Grants

NSF

The final stages in the evolution of the most massive stars play a crucial role in astrophysics, injecting energy and enriched metals into the interstellar medium, and producing compact objects – neutron stars, black holes, and the wide range of transient phenomena associated with them. The predictive power of theoretical models for massive stars, however, is severely limited by the large uncertainties associated with this regime of stellar evolution. The project will constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss and the impact of supernova kicks. This project will develop new tools that can maximize the science extracted from the wealth of data on resolved stellar populations and object catalogs that will be available in the coming decade with the advent of facilities, like the Vera C. Rubin Observatory. The project will also broaden the impact of the research through a mixture of new and well-established outreach projects designed to foster engagement by educators and students in science. The spatially resolved stellar populations in Local Group galaxies will be used to place unprecedented constraints on the evolution of the most massive stars. To this end, the investigators will precisely measure the formation efficiencies and evolutionary timescales (i.e., the delay time distributions, or DTDs), for the main outcomes of massive stellar evolution: Wolf-Rayet and Intermediate Mass Stripped Stars, Blue, Yellow and Red Supergiants, X-ray Binaries, and Supernova Remnants. The investigators will model each of these DTDs using the state-of-the-art population synthesis code COSMIC. A systematic comparison between measured DTDs and COSMIC predictions will therefore constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss, the effects of the common envelope phase, and the impact of supernova kicks. These constraints will provide a new level of detail in our ability to trace and quantify the energetics and enrichment from the progenitors of many astrophysical transients and gravitational wave sources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The final stages in the evolution of the most massive stars play a crucial role in astrophysics, injecting energy and enriched metals into the interstellar medium, and producing compact objects – neutron stars, black holes, and the wide range of transient phenomena associated with them. The predictive power of theoretical models for massive stars, however, is severely limited by the large uncertainties associated with this regime of stellar evolution. The project will constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss and the impact of supernova kicks. This project will develop new tools that can maximize the science extracted from the wealth of data on resolved stellar populations and object catalogs that will be available in the coming decade with the advent of facilities, like the Vera C. Rubin Observatory. The project will also broaden the impact of the research through a mixture of new and well-established outreach projects designed to foster engagement by educators and students in science. The spatially resolved stellar populations in Local Group galaxies will be used to place unprecedented constraints on the evolution of the most massive stars. To this end, the investigators will precisely measure the formation efficiencies and evolutionary timescales (i.e., the delay time distributions, or DTDs), for the main outcomes of massive stellar evolution: Wolf-Rayet and Intermediate Mass Stripped Stars, Blue, Yellow and Red Supergiants, X-ray Binaries, and Supernova Remnants. The investigators will model each of these DTDs using the state-of-the-art population synthesis code COSMIC. A systematic comparison between measured DTDs and COSMIC predictions will therefore constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss, the effects of the common envelope phase, and the impact of supernova kicks. These constraints will provide a new level of detail in our ability to trace and quantify the energetics and enrichment from the progenitors of many astrophysical transients and gravitational wave sources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The final stages in the evolution of the most massive stars play a crucial role in astrophysics, injecting energy and enriched metals into the interstellar medium, and producing compact objects – neutron stars, black holes, and the wide range of transient phenomena associated with them. The predictive power of theoretical models for massive stars, however, is severely limited by the large uncertainties associated with this regime of stellar evolution. The project will constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss and the impact of supernova kicks. This project will develop new tools that can maximize the science extracted from the wealth of data on resolved stellar populations and object catalogs that will be available in the coming decade with the advent of facilities, like the Vera C. Rubin Observatory. The project will also broaden the impact of the research through a mixture of new and well-established outreach projects designed to foster engagement by educators and students in science. The spatially resolved stellar populations in Local Group galaxies will be used to place unprecedented constraints on the evolution of the most massive stars. To this end, the investigators will precisely measure the formation efficiencies and evolutionary timescales (i.e., the delay time distributions, or DTDs), for the main outcomes of massive stellar evolution: Wolf-Rayet and Intermediate Mass Stripped Stars, Blue, Yellow and Red Supergiants, X-ray Binaries, and Supernova Remnants. The investigators will model each of these DTDs using the state-of-the-art population synthesis code COSMIC. A systematic comparison between measured DTDs and COSMIC predictions will therefore constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss, the effects of the common envelope phase, and the impact of supernova kicks. These constraints will provide a new level of detail in our ability to trace and quantify the energetics and enrichment from the progenitors of many astrophysical transients and gravitational wave sources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project investigates the principles and methodologies for Artificial Intelligence of Things (AIoT) devices to make decisions quickly and accurately, to meet the requirements for safety, accuracy, and latency even with limited communication and computing power. By developing new machine learning techniques that work well in the presence of communication resource limitations, this project will spur a new line of thinking for a variety of AIoT applications that face the challenges of communication-constrained machine learning, such as smart health, connected cars, augmented reality, and smart city, benefiting society at large. This project will also contribute to skilled workforce development in this area of national needs, by integrating the research findings into undergraduate- and graduate-level education and organizing various summer programs on data science, AI and machine learning to broaden the participation of K-12 students. This project aims to develop innovative Communication-constrained Adaptive Machine Learning (CAML) methods that adapt to varying communication bandwidth, computational power, and data size of edge devices, thereby enabling heterogeneous devices to work in concert. Specifically, the project will investigate CAML for two popular edge network settings: edge networks with a central coordinator and fully decentralized edge networks. For edge networks with a central server, this project studies distributed fine-tuning for on-device machine learning model adaptation through dynamic tier-based low-rank model adaptation, allowing each device to train a local adapter of a suitable rank. This approach ensures synchronized model updates across devices with varying capabilities, improving the training efficiency of large machine learning models. Cross-layer optimization schemes will be developed to speed up the learning process while ensuring accuracy. To address the scenarios with extreme communication resource limitations, the project introduces seed-aided machine learning model tuning algorithms that enable meaningful model updates with minimal data exchanges. For decentralized edge networks, the project develops communication-efficient workload balancing algorithms to reduce synchronization delays. The innovations in this project promise to advance federated and collaborative learning across diverse edge environments, enhancing scalability, efficiency, and robustness in real-world AIoT 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

This project aims to make the Internet more reliable by giving operators more control over how data reaches their network from other parts of the Internet. Today, network operators rely on trial and error, and often settle on subpar outcomes, because they do not have direct control over or visibility into which geographic locations and other networks the data will traverse to reach their network. This project aims to change that by creating a system where operators can simply specify their goals, such as improving performance or avoiding particular countries, and then the system figures out how to make that happen. This collaborative project brings together investigators from Columbia University, Northeastern University, and Federal University of Minas Gerais (UFMG) in Brazil. The project envisions a system that allows a network operator to describe the policy for how to set preferences for possible ingress routes (which the project calls “intents”), then automatically configures routing announcements to achieve the most preferred (feasible) outcome. To realize this vision, the project will address the following research questions: 1) What intents are desired, and how can it be made easy for operators to express them? 2) How can a system predict the routes and traffic engineering metrics that will result from an announcement? 3) How can a system automatically learn which configurations are possible and what their semantics are? 4) What are efficient ways to search through large numbers of configurations spanning multiple networks to satisfy general intent? The project will enable new ways for Internet providers and cloud services to improve the reliability and performance of Internet services on which society increasingly relies. For example, our project can help Internet providers maintain service during and after natural disasters, as well as identify and block Internet attacks. It will help improve performance for a wide range of services that include online educational technologies, telemedicine, remote work, and/or various forms of e-commerce and entertainment. The research outcomes will also serve as a foundation for future academic and industrial innovation in Internet routing. Project updates and outcomes will be published at https://ingress-routing.ee.columbia.edu/, with the plan to maintain the site for at least three years beyond the award period. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This project aims to make the Internet more reliable by giving operators more control over how data reaches their network from other parts of the Internet. Today, network operators rely on trial and error, and often settle on subpar outcomes, because they do not have direct control over or visibility into which geographic locations and other networks the data will traverse to reach their network. This project aims to change that by creating a system where operators can simply specify their goals, such as improving performance or avoiding particular countries, and then the system figures out how to make that happen. This collaborative project brings together investigators from Columbia University, Northeastern University, and Federal University of Minas Gerais (UFMG) in Brazil. The project envisions a system that allows a network operator to describe the policy for how to set preferences for possible ingress routes (which the project calls “intents”), then automatically configures routing announcements to achieve the most preferred (feasible) outcome. To realize this vision, the project will address the following research questions: 1) What intents are desired, and how can it be made easy for operators to express them? 2) How can a system predict the routes and traffic engineering metrics that will result from an announcement? 3) How can a system automatically learn which configurations are possible and what their semantics are? 4) What are efficient ways to search through large numbers of configurations spanning multiple networks to satisfy general intent? The project will enable new ways for Internet providers and cloud services to improve the reliability and performance of Internet services on which society increasingly relies. For example, our project can help Internet providers maintain service during and after natural disasters, as well as identify and block Internet attacks. It will help improve performance for a wide range of services that include online educational technologies, telemedicine, remote work, and/or various forms of e-commerce and entertainment. The research outcomes will also serve as a foundation for future academic and industrial innovation in Internet routing. Project updates and outcomes will be published at https://ingress-routing.ee.columbia.edu/, with the plan to maintain the site for at least three years beyond the award period. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This collaborative project, involving investigators from the Massachusetts Institute of Technology and the University of Washington, focuses on developing new ways to quickly and accurately predict how computer networks perform. Traditional methods for predicting network behavior either simulate each network event in detail, which is extremely slow, or simplify too much, sacrificing accuracy. This project uses machine learning to build fast yet accurate models of network performance. These models will help operators design better, more efficient networks, improving the reliability and speed of services such as online applications, artificial intelligence, and cloud computing. The intellectual merit of the project lies in its innovative use of machine learning to overcome the trade-offs traditionally faced in network modeling. It is structured into three main research activities. First, it develops models that accurately predict critical performance metrics like latency and packet loss using a large corpus of training data. Second, it creates deep learning models capable of forecasting how network performance evolves over time, especially for dynamic applications whose behavior depends on network performance. Lastly, the project investigates how these learned models can be utilized to optimize network configuration in real-time, ensuring networks consistently meet desired performance objectives despite changing conditions. The broader impacts of this project include benefits to both industry and education. Improved network models can lead to significant enhancements in data center efficiency and quality of service, improving user experience, reducing operational costs, and decreasing environmental impact through reduced energy consumption and electronic waste. Industries such as finance, cloud computing, and large-scale AI will particularly benefit from increased efficiency, reliability and real-time adaptability, reducing outages and enhancing resilience against unexpected conditions. For education, the project will generate open-source educational resources that help students and practitioners better understand network dynamics and performance. More information, along with data, code, and results from this project, will be available online at https://github.com/netiken. The repository and related resources will be actively maintained and updated for at least five years beyond the duration of the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This collaborative project, involving investigators from the Massachusetts Institute of Technology and the University of Washington, focuses on developing new ways to quickly and accurately predict how computer networks perform. Traditional methods for predicting network behavior either simulate each network event in detail, which is extremely slow, or simplify too much, sacrificing accuracy. This project uses machine learning to build fast yet accurate models of network performance. These models will help operators design better, more efficient networks, improving the reliability and speed of services such as online applications, artificial intelligence, and cloud computing. The intellectual merit of the project lies in its innovative use of machine learning to overcome the trade-offs traditionally faced in network modeling. It is structured into three main research activities. First, it develops models that accurately predict critical performance metrics like latency and packet loss using a large corpus of training data. Second, it creates deep learning models capable of forecasting how network performance evolves over time, especially for dynamic applications whose behavior depends on network performance. Lastly, the project investigates how these learned models can be utilized to optimize network configuration in real-time, ensuring networks consistently meet desired performance objectives despite changing conditions. The broader impacts of this project include benefits to both industry and education. Improved network models can lead to significant enhancements in data center efficiency and quality of service, improving user experience, reducing operational costs, and decreasing environmental impact through reduced energy consumption and electronic waste. Industries such as finance, cloud computing, and large-scale AI will particularly benefit from increased efficiency, reliability and real-time adaptability, reducing outages and enhancing resilience against unexpected conditions. For education, the project will generate open-source educational resources that help students and practitioners better understand network dynamics and performance. More information, along with data, code, and results from this project, will be available online at https://github.com/netiken. The repository and related resources will be actively maintained and updated for at least five years beyond the duration of the project. 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

Quantum sensors capitalize on the strong sensitivity of quantum systems to external disturbances to measure various physical phenomena with extreme precision. Quantum sensing is a rapidly emerging field with many applications, including detecting gravitational and magnetic fields, biological measurement, imaging, etc. The full potential of quantum sensing is realized by using a distributed network of quantum sensors to estimate physical phenomena; in particular, when a network of quantum sensors allows the sensors to be in an entangled (correlated) state, its precision is further improved. There has been recent work using multiple quantum sensors, but the use of a distributed network of quantum sensors working collaboratively to estimate complex physical phenomena, as in many classical sensor network applications, has remained largely unexplored. This proposal seeks to fill this gap and investigate many scientific challenges that arise in developing efficient sensing protocols for a quantum sensor network (QSN). In addition, the project helps develop the workforce in this emerging quantum sensing and communication field by designing and offering educational programs targeting a wide variety of students ranging from those in high school to those in graduate school and beyond. The goal of this project is to tackle the main challenges and problems that arise in building QSNs. The research work consists of the following thrusts: (i) Initial State and Measurement Optimization. The initial state of the quantum sensing system can strongly affect the estimation error of the sensed parameter. Thus, the project will investigate the optimization problem of determining the optimal initial state and global measurement that minimizes the estimation error. (ii) Event Localization Schemes via QSNs. The project will design efficient schemes for event localization using QSNs. (iii) Distribution of Quantum Circuits in QSNs. The project will develop techniques for the distributed implementation of quantum sensing circuits in QSNs, with an objective to minimize aggregate quantum error or execution latency of distributed circuits. (iv) Declarative Framework for Specifying QSN Protocols. The project plans to develop a programming framework for specifying, evaluating, and reasoning over QSN protocols. (v) QSN Simulator, System Evaluation, and QSN Testbeds. The project will develop a QSN simulator that can be used to simulate general QSN applications. In addition, the developed techniques will be evaluated over three platforms: large-scale simulations over the QSN simulator, moderate-scale simulations over a cloud quantum computer, and small-scale experiments on two QSN testbeds. 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

Quantum sensors capitalize on the strong sensitivity of quantum systems to external disturbances to measure various physical phenomena with extreme precision. Quantum sensing is a rapidly emerging field with many applications, including detecting gravitational and magnetic fields, biological measurement, imaging, etc. The full potential of quantum sensing is realized by using a distributed network of quantum sensors to estimate physical phenomena; in particular, when a network of quantum sensors allows the sensors to be in an entangled (correlated) state, its precision is further improved. There has been recent work using multiple quantum sensors, but the use of a distributed network of quantum sensors working collaboratively to estimate complex physical phenomena, as in many classical sensor network applications, has remained largely unexplored. This proposal seeks to fill this gap and investigate many scientific challenges that arise in developing efficient sensing protocols for a quantum sensor network (QSN). In addition, the project helps develop the workforce in this emerging quantum sensing and communication field by designing and offering educational programs targeting a wide variety of students ranging from those in high school to those in graduate school and beyond. The goal of this project is to tackle the main challenges and problems that arise in building QSNs. The research work consists of the following thrusts: (i) Initial State and Measurement Optimization. The initial state of the quantum sensing system can strongly affect the estimation error of the sensed parameter. Thus, the project will investigate the optimization problem of determining the optimal initial state and global measurement that minimizes the estimation error. (ii) Event Localization Schemes via QSNs. The project will design efficient schemes for event localization using QSNs. (iii) Distribution of Quantum Circuits in QSNs. The project will develop techniques for the distributed implementation of quantum sensing circuits in QSNs, with an objective to minimize aggregate quantum error or execution latency of distributed circuits. (iv) Declarative Framework for Specifying QSN Protocols. The project plans to develop a programming framework for specifying, evaluating, and reasoning over QSN protocols. (v) QSN Simulator, System Evaluation, and QSN Testbeds. The project will develop a QSN simulator that can be used to simulate general QSN applications. In addition, the developed techniques will be evaluated over three platforms: large-scale simulations over the QSN simulator, moderate-scale simulations over a cloud quantum computer, and small-scale experiments on two QSN testbeds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The exploration of Terahertz (THz) communications (above 100 GHz) is promising for achieving ultra-high data rates and bandwidth in next-generation wireless networks. The combination of large transmitting arrays and smaller wavelength in THz communications introduces new challenges where the near-field range of a transmitter can extend to several meters. A wireless base station or access point may serve multiple near-field and far-field users. This project investigates the impacts of near-field complexities and the interplay between near- and far-field communications. It leverages near-field properties, such as the generation of curved beams for going around the obstruction or reconstructing the beams after interacting with an obstacle, to improve blockage resilience and system performance of THz wireless networks. This project also includes significant efforts to motivate participation in STEM, at all educational levels. This project aims to explore near-field propagation properties of wireless signals to enable blockage-resilient high-speed multi-user wireless communications above 100 GHz. The proposed research comprises four thrusts, (1) understanding and modeling the fundamental limits of near-field wavefront shaping for realizing agile and high-speed communications in the scenarios with link blockage, (2) investigating the design and fabrication of a new programmable array architecture that can generate near-field wavefronts and dynamically adapt them according to the mobility and signal transmission blockage conditions, (3) designing and implementing digital signal processing algorithms to support 10s of GHz of bandwidth in a power-efficient manner, and (4) developing foundations for interference modeling and multi-user communications with the consideration of both near- and far-field regions. Through the physics of wave propagation, mathematical modeling, hardware design, and experimental demonstration, this project will lay out the foundation to bridge the theory of near-field electromagnetics with practical wireless 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

The exploration of Terahertz (THz) communications (above 100 GHz) is promising for achieving ultra-high data rates and bandwidth in next-generation wireless networks. The combination of large transmitting arrays and smaller wavelength in THz communications introduces new challenges where the near-field range of a transmitter can extend to several meters. A wireless base station or access point may serve multiple near-field and far-field users. This project investigates the impacts of near-field complexities and the interplay between near- and far-field communications. It leverages near-field properties, such as the generation of curved beams for going around the obstruction or reconstructing the beams after interacting with an obstacle, to improve blockage resilience and system performance of THz wireless networks. This project also includes significant efforts to motivate participation in STEM, at all educational levels. This project aims to explore near-field propagation properties of wireless signals to enable blockage-resilient high-speed multi-user wireless communications above 100 GHz. The proposed research comprises four thrusts, (1) understanding and modeling the fundamental limits of near-field wavefront shaping for realizing agile and high-speed communications in the scenarios with link blockage, (2) investigating the design and fabrication of a new programmable array architecture that can generate near-field wavefronts and dynamically adapt them according to the mobility and signal transmission blockage conditions, (3) designing and implementing digital signal processing algorithms to support 10s of GHz of bandwidth in a power-efficient manner, and (4) developing foundations for interference modeling and multi-user communications with the consideration of both near- and far-field regions. Through the physics of wave propagation, mathematical modeling, hardware design, and experimental demonstration, this project will lay out the foundation to bridge the theory of near-field electromagnetics with practical wireless 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

The exploration of Terahertz (THz) communications (above 100 GHz) is promising for achieving ultra-high data rates and bandwidth in next-generation wireless networks. The combination of large transmitting arrays and smaller wavelength in THz communications introduces new challenges where the near-field range of a transmitter can extend to several meters. A wireless base station or access point may serve multiple near-field and far-field users. This project investigates the impacts of near-field complexities and the interplay between near- and far-field communications. It leverages near-field properties, such as the generation of curved beams for going around the obstruction or reconstructing the beams after interacting with an obstacle, to improve blockage resilience and system performance of THz wireless networks. This project also includes significant efforts to motivate participation in STEM, at all educational levels. This project aims to explore near-field propagation properties of wireless signals to enable blockage-resilient high-speed multi-user wireless communications above 100 GHz. The proposed research comprises four thrusts, (1) understanding and modeling the fundamental limits of near-field wavefront shaping for realizing agile and high-speed communications in the scenarios with link blockage, (2) investigating the design and fabrication of a new programmable array architecture that can generate near-field wavefronts and dynamically adapt them according to the mobility and signal transmission blockage conditions, (3) designing and implementing digital signal processing algorithms to support 10s of GHz of bandwidth in a power-efficient manner, and (4) developing foundations for interference modeling and multi-user communications with the consideration of both near- and far-field regions. Through the physics of wave propagation, mathematical modeling, hardware design, and experimental demonstration, this project will lay out the foundation to bridge the theory of near-field electromagnetics with practical wireless 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

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

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

Cooperation of multiple devices to learn and make decisions based on their environment is especially valuable for Internet of Things (IoT) and other applications. Existing algorithms for cooperative learning often assume that all devices face the same set of decision choices, which is not often the case in networked system settings. This project, CoLeNe (Cooperative Learning in heterogeneous Networks), proposes to design and evaluate algorithms for multiple devices to cooperatively learn for decision making over a large set of choices in computer networks as the agents may face different sets of decisions. The developed algorithms allow devices to collaboratively explore the decision space and identify the best option from a large set faster. The project also applies these algorithms to the decision-making cases in networks and demonstrates their usefulness through the examples. In addition, the research effort is paired with educational and outreach initiatives that introduce students to the theory and practice of cooperative learning in networks. This proposal aims to develop cooperative online learning algorithms that are communication-efficient and robust to heterogeneity in computation, data, and privacy across agents. It consists of two research thrusts: (1) theoretical foundations and algorithms. The project will extend existing theoretical work on online learning to the settings of multiple heterogeneous agents with different sets of decision choices and privacy constraints on the information they can share with other agents. It develops algorithms for multiple devices to cooperatively learn to make optimal decisions. (2) Implementing two network applications. The developed cooperative online learning algorithms will be adapted to two applications in edge networks: distributed placement of computing applications across a network of devices, and optimization of wireless network configurations and transmission schedules. The learning framework developed through this project is expected to have broad applicability across IoT as well as other domains involving distributed, heterogeneous learning 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

Cooperation of multiple devices to learn and make decisions based on their environment is especially valuable for Internet of Things (IoT) and other applications. Existing algorithms for cooperative learning often assume that all devices face the same set of decision choices, which is not often the case in networked system settings. This project, CoLeNe (Cooperative Learning in heterogeneous Networks), proposes to design and evaluate algorithms for multiple devices to cooperatively learn for decision making over a large set of choices in computer networks as the agents may face different sets of decisions. The developed algorithms allow devices to collaboratively explore the decision space and identify the best option from a large set faster. The project also applies these algorithms to the decision-making cases in networks and demonstrates their usefulness through the examples. In addition, the research effort is paired with educational and outreach initiatives that introduce students to the theory and practice of cooperative learning in networks. This proposal aims to develop cooperative online learning algorithms that are communication-efficient and robust to heterogeneity in computation, data, and privacy across agents. It consists of two research thrusts: (1) theoretical foundations and algorithms. The project will extend existing theoretical work on online learning to the settings of multiple heterogeneous agents with different sets of decision choices and privacy constraints on the information they can share with other agents. It develops algorithms for multiple devices to cooperatively learn to make optimal decisions. (2) Implementing two network applications. The developed cooperative online learning algorithms will be adapted to two applications in edge networks: distributed placement of computing applications across a network of devices, and optimization of wireless network configurations and transmission schedules. The learning framework developed through this project is expected to have broad applicability across IoT as well as other domains involving distributed, heterogeneous learning 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

The convergence of sensing and communication technologies is becoming increasingly critical in emerging multi-agent systems, such as robot swarms, autonomous vehicles and virtual reality. These systems require both precise spatial awareness for coordination and real-time data exchange for collaborative decision making. However, existing solutions face fundamental limitations. Optical tracking systems are expensive and sensitive to occlusion, while Radio Frequency-based systems struggle to achieve comparable accuracy. Moreover, existing wireless solutions cannot simultaneously deliver the high-precision spatial tracking and high-throughput data exchange needed for real-time coordination in dynamic environments. A unified solution that enables both high-precision tracking and efficient communication remains an open challenge. This project tackles the above problem. Developing such new wireless technology is strategically important for setting the US as the leader in 6G and its applications. The proposed research is interdisciplinary in nature and will have a significant impact on different aspects of the society such as education, smart environment, health, robotics, warehouse automation and emergency response. The goal of this project is to enable precise spatial awareness and real-time sensor data communication using millimeter-wave (mmWave) backscatter technology. To achieve this, it first designs and fabricates novel backscatter tags that overcomes environmental interference and achieves submillimeter position accuracy and sub-degree orientation tracking. Then, to enable communication, it incorporates ultra-low power designs that directly encode analog sensor data into backscatter signals and develops an add-on receiver module for radars that enables data streaming for digital sensors. Finally, the system will be integrated into multi-robot systems performing collaborative perception tasks like 3D object detection and mapping. By combining hardware innovations, advanced algorithms, and system integration, this project provides a robust and energy efficient platform that enables new possibilities in networked multi-agent applications where both precise spatial coordination and real-time data sharing are essential. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This collaborative project explores immersive video streaming technology that lets people see and interact with lifelike 3D environments. Instead of watching a regular video on a flat screen, users can move around a scene, change their viewpoint, and experience the environment from different angles in real time. This is made possible through advanced techniques that simulate how light travels through a scene using artificial intelligence. By leveraging computer vision, computer graphics, and machine learning, the project aims to make these 3D experiences smooth, detailed, and fast, even over current Internet connections. This project builds a high-performance immersive video streaming system using neural radiance fields, an advanced 3D scene representation powered by machine learning. It is structured around three key thrusts: improving resiliency, optimizing bandwidth, and reducing latency. Resiliency is addressed through lightweight models that recover lost content, adaptive encoding based on content robustness, and artifact reduction via reprojection. Bandwidth is optimized by filtering less important content, prioritizing semantically significant regions, and applying hybrid upsampling. Latency is reduced through intelligent packet ordering, caching intermediate results, and collaborative edge-client rendering. These innovations are integrated into a unified framework called NeuVol, which will be evaluated on diverse hardware and real-world network conditions. The project’s broader impacts include both technological innovation and societal advancement. It will enhance immersive applications in fields such as remote training, collaborative design, and medical diagnostics by enabling efficient delivery of high-quality 3D content. Educationally, it will integrate research outcomes into courses on multimedia systems and immersive computing, offering students hands-on experience with emerging technologies. The project will actively engage undergraduate researchers through active mentorship. Public outreach will feature demonstrations at schools, community centers, and science fairs to promote awareness of immersive technologies and inspire interest in engineering and computer science among the broader public. The project brings together researchers from George Mason University and the University of Illinois Urbana-Champaign to design, build, and test NeuVol and make it available to a wide audience through open research. All research artifacts developed through this project will be made publicly available via a dedicated project website at https://neuvol.github.io. The website will be actively maintained throughout the project duration and archived for continued access beyond the project’s conclusion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This collaborative project explores immersive video streaming technology that lets people see and interact with lifelike 3D environments. Instead of watching a regular video on a flat screen, users can move around a scene, change their viewpoint, and experience the environment from different angles in real time. This is made possible through advanced techniques that simulate how light travels through a scene using artificial intelligence. By leveraging computer vision, computer graphics, and machine learning, the project aims to make these 3D experiences smooth, detailed, and fast, even over current Internet connections. This project builds a high-performance immersive video streaming system using neural radiance fields, an advanced 3D scene representation powered by machine learning. It is structured around three key thrusts: improving resiliency, optimizing bandwidth, and reducing latency. Resiliency is addressed through lightweight models that recover lost content, adaptive encoding based on content robustness, and artifact reduction via reprojection. Bandwidth is optimized by filtering less important content, prioritizing semantically significant regions, and applying hybrid upsampling. Latency is reduced through intelligent packet ordering, caching intermediate results, and collaborative edge-client rendering. These innovations are integrated into a unified framework called NeuVol, which will be evaluated on diverse hardware and real-world network conditions. The project’s broader impacts include both technological innovation and societal advancement. It will enhance immersive applications in fields such as remote training, collaborative design, and medical diagnostics by enabling efficient delivery of high-quality 3D content. Educationally, it will integrate research outcomes into courses on multimedia systems and immersive computing, offering students hands-on experience with emerging technologies. The project will actively engage undergraduate researchers through active mentorship. Public outreach will feature demonstrations at schools, community centers, and science fairs to promote awareness of immersive technologies and inspire interest in engineering and computer science among the broader public. The project brings together researchers from George Mason University and the University of Illinois Urbana-Champaign to design, build, and test NeuVol and make it available to a wide audience through open research. All research artifacts developed through this project will be made publicly available via a dedicated project website at https://neuvol.github.io. The website will be actively maintained throughout the project duration and archived for continued access beyond the project’s conclusion. 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

Hardware resource disaggregation separates individual hardware components from traditional, monolithic servers and connects them via a network. Disaggregation has been used in the context of compute, memory, and storage resources to bring greater flexibility, decrease energy consumption, and reduce costs. This project extends the concept of disaggregation to data center networks with a key research question: Can data centers and data-center applications benefit from disaggregation by moving network functionalities out of end hosts and pooling them together in another system layer? This project explores this question by introducing a rack-level disaggregated network solution called NetFusion, which consists of a pool of programmable Network Interface Cards (NICs) or SmartNICs, each of which can execute network tasks on behalf of end-host applications. NetFusion allows for the consolidation of networking demands associated with both packet processing and network-function processing, enabling statistical multiplexing of networking tasks and bringing disaggregation benefits to data centers and data-center applications. The project addresses a number of challenges in realizing this vision: How to support the safe and fair sharing of NetFusion? How can the system address both the long-term traffic needs and the short-term bursts of applications? How to allocate resources across the pool of SmartNICs? How to support different SmartNICs with varying resources? How can the system provide fault tolerance and react in a timely manner to workload changes? How can applications benefit from network disaggregation? This collaborative project brings together investigators from University of California at San Diego and University of Washington to optimize the network-intensive datacenter applications used by billions of people around the globe on a daily basis. By improving the efficiency of network operations, one can dramatically reduce the cost of provisioning existing datacenter infrastructure as well as make it much cheaper to deploy new public services. The project integrates industry collaborators who will provide access to cutting-edge network technologies and assist in technology transfer to the industry. For the broader community of users and society at large, the project’s artifacts will be made publicly available at https://sites.google.com/cs.washington.edu/netfusion/home, enabling the development of high-performance data center 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

Hardware resource disaggregation separates individual hardware components from traditional, monolithic servers and connects them via a network. Disaggregation has been used in the context of compute, memory, and storage resources to bring greater flexibility, decrease energy consumption, and reduce costs. This project extends the concept of disaggregation to data center networks with a key research question: Can data centers and data-center applications benefit from disaggregation by moving network functionalities out of end hosts and pooling them together in another system layer? This project explores this question by introducing a rack-level disaggregated network solution called NetFusion, which consists of a pool of programmable Network Interface Cards (NICs) or SmartNICs, each of which can execute network tasks on behalf of end-host applications. NetFusion allows for the consolidation of networking demands associated with both packet processing and network-function processing, enabling statistical multiplexing of networking tasks and bringing disaggregation benefits to data centers and data-center applications. The project addresses a number of challenges in realizing this vision: How to support the safe and fair sharing of NetFusion? How can the system address both the long-term traffic needs and the short-term bursts of applications? How to allocate resources across the pool of SmartNICs? How to support different SmartNICs with varying resources? How can the system provide fault tolerance and react in a timely manner to workload changes? How can applications benefit from network disaggregation? This collaborative project brings together investigators from University of California at San Diego and University of Washington to optimize the network-intensive datacenter applications used by billions of people around the globe on a daily basis. By improving the efficiency of network operations, one can dramatically reduce the cost of provisioning existing datacenter infrastructure as well as make it much cheaper to deploy new public services. The project integrates industry collaborators who will provide access to cutting-edge network technologies and assist in technology transfer to the industry. For the broader community of users and society at large, the project’s artifacts will be made publicly available at https://sites.google.com/cs.washington.edu/netfusion/home, enabling the development of high-performance data center 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

The goal of this proposal is to design and deliver improved measurements of the microwave continuum emission from circumstellar disks to better understand the evolution of their constituent solids during the epoch of planet formation. The project will exploit synergies between centimeter continuum observations of protoplanetary disks with the VLA and millimeter continuum data from other facilities (e.g., ALMA) to significantly improve characterizations of the radial variations in temperatures, densities, and dust properties. The project will develop a new Course-Based Undergraduate Research Experiences in radio astronomy to connect research programs to the classroom to train junior scientists in radio observations. This proposal advocates taking advantage of the lower optical depths at < ∼ 100 GHz (∼cm-wavelength), focusing on data from the VLA, to (A) improve some of the fundamental methods for analyzing microwave continuum data; (B) leverage the novel combination of resolved multifrequency images and dense spectral coverage to enhance the precision and accuracy for inferences of key properties (e.g., the radial profiles of dust densities and particle size distributions); and (C) survey the cm-band emission from a large, diverse disk sample to better understand the origins of foundational demographic trends identified from data at higher frequencies. The project will produce a comprehensive census of the cm-band luminosities and spectral indices from dust and radio contamination in a single disk population and will create a new set of benchmarks to guide the development of next generation dust evolution population synthesis models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The Arctic Ocean has experienced a significant loss in sea-ice coverage since the 1970s, with impacts on marine ecosystems, weather patterns, and economic activities such as fishing, shipping, and resource exploration. Understanding how Arctic sea-ice conditions have varied in the past provides valuable insights into natural patterns in ice coverage and helps refine scientific models used to project future ice cover. This project will investigate Arctic sea-ice cover during past warm periods in the geologic record using advanced geochemical techniques to reconstruct historical ocean conditions. The research will also support workforce development by training graduate and undergraduate students in cutting-edge scientific methods and equipping high school educators with new resources to enhance STEM education. This study will analyze ocean sediment cores to examine Arctic sea-ice coverage and ocean conditions over the past 150,000 years, focusing on two key warm intervals: the Holocene Thermal Maximum (~8,000 years ago) and the Last Interglacial (~130,000 years ago). The research team will use helium and thorium isotopes as a novel, inorganic method for estimating past sea-ice extent, while nitrogen isotope analyses of marine microfossils will provide insights into ocean nutrient levels and biological activity. These data will be collected from three sediment cores spanning a range of Arctic conditions, allowing scientists to assess changes in ice coverage, ocean structure, and nutrient cycling. By integrating these findings with existing data, the project will improve understanding of Arctic environmental history and enhance the accuracy of models used to study changing polar regions. 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.