University Of Illinois At Urbana-Champaign

NSF Awards · FY 2025 · 2025-07

Rotary systems, such as spinning blades on helicopters, drones, energy systems, and even artificial heart pumps, play a critical role from mechanical and aerospace engineering to biomedical applications and energy systems. However, testing these spinning devices to ensure they operate efficiently and safely is expensive, especially at full-size. This project tackles that problem by enabling engineers to use small-scale models in lower-cost wind tunnels while still capturing the full-scale behavior of the flow. This advancement will be transferable to a broad range of rotary systems for long-term cost reduction of wind tunnel testing. In addition to addressing a critical knowledge gap in fluid mechanics, the project will help educate the future workforce through a modernized curriculum, hands-on research experiences, and outreach activities. The proposed research seeks to validate the hypothesis that rotor thrust and induced power coefficient, rather than total power coefficient, provide sufficient conditions for replicating vortex wake turbulence and stability across scales. The research integrates advanced computational approaches, including inverse blade design, Large Eddy Simulation (LES) of rotary system wakes using actuator lines, and blade-resolved hybrid Unsteady Reynolds-Averaged Navier-Stokes (URANS)/LES methods, with extensive experimental campaigns. Testing will be conducted in three distinct wind tunnel facilities: a compressed-air tunnel for studying Reynolds-dependent roughness and rotational effects, a boundary-layer wind tunnel for exploring sheared turbulent inflow and platform motion effects, and an aerodynamic wind tunnel for analyzing blade loads and wake stability. This research will generate a publicly accessible database, advancing the fundamental understanding of rotary system wakes and providing critical insights for applications ranging from rotorcraft and propellers to distributed propulsion systems and energy technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project aims to study interactions between model theory, geometry, and combinatorics. Here, model theory refers to a branch of mathematical logic: roughly speaking, model theorists study mathematical structures by studying the expressive power of abstract languages that describe them. Over the last few decades, the abstract perspective of model theory has led to the discovery of certain surprising patterns across different areas of mathematics. A key such pattern is Zilber's trichotomy: this is a general observed phenomenon in which sufficiently well-behaved mathematical structures (in the sense of model theory) tend to have one of just three basic forms. This is an imprecise phenomenon -- but recent work has increased our understanding of when and how it can be formally studied. This project aims to expand on such recent work, using the latest techniques to find more precise and true instances of the trichotomy. The PI then plans to use new cases of the trichotomy to expand applications of model theory in other areas of mathematics, with a particular focus on certain aspects of geometry and combinatorics. The project will provide research opportunities for graduate students. More specifically, the project has four main goals. First, building on recent work, the PI will seek to prove new instances of the Zilber trichotomy for relics of structures of geometric interest. The main questions of this form concern relics of o-minimal structures and relics of algebraically closed valued fields; in each of these cases, the status of the trichotomy has seen substantial recent progress but remains open. Second, the PI will use new instances of the trichotomy to study reconstruction problems in geometry -- focusing particularly on reconstructing algebraic groups over algebraically closed fields from partial data. This will generalize the work of Zilber on recovering a curve from its Jacobian variety: the goal is to prove stronger and more general statements of the same type, using the latest trichotomy results as a tool. Third, the PI will investigate broader interpretations of the trichotomy, weakening the usual assumption of strong minimality to similar notions of tameness, such as geometric structures—the hope being that a wider array of trichotomy-style theorems will allow for a broader array of geometric applications. Finally, the PI will investigate pseudo-finite variants of the trichotomy in the hope of finding new applications to incidence problems in combinatorics. 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

While recent advancements in large-scale machine learning models have shown impressive capabilities, they often rely on hundreds of millions of labeled samples. However, obtaining high-quality labels in many fields is extremely costly, so most available data remain unlabeled. For example, although millions of images and videos can be easily collected from social media platforms, manually labeling them is a tedious and time-consuming process. To address the challenge of limited labeled data, self-supervised representation learning has emerged as a promising approach in computer vision and natural language processing. It has already played a key role in the success of recent large language models. Despite its strong performance in practice, the theoretical understanding of self-supervised representation learning remains limited. Moreover, the problem of scarce labeled data also affects biomedical research, but the existing self-supervised methods cannot be directly applied due to the unique nature of biomedical datasets. This project aims to address these gaps by developing new theoretical frameworks for self-supervised representation learning, along with computational tools tailored to biomedical studies. It also includes educational efforts to engage students and the broader public with this growing area of research. This project aims to advance the theoretical foundations of self-supervised representation learning and transform how unlabeled data are utilized in biomedical research. On the theoretical front, the project will investigate self-supervised learning on a low-dimensional nonlinear model, which effectively captures the invariant and intrinsic low-dimensional structure underlying observed data. Building on this nonlinear modeling framework, this project will develop a novel theory that explains the empirical success of self-supervised learning and clarifies the role of pseudo labels generated from unlabeled data. This theoretical foundation will inform the design of innovative and principled learning methodologies. On the application side, the project will integrate self-supervised representation learning into biomedical applications, including microbiome studies and omics-based longitudinal (trajectory) data. The project will develop new computational tools and software tailored to these contexts, enabling the effective use of large-scale unlabeled biomedical data. These advancements are expected to help address critical scientific questions and contribute to a deeper understanding of biological systems and human health. 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 · 2025-07

Contemporary organisms inherited their biochemistry and basic metabolic pathways from ancestors that lived in an anoxic, iron-rich world. The subsequent aeration of the planet led to problems, because oxygen is a reactive chemical that can disrupt iron-dependent enzymes. It has been the goal of our lab to detail the threats that oxygen poses, the defenses that organisms have evolved, and the situations in which oxidative stress exerts a strong impact upon microbial fate. We learned that molecular oxygen can intercept electrons as they move through redox enzymes. A mixture of superoxide and hydrogen peroxide is formed. These reactive oxygen species (ROS) are stronger oxidants than is molecular oxygen itself, and they can inappropriately disable the iron cofactors of certain enzymes. To defend themselves, cells evolved layers of defenses: scavenging enzymes that keep ROS scarce, and adaptive systems that repair damaged enzymes. ROS also threaten DNA. Hydrogen peroxide oxidizes loose iron inside the cell, forming hydroxyl radicals that can abstract electrons from DNA. Cells protect themselves by limiting the loose-iron pool and by maintaining a fleet of DNA-repair enzymes. This view is coherent and explains a lot of phenomena, but important issues are unsettled. First, we showed that different organisms produce ROS at different rates and that this variability influences oxygen sensitivity. But we do not know why ROS production varies. We are working to identify the particular enzymes that are predisposed to leak electrons to oxygen; we anticipate that ROS production will be highest in organisms that in which such enzymes are abundant. Second, we want to know which DNA repair enzymes act upon oxidative lesions. Until now, many repair mutants have not shown ROS sensitivity—but we recently developed a better understanding of how to obtain a relevant phenotype. This approach has already yielded surprising insights and suggests that oxygen was an impetus to the evolution of familiar DNA defenses. The most obvious example of oxidative stress is the phenomenon of obligate anaerobiosis. We found that aeration generates overwhelming ROS in a model anaerobe, poisoning the vulnerable enzyme families that had been identified previously. However, molecular oxygen itself also directly attacks several special enzymes that are critical for anaerobic fitness. As a test of our understanding, we aim to recapitulate evolution and fix these trouble points one by one, by engineering changes that nudge an anaerobic bacterium toward oxygen tolerance. Finally, we detailed how one redox-active antibiotic oxidizes DNA via mechanism that is shielded from cellular defenses. We will test whether other clinical antibiotics/antitumor agents do so as well. In toto, we feel we are arriving at a view of oxygen toxicity and resistance that is thorough and detailed. With that understanding may come the ability to manipulate oxidative stress in ways that are beneficial.

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.

NSF Awards · FY 2025 · 2025-06

Research computing stakeholders have grown to consider The Practice and Experience in Advanced Research Computing (PEARC) Conference series as a forum for discussing challenges, opportunities, and solutions specific to research computing. Also, the consistent growth in data and the ubiquity of research instruments has increased the array of research areas that rely on advanced research computing. Thus, access to reliable, robust and resilient high-performance computing (HPC) resources as well as data solutions is no longer a luxury, but an expectation and requirement for researchers to stay competitive in their domain. Highly skilled individuals are needed to facilitate computation and accompany users in their usage of research computing tools. Thus, the theme for this year’s PEARC conference is “The Power of Collaboration”. Building on the success of the XSEDE and PEARC conference series, this year’s conference aims to highlight the role and importance of the power of collaboration while broadening the community by introducing the next generation to the world of Research Computing. The conference exposes students and scientists to professional networks and other resources that facilitate research workforce development. Students are paired with mentors from a community of experts, who provide support and networking opportunities at and beyond the conference. Research presentations and lightning talks are additional opportunities available to attendees. PEARC25’s program offers students with a unique opportunity to gain hands-on-experience in advanced research computing and encourages them to pursue a career in it. 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 project aims to develop new methods to support community-determined behavior norms in online communities consistent with the goal of helping communities reward behaviors that serve the community's goals. Current approaches to online community management typically focus on detecting and discouraging undesirable behavior with sanctions like bans, quarantines, and content removals. These punitive approaches do not directly foster an environment for community members to engage in positive ways around the group's norms. This project will look at ways to use positive reinforcement to proactively incentivize community-determined behavior. The work will advance knowledge about how communities recognize and encourage behavior while developing novel technologies to promote community norms in online spaces. The education and outreach plan is deeply tied to the research activities, focusing on scaling up the broader impacts of the research. A public application programming interface will enable developers and moderators around the world to integrate the computational approaches developed by this research into their own communities. The project will be carried out in three phases. Phase 1 will involve the development of new computational models to detect community-determined behavior across different types of online communities. This phase will uncover the structure of encouraged behavior within online conversations and develop community- and context-sensitive computational models to identify behavior. Phase 2 will employ causal inference frameworks to evaluate the efficacy of current feedback mechanisms in terms of their effects on positive reinforcement and promoting desirable behavior. This phase will draw on theories from human-computer interaction and psychology to examine the causal effects of various forms of positive feedback in motivating users and reinforcing normative behavior. Phase 3 will involve the building of a new AI-backed system that will allow moderators to easily identify instances of valuable, normative behavior within their communities and respond with timely feedback that serves as positive reinforcement. Easy access to pre-trained models and carefully designed human-centered tools developed as part of this research will allow moderators to incorporate high quality inputs suggested by state-of-the-art AI methods into their workflows to help achieve their community'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 · 2025-06

NON-TECHNICAL SUMMARY Shape memory alloys (SMAs) experience reversible transformations, shifting back and forth between different arrangements of atoms, which in term dictate their properties. Once stretched, they undergo a change and then upon removal of the load, they can return to their original shape. Such a phenomenon finds applications in cardiovascular stents, structural dissipation under impacts, and actuators for motion control. Consequently, these kinds of materials can be used in defense, healthcare, aerospace, and structural engineering. However, for these materials to be trusted as reliable, they need to function over many cycles. However, the inability to return to its original shape is termed an “irreversibility”. Irreversibilities can eventually result in fatigue cracks, compromised performance and reduced durability. This work focuses on improving the understanding of atomic level changes in shape memory alloys such as NiTi and NiMnTi. The aim is to mitigate these irreversibilities via changes to elemental composition, introducing precipitates, and modifying atomic arrangements to reduce the defects that lead to irreversibilities. The introduction of precipitates in these materials is particularly promising, as they have been shown to facilitate transformation, imparting additional reversible strains especially under high stress applications. In this proposal, the advances in science-based understanding of SMA interfaces is establishing high-accuracy results built upon experiments using lattice scales and supported by modeling efforts that drive innovation. Additionally, this work is revamping our educational efforts in materials science, by enhancing pedagogical understanding and inspiring a new generation of students through the completion of a textbook on phase change materials and participation in a senior design project focused on fatigue testing. Ultimately, these efforts serve the national interest in different economic sectors and align with the National Science Foundation (NSF) mission by promoting research at the forefront of materials innovation. The research alos trains graduate students, allowing them to interact with others in the field and prepare them for the future workforce. TECHNICAL SUMMARY The intellectual contribution of this work centers on an advanced understanding of reversible phase transformations in shape memory alloys (SMAs). The aim is to develop the foundations of strain accumulation by accounting for the complexity of interfaces, specifically focusing on four characteristic twin intersections. This complexity is being studied with advanced high-resolution transmission electron microscopy and atomistic simulations. The local strains at characteristic twin interfaces of the monoclinic martensite phase are being established with a level of accuracy and high physical fidelity from atomic displacements using template matching methods and developing advanced algorithms to characterize displacements at twin interfaces. The creation of defects such as dislocations at these interfaces as an outcome of displacement incompatibilities will be established. The ab-initio calculations are also providing the basis for the determination of energy barriers for transformation motion, martensitic twinning, and dislocation slip progression, leading to the calculation of Critical Resolved Shear Stress (CRSS) that is being compared to experimental measurement. Modeling is pinpointing the minimum energy interface configurations considering the lattice offsets at interfaces, whereas the imperfect mis-alignments are providing crucial understanding of slip-induced irreversibility in shape memory alloys (SMAs). The experimental findings are checking the validity of these interface configurations and critical stress levels are being corresponded to the motion of such interfaces. Ultimately, the proposal focuses on an in-depth understanding of the microstructure of NiTi and the promising NiMnTi SMAs with varying compositions, particularly regarding the unexpected transformation of the precipitates (which have an initial rhombohedral structure) in the quest to achieve further functionalities. In summary, by deciphering the evolution of internal twinning interfaces in martensite, the proposed study’s is charting out a route to advanced shape memory alloys by curtailing dislocation slip effects. Additionally, this work is revamping our educational efforts in materials science, by enhancing pedagogical understanding and inspiring a new generation of students through the completion of a textbook on phase change materials and participation in a senior design project focused on fatigue testing. The proposal is aligned with national interest and the NSF mission by contributing to the science of advanced materials and accelerating materials discovery in the field of transforming alloys. 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 project investigates the subsurface structure beneath the Yellowstone volcanic province in northwestern Wyoming. Much of Yellowstone National Park, which attracts more than 4 million visitors annually, lies within a volcanic caldera formed by a large explosive eruption 630,000 years ago. Present-day geologic activity at Yellowstone indicates the presence of a magma body beneath the caldera. Uncertainties persist, however, regarding the volume and distribution of magma, and how current conditions compare to those preceding past eruptions. Seismic imaging has revealed the existence of a magma reservoir in the mid-to-upper crust, but limitations in spatial resolution have hindered its accurate mapping. Recent advances in computational imaging techniques, such as full waveform inversion, combined with unprecedented seismic data coverage provided by a deployment of over 650 nodal seismic instruments, now offer new opportunities to image Yellowstone’s magmatic system at previously unattainable scales. This study will create a new three-dimensional image of subsurface wave speeds in Yellowstone’s magma reservoir using full waveform inversion, with the goal of uncovering new insights into crustal magma storage and enhancing our understanding of volcanic hazards. This project will support a graduate student and provide opportunities for an undergraduate student. New computational code will be released for community use, enabling similar approaches on other volcanic systems. The primary objective of this project is to resolve fine-scale intra-reservoir structures that could identify potential regions of concentrated melt within Yellowstone’s mid-to-upper crustal magma reservoir. The project will leverage data from long-term broadband seismic networks and dense temporary nodal deployments to create a high-resolution 3D image of subsurface seismic wave speeds. Both short-period noise correlation functions (T > 3 s) and local earthquake waveforms will be inverted using a full waveform inversion approach that incorporates radial anisotropy. While radial anisotropy is commonly observed in seismic investigations of large continental magmatic systems, the anisotropic signatures of complex reservoirs—and the ability of tomographic methods to resolve them accurately—remain underexplored. To address this gap, the project will also perform systematic forward modeling experiments to investigate crustal melt storage configurations capable of producing the observed positive radial anisotropy at Yellowstone. The results of this project will develop code and establish a framework for applying computationally advanced imaging techniques to other densely instrumented volcanic systems of significant scientific interest. 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

Today's data science systems, ranging from batch jobs to interactive interfaces, are surprisingly fragile. Data scientists typically use dozens of libraries, but a single bug in any can destroy hours or even days of computation, causing significant pain. This issue has been widely discussed in the data science community and academic literature. Yet, no principled mechanisms have been proposed to address the issue which might be puzzling to database researchers because existing databases implement checkpointing to periodically save changes in data for future recovery. Why haven't data science systems adopted checkpointing? What are the unique properties of data science systems that challenge the adoption? This project will answer these questions and bring checkpointing to data science systems with zero modifications to existing libraries and programs. If successful, this project can enable checkpointing, for the first time, in today's data science ecosystems. It will enable recovery from crashes, execution “undos”, suspending cloud resources without losing data, etc. This project first identifies a critical challenge: data science systems lack mechanisms for detecting changes in data, an important premise of checkpointing. Existing databases achieve this with centralized buffer pools. In contrast, data science systems intentionally omit centralized data spaces, allowing individual libraries to manage data using shared memory, GPUs, and remote machines for high performance. The changes in these library-managed data must be identified for checkpointing. This project will achieve this identification by developing a nonintrusive state manager that can act like conventional buffer pools without forcing data to be placed in central places. The key idea is to build a mathematical map of library-managed data, including dependencies between data, using graphs. These graphs will enable new algorithms to identify changes, save them partially, and restore states correctly. This project will develop an open-source system, Kishu, to benefit all data practitioners. This project will also pursue its adoption into the National Center for Supercomputing 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.

NIH Research Projects · FY 2026 · 2025-06

Abstract Maintaining the integrity of the genome is essential to cell survival. As bacteria grow and divide, they must coordinate numerous molecular machineries for each daughter cell to inherit an intact copy of the chromosome. Miscoordination can be lethal, making these processes potential targets for novel antibiotics. The Mera laboratory uses a systems approach to untangle the complex temporal and spatial coordination that drive the progression of the bacterial cell cycle. The work combines classical genetics, biochemistry, and high- resolution imaging to examine at the molecular level cell cycle coordination. The bacterial model system used is Caulobacter crescentus, which exhibits a dimorphic life cycle. The project proposed has two main goals for the next 5 years: (i) define mechanistic details for how the replication initiator DnaA with the partitioning system ParABS coordinate the initiation of chromosome replication with centromere segregation, and (ii) uncover mechanisms that bacteria use to coordinate chromosomal maintenance to the regulation of cell size and shape over the cell cycle. Furthermore, the proposed work will provide important insights to how cells integrate environmental information (e.g., nutrient availability) into the cell cycle network. The vision of the research program in the Mera lab is to uncover the molecular rules that govern the coordination of the bacterial cell cycle. The understanding of these fundamental rules has the potential to transform our ability to control the growth of bacteria and to drive biomedical innovations.

NSF Awards · FY 2025 · 2025-06

The information revolution is largely driven by semiconductor industry’s capability of incorporating electronic device components in microprocessor chips with increasingly higher density, as described by Moore’s law, during the past 70 years. However, it has become more and more difficult to sustain this progress by further reducing the size of each transistor since the lateral dimension scaling is approaching its physical limits. This project aims to address this challenge by developing a set of enabling technologies for the vertical stacking of high-performance silicon devices in future integrated circuits. This three-dimensional (3D) stacking approach allows us to achieve higher device integration density without reducing the size of individual transistors and enables a higher communication bandwidth with lower energy consumption between circuit blocks for better performance. This project serves the U.S. national interest in helping US-based semiconductor companies to sustain their technology leadership in the worldwide competition through accelerated technology transfer. It also provides summer research internships for high-school students and develops research-based laboratory modules for the enrichment of undergraduate curriculum. Monolithic 3D integrated circuits offer multiple advantages over conventional planar 2D architecture or 3D systems realized via wafer or chip stackings. However, their implementation and manufacturing face the fundamental challenge of forming high-performance n- and p-channel transistors on top tiers under the constraint of limited thermal budget to preserve the bottom-tier devices and interconnects from degradation. Low-temperature processed transistors built on laser-annealed poly-crystalline silicon, metal oxides, and low-dimensional nanomaterials have been explored as potential candidates, but their performance is far inferior to transistors built on single-crystalline silicon. The objective of this research is to fill this technology gap by developing a process to transfer-print ultrathin (<10 nm thick) single-crystalline silicon nanomembranes with uniform doping on wafer scale at low temperature (<200 oC) on top of a silicon-CMOS wafer with completed devices and interconnects. The transferred silicon nanomembrane is then utilized to build high-performance junctionless transistors without extra doping process to stay within a limited thermal budget <400 oC. They will connect with the bottom-tier transistors through ultra-dense interlayer vias to form functional logic and memory circuits with performance and chip-area footprint unattainable in 2D architectures. This project will enable a breakthrough in realizing high-performance 3D monolithic integration, overcoming thermal constraints associated with both the formation of high-mobility semiconductor layers and the fabrication of high-performance transistors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Clay-laden flows play a significant role in shaping rivers, estuaries, coastal systems, and deep-sea environments by controlling erosion, sediment movement, and water quality. However, despite their environmental and engineering importance, the influence of clay on flow behavior remains poorly understood. This award will explore how clay interacts with fluid motion across a range of real-world conditions by studying how these flows behave under controlled high-frequency oscillations. Laponite, a synthetic clay that forms a transparent suspension in water, will be used to enable researchers to observe and measure particle motion and turbulence development using advanced optical techniques. The project will lead to new models for predicting sediment transport, erosion, and flow stability in both natural systems and industrial settings. The findings can improve flood risk assessments, water quality monitoring, and the design of hydraulic structures. The award will also support hands-on learning for students in engineering and environmental science, encouraging interest in research careers and helping train the next generation of scientists and engineers in fluid dynamics and sustainability. This award focuses on the fundamental physics of clay-laden flows subjected to transitional flow conditions, where turbulence develops under intense, time-dependent shear driven by high-frequency, small-amplitude oscillations. The project aims to systematically quantify how clay concentration alters the onset, structure, and evolution of turbulence in such flows. Using a custom-designed laboratory flume, the study will explore three primary regimes: (1) the transition from laminar to turbulent flow as a function of clay concentration and oscillatory shear, (2) the development and scaling of boundary layers over smooth surfaces, and (3) the influence of organized and random roughness on turbulence modulation in clay-laden boundary layers. Experimental results will be used to derive functional relationships linking turbulence characteristics to key control parameters: clay concentration, shear frequency, amplitude, and surface roughness. Emphasis is placed on identifying instability mechanisms, characterizing coherent structures, and quantifying energy redistribution across flow scales and regimes. These findings will inform predictive scaling laws for transitional clay flows and support models of sediment transport and boundary-layer behavior in engineered and natural systems. By bridging fundamental fluid mechanics with environmental and engineering relevance, this award will offer critical insights applicable to natural sediment-laden flows, infrastructure resilience, slurry transport systems, and water quality management across multiple scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This Faculty Early Career Development (CAREER) project intends to develop new legged robots that combine agility over rough terrain with speed and efficiency, enabling robots to go wherever human workers can. Robots with legs can traverse rougher terrain than wheeled vehicles and carry heavier loads for longer periods than flying drones. However, current legged robots are significantly slower and less efficient than their wheeled and flying counterparts. The hypothesis is that robot legs driven by a small number of powerful motors — similar to a car’s wheels or a boat’s propellers — can achieve higher speeds and efficiency compared to conventional robot legs powered by many smaller, less powerful motors. Achieving this goal requires new knowledge in controlling balance and steering with fewer motors, as well as investigations into efficiency, robustness, and payload capacity. The resulting robots aim to match human speed, even over long distances, to assist with tasks such as package delivery, emergency response (e.g., wildfire fighting), and scientific exploration. This project will encourage K-12 students' interest in engineering through hands-on activities with walking linkages and by providing training to expand access to the FIRST LEGO League. This CAREER project will attempt to combine a single-degree-of-freedom, linkage-based legs that recirculate in a stepping motion through continuous crank rotation with underactuated balance control algorithms based on reduced-order models to enable high-speed walking, low cost of transport, and flexible navigation over rough terrain. Investigations into passive dynamic walking and actuator coordination will focus on improving efficiency and payload capacity. Bipedal and multi-legged robot designs, along with control architectures developed around shared reduced-order models of walking locomotion, will be evaluated through simulation, physical prototyping, and hardware experiments. These efforts aim to set new records for walking robot speed and endurance. This work's improvements to the foundations of legged robot performance could enable future robot locomotion over uneven terrain for service in the home, transportation outdoors, and planetary exploration beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This Faculty Early Career Development (CAREER) project supports research that aims to advance robotic tactile sensing technology by enabling robots to actively explore and perceive the physical properties of objects, such as hardness, texture, and slipperiness. This capability is essential for robots to handle complex tasks, ranging from manipulating delicate fabrics to working with irregular objects like sauces or seasoning particles. The research looks to develop innovative frameworks that enable robots to co-optimize their actions and observation models, leading to improved perception and manipulation capabilities. This work has significant societal and educational benefits. Enhanced tactile sensing will expand the range of tasks robots can perform in healthcare, manufacturing, and everyday life, making them more versatile and impactful. Additionally, the project will integrate its findings into new robotics courses, fostering the education of a new generation of roboticists. Openly accessible educational materials will promote broader community engagement and diversity in robotic tactile sensing research, contributing to national prosperity and workforce development. This CAREER project addresses fundamental challenges in robotic tactile sensing by developing methods for active perception that integrate exploratory actions with tactile signal interpretation. Co-optimization frameworks will be formulated to minimize perception uncertainty by refining both exploratory actions and observation models. These frameworks will draw upon insights from psychological studies, physical modeling, and data-driven optimization, enabling robots to perceive complex object properties. The research introduces implicit feature representations for properties that are difficult to measure directly, utilizing these representations for both perception and manipulation. This project will provide the first general solution for using active touch to perceive physical properties and for integrating active touch perception with precise manipulation. By framing the perception-manipulation system as a co-optimization problem, the methods will advance both theoretical understanding and practical applications. A sensorized dexterous hand will validate the approach through challenging tasks involving both solid and formless objects, pushing the boundaries of current robotic perception and manipulation technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This research will study fast and cost-effective manufacturing of commonly used part geometries. Today, most plastic and rubber parts are made by molding: A liquid polymer resin is poured into an expensive mold, let to cure or solidify, then removed from the mold while ensuring that the final part meets the expected quality. Examples of such parts include the casings of computer keyboard, mouse, monitor, webcam, car dashboard, kitchen mixer, tennis rackets, and shoe soles. However, these molds are expensive because they are made of metals using elaborate machining processes, and they require frequent maintenance to ensure that they can be used for many parts. On the other hand, plants and trees simply grow to their geometries without needing molds. This project will study a mold-free production of parts, called growth printing, which could eliminate the cost of the mold and its maintenance. This Additive Manufacturing (AM) method is anticipated to be very fast because it uses a chemical liquid resin which can solidify instantaneously on demand using laser triggering. It is anticipated to be 100 folds faster than current 3D printers, which leads to considerable savings in cost and time to make useful strong and tough parts. This research studies a mold free manufacturing process which relies on a propagating reaction front, naturally self-driven by the exothermic polymerization of the monomer dicyclopentadiene (DCPD). This polymerization front is thermally triggered from a point heat source, such as a laser. This front propagates radially in the resin vat, curing the monomer into poly-DCPD, a high-performance cross-linked polymer. A metal initiator is connected to a motion stage such that it pulls the solidified part from the liquid with a vertical velocity profile which defines the part geometry. This research looks to use multiple point laser initiation to trigger the nucleation of an array of curing fronts, which then grow and merge. The merging fronts define the cross-section geometry of the part while the height is defined by the upward motion of the initiator tip. The tasks will study the laser initiation of frontal polymerization experimentally, analytically, and numerically; the geometry and physics of merging fronts; and the applicability to industrially relevant part designs. Education activities include a design competition for K-12 students to convey the importance of manufacturing speed and cost; develop a new lecture on constructive controversy on manufacturing and industry; and lab open house activities for visitors of all ages. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This award is funded by NSF Global Centers program, an innovative partnership with other funding agencies in Canada, Finland, Japan, Republic of Korea, and the United Kingdom, to jointly support use-inspired research addressing global challenges through the bioeconomy. These partnerships leverage resources to tackle challenges at a larger scale than would be possible for one funding agency alone. This Center is jointly supported by NSF Office or International Science and Engineering and the Directorate for Biological Sciences, the Research Council of Finland and Business Finland, Japan Science and Technology Agency, and UK Research and Innovation. Faster progress towards a sustainable bioeconomy is essential to reduce carbon emissions and ensure the production of cleaner energy. One key goal is improving feedstock-crop performance and resilience. Plant lipids store substantial energy that can be easily converted into fuels and other products. However, feedstock crops need to have greater energy density, increased resilience to environmental stresses and higher yields. Here, the Global Center for "Alliance for Socially-Acceptable & Actionable Plants" (ASAP) exploits natural biodiversity in gene sequences to engineer crops with increased lipid content and greater water use efficiency (WUE). WUE is the ratio of biomass produced relative to water used. It is determined by stomata, small pores on leaves that balance CO2 uptake with water loss. This transformational advancement integrates recent breakthroughs in genetics, protein modelling, synthetic biology, AI, and biotechnology. It draws on the expertise of a multi-disciplinary team of scientists from four countries. ASAP also investigates attitudes among stakeholder groups toward biotechnology to achieve sustainability goals. The project directly leverages natural biodiversity across the tree of life. It employs a biofoundry and artificial intelligence tools to accelerate the crop improvement cycle. It trains the next generation of scientist expert in this field. Public and industry engagement strengthen its technological enterprise, accounting for consumer attitudes and market preferences. The project also provides support and training to undergraduate and graduate students at University of Illinois - Urbana Champaign, and to postdoctoral associates at bot University of Illinois and Stony Brook University. Progress towards improving feedstock crops for a viable bioeconomy is limited by the slow speed of the design-build-test-learn (DBTL) cycle and the urgent need to discover genetic variants that confer trait improvements. ASAP delivers a synthetic biology solution to produce high-energy, water use efficient crops by: 1) genomic screening of diverse natural variation to identify amino acid changes likely to improve oil production (lipid biosynthesis) or WUE (via stomatal characteristics); 2) using AI-based modelling of protein structure to predict enzyme properties to prioritize functioning gene variants; 3) developing deep-learning models to predict plant regulatory protein-DNA binding; 4) testing targets using the biofoundry to build genetic designs; 5) gene function testing using robotic and AI driven phenotyping. A key part of ASAP is reducing the gap in understanding of societal attitudes towards gene edited products for the bioeconomy. Consumer attitudes and market preferences will be evaluated using multi-national surveys, whilst emotional responses are examined via cognitive image elicitation. Translation of successful genetic designs into productive crops is facilitated by engagement of a Stakeholder Advisory Board, whilst broad impacts are realized through the development of new technologies and training of a diverse bioeconomy 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 · 2025-05

With support of the Chemical Catalysis program in the Division of Chemistry, Professor Scott E. Denmark at the University of Illinois at Urbana-Champaign is studying the development of new methods for catalyzing the synthesis of high-value ring systems that are prominent in complex natural products, pharmaceutical and agrochemical compounds. The research aims to use selenium-based catalysts to anneal doubly functionalized reagents (featuring nitrogen, oxygen, and/or sulfur) onto readily available alkenes while controlling the orientation and three-dimensional arrangement of all elements. The research activities in organic and inorganic catalysis and synthesis are ideal for the intellectual and practical training of undergraduates, graduate students and postdoctoral coworkers. The actionable roadmap for the project is to construct the mechanistic / physical organic foundation for developing general and selective selenium-catalyzed alkene difunctionalization reactions. The overall project objective is divided into three Specific Aims: (1) catalytic, enantioselective syn-1,2-diamination to construct piperazines and tetrahydroquinazolines; (2) catalytic, enantioselective syn-1,2-oxyamination to construct morpholines and oxazolines; and (3) catalytic, enantioselective, intramolecular 1,2-oxyamination to construct polycyclic oxazolidin-2-ones. For each Aim, the project team will carry out detailed mechanistic (kinetic, spectroscopic, crystallographic, computational) investigations of the catalytic reactions to learn the rules for achieving high catalytic activity (TOF and TON) for the target reactions; design chiral catalysts that will impart high stereoselectivity and high chemical conversion for the introduction of new stereocenters; and demonstrate generality in a variety of substrate classes that represent broadly useful structural motifs. 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 · 2025-04

Project Summary Bone regenerative biomaterials are of critical importance in treatment of bone defect, enhanced bone healing, reduction of pain and disability, replacement of damaged or diseased bone, and development of new orthopedic treatment options among others. With many years of endeavors, the field of orthopedic regenerative biomaterials has seen tremendous progress, but many challenges remain. For instance, the currently available bone regenerative biomaterials are featured with inabilities to mimic the native tissue composition, and slow bone regeneration among others. The goals of this proposal are to elucidate an unexplored synergistic optoelectrostimulation and metabonegenic regulation for bone development, and to translate these understandings towards the design of novel biomimetic optoelectroactive citrate-presenting bone biomaterials for orthopedic applications for promoted bone repair. The proposed optoelectroactive citrate biocomposites (OCBs) that will enable synergistic osteopromotive effects between exogenous citrate supplement and in situ electrostimulation by wireless untethered photo-illumination to provide increased cellular energy supply in osteodifferentiated stem cells, as referred to a novel strategy of optoelectro-metabonegenic regulation. The OCBs will allow for citrate release in a control rate and in situ optoelectrostimulation through µ-solar cell pellets, where the µ-solar cell pellets will also be designed to be biodegradable to match the time frame of bone regeneration.

NSF Awards · FY 2025 · 2025-04

This award will support the participation of students from institutions of higher learning in the United States in the 16th Power and Energy Conference at Illinois (PECI), to be held at the University of Illinois, Urbana-Champaign in April 2025. The conference program will include paper presentations, keynote lectures, tutorial sessions, and panel discussions. Networking events between students and power system researchers from industry and government laboratories are also planned. The conference will provide students with the unique opportunity to engage with field experts through attending session talks, immersing themselves in enlightening keynote presentations, and fostering valuable connections during social events. PECI is the oldest annual, student-run IEEE international conference about power and energy technology. The conference is organized by the IEEE PES/PELS/IAS Joint Graduate Student Chapter at the University of Illinois at Urbana-Champaign, and has become a perfect place in the Midwest for industry and academic researchers to discuss future power and energy topics such as renewable energy, electrified transportation, smart grid reliability, security of cyber-physical systems, and power electronics for emerging technologies. Attendees will have the opportunity to present their research and to learn about each other's work to gain a broad perspective on ongoing topics in energy systems research. 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 · 2025-04

PROJECT SUMMARY Sex differences are present in many forms of epilepsy, both in humans and in preclinical animal models. The origins of these sex differences remain poorly understood. Although gonadal hormones strongly influence neural excitability and seizure activity in both males and females, the contributions of sex chromosomes, the other component of biological sex, in driving differences in seizures between and within sexes are largely unknown. The studies in this project will apply a unique mouse model, in which gonadal and chromosomal sex effects can be dissociated, to the study of seizures and epilepsy for the first time. Using a combination of in vivo video-electroencephalography, RNA sequencing, and immunostaining, we will determine the contributions of sex chromosomes and gonadal hormones in shaping acute seizure susceptibility and seizure-induced neuropathology (Aim 1) and spontaneous recurrent seizures and hippocampal gene expression in a mouse model of chronic temporal lobe epilepsy (Aim 2). This work will have positive translational impact by addressing the 2021 NINDS Epilepsy Research Benchmark to “identify and understand mechanisms of sex modulation of epilepsy risk,” and will provide a basis for future mechanistic investigations of the interactions between sex chromosomes and specific gonadal hormones in shaping neural excitability, seizures, and epilepsy in both males and females.

NSF Awards · FY 2025 · 2025-04

This award will support student travel to the 46th edition of the IEEE Symposium on Security and Privacy (IEEE S&P) this conference, to be held in May 2025. IEEE S&P is recognized as a premier platform for showcasing advancements in computer security and electronic privacy across a wide range of related topics and disciplines. Participation in premier venues is critical to the success of student researchers. Attendees will have the opportunity to showcase their research, receive invaluable feedback, attend high-quality talks, and interact with top researchers across the field of security and privacy. This will provide both intellectual and professional resources to advance the quality of their ongoing work and careers as security and privacy researchers. This grant will provide travel support to about 15 students who otherwise have limited travel funding and so might not be able to attend. The committee will widely advertise the availability of travel funding to solicit applications from students of a range of institutional, disciplinary, topical, and personal backgrounds. Criteria for selection include evidence of a serious interest in the field as demonstrated by coursework and/or project experience and financial need. The committee will also limit the amount of funding given to students from any particular advisor or institution in order to expand the set of students and institutions attending the conference. 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 · 2025-03

Endometriosis is a multifactorial estrogen-dependent disease. Endometriotic lesions occur throughout the peritoneal cavity and on the ovaries, leading to chronic pelvic pain and infertility. Clinical management focuses on surgical resection but improved care is severely limited by our poor understanding of processes driving lesion formation, persistence, and recurrence. While ovarian endometriosis lesions (endometriomas) are the most common, essential studies of lesion initiation and growth are largely intractable in vivo. There is a critical need for experimental tools to investigate processes that shape endometrioma lesion formation, treatment, and recurrence. The long-term goal of this research program is to develop a tissue engineering model of the endometrioma lesion microenvironment to study processes that shape shape lesion initiation, ovarian stroma invasion, and eventual persistence in a chronic inflammatory tissue microenvironment. Retrograde transport of endometrial tissue and menstrual effluent containing endometrial epithelial and stromal cells through the fallopian tubes into the peritoneal cavity is believed to contribute to lesion initiation. However, while retrograde menstruation is common, only a fraction of patients develop endometriosis. Further, lesions are not ubiquitously spread through the peritoneal cavity and on the ovaries in each patient. Hence, while retrograde menstruation is likely necessary, it is not sufficient: lesion initiation must be triggered by more than just the presence of these cells. We focus on uncovering multicellular interactions that shape endometrioma lesion initiation and invasion that may explain the idiosyncratic nature of lesion distribution. We hypothesize tissue tropism associated with the ovarian microenvironment provides cues that influence the activity of multicellular cohorts of endometriotic epithelial and stromal cells responsible for lesion initiation. And subsequently, angiocrine signals from the underlying ovarian perivascular environment accelerate lesion invasion. The objective of this project it to demonstrate a physiomimetic model of the endometrioma lesion microenvironment, combining 2D and 3D biomaterials tools with human menstrual effluent specimens to investigate processes responsible for lesion initiation and invasion. To accomplish this goal, we will first identify cues that inform adhesion of endometriotic epithelial and stromal cell cohorts responsible for lesion initiation (Aim 1). Then we will define patterns of endometrioma invasion in response to the ovarian microvascular environment (Aim 2). We will generate unprecedented data regarding variation of lesion initiation and invasion not possible in vivo. Understanding how tissue tropism and ovarian vascular signals shape patterns of lesion initiation and invasion is essential for the design of effective therapeutic strategies. Tissue engineering models benchmarked here will also provide an essential foundation for future study of the influence of chronic inflammatory signals on lesion persistence and recurrence.

NSF Awards · FY 2025 · 2025-03

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry and co-funding from the Quantum Information Science Program in the Division of Physics, Professor Backlund's group at the University of Illinois Urbana-Champaign is combining theory and experiment to elucidate the “speed limits” that govern the measurement of molecular processes with light. Light-based chemical measurement and imaging is central to a vast array of technologies that find use both inside and outside of the laboratory, including in the areas of health, defense, and energy. This work guides the design of optimal measurement schemes, which in turn will enable the development of powerful new devices that might be used, for example, to improve disease diagnoses or detection of chemical weapons, or to enhance the efficiency of energy capture from light. In addition to these advancements in science and technology, the group seeks to broaden participation in, and understanding of, quantum information science (QIS) in disciplines beyond physics and communities beyond the university setting. The Backlund group seeks to theoretically establish and experimentally realize the fundamental precision bounds of single-molecule spectroscopy within the framework of quantum metrology. Their approach probes the limits to single-molecule measurement set by quantum parameter estimation and detection theory. Specific measurement tasks addressed include 1) assessing the limits of spatial resolution of non-photoswitching molecules, 2) discrimination of molecules based on the spectro-temporal properties of emission, and 3) assessing the fundamental limits to single-molecule chiroptical discrimination. Research efforts are complemented by educational initiatives that encourage undergraduate and graduate chemistry students to participate in QIS, and by broadening participation in QIS through engagement with the local community. 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.