University Of Illinois At Chicago

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY The vasculature is lined by endothelial cells (ECs). Emerging studies have revealed remarkable endothelial heterogeneity across distinct tissues, in part dictated by environmental signals from the tissue microenvironment. EC homeostasis is also tightly controlled by various transcription factors leading to cell-autonomous changes in gene expression. Although the field of transcriptome analysis has greatly expanded our understanding of endothelial biology, the extent to which mRNA translational control contributes to the dynamic regulation of ECs, especially under stress, remains less characterized. Translational control is a key regulatory mechanism that controls protein abundance and thereby dictates identity and functions across cell types and tissues. In the present study, we sought to investigate the molecular mechanisms by which sunitinib, a tyrosine kinase inhibitor (TKI), induces endothelial dysfunction. Sunitinib, a multireceptor TKI, is used as the first-line therapy for solid tumors such as metastatic renal cell carcinoma. Despite its effectiveness, a considerable number of patients receiving sunitinib suffer from cardiac and vascular toxic effects. While mechanisms of cardiomyocyte toxicity have been identified, much less is known about the effects of sunitinib on vascular dysfunction. Thus, a better understanding of sunitinib-induced vascular toxicity and its underlying mechanisms is critical to mitigate its risk. As a convergent mechanism downstream of most oncogenic signals, translational rewiring is known to play a key role in promoting cancer onset and progression. Whether such a regulatory program functions in the context of cardio-oncology remains largely unknown. Our preliminary data suggest that translation is dysregulated in sunitinib-treated ECs, leading to translational inhibition of SND1 (Staphylococcal nuclease domain-containing protein 1), a gene with unknown function in ECs. We established SND1 as a potential novel regulator of endothelial health, in part due to its cooperation with UBE2N in regulating the DNA damage response signaling pathway upon sunitinib exposure. Using in silico repurposing of FDA-approved drugs followed by in vitro testing, ramipril, an angiotensin-converting enzyme inhibitor, mitigates sunitinib-induced vascular damage. Therefore, in Aim 1 of the proposed studies, our goal is to define the molecular roadmap by which sunitinib translationally regulates SND1. In Aim 2 of the proposed studies, we aim to dissect the role of SND1 as an important mediator of endothelial tolerance against sunitinib through its interplay with UBE2N in regulating DNA damage repair. In Aim 3, we will validate the cardioprotective effects of ramipril in vivo and confirm that it will not interfere with the effectiveness of sunitinib against tumor growth. Completion of the proposed studies will produce critical insights into the role of translational control in sunitinib-induced vascular dysfunction and will fundamentally advance our understanding of the interaction between translation and endothelial homeostasis in general.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Physiological and oncogenic signals are propagated through a chain of protein interactions. The assembly and disassembly of key signaling complexes are often transient and can occur within minutes or even seconds. Mapping dynamic changes in protein interactions is one of the main approaches for deciphering the regulation of critical biological processes and for the identification of potential therapeutic targets in the treatment of cancer. However, the interrogation of rapid changes in protein interactomes remains a significant challenge due to the poor sensitivity and/or insufficient temporal resolution of current tools. We propose to develop a set of tools that will enable the facile analysis of changes in protein interactions occurring within one minute or less and will significantly improve the sensitivity of existing approaches. Currently, the most comprehensive characterization of protein interactomes is achieved through the application of the biotin proximity labeling approach. This method uses promiscuous biotin ligases, such as BioID or TurboID, that can biotinylate any protein within a 10Å radius. By attaching a biotin ligase to a protein of interest, researchers can detect even weakly associated protein complexes in living cells. However, detection of dynamic changes in the protein interactome using this strategy has been challenging due to a poor signal-to-noise ratio when cells are labeled with biotin for short periods of time (1-2 minutes). We will develop three orthogonal methods that dramatically reduce the background signal during biotinylation and enable fast labeling of samples within one minute or less. One approach will employ a light-regulated biotin ligase that demonstrates no activity in the dark, thus minimizing the background biotin signal to the lowest level. Two other strategies will employ engineered biotin ligases capable of labeling proteins with biotin analogs, desthiobiotin and iminobiotin. These two analogs offer a significant advantage because they can be separated from biotin by affinity chromatography using selective elution condition. This will allow us to reduce the background signal caused by endogenous biotin present in mammalian cells and thus significantly improve signal-to-noise ratio. Furthermore, since all three technologies utilize different biotin analogs, we can apply three different labels within the same cell and separate them during the sample preparation. The application of this novel strategy will enable the labeling of different time points in the same biological sample or the analysis of interactions for three different proteins within the same cells. Overall, the combined advantages of the proposed tools will significantly enhance our capabilities for dissecting the signaling processes driving tumor formation and progression.

NSF Awards · FY 2025 · 2025-01

Human-driven vehicles (HDVs) and automated vehicles (AVs) of all levels (Level 1-5, AVs1-5) may share the highways in the long and foreseeable future. The increasing vehicle autonomy heterogeneity and diversity may jeopardize the safe and harmonious interaction among such vehicles with mixed autonomy on highways and pose a threat to the safety of all vehicles. This may exacerbate an already growing and alarming national concern on traffic safety. This project aims to advance the state of the art in the Cyber-Physical Systems (CPS) research areas of Autonomy, Safety, and Transportation by ushering in a new CPS paradigm of harmonious and safe integration of highway vehicles with heterogeneous, varying, and mixed human / machine autonomy. Through collaborative research, the project may create new methods and tools to enhance the highway driving safety of heterogeneous vehicles. The outcomes of this work may also be extended to advance other CPS in manufacturing, warehousing, and healthcare applications where interaction among humans and heterogeneous autonomous robots is pervasive and safe coordination among them is critical. The project seeks to address the emerging challenges associated with vehicles of heterogeneous autonomy in highway transportation by creating a universal framework that can augment AVs1-5 systems to enable safe and harmonious integration of vehicles in highway traffic. The research team will use an interdisciplinary research approach to understand driving behaviors and assess individual perceived safety of other HDVs and AVs1-5, as well as to achieve cooperative, decentralized behavioral coordination and verifiably safe control in highway traffic scenarios. Human-in-the-loop driving simulation experiments and scaled vehicle-traffic system experiments will be conducted to investigate and evaluate the developed methods. Educational activities such as curriculum development and graduate/undergraduate student research participation will be conducted. Research dissemination and K-12 outreach activities will also be pursued to further increase the broader impact of the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The ability to precisely separate and characterize nanoscale objects, such as molecules, inorganic particles, and biological particles (e.g., exosomes), is crucial in fields like analytical chemistry, environmental science, and medical diagnostics. Chromatography is a key method used for these purposes. As such, it remains a vibrant academic and industrial research and development field. In fact, the chromatography industry generates approximately $9 billion in net global revenue, which is expected to grow by 7% annually over the next decade. Liquid chromatography separates the nanoscale objects (solutes), which are dissolved in a liquid, by passing the solute-laden fluid over the separation medium. The interactions between the solutes and the separation medium govern the effectiveness of the separation. Despite the economic and scientific importance of chromatographic separations, a detailed understanding of the dynamic interactions between solutes and the separation medium at a microscopic level has not been established, hindering progress toward developing more robust and effective techniques. This knowledge gap exists primarily because few experimental techniques can reliably observe these interactions in situ at the interfacial layer. This research project brings together an international team of scientists from the U.S. and Germany to address this gap. The team will use their newly developed, state-of-the-art instruments to directly observe the chromatographic steps at the fundamental, single-particle level during the separation process. Graduate students will gain valuable technical and professional experience through this international collaboration. Liquid chromatography is an important separation technique. Its wide-ranging applications include chemical purification, pharmaceutical analysis and production, and environmental monitoring, among others. Furthermore, it is foundational in understanding complex biological systems, developing new materials, and optimizing chemical processes. The basic principle of liquid chromatography involves partitioning components between a stationary phase and a mobile phase, with differential interactions leading to their separation based on relative retention times. The general view of the underlying principle is that smaller flexible molecular species are segregated by their retention in the porous environment of the solid substrate. This notion that entropic and enthalpic contributions can be separated in a purely size-exclusion process or by affinity chromatography is now being challenged by the development of liquid chromatography for hybrid nanomaterials with inorganic hard cores and functional soft organic shells. New studies reveal a complex interplay of entropic and enthalpic interactions, the latter resulting from the hybrid materials’ functional shells. Unfortunately, a comprehensive microscopic picture of this interplay cannot be developed because of the limited experimental techniques capable of in situ observations at the interfacial layers governing the chromatographic process. This project aims to shed new light on the interfacial layer dynamics that occur during the chromatographic separation of nanomaterials. Recent advancements in nanomaterials synthesis and functionalization, high-resolution three-dimensional (3D) fluorescence microscopy, and boundary layer thermofluidic manipulation enable this project. The enthalpic contributions will be controlled by synthesizing functional quantum dots (QDs) and modifying the stationary phase of the chromatography column with complementary DNA strands. The 3D dynamics of the functionalized QDs will be explored in situ with unprecedented spatial (10 nanometers) and temporal resolution (10 microseconds). The dynamics and enthalpic interaction of functionalized QDs will be further investigated with complementary planar interfaces by controlling thermo-osmotic flows induced directly at the liquid-solid interface. The results are expected to lead to a new fundamental understanding of chromatography that will considerably improve performance and trigger the development of more efficient or selective separation methods based on novel osmotic flows in microfluidic environments. This project was awarded through the “Measurements of Interfacial Systems at Scale with In-situ and Operando aNalysis (NSF-DFG MISSION)" opportunity, a collaborative solicitation that involves the National Science Foundation and Deutsche Forschungsgemeinschaft (DFG). 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

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Relaunch project will use modeling to study the mechanical behavior of two-dimensional materials formed by laterally interfacing sheets that are one atomic layer in thickness. When the sheets are laterally interfaced, they may form defects within the interfacing region. These defects influence the mechanical behavior, such as elasticity and strength, which are key properties that must be understood for the materials to be integrated into new technology. Advances in artificial intelligence and machine learning will be used to overcome challenges in computational time and resources that are usually needed for traditional modeling of these materials. Accelerating the modeling will advance researchers’ efforts in making new two-dimensional materials. The research will be made widely available to benefit the US economy and society. This BRITE Relaunch addresses fundamental barriers to predictive mechanics of materials and structures needed for modeling of two-dimensional nanomaterials integrated into new devices, such as for flexible electronics or batteries. Specifically, the research will focus on a workflow to predict elastic properties, mechanical stress-strain behavior, and fracture strength of two-dimensional lateral heterostructures with defects at the interface between two-dimensional atomic sheets. The project integrates novel artificial intelligence and machine learning techniques to predict properties that can match the accuracy of traditional modeling with density functional theory. The same workflow will automate the search for defects at interfaces through geometry optimization and automate the data clustering for training of algorithms. This project will serve as the foundation for future research on predictive modeling and inverse design (in which the structure of the material is computed from the desired properties, a long-term goal of the community) of two-dimensional materials by developing a materials agnostic workflow. The findings will accelerate the research communities’ efforts on modeling stress-strain and fracture strength for two-dimensional lateral heterostructures needed to predict the mechanical behavior of new 2D material systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Endothelial cells (ECs) form the innermost lining of blood vessels to actively regulate vascular health. Fully receptive to environmental cues, ECs sense alterations in blood flow patterns and transmit hemodynamic information to the underlying vessel wall. Unidirectional laminar blood flow promotes EC quiescence and vascular health via activation of endothelial Akt signaling and corresponding changes to the transcriptome. It remains, however, unclear how mechanical forces are conveyed to the nucleus to affect transcriptional output. During the resubmission period, my laboratory identified nucleoporin93 (Nup93), a major component of the nuclear pore complex (NPC), as an indispensable player for endothelial protection. We reported aberrant nuclear Yap accumulation, a transcriptional cofactor known to trigger atherosclerosis, as the major mechanism driving endothelial inflammation in Nup93-deficient ECs. Demonstrating the importance of Yap subcellular localization, laminar flow conditions are well-known to prevent nuclear Yap localization to promote an anti-inflammatory and atheroprotective genetic profile. Intriguingly, we have identified and validated a highly conserved Akt phosphorylation motif on Nup93. Endothelial Nup93 phosphorylation may therefore occur to prevent nuclear Yap accumulation, thereby uncovering a novel mechanism of vascular protection. To understand the impact of Nup93 phosphorylation on Yap signaling and endothelial inflammation, we introduced Nup93 point-mutant constructs (phospho-impaired [Nup93-AA]; phospho-mimetic [Nup93-DD]) into primary ECs. Indeed, we find that impaired Nup93 phosphorylation (Nup93-AA) leads to nuclear Yap accumulation, EC inflammation, and endothelial permeability. Mechanistically, Nup93-AA ECs exhibit increased binding to Nup62, a nucleoporin anchored at the NPC involved in cargo transport. More importantly, endogenous mutation abolishing Nup93 phosphorylation (Nup93-AA) enhances atherosclerotic lesion formation, revealing Nup93 phosphorylation as an atheroprotective event. Based on these observations, we hypothesize that impaired Nup93 phosphorylation, coupled with increased Nup62 interaction, promotes nuclear Yap accumulation to prime the vasculature for inflammation and atherosclerosis. To test this, AIM1 will investigate Yap subcellular localization, downstream activity, and Nup93- Nup62 interaction in Nup93 mutant expressing ECs under conditions of endothelial laminar flow. AIM2 will elucidate the role of Nup93 phosphorylation in Yap-mediated endothelial inflammation and barrier permeability using classic in vitro functional models. We will also integrate our novel Nup93 phospho-deficient point-mutant (Nup93AA/AA) mouse model to define the in vivo importance of Nup93 phosphorylation in vessel homeostasis. Lastly, AIM3 will detail the consequences of impaired Nup93 phosphorylation on atherosclerotic lesion formation. These studies will include bone marrow transplant experiments and use of our newly generated Nup93 floxed conditional model to investigate the vascular and endothelial-specific importance of Nup93 phosphorylation, respectively. Overall, this proposal aims to elucidate how Akt-directed phosphorylation of Nup93, a critical nuclear pore protein, promotes endothelial and cardiovascular health, thus opening a new area of research in vascular biology.

NSF Awards · FY 2025 · 2025-01

Neuromuscular disorders affect millions of individuals worldwide, yet efficient tools to accelerate diagnosis and assess therapeutic interventions are currently lacking. Existing methods for evaluating muscle health face significant limitations, including clinical impracticality due to cumbersome procedures, reliance on highly trained personnel, high costs, and safety concerns stemming from associated pain or the use of ionizing radiation. The emergence of electrical impedance myography (EIM) offers a promising avenue for assessing muscle health. EIM is sensitive to changes in muscle structure and composition brought about by a variety of neuromuscular disorders as well as by disuse, producing unique disease signatures that will vary with muscle status. Thus, EIM analysis can provide a method to rapidly, quantitatively, and reliably diagnose and monitor neuromuscular diseases at the bedside, act as a tool to help tailor care for individual patients and streamline and improve clinical drug trials. This CAREER project integrates research with educational outreach by offering students hands-on experience in innovative translational research. This is interlaced with a long-term educational objective of mentoring new generations of students by providing them with experiences in cutting-edge research, developing and implementing activity-based style courses to motivate students’ self-learning in the classroom, encouraging students to choose a science, technology, engineering or math (STEM) degree by participating in research, and assisting undergraduate students in their own translational research efforts. This CAREER project will establish the scientific foundations of future generation EIM tools and enhance diagnostic accuracy by integrating artificial intelligence algorithms with simulation and analytical methods to extract quantitative muscle insights that are currently inaccessible. The tools developed and data collected are expected to lead to a deeper understanding of the role played by muscle electrical properties in EIM, understanding that is needed for the development of new and more accurate EIM tools for evaluating neuromuscular disorders (NMD). Research objectives include (1) developing a physics-informed analytical and simulation framework to model the entangled multicellular architecture underlying tissues and automate the extraction of relevant physical and biological information from EIM data, (2) assessing EIM biological variability in silico, and (3) evaluating the robustness of models generating EIM data. In silico simulations and ex vivo measurements will provide proof of principle to optimally determine the minimal yet sufficiently biophysical relevant mechanisms needed to build robust virtual EIM predictions for healthy and prototypical diseased conditions necessary to interpret EIM outcomes in patients. 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

This Engineering Research Initiation (ERI) grant will support fundamental research that aims to improve reliable coordination for a team of autonomous mobile robots operating in realistic and possibly adversarial environments. Robots that can collaborate have shown great potential in many applications from search and rescue missions to precision agriculture. Due to limited sensing, communication, and processing capabilities, autonomous robots in a collaborating group often need to make collective decisions through communications in proximity. For example, exchanging sensing data or control commands with neighboring robots through range-constrained wireless communications. The integral role of wireless communications requires robots to coordinate and react to the changing environment for reliable information sharing in addition to their original tasks. However, existing research paradigm often assumes that the communication environment is well modeled and can be pre-programmed into robotic systems for effective coordination without disruptions. This significantly limits the capabilities of robot teams and makes them vulnerable in real-world environments that can often be unpredictable. To address the challenges, this project seeks to create a set of methods and tools that may augment many multi-robot coordination tasks, by enabling robots to learn the environment on the fly, adapt their motion to environmental changes, and maintain communications when unexpected robot failures happen. The project will also support education and outreach activities such as curriculum development, broadening participation of students from underrepresented groups, and local community engagement in the Charlotte metropolitan area. The objective of this project is to create novel methods and algorithms that enable the co-design of learning, communication, and motion control for mobile robot teams. This may allow for robust operation of robots with provable assurances on communication capabilities and multi-robot network resilience adaptive to uncertain environments, positively supporting the primary task execution. In pursuit of this goal, the project will make two main contributions: (i) Developing data-driven methods for robots to collaboratively learn the spatially varying realistic communication performance online, and (ii) Developing new approaches that specify the impact of realistic communication constraints and network resilience on the task-related robots’ motion, to enable joint optimization for more efficient multi-robot coordination with performance guarantees. The work will be evaluated in simulations and experiments on physical robotic platforms. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). 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 · 2025-01

PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) remains a devastating illness for which there are limited prevention and treatment options. Vascular endothelial dysfunction as manifested in the loss of the blood brain barrier (BBB) integrity can precede symptomatic cognitive decline in AD patients and thus suggests that vascular pathology may be involved early on in AD development and progression. Endothelial cells (ECs) play several critical roles such as tightly regulating the transport of molecules across the BBB as well as the influx of immune cells which can induce inflammation and thus promote AD. Recent data suggest that viral or bacterial infections can increase the incidence and progression of Alzheimer’s disease. There is also growing evidence that premature cellular senescence impairs blood brain barrier function and promotes neuroinflammation. However, the mechanistic roles of cellular senescence and blood-brain barrier dysfunction in mediating pathogen-induced AD progression remain unclear. Our preliminary data suggests that viral and bacterial pathogens can initiate blood brain barrier dysfunction by direct activation of inflammatory signaling as well as by increased expression of endogenous retroviruses which in turn initiate a cascade of hyperinflammation. We have developed a novel computational algorithm SenePy to analyze cellular senescence in single cell transcriptomic data and found significant increases in cellular senescence associated with expression of endogenous retroviruses in experimental mouse models of Alzheimer’s disease as well as human brains with Alzheimer’s disease We have thus formulated the overall hypothesis that pathogen-induced cellular senescence and blood -brain barrier dysfunction mediate Alzheimer’s disease. The proposed experiments will leverage experimental mouse models of Alzheimer’s disease, molecular characterization of endogenous retrovirus RNA sensing as well as human iPSC models.

NIH Research Projects · FY 2026 · 2025-01

Abstract Obesity is linked with a variety of cancer types and is particularly prevalent among Black American (BA) women compared to other subgroups (57.2 5% for BAs). In weight management interventions, BA participants lose less weight than non-Hispanic whites. Obesity interventions co-created with community stakeholders show promise, but are difficult to bring to scale. A novel approach to treatment response involves engaging intervention participants as change agents. Our pilot data reflect that providing current participants with the skills to share intervention messages with their social networks (e.g., family, friends) results in greater personal health effects relative to treating them as passive recipients. Participant-driven dissemination of evidence- based information may also lead to network- and population-level health effects. These “spill-over effects” may benefit other BA women with obesity in participants’ networks, given BA women’s highly homogeneous networks and BA cultural values that prioritize social connectedness and community mobilization. We are unaware of research examining: (1) if training intervention participants as change agents augments interventions’ effects on obesity and associated lifestyle behaviors; and, (2) how non-participant network members may benefit from interventions through participant-driven dissemination. Molina tested a navigation social network evidence-based intervention (Nav-SN) that provided BA women with skills to share intervention messages (R21CA215252). Fitzgibbon and colleagues developed an evidence-based Mediterranean Diet weight management intervention designed for BA women (MedDiet-WL, R01HL129153). Our participant advisory board and our team successfully integrated and implemented Nav-SN with the Med Diet-WL intervention. We propose three specific aims: Aim 1. Conduct a 6-month (24-session) RCT comparing MedDiet-WL alone to MedDiet-SN among 268 BA women with obesity. We hypothesize that: 1) Participants in MedDiet-SN will show improvements in bodyweight/BMI, and body composition compared to MedDiet-WL at post-intervention (6-months); Aim 2. Use social network methods to compare differences in weight management information dissemination by 268 women randomized to MedDiet-WL and MedDiet-SN. We hypothesize that women in MedDiet-SN will share information to more network members compared to MedDiet-WL women. Aim 3. Use social network methods to compare MedDiet-SN and MedDiet-WL in 268 social network members. We hypothesize that MedDiet-SN network members will show, greater improvements in BMI, body fat, diet, and physical activity than MedDiet-WL network members at 6 months.

NSF Awards · FY 2025 · 2025-01

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Neal Mankad of the University of Illinois Chicago is studying Earth-abundant metal complexes that can efficiently harvest energy from light to catalyze chemical transformations of importance to industrial chemical synthesis. To achieve this objective, the research team will create bimetallic reaction centers that are encapsulated by naturally occurring sugar molecules with cage-shaped structures called cyclodextrins. The encapsulated reaction centers will be tested for their ability to promote light-driven chemical reactions. The surrounding cage structures are expected to increase efficiency by preventing unwanted side reactions and increasing catalyst stability. Not only is industrial chemical synthesis highly energy-intensive, but it also is dependent in many cases on precious metal resources. Therefore, the use of Earth-abundant catalysts that harvest energy from light is a welcome advance that could positively impact energy efficient and environmental impact of society. This project is an international effort involving Drs. Sylvain Roland and Matthieu Sollogoub, both from Sorbonne University in France, who are funded by the French National Research Agency (ANR) and who possesses expertise in the synthesis of cyclodextrins and their derivatives, including those necessary for this collaborative project. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Neal Mankad of the University of Illinois Chicago is studying bimetallic complexes that (i) include exclusively Earth-abundant metals and (ii) have the highest possible photoactivity in the visible light region. Emphasis will be placed on different designs of copper(I) complexes supported by modified cyclodextrins (CDs) as supramolecular scaffolds in which the copper centers are encapsulated. In many cases, copper(I) will be connected to a second Earth-abundant metal through different strategies. In-depth studies of the encapsulated bimetallic complexes will provide fundamental knowledge about the factors affecting reactivity and light-harvesting ability, including details of both cooperativity between metal centers and supramolecular synergy between the binuclear reaction centers the CD cavities. A major goal is to increase the efficiency of the photocatalytic systems at long wavelengths. In the long term, the project aims at developing sustainable photocatalytic systems leveraging properties of both the paired Earth-abundant metals and the CD cavity. While this project is fundamental in nature, its implications might be very applied and could enable emergence of sustainable industrial processes using environmentally benign metals. 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

The overall goal of this K24 application titled “Mentoring & Patient-Oriented Research in Sickle Cell-Related Kidney Disease” is for a mid-career hematologist and physician-scientist, Santosh L. Saraf, MD, to develop necessary skills to mentor the next generation of junior investigators focused on sickle cell disease (SCD)- related research. Chronic kidney disease develops in over half of adults with SCD and leads to increased morbidity and early mortality. The mechanisms for how chronic kidney disease develops are, unfortunately, poorly understood and therapies to prevent and treat sickle cell nephropathy are urgently needed. Further complicating the care of individuals with sickle cell disease is the dearth of clinicians focused on SCD-related research and clinical care. The applicant’s goals for this K24 award are to train the next generation of clinician scientists by leveraging preliminary work and multi-center collaborations of carefully phenotyped SCD cohorts to identify high-risk patients and targetable pathways for SCD-related kidney disease. The applicant has demonstrated that chronic exposure of the kidneys to cell-free hemoglobin and heme are associated with progression of chronic kidney disease. The new research that is proposed in this award is a natural extension of Dr. Saraf’s ongoing work and consists of investigating 1) whether genetic variation of the haptoglobin allele and heme oxygenase-1 GT-repeats predicts chronic kidney dysfunction and its progression and 2) if complement activation is implicated in the pathophysiology of cell-free hemoglobin catabolism and SCD- related kidney disease. Several of Dr. Saraf’s active and completed projects have developed longitudinal SCD cohorts with harmonized phenotypes, including kidney, heart and lung function. These cohorts are available for mentees to address the research aims in this proposal as well to develop their respective independent research trajectories. Dr. Saraf will apply several resources available at the University of Illinois at Chicago, such as T35, TL1, R38, and KL2 programs, to train and produce outstanding clinical scientists. Dr. Saraf has a strong record of funded clinical research and mentoring trainees but requires additional protected time and training to become an inspiring mentor for this underserved and high-risk patient population. The K24 award will provide Dr. Saraf with the additional time and resources to amplify his efforts in understanding risk factors and targetable pathways for SCD-related chronic kidney disease and in training the next generation of clinician scientists in SCD-related research and clinical care.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY Patients with chronic kidney disease (CKD) are at increased risks of developing cardiovascular disease, progressing to end stage kidney disease, and dying prematurely. New strategies are needed to non-invasively identify targets to develop new therapies. Prior studies implicate decreased kidney perfusion and resultant chronic hypoxia in the pathogenesis of kidney fibrosis. The resulting chronic histopathologic lesions, which include interstitial fibrosis/tubular atrophy, global glomerulosclerosis, and microvascular disease lead to CKD progression. While chronic histopathologic lesions carry prognostic value, assessment requires a native kidney biopsy that is not performed in patients with common forms of CKD since a kidney biopsy carries risks. Non- invasive imaging biomarkers of chronic histopathologic lesions may enable selection of high-risk individuals to test new therapies or serve as surrogate endpoints to assess effectiveness of targeted interventions. However, non-invasive imaging remains underdeveloped in nephrology. Gadolinium-free kidney functional magnetic resonance imaging (fMRI) provides non-invasive assessments of kidney fibrosis, oxygenation, and perfusion. In prior work, our team incorporated kidney fMRI into a multicenter study and showed that kidney fMRI-derived biomarkers of fibrosis, hypoxia, and malperfusion associated with worse kidney function, differentiated patients with CKD from healthy volunteers, and may have prognostic value. Advancement of kidney fMRI in research and clinical practice requires further testing in a large cohort of patients with CKD that have gold standard assessments of histopathology. The Kidney Precision Medicine Project (KPMP) offers a unique opportunity to fill this gap. KPMP interrogates tissue obtained from research native kidney biopsy to identify critical pathways and targets that may ultimately lead to new therapies for patients with common forms of CKD. In preliminary data, we piloted kidney fMRI at a KPMP Recruitment Site (n=13) and showed high intra-reader, inter-reader, and within-participant agreement of the kidney fMRI biomarkers. In this proposal, we will perform kidney fMRI on 420 KPMP participants with CKD recruited from 7 Recruitment Sites and perform a 2-year follow-up kidney fMRI scan in a subset of participants (n=210). We will test the associations of kidney fMRI biomarkers with chronic histopathologic lesions and change in kidney function over time. We will extract kidney fMRI radiomic features to determine whether unsupervised clustering of radiomic features identifies imaging sub-phenotypes of CKD. To externally replicate our results, we will leverage de-identified data from a multicenter observational study of ~500 patients with diabetic kidney disease undergoing kidney fMRI in Europe. This proposal builds upon the work of an early-stage PI and fosters an ongoing cross-disciplinary collaboration among investigators experienced in clinical phenotyping, biomarker development and testing, and imaging. The results have the potential to inform whether kidney fMRI biomarkers identify sub-phenotypes of CKD or serve as surrogate markers of chronic histopathologic lesions in future drug development trials in patients with CKD.

NIH Research Projects · FY 2026 · 2024-12

Project Abstract A tumor is complex ecosystem in which cancer cells interact with diverse immune cells, creating a cancer- supportive microenvironment. The locations of these immune cells within a tumor significantly affect their functions, which are associated with cancer progression, metastasis, and response to therapy. We propose to develop a novel spatial proteomics approach by integrating 3D microscopy techniques with bottom-up proteomic assays, which can offer deep proteome profiles of specific immune cells at various subregions of a tumor. In Aim 1, we will develop a workflow involving 3D photobleaching-mediated spatial fluorescence encoding of immune cells located in different subregions of a mouse tumor. In Aim 2, we will apply the spatial proteomics method to identify macrophage niches associated with therapeutic responses in the tumor microenvironment. If successful, this innovative approach will revolutionize our ability not only to understand the spatially associated functions of immune cells in tumors but also to discover new immune cell niches as biomarkers and targets for accurate diagnosis and effective therapy for cancers.

NIH Research Projects · FY 2026 · 2024-12

Abstract Many COVID-19 survivors experience long-lasting neurological post-acute sequelae of COVID-19 (NeuroPASC) including cognitive, cerebrovascular, and neurological disorders. The causes of NeuroPASC are not understood. However, evidence suggests that blood-brain barrier damage may contribute to NeuroPASC. Identifying mechanisms that regulate the brain endothelial cell response in NeuroPASC is therefore important. Wnt/β-catenin signaling plays a critical role in maintaining integrity of the blood-brain barrier. This grant will test the novel mechanism that Wnt/β-catenin dysregulation in brain endothelial cells contributes to NeuroPASC by increasing blood-brain barrier permeability and neuroinflammation. We will determine the effect of age on brain endothelial cell signaling and blood-brain barrier permeability for the resolution of NeuroPASC. We will define the mechanism by which Wnt/β-catenin activation reverses blood-brain barrier leakage and memory impairment in NeuroPASC. We will determine the extent through which transcellular blood-brain barrier permeability contributes to NeuroPASC. These studies could identify future therapeutic strategies leveraging Wnt/β-catenin signaling to improve chronic post-infectious neurological diseases.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSTRACT Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related mortality worldwide. Lung adenocarcinoma (LUAD) is the most common subtype of NSCLC. Genetically engineered mouse models (GEMMs) in which expression of mutant oncogenes and/or deletion of tumor suppressors leads to the formation of autochthonous tumors are the most commonly used models in lung adenocarcinoma research. Although the lesions generated in these models histopathologically resemble human tumors, they differ in their molecular characteristics. Unlike human tumors, murine ones lack high mutational burden. Our preliminary evidence highlights that the anatomical region from which lung adenocarcinoma arises is significantly different in murine and human lungs. Further, the epithelial stem and progenitor composition and their lineage trajectories are distinct in mice and people. Notably, previous studies have shown that GEMMs are non-responsive to single ICI therapies. All these reasons point towards the need to develop novel human-relevant lung adenocarcinoma models. As an alternative model to GEMMs, patient-derived organoids (PDO) and patient-derived xenografts (PDX) in mice have been proposed to evaluate tumors. Although well suited for personalized therapy, PDO and PDX models are limited in availability, lose their original characteristics with passaging, and are not easily controlled for the mutations and tumor stage being studied. These technical limitations have made it difficult to dissect the mechanisms influencing human lung tumor cell plasticity. To overcome these limitations, we seek to model tumors by genome editing primary human lung cells and culture them as tumor organoids or orthotopically xenograft them into mice. In Aim 1, we seek to introduce oncogenic mutations into two primary human lung stem and progenitor populations and generate organoid models. These models are amenable for high throughput drug testing in the future. Further, in Aim 2, we will xenograft the mutated stem/progenitor populations into lungs of immunodeficient mice to develop an in vivo model. We will perform histopathological analysis to compare the resulting tumors to primary human lung adenocarcinoma. We will further perform single cell transcriptomics to characterize the tumor cell states and dissect the role of cell of origin in determining histopathology of tumors and tumor cell states. In brief, we seek to develop novel lung adenocarcinoma models that better capture the initiation, progression and composition of primary human LUAD tumors, and characterize the cell of origin dependent effects on the resulting tumors.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY Aging is an inevitable progressive series of events that effect all living organisms. Structural and compositional changes that occur to mineralized tissues during aging can deteriorate their function and regeneration. Cementum is a connective mineralized tissue that provides support, by attaching collagen fibrils from the periodontal ligament to the tooth, and function by absorbing mechanical loads during mastication. Periodontal disease is an infections chronic inflammatory condition that is highly prevalent in the older population and directly effects the biology and function of the cementum. However, the underlying functional properties of cementum with aging are not fully elucidated. The hypothesis is that modifications to the composition and structure will degrade the functionality and regeneration of the cementum with aging, and potentially enhance the progression of periodontal disease. The proposed aims consist of identifying the biochemical, structural, and mechanical features of the aging cementum (Aim 1), defining the state of periodontally involved cementum as a function of age (Aim 2) and delineating in vivo biochemical, physico-mechanical and molecular mechanisms in cementum to unveil the impact of aging and periodontal disease using ligature-induced periodontitis model (Aim 3). The aims of this proposal integrate important events (aging and disease) of living organisms, in particular their effects in oral health. This proposal is elaborated based on the candidate’s biomaterials and matrix biology experience and plans to support the achievement of specialized skills in cementum biology, structural and protein analysis, and in vivo animal models to pursue an independent research career. An experienced advisory team was assembled to provide expertise in tooth ultrastructure and biomechanics (Dr. Ana Bedran-Russo, primary mentor), protein analysis and murine periodontitis model (Dr. Afsar Naqvi, co-mentor), immunohistochemistry and regeneration (Dr. Xianghong Luan, co-mentor), morphometrics (Dr. Jeffrey Toth, collaborator), clinical periodontics support (Dr. Vrisiis Kofina) and ECM proteomics and bioinformatics (Dr. Alexandra Naba, consultant). The career development plan is structured to advance in the gaps of scientific and professional skills related to the aims of the proposal. It is composed of didactic courses, workshops and activities that combined with an organized mentoring dynamic will allow the candidate to achieve specific milestones to obtain an independent faculty position. The completion of the proposed aims will reveal uncharted areas of the structure, biology and biomechanics of the cementum as a function of age and disease, and the impact of these processes on cementum regeneration. Potential molecular mechanisms of cementum aging elucidated in this proposal could build a foundation for understanding mineral-to-matrix interactions and their probable roles in the functionality and regeneration of mineralized tissues.

NSF Awards · FY 2024 · 2024-11

Model theory is a branch of mathematical logic which studies common properties shared by different types of mathematical structures. A crucial idea in this area is the notion of a dividing line, meaning a fundamental dichotomy among mathematical structures. Historically, the most important of these dividing lines is stability, a notion which has found extensive applications in the setting of infinite structures. In contrast, the fields of extremal and arithmetic combinatorics focus mainly on finitary problems, but have thematic elements in common with model theory. In recent years, extensive interactions have begun between model theory and these fields, leading to surprising new results. This project will further explore these connections by establishing a model theoretic understanding of important tools from arithmetic and extremal combinatorics. Understanding these tools from a model theoretic perspective has the potential to lead to novel applications in both fields. The educational component of this project focuses on broadening participation efforts utilizing Ohio State infrastructure and the organization of a summer school for graduate students. In extremal and additive combinatorics, hypergraph regularity and higher order Fourier analysis have proved to be powerful tools. The goal of this project is to develop connections between these tools and generalizations of stability theory. The PI will prove theorems connecting tame behavior in hypergraph and arithmetic regularity lemmas to new generalizations of stability. Complementing this, the PI will develop the pure model theory of these higher order notions of stability, as well as higher order analogues of stable group theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The cellular networks are crucial for the national economy and security. With each generation, the networks are designed to support higher performance levels. The next-generation networks, namely sixth-generation (6G) networks, will target higher data rates and new features like city-scale perception. To achieve the desired performance in the next-generation networks, new spectrum in the 7-24GHz frequency range, also known as the FR3 band, is being considered. However, the FR3 spectrum band is highly fragmented, which means that the design of wireless radios is poised to be even more challenging than the existing systems. In particular, this project will address two important questions for the new FR 3 band - How to formalize a mixed-domain system design methodology that leverages programmable baseband and analog (i) to meet the need of next-generation communication systems in FR3, and (ii) to enable multi-function operation with communication and sensing over such diverse bands in a power-efficient manner? This project will develop MD2 (Mixed Domain Design) - a novel theoretical framework and practical designs for next-generation networks using the FR3 wireless systems. The project thrusts will address foundations, algorithmic methods and prototype designs, using the platform of pixel antennas. The new designs will target communications-only, sensing-only, and joint communications and sensing wireless applications. A key innovation of MD2 framework will be to allow joint and systematic design across antenna, analog and digital domains, with practical designs using pixel antennas optimized for FR3 spectrum. The team consists of investigators from the USA and Finland who bring complementary expertise across multiple disciplines. This project will strengthen international collaboration between the US and Finland, with a significant impact on next-generation wireless communications systems.The project will involve undergraduate students to improve student retention in engineering programs and integrate new research modules into wireless communication courses. 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

This project aims to serve the national interest by (1) improving programs preparing novice college mathematics instructors and (2) establishing leadership development for faculty who are the Providers of teaching-focused professional development (TPD) for those novices. Extensive educational research has identified evidence-based instructional practices that support undergraduates' persistence and learning in science, technology, engineering, and mathematics (STEM). For undergraduates to benefit from advancements in instructional practices, novice instructors (e.g., graduate students) need opportunities to develop expertise in those practices. For novice instructors to develop that expertise, Providers (i.e., those who facilitate TPD for instructors) themselves need opportunities to develop expertise in teaching about teaching. Providers face daunting challenges: no curricular packages (e.g., textbook, assessment items) exist for teaching graduate students how to teach mathematics. This effort builds upon previous work addressing these needs through workshops for Providers and creating a library of individual activities for TPD. Experienced Providers will assemble lessons from the library of activities, create assessments of learning about teaching, and teach new Providers about use of these packages. An innovation in the project is attention to a particular group of Providers, whose ambitions include scholarly work related to the development of novice instructors. These Provider-Scholars will be the next generation of leaders in this field. Greater Provider skill will improve instruction by novices and boost learning opportunities and outcomes for undergraduates. The goals of the project are (1) to develop curricular packages for learning about teaching college mathematics which will be piloted by Providers and (2) to build new research-based understanding of the knowledge, skills, dispositions, and communities Providers develop as they grow professionally into Provider-Scholars and Stewards (i.e., Provider-Scholars who also have leadership roles). Project research and evaluation will use a mixed-methods convergent design so complementary data are collected concurrently or, as appropriate, sequentially. This approach combines the strengths of quantitative data collection and analysis (e.g., large sample, repeated measures) with those of qualitative methods (e.g., participant voices, rich detail). In particular, the exploratory research questions are: (RQ1) What is the nature of Provider-Scholar knowledge, skills, and dispositions for engaging in scholarly work as Stewards? (RQ2) What is the nature of Steward, Provider-Scholar, and Provider engagement in the work and community growth? Project evaluation questions are: (EQ1) To what extent is project exploratory research implemented as planned? (EQ2) To what extent is the project succeeding in developing and piloting starter packages and Provider orientation with target communities? (EQ3) How can the project do better in supporting the professional community, including stewardship and leadership capacity development? The project intends to build professional community through collaborative working groups of experienced Provider-Scholars and education researchers. Mathematics graduate students (94% of whom have teaching related responsibilities while in graduate school) will benefit from the strengthening of TPD programs achieved by equipping new Providers with “starter packages” of resources informed by research findings about student-responsive teaching and learning. A robust community of Providers whose scholarly activity is about TPD will seed the next generation of leaders. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

Falls are a significant cause of morbidity and mortality in the elderly. A robust and low-cost solution for the estimation of fall risk and detection of falls will allow seniors to live independently and reduce medical costs due to falls. Wearable devices have been developed to detect “hard falls”, namely falls that cause injury. However, many falls in the elderly do not cause physical injury (“soft falls”). These occur in association with weight transfer activities such as turning and sit-stand transitions. Indeed, the ability to control the position and movements of the trunk (“core”) is essential for coordinating the movements of the limbs during weight transfer. The goal of this project is to combine real-world limb-core dynamics of an individual with data collected by accelerometer via a commodity wristwatch and a cell phone on the opposite hip to improve the detection of hard and soft falls. A personalized fall risk analysis and detection model will be created for each user via real-time learning of the limb-core dynamics using state of the art machine learning algorithm. We will also assess the perceptions and preferences of elderly patients using this technology and evaluate their attitudes towards continuous data collection and sharing of health data for improved health. The software system, the real-world gait and weight transfer movement and the associated accelerometer data will be made freely available to any institution, investigator or research student interested in the study of machine learning on health conditions as well as on fall risk and analysis. This project will train graduate and undergraduate students in technical skills (machine learning, wearable technologies and data analysis skills) as well as in people skills for working with the elderly who live in long-term care facilities. While numerous fall detection devices incorporating artificial intelligence (AI) and machine learning algorithms have been developed, this project focuses on personalizing fall risk detection. This project will explore the use of kinematic measurements of an elderly individual’s movements associated with weight transfer to enable multi-task and multi-modal machine learning algorithms to personalize fall risk detection. A small-sample-based deep learning algorithm optimized to incorporate individual kinematic characteristics using multi-task and multi-modal learning frameworks is developed. Second, the team will analyze the movement transitions captured by the Azure Kinect system in order to identify relationships between the accelerometer data and the complete skeletal frame with an emphasis on the limb-core dynamics. Specifically, our goal is to determine whether or not Generative Adversarial Networks (GANs) can be used to augment missing modality from a small amount of body motion data, smartwatch and phone acceleration data collected directly from elderly participants who are at most risk of falling, namely those living in an assisted living center. Finally, we will evaluate the perception and attitudes of the elderly participants towards the continuous use of wearable devices for fall risk analysis and detection. 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

Advanced cyberinfrastructure (CI) is undergoing disruptive changes in system architectures and application workloads. The landscape of cyberinfrastructure workloads is rapidly expanding beyond traditional computational simulations to include a hybrid mix of applications. CI facilities now host diverse high-performance systems with heterogeneous configurations, leading to a complex mix of computing, memory, and storage components. Existing CI management methods, which are heavily heuristic or manual-based, struggle with these evolving challenges. This project addresses the complex challenges of CI resource management by integrating artificial intelligence (AI) technologies with human expertise. UIC is a federally designated Minority-Serving Institution (MSI). An integrated education plan can strengthen diversity-focused programs at UIC, thus promoting greater diversity and inclusion within the scientific community. The project transitions from managing isolated single clusters to coordinating facility-wide management, orchestrating the entire facility as a unified pool of diverse resources for a broad spectrum of applications with various resource requirements. Specifically, it aims to design and evaluate an AI-guided framework named AIMCI (Artificial Intelligence for Managing Cyberinfrastructure). Key research thrusts are: (1) developing new AI models for predictive analysis of resource usage patterns and user behavior, (2) applying reinforcement learning methods to optimize resource management in a complex and dynamic computing environment, and (3) building a discrete event-driven simulator for exploratory simulation of CI resource management with human-in-the-loop interaction. 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

This project focuses on research connecting multiple fields of mathematics, namely, analysis, combinatorics, and model theory. Analysis and combinatorics can be seen as two different approaches toward using mathematics to study the physical world. In analysis, which evolved from calculus, the approach is based on continuous and dynamical methods of an infinite nature. By contrast, combinatorics seeks to understand complicated and subtle patterns in discrete (and often finite) systems. The proposed research centers on using model theory, a branch of mathematical logic, to bridge these two different perspectives. Model theory is the abstract study of mathematical objects using properties that can be described with formal language and semantics. The leverage provided by model theory stems from the fact that two mathematical objects can appear substantially different in nature, but share enough semantic properties so that an understanding of one object leads to understanding of the other. This approach has led to significant breakthroughs in mathematical research, which will be further developed in this project. The interdisciplinary nature of this project will also allow for collaboration between researchers and students from a variety of mathematical backgrounds and levels. The broad theme of the research proposed is the use of continuous logic (model theory for metric structures) to develop a stronger foundation for the interaction between analysis and combinatorics described above, with a focus on arithmetic combinatorics in noncommutative groups. There are two main goals. The first is to prove a fully general arithmetic regularity lemma valid for arbitrary groups using a Radon-Nikodym-type strategy (similar to the nonstandard proof of Szemeredi's regularity lemma for graphs). Previous attempts toward such a theorem have been impeded by fundamental drawbacks of classical (discrete) logic, and this project proposes a new strategy based on continuous logic. This theorem is envisioned as a necessary step in the ongoing development of a model-theoretic framework for arithmetic combinatorics. The second main goal is based on recent work on the structure of stable functions on groups, which establishes a connection to existing results in arithmetic combinatorics (e.g., on approximate groups). A majority of these results are only currently understood at a qualitative level, and thus a quantitative understanding of stable functions on groups should lead to quantitative breakthroughs in these other areas. Moreover, model theoretic ideas were previously successful in obtaining a quantitative analysis of stable sets in groups. This project will pursue an analogous quantitative analysis of stable functions, motivated by applications to arithmetic combinatorics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The Internet of Things (IoT) promises to advance smart homes, cities, industries, and healthcare. It is projected to reach a market value of $4.5 trillion by 2035. However, challenges include the environmental impact and disposal issues of battery-powered devices, and their limited processing power and memory. This highlights a need for sustainable, efficient IoT solutions. This CAREER project innovates duty-cycle-variable computing, proposing Elastic Intermittent Computation foundations, which will be advanced through application, architecture, and circuit-level design flows, by: (1) understanding and modeling the intermittent behavior of batteryless edge intelligence, and designing efficient intermittent-robust IP cores to ensure uninterrupted operation via a novel automated framework; and (2) exploring existing machine learning training algorithms and developing hardware-software co-design strategies for energy/intermittent-aware scheduling and execution. The technologies developed through this CAREER project promise to reduce electronic waste and carbon emissions as the world moves toward the era of a trillion connected, batteryless things. This research aims to advance sustainable computing, supporting the shift towards a carbon-neutral society. Developing batteryless processing and learning systems will enhance innovations in key sectors such as agriculture, healthcare, and defense, focusing on affordability and reliability. Alongside the research on sustainable smart devices, this CAREER project will launch hands-on education programs, from high school through the graduate level, exploring real-world applications of the underlying concepts. A Practice for Practical Problems initiative will also be introduced, emphasizing computational thinking and real-world challenges, assisting in bridging the gap between high-tech industries and conventional teaching programs. It seeks to prepare a diverse future workforce in computer science and engineering, with programs designed for high-school teachers and students. This initiative will particularly benefit rural students with limited access to such opportunities, thus broadening their educational and career horizons in technology and engineering. 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

Many low-income households in the United States face high energy bills, with some spending up to 13% of their income on energy, compared to the national average of 2.9%. This disparity is often due to inefficient windows and poor insulation, especially in older homes built before the 1960s. These homes suffer from air leaks, drafts, and inconsistent indoor temperatures, leading to increased energy consumption and higher bills. Additionally, the residents' health is adversely affected during extreme weather conditions such as heatwaves and wildfires. Traditional methods to measure window air leakage, like the blower-door test, are expensive and disruptive, making them impractical for many low-income communities. Without documented leakage data, these communities miss out on retrofit grants meant to improve energy efficiency and climate resilience. This project aims to fill this gap by developing a cost-effective, community-driven method to accurately measure window air infiltration and leakage using drone-mounted infrared/thermal imaging combined with artificial intelligence (AI). The project will co-develop an innovative approach for rapid data collection and analysis by focusing on overburdened communities. The objectives are to conduct stakeholder focus groups, coordinate future window replacements, and perform in-lab and field experiments to calibrate thermal imaging for air leakage detection. The project aims to empower these areas to access retrofit grants, enhance climate resilience, improve energy efficiency, and ultimately reduce energy costs and improve residents' health and safety. This initiative also addresses environmental injustice, ensuring that affordable and sustainable housing is accessible to those who need it most. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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.