University Of Illinois At Urbana-Champaign

NSF Awards · FY 2026 · 2026-09

Scientific cyberinfrastructure enables discoveries that depend on high-performance computing, research portals, application programming interfaces, cloud platforms, and large-scale data collaborations. These environments are increasingly targeted by adversaries, and Security Operations Centers must detect, understand, and respond to threats while supporting open and collaborative science. Cyber deception provides a proactive defense by using decoys, deceptive data, and honeypots to mislead attackers and collect intelligence, but current approaches are difficult to adapt across dynamic and heterogeneous scientific computing environments. This project develops a large language model-powered adaptive cyber deception framework for cyberinfrastructure Security Operations Centers. The framework supports realistic deceptive interactions, self-learning deception environments, and automated threat intelligence. It generates context-aware responses during controlled attacker engagement, adjusts decoys and honeypots based on observed adversary behavior, and translates deception-generated evidence into actionable intelligence for security analysts and automated response systems. The research integrates advances in cybersecurity, machine learning, and computer systems to transition adaptive deception methods into operational cyberinfrastructure defense. The system is deployed and evaluated with the National Center for Supercomputing Applications to assess its effectiveness in protecting scientific research infrastructure. Project results are shared through publications, open-source tools, and new undergraduate and graduate course modules. The work strengthens the resilience of scientific cyberinfrastructure, reduces the workload of security analysts, and helps prepare the next generation of cybersecurity professionals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

This NSF CAREER project aims to develop a new class of energy-efficient artificial intelligence (AI) models that can infer the internal behavior of complex energy systems from limited sensor measurements. Many critical infrastructures, including power grids and thermal energy systems, contain regions that cannot be directly instrumented, yet safe and reliable operation requires knowledge of internal conditions such as temperature, voltage, and power balance. Existing AI methods are often computationally intensive, tailored to a single system or component, and challenging to deploy in power-constrained environments such as substations, industrial facilities, and remote monitoring stations. The project will bring transformative change by creating compact foundation models that learn the governing structure of dynamic energy systems and reconstruct unmeasured states in real-time while operating within strict energy budgets on facility-grade or fog-level compute. This will be achieved by integrating physics-aware learning methods, adaptive modeling strategies, and energy-efficient architectures. The intellectual merit of the project includes advancing the theoretical foundations for compact foundation models that generalize across heterogeneous dynamic energy systems and establishing principles for energy-efficient AI. The broader impacts of the project include improving the reliability and resilience of national energy infrastructure, enabling real-time monitoring of critical systems with limited sensing coverage, developing open-access educational curricula on AI for energy systems, and training the next generation of students at the intersection of artificial intelligence, energy engineering, and infrastructure resilience. The project addresses a fundamental challenge in modern energy infrastructure: many systems are governed by differential–algebraic equations, which couple dynamic physical processes with network constraints, as seen in electric power grids where dynamic generator behavior must satisfy algebraic power-flow balance constraints. Traditional monitoring and simulation approaches are often too computationally expensive for real-time deployment and too specialized to generalize across different energy technologies. This research develops a compact neural-operator foundation model that learns mappings between sparse sensor observations and full system state fields while incorporating physical constraints through topology-aware representations and constraint-aware calibration. A neuroscience-inspired spiking neural layer enables event-driven inference that reduces energy consumption relative to conventional neural networks, supporting deployment on embedded edge platforms. The framework will be validated on representative dynamic energy systems, including power-grid state estimation using sparse phasor measurement unit observations, together with hardware-in-the-loop experiments that demonstrate real-time operation under strict power constraints. Together, these CAREER activities will establish energy-efficient foundation models that can be deployed across diverse energy infrastructures without reliance on centralized cloud computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Satisfiability Modulo Theories (SMT) problems are akin to mathematical puzzles: given a mix of equations and logical rules, an SMT solver determines whether there exists an assignment of variables that satisfies everything at once. SMT solving underpins high-stakes assurance tasks: for example, proving that a self-driving car cannot steer into an obstacle under modeled operating conditions, and supporting safety checks in domains like avionics, medical devices, and power systems. These problems become increasingly challenging as modern software and systems incorporate machine learning (ML) and artificial intelligence (AI), which introduce large, highly nonlinear mathematical constraints that often exceed the scalability of traditional solvers. To address this gap, the project develops a new solver framework that transfers highly scalable, hardware-accelerated techniques originally created for neural-network verification into general SMT solving. The project’s novelties are the fundamental re-architecting of traditional SMT solving procedures around a new concept called linear bound propagation, which effectively handles large SMT problems with nonlinear arithmetic and enables massive parallel computation. The project's impacts are an open-source, high-performance reasoning engine that strengthens safety and reliability across software and cyber-physical domains where nonlinear constraints and rigorous safety requirements are central, including next-generation AI-enabled systems, thereby fostering public trust in autonomous technologies. Technically, the project advances formal verification for nonlinear real arithmetic (NRA) SMT through three synergistic thrusts. Thrust I builds a new NRA decision procedure grounded in linear bound propagation, introducing novel methods to efficiently account for correlations among constraints, infer bounds even when variables are only implicitly constrained, and tighten relaxations through computation-graph optimizations and cross-constraint bound refinement. Thrust II elevates this procedure into a delta-complete solver by specializing branch-and-bound for NRA and integrating the resulting engine into a DPLL(T) architecture with linear-bound-aware propagation, clause learning, and unsatisfiable core extraction. Thrust III delivers a practical, high-performance implementation via an asynchronous DPLL(T) pipeline that keeps parallel numerical evaluation highly utilized. The approach is validated on previously intractable verification workloads (including those with AI/ML components) while contributing new community benchmarks, accelerating progress in building toolchains for formally verified systems that integrate code, physics, and AI 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 2026 · 2026-07

Artificial intelligence (AI) systems are increasingly deployed in real-world physical environments to assist in factories, hospitals, warehouses, and other dynamic settings, where safety, reliability, and effective human-AI collaboration are essential. Yet current AI agents still fail in ways that are difficult to detect, explain, and correct. They often misinterpret instructions, fail to recognize when their own reasoning is flawed, struggle to recover from errors, and persist in unsafe or ineffective behavior. This project addresses these limitations by developing a new generation of AI agents that can monitor their reasoning, draw on past experience to evaluate and explain their decisions, and adapt their internal understanding of the world based on corrective feedback. Such self-regulating AI agents will be more reliable, more transparent, and safer to deploy in complex physical environments. Beyond advancing the science of AI, this project translates research into learning experiences, student mentoring, interactive demonstrations, and community engagement activities that expand AI literacy and strengthen AI workforce readiness. This project develops a unified framework for embodied intelligence in which language serves as a cognitive mechanism for self-monitoring, self-evaluation, and self-regulation. First, the research develops introspective reasoning methods that enable agents to detect, attribute, and mitigate perceptual and reasoning failures during task execution. Second, the research develops reflective explanation mechanisms grounded in episodic memory, enabling agents to retrieve relevant past experiences and their associated reasoning to assess ongoing decisions and proactively seek targeted human feedback. Third, the research develops language-guided self-organizing world models that enable agents to revise their internal beliefs and dynamically adapt decision-making in response to evolving feedback and introspective diagnostics. The project will produce new algorithms, benchmarks, evaluation protocols, and openly shared resources for reliable embodied AI. By advancing our scientific understanding of the fundamental principles underlying intelligent embodied behavior, this work establishes new foundations for how language enables self-regulation and adaptive decision-making in physical environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The rational design of new biomolecules, such as enzymes and therapeutic proteins, is essential for advancing medicine and creating sustainable chemical processes. However, conventional frameworks for discovering these molecules remain slow, fragmented, and largely rely on trial-and-error. This project promotes the progress of science by creating a new class of artificial intelligence (AI) models that can design complex biological molecules with unprecedented precision and speed. Specifically, this research will build an autonomous discovery engine that unifies the design of molecular sequences and their three-dimensional shapes into a single, self-improving system that can efficiently design molecules, learn from real-world experimental feedback, and continuously improve its own performance. By shifting the scientific process from manual orchestration to automated AI-driven discovery, this research will significantly reduce the time and cost required to develop life-saving therapeutics and sustainable biochemicals, thereby advancing the national health and prosperity. The project also contributes to the national interest by strengthening the biotechnology workforce through interdisciplinary training for students and public outreach programs that introduce young learners to the intersection of computer science and biology. Finally, all AI models, datasets, and software developed during this project will be shared publicly to broadly enhance the national research infrastructure. The technical goal of this project is to develop a unified, multimodal, and self-improving artificial intelligence framework for high-fidelity biomolecule sequence and structure co-design, alongside iterative optimization. The research activities are organized into three synergistic aims. First, the investigator will develop a novel generative paradigm that natively supports multimodal variable-length co-design through evolution-inspired edit operations, coupled with multi-scale geometric representations and fast-sampling consistency models. Second, the project will create a suite of data-efficient optimization algorithms to intelligently steer and improve these generative models. This includes a unified guided generation framework based on stochastic optimal control, uncertainty-aware sequential Monte-Carlo sampling, and a co-evolutionary reinforcement learning framework that allows the system to continuously improve from experimental feedback. Finally, these computational methods will be integratedinto a fully autonomous design, build, test, and learn cycle. This closed-loop platform will be deployed to solve high-impact biochemical engineering challenges, specifically targeting the design of noncanonical cyclic peptides, new-to-nature enzymes, and high-affinity antibodies. The potential contribution of this work is a fundamental shift from static, fragmented pipelines to an autonomous discovery engine capable of rapidly creating highly functional biomolecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The amount of data processed by AI-based analytics has grown exponentially year over year. Unfortunately, this has unveiled a critical deficiency of contemporary computers: they physically separate their computation chips from their data storage chips, requiring each piece of data to move between the two whenever it needs to be processed. This separation wastes an increasing amount of energy and time on data movement, often 100 to 1000 times more than the computation itself, hindering the performance, efficiency, and potential uses of computers. For mobile computers such as smartphones, small drones, and sensors, the waste forces them to offload most analytics to the cloud. Recent breakthroughs in computer hardware have realized a near-60-year vision to unite computation and storage onto the same chip, potentially eliminating most data movement waste. If fully realized, this new hardware could unleash the next revolution in mobile computing, with ultra-low-cost devices that no longer need the cloud or an Internet connection to perform AI data analytics in mobile computers. This project addresses the major challenges preventing this realization, by jointly designing the hardware and software foundations required to support and integrate these new chips. The research produced by this project can enable many new uses of computers, from automated crop monitoring to all-day extended reality gaming to rural healthcare to infrastructure anomaly detection. All research findings will be publicly disseminated through conference publications, openly available software tools, and a project website. The project will introduce new university classes, K-12 programs, and public outreach campaigns to introduce learners to hardware/software co-design skills that are critical to train the next generation of computer engineers. Specifically, this project will make cloud-free edge AI analytics a reality by evolving today's processing-using-memory (PUM; a.k.a. in-memory computing) accelerators into standalone, scalable systems. PUM accelerators use electrical interactions inside memory arrays to perform approximate analog multiplies or precise general-purpose Boolean operations. While prior research has developed PUM datapaths that can execute a limited range of parallel microkernels, the research team will use hardware/software co-design to tackle three critical challenges for Boolean-PUM-based edge AI analytics. First, the team will design cross-stack abstractions that enable in-PUM execution of thread-based software and allow PUM to execute applications without CPU assistance. Second, the team will design data protection mechanisms that enable PUM virtual memory and address security and privacy concerns with today's PUM accelerators. Third, the team will design a meta-manager that allows system developers to cohesively coordinate edge analytics across many PUM chips in a scalable manner. The work will introduce several firsts, including end-to-end application execution in PUM without a CPU, a software stack for general-purpose PUM, the unification of memory allocation and thread scheduling for data-centric systems, generation-based virtual memory management, and multi-PUM-chip coordination. Together, these solutions can enable the deployment of standalone cloud-free PUM edge platforms that deliver several-orders-of-magnitude higher energy efficiency while significantly reducing programming complexity; and catalyze new research areas in PUM systems, programming languages, and ubiquitous edge analytics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

As artificial intelligence (AI) increasingly accelerates scientific discovery and engineering design, there is a growing need for models that are not only computationally fast but physically reliable. Many current AI approaches rely purely on massive datasets, predicting physical phenomena without incorporating the underlying laws of nature. This purely data-driven approach can lead to predictions that are unstable or physically impossible. This project develops 'physics-preserving' machine learning models that embed geometric and physical constraints directly into the AI's architecture. By ensuring these models obey fundamental physical laws by design, the research yields simulators that operate thousands of times faster than traditional computational methods without sacrificing accuracy. These advancements directly support Federal strategic interests in artificial intelligence and advanced manufacturing by enabling the creation of real-time, highly accurate digital twins for complex systems in aerospace, materials science, and energy. Additionally, the project supports workforce development by training a new generation of scientists, spanning high school, undergraduate, and graduate levels at the critical intersection of computational mathematics and machine learning.
 This project will create structure-preserving scientific machine learning (SciML) architectures to learn reduced partial differential equation (PDE) models incorporating constrained tensors. Examples include the stress and strain tensors in linear elasticity (symmetric), deviatoric stress (trace-free), and internal stress or linearized Riemann curvature (antisymmetric in pairs, pair exchange symmetry, algebraic Bianchi identities). The PDEs and tensor constraints will be formulated using higher-order differential complexes created by combining de Rham complexes via the Bernstein-Gelfand-Gelfand (BGG) technique. This ensures data-driven surrogates inherit the geometric structure inherent in the tensor objects. Rather than treating physics as data-driven regression, the approach performs learning at the level of the physics to build transformer models thousands of times faster than forward simulators, with geometric structure inherited from the BGG complexes providing guarantees of trustworthy performance. This work will lay the foundation for a unified theory of data-driven physics matching the rigor of finite element methods while preserving the approximation power of modern transformer methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Chromosomes carry genetic instructions that allow cells to grow, respond to the environment, and pass information to future generations. Just as the function of a protein is understood through its amino acid sequence, three-dimensional structure, and conformational dynamics, a complete understanding of chromosome function requires knowledge of DNA sequence, chromosome structure, and dynamic behavior. Modern sequencing and imaging methods have transformed the ability to read genomes and capture snapshots of chromosome architecture, but much less is known about how different regions of the chromosome move inside living cells. The project will address this gap in knowledge by creating a genome-scale view of chromosome dynamics in the bacterium Escherichia coli and establishing physical principles that explain why different chromosomal regions move in different ways. The outcomes have broad implications for the U.S. national interest by promoting fundamental discovery at the interface of physics and biology, strengthening quantitative approaches in biotechnology, and helping build a foundation for future efforts to predict and engineer genome function. Integrated education and outreach activities will train students across multiple levels in quantitative biophysics, broaden access to research experiences, disseminate protocols and analysis tools, and highlight the contributions of physicists to biology and medicine, including Nobel laureate and Illinois alumna Rosalyn Yalow. The project will combine high-resolution single-molecule tracking, MINFLUX super-resolution microscopy, quantitative polymer-physics modeling, and in vitro reconstitution to decode how active biological processes and passive physical constraints shape bacterial chromosome dynamics. In living cells, the project will measure fast-timescale motion of many genomic loci to build a high-resolution atlas of chromosome motion. These measurements will be combined with complementary genomic and cellular data to determine links among chromosome organization, cellular activity, and locus-specific dynamics. In vitro experiments will then test the contributions of specific biological and physical factors to chromosome dynamics in a controlled setting. The resulting data will be used to develop and validate predictive models that connect measurable chromosome dynamics to underlying molecular mechanisms and polymer properties. By linking dynamics, structure, and function in the bacterial genome, this project will provide new concepts and tools for understanding genome regulation and may inform future efforts to engineer synthetic gene expression systems and other biotechnology 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 · 2026-06

Summary Parkinson’s disease (PD) is a neurodegenerative disorder characterized by progressive motor and cognitive decline. Despite extensive research efforts, current treatment protocols are limited to symptomatic management, and, therefore, there is a high need to develop new therapies that can address the underlying pathogenic mechanisms. Since mutations in the LRRK2 gene are major contributors to both familial and sporadic PD, several therapeutic approaches aim to reduce LRRK2 activity using small-molecule inhibitors or antisense oligonucleotides. However, these strategies face substantial obstacles, including safety concerns, suboptimal biodistribution, and a lack of durable effects. One strategy that can overcome some of those problems is genome editing. Base editors are a class of genome editing tools capable of introducing specific nucleotide substitutions without generating double-strand DNA breaks. Given the precision and ability to induce permanent modifications following a single administration, this project proposes to develop a base editing gene therapy that either silences LRRK2 expression or precisely modulates its activity using genome editing. Using an intein-split base editor delivered by adeno-associated viruses (AAVs), we will test two therapeutic strategies in disease-relevant cellular and animal models of PD. In Aim 1, we will evaluate the feasibility and impact of knocking out LRRK2 using base editors to introduce premature stop codons. In Aim 2, we will assess whether base editing can create LRRK2 variants with reduced LRRK2 activity and attenuate PD-related cellular phenotypes in genetic and sporadic PD models. These studies will clarify whether tuning or eliminating LRRK2 activity offers therapeutic benefit and will address key safety and efficacy questions related to base editing in the central nervous system.

NSF Awards · FY 2026 · 2026-06

The construction and control of large-scale quantum systems is one of the grand challenges in contemporary science. The ability to do so would enable us to unlock fundamentally new technological capabilities in the form of quantum simulation and computation. Once available, these technologies are widely anticipated to lead to solutions to so-far unsolvable problems across a multitude of fields such as materials science, drug discovery, or computer science. As such, quantum technology has exceptional potential to benefit society. The critical challenge for building suitable quantum devices lies in the inherent fragility of quantum states: we must find physical quantum systems that represent the logical units of a quantum machine, quantum bits or qubits, with long, intrinsic coherence times (i.e., the time that a quantum state can be kept intact) while at the same time maintaining the ability to control them. This proposal aims to find the means to effectively enhance the available coherence in one of the most promising platforms for quantum computation, superconducting qubits, by developing quantum memories based on naturally extremely coherent spins. Through the design of specifically tailored coupling circuits, this proposal will realize efficient interfaces between superconducting qubit devices and ensembles of spins and allow storage and retrieval of quantum states that are suited for quantum computation. In superconducting circuits, a leading platform for quantum computation, coherence limits are set by materials and methods to fabricate the circuits. The central research goal of this project is the experimental realization of the storage of logically encoded qubits in long-lived spin ensembles. Using state-of-the-art parametric couplers, this work aims to efficiently transfer quantum information from superconducting qubits to harmonic oscillator modes realized by the ensembles. This capability will result in the creation of logical qubit states in the form of so-called cat-qubits. The research aims to provide a pathway for the storage of error-correctable quantum states with orders-of-magnitude improvements compared to superconducting qubits and cavities. Research objectives are: (1) Encoding cat states in ensembles of Ytterbium dopants through parametrically induced strong coupling between spins and a superconducting qubit using Josephson parametric couplers; and the storage of multiple cat states on the millisecond timescale in the spin ensemble using spin echoes. (2) Realization of new parametric couplers based on nonlinear kinetic inductance in superconducting thin films; and the quantum control of spin ensemble modes in externally applied magnetic fields using these couplers. The proposed research will result in new insights across fundamental and applied science, as well as quantum engineering and quantum computing. It will further the understanding of driven, nonlinear quantum systems and limits of gate speeds; aim to demonstrate the feasibility of achieving net benefits in hybrid quantum systems; and significantly advance available storage times of logical qubit states for improved 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 2026 · 2026-06

River deltas are dynamic landscapes shaped by branching channel networks that transport water, sediment, and nutrients to the coast, supporting communities and ecosystems. These systems are increasingly vulnerable to sea-level rise, reduced sediment supply, and human activities, which can trigger rapid changes such as channel shifts or continuous land loss. While long-term delta evolution is relatively well understood, the short-term processes controlling channel network behavior over decades remain poorly constrained. This CAREER project develops a process-based understanding and predictive framework for delta channel network morphodynamics at timescales relevant to human observation and decision-making. The research integrates field observations, remote sensing, laboratory experiments using 3D-printed channel networks, and modeling approaches that combine physical theory with machine learning. The resulting framework will improve predictions of delta evolution, including in Arctic systems undergoing rapid change. Broader impacts include informing more effective and cost-efficient delta restoration and management strategies, with implications for coastal resilience and infrastructure stability. An integrated education program, including a publicly accessible “Delta Morphology Simulator” and hands-on student design challenges, will enhance public understanding and help train the next generation of scientists and engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Surrounding Earth is the exosphere, a vast cloud of hydrogen atoms that forms the outermost edge of our atmosphere and extends tens of thousands of kilometers into space. This region plays important yet poorly understood roles in atmosphere near-space interactions, including how Earth recovers from geomagnetic storms — solar-driven disturbances that can disrupt satellite communications, GPS navigation, and power grids. The project serves the national interest by improving space weather prediction and resilience, helping to protect satellites, critical infrastructure and astronauts. It advances fundamental understanding of how Earth’s atmosphere interacts with the space environment, with broader implications for atmospheric escape and planetary habitability. The work aligns with national priorities for distributed ground-based observing systems and supports student training through a multi-institutional collaboration with openly accessible data products. This project leverages a rare, time-sensitive opportunity created by recent start of science operations for NASA’s Carruthers Geocorona Observatory, coinciding with the decline from the peak of solar cycle 25. During the period of elevated solar activity, overlapping space- and ground-based observations of Earth’s extended hydrogen atmosphere are planned. Because spacecraft cannot fully observe the nightside of Earth, a distributed network of ground-based observatories across North and South America would be used to fill these gaps. Together, these measurements will produce a coordinated, three-dimensional view of the exosphere not achievable by existing observations alone and reveal its storm-time response. This project investigates the structure and dynamics of Earth’s hydrogen exosphere, where charge exchange with magnetospheric hydrogen and oxygen ions plays a central role in geomagnetic storm recovery. The project enables new constraints on exospheric hydrogen density by combining Lyman-alpha observations from the Carruthers Geocorona Observatory with near coincident ground-based measurements of Balmer-alpha and Balmer-Beta emission obtained using Fabry–Perot interferometers and narrow-band photometers. This effort represents a unique opportunity to obtain complementary measurements that are not achievable by the space-based mission alone. 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 · 2026-05

PROJECT SUMMARY Genome editing is rapidly transforming biology and medicine by enabling the precise modification of DNA sequences in vivo. The technologies used for genome editing have also provided a means to modulate the expression of a target gene by facilitating the recruitment of transcriptional effector domains to a target site. One such tool capable of recreating the native mechanisms of gene activation consists of catalytically inactive Cas9 (dCas9) fused with the histone acetyltransferase p300, which, by acetylating histones near a target site, can activate gene expression through a nearly physiological approach. Nonetheless, current dCas9-p300 systems are inefficient, unpredictable, and incompatible with adeno-associated virus (AAV) delivery, which hinders future applications of this technology. Here, we propose to integrate state-of-the-art techniques including protein engineering, machine learning, and viral-vector design to create an innovative toolkit of epigenome editors that can be used to activate gene expression in vivo. Aim 1 will leverage phylogenetic diversity and directed evolution in mammalian cells to identify p300 variants with enhance gene activation capabilities. In Aim 2, we will develop machine-learning models that integrate target-site sequence features and nucleosome architecture to predict actionable target sites for efficient acetylation and gene activation. And, in Aim 3, we will engineer split-intein and ultracompact epigenome editors that can be packaged within single or dual AAV vectors to enable in vivo gene activation. To accomplish these objectives, we have assembled a multidisciplinary team with collective expertise in epigenome editing (Dr. Perez-Pinera), computational biology (Dr. Song) and AAV gene delivery (Dr. Gaj). Our collaborative efforts will: (1) yield a toolkit of programmable, tunable, and deliverable gene-activation tools, (2) facilitate the discovery of fundamental principles for epigenome editor design, and (3) enable the development of therapeutic applications for multiple disorders including developmental and metabolic disorders. We anticipate that the innovative and interdisciplinary nature of this proposal will yield technologies that will broadly impact biotechnology and medicine.

NIH Research Projects · FY 2026 · 2026-05

Project Abstract The protozoan parasite Cryptosporidium is a leading cause of diarrheal disease (cryptosporidiosis) and death in young children and immunocompromised individuals. Currently, there are no vaccines or effective drugs to prevent or treat cryptosporidiosis. Our understanding of the interactions of Cryptosporidium with its host cell is very limited. Cryptosporidium has a unique mode of invasion that differs from other related apicomplexan parasites. The parasite creates an ‘intracellular but extracytoplasmic’ niche where it sits on top of the intestinal epithelial cell, engulfed by the host cell membrane, and is separated from the host cell cytoplasm. A key feature of Cryptosporidium invasion is the rapid polymerization of host actin and cell remodeling, which leads to the formation of the trophozoite with an ‘actin pedestal’ underneath at the host-parasite interface. Although Cryptosporidium releases hundreds of secretory effector proteins from four organelles—rhoptries, micronemes, dense granules, and the newly discovered ‘small granules’—the role of these proteins in host-parasite interactions remains largely unknown. Notably, the parasite effectors that manipulate the host machinery to promote actin polymerization during invasion are not known. To address this knowledge gap, we have identified Cryptosporidium secretory proteins that are potentially involved in manipulating host actin polymerization machinery during invasion for the parasite to establish its unique niche. The goal of this exploratory proposal is to utilize a combination of molecular genetics and cell biology approaches, and animal infection experiments to investigate the role of the top identified candidates and their interacting host proteins in promoting actin polymerization. The results of this study will improve our understanding of Cryptosporidium biology and host- parasite interactions. The new knowledge gained from this work will support future development of vaccines and effective therapies against cryptosporidiosis.

NSF Awards · FY 2026 · 2026-05

This REU Site award to the Prairie Research Institute’s Illinois Natural History Survey (INHS), located at the University of Illinois Urbana-Champaign in Champaign, Illinois will support the training of 10 students for 10 weeks during the summers of 2027–2029. This program will provide undergraduate students with immersive, hands-on research experiences centered on the innovative use of biological collections and the “extended specimen” approach. Biological collections are libraries of preserved plants, animals, fossils, and identifying records that span decades, even centuries and the globe. Collections serve as powerful scientific resources for understanding how biodiversity, ecological communities, and environments change across time. The extended specimen lens enhances the impact of such collections by linking specimens with additional data such as genetic and environmental information, and species interactions. Integrating these data allows scientists to address complex challenges such as emerging diseases, food security, and shifting ecosystems. Through interdisciplinary research projects, students will experience the full scientific data life cycle from field research and specimen curation to data analysis and communication of their results. Students will develop skills in data literacy, scientific reasoning, and teamwork while preparing for careers that address global challenges and that will strengthen the STEM workforce. The program will broaden access to research opportunities for all, particularly for students from institutions with limited research infrastructure. Students will learn how research is conducted, and many will present their findings at scientific conferences. Program assessment will use surveys, interviews, and career-tracking to evaluate outcomes, and students will apply through NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with NSF priorities in Artificial Intelligence and Biotechnology. The scientific theme of this REU Site is the use of collections and the extended specimen framework as a platform for interdisciplinary research training. Students will conduct projects using the world-class collections at INHS that include plants, insects, fishes, reptiles, mussels, and fossils to investigate questions in evolution, ecology, environmental change, and disease dynamics. Projects may involve field sampling, specimen identification and digitization, genomic or ecological data analysis, and development of biodiversity informatics workflows. Students will be mentored by scientists across multiple disciplines, including evolutionary biology, museum science, data science, and environmental biology. Professional development will include workshops in coding, data management, reproducible and responsible conduct of research, as well as training in scientific communication and career development. Through these integrated activities, the program provides a comprehensive research experience spanning specimen collection, data integration, analysis, and communication while advancing innovative digital approaches to collections-based science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Brain disorders affect millions of people worldwide, yet effective treatment options remain limited. Ultrasound neuromodulation is a promising technique that uses sound waves to gently influence brain activity without surgery. However, scientists do not fully understand how ultrasound affects individual brain cells, which limits its safe and effective use in medicine. This project will uncover how mechanical forces created by ultrasound interact with neurons at length scales smaller than a single cell. The research will explore whether, where, and how the mechanical energy is delivered to a neuron and how that neuron responds. The results of the project will guide the development of new brain therapies. The project will also include educational activities designed to foster curiosity in bio-inspired photonics and neuroscience. Together, the outcomes of this project will inform the development of future healthcare biotechnology and spur interest in bioengineering in the next generation of scientists and engineers. The project will develop a new laboratory tool that will allow scientists to apply highly controlled mechanical forces to specific regions of individual neurons and monitor their response in real-time. This tool will use light to generate forces, enabling precise control over where the force is applied and how it changes over time. These light-driven forces are designed to mimic key features of ultrasound stimulation, including force strength, timing, frequency, and duration. Unlike existing methods, which often rely on blunt mechanical probes or large ultrasound devices, this new approach will allow each parameter to be adjusted independently. It will combine a gold nanostructure that converts light into localized sound waves with a highly sensitive microscope capable of measuring very small forces. By carefully measuring how neurons respond to well-defined mechanical signals, the research will identify which force characteristics are most important for activating or suppressing neuronal activity. The knowledge gained from this research will help clarify the physical principles behind ultrasound neuromodulation and guide the design of future brain stimulation technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Polyamines are organic cations important in all domains of life, but their physiological roles are not well understood. Salmonella Typhimurium is a major food-borne pathogen that replicates within macrophages, and adaptation to the low magnesium environment of the macrophage phagosome is key to Salmonella virulence. Our recent data support a new paradigm for the overall role of polyamines in cell physiology. Our primary hypothesis is that polyamine and magnesium concentrations are coordinately controlled in the cell and that polyamines replace magnesium in critical systems under low magnesium stress. Upon magnesium starvation, polyamine synthesis is induced, as is production of high affinity magnesium transporters. Either polyamine synthesis or magnesium transport is required to maintain viability. Once magnesium levels are re-established, the excess polyamines must be excreted, or cells lose viability in stationary phase. This lethality is suppressed by blocking magnesium transport and is exacerbated by increased magnesium in the medium, indicating that it is the total concentration of magnesium and polyamines that is detrimental. Importantly, these results are recapitulated during infection, confirming that, during infection, magnesium and polyamines are required for the same processes. Although critical for viability and pathogenesis, the mechanisms by which cells coordinately control polyamine and magnesium levels are completely unknown. Our long-term goals are to understand the regulation of polyamine synthesis and export, and the consequences to Salmonella physiology when this regulation is disturbed. Our expertise in gene expression and bacterial genetics, combined with the strong phenotypes observed in our Salmonella polyamine mutants, put us in a unique position to address these questions. Aim 1: We have shown that the PhoPQ two-component system, the primary regulator of magnesium homeostasis in the cell, is dependent on the polyamine putrescine for function in low magnesium conditions, the normal inducing signal. Specifically, lack of putrescine locks PhoQ into a state where it dephosphorylates the transcriptional regulator PhoP. We will dissect the site and mechanism of putrescine action in PhoQ. Aim 2: The biosynthetic enzyme SpeA catalyzes the first step in putrescine synthesis. We have discovered that speA transcription is repressed by putrescine. We will address the mechanisms of this previously unexplored regulation, including identifying the regulatory proteins that are responsible. Completion of the specific aims will provide novel insights into Salmonella adaptation to low magnesium, critical for cell viability and virulence of this important pathogen, and likely relevant in all cells.

NSF Awards · FY 2026 · 2026-04

This project is focused on advancing the goals of the Executive Order on Advancing Artificial Intelligence (AI) Education for American Youth by providing resources for K-12 AI education, as called for in the Dear Colleague Letter NSF 25-035. Specifically, the project will expand AI curricular resources for students taking the Advanced Placement Computer Science A (APCSA) course. APCSA classrooms provide a wide audience for this work; in 2025, almost 100,000 students took the APCSA exam. Additionally, the research team will support 130 teachers in implementing these AI curricular resources in their courses. The AI curricular resources will focus on two essential sets of knowledge: (i) how AI works and (ii) how to use AI to write code. All training and curricular materials will be publicly shared on a project website, CSTeachingTips.org, as well as on relevant AI and CS curriculum repositories. There is broad interest among teachers in integrating AI, and this project aims to meet the needs of teachers and students. However, three key questions remain that can guide AI curricular resources to be more effective. (1) What AI content are students and teachers most interested in? (2) What of students' existing knowledge can we leverage to support robust AI knowledge? (3) What opportunities are there to improve the AI curricular resources to be more effective and better aligned with students' interests and existing knowledge? This project addresses these key questions and will substantially expand high school students’ access to AI learning by providing AI curricular resources and teacher training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Earthquakes result from the repeated buildup and release of stress along faults in the Earth’s crust. These processes are controlled by friction between rocks. The way rocks change between earthquakes governs when and how slip initiates and propagates. Water’s effect is widely recognized in fault processes. The complexity of natural fault systems makes it difficult to isolate individual factors. This project will address a central gap in earthquake science by examining how temperature, fluid chemistry, and surface roughness influences frictional strength along a common material in faults. By conducting laboratory experiments, the research will isolate the processes that govern how contacts between mineral surfaces evolve over time. These experiments will clarify how fault surfaces strengthen or weaken under conditions that are difficult to observe in natural settings. The gained knowledge will thus contribute to developing more reliable prediction of seismic processes to support national welfare, security, and resilience. The project will strengthen the STEM workforce by training a graduate student and several undergraduate students. This project will determine how interfacial properties, pressure solution, and plastic creep influence friction, adhesion, and wear in nanoscale contacts as a function of temperature and fluid chemistry. The project hypothesis is that elevated temperature and specific fluid chemistries promote contact aging and velocity-weakening friction. To bridge the gap between nano- and macroscale frictional behavior, this project determines the contribution of roughness to the frictional characteristics of single calcite crystals under selected hydrothermal conditions. The project will develop a model for kinetic friction, informed by experimental results and existing microphysical theory to capture both contact quality and quantity. Experimental studies of the interfacial properties and measurements of friction, adhesion, deformation, and wear will be carried out using Atomic Force Microscopy and a Surface Forces Apparatus in a temperature-controlled fluid cell. A modeling framework will also be also developed to describe friction in single- and multi-asperity contacts using microphysics-based parameters. By linking these insights to rate and state friction equations, the work will bridge microscale mechanisms and macroscale fault behavior. Collectively, the project will generate new knowledge on hydrothermal effects in calcite friction with broad implications for earthquake physics and geomechanics. 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 · 2026-04

Paragons of Conformational Control in Metalloenzyme Reactivity Lisa Olshansky Project Summary. Recent decades have witnessed a revolution in what was once a static picture of biology. For example, the tenets of biochemistry once taught that sequence determines structure, but we now know that sequence and cellular environment determine conformational sampling. Evolutionary selection acts on dynamic rather than static features. The implications for this dynamic biochemical world permeate all aspects of human health. Therefore, it is essential that contemporary research explore the roles, mechanisms, and structure- function paradigms at play therein. However, the complex interplay between structural changes and changes in reactivity make exploring these paradigms in natural systems incredibly challenging. At the same time, simplified synthetic models typically fail to capture the key elements of control leveraged in Nature to regulate activity. My approach is to combine these tactics. By incorporating synthetically prepared metal complexes into proteins that have evolved to undergo allosterically driven conformational changes, we are preparing switchable artificial metalloproteins (swArMs) that represent paragons for conformational control in metalloenzyme reactivity. By creating artificial systems in which changes in structure are directly linked to changes in function, we aim to quantify the effects of conformational control in terms of thermodynamic and kinetic parameters underlying reactivity. Ultimately, this understanding can then be harnessed in the development of new biocatalysts, bioimaging agents, and new systems for biomimetic energy conversion. Our work is poised for the exploration of key unanswered questions in enzyme catalysis, such as how allosteric binding events are converted into metallocofactor activation, or how entropic factors regulate radical chemistry, or how energy conversion occurs in mitochondrial respiratory proteins. Using a wide range of biophysical and spectroscopic methods, swArMs provide a platform with which to explore all of these questions, and to examine the mechanisms of regulation underlying function and dysfunction in metalloenzyme reactivity that is critical to human health.

NIH Research Projects · FY 2026 · 2026-04

Technological advances over the past few decades have transformed our everyday lives. A remarkable opportunity also exists to use these advances to engineer solutions to end cancer as we know it. The Cancer Center at Illinois (CCIL) harnesses the combined power of engineering and basic sciences to transform cancer research, detection, and treatment. Our engineering mindset seeks to resolve problems in cancer through novel technological solutions; our overarching goal is to provide accessible solutions with high capability, high quality, low costs, and increased personalization. Organized under a “Discovery-to-Use” paradigm, the CCIL’s Strategic Plan (2020-25) prioritizes the following outcomes: (a) drive innovation through the convergence of engineering, chemistry, and biology; (b) support translation of findings to clinical, research, and industrial use; and (c) build a collaborative community that sustains a steep upward trajectory of impact. The CCIL spearheads the institutional cancer research capabilities of University of Illinois Urbana-Champaign (UIUC) by integrating meritorious research and education across boundaries. The two CCIL research programs, Cancer Technology & Data Science and Cancer Engineering & Biological Systems are supported by three powerful shared resources and are augmented by comprehensive educational and mentoring programs across the career arc. Organized as a university-level unit spanning traditional colleges and disciplines, the CCIL has robust external funding, interdisciplinary physical space highly conducive to our mission, long-term institutional support, proactive use of developmental funds, and effective leadership. As of October 1, 2024, 82 CCIL members are supported by $34.7M in cancer-relevant direct costs from external peer- and non-peer reviewed funding sources and $53.8M in total direct costs. Among members’ peer reviewed publications, 70% were collaborative overall, 24% were within-CCIL collaborations, and 25% were published in high-impact (impact factor ≥10) journals. CCIL members actively translate discoveries, holding >350 patents (108 licensed) and starting 47 companies. Formal partnerships with regional and national clinical centers help move CCIL discoveries to use. Trainees, including over 42 postdoctoral fellows, 163 graduate students, and 133 undergraduates, benefit from CCIL educational and mentoring programs and financial support. A visionary and experienced Director leads the CCIL. He serves as a senior campus leader with authority above the level of department heads. CCIL senior leaders form a Steering Committee and work with highly engaged advisory committees to develop strategy, supported by a dedicated Administrative Core Team for all Center operations. With a Cancer Center Support Grant, the CCIL will increase the breadth of expertise, intellectual diversity, and research capacity of the national cancer community as the 8th NCI-designated basic cancer center, and the first new one since 1987. During the proposed project period, the CCIL will increase our research, education, and training portfolio, expand our translational reach, and lead the growth of cancer engineering across the national cancer community.

NIH Research Projects · FY 2026 · 2026-04

Project Summary The native biomembrane of red blood cells (RBCs) has long been considered an attractive engineering target for drug delivery, immune modulation, hemostasis, vaccination, and many other applications due to the natural abundance, long life-span, and excellent tissue accessibility of RBCs. However, current RBC membrane engineering approaches are only applicable to isolated RBCs under in vitro conditions, and there is a lack of effective methods for in vivo tagging or modification of circulating RBCs under physiological conditions. Here we propose to develop an innovative platform technology for in vivo metabolic tagging of RBC membrane and subsequent targeting of molecular imaging and therapeutic cargos via efficient click chemistry. The proposed work builds upon our preliminary data that RBCs can metabolize unnatural sugars and express chemical tags (e.g., azido groups) in the form of glycoproteins and glycolipids. This finding is somewhat counter-intuitive considering the RBC’s nucleus-free structure and low metabolic activities, but it is not surprising since glycoproteins and glycolipids are essential components of RBC membranes, so there must be some active pathways for metabolism and glyco-synthesis associated with RBCs. We further hypothesize that intravenous administration of unnatural sugars could also result in metabolic labeling of circulating RBCs with chemical tags, which would then allow direct conjugation and targeting of various imaging and therapeutic agents via click chemistry. Thus, the proposed work has three specific aims. In Aim 1, we will elucidate the fundamental mechanisms of metabolic glycan labeling, optimize its efficiency, evaluate the effects of metabolic glycan labeling on RBC structure and functions, and investigate the conjugation efficiency and membrane retention of cargos on chemically tagged RBCs. In Aim 2, we will explore metabolic glycan labeling in vivo, measure the in vivo conjugation efficiency of DBCO-molecules of different sizes to azido-labeled RBCs, and determine in vivo retention of the conjugated molecules. In vivo labeling of RBCs in dogs will also be studied. In Aim 3, we will demonstrate the promise of our membrane labeling and targeting technology to enable (1) long-term magnetic resonance imaging (MRI) with one dose of contrast agent, and (2) enhanced blood circulation and pharmacokinetics of drugs such as insulin. If successful, the proposed tagging technology is expected to have a broad range of biomedical applications including molecular imaging, long-acting drug delivery, and immune modulation.

NSF Awards · FY 2026 · 2026-03

IEEE Symposium on Security and Privacy (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 and the workforce development for cybersecurity and national security. This project supports student travel to IEEE S&P and enable students to showcase their research, receive invaluable feedback, attend high-quality talks, and interact with top researchers across the field of security and privacy. This travel grant provides students both intellectual and professional resources to advance the quality of their ongoing work and careers as security and privacy researchers. The project's broader significance and importance include professional development for students as well as continued growth of the symposium. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

There is a growing tension between the collaborative, open nature of academia and national security risks. Current cybersecurity threat models fail to predict or prevent actions most likely to occur in research settings and don't account for the social challenges of research security. Research security frameworks such as NSPM-33 emphasize compliance and traditional risk assessment but often overlook the context-specific human behaviors that lead to security breaches. Effective research security requires an approach that accounts for the human factors that drive researcher decision making and distinguish between honest mistakes and genuine threats. The REDTEAM project uses scenario-based tabletop exercises to conduct threat modeling for research security. This approach builds on established red teaming traditions from military and intelligence communities while employing Role Playing Game (RPG) architectures proven effective for exploring novel strategic problems without predetermined solutions. Two facilitated workshops will use nuanced, realistic scenarios led by experts to uncover behavioral and contextual drivers of security vulnerabilities. Unlike traditional analytical methods that presume rationality, interactive workshops allow participants to explore real trade-offs between collaboration, loyalty, financial incentives, career success, and security obligations. The results are research security frameworks that preserve the openness essential to science while safeguarding research investments from foreign interference and provide evidence-based recommendations to enhance NSF's TRUST framework. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Non-Technical Summary High-performance materials ranging from composites for structural and defense capabilities to biomaterials for biotechnology and agricultural applications rely on long-chain macromolecules ‘stitched’ together into networks. The resulting polymer networks show high mechanical strength but are typically challenging to process in energy and cost-efficient ways that prevents manufacturing and use. The incorporation of chemical linkages that can rearrange on the molecular level known as ‘dynamic bonds’ presents a strategy to impart processability when bond exchange is activated while preserving mechanical strength when suppressed. However, understanding of how microscopic chemistry translates to observed bulk behavior remains poorly understood. This CAREER work combines chemistry and characterization tools to enable dynamic networks with co-optimized processability and mechanical stability. This will be achieved by understanding how local molecular events define deformation behavior that is key to processing and manufacturing without compromising mechanical strength. The new materials and understanding developed in this project will broadly benefit the US by advancing national health, prosperity, and welfare by enabling to methods to make and deploy advanced materials. This research will further be combined with K-12 outreach and student training to prepare the next generation STEM workforce. Technical Summary This project will establish how mixtures of dynamic covalent bonds and topological entanglements can be harnessed to co-optimize viscosity and elasticity. New characterization methods and synthetic tools will be developed to understand the physics of network relaxation and the fundamental insight gained will subsequently be used to control chemical and physical interactions. Coupled rheological and spectroscopic measurements will be used to reveal the ways in which segmental motion and configurational constraints affect local bond exchange events. These measurements will inform the design of dynamic covalent bond pathways to decouple viscous relaxation and elastic mechanics. Finally, the effect of topological motifs including entanglements and defects on emergent viscoelasticity will be explored. The goal of this work is to provide measurements to definitively link microscopic exchange events, mesoscopic chain topology, and macroscopic properties. Importantly, such data could inform future AI/ML frameworks for the design of materials for advanced manufacturing, composites, and biomedicine. Additionally, this research will be integrated with a school mentorship program for fostering polymer science and building connections to motivate and support the STEM 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.