students and postdocs in basic but industry-need-inspired research. Engineering research in this center will be inherently multidisciplinary spanning the fields of electrical, mechanical, civil, ocean, materials, and environmental engineering. This multi-university collaboration provides expanded access to world-class schools of naval architecture and engineering, state-of-the-art ocean technology test facilities, and computational facilities to Center faculty and students, regardless of home institution, as well as to members of the Center Advisory Board through faculty-initiated research projects of high priority to the marine energy economic sector. The geographical distribution of the three partner universities: The Great Lakes (Michigan), ocean (Stevens Institute), Gulf of Mexico (Texas A&M Corpus Christi), creates a national ensemble that allows the Center to tackle and experiment with new ideas, technology, and energy implementation scenarios in vastly different marine/coastal/large freshwater lake environments and settings. Center research ideas come from faculty who listen to the needs of the marine energy economic sector, as represented by dues-paying members of the Center Advisory Board. Faculty research is funded by pooled Advisory Board membership fees for projects of high priority to the sector, as indicated by the collective members of the Center Advisory Board. Center research thrusts are marine energy technology for renewable energy production, powering the blue economy which includes power generation and marine transport, and marine energy societal acceptance; economic viability; and environmental sustainability. This award is co-funded by the Division of Engineering Education and Centers in the Directorate for Engineering, and the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences 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 serve the national interest by working to shift the narrative nationally in engineering education from surviving in a culture of stress to a narrative of thriving. Higher education is facing a mental health crisis, with increasing rates of mental health and well-being (MHWB) challenges both nationally and globally. This change towards a culture of thriving in engineering is particularly important because MHWB impacts every aspect of engineering culture. Most approaches to support MHWB use faculty training in recognizing students in distress, which is a deficit approach instead of a proactive, asset-based approach. This Level 2 Institutional and Community Transformation project is designed to build on an existing community of practice that includes over 100 people, including faculty, staff, graduate students, postdoctoral fellows, and university administrators. This group empowers members to discuss MHWB in engineering and share strategies to support student, faculty, and staff MHWB. Strengthening and expanding this community will have an asset focus on how to promote and celebrate well-being, normalizing these conversations in engineering education. This project has the potential to directly support the MHWB of students, faculty, staff, and administrators in engineering education. The main research question is: How can faculty, staff, and administrators act as agents of change for cultures of well-being in undergraduate engineering education? The design consists of (1) a programmatic expansion of the Wellness in Engineering Community of Transformation (WE-CoT) with well-being advocate mini-grants to support the integration and assessment of MHWB interventions as well as (2) qualitative research that assesses the experience and advocacy development of participants via focus groups, interviews, and photovoice, a method centering members’ voices. The programmatic expansion plans to include a monthly meeting series and development of a sustainable community structure. The project includes support for development of a MHWB intervention, assessment plan, and data analysis for mini-grant recipients. Each project will receive mentorship support with experts in educational research methods and/or intervention development and counseling. This programmatic expansion is positioned to support the continuation of MHWB workshops for engineering faculty, staff, and administrators nationally. The project plans to utilize a qualitative research design focused on centering the voice of engineering faculty, staff, and administrators to answer the research questions. These will consist of photovoice interviews and focus groups with both members in the WE-CoT and also with non-community students, faculty, staff, and administrators who are engaged in engineering education. If successful, the results from this research will inform WE-CoT initiatives to increase the effectiveness of the community in supporting change agents around mental health in engineering. Research results will be disseminated in scholarly publications as well as through WE-CoT monthly meetings and conference MHWB workshops. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary 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.

NSF Awards · FY 2024 · 2024-10

This Growing Convergence Research project brings together researchers from engineering, physics, geo- and materials sciences with the goal of establishing a convergence framework to address the feasibility of mineral detection (MD) of dark matter. There is overwhelming evidence from astrophysics and cosmology that there is about five times as much dark matter as there is ordinary matter, i.e. the stuff we are made of. In MD, one studies geological samples of gram- to kilogram-scale, which have been exposed to dark matter interactions for billions of years. This allows MD to have the potential to match or exceed the sensitivity of conventional experiments. MD may therefore provide a path to answering the question of what dark matter actually is. In MD, the interactions of crystals with dark matter results in permanent changes to the crystal lattice which can be measured much later than the original interaction. This long intervening time combined with the geological changes the samples have encountered is a challenge for the interpretation of dark matter signals in MD. The changes to the crystal lattice are happening at the nano-scale and thus methods which can record nano-scale features scattered over a large, cubic millimeter to cubic centimeter, volume are required. This also implies a challenge in terms of data volumes and subsequent analysis. In addition, a dedicated simulation effort, from particle transport to molecular dynamics, is required to gain a theoretical understanding of damage formation and permanence. This project will test the feasibility of the MD approach to detecting interactions between ordinary and dark matter. 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 serve the national interest by improving the educational experience of undergraduate engineering students through the development of wellness-focused curricular resources. Studies of engineering students reveal engineering education to be a high-stress environment that hinders retention, persistence, and learning. It is important to understand how wellness interventions can engage and benefit engineering students. The overall goal of this project is to change the narrative from "surviving" engineering education to "thriving" in it by providing students with research-based tools and strategies to support their wellness. The project team will develop a novel course and related curricular resources designed for adaptation at other institutions and across STEM disciplines. These resources, including lessons, workbooks, and workshops, will support instructors who may not have the time or expertise to develop wellness curricula themselves. A more inclusive and empowering engineering curriculum should enhance persistence and diversity in engineering. A thriving mindset built on wellness will also lead to a better-prepared engineering workforce and engineers who are better-equipped for the complexity of professional practice. Increasing engineering students' wellness will also improve the public's views of engineering as a profession that desires a diverse and thriving U.S. engineering workforce. The investigators aim to identify strategies and implement interventions in first-year engineering curricula that will support engineering students' thriving and wellness. This effort will involve an integrated research and educational approach to investigating wellness and developing instructional resources. Guided by social cognitive theory as a theoretical framework, the research findings will inform content and delivery of curriculum to support students' wellness and lead to the creation of resources for faculty. Knowledge gains from interviews of engineering students (qualitative data) will enable insights into the opportunities and barriers that students face, as well as how they engage with wellness practices. Knowledge gains from interviews of engineering faculty (qualitative data) will enable insights into best practices to support faculty in adopting wellness education in their classrooms. The results will inform the development of a course and other curricular resources that will be shared broadly for wider adoption. Undergraduate student researchers will be trained in education research methods, and they will support the development of curricula and resources. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and 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 Awards · FY 2024 · 2024-10

Machine learning has grown to increase prominence over the past years, finding applications in various domains from image and speech processing to disease diagnosis. Despite the great success of machine learning techniques, massive amounts of data are collected and used to train the machine learning models. The privacy of sensitive data has become a big concern. Existing efforts are still preliminary, and enormous challenges remain to be resolved. Crucially, stronger privacy protection guarantees often sacrifice important properties of machine learning models, such as predictive utility and fairness, which can be undesirable or completely unacceptable. This project develops a consolidated privacy protection framework for machine learning systems that comprehensively considers the optimal trade-offs between computational privacy and several critical properties of machine learning, including utility, fairness, and distributed learning. The project will provide a comprehensive set of tools to protect data privacy for real-world machine learning applications under different circumstances. The privacy-preserving techniques will have a transformative impact on machine learning systems used by various sectors, allowing companies and hospitals to enjoy the advantages of machine learning techniques on big data while protecting data privacy under corresponding regulations. The research project thoroughly examines and discusses the real-world complicacy or restrictions when applying differential privacy, from privacy-utility trade-off, privacy-fairness relation, privacy in distributed learning, to post-learning privacy protection. The framework developed by the project takes deep root in rigorous optimization frameworks, often accompanied by theoretical guarantees and aided by cutting-edge algorithmic tools such as meta-learning, adversarial learning, and federated learning. Besides, the framework carries the following methodological innovations: differential privacy tailored to learning problems; customized privacy addressing heterogeneity in collaborative learning; privacy-protection of learned models through unlearning; consolidated privacy and fairness in learning. Those efforts will significantly augment the practicality and scalability of differential privacy. The project will be systematically evaluated on various real-world medical applications, and the tools will be readily used to tackle critical challenges in medical research. The outcomes will be incorporated into multiple courses at both undergraduate and graduate levels. The research outcomes will be disseminated broadly and comprehensively through open-source software releases and workshops, the involvement of undergraduate research, and outreach to K-12 education, focusing on minorities and under-representative groups in STEM education. Students at different levels and disciplines, STEM and liberal arts, will be participating in the research on privacy and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The proposed research-centered pilot will support the deployment of data-driven analytics to improve the efficiency, access, sustainability, and equity of the largest community carshare service in the nation and the development of fundamental knowledge about the science and engineering of community carshare. In 2022, the cities of Saint Paul and Minneapolis, Minnesota launched the EV Spot Network in collaboration with HOURCAR, the largest nonprofit carshare provider in the US. The EV Spot Network provides publicly owned electric vehicle (EV) carshare and chargers. The vehicles and chargers are owned by the cities, while HOURCAR operates the service. The system was designed from inception to serve those with the greatest transportation need, with at least half of serviced neighborhoods designated as disadvantaged. The service is planned to grow significantly over the next three years with up to 200 additional EVs to be added in 2025 (and potentially more in subsequent years). Planned growth will allow for expanding the service region, extending the reach of public transit and making cars more available. If successful, the pilot can serve as a template for how community carshare can be scaled up in a cost-effective way while increasing access, reducing environmental footprint, and improving equity. This scale-up poses challenges that have not been adequately addressed previously in the academic literature or practice and thus provides a unique opportunity to conduct a research-oriented pilot project. The proposed research-centered pilot will deploy data-driven analytics, developed by the research team, to support HOURCAR in making decisions as it seeks to densify of its service, expand its service region, and extend the reach of public transit. Specifically, we envision working with HOURCAR and its stakeholders, to develop and deploy AI based decision support tools for demand estimation, design of the system network, pricing and incentive design, and overall system management and planning. The research will also test the effectiveness of various behavioral modification interventions and incentives that can promote the use of carsharing and enhance the body of social science research on this topic. The approach developed here will demonstrate how community carshare can be scaled up in a cost-effective way while increasing access, reducing environmental footprint, and improving equity. It can also serve as a blueprint for how cities, transit authorities, and community-based organizations can partner to bridge the gap between essential resources and services and community needs. 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

Green Era Educational NFP (Green Era) and the University of Michigan are working together in partnership to transform the landscape of waste management by fostering community resilience and social equity through the innovative use of bioenergy solutions. Green Era has developed a 9-acre vacant brownfield lot in the South Side of Chicago into a vibrant campus, hosting a renewable energy biodigester facility that uses food waste for operation and hub for urban agriculture and green jobs. Together, we envision a Research-Centered Pilot Project that will focus on optimizing their technological processes, engaging with their local communities, and developing educational materials to strengthen and amplify their positive community impact now and in the future. The work proposed will take a community-centric approach and contribute to broader positive impacts in the local community and beyond, such as increased economic opportunities, offset carbon, and support for healthier lifestyles. The outcomes of this work can be used to empower other communities and support economic and environmental prosperity, as well as community resilience. In the planning scope of this project, we are motivated by the following research questions: 1) How might we characterize food waste streams and design an optimal blend of food waste sources as the substrate for biodigestion operation for Green Era? and 2) What are community priorities related to Green Era’s bioenergy operations and how do their priorities align or conflict with Green Era’s current processes? To answer these questions, we plan to run two workshops at Green Era’s campus to conduct technical analysis and community mapping to gather information and deepen our collective understanding. This information gathering will include community consultations, which will include semi-structured interviews with identified community partners, and technical feasibility studies, which will include laboratory characterizations of food waste and process optimization trials. Ultimately, the goal of the planning phase is to co-develop a strategic plan for Green Era based on our sociotechnical learnings that will be implemented in the project's next phase. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

Students are typically taught to derive equational proofs by hand, with little or no direct feedback or mechanical assistance during the process. The project’s novelties are the development of a customizable classroom proof assistant, called the Hazel Prover, that provides classroom-appropriate automated assistance and feedback to students as they derive equational proofs. The project’s impacts are the development, deployment, and evaluation of customized instances of the Hazel Prover into a representative sample of courses offered at all levels in the College of Engineering at the University of Michigan that collectively enroll several thousand students per year. With the aid of these tools, students benefit from a much tighter and more accurate feedback loop, improving learning. The technology and course material, which are made broadly available to researchers and instructors, advance the fields of educational technology and automated theorem prover design. The project addresses several challenges that have limited the impact of existing proof assistants in the classroom. In particular, modern proof assistants (1) present an excessively steep learning curve, particularly in courses where programming is not a prerequisite, (2) require shifting from conventional and domain-specific mathematical notation to textual programming notation, (3) are intolerant of errors, unlike human graders, and (4) are distractingly pedantic, requiring users to satisfy proof obligations that humans typically elide. The proposed research refines and deploys a customizable classroom proof assistant, the Hazel Prover, that solves or can be customized to solve each of these problems. The Hazel Prover builds incrementally on the recent work by the investigator on the Hazel educational programming environment, including promising preliminary work on the Hazel Stepper tool that has already been deployed successfully into the classroom. The system interfaces with the Coq theorem prover and existing math proof libraries, while only exposing students to structured easy-to-use proof construction affordances. 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 project seeks to design, develop, and deploy Artificial Intelligence (AI) responsibly. Rapid technological growth has often overlooked the importance of community input. To address this, this project uses an approach that recognizes community members as experts with valuable insights. The project uses human-computer interaction to change how communities are involved in developing AI. The project will explore these changes and develop new ways to include community voices in AI design. This project will foster AI application and methodological advancements through established community-based partnerships. Focus areas include public health, education, and work. Investigating how AI impacts these areas will help solve important social problems. It will also improve research on responsible technology development. Using methodologies such as Community-Based Participatory Research and participatory design, this research focuses on residents of the east side of Detroit to investigate community members’ priorities for Artificial Intelligence (AI) technologies. This project also employs educational sessions, reflective surveys, and the innovative Choosing All Together (CHAT) deliberative tool to co-create planning processes with academic researchers and community members that account for technological advances and societal impacts. In collaboration with community members, the project will investigate how to use Large Language Models (LLMs) like ChatGPT. It will uncover their benefits as well as potential harms while focusing on applications pertinent to the context. The objectives are to: (1) Establish a baseline understanding of AI, its current use, and its impact on communities; (2) Shift towards more responsible, community-focused research and development in AI; (3) Identify and prioritize the most critical areas for future investigation; and (4) Move toward responsible AI integration within community-engaged research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Understanding the dynamics of radiation belt electrons is critical for our ability to predict the effects of solar storms on the near-Earth space environment and our upper atmosphere. In the radiation belts, these electrons can pose problems for satellite operations. Plasma waves direct these electrons into our atmosphere, where they can influence upper atmospheric chemistry and dynamics. This project will incorporate methods from the weather and climate fields (stochastic parameterization and ensemble modeling) to better model and predict the result of these wave-particle interactions. This project will expose the broader space physics community to these compelling tools from the weather and climate fields. This project will support a first-time early career PI. Lightning-generated whistlers (LGWs) are one of the primary drivers of radiation belt electron precipitation within the plasmasphere. The primary objective of this project is to study the interaction of LGWs with radiation belt electrons using event-specific diffusion coefficients to more accurately capture the rate of pitch angle scattering and precipitation into the Earth’s atmosphere for eventual implementation into a stochastically parameterized diffusion model. Stochastic parameterization is a modeling scheme where the variables representing the sub grid physics (i.e., diffusion coefficients representing wave-particle interactions) are selected randomly from a distribution instead of using a deterministic average. This allows for explicit variance of the sub-grid physics and better error quantification through ensemble modeling. Quantifying and understanding the temporal and spatial scale sizes of the variability of each wave mode is a crucial first step in implementing a complete stochastically parameterized radiation belt diffusion model. This project seeks to accomplish this for LGWs, an important wave mode inside the plasmasphere. 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

Liquid metals are fertile platforms for inorganic materials synthesis and design. Recently, room temperature liquid metals have proven uniquely suited for the electrochemical syntheses of novel nanomaterials, new compositions of matter, and potent (electro)catalysts. A critical factor in expanding the palette of materials chemistry possible with liquid metals is a clearer understanding of the structure of the interface with molecular solutions. With support from the Division of Chemistry at NSF and DFG, Dr. Maldonado at U Michigan, Dr. Ocko at Brookhaven National Lab in US and Dr. Magnussen at Kiel University in Germany will work together to define the microscopic details of the evolution of the liquid/liquid interface between metallic gallium (Ga) and water during electrochemical reactions. The successful completion of the proposed work will lead to the development of supported electrode platforms that enable smooth, thin liquid metal films that are amenable for more precise operando and electrocatalytic studies and that are applicable to any liquid metal composition. The students involved in the research project will have the opportunity to enhance students exposure to global research and practices. This work will advance the fundamental understanding of liquid metal/liquid electrolyte behaviors on two distinct fronts. First, the electrodeposition of various metals cations onto liquid Ga electrodes will be performed to examine the physicochemical and electrochemical factors at the interface relevant to observing quasi-epitaxial crystal growth. The atomistic details of quasi-epitaxy will be unraveled by operando X-ray reflectivity and grazing incidence X-ray diffraction. As an important prerequisite for these studies, the team will investigate the interface structure of liquid Ga in metal-free aqueous base electrolyte as a function of potential and pH, focusing in particular on the presence and structure of Ga surface oxide layers. Second, the formation and activity of supported catalytically active liquid metal platforms based on Ga will be studied. The interplay of the interface structure of the liquid metal interfacial structure and the distribution and dynamics of isolated catalytic solute atoms will be determined for the case of hydrogen evolution and electrochemical CO2 reduction. 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

Human activities lead to increased contamination in both terrestrial and aquatic ecosystems. Contaminants can pose harm to both people and wildlife, with specific harms and exposures dependent on complex socio-ecological factors. This study investigates environmental risk vis-a-vis mercury (Hg) released from artisanal and small-scale gold mining (ASGM) sites. Mercury is a potent neurotoxin used to amalgamate gold in ASGM contexts globally. ASGM has become the leading cause of mercury emissions worldwide and is particularly harmful when burned off gold and emitted as gaseous elemental mercury (GEM). However, little is known about the fate of mercury from ASGM activities, including if and how mercury accumulates in crops, or local knowledge of mercury dangers and exposure routes, all of which contribute to actual and perceived environmental risk. This research asks: (1) What is the local and regional fate of Hg vaporized from ASGM in terrestrial ecosystems? (2) To what extent is Hg accumulating in crops grown near ASGM, and how do landscape and soil characteristics impact crop Hg concentrations? (3) How do local people understand Hg contamination and exposure, and how do spatialities of local knowledge articulate with environmental contamination variability? Results from this research will provide direct benefits related to the goals of the recently ratified International Minamata Convention on Mercury. The research focuses on mercury in Ghanaian ASGM systems with contrasting geology, mining type, climate, and ecosystem. Ghana possesses extensive ASGM activities, which increased dramatically in the last 15 years. To query the research questions, the researchers employ mixed methods including community mapping activities and semi-structured interviews, biogeochemical data collection (passive air samples, bulk deposition, throughfall, litterfall, soil, and crop) and modeling (empirically constrained mass budgets, Lagrangian plume models). As farmers grow crops directly adjacent to ASGM sites, this project models where mercury travels, and how it accumulates in agricultural fields and specific staple crops grown in both communities (e.g. cassava, cocoa yam, plantain). The data generated from this proposal will improve Hg models, allowing for better understanding of environmental risks and contamination dynamics (i.e., Hg use, atmospheric transport, deposition, soil storage, and crop accumulation) associated with localized ASGM activity. Furthermore, this research systematically investigates what soil and landscape features lead to the accumulation of Hg within crops to better understand potential human exposure pathways to Hg beyond occupational exposure and consumption of contaminated fish. At the end of the project, workshops and training will be organized to inform harm reduction practices, including agricultural and mining practices to reduce mercury accumulation in crops, and other local exposures. This research links together concepts from political ecology and landscape ecology in a dynamic bi-directional coupling of socio-environmental systems that determines where and when contaminants deposit and accumulate on the landscape. The concept of “environmental risk landscape” can be further developed to evaluate environmental risk in other socio-environmental contexts, including environmental injustice in the U.S. 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

Rehab for America: Housing Resilience for Detroit Communities addresses the nation’s acute housing crisis through the rehabilitation of vacant homes. Potential homebuyers are being priced-out of even the most affordably built new housing. Yet, in many communities, unoccupied, salvageable structures are ready to be converted to viable housing. Through Rehab for America, University of Michigan researchers will work with the Detroit Land Bank Authority (DLBA), Community Development Advocates of Detroit (CDAD), and aspiring home purchasers/rehabbers to make the rehabilitation process more attainable. Using innovative construction methods and materials, toolkits and training, and funding sources, homeowners will be empowered to conduct repairs and/or work more effectively with contractors, tapping the wealth of DLBA structures (currently 6841), contributing solutions that are expeditious, equitable, and resilient. Our vision is to increase homeownership for Detroit residents by making rehab construction affordable and homes more sustainable so they’re cheaper to heat, cool, and maintain in the service of increasing mental and physical well-being, household wealth, and neighborhood property values. Rehab for America addresses an important component of the nation’s housing affordability crisis by creating resources for and efficiencies in the process of rehabilitating vacant homes in America’s post-industrial cities. It envisions new service design; construction materials, methods, and efficiencies; and recommendations for financing to improve the success of homeowners and nonprofit developers purchasing and rehabilitating DLBA held properties. We aim to lower the barriers to structural rehabilitation through a three-pronged approach: the development of a better communication strategy and servicing by the DLBA, the creation of tactical approaches to aid in construction, and identification of funding models. The team of architects, engineers, and planners will partner with the DLBA to develop more robust forms of communication with buyers and nonprofit developers about rehabilitating properties. Working with the non-profit Community Development Advocates of Detroit (CDAD) we will engage community partners to identify new or underutilized funding sources to offset the cost of home repair. During the pilot project (Stage 2) we will rehab a DLBA property creating models and demonstrations for best practices. The goals to be assessed during the project include: lowered energy costs through life-cycle building performance and long-term maintenance through material and method specification; increased home value; rebuilding of community; better health outcomes; increased trust among stakeholders, and job training. Piloting the project in Detroit, the service design and products will be modeled for applicability to communities across the United States. 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 is a planning workshop that will kick off an Advocacy Building Campaign for Engineering Education Research (ABC for EER) to promote the recognition and support of EER by universities and colleges nationwide. The ABC for EER seeks to address the problems facing the field of EER in gaining widespread acceptance and support within academia as a valued field of study. The field of EER includes educators and researchers with experience in the practices of both engineering and teaching and with expertise in education research, psychology, and social sciences. EER focuses on complex problems such as recruiting students in engineering with diverse perspectives and backgrounds, embracing new technologies and approaches to teaching engineering, and preparing diverse types of engineering students for the global challenges they will face as practicing engineers and leaders. Research by EER scholars provides evidence and knowledge to help universities and colleges address these complex problems; yet their work is often dismissed by administrators and faculty in engineering programs at universities and colleges as not being robust or relevant. This project aims to start the process of advocating for EER through a targeted campaign that will clearly articulate the value of EER to scholars outside the field, to leaders in higher education, and to policymakers who make decisions that affect higher education. This project aligns with the focus of the Engineering Education and Centers Division to promote research that creates and supports an innovative and inclusive technical workforce for the future, particularly in terms of understanding systemic and structural changes in engineering education programs, departments and colleges. This planning workshop for the Advocacy Building Campaign for Engineering Education Research (ABC for EER) aims to address the challenges facing the field of EER, which can be characterized as a disconnect between the tangible benefits and impact of EER and the metrics typically prioritized by engineering programs, colleges and universities. These challenges often result in a lack of institutional support and recognition for EER scholars, they hinder the career advancement of EER scholars and the growth of EER as a discipline, and they limit the integration of innovative educational practices within higher education. The planning workshop will convene EER scholars, stakeholders and leaders at different levels of power and influence in higher education, policymaking, advocacy, and legislation. It will be conducted as a series of three sessions structured to build trust, foster collaboration, outline critical paths for implementation, and establish evaluation metrics to assess the success of the ABC for EER. Interim steps between workshop sessions are proposed to synthesize, communicate, and gather feedback on the planning process at different time points. The workshop sessions will be guided by elements of campaign strategy to identify and articulate the value of EER to a broad audience both within and outside of academia, develop a strategic communication plan, and lay out actionable objectives for promoting EER within the higher education community. The two anticipated outcomes of this workshop series are: (1) a list of key stakeholders in the EER ecosystem to participate in the future, ongoing ABC for EER, and (2) a plan for a series of activities to successfully conduct the ABC for EER as an advocacy campaign. 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

A nontechnical description: Society is experiencing a widening gap between artificial intelligence (AI) demand and computing hardware capabilities, which must already catch up with Moore's Law. This immense hunger for computing power is driven by continuous advancements in computing hardware, which now faces the constraint of interconnect inefficiency, exemplified by the infamous “Memory Wall.” Under the Future of Semiconductor 2 program, this project proposes a highly integrated photonic chip solution to enable ultra-high bandwidth, energy-efficient data transmission between computing nodes. The approach incorporates multiple innovative advancements across materials, devices, integration, architecture, and network topology. This collaborative research, conducted by three leading universities in partnership with four industrial IT companies and a national lab, aims to achieve a significant leap in performance metrics over current state-of-the-art technologies. The broader impacts of the proposed research, driven by a mixture of academic and industrial veterans, also include expediting tech-to-market transfer and nurturing next-generation minds with a real-world perspective, co-designed execution flow, and practical know-how build-up. The effort ensures a holistic approach to education and innovation. Participated student researchers will heavily benefit from and further propel various regional and national semiconductor training and research programs. A technical description: The project aims to develop a novel system-in-package technology with a heterogeneous multi-functional in-package photonic platform/interposer, accommodating CPUs, TPU, or GPU (collectively, Processing Units (PU)) chips, high-bandwidth memory (HBM) stacks, and accelerator/ASIC chips, etc. Each PU and HBM chip is intimately integrated with a heterogeneous optical transceiver, composed of ultra-compact high-speed modulators and photodetectors, which provide up to 400 Gb/s/lane communication bandwidth. Arrays of 200-400 Gb/s heterogeneous modulators support a record-large 9.6-19.2 Tb/s total communication bandwidth for each PU chip. Moreover, the project will develop on-chip lithography-free photonic routing circuits and heterogeneously integrated waveguide-free optical switches with optical gain to enable reconfigurable on- and off-chip optical switching networks that can be dynamically programmed to realize various network topologies for different AI algorithms. The hardware platform will be developed with seamless co-design of materials, devices, fabrication, system, architecture, and network topology and benchmarked with an automatic network mapping framework. 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

Data uncertainty is ubiquitous in several algorithmic applications such as healthcare, retail, transportation and robotics. For example, medical diagnosis involves performing tests without precise knowledge of the underlying condition, capacity planning in the retail industry involves coming up with inventory levels without full knowledge of future demand, and path planning in robotics involves finding routes with limited information about the ambient space. In these and many other applications, one needs to design algorithms using only partial input information (typically obtained from historical data). This project aims to design algorithms with theoretical performance guarantees for some fundamental problems arising in these applications. Results from this project will contribute to and establish new connections between theoretical computer science, operations research, and machine learning. The project also involves mentoring graduate and undergraduate students, developing new course material, and organizing workshops to broaden participation in graduate programs. This project models uncertainty using stochastic optimization, where unknown input parameters are treated as random variables. Algorithms for such problems are often very sequential, where each step makes some decision and observes a corresponding random variable. The first goal in this project is to obtain parallelizable algorithms for stochastic optimization, which corresponds to making decisions in a small number of sequential rounds. This research direction involves quantifying the tradeoff between approximation quality (relative to the optimal sequential solution) and the number of sequential rounds. The second goal in this project is to obtain algorithms for stochastic optimization when the probability distribution of the input is unknown. This research direction is motivated by applications where historical data is inaccurate or unavailable. This direction will utilize and combine techniques from approximation algorithms and online learning. The project will address both these research directions in the context of fundamental combinatorial optimization problems such as knapsack, set cover, and influence maximization. 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 · 2024-09

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. Microbusinesses, those with less than 10 employees, make up 92% of U.S. businesses and are an economic lifeline, particularly for people of color. Microbusinesses generate 41.3 million jobs and $5 trillion in economic impact. Yet, learning to use digital technologies, such as online ads and point-of-sale systems, is one of their top challenges. The widening gap in digital engagement between under-resourced microbusinesses and higher-resourced larger businesses has long-term implications for profitability and community development. Despite the nation's major investments in broadband development, building digital capacity among microbusinesses will require more than just providing broadband access -- it will also involve additional infrastructure to facilitate adoption and sustained use. This project brings together a diverse team of researchers from Human-Computer Interaction, Social Work, and Computer Science to propose the Community Tech Workers model as a form of infrastructure for supporting under-resourced microbusinesses' digital capacity. The Community Tech Workers (CTW) model is inspired by the validated Community Health Worker model. It employs and trains local residents to provide 1:1 support for increasing community members' confidence and autonomy with using digital technologies. The goals of this project are three-fold: (1) Create a taxonomy of barriers and supports to equitable participation in using digital technologies among under-resourced microbusiness owners; (2) Develop culturally relevant workforce training for cultural competency skills in technology jobs; and (3) Produce a conceptual model outlining factors that might impact a community's potential to adopt the CTW program. Traditional technology adoption models often use a deficiency-based approach by highlighting the problems of those receiving services, thus setting up communities to rely on long-term external help. CTWs promote a more sustainable model of digital capacity building that leverages community assets. Outcomes of this research include a more scalable and cost-effective CTW model for under-resourced communities to adopt and implement on their own. 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.