Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2026 · 2026-01

This award aims to revolutionize the design and manufacturing of advanced materials using artificial intelligence (AI), by improving the mechanical performance of nanocomposites (advanced materials made by combining different substances at extremely small scales). Research enabled by this award focuses on understanding and controlling a specific type of internal structure in these materials – called amorphous-crystalline interfaces – that can significantly enhance strength, durability, and reliability. If successful, the research findings could impact a wide range of critical applications in the areas of energy, defense, transportation, and others. By applying AI and advanced manufacturing techniques, the award seeks to uncover how processing methods can be used to tailor the structure and behavior of these interfaces for optimal performance. This award aims to develop a physics-based framework for the tunability of metastable amorphous-crystalline interfaces (ACIs) in nanocomposites through physical vapor deposition (PVD) processing. Research tasks focus on investigating how PVD parameters – such as chemical composition, deposition rate, temperature, and incident velocity – affect the local structural and chemical environments at ACIs, which in turn control deformation mechanisms like plasticity and shear banding. Self-propelling energy landscape sampling algorithms are employed to explore atomic-scale rearrangements without prior assumptions, combined with transition state theory to quantify the kinetics of transitions among various metastable micro-states. Machine learning models and Bayesian optimization will guide intelligent data acquisition and accelerate exploration of complex phase spaces. These computational approaches will be integrated with precision magnetron sputtering experiments, high-resolution electron microscopy, and nanomechanical testing to validate predictions. The resulting predictive, testable processing-structure-property loop could enable the design of high-performance, ACI-rich nanocomposites for advanced manufacturing 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 2026 · 2026-01

This award supports research on new models and a comprehensive framework to capture the complexities and interdependencies of infrastructure systems, while accounting for technological innovations and policy changes. Infrastructure systems, including energy, transportation, water, and telecommunication, form the backbone of modern society and are becoming increasingly complex due to emerging technologies such as renewable energy and artificial intelligence, as well as rapidly evolving public policies. By creating new models, theories, and tools to understand how these systems interact and respond to change, the research aims to enhance system performance, improve resilience to risks, and promote more efficient resource utilization. The outcomes support the development of robust infrastructure systems that can advance economic growth, public safety, and disaster risk reduction. The project also involves students in hands-on research and produces open-access educational tools to inspire the next generation of engineers and planners. Research findings will be broadly disseminated through publications, open-source code, shareable datasets, presentations, and cross-disciplinary collaborations. This research addresses the challenge of designing and operating interconnected infrastructure systems under uncertainty in future technological and policy directions. The project employs a broad set of methodologies, including network optimization, stochastic and robust optimization, and reinforcement learning, to develop scalable and reliable approaches for system integration and risk management. The research advances the field in three primary directions: (1) enabling dynamic and sequential modeling of design and operational decisions in integrated infrastructure systems; (2) identifying optimal or near-optimal solutions to complex system behaviors under both exogenous and endogenous uncertainties; and (3) evaluating the resilience, efficiency, and reliability of integrated systems through model validation and simulation. The models and algorithms are demonstrated in two key application domains: (i) the integration of power and transportation systems, and (ii) the coordinated monitoring, planning, and operation of multiple infrastructure networks. 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 2026 · 2026-01

Technology translation is the process of converting scientific research and technical innovations to practice. In order to move basic and use-inspired research into society most effectively, it is imperative that innovators and entrepreneurs have access to facilities that enable testing and validation of their new technologies under real world conditions. Such testing requires a safe and controlled environment to ensure the technology is robust, reliable, and ready for use. National “test beds” could include fabrication facilities and cyberinfrastructure to advance the development, operation, integration, testing, deployment, and, as appropriate, demonstration. This effort supports a workshop series facilitating conversations among critical test bed stakeholders from academia, industry, government, and non-profits. The stakeholders offer their unique perspectives on strategies and models for designing and using test beds to scale up technologies and accelerate the translation of innovations into the marketplace. This workshop series will provide opportunities for open dialogue about opportunities and challenges of bringing emergent technologies to practice using test bed facilities. Topics include lessons learned from existing test bed efforts, novel operational models, gaps in existing infrastructure, and how to expand access to physical and virtual resources, investment, and multisector collaboration. The workshop series brings together stakeholders to share community-wide perspectives in a large number of technology fields – from advanced communications to biotechnology and materials development. The workshop deliverables will include at least one white paper that will capture the conversations at the sessions. The workshop series includes virtual events and in-person sessions hosted by Iowa State University, the University of Alabama, and the University of Michigan in Spring 2026. 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 2026 · 2026-01

Billions of people use encrypted messaging apps every day. These apps generally do not interoperate - users must all have accounts on the same app to communicate. Users view this as a major pain point; increasingly, policymakers and industry groups do as well. In Europe, the Digital Markets Act (DMA) will require interoperability; in the US, similar legislation has been considered and Apple and Google are jointly designing a protocol that will allow encrypted text messaging between iPhone and Android devices. However, making encrypted messaging interoperable is challenging - existing apps were not designed to interoperate and the security of the few preliminary proposals for doing so is unclear; this is in part because interoperability enables new kinds of attacks that are not well-understood. More fundamentally, the formal cryptographic work on encrypted messaging does not consider interoperability, and it is not easy to generalize. This project’s novelties are building a practice-oriented foundation for the design of interoperable encrypted messaging systems. By both examining existing proposals and building new theory and protocols, the project’s broader significance and importance are to retain the strong privacy guarantees of existing encrypted messaging apps into the interoperable future. The project contributions are along three lines. First, the project analyzes existing proposals for interoperable encrypted messaging - specifically the proposals of Apple/Google, Meta, and the ongoing design of the IETF’s interoperability working group. It evaluates the handling of core security issues like identity management and the encryption protocol itself. Using the lessons learned from this evaluation the project develops novel cryptographic protocols that reduce metadata exposure and provide cross-platform key transparency. In parallel, the project investigates the group messaging setting; to resolve challenges like interoperable metadata management it designs protocols that distribute group metadata among multiple servers to ensure privacy. Finally, the last effort extends from text-based messaging protocols to encrypted interoperable voice and video calls. The project adopts the provable-security tools and insights from other areas of the research to understand them. The unique nature of the US-German collaboration allows the research to have broad impact to both US- and EU-based technologists and policymakers. 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 2026 · 2026-01

Abrupt weather extremes, changing climate, and frequent natural hazards, such as floods and droughts, have created new challenges for the effective, sustainable, and flexible operation of our nation’s reservoir systems. To avoid reservoir failures due to insufficient operational flexibility and unpredictable water fluxes during extreme events, dam operators need two essential items: (1) accurate and reliable hydrological forecasts at extended lead times (ranging from days to months in the near future); and (2) powerful and adaptive decision support tools, which not only could assist real-time decision making about how much water to release at a certain time, but also allow reservoir operators to nimbly incorporate engineering constraints and hydroclimatological forecast scenarios into flexible release planning. Over the past four decades, significant scientific advancements have been made in deterministic forecasts, linear programming, optimization algorithms, and rule-based simulation models to guide reservoir operations. However, these approaches are unable to address future operational challenges due to current limitations in understanding the variabilities of Subseasonal-to-Seasonal (S2S) hydroclimatological forecasts and a lack of modeling capabilities that utilize ensemble forecasts for more effective water release decision-making. Therefore, the goals of this CAREER project are twofold: 1) to develop an integrated solution that can account for the spatial and temporal variability of precipitation and its uncertainty; and 2) to develop a novel Artificial Intelligence & Data Mining (AI&DM) decision support tool that allows reservoir operators to use improved ensemble forecasts to develop adaptive release strategies. This research targets enabling better response to, and mitigation of the impacts of, extreme weather events and climate uncertainty in reservoir operation and planning. The project will (1) leverage the advantages of state-of-the-art deep learning models to discover and correct the spatial and temporal errors associated with S2S precipitation forecasts from multiple forecasting models in the North American Multi-Model Ensemble dataset; and (2) develop an adaptive Ensemble Boosting Tree-based Predictive Control Model, which can effectively incorporate improved ensemble forecasts into scenario-based reservoir release simulations for planning purposes. Hydrological modeling and uncertainty analysis will be performed to help understand how meteorological uncertainty propagates from atmospheric conditions into water resources planning and infrastructure management. Large-scale hydrological validation experiments (over 671 watersheds) and reservoir simulations (over 316 dams) across the U.S. will be conducted. The results will be used to validate the improved forecasts, quantify the ensemble hydrological forecast uncertainty, and evaluate the forecast-informed reservoir decision support tool. The AI&DM models will be comprehensively tested in collaboration with the U.S. Bureau of Reclamation (USBR) and the U.S. Army Corps of Engineers (USACE), which are two major reservoir agencies in the USA. The expected outcomes are aimed to allow reservoir operators to develop suitable reservoir storage and release strategies that address sudden fluxes of incoming water or a lack of water supply, while simultaneously meeting various demands and constraints. Educational tasks are tightly coupled with research. Active learning activities will help graduate students develop the ability to tackle complex research problems. Undergraduate students will obtain skills in programming. Outreach includes hosting an annual “Water Festival” exhibit at the National Weather Museum and Science Center (NWMSC) in Norman, Oklahoma. During and beyond this CAREER project, museum visitors and children will witness the importance of hydrology, meteorology, water resources management, and the impacts of extreme weather and climate. The NSF-funded CUAHSI organization will also collaborate with the project to maximize the broader impacts of developed data, models, and algorithms via various educational and outreach 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 2026 · 2026-01

This award supports the third biennial Ecosystem for Collaborative Leadership and Inclusive innovation in Plasma Science and Engineering (ECLIPSE) meeting, planned to take place from May 18-20, 2026 on the campus of the University of Michigan in Ann Arbor. The meeting is intended to bring together the community supported by NSF and Partner Agencies across the breadth of plasma science and engineering. The meeting will provide a unique opportunity for the plasma science and engineering community to exchange ideas and scientific results on the full range of topics in the field from high-energy plasma astrophysics and geospace plasmas, to the fundamental physics of light-matter interactions and plasma-based particle acceleration, to environmental and industrial applications of low temperature plasmas. The goals of the ECLIPSE meeting series include (1) broadening scientific community’s exposure to the field of plasma science and engineering, (2) highlighting the interconnections between the physics foundations of the field and its many applications and (3) enabling members of the plasma science and engineering community to network across traditional disciplinary boundaries. The meeting will include invited and contributed talks with generous time for questions and answers, a poster session, facility tours, and opportunities for discussion of potential for future cross-agency collaboration in addressing national science and technology challenges and priorities. The meeting presentations will be webcast with the opportunity for online viewing and an online discussion forum for members of the community who are not able to participate in person. This award is jointly supported by the Division of Physics, the Division of Astronomical Sciences, the Division of Atmospheric and Geospace Sciences, and the Division of Chemical, Bioengineering, Environmental and Transport Systems. 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 2026 · 2026-01

Quantum information technologies are progressing at an ever-increasing pace. The potential for the quantum information and computing paradigm to solve complex problems exponentially faster than classical computers promises revolutionary advancements in data analysis and optimization, medicine, material science, and security and communications. This has led the government, the technology industry and the academia to invest in research towards realization of robust operations on quantum networks capable of processing quantum information in a distributed way. The QUANTINT project envisions an interconnected network of quantum devices exchanging qubits and utilizing unique quantum properties such as entanglement to enhance information processing algorithms. The project aims to design efficient universal algorithms and strategies to (i) compress distributed qubits and (ii) harness distributed entanglement in emerging tasks such as distributed learning. Achieving the above vision will amplify the performance of distributed learning and other information processing tasks by several orders of magnitude, with a great reduction of the burden on network capacity. The objective of QUANTINT is to characterize and reach the fundamental limits of quantum information and learning theory and to achieve performance breakthroughs in distributed information processing. The quantum toolbox from which the project draws includes phenomena such as superposition, entanglement and non-locality. Motivated by this, QUANTINT undertakes a comprehensive exploration of two centrally important tasks - compression and inference - in the context of quantum information. 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 · 2025-12

Stem cells have the ability to develop into different types of cells. These cells are necessary to build and maintain normal tissues. However, damaged tissues often do not heal themselves. This Faculty Early Career Development Program (CAREER) project will pioneer new approaches to understand how stem cells can be precisely guided to regenerate tissues by building instructive niches (microenvironments) around them, one cell at a time. The approach developed can potentially be integrated with other existing biomanufacturing approaches to fabricate new tissues by providing gel-coated cells with locally defined properties as building blocks. The educational program will provide both high school teachers and students from diverse backgrounds with opportunities to be engaged in hands-on research activities that integrate multiple fields in science, technology, engineering and math (STEM). This integrated effort is designed to promote early exposure to multidisciplinary thinking and learning processes that are critical to solve challenging problems in biological systems. The investigator's career goal is to achieve a paradigm shift by showing that microgels can be designed as instructive cues to precisely understand and control single stem cell functions. Toward this goal, this CAREER project is based on the central premise that building instructive niches that recapitulate physiologically relevant extracellular matrix properties around single cells offers a unique direction that will enable investigations into how microenvironments regulate stem cell functions at an unprecedented resolution. To confirm this premise, the project will investigate how three-dimensional microenvironments can be designed to precisely direct fundamental processes that are essential for cell fate decision, including cell growth, symmetry breaking, asymmetric division and differentiation at the single cell level. Multidisciplinary approaches will be employed to pursue this project, including biomaterial design, droplet microfluidics, biophysical methods, mathematical modeling, imaging and genetic engineering. The research will yield a library of designed microenvironment models that can be used to recapitulate and control specific biological processes of cell fate decision in a deterministic manner. A greater understanding of cell fate decision enabled by the tools developed in this research will aid in endeavors to develop effective stem cell-based therapies and biomanufacturing approaches for tissue regeneration. 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 · 2025-12

Coral reefs nurture fisheries, protect coastlines, and support rich ecological communities. Reef-building corals depend on microbes living in their tissues to keep them healthy and thriving. This community of microbes – the coral's microbiome – includes algae that provide food and bacteria that promote coral health. Changes in reef conditions can affect the makeup of this microbiome. In coastal regions near coral reefs, environmental stresses have intensified, especially over the past several decades. These changes have impacted coral health. Some corals can live for decades or centuries and have survived these changes. The chemistry of a coral's skeleton reflects the conditions under which it grew. Researchers will measure the chemistry of the coral skeleton from its earliest growth to today. These data will provide a history of warm and cool extremes, periods of fast and slow growth, and changes in seawater salinity, nutrients, pollution, and clarity. Coral skeletons also preserve the DNA of the coral and its associated microbiome. Researchers will use the DNA preserved in the skeleton to describe the composition of the entire community. By pairing DNA analysis with histories of reef conditions, this research represents a major breakthrough to understand how corals and their microbes are surviving in a changing ocean. This study will reconstruct the recent environmental and ecological history of massive corals in the Caribbean (Siderastrea siderea) and the Great Barrier Reef (Porites lobata). Using paleoenvironmental and paleometagenomic reconstructions, this research will elucidate how the coral holobiont - the coral and its associated microorganisms - responds to specific changes in the reef environment. The use of corals from different reef locations will highlight how different species and different ecosystems may show distinctive types of responses to environmental stressors. Historical reconstructions that compare holobiont communities from the pre-industrial era (1600-1850’s) to present day will be used to assess and identify microbial taxa that are robust to environmental stressors, as well as gene functions that appear to be adaptive over time to specific environmental conditions. This will be the first research to document how the dynamics of coral holobionts respond to disturbance events at fine temporal scales, applying new ancient DNA techniques to long-lived coral skeletons over decades to centuries. Broader Impacts of this research will facilitate the creation of a Global Coral ancient DNA Paleobiology Network that will include several teams already in possession of coral cores from different oceans, thus expanding the reach of our efforts to planetary scales. 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 · 2025-12

Wave-particle interactions are a fundamental process underlying phenomena across the plasma universe, from laboratory plasmas to the magnetosphere. Understanding how energetic particles interact with waves in space and laboratory plasmas has the potential to improve our ability to protect satellites, design cleaner energy sources, and develop technologies that rely on controlling high-temperature plasmas. This award supports a collaboration between Columbia University, West Virginia University, and New York University to study how modulations of the background magnetic fields can impact the interactions between energetic particles and plasma waves. Machine learning techniques will be leveraged to discover simplified models that capture the relevant dynamics. In addition to advancing science, this project will support the training of students and early-career researchers, develop interactive classroom tools for K-12 and graduate education, and promote open, accessible science through videos, software, and tutorials. This project will bring together expertise from energetic particle dynamics in magnetic confinement fusion, radiation belt electron transport, and data-driven reduced models to address two fundamental questions: How are resonant wave-particle interactions (WPI) modified by three-dimensional (3D) structure of magnetic fields? and How do 3D magnetic fields modify wave-induced particle transport? These questions will be addressed using two model problems: resonant interaction of energetic particles with Alfvén waves and transport of radiation belt electrons by ultra low frequency (ULF) waves. The project will develop a reduced particle-based simulation framework to address these questions, taking advantage of the separation of timescales between the background evolution and resonant population evolution. This analysis will be complemented by data-based development of reduced-order models of WPI. An interpretable machine learning paradigm, sparse identification of nonlinear dynamics (SINDy), will be used to discover reduced models for particle transport due to WPI and 3D fields. These reduced transport models will fill the gap between quasilinear diffusion coefficients and particle tracing simulations, while also informing global magnetospheric modeling, where a neural network with an autoencoder architecture will be used to identify a nonlinear low-dimensional latent space where the nonlinear behavior of WPI can be mapped. 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 · 2025-11

Abstract Title: Ultrasensitive sensors for detecting gases at the single molecule level Gas sensing has a broad range of applications in environmental protection/monitoring, healthcare (such as breath analysis and body odor analysis), homeland security (explosive detection), agriculture, and industries (food, winery, and petroleum). Current gas sensors are limited by size, weight, sensitivity, and power consumption. The project aims to develop a miniature gas sensor that can detect gases at the single-molecule level. To achieve this, a semiconductor structure will be engineered to capture ions generated by ionizing gas molecules and then amplify them into an enhanced signal approximately one million-fold. Such a device will enable field-deployable gas sensing applications with a sensitivity similar to or even better than that of benchtop gas sensing instruments. The project is highly interdisciplinary, involving electrical engineering, micro/nanofabrication, semiconductor physics, material sciences, analytical chemistry, and gas analysis. It will provide vast opportunities for the participating students to learn how to synergize the knowledge and skills to advance sciences and technologies. The project will include a prominent outreach and education program, promoting awareness of and interest in engineering, physics, and bio/chemical sensing among K-12 and undergraduate students. The goal of the project is to (1) develop a miniature avalanche gas ionization detector (AGID) capable of detecting single negative/positive ions in an ambient environment (1 atm. and room temperature) with a low voltage (<50 V) and (2) integrate the AGID with micro-gas chromatography (micro-GC) for rapid and field-deployable gas analysis with unprecedented size, weight, and sensitivity. Gas sensing has a broad range of applications. Many widely used gas analysis instruments, such as gas chromatography (GC), mass spectrometry (MS), and ion mobility spectrometry (IMS), rely on “universal” gas detectors that respond to a wide range of gases after they are separated by those instruments. However, existing “universal” gas detectors either lack sufficient sensitivity or require high voltage and vacuum operation. The proposed AGID uses avalanche processes in a semiconductor P/N junction to detect positive/negative ions and electrons produced by ionizing gas molecules to achieve an ultrahigh sensitivity. The AGID can be operated in linear amplification mode (also known as analog mode) with an internal gain of >100 and Geiger mode (or counting mode) with an internal gain of ~106. The project will accomplish three aims. (1) Design/fabricate AGID to detect electrons (or negative ions) using Silicon-based CMOS technology. The AGID will be characterized and operated in both analog and Geiger mode. (2) Design/fabricate AGID to detect positive ions using Silicon- or Germanium-based CMOS technology in which holes (instead of electrons) will be used in avalanche processes. The AGID will be characterized and operated in both analog and Geiger mode. (3) Construct and benchmark an automated, fully functional, battery-powered micro-GC-AGID. The AGID enables ultrahigh sensitivity to detect single ions/electrons generated from gas molecules, while achieving a small size and low voltage/power operation at ambient pressure. A novel vertical-collection-lateral-multiplication AGID structure will be explored to leverage mature Si and Ge materials and CMOS technology, which will result in a low avalanche breakdown voltage, low fabrication cost, compact form factor, and significantly improved device reliability, scalability, and manufacturability. More importantly, Ge-based design targets positive ion detection, which has rarely been studied. The AGID can be used broadly in analytical instruments to help increase their sensitivity, reduce size/weight/power consumption, and improve operational conditions. The micro-GC-AGID synergizes AGID and micro-GC technologies to achieve an unprecedented size, weight, and sensitivity. It will be battery-powered and can be field-deployed for rapid and in situ gas analysis. 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 · 2025-11

EEC 2508612 - Public Abstract Achieving environmental sustainability is one of the greatest challenges facing society. Engineers are critical to pursuing this goal because of their central role in creating new technologies. The challenge of achieving environmental sustainability is particularly acute in the current healthcare system and the associated medical technology industry, which require large amounts of energy and produce large amounts of waste. Biomedical engineers are broadly involved in developing and implementing new medical technology and therefore have the potential help achieve environmental sustainability in the healthcare field. This project will provide an understanding of the extent to which biomedical engineering students recognize environmental sustainability as an engineering problem, and how they interpret it with respect to their professional responsibility in the medical technology industry. There have been increasing efforts to incorporate elements of environmental sustainability into engineering academic programs. This project will contribute to this effort and will strengthen the United States workforce in the medical technology industry by studying how engineering students perceive sustainability as part of their career preparation. It will also explore the needs and expectations of the medical technology industry so that the academic preparation of engineers can be aligned with these needs. This project will therefore support the goals of the Research Initiation in Engineering Formation (RIEF) program by studying the professional formation of engineers and augmenting the community of researchers in the field. Results from the project will inform curriculum development in engineering to meet the evolving needs of industry, in direct alignment with NSF priorities to advance national health, strengthen the domestic workforce, and fuel economic prosperity. This project will identify how undergraduate biomedical engineers perceive environmental sustainability as an element of their professional identity and future careers in the engineering industry, specifically in medical technology. The study is theoretically grounded in Social Cognitive Career Theory and will leverage a qualitative design to answer three main research questions: 1) How do undergraduate biomedical engineering students understand the responsibility of environmental sustainability as related to their professional formation and careers as engineers?, 2) How do undergraduate BME students develop interest in the area of environmental sustainability as part of their engineering career?, and 3) To what degree are undergraduate engineering students’ outcome expectations related to environmental sustainability in alignment with the needs of the engineering industry? This mentored project will collect data from both undergraduate engineering students and industry professionals. The project team will analyze student data using a priori and emergent thematic analysis and will identify similarities and differences in the outcome expectations of students and professionals as related to sustainability. The findings will inform how engineering educators design and frame sustainability content in the undergraduate curriculum, including mapping skills and outcomes that are identified as needs by the medical technology industry. The intellectual merit of this project lies in its specific contribution to our understanding of the role of environmental sustainability in influencing engineers’ self-efficacy, outcome expectations, and career choices. The broader impacts derive from its potential to achieve desired societal outcomes by aiding in the development of engineers who are responsive to one of the greatest challenges of our age: achieving environmental sustainability. Collectively, the results of the project will inform how engineering curricula are developed and will lay the groundwork for future work to align engineering education with the needs of industry to train the United States engineering workforce to meet current and emergent challenges. 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 · 2025-11

With the support of the Macromolecular, Supramolecular and Nanochemistry Program of the Division of Chemistry, Prof. Milliron and Prof. Truskett at the University of Michigan seek to develop a fundamental understanding of the forces that keep nanoscale particles of metal oxides (nanocrystals) suspended as soluble inks or can lead to their precipitation. The results will be relevant for the understanding how nanocrystals can be effectively integrated in electronic or optical devices, and the concepts can be extended to help rationalize biomolecular interactions. Researchers participating in this project will receive training in computation and analytical techniques. A related symposium and webinar series will be organized, with a corresponding podcast to share knowledge across the scientific community and with a broader public audience. Since nanocrystal interactions are challenging to measure directly, the team will combine information from small angle X-ray scattering measurements, optical spectroscopy, and simulations to develop and validate models. The team will apply this strategy to analyze attractive and repulsive interactions between nanocrystals as they depend on experimentally accessible parameters like the concentration of added polymers and salts, pH, and solvent dielectric constant. Each of these parameters will be explicitly included in the models for interaction potentials, and the predicted trends in how they influence the nanocrystal dispersions will be quantitatively compared to the experiments. Further, the team will apply a recently developed simulation method to predict trends in the plasmonic optical spectra of the nanocrystal ensembles, providing additional validation of the interaction potentials inferred from small angle X-ray scattering. 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 · 2025-10

Optical spectroscopy plays a crucial role across various scientific fields, from chemical process analysis to material identification and fluorescence detection. Driven by the demand for portable and field-deployable tools, miniaturizing spectroscopic systems onto chip-scale platforms has become a major research focus. This project leverages cutting-edge machine learning techniques for spectral reconstruction to develop a compact, on-chip spectrometer supporting ultraviolet-visible fluorescence, chemi-/electro-luminescence, and a broad range of optical sensing applications. To overcome the major challenges of limited labeled experimental data and inherent measurement noise for training machine learning models, we will pursue two synergistic strategies: (i) training modern machine learning models on simulated data calibrated to device-specific characteristics to bridge the simulation-to-real gap via in-context learning, and (ii) co-designing the spectrometer and learning framework to jointly optimize hardware and software for accurate, miniaturized spectral analysis. By pioneering training methodologies that utilize realistic simulations, we will demonstrate the efficacy of our on-chip spectrometer across a diverse range of scientific applications. Technically, this project aims to develop an ultra-compact, chip-scale spectrometer with high reconstruction accuracy through the integrated co-design of hardware and modern machine learning algorithms. Our proposed approach will minimize system size while enabling fast, robust, and accurate spectral reconstruction. The project has two primary objectives: (i) development of device-informed Sim2Real in-context learning methods: to address data scarcity and corruption challenges in real-world datasets, we will develop advanced Sim2Real in-context learning techniques. These methods will leverage realistic simulated data—tailored to device characteristics—to train models that generalize effectively to real-world conditions. In particular, we will harness the emerging in-context learning capabilities of large language models to bridge the domain gap between simulated and real data, enhancing model robustness and reliability in deployment. (ii) Co-design of machine learning framework and compact spectrometer: we will jointly design the machine learning algorithms and the chip-scale spectrometer to maximize efficiency and reconstruction performance. This hardware-software co-design will enable compact, low-power deployment with fast and accurate inference. We will validate the proposed spectrometer system across a range of scientific applications, including use cases in healthcare and chemistry. 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 · 2025-10

The aim of this project is to research a novel high-order nonlinear resonance wireless power transfer (WPT) approach for implementation in practical position-agile WPT applications ranging from dynamic and opportunistic electric vehicle (EV) charging to powering biomedical devices. Inductive coupling near-field WPT is an emerging technology with an immense potential for a wide range of applications. WPT systems use dedicated sources or transmitters for contactless electrical power transfer at different power levels ranging from milliwatts to kilowatts. Although some commercial products have adopted WPT technology, the technology remains underdeveloped because of the limitations imposed by sensitivity to alignment and position in current WPT systems. For example, in wireless EV charging, the system's sensitivity to vehicle's tire size, speed, and position could significantly degrade power transfer efficiency. This project will develop a new position-agile WPT technology based on high-order nonlinear resonance, founded on a unique topology. The new WPT technology offers an effective solution to address the sensitivity of WPT efficiency without any complex feedback or sensor circuitry requirement. Within the field of biomedical devices, when wirelessly powering implants, this approach provides a robust WPT solution that does not hinder patient's mobility and can wirelessly power a host of biomedical devices, which not only improves patients' lives but also reduces the burden on the healthcare industry. The multidisciplinary nature of the project involves nonlinear circuit analysis, mathematics, power electronics, radio frequency engineering, and applications ranging from automotive to biomedical engineering. The project will involve students of various levels in research, including graduate students, undergraduate students through Research Experience for Undergraduates (REU) program, and K-12 students from local community. The goal of this project is to demonstrate a fundamentally new "position-agile" WPT paradigm employing high-order nonlinear resonance for highly efficient power transmission that is insensitive to misalignment and transfer distance. The proposed research centers on a detailed theoretical and experimental study of the nonlinear circuits in WPT to automatically compensate for the variation in the coupling factor due to changes in distance and alignment between the transmitter and the receiver. In contrast to conventional methods, this approach neither varies the operating frequency nor must use any active matching circuitry involving feedback and control algorithms. Additionally, this approach has the advantage of providing a low-cost, low-complexity, rapid-response and highly reliable solution for practical position-agile WPT design. The first research task of this project is to design and construct a WPT system prototype for demonstration and validation of the novel approach. The second research task is to design, fabricate and test a position-agile multi-input, multi-output WPT system, where the high-order nonlinear resonance innately balances the power transfer to multiple receivers simultaneously. The third research task is to design and develop experimentation for a position-agile high-power WPT system with distributed nonlinear devices to study the capability of this approach for achieving high-power rapid charging. 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 · 2025-10

Modern Algorithms, including those for artificial intelligence (AI) and Scientific Computing, rely on efficient optimization. This project addresses current challenges in optimization, with the goal of developing techniques that enable faster and more accurate algorithms than are currently known to be possible. The target problems are at the intersection of computer science with other theoretical disciplines, including convex geometry, analysis, statistics, and operations research. This project also includes research opportunities for undergraduate students and early exposure to computing concepts for K-12 students at local schools. The development of the theory of algorithms and complexity has gone hand-in-hand with the development of techniques for optimization. This project focuses on three related thrusts, all building on recent breakthroughs: (1) Understanding the complexity of the widely used interior-point method in terms of the number of iterations, in the worst case, on average and for sparse inputs; (2) developing continuous methods for solving discrete problems, particularly those at the frontier of discrepancy minimization, satisfiability and spectral optimization; and (3) improving approximation algorithms via better analysis of convex relaxations, as well as the analysis of practical cutting-plane methods for solving them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Machine learning (ML) is increasingly used to combat cyberthreats. ML enables tools known as security classifiers to identify potential cyberthreats, e.g., to detect malicious software ("malware") or a network intrusion. Such classifiers are typically developed by collecting data on threats (e.g., malware samples) and benign entities (e.g., legitimate software), then building an ML model that learns patterns in the gathered training data that suggest the presence of threats. The model is then used in real systems to help identify new undetected threats. However, for many security problems, good training data is hard to find. Threats may be relatively rare, or not shared by people and companies that experience them. This leads to unbalanced datasets that contain mostly benign cases, which ML models often struggle with. Threats also change over time, as malicious software is constantly evolving, and models may quickly go out of date. This project will develop ways to address these data challenges by developing methods for Generative Artificial Intelligence (GenAI) tools to create synthetic but useful data for network and application security tasks. Through this, the project will advance knowledge of both GenAI systems and more practical, effective defenses against cyberthreats. The project team will also create novel educational resources on AI and security topics and provide educational opportunities for pre-college teachers and students and research opportunities for undergraduate students. The project's goal is to boost and maintain the performance of a security task by addressing training data challenges. The work is structured around three research thrusts. The first thrust focuses on conducting an in-depth study to evaluate the effectiveness of existing GenAI schemes in addressing data challenges in ML-based network and application security tasks, highlighting cases where they fall short and where there are opportunities for improvement. The second thrust is to develop a novel GenAI framework called Aura, which will be purpose-built for the security domain to generate high-quality synthetic data, even when training data are limited, biased, or have noisy labels. The third thrust will extend Aura to support security operations after deployment by designing novel techniques to mitigate concept drift and by enabling continual learning against evolving security threats. Aura will also provide novel model interpretation schemes to attribute predictions to synthetic data in the training set. Beyond the contributions to the specific problem of generating useful synthetic data, the project will also provide a case study of the larger goal of leveraging AI-based techniques to support security and privacy, an area of high interest to the research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Extended reality (XR) technologies have shown significant promise in increasing user engagement and skill acquisition in a variety of domains. The goal of this project is to accelerate adoption of innovative XR applications for rehabilitation, for example, to enhance user experiences in treatments to improve motor function for diseases with motor disabilities. This project develops immersive XR exercise environments that enable users to move and interact in 3D space with each other and with virtual elements. A key goal is to make the experience of rehabilitation more enjoyable and effective by incorporating social interactions between remote users that can boost engagement and skill acquisition. The technology also allows clinicians to guide and interact with their patients remotely. To create a virtual environment that users experience as fast and seamless, this project develops novel approaches to the underlying networking infrastructure needed to run the application. Collaboration with industry partners will support technology adoption for XR-enabled rehabilitation technology and for the XR industry. Realizing multi-user, geo-distributed XR technology is challenging due to stringent motion-to-photon latency requirements for good user experience. Current wide-area Internet routing and cellular wireless management are one-size-fits-all across applications, hurting latency. A key insight of this project is that not all types of XR traffic require uniformly low latency; instead, the project enables prioritization of delivery of important XR traffic while intelligently managing lower-priority XR traffic. The project’s core technical contributions are an adaptive XR application with new delivery mechanisms, leveraging Internet path selection on the testbed, and programmable wireless resources using Open Radio Access Networks (Open RAN) and xApps. Performance of the XR environment will be assessed via user experience and key network performance indicators. The expected outcome is achieving key performance targets currently deemed impossible on today’s Internet by demonstrations of network-supported, multi-user XR technology for rehabilitation at different sites. 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 · 2025-10

This NSF IUSE:EDU Level 1 project aims to serve the national interest by developing design principles by which educational tools powered by generative AI can support the goal of personalized learning in STEM education. Generative AI models, such as large language models, have presented educators with a tangible step toward addressing the National Academies of Engineering's grand challenge of personalized learning. In particular, chatbot-style AI tools (e.g., ChatGPT) have gained significant attention as mechanisms to enhance learning environments within and outside the classroom. Much effort has been placed into developing specific chatbots for disciplinary contexts, which lean heavily on the purported problem-solving abilities of modern large language models. This project plans to question design processes when creating these systems and aims to develop and test design principles for developing AI-empowered educational tools, focusing on providing personalized and humanized interactions and feedback grounded in theories of social presence and self-regulated learning. Computational thinking is recognized as a core 21st-century skill and a vital part of STEM education. As a context for testing the design principles, the project team intends to focus on courses aimed at developing computational thinking. The project team plans to build a generative AI tool called the "coachbot" utilizing the proposed education-informed design principles and will be tested in two complementary computing-focused courses at the University of Michigan and University of Cincinnati. The project aims to synthesize current literature to document how current educational AI tools are currently developed to extract preliminary design principles and gather insights related to their efficacy using a combination of learning analytics, student interviews, and pre-post surveys. The goal of this project is to develop design principles for AI-driven synchronous tutoring systems, focusing on computing education, by emphasizing broader, theory-based frameworks rather than niche, one-off chatbot solutions. Grounded in self-regulation theory and social presence theory, the initiative seeks to enhance students' metacognition and motivation in STEM while fostering acceptance of human-like digital tools. The resulting principles and AI model are intended to guide the development of future intelligent tutoring systems that prioritize educational outcomes beyond technological innovation. The project focuses on three research questions: (1) What design principles have been used to create educational AI tools? (2) How do students' experience interacting with the generative AI-powered tool called a “Coachbot” align (or not) with the elements of social presence? (3) How does the Coachbot support students' learning to solve computational problems? The project intends to address challenges in creating effective personalized educational experiences. To help to answer these questions, the project plans to: (1) conduct a scoping review to propose a set of evidence-based design principles for AI educational tool creation, (2) refine and implement the Coachbot as a specialized, personalized, principle-informed intelligent tutoring system for computationally focused courses, and conduct a case study centered on students’ use of the Coachbot of the proposed design principles from the perspective of students’ user experiences and learning outcomes; (3) revise the design principles based on the findings synthesized in Phase 2 and disseminate feedback from the greater design, AI, and education communities. 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 2025 · 2025-10

When clinicians receive high-quality team training for managing healthcare emergencies, such as in-hospital cardiac arrests, patients have a better chance of surviving. However, the high cognitive demands involved in complex decision making and team management can harm performance, particularly among healthcare professionals in training or in new roles. This project aims to understand and improve how medical professionals learn to work as an effective team by detecting and managing the mental demands they face during high-stakes events. By leveraging multimodal data (e.g., heart rate, speech, gaze) within team-based immersive virtual reality, this project enables trainee teams to practice in a controlled, simulated environment while receiving "just enough, just in time, and just for you" feedback at both individual and team levels. The ultimate goal is to equip trainees with strategies for making rapid, accurate, and repeatable decisions while effectively executing tasks to save lives. The project's outputs, including an open-source database documenting types of cognitive load triggers and corresponding strategies for regulating cognitive load, are designed to support a wide range of stakeholders, including medical educators, quality and safety professionals, human factors engineers, and those developing cardiac arrest response guidelines. The training methods developed in this research could also benefit other fields that rely on expert teams, including aviation, emergency rescue operations in the military, and wildfire management, leading to safer and more effective teamwork in high-stakes situations. To meet these goals, this project integrates multimodal sensing, modeling, and instructional strategies to support regulation of cognitive load at both individual and team levels during collaborative learning tasks. Unlike prior work, which relied on noisy single modalities and self-report measures after performance events, this integrated approach provides a comprehensive framework for detecting, modeling, and responding to cognitive load in a complex VR simulation-based training environment. In its first phase, the project will model cognitive load using multimodal signals such as visual, linguistic, and physiological responses, including interactions between team members. The second phase will involve qualitative interviews with learners to elucidate their cognitive overload experiences that correspond to the cognitive load peaks and behavior patterns identified in the first phase. These findings, along with the extracted multimodal features, will be used in phase three to detect and model cognitive load and develop AI-driven strategies. Finally, phase four will evaluate the impact of these findings on learners through a quasi-experimental study. Understanding markers that may predispose learners to errors or delays in therapeutic interventions will provide significant insight into a more holistic assessment of individual and team learning processes and provide unique opportunities for feedback, practice, and/or remediation. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and 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 2025 · 2025-10

Nontechnical Description The rapid rise of generative artificial intelligence (AI) has ushered society into a new era of supercomputing-driven data exploration. This tipping point is intensifying the gap between the exploding size of AI models and the limited computing throughput available today. A major bottleneck lies in data movement, specifically, the limitations of current interconnect technologies, further constrained by the aging von Neumann architecture. To address this, this project will employ a co-designed approach spanning architecture, packaging, and device innovation to meet the demands of next-generation interconnects, including high bandwidth, low energy use, low latency, scalability, and reliability. Supported by the ASCENT program, this project introduces a novel 3.5D integrated photonic interconnect solution that combines breakthroughs in network architecture, photonic devices, and advanced packaging. By leveraging the complementary expertise of academic and industry collaborators across several ECCS clusters, this effort drives interdisciplinary innovation, trains the next generation of engineers, and enables more powerful, efficient, and scalable computing systems that benefit society. Technical Description This project advances the field of integrated photonics by introducing a co-designed solution across architecture, packaging, and devices to meet the demands of next-generation computing. It proposes a transformative photonic interconnect-switching architecture based on a novel wavelength-mode division multiplexing scheme, enabled by athermal, energy-efficient, high-speed modulation and advanced hybrid Cu-Cu bonding techniques in 2.5D/3.5D integration. The goal is to achieve terabit-per-second data transmission with dynamic AI workload optimization. Key research tasks include the design of a reconfigurable and resource-aware photonic interposer network, exploration of 2.5D/3.5D network architectures with AI workload analysis, fabrication of heterogeneous athermal capacitive modulators and switches, development of microring-based transceiver and switch testbeds, and advancement of hybrid Cu-Cu bonding technology. This tightly integrated, interdisciplinary effort will drive innovation across multiple fronts, directly aligning with the core mission and priorities of the ASCENT program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Software systems, including Artificial Intelligence (AI) systems, have been rapidly and increasingly adopted to enact policy in public administration. The resulting interactions between policymaking and AI adoption are complex and social, legal, and technical, yet research-driven policy guidance is sorely lacking. This research intervenes on the lack of empirical evidence about how accountability structures interact and evolve when automated decision systems are deployed in public sector organizations. The project will contribute to accountability in software systems in public administration, supporting economic competitiveness across government and industry contexts. The proposed research will generate actionable insights for ensuring government software systems facilitate efficient and legitimate public service delivery. Focusing on the case of fraud detection systems for government benefits, the project will establish a theoretical framework and an empirical approach to understand how software systems influence how governance is performed on the ground in a highly contested administrative context. The research will serve two cross-cutting aims: (1) empirically identifying and interrogating the conflicting incentives, organizational practices, and understandings of accountability at play in increasingly automated public administration contexts; and (2) identifying research-driven frameworks for supporting accountability. This includes socio-legal frameworks as well as technical guidance for meaningful evaluation and procurement strategies to ensure accountable adoption and use of software systems. 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 · 2025-10

Augmented Reality (AR) applications place virtual contents, such as markers, avatars, images or videos, in users’ views of their surroundings. Users of outdoor AR applications can use these while walking, riding a bike, or driving in a vehicle to, for example, navigate dense environments safely. Today’s AR frameworks lack comprehensive support for developing outdoor AR applications. This project will produce a suite of techniques that will enable advanced AR applications, especially those that permit multiple users to interact with virtual objects in the environment, and deliver continuously updated views in near real-time of moving objects in the environment. Every AR application involves three different entities: the environment, the user, and the virtual objects introduced by the AR application. The collaborative project, which brings together researchers from the University of Southern California and the University of Michigan, will systematically develop techniques to model the environment, determine human pose, and model virtual object interaction with the physical environment. It will explicitly focus on fast algorithms, resource-efficient realizations on mobile devices, and employ a judicious combination of latency hiding, offload, pre-computation and pre-fetch techniques to simplify the development of usable outdoor AR applications with rich functionality. Outdoor AR applications can potentially improve public safety, enhance education, promote public health outcomes, provide entertainment, and support business activity. In addition, this project will involve undergraduates in community-building, by developing outdoor AR applications to facilitate the work of local non-profits, and expose these undergraduates to research. Its collaboration with industry will result in the transfer of AR technology to improve vehicular safety. The project's products, including papers, data and software artifacts are available at https://nsl.usc.edu/projects/pervasive-outdoor-ar. This website shall be available for at least three years after the conclusion of the grant, but products such as published papers and software may be available in other repositories for longer. 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 · 2025-10

Cyber-physical systems (CPS) form the foundation of modern intelligent infrastructure by tightly integrating physical processes with computation, communication, and control. The increasing complexity of CPS, particularly in transportation, robotics, disaster response, which are focal domains of this project, necessitates advanced decision-making frameworks capable of modeling and optimizing hierarchical, interactive behaviors under uncertainty. As urban transportation systems become more connected and autonomous, and as robotics platforms evolve toward multi-agent, distributed architectures, the role of bilevel optimization becomes particularly salient. This project will build a novel, scalable, and trust-aware bilevel optimization framework tailored for multi-user CPS operating under uncertainty and ambiguous user trust. By explicitly embedding human trust and behavioral uncertainty into optimization models, the research aims to advance the design of smarter, safer, and more adaptive CPS. The outcomes have the potential to transform sectors where human-machine interaction plays a central role. Beyond research contributions, the project will support education and workforce development through interdisciplinary student training, the creation of interactive games to introduce K–12 students to human-in-the-loop control and optimization, and the development of new graduate-level courses on CPS optimization and computation, which will be made available as open-access online content. The development, validation, and calibration of the research will push the frontiers of multi-user CPS studies in three directions: (1) the ability of modeling multi-user decisions dynamically and sequentially with dynamic user trust update; (2) finding optimal and risk-averse policies to bilevel programs under exogenous and endogenous uncertainties; (3) analyzing the resilience, operational efficiency, and reliability of example CPS applications via simulation and computation. A key innovation of this research is the integration of trust as an endogenous and dynamic source of uncertainty, directly influencing users’ risk assessments and decision optimization. The research will introduce a new class of bilevel optimization models that integrate behavioral and learning-based mechanisms, uniting stochastic programming, risk modeling, and distributionally robust optimization within a multi-agent decision framework. By addressing challenges such as partial observability, adaptive learning, and sequential interactions in decentralized environments, this research will generate new theoretical insights and broaden the landscape of tractable solutions in dynamic, human-in-the-loop CPS. 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 · 2025-10

Virtual Private Networks (VPNs) are one of the most fundamental security and privacy tools that have found their way into regular Internet users' toolboxes. Despite widespread reliance on these tools, the mobile VPN ecosystem is rife with VPNs, posing several privacy and security issues. There is no visibility into the extent of the problems in mobile VPN apps beyond a few isolated examples based on manual reverse engineering efforts. The project’s novelties are new tools and methods to uncover hidden risks in mobile VPNs, which many people use to stay private online. The project’s broader significance and importance are in bringing together researchers, advocates, and VPN providers to better understand the risks and strengthen the safety and privacy of VPN services for everyone. This effort advances technical solutions needed for assessing and mitigating the risks associated with the mobile VPN ecosystem. The current Reverse engineering tools are less developed for mobile environments, and their effectiveness is further limited by the complexity of the mobile VPN ecosystem. This project facilitates systematic investigation into the mobile VPN ecosystem by understanding current practices and designing and building novel frameworks capable of conducting large-scale analysis. The project also uses this framework to explore and evaluate different components of the mobile VPN ecosystem, such as free VPNs. Finally, the project also focuses on advancing the understanding of detection and interference attacks on mobile VPNs. 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.