William Marsh Rice University

NSF Awards · FY 2026 · 2026-08

This Faculty Early Career Development Program (CAREER) award will advance the predictive design of concrete, the most widely used construction material, by developing new data-driven approaches to improve performance, cost efficiency, and resource utilization. Concrete production currently relies on empirical, trial-and-error methods that often lead to suboptimal mixtures and increased costs. These limitations are becoming more significant as the industry incorporates diverse supplementary materials derived from industrial byproducts and natural resources, introducing greater variability and complexity. This project will integrate data science with fundamental materials science to enable faster, more reliable, and more adaptable concrete design. By supporting the use of locally available materials and reducing reliance on standardized formulations, the work will enhance efficiency and flexibility in infrastructure development. The project also integrates research and education through a novel design challenge that engages undergraduate and high school students in solving real-world engineering problems while developing skills in data science and materials design. These efforts will expand participation in science and engineering and contribute to a future-ready workforce aligned with national priorities. The project will develop physics-informed, data-driven frameworks for the predictive and inverse design of blended cement concrete systems. The research will: (1) build a large-scale, open-access data infrastructure through automated literature mining and guided experiments; (2) develop mechanistic descriptors of binder reactivity and porosity by integrating atomistic simulation, diffraction-based characterization, thermodynamic modeling, and machine learning; and (3) incorporate these descriptors into predictive models to enable accurate property prediction and multi-objective optimization of concrete mixtures. These models will support rapid identification of optimal mixtures that meet performance targets while minimizing cost and environmental impact and maximizing resource efficiency across diverse material systems. The resulting framework will improve the reliability, interpretability, and transferability of data-driven models and establish a foundation for next-generation materials design. This work will advance fundamental understanding of cementitious materials while enabling scalable, automated approaches to infrastructure material design. 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

The objective of this Faculty Early Career Development Program (CAREER) project is to support research on how combinations of flood and stormwater infrastructure perform as a system under realistic storm conditions. Flooding causes more economic damage than any other US natural hazard. Communities invest in stormwater drainage, detention basins, and river modifications, but these projects are typically designed one at a time using simplified design storms that do not capture how risks to homes, roads, and critical services vary from storm to storm, or how infrastructure interactions redistribute those risks. A project that reduces flooding in one area during one storm can worsen it elsewhere during another. Using Greater Houston -- where recent floods have motivated over $2.5 billion in adaptation -- as a testbed, this project seeks to develop methods for evaluating how these projects perform together across a wide range of storms. The project promotes education and workforce development through open-source teaching modules, a Teaching Fellows program that trains professionals to evaluate computational flood risk models, and a Vertically Integrated Project that engages undergraduates in research. All code, datasets, and teaching materials will be released as open-source. The project develops physics-informed machine learning emulators trained on detailed hydrodynamic flood simulations to evaluate how large ensembles of storms -- varying in intensity, duration, spatial pattern, and movement -- interact with spatially distributed infrastructure configurations to produce flood hazards and risks. The research addresses three questions: (1) what spatial patterns of flood risk emerge from variability between and within storms, and how well do design storm methods capture these patterns; (2) what storm characteristics drive different types of infrastructure failure, and can those thresholds be predicted; and (3) when and how do combinations of infrastructure provide benefits beyond what individual projects achieve. To address these questions, the project constructs probabilistic storm catalogs from historical and synthetic records, couples them with multi-scale flood models that represent infrastructure operations, and trains physics-informed machine learning models to efficiently simulate thousands of storm-infrastructure combinations. 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

This award supports the 2026 edition of the Seminar on Stochastic Processes, held March 25-28, 2026 at Union College, in Schenectady, NY. This annual meeting has had tremendous impact on the probability community since its inception in 1981, both in North America and internationally. The conference brings together a diverse group of accomplished and early-career researchers and graduate students working in the fields of probability and stochastic processes. There are six invited speakers, delivering four plenary lectures, one distinguished plenary lecture known as the “SSP Founders lecture”, and one tutorial speaker giving a set of two 90-minute deeper-dive lectures aimed at Ph.D. students working on stochastic processes. There are two poster sessions with brief introductory talks, two open problem discussion sessions and a panel session on career development. The conference provides all the participants an opportunity to interact and discuss recent advances in probability theory and stochastic processes, and their applications. As such, the conference represents an important networking opportunity for the many dozens of early-career researchers in attendance and it will enhance the careers of the next generation of researchers in stochastic processes. The scientific committee has chosen invited speakers who represent a wide breadth of research areas in probability and stochastic processes, including stochastic analysis, stochastic partial differential equations, potential theory and the intersection of probability and analysis, probability on graphs, interacting particle systems, percolation, and other discrete probability and combinatorial topics, Artificial Intelligence (AI) via probabilistic generative models and latent variables, probability and geometry, and the theory of large deviations. The topics also cover a range of application areas including biology, data science, physics, and human medicine, particularly cancer research. Recent research work by other participants is presented at poster sessions. The open problem sessions provide opportunities for discussions about emerging and challenging topics in probability and stochastic processes and the formation of future collaborations. https://www.math.union.edu/~marianop/SSP2026/ 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

Many industries, such as semiconductor manufacturing, mining, and electroplating, produce wastewater that contains salt and heavy metals. It is difficult and expensive to treat this type of wastewater. This project will develop a new technology called Electrochemical Ion Pumping (EIP) to remove salt and recover valuable metals like copper and nickel in a single step. EIP uses electrical control to separate salts from metals. This method is energy-efficient, scalable, and has low waste generation. It can help reduce industrial pollution, recover useful materials, and support a circular economy. The project will develop hands-on educational kits to teach high school and college students about electrochemical separation to prepare the next generation of STEM workforce. Many industries that are important to the U.S. economy and supply chains produce wastewater that contains salt and heavy metals. Treating these wastewaters involves complex, multi-step processes that use large amounts of chemicals. The project will establish a new electrochemical platform based on EIP that combines high-frequency circuit switching with narrow-window electrode potential control to enable pseudo-continuous desalination and electrowinning of heavy metals. The research will define mechanistic principles for stabilizing electrode potentials through ultrashort cycling at specific electrode saturation levels and apply these principles to selectively recover redox-active metals from saline wastewater. A multi-electrode EIP stack will be developed and tested for scalability and long-term performance. By integrating desalination and redox-selective metal recovery in a single process, this project will advance electrochemical separation science and provide a sustainable approach based on process intensification for treating complex industrial wastewater. 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

Injuries to the nervous system, such as spinal cord injury or stroke, can limit a person’s ability to control their muscles. Functional electrical stimulation (FES) is a therapy that uses electrical pulses applied through electrodes on the skin to activate muscles. FES can improve strength and coordination, but it is difficult to use. Electrodes must be placed precisely over specific muscle locations. This is a time-consuming process that relies on skilled clinicians and must be repeated each session. This project will develop a smart wearable garment that can automatically find the best locations for stimulation. By testing different stimulation patterns and measuring responses, the system will learn how to activate muscles effectively without manual setup. This technology will allow more people with movement impairments to benefit from therapy at home, improve comfort and consistency of treatment, and reduce healthcare costs. The project will also engage students in hands-on research and create teaching materials on wearable medical devices. The project will enable new advances in rehabilitation technology and help train the next generation of engineers and scientists. This project will advance a self-optimizing system for FES that eliminates the need for manual electrode placement. The system is designed to maintain reliable skin contact across different anatomies and movements. Real-time, machine learning-based control algorithms will be developed to identify optimal electrode placement corresponding to motor point locations on muscles. The system will automatically tune stimulation parameters and location to account for individual variability, posture changes, and session-to-session differences. The integrated hardware and algorithms will be evaluated against traditional manually placed FES. The project will advance the understanding of neuromuscular physiology and human-centered engineering. The results of this project will help guide the design of future stimulation technologies. 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

This project aims to develop the mathematical foundations for a digital twin (DT) system for individuals with autism spectrum disorder (ASD), focusing on dynamic modeling, prediction, uncertainty quantification, and treatment or intervention recommendation through DT-based optimization. ASD is characterized by challenges in social interaction, communication, and behavior, such as difficulties in forming relationships, understanding nonverbal cues, speech development, repetitive behaviors, and sensory sensitivities. The project will create a unified system integrating clinical and neuro-developmental data, analyzed using a DT healthcare paradigm. The DT technology will enable individualized models, and its predictive capabilities will allow healthcare providers to anticipate progression and adjust treatment or intervention proactively. Additionally, the continuous feedback loop from real-time data will enhance therapeutic outcomes. The developed methods and theories will have broader applicability to other medical areas, improving healthcare efficiency, reducing system burdens, and informing public health strategies. This will ultimately enhance care and promote community well-being. The project will also develop quality cyberinfrastructure to share algorithms, data, and open-source software with the community. Furthermore, the investigators plan to expand scientific impacts through collaborating with medical experts and industry scientists, training undergraduate and graduate students, and integrating research findings into course development. The project will develop a DT framework by modeling brain activities with a unified data structure, linked to behavioral characteristics and interventions aligned with individuals' neuro-developmental processes. This system will integrate multimodal and multi-source data related to human health and development. It will establish foundational models for training and generating synthetic data from DT models, enabling personalized predictions of progression and uncertainty quantification through novel interdisciplinary approaches. The DT system consists of four research modules: (1) Develop computational models based on conditional variational auto-encoders (CVAE) and longitudinal CVAE to analyze brain activities, integrate diverse imaging data, and model neurodevelopmental processes. (2) Create a novel bilevel formulation for multi-distribution fine-tuning techniques on pretrained foundational models and a fast algorithm to learn from heterogeneous data sources to predict ASD outcomes. (3) Develop a model-free conformal prediction procedure to ensemble predictions from multiple models obtained with different modalities and progression simulations, integrating various types of uncertainties into one framework. (4) Develop a DT-based reinforcement learning framework to recommend personalized treatment/intervention plans that significantly improve online learning efficiency and clinical outcomes. The project will address challenges such as multimodality and multi-source data, high-dimensional features, dynamic progression of ASD symptoms, brain functional connectivity, and the need for personalized intervention or treatment recommendations and uncertainty quantification. This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems 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 2026 · 2026-01

The digital revolution has generated a vast volume of interconnected data, often represented as graphs, which is pertinent to numerous critical real-world applications. This has led to the increasing prevalence of Graph Neural Networks (GNNs), a technique that extends the benefits of Artificial Intelligence (AI) to graph-based applications. GNNs hold promising potential to significantly impact society, from accelerating drug discovery and preventing supply chain disruptions, to averting cascading power grid failures and identifying misinformation on social media. However, the actualization of such potential is currently impeded by computational inefficiencies caused by the colossal size and intricate nature (such as extreme sparsity and irregularity) of graphs, which pose challenges to the practical deployment of GNNs. This project aims to bridge the gap between the computational efficiency required for GNNs and their current performance, primarily due to the uniquely heavy load of communication required in GNN computation. In addition, the project enriches the educational experience of undergraduate and graduate students in the US by enhancing the quality of AI and system-related courses and outreach activities at the University of Rochester and Indiana University. Successful completion of this research project can unlock the immense potential of GNNs to solve problems in fields of medicine, public infrastructure, and economic development, among many other issues critical to the well-functioning of the republic and the prosperity of its economy. This project aims to develop a revolutionary communication reduction method that organically integrates on-the-fly versatile graph locality enhancement and high-ratio compression through software-hardware co-design. The research is structured around three primary thrusts: (1) The development of an on-the-fly graph locality enhancer via hardware-software co-design, providing significant versatility and additional reductions in communication demands compared to current leading methods. (2) The creation of an efficient lossy compressor that enables high-ratio, error-bounded compression and decompression for graph data, including both graph embedding and topology information. (3) The investigation into methods for effectively combining the graph locality enhancer and graph compressor, allowing them to mutually benefit each other. These strategies together directly address the persistent communication bottlenecks in GNNs and unleash their potential for societal benefits. Moreover, this project aims to resolve the following query: whether a collaborative integration of locality enhancement and data compression, the two most prevalent communication optimization approaches, can provide a ground-breaking solution to general graph problems. 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

Human attention is limited and when distractions occur, they frequently result in accidents and other adverse events. Recent advances in Human-AI teaming aim to overcome limitations of human attention by combining the strengths of humans and AI to synergistically work together to accomplish shared objectives. For human-AI teaming to be truly successful in everyday life, more knowledge is needed about how humans shift attention between competing sources of information. For example, when driving, irrelevant distractions such as flashing billboards pull attention away from the road. To safely continue, the driver needs to disengage their attention from the distraction and direct it back to the road. The current work evaluates competing theories of how humans successfully disengage attention from visual distractions. One aim is to guide development of new technologies, such as computer vision and augmented reality, that aim to overcome limitations of human attention to improve performance in high-stakes situations (e.g., detecting potential threats within TSA scans or satellite images). Two competing explanations of how distractions compete for attention are tested using behavioral and neural (EEG) measures. The first explanation posits that visual distractions impair behavior by causing multiple shifts of attention, whereas the second explanation is that distractions impair behavior by overloading working memory. These ideas generate unique predictions for how distractions will impact behavioral and neural markers of attention and working memory. To test these ideas, the investigator combines a novel, large-scale EEG dataset (i.e., thousands of measurements per individual) with targeted manipulations of visual displays. The work informs understanding of the functioning of human attention, providing key information for building adaptive human-AI teaming applications that function safely and effectively. 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

2420676 (Tong) and 2420677 (Lin). Climate change is threatening water sustainability by causing more droughts and limiting water access to people around the world. For example, the Western United States has suffered from severe droughts and heat waves, and the Colorado River has recently experienced record low water levels. Brackish water desalination (BWD) is a promising approach to produce more freshwater, but it is inhibited by the lack of effective strategies for brine management. The goal of this research is to develop cost- and energy-efficient brine treatment technologies that enable decarbonized BWD for climate-adaptive water supply. This goal is targeted to be achieved through interdisciplinary research that integrates fundamental interfacial processes and thermal transport to achieve a solar driven zero liquid discharge (ZLD) system. The environmental impacts of this system will be evaluated by techno-economic analysis, life-cycle assessment, and assessing public acceptance. Further benefits to society will result from research training of college students from underrepresented groups, curriculum enrichment, and outreach and public engagement activities. The accelerating global effects of climate change have resulted in an immediate need of adapting water supplies to the rapidly intensified drought conditions. The nationwide adoption of BWD as a feasible strategy to augment freshwater supply is hindered by the challenge of brine management. Minimizing brine volume via ZLD is the key to render BWD a practical and viable means to mitigate the adverse impact of climate change on water security and resiliency. The overarching goal of this project is to achieve solar driven ZLD for decarbonized inland freshwater production as part of a strategy to address climate change. Specific objectives of the project are to 1) develop a novel process integrating nanofiltration and reverse osmosis to enable cost-effective brine volume reduction; 2) design an innovative interface enhanced crystallizer for energy-efficient and robust brine crystallization, guided by fundamental understanding of interfacial salt crystallization, 3) develop a novel high-efficiency heat pump to power ZLD with interface enhanced crystallizer; and 4) evaluate the sustainability of off-grid, decarbonized inland BWD with ZLD with concurrent techno-economic, lifecycle, carbon flow, and social acceptance assessments. To achieve these objectives, this project will integrate and converge knowledge and approaches from multiple disciplines including environmental engineering, environmental sustainability, interfacial engineering, thermal transport processes, systems engineering, and social science. The successful completion of this project has the potential for transformative impact through enabling decarbonized ZLD to support the wide adoption of climate-resilient inland desalination that improves water resilience against a changing climate. The project will provide undergraduate and graduate students from underrepresented groups with opportunities of preforming interdisciplinary, convergent research to solve an environmental and sustainability challenge of global concern. 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

This project focuses on special materials called "polymer brushes," which are tiny, hair-like chains attached to surfaces. These brushes can change how they behave based on their environment — such as changes in acidity or salt levels. Because they are low-cost, flexible, and easy to make, polymer brushes have the potential to be used in water purification and environmental sensing. However, scientists do not yet fully understand how to design these brushes to control how small molecules move in and out of them. This project will identify how characteristics of the brush — such as whether they carry an electric charge, how they interact with water, the size of the building blocks, and how many chains are present — affect their structure and behavior. The goal is to better understand how the makeup of these brushes affects their response to environmental changes and how they allow molecules and particles to pass through. Project outcomes could help improve materials used in water purification systems and biological separation processes. The project will also provide training for undergraduate and graduate students at Rice University and the University of Houston. In addition, the team will lead public outreach activities on filtration and clean water at local centers and science festivals. This project will help understand the influence of charge state on the transport of penetrants within charged polymer brushes. The team will synthesize random copolymer brushes with charged, neutral hydrophilic, and/or neutral hydrophobic monomers. Polymer structure and charge distribution in various solution conditions will be characterized using in situ (wet) ellipsometry, neutron scattering, and molecular simulation. This information will be used to test theories coupling penetrant transport, to be assessed through microscopic imaging and simulation, to the dynamics of polymer brushes under quiescent conditions. Finally, penetrant transport will be quantified under flow conditions using microfluidics and simulation. This project will thus provide the fundamental knowledge needed to molecularly design polymer brushes to control penetrant transport. This information will be used to control the local monomer interactions within brushes and thereby tailor the interfacial properties of separations and sensing devices. Results will be disseminated at local meetings that attract participants from Houston’s petrochemical, biomedical, and materials industries, including the Texas Soft Matter Meeting. The PIs will partner with the Rice Office of STEM Engagement (R-STEM) to develop outreach modules on water purification for the Energy Explorations Academy to present hands-on demonstrations at the annual Houston Energy Festival. 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 award will fund a research project to improve the methodology for testing coefficients in linear regressions. Linear regression has been the workhorse of empirical research in economics but existing methods of making inference about estimates cannot produce results that are appropriate without making restrictive assumptions about the distribution of error terms in small samples or relying on infinitely large sample properties. This research will develop new test methods that are appropriate without regard to assumptions about the distribution of the underlying error terms or sample size. The new approach will allow researchers to construct test statistics that are valid under weaker assumptions than current methods. These methods will be useful for the analyses of observational data as well as guide the design of experiments. The results of this research project will lead to more precise coefficient estimates, hence improve decision making, increase economic growth, and improve the living standards of many citizens. This award will fund a research project that will develop a complete small sample and asymptotic theory for randomization-based inference for linear regressions. This estimator is not only asymptotically robust to heteroskedasticity but also to serial dependence, making it an omnibus procedure with small-sample guarantees. The conceptual aspect of the research will frame the randomization inference, which is tailored to observational data and Fisher tests and experimental data in the same framework to better understand the connections between the two. The research results will show that randomization inference has a natural interpretation as a robust alternative to Fisher tests, which in turn suggests important methodological developments. The results of this research project will lead to more precise coefficient estimates, improve decision making, increase economic growth, and improve the living standards of many citizens. 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

Cement is the backbone of modern infrastructure, used in everything from buildings and roads to bridges and energy systems. Yet, its production is among the most energy-intensive industrial processes, accounting for 2-3 percent of global energy use and approximately 9 percent of human-made CO2 emissions. Traditional cement manufacturing relies on fossil-fuel-based kilns that heat materials to extreme temperatures for hours, making the process inefficient, costly and difficult to electrify. As global demand rises due to population growth and aging infrastructure, there is an urgent need for new production methods that improve energy efficiency, reduce cost and environmental impact, and maintain high performance. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project introduces flash Joule heating (FJH), an electrified process that rapidly heats raw materials to extreme temperatures in seconds—enabling fast, energy-efficient synthesis of cement clinker. Its compact, modular nature supports decentralized production, reducing transportation-related costs and emissions while enabling local use of raw materials and industrial wastes. By integrating advanced synthesis, modeling, experiments, and AI-guided optimization, this project seeks to revolutionize cement production while training the next generation of engineers and scientists in materials science, civil engineering, and artificial intelligence. This project will establish flash Joule heating as a scientifically grounded, electrified method for synthesizing cement clinker and minerals with ultrahigh energy efficiency and phase selectivity. Unlike conventional kilns, FJH applies short, high-power electrical pulses to heat raw materials above 3000 K in seconds, enabling the rapid formation of reactive clinker phases present in conventional cement clinker, such as tricalcium silicate, dicalcium silicate, and tricalcium aluminate, but at significantly lower energy cost. The research integrates thermodynamic modeling, atomistic simulations, and advanced characterization to uncover high-temperature reaction mechanisms and guide FJH process optimization. The resulting FJH clinker will be evaluated in terms of mineralogy, atomic structure, reaction kinetics and mechanisms, pore structure and engineering performance (e.g., workability, strength development, durability). These insights will inform the design of blended cements incorporating FJH clinker and supplementary cementitious materials to deliver high performance at low cost. The work will be supported by life cycle and techno-economic analyses, along with AI-driven modeling to accelerate synthesis optimization and formulation discovery. This closed-loop framework supports electrified, decentralized cement production with unprecedented energy efficiency. By tightly integrating synthesis, multiscale modeling, experimental validation, and AI-guided design, the project directly advances the goals of DMREF and the Materials Genome Initiative—accelerating materials discovery and deployment in a critical industrial sector. 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 award will support students from institutions of higher learning in the United States to participate in the 64th IEEE Conference on Decision and Control (CDC), to be held in Rio de Janeiro, Brazil, December 9–12, 2025. The CDC has been for over fifty years the world's leading annual forum for scientific and engineering researchers interested in systems and control theory and the foundations of automation technology. As in previous years, the CDC will feature presentations of contributed and invited papers, tutorial sessions, plenary and semi-plenary sessions, and pre-conference workshops. We anticipate that the conference will draw over 1,500 participants, including more than 500 students. Recognizing the importance of students to the present and future of the IEEE Control Systems Society (CSS), this proposal seeks continued NSF support to facilitate student travel and engagement through the longstanding student travel award program. The topics covered at the annual CDC span a broad spectrum, reflecting the many applications of control and systems theory. The systems-theoretic approach has played a central role in advancing technologies that impact daily life, from managing communication networks and transportation systems to ensuring the robustness and efficiency of cyber-physical systems. Tools from systems and control theory are now essential in addressing modern challenges such as optimizing large-scale networks, enhancing security and resilience, and integrating learning-based methods into feedback and decision-making processes. The CDC provides students with unique opportunities for intellectual growth, professional development, and community engagement. The conference includes numerous student-centered activities such as a Newcomers' Reception to welcome first-time attendees, a Meet the Faculty Candidate Poster Session to highlight job-seeking students, a Networking Breakfast, and the CSS NextCom Early Career Welcome Event and Orientation. These structured events are designed to help graduate students, postdoctoral researchers, and early career professionals build peer networks, receive mentorship, and plan their conference participation effectively. This environment supports rigorous technical training and strengthens the pipeline of highly skilled researchers and practitioners in the United States. By enabling students to engage with global leaders in systems and control, this program enhances U.S. scientific leadership and contributes to the development of advanced technologies critical to national priorities in areas such as energy, transportation, defense, and 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

The project will help meet the nation’s growing need for a highly skilled STEM workforce, particularly in the emerging fields of biotechnologies and artificial intelligence (AI) in health. To increase participants’ access to emerging career pathways, this project seeks to bridge the gap between academic learning and workforce needs, particularly in the field of health technologies, through the Rice University and Methodist Hospital partnership. The project will engage high school teachers, high school students, and community college students in hands-on learning that combines real-world applications in health technologies and artificial intelligence to help them discover and prepare for exciting careers in digital health. It will also strengthen problem-solving, teamwork, and communication skills, while giving teacher participants new tools and strategies to bring engaging, real-world learning into their classrooms. In this year-long program, participants will explore real-world healthcare challenges by conducting needs assessments, designing and prototyping hardware-based solutions, and using AI tools to support diagnostics and decision-making. They will work in collaborative teams to develop functional prototypes within time and material constraints, and will gain experience in iterative design, problem-solving, and project execution. Formal training in technical communication will prepare participants to present their solutions through oral, visual, and written formats to expert and public audiences. Teacher participants will participate alongside students and deepen their understanding of digital health content and develop classroom-ready lesson plans grounded in experiential learning pedagogy. Expected outcomes include increased proficiency in digital health technologies, deeper understanding of the needs assessment process, experience in designing and prototyping of hardware-based solutions, improved teamwork and communication skills, and the dissemination of project-based lessons by participating educators. The ExLENT Program, support by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in, and their access to, career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Quantum networking represents an enabling technology to build distributed quantum systems through remote entanglement. It offers transformative capabilities in secure communications and constructing large-scale quantum computers. T centers in silicon emerge as a novel type of qubit for quantum networking applications, owing to their preferrable telecom optical transitions and long spin coherence times. This research endeavor seeks to investigate and control the spin-photon interface properties of device-integrated single T centers via externally applied electric fields. The knowledge and methods gained about the electric field control of T center electrical environment will help to tackle the critical spectral diffusion issue for single T centers and boost the advancement of constructing scalable quantum networks based on device-integrated T center qubits. Beyond the scientific objectives, this project is also committed to training the next-generation quantum workforce. We will incorporate interdisciplinary education initiatives – spanning quantum curriculum development, capstone and REU projects, lab summer internship, and high school outreach – aimed at engaging students from different academic backgrounds in cutting-edge quantum information science and technology research. Technical Description: Single T center spins in silicon are promising candidates for building quantum repeater devices for quantum networking applications. Photonic device-coupled single T centers experience significant spectral diffusion, broadening their optical linewidths far beyond the transform limit and impeding critical quantum networking protocols. This research project will utilize electric fields to control the electrical environment for cavity-coupled single T centers and tune their optical transitions via DC Stark effect. The research aims to narrow down the T center optical linewidth and elucidate how electric field affects the T center spin-photon interface properties. The team will implement microscopic electrodes with tight spacing to enable large electric fields and explore the charge noise depletion and its influence on T center spectral diffusion. The team will further investigate how electric field affects the T center photon indistinguishability and spin coherence. The work will shed light on mitigating T center spectral diffusion issue and accelerate the development of quantum network nodes and repeaters based on cavity-coupled T centers for building scalable 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 · 2025-10

Algebraic geometry is the study of geometric objects, called varieties, which are defined by the solution sets of systems of polynomial equations. It is a far-reaching branch of mathematics, making connections with many other research areas such as commutative algebra, number theory, differential and complex geometry, representation theory, and mathematical physics. In this project the PI will study certain families of varieties that play an important role in the classification of all varieties, namely hyperkaehler varieties and rational varieties. This project focuses on arithmetic questions about these two families. The project includes research training opportunities for undergraduate and graduate students, as well as outreach activities to strengthen the community of individuals in algebraic geometry from underrepresented backgrounds. This project is jointly funded by the Algebra and Number Theory Program and the Established Program to Stimulate Competitive Research. This research program is centered around three projects. In the first, birational transformations of hyperkaehler varieties will be used to study Brauer classes on K3 surfaces in order to identify which Brauer classes can arise as exceptional loci in hyperkaehler contractions. This makes connections to questions about the rationality of families of cubic fourfolds. The second is to study the behavior of rationality of fourfolds in arithmetic families, giving an analogue to previous results in families over the complex numbers. The third project is centered around the intermediate Jacobian torsor obstruction to rationality for geometrically rational threefolds, with the goal of characterizing rationality for a certain family of conic bundle threefolds. 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

Long-term sustainable, energy-efficient, and high-performance cloud computing network infrastructures are crucial to advancing national prosperity and promoting the progress of science. To meet this challenge, this project develops a new family of networks that employs optical circuit switching technologies. The fundamental issues this project seeks to address include the design of the new network structures that are highly resilient and flexible to application requirements, the design of the algorithms and software that optimize the use of the new networks, and the design of the methodology and software that accurately measure the performance of the new networks. This project not only provides exciting hands-on education opportunities for students, but it also benefits the scientific community by improving the efficiency of scientific computational tasks. Optical circuit switching technologies have negligible power consumption with data-rate agnostic properties, thus have the potential to overcome the inherent challenges of conventional network architectures consisting of power-hungry electrical packet switches that must be upgraded frequently. Furthermore, optical circuit switching enables tuning of the network topology by dynamically reconfiguring the circuits at runtime, leading to high performance. However, approaches for achieving tunability in optical network architectures are nascent and the community lacks standard performance benchmarks. This project develops a new family of tunable optical network architectures using optical switches at the network edge to support diverse applications with high scalability, reliability, and low control-plane overhead, and develops standard benchmarks along with general methodologies to quantitatively compare different optical network architectures based on diverse performance metrics. Further, this project aims to cover a broad range of evaluations such as workload-independent analysis, large-scale simulation, and prototype implementation with real applications for ensuring the practical viability of novel optical network architectures. 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

Non-technical description: The goal of this project is to build and steer swarms of micron-scale magnetic colloidal particles that come together and move cooperatively through complex environments, much like schools of fish, flocks of birds, or swarms of insects. These swarms are activated by a time-varying magnetic source (for example, an electromagnet or a moving permanent magnet) which functions as an external remote controller. The magnetic controller can direct swarms to propel through fluids, maneuver over surfaces and around obstacles, detect and respond to changes in their surroundings, and carry passive cargo. This project aims to advance the field of magnetic swarms by integrating large computer simulations, theoretical modeling, and experimental approaches within a cohesive framework. Mastering life-like swarm behavior could enable miniature ARMS robots that deliver medicine inside the body, inspect subsurface pipelines, or remove contaminants from water supplies. By opening new frontiers in materials science and programmable matter, this project advances the nation’s health, prosperity, and security while strengthening technological leadership. This project will also provide K-12, undergraduate, and graduate students with interdisciplinary training in computational and experimental techniques for materials science, physics, and engineering to develop our domestic workforce, improve public scientific literacy, and stimulate engagement with science and technology. Technical description: While magnetic swarms capable of dynamic reorganization have been demonstrated, a systematic approach to designing swarms with increasingly sophisticated functions in porous environments and unbounded 3D fluids remains a challenge. Large-scale simulations will capture the coupled magnetic, hydrodynamic, and contact interactions that drive collective motion across multiple length and time scales. Analytical theory will translate these data into design rules, while inverse-design algorithms will search efficiently for particle shapes, magnetic moments, and field protocols that enable adaptive aggregation to move through complex structures. Lithographically fabricated and chemically synthesized particles will test these predictions; high-speed imaging, particle tracking, and force mapping experiments will measure swarm structure, flow fields, and cargo transport efficiency. By combining computational analysis with experimental methods, swarm functionalities for advanced applications, such as adaptive organization, precise navigation, and targeted cargo transport in complex environments will be expanded. These advancements will create a foundation for future applications of colloidal swarms in sensing and delivery, turning theoretical insights into practical outcomes. More broadly, the proposed methods to accelerate swarm design will benefit other active material systems where flows of energy, matter, and information animate material structures to enable life-like 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 · 2025-09

This I-Corps project focuses on the development of wearable textile systems that communicate through the sense of touch to enhance awareness, safety, and performance in high-risk environments. These garment-integrated systems discreetly transmit information to the wearer using programmed physical cues such as vibration, warmth, or pressure, directly by the wearer’s clothing. This solution improves cognitive bandwidth and reduces situational blind spots to improve performance and reduce risk for personnel in military, emergency response, and/or industrial safety roles. The system is lightweight, unobtrusive, and designed to operate with or without vision or hearing, which is ideal for scenarios where conventional communication tools are overloaded or impaired. These issues affect millions of workers and represent a national concern in health, security, and operational effectiveness. By providing enhanced communication through the sense of touch, this technology enables faster decision-making, improved coordination, and reduced exposure to risk, with broad implications for public safety and national defense. 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 fully-textile haptic systems that combine fluidic circuits and electronic circuits embedded within smart fabrics to deliver programmable signals, such as compressions, vibrations, and thermal cues. The garment-integrated haptic systems contain no rigid components and apply stimuli directly to the skin in response to signals from remote command inputs. Scientific advances include embedded fluidic logic, multimodal actuation, and programmable feedback mechanisms through scalable, garment-compatible platforms. In addition to distributing haptic communication across the body in a variety of modes, these haptic textiles outperform current state-of-the-art haptic systems in terms of size, weight, power consumption, and cost, while also being durable, body-conforming, and easily integrated into standard uniforms or gear. 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 · 2025-09

This project seeks to understand how communities flourish in seasonally dynamic landscapes over long periods of time. Researchers investigate the details of past livelihoods that required people to be mobile and sedentary at different times, for both economic and social reasons, and how these contrasting practices may have enabled the long-term success of these communities. Archaeology is particularly well-suited to investigate these issues, with access to economic and subsistence data from centuries-long timeframes. Situated in a seasonally active floodplain, this research investigates the ways that humans came to thrive within these complex ecosystems and thus provides important insights for contemporary communities around the world as they seek to make their communities more resilient in similar dynamic floodplain environments. The research also provides crucial comparative data for environmental assessment and histories for settlement in dynamic floodplains. Research experiences and training opportunities for graduate and undergraduate students are included in the project; students create a public database and description that will make the work accessible to a wide audience and allow for comparative research. The research team examines evidence from permanent settlements, mobile activities, and the movement of goods within and across a dynamic floodplain region to reveal how mobility and long-distance linkages were key to settlement permanence. The research uses archaeological data already collected where a series of fourteen mounded settlements were founded and inhabited during a long period of stable occupation. Laboratory analyses of excavated samples (including charred seeds, soil samples, animal bones, and shell beads) provide evidence of the movements and activities of people, animals, and objects within and across the region through time. The analysis of these data show how people built a subsistence and economic base over time, utilizing various forms and scales of mobility. This project produces the largest regional database of its kind offering a landmark comparative dataset that will allow others to explore how mobility and sedentism help people build economies. 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 · 2025-09

Water is essential for life and connects many parts of Earth’s complex system. Yet scientists still struggle to predict how the water cycle will change in the future because current models do not fully and accurately capture how water moves through and is transformed by the Earth system. Stable water isotopes---special forms of oxygen and hydrogen in water---carry unique "fingerprints" that reveal where water comes from, how it travels through the atmosphere, ocean, and land surface, and how it interacts with the environment. However, most of today’s Earth system models do not simulate these isotopes, leaving valuable data from field campaigns, satellites, and ancient geological records underused. Past efforts to build isotope-enabled Earth system models have faced several obstacles; outdated software and software practices make the models difficult to maintain, and a lack of training resources makes it challenging to help new researchers collaborate effectively to enhance water cycle science. This project aims to solve these problems by using advanced software concepts and practices to develop the isotope-enabled Community Earth System Model (iCESM). The innovative Earth system model is rigorously validated using diverse observational datasets. The project demonstrates the profound opportunities afforded by iCESM by providing new insights into atmospheric dynamics, plant-water and ecosystem hydrology, and changes to ice sheets and glaciers. It enables broader research on water processes and improves predictions of water-related changes, hazards, and risks, driving new scientific discoveries across multiple disciplines. The project also supports global training for researchers and developers and helps engage the public in understanding water on a changing planet. iCESM leverages novel measurements of stable oxygen and hydrogen isotope ratios, which contain crucial information on water-related processes (e.g., cloud formation, evapotranspiration, or heavy rainfall) across time and space where measurements of traditional bulk water fields and fluxes may fall short. iCESM enables tracking and perturbation of hydrological processes in a complex Earth system model for deeper mechanistic understanding across multiple disciplines. Leveraging the community nature of CESM and flexible physics functionality such as the constituents object in the Common Community Physics Package, the project offers portable and sustainable isotope functionalities, comprehensive documentation, and user-friendly analysis tools to support community adoption and sustain co-development in the future. The project further aims to demonstrate iCESM’s innovative capabilities in three science use cases: 1) improving understanding of shallow convection and cloud feedbacks, 2) investigating hydroclimate processes in arid and semi-arid regions to disentangle land-surface processes from atmospheric transport, and 3) exploring post-depositional atmosphere–snow–ice interactions and ice-sheet dynamics to enhance interpretation of critical polar ice-core records. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Atmospheric and Geospace Sciences in the Directorate for Geosciences. 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 · 2025-09

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor László Kürti of Rice University is studying the development of reactions to build nitrogen-containing molecules—compounds commonly found in medicines, crop-protection agents, and functional materials. These new synthetic strategies break weak N-O bonds to drive the formation of stronger bonds, use readily available building blocks and avoid reliance on expensive precious metal catalysts or harmful reagents. In addition, Professor Kürti leads a long-standing K–12 outreach program called “Fun with Chemistry,” which reaches over 8,000 students annually in the Houston area. The program promotes STEM education through live demonstrations and hands-on activities. This project focuses on reagent-controlled strategies to construct diverse N-heterocycles by harnessing the cleavage of weak N–O bonds to drive C–C and C–N bond formation. Key approaches include the development of [3,3]-sigmatropic rearrangements of N-alkenyl and N-aryl O-vinylhydroxylamines and O-dihalocyclopropyl hydroxylamines to access fully substituted pyrroles, indoles, and halogenated indolines. Additionally, new intermolecular and intramolecular amination reactions using amphoteric O-sulfonyl hydroxylamines are being explored for their ability to engage both electrophilic and nucleophilic partners. These activities are expanding the toolkit of operationally simple, catalyst-free reactions and providing broadly useful methods for the synthesis of azaheterocycles relevant to pharmaceuticals, agrochemicals, materials science and catalysis. 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 · 2025-09

An award is made to William Marsh Rice University to enable the development of a safe, programmable RNA-based system for transient protein production in bacteria. Harnessing the replicative machinery of single-stranded RNA bacteriophages and incorporating regulatory control elements, this project will produce self-replicating RNA (srRNA) scaffolds that function across a broad range of bacterial species. This platform enables precise, short-term protein production without the risks of horizontal gene transfer or permanent genomic integration. The technology has wide-ranging applications in environmental remediation, sustainable agriculture, and therapeutic delivery by enabling temporary, task-specific microbial functions with improved biosafety. Through partnerships with academic institutions, industry, and non-profit organizations, the project will contribute to global efforts in environmental sustainability, food security, and public health. The resulting srRNA toolkit will be openly shared with the scientific community to promote innovation, cross-sector collaboration, and responsible biotechnology development. The intellectual merit of the research lies in its transformative approach to overcoming key limitations of existing RNA-based expression systems in bacteria. Current srRNA platforms are largely restricted to E. coli, limiting their broader utility. This project will engineer a diverse set of srRNA scaffolds derived from newly characterized RNA bacteriophages to extend compatibility across a wider range of bacterial hosts. It also addresses longstanding technical challenges such as cell toxicity due to uncontrolled RNA replication by integrating regulatory elements, such as riboswitches, to provide precise temporal control over RNA replication and protein synthesis. By bypassing the need for DNA and transcription, the system enables rapid, direct protein production and ensures complete degradation of constructs after use. Such features are especially valuable in field-based or ecologically sensitive applications. Together, these innovations unlock new capabilities for safe and targeted microbiome manipulation and facilitate the development of novel applications in microbial and environmental biotechnology. 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 · 2025-09

Magnetars are neutron stars with incredibly strong magnetic fields, the like of which are not possible to recreate in an Earth-bound laboratory. Currently only about 30 have been identified, and much about them remains unknown. A collaborative group of researchers at Rice University and Hope College will take significant strides in increasing the understanding of magnetars by developing a comprehensive simulation study of the emission from strongly-magnetized atmospheres straddling their surfaces. The project will provide training and support for the next generation of scientists, including a graduate student at Rice University and undergraduate students at Hope College, a primarily undergraduate institution. An on-line mini-course on compact object astrophysics for senior high school students will also be developed. This program will develop state-of-the-art models for the atmospheric emission of magnetars. An extant Monte Carlo simulation for polarized X-ray transport in fully ionized light element atmospheres will be upgraded to treat hydrostatic support by gas, radiation and magnetic pressure, and incorporate bremsstrahlung opacity and ion cyclotron absorption lines. It applies to arbitrary magnetic field orientations, addressing all locations on the neutron star surface, from the magnetic pole to the equator. Emissivities and polarization signals from hot active zones will be integrated, and as the light passes through the magnetosphere, modifications due to general relativity and the polarized birefringent vacuum will be tracked. The prime objective is to deliver a suite of signal predictions to enhance the interpretation of intensity and polarization data from X-ray telescopes. Tracking the polarization signatures enhances the precision of results, profoundly increasing the diagnostic potential of the modeling. Specifically, encapsulating polarization content will help enable the discrimination of geometrical atmosphere information from the signatures of strong-field QED physics, such as birefringent vacuum polarization, in their magnetospheres. Moreover, in combination with general relativistic lensing of light, this improved precision can afford potential probes of the mass-to-radius ratio of magnetars. 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 · 2025-09

Non-Technical Abstract: This project partially supports a Workshop on Hidden Order and Quantum Entanglement to be organized by Rice University’s Extreme Quantum Materials Alliance from Oct. 6 to Oct. 8, 2025 on the campus of Rice University. This workshop will survey the recent progress and prospects for research in this emerging cross-cutting field. It will bring together experts and early-career researchers from communities with varied perspectives and involving different physics communities including condensed matter physics and atomic physics. The conference will promote participation of junior scientists and therefore enhance their future career goals. Technical abstract: In quantum materials, quantum fluctuations reduce the tendency towards conventional orders that fall within the classification of spontaneous symmetry breaking. They may instead drive more exotic orders that are beyond the Landau framework, such as quantum spin liquids and quantum Hall states. They can also give rise to the regime of quantum criticality and, in gapless settings, to strange metallicity. There is increasing recognition that such hidden-order systems are highly entangled and, importantly, recent developments have allowed for the detection of quantum entanglement in such many-body settings. The interplay between highly fluctuating quantum materials and quantum information has the potential to bring about new advancements in both fields. This workshop will survey the recent progress and prospects for research in this emerging cross-cutting field. It will bring together experts and early-career researchers from communities with varied perspectives and involving different platforms, which include: Entanglement witnesses; Quantum spin liquids; Strange metals; Quantum Hall systems; Quantum emulators. The workshop, organized by Rice University’s Extreme Quantum Materials Alliance (eQMA) in collaboration with the Smalley-Curl Institute (SCI), will bring together a diverse pool of theoretical and experimental experts on these topics. 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.