University of Texas at Dallas

NSF Awards · FY 2026 · 2026-09

This project studies rational functions. Rational functions arise in modeling and computation in complex systems and provide a flexible yet numerically tractable means of describing a wide range of phenomena. Their study is key to advancing knowledge in areas such as signal processing, dynamical systems, and scientific computing. This project seeks to contribute to broader progress in the mathematical sciences by developing new understanding of the structure of the space of rational functions, with an eye towards applications as well as advancing foundational knowledge in analysis and dynamics. As part of the project, future generations of mathematicians are trained through the establishment of a yearly undergraduate summer research program and through engagement in outreach activities involving the broader community. Rational functions are usually described using zeros and poles as parameters, or by coefficients in a representation as the quotient of two polynomials. These descriptions do not capture important aspects of the structure of this space. The investigator plans to use the theory of quasiconformal mappings in one complex variable to parametrize spaces of rational functions using critical points as parameters, together with combinatorially defined objects needed to distinguish between functions sharing a common set of critical points. This approach is expected to lead to new understanding of the space of rational functions, with potential applications to analysis, dynamical systems, and topology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

The conference “Billiards and Stars: Geometry and Dynamics” is going to take place in Guanajuato, Mexico on August 10-14, 2026. It will focus on recent developments and interactions of two broad mathematical domains: one of them is mathematical foundations of Celestial Mechanics, the area rooted in sky observations since antiquity, later boosted by Newton's discoveries, and flourished during the last century with novel applications of topology, geometry, control theory, and dynamics to study both regular and chaotic motions of celestial bodies, as well as the discovery of many explicit solutions to seemingly intractable problems. The other area is that of Mathematical Billiards, a class of dynamical systems owing its birth to Poincare and Birkhoff and which recently developed into a remarkable domain of symplectic geometry and dynamical systems. This event is going to bridge research communities that rarely meet, to inspire young researchers, and broaden participation across demographics and regions. The conference will foster interaction of scientists and students working in those domains, which share a remarkable geometric structure: both areas involve the study of trajectories governed by geometric and topological constraints. The study of periodic orbits, conserved quantities, and chaos in these systems has produced tools of broad applicability in topology, mechanics, Hamiltonian systems, and discrete dynamics, it inspired new approaches in mathematical physics and robotics. Its recent successes are related to discoveries of new classes of periodic orbits, partial proofs of the Birkhoff conjecture on its integrable cases, and discovery of new billiard-type systems in fluid dynamics. The conference will also celebrate the 70th birthdays of two world experts in those respective domains, Richard Montgomery and Sergei Tabachnikov. The conference website is https://www.cimat.mx/~gil/GD2026/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Modern vehicles increasingly rely on millimeter-wave (mmWave) radar sensors to support driver-assistance and automated-driving capabilities. However, each vehicle still perceives the world only from its own vantage point. Buildings, large trucks, adverse weather, clutter, and simple distance limits can therefore hide critical hazards, such as a pedestrian entering a crosswalk, a vehicle approaching a blind intersection, or a fast-sudden lane merging conflict. While connected-vehicle technologies allow vehicles to exchange messages, they do not currently enable vehicles to share radar-based understanding of the surrounding scene in a way that is timely, compact, and directly useful for safety‑critical decision making. This project explores a new paradigm in which the radar already installed on a vehicle becomes an active part of a wireless network. Nearby vehicles and roadside infrastructure cooperatively share information to construct a richer and more reliable view of the roadway than any single platform could form alone. This project will develop artificial‑intelligence methods that allow each vehicle to convert its radar measurements into compact summaries of the surrounding environment, determine which information is most important and urgent to share, and transmit that information efficiently over bandwidth‑limited and rapidly changing wireless links. The project will combine theory, large‑scale simulation, and laboratory‑scale testbed prototyping. It also includes outdoor experimentation on national wireless research platforms. Together, these efforts will test whether cooperative radar networking can extend perception beyond line of sight without requiring expensive new sensing hardware. If successful, this work could improve roadway safety and inform the design of future cellular vehicle‑to‑everything (C‑V2X) and 6G networks. The underlying ideas also apply to drone swarms, warehouse robotics, and smart‑city sensing systems. The project will train students at the intersection of radar, wireless networking, and machine learning. It will also release open datasets, software, and educational materials. These resources are intended to accelerate research and workforce development in this emerging area. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The project will support student travel to participate in the Project Connect program during the 2026 IEEE International Microwave Symposium, which will be held in Boston, MA on June 7-12, 2026. The conference is the largest flagship meeting of microwave engineering in IEEE, the world's largest professional society covering multiple fields in electrical and electronics engineering. The conference has a long history started in 1957 and now includes an industry exhibition of several hundred companies developing products or providing services in wireless and semiconductor industries. The week-long conference features plenary and invited talks, paper presentation sessions, interactive poster sessions, workshops, tutorials, panels, student design competitions, and many professional networking events. The conference attracts several thousand attendees each year from industry, academia and government research labs to share research findings, foster collaborations, and identify new directions for research and development. The Project Connect program will include a variety of activities to help student participants build their network with other professionals in the technical community. The travel support of this project will provide students many exciting opportunities to learn the state-of-the-art technology advancements and interact with potential mentors in the microwave and wireless technological areas covering a wide range of spectrum from megahertz to terahertz. The project aims to develop the technical interests of the student participants, motivating them to be involved in undergraduate research and to pursue further studies in graduate programs. Through the Project Connect program, the student participants will develop a successful long-term professional career and contribute to the U.S. STEM workforce in the critical industries requiring expertise in microwave, wireless, and microelectronics/semiconductors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This award provides support for the conference “Recent Perspectives on Moments of L-functions,” which will be held at the University of Texas at Dallas from June 1-2, 2026. The concept of an L-function is a mathematical object in the field of Number Theory used to study arithmetic data such as the prime numbers. The conference will focus on the broad theme of understanding L-functions through statistical information known as “moments.” The conference will bring together experts in the field to present recent advances, and other researchers in the area, to exchange ideas and build collaborations. Most of the participants will be early-career researchers--graduate students, postdoctoral researchers, and assistant professors--who will benefit from the conference’s stimulating environment. The study of moments of L-functions, both on the critical line and in families at the central point, has been a fruitful and robust way to understand L-functions and obtain a myriad of applications, including subconvexity bounds and non-vanishing results. This conference will showcase various distinct approaches to moments of L-functions that have been remarkably successful in recent times. Some of these approaches include reciprocity formulae for moments of L-functions, the delta symbol method, higher rank trace formulae and families, and asymptotics for moments of L-functions over special families. The conference will encourage researchers working with one approach to learn about other approaches, with the goals of establishing new links between the different techniques and the development of novel methods. More information on the conference can be found at https://sites.google.com/view/l-functions-utd/home. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

We live in an era of unprecedented data explosion, where autonomous intelligent systems constantly generate and process enormous amounts of data in real-time. For example, today a single autonomous vehicle can generate nearly 40 terabytes of sensor data per hour, which is equivalent to streaming 6,000 Netflix movies simultaneously. Interpreting such massive data with artificial intelligence (AI) requires computing hardware that is both extremely fast and highly energy efficient while providing large storage capability. However, today’s computer chips face fundamental limitations. Most chips are built like a flat city, where different components responsible for computing, storage, and communication sit side by side. This arrangement forces large amounts of data to travel long distances between different parts of a system, consuming significant energy and slowing performance. One promising solution is to build computer chips more like a multi-story building, where different layers of electronics are stacked vertically. This three-dimensional (3D) design can dramatically shorten communication distances, enabling faster operation and lower energy consumption. Achieving this vision requires new types of electronic switches, called transistors, that can be manufactured at low temperatures so they can be safely built on top of existing circuits. This project studies a new class of materials that can enable such vertically stacked chips while maintaining high performance and extremely low energy consumption. The goal of this CAREER proposal is to advance the science of amorphous oxide semiconductor (AOS) nanoelectronics and enable their use in 3D integrated systems. The research will investigate four fundamental aspects: (1) scaling limits of AOS transistors with stackable non-planar geometry, (2) vertical 3D integration, (3) fundamental understanding of AOS device physics including transport, reliability and thermal, and (4) AI-driven acceleration of AOS technology development. The project will first demonstrate stacked nanosheet AOS transistors and experimentally study their electrical, thermal, and reliability characteristics. Detailed characterization will be used to uncover transport mechanisms, self-heating effects, and degradation behavior in ultra-scaled devices. Based on these insights, physics-based transistor models will be developed and integrated into compact models suitable for circuit-level simulation. These models will enable multi-scale simulations to evaluate device performance and system-level implications in vertically integrated architectures. Finally, the project will develop a digital twin framework for AOS nanoelectronics that integrates fabrication, characterization, modeling, and simulation within a unified data-driven platform to accelerate design space exploration, significantly reducing experimental cost and accelerating technology optimization. Together, these efforts will establish the fundamental device physics, predictive modeling tools, and data-driven methodologies needed to enable scalable AOS technologies for next-generation energy-efficient computing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The ionosphere is a region of the space near-Earth (geospace) environment characterized by free ions and electrons produced, in most part, by solar photoionization. Studies of the ionosphere are motivated by a need to better understand (a) the role of fundamental physical processes responsible for ionospheric variability caused by different drivers, (b) impacts of ionospheric variability on radio signals used by civilian and military applications (e.g., GPS, over-the-horizon radars). Ionospheric variability is an important component of space weather. This project will advance the current understanding of ionospheric variability utilizing existing and new measurements made by a radar system that is deployed at the Jicamarca Radio Observatory. The observatory is located at the magnetic equator where severe ionospheric disturbances originate before extending to low and mid latitudes. Improved understanding of ionospheric variability will enhance the reliability of communication, navigation, and remote sensing systems, support space weather prediction, and contribute to national security and workforce development. The project will advance understanding of ionospheric irregularities observed over a wide range of scale sizes and referred to as equatorial spread F (ESF). More specifically, it will advance understanding of the behavior of late-night and post-midnight ESF irregularities and their response to different geospace conditions. The project will create and utilize two-dimensional Ultra-High Frequency (UHF) radar observations of ESF made by a 14-panel version of the Advanced Modular Incoherent Scatter Radar system (AMISR-14) over a wide range of geophysical conditions. The project will also utilize collocated equatorial drift measurements made by a new medium power mode of the Jicamarca Very High Frequency (VHF) incoherent scatter radar. The project will also investigate the derivation of ionospheric parameters (e.g., electron density, electron and ion temperature) from incoherent scatter radar measurements made by AMISR-14. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Reliable, safe, and affordable energy storage is essential for maintaining a resilient electric grid and supporting economic growth. Many rechargeable batteries fail due to the formation of needle-like metal structures called dendrites. Dendrite formation reduces storage capacity and shortens battery life. Aqueous zinc-based batteries use abundant materials, have low safety risks, and offer cost advantages. However, uncontrolled zinc metal growth on the battery electrode limits their practical use. This project will study a new class of electrolytes. They combine liquid-like ion transport with solid-like mechanical strength to resist dendrite formation. The project will identify processes that control metal deposition and long-term performance. The results will support the development of safer, long-lasting, reliable energy storage systems. The results will also benefit manufacturing of high performance energy technologies. The goal of this project is to understand and control zinc dendrite formation by linking electrolyte mechanical properties to interfacial metal growth dynamics. The project will design quasi-solid electrolytes containing swelling clay particles and tune their physical and chemical properties to regulate zinc ion transport and deposition behavior. Specialized in-situ battery cells will be developed to enable three-dimensional X-ray imaging of zinc nucleation, growth, and degradation during battery operation. This non-destructive approach allows direct visualization of early-stage metal growth under realistic conditions. Because dendrite formation is localized and sporadic, machine learning methods will be applied to analyze large image datasets, enabling automated detection of nucleation events, identification of growth patterns, and recognition of failure indicators. By integrating electrolyte design, operando imaging, and data-driven analysis, the project will establish fundamental design principles for suppressing dendrite growth and improving the stability and lifetime of zinc metal batteries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Part 1: NON-TECHNICAL SUMMARY The development of new semiconductors with outstanding properties is of critical importance to modern technology and energy security. With support from the Solid State and Materials Chemistry Program in NSF’s Mathematical and Physical Sciences Directorate, this CAREER project investigates "soft" metal halide and chalcohalide semiconductors, which have shown great promise as emerging materials for electronics and energy applications. These compounds exhibit unusual behavior due to local distortions of the atomic structure, but these distortions are challenging to characterize because they are invisible to most routine characterization techniques. This limits the ability of scientists and engineers to tailor properties critical to electronic performance. Through this award, Prof. Kyle McCall and his team will characterize the local structure of these materials using X-ray scattering techniques sensitive to the local atomic environment. These structural analyses will be complemented by optical and electronic property evaluation, linking the hidden local structure distortions to physical properties, and thereby advancing the understanding needed to engineer desirable semiconducting properties based on local atomic distortions. Additionally, graduate and undergraduate students will receive training in materials chemistry and electronic characterization, strengthening the semiconductor workforce in the Dallas-Fort Worth Metroplex. This CAREER project will expand materials research opportunities with a 4-semester undergraduate team research course and by establishing an Emerging Materials Workshop that provides lesson plans and demonstrations for educators, fostering interest in materials research for undergraduate and K-12 students in the DFW area. Part 2: TECHNICAL SUMMARY The development of emerging technologies including semiconductors and quantum materials requires in-depth understanding of the atomic structure underlying their performance. Heavy post-transition metal halides and chalcohalides host stereoactive lone pair electrons and large spin-orbit coupling, imparting anharmonicity that enables unusual properties and yields outstanding photovoltaics, ferroelectrics, and topological insulators. However, predicting and engineering such chemically "soft" anharmonic materials is an immense challenge due to limited understanding of local atomic structure deviations driven by the lone pair electrons. Long-range crystallography is blind to potentially active lone pairs, especially in materials with complex environments, and the resulting lack of quantitative information on local hidden lone pair stereochemistry is a critical knowledge gap. This CAREER project, with support from the Solid State and Materials Chemistry Program in NSF’s Mathematical and Physical Sciences Directorate, addresses this knowledge gap by experimentally characterizing the local structure of anharmonic lone pair electrons in heteroleptic halide and chalcohalides using total X-ray scattering. Analysis of select chemical series will elucidate the role of specific chemical features on the local structure, providing design principles for tuning the degree and dynamics of anharmonic distortions. These structure-property relationships will be tested through design of new chalcohalides targeting high symmetry environments. This CAREER project will yield fundamental understanding of the local chemistry underlying anharmonic dynamics in heteroleptic halides and chalcohalides, contributing to chemical control of complex semiconductors for optoelectronic and ferroic applications. Integrated with these research objectives is an educational program comprising two initiatives: 1) a team research course on Materials Chemistry and Crystallography, enabling undergraduates to work with national user facilities, and 2) an Emerging Materials Workshop that will provide experiments, demonstrations, and lesson plans to educators in the Dallas-Fort Worth area. These efforts will bolster research opportunities and foster interest in STEM careers, contributing to workforce development needs of the DFW semiconductor industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Microburst precipitation is a key loss mechanism for electrons in the Earth’s inner magnetosphere and a significant energy source for the ionosphere. Chorus waves play a crucial role in driving electron microbursts. This study employs a simulation model to quantify microburst generation by chorus waves. The project will have a broader impact on human communication, as electron microbursts enhance ionospheric electron density, altering conductivity and consequently affecting communication signals. The project will support a scientist who obtained a PhD in 2021 and a graduate student. This project aims to quantify electron microbursts driven by chorus waves and to investigate their temporal and spatial evolution using a self-consistent simulation model. The study has three scientific objectives: 1) identify the dominant mechanism for electron microburst formation, 2) characterize the temporal evolution of electron microbursts, and 3) determine the spatial characteristics and dependencies of electron microbursts. To achieve these goals, General Curvilinear Particle-In-Cell (GCPIC) simulations in a dipole field will be performed. The motion of resonant electrons will be analyzed, with a focus on nonlinear physical processes. Electron microbursts will be quantified in both the meridian plane and an L-shell-fixed surface, and their spatial scale dependence will be examined. Simulation results will be validated through microburst observations from the Electron Loss and Fields Investigation (ELFIN) satellite. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This project seeks funding to enable participation of U.S. scientists at the International Symposium on Equatorial Aeronomy (ISEA) meeting, which offers a unique platform for dissemination of scientific knowledge and promotes innovative discussions on emerging research trends. This symposium represents an opportunity for researchers to share and discuss the most recent results and scientific advances in theoretical and experimental areas relevant to geospace research. Studies by US investigators often require collaborations with scientists from countries at various locations to obtain a global context. Therefore, this meeting provides an excellent opportunity for discussions about new observational capabilities, observational campaigns and experiments. This funding enables participation of early-careers scientists at the ISEA. Early career participants will benefit from discussions relevant to new geospace research topics and it will facilitate new connections, collaborations, which is crucial for their professional growth and promotes workforce development. The topics of research to be covered by the ISEA-17 are aligned with the current NSF priorities: space traffic and sustainability, global- and large-scale variability in the upper atmosphere, equatorial E- and F-region irregularities that enables accurate forecasting of space-weather impacts, their causes and effects, atmosphere-ionosphere-magnetosphere coupling, recent advances in instrumentation and observation, future trends, opportunities, and challenges. The support will enhance US visibility, enable new collaborations with leading scientists, and strengthen US global leadership. The effort will allow the participation of talented scientists, particularly early-career investigators, that often do not have opportunities to attend such meetings. In addition to benefiting the advancement of fundamental scientific knowledge, the meeting will contribute to areas related to effects of the geospace environment on technological systems (e.g., GPS) that are relevant to society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Understanding the Earth’s subsurface is essential for society’s ability to monitor natural hazards, manage environmental risks, and support sustainable energy development. Seismic waves generated by earthquakes and other sources can provide clues about underground structures, but interpreting these waves remains complex and computationally intensive. This project will advance artificial intelligence (AI) methods for imaging and monitoring the Earth’s subsurface. By combining advances in geophysics and machine learning, the research will enable more accurate and efficient interpretation of seismic data. These innovations may help scientists detect changes beneath the Earth’s surface more quickly and with greater clarity, benefiting efforts in natural hazard monitoring, such as volcanic or earthquake activity, and informing future strategies for environmental management and energy exploration. Aligned with these goals, the project will provide hands-on training in geoscience and AI for students and researchers, fostering cross-disciplinary innovation, education, and collaboration. Technically, the research team will develop a multi-task deep learning inversion framework that jointly estimates subsurface velocity structures and earthquake source parameters using passive seismic data. Conventional full-waveform inversion methods require accurate initial models and typically alternate between updating velocity and source parameters. Existing AI approaches often handle the two tasks independently by assuming that either the velocity model or the source parameters are known, thereby limiting their applicability. This project will address these limitations by introducing a unified framework that captures the intrinsic coupling between earthquake sources and velocity structures in seismic inversion and enables their simultaneous estimation within a single learning system. By integrating deep learning with conventional full-waveform inversion, the proposed approach aims to reduce dependence on initial models and improve computational efficiency after a one-time model training. Additionally, the team will design a multi-scale inversion strategy that enables the AI model to help resolve subsurface features across a range of domain sizes and depths. The framework will be validated using real-world data from seismically active regions where accurate monitoring is essential for public safety. The project will also produce benchmark datasets and open-source tools to support continued research in AI-enhanced Earth imaging and monitoring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

The objective of this project is to establish a multi-university, Phase III I-UCRC (Industry-University Collaborative Research Center) for wind energy research, education, and outreach. The effort is based on a successful ten-year operation led by two university sites (UMass Lowell and the University of Texas at Dallas). Together these two universities have conducted wind energy research, established long-term partnerships within the wind industry, trained undergraduate and graduate students to perform state-of-the-art industry relevant research, engaged in outreach to K-12 students and the international wind energy community. The Center contributes to the nation’s research infrastructure and enhances the intellectual capacity of the energy workforce. An experienced group of scientists, engineers, and practitioners will execute a program of research and education focused on the design, operation, and maintenance of wind energy systems for electricity production. The Center will be aimed at: (a) enhancing national excellence in wind energy research and development that has direct relevance to industry, and (b) developing a cadre of diverse undergraduate and graduate students with world-class training who will support and eventually lead in the analysis, design, manufacture, and successful operation of wind energy systems. This Phase III I-UCRC integrates engineering with fundamental research to support the development of low-cost and high availability wind energy systems. The partners will engage in cooperative research and education in the following key thrust areas: (a) Composites, Blade and Rotor Design & Manufacturing, (b) Structural Health Monitoring and Non-Destructive Inspection, (c) Wind Plant Modeling and Measurements, (d) Control Systems for Wind Turbines and Wind Plants, (e) Energy Storage and Grid Integration, (f) Foundation and Towers, and (g) Topics Beyond the Levelized Cost of Energy Metric. Research led by the UT Dallas site is expected to result in: (1) mechanics-informed machine learning models for prediction of defects in wind blade manufacturing; (2) AI-assisted digital twins to evaluate and predict the condition of critical components; (3) characterization and modelling Farm-to-Farm interactions to inform plant design and control; (4) design for repowering of wind plants to increase capacity factors and guide future designs, (5) fault tolerant wind energy systems; (6) passive devices for performance improvement. The expertise of the site includes high-fidelity simulation of wind power systems; LiDAR measurements and analysis of wind fields for diagnostics and model validation; wind tunnel testing; control system design for wind turbines and wind farms, large rotor design, grid integration and energy storage; applied machine learning for estimation and forecasting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Electric machines are critical to society, converting power between electricity and motion. However, innovations in electric machines are necessary to reduce costs, increase efficiency, and improve their capabilities for challenging applications, such as space exploration. Therefore, this project will develop and integrate fast, flexible, and accurate electromagnetic, structural, and thermal equivalent circuit models of electric machines. These models will be tailored for topology optimization (TO), which yields novel shapes that would not result from conventional optimization. Previously, TO has only been applied to portions of electric machines and has not considered electromagnetic, structural, and thermal performance simultaneously. However, the speed, flexibility, and accuracy of the developed models will enable TO of entire electric machines considering electromagnetic, structural, and thermal performance simultaneously. The resulting new designs will reduce costs and losses for existing applications and enable the use of electric machines in new applications. Other broader impacts of the project include research opportunities for high school, undergraduate, and graduate students and the development of assignment modules teaching students to combine coding and discipline-specific knowledge to develop models for solving engineering problems; these assignment modules will be designed to be incorporated into various engineering classes. This project will develop a new approach for multiphysics analysis of electric machines that is significantly faster than finite element analysis (FEA) with the accuracy and flexibility required for TO. TO, unlike conventional optimization, yields fundamentally new geometries that can take advantage of additive manufacturing. However, true TO of electric machines requires fast and extremely flexible multiphysics analysis. Thus, this project will develop high-resolution lumped parameter magnetic, electrical, structural, and thermal network models. These models will enable multiphysical TO of entire electric machines. Each network will divide the geometry into a grid of many small node cells, which consist of lumped sources (magnetomotive force, electromotive force, mechanical force, or heat) and impedances (reluctances, resistances, springs, or thermal resistances), and solve for the magnetic scalar potential, electrical current, mechanical deflection, or temperature of each node cell. Each network will share the same grid of node cells as the TO algorithm. To perform multiphysics analysis, each network will be solved individually and its results used to update the other networks iteratively until the analysis converges. To test the models’ accuracy, the networks will be applied with TO to design different electric machines, and the performance of the optimal designs will be verified with FEA and experimental testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Thermal noise presents the most fundamental limit to the achievable signal-to-noise ratio (SNR) in analog electronic circuits. To suppress thermal noise, many scientific instruments used in fields such as radio astronomy, high-energy particle colliders, and emerging superconducting quantum computers require cryogenic cooling, which significantly increases their cost and consumes more energy. For most commercial and industrial applications where cryogenic cooling is too expensive to employ, the only solution for reducing sampled thermal noise in discrete-time electronic circuits is to use a larger sampling capacitor, which significantly increases the size and cost of electronic components and is impractical for integrated semiconductor chips. This underscores the need to search for a new game-changing circuit design solution, which is the focus of the proposed thermal noise cancellation research in this project. The intriguing challenges and intellectual allure surrounding the noise-cancellation analog electronic circuit design have the potential to motivate undergraduate and graduate students toward this electrical engineering discipline, igniting interest not only in analog electronics but also in the broader spectra of science and engineering. Building upon this initiative, the outreach plan involving both the University of Texas at Dallas and the University of Texas Rio Grande Valley holds the potential to impact both institutions and the broader communities in the North and South Texas regions, thereby fostering a strong educational component that will contribute to the future workforce development in the semiconductor industry. In this project, the principal investigator (PI) introduces a closed-loop noise-cancellation technique to suppress thermal noise in signal-sampling circuits (a.k.a. kT/C noise, where k is the Boltzmann’s constant, T is the absolute temperature, and C is the sampling capacitance) while improving the linearity of the sample-and-hold (S/H) circuit over a prior open-loop noise-cancellation technique. The insight of the closed-loop technique is based on and derived from an accurate analysis of the existing open-loop counterpart’s limitations, primarily its signal-feedthrough nonlinearity problem associated with the delayed secondary sampling operation. The necessity of employing a large ac-coupling capacitor for noise storage and a two-stage operational amplifier presents several design challenges, including higher power, larger area, and compromised analog performance. The project will develop a new closed-loop architecture to eliminate signal-feedthrough distortion without a large noise-storage capacitor and a two-stage amplifier. Additionally, the research team will incorporate a digital predictive amplifier-swing neutralization technique to further enhance the noise-cancellation performance of and improve signal linearity in the closed-loop architecture. The project will demonstrate the new closed-loop architecture using a semiconductor chip prototype implemented in a 16-nm silicon CMOS technology, with a targeted size reduction of front-end sampling capacitance by 20 times as compared to a state-of-the-art commercial product. To facilitate benchmarking the high dynamic range of the S/H circuit achieved by the new noise cancellation technique, a 16-bit pipelined successive-approximation-register (SAR) analog-to-digital converter (ADC) will be integrated on the same prototype 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 · 2025-10

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at University of Texas at Dallas. A total of 30 scholars pursuing Master of Science degrees in Actuarial Science, Artificial Intelligence for Biomedical Sciences, Bioinformatics and Computational Biology, Biotechnology, Data Science and Statistics, Geosciences, Bioengineering, Computer Science, Electrical Engineering, and Mechanical Engineering will receive scholarships averaging $20,000 per year for up to five years. Scholars will receive faculty mentoring, and the project will build strong scholar cohorts through cohort-based career development activities and project-based learning experiences. Additional activities for scholars include student-to-student engagement opportunities. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income graduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in artificial intelligence, biotechnology, and other key areas of need. The project will be assessed by an experienced evaluator who will investigate barriers associated with the attainment of a master's degree in STEM, and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The evolution of Internet of Things (IoT) is transforming the field of industrial automation including process control and smart manufacturing into an important class of Industrial IoT (IIoT). Today, wireless solutions for industrial automation are based on short-range wireless technologies (e.g., WirelessHART, ISA100). To cover a large area with numerous devices, they form multi-hop mesh networks at the expense of energy, cost, and complexity, posing a big challenge to support the scale and wide-area of today’s IIoT. For example, the East Texas oil-field extends over 74x8 square kilometers requiring tens of thousands of sensors for automated management. Also, in process industries, many silos, tanks, and plants are often positioned far from the center, at inconvenient locations in difficult terrain or offshore. Pipelines can be hundreds of miles long and pass through difficult terrains, making it difficult to monitor gas and chemical leaks in real-time. This project proposes to adopt the Low-Power Wide-Area Network (LPWAN) technologies for industrial automation. Due to long-range, LPWANs can be adopted without complex configuration and at a fraction of costs for wide-area IIoT applications, compared to multi-hop solutions. This project will develop theoretical foundations and systems for enabling industrial automation using LPWANs. Its important findings will be shared with the standards bodies and industries. The developed technologies will be made open-source. This project will particularly consider LoRa, a leading LPWAN technology. Adopting LoRa for industrial automation poses some evolutionary challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. Due to severe energy-constraints, LoRa uses a simple media access control protocol that is unsuited for real-time communication. It needs to adopt low duty-cycling in several regions (e.g., Europe). In addition, to optimize performance, industrial automation needs a codesign of real-time scheduling and control. Such a codesign becomes specially challenging in LoRa because it is large-scale and has energy-limitations. This project will address these challenges and make the following contributions: (1) an autonomous real-time scheduling technique and analysis using the demand bound function theory for LoRa; (2) a scalable scheduling-control codesign that jointly and dynamically determines control input and sampling rates; (3) a highly energy-efficient codesign by maximizing the sleeping times of the devices through a combination of self-triggered and even-triggered control adopting state-aware communication; and (4) an evaluation of the results through experiments using industrial process control use-cases on a physical testbed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Energy consumption is one of the most pressing challenges for semiconductor technologies. This is a result of applications like artificial intelligence (AI) leading to exponential growth in energy consumption. At this rate, energy demand from computing may outpace energy production within a few decades. In addition to making transistors more energy efficient, it is also necessary to integrate them in new ways. The greatest advancements are expected when transistors are directly processed in the back-end-of-the-line (BEOL). In comparison to the stacking of chips, BEOL processing minimizes energy consumption through short and fine interconnects. However, BEOL processing requires semiconductors that can be grown below approximately 450 ºC to prevent damage to the underlying chip. This is a fundamental challenge for traditional semiconductors and other two-dimensional (2D) semiconductors like transition metal dichalcogenides (TMDs). This project is developing a new kind of 2D semiconductor compatible with BEOL processing. By combining theoretical and experimental approaches, it is overcoming key hurdles for new 2D semiconductors like (1) controlled doping and (2) low electrical contact resistance. The project is also integrating workforce development for the United States semiconductor industry by collaborating with the North Texas Semiconductor Institute to create programs that address the regional shortage of skilled workers, accelerating the transition from technician to operator roles. This project is developing a new class of 2D oxyhalides for BEOL-compatible electronic devices. It is investigating the low-temperature growth of these oxyhalides and correlating relationships between low-temperature growth kinetics and electronic properties of these thin films. This project is also bridging major gaps in the present knowledge of how to fabricate 2D oxyhalides into transistors. Any new 2D material will likely face challenges to control the chemical doping and form the electrical contacts. These properties can be difficult to compute as they are often set by the rich structural and chemical complexity of their interfaces. For example, manipulating 2D semiconductor-metal interfaces for low electrical contact resistance has taken a decade for 2D transition metal dichalcogenides (TMDs). This project will significantly advance our control over chemical doping by identifying extrinsic dopants and those that can be controllably introduced during growth. It will also decode important relationships between contact metals, the oxyhalide-metal interface chemistry, and the electrical contact resistance, which are critical for short-channel transistors. Beyond their applications as classical electron devices, these 2D oxyhalides also present possibilities for quantum devices. Their tunable bandgaps and novel charge transport behaviors that can be applied to quantum tunneling devices, excitonic devices, and even platforms for quantum information processing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Global digital data is increasing immensely, reaching 291 zettabytes by 2030, as most human activities today are captured digitally. However, the longevity of digital storage media is limited, typically not exceeding 15 years, which poses a significant challenge for preserving valuable data. Given these challenges, synthetic deoxyribonucleic acid (DNA) emerges as a promising alternative due to its high density and longevity, making it an ideal medium for archival storage. With the development of biotechnologies over recent decades, DNA storage has transitioned from theoretical to practical. To fully utilize the advantages of DNA storage, this project will develop new algorithms and systems through cross-layer optimization by leveraging DNA storage properties, architecture design, and storage system design. The following innovations will be pursued: 1) Designing novel DNA storage algorithms for bio-domain optimization to enhance scalability; 2) Creating a novel DNA storage architecture to increase reliability and performance; and 3) Developing system management solutions for DNA storage based on traditional storage technologies. These efforts will collectively advance the scalability, reliability, and performance of DNA storage. This research aims to advance DNA storage systems, preserving human activities through centuries-long DNA storage, and deepening our understanding of the trade-offs and efficiencies necessary for scaling up DNA storage. Developing new DNA storage platforms through algorithmic and systems innovations will make the goal of preserving the world’s digital data one step closer. This project will offer a rich interdisciplinary platform for teaching and learning, equipping computer science students, both graduate and undergraduate, with critical skills in system building and experimentation, which are essential for the modern and future information technology workforce. Research findings from this project will be integrated into the curriculum, enriching both specialized projects and core courses in computer science and engineering, which will provide students with expertise in emerging technologies while fostering a deep understanding of comprehensive system design principles, preparing them to excel in both current and future technological landscapes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Today's quantum circuit designs are akin to classical circuits in their early stages, which were designed by hand and manually laid out, while the power of classical computing hardware was not fully unleashed until the emergence of Electronic Design Automation (EDA) in the 1950s, enabling the scalable design of integrated circuits. Although quantum computing holds great promise to dramatically speed up many chemical, financial, cryptographic, and machine-learning applications, we are witnessing that the existing quantum computing design workflow significantly relies on human designs, such as manually implementing and verifying quantum circuits on the gate level for quantum algorithms. As such, domain experts from other fields without a sufficient fundamental understanding of quantum operations can hardly leverage the power of quantum computers for their domain applications, and more importantly, they lack toolkits to test the correctness of an ad-hoc designed quantum circuit. Furthermore, since quantum computing has a fundamentally different computing scheme, which relies on superposition and entanglement, the traditional EDA techniques cannot be directly applied to quantum circuits. To close the gap between quantum hardware (in physics) and quantum algorithms (in computer science), we envision the necessity of a quantum EDA framework, which will play a role similar to that of EDA in revolutionizing classical Silicon-based hardware design. Beyond the technical impact, the fundamentals of the design automation tools can help beginners understand how a quantum system is designed and how it works, which are compiled in the education activities in this project for public access. To carry out pilot research on the quantum EDA, this project proposes to develop an automated framework, namely SPV, to efficiently synthesize, profile, and verify quantum circuits, which include a set of quantum EDA tools: (1) We develop an automated quantum circuit construction toolset to optimize quantum circuit design in modern quantum processors. The toolset supports end-to-end quantum circuit design, including both quantum state preparation and function synthesis using available quantum gates. (2) We develop both formal and simulation-based approaches to verify quantum circuits at scale. Specifically, we utilize the widely adopted ZX calculus to optimize quantum circuits for equivalence checking, and we develop a scalable, simulation-based verification methodology tailored for larger circuits. Moreover, it will comprise methodologies to verify quantum circuits in the presence of quantum error correction (QEC). And (3) we build a benchmark test platform with circuit property profiling and performance validation. To address the shortage of QEC designs in existing benchmarks for quantum verification, we integrate a set of state-of-the-art QEC code designs into the benchmark tool. After all the synthesis, profiling, verification, and benchmarking tools are developed, we integrate them into a holistic quantum design automation toolchain. With a completed toolchain, SPV can benefit researchers in deploying and testing domain-specific quantum algorithms on available quantum computers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Safety-critical systems that have strict “real-time” requirements are becoming increasingly ubiquitous and complex. Epitomizing this recent trend toward sophisticated real-time systems are autonomous vehicles, which must perform image recognition, machine learning, routing, and planning tasks, simultaneously and with minimal delay. Furthermore, these real-time computational tasks must execute upon shared hardware (e.g., processors, memory, storage) due to the severe constraints on the size, weight, and power of the entire system; however, the sharing of computer resources creates tremendous contention and competition between tasks. This project addresses a fundamental challenge of how multiple real-time, safety-critical tasks can effectively share the underlying memory architecture and still meet timing constraints. In particular, this project will develop a novel system design and analysis framework called PARSEC (Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache). PARSEC contributes to the state-of-the-art with (a) new multicore scheduling algorithms that explicitly manage how contending tasks share memory resources; (b) new formal analysis techniques that verify that a system’s timing constraints are satisfied with existing memory resources; and (c) a set of open-source automated tools that will enable system designers to utilize the framework on commercial off-the-shelf processing architectures. PARSEC will be implemented and evaluated upon the popular RISC V architecture to facilitate wide dissemination to the public. This project will result in safer, more efficient designs of time-sensitive systems, including autonomous vehicles and robotics. Furthermore, the resulting research and system design techniques in this project can be applied to any real-time, safety-critical systems executing concurrent computational tasks upon a shared processor and memory. The reduction in contention in the memory hierarchy obtained from project artifacts will potentially lessen demands on power and fuel in safety-critical systems, decreasing their carbon footprint. The project will benefit the educational missions of University of Nevada Las Vegas and Wayne State University by providing a unique training, education, and experiential learning opportunity for undergraduate and graduate students via course projects related to safety-critical system design. To aid other researchers, this project will also disseminate research results through publications, public talks, tutorials, project websites, and online videos. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

NSF Awards · FY 2025 · 2025-10

Software vulnerabilities, which are flaws or weaknesses in code that can be exploited by attackers, pose significant risks to computing infrastructures across industry, government, and academia. Current research on vulnerability detection and remediation faces several key challenges, including keeping pace with rapidly evolving software, enabling data-driven methods (e.g., artificial intelligence-based techniques), and integrating various types of vulnerability-related metadata. To address these gaps, this planning project will lead to the construction of a robust, community-supported infrastructure and shared dataset that advance software vulnerability research, ultimately enhancing the security of diverse computing systems critical to national defense and prosperity. The project will also develop accessible security training resources for students and professionals. This project will plan an infrastructure featuring a continuous collection framework that captures scalable and multimodal data to facilitate high-impact software vulnerability research through a series of planning activities. First, the project team will conduct surveys and interviews with the security, software engineering, and human-computer interaction communities to understand researchers’ practical needs and how an infrastructure and dataset can reduce barriers in their work. Second, the project team will host workshops to gather feedback and share best practices on the initial infrastructure design. Third, the project team will conduct summative surveys and form a working group to assess, refine, and improve the design. By identifying community needs and priorities, the project will inform the infrastructure design that benefits and accelerates research on software vulnerability detection and remediation. Long-term collaboration with participants will also be fostered to support the establishment of the new 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 · 2025-10

In this ASCENT project, the team aims to develop a set of semiconductor technologies, including new device fabrication, large-scale heterogeneous integration, and robust beam-alignment system architectures, to achieve electrically controlled collimation of electromagnetic waves in sub-terahertz (sub-THz) frequency bands at low cost. Compared to the existing 5G wireless bands below 100 GHz, the sub-THz bands offer unprecedented wide bandwidth that can enable ultra-fast data rate for data center networking and wireless infrastructure, as well as high precision for radar and imaging. The electronic hardware developed in this project provides a highly desirable function for most sub-THz systems -- focusing the beam power within one degree in space (hence the term "needle beam" in the project title) with high-precision electronic control of the beam direction. The new hardware architecture enables wireless communication systems to achieve a high data rate up to 120 Gbps over a distance greater than one kilometer. It also enables radar imaging systems to achieve high-resolution sensing of the ambient environment, which is critical for all-weather safe operation of autonomous vehicles. In addition, this project not only provides extensive graduate researcher training in high-frequency circuit designs and advanced semiconductor manufacturing but also promotes STEM education through various programs. Needle beam forming at 140 GHz requires large (> 70x70 millimeter square) electronic phase-controlling surfaces with low signal loss at high frequencies. Therefore, transistors fabricated using advanced lithography are needed. Recent demonstrations of sub-THz reflectarray using either tiled complementary metal-oxide-semiconductor (CMOS) FinFET chips or chip package modules have excessively high fabrication and assembly costs. In this project, the team explores a new direct wafer-scale integration approach through low-temperature fabrications of custom metal-insulator-semiconductor-insulator-metal (MISIM) variable capacitance devices and high-efficiency sub-THz antennas on top of a foundry-processed integrated-circuit glass substrate. Without using any expensive advanced lithography, the devices can still achieve low-loss phase shifting of sub-THz signals, hence significantly reducing the cost of the needle-beam-forming system. The project also investigates new reflectarray circuits that perform self-correction of device defects and process variations, as well as new reflectarray transceiver architectures that enable compact overall system form factor and high-precision beam alignment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Surface waves and their resulting turbulent processes play a central role in regulating the exchanges of mass, momentum, and energy between the atmosphere and ocean, directly influencing sea states, weather patterns, and climate systems. Although integrating the dynamics of surface waves into the coupled atmospheric-oceanic models is crucial for accurate weather and climate forecasts, the current understanding of fundamental processes that link the turbulent flow structures above and below the surface within the coupled air-sea boundary layers is limited. This is due to challenges in resolving the dynamics of small-scale turbulence in the vicinity of the air-sea interface. This project will combine high-resolution laboratory experiments, high-fidelity numerical simulations, and foundational AI techniques to examine the multiscale turbulence above and below ocean surface waves and quantify the two-way coupled air-sea momentum and energy fluxes at the air-sea interface. This project will examine the coupling of wave-induced flow structures above and below the air-sea interface. A synergistic experimental and AI-driven approach will be used to develop a comprehensive parameterization of wave and turbulent stresses at the air-sea interface. The aim is to address the turbulent closure problem in the governing equations and improve predictive models of air-sea fluxes. A combination of novel experiments that concurrently measure air- and water-side flow velocities and an advanced multiscale AI-driven framework that completes the partially measured statistical signatures of the flow will be employed. The AI model will integrate attention mechanisms with physics-informed neural networks (PINNs) to enhance the two-dimensional planar velocity measurements by reconstructing the third velocity component and the pressure field. The resulting dataset will support the development of data-driven surrogate models for air-sea fluxes with enhanced physical consistency and superior generalizability. 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.