University of Maryland, College Park

NSF Awards · FY 2025 · 2025-07

This award is to sponsor the "Conference: Understanding Regional Opportunities and Partnerships to Drive American Competitiveness". The conference convenes representatives from academia, government, nonprofits, policy makers, various non-profit funders, entrepreneurs, NGOs, and publishers to discuss and disseminate their findings on innovation, entrepreneurship, and use-inspired translational research necessary to address society's grand challenges. An essential component of the conference's workshops is the role and importance of the social sciences and humanities when developing and optimizing the different technologies. The 2025 conference will particularly focus upon intersection of artificial intelligence innovations, the issues of trustworthiness and ethics, and advancing societal good. The event has several key objectives, such as creating/strengthening public-private partnerships that will advance and accelerate economic development through use-inspired research. Another key objective is to identify opportunities for researchers to contribute to the successful development and implementation of artificial intelligence and other emerging technologies that are also ethical and human-centered. As part of this conference, participants will expand communities-of-practice and identify models that measure the societal impact and economic growth in regional ecosystems through collaboration and engagement. 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 · 2025-07

Partial differential equations describe dynamic relationships between several mutually dependent quantities. As such, they are ubiquitous in the mathematical modeling of systems in the sciences, engineering, and social sciences. This project will explore the question of unique continuation of maps in several complex variables which, roughly speaking, asks when infinitesimal data about a function that maps one higher dimensional surface to another determines the macroscopic behavior of the function. The project will also support the PI in writing a book for graduate students on recent developments and open questions in function theory. The focus of the award is the theory of CR mappings between CR manifolds and CR functions. Both are fundamental objects in the theory of several complex variables. Specifically, the project will explore four types of problems, namely, unique continuation for CR mappings, regularity of CR mappings and functions, the Rado property for continuous CR functions, and the strong maximum principle for CR functions. Unique continuation will be explored for CR mappings and the closely related second order subelliptic partial differential equations with real analytic coefficients and second order elliptic equations with Gevrey coefficients. The general problem on unique continuation for CR mappings is the following: determine the geometric conditions on two embedded CR manifolds M and M’ so that if f is a CR mapping from M to M’ that vanishes to infinite order at a given point in M, then f vanishes identically in a neighborhood of the point in question. 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 · 2025-07

This research investigates how shifts in Arctic environments and human activities have affected the feeding behaviors and food chain positions of Atlantic cod over the past thousand years. Understanding these changes is important because Atlantic cod play a crucial role in the Arctic marine ecosystem. Atlantic cod are an essential food source for both marine animals and people. Cod also contribute significantly to the regional economy and cultural identity of Arctic communities. Recent environmental shifts have intensified habitat changes and increased fishing pressures in Arctic regions, impacting species like Atlantic cod and the ecological and economic systems they support. This study uses an innovative technique to track dietary shifts in cod by analyzing chemical signatures in ancient cod bones from archaeological sites. The results will help determine whether the changes in cod’s feeding patterns align with historical increases in fishing, changes in fishing methods, or other natural fluctuations. The outcomes can provide valuable data for fisheries science and policy, supporting efforts to build sustainable fishing practices in response to long-term environmental variability. This project will use segmental isotope analysis of fish vertebrae (SIAV) to reconstruct high-resolution dietary life histories for Atlantic cod (Gadus morhua) from archaeological sites spanning the last millennium. By analyzing sequential stable isotope values of nitrogen (δ15N) and carbon (δ13C) in individual vertebrae, this project will trace the trophic positions of individual fish throughout their lives. This approach offers greater detail than bulk stable isotope analysis, which typically provides an average dietary signature that may mask specific ecological events. This research will address two primary questions: 1) Have trophic positions of Atlantic cod populations changed over the last millennium? and 2) Are these changes associated with increased fishing pressure, shifts in fishing practices, or natural environmental variability? Additionally, this project highlights the potential of fish vertebrae as a data source in archaeological studies, providing a new model for analyzing historical and ecological changes across Arctic marine 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 · 2025-07

NSF’s Secure and Trustworthy Cyberspace (SaTC) program is interdisciplinary and highly competitive. New principal investigators who do not have experience with SaTC expectations and norms often struggle to write successful funding proposals. This workshop offers junior researchers without prior SaTC funding training and experience in proposal development. The workshop’s novelties include providing not just lectures, but hands-on experience such as writing a short mock proposal and participating in a peer-review session modeled on real NSF review panels. This provides participants with direct knowledge of the process as well as peer feedback on their proposed ideas. The workshop’s broader significance and importance lie in allowing junior researchers, especially those without pre-existing networks of successful SaTC researchers, to access implicit knowledge they may not otherwise have access to, enabling them to present their ideas in the best light. This helps ensure that promising ideas for new SaTC research will contribute to our overall understanding of cybersecurity. The 1.5-day workshop includes panel discussions and small-group mentoring from established SaTC researcher “coaches,” as well as mock panel review led by these coaches. Participants write mock proposals before arriving, edit them on day 1 after learning more about proposal writing, and review each others’ proposals on day 2. Participants leave the workshop with an increased understanding of the panel review process, explicit instruction in how to structure and format proposals to meet SaTC expectations, and an expanded peer and mentoring network. The workshop will help new investigators establish themselves within the security and privacy research community so that they can continue to contribute valuable research over the course of their careers. 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 · 2025-07

Partial Differential Equations play a pivotal role in a wide range of applications, facilitating the study of many questions in physics, geometry, meteorology, biology, economics, and engineering sciences, to name a few. This project aims to advance the current understanding of fundamental questions that arise in the context of three canonical classes of nonlinear partial differential equations, which model (i) emergent phenomena; (ii) conservation laws; and (iii) the pressure-less early universe model. While these three classes of evolution equations are well-understood in the one-dimensional spatial setting, the questions of existence, regularity and large-time behavior of solutions for the more realistic multi-dimensional models are mostly open. The plan of this project is to develop novel paradigms to address these questions with emphasis on the multi-dimensional setting. This project also involves mentoring graduate students who will be involved in this research. This project is concerned with the following time-dependent partial differential equations in multiple spatial dimensions. (i) Euler Alignment. The system of Euler Alignment arises as the large crowd dynamics of the Cucker-Smale alignment model. The goal is to study the open question of existence of multidimensional strong solutions, subject to sub-critical initial data, and their large-time behavior with short-range communication kernels. (ii) Nonlinear Conservation Laws. Nonlinear scalar conservation laws admit a regularization effect, where the entropic time evolution of bounded data gains spatial regularity of fractional order s< 1/3. The 1/3 barrier, at least in two or more dimensions, remains an open problem. The project will address this open problem by refining a velocity averaging lemma adapted to deal with one-signed measures and quantify fractional regularity in proper Morrey spaces. (iii) The Pressure-less System. In one-dimension one encounters the inviscid Burgers’ equation. A standard embedding of the system into vanishing pressure is limited to one dimension by the formation of delta-shocks. The goal is to construct vanishing viscosity solutions of the Zeldovich pressure-less system in two or more dimensions. Note that for two or more dimensions, the equations form a non-conservative system. The development of such two-dimensional existence theory is based on spectral dynamics and priori bounds on the spectral gap coupled with compensated compactness arguments. 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 · 2025-07

This research project concerns questions in geometry, analysis, and algebra that can be formulated in terms of nonlinear partial differential equations. One of the research themes is the existence of canonical shapes and structures on manifolds or geometric spaces, related to the original work of Riemann on curvature and Einstein's equations of general relativity. The analytic techniques to be developed in this project are expected to be useful to researchers working in geometry, physics, and related areas. Additionally, the project aims to develop better understanding of relations between complex geometry and convex geometry, with applications to algebraic geometry of thresholds. Some of these relations involve novel generalizations of the Legendre transform, which could be useful in solving a range of partial differential equations, generalizing the theory for the Legendre transform that is a classical tool in mathematics, mechanics, and economics. The project involves research training of graduate students in related topics. Understanding Special Lagrangian submanifolds of Calabi-Yau will deepen understanding of minimal submanifold theory as well as the Lagrangian mean curvature flow and its singularities. Understanding singular Kähler-Einstein metrics and spaces will deepen understanding of smooth Kähler-Einstein metrics on both compact and non-compact Kähler manifolds, including Fano and Calabi-Yau spaces. These spaces are central in a wide variety of fields, ranging from algebraic geometry and number theory to theoretical physics where the Eguchi-Hanson metric appears. Monge-Ampère type equations arise in a wide variety of questions in pure and applied mathematics and have a wide range of practical applications. This project aims to develop methods to construct and approximate such solutions and to study their regularity, which will have applications in other instances where these equations appear. The project also intends to develop novel connections with algebraic geometry, convex geometry, and micro-local 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 · 2025-07

Human microbiomes contain a mixed population of bacteria. These bacteria communicate with each other and with the host. Bacteria produce enclosed capsules, called vesicles, that shuttle information (DNA, protein, RNA) between cells and between the bacteria and the host. The communication can affect the types and relative numbers of bacteria present. They can affect the health of the host by stimulating or suppressing immune responses. They impact the local environment, which can also impact reproductive disease resistance, for example. This project is designed to understand bacterial signal production and transmission in a vaginal microbiome. How bacterial vesicles are produced will be investigated. How these capsules move through barriers such as mucus will be evaluated. How they affect the bacterial community composition, and ultimately, health, is of critical importance and will be studied. The project will also provide research experiences for local high school students and undergraduates. This project is designed to advance understanding of how microbiomes contribute to health, with a specific focus on bacterial extracellular vesicles (bEVs). bEVs are a poorly understood mode of cross-kingdom cellular communication. As membrane-bound, nano-sized particles that transport proteins, small molecules, and nucleic acids, bEVs facilitate cellular communication both locally and at distant sites throughout a host. The model system to be studied is the vaginal microbiome. This project has three technical objectives. The first is to characterize bEV biogenesis. The second is to evaluate how the mucus barrier impacts the fate of bEVs. The third is to determine how bEV function is affected in physiologically relevant environments. The proposed research will advance fundamental understanding of bEV loading and perturbations to bEV-based signaling in response to changes in the host environment. This work differs from current efforts as it examines bEV biogenesis, fate, and function in physiologically relevant model systems, allowing for a comprehensive understanding of the role of bEVs in microbe-host communication. Microcalorimetry, transcriptomics, lipidomics, and proteomics will be used to map changes to bEV loading and production in response to environmental perturbations. Multi-particle tracking technology will quantify the transport of bEVs across mucus barriers. Finally, mucus-based models will probe microbe-host interactions in physiologically relevant systems. Knowledge gained from this research will generate new insights and provide a deeper understanding of bEVs that will prove useful for engineers, microbiologists, and health researchers. 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 · 2025-07

The use of modern computing and data infrastructure is critical to harnessing the full potential of instruments, data, and tools offered by state-of-the-art laboratory facilities, but many scientists do not have the necessary knowledge of data management, scientific programming skills, or the ability to use computing resources to bring them to bear for data analysis, leading to new discoveries. This project - CITEAM - addresses the gap by developing an innovative training program targeting the materials science research community that relies on advanced microscopes for research and needs to process and manage large data volumes to make fundamental advances in materials science. CITEAM provides training for microscope data processing, the use of Artificial Intelligence methods in data analysis, and effective data management, thereby reducing time-to-science. The project helps researchers in overcoming challenges in handling large-scale datasets and utilizing novel computing methods and resources. The project increases computing skills, awareness, and literacy for researchers with limited computing expertise, thereby accelerating the scientific innovations in materials science. The CITEAM project brings together a team of researchers with expertise in cyberinfrastructure (CI) as well as in imaging-enabled materials science to develop an innovative training program targeting the materials science community that relies on advanced microscopes (e.g. Transmission Electron Microscopes (TEM)) for research. This project aims at optimizing return on a state-of-the-art investment in physical infrastructure - a new aberration-corrected Transmission/Scanning Electron Microscope (AC TEM/STEM) at UMD. The training program covers several relevant thematic areas - TEM instrument software, image analysis, scientific computing, application of AI in TEM image and data analysis, diffraction and spectroscopy data analysis, distributed computing for microscope data processing, data curation, and FAIR principles. The training program includes an additional element of "training the trainers" by exposing the research facilitators and laboratory staff scientists to advanced CI topics, empowering them to guide others and innovate in the use of CI for materials science. Training is offered for both users and trainers in a multitude of modalities to promote efficient learning - self-paced modules, video lectures, templates and catalogs, office hours, training sessions at annual CITEAM Users' workshop, and tutorials at domain science conferences. CITEAM promotes community building by developing a coordination network comprising similar imaging laboratories, different domain science communities that use advanced microscopes, and experts from national CI resource providers. The CITEAM coordination network helps in adapting and disseminating training materials beyond the participating institutions, ensuring both scalability and sustainability of the 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 · 2025-07

Non-interactive Zero-Knowledge (NIZK) proofs provide a powerful means of verifying properties of private data without revealing any of that data: anyone can check the resulting proofs at any time in the future. Rapid advancements in recent years have made NIZK technology a promising tool for verifiable computation, privacy enhancing technology, and verifiable machine learning; however, these systems are built and optimized in an ad-hoc manner, with very little exploration of the fundamental programming abstractions that NIZK proofs enable. The project’s novelties are programming abstractions that simplify the development and maintenance of projects using NIZKs, techniques for reasoning about the security of large programs using NIZKs and the performance implications of using NIZK technologies, and languages for describing the security policies of realistic applications. The project's impacts are increased productivity of developers and increased software quality of software artifacts that leverage NIZK technologies. The project will also train graduate students. This project will develop techniques for (1) combining code that efficiently generates a NIZK proof and code that verifies it, tackling its inherent duality head on; (2) expressing the security guarantees provided by NIZKs in the language of information-flow control (IFC), addressing both how such guarantees compose in larger systems, as well as exploring the performance overhead of the new abstractions using static amortized resource analysis; and (3) integrating advances in realistic applications, verifying end-to-end security properties of systems that rely on NIZK proofs, such as anonymous credential systems and private payment systems with anti-money laundering protections. 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 · 2025-07

The project focuses on the behavior of disordered systems in statistical physics, which arise when studying physical systems in inhomogeneous media. The research aims to understand the effects of inhomogeneity on the systems’ behavior. Focus is given to membranes in random environments and the study of their fluctuations. Such membranes arise naturally when classical models, such as the Ising or XY models, are either assigned random inhomogeneous interactions or are placed in a random magnetic field. They have also recently been discussed in the study of random quantum circuits. The research aims to explore the emergent physical phenomena from a mathematically rigorous standpoint. The project also provides research opportunities for graduate students. The large-scale properties of equilibrium statistical physics systems, such as the classical Ising or XY models, can change significantly when the models are placed in an inhomogeneous environment. Such environments are often modeled by quenched (frozen-in) disorder and lead to random-bond or random-field versions of the classical systems. Another instance of this phenomenon is first-passage percolation, where the metric structure of the space is altered by random local perturbations. This project will study fundamental problems in these areas with rigorous mathematical tools. The problems are centered on minimal surfaces in random environments, which are models for both the interfaces between different spins in random-bond and random-field Ising models, for the spins of the random-field XY model, and for geodesics in first-passage percolation. The project aims to capitalize on recent progress to obtain an in-depth analysis of the properties of minimal surfaces in random environments. 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 · 2025-07

This I-Corps project is based on the development of a photonics (light-based) analog-to-digital converter (ADCs) system used in telecommunications, sensing, and computing. Analog-to-digital converters are indispensable hardware in modern technology as they play a crucial role in connecting the signal from an analog device such as a sensor with the digital world. However, current ADCs operate in the electrical domain and have strict trade-offs between speed, precision, and power due to the nature of electronic architectures. This technology is a light-based solution utilizing photonic integrated circuits (PICs) to address this challenge. To date, no photonic ADCs are available on the market due to fabrication and energy consumption issues associated with existing methods. The technology may provide the high-speed data processing required by artificial intelligence (AI) and internet of things (IOT) applications and may satisfy the need for energy-efficient, high-performance solutions across many industries. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of photonic analog-to-digital converters (ADCs). The technology is focused on two approaches: all-optical passive (zero-power) and high-speed electro-optical converters using scalable and Complementary Metal-Oxide-Semiconductor (CMOS)-compatible photonic integrated platforms. The designs incorporate photonic integrated resonators modulated via thermo-optic or electro-optic effects to trigger a single output channel, selected based on the power of an analog input signal. The output channel is then guided through photonic circuitry to photodetectors that identify each binary bit. Both methods are expected to demonstrate efficient performance, CMOS compatibility, and scalability that surpasses conventional electronic approaches. Current ADC technologies are impractical due to energy speed requirements. The ADC technology addresses these challenges as it is also light-based, and avoids electro-optical conversions entirely. This technology may help to unlock the full potential of any photonic computing chip. 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 · 2025-06

This I-Corps project focuses on the development of a software platform that assists in the design of cooling systems for data centers. Data centers serve as the core infrastructure supporting communication networks, online services such as cloud storage and data processing, and high-performance computing. As demand for these facilities rises due to applications such as artificial intelligence, the power required to operate them is skyrocketing, with nearly half of this power being used for cooling. This digital twin software, which provides a virtual online representation of a data center’s cooling system, offers significant advantages to the data center industry, by aiding in more energy efficient operations, longer equipment lifespan, and lower operational costs – all of which are crucial in such a competitive and energy-intensive industry. The technology can also improve the reliability of services that use these data centers, benefiting government and commercial users as well as the general public by ensuring better, more consistent access to digital services and applications. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a modular simulation framework for analyzing and optimizing the thermal performance, energy efficiency, durability, and cost of electronic cooling networks. The framework integrates several scientific techniques into a unified workflow. A reduced-order thermal model is used to approximate three-dimensional temperature distributions across components, servers, racks, and rooms based on high-fidelity simulation data, enabling rapid assessment of new design configurations. This model is dynamically coupled with a flow solver that computes pressure, temperature, and mass flow rate across interconnected cooling components, including channels with phase-change behavior. The solver supports both single- and two-phase working fluids, making it applicable to traditional air and liquid cooling systems as well as more advanced boiling/evaporation-based techniques. Reliability prediction is incorporated using statistical degradation models and system-level availability metrics. The platform also includes cost estimation routines that compute key financial indicators, such as operational expenses, capital investment requirements, and investment returns, based on modeled performance. This integrated approach enables users to compare cooling strategies not only based on technical performance, but also in terms of long-term cost-effectiveness and system reliability, thus enabling more informed decision-making in the design of data center infrastructure. 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 · 2025-06

This I-Corps project is based on the development of a cancer treatment for locally advanced cancer that cannot be removed by surgery. This treatment is aimed at cancers involving tumors spread to nearby tissues or lymph nodes, but not to distant organs. Currently, over 40,000 cases of liver and pancreatic cancers that cannot be removed by surgery are diagnosed in the U.S., often with poor prognoses and high recurrence rates. This technology provides a targeted, minimally invasive solution that can be integrated into current clinical practice to improve outcomes. The technology is based on removing diseased tissue with light activation, which may enhance survival while reducing treatment-related toxicity. As a result, patients may experience fewer complications and hospitalizations. This technology may help advance cancer care, lower healthcare costs, and expand access to more effective, less toxic therapies for patients with locally advanced disease. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a light-triggered ablation technology for cancer photodynamic therapy. While photodynamic therapy is effective at killing primary tumor cells, addressing tumor spread to nearby tissues and lymph nodes remains a challenge, as most cancer deaths result from metastasis. This technology combines two non-heat-based chemical ablative methods that have been proven safe with low adverse effects for unresectable tumors: photodynamic therapy, which involves delivering light-activated, tumor-killing photosensitizers, and percutaneous ethanol injection (PEI), which involves the direct injection of pure ethanol into tumor nodules. This therapy is designed for localized tumors that cannot be surgically removed, such as unresectable liver cancer, pancreatic cancer, and soft tissue sarcoma. This approach ensures the treatment remains confined to the tumor site, thereby significantly minimizing side effects, compared to standard chemotherapy. Light activation enables localized tumor destruction and may also stimulate immune-mediated tumor killing. When integrated with computerized tomography (CT) imaging, this platform allows real-time monitoring of drug delivery and dosing adjustments, supporting personalized therapy. This technology may advance cancer care and overcome the limitations of current cancer treatments. 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 · 2025-06

Science education at the elementary level is crucial but often challenging for both young students and teachers. Traditional methods of teaching biology can be difficult for students to understand and engage with because they often involve a “one-way” approach that lacks meaningful learning contexts. Similarly, teaching computational thinking (CT) faces its own set of challenges: many teachers and schools lack affordable resources to integrate CT concepts into the existing curriculum. This project promotes science education using affordable, programmable, and easy-to-deploy embodied robotics as part of the culturally responsive curriculum to teach both biology and CT in elementary schools. The project aligns with four of NSF’s 10 Big Ideas, including Future of Work at the Human-Technology Frontier, Growing Convergence Research, NSF INCLUDES, and Understanding the Rules of Life. This project tackles multifaceted and interdisciplinary approaches across STEM education, computational thinking, embodied learning, robotics, and end-user programming, laying the foundation for future elementary education by integrating interactive and embodied learning into the curriculum. The project centers around three main research objectives. Firstly, it identifies the challenges and opportunities within the current curriculum and designs innovative learning strategies. Employing a co-design approach, the investigators engage local classroom teachers and students to integrate Biology and Computer Science (Bio+CS curriculum) through embodied robotics. Secondly, the project focuses on developing novel, low-cost, intelligent, and easy-to-deploy embodied robotics and software tailored for elementary classrooms. The embodied robot is designed to move across a student’s body, visualize different bio-signals in situ and on-body, provide direct visual and tangible feedback, and support embodied programming activities. This design and development process follows a human-centered iterative design approach, with early prototypes tested with teachers and students in small batches. Thirdly, through design-based implementation research, investigators implement the Bio+CS curriculum and examine how students from diverse backgrounds interact with it. The project identifies effective strategies for teaching complex scientific concepts using innovative technology, thereby bridging educational gaps and fostering a more inclusive approach. Integrating biology and computational thinking through embodied robotics thus broadens participation in STEM education. 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 · 2025-06

Nontechnical: Commercialization of quantum networks is a key goal for the Engineering Research Center for Quantum Networks (CQN). Toward identifying the commercial applications of the quantum networks, CQN is actively working on developing corresponding application roadmaps. In an early effort, the industry partners at CQN developed the Quantum Networks Application Roadmap (QNAR) 1.0 published in 2022. In a new attempt to update the roadmap, CQN has partnered with the Quantum Economic Development Consortium (QED-C) for a one-year project to expand the previous effort and develop QNAR 2.0. QED-C is the largest international public-private consortium for quantum technologies with 188 member companies. This partnership combines CQN’s technical leadership in quantum networks with QED-C’s experience with industry roadmap development toward CQN’s mission for commercialization. Also, such a partnership further establishes CQN as a leading center for quantum networking technology. Technical The technical goal of CQN ERC is to develop and integrate all the key technologies to realize fault-tolerant quantum networking, by demonstrating: (1) the underlying device functionalities—viz., to entangle remote telecom-compatible quantum memory registers—built with integrated-photonic chips that are high-efficiency-coupled to millions of Silicon- and Tin-vacancy color centers—mediated by high-fidelity high-rate photonic entanglement generated by the zero-added-loss-multiplexing source, (2) scale these up to demonstrate quantum repeater networks designed using scalable multiplexed color-center repeater nodes interspersed with quantum-logic-rich trapped-ion-qubit-based repeater nodes to facilitate quantum error correction, and (3) design architectures and protocols for the quantum internet that seamlessly interoperate with the classical internet, to demonstrate a suite of carefully-chosen multi-user-group algorithms for routing entanglement, scheduling parallel quantum communication and use cases in distributed sensing, at our testbeds in Tucson, Boston and Maryland. These testbeds serve as a proving ground for our research, education and workforce development, and facilitate access to R&D partnerships with CQN’s 20+ industry-member innovation ecosystem. CQN’s engineering research coordinates with social science research on security and privacy laws, developing a measurable index to quantify the goodness of quantum networks as they develop and grow, studying unintended biases in quantum-network-driven applications, and implications of open-source quantum cloud access. As a public-private partnership of academia, the industrial base, leading international partners, national labs and partners, the CQN ERC is a world leading full-stack quantum networking program that serves as an American national hub for advancing the development of the quantum internet and road mapping its anticipated applications and societal impacts. CQN will continue to build on its successes in developing a new discipline--Quantum Information Science and Engineering (QISE), with interdisciplinary courses spanning our member universities, to train the future US quantum engineering workforce. CQN’s EWD program will also build on its highly popular Winter School to create an internationally renowned resource for quantum networking education, and an EWD Fellowship that funds innovative ideas in quantum education. CQN, in concert with its industry advisory board, will develop a certificate program to equip industry engineers to pursue quantum upgrades to appropriate technologies and use cases. Under the unique leadership of a quantum information scientist and a quantum device engineer, this highly interdisciplinary University of Arizona led ERC draws from core partner institutions MIT, University of Maryland, University of Massachusetts Amherst and Harvard University - along with member institutions University of Oregon, Northern Arizona University, University of Chicago, and Brigham Young University. CQN also enjoys the support of a strong industry consortium and the leading international partners in advancing quantum internet technology through advisory boards. The CQN ERC will help to support the strategic vision that is laid out in a 2020 White House memorandum on America's Quantum 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 2025 · 2025-05

This I-Corps project focuses on the development of a software-based tool that enables health-focused applications and medical devices to detect health-related features in a person's voice. This tool aims to solve a widespread problem in healthcare: the lack of accessible, non-invasive, and continuous methods for monitoring chronic and emerging health conditions. By analyzing speech for vocal patterns linked to conditions such as neurological disorders, respiratory diseases, and mental health concerns, this tool supports early detection and personalized treatment. The ability to monitor these conditions using only voice recordings has broad implications for improving patient outcomes, reducing healthcare costs, and extending high-quality care to in-person and remote communities. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on an application programming interface that enables medical systems to extract and analyze voice biomarkers from speech audio using advanced signal processing and machine learning techniques. The tool identifies features derived from articulatory dynamics and acoustic characteristics of the voice that correlate with various health, wellness, and medical states. The solution incorporates near real-time, cloud-enabled processing and uses state-of-the-art models to analyze subtle vocal changes, including deep learning frameworks that go beyond traditional spectral features. This technology enables seamless integration into healthcare systems, offering high sensitivity in detecting early signs of health problems and / or deterioration over time. The solution is designed to be efficient, scalable, and adaptable across a wide range of healthcare applications, offering care providers, clinicians, and developers a new, data-driven tool for enhancing diagnostic and monitoring capabilities. 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 · 2025-05

This I-Corps project focuses on the development of time and energy efficient solutions for mathematical optimization, a cornerstone of industries such as finance (portfolio optimization), supply chain management (routing and scheduling), medical imaging and treatment development, power grid control, and more. Traditional solvers often fail to fully leverage emerging hardware like graphics processing units and quantum devices due to limitations in both theoretical algorithm design and implementation strategies. The optimization engine solution is uniquely suited to harness the potential of such hardware, aiming to significantly enhance the efficiency of solving complex, industry-scale optimization problems. By providing critical decision-making intelligence in industrial production processes, this optimization solution has the potential to drastically reduce production costs and increase revenues. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a novel algorithm for optimization called quantum Hamiltonian descent which is inspired by physical processes and designed to be compatible with quantum devices (e.g., quantum Ising machines) and high-performance classical processors like graphics processing units. The prototype for graphics processing units has demonstrated a 100x performance improvement over the state-of-the-art commercial solvers on well-established benchmarks for quadratic programming problems. Additionally, prototypes for quantum and semi-quantum devices have shown orders-of-magnitude improvements in either time efficiency (quantum) or energy efficiency (semi-quantum) for small-scale problem instances allowed by current hardware. To ensure seamless integration, a user-friendly software interface has been developed, which is tailored to existing optimization software ecosystems. 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 · 2025-05

Information about the evolutionary relationships between species and populations is critical for many biological analyses. For example, evolutionary trees and networks are commonly used to study the evolution and genetic basis of traits, with applications in fields ranging from medicine to agriculture and conservation. Despite significant progress over the past decade, many important parts of evolutionary history remain unresolved or are subject to debate. An opportunity to address these open questions has recently emerged. Technological advances have led to more complete and high quality genome assemblies than was previously possible, even resolving highly repetitive regions, like the 30-50% of vertebrate genomes made up of retrotransposons--DNA sequences that when active can copy-paste themselves to new locations. This development is significant because retrotransposons are major drivers of genome expansion and innovation--and their presence or absence at related positions across the genomes of different species are powerful markers of evolution. However, new computational methods are needed that leverage retrotransposon data for species tree and network reconstruction, an emerging field called retrophylogenomics. Only a few methods have been developed to date, but they are based on simple models that do not reflect retrotransposon dynamics. This project will address such challenges, building the computational infrastructure needed for fast and accurate evolutionary analyses of retrotransposons. This research will be integrated with an education plan to provide hands-on research and training opportunities in interdisciplinary computing and data science for undergraduate and graduate students. The software tools and curated data sets from this project will be incorporated into outreach for middle and high school students, connecting computing fundamentals with scientific discovery. The objective of this project is to address the core algorithmic and statistical barriers to retrophylogenomics, an emerging field concerned with leveraging retrotransposons to understand the evolution of species and populations. This goal will be pursued through the following activities. (1) Development of fast and user-friendly data processing workflows for calling retrotransposon variants from whole genome alignments, alleviating the need for manual curation. (2) Development of realistic models of retrotransposon evolution, integrating knowledge of retrotransposon dynamics with evolutionary models acting on multiple scales from the species/population-level to the molecular level. (3) Data simulations and model validation. (4) Design and analysis of methods for inferring species trees and networks from retrotransposons, blending statistical techniques with discrete algorithms. (5) Implementation of methods in open-source software. (6) Evaluation of method accuracy and scalability on simulated and real data. (7) Application of methods to vertebrate genomes to generate the largest catalog of retrotransposons variants, along with reconstructed species trees or networks. Graduate students will fully participate and be mentored in these activities. This project will create research opportunities for undergraduate students focused on data exploration and error detection via visualization and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This program will recruit 10 to 20 undergraduates per year to come to the University of Maryland at College Park where they will work for 10 weeks on team research projects with expert mentors. The goal of these projects is to take theoretical results and make progress towards using them in practice. To illustrate our approach, we list four real examples of topics studied in this project and describe how they serve the national interest. (1) Making programs run faster (e.g., by making them parallel so they can be run using multiple computers or cores). This will benefit all sciences as more and more work in science relies on fast computation. Note that the most recent Nobel prize in Chemistry went to work that used computation in a major way. (2) Making computer more secure (e.g., by studying secure cryptography and efficient communication schemes). Advancing secure communication is important for both the Military and Financial sectors. (3) Getting Artificial Intelligence (AI) to make less errors. AI now plays a critical role in healthcare, e.g., in disease diagnosis, selecting treatment, clinical lab testing, drug discovery, and many other tasks. Many such applications are hampered by the fact that AI can make errors and harm people as a result. Practical research on AI aimed at reducing mistakes is important for the health and prosperity of our nation. (4) Make quantum computers practical. Sometime in the future practical quantum computing may be a reality. We need to both hasten this advance and know how to take advantage of it. Two important applications are simulating quantum mechanics (which will help science) and cryptography (which is important for security). This program will give undergraduate students a unique opportunity to do research. This is especially important for students who normally do not have that opportunity, in particular, students from non-research-focused schools and from underrepresented groups. There will be a special effort to recruit such students. This effort will increase the number of researchers which will help science and technology progress faster. This program will give students an idea of what graduate school is like in two ways: (1) their research projects are scaled down versions of Ph.D. theses; and (2) there will be interaction with other students in Research Experience for Undergraduates (REU) programs, current graduate students, and faculty. The students will be offered a variety of research projects, and will also be given a strong voice in the creation of their own projects. We list sample projects below: (1) Parallel Computing: If one computer or core can solve a problem in time T then perhaps p computers (cores) can solve that problem in T/p time. In this project students will study how to design practical and efficient algorithms that can be run on real-world parallel computers and obtain large speedups. (2) Cryptography and Security: Most current cryptosystems are based on factoring (or other problems in number theory) being hard. Since factoring might become easy due to quantum computers becoming realistic, there has been much work on designing alternative cryptosystems. This project will code up these alternatives to test if they are easy to use, efficient, and secure. (3) Question-Answering Systems (AI): There are many AI programs that will, given a question, try to answer it. If the question is given as text, they often do very well. If the question is given as an image (e.g., "is this a dog?") then they often do poorly. This project will discern which questions they do badly on and strive to make the system better. (4) Quantum Error Correction: If Alice sends a string of (classical) bits over a noisy line then she can use error-correction techniques that will detect and correct errors. One obstacle to real quantum computers is the lack of good error-correction for qubits. This project will study known quantum error correction techniques and extend 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 · 2025-04

Many industrial and environmental processes involve thin liquid films to transport and deposit solid particles. However, the particles can destabilize the film, reducing the transport efficiency and resulting in an ill-controlled deposition and contamination of substrates. The classical description of films of pure liquid fails to capture the interplay between the air-liquid interface and the particles. This CAREER project will characterize and model the role of interfaces in suspension dynamics, using state-of-the-art experimental studies coupled with numerical simulations. The results will advance the fundamental understanding of suspension flows under confinement, establishing when and how particles disturb thin liquid films. The proposed work will provide a framework to model and control advanced coating, aerosol-based treatments, and transport in porous media. As a result, this CAREER project will have a broad environmental and economic impact. The research will provide educational opportunities for high school, undergraduate, and graduate students, in particular from under-represented groups. An integrated curriculum on water contamination will inspire the participation of local K-12 and undergraduate students in STEM. The goal of this CAREER award is to develop a new understanding of capillary dynamics when solid non-Brownian particles are dispersed in a Newtonian liquid phase. This configuration encompasses a variety of fundamental problems and practical situations. Past studies have focused on surface tension effects during the formation of liquid films and drops of a pure Newtonian liquid. However, when the liquid contains solid particles, the rheological description of a suspension fails to capture the interfacial dynamics at play when the thickness of the liquid becomes comparable to the particle size. The experimental approach bridging different length and time scales will describe how the bulk behavior and local heterogeneities contribute to the dynamics of capillary objects. More specifically, the study will consider model thin films, self-suspended or bound by a solid surface. First, the formation of a thin-film of suspension on a substrate will illustrate how the particles are entrained and deposited depending on the dynamic wetting and stability of suspension films. The researchers will then examine suspension sheets and their atomization to provide guidelines to describe sprays. The fundamental knowledge obtained through the proposed work will lead to a better description of multiphase flows involving a solid dispersed phase during the formation, flow, and fragmentation of suspension films and sheets. In addition to improving process efficiency, the knowledge should bolster the development of new coating processes and inspire further research on heterogeneous capillary flows. 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 · 2025-04

This I-Corps project addresses the critical thermal challenges in data centers. Data centers house thousands of computer servers and electronic components that generate significant heat. Data centers currently consume vast amounts of energy and water for cooling, which is essential to prevent overheating, maintain performance, and ensure reliability. The increasing demand for artificial intelligence (AI) and cloud computing is driving the development of more powerful logic chips and the expansion of data centers worldwide. However, these advancements result in generating even more heat, which must be effectively managed to ensure optimal performance. This technology, a direct-to-chip, two-phase, liquid cooling technology, could enhance the cooling efficiency of high-powered chips, potentially reducing the energy consumption of data centers. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a novel, direct-to-chip, two phase, evaporative cooling system for thermal management of high-powered logic and power electronic semiconductors. A cold plate, placed directly on the chip, routes and creates microdroplets on top of a set of micropillars. These microdroplets evaporate in response to heat from the chip, dissipating heat in the system. Compared to state-of-the-art direct to chip single- and two-phase liquid cooling technologies, this two-phase evaporative cooling module operates under a significantly lower coolant pumping power, potentially reducing the energy consumption of data centers. A coolant flow loop and flow control algorithm are also implemented to harness the maximum benefit of the direct-to-chip evaporative cooling technology. 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 · 2025-04

Rare events play a critical role in fundamental scientific processes, such as diffusion and chemical reactions, which have broad applications in materials science, chemistry, and biology. However, predicting these events remains a major computational challenge due to their rarity and the complexity of high-dimensional potential energy landscapes. This project aims to develop a foundation model for predicting rare events across diverse atomistic systems, significantly reducing computational costs and enabling more efficient scientific discoveries. By leveraging recent advances in artificial intelligence (AI) and computational materials science, this research aligns with the National Science Foundation’s mission by promoting the progress of science and advancing national prosperity. The project will accelerate the discovery of new materials and chemicals, benefiting industries such as clean energy and sustainability. Additionally, the open dissemination of models and datasets will foster education and contribute to workforce development in AI and materials science. As part of its outreach efforts, the project will engage students from different levels through educational workshops, mentorship programs, and open-access learning resources, equipping the next generation of researchers with cutting-edge computational tools. This project focuses on the development of a general-purpose foundation model for predicting rare events in atomistic simulations. Unlike conventional machine learning approaches that require extensive retraining for specific materials, this model leverages advanced AI techniques—such as equivariant Transformers, generative models, and multimodal learning—to enhance prediction accuracy and generalization. To address data scarcity, the model integrates high-fidelity graph neural network interatomic potentials, large density functional theory databases, and synthetic data from generative models. The proposed workflow enables the prediction of transition states, pathways, and reaction rates for rare events. In its initial phase, the project will focus on diffusional rare events in inorganic solid-state materials, demonstrating applications in energy storage technologies such as batteries and fuel cells. The outcomes will provide a computational foundation for modeling and predicting rare events across multiple scientific disciplines, accelerating breakthroughs in materials discovery and beyond. 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 · 2025-02

Recent years have witnessed significant progress of learning in dynamic environments. Many such success stories, e.g., AlphaGo, autonomous driving, and robot learning, naturally involve “multiple agents”. Note that these agents usually operate under “Information Constraints” as the underlying system state is only partially observable, and each agent only has local information that differs across agents. Despite the practical relevance, theoretical foundations for such settings are not well developed. The PI proposes three novel research thrusts (RTs) that bridge the insights from Control Theory and Machine Learning (ML). In RT 1, PI will formally introduce “Information Structure”, a well-studied notion in Decentralized Stochastic Control, into the theoretical studies of dynamic multi-agent learning. In RT 2, PI will theoretically ground several new empirical paradigms that address Information Constraints in multi-agent dynamic learning, with non-asymptotic complexity analyses. In RT 3, PI will introduce the perspective of “Learning-in-Games”, justifying equilibrium as the “emerging” outcome from agents’ independent learning, into this information-constrained and dynamic setting. The principles and algorithms will be validated on new testbeds both in simulations and on robotic platforms. This program is expected to bring significant changes to the foundations of multi-agent learning in dynamic environments. From a ML perspective, the introduction of Control principles as Information Structures will help ground many empirical advances systematically. From a Control perspective, the formalization of the new ML regimes will open up new research problems to further understand the “Value of Information” for “Learning” purposes in multi-agent systems, in addition to the well-studied subject of its value for “Optimization” purposes. Instantiating the “Learning-in-Games” perspective necessitates completely new solution concepts and learning dynamics. The proposed research is interdisciplinary, integrating fundamentals from Control Theory, Game Theory, Statistics, Theory of Computation, and Economics. This program will advance the fundamental science of principled Large-Scale Autonomy, with broader impacts on socio-technical applications, including transportation systems, power networks, robotics, and supply chains. The program will design new curricula particularly on multi-agent dynamic learning, mentoring undergraduate students for robotic competitions, outreach to K-12 students, and building the community of multi-agent learning through new academic events and industry collaborations. 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 · 2025-02

The broader impact of this I-Corps project is based on the development of a robotic-assistive technology designed to ease the workload of healthcare staff by automating routine and complex tasks in healthcare settings. As the world's elderly population grows, increasing long-term care needs and rising patient-to-nurse ratios are putting a strain on healthcare systems. These pressures can lead to nurse burnout, diminished quality of care, and a higher rate of patient readmissions within 30 days. The technology aims to alleviate these pressures by performing both direct patient-facing tasks such as assistive feeding and indirect tasks like the delivery of medicines and room turnover. The solution promises to return valuable time to healthcare staff, enabling them to manage more patients efficiently and improve the overall quality of care. This innovation holds commercial potential in a healthcare industry increasingly reliant on technological solutions to meet growing demands, thereby enhancing the sustainability of healthcare systems and improving patient outcomes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of advanced artificial intelligence technologies integrated into a mobile, robotic-assistive technology platform capable of adaptive, responsive interactions within varied healthcare environments. The intellectual merit of this project lies in its novel application of Learning from Demonstration (LfD) algorithms, which are tailored for dexterous, long-horizon manipulation tasks requiring minimal human intervention. These algorithms enable the robotic-assistive technology to quickly learn and adapt to new tasks from limited demonstrations. Additionally, the integration of Large Language Models facilitates natural interactions between healthcare workers and the robotic-assistive technology, ensuring ease of use and enhancing operational effectiveness. The initial focus has been on assistive feeding, with demonstrated ability to handle a range of food consistencies to serve a variety of patient feeding needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Next-generation communications and computing, both in the classical and quantum regimes, stands to gain significantly from compact, dense encoding and manipulation of information using scalable, tightly integrated photonic circuits in silicon-based and beyond-silicon materials. Photonic integrated circuits (PICs) enable such information processing using many degrees of freedom of light, such as the wavelength (i.e., the color). Despite being well understood, the transverse modes of multimode waveguides, another degree of freedom, remain vastly underexplored, with current demonstrations limited to passive, bulky (0.1mm - 1mm-scale) conversion between a few modes. To enable their full potential, new materials with active reconfiguration are clearly needed. Phase change materials, which can dramatically change their electronic, optical, and physical properties during solid-solid phase transitions, offer a promising solution. This project will explore a new class of low-loss phase change materials that allows to control light propagating within photonic circuits and, in particular, trigger the transitions between modes within a waveguide. Furthermore, this project aims to use these mode conversions in creating “synthetic dimensions”, which expand the possibilities for processing and computing with light while preserving the compactness and low energy of a single device. The project has a comprehensive educational plan. The team will jointly provide a 10-week research experience for undergraduates on nonlinear integrated photonics through the UMD TREND program supported by NSF. Moreover, the team’s efforts would support the development of a workforce in photonics, from unconventional undergraduate programs in Materials and Mechanical Engineering, to alleviate the shortage of Electrical Engineers in this field; thus, supporting current national needs. This project leverages interference between many transverse spatial modes of multimode waveguides to create a “synthetic modal dimension” and dynamically reconfigure their connectivity using nonvolatile phase-change materials (PCMs). The central hypothesis is that suitable design of transverse mode converters will enable a wide range of lattice connectivities due to the ability to design any desired coherent coupling between these modes, while the large index tunability of PCMs will enable compact, active reconfiguration of this coupling. This research includes the design, fabrication and demonstration of passive multimode synthetic dimension platforms and the realization of topological Hamiltonians, all on a low-loss, high-confinement photonic integrated circuit. Moreover, the team will explore PCM-driven dynamic reconfiguration of optical modes through suitably patterned PCMs for ultra-compact post-fabrication mode coupling and conversion, and eventually use them to realize reconfigurable synthetic modal dimensions for fundamental studies of phase transitions in topological nanophotonics and applications in quantum and analog unconventional optical computing. 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.