University Of California-Irvine

NSF Awards · FY 2026 · 2026-08

Modern data science, quantum computation, and high-dimensional probability rely on mathematical tools for understanding functions of many yes/no variables and their continuous analogues. This project studies such functions on the discrete cube and in Gaussian space, where approximation, learning, randomness, and boundary structure can be analyzed precisely. The work addresses basic questions about how much information is needed to learn a low-complexity function, how well complicated functions can be approximated by simple polynomials, and how the shape of a high-dimensional set controls its boundary. These questions are central to mathematics and also inform learning theory and quantum computing. This project promotes the progress of science and advances national prosperity and welfare by strengthening foundations for reliable computation, high-dimensional data analysis, and artificial intelligence. The project also supports education and workforce development through training of graduate students and postdoctoral researchers, summer schools and research programs for early-career researchers, and dissemination through seminars, webinars, lecture notes, and preprints. The investigator develops a unified program in analysis on discrete and Gaussian spaces, using semigroup methods, Fourier analysis on the Hamming cube, hypercontractivity, and sharp inequalities. The project seeks sharper Bohnenblust-Hille and hypercontractive inequalities, with applications to PAC learning of low-degree Boolean functions, learning with small spectral support, polynomial threshold functions, and the Aaronson-Ambainis conjecture in quantum query complexity. It develops Jackson- and Poincare-type approximation theorems with dimension-independent bounds and transfers discrete approximation principles to Gaussian weighted approximation. It pursues sharp isoperimetric and influence inequalities, including progress on the Kahn-Park conjecture and the remaining range of Weissler's complex hypercontractivity conjecture. The project also studies discrete additive inequalities, including optimal sumset growth and reverse sharp Young convolution inequalities, and develops a multiversion Hausdorff-Young theory for correlated Gaussian and discrete inputs. Across these themes, the investigator uses heat-flow and Ornstein-Uhlenbeck semigroups, interpolation, invariance and decoupling methods, Bellman-function ideas, and sharp two-point inequalities. Expected outcomes include new constants and exponents, progress on long-standing conjectures, and transferable tools linking harmonic analysis, probability, Banach-space methods, additive combinatorics, learning theory, and quantum computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Modern society increasingly relies on technologies that use waves to see inside complex environments that cannot be observed directly. Medical imaging, seismic exploration, remote sensing, and astronomical imaging all depend on interpreting how waves propagate through complicated materials in order to detect hidden structures or abnormalities. This project will develop new mathematical and artificial intelligence (AI) methodologies that will make such imaging technologies more accurate, reliable, and computationally efficient. A major focus will be on biomedical imaging, where the methods will help identify malignant tissue and improve early diagnosis of disease. The project will establish a rigorous mathematical foundation for emerging generative AI techniques used in imaging and uncertainty quantification, thereby improving confidence in AI-assisted scientific and medical decision making. The research will also strengthen connections between mathematics, physics, engineering, and data science, while training graduate and undergraduate students in interdisciplinary research areas of growing societal importance. The project will develop new mathematical theory for Bayesian inverse problems and generative modeling in infinite-dimensional settings arising from wave propagation in complex media. The research will analyze stochastic differential equations, their time reversal, and associated sampling dynamics in order to construct efficient algorithms that rapidly approach conditional probability distributions informed by observational data and prior information. The work will incorporate optimal annealing strategies, preconditioning methods, and generalized Lévy driving processes to improve sampling efficiency and robustness. A particular emphasis will be placed on Magnetic Resonance Elastography and related tissue imaging techniques, where dispersive elastic wave effects will be exploited to infer tissue morphology and detect pathological structures. The project will further mathematical theory for waves in random media and develop computational tools for rapid conditional sampling and generative AI more broadly. The resulting techniques will have applications extending beyond medical imaging to remote sensing, reflection seismology, and other imaging sciences. The project will also support a seminar series and a Southern California inverse problems workshop, foster collaborations across applied mathematics, biomedical engineering, and physics, and provide research opportunities for graduate and undergraduate students. The biomedical imaging advances developed in the project will also create strong opportunities for future technological translation and commercialization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

This award supports participation in the workshop "Theory and Applications of Elliptic Partial Differential Equations" held August 17-21, 2026 at University of California, Irvine. The workshop consists of three short courses given by experts in elliptic partial differential equations (PDE). Elliptic PDE play an important role in problems from physics, biology, and geometry, and the field has seen exciting advances in recent years. The overall goals of the courses are to bring graduate students up to speed in the most active areas of elliptic PDE, to promote a sense of community within the field, and to set the course for possible future research directions. At a technical level, the courses present recent advances made on regularity and stability in fluid dynamics, fully nonlinear geometric PDE, and homogenization. All of these areas have experienced spectacular developments in the past few years, some examples being classification results for steady solutions to the Euler equations in two dimensions, Bernstein type theorems for the Monge-Ampère equation with periodic right-hand side, and optimal convergence rates for periodic homogenization of Hamilton-Jacobi equations. The event provides students an opportunity to forge new research directions and to make connections with fellow students as well as established mathematicians. The participants also have an opportunity to give short talks and poster presentations on their research. This serves as training in scientific communication, and as a catalyst for networking. Products such as sets of lecture notes and lecture videos will be made accessible to the broader mathematical community. A website for the workshop is available at https://ucipde2025.github.io/2026-PDE-Summer-School-Website-Public-keep-public/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Few who have observed large groups of schooling fish have failed to be impressed by their degree of coordination. Schools depend on rapid communication and synchronized movement among individuals to improve predator avoidance, foraging success, and swimming efficiency. Despite the ecological importance of schooling, it is not fully understood how these highly coordinated behaviors evolve or how interactions among individuals generate diverse group-level patterns. Further, schooling behavior is one of the most widespread and important forms of social coordination in fishes. This project will investigate the evolution and mechanics of schooling in Neotropical tetras, a diverse group of fishes that contains a range of behaviors from weakly aggregating species to highly synchronized schoolers. By examining both real-time coordination among individuals and broader evolutionary variation across species, the project will provide new insight into how complex social systems originate, function, and persist in nature. Findings from this work may also inform fields beyond biology, including the development of coordination and communication algorithms for autonomous vehicles and drone swarms. The project will support the training of graduate students, postdoctoral researchers, and undergraduate students at the University of California, Irvine and the University of Southern California. Educational activities will include new teaching modules that integrate biomechanics, evolution, and collective behavior into biology and engineering courses, as well as a workshop on phylogenetic comparative methods for physiology research. This project will combine biomechanics, computational modeling, and comparative methods to investigate the mechanisms underlying the evolution of schooling behavior. The research has three primary objectives: (1) identify macroevolutionary trends in schooling performance across Neotropical tetras, (2) determine the individual-level behavioral rules that govern collective movement, and (3) evaluate how environmental and social factors influence schooling dynamics. High-speed video recordings collected in a custom experimental arena will be used to quantify kinematic traits describing individual and group movement patterns. Comparative analyses will integrate behavioral measurements with evolutionary relationships to test associations among schooling performance, ecology, morphology, and social structure. Species representing the range of observed schooling behaviors will be selected for additional experiments examining how environmental conditions and social interactions affect coordination dynamics. The project will also develop mathematical and computational frameworks linking individual behavioral interactions to emergent group properties and social network structure. Together, these approaches will provide a mechanistic and evolutionary understanding of collective movement in fishes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project is funded through the NSF Translation to Practice (TTP) program, which supports efforts to translate research discoveries into practical tools that benefit communities, industry, and society. Many everyday products, from plastic bottles and packaging to car parts and clothing, are made from chemicals called olefins. These important building blocks of modern industry must be separated and purified before they can be used. Today, this is primarily done using a method called distillation, which requires expensive equipment and is energy intensive. Additionally, smaller amounts of valuable olefins simply cannot be separated economically, so they are burned off as waste, resulting in over $100 million in lost value every year. The researchers are pioneering a new, low-energy method to separate these chemicals using electricity instead of heat, called "electro-swing" separation. During this TTP-T project, the research team is collaborating with a large chemical company to build and test a working prototype of this process. This technology could save the chemical industry millions of dollars, reduce waste, and lower costs for consumers of products made from olefins. This project develops an electrochemically driven reactive absorption process for separating olefins from paraffins and difficult olefin/olefin mixtures - applications where conventional distillation is either uneconomical or technically impractical due to extremely close boiling points. The core innovation is a metal-complex sorbent solution whose affinity for olefins is governed by the metal oxidation state: the metal complex selectively captures olefins via π-complexation in one oxidation state while releasing it in another. By cycling between these two states using an electrochemical flow cell, rather than through heating or pressurization, the process eliminates the sorbent decomposition and thermodynamic efficiency losses that have historically limited reactive absorption systems. The project will proceed in two phases: Phase 1 will target the selection of continuous redox cycling parameters, including current density, flow rate, and copper concentration, and characterize sorbent capture capacity, selectivity, and impurity tolerance for industrially relevant olefin mixtures including ethylene/ethane, propylene/propane, and 1-butene/isobutene. Phase 2 will integrate these conditions into a scaled prototype targeting separation of greater than 5 grams/day of product at greater than or equal to 99% purity with greater than 100 hours of stable operation. A detailed technoeconomic analysis will also be constructed. Successful completion of these milestones will establish electro-swing separation as a robust, commercially viable alternative for olefin separations where distillation is not cost-effective. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

CAREER: Precision Theory for Interpreting Neutrino Experiments Neutrinos are among the most abundant particles in the universe, yet many of their fundamental properties remain unknown. Answering basic questions—such as whether neutrinos violate fundamental symmetries or reveal new particles beyond our current knowledge—requires a new level of precision in how neutrino experiments are interpreted. Over the next decade, major U.S.-led experiments will collect unprecedented data, but fully realizing their discovery potential requires reducing theoretical uncertainties in how neutrinos are produced, propagate, and interact with matter. This CAREER project aims to remove these barriers by developing precision theoretical tools that enable neutrino measurements to be interpreted with the same rigor as the data themselves. By combining modern computational methods with first-principles modeling, the research will enable more reliable tests of fundamental symmetries, sharpen searches for new physics, and maximize the scientific return of national investments in neutrino experiments. At the same time, the PI will develop immersive virtual-reality tools for public engagement, build quantum information science training opportunities that strengthen the STEM workforce, and create undergraduate-accessible research experiences that broaden participation in fundamental physics. By integrating theory with education and outreach, this CAREER project advances both scientific discovery and STEM workforce development. The research program focuses on three interconnected challenges in precision neutrino physics. First, it develops machine-learning–based frameworks to reduce dominant theoretical uncertainties in neutrino–nucleus interaction modeling, a critical limitation for long-baseline experiments searching for leptonic charge-parity violation. Second, it establishes first-principles calculations of quantum decoherence effects in neutrino oscillations, enabling robust predictions of standard physics signals while preventing these effects from mimicking or obscuring new phenomena. Third, it constructs theoretically controlled observables for neutrinos from a future galactic supernova, with quantified uncertainties for probing neutrino properties and physics beyond the Standard Model. Together, these efforts establish a unified precision framework that connects neutrino sources, propagation, and detection. This unified approach enables precision tests of neutrino properties while strengthening the reliability of searches for physics beyond the Standard Model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Living cells receive signals from other cells and process this information to make decisions and take actions. One example is provided by immune cells processing information about other cells to decide whether to mount an immune response. Decades of mathematics have revealed how parts of this information processing system work, but the insights were limited by the mathematical technology of the time. Recent artificial intelligence and machine learning methods have powerful abilities to predict how cells respond to signals, but do not have a straightforward way to harness the insights from previous decades. This project develops a method that combines the insights from previous decades with modern machine learning. In doing so, the method achieves higher accuracy predictions, even in the face of complex signals. The method is applied to immune cells receiving complex signals of different frequencies, and complex combinations of primary signals with secondary signals (so-called accessory receptors or co-signaling receptors). The secondary signals were previously particularly challenging to understand, because multiple signals act simultaneously, creating a high number of combinations. The project will train graduate students in machine learning, immunology, and applied mathematics, and develop a course for coding practices for reliably using modern tools such as artificial intelligent coding assistants. A central goal of mathematical biology is to build quantitative, predictive models of how cells respond to signals. The need is especially acute for T cells, given their role in cell-based immunotherapies. Recent high-dimensional models fit data better but raise three concerns: computation and data needs, overfitting, and interpretability. The first two have seen progress, but the third has remained challenging. This project adopts the view that interpretability is the ability to explore, reject and use hypotheses expressible in plain language, including hypotheses from previous decades of mathematical biology research. From that perspective, more model flexibility is not better if the model can no longer reject a false hypothesis. This project develops classes of models whose flexibility is tunable to the hypothesis being tested. To do so, the project develops families of functions with adjustable flexibility for use in trainable models. This method is applied to predictively understand the response of T cells stimulated with temporal pulses at varying frequencies, a technique borrowing from classical control theory, and T cells exposed to combinatorial mixtures of accessory ligands. The mathematical novelty lies in working with intermediate-flexibility functions, which are not amenable to either gradient descent or Monte Carlo training algorithms. Flexibility is measured by a model's ability to fail to fit data, by introducing a design-specific Rademacher complexity metric. The project also extends the NSF-funded "DevOps for Mathematical Biologists" program, shifting toward widely accessible resources in the era of AI-assisted scientific computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project will establish the BRIDGE National Center to broaden access to modern data science and artificial intelligence for scientists across many research fields. Researchers in data-intensive domains, such as criminology, public health, and neurobiology, increasingly need to analyze large datasets and use advanced AI and machine learning methods. The Center will directly address these needs, serving as a vital resource for domain researchers who require additional programming expertise, computing infrastructure, or technical support. Specifically, BRIDGE will eliminate traditional barriers to entry by providing an open, supported, and shareable infrastructure that enables researchers to build, reuse, and collaborate on data-analysis workflows, datasets, and machine learning models. By lowering technical barriers and expanding access to cloud-based computing resources, the Center will empower a broader range of scientists to participate in data-driven discovery. Ultimately, the project serves the national interest by promoting the progress of science, strengthening research infrastructure, advancing education and workforce development, improving reproducibility, and enabling discoveries that benefit national health, prosperity, and welfare. The Center will build on "Apache Texera (Incubating)", an open-source, web-based workflow system that supports data science, real-time collaboration, workflow sharing, and elastic access to cloud computing resources. BRIDGE will operate and coordinate Texera-based platforms across multiple scientific communities while providing documentation, tutorials, training, office hours, and technical support. The project will continue to strengthen the usability, scalability, reproducibility, privacy, security, and efficiency of these platforms during the operations. Technical goals include developing more intuitive workflow construction and execution interfaces, domain-specific AI assistants and co-pilots with privacy and safety protections, mechanisms for protecting data and workflows, automated migration of script-based analyses into reusable workflows, and seamless use of cloud resources for large-scale and GPU-intensive computation. The resulting infrastructure will enable scientists to create, execute, share, and reproduce complex data science and AI pipelines more easily, thereby accelerating multidisciplinary research and expanding the impact of open, collaborative scientific computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Collaborative immersive systems use augmented, virtual, and mixed reality to merge physical and digital worlds, allowing multiple users to share and interact within the same three-dimensional space in real time through natural actions such as gaze, gestures, and spatial manipulation. By creating a sense of shared presence, these systems enable new forms of collaboration in critical domains such as healthcare, education, and workforce training. However, unlike web or mobile platforms where users interact independently through a screen, each user's actions in these systems are sensed and directly affect what every other user sees and experiences. This introduces security and privacy risks that are fundamentally different from those in traditional computing, as a malicious participant can manipulate what others see and experience, impersonate trusted users, or exploit the sensitive data that these systems continuously capture about users' bodies, movements, and surroundings, risks that are amplified as these systems integrate AI-driven features. Existing security approaches cannot address these risks because they do not account for the multi-user, embodied nature of immersive interaction. The project's novelties are a principled framework that formally models the multi-layered interactions unique to collaborative immersive environments and provides integrated mechanisms for trust establishment among participants, secure provenance of shared interactions, and adaptive security and privacy policy enforcement. The project's broader significance and importance are in enabling trustworthy immersive collaboration that people can safely rely on in high-stakes settings, contributing to industry-wide security standards, and cultivating a workforce skilled in securing next-generation computing platforms. The specific goals of this project are divided into three research thrusts. The first thrust constructs formal models of embodied multi-user behavior to enable the systematic discovery and analysis of emergent threats that arise from the composition of individual user actions in collaborative immersive systems. The second thrust designs novel mechanisms to establish and maintain trust among participants throughout the lifecycle of a collaborative session, addressing the challenges of verifying user identity and managing group membership in real-time embodied environments. The third thrust develops methods to track the integrity of shared interactions and enforce security policies without disrupting the user experience, ensuring that the collaborative environment remains accurate and tamper-resistant. The outcomes of this project will enhance the current security practices for immersive systems and guide the design of future real-time, multi-user computing platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Early life stress (ELS), particularly during fetal development, is a critical risk factor for long-term health, including obesity and metabolic disorders. This project investigates how prenatal stress exposure is biologically embedded, leading to increased vulnerability to abdominal adiposity and metabolic dysfunction. Our long-term goal is to eluci- date cellular and molecular pathways that mediate the developmental origins of metabolic disease, supporting early identification and prevention strategies for at-risk children. Despite known associations between ELS and adult dis- ease, current research is limited by inconsistent findings in early life, inadequate biomarkers of fetal stress exposure, and poor measurement of adiposity in infants. Traditional reliance on weight-based metrics fails to capture fat dis- tribution, which is key to metabolic risk. Moreover, stress exposure during pregnancy is typically estimated from basal circulating biomarkers, neglecting dynamic physiological stress responses. To address these gaps, we employ a translational, multi-level design integrating basic science and clinical research. Using umbilical-derived mesenchy- mal stromal cells (MSCs) from human newborns, we will model individualized cellular vulnerability to ELS. In par- allel, we will track in vivo adipose development using serial MRI assessments and metabolic profiling in infants. Our specific aims are: Aim 1: Determine if biological stress during pregnancy predicts infant adiposity, distribution, and metabolic function using state-of-the-art MR imaging at birth and 5–6 months. Aim 2: Test whether MSCs from high-stress exposed infants exhibit greater cellular vulnerability under in vitro adi- pogenic challenge conditions. Stress exposure will be comprehensively quantified using ex vivo glucocorticoid-cytokine stimulation, diurnal sali- vary cortisol sampling, and maternal blood assays during early and late pregnancy. These data will be synthesized into a composite (PCA) biological stress exposure score. We hypothesize that dynamic, functionally derived measures of maternal stress will better predict infant abdominal adiposity and metabolic function than static bi- omarkers, and that stem cells from high-stress-exposed infants will exhibit greater vulnerability—reflected by in- creased lipid accumulation and hypertrophy—especially under in vitro challenge conditions. This integrated ap- proach will illuminate mechanisms of biological embedding and identify novel markers of metabolic risk. Findings will advance precision health by enabling targeted early-life interventions. This project will also establish a scalable human newborn stem cell biobank for future studies of stress-related disease pathways.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT This project is an investigation of vascular contributions to cognitive impairment and dementia (VCID) due to cerebral microbleeds (the pathologic substrate of which is cerebral microhemorrhage) in the context of periodontitis. The proposed project will utilize a combination of novel animal models of cerebral microhemorrhage to identify mechanisms of the dementia-cerebral microbleeds link specifically related to periodontitis, an emerging risk factor for dementia and stroke, as well as hypertension, the most important treatable risk factor for cerebral microbleeds. The project investigators are a unique group with expertise in stroke neurology, vascular neurobiology, brain aging, bioengineering, brain imaging, and biostatistics, all with capabilities relevant to animal studies. We specifically propose the following aims and hypotheses: Specific Aim 1: To determine effects of periodontitis on cerebral microhemorrhages in a mouse model of hypertension and aging. Hypothesis 1A: Periodontitis enhances cerebral microhemorrhage development. Hypothesis 1B: Periodontitis alters size and number of source vessels of cerebral microhemorrhages. Hypothesis 1C: Periodontitis-enhanced cerebral microhemorrhage formation provokes cognitive decline. Hypothesis 1D: Periodontitis-enhanced cerebral microhemorrhage formation is inhibited by CXCR3 antagonists. Specific Aim 2: To determine the effects of periodontitis on microglial activation in a mouse model of hypertension and aging. Hypothesis 2A: Periodontitis enhances microglial activation. Hypothesis 2B: Microglia mediate periodontitis-induced microhemorrhage formation. This project addresses VCID from the perspective of the highly prevalent hemorrhagic microvascular disease of the brain. Our work and that of others have shown the near-ubiquitous presence of microhemorrhagic changes in the aging human brain. Our multi-disciplinary team is uniquely capable of taking this project to completion with the expectation that robust insights will emerge to assist the management of VCID and microvascular disorders of the brain which, along with Alzheimer’s disease, represent some of the most common causes of neurological morbidity of aging: cognitive decline, vascular cognitive impairment, and dementia.

NIH Research Projects · FY 2026 · 2026-06

Contact PD/PI: LEE, ABRAHAM My research focuses on the development of novel microfluidic tools for precision medicine, including cell sorting and enrichment, single cell analyses, cell-cell interactions, cell engineering, artificial cells, and vascularized micro organs. Over the years my lab has developed a versatile set of microfluidic platforms for biological applications: 1. droplet microfluidics for molecular and cellular analysis, 2. acoustic microstreaming for sample preparation, cell sorting, and rare cell enrichment, 3. dielectrophoresis (DEP) for label-free separation and enrichment of targeted cell populations, 4. microfluidic platforms for single cell analysis and vascularized organs-on-a-chip. Based on these techniques, the three major projects currently in my lab are: (1) acoustic- electric shear orbiting poration (AESOP) platform that modulates acoustic and electric fields to induce size- controlled membrane pores for non-viral intracellular delivery (NIGMS (R01GM145987-01). The AESOP device has allowed us to control dosage and sequentially deliver multiple genetic coding cargoes into cells efficiently and gently. (2) artificial antigen presenting cells (aAPCs) with conjugated targeting ligands that form cell-to-cell synapses for antigen-specific T cell activation (R33CA267258-01A1). This aAPC platform enables the de novo design of cells for various research applications with potential biomedical applications in cell therapy for cancer, autoimmune diseases, and even Alzheimer’s Disease. (3) arrayed-droplet optical projection tomography (ADOPT) platform capable of capturing the 3D morphology of single cells both of the membrane as well as the intracellular organelles. This platform is the driver for this grant and will provide rich biological content for cellular studies. For the next 5 years, we will tackle challenging biological questions and bottlenecks in the field: 1. Can we rapidly acquire a panel of 3D single cell morphology with a drop of blood? 2. How does 3D single cell morphology correlate with existing single cell analysis tools such as dropseq? 3. How does 3D morphology of cell membrane and intracellular organelles correlate to cell function and cell state? 4. How does shear force affect 3D morphology and mechanosensitive pathways? 5. Can one precisely program cells for specific cell function or cell state? 6. Can 3D single cell profiling (size, shape, morphology) of immune cells and tumor cells reveal comprehensive health status (immune health, metabolic health)? The vision of the CPU for cell-based health assessment and intervention: The bioengineering “CPU” (cell processing unit) is analogous to the CPU (central processing unit) for computer engineering. The bio-CPUs are microfluidic processors capable of cell sorting, cell engineering, and cell sensing. This MIRA grant provides a “GPU” (graphics processing unit) by producing 3D morphologies of single cells that will be powered by the bio- CPUs. This is how the AI revolution was started and how the BI (biological intelligence) revolution may lie.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Antibodies have proven to be a powerful and effective therapeutic strategy, with successful drugs targeting a variety of pathways. New antibodies can be developed by animal inoculation or by ex-vivo methods such as phage-display. The former harnesses the power of somatic hypermutation that is a natural component of the mammalian immune system, while the latter circumvents the labor and cost associated with animal experimentation. Both approaches produce antibodies characterized by strong binding to the antigen target of interest. Strongly binding antibodies must then be further screened to determine their functional activity, namely their ability to either stimulate or block activity in a specific cell signaling receptor. Importantly, these approaches do not harness the power of evolution to optimize the functional characteristics of the antibody. The goal of this project is to develop an in vitro method to perform rapid antibody evolution, where selection is performed based on the function of the antibody rather than just the strength of antigen binding. Specifically, the project will aim to develop allosteric modulators for G protein-coupled receptors (GPCR), which are the largest family of membrane proteins in humans and broadly represented across nearly all physiological and pathophysiological processes. The project will leverage an engineered system in which a yeast plasmid replicates orthogonally from the rest of the yeast genome such that the plasmid is copied with a high error rate to produce rapid evolution while the rest of the yeast genome is replicated with low error rate to maintain the health of the organism. Antibody fragment sequences will be encoded on the rapidly mutating plasmid and secreted by the yeast. The yeast will be encapsulated in microfluidic double-emulsion droplets together with mammalian GPCR reporter cells. Antibody function will thus be quantified by fluorescent reporter, and droplets containing yeast with a desired functionality can be selected via droplet sorting by conventional fluorescence-activated cell sorting. After droplet demulsification, the selected yeast can be further expanded to continue the evolutionary process.

NIH Research Projects · FY 2026 · 2026-06

PROJECT ABSTRACT We seek to refine, field test, and deploy an intelligent AI-based chatbot in small, peer-to-peer mobile support groups for smoking cessation. The chatbot will complement the human support group members by responding to posts when no human can. We expect the chatbot to improve engagement by ensuring no post goes unanswered. By developing and testing this chatbot, we hope to breathe new life into research on mobile health support groups which have been challenged by low engagement. We have already built a chatbot using a state-of-the-art open-access LLM (large language model) and set it up on a local, dedicated, secure server. The LLM does not store data on the cloud and all posts are encrypted before server storage. We have trained our chatbot on 77400 posts from our past mobile support groups to accurately comprehend the 25 most common post types. We have developed 25 response libraries for the chatbot that contain over 1k responses developed from knowledge bases, e.g., it provides support for quitting, assists with study-provided NRT, and advises on coping with cravings and stress. Our chatbot intervention is based on the Supportive Accountability Model of Mobile Health. We expect the peers in the group to provide legitimate information that is relevant, trustworthy, and expert, and to form social bonds by being caring, nonjudgmental, and timely, increasing accountability and adherence. By adding a chatbot, we hope to provide additional legitimate information and social bonding. Aim 1 is to refine the chatbot using human-centered design methods. Before putting the chatbot into groups, we want to ensure it can communicate 1:1. We will recruit 4 groups of 5 smokers (N=20), and ask each person to interact with the chatbot, using all 25 post types. They will think aloud in the presence of trained staff to identify and solve usage problems. The sessions will be taped, a survey conducted, and usability metrics assessed. Aim 2 to conduct a one-armed field trial to assess the chatbot’s feasibility and acceptability within human support groups. We will recruit smokers in 2 groups of 10 (N=20). Participants will be placed in a mobile group with the chatbot and asked to support each other in quitting for 2 weeks. We will download and analyze posts and conduct exit interviews. Aim 3 is to conduct a pilot RCT of chatbot efficacy. We will recruit 4 cohorts with 30 smokers per cohort, randomizing 15 smokers to intervention arm (support group with chatbot) and 15 to control arm (support group without chatbot). We will measure primary and secondary engagement outcomes and will have adequate power to compare intervention vs. control. We will also measure intervention-end bioconfirmed abstinence to power a larger RCT.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT This project aims to decipher the efficacy of a lifespan inulin-supplemented diet on the metabolic pathophysiology of aging and elucidate what major biochemical pathways mediate inulin’s effects. In my preliminary studies, I have discovered that lifespan inulin feeding decreases multiple aging markers. Isotope tracing revealed that inulin feeding enhances glucose clearance through both increased catabolism (oxidation via TCA cycle) and anabolism (fatty acid and amino acid synthesis). Also, inulin-fed mice exhibited enhanced oxidation of dietary fatty acids, indicating improved usage of available nutrients. Finally, inulin-fed old-aged mice showed markedly reduced arachidonic acid (ARA) levels while increasing its downstream metabolites, including anti-inflammatory prostaglandin (PG) species. Thus, I hypothesize that lifespan inulin supplementation invigorates tissue macronutrient usage and alters ARA metabolism to preserve metabolic fitness and mitigate aging-associated inflammation. Therefore, Aim 1 is to determine the effect of lifelong inulin provision on tissue glucose metabolism in old-aged mice. To decipher which organ(s) mediate inulin’s effect on improving systemic glucose homeostasis, I will perform a 13C-glucose tracing coupled with comprehensive metabolomics analysis across key organs utilizing our in-house quadrupole-orbitrap mass spectrometer (Q-Exactive Plus Orbitrap LC-MS, Thermo Fisher) in young and old-aged mice exposed to lifelong inulin supplementation. I will also employ indirect calorimetry to determine the effect of lifelong inulin supplementation on metabolic flexibility. My proposed Aim 2 is to determine whether arachidonic acid metabolism and inflammation regulation mediate inulin’s anti-aging effects. I aim to quantitatively measure the impact of inulin feeding on ARA synthesis and utilization for PG production using dual stable isotope tracing techniques. Also, I will determine the changes in systemic inflammatory cytokine profiles across the lifespan using longitudinally sampled blood samples every 2-3 months from 3 months to 24 months of age. Third, a successive nutritional intervention will be conducted with simultaneous administration of lifespan inulin and ARA supplementation to decipher how ARA metabolism and its bioactive lipid-derived metabolites influence the inulin’s anti-aging effects. Collectively, these studies will determine how lifelong inulin supplementation improves macronutrient usage for metabolic health and whether the ARA-PG axis regulating inflammation contributes to such anti-aging effect of inulin.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Valosin -containing protein (VCP) multisystem proteinopathy 1 or MSP1 is an autosomal dominant disorder associated with inclusion body myopathy, Paget's disease of the bone, frontotemporal dementia and amyotrophic lateral sclerosis (ALS). It is caused by missense mutations of the valosin containing gene, and in vitro assays of VCP mutants have shown enhanced ATPase activity, suggesting a gain of function mechanism. VCP shares common pathologies including disrupted autophagy and TDP-43 mislocalization with more common neuromuscular diseases. Using antisense oligonucleotides (ASOs) for the treatment of neuromuscular diseases is a burgeoning field with promising research. Currently there are clinical trials involving ASOs for various neuromuscular diseases with gain of function mutations in genes such as SOD1, and FUS. Major gaps: There is currently no treatment for the neuromuscular component of VCP disease which results in severe muscle weakness, and early death. The Kimonis lab has taken the lead in mechanistic and translational research in VCP disease. The rationale for this study is to decrease VCP activity using ASOs to a level commensurate with the gain of function to ameliorate disease pathology. Our ultimate goal is to develop a therapy to improve the progressive myopathy in VCP disease. Preliminary results: ASO technology has emerged as a powerful direct treatment of genetic disorders such as spinal muscular atrophy, Duchenne muscular dystrophy, and ALS. ASOs targeting VCP was designed by Ionis Pharmaceuticals Inc. and preliminary studies in the patient iPSCs derived myoblasts and humanized overexpressed VCP A232E mouse showed that ASO2 had the best safety and improvement in TDP-43 levels, the hallmark of VCP pathology. Treatment with ASO2 reduced VCP mRNA expression by ~ 48% and protein expression by ~ 40% in myoblasts generated by differentiating patient derived iPSCs. On treating the VCP A232E overexpressed mouse with ASO2, VCP mRNA level in the muscle reduced by 50% and the protein level reduced by 38%, additionally the TDP-43 and autophagy pathological markers in tissue improved. Hypothesis: We propose that early treatment with the optimum dose of ASOs in the patient iPSCs derived myoblasts and motor neurons and humanized VCPA232E mice will correct VCP pathology. Thus, we propose these two specific aims: Aim 1: Correction of the TDP and autophagy pathology using VCP ASOs in patient iPSCs derived myoblasts and motor neuron cells. Aim 2. Correction of muscle pathology and weakness in the humanized VCPA232E mice using ASOs. Success in this study will provide a novel effective treatment for VCP and other dominant diseases Robust preclinical data in patient derived myoblasts and the VCP knock-in mouse model will thus pave the way for regulatory approval for a patient trial of ASOs. Successful therapeutics in VCP disease also has huge translational potential for more common diseases with which it also shares common pathologies including disrupted autophagy and TDP-43 pathology.

NIH Research Projects · FY 2026 · 2026-05

Project Summary The goal of this collaborative research program is to develop antimalarial and antibabesial clinical candidates based on the lead compound leelamine isonitrile (GB-79), which exhibits potent activity against Plasmodium falciparum and Babesia species, the causative agents of human malaria and babesiosis, respectively. These intraerythrocytic parasites, which share key biological features and belong to the same phylum, represent major and growing threats to human health. Malaria continues to cause widespread morbidity and mortality and is increasingly difficult to treat due to drug resistance. At the same time, human babesiosis is emerging as a significant public health concern in the United States and globally, with no effective therapies and rising incidence due to expanding tick populations. Together, these challenges underscore the urgent need for new chemical classes and novel therapeutic strategies targeting both pathogens. Preliminary data from our laboratories demonstrate that GB-79 and its analogues have strong potential as dual-acting agents. Specifically: (i) they exhibit potent activity against blood stages of both drug-sensitive and drug-resistant P. falciparum strains, with IC₅₀ values in the low nanomolar range; (ii) they are similarly effective against blood stages of Babesia duncani and B. divergens, key human-infective species; (iii) their synthesis from inexpensive leelamine enables facile chemical diversification to support structure-activity relationship (SAR) studies; (iv) they show favorable therapeutic indices in preliminary safety assessments; and (v) they likely operate via mechanisms distinct from existing antiparasitic drugs, supporting their potential as a novel chemotype. Building on this foundation, we propose the following aims. In Aim 1, we will evaluate the biological activity and pharmacological properties of GB-79, its more potent congener GB-120, and other analogues already in hand. We will assess their ability to inhibit intraerythrocytic growth of both P. falciparum and Babesia species, including drug-resistant strains; to block sexual differentiation and transmission stages (in malaria); and to clear infections in mouse models of both diseases. In Aim 2, we will employ a modular synthetic platform to generate a diverse library of leelamine-derived isonitriles, optimizing for potency, pharmacokinetics, and safety. Compounds will be prioritized for in vivo evaluation based on in vitro performance and drug-like properties. In Aim 3, we will elucidate the mode of action and potential resistance mechanisms of lead compounds using a combination of chemical biology, target identification, and systems biology approaches across both parasite models. This project directly addresses critical gaps in the treatment of malaria and babesiosis by advancing a novel, dual-active class of compounds with promising pharmacological and mechanistic profiles. The outcomes are expected to deliver validated preclinical leads and new insights into therapeutic strategies for intraerythrocytic parasitic infections.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Brain metastasis is a lethal disease and major clinical unmet need for breast cancer patients. There is growing interest in immunotherapies to treat central nervous system (CNS) cancers, but greater understanding of the unique immune microenvironment of the brain is needed to develop effective treatment strategies. The brain contains a unique type of tissue resident macrophage called microglia that constitute 10-15% of brain cells and play diverse functions in CNS homeostasis and disease. In recent work, we found that microglia are central regulators of the anti-tumor T cell response and are critical to suppress brain metastasis (Evans et al., Nature Cell Biology, 2023). In this renewal proposal application, we will investigate three key gaps in knowledge raised during this work: 1) how do microglia promote the T cell response? Although microglia are principally known as phagocytes, but we observe a robust upregulation of antigen presentation machinery in response to brain metastasis. In Aim 1, we will test the hypothesis that microglia promote the T cell response primarily through local antigen presentation in the CNS and that they are main source of this function. 2) Why does disease continue to progress in many animals despite a robust immune response? In Aim 2, we will test the hypothesis that our observed accumulation of Tregs is a main mechanism for disease progression through direct suppression of microglia in the CNS. 3) How do microglia initially become activated in response to brain metastasis? We observe that they rapidly sense and home to sites of infiltrating cancer cells. In Aim 3, we will test the hypothesis that the purinergic receptor P2RY12 that mediates chemotaxis in other brain disease settings is essential for microglia activation and chemotaxis to metastasis. Each of these phases of the microglia response – their initial activation, their regulation of adaptive immunity, and their potential suppression in advanced metastasis - present new potential opportunities for therapeutic targeting in brain metastasis.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Despite major advances in antiretroviral therapy (ART) treatment and distribution, latently infected “reservoir” cells remain a major barrier to developing a cure for human immunodeficiency virus (HIV) infections. Replication- competent HIV reservoirs are rare and remain stable for decades during ART, providing opportunities for viral rebound in the absence of ART. Multiple strategies have been explored in the field to eliminate or permanently silence HIV reservoirs, including the “kick and kill” cure approach. This two-pronged approach consists of inducing HIV reactivation from latency (kick) using a latency reversing agent (LRA), enabling a subsequent “kill” through immune mechanisms or viral cytopathic effects. However, no LRAs that completely reactivate all latent HIV have been identified, and the most successful introduce potentially harmful side effects such as proinflammatory cytokine induction. Thus, there remains a need to not only identify improved LRAs that balance HIV reactivation and immune activation, but also develop a greater understanding of the pathways involved in maintaining HIV latency. In this project, we propose to improve “kick and kill” LRAs through two approaches. Previous studies demonstrate protein kinase C modulators (PKC) are a particularly potent LRA class that strongly reverse HIV from latency but simultaneously induce high levels of proinflammatory cytokines upon activating T cells. Synthesized analogs of the natural PKC modulator bryostatin-1 have also been shown to possess improved tolerability and in vivo HIV reservoir reduction, highlighting the potential of optimizing compounds for the “kick and kill” approach. Building upon this, in Aim 1, we will evaluate promising next- generation PKC modulators for in vitro and in vivo latency reversal. To explore new molecular mechanisms of HIV latency, we propose to evaluate a novel polyadenylation-driven pathway using JTE-607, a small molecule with anti-inflammatory effects. In Aim 2, we will evaluate JTE-607 as a HIV LRA in vitro and in vivo and to complement strong but inflammatory PKC modulators by downregulating proinflammatory cytokine expression. By designing and working on this project, I will contribute enhanced LRAs that advance the “kick and kill” approach towards wider, safer implementation while enhancing fundamental knowledge of HIV gene regulation. Simultaneously, when completed, this project will yield improved HIV reservoir depletion strategies, complementing other cure approaches and bringing the field closer to a definitive HIV cure.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract The long-range goal of this study is to test the novel hypothesis that alterations in meibum lipid synthesis cause an obstructive meibomian gland dysfunction (O-MGD) leading to duct dilation and a psoriasis-like epithelial inflammatory disease. This hypothesis is based on recent evidence showing that acinar meibocytes undergo a stratified differentiation process in which early differentiating meibocytes express enzymes that synthesize immature or precursor lipids (Far2/fatty alcohols), while later differentiating meibocytes (D2) express enzymes that synthesize mature meibum lipids (Awat2/wax esters). Further, alteration in the expression of these enzymes in knockout mice dramatically alter the quality of meibum leading to increased viscosity, plugging and ductal dilation, hallmarks of O-MGD, and induce a psoriasis-like, epithelial inflammatory disorder that leads to a dry eye disease. While the enzymes necessary for synthesizing meibum lipids have been identified, the molecular mechanisms controlling meibocyte differentiation and the stepwise expression of meibum lipid enzymes are unknown. Further, the consequences of abnormal meibocyte differentiation and altered meibum quality leading to a downstream psoriasis-like epithelial inflammatory response remain critical gaps in our knowledge of meibomian gland function. The objectives of this exploratory proposal are to: 1) Confirm that hMGEC organoids recapitulate acinar development and follow a sequential differentiation program from basal progenitor cells to immature (Far2) and mature (Awat2) meibocytes, 2) Identify the molecular mechanisms controlling meibocyte differentiation, 3) Determine if conditional KO of Far2 induces an obstructive MGD phenotype, and establish that altered meibum quality directly initiates a psoriasis-like meibomian gland epithelial inflammatory disease.

NSF Awards · FY 2026 · 2026-05

Enzymes catalyze chemical reactions. They can improve chemical processes, but there are major challenges to realizing their full potential. Most chemical processes involve harsh conditions. These can be high temperatures, extreme pHs, and the presence of organic solvents. This project will identify enzymes that work under harsh conditions by exploiting two characteristics of spores. Spores are highly resistant to harsh conditions, and they can be engineered to display proteins on their surfaces. The experimental strategy will be to generate a large number of mutated enzymes that are displayed on spore surfaces. The spores will then be subjected to a variety of harsh conditions mirroring those found in chemical processing facilities. The mutants that exhibit the desired activity under those conditions will be selected for characterization and additional rounds of mutations. The project will support outreach activities for local high school and community college students that will encourage them to join the biomanufacturing workforce. Combining directed evolution with enzyme immobilization is challenging. Immobilization can cause physical changes that diminish the improvements achieved through enzyme mutation. Traditional evolution methods are low-throughput and incompatible with high-throughput screening due to detrimental effects on cell viability. The overall goal will be to establish a directed evolution workflow for surface-displayed enzymes on Bacillus subtilis spore particles. Directed evolution will be integrated with enzyme immobilization on the spore surface, leveraging the chemical resilience, retained genetic information, and proliferation capability of bacterial spores. The first step will be to develop a high-throughput fluorescence-activated cell sorting (FACS) screening workflow using engineered chemical probes to directly visualize enzymatic activity. Then, a randomized enzyme library will be generated and displayed on the spore surface. This will be followed by identification and characterization of enzyme variants with enhanced performance in harsh environments. The major developments of this research are expected to be three-fold. First, the FACS screening workflow will dramatically improve throughput for directed evolution studies. Second, the spore display strategy will further increase throughput by affording the simultaneous screening for activity in harsh conditions while immobilized. Finally, characterization of mutations that confer enhanced activity under harsh conditions could lead to design rules applicable across a wide range of enzymes with industrial relevance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Wearable and point of care biosensors can deliver earlier and more personalized health information than laboratory tests. However, many sensors cannot verify that the signals they produce truly come from a targeted molecule, especially in a complex and changing biofluid. This project will develop a built in “specificity check” for sensors operating under the skin. The Enzymatic Perturbation Specificity Test (EPST) will measure a sample and then measure it again after an enzyme briefly converts or removes the target molecule. A predictable drop in signal provides evidence that the sensor response is driven by the correct target. The project will pair EPST with reusable synthetic receptor sensors that can be electrically reset between measurements. To show that the method applies across chemistry types, the work will focus on two stress related biomarkers from different molecular classes: cortisol (a steroid hormone) and neuropeptide Y (a peptide). Outcomes will include openly shared protocols, datasets, and analysis tools, plus training for students in materials, microfluidics, biochemistry, and data analysis. By improving confidence and reducing false positives, EPST could help advance reliable, affordable biosensing that supports the national interest in health, prosperity, and welfare. This EAGER project will develop an Enzymatic Perturbation Specificity Test (EPST) to self-validate biosensors based on nanoengineered molecularly imprinted polymers (NCMIPs). EPST will collect paired native and enzyme perturbed measurements in which the free target concentration is reduced by a verified conversion/depletion fraction. It will use the differential suppression to compute a Specificity Confidence Metric. The project will fabricate restorable NCMIP recognition layers and will optimize an in situ restoration cycle for stable repeated operation in buffer and interstitial fluid mimicking matrices. Then, the project will build interchangeable immobilized enzyme microreactors with composition matched ON/OFF controls and will map conversion/depletion fraction as a function of residence time using orthogonal chromatography/mass spectrometry assays. A time multiplexed single sensor architecture will be the primary testbed. Split path sensing and norepinephrine perturbation will be conditional stretch targets. EPST performance and Specificity Confidence Metric thresholds will be calibrated to a ≤5% false positive rate using designed negative controls across independent sensor batches. Deliverables will include a transferable EPST playbook with open datasets and code for reproducible specificity auditing This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

As the demand for additional compute power and memory continues to increase, the semiconductor industry is shifting towards ultra-large-scale systems capable of providing orders of magnitude greater compute-memory capacity. Applications such as high-performance compute, neural networks, and large language models, stand to benefit significantly from such systems. Some homogeneous ultra-large-scale systems exist today, however, due to yield challenges and lack of heterogeneity, the industry is clearly shifting towards chiplet-based heterogeneous integration platforms. The technology for chiplet-based systems is becoming rapidly available. However, the design aspects of such systems are yet to be addressed. One of the key challenges of ultra-large-scale systems is the communication among chiplets. To address the communication challenge, a network is required that considers the specificity of the technology, chiplets, and scale of the systems. This project stands to significantly impact the way we design computational systems, stepping away from classic architectures and enabling heterogeneous plug-and-play chiplet-based design. The following are the main thrusts of this project: 1) A communication network architecture for chiplet-based ultra-large-scale systems that includes compatible network topologies and allocation of high-bandwidth domains. 2) Routing and built-in self-test algorithms that will consider the limitations of advanced integration technologies. 3) A unified memory architecture where not only memory is shifted, as is typical in modern architectures, but rather both compute and memory can be relocated to enable efficient computation and enhanced performance. 4) Standards interfacing enabling efficient communication among heterogeneous components such as high-bandwidth memory stacks and compute chiplets. 5) Validation of the proposed topologies, methodologies, and algorithms through circuit design and simulation. 6) A comprehensive education, training, and mentoring plan integrated with the proposed research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Facial variations play a crucial role in human identity and social interactions, with craniofacial malformations being among the most prevalent congenital disorders. However, our understanding of the genetic factors governing mammalian craniofacial morphogenesis is still limited. The complexity in deciphering the genetic architecture of facial shape comes from its highly polygenic nature, as indicated by genome-wide association studies (GWAS). Many quantitative trait loci (QTLs) associated with facial shape and abnormalities are located in non-coding regions, which are thought to be cis-regulatory elements like enhancers. Enhancers can influence gene expression through alterations in sequences and/or enhancer–promoter interactions, and consequently affect phenotypic outcomes. Nevertheless, identifying the causal variants within QTLs is challenging because physically close non-causal ones can also reach statistical significance as a result of linkage disequilibrium. Understanding the impact of enhancer variants is further complicated by the fact that, in mammalian genomes, most developmental enhancers regulate more distal genes rather than the nearest ones, as shown in my preliminary data. Therefore, this proposal aims to identify and functional test non-coding elements and their variants to advance our understanding of genetic mechanisms underlying craniofacial variations and anomalies, ultimately informing targeted therapeutic interventions. For the K99 phase in this proposal, the objective is to use comparative genomics and dog breeds as a model system to uncover enhancers associated with craniofacial variations and validate their function through transgenic reporter assay (Aim 1.1). The impacts of specific variants on enhancer function and target gene expression will be tested using CRISPR/Cas9 genome editing (Aim 1.2). For the R00 phase, the research goal is to employ novel Micro-C technology to link thousands of enhancers to their target genes during craniofacial development (Aim 2.1), and to identify the variants potentially affecting transcription factor binding on enhancers (Aim 2.2). Completion of this proposal will provide a more comprehensive view of the genetic architecture underlying craniofacial morphogenesis in mammals. The research training will include: 1) Learning statistical methods for association studies and QTL mapping 2) Genetic control of craniofacial development and morphogenesis, and 3) Micro-C technology development in embryonic facial tissues. My career development plan will focus on enhancing skills critical to the proposed research, attending related courses and workshops, developing leadership and mentorship skills, and securing a faculty position. To ensure rigorous oversight of my progress, a distinguished research advisory committee has been assembled, consisting of my primary mentor, Dr. Evgeny Kvon, and co-mentor, Dr. Thomas Schilling, along with esteemed collaborators Drs. Licia Selleri, Timothy Cox and Anthony Long, which will provide me both research training and career guidance, facilitating my transition to independence.

NSF Awards · FY 2026 · 2026-05

The increased use of lithium-ion batteries has increased the risk of large-scale battery fires in storage facilities. Recently, a fire occurred at a lithium-ion battery storage facility in coastal California, damaging more than half of the facility's batteries. This fire may have released metals and fluorinated chemicals into the environment. This project will conduct rapid-response field sampling of soil, sediment and shallow groundwater to assess the presence of contaminants. The project will help to implement remediation activities to reduce effects of the fire. The project will train doctoral and undergraduate students in field environmental chemistry and advanced analytical techniques. This research will inform for future national safety policy on storage facilities for lithium-ion batteries. This research will quantify per- and polyfluoroalkyl substances, including novel lithium-battery-associated fluorinated compounds, as well as metals and metalloids in soils, sediments, estuarine waters, and shallow groundwater that may have affected by the recent lithium-ion battery storage facility fire in the Central Coast of California. The research team hypothesizes that differential per- and polyfluoroalkyl substance transport based on perfluoroalkyl chain-length will be modulated by salinity gradients, tidal dynamics, and bank filtration processes characteristic of the Elkhorn Slough coastal system. Approximately 50 primary sampling locations have been identified within 15 kilometers of the fire site to measure contaminant distributions prior to hydrologic redistribution following precipitation. Per- and polyfluoroalkyl substances will be quantified using liquid chromatography-tandem mass spectrometry standard methods (United States Environmental Protection Agency Method 1633) and nontargeted approaches. Metals and metalloids will be analyzed using inductively coupled plasma-based techniques. Spatial distributions will be mapped using geographic information systems and interpreted in relation to soil properties, groundwater gradients, and estuarine salinity regimes. This research will generate the first field-based dataset characterizing early-stage fate and transport of lithium-ion battery contaminants due to fire in a coastal setting. The research will also provide scientific evidence for exposure assessment, emergency response planning, and long-term remediation strategies. 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.