University Of Illinois At Urbana-Champaign

NSF Awards · FY 2025 · 2025-03

The human brain depends on a vast network of tiny blood vessels to deliver the oxygen and nutrients that keep it functioning. Interruptions in blood flow are tied to various neurological conditions, including autism and Alzheimer’s disease. Studying how blood flows in animal brains during different activities could unlock new treatment approaches. However, today’s imaging methods lack the ability to capture high-resolution images of blood flow changes in real-time, especially while animals perform tasks. This CAREER project seeks to bridge that gap with a new super-resolution imaging technique that can map brain activity in unprecedented detail by combining ultrasound and photoacoustic imaging. This new system will empower researchers to study how blood vessels respond to neurological abnormalities, deepening our understanding of brain disorders. The project will engage middle school students through a summer camp for focused on imaging sciences, interactive demos for K-12 students, and hands-on courses using real research data to expand the science and engineering workforce and train the next generation of imaging experts. The primary research objective of this proposal is to create a multiparametric, high-resolution brain imaging platform that utilizes both photoacoustic (PA) and ultrasound localization (UL) imaging technologies. This new platform, termed Super-Resolution PAUL (sPAUL), will allow researchers to capture detailed images of cerebral blood flow, volume, and oxygenation in awake animals while they perform specific tasks. Specific objectives include enhancing photoacoustic oxygen imaging with co-registered ultrasound, translating PAUL signals into functional cerebrovascular maps in a behavior-compatible setup, and investigating the cerebral vascular response in rodent models of neurological disorders. The expected outcome of this research could reveal critical insights into the mechanisms behind many neurological conditions and lay the groundwork for new therapeutic approaches. A new graduate-level course on advanced imaging methods and a lecture series with a lifelong learning center will inspire a broad spectrum of individuals in the 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.

NSF Awards · FY 2025 · 2025-02

This project supports the broader efforts of the National Artificial Intelligence Research Resource (NAIRR) Pilot to address how powerful Artificial Intelligence (AI) resources can be used to accelerate scientific understanding and discovery and further the capabilities of AI models. It also develops efficient processes for providing these resources to researchers. This project builds on the successful model of, and lessons learned from, the COVID-19 HPC Consortium (C19HPCC), which demonstrated the power of public-private partnerships in addressing global challenges. By applying these lessons to the NAIRR Pilot, the project creates a robust framework for future government-academia-industry collaborations. This not only enhances the NAIRR Pilot but also paves the way for the full NAIRR program, ultimately supporting a broader range of research efforts and fostering innovation in artificial intelligence. The project leverages lessons learned from the C19HPCC to enhance the National Artificial Intelligence (NAIRR) Pilot. The C19HPCC was a collaborative effort that brought together high-performance computing (HPC) resources from government, academia, and industry to accelerate research and discovery in the fight against COVID-19. The primary goals are to develop efficient processes for allocating AI resources, improve proposal review mechanisms, establish effective reporting methods, foster partnerships across government, academia, and industry, and establish and evolve governance structures and coordination mechanisms to manage the diverse set of resources and stakeholders involved. The scope includes leveraging prior policies, procedures, and tools from the C19HPCC to support the NAIRR Pilot and ultimately the full NAIRR program. By applying these methods, the project aims to create a robust framework for future AI research and innovation. 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 · 2025-02

Nitrogen is an essential nutrient for plant growth. Each year, millions of tons of fertilizer nitrogen are applied across the U.S. Midwest to support crop production. However, less than half of this applied nitrogen is utilized by crops within the same year, and it remains unknown where the excess nitrogen is stored or how it is lost from intensively managed agricultural watersheds. This lack of knowledge prevents the closure of nitrogen budgets in many watersheds, posing a significant challenge to effectively managing nitrogen as a critical resource for agriculture while minimizing its loss to sensitive receiving waters, where it can cause a range of environmental and health issues. This research will bridge key knowledge gaps regarding the sources, fates, and storage of nitrogen in watershed systems. This will be achieved by integrating water age – a fundamental descriptor of how water transits through a watershed – and nitrate isotopes, which are intrinsic tracers of nitrogen sources and reactions, into a unified framework for quantifying and modeling nitrogen cycling at the watershed scale. By incorporating findings into public exhibits and outreach events through multiple online platforms, the project will establish a regional hub dedicated to advancing public literacy in the complex interplay of agriculture, water resources, and society. It will also foster the development of an interdisciplinary hydrologic science workforce by enhancing isotope tracer applications in undergraduate and graduate curricula. The primary objective of the proposed research is to better understand the linkages between subsurface storage, flow path variations, and biogeochemical nitrogen cycling and, thus, to better quantify the source-sink strength and mass balance of nitrogen in tile-drained Midwestern agricultural watersheds. A multiscale design, ranging from soil columns to nested watersheds, will be used in conjunction with high-frequency water and nitrate isotope measurements to estimate time-variable water transit times and their relationship with nitrogen cycling dynamics. The resulting data and insights will be used to develop a water age-based and isotope-aided watershed reactive transport model to partition nitrogen source input into various loss and retention pathways and to assess changes in watershed nitrogen budgets and nitrogen use efficiency as a function of hydroclimatic and management forcing intensities. Through model benchmarking tests, the proposed research will lead to a theoretical advance in using nitrate isotopes to reveal macroscale biogeochemical mechanisms under complex watershed conditions. The outcomes of the research will support informed decision-making in sustainable water and nitrogen management in the agricultural Midwest. 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 · 2025-02

Machine learning and deep learning are advanced technologies employed to address complex problems that were previously deemed too difficult for computer solutions. Students enrolled in this Research Experiences for Undergraduates (REU) program acquire practical experience across various machine learning domains, from developing open-source models to applying them in real-world scenarios. They contribute to research advancements spearheaded by researchers at the University of Illinois, with their developed models and tools being made open source to facilitate further innovations in other fields. This project aims to cultivate a proficient workforce in advanced machine learning techniques and open-source contributions. The training provided equips students from diverse backgrounds and quantitative disciplines with the skills necessary to apply machine learning in research, preparing them to utilize these methods across multiple domains and encouraging them to pursue higher education. The objectives of this project are to train undergraduate students, particularly those from communities with limited access to relevant resources, in the fields of machine learning and open-source software. These students subsequently apply their acquired skills to mentor-guided research projects. This on-site summer program at the University of Illinois Urbana-Champaign hosts 10 students per year, matching their preferences and interests with those of a group of mentors. Each student is paired with two mentors—one specializing in the project's research area and the other an expert in machine learning. The program aims to enhance the students' understanding of research and graduate school, often inspiring them to pursue further education. Additionally, it equips students with valuable skills for data science and data analysis careers in industry, thereby increasing diversity in both graduate programs and professional settings. By participating in the program, students foster connections between the University of Illinois and their home institutions, encouraging future involvement in the program and laying the groundwork for future collaborative research initiatives. 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 · 2025-02

Recently, the advancement of Large Language Models has facilitated the development of AI models for scientific discovery. However, many scientists today face two major challenges in building high-quality AI-based scientific models: (i) it is very challenging to fully utilize the compute power offered by modern hardware with massive parallelism because many science-informed model architectures often exhibit high-order computation and memory complexity, and programming such hardware is extremely complex, requiring advanced system technologies to effectively utilize all available hardware resources simultaneously to achieve optimal performance; (ii) the challenge of the humans in the loop -- non-CS scientists. The project's novelties are in addressing these two major challenges: empowering non-CS researchers to harness the power of modern hardware with massive parallelism for training science-informed AI4Science models, and simplifying the complex programming required to achieve optimal performance. The project’s broader significance and importance are in making training advanced AI models more accessible and effective for scientific research, thereby accelerating scientific discovery and innovation. The project includes the following synergistic components. First, it enables capturing unprecedented high-order, extremely long-range, and high-volumetric interactions in scientific data by developing novel memory-efficient and hardware-friendly kernels on a single GPU, effectively changing the state of many advanced models from impossible to possible to train with limited GPU resources. Second, it creates novel hardware and model-conscious 4D parallelism, which further unlocks the performance potential of AI4Science models in multi-node multi-GPU distributed environments. Finally, the project builds an automated pipeline through new techniques in performance adviser, deep learning compilation, and auto-tuner, which lowers the burden on non-CS scientists from dealing with complex parallel hardware. Together, this project paves the path to generalize AI system technologies so they can broadly address major system pain points and promote progress in large-scale scientific discoveries. 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 · 2025-02

Abstract Seasonal influenza viruses (IVs) cause hundreds of thousands of deaths every year, despite widespread pre- exposure and vaccination. To design more effective vaccines against both seasonal and pandemic IVs, there is an urgent need to better understand how the complex interplay between viral and immune dynamics in humans influence infection outcomes and transmission risk. Several critical fundamental knowledge gaps hobble the development and evaluation of next-generation IV vaccines. One is that we have a very poor understanding of which specific features of the enormously complex human immune response to IV can serve as reliable correlates of protection from severe infection outcomes and forward transmission risk. This makes it difficult to predict the performance of vaccine candidates in the human population. Similarly, we do not understand how infection or vaccination drive the evolution of immune memory repertoires across highly heterogeneous individuals. This makes it hard to design/evaluate novel antigens that reliably reshape the immune memory repertoire to effectively protect against future strains. To address these critical gaps in our understanding of functional IV immunity in humans, we will build upon a unique clinical study already underway on the University of Illinois campus that uses a frequent PCR screening program to identify individuals during the first few days of IV infection. By combining repeated blood draws with daily virus sampling following infection, we can generate both an unprecedented high-resolution longitudinal profile of viral shedding dynamics and a rich, highly multidimensional profile of immune status before, during, and after infection. Our multidisciplinary team of experimentalists and computational modelers will fit the virological and immunological data we collect to mechanistic models of virus-immune dynamics to infer key viral and immune dynamics features from each infected individual. We will then apply novel statistical and machine learning approaches to achieve two primary aims: (1) identify reliable immune correlates of reduced viral shedding and transmission risk, and (2) define how both viral and host factors shape the evolution of the anti-IV B cell repertoire and functional antibody landscape. Together, these aims will identify new host and viral features that correlate with desirable, protective memory responses against IV that can be incorporated into the design and evaluation of next generation, universal IV vaccines.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract Reproduction is crucial for the survival of all species. Reproductive success can be influenced by genetic and environmental factors. In humans, at least 30% of infertile couples are diagnosed with unexplained infertility, arguing for the existence of novel mechanisms that are critically important for reproduction. The oocyte-to- embryo transition (OET) is crucial for reproduction. Prior to the OET, the oocyte accumulates large amounts of maternal products during oogenesis and is maintained in the ovary in a quiescent state. During the OET, the cellular organelles and maternal gene products stored in fully-grown oocytes are precisely remodeled to orchestrate the OET. Currently, the mechanisms responsible for these cytoplasmic remodeling events during the OET remain largely unclear. In an effort to study the cytoplasmic remodeling during Xenopus OET, we uncovered a novel function of the ER in controlling the localization of maternal mRNAs. We found that maternal RNAs are abundantly associated with the ER in the oocyte. After oocyte maturation, ER-associated mRNAs are released into the cytosol. The remodeling of the ER and decreased mRNA-ER association during oocyte maturation offer an important mechanism to sort maternal RNAs and correct localization errors that may have occurred during early oogenesis. It also contributes to the overall increase in protein synthesis rate after oocyte maturation. Furthermore, our preliminary results demonstrate that a subset of maternal mRNA undergoes phase transition during the OET. Based on these exciting findings, here we propose to test the hypothesis that dynamically regulated ER remodeling, mRNA-ER association, and RNA phase transition represent novel cytoplasmic remodeling mechanisms that are fundamentally important for vertebrate OET. To test this hypothesis, we propose 1) To determine the extent to which the remodeling of the ER, regulated mRNA-ER association, and RNA phase transition during the OET are evolutionarily conserved, 2) To investigate the role of the Cdc42-Wasl-Arp2/3 signaling cascade in regulating ER remodeling during the OET, and 3) To determine the extent to which exposure to an environmentally relevant phthalate mixture impairs cytoplasmic remodeling during the OET. The proposed work is expected to identify novel mechanisms that act during the OET to influence vertebrate reproductive success. This could have a major impact on improving human reproductive health.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Salmonella Typhimurium is a major foodborne pathogen that causes gastroenteritis and potentially lethal systemic infection. Colonization and invasion of the intestinal epithelium is dependent on the direct injection of effector proteins into host cells via a Type Three Secretion System (T3SS) encoded on Salmonella Pathogenicity Island 1 (SPI1). This critical system is controlled in response to numerous environmental and regulatory signals that dictate expression of the system at the proper time and place in the host. Our long-term goal is to understand the mechanisms of overall signal integration that allow this precise regulation. Our work has defined the SPI1 regulatory circuit. The three AraC-like regulators, HilD, HilC, and RtsA, act in a complex feed-forward regulatory loop to control expression of hilA, encoding the direct regulator of the SPI1 structural genes. Much of the regulatory input is integrated at the level of HilD, including at hilD mRNA translation or stability. The hilD mRNA has an unusual 300 nucleotide 3’ untranslated region (UTR) that acts as an independent module to confer instability to the mRNA. A primary hypothesis is that the hilD 3’ UTR serves as a critical node for integration of regulatory signals. Preliminary data show that mRNA stability is regulated by a novel mechanism involving interaction between Rho- mediated transcriptional termination at the 3’ UTR and RNA Degradosome-dependent degradation. Moreover, we have identified seven small RNAs (sRNAS) that bind to the hilD 3’ UTR to differentially control these factors. The first aim of this proposal is to understand the regulation of the identified sRNAS and how they affect the overall control of the SPI1 system. Expression of the sRNAs and confirmation of transcriptional regulators will be addressed using transcriptional lacZ fusions. Phenotypes conferred by deletion of the sRNA genes and/or their regulators, both in vitro and using the well-established animal model, will determine the specific roles of the sRNAs in SPI1 regulation. The second aim is to characterize the mechanism of post-transcriptional regulation via the hilD 3' UTR. Deletion analysis will identify the site of Rho action in the 3’ UTR. The roles of Rho, the RNA Degradosome, and the small RNAs in the creation and/or processing of the 3’ ends in the hilD 3’ UTR will be monitored using tagging and deep sequence analysis. In vitro transcription will more precisely define the action of Rho in creating terminated hilD transcripts. The interactions of these factors will reveal the mechanistic details of this novel regulation. The SP1 T3SS regulatory circuit serves as a paradigm for understanding the integration of host environmental signals to control a complex virulence phenotype and analysis of this system is critical to our understanding of this important pathogen.

NSF Awards · FY 2025 · 2025-01

Quantum physics provides a powerful framework for exploring the natural world and creating transformative technologies. However, current quantum platforms are small, noisy, and isolated. This proposal seeks to overcome these challenges by developing scalable, coherent, and networked quantum systems based on optically active spin qubits in silicon carbide (SiC). These qubits emit photons that enable long-distance entanglement for quantum communication, while solid-state photonic integrated circuits offer a scalable and manufacturable approach to building quantum devices. The unique advantages of photons: high-speed, low-loss transmission and room-temperature operability— make them ideal quantum information carriers, unlocking applications such as unhackable secure networks, distributed sensors, and modular quantum computing. The educational and outreach components of this project are synergistic, developing new activities for middle school students, at the interface of materials engineering and quantum science — combined with efforts to expand the quantum ecosystem. As a result, recruitment and retention of students will be improved, shoring up the leaky pipeline to STEM careers. The proposed research addresses a grand challenge in quantum photonics: coupling optically active quantum states to scalable photonic platforms while minimizing decoherence caused by device integration. Noise and decoherence, arising from multidisciplinary phenomena at the bulk and interfaces of solid-state materials, are fundamental obstacles across all quantum technologies. This work aims to develop a novel approach to suppress bulk and surface noise in solid-state quantum systems, enabling coherent qubits in devices operating near noisy interfaces. Our research will pave the way for wafer-scalable, 4 K operable spin-photon interfaces in thin-film silicon carbide (SiC). By creating in-house silicon carbide on insulator (SiCOI) substrates, we will fabricate photonics compatible lateral p-i-n devices and investigate the optical coherence of near-surface emitters, enhanced through semiconductor depletion. Additionally, we will develop existing alternate defect candidates enabled by photonic integration and explore recent proposals for experimentally unexplored defect qubits. Finally, we will demonstrate the first on-chip entanglement of optically active spin qubits in SiC by interfering photons emitted from two isolated emitters in separate SiCOI waveguides, providing the foundation for scalable quantum photonics with SiC. 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 · 2025-01

During the COVID-19 pandemic, it was observed that individuals did not always follow mitigation policies closely. Instead, they behaved according to their own objectives, where demographic and socioeconomic factors seemed to have influenced their responses to the set policies. Therefore, this project aims to improve the policymaking processes to mitigate the transmission of respiratory pathogens by incorporating the individuals’ decision-making and socio-demographic heterogeneities. To do this, the investigators propose to develop and study game theoretical mathematical models, as well as simulation tools and numerical approaches that can be adapted to specific public health problems of interest to practitioners and researchers. These tools will be made publicly available. This project will also involve interdisciplinary training for graduate students in applied mathematics, statistics, operations research, epidemiology, and quantitative biology. To model many interacting agents, the investigators will develop and study extensions of mean field games (MFGs). First, they will focus on building multi-population MFGs and graphon games to incorporate socio-demographic heterogeneities while finding the Nash equilibrium responses of individuals under different disease mitigation policies (e.g., vaccination policies and non-pharmaceutical interventions). Furthermore, different equilibrium notions to incorporate altruism in the populations will be explored through the introduction of mixed multi-population MFGs that include both cooperative and non-cooperative individuals. Later, the investigators will focus on finding optimal mitigation policies by using Stackelberg MFGs that include the optimization of a regulator (e.g., a governmental institution). The extensions of Stackelberg MFGs that include heterogeneities in the mean field populations, altruistic behaviors, and possible state variables for the regulator will be developed and analyzed. Surveys and analyses of publicly available data will be conducted to calibrate and parameterize the mathematical models to capture real-life patterns. Finally, numerical approaches and simulation toolboxes will be implemented to solve large dimensional and more complex models, which will allow policymakers to adapt and parametrize our models according to their specific needs. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD). 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 · 2025-01

Cornell Tech, in collaboration with Hofstra University and the University of Illinois, seeks to expand its proven Break Through Tech AI Program designed to equip all undergraduate students with the skills needed to thrive in the fast-evolving fields of artificial intelligence (AI) and machine learning (ML). By expanding participation in high-quality AI education through a network of Instructional Hubs, this project aims to double the number of students served annually. This new generation of AI leaders will help ensure advances in responsible AI and promote US competitiveness in this exploding technical field. This project focuses on scaling up the ML Foundations component of the Break Through Tech AI program. This nine-week, skills-based training course is delivered by faculty and graduate students from newly established Instructional Hubs at various institutions. The expansion will involve recruiting five new Instructional Hubs, training instructors through a “Train the Trainer” program, and delivering synchronous lab sessions to ensure students gain practical, industry-relevant skills. By the end of the three-year grant period, the program aims to serve 1,500 students annually, significantly enhancing the readiness of the STEM workforce. This project will contribute to the field by providing a scalable model for AI/ML education and generating valuable data on the effectiveness of distributed instructional hubs in expanding participation in cutting-edge AI education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

This award is jointly supported by the Major Research Instrumentation (MRI) program, the Division of Chemistry Research Instrumentation program, and the Directorate of Mathematical and Physical Sciences Office of Strategic Initiatives in response to the solicitation of proposals that promote the recovery, recycling, and/or reuse of helium initiated by the CHIPS and Science Act of 2022. The University of Illinois Urbana-Champaign, led by Professors Jeffrey Filippini and Liviu Mirica and Dr. Dean Olson, is developing upgrades to the Central Helium Liquefier and Recovery Facility (CHLRF) which supports research in the areas of chemistry and biochemical sciences, notably through a shared-use Nuclear Magnetic Resonance (NMR) spectroscopy facility, materials science and engineering, physics and quantum information sciences, astronomy, and nuclear physics. Established in 1958, the CHLRF ensures a stable and cost-effective supply of liquid helium to researchers across campus. This development project upgrades the CHLRF on several fronts to improve operational efficiency and system longevity, and to expand the system’s capacity for storage of liquid and recovered gas. The CHLRF distributes between 16,000 – 28,000 L of liquid helium annually serving more than 500 individual researchers spanning several dozen research groups, as well as supporting the development of the future scientific work force through advanced coursework and the training of research students. This project supports the development and improvement of helium liquification equipment necessary for significantly increasing the campus-wide helium recovery efficiency of the CHLRF from the current ~74% to a target of 85-90%. This award strengthens the research infrastructure of the university and region, to benefit a wide range of multidisciplinary research. Some of the research projects supported by this project include (1) developing inorganic nanomaterials for biological and energy-related applications, and understanding the chemical interactions of these nanomaterials with their surroundings; (2) developing spectroscopically-guided synthetic approaches to installing well-defined, molecular active sites in porous materials; (3) developing a new atomic-resolution, time-reversal-invariant spin-polarized probe that can be used in a host of quantum applications, including the detection and characterization of Majorana fermions; (4) developing a novel experiment to measure the mass of the electron neutrino using a precision measurement of cyclotron radiation; (5) developing balloon-borne, helium-cooled telescopes to observe the polarization of the cosmic microwave background and the history of cosmic star formation; and (6) characterizing the structural and electronic properties of aluminum oxide-based Josephson junctions at cryogenic temperatures incorporated into resonators and qubits. This project also supports the training of undergraduate students on the maintenance of helium recovery equipment, and of interns through a partnership with a local community college. 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 2024 · 2024-12

Quantum information theory studies how quantum-mechanical systems process information. A central goal is to understand how much information can be preserved when the information-carrying systems are affected by noise, which is modeled as a quantum channel. The capacity of a channel quantifies the optimal rates of faithful information-processing. However, solving the optimization problems characterizing these capacities is challenging due to the complicated structure of correlations in large quantum systems. This project aims to contribute to our understanding of quantum channel capacities. It will develop flexible methods to construct simple testbed channels with interesting yet tractable information-theoretic properties; devise methods to efficiently approximate quantum channel capacities of general channels; and study new ways of transmitting information through channels based on insights from classical network information theory. As a collaborative project between the US and Canada, it will strengthen international collaboration and communication. Visits between the two investigators' institutions will benefit workforce training. There will also be workshops and review articles, which will greatly benefit the research community and students in particular. Quantum channel capacities can be expressed in terms of an optimization problem involving entropic quantities. These entropic quantities are generally non-additive due to non-trivial interactions of the channel with the quantum correlations in many-body quantum systems. As a result, the entropic optimization problems determining quantum channel capacities become intractable to solve exactly. This project aims to address this challenge from different angles. First, it proposes a method of constructing quantum channels with rich information-theoretic properties but for which the corresponding optimization problems are solvable. Second, it will develop new methods to estimate channel capacities of general unstructured quantum channels. The chosen approach relies on a channel capacity bound that is additive but involves auxiliary systems of unbounded dimension. The main technical focus is to further develop an existing method of handling such unbounded optimizations through a converging hierarchy of bounded convex optimization problems. Third, it will study new coding techniques for quantum information transmission that are inspired by broadcast channel coding, the classical analogue of quantum channel coding. Novel coding techniques based on this correspondence will provide new insights into the limitations of faithful quantum information transmission. 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 · 2024-12

This project will define how interactions between herbicide exposure, the microbiome, and the host promote adiposity. Billions worldwide are overweight or obese. The gut microbiota regulate host energy metabolism, and imbalance in these communities correlates with increased body mass, adiposity, and altered lipid metabolism. Common herbicides are known potent disruptors of gut microbial communities and the patterns of microbial community disruption induced by herbicides are consistent with those that manifest in obese individuals. Moreover, disruption of the gut microbiota during key developmental windows has been linked with increased risk of obesity. However, it remains unclear if herbicide induced disruptions of gut microbiomes or their functioning yields obesogenic effects. We hypothesize that early life exposure to environmentally relevant concentrations of three widely used herbicides (glyphosate, atrazine, and 2,4-D) induces disruption of the microbiome favoring the assembly of a microbiota that contributes to elevated adipogenesis. We propose a series of innovative experiments that integrate multiomic microbiome data with measures of host physiology to identify herbicide disrupted microbial pathways that correlate with adiposity. Germ-free animals, microbiome transplants, and in vitro assays will allow us to resolve causal relationships between microbiota and obesity. Additionally, this project will define the role of the gut microbiome in mediating the acute toxicological effects of herbicide exposure, identify the molecular pathways that mediate this functioning, and isolate and characterize the microbes responsible for its execution. Consequently, the proposed work will clarify herbicide exposure hazards by defining concentrations that sufficiently disrupt microbiota to contribute to obesity, define mechanistic links between herbicide associated microbiome disruption and adiposity, and propel subsequent research in mammals towards generating microbiome targeting or mimicking therapeutics capable of preventing or treating obesity.

NSF Awards · FY 2024 · 2024-12

This award is funded by NSF Global Centers program, an innovative partnership with 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, , the Research Council of Finland and Business Finland, Japan Science and Technology Agency, the National Research Foundation of Korea, and UK Research and Innovation. Biofoundries stand to be as transformative to biotechnology as computers are to information technology. However, their wide adoption for biomanufacturing and global bioeconomy faces a challenge. It is hindered by the lack of standards and metrics in data, workflows, ontologies, and regulatory considerations. This project addresses this challenge, opening the way to the full-scale adoption of biofoundry applications throughout society. The Center leverages the international expertise of seven successful biofoundries from the US, Finland, Japan, Republic of Korea, and the United Kingdom. It engages investigators from academia and the industrial sector across borders. Beside improving the reliability and scalability of biofoundries, the investigators enable scientific breakthroughs. They develop genetic design rules, microbial cell factories, and artificial intelligence (AI) tools for biotechnology. The Center also promotes the training of a workforce for the bioeconomy with a global view and unique skills at the interface of AI, synthetic biology, and robotics. One of the biggest scientific and technical challenges in synthetic biology for the bioeconomy is to establish robust, predictive, and reproducible genetic design rules for different applications. Biofoundries are well-suited to address such challenge. They facilitate large-scale, fully annotated and robust biological design and measurement at different scales. However, due to vastly different configurations, instruments and software used by the growing global network of biofoundries, as well as a lack of standards and metrics, comparable data acquisition at scale is not possible. This Center leverages international expertise to address this limitation and unlock the bioeconomy. It develops standards and metrics, both technical and non-technical. Standardization and metrics ensure interoperability and reproducibility, improve efficiency in innovation pipelines, support policies and legislation, and accelerate commercialization. The team consists of 40 investigators from five countries and 17 institutions. These include universities, national labs, private companies, and non-for-profit organizations. The Center brings together experts in synthetic biology, biofoundry, automation and robotics, AI/ML, software engineering, governance, education/training, diversity, public policy, and outreach. It focuses on four thrusts: (1) develop and validate cross-national standards and metrics for biofoundry applications; (2) perform cross-national comparison of governance and regulatory frameworks for biofoundries to establish best practices; (3) develop cross-national programs for industry partnerships and public outreach; (4) develop cross-national programs for education and workforce development. 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 2024 · 2024-12

This project aims to enable upper atmospheric investigations by operating the red- and green-line imagers and Fabry-Perot Interferometers (FPIs) in the existing Mid-latitude All-sky-imaging Network for Geophysical Observation (MANGO) network to collect data in the current solar cycle. The MANGO network was developed with the support of the National Science Foundation (NSF) that observes the light from the airglow and aurora at night across the continental United States. The earth’s upper atmosphere receives energy and momentum inputs from above and below, which manifest in the form of traveling atmospheric/ionospheric disturbances in the thermosphere-ionosphere region. The MANGO observations allow us to understand what energetic events in the lower atmosphere and the sun impact the upper atmosphere over the United States and how. The MANGO observes the low-latitude aurora and waves, and measures the winds and temperature in the upper atmosphere. The data from these observations is made available in near-real time for scientific and public use. This project will continue to operate and maintain the 19 instruments that make up the MANGO network – 15 all-sky imagers and 4 FPIs, process and share the generated data, create higher-level data products, and interface with the scientific community to make progress in understanding the earth’s space weather. Broader impacts of the project include open curated datasets with no embargo period, an open-source software repository maintained on GitHub, and training the next generation of scientists (post-docs and undergraduate students). This five-year project is a collaboration between SRI, University of Illinois Urbana-Champaign, and the University of California, Berkeley. Under this project, the team will operate and maintain the MANGO network established through the NSF Distributed Array of Small Instruments (DASI) program, which includes both red- and green-line all sky imagers (15 at completion) and 4 Fabry-Perot Interferometers, maintain the data infrastructure to collect and share the data to the broader scientific community, create higher-level data products, and interface with the scientists and general public to advance our understanding of the geophysical and geomagnetic processes in the nighttime mid-latitude ionosphere. The MANGO network enables the following scientific investigations: (1) determine spatial scales of the lower and upper thermospheric winds, (2) investigate vertical propagation of thermospheric variability relative to F-region dynamics, and (3) study the relative impact of lower atmospheric forcing with respect to magnetospheric forcing on the mid-latitude thermosphere and ionosphere. 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 2024 · 2024-12

The design of materials and structures has become increasingly dominated by the adoption of optimization methods, which allow maximizing certain measures of mechanical performance, such as stiffness and energy absorption. Optimization is crucial in engineering applications involving components that are expected to execute complex mechanical operations, such as aerospace systems, biomedical devices and soft robotics. Together with the fabrication flexibility in additive manufacturing, optimization boosts the opportunities for design by tapping into a nearly unbounded design space. This award supports research with the goal to design structures that can be programmed to display variable levels of material softness and rigidity, allowing them to manage, intelligently, the loads applied by the outside environment. The advances enabled by this study will affect many technological applications of industrial and societal interest, such as the design of tires space vehicles or operating in hazardous environments, protective equipment that can sustain impacts from projectiles, and soft robotic devices with sensing capabilities. The knowledge will enrich understanding of important concepts in mechanics and optimization, with educational impact on how these topics are taught in the classroom. Topological metamaterials are systems whose functionalities are controlled and protected by the topology of their phonon bands. They display unconventional elasticity regimes characterized by robustness against defects, damage and randomness. This project is especially concerned with so-called topological polarization, a property of certain lattice materials that manifests as a dichotomy between edges, whereby one edge displays an excess of softness, promoting extreme localization of deformation, while the opposite edge behaves rigidly. This property enables the design of materials with soft boundaries that can handle asperities and sharp loads, without compromising the global stiffness of the entire structure. The project will investigate polarization through the lens of optimization with a double objective: acquiring a deeper understanding of the mechanistic relations between the geometric features of the materials and the emergent polarization, and designing families of metamaterials with maximized and programmable polarization, beyond the canonical kagome paradigm. 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 2024 · 2024-12

Many of the key challenges facing society – such as enhancing human health, conserving biodiversity, and improving domesticated species – can be addressed through a better understanding of genetic variation. Luckily, genetic tools, like genome sequencing, gene editing, and robotic trait measurements, have advanced rapidly in recent decades. But genetic theory has not kept pace with discoveries on the molecular and cellular basis of organisms’ traits. Instead, the standard theory that geneticists use to try to understand quantitative genetic variation and predict its effects is largely from the first half of the 1900s, and it ignores insights from molecular and cellular biology. Recently, however, geneticists have been exploring exciting new models of genetics, which better incorporate biological knowledge into quantitative trait genetics. This project will develop new tools for geneticists to incorporate these emerging new models in genetics into their research, and rigorously test the underlying concepts. The tools will be tested in laboratory plants and in crops, which are ideal systems to develop and test concepts and tools that can later be used in hard-to-study organisms, such as humans or wild organisms. This project will directly benefit society by making new tools for genetic mapping, prediction, and simulation available to global crop improvement programs; as well as improving both the understanding of genetics and the scientific method in public-school students and trainee scientists. Understanding the genetic architecture of complex quantitative traits is a central goal of biology. However, standard quantitative genetic theory and practice does not incorporate molecular and cellular biology knowledge, such as gene expression patterns and gene regulatory networks. Further, existing tools do not provide functionality to test emerging models, such as the omnigenic model. The goal of this project is to develop genetic analysis tools that incorporate molecular and cellular biology knowledge directly into statistical models used to map genes, predict traits, and simulate changes in the genotype-to-phenotype relationships. These tools will be used to test the hypotheses on the impact of various forms of gene interactions (epistasis) and test the hypothesis that the omnigenic model accounts for differences in genetic architecture of traits across subpopulations. This research will provide insight into how and why genetic architecture differs across subpopulations, a key question in several areas of basic and applied genetics. Research will be conducted using simulated traits as well as real genotype and phenotype from the model plant Arabidopsis thaliana and the crop, Sorghum bicolor. The broader impacts of this project will focus on developing both graduate student and high school level activities that teach fundamental concepts in genetics and scientific method. This project will also work with public crop breeding programs to ensure that the research findings will be diffused to the applied plant genetic 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.

NSF Awards · FY 2024 · 2024-11

Many geoscientific models—such as those used to study air pollution or climate—are computationally intensive to run, and this limits their usefulness. Often, limited availability of computational resources limits scientific progress; additionally, these models are not usable by scientists without access to high-performance computing clusters. This work aims to increase the computational speed of these models by creating simpler versions of each model component via machine-learned (ML) “surrogate models”. This will allow improved tradeoffs to be made between accuracy and computational cost in geophysical modeling, resulting in more accurate and efficient virtual models of Earth. At the same time, results of the project will greatly decrease the computational expertise and resources required of new model users and developers, increasing the number of people able to engage with geoscientific modeling. Removing barriers to model use in educational and policy settings will increase the fraction of the population familiar with the workings of geoscientific models, improving public trust and perception of the transparency of models and their outputs. Project research will be organized in three Thrusts. Thrust 1 will develop surrogate models for atmospheric chemistry—the most computationally intensive component in models of atmospheric composition—and will also develop improved dimensionality reduction methods for these systems. Thrust 2 will use the same methods to develop ML models for wildfire plume rise, which is a key determinant of wildfire smoke transport (which is in turn an increasingly important determinant of public health) but is not well characterized in current models. Thrust 3 of the proposed project will develop an “equation-based” platform for atmospheric chemical transport modeling which expands the state of the science in performance, modularity, and differentiability for geoscientific modeling, thus allowing improved integration between physics-based and ML modeling components. This platform will also remove barriers to the broader use of geoscientific models by making models easier to use, understand, and develop. 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 2024 · 2024-11

The Chemical Synthesis Program of the Chemistry Division of NSF supports the project by Professor Gregory S. Girolami of the University of Illinois at Urbana-Champaign to develop new and better molecular precursors for the chemical vapor deposition (CVD) of thin films. The development of new and better molecular precursors for the CVD of thin films is a important need of the microelectronics industry. The current work will develop new classes of transition metal compounds that can be used in the CVD of metals and metal oxides. The research will lead to deeper understandings of the chemistry of volatile transition metal compounds and lead to better ways to deposit the wires and insulators that are key components of modern-day computer chips. The work will enhance already existing interactions between Professor Girolami’s group and leading companies in microelectronics development. This project will contribute directly to the education and training of B.S. and Ph.D. students, and the results will be disseminated by presentations at international conferences, and by publication in scientific journals. This award will contribute to the education of a diverse group of undergraduate and graduate students and enable Professor Girolami to write short radio spots highlighting recent scientific advances of interest to the public. The project focuses on developing new volatile precursors for the CVD of thin films of electronic materials, investigations of their chemical reactivities, and studies of their volatilities and utilities as thin film precursors. A major thrust will address the synthesis and chemistry of metal CVD precursors containing new chelating borohydride ligands. The guiding hypothesize is that such complexes should have reduced London forces owing to the presence of the BH3 group, but that the substituent group can be chosen to tune the ligand’s steric and electronic properties. Initial targets include metal complexes of the boranatodimethylaminomethyl group. The hypothesis is that these will form volatile complexes of easily reducible metals due to the donor ability of the alkyl or amido group at one end of the chelate. The behavior of these complexes will be investigated by solution mechanistic studies and theoretical investigations in collaboration with Prof. Kostantinos Vogiatzis at the University of Tennessee – Knoxville. The CVD depositions of the compounds will be studied with collaborator Prof. John Abelson at the University of Illinois. These studies are aimed at elucidating the chemical steps by which CVD precursors transform into the finished films on the growth surface. The development of these new molecules will lead to better performing materials and new fabrication methods that will enable the continued miniaturization of integrated circuits. The project lies at the interface of inorganic and materials chemistry and is well suited for the education of scientists at all levels. Professor Girolami’s group is also well positioned to provide the highest level of education and training for students underrepresented in science. Outreach activities involving the production and broadcast of TinyTech radio spots will also be part of the funded project. 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 2024 · 2024-11

Homogeneous catalysts are crucial for producing everyday products like chemicals, solar panels, and medicines. For instance, palladium-based catalysts are instrumental in creating and manufacturing new drugs. However, most of these catalysts cannot be reused after the reaction due to the difficulty in separating them from the products they help make. Current recycling methods are costly and can damage the catalysts. Finding better ways to recycle these catalysts, especially those used to produce organic compounds, is essential for sustainable chemical manufacturing and larger-scale uses. One promising solution is using electrochemical methods to separate the catalysts from complex mixtures. This approach can be integrated with renewable energy sources and allows the catalyst to be reused. This project aims to develop a sustainable electrochemical recycling method for important industrial catalysts, which will help minimize manufacturing waste and promote the reuse of valuable critical metals. The project also provides educational opportunities for high school and graduate students and builds a globally competitive workforce through international collaboration with researchers at the University of Chemistry and Technology (UCT), Prague. Importantly, the project has the potential to enable the use of previously uneconomical reactions, driving innovation and economic growth. The project aims to advance an electrochemical recycling approach for major classes of homogeneous catalysts for oxidation and cross-coupling while understanding the interfacial interactions of the catalyst species with the electrode across multiple scales. The project will pursue three parallel aims: (i) designing nanostructured, high-accessibility electrosorbents for recovering various palladium homogeneous catalysts, (ii) investigating the nanoscale charge-transfer and mechanistic binding of the catalysts at the surface of the electrodes, and (iii) developing multiscale modeling tools to optimize the design of a flow-through sorption cell. This interdisciplinary project combines the University of Illinois Urbana-Champaign team’s expertise in electrochemical separations and polymer design with the UCT Prague team’s knowledge of electrochemical engineering and interfacial electrochemical spectroscopy. The project is expected to contribute to global decarbonization efforts by substituting conventional thermal and chemical-based separation methods with electrochemical processes, especially in the context of industrial-scale reaction and separation processes. Finally, enabling homogeneous catalyst recycling can unlock access to new, previously uneconomical reactions that perform advantageously at an industrial scale. Educational and broader impact goals include (i) creating summer camp modules on electrochemistry, (ii) enhancing undergraduate and graduate mobility and student exchanges between the U.S. and Czech Republic, and (iii) creating joint international classes on electrochemical engineering and separations. 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 2024 · 2024-11

High performance microelectronic devices require stacking of individual silicon dies into complex three-dimensional assemblies to meet the needs of high rates of data transfer between logic and memory. Interfaces between dies, however, create thermal resistances that impede the transfer of up to 1 kilowatt of heat from the three-dimensional device into the adjacent heat exchanger. Limitations on device performance that are imposed by inadequate heat transfer are ubiquitous in the microelectronics industry. Thermal metrology tools that are applicable in an industrial setting will improve the ability of engineers to design thermal management solutions, monitor assembly processes, and analyze failure mechanisms. The objective of this project is to advance the science and engineering of thermal property measurement and develop a metrology tool that can meet the needs of industry. This work will also provide training for PhD students in the science and engineering of thermal management in the microelectronics industry. This project will develop lock-in infrared thermography for the measurement of thermal resistances in three-dimensional integrated circuits. Lock-in infrared thermography leverages recent advances in high performance infrared cameras and can effectively address the measurement challenges outlined above. A key aspect of the research is the development and validation of an analytical model for heat conduction in multilayer die stacks and the refinement of a measurement approach to rapidly acquire data and extract the thermal resistance of individual interconnect layers. The outcomes of the research will include improved metrology tools that will accelerate research and development in the packaging and thermal management of microelectronics. 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 2024 · 2024-10

Simulation technology for nonlinear interface phenomena enabling high, managed accuracy with low cost is an urgent need in many fields of science and technology. This project seeks to develop new numerical methods that address these needs. The methods under consideration belong to the family of integral equation methods, which attain asymptotically optimal cost in the solution of certain ("linear homogeneous exterior elliptic boundary value") problems. The project seeks to extend them to challenging nonlinear settings, while improving their efficiency when modeling boundary layers, and developing new methods for the case where volume contributions are needed. Examples of technical fields in which such methods are needed include the project's motivating applications, which will be used to demonstrate our methods' efficacy: (1) Wetting problems, relevant across chemical engineering and biology. (2) Nonlinear plasmonics, a promising avenue for the construction of optical networks. Accurate computer simulation can help confirm or refute scientific theories by comparison with experiment, can replace experiments, and can be used in engineering design processes. The PhD students trained under the project will add to the nation's scarce expert labor supply, and the methods and open-source software released under the project will enable science and industry users around the world to deploy the newly-developed methods for the advancement of science. Since they are based on the superposition principle, integral equation methods (IEMs) are not often used to solve partial differential equation (PDE) problems with nonlinearities. This project removes important obstacles to the adoption of IEMs in such a setting, and it validates the case for them through two ambitious motivating nonlinear model applications involving interfaces. The efficient solution of elliptic (i.e. globally coupled) computational problems remains a major challenge, and IEMs have crucial strengths in this area. While one major strength of IEMs is the use of boundary (i.e. lower-dimensional) unknowns to represent volume solutions, the presence of nonlinearities invariably necessitates the use of volume unknowns. We demonstrate that this use can often be kept localized, particularly in problems modeling interfaces, while maintaining IEM's suitability for problems on unbounded domains. We propose a new method for the evaluation of the resulting volume potentials that retains high-order accuracy in the presence of complex geometry. The project builds on recent advances made by the PI on high-order accurate fast algorithms for the evaluation of layer potentials, the building blocks of IEMs, in the presence of complex geometry in two and three dimensions. We further propose research leading to major efficiency gains in the underlying singular quadrature method and, motivated by empirical observations, a theoretical investigation of the influence of geometry on the accuracy of that method. A final line of proposed research concerns the reduction of resolution demands posed by boundary layers, embodied in IEMs by rapidly-decaying Green's functions, which often result in increases of computational cost that threaten to make certain simulations infeasible. 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 2024 · 2024-10

The goal of this project is to understand how neighborhoods at the geographical margins of a city may or may not be socially peripheral to that city and how that affects the historical trajectory of that city. Specifically, the research team will undertake research to study the relationship between people’s investment of time, energy, and resources into urban neighborhoods, their identity in relation to their neighborhood and the city overall, and the length and intensity of their occupation. Archaeology is uniquely situated to address these questions over the course of a city’s history, with a focus on material evidence for engagement, identity, and inequality. By considering neighborhood diversity in relation to level of investment in the city, one can understand whether social, economic, and political ties created through intentional engagement lead people to stay and continue to participate in that city or, conversely, whether a lack of such ties may lead to more rapid abandonment of those neighborhoods. This may help people understand the nuanced relationship between social investment and local participation, diversity of neighborhood identities, and the futures of modern cities from a bottom-up, grass-roots perspective. Additionally, this project will provide a space for students to learn techniques of archaeological survey and excavation as well as opportunities for the local community, which is largely comprised of immigrant or first-generation community members, to engage with and understand the similarities and differences between their modern experience of a city and those of people living in that space in the past. Focusing on the lived experiences of people in different neighborhoods in an ancient city, researchers will consider the local investments (e.g., architecture, infrastructure, neighborhood organization) and the potential benefits (e.g., access to certain goods, spaces, or activities) that coincide with actively participating in the city and its goings-on. Researchers will address these questions at a prehistoric city which was occupied by both local and non-local peoples for roughly three centuries. Using non-invasive geophysical survey to search for subterranean archaeological features, soil coring, and targeted excavation, researchers will compare two neighborhoods at the physical periphery of the city to gauge whether occupants were also socially peripheral or whether they were fully engaged in central city projects, activities, and ideas. Researchers will examine architectural style and neighborhood organization, including distribution of public buildings, proximity to causeways connecting areas of the site, mound-and-plaza groups where community ceremonies would have occurred, and areas of leveling or filling for habitation which are indicative of local level of physical and social investment. Finally, researchers will compare the proportions of material types that are typical of the central portion of the site to see if people living in these peripheral neighborhoods had access to the same kinds of objects, raw materials, and food as people living closer to the city center, i.e., is there a social benefit to fully engaging in the urban project. 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 2024 · 2024-10

The goal of this project is to synthesize existing tree-ring proxy data (ring width, stable oxygen isotope ratios, blue light intensity, and gridded Palmer Drought Severity Index (PDSI) estimates) to create a seasonal-resolution paleo-streamflow reconstruction for Southeast Asia. The investigators will compare the reconstruction to paleo-streamflow estimates generated by forcing a Southeast Asia hydrological model with Paleoclimate Modelling Intercomparison Project Phase 4 (PMIP4) hydroclimate variables for the last millennium. The comparisons will include assessment of variability in modeled streamflow and comparison of dominant modes and spatial/temporal coherency of variability in the proxies and models. These analyses seek to illuminate the mechanisms that drive the observed dynamics. The project includes support of two early career researchers from underrepresented groups, a graduate student and plans to include undergraduates in the research, development of an interactive public-facing web application with educational materials, and visualization of streamflow through space and time as part of an effort to help create more effective water management strategies for a densely populated region. 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.