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NSF Awards · FY 2024 · 2024-09

This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral and Economic Sciences (SBE). It has both scientific and societal benefits in addition to integrating research and education. The Data Justice Academy (DJA) is a ten-week summer program at the University of Virginia (UVA) that provides undergraduate students from underrepresented backgrounds with research experiences, technical training, and professional development. The program seeks to encourage students to see Data Science and Computational Social Science as avenues for helping their communities and making a difference in the world. Research teams are led by faculty trained in culturally responsive mentoring and with deep knowledge of experiential pedagogies. The DJA program continuously improves its curriculum for student development in data skills, sociotechnical knowledge, and research acumen but also emphasizes social and cultural capital. Mentoring is at the heart of the program, with rigorous training of faculty and graduate student mentors. Overseen jointly by UVA’s Equity Center and School of Data Science, the DJA supports faculty-mentored projects in three broad areas: (1) employing the tools of data science to document, study and combat social inequalities, (2) advancing the development of ethical data science tools and data sets, and (3) studying data practices as socially constructed and contested spheres of human activity. With the proliferation of automated decision systems and artificial intelligence in everyday life, such research is vital to ensuring that the data and algorithmic practices that underly these tools are socially beneficial. Data Justice takes as its first and fundamental question, “For whom does this model fail?” (O’Neil, 2016, 2017). By centering vulnerability to harm, data justice advocates for better technologies, with better outcomes and lower risks for all. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Prof. Huiyuan Zhu of the University of Virginia aims to develop advanced chemical synthesis methods to produce nanocrystals. Each of the nanocrystals has a non-precious metal core with a thin (a few-atomic-layers) shell consisting of ordered intermetallics and their corresponding hollow structures. The outcomes shall address a challenge in current nanochemistry, i.e., a lack of fundamental understanding of the chemistry processes involved. The results are of generic importance in making complex inorganic nanostructures. These nanostructures hold great promise in a diverse range of applications in clean energy catalysis and environmental remediation. This project will provide research training and education to undergraduate and graduate students at the cutting-edge of nanochemistry, nurturing future leaders in academia and industry. The team will stimulate excitement for the science and engineering of the next generation through STEM-interest-instilling K-12 outreach programs and hands-on demonstrations designed to emphasize the relevance and impact of nanoscience in daily lives. Under this award, the team will monitor the nucleation and growth processes of these core/shell nanocrystals and hollow nanocages. Integrating the Zhu’s expertise in precision colloidal synthesis with an array of advanced characterization techniques, including in-situ spectroscopic probes, quasi-in-situ electron microscopic probes, and computational approaches, thermodynamic and kinetic information will be attained. The proposed retrosynthetic analysis is highly advantages to streamline the synthesis of complex nanostructures by transforming a target nanocrystal into metal precursors and ligands and sequential chemical reactions. This approach paves the way to establish a synthesis library similar to diverse arrays of organic reactions. The outcomes also include an in-depth understanding of the interfacial chemistry that governs the colloidal reactions and revealing of the underlying formation mechanisms and transformation pathways of these nanostructures featuring ordered intermetallic shells. Collectively, the anticipated advances in nanochemistry enable the design of retrosynthetic routes into synthetically tractable steps and guide the selection of synthons (metal precursors and ligands). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

A nontechnical description of the project: Avalanche Photodiodes (APDs) are widely used for optical communications, environmental monitoring, imaging, and night vision. They operate by employing a large electric field to crash photogenerated carriers into each other, causing an avalanche of free carriers by impact ionization, leading to a large gain in photocurrent that can increase the sensitivity of optical receivers to the extreme of single photon detection. However, since these impact ionization events are random, they also amplify shot noise arising from the granularity of charges. A key goal in APD research is to lower the noise arising from this gain mechanism to near-silicon values, albeit at small material bandgaps sensitive to longer infrared wavelengths. The goal of this proposal is to examine if the random noise can be reduced deterministically by correlating the charges. Recent experiments in mercury and antimony-containing APDs show dramatic noise reduction. This proposal will explore a possible origin due to ‘dead spaces’, over which the mobile charges build up adequate energy and momentum to tear away other bound charges from their parent atoms. In heavy element-based materials with relativistic spin-charge interactions and well-separated bands, the high internal fields can localize the charges to the start of these dead spaces, which will periodically correlate the charges and reduce the noise. A thorough understanding of the impact of correlated noise – both theoretically with high-power computational models as well as experimentally with material growth, fabrication, and characterization, will provide insight into fundamental device physics and enable design of ultralow noise APDs. The result will be a significant breakthrough in optical receiver sensitivity across a broad range of commercial, military, and research applications, including imaging arrays, optical communications, chemical and biological sensing, astronomical observations, and quantum optics. Educational tools, training videos, and outreach measures will bring the research and the underlying science to the mainstream scientific community and the next generation of student practitioners in this area. A technical description of the project: Avalanche Photodiodes (APDs) use impact ionization under high-bias fields to amplify the current from a few photogenerated carriers. However, the stochastic nature of the underlying gain mechanism inevitably amplifies shot noise owing to the granularity of charges. McIntyre’s local field model has been used successfully for 60+ years to characterize this noise in APDs, using the excess-noise-factor figure of merit. The excess noise is primarily controlled by the average gain and the ratio of the minority to majority carrier ionization rates (i.e., how bipolar the chain reactions are). The aim of this proposal is to explore, explain and exploit a series of persistent observations in homojunction APDs containing antimony and mercury, and impact ionization engineered (I2E) heterojunctions with negative band-offsets, where the measured excess noise consistently lies below the fundamental noise limit predicted by McIntyre’s model, especially for lower gain values. In particular, the homojunction APDs exhibit this sub-McIntyre noise characteristic up to high gains. Viewed through the conventional lens of uncorrelated noise, this observation suggests that one of the two ionization rates is unphysically negative. In the proposed program, we will combine state-of-the-art material and transport modeling with digital and random quarternary and ternary alloy growth, APD fabrication, and characterization of current gain and excess noise, to demonstrate that a likely origin of sub-McIntyre noise is the spatial correlation between individual impact ionization events imposed by high field non-local effects (‘dead space’) in homojunction APDs, and the abrupt threshold reduction and charge heating in heterojunction I2E APDs. Educational tools, training videos and outreach measures will bring the research and the underlying science to the mainstream scientific community and next generation of student practitioners in this area. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This grant supports a program of precise experiments with low energy (cold) neutrons. With the first project, the Nab and pNab experiments, the PI and his team continue a new study of neutron beta decay. The goals of this project tackle two of three items in a list of priorities listed in the Weak Interaction section of the new Long-Range Plan for Nuclear Science. The main purpose of the measurements is to shed light on recent evidence for a failure of the Standard Model of Elementary Particle Physics. The discovery of Beyond-Standard-Model physics would have important implications for High Energy Physics and Cosmology. With the second project, the PI and his team study states of cold neutrons with the goal to detect new short-range forces. Furthermore, the project has a valuable impact on higher education: Most of the work is done by students at undergraduate and graduate levels, and these students will earn valuable skills and experiences for their future career. The redundancy inherent in the Standard Model description of the neutron beta decay process allows uniquely sensitive checks of the model’s validity and limits: The neutrino-electron-correlation coefficient “” will allow an extraction of “ud”, the upper left element of the CKM matrix, with the purpose to test the matrix’s unitarity. A recent re-evaluation of radiative corrections made it appear that the CKM unitarity is violated. A non-unitarity CKM matrix is not possible within the SM. It would signal that either physics at a scale of several TeV affects how CKM matrix elements are computed from observables, or that we have more than three quark generation even if those extra quarks have masses that makes them undetectable directly. The other observable, the Fierz term, allows verifying hints for non-SM effective scalar or tensor interactions at low energies, in a complementary way to what is expected from LHC. Measurements of electron and proton asymmetry with a polarized beam with pNab add precision and redundancy. The studies are performed by the Nab collaboration, of which the PI is the experiment director. The grant funds the PI’s group’s specific contributions to the Nab and pNab project in leadership, simulation, data analysis, neutron beam polarization and in the characterization of the proton detection efficiency. With the whispering gallery experiment, spectroscopy of gravitationally bound quantum states of ultracold neutrons will be performed. The results are sensitive to new short- range interactions at a length scale of nanometers which could be mediated by so far undiscovered pseudoscalar bosons (axion-like particles). Those would contribute to the answer to the question about the nature of dark matter. The PI is strongly committed to attracting members of underrepresented groups and is leading a diverse research group. The PI will try to obtain funding for a REU program at UVa. 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

This grant supports United States (U.S.) student attendance at the 2024 version of the ACM International Conference in Ubiquitous and Mobile Computing (Ubicomp) and the International Symposium on Wearable Computers (ISWC), which will take place together in Melbourne, Australia. Ubicomp and ISWC are leading events in ubiquitous, mobile, wearable, and interactive computing that foster the exchange of cutting-edge research and innovations among global researchers and practitioners. The conference serves as a platform for advancing knowledge and technological applications in this field, as well as a place to build the research community. Having a strong representation of U.S. student researchers at the conference is crucial to both develop the community and to uphold U.S. competitiveness in this important field. The grant funds will be used to facilitate up to 16 U.S.-based students attending the doctoral colloquium associated with the conferences. Students will be selected primarily based on the potential of their work's contributions to the field and to society more generally. The selection committee will also give some weight to selecting a set of attendees from a diverse set of institutions and backgrounds, as well as to promising first-time attendees who do not yet have papers at the conference. Attending students will benefit from receiving experienced mentors' feedback on their work as well as the chance to connect to more senior researchers in the 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 · 2024-08

National frameworks for science education in the United States advocate for bringing science, technology, engineering, mathematics, and computer science (STEM+CS) disciplines together in K-12 classrooms. Although curricular materials are emerging to support STEM+CS integration, research demonstrates that teachers need support to engage students in authentic STEM+CS practices that leverage and sustain student and community assets. This project aims to support middle school teachers in their enactment of an integrated science, engineering, and computational modeling curriculum unit and understand how teachers customize computationally-rich, Next Generation Science Standards (NGSS)-aligned curricular materials to their own schools and classrooms. Through this work, the project team seeks to develop a better understanding of the supports that teachers need to successfully adapt science curricular materials to different classroom, school, and community contexts. The project involves collaborating with school districts to implement the Water Resource Challenge (WRC), developed based on existing NSF-supported curricular materials, and revise it to enable customization for different teachers and school settings. As more NGSS-aligned STEM+CS materials are developed for teachers, this work is important for understanding how teachers adapt and customize curricular materials to meet the needs of their students. The project leverages a participatory design paradigm to engage middle school teachers in professional learning experiences focused on the development of STEM+CS curricular materials. Through design-based research, the project examines the kinds of support teachers need to customize STEM+CS curricular materials for their specific contexts. Researchers will investigate implementation integrity and explore factors that shape teachers' enactment and adaptation of STEM+CS curricular materials, including use and customization of educative materials and teacher guides, teacher characteristics (e.g., STEM+CS experience, beliefs, and perceptions of students), and classroom contexts (e.g., student assets and resources, community resources, district, and state requirements). Specifically, the project will investigate: (1) the kind of supports teachers need to be able to customize STEM+CS curricular materials; (2) how teachers customize and enact STEM+CS curricular materials in their classroom; and (3) students' learning of STEM+CS concepts and practices and their STEM+CS identity when teachers use customized and non-customized materials. The project will involve 18 teachers and impact up to 4,800 students in school districts in Virginia and Tennessee. Project outcomes include co-designed STEM+CS curricular materials, a computational modeling environment, and teacher resources to support STEM+CS integration in curricular materials and practice. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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 · 2024-08

Providing students with exposure to high quality CT activities within science classes has the possibility to create transformative educational experiences that will prepare students to harness the power of CT for authentic problems. By building upon foundational research in human-AI partnership for classroom support and effective practices for integrating computational thinking (CT) in science, this collaborative research project will advance understanding of how to empower teachers to lead computationally-enriched science activities with adaptive pedagogical tools. This project will also advance knowledge of how to prepare teachers to engage in AI-augmented teaching and human-AI partnerships for classroom support. The project will involve three yearly cycles of teacher professional development, iterative co-design, development of an AI tool (called TRACES), and classroom implementation of the designed learning activities and AI tool within middle school classrooms. Using state-of-the-art AI methods, real-time classroom data will be used to help teachers modify pacing, help specific students in need, and identify students who can act as peer mentors. The team’s prior research has shown that teachers can be successfully prepared to use and teach with AI in their classrooms, allowing them to notice and respond to these classroom and individual needs. The project will leverage these recent advances to transform the landscape of CT in science education. The project will co-design and test 28 new CT-integrated science activities with teachers and provide learning experiences to 84 science teachers by developing and implementing professional development for CT in science. The project will also iteratively co-design and test the proposed AI-powered support toolset that aids teachers in making data-driven instructional decisions during these activities. The AI-powered toolset co-designed with teachers will provide real-time information to support their ability to notice and respond to student work. 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 · 2024-08

Extrachromosomal DNA (ecDNA) is DNA that exists and functions separately from the main genome. In the malaria parasite, ecDNA is circular, unlike the linear chromosomes. Some ecDNAs encode instructions that confer new abilities such as resistance to a drug. They mutate over time, conferring newfound functions, and can eventually rejoin the main chromosome. This project will study this DNA form in a single cell organism to better understand how it arises and changes to confer new abilities. The Broader Impact of the work includes intrinsic biological discoveries in a group of organisms that cause a range of diseases such as malaria. Additional activities seek to improve scientific and technical literacy by building virtual reality (VR) skills in the future workforce. This research will generate a comprehensive framework to study extrachromosomal ecDNA (ecDNA). The generation, maintenance, and replication of these molecules vary across different organisms, making the integration of information from diverse contexts challenging. Studies in this proposal bring together a range of cutting-edge microscopy, molecular genomics, and computational techniques to study the complete lifecycle of ecDNA in the Plasmodium protozoan. Research activities will take advantage of exceptional aspects of Plasmodium biology to gain key insights into the purpose and lifecycle of ecDNA. Studies will specifically evaluate: 1) the connection between chromosomal genes and ecDNA, 2) the propensity for ecDNA diversity, 3) the contribution of cellular pathways and sequences to ecDNA generation and stability, and 4) the clustering of ecDNA within the nucleus. Results from the project will contribute to knowledge about ecDNA structure and frequency and how genome characteristics directly impact adaptive potential. Novel approaches and concepts will inspire new insight into ecDNA biology across diverse organisms. 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 · 2024-08

Nontechnical Organic semiconductors and metal halide perovskites are intensively studied materials for a range of clean energy and consumer applications such as solar cells, flexible electronics, and sensors. Although organic semiconductors have great promise, they exhibit significantly different electrical and optical properties depending on the crystal structure created when assembled into solid films. This impacts their potential for use in optoelectronic devices. Although there is a well-recognized need to precisely control crystal structure to manipulate or optimize material behaviors, there is a very limited experimental toolkit for doing so. In this project, the researchers aim to use perovskites as a templating layer that can control the crystal properties of organic films deposited on top. X-ray scattering and ultrafast spectroscopy are used to evaluate the structural properties of organic thin films, and their effect on the optical and electronic properties. This approach allows investigators to finely tune the underlayer periodicity and chemistry simultaneously and thereby optimize the efficiency of energy and charge transport. This paradigm can be used to design better solar cells, light-emitting diodes, and other optoelectronic devices. Students are engaged in the research through training and guidance by the principal investigators and participate in regular meetings between the groups. In addition, the project team is committed to promoting diversity by encouraging recruitment and retention of underrepresented groups to the project, and leading outreach efforts in the community targeted towards middle and high school students. Technical The goal of this project is to develop two-dimensional metal halide perovskites (2D MHPs) as a crystallization templating tool for organic semiconductor (OSC) thin films, to reduce the disorder in crystalline packing, and to control packing geometries for tuning optoelectronic behaviors, including singlet fission and exciton transport. These controlled heterostructures can then be utilized for photovoltaic devices. Numerous research thrusts have focused on how changing the OSC chemistry can impact exciton transport and energy transfer, which generally result in large crystal structure changes, but there is relatively less information on how to finely control the solid-state structure of the OSC to control optoelectronic behavior, and by extension, device performance. This understanding is necessary for controlling processes such as singlet fission and exciton transport, as sub-Angstrom changes in molecular packing can cause significant changes in these behaviors. This project aims to address three objectives: 1) Understanding how 2D MHP thin films can control various OSC thin-film crystalline properties (order, polymorphism, orientation) through lattice registry, 2) Controlling exciton transfer (including singlet fission) and transport in 2D MHP templated OSCs by utilizing these sub-Angstrom changes, and 3) Controlling the heterostructure transport between the 2D MHP layer and the OSC. The research focuses on the prototypical OSC molecule perylenediimide, but the knowledge gained is relevant for other OSCs as well. The success of this project can result in a novel method of controlling the order, packing, and orientation of OSCs and understanding and manipulating structure-property relationships between templated OSCs and optoelectronic properties. There is a scarcity of fundamental knowledge relating to sub-Angstrom changes in OSC solid-state packing and their resulting optoelectronic behavior. The work addresses this gap and advances the knowledge of crystal structure and optoelectronics. The use of a 2D MHP template to reduce defects as well as change the crystal packing while still creating stable structures would be a significant departure from the current understanding of creating ordered OSC thin films, and the optoelectronic control afforded by these structures can open new doors for device 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 · 2024-08

Plants have a wide variety of leaf traits, yet only a small set of trait combinations are viable in today’s changing world. Predicting the flow of carbon, water, and energy between plants and the atmosphere (i.e., ecosystem functioning) requires describing leaf traits and how leaves are arranged within a canopy. Leaves are not isolated units on a canopy, but rather, they interact with thousands of other leaves. Much of that interaction, and therefore ecosystem functioning, is determined by the position and orientation of leaves within the canopy. This project will leverage a new technology to map canopy details in three dimensions in forests in the eastern United States. The mapping effort will provide the position and orientation (angle relative to the horizontal plane) of every leaf in a canopy. The work will reveal how plant traits influence tree growth and resource use, deepening our understanding of how forest trees compete for water and light. The broader impacts of this project include creating virtual reality forests for eight National Ecological Observatory Network (NEON) sites. The use of virtual reality will provide an accessible and equitable experience for people who cannot visit forests in person. Researchers will work with collaborators at local schools to include these virtual forest galleries in their programs. The project will also support new class modules for field remote sensing courses at the University of Virginia. The research bridges the leaf and ecosystem scales, thus bringing the whole canopy perspective to studies of functional traits. The overarching question this project seeks to answer is: To what extent do canopy structural traits and leaf traits converge at the ecosystem scale? With new technology and algorithm development, this project will bring novel insight into the role of canopy structure metrics in the functional trait spectrum. These metrics include the statistical distribution of leaf angles, leaf area, and leaf clumping. Researchers will focus on eight forest NEON sites in the eastern United States. They will investigate the relationship between canopy structural metrics and leaf traits estimated by the trait maps generated from imaging spectroscopy data. Researchers will map canopy structural traits (leaf angle and clumping) using algorithms calibrated by co-located NEON Airborne Observation Platform canopy reflectance and Terrestrial Lidar Scanning data. The second part of this project focuses on three sites to assess the extent to which canopy structural traits optimize canopy photosynthesis. Testing the extent of optimization of canopy structural traits along the vertical gradient will shed light on how plants optimize (or not) photosynthesis, given the constraint of water and competition for light. Canopy structural trait maps and the vertical trait data set generated will provide durable benefits to the scientific community. In addition, valuable canopy structural information will be extracted from the Terrestrial Lidar Scanning data, such as branching patterns and biomass, providing essential datasets for forest ecologists. Through collaboration with K-12 institutions, researchers will develop educational materials that will elevate opportunities. 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 · 2024-08

This project will support the efforts of a team of undergraduate students from various STEM disciplines at the University of Virginia. The team will develop a low-cost treatment option for exocrine pancreatic insufficiency for the International Genetically Engineered Machines (iGEM) competition in synthetic biology. The goal of the project is to design and build a probiotic that secretes active synthetic pancreatic enzymes. The development and production of this treatment system will provide undergraduate students with hands-on training in a variety of scientific areas, including synthetic biology, molecular biology and computational modeling. The project supports the bioeconomy by introducing students to and creating an opportunity for student-driven entrepreneurship within the field of synthetic biology. In addition, student participants acquire a global perspective on innovation, strategic road mapping, and professional community, while recognizing structural barriers to fair resource sharing and access to new technologies. Broadening participation in STEM research is an important goal of this project. This project supports the interdisciplinary research training of a diverse team of undergraduate students at the University of Virginia. The team will develop a low-cost treatment option for exocrine pancreatic insufficiency. The project will be developed for the International Genetically Engineered Machines (iGEM) competition in synthetic biology. The participants will receive training in a variety of STEM disciplines, acquire a global perspective on innovation and strategic road mapping, and have many opportunities to network with leaders in STEM fields such as synthetic biology, molecular biology and computational modeling. 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 · 2024-08

Populations of organisms in different locations are connected by the dispersal of offspring, such as seeds or larvae. Dispersal from populations in favorable environments ('sources') to populations in poor environments ('sinks') can allow the latter to persist where they would otherwise fail to survive. Knowledge about source-sink dynamics is important to conservation and natural resource management. For instance, protecting important source habitats or restoring habitats in locations that enhance connectivity with sinks can improve regional population stability and resilience. Environmental variability and climate change may alter the dynamics of source-sink populations, but the effects of these changes are not well resolved, challenging effective decision-making for habitat conservation and restoration. This CAREER project is using long-term data, multiyear field experiments, biophysical dispersal simulations, and mathematical population models to address several gaps in the understanding of source-sink dynamics in oysters. By partnering with restoration practitioners, the investigator is generating knowledge to optimize oyster reef management in Virginia coastal bays, where the marine environment is rapidly changing. Research methods, data, and specimens are being used to develop a 5th-grade lesson plan on oyster restoration ecology and a 7th-grade field-trip module on oyster biology for public-school students in a rural, high-poverty area of coastal Virginia. Research is also incorporated into an undergraduate restoration ecology class and a course-based research experience for community college students. Research and teaching in this CAREER award are integrated such that students generate data used to advance the science, students receive hands-on training in marine ecology, and scientific samples and findings are used to develop classroom lesson plans. Temporal variation in the demography and connectivity of source-sink populations has rarely been explored empirically beyond straightforward lab studies. In this CAREER project, the investigator is resolving the patterns, causes, and consequences of temporal dynamics in oyster (Crassostrea virginica) source-sink structure and informing oyster restoration in coastal Virginia. The investigator is testing the hypothesis that temporal heterogeneity -- variation, autocorrelation, and trends over time -- in reproductive output, reproductive timing, and oceanographic forcing alters demographic connectivity through development of a biophysical model of oyster larval dispersal that is parameterized using two decades of environmental and population time series data. Five years of field observations and experiments are determining the drivers of spatial and temporal heterogeneity in the demographic rates that govern source-sink structure -- oyster recruitment, survival, growth, and fecundity. A Bayesian state-space integral projection model parameterized with dispersal estimates and empirical data is being used to test the hypothesis that temporal heterogeneity in dispersal and demography determine regional oyster population dynamics. The investigator is combining models and data with feedback from restoration practitioners to develop spatial planning scenarios that improve oyster population size and stability under future climate conditions. 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 · 2024-08

In the standard Big Bang model, the first few instants of the early universe were filled with quark-gluon plasma -- an incredibly high-temperature gas of the constituents of protons and neutrons. In the laboratory, tiny nucleus-sized droplets of quark-gluon plasma have been produced briefly by colliding large atomic nuclei at extremely high energy. The droplets live too briefly to study directly and must instead be studied from the high-energy signatures of the collisions. For instance, some collisions produce additional pairs of extremely high-energy constituents that lose energy by flying through the quark-gluon plasma droplet. So, one way to study properties of quark-gluon plasmas is from measurements related to the amount of such energy loss and its comparison to theoretical calculations. This project tests the validity and self-consistency of methods typically used in theoretical calculations by calculating how thoroughly quantum mechanical corrections influence the energy loss. In addition to studying the theory of probing quark-gluon plasmas, the PI will mentor students engaged in this research and will refine and make publicly available educational materials designed to excite advanced undergraduates and early graduate students into a first exploration of quantum field theory, a fundamental approach to theoretical particle physics. High-energy particles traveling through matter lose energy mainly by showering via splitting processes like bremsstrahlung and pair production, similar to a cosmic ray shower in the atmosphere. In the case here, typical splittings are induced by glancing collisions of the high-energy particle with the constituents of the quark-gluon plasma. In principle, each collision offers a chance for a high-energy particle to split. However, the quantum mechanical duration of that splitting, known as the formation time for the splitting, turns out to grow with energy. At high enough energies, it grows so much that multiple collisions with the plasma occur within the formation time. This causes a large suppression of the splitting rate known as the Landau-Pomeranchuk-Migdal effect, worked out in the 1950s for electromagnetic interactions (quantum electrodynamics, QED) and the 1990s for the strong interactions (quantum chromodynamics, QCD). This project will study whether formation times can become so large that there is also quantum interference between successive splittings of the shower. This question bears on whether such showers can really be modeled as a growing collection of well-defined numbers of high-energy particles -- a question the project addresses by improving and extending calculations of such quantum interference effects. 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 · 2024-08

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Profs. Sen Zhang and Michael Hilinski of the University of Virginia, and Prof. Xu Zhang of the California State University Northridge will expand the synthetic toolbox of the nanomaterials chemistry. Controlling the sizes, compositions, and shapes of nanocrystals is crucial for applications in catalysis, optoelectronics, plasmonics, magnetism, and biomedicine. This project will incorporate designed N-heterocyclic carbenes (NHCs) as a new ligand to control over nanocrystal surface facets and sizes. The results shall offer new insights into the fundamental nanochemistry pathways especially the mechanism of NHCs in the formation and modulation of nanocrystals. The knowledge generated could significantly advance research and applications in various fields utilizing functional nanocrystals. Additionally, the project will foster the teaching and training of the next-generation workforce by integrating research and education activities, including outreach to minority-serving institutions. This collaborative project focuses on developing NHC ligands for the colloidal synthesis of noble metal nanocrystals with high-index surfaces. The steric and electronic properties of the NHCs will be independently and systematically adjusted to understand how these perturbations affect nanocrystal formation. By leveraging the combined expertise in nanocrystal synthesis, organic synthesis, catalysis, and quantum mechanics-based multi-scale calculations, the team aims to achieve fundamental advancements in understanding NHC-controlled nanocrystal synthesis. Three specific tasks will be pursued: [1] Preparing a series of NHC ligands and developing the colloidal synthesis of noble metal (Pt, Pd, Au, Ag) nanocrystals under the control of NHC ligands; [2] Elucidating the controlled synthesis mechanism via in-situ characterizations, density functional theory calculations, and machine learning; [3] Understanding the catalytic properties of noble metal nanocrystals with high-index surfaces for CO2 reduction and furfural reduction reactions. Based on a deep understanding of NHC-surface interactions, this project will provide new families of NHC designer ligands that enable the direct synthesis of nanocrystals with controlled surfaces and structures. 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 · 2024-08

The ACCORD cyberinfrastructure project at the University of Virginia successfully developed and deployed a community infrastructure providing access to secure research computing resources for users at under-served, minority-serving, and non-PhD-granting institutions. ACCORD is built around balancing security with accessibility. While the ACCORD expedition achieved its technical and operational goals, its broader mission of broadening access had limited success due to real barriers that remain prohibitive for a large group of users and institutions. Building on the progress and lessons of ACCORD, this effort will augment ACCORD with enhanced security and policies for broadening access to research computing (i.e., ACCORD-SPARC). ACCORD- SPARC comprises three components that streamline access and enhance controls for the current ACCORD system: (1) a responsive security framework to lower user access barrier; (2) new policies and controls to support data rights; and (3) pilot ACCORD-SPARC with current ACCORD users and HBCU partners in the State of Tennessee. The exploration and development of ACCORD-SPARC is an endeavor in discovery. Through this effort, the project will synthesize a minimal framework of responsive and effective security for accessing UVA’s secure infrastructure. This framework can be adjusted to address threat models and security requirements for other institutions. On the policy side, ACCORD-SPARC will result in an articulation of policies on data rights and sovereignty – specifically, on finite consent and transparent purposes. The new policies, and associated controls will be piloted and evaluated. Lessons from these practical steps will be helpful to the community broadly as it grapples with these issues. On the other hand, by increasing access to important computing infrastructure to users and institutions where resources are not currently available, ACCORD-SPARC will support the long tail of science and enable new discoveries in multiple fields of sciences and improve research and teaching outcomes. The ability to work on sensitive data is especially important for research involving human subjects or describing human activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award funds research in an area of abstract algebra. Throughout history, mathematics and physics have had profound influences on each other. In the late 20th century, physicists discovered a deep connection between quantum physics and three-dimensional shapes, leading to the concept of topological quantum field theory (TQFT). While these 3D theories cannot fully describe our 4D universe, condensed matter physicists have found surprising applications of them in the field of quantum computing. In an effort to bridge the gap between these three-dimensional theories and our actual universe, Crane and Frenkel introduced a program called "categorification" in the late 1990s. This program aims to lift three-dimensional TQFTs to four dimensions, making it a more direct reflection of our physical reality. The PIs will involve students and postdocs in this research, with particular focus on students from underrepresented minorities. The first significant development in categorification was the discovery of Khovanov homology. This is a powerful invariant of links whose graded Euler characteristic is the Jones polynomial. The investigators plan to use the technical machinery of hopfological algebra to extend a dual version of Khovanov homology to a homological invariant of three-dimensional manifolds whose graded Euler characteristic is the Witten-Reshetikhin-Turaev invariant. Ideally, this construction will be fully functorial, giving rise to an invariant of four-dimensional manifolds, while remaining computationally accessible. These invariants are expected be sensitive to smooth structures and should give insights into smooth topology not provided by gauge theoretic invariants like Donaldson and Seiberg-Witten invariants. This direction will build upon the investigators' previous work on categorified quantum groups and their representations at roots of unity. It is an open question of how to incorporate hopfological structures into Khovanov homology. This should lead to new homotopic notions. The investigators also plan on continuing to develop non-semisimple versions of three-dimensional topological quantum field theories with an eye toward applications to quantum computation. These non-semisimple invariants have certain topological advantages over their more classical semisimple counterparts. This line of research will also build upon their work on the centers of small quantum groups which has recently been an active area of research in geometric representation theory. 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 · 2024-07

Autonomous vehicles, such as self-driving cars, are complex and advanced cyber-physical systems that are part of a significant economic sector. With the goal of moving towards full autonomy without human drivers, the reliability and safety of these systems are of highest importance. The safe operation of autonomous vehicles depends not only on their physical components, such as sensors and brakes, but also on the proper operation of their autonomous control software and machine learning components. System faults such as transient faults (soft errors) in these components may lead to safety hazards and accidents. This project aims to enhance the safety of autonomous vehicles by thoroughly examining and improving their controller and machine learning components. This project brings classical reliability research methodologies to autonomous driving systems and improves them to understand the reliability behavior of autonomous vehicles with complicated software and hardware. This understanding will be used to identify vulnerabilities (that could lead to hazards) and to improve the reliability of autonomous vehicles. The involved research spans a broad range of theoretical and experimental approaches that are also applicable to other complex cyber-physical systems. This project will combine model-based and data-driven approaches for end-to-end strategic resilience assessment and multi-level selective resilience enhancement in autonomous vehicles through a holistic focus on temporal and spatial aspects of vulnerabilities within the software and hardware components. The project will start with a spatial vulnerability assessment to pinpoint critical fault locations inside the vast software space in autonomous vehicles, hence accelerating the process of identifying vulnerabilities in their machine learning models and the vulnerable functions and variables in the controller. Meanwhile, temporal vulnerability assessment will be performed to identify the underlying system contexts that are critical in the activation and propagation of faults and safety violations for the purpose of bridging the gap between in-lab reliability assessment and practical system development in the real world. Based on the spatial and temporal vulnerability assessment, the project will explore mitigation techniques through efficient selection protection to enhance the resilience of autonomous driving systems based on the knowledge of spatial and temporal criticality of vulnerabilities to address the challenges of real-time requirements and resource constraints in AVs. The research in this project will be tested and validated through the integration of strategic fault injection and selective protection mechanisms with end-to-end AV testbeds, comprising realistic control software, driving simulators, and safety intervention simulators. 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 · 2024-07

The AI for Youth ExLent Explorations project aims to democratize access to cutting-edge artificial intelligence (AI) education, providing students from diverse backgrounds and educational profiles with the opportunity to learn vital AI topics. In a world where AI technologies are increasingly ubiquitous, ensuring inclusivity in AI education is paramount for building future technologies on principles of equity and fairness. Despite this imperative, challenges such as the lack of teacher training in AI and a comprehensive pedagogical framework impede the integration of AI into K-12 curricula, specifically in under-represented schools. AI for Youth aims to prepare the next generation of AI innovators irrespective of gender, ethnicity, or socioeconomic status. By targeting underrepresented minority and low-income high school students, the project aims to demystify AI career pathways and empower students to address social issues through AI-powered solutions. AI for Youth also empowers high school teachers to implement an innovative AI curriculum, thereby inspiring and motivating high school students, particularly those from underserved groups, to pursue careers in AI. Through a multifaceted approach, the initiative equips teachers with essential AI skills, offers transformative learning experiences for students, and fosters proficiencies in communication, leadership, and teamwork necessary for success in higher education and AI-related careers. The initiative includes a comprehensive paid internship program for high school students and a mentorship model pairing faculty members and graduate students with teachers and students. The project encourages leadership, problem-solving, and critical thinking by engaging students in action-oriented, team-based activities. Overall, AI for Youth seeks to cultivate a diverse, creative, and ethical AI workforce while empowering students to address real-world challenges through AI innovation. This initiative is in accordance with the NSF ExLENT Program, which is supported by the NSF TIP and EDU Directorates. 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 · 2024-07

Across the United States, different places have different levels of environmental benefits (such as clean air) and risks (such as groundwater pollution). These differences have important implications for health, productivity, and economic opportunity. This CAREER project uses a newly constructed individual-level data set that includes rich demographic, environmental, and economic information. These data will be analyzed to answer important questions about how environmental factors affect the extent, causes and consequences of the distributional impacts of risks and benefits. The first part of the research studies individual exposure and the second analyzes the mechanisms that result in the observed demographic distribution of environmental risks and benefits. The third and final part studies how this distribution affects health, productivity and economic opportunity. The data can also be used to evaluate the effects of natural disasters on specific parts of US population. This CAREER award funds research using a combination of different methods to study the causes and consequences of the distribution of environmental goods on the well-being of the US population. The first set of projects produce new descriptive facts about individual exposure to environmental benefits and hazards. These insights provide the context and framing to think about the models and mechanisms underlying the distribution of benefits and hazards. The second set of projects answer questions about the mechanisms that cause this distribution. The third part of the project provides new evidence on how environmental factors shape health, productivity, and economic opportunity in the U.S. This effort pays particular attention to combining microeconomic data and research designs with macroeconomic models to better understand aggregate and distributional effects. The results of this research will help us understand the costs and benefits of environmental policies and how decisions by firms and consumers can increase labor productivity and overall well being. 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 · 2024-07

Recycling carbon dioxide would help reduce the impact of climate change. It might also help establish a circular economy. This project will evaluate a process to capture CO2 and transform it into a feedstock for microbes. This will be accomplished using a membrane with two functions. The membrane will absorb CO2 from the environment. It will also contain a catalyst to transform the CO2 to acetic acid. The microbes will be modified to produce a range of high value bioproducts using acetic acid as a starting material. Graduate and undergraduate students will participate in research and scientific workshops. Outreach to middle and high schools will encourage women and underrepresented students to participate in STEM-related careers. The goal is to create a process that produces microbial feedstock from recycled CO2. The emphasis is directed towards closing the carbon cycle for acetic acid-based microbial synthesis of high-value products (HVPs) or precursors. Integrated bioelectrochemical cells will be developed and evaluated. The results will be subject to systems level and technoeconomic analyses. The strategy is to use a composite of polymer and metal-organic framework (MOF) as a gas-diffusion layer to increase the CO2 uptake from dilute streams. CO2 will be delivered to a cathode in a single-atom catalyst (SAC) based electrocatalysis configuration to produce acetate. Integrated acetate separation from electrolyte and enrichment of minimal media is then followed by engineered Escherichia coli strains for sustainable synthesis of biomaterials. The economic viability and environmental impact of the integrated bioelectrochemical manufacturing will be assessed. The structural and chemical properties of gas-diffusion electrodes decorated with polymer-MOF and SAC electrocatalysts will be identified. Acetate bioconversion efficiency will be maximized, and cross-contamination between the electrolyzer and bioreactors will be minimized. 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 · 2024-06

This award provides support for US-based researchers to attend the Conference on New Developments in Probability (CNDP) at the Centre de Recherches Mathématiques (CRM) at Université de Montréal September 26-28, 2024. This meeting is the third CNDP, a conference series jointly organized with Women in Probability. This conference has two main goals. The first is to bring together leading experts, researchers, and scholars to explore the latest advancements in the field of probability theory and to share cutting-edge research. The second goal is to provide a platform for early career researchers in probability theory to present their work in an environment which cultivates collaboration. Probability is the backbone of many mathematical disciplines, providing the language and tools for reasoning about uncertain outcomes and making predictions based on available information, with applications in diverse fields of science, engineering, and economics. Over the years, this area has witnessed remarkable growth, with novel methodologies, techniques, and applications that transform our understanding of uncertainty and randomness. The conference is expected to have a lasting impact on the academic community of researchers in probability theory, and to foster a collaborative environment that encourages the exchange of ideas and knowledge among experts. The 2024 Conference on New Developments in Probability seeks to highlight recent breakthroughs in the field of probability, in particular, advancements in the areas of stochastic processes and random matrices, interacting particle systems, statistical inference and machine learning, random graphs and networks, high-dimensional probability, and stochastic analysis. This meeting will contribute to advancing the field of probability through diverse perspectives and innovative ideas, fostering the exchange of ideas and opportunities for collaboration. The conference will feature research presentations from speakers representing a range of career stages. This includes short talks by early career participants (postdocs and graduate students) who will receive advising and feedback on delivering research presentations, providing them with invaluable networking opportunities and mentorship experiences. There will be emphasis on both theoretical foundations and practical applications, leading to new research directions and interdisciplinary collaborations. The CNDP also seeks to highlight the contributions of researchers from underrepresented groups in probability, increasing their visibility within the academic community, which will lead to more opportunities for collaborations, grants, and academic positions, ultimately empowering them to progress in their careers. More details can be found at the conference website http://womeninprobability.org/CNDP.html 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 · 2024-06

Robotic and autonomous systems (RAS) are becoming more common in our daily lives. Making sure they operate safely, however, remains challenging. RAS code is often messy, and the real world in which these systems operate is complex and unpredictable. This research project focuses on creating a family of methods to find faults in these sophisticated systems by analyzing: 1) the program artifacts they are built from to extract models that can be more easily checked for faults, and 2) the real-world data they encounter (like images taken by cameras) to determine what portions of the environment are worth simulating more accurately. If successful, the research findings will inform solutions to challenges faced in the development of RAS, affording a path to improving public safety. The research will be integrated into educational curriculum and training. The key insights enabling the project are two-fold. First, although robotics code is often messy and complex, model-relevant behavior is typically implemented via a subset of application programming interfaces and configuration files with clearly-specifiable semantics. Second, field data encodes key spatial-temporal physical constraints imposed by the real world, which provides hints on how to steer simulation to reduce the gap with reality. The investigators will leverage these insights to effectively lift useful models from real code to detect compositional faults, identify and construct simulation scenarios that capture constraints imposed by the real world, and inform and validate the evolution of robotic systems. The proposed work will produce: 1) Techniques to infer rich behavior models with physical attributes from RAS code and artifacts, and to check those models against component and system properties; 2) Techniques to infer specifications from messy real-world data, to contrast those specifications against simulation states, and to synthesize missing simulation environments; 3) Techniques to inform RAS' evolution, to understand the impact of code, physical, and world changes, and to cost-effectively correct and test changing RAS; and 4) Studies with RAS to assess the techniques, quantifying their effect on the gaps between code and models, between simulation and reality, and during evolution. 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 · 2024-06

With the advent of federated systems, it is now common to have devices that operate at the edge of the networks (example: mobile phones, sensors on bridges). While federated learning is an advance over moving data to one place for analysis, machine learning in federated systems creates challenges to privacy consideration at the edges. New machine learning techniques are emerging that can address these privacy concerns at the edges. Federated learning is a prominent distributed learning approach to address the privacy issue through collaborative training. It enables data owners (clients) to jointly train a machine learning model without sharing their private data, orchestrated by a central server. In the real world, data samples within a client often exhibit strong relational dependencies and naturally form a local graph. In this project, we consider learning over such distributed graph data in a federated manner, termed Federated Graph Machine Learning (FGML). Typically, Graph Neural Networks (GNNs) are widely adopted for collaborative training over graph data in FGML because they excel at modeling relational information. However, federated training on graphs faces several unique challenges, including (1) data heterogeneity—significant graph data heterogeneity across clients severely degrades the performance of GNN models in FGML; (2) label deficiency—the scarcity of labeled data for each client, when combined with complex graph structures, complicates training GNNs in the federated setting; and (3) data privacy protection—privacy leakage may arise for both attributes and structures, especially when cross-client relations exist. Our proposed framework systematically investigates these challenges and develops innovative algorithms to enhance model utility and strengthen data privacy protection in FGML. This project will develop novel and significant advances in the scientific fields of graph mining, federated learning, and distributed machine learning. Each advance will dramatically advance current machine learning research in addressing the multifaceted challenges presented in the emerging collaborative training paradigm over graph data. First, this project aims to thoroughly study data heterogeneity in FGML across three different modalities (i.e., label, structure, and feature) and develops novel solutions to address these issues in FGML. Second, this project designs novel algorithms to tackle the label deficiency issue across three scenarios (i.e., few labels, noisy labels, and no labels) to enhance the generalizability, robustness, and effectiveness of GNN models in FGML. Third, this project aims to strengthen data privacy protection in FGML concerning node attributes and graph structures and proposes two effective techniques based on attribute perturbation and adversarial training. This project has the potential to impact a wide range of industries and applications, including social networks, e-commerce, healthcare, and more. Finally, this project will play an integral part in educating and training students. The research will be tightly integrated with existing and new courses related to data mining and machine learning. The investigators will actively encourage undergraduate participation in the project, host more REU students at their institutions, and continue ongoing efforts to advise female and underrepresented 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 2023 · 2023-12

The project aims to serve the national interest by addressing the shortage of talent in the emerging technology of artificial intelligence (AI) through reaching out to untapped populations, such as autistic STEM community college students. Autistic people often demonstrate a strong affinity for STEM; however, they suffer from an 80% unemployment rate due to social stigma and discrimination. Carnegie-Mellon University and Pennsylvania State University at University Park has assembled a team of experts to develop innovative pedagogical materials and scalable tools to teach AI technical skills through a "learning-by-doing" experience, teach crucial team collaboration and communication skills through AI-focused project-based learning, and facilitate autistic community college students to obtain AI-focused summer internships and increase their access to AI careers. The program will use a strengths-based approach to provide strategies and supports that allow individuals with autism to engage confidently, competently, and with a positive sense of self when navigating the complex and challenging social environment of the workplace. The project’s pedagogical innovations include the human factors and user interface design to support AI subject-matter experts, mentors, and teachers learning to teach autistic students. Special education experts in autism who specialize in teaching communication, social, and teaming skills will support project mentors in their day-to-day learning experiences. Online training will be employed to reach more autistic students and also make the online course materials available to community college teachers to offer this course at their own schools. This project will not only address the employment gap by establishing innovative pathways for autistic students to specialize in AI careers, but more importantly, debunk the harmful stereotypes and change the exclusionary norms that impact their employment. This project aligns with the NSF ExLENT Program, funded by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from diverse professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology 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 2023 · 2023-12

This Future of Work at the Human-Technology Frontier - Research: Large (FW-HTF-RL) award supports research to address the critical shift towards digital transformation in the facility management industry, a sector grappling with technological and workforce changes. The industry is on the verge of adopting advanced Information and Communication Technology, Internet of Things, and Big Data paradigms, aiming to make traditional operations more data-driven to meet organizational and national energy reduction goals. These advances target multiple improvements, including occupant comfort and health, system resilience, energy performance, and operational cost reductions. The transition necessitates facility managers to adapt to increased data volumes and new forms of human-machine interaction, thereby necessitating upskilling and reskilling. The project also recognizes the skills shortage in the industry, highlighting the need for efforts from industry leaders, educators, and policymakers to prepare a workforce for the future of facility management. The technical goals of this project aim at addressing the digital transformation challenges within facility management. The first objective is to construct a digital twin ecosystem for a facility, enhancing the role of facility managers by providing them with physical and cognitive assistance. The second objective involves developing a multi-modal user interface to promote effective interaction within the digital ecosystem, allowing communication between occupants, FMs, and other stakeholders. Thirdly, the project will gauge facility managers' readiness to utilize the digital twin technology, intending to refine the technology continuously and identify any barriers to adoption. The final objective is to study the impacts of adopting this technology on facility managers, occupants, and facility owners, incorporating different perspectives to develop comprehensive solutions to the industry's challenges. Additionally, project work with industry leaders and facility managers will identify the necessary training and educational initiatives that can equip the current and future workforce with the skills required to keep pace with digital transformation. Such initiatives will involve curating curricula that are aligned with technological advancements, enhancing existing training programs, and designing new ones to fill the skills gap. Collaboration activities will also seek to inspire and attract diverse talents to the industry, thus fostering a work environment that is ready for the future of digital facility management. 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.