William Marsh Rice University

NSF Awards · FY 2025 · 2025-09

Critical minerals are essential for products such as electronics, motors, and batteries. The demand for critical minerals could be met in part by recovering them from waste and process streams. However, current recovery processes are expensive, and valuable materials in waste streams often are unrecovered. This project will develop polymeric membranes for use in low-cost filtration-type processes. These membranes will improve the recovery of target ions of critical minerals from aqueous streams. Most polymer membranes are manufactured by methods that result in pores and voids of varying sizes. This project will develop experimental methods to precisely control and measure the sizes of pores and voids in membranes. Using computations and experiments, the research will show that suitable pore sizes can improve recovery of the target ions. This project will improve national security and improve economic competitiveness in critical minerals. Additional benefits include the training and mentoring of teachers and community-college students on modern separation technologies. Polymeric membranes have emerged as versatile, low-cost, and scalable materials systems for a wide variety of gas and liquid separation applications. Such membranes are generally prepared using free radical polymerization, which produce membranes with a heterogeneous pore and network structure. The influence of network heterogeneity on ion transport in hydrated membranes is poorly-understood. This project will pursue a combination of experiments and computer simulations to systematically tune and characterize network heterogeneity in polymer networks and understand its impact on ion transport and separations. Experimental work will implement methods for controlling and quantifying membrane heterogeneity, and measuring the impact of heterogeneity on ion transport and selectivity. Molecular simulations will capture the effects of network heterogeneity and ion-specific interactions. The resulting insights on the role of water-filled voids on ion diffusion and sorption will guide the experimental work. The knowledge generated in this project will immediately impact the field of membrane separations. Unraveling the interplay between membrane heterogeneities and ligand-ion interactions on ion selectivity will impact a variety of contemporary applications of extraction, recycling and reuse of critical minerals. The project includes training of K-12 science teachers on activities relating to membrane separations, development of a hands-on activity to be used in K-12 community outreach events, and training undergraduate researchers from Houston and Austin area community colleges. 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

Therapeutic cells produce proteins that aid in the treatment of disease. A promising strategy for their use is direct injection of these cells into the human body. Once established inside the body, they would be a constant source of the therapeutic agent. Low cellular production rate is the major limitation of this strategy. Increasing the secretion performance of these cells would make adoption of such strategies more practical. Natural plasma cells (PCs) have much higher antibody secretion rates that other cell types but pose cost and safety concerns. The objective of this project is to modify existing therapy-grade cells with the hyper-secreting capabilities of PCs. Educational and outreach activities will focus on training early-stage researchers. The strategy is to understand the mechanisms that drive the transition of B cells into high antibody-producing PCs and implement them in existing therapy cells. The expression of key transcription factors (TFs) and other regulatory factors will be manipulated. Methods will be developed to systematically tune the expression strengths, ratios and timing of these regulatory elements. This will make it possible to both investigate and optimize the utility of TF expression perturbations for maximizing biologic transcription, translation, post- translational processing, and trafficking through the endoplasmic reticulum. Analyses will be designed to identify combinations of TF- perturbations that maximize the functional secretion properties of cells. Methods to controllably trigger therapy cell reprogramming will also be established to address challenges stemming from growth arrest and the development of post-mitotic phenotypes. Development of this capability will enable comprehensive temporal analyses of cell adaptations and address challenges affecting the deployment scaling the production of hyper-secreting engineered cells for ultimate use in therapy. 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

The aim of this project is to study and improve how Artificial Intelligence (AI) chatbots evaluate job candidates. AI chatbots increasingly are used in workplace settings to interview job candidates, offering efficiency and standardization in hiring. AI-based interview systems may unintentionally rely on irrelevant information, however, leading to inappropriate outcomes. This research investigates how AI systems might produce different outcomes based on individual characteristics, even when qualifications are equal. It also explores how people perceive the balance and transparency of such AI interview experiences. The findings inform the development of more robust AI systems and support the deployment of ethical AI in hiring practices, ultimately contributing to a stronger workforce. The project trains students in responsible AI, offers outreach through public forums, and develops interactive dashboards to help human resource professionals make better use of AI tools in hiring. The research in this project analyzes AI-based interview systems through the lens of predictors (e.g., language model embeddings), outcomes (e.g., scores or hiring decisions), and user perceptions (e.g., trust). Drawing on an existing conceptual framework and psychometric natural language processing methods, the research team examines differential functioning of AI predictors across groups, detecting group differences in outcomes, and evaluating candidate reactions to chatbot interviews. Data from both university seniors and working professionals are collected to ensure generalizability. By integrating expertise from psychology, machine learning, and business analytics, the project produces validated metrics, statistical models, and explainable AI tools that enhance transparency and balance in AI-chatbot-based interview systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Engineered multicellular bacterial systems have a wide range of potential applications, including gut microbiome maintenance, cancer therapy, environmental remediation, and engineered living materials that can sense and respond to local conditions. However, such bacterial systems are difficult to design due to our lack of a mechanistic understanding of how cells within a bacterial colony interact and communicate across time and space. This work will address this issue by studying simplified engineered bacterial systems and formulating novel mathematical frameworks that will allow engineers to better predict colony behavior. More broadly, this research will expose undergraduates, graduate students, and postdoctoral researchers to cutting edge synthetic biology research and train them to enter the growing biotechnology industrial sector. Additionally, the findings will be incorporated into undergraduate and graduate classes. Synthetic biologists have long strived to create engineered multicellular systems with unicellular bacteria for industrial and biomedical applications. Towards this goal, the PIs will use a combination of experimental and theoretical synthetic biology to develop mathematical modeling techniques that describe the spatiotemporal dynamics of intercellular signaling in spatially extended bacterial systems. In previous work, the PIs have developed numerous spatially extended synthetic bacterial systems and methods for monitoring intercellular signaling, gene expression, and cellular growth within them. The PIs will use these techniques in a series of increasingly complex experiments to determine how mathematical models need to be altered to better reproduce and predict the spatiotemporal dynamics of spatially extended synthetic multicellular systems. Overall, this research will lead to more accurate mathematical models that will enable us to better design large-scale, complex synthetic systems capable of coordinating their spatiotemporal gene expression patterns. 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 Rice University to enable the development of PhyNetPy—a comprehensive, open-source Python library, available to developers and biologists working on or using phylogenetic networks. The library will offer user-friendly inference and analysis methods, fundamental data structures, and essential components for developers to rapidly implement their ideas and contribute to the expansion of the phylogenetic network toolkit. This project will foster research in software engineering, mathematical modeling, and algorithm design while providing valuable training opportunities for graduate students and postdoctoral researchers at the intersection of computing and biology. Research findings will be incorporated into courses taught by the principal investigator at Rice University and shared through publications in peer-reviewed journals and conference proceedings. Additionally, the software library will be made accessible via a dedicated website that includes user and developer manuals, tutorials, demonstration videos, and a discussion forum. Researchers from the biology and computing fields have long been creating mathematical models, algorithmic solutions, and software tools to reconstruct the evolutionary histories of genes, genomes, and species from genomic data. Notably, extensive libraries of data structures and algorithms have been developed to enable broader community contributions in creating tools for this purpose. A salient feature of almost all of these efforts is the mathematical modeling of evolutionary history as a tree. While tree models are adequate for representing certain evolutionary histories, more complex processes such as hybridization and horizontal gene transfer are better modeled using phylogenetic networks. However, the development of software tools for inferring and analyzing phylogenetic networks has not kept pace with the plethora of phylogenetic tree software. Specifically, there is a notable absence of a general-purpose software library that enables developers to quickly prototype new algorithms and methods, hindering progress in this area. This project aims to rectify this gap by developing PhyNetPy. 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 grant supports research that will contribute to the advancement of national prosperity and social and economic welfare by developing adaptive control and learning algorithms to solve complex, practical problems arising from networked systems with uncertainties. Large-scale service operations, manufacturing and production systems, inventory and logistics, healthcare patient flows, telecommunications, and cloud computing all have complex network structures and often face various challenging operational risks such as sudden changes in demand or disruptions in service. Unlike traditional methods that assume full knowledge of system behavior, this research will create new algorithms that can learn from data and adapt in real time, while also accounting for risk and variability in outcomes - weighing in on the potentially high fluctuations around the average values of certain performance metrics. Beyond the technical contributions, the project will enhance STEM education by integrating cutting-edge research into both undergraduate and graduate curricula. It will prepare students with advanced mathematical and engineering skills needed to lead in fields like artificial intelligence, operations research, industrial and systems engineering - strengthening the U.S. science and engineering workforce. This research will advance the computational and learning methods of risk-sensitive control of Markov chains and diffusions and their applications in stochastic networked systems. There has been substantial development recently in the theory of ergodic risk-sensitive control of Markov chains and diffusions. However, this theory requires that the underlying dynamics and parameters, as well as model specifications, are known, which is not usually fulfilled in practice, and therefore, a synergy of learning and adaptive control simultaneously is highly demanded. This project will develop a variety of adaptive control and computational algorithms to solve risk-sensitive control problems for Markov chains and diffusions, as well as the associated heuristic adaptive rolling horizon algorithms. These computational methods will be augmented with advanced learning algorithms, which include recursive confidence region learning algorithms for the parameter ranges, learning of the model specification to account for model uncertainty, and learning of the reward or cost functions in the objectives. Furthermore, learning-augmented adaptive control algorithms will be developed particularly for stochastic networked systems under the ergodic risk-sensitive criteria, to tackle the challenges from complex network structures. 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 · 2025-08

This project aims to better understand how cells determine their fate when they are under stress, specifically stress in a part of the cell called the endoplasmic reticulum (ER), the cell's power plant, which produces and folds proteins. Since proteins support all cellular functions, the production of properly functioning proteins is critical to cellular health. When the ER becomes overwhelmed due to an increase in protein production needs, it triggers a response called the unfolded protein response (UPR). This type of response plays a crucial role in maintaining cell health. If the UPR goes awry, then cellular fate is altered, potentially leading to cell death. This research project develops new tools that enable the investigation of how genes behave in real-time and create systems that help cells better manage protein production under stress. Unlike older trial and-error methods, these approaches provide precise control over how cells respond, utilizing built-in feedback systems. The results of this project could lead to new ways to engineer healthier, more resilient cells. The highly multidisciplinary research environment provides broadly reaching educational and training opportunities to graduate, undergraduate, and high school students. The secretory pathway is responsible for synthesizing approximately one-third of all proteins in eukaryotic cells. As physiological demands and pathological insults constantly challenge endoplasmic reticulum (ER) homeostasis, the unfolded protein response (UPR) activates adaptive mechanisms to maintain an optimal protein production rate, reacting to diverse stimuli and leading to opposite cell fate decisions (survival or cell death). Current approaches for manipulating the UPR and investigating the link between UPR and cell fate are based on the deregulated or exogenously controlled modulation of specific UPR genes, which results in cell adaptation. This research project uses previously built feedback-regulated cells that detect the UPR status and, in response, modulate specific UPR signaling pathways to mitigate stress and enhance cell viability. This synthetic biology platform is used to investigate the relationship between the temporal progression of the UPR and cell fate. The project also engineers cells that continuously and dynamically adjust the innate cellular capacity to buffer proteotoxic stress in response to diverse stimuli, including environmental stresses (glucose deprivation, oxidative stress), the overexpression of secretory proteins, and viral particle replication and assembly. 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 · 2025-08

The investigator studies problems in spectral theory, the mathematical theory that pertains to physical notions such as energy levels of quantum systems and vibration frequencies of mechanical systems. The research is motivated by universality, which is the appearance of certain common local statistical behaviors for different systems, and integrability, which is the existence of many conserved quantities in certain nonlinear systems used to describe their behavior over time. The project focuses on commonly studied one-dimensional systems, such as orthogonal polynomials and Schrödinger operators. One focus of this project is the study of spectral properties of large finite truncations of an infinite system, on a microscopic scale. These problems have immediate interpretations in terms of random matrix theory and one-dimensional quantum mechanics; moreover, mathematical methods developed on these systems have the potential to illuminate other mathematical models and physical applications. The project provides research training opportunities for graduate students and postdoctoral scholars. Asymptotic local zero distributions of orthogonal polynomials, as the degree goes to infinity, are described by scaling limits of Christoffel-Darboux kernels. Analogously, local eigenvalue distributions of truncations of some operators are described by scaling limits of corresponding reproducing kernels. A modern approach based on de Branges canonical systems gives optimal results for scaling limits in the fixed measure setting; one goal of this project is to develop this approach in the varying measure setting. Another goal is to describe new scaling behaviors beyond the standard universality classes, where instead of a scaling limit there may be a limit cycle or a one-sided characterization of allowed behavior; this is motivated by almost periodic operators in critical/supercritical regimes and ergodic operators with zero measure spectrum. The investigator also studies related questions in direct spectral theory, to develop techniques for fine control of spectral measures around a point in the spectrum, and sum rules with respect to essential spectra with infinitely many gaps, building upon recent applications of potential theory to differential operators. 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 · 2025-08

In the study of physical and mathematical objects, a key role is often played by the symmetries of the object, particularly when the object has many symmetries. This project investigates ways of characterizing, describing, and studying spaces with many symmetries in various dynamical, geometric, and topological settings. These questions often require learning, adapting, and applying ideas and techniques from multiple areas of mathematics. As a result, this research has connections with many areas from differential equations to theoretical computer science to descriptive set theory to number theory. The PI will also continue to organize many conferences, summer schools and other events in order to increase participation in mathematics. The main thrust of the project is to exploit connections between a wide set of areas to further understand fundamental structures related to lattices in Lie groups, a paradigmatic model of symmetry. A major focus is the study of group actions on manifolds where the PI recently made significant advances on conjectures of Zimmer's. Another major focus is on the structure of negatively curved manifolds where the PI made several breakthroughs and discovered new connections. An additional topic is studying how hidden symmetries relate to several classical questions in geometry, topology, and group theory. 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 · 2025-08

This award will support research to develop new platforms for biological-electronic communication that can be harnessed for biological computing, based on microbial sensing and communication. Microbes both sense and respond to their environments. These processes can be complex, even for a single microbe. Microbes also communicate with one another, often through chemical signaling, but sometimes using a form of electrical signaling. This communication can result in a collective response. Viewing each individual microbe as an information processor offers the possibility of connecting the microbes together to create a complex living computer. This project seeks to connect microbes through electronic networks, organized to perform intelligent behaviors, such as learning complex patterns. Such microbe-based networks could serve as the basis for smart sensors, for example, harnessing biological-electronic communication for biological computing. Importantly, this technology must be developed safely and in alignment with public values. In support of this goal, the ethical, legal, and social implications (ELSI) of novel biological computers will be investigated. This research could enable development of programmable living biocomputers that could have applications in medical monitoring. Microorganisms are capable of sensing, responding and adapting to their environments. Such biological sensing and information processing tools could be harnessed to detect and interpret complex chemical signatures such as biomarkers in patient samples or contaminants in environmental samples. To unlock this potential, a biocomputing platform for high-dimensional chemical pattern recognition called the electrogenetically-networked Cyber-Bacterial Organoid will be developed. These synthetic microbial consortia will be capable of integrating chemical and electronic inputs into electronic outputs, which are then relayed to a network of parallel consortia. To implement chemical pattern recognition, a theoretical framework that combines principles of distributed and biological computing will be developed. Next, the computational capabilities of the bio-processors will be extended through implementation of cellular memory and adaptive learning. This will require development of continuous culture systems that maintain long-term microbial activity and support electronic interfacing. These will allow learning and iterative response refinement through long-term electrogenetic interfacing and transcriptional feedback that sensitizes cells to inputs. These intelligent behaviors will be used to classify chemical signatures and adapt to changing environments. Given that intelligent biological computing systems raise novel questions about responsible development and use, this project will explore the ethical, legal, and social implications (ELSI) of biocomputing, focusing on regulatory frameworks, public perception, and responsible development of bacterial organoid systems. This integrated approach should advance both the technical capabilities and the societal readiness of programmable living biocomputers with applications in diagnostics, sense-and-respond therapeutics and other monitoring applications This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Microbes share information through exchanging DNA elements called plasmids, which are critical for how microbes adapt and evolve new abilities. Researchers use plasmids as tools to give microbes new instructions, like producing medicines or cleaning up pollutants. However, just as software only works on certain types of hardware, different plasmid types can only function in specific types of microbes. While a vast variety of plasmids exist in nature, researchers primarily use a select few, which limits our ability to program different microbes for societal benefits. To overcome this challenge, this project will develop new methods to isolate, study, and use different plasmids. This research will be used to expand the current plasmid toolbox from a few plasmids to hundreds and potentially even thousands, which will enhance our ability to program microbes for beneficial purposes. The beneficial impact of this research will be to expand our ability to address grand challenges using microbes related to public health, food and water security, and energy transitions. In addition, broader outcomes will include the development of new training resources, facilitation of workshops to disseminate research innovations, and the creation of new summer research opportunities for high-school students and community college students. The goal of this research is to expand the available plasmids for researchers to use as tools by establishing a high-throughput platform for isolating and engineering plasmids and characterizing their properties (i.e., host range, stability, connectivity). Novel methods will be developed to achieve this research. First, more efficient and effective methods for isolating DNA plasmids from environmental microbes will be established. Second, an innovative synthetic biology tool will be established to measure the key properties, such as host range, of these novel DNA plasmids. The new plasmid tools will then be used as resources to study the genetic determinants of plasmid host range and plasmid stability in microbial communities. Additionally, we will demonstrate the use of these plasmids as tools to introduce a non-native catabolic gene for phenol pollutant biodegradation into a microbial community, and assess its impact on pollutant biodegradation rates. This project is supported by the Systems and Synthetic Biology Cluster of the Division of Molecular and Cellular Biosciences. 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 · 2025-08

Technical Abstract: The stimuli-responsive behavior of polymers and other networks of flexible chains is governed by properties such as supramolecular interactions (including hydrogen bonding), entanglements, and system dynamics. This proposal aims to advance the fundamental understanding of stimuli-responsive materials by investigating the structure-function relationships in entangled polymer chains and fibrous systems across scales. To generate new knowledge in fundamental polymer science, this project will focus on three main objectives addressing the following research questions: (1) How is supramolecular hydrogen bonding (H-bonding) disrupted or promoted through interactions with other species? (2) How do the thermal properties and heating profile dynamically affect polymer morphology and its relationship to bulk behavior? and (3) How do random entanglements of Shape Memory Polymer (SMP) fiber “chains” contribute to motion in textiles? In parallel, this project will support the PI’s mission to foster a diverse and inclusive lab environment. Through mentorship, outreach, and research opportunities, the PI will expose a broad range of students to soft materials, cultivating a collaborative space for emerging talent. Additionally, this project will aid in the setup of the Sanchez (“Theory to Execution”) texlab and support the generation of preliminary data, positioning the PI to pursue future work aligned with DMR’s goals. Non-Technical Abstract: This proposal aims to advance the fundamental understanding of stimuli-responsive materials by investigating the structure-function relationships in entangled polymer chains and fibrous systems across scales. This project will support the PI’s mission to foster a diverse and inclusive lab environment. Through mentorship, outreach, and research opportunities, the PI will expose a broad range of students to soft materials, cultivating a collaborative space for emerging talent. Additionally, this project will aid in the setup of the Sanchez (“Theory to Execution”) texlab and support the generation of preliminary data, positioning the PI to pursue future work aligned with DMR’s goals. 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 · 2025-08

Novel computational methods will be developed and analyzed for modeling coupled flow and transport phenomena occurring in networks of one-dimensional lines embedded in a three-dimensional porous domain. The scientific outcome is a foundational basis for models used in biotechnology and in geosciences: for instance, for the modeling of organ perfusion, the modeling of embolization and drug delivery in damaged, unhealthy organs, and the modeling of flow of solvent mixed with resident fluid in fractured subsurface for applications. The computational models are efficient thanks to the reduced cost of one-dimensional solutions. The numerical analysis of the methods provides a guaranteed accuracy of the computational models. One outcome of the project is the convergence analysis of a numerical scheme that employs the interior penalty discontinuous Galerkin methods in space and backward Euler in time for solving the miscible displacement problem in coupled domains of codimension equal to two. The numerical analysis is non-standard because of the low regularity of the weak solution and the lack of consistency of the scheme. Additional difficulties for the derivation of error bounds include the coupling between flow and transport via nonlinear coefficients and the unboundedness of the diffusion-dispersion matrix in the three-dimensional concentration equation. Depending on the assumptions on the data, convergence is obtained by the derivation of a priori error estimates or by a compactness argument. Via a python-based implementation, robustness and accuracy of the schemes are investigated for several numerical and physical scenarios, such as evaluating the effect of different time step sizes for pressures and concentrations, and investigating the amount and stability of the overshoot and undershoot phenomena. Another outcome of the project is the formulation and analysis of multinumerics schemes that combine finite element methods and two variants of discontinuous Galerkin methods for single-phase flows in coupled three-dimensional domains and metric graphs. Two different treatments of the bifurcation conditions are introduced. A priori error estimates are derived. Robustness and accuracy of the multinumerics schemes are numerically investigated. Of particular interest is the verification of the Neumann-Kirchoff conditions as well as how the schemes perform on networks with increasing complexity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Understanding quantum materials is essential for developing emerging quantum technologies. Many of the quantum materials belong to the class of quantum many-body systems, which consist of quantum particles interacting with each other. The challenge here lies in the fact that the study of quantum many-body systems is in general extremely difficult and, in fact, this serves as a strong motivation for the development of quantum computing. The research supported by this award will try to provide a deeper understanding of quantum materials via quantum simulation, that is, to use simple and easy-to-control laboratory quantum systems (e.g., ultracold neutral atoms or electrically charged atomic ions confined inside vacuum chambers) to simulate complicated quantum many-body systems. Such a quantum simulator could be regarded as a special-purpose quantum computer. In addition, through student involvement, this research will also contribute to education and technical training in the STEM fields. More specifically, this research program consists of two major thrusts. Thrust 1 concerns some peculiar properties of quantum many-body systems confined in one dimension. Such 1D systems often exhibit unusual quantum behavior not seen in higher dimensions. Physics to be explored include the transport dynamics that displays spin-charge coupling, as well as a proposal and investigation of particles obeying anyonic quantum statistics. Research in this Thrust can be implemented in cold atom experiments. Thrust 2 concerns with spin-boson coupling that can be readily realized in trapped ion experiments. The focus will be on the engineering of quantum entanglement, quantum squeezing, and quantum state preparation via engineered dissipation. These studies could have significant impact on quantum technologies across various platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Nonlinear wave equations in second-order form are fundamental to understanding phenomena in geophysics, plasma physics, quantum science, and beyond. However, accurately and efficiently simulating these equations remains a major challenge due to their complexity and sensitivity, which demand a careful balance of precision and speed, along with the use of stable numerical schemes to ensure reliable results. This project develops robust and efficient numerical algorithms for solving wave equations, optimized for high performance on both current and next-generation computing platforms. These computational tools will advance foundational research and have wide-ranging applications in areas where accurate wave prediction is critical. Beyond technical innovation, the project supports the development of a skilled scientific workforce by training graduate researchers and engaging students through reading groups and seminars. These educational initiatives promote participation in computational mathematics and contribute to the nation's continued leadership in science, engineering, and technological innovation. The main computational challenges associated with nonlinear second-order wave equations stem from their rich and intricate range of behaviors. These equations can exhibit solitary waves, solitons, finite-time blow-ups, singularities, and rapid oscillations. These equations, often derived from Euler–Lagrange equations, carry intrinsic geometric and energetic structures that critically shape their dynamics. Standard numerical approaches typically reformulate them into first-order systems, which increase computational cost and potentially compromise key physical properties. Building on the investigator’s prior success with numerical methods for (semi-)linear second-order wave problems, this project aims to address these challenges. The goal is to design numerical schemes specifically tailored to the nonlinear second-order formulation, emphasizing stability, high accuracy, computational efficiency, and fidelity to the underlying physics. In particular, the research will focus on constructing fully discrete, structure-preserving energy discontinuous Galerkin methods and applying them to complex physical systems, such as geometric wave models and coupled first- and second-order hyperbolic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This research develops new statistical theory, methodology, algorithms, and software to produce more robust survey estimates. The new methods address a central challenge in the field of survey methods: the widespread problem of survey‐taker satisficing. Satisficing respondents provide low-effort responses without sufficiently considering the survey questions. Improving survey estimates benefits a range of disciplines that rely heavily on surveys or experiments with survey-based measures, including the social sciences, public health, and education. The research assists the countless decision makers and organizations that rely on survey data to inform their decision making. The project’s education component invests in the next generation of survey researchers through short courses, software development, and summer research experiences for undergraduates. By forging methodological innovations and training the next generation of survey researchers, the project expands survey methods expertise and produces widely disseminated tools. This research draws on techniques from causal inference, econometrics, and convex optimization to develop new statistical estimators that are robust to measurement error caused by satisficing. While survey researchers have developed ad hoc techniques, such as attention checks, to try and exclude satisficing respondents from survey samples, these techniques are often detached from statistical theory. This research demonstrates the inadequacy of existing solutions and the necessity of integrating statistical theory and survey design to produce more robust survey estimates. In particular, the investigator works on three main objectives. First, the investigator develops new statistical theory and estimators for common survey data collection techniques that address satisficing though attention checks, panel data, and the like. Second, the investigator extends the new statistical framework to question experiments—where response options are permuted—and by doing so relaxes the strong assumptions needed for screeners or panel data to detect satisficing. Third, the investigator develops new tools to make experiments with survey-based measures more precise despite the presence of measurement error. The output of the research includes theory, methodology, algorithms, and software that can be deployed to estimate population-level attitudes and beliefs and to estimate causal effects on survey-based outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This research investigates how and when scientists and other technical experts have affected decisions related to science, technology, and innovation (STI) decisions of national importance, such as research security, science education, and public health. Decision makers increasingly rely on scientists and other technical experts to inform U.S. national STI decisions. However, the influence of science and scientists in public decision making remains difficult to evaluate due to the wide range of evidence used and the delay between ideas and outcomes. The research will focus on the role of advisors and entrepreneurs in advising and coordinating STI research and development (R&D). The goal is to inform the science decision making system and increase the appropriate uptake of scientific data and analysis into STI decision making. A more effective U.S. science decision making system will lead to improved outcomes for the American public and strengthen U.S global leadership in STI R&D. This research draws on complementary methods in science of science, the digital humanities, and computational sociology to build a novel methodology for identifying and measuring expert influence on decision making related to STI. Utilizing a large corpus of digital textual records, the research team uses a relational database (RDB), which connects individual actors and institutions to documents and outcomes through linked metadata, to trace systematically the decision-making process across time. The analysis of the RDB is complemented by semi-structured and oral history interviews. The interviews and oral histories are analyzed using computational grounded theory to identify patterns in interview responses, providing a means to validate findings from the text analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The ability to efficiently conduct high-fidelity simulations of complex physical phenomena simultaneously reflects our increased understanding of the underlying physics and enables future technological developments based on rapid iterative/inverse design. This project concerns a class of simulation techniques that rely on fundamental solutions (that is, by expressing solutions as a complicated superposition of `point sources' of light, sound, etc.) which have been highly effective when applicable as they have enabled transformational simulations of problems in electrostatics, wave phenomena as well as in human blood flow contexts. But more complicated phenomena (e.g. featuring nonlinearities or spatially-varying media), which are increasingly relevant in applications in medical imaging and also have long-standing intrinsic importance in geophysical exploration, have posed a substantial barrier to this class of methods---which thus have significant untapped potential in these application domains. This project will develop numerical methods with rigorous approximation guarantees to solve these physical problems and enable new scientific questions to be answered / for new technology to be designed, thereby strengthening the U.S. competitiveness and national defense. On the education front, the project will involve training in modern scientific computing generally and for their use in integral equation methods and applied to wave propagation particularly, all rare skills highly valued by industry. Integral equation formulations of nonlinear and/or variable-coefficient problems typically involve one or more volume integral operators (VIOs) featuring the free-space Green's function over the (generally complicated) domain of interest. This project proposes a novel class of volume regularization methods for accurately discretizing any of these VIOs, and if necessary to allow for their simultaneous computation. By suitably exploiting Green's identity the proposed methods allow for singularity-oblivious quadrature to nevertheless be used for these erstwhile singular integrals; accompanying error analysis in two and three dimensional contexts and in curvilinear domains will place the proposed methods on a firm theoretical foundation, and they will be integrated with fast N-body interaction methods. These methods will be coupled into advanced solvers that enable treatment of complex nonlinearities that arise in acoustic problems found in medical imaging. More generally, the methods will allow the extension of the integral equation methods to nonlinear and possibly time-dependent PDEs, as VIOs reduce inhomogeneous linear PDEs to corresponding homogeneous problems---for which surface integral methods have always been highly advantageous. Implementation of the numerical methods will be publicly distributed via the open-source integral equations software project Inti.jl. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Jeffrey D. Hartgerink of Rice University aims to reveal the atomic structure of collagen, the most abundant protein in the human body. Collagen is a remarkable protein which undergoes multiple levels of assembly. The beautiful final product controls an incredibly wide range of biological behavior including tissue integrity, cancer metastasis, wound healing, effective response to viral and bacterial invaders, and even weight regulation. Amazingly, despite early studies dating back to the 1950’s, collagen is very poorly understood. Without an accurate structure, advances in all these areas will be limited. The research team has recently discovered that bundled collagen can take on a never-before-observed shape. The team will explore the circumstances under which this novel shape is formed, undertaking studies with potential to reveal the mysteries of collagen superstructure that have evaded sight for over seventy years. This work expects to lay out chemical methods allowing scientists to mimic biological materials, which may lead to breakthroughs in tissue regeneration, immunology, and cancer research. This program will also serve as the basis to train the next generation of scientists including graduate students pursuing a PhD, undergraduates pursuing Chemistry and Bioscience degrees, and enhancing the pipeline of STEM focused students by engaging high school students and their teachers from Houston area schools. Recent discoveries by the Hartgerink lab related to collagen structure suggest that the fundamental structural unit – the collagen triple helix – can adopt a much wider range of superhelical twists than previously appreciated. This research will study the extent to which the superhelical pitch can be altered, its impact on the self-assembly of high molecular weight complexes, and the sequential circumstances in which this surprising flexibility is observed. The hypothesis is that superhelical unwinding is more likely to occur under the following conditions: 1) when helix-helix packing forces are significant, 2) at or near discontinuities in the required (Xaa-Yaa-Gly)n primary sequence of collagens, 3) at or near the N- and C- termini of a protein, 4) in regions with a low fraction of proline in the Xaa position, or in circumstances that combine two or more of the above. To test these hypotheses, the sequences of natural collagen in which one or more of the criteria above are present will be examined, and, using sequence-structure rules elucidated in this way, the de novo design of these unusual collagen assemblies will be performed. This project will make use of extensive solid-phase peptide synthesis, which allows for the high frequency incorporation of hydroxyproline, which is not genetically encoded. These peptides will be assessed for their ability to self-assemble into all varieties of triple helices using circular dichroism polarimetry and their ability to form higher-order assemblies first by size exclusion chromatography and, subsequently, by methods which allow for atomic precision including nuclear magnetic resonance spectroscopy, X-ray crystallography, and cryo-electron microscopy. The fundamental chemistry knowledge gained from this research is expected to expand the current understanding of collagen assembly, which could have transformative effects in a range of biomedical applications and materials science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Most bacteria live in microbial communities commonly found in various environments, affecting processes such as crop output, human health, and soil nutrient cycling. Within these communities, bacteria compete for resources. A method to antagonize competitors is the contact-dependent delivery of toxins, where bacteria directly transfer toxins to neighboring cells. Understanding how bacteria compete using toxins and the outcome of these interactions is fundamental to understanding the natural processes involving microbial communities. In this project, we will study a group of DNA-targeting toxins, elucidating their activity, their impact on bacterial cells, and developing novel tools to study interactions in microbial communities. This project will also contribute to the participation of students in science by providing research opportunities assisting in the design and create assistive devices that facilitate differentially abled persons to perform lab work. This project investigates the bacterial deaminase toxin families (BaDTFs). This diverse group of toxins is of special interest as it can antagonize competing bacteria, induce mutations in targeted cells, and can be used for DNA-modifying technologies. As a recently discovered group of toxins, many questions remain regarding their activity and biological role. In this CAREER program, we will study BADTFs to characterize their substrate preference and structure, explore their biological roles independent of secretion, and employ their biotechnological potential to develop tools to track toxin-mediated interbacterial interactions. At the conclusion of this project, we expect to gain new knowledge of phenomena associated with bacterial toxins, as well as their application as biotechnology tools that can be translated to processes that involve microbial communities and synthetic biology applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project examines the complex relationship between the social and built environment in the United States. In particular, it considers how disconnected roads and physical barriers can vary by residential location. The role of the built environment in facilitating social and spatial variation has received little attention. This project provides new insight into how historical and contemporary views of the spatial landscape can become embedded in the urban infrastructure and contribute to variation in how places may differ. In addition to advancing scientific understanding of these processes and developing innovative methods to analyze them, the project also emphasizes education and outreach by mentoring students in computational social science and creating an interactive web tool to share findings with the public, policymakers, and community stakeholders. This research addresses four key questions. First, how is road network connectivity related to spatial variation and proximity to local public services? Second, what types of built environment features facilitate social and spatial variation, and what are their origins and evolution? Third, how have these features influenced home valuation? And fourth, how do valuation practices persist in shaping property appraisals? This project analyzes large-scale datasets, including demographic and geographic data, road networks, historical maps and records, housing market data, and satellite and street-view images. It utilizes a multidisciplinary approach that integrates scholarship from the social and computational sciences. Methods include spatial and network analysis to measure road connectivity, artificial intelligence (AI) tools for image classification, and machine learning to analyze texts. The project will produce novel computational methods and a public-facing interactive web application to engage diverse audiences in the findings and tools developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Wearable medical imaging devices capable of continuously monitoring health in daily life will significantly advance healthcare. Traditional medical imaging equipment, although powerful, tends to be bulky, expensive, and energy-intensive, limiting its accessibility and usability. This innovative research proposes to miniaturize a photoacoustic imaging (PAI) system—a promising technology combining ultrasound and light—to visualize deep tissue functions. By developing a novel signal encoding and decoding framework, the hundreds of detection channels needed in conventional PAI can be drastically reduced to just one. This advance makes it possible to create lightweight, affordable, and energy-efficient wearable imaging devices. Such devices will greatly benefit telemedicine, enhance early diagnosis and timely intervention of diseases, particularly in underserved and remote communities, and ultimately contribute to better patient outcomes. Additionally, the project incorporates educational activities designed to inspire undergraduate and high-school students to pursue careers in medical device innovation and STEM fields. The technical goal of this CAREER project is to develop a novel spatiotemporal signal encoding and decoding framework enabling the miniaturization of PAI. Conventional PAI requires arrays of hundreds to thousands of ultrasonic detectors for high-resolution imaging, resulting in large, expensive, and power-hungry equipment. To overcome these limitations, the project will implement a specialized encoding medium (hardware) to uniquely tag photoacoustic waves with distinct spatiotemporal signatures, enabling serial detection by a single-element detector. Along with this hardware innovation, a mathematical decoding algorithm (software) will be developed to reconstruct the original signals, effectively creating over one thousand virtual detection channels from a single physical detector. This integrated hardware-software approach will maintain imaging performance comparable to traditional multi-detector systems while dramatically reducing the device size, cost, and energy consumption by one to two orders of magnitude. Successful completion of this research will provide foundational knowledge for designing compact and efficient wearable PAI and ultrasound devices, promising wearable applications for clinical diagnostics and healthcare delivery. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Hans Renata of Rice University is studying the development of novel strategies to prepare complex terpenoids, which are natural, hydrocarbon-based small molecules. Owing to their structural complexity, synthesizing these molecules with purely chemical means remains a challenging endeavor. The proposed research will combine the use of enzymes, which are nature’s catalysts for chemical reactions, with modern chemical transformations to provide efficient access to many topologically complex terpenoids. This investigation will provide new knowledge in chemical reactivity and new insights in synthetic strategy development that will inform the synthesis design of other topologically-challenging molecules. Trainees working on this project will be exposed to cutting edge techniques in contemporary chemical synthesis and biocatalysis to prepare them for future careers in the chemical industry. In conjunction, Professor Renata will organize outreach efforts and scientific demonstrations to high school students, and virtual lecture series by faculty members from primarily undergraduate institutions (PUIs). Research in the Renata lab focuses on the development of hybrid chemoenzymatic strategies in the synthesis of complex molecules, especially natural products. The goal of this project is to develop several hybrid synthetic approaches to complex terpenoids with various skeletal connectivities and topologies. Specifically, site-selective enzymatic oxidations with engineered P450 variants will be used in combination with modern methodologies in organic chemistry, such as radical cross-couplings, to provide rapid synthetic access to the proposed targets. Additionally, they will explore the use of enzymatically installed hydroxyl groups as a starting point to effect skeletal reorganization to prepare a diverse range of terpenoid frameworks. To complement the research activities, educational activities such as scientific demonstrations to promote interest in the STEM field from the local community, virtual lecture series and workshops on biocatalysis will be carried out. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This grant supports the next generation of researchers in control systems, automation, and robotics by funding graduate students and junior researchers to attend the 2025 American Control Conference (ACC), held in Denver, Colorado, 8-10 July 2025. The ACC is a premier venue for disseminating research on control theory, learning-based control, optimization, and intelligent systems design. The conference brings together researchers from academia, industry, and government laboratories to discuss recent advances in autonomous systems, energy networks, and cyber-physical security. The primary objective of this grant is to broaden participation by enabling students from a wide range of institutions to present their work and engage with experts in the field. Student travel support expands access to professional development opportunities and allows participants to engage with the latest scientific developments and establish valuable professional connections. The conference also includes structured professional development activities to equip students with the skills necessary to contribute to future technological advancements, supporting national interests in economic growth, infrastructure resilience, and scientific progress. The 2025 ACC features a broad technical program that includes learning-based control, networked and distributed systems, and the intersection of artificial intelligence with control engineering. Core topics include advances in data-driven control, robust and adaptive system design, and novel transportation, healthcare, and manufacturing applications. The conference will also include dedicated sessions for career development, mentorship opportunities, and networking with leaders from both academia and industry. Attendees will have the opportunity to participate in sessions focused on broadening participation and technical panels that highlight emerging challenges and opportunities in the field. This initiative strengthens the pipeline of future control engineers and researchers by supporting student attendance, ensuring that the next generation of innovators can engage in high-impact scientific discussions and contribute to advancing the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Over the past two decades, considerable interest has focused on developing engineered living materials (ELMs). This emerging class of materials leverages embedded living cells to achieve functionalities such as self-assembly, self-repair, sensing, manufacturing, and energy generation. ELMs offer potential solutions to issues sometimes associated with conventional materials through (1) manufacture using low-resource/low-waste methods, (2) engineered multifunctionality, and (3) onsite production for reduced transport distances and remote area capability. This approach affords the opportunity to reduce reliance on non-renewable resources and enable resilient infrastructure. Nevertheless, several challenges must be overcome to advance ELMs as a viable alternative to traditional materials, including demonstrating safety, improving longevity, stiffness, and strength, and scaling up manufacturing. To enable the creation of an ELM industry responsive to the Dear Colleague Letter “Funding Opportunities for Engineering Research in Biotechnology” (NSF 24-040), the developmental bottlenecks must be addressed. This will require inputs from experts in synthetic biology, microbiology, materials science, multiple engineering disciplines, as well as the social sciences, architecture, and law. This award will support an ELM Workshop, a unique US-based opportunity for all stakeholder groups to engage in discussion of research needs and translational opportunities. This workshop will solidify the USA’s leading role in the nascent ELM industry, foster ideation around the creation of ELM research institutes, and support development of multidisciplinary curricula to educate a workforce in this field. The ELM Workshop will advance knowledge through sharing of current research, identifying the principal challenges to manufacturing and deployment of ELMs, and building a community that includes both researchers and industry affiliates, as well as government representatives. The workshop is scheduled to take place on October 22-24, 2025 in Houston, Texas at the Bioscience Research Collaborative, a space dedicated to collaborative life science research between Rice University and the Texas Medical Center. = The program will include keynote addresses and other research presentations, discussion panels, breakout discussions, networking opportunities, and a poster session featuring early career researchers. Keynote presentations will cover a range of topics including artificial intelligence and computation, off-earth applications, molecular assembly, and use of consortia. An immediate outcome of the 2025 ELM Workshop will be increasing the focus on ELM research and development. Additionally, a recurring ELM Workshop series will be established for sustained progress and online activities expanded for consistent communication in the field. Results of the event in the form of recordings of the event will be shared (with presenter approval) via a YouTube channel. 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.