University Of Illinois At Urbana-Champaign

NSF Awards · FY 2025 · 2025-08

Non-Technical description Engineering living materials is a promising strategy to develop smart, resilient materials that can grow, adapt, and repair themselves, expanding possibilities in next-generation materials science and engineer-ing. A striking example of nature’s ingenuity in creating living materials is the way it regulates bio-mineralization in coral reefs. These dynamic structures not only provide crucial support for marine life, coastal protection, and local economies, but also serve as a source of bioactive compounds with poten-tial as drugs for treating cardiovascular diseases and cancer. If a coral fragment is detached from the main colony and attached to a substrate, the wound heals, allowing the fragment to grow into a new cor-al. While this process, known as coral fragmentation (or fragging), presents a promising strategy for cor-al conservation and restoration, fragging also offers a pathway for developing coral-inspired living mate-rials. However, the fundamental understanding of the processes involved remains quite limited from a materials science perspective. The goal of this project is thus to advance from infancy to maturity the understanding and control of coral fragment biomineralization. The project outcomes advance the sci-ence of biomineralization and how it can be used to design materials that live, grow, and heal— inspired by corals. Broader impacts of this project include training two graduate students at the intersection of materials science and biomineralization and hosting undergraduate students in the principal investiga-tors´ lab to further develop their interests in materials science, thereby enhancing the STEM pipeline. Discoveries in coral-inspired living materials have the potential to drive economic prosperity through product innovation and new markets, while simultaneously improving societal welfare by enhancing health and safety. Technical description The goal of this NSF project is thus to advance the knowledge of the biomineralization processes in cor-al fragment healing and growth and how these processes relate to the resulting material properties. Moreover, the project provides insights into how to influence early-stage coral biomineralization and material properties by tuning the substrate composition and/or water chemistry, i.e., the local chemical environment around the fragment. To achieve this goal, the project tasks are to (i) study the self-attachment of (micro)fragments and the mineralization at the body basal wall, (ii) characterize fragment growth and the evolution of skeletal morphology, (iii) elucidate the evolution of fragment microstructure during early growth (2–3 years) after attachment and the biomineralization pathway, and (iv) understand the effects of extrinsic parameters (substrate composition, water chemistry) and fragment size on growth, properties of (micro)fragments, and the biomineralization pathway. Focusing on fragments of two spe-cies of reef-building scleractinian corals (Echinopora lamellosa and Favia fragum), the experimental plan includes an in-depth study of early-stage calcification during self-attachment using advanced liquid-cell transmission electron microscopy. Additional knowledge is obtained from a comprehensive dataset encompassing coral morphology, growth rates, and skeletal microstructure and composition using in-house techniques such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, and nano-computed tomography with submicron resolution. These studies are complemented by experiments with collaborators at Argonne National Laboratory to determine the skeleton composition. Overall, the insights gained from this research contribute to establishing design principles for coral-inspired living materials and advance the knowledge about biomineralization processes. 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 ability to store particles of light is important for new methods of computing, communicating, and sensing based on quantum physics, which describes how things behave at the smallest scales. Making memories for light particles, or photons, that keep their properties remains a challenge. This is especially true for the very short pulses of light that are created in some of the most commonly used photon sources. This research will study how to build memories for such photons using atoms, explore ways to improve how long they can be stored, and study the quantum nature of the photons. The work will provide training to graduate and undergraduate students in quantum theory and experiment. The team will engage the greater population in quantum technology through the recently created Public Quantum Network that sends entangled photons to public spaces. New courses for a master’s program for high school teachers will also be developed to bring quantum physics to high school classrooms. This research will span fundamental theory, simulation, and experiment towards enhancing the resources for quantum processing through investigation of quantum memory operation in the GHz-THz bandwidth regime. Through this research an atomic-ensemble-based quantum memory platform will be extended to enable increased total efficiency, longer lifetime, and storage and retrieval of telecom-wavelength single photons. The memory will be applied to qubit storage and retrieval in the polarization, frequency, and spatial degrees of freedom, and frequency transduction. A recently developed theoretical model and experimental study of the quantum correlations inherent in Raman-based photon-pair generation will be extended to investigate Stokes-anti-Stokes photon-pair correlations, which determine the degree of quantum state purity of the produced single-photon states. By studying methods to enhance quantum memory operation, investigating its application in quantum information processing tasks, and elucidating and engineering its quantum correlations, this work will advance the broadly impactful goals of improved computation, communication, and sensing. 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

Teams appear in almost any organization such as universities, corporations, and governments. The importance of teams is even more evident with the work practice has been evolving to a new hybrid mode – a combination of work in office and from home which inevitably changes how people collaborate as a team. Consequently, it presents new challenges to team collaborations, in that it increases difficulty of communications, stifles innovation, and affects collaboration. Despite an organization as well as an individual’s profound dependency on teams and the rapid changing landscape of team-enabled operations, computational models, algorithms and tools to optimize the team collaboration are lacking and lagging. To name a few, how to model the multi-channel, multi-platform team collaboration data? How to foresee the rising or the falling of a team at an early stage? How to form a high-performing team as well as to enhance the performance of an existing team? This project develops data mining models, algorithms and tools to optimize team collaboration facing novel challenges in a new hybrid working environment. It consists of three mutually complementary and synergistic research tasks. The first task models the raw team collaboration data to provide a worldview representation of how complex tasks are conducted by teams in multiple channels and platforms. The second task builds multi-task, multi-target predictive models to forecast the performance of a given team. The third task develops algorithms and tools to optimize teams. Specially, it develops data-driven approaches to form and enhance teams. Based on that, it develops reinforcement learning based methods to proactively optimize teams and game-theoretic methods to interactively optimize teams by incorporating user feedback. This project helps improve team efficacy, and optimize human resource allocation, thereby mitigating the challenges that the post-pandemic age has posed to the workforce. The project team actively seeks to engage under-represented students. The research outcome of this project is disseminated through publications, tutorials and open-source software. 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.

NIH Research Projects · FY 2025 · 2025-08

Abstract Antibiotics were wonder drugs of the 1900s and largely responsible for the dramatic extension of life-span observed during that century. Unfortunately, drug-resistance is now rampant, limiting options for treating certain infections. This problem is exacerbated for Gram-negative pathogens, and there are only a handful of antibiotic drug classes for treating Enterobacterales (including E. coli, K. pneumoniae, and E. cloacae) and Acinetobacter, with even fewer options for P. aeruginosa. Discovering novel antibiotics for Gram-negative pathogens has been challenging, due to the ability of these organisms to severely restrict the types of compounds that traverse the outer membrane and evade efflux. We have pioneered a novel workflow that has focused on understanding small molecule accumulation in Gram-negative bacteria, leading to the discovery of the eNTRy rules (for E. coli), and very recently the PASsagE rules (for P. aeruginosa). The eNTRy rules have been used by researchers all over the world to design and discover >25 novel Gram-negative-active antibiotics, including several that are advancing toward clinical trials. However, it is clear that the majority of antibiotics – even ones with excellent influx – have an ‘efflux liability’, that is, are pumped out of the Gram-negative cell by promiscuous efflux pumps, reducing their ability to act as antibiotics. If there were rules for efflux pump evasion, then highly potent antibiotics from multiple compound classes could be readily designed. Here we propose to apply our successful experimental workflow to identify physicochemical traits that allow efflux evasion. For this work we will measure the accumulation of thousands of structurally diverse compounds in efflux-deficient and efflux-proficient isogenic Gram-negative strains, allowing calculation of an Efflux Ratio. This data will be analyzed with the aid of sophisticated chemoinformatics, followed by validation experiments, allowing the derivation of the ‘EXPEL rules’, chemical features that allow compounds to evade efflux. We will conduct separate experiments and devise separate EXPEL rules for E. coli, P. aeruginosa, and A. baumannii; in important preliminary results we have already made an excellent start to this for E. coli. With an excellent understanding of both influx and efflux in place, we will apply the combined learnings from the eNTRy, PASsagE, and EXPEL rules to develop novel antibiotics from 4 different compound classes. We anticipate at least 4 different optimized antibiotics will arise from this work, compounds that can then advance to help patients. As importantly, these demonstrations will provide a blueprint for other researchers to implement these learnings to develop maximally potent Gram-negative active antibiotics. Our team has expertise in all facets of this work, and collaborating together we have made breakthrough discoveries in understanding compound accumulation and have developed novel antibiotics for the most difficult-to-treat Gram-negative pathogens. We will now bring this experience to bear on the efflux problem, an understanding of which will greatly facilitate Gram-negative antibiotic discovery and development.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Adults with Down syndrome (DS) face significant health disparities relative to the general population. However, their perspectives are not well-represented in DS health research because almost no studies have included them as direct respondents, especially in the U.S. Further, adults with DS from underserved communities (i.e., individuals of color; rural communities) experience even greater health disparities, yet they are underrepresented in DS research, as are individuals with DS who have more extensive support needs. To enact change that reduces health disparities and improves outcomes for adults with DS, including those who are underserved and underrepresented in research, it is critical to reflect their perspectives and lived experiences. The proposed project takes this step by using community-based participatory research (CBPR) and thus is also responsive to NOT-OD-22-142. Using CBPR, this project partners with young adults with DS in the entire research process, from development through dissemination, to ensure that it is meaningful and impactful to the DS community. This proposal was developed with a national Steering Committee of 12 young adults with DS who identified the following key topics: employment, community living, social opportunities, healthy living, and self-advocacy. These topics align with a multi-construct, multi-dimensional model of health in DS (Santoro et al., 2023) and represent specific constructs within the broader dimensions of social (employment, community living, social opportunities), physical (healthy living), and mental (self-advocacy) health. To identify what impedes or facilitates these health constructs, we will use the National Institute on Minority Health and Health Disparities Research Framework to examine domains (biological, behavioral, physical/built environment, sociocultural environment, health care system) and levels (individual, interpersonal, community, societal) of influence that shape their social, physical, and mental health. Thus, this project aims to (1) explore the experiences of young adults with DS, including the domains and levels of influence that impede or facilitate their social, physical, and mental health, (2) develop and disseminate resources for the DS community about pathways to improve social, physical, and mental health, and (3) develop and disseminate a toolkit for researchers to engage in CBPR with co-researchers with DS. We will conduct individual, PhotoVoice-informed interviews with 45 young adults with DS (ages 18-35) including individuals of color, individuals from rural communities, and individuals with varying support needs, and we will identify patterns across (a) underserved backgrounds and (b) the extent of support needs. These findings will inform the resources and dissemination methods developed in Aim 2. Each aim will be conducted in partnership with the Steering Committee and an inclusive research team of co-researchers with and without DS. This proposal aligns with the NIH INCLUDE DS Research Plan theme of “Living and Aging with DS” and its objective to “increase inclusion of people with DS in research”. The knowledge gained will fill a gap in representing the lived experiences of young adults with DS and will accelerate inclusive research to improve their health.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY The ongoing paradigm shift in healthcare towards personalized and precision medicine is posing a critical need for noninvasive imaging technology that can provide quantitative tissue and molecular information. The primary goal of this proposed Bioengineering Partnership with Industry (BPI) project is to develop and validate a new magnetic resonance-based multimodal imaging technology that can simultaneously acquire a large set of molecular and tissue property biomarkers from the whole brain in high spatial resolution and imaging speed. To this end, we have established an Academic-Industrial Partnership and assembled a special project team consisting of four PIs and seven co-investigators from University of Illinois at Urbana-Champaign, Yale University, Emory University, Johns Hopkins University, and Siemens Healthineers. The project team has unique complementary technical, medical and industrial expertise and resources in magnetic resonance spectroscopic imaging (MRSI) technology development and clinical applications. The project team will collaborate closely to: a) develop and deliver a next-generation MRSI data acquisition sequence for simultaneous multimodal imaging, b) develop and build a novel machine learning-assisted data processing pipeline, and c) carry out a multisite experimental study to evaluate and characterize the performance of the proposed multimodal imaging technology and assess its clinical potential. The proposed technology is based on innovative development of 1H-MRSI without water suppression and machine learning-assisted processing of multimodal data. By utilizing the unsuppressed water spectroscopic signals, the technology will generate quantitative tissue property maps, including T1, T2, spin density, myelin water fraction, and magnetic susceptibility at 1×1×1 mm3 resolution, which are important imaging markers for brain diseases. The technology will also simultaneously measure and quantify multiple endogenous brain metabolites and neurotransmitters, including N-acetyl aspartate, Myo-inositol, Choline, Creatine, Glutamate, Glutamine, Lactate, and γ-aminobutyric acid at 2×3×3 mm3 resolution. These molecules provide important biomarkers for brain neuronal integrity, glial proliferation, cell membrane turnover, astrocytosis, inflammation, hypoxia, and excitatory/inhibitory synaptic neurotransmission. When fully developed and validated through this partnership project, the proposed technology will provide about six-fold reduction in scan time compared with the existing state-of-the-art imaging technology that performs quantitative tissue property imaging and metabolic imaging independently. The new imaging capability is expected to greatly enhance our ability to diagnose and characterize brain diseases and assess their treatment response and efficacy. Although the proposed development and partnership target the Siemens MRI platform and use a brain tumor application as a testbed, the developed technology will readily be ported to other vendor platforms and used for other applications, such as neurodegenerative diseases.

NSF Awards · FY 2025 · 2025-08

This project aims to advance simulations of gas-liquid mixtures, especially when they break apart into small droplets or bubbles and when small droplets or bubbles come together. These fluid behaviors are commonplace in nature, for example in the breaking of waves at the ocean surface or formation of raindrops in clouds. They are also crucial for many engineering applications, such as the injection of fuel in combustion engines, the spraying of crop protection products, or the production of powders in the food and pharmaceutical industries. The most common simulation methods struggle to capture small but important details, such as very thin liquid sheets or tiny droplets, which limits accuracy and utility of the results. This project will develop new ways to represent and model these fine details of fluid behavior, resulting in more accurate simulations without requiring expensive computer resources. The approach will allow scientists and engineers to better predict how gas-liquid mixtures behave in complex situations, making engineering design more affordable and more accurate. The proposed research will also contribute to modernizing course content for training undergraduate and graduate students, while fostering collaboration with industry to promote the widespread adoption of open-source software tools. Multi-scale two-phase flows play a central role in many natural phenomena but also in several key industrial sectors, such as energy production, transportation, manufacturing, and the food and pharmaceutical industries. Traditional Eulerian interface capturing methods fail to accurately predict topology changes of the gas-liquid interface due to mesh resolution limits and numerical errors. To address these limitations, this project proposes two key innovations: (1) a new piecewise-quadratic interface representation that enables the capture of sub-grid scale structures such as thin films/sheets and ligaments, and (2) a new volume-filtered framework in which sub-grid scale surface tension-driven physics are accounted for through closure models. The outcome will be a framework capable of accurately predicting break-up and coalescence events as well as droplet size distributions in multi-scale two-phase flows, a feat that has so far remained elusive to even the most refined simulation frameworks. This project marks a shift from expensive and often insufficient direct numerical simulation to efficient, physics-informed modeling, through the introduction of novel sub-grid scale interface representation. This promises both more affordable and more predictive simulations of multi-scale two-phase flows. Industrial impact will be maximized by the open-source release of the developed numerical tools, their integration in commercial codes, and the organization of user workshops. The project will also modernize course content on multiphase flows to benefit engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-08

SUMMARY ABSTRACT Three-dimensional (3D) imaging results in better diagnostics and clinical benefit. However, 3D ultrafast volumetric imaging using ultrasound has lagged other modalities such as MRI and X-ray CT. Ultrafast volumetric imaging enables functional imaging modes like volumetric color flow imaging, volumetric shear wave imaging, and volumetric functional ultrasound. The major factor hindering the development of 3D ultrafast ultrasound is the cost associated with hardware platforms that can control probes (2D arrays) with large channel counts, i.e., 1024 channels or more. To overcome these issues and lower costs, different solutions have been proposed that result in tradeoffs in image quality and speed of image generation, e.g., freehand movement of linear arrays, mechanical translation of linear arrays, row-column arrays. However, these solutions are sub-optimal. The optimal solution, in terms of performance, is a 2D array. A 2D array can provide ultrafast volumetric imaging, low side lobes, improved contrast, and a consistent point spread function in both lateral and elevation directions. The tradeoffs with a 2D array are that it requires a larger element count and channel count to operate the array, which is more expensive to manufacture, both for the array and the electronics to operate the array. The cost of a 2D array (32x32 elements) is $20-30k or more, which makes it an expensive array, but still affordable. Although multiplexing solutions are available with a tradeoff in volume rate, to fully operate the array with its maximum potential, each element should be controllable through electronics. Currently, to operate a 32x32 element array would require 1024 channels. One way that this has been accomplished is by linking several research imaging platforms together, such as 4x256 Verasonics Vantage systems. The cost of purchasing and combining four of these systems together is ~$1,000,000, which is out of reach for many ultrasound research laboratories and is cost prohibitive for use in clinical scenarios. These high costs have limited access to these devices and hindered progress in ultrafast, volumetric ultrasound imaging capabilities and innovations. Our proposed project is to develop a low-cost, novel, open-source ultrasound imaging research platform with 1024 individually addressable channels that emphasizes the production of fast 3D volumetric data acquisition and image formation, i.e., real time and ultrafast volumetric imaging. We estimate the cost of hardware to produce our 1024-channel ultrasound research platform will be <$70k. To accomplish this goal, four specific aims are proposed. In the first specific aim, we will design and validate our FPGA-based distributed volumetric beamformer and ultrasound over ethernet protocol. This will allow real time volumetric imaging and a low power budget. The second aim is to design, construct, and test a 1024-channel ultrasound scanning system capable of operating a 32x32 2D array at a cost of under <$70k. The third aim is to develop a user-friendly toolchain to allow user-defined customized imaging tasks. The final aim is to demonstrate in vivo real-time ultrafast volumetric imaging using a 2D matrix array and our novel system. The new system will democratize research in 3D volumetric imaging for ultrasound and facilitate innovations in this critical area.

NSF Awards · FY 2025 · 2025-08

Pollen and spores isolated from modern environmental and geologic samples are a critical data source for a broad range of fields in research and industry. Pollen is widely used to determine provenance and history in forensic analysis and archeological research. Fossil pollen is used to reconstruct ancient terrestrial environments and changes in paleoclimate. It is used to study the timing of plant originations and extinction and the evolution of plant biodiversity. Fossil pollen samples are also used to date the age of geologic sediments and are a critical resource for hydrocarbon exploration. However, despite the widespread use of pollen in biological and geological research, pollen identification remains a highly specialized, time-intensive, and primarily visual skill. This has impeded progress in the many fields that rely on these data. Automated the analysis of pollen samples, therefore, would vastly improve the quality and consistency of pollen data available for multiple areas of scientific research. This project will develop an intelligent web-accessible palynology image analysis platform (PALYIM) for hosting published computer vision tools for automated pollen identification. PALYIM will serve as a centralized repository of vetted, reproducible machine learning workflows specific to pollen identification and classification, drawing attention to the successes of this emerging field of research and crediting innovators within the community. Consolidating these efforts will allow us to build upon and expand the community knowledgebase. It will serve as a user-friendly gateway to machine learning analysis for the diverse communities that employ pollen data and automate and streamline curation of modern reference and fossil specimen images and image analysis workflows. The result will be a community platform that is accessible to researchers without experience in programming or machine learning. The project will increase the efficiency, replicability, and transparency of palynological research by allowing collaboration on a global scale. The platform will transform the dynamics of pollen research by incentivizing the sharing of analysis results and images by integrating public archival into the analysis workflow. Automation will reduce the research effort needed for pollen analyses and as a result will further encourage data sharing and large-scale analyses. The proposed project represents a unique in-demand and interdisciplinary training opportunity for early-career researchers and students who will participate in the development of the platform. 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

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Liviu M. Mirica of the Department of Chemistry at the University of Illinois Urbana-Champaign aims to develop new odd-electron reaction processes for palladium metal, which normally favors even-electron processes. The proposed research will lead to better fundamental understanding of the electronic properties and reactivity profiles of the uncommon paramagnetic (odd-electron) palladium systems, which impacts how chemists build complex molecules more efficiently. The proposed studies comprise synthetic inorganic and organometallic chemistry and physical inorganic spectroscopy, thus providing interdisciplinary training and the opportunities students to develop a broad range of scientific skills. Palladium (Pd) complexes play an important role organometallic catalysis of a wide range of synthetically useful organic transformations such as C-H functionalization, C-C coupling, and C-heteroatom bond formation reactions. The vast majority of these catalytic processes formally involve Pd(0)/Pd(II) oxidation states, while the Pd(IV) oxidation state has been invoked more recently in several chemical transformations. By contrast, the chemistry of odd-electron Pd(I) and Pd(III) oxidation states is less appreciated and understood. Herein, the PI aims to develop (1) a fundamental understanding of the electronic properties and reactivity profiles of paramagnetic Pd(I) and Pd(III) systems, and (2) methods to control one- vs. two-electron processes at these Pd centers. Spectroscopic techniques including EPR, low temperature UV-vis, low temperature electrochemistry, X-ray crystallography, solution X-ray absorption spectroscopy, and computational studies will be applied to study these uncommon paramagnetic Pd systems. The specific activities include: (1) perform ligand-enabled C-H activation/functionalization studies; (2) investigate reactivity of photogenerated Pd(I) species; and (3) perform mechanistic studies of reactions at Pd(I) and Pd(III) centers. 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

Artificial Intelligence(AI)-driven cyberattack detection is essential for safeguarding the U.S. supercomputing infrastructure. Research in AI relies on the national supercomputing infrastructure, but this critical resource is vulnerable to cyberattacks. Securing this infrastructure requires an extensive understanding of historical security incidents, providing a longitudinal perspective on trends, seasonality, and the evolution of cyberattacks. Without this historical context, the research community is left to react rather than preempt futuristic threats, such as AI-driven malware, quantum-resistant vulnerabilities, and machine learning model supply chain backdoor attacks, leaving scientific breakthroughs vulnerable. The AICyberLake project curates a security data lake by sourcing cyberattacks from the DeltaAI system at the National Center for Supercomputing Applications (NCSA) and its peer supercomputing centers. The data includes Zeek network cryptographic metadata, graphics processing unit (GPU) interconnect vulnerabilities, and ground truth incident reports. The resulting data lake provides a real-time, anonymized stream of attack attempts to vetted research teams for evaluating their agentic AI-based detection models against unseen adversaries. An API (Application Programming Interface) helps inform the broader community by contributing attack metadata to policymakers such as the National Institute of Standards and Technology (NIST) and the Cybersecurity and Infrastructure Security Agency (CISA). Ultimately, the project aims to reinforce public trust in running AI workloads within cyberinfrastructure, provide practical exercises by reproducing novel attack traces, and helping educate the next generation of the cybersecurity AI research workforce. 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 supports research that aims to break new ground in the analysis of dynamical systems with time-varying signals and parameters. This variation can happen on two time scales: fast and slow. Moreover, abrupt switches in the values of the signals and parameters are allowed. Such systems arise in a variety of application-motivated contexts. One example is that of medical drug delivery, where slow variation corresponds to a desired schedule of drug concentration in the body eventually decreasing to zero, while fast variations are due to the drug concentration increasing rapidly after an intake and then diminishing gradually. The results of this research are expected to become part of the core theory of nonlinear systems, as well as help bridge the gap with applications that current theory is unable to handle. The latter include, in addition to medical drug delivery, a variety of control applications where control action corresponds not to exact instantaneous motion of the actuator but to its average effect, which frequently occurs in power electronics and mechanical systems. The award also includes components for integrating the research with personnel training and educational activities. The overall goal of the research is to develop a theory that can handle the presence of both slow and fast time-varying signals or parameters. The research seek to accomplish this goal by suitably combining key features of the averaging theory with those of stability analysis of slowly varying systems, and especially, by incorporating techniques employed in the study of switched systems to allow the presence of frequent discontinuities in these signals/parameters. A novel combination of state-of-the-art tools from nonlinear system theory, switched and hybrid systems, and Lyapunov stability will be developed for this purpose. A distinguishing feature of the approach is that explicit stability conditions are formulated based on two components: an appropriate Lyapunov function for the slow dynamics, and an upper bound on the total variation (including jumps) of the slowly-varying signal. Beginning with systems exhibiting rather special structure, progressively more general and realistic system classes will be considered, incorporating general nonlinear dynamics, non-periodic fast-varying signals, and exogenous disturbances. 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

Limited availability of raw materials including cobalt and lithium is a roadblock for widespread adoption of Li-ion batteries. One way to circumvent this roadblock is to develop metal-ion batteries beyond lithium. Water-in-salt electrolytes (WiSEs), which are highly concentrated solutions where the salt content is much higher than the water content, are promising electrolytes. These electrolytes offer improved safety, reduced flammability, efficiency, and performance, while also reducing the cost compared to other highly concentrated electrolytes. This collaborative project will use experiments and theoretical modeling to gain a fundamental understanding of the behavior of WiSEs at electrified interfaces. Discoveries in battery electrolytes have the potential to drive economic prosperity through economic growth and technological advancement, while simultaneously improving societal welfare by enhancing safety. The project will support training of two graduate students at the intersection of electrochemical systems and interfacial engineering and will provide opportunities for undergraduates to participate in the research. This project will advance fundamental knowledge of WiSEs at electrified interfaces, focusing on K- and Na-based WiSEs and model electrode materials. The research hypothesis is that tuning the WiSE electrical double layer (EDL) provides a pathway to modulate the heterogeneous electron transfer rate. This project will provide insight into how surface potential, ion specific effects, the amount of solvent and electrode material determine the EDL structure and cluster composition, as well as the screening of electrode charge. Task B will deliver how the species associations and voltage drop at the interface determine the activity and reactivity of each species, and thereby, modulate the interfacial electron transfer. In experiments, the EDL will be studied via attenuated total reflectance–surface enhanced infrared absorption spectroscopy, electrochemical impedance spectroscopy, and potential-dependent force spectroscopy by Atomic Force Microscopy. This will be combined with surface-force measurements using a surface forces apparatus and wide/small x-ray scattering to determine the 3D structure from the interface into the bulk. Mechanistic insight into electron transfer will be gained using in-situ voltammetry with an ultramicroelectrode. For the theory, the current EDL framework will be extended to account for surface effects, divalent cations and redox molecules, calculate species and clusters activities, and incorporate these predictions into an interfacial reactivity model based on the coupled ion-electron transfer theory. The theory will give access to information that is difficult to access experimentally, e.g., molecular configurations at the interfaces, association constants, cluster distribution; this insight will help test and revise hypotheses. The experiments will provide data to validate the 3D interfacial structures, to determine the accuracy of the models, and, in turn, will help improve and tune the theory and models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Machine learning (ML) technologies continue to permeate diverse aspects of the world. Their impact is large and impacts areas from personalized recommendations to critical decisions in healthcare, finance, and legal systems. The ability of these systems to make accurate and interpretable predictions, therefore, is of paramount importance. However, contemporary ML models, such as large-scale neural networks, often struggle with robust generalization — they fail to maintain performance consistency when faced with unseen and unexpected (i.e., out-of-distribution) data. Furthermore, due to the increasing complexity of model structure and the growing volume of training data, it is often challenging to interpret models' decision-making processes. The triple challenges of accuracy, robust generalization, and interpretability constitute significant barriers to the trustworthiness of ML models, raising crucial questions about their wide real-world applications. Motivated by the need to address these concerns, this project outlines a concerted plan on the three aspects of trustworthy ML, group fairness, robust generalization, and data attribution via the algorithmic paradigm of post-processing. The outcomes of this project will be integrated into both undergraduate and graduate courses in trustworthy ML to bolster the technical course material, available to all the students. The technical aims of the project contain three key thrusts: (1) Group fairness, which aims to develop a unified framework for understanding and analyzing the trade-offs between statistical parity, equalized odds and model accuracy in classification tasks, leading to novel algorithmic solutions that achieve optimal trade-offs; (2) Robust generalization, which focuses on developing theories and algorithms to ensure that ML models can generalize well across diverse tasks and domains, particularly under distribution shifts; and (3) Data attribution, which seeks to provide an efficient and principled approach to explain the decision-making process of complex models by attributing the model's predictions to the training data, thereby enhancing interpretability and trustworthiness. All the proposed research will be conducted through the lens of post-processing techniques, which are practical and scalable to large-scale models, including large language models (LLMs). The proposed methods will be tested using the open-source LLM framework LMFlow, ensuring practical application and community accessibility. The team will share all the research outcomes through open-source software packages, and creating new tools for broader access. 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

Algebraic varieties are geometric objects defined as the solution sets of systems of polynomial equations in several variables. Classifying algebraic varieties is a fundamental problem, and from this perspective, there are three basic building blocks of varieties. One of these building blocks consists of Fano varieties, which are a class of varieties with many important applications in geometry and number theory. In the case when the variety is defined by a single polynomial equation, the Fano condition states that the degree of the polynomial is low compared to the number of variables. This project will study Fano varieties, their geometric properties (over the complex numbers), and their arithmetic properties (such as what types of numbers arise as solutions to the system of polynomials). The project will also include the training of graduate and undergraduate students. Specifically, the project will study the following three directions. The first direction will be the study of rationality questions for varieties over non-closed fields. This will focus on varieties that admit Fano fibration structures, in particular for a class of such threefolds, by pursuing generalizations of the intermediate Jacobian torsor obstruction. The second direction will focus on the relationship between rationality and group actions. Thirdly, this project will also study geometric and arithmetic aspects of pencils of quadrics, which are an important class of Fano varieties with numerous applications to the existence of rational points and to moduli spaces. 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 supports research on nonlinear dynamical systems that can model a wide range of engineered and natural processes in the real world, thereby promoting the progress of science, and advancing prosperity and welfare. Compared to linear systems, nonlinear dynamic systems are notoriously more challenging to analyze and control. The objective of this project is to leverage the more comprehensive theory of linear systems to address outstanding challenges in nonlinear dynamics. The project looks to achieve this goal through the concept of super-linearization of a nonlinear dynamical system. The application domains where such linearizations can be utilized include fluid dynamics, epidemiology, biology, neuroscience, chemical processes, plasma dynamics, finance, logistics, robotics, and power grids. In addition, the project includes an outreach plan that comprises organizing tutorials to introduce a larger part of the control community to super-linearization, and research opportunities to undergraduate students interested in dynamics and control. This research aims to establish the groundwork for a unified theory of super-linearization, advancing the field forward. In essence, super-linearization of a nonlinear dynamical system involves transforming it into a linear system operating in a higher-dimensional state space, where its trajectories align with those of the original system after projection. This project comprises three research thrusts to address specific questions related to the existence, computation, and implementation of super-linearization. These thrusts are interconnected, yet none relies on the success of others to proceed, thereby mitigating the inherent risks associated with this research endeavor. More precisely, the first thrust employs algebraic methods to study super-linearizations, particularly focusing on the case of polynomial vector fields. The second thrust explores geometric aspects, to explore the properties of the space of super-linearizable vector fields, such as whether it is locally an infinite-dimensional manifold. The final thrust combines algebraic and graphical invariant theory to obtain practical insights into super-linearization and its implications to other relevant fields, including optimal control 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

Campi Flegrei is one of the most dangerous volcanoes in the world. It is located near Naples, Italy, close to the famous sites of Pompeii and Mount Vesuvius. More than 1.3 million people live in the area, and millions of tourists visit each year. The ground at Campi Flegrei has risen by more than 4 meters since the 1950s, and the area has experienced many earthquakes. These warning signs have become stronger in recent years, raising concern about a possible eruption. This project will build a computer model to help scientists understand whether the volcano is getting closer to erupting. The study will focus on modern times, when satellite and ground data are available, and the centuries before the last eruption in 1538 using historical and archaeological records. The model could also help forecast activity at similar volcanoes in the United States, like Yellowstone and Long Valley in California. This research will provide training for an early-career scientist and a student intern and will strengthen collaboration between scientists in the United States and Italy. This grant supports scientific progress and helps protect lives and communities from natural disasters. This study aims to improve our understanding of the volcanic and seismic hazard associated with the ongoing unrest at Campi Flegrei, one of the most densely populated volcanoes of the world. The goal of the project is to conduct a series of numerical data assimilation experiments using the Ensemble Kalman Filter (EnKF) with high-fidelity, multiphysics 3D finite element method (FEM) models to evaluate Campi Flegrei’s deformation data from 1946 to present and its historical unrest from 1251 to 1538. The EnKF-FEM approach will allow calculation of variations in the stress field from the magmatic system while also considering the effects of topography, rheology, and pre-existing weakness due to caldera faults. The proposed investigation will be the first study of its kind at Campi Flegrei and the first investigation to use the EnKF-FEM technique to calculate stress evolution of a magma system over such a long-time series. An aim is to demonstrate that the EnKF can significantly improve our ability to track the stress field over time at long-term deforming volcanoes. Additionally, the project will address critical questions regarding the Campi Flegrei magma system: Q1. How have repeated unrest episodes at Campi Flegrei impacted the evolution of stress? Q2. What is the estimated state of stress presently? Q3. How might the ongoing unrest impact the state of stress in the future? Q4. What was the state of stress prior to the 1538 eruption? The expected results will provide critical insights into ongoing unrest at Campi Flegrei. This work will provide a tool to evaluate unrest episodes in large felsic calderas worldwide (e.g., Toba, Long Valley, Yellowstone). This project will support the career development of a postdoctoral researcher and fund a summer intern who will participate with a cohort of students to explore opportunities for graduate study and careers in the geosciences. Finally, an educational module on Campi Flegrei will be developed for 100-level Natural Disasters courses and made available via GETSI (GEodesy Tools for Societal Issues) hosted by SERC (Science Education Resource Center). 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 will advance next generation wireless technology to substantially reduce communication delay, known as latency, such that low latency communication service can become economically and broadly available. The project develops this technology for remote medical collaboration applications, to make medical specialists and clinicians available when they are urgently needed. Extended reality (XR) technology, including virtual reality and augmented reality, could allow remote specialists to assist on-site personnel, but enabling such real-time shared experiences critically depends on the availability of low latency wireless networking which is not generally deployed. This project will create an innovative system and platform that delivers low-latency wireless XR for remote medical collaboration, or LXM. LXM will solve technology barriers that ease deployment of low latency service for a wide range of applications even beyond medicine. The project is thus advancing U.S. global leadership in next-generation wireless technology, enabling timely patient medical response, and developing the U.S. workforce in wireless networking and XR-based medical collaboration. The project will develop both XR-based medical applications and the underlying networking technology stack to enable practically deployable low latency wireless service. It will also develop surgical planning and medical training applications that support remote collaboration and cross-layer performance requirement signaling. These will leverage new capabilities in underlying systems to provide (1) fine-grained heterogeneous network service, to enable using low latency service judiciously which results in an improved tradeoff between network latency and network bandwidth; (2) dynamic adaptivity to deal with variable network conditions; and (3) cross-layer performance intent signaling, so the technology stack can make good context-sensitive, application-aware decisions. Achieving these properties will involve new innovations in the XR runtime, transport protocol, 5G radio access network (RAN) and user equipment (UE), and will leverage advanced low-latency WiFi. 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 aims to serve the national interest by fostering interinstitutional connections among two-year college educators to expand access and drive innovation in STEM education. The University of Illinois at Urbana-Champaign partners with two-year colleges across the state to share current initiatives and provide professional development for STEM educators in Illinois and neighboring states. The ITYC Conference project includes a plan to disseminate findings to the broader STEM education community. Additionally, these findings inform the development of a statewide Illinois Community College STEM Consortium, establishing a long-term framework for enhancing connectivity and professional development among two-year college STEM educators. The main goals of this project include: 1) gathering input to inform the creation of a statewide Illinois two-year college STEM consortium, 2) engaging Illinois two-year college STEM educators in discussions on undergraduate research and fostering connections with four-year institutions, 3) providing opportunities to share recent and ongoing initiatives, and 4) increasing awareness and engagement of two-year college STEM educators with external funding opportunities. To achieve these goals, the project facilitates pre-conference activities, including surveys to assess educator needs and interests related to undergraduate research and external funding. This information guides the development of a responsive conference schedule and helps identify potential presenters. The conference's assessment and evaluation component gathers feedback on its impact while advancing understanding of two-year college STEM educator interests, initiatives, and challenges related to undergraduate research. Findings from the project are shared with participants and the broader STEM education community through conference presentations and a publicly available website. Additionally, these results help shape the future formation of the statewide consortium. The NSF IUSE: Innovation in Two-Year College STEM Education (ITYC) Program seeks to accelerate the impact of and advance knowledge about emerging and evidence-based practices in undergraduate STEM education at two-year 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 · 2025-07

Photons are excellent carriers of quantum information and will certainly play a critical role in quantum technologies spanning communication, computing, and metrology. Such quantum technologies will play (and are playing) an important role in advancing science as well as the national health, prosperity and welfare, along with securing the national defense. Producing efficient sources of pure single- and multi-photon states is a key step towards the broad deployment of such quantum technologies. However, creating these quantum states of light efficiently remains a challenge, preventing their widespread use. This project seeks to combine established photon sources with low-loss optical buffers and switches to efficiently generate these complex forms of light. To confirm that our sources indeed generate the desired quantum states, we will also develop advanced techniques to characterize the produced light. Lastly, we will demonstrate how our efficient and well-characterized sources of single and multi-photon states can enable new advances in communication, computing, and metrology. We leverage repeated “photon-addition” operations with nonlinear-optics sources of heralded photons to efficiently create multi-photon states of light such as Fock states, heralded bipartite entangled states, and “N00N” and related states. Similar multiplexing methods also allow near-deterministic photon subtraction, which enables another entire class of states to be generated by starting with coherent or squeezed states. Combined, these techniques allow the bottom-up engineering of a wide variety of exotic quantum states that are otherwise nearly impossible to generate. To assess the quantum mechanical nature of these states, well-established techniques for basic single-photon states need to be extended to and validated for multi-photon states. Examples include multi-photon interference, photon autocorrelation measurements, and homodyne detection. Lastly, we will illustrate the value of these multi-photon states via several demonstrations, including calibrating photon-number-resolving detectors with Fock states and validating phase sensitivity beyond the classical shot-noise limit with N00N states. Long-term impacts of this work include facilitating new technologies such as quantum error correction codes based on N00N and related states (potentially useful for future quantum networking applications), quantum simulations with Fock states, and practical quantum metrology beyond the classical shot-noise limit. Additionally, a hybrid continuous-variable-discrete-variable (CVDV) state-characterization system based on homodyne detection could enable the exploration of CV-encoded quantum states (e.g., squeezed states or cluster states) as well as hybrid CVDV states (e.g., cat states), with applications in measurement-based quantum computation. 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 collaborative project explores immersive video streaming technology that lets people see and interact with lifelike 3D environments. Instead of watching a regular video on a flat screen, users can move around a scene, change their viewpoint, and experience the environment from different angles in real time. This is made possible through advanced techniques that simulate how light travels through a scene using artificial intelligence. By leveraging computer vision, computer graphics, and machine learning, the project aims to make these 3D experiences smooth, detailed, and fast, even over current Internet connections. This project builds a high-performance immersive video streaming system using neural radiance fields, an advanced 3D scene representation powered by machine learning. It is structured around three key thrusts: improving resiliency, optimizing bandwidth, and reducing latency. Resiliency is addressed through lightweight models that recover lost content, adaptive encoding based on content robustness, and artifact reduction via reprojection. Bandwidth is optimized by filtering less important content, prioritizing semantically significant regions, and applying hybrid upsampling. Latency is reduced through intelligent packet ordering, caching intermediate results, and collaborative edge-client rendering. These innovations are integrated into a unified framework called NeuVol, which will be evaluated on diverse hardware and real-world network conditions. The project’s broader impacts include both technological innovation and societal advancement. It will enhance immersive applications in fields such as remote training, collaborative design, and medical diagnostics by enabling efficient delivery of high-quality 3D content. Educationally, it will integrate research outcomes into courses on multimedia systems and immersive computing, offering students hands-on experience with emerging technologies. The project will actively engage undergraduate researchers through active mentorship. Public outreach will feature demonstrations at schools, community centers, and science fairs to promote awareness of immersive technologies and inspire interest in engineering and computer science among the broader public. The project brings together researchers from George Mason University and the University of Illinois Urbana-Champaign to design, build, and test NeuVol and make it available to a wide audience through open research. All research artifacts developed through this project will be made publicly available via a dedicated project website at https://neuvol.github.io. The website will be actively maintained throughout the project duration and archived for continued access beyond the project’s conclusion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-07

PROJECT SUMMARY/ABSTRACT Otitis media (OM) is the most common diagnosis in pediatric patients seen for illness in the United States (1,2), affects 90% of all children (3,4) and is the most common indication for antimicrobial therapy and surgery (5) in young children. Despite many attempts to improve diagnosis, treatment, and prevention, OM continues its highly prevalent impact on children and substantial ongoing morbidity (1,3,6-30). OM continues as the most common cause of hearing loss (HL) in children and leads to speech, educational and other developmental delays (31-37). OM causes life-threatening complications (22,27) and is expensive, resulting in over $5 billion annually in U.S. health care expenditures (3,38). Despite the prevalence and difficulties with OM, diagnostic accuracy to allow appropriate treatment is lacking, leading to misplaced resources in treating OM. This proposal builds on our central hypothesis that enhanced diagnostic tools, specifically, optical coherence tomography (OCT), will yield improved diagnosis and lead to reduced need for antibiotics to treat acute OM, reduced surgical interventions for chronic otitis media, and overall fewer complications and cost associated with this disease. In this proposal we will explore three specific aims. The first aim, part A, we will perform a comparative assessment of middle ear (ME) pathology using pneumatic otoscopy (PO) and optical coherence tomography (OCT) in pediatric patients that present to a primary care clinic with complaints of otalgia (ear pain) or OM, with the hypothesis that OCT added to standard PO will improve diagnostic accuracy and reduce overall antibiotic prescriptions. In part B of this aim, a comparative assessment of ME pathology using PO along with audiology/tympanometry (TY) and OCT will be performed in pediatric patients that present to the pediatric otolaryngology clinic with a referral for chronic otitis media with effusion (OME), with the hypothesis that OCT added to standard PO and TY will improve diagnostic accuracy and reduce overall need for surgery in patients with OME. In the second aim, using the OCT images captured in the previous aim, we will develop image processing and machine learning algorithms for automated identification of effusions and biofilms in OCT image data to augment OM diagnosis for medical decision making. Finally, using the OCT images captured previously, along with our machine learning algorithms, we will establish OCT B-mode and M-mode image-based features that predict the resolution or persistence of middle ear effusions over time. Collectively, this project will demonstrate how these advances in diagnostic tools and algorithms will improve diagnosis and provide added information for clinical decision making in the management of OM.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT This application describes two programs of research aimed at improving the efficiency and selectivity of chemical synthesis relevant to small molecule drug discovery efforts. The first program seeks to invent ste- reodefined building blocks that can be used to optimize therapeutic properties of small molecules at various stages of drug discovery and development efforts. The second program seeks to invent a new, modular, pro- grammable synthesis of monosaccharide building blocks which are constituents of glycosylated natural prod- ucts of increasing importance as therapeutic agents and probe molecules for studying cell-cell communication. The primary objective of first program is the creation of a small library of stereodefined, functionalized, three-dimensional building blocks that can be introduced as plugins for the optimization and diversification of small molecule candidates in drug discovery programs. One of the major problems facing the research and discovery efforts in the pharmaceutical industry is the mismatch between the chemical characteristics of avail- able screening libraries and the kinds of characteristics needed to intervene by association and interaction with biomolecular targets. This problem arises from the lack of robust methods that reliably and predictably install three-dimensional carbon centers bearing appropriate functionality (oxygen, nitrogen, sulfur) in both manual and automated platforms. By systematic examination of the stereochemical outcome of the coupling of small, stereodefined boron-containing building blocks and a rigorous understanding of the mechanisms of their intro- duction, this program will provide the medicinal chemistry community with reagents that constitute “stereocen- ters in a bottle”; namely off the shelf plugins to accelerate discovery programs. The primary objective of second program is the creation of a modular, programmable synthesis of mono- saccharides that contain a diverse array of substituents and stereochemical relationships not readily accessible by current methods. The ability to access monosaccharide precursors with programmable substitution and ste- reochemistry is paramount to the precise tuning of properties. This program of research will develop a family of allylic boron reagents that will enable stereocontrolled introduction of three-carbon allyl units bearing a wide variety of substituents. These reactions take place in water using only trace amounts of indium metal as the catalyst. A critical secondary objective of this program is the application of this mild modification method in gly- cochemistry and bioconjugation. Carbohydrates are fundamental to a variety of biological processes including cell-cell recognition, protein folding, neurobiology, inflammation, and infection. Modifying carbohydrate struc- tures to enhance their physiological properties has become a key strategy in the development of innovative pharmaceuticals. Further modification of glycans is necessary to introduce functional groups that enhance their separation and detection. Similar manipulations are used in the synthesis of glycoconjugates by introducing bifunctional chemical linkers to facilitate the conjugation reaction.

NSF Awards · FY 2025 · 2025-07

Proteins play a central role in many processes essential to life and have wide-ranging applications in medicine, energy, agriculture, and biotechnology. However, natural proteins are often not ideal for these practical uses. Protein engineering, a field that aims to design proteins with improved or novel functions, has transformed industries by creating tailored proteins. While traditional approaches, such as the Nobel Prize-recognized directed evolution method, have been remarkably successful in numerous protein engineering applications, they are typically slow, costly, and resource-intensive. This project seeks to advance protein engineering by combining cutting-edge artificial intelligence (AI) methods with advanced laboratory automation. By harnessing the power of AI to predict and design protein sequences and integrating it with an automated experimental platform, this research aims to greatly accelerate the discovery of new proteins, offering immense potential across multiple scientific domains with significant commercial and societal impact on medicine, biotechnology, energy, agriculture, chemical manufacturing, consumer products, and more. This project introduces a novel interdisciplinary approach leveraging recent AI breakthroughs in large language models and generative models, to guide protein function analysis and protein engineering, unlocking an unparalleled efficiency for functional protein discovery. The research focuses on developing new AI techniques tailored to the unique challenges of protein engineering, such as sparse data and the need to balance multiple complex protein properties. By leveraging protein evolution insights and generative modeling, the AI system will guide the design of functional proteins with enhanced properties. An integrated automated Biofoundry will design, create and test AI-designed proteins, validating and then refining the design as needed, enabling a high-throughput, closed-loop discovery process. Beyond advancing the field of protein engineering, the project's algorithmic innovations will contribute to foundational research in AI and computing, with the potential for broad applications in other scientific and technological domains. This award is co-funded by the Directorate for Computer and Information Science and Engineering and by the Directorate for Biological Sciences. 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 of the plants on Earth – around 374,000 species – are flowering plants, also known as angiosperms. The appearance of angiosperms and their diversification changed the planet in important ways. Understanding the history of how angiosperms evolved is key to explaining how they became so widespread and diverse. However, we still do not fully understand when or how angiosperms began to diversify, which prevents us from determining what caused their rapid spread. Studies using molecular data show that flowering plants may have evolved much earlier than the first fossil evidence indicates. This project aims to bridge the divide between molecular studies and fossils by focusing on the pollen records of the Chloranthaceae. The Chloranthaceae comprise a flowering plant family that includes some of the oldest angiosperm fossils. The project will use advanced techniques like optical superresolution microscopy, deep learning, and new methods for reconstructing evolutionary trees. It will train two postdoctoral researchers in these cutting-edge biological and computer science methods. In addition, the project will work with non-profit groups and the UK Open University’s OpenLearn platform to offer free online educational materials about the evolution of flowering plants to the global community. This study will build a new phylogenomic tree for Chloranthaceae and use phylogenetically-informed deep learning models to place Chloranthaceae fossils within this new phylogeny. Placing fossils within the phylogeny will establish the timing of diversification within the plant family and whether Early Cretaceous (125 million years ago) Chloranthaceae pollen, which are the earliest fossil evidence for this group, represent early- or late-diverging species. If the fossils represent early-diverging species, this will support an Early Cretaceous origin for the Chloranthaceae. If the fossils represent late-diverging species, this will support a much older origination date in line with molecular estimates. Establishing the age of Chloranthaceae will also help resolve the timing of diversification of early angiosperms more broadly by constraining the ages of lineages at the base of the angiosperm phylogeny and will address the debate on when the first flowering plants appeared. 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.