University Of Chicago

NSF Awards · FY 2025 · 2025-08

NONTECHNICAL SUMMARY This award funds research and education into the study of novel materials, where strong interactions between particles give rise to unique and complex behaviors that challenge traditional understanding. Metals are a cornerstone of condensed matter physics and materials science, with wide-ranging applications; their efficient conduction of electricity and heat arises from quantum mechanical laws that create a coordinated, collective motion among particles. However, current theories struggle to explain what happens when particles within these metals interact strongly. Such strongly interacting metals are known to exhibit certain remarkable properties, including high-temperature superconductivity. Yet, the diversity of these phases and their possible behaviors remains unexplored, largely due to a lack of suitable theoretical tools, placing them at the forefront of challenges in theoretical physics. This research will develop new theoretical tools to better understand and predict these unusual states of matter. By building on the latest advances in quantum field theory, it will provide new ways to explore how the structure of these materials affects their behavior. A key focus will be on understanding the unique patterns of correlation that can arise in metals, especially in strongly interacting phases. Some of these correlations involve quantum entanglement, a fundamental property that links particles in ways that classical physics cannot describe. Metals are among the most entangled states known, and understanding this entanglement is crucial, as it serves as a valuable resource for advancing quantum materials and quantum information science. This work will also shed light on fundamental quantum limits to how quickly these systems can reach thermal equilibrium, providing a deeper grasp of quantum mechanics. Ultimately, the insights gained may expand our understanding of the quantum world and open new avenues in the study of exotic phases of matter. The educational aspect of the project focuses on expanding access to education on modern quantum physics. Through the "TeachQuantum" program, high school teachers, especially from underserved communities in Chicago's South Side, will engage in quantum research experiences and bring these concepts to their classrooms, inspiring the next generation of scientists. Additionally, the project will revamp university-level courses to make quantum field theory more accessible to a broader audience. These efforts aim to cultivate a diverse and scientifically literate workforce. TECHNICAL SUMMARY The integration of Quantum Field Theory (QFT) into condensed matter physics has profoundly expanded our understanding of material phases and their underlying mechanisms. However, despite significant progress, critical challenges remain, particularly in the experimentally relevant context of compressible phases of matter, such as Fermi liquids and non-Fermi liquids. The combination of extreme gaplessness and strong correlation makes the dynamics of non-Fermi liquids one of the most challenging problems in condensed matter physics, and constitutes a true frontier in QFT research. This research builds on recent developments in QFT to construct innovative approaches that can handle the extreme gaplessness of Fermi surfaces. It will exploit and refine generalized symmetries and their anomalies to identify the appropriate structure that underpins the nonlinear dynamics of Fermi liquids. It will also leverage recently discovered effective field theories (EFT) for Fermi liquids to establish controlled perturbative and non-perturbative approaches to non-Fermi liquids. One key objective is to understand protected geometric observables in Fermi liquids, and extend these insights to non-Fermi liquids. The proposed research will make use of UV/IR constraints to study these phases and their dynamics, with another goal being to prove the Planckian thermalization bound, a conjectured fundamental limit on how quickly systems can thermalize. It will furthermore investigate thermalization through the lens of quantum information and effective field theory, aiming to propose novel applications of quantum technologies. The educational aspect of the project focuses on expanding access to education on modern quantum physics. Through the "TeachQuantum" program, high school teachers, especially from underserved communities in Chicago's South Side, will engage in quantum research experiences and bring these concepts to their classrooms, inspiring the next generation of scientists. Additionally, the project will revamp university-level courses to make quantum field theory more accessible to a broader audience. These efforts aim to cultivate a diverse and scientifically literate workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Long-term depression (LTD) at cerebellar parallel fiber (PF) to Purkinje cell (PC) synapses has been studied in great detail, because a) it constitutes a major form of cerebellar plasticity that may underlie various forms of motor adaptation and learning, b) it may initiate synaptic pruning and thus shaping of essential connectivity, and c) it provides the perhaps best example of supervised learning due to is dependence on co-activity of the climbing fiber (CF) input. According to the theories of Marr, Albus and Ito, the instructive CF signal in some conditions presents an error that for its correction requires a change in PF synaptic input weights. LTD has been first described in vivo in decerebrate rabbits. Most subsequent studies have been performed in slices, where whole-cell patch-clamp recordings from PCs enabled a characterization of LTD in molecular detail. The goal of this study is to assess the conditions under which LTD is evoked in the cerebellum of intact mice. The motivation to do so is twofold, one is more general, and one specific. First, we have recently determined LTD induction rules in slice, but under more realistic (physiological) recordings conditions, including the use of an accurate extracellular ionic milieu ([Ca2+]o =1.2mM; [Mg2+]o =1.0mM) and near-physiological bath temperature. These ‘updates’ changed LTD rules substantially, and we became aware that recordings in vivo might reveal further differences to the current understanding. Second, a new physiological phenomenon has been observed in vivo that we predict will have a substantial impact on LTD: ramping in the activity of granule cell / PC activity over hundreds of milliseconds up to seconds, which was described during motor planning and in learned behaviors triggered by sensory cues. As a result of prolonged calcium elevation, we predict a shift in plasticity threshold values via a ‘leaky integrator’ mechanism. We will use two-photon measurements of GCaMP7b- encoded calcium transients in PC dendrites (PC-specific promoter L7) to compare plasticity outcomes between classic PF single-pulse or burst / CF protocols and new protocols, in which prolonged PF activity ramps are paired with CF stimulation (aim 1). Here, the PF and CF inputs are electrically stimulated, and plasticity is determined by a dendritic calcium signal measure. We will pair this approach with patch-clamp recordings from PCs in slices, where we mimic ramps by PF stimulation (aim 2). This approach is important to obtain an electrophysiological LTD readout from the somatic patch electrode that complements the imaging results obtained in vivo. Prolonged PF ramps lasting ³ 1s will be ‘interrupted’ by spontaneous CF-evoked complex spikes. There is no current hypothesis of the purpose of these complex spikes, but we do hypothesize that their firing erases prior calcium build-up that results from ramping (‘windshield wiper effect’). Using both in vivo and in vitro imaging approaches, we will study how interfering complex spikes affect PF ramps and plasticity outcomes (aim 3). This study will be the first to assess cerebellar LTD in intact mice with consideration of activity ramping, a motif in temporal input structures that is typical for learned PF input patterns.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT: Loss of motor neuron (MN) function leads to debilitating diseases like amyotrophic lateral sclerosis (ALS). Victims of such diseases suffer from a lack of effective treatments in part due to our poor understanding of the molecular mechanisms that enable MNs to terminal differentiate and maintain function throughout life. The discovery of terminal selectors represented a pivotal advance in our understanding of these mechanisms. Terminal selectors are a class of transcription factors that establish and maintain neuronal terminal identity by directly activating expression of terminal identity genes encoding proteins that define the functional properties of neurons. Mutations in terminal selectors are implicated in severe neurodevelopmental disorders. Our understanding of terminal selector mechanisms remains limited by two main challenges: (1) they have only been studied using biased approaches, and (2) potential non-cell autonomous functions for terminal selectors are unknown. To overcome these two challenges, this proposal focuses on UNC-3 (Collier/Olf/Ebf), which functions as a terminal selector in C. elegans cholinergic MNs, and which is linked to a neurodevelopmental syndrome through mutations in its human ortholog, EBF3. Our recent ChIP-Seq study predicted > 3,500 genes as putative direct UNC-3 targets, only a fraction of which (2%) have been experimentally validated. To identify the transcriptional targets of UNC-3 in an unbiased manner, we performed single-cell RNA sequencing (scRNA-Seq) in adult wild- type and unc-3 mutants. This approach revealed thousands of up- and downregulated terminal identity genes in cholinergic MNs, suggesting a dual activator-repressor role for UNC-3 (Aim #1). Because our scRNA-seq strategy broadly profiled MNs in the mutant, we also uncovered massive transcriptional disruptions in GABA MNs, which do not express unc-3, suggesting that UNC-3 has non-cell autonomous functions. To uncover the mechanism through which UNC-3 affects GABA MN development non-cell autonomously, I will validate these effects in vivo and conducting a suppressor genetic screen (Aim #2). It is vital to extend insights from C. elegans to human MNs, as human UNC-3 ortholog EBF3 is linked to a neurodevelopmental syndrome. To do this, we employed human embryonic stem cell (hESC) technology. My preliminary data show that EBF1 and EBF3 are expressed in human ESC-derived MNs, but their function remains unknown. Based on our C. elegans studies, I hypothesize that EBFs controls terminal identity genes in human MNs. To test this, I will use small hairpin RNA (shRNA) to knockdown the expression of each human EBF gene in hESC-derived MNs paired with scRNA-Seq (Aim #3). Completion of this proposal will provide extensive training opportunities and valuable insights into gene regulatory mechanisms underlying MN terminal differentiation, which may aid the development of in vitro protocols to generate MNs and advance our molecular understanding of the EBF3 Syndrome and other neurodegenerative diseases.

NSF Awards · FY 2025 · 2025-08

Generative artificial intelligence (AI) has transformed information processing and has demonstrated remarkable capacities for creativity in language and imagery. However, despite these advancements, generative AI continues to struggle when it comes to reasoning about cause and effect. Causality is the ability to understand how and why things happen. For example, recognizing which factors bring about a particular outcome, as well as what might happen if conditions change. This lack of the deeper causal understanding in generative AI is an issue because it is often needed for more complex decision-making. This type of reasoning is essential for building AI systems that can generalize more reliably, predict the results of actions or changes, and offer deeper understanding of the systems they are meant to model. This project will address these shortcomings of generative AI systems by developing a new generation of AI models designed specifically to learn causal models. By integrating theoretical principles from causality with recent advances in deep learning, these models will enable robust causal reasoning and meaningful abstraction. Ultimately, this research aims to deliver generative AI systems that are interpretable, verifiable, and robust, significantly enhancing their applicability to reason across a broad range of real-world scenarios. Current generative AI models, which leverage end-to-end deep learning over large, unstructured datasets, demonstrate impressive scalability and expressivity, successfully capturing latent structures useful for various applications. However, these models typically produce representations that are difficult to interpret and reliant on spurious correlations rather than robust causal relationships. Such limitations hinder their effectiveness in mission-critical settings, where interpretable causal reasoning and reliable explanations are essential. This research will develop a new generation of models capable of both causal reasoning and abstraction through automated, data-driven learning of causal representations. This approach maintains expressivity while incorporating explicit statistical guarantees to ensure learned representations are causal, interpretable, and reproducible. Unlike existing approaches that rely heavily on manual specification, known causal graphs, or explicit supervision, our framework is built upon a theoretically rigorous method for discovering causal relationships directly from data. By clearly articulating underlying assumptions within causal graphical models and avoiding reliance on prior knowledge, the framework enables training generative models that intrinsically learn meaningful causal representations from scratch. Incorporating causal reasoning into generative models enhances their ability to make more informed decisions, benefiting a wide range of sectors by improving accuracy and effectiveness across various applications. In education, the impacts will be achieved through the integration of undergraduate and graduate students in research, fostering a deeper understanding of causal relationships and encouraging hands-on learning in cutting-edge areas of AI. 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 · 2025-08

PROJECT SUMMARY, ABSTRACT This proposal outlines a comprehensive strategy for advancing novel methodologies and innovative strategies for the chemical synthesis of intricate, bioactive alkaloid natural products derived from Veratrum and Hapalindole families, among others. The research plan is divided into three distinct projects, each providing access to unique classes of natural products. Project 1 focuses on the development of novel synthetic strategies for Veratrum alkaloids, a group of bioactive alkaloid natural products. This project has been inspired by the structural challenges posed by these alkaloids and aims to develop concise syntheses of several members. Project 2 details the further exploration of dearomative-Claisen rearrangement, which allows functionalization of the C2 position of indoles, enabling the stereocontrolled, asymmetric synthesis of 2,2- disubstituted indoline derivatives. Project 3 outlines our continued study and application of two [4+3]- cycloaddition-based routes for the synthesis of indole-fused cycloheptanes and their application to the synthesis of the complex, bioactive ambiguine natural products. The research plan is backed by compelling preliminary results and aims to maximally utilize the proposed methodology while addressing unmet synthetic challenges posed by the complex targets. The resulting chemistry is expected to be broadly beneficial to chemists involved in drug development, chemical synthesis, and natural product research. Over the next five years, we will especially focus on solving the key structural challenges presented by jervine-type alkaloids (Veratrum family) and ambiguines (Hapalindole family), providing unique solutions, and recording their synthesis using methods developed in our laboratory. The proposed projects will provide excellent training to graduate students and postdoctoral associates in chemical synthesis and problem-solving, preparing them for successful careers in the pharmaceutical industry and academia.

NSF Awards · FY 2025 · 2025-08

The detection of gravitational waves has opened an entirely new window on the universe. This award will support the analysis of the rapidly growing catalog of gravitational-wave sources—from the current catalog of 90 confirmed events to an expected 300+ by 2027—to unlock fundamental insights into black holes, neutron stars, and the expansion of the universe. By studying the population characteristics of these cosmic collisions, this research will advance our knowledge of how the most extreme objects in nature form and evolve, while simultaneously providing new ways to measure cosmic distances and test Einstein's theory of general relativity. This award directly serves the national interest by maintaining U.S. leadership in gravitational-wave science, training the next generation of scientists in cutting-edge data analysis techniques, developing innovative computational tools that benefit the broader scientific community and beyond, fostering innovation in data analysis, and establishing extensive educational outreach programs that serve communities in Chicago and nationwide. This award focuses on the power of hundreds of gravitational-wave detections to provide qualitatively and quantitatively new insights into the field of gravitational-wave science. One theme of the work is gravitational-wave population astrophysics, which focuses on building the tools and analysis pipelines to answer some of the most exciting astrophysical questions related to LIGO sources. The award delves into the existence of a putative gap between the masses of the biggest neutron stars and the smallest black holes, and further explores implications for the formation mechanisms of both classes of compact objects. The award also studies the largest component black holes in the gravitational-wave catalogs. These sources are among the most surprising gravitational-wave discoveries to date, and there is much to learn about their properties and their astrophysical implications. The award will examine the gravitational lensing of gravitational waves, an entirely new scientific avenue which is only enabled by populations of at least hundreds, if not thousands of gravitational-wave sources. Work will also focus on standard siren cosmology, a uniquely clean and powerful gravitational-wave probe. Research will explore four standard siren approaches: bright sirens, dark sirens, spectral sirens, and Love sirens, including further development of the methodologies and implementations as the data sets grow and improve. Precision measurement of cosmological distances remains a topic of intense scientific fascination, and gravitational-wave sources are poised to make important contributions to this field. This award is at the forefront of gravitational-wave science and cuts across broad swaths of physics and astronomy, including nuclear physics, general relativity, astrophysics, cosmology, statistics, and data analysis. 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 · 2025-08

The Institute for Mathematical and Statistical Innovation (IMSI) applies mathematics and statistics to important scientific problems, and works to increase engagement of mathematicians and statisticians in interdisciplinary approaches to these problems. The Institute aims to catalyze new mathematical and statistical approaches to further understanding of complex phenomena and issues of great societal interest. As part of its mission, IMSI facilitates rapid and effective dissemination of these advances to both the mathematical sciences research community and broader audiences. IMSI contributes to training a new generation of mathematicians and statisticians equipped to advance multidisciplinary research through immersion in the challenges encountered within and beyond the mathematical sciences. IMSI addresses major challenges facing society by building conduits from the core disciplines of mathematics and statistics to a broad range of disciplines and applications that need mathematical and statistical insights to understand the dynamics of data- and computation-intensive phenomena. At the same time, these interactions will help to enrich research in fundamental mathematics and statistics. During programs ranging in length from a few days to several months, IMSI researchers immerse themselves in major projects, organized around themes, and driven by challenges in areas such as artificial intelligence and reinforcement learning, digital twins for complex systems, understanding the structure and function of the human brain, modeling traffic and transportation systems, quantum computing, and the design of new materials. Institute activities will contribute to scientific and technological innovation and national prosperity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Immunoprevention has emerged as a promising approach to reducing cancer risk, with most efforts in vaccines development, or repurposing of immune checkpoint inhibitors (ICIs). However, whether dietary small molecule substances as effective immunomodulators for cancer immunoprevention is largely unknown. Emerging evidence suggests a link between the de novo substances generated from food processing and immunomodulation and a reduced risk of certain cancers. Considering the vital immunosurveillance roles of NK cell against tumorigenesis, we hypothesize that certain de novo substances generated from food processing may act as bioactive compounds (named deFPBioC) with immunomodulatory functions, exerting a direct and specific influence on NK cells and related immunity to inhibit tumorigenesis. we assembled a novel deFPBioC library and systematically examined their direct effects on NK cell-dependent cytotoxicity against cancer cell. Sumiki’s acid (SKA), generated from Millard Reaction during food processing by hearing, or microbiota biosynthesis, was identified as top candidate, which can significantly enhance the activation and cytotoxic functions of both primary human NK cells and mouse NK cells. The oral administration of SKA boosts NK- mediated anti-tumor immunity in various therapeutic mouse models, which encourages me to further determine its potential as a cancer-preventive agent in spontaneous GEMM and carcinogen-induced colon cancer models (Aim 1), and elucidate the underlying molecular mechanism (Aim 2). In addition, I aim to perform medicinal chemistry studies for newly designed molecules with improved potency, and bioengineering of gut-microbiota for SKA in situ production for more effective and sustain cancer-preventive effects (Aim 3). The scientific knowledge and technical skills gained from these studies as well as my research training plan will help me develop a unique research direction and facilitate transition into independence. I will extend my independent research by focusing on the gut-resident immunity, investigating how de novo metabolites generated from microbiota-mediated nutrient processing (named MicroNutriMetabolites, MNMs) regulate diverse gut residential immune cells and related immunity response for colon cancer immunoprevention.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT: The exocrine (e.g., acinar cell) and endocrine (e.g., islet b-cell) compartments of the pancreas have long been considered functionally distinct. However, there is now overwhelming evidence for exocrine-endocrine crosstalk in the development, physiology, and dysfunction of the pancreas. For example, pancreas size (99% of which is exocrine tissue) is significantly reduced at type 1 diabetes (T1D) onset, in islet autoantibody-positive donors without diabetes, and in first degree relatives of T1D patients elements for exocrine-specific digestive enzymes pathogenesis, but mechanisms remain unknown. In response to RFA-DK-23-007, our Team brings deep . Multiple T1D risk variants map to cis- regulatory . These findings implicate the exocrine pancreas in T1D expertise in the biology, immunology and imaging of the exocrine/endocrine pancreas to unravel the molecular crosstalk among these cellular compartments in b-cell health and immunity. Our Team recently discovered that unregulated pancreatic elastase activity from acinar cells has detrimental effects on b-cell biology and immunity. Mechanistically, we have evidence that elevated pancreatic elastase proteolytically inactivates a cell surface growth receptor on b-cells to trigger a signaling cascade that halts proliferation, increases inflammatory cytokines, and leads to cell death. Furthermore, our Team discovered a missense mutation in pancreatic elastase that increases its expression and is linked to an inherited syndrome of pancreatitis, diabetes and pancreatic ductal adenocarcinoma. In parallel, we have identified small molecules that inhibit pancreatic elastase expression or activity, increase regulatory T-cell numbers in vivo, and promote murine and human b-cell health to prevent diabetes in mice. Taken together, our preliminary data support the over-arching hypothesis that elevated pancreatic elastase activity is directly pathogenic to islet b-cells in part through impairing growth signaling to induce secretion of inflammatory cytokines, autoimmunity and cell death; and that attenuating elastase activity will promote b-cell health and protect against T1D. Building upon these results, the goals of this project are to determine the human T1D relevance of the pancreatic elastase signaling circuit by defining how it: (1) predicts T1D risk in patients, (2) triggers inflammation and autoimmunity, and (3) can be effectively targeted to prevent and/or reverse T1D. These studies will provide insights into the mechanisms responsible for pancreatic exocrine-endocrine crosstalk in T1D by understanding how pancreatic elastase impacts human b-cell health and autoimmunity, and test new pharmacologic approaches to limit elastase activity as a novel therapeutic strategy for T1D.

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT Inherited genetic variation within a highly pleiotropic locus at 5p15.33 impacts risk for >15 cancer types, >10 blood cell traits, telomere length (TL), epigenetic aging, clonal hematopoiesis, pulmonary fibrosis, uterine fibroids, and other disease-related traits. This ~150 kb region contains the TERT (telomerase reverse transcriptase) gene, which encodes the catalytic subunit of the telomerase enzyme, responsible for maintaining TL in stem cells. Epidemiological research suggests that individuals with longer TL are at increased risk for multiple types of cancer, likely due to increased proliferative capacity and the accompanying burden of replication errors in cells with longer TL. The striking pleiotropy observed for the many causal variants at 5p15.33 points to etiological connections between cancer and aging-related changes in the proliferative potential of cells. However, no attempts have been made to systematically characterize and compare the many association signals across all 5p15.33-associated traits, and we know little about the biological mechanisms at this locus. This knowledge gap is due, in part, to the fact that TERT is primarily expressed in stem and progenitor cells, making the identification of regulatory variants challenging. To elucidate the biological mechanisms at 5p15.33 and characterize relationships among TL, cellular aging, and cancer risk, we will integrate and analyze (1) large-scale genetic association data across many phenotypes, (2) multi-omic QTLs in stem/differentiating cells and other contexts and (3) somatic events across multiple tissue types. Aim 1 it to characterize the extensive pleiotropy and allelic heterogeneity at 5p15.33 by conducting co-localization analyses and fine mapping across multiple phenotypes, leveraging multi-ancestry individual-level data and summary statistics for >15 cancer types, >10 blood cell traits, TL, epigenetic clocks, clonal hematopoiesis, and additional phenotypes. Aim 2 is to identify effects of 5p15.33 variants on gene regulation using multi-omic cis- QTLs in induced pluripotent stem cells and ‘heterogeneous differentiating cultures’, leveraging internal resources. We will also determine if 5p15.33 variants influence local mutability at the TERT mutation hotspot (using TCGA tumor data) or genome-wide mutation burden (using data from cancer-free GTEx tissues). Aim 3 is to assess the roles of TL and TL-associated variants beyond 5p15.33 in risk for somatic events, biological aging, and cancer using (1) tissue-specific TL, mutation, and epigenetic data from GTEx and (2) Mendelian randomization and genetic correlation approaches applied large-scale genetic association data. Our team has extensive expertise related to cancer, telomeres, QTL mapping, stem and differentiating cell culture, and cancer GWAS and fine mapping. We routinely work with the datasets relevant to this proposal, and we contributed to their creation, including cancer and TL GWAS, TL in diverse tissue types, and multi-omic QTLs, making us exceptionally well positioned to complete our aims. The results of this project will allow us to build a stronger scientific foundation for the role of TERT and TL in cancer susceptibility, aging, and human health.

NSF Awards · FY 2025 · 2025-08

The rapid development of GPU hardware has promoted scientific supercomputing, enabling exascale data production on heterogeneous supercomputing systems. With GPU dominance in heterogeneous computing, the cyberinfrastructure of GPU-based scientific data compressors is still maturing, and several gaps need to be addressed: existing frameworks lack adaptations to many scientific data analysis requirements; there are no user-friendly interfaces and off-the-shelf solutions for GPU-based scientific data compressors; and the compressors that support non-NVIDIA GPU architectures are very limited. This project develops a user-friendly, high-performance, and portable GPU-accelerated data reduction cyberinfrastructure for all primary GPU-equipped supercomputing systems. It will mitigate data challenges on GPU-equipped supercomputing systems, improve data analysis efficiency, and eventually accelerate scientific discovery. This project will continuously contribute to the education and training of graduate students by enhancing the quality of computing-related curricula in heterogeneous scientific computing, data management, and visualization. This project builds Scientific GPU Compression Cyberinfrastructure (SGCC), a user-friendly end-to-end cyberinfrastructure of GPU-based data compression for scientific data workflows, by porting, extending, and optimizing multiple existing capabilities, including but not limited to: the cuSZ family of error-bounded lossy compressors, GPU-based lossless encoders, QCAT (a CPU-based compression quality assessment toolkit), the Kokkos ecosystem (a multi-backend performance-portability framework), LibPressio (the unified programming interface of scientific compressors), and HDF5. To create SGCC, the project combines three thrusts: (1) SGCC ensures its efficiency and effectiveness in practical scientific data analysis workflows, providing adequate support for diverse data formats and compression quality targets; (2) SGCC improves the usability of the GPU-accelerated data-reduction ecosystem by providing high-level language bindings, command line interface, and user-interface integrated with visualization functionality; and (3) SGCC enables state-of-the-art GPU-accelerated scientific data compressors on multiple heterogeneous computing platforms, such as NVIDIA, AMD, and Intel. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT While exposure to a traditional farm environment has been robustly demonstrated to reduce the risk of childhood asthma, and immune cells are key drivers of asthma pathogenesis, the mechanisms by which the farm environ- ment affects immune cells is not well characterized. This proposal focuses on children from two unique U.S. farming populations, the Amish, who have extensive early life exposure to barn animals and a low prevalence of asthma, and the Hutterites, who live on industrialized farms without early life exposure to barn animals and their microbes and have over 4-fold higher prevalence of asthma compared with the Amish. While prior studies have indicated that both innate and adaptive immune cells differ between Amish and Hutterite children, suggesting that farm exposures may perturb the immune system early in life, these were limited to focusing on specific subsets of immune cells. The current proposal seeks to leverage the natural environments of Amish and Hutterite children and to generate multi-omics datasets to characterize the immune cells in these two populations, thereby discovering novel immune cells and states in the Amish that are associated with protection from asthma. Aim 1a will define the transcriptional landscape of Amish and Hutterite peripheral immune cells using single cell RNA sequencing to annotate distinct cell populations and ask: i) What are the proportions of immune cell types thus identified? ii) Do these proportions differ in Amish and Hutterite children? iii) What genes differ between Amish and Hutterite within each annotated immune cell type? Aim 1b will complement and extend Aim 1a by measuring open chromatin using single nucleus assay for transposase-accessible chromatin sequencing in the same cells used in Aim 1a so as to delineate the regulatory chromatin underpinning gene expression and ask: i) What genomic regions demonstrate differential chromatin accessibility (DCA) in the major immune cell populations between high and low risk environments? ii) Are differentially expressed genes in Aim 1a in regions with DCA? iii) Which transcription factor binding motifs are enriched in DCA regions in high and low risk environments? Aim 2 will focus on genes at childhood-onset asthma-associated genomic loci and address: i) What cell types are enriched for expression of genes at childhood-onset asthma loci? ii) What regions of the genome and in which immune cells are there DCA regions between high and low risk environments that overlap with childhood-onset asthma loci? This contribution will determine the transcriptional and chromatin states of immune cells in two unique sets of children with different risk of asthma due to environmental exposures and will provide substantial clues about asthma risk. Insights from these studies will generate subsequent hypotheses that can then be empirically tested using functional studies of blood immune cells and will be the basis for subsequent R01 grant application(s), thereby paving a pathway from mentored to independent investigation. Achieving these goals will lead to the discovery of new mechanisms of immune regulation in asthma, ultimately leading to novel treatments for asthma.

NSF Awards · FY 2025 · 2025-08

Laura Gagliardi of the University of Chicago and Donald G. Truhlar of the University of Minnesota are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop electronic structure theories for studying strongly correlated systems relevant to energy growth and sustainability. Gagliardi and Truhlar will develop advanced methods to enhance the understanding of catalysis, photochemistry, spectroscopy, materials science, and atmospheric chemistry. Their work aims to explain phenomena observed by experimentalists and predict new chemical behaviors in these fields. The proposed research targets unprecedented levels of accuracy and applicability for quantum chemical methods. Additionally, they will continue to develop and improve open-source computational codes, making them freely available to the scientific community with extensive documentation. The collaboration between these two teams will also provide students and postdoctoral researchers with a broad range of ideas and experiences, fostering well-rounded career development. Gagliardi and Truhlar will focus on developing multiconfiguration nonclassical-energy functional theory (MC-NEFT), optimizing its functional components by creating databases for multiconfiguration systems and training the method against these datasets to improve both accuracy and scope. They will also develop automated procedures for selecting active spaces, streamlining calculations for excited states and reactivity studies while reducing reliance on human intuition. Additionally, they will enhance methods for using MC-NEFT to model excited-state dynamics in systems involved in photo-induced processes, such as light-harvesting, photocatalysis, photosensing, vision, and DNA photostability. This density-functional-based approach will enable quantitative modeling of larger systems over longer time scales, incorporating extensive ensemble averaging for greater predictive power. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Project Summary A growing scientific literature concerning the influence of olfaction on cognitive health shows that olfactory dysfunction is involved in a myriad of diseases, including depression and dementia. Smell deficits are often the first symptom of Alzheimer’s Disease and other types of dementia. Many of these diseases exhibit sex differences in incidence and/or clinical presentation along with striking time-of-day dependent changes in symptom severity. Therefore, how biological factors (sex) and environmental cues (light, time-of-day information) affect olfactory system function will inform understanding of cognitive health. Despite mandates to include sexes equally in biomedical research, few studies address sex differences in primary olfactory function relevant to non-sexual odors. Likewise, circadian function is largely ignored in olfactory research. The majority of behavioral testing occurs during the light phase, even though lab rodents are typically nocturnal, and behavior and physiology vary strikingly across the day. This proposed work will examine cognitive and physiological factors underlying sex differences in olfactory behavior and interactions between time of day and sex in odor processing in rats. Preliminary data indicate that female rats sample odors as much as 40% longer than males when they are rewarded to learn an odor discrimination task, but this sex difference is reversed when rats learn new odors without any reinforcement, suggesting that odor sampling is not simply dictated by sex, but instead the sex difference depends on what is being learned. Moreover, these sex differences in behavioral strategy fluctuated based on the time of day. Preliminary neurophysiological recordings show that females produce lower amplitude fast cortical oscillations than males when sniffing odors, and that female and male brains show different olfactory-hippocampal network configurations when performing an odor discrimination task. Here we will test the hypotheses (1) that females and males utilize distinct behavioral and neurophysiological mechanisms to solve olfactory learning tasks, and (2) that olfactory cognitive skills vary over the circadian cycle. Aim 1 examines sex differences in olfactory behavior and neurophysiology, using odor habituation and two operant associative odor discrimination tasks; these studies will identify interactions among sex, task difficulty, and the cognitive structure of the task itself. Studies will also define the role of circulating sex hormones in driving sex differences. Aim 2 interrogates how circadian time affects olfactory cognition; these studies will characterize how behavior and neurophysiology vary when rats are tested across multiple phases of the light and dark cycle. Male and female rats will be included in equal numbers in all experiments. To permit analysis of neural signals from olfactory and hippocampal regions, electrophysiology will be performed in a smaller group of rats as they learn odors and perform tasks. Sex and time-of-day currently contribute an unknown degree of variance to research in olfactory neuroscience. These innovative experiments will quantify these sources of variance and thereby enhance rigor and reproducibility in research on odor perception and olfactory neurophysiology. The outcomes will inform basic questions about how sex and time affect the brain and will serve as a foundation for future practices in olfactory neuroscience research.

NSF Awards · FY 2025 · 2025-08

Collisionless shocks are ubiquitous in space and astrophysical plasmas: they dissipate kinetic energy into heat, produce energetic particles, and generate magnetic turbulence. Shock-accelerated particles are key for the non-thermal emission (from radio to gamma-ray bands to high-energy neutrinos) observed in many astrophysical objects and events, such as novae, supernovae, pulsar wind nebulae, clusters of galaxies, and winds and jets launched by active galactic nuclei. Shock acceleration is usually attributed to the first-order Fermi mechanism, involving particle scattering around two sides of the shock to gain energy. Despite its universality in astrophysics, the conditions for operation of this process, and particularly its efficiency, is not understood from first principles, as they depend on microscopic plasma physics of the shock transition. A research collaboration between Princeton University and the University of Chicago will address this problem from first principles by performing large-scale multidimensional kinetic particle-in-cell and hybrid simulations of astrophysical collisionless shocks and studying the development of self-consistent particle acceleration. The work will integrate research and education through the involvement of graduate and undergraduate students and postdocs. The students will be trained in numerical modeling of multiscale systems; this experience will prepare them for careers in science and technology fields, where large-scale computing increasingly plays an important role. The outreach efforts will engage public interest in plasma physics and astrophysics. The research program will answer several fundamental questions about shock acceleration: 1) how the acceleration efficiency of electrons and ions depends on shock parameters and dimensionality; 2) how the internal structure of a shock changes with magnetic turbulence generated by accelerated particles; 3) what are the proton and electron spectra generated at realistic astrophysical shocks? The researchers will develop parameterizations of the results of kinetic simulations for inclusion into a semi-analytical model of shock-accelerated spectra that will bridge the scales between microscopic shock physics and macroscopic emission modeling for astrophysical sources. The results of this research are also of broad significance to space physics, where particle acceleration in heliospheric shocks is observed in-situ by spacecraft. Collisionless shock physics is also being studied through laboratory experiments with intense lasers, and the findings of this research program will be relevant to the interpretation of shock experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

Abstract Despite therapeutic advances, multiple myeloma (MM) remains an incurable malignancy with heterogeneous outcomes. While molecular features like cytogenetic abnormalities and minimal residual disease status provide important prognostic information, these tests are often unavailable or delayed. In contrast, bone marrow histology slides are routinely collected and contain rich visual information about disease biology. Deep learning models have shown promise in analyzing histology across many cancers, but their application to hematologic malignancies remains limited. Recent advances in foundation models - deep learning systems pre-trained on vast histology datasets - offer potential solutions, but have been developed primarily for solid tumors. We hypothesize that bone marrow histology contains morphological features predictive of molecular characteristics, and that deep learning models can be refined to extract this information. First, we will evaluate and optimize contemporary foundation models for hematologic histology using a dataset of of over 2000 bone marrow specimens with matched molecular and clinical annotations. Second, we will apply deep learning models to identify morphological features in myeloma (using over 200 multiple myeloma cases) that correlate with established prognostic markers, including cytogenetic abnormalities and minimal residual disease status. Through novel explainability methods, we will characterize these morphological patterns to provide biological insights. This work will develop interpretable artificial intelligence tools for hematology while advancing our understanding of myeloma biology.

NIH Research Projects · FY 2025 · 2025-07

Summary Donor-specific transplantation tolerance is an important goal for improving the quality of life of transplant patients, as it eliminates side-effects from chronic immunosuppression. Transient regimens that use a blocking CD154 antibody have been shown to promote donor-specific transplantation tolerance in mice and non-human primates. Although initial clinical trials testing CD154 had been halted due to thromboembolic complications in humans, new humanized Fc-mutated antibodies avoid FcR cross-linking and platelet aggregation and are safe. Three such antibodies are in clinical trials, frexalimab (Sanofi), tegoprubart (AT-1501, Eledon Pharmaceuticals), and TNX-1500, with both TNX-1500 and AT-1501 being slated to start phase 2 trials in kidney transplantation. As such, there is considerable renewed interest in mechanistic experiments to fully understand the consequences of CD154 therapy. We and others have shown that transient administration of CD154, when in combination with injection of donor splenocytes (DST) as a source of systemic alloantigen, results in long- term donor-specific transplantation tolerance of fully mismatched cardiac allografts in mice. This treatment expands Tregs, prevents germinal center formation and therefore development of donor-specific antibodies (DSA), and results in a hypofunctional state of alloreactive conventional T cells (Tconvs) akin to anergy. We have shown that acquisition of this T cell anergic state requires both blockade of CD154/CD40 interactions and persistent expression of the cognate alloantigen in the graft. Our preliminary results also show that CD154+DST-mediated graft acceptance and expansion of Tregs both depend on the presence of classical dendritic cells 1 (cDC1), as they fail to occur in IRF832 mice that lack cDC1s. Although we initially thought that Tregs would function as suppressor cells in this model (reducing the activation of functional Tconvs), our preliminary results reveal an exciting new property of Tregs: their ability to program and maintain anergy in alloreactive Tconvs. Indeed, CD154+DST fails to induce alloreactive Tconv anergy in mice lacking Tregs, and Treg depletion long after establishment of tolerance reinvigorates Tconvs, along with de novo DSA development. We hypothesize that the crosstalk between Tregs, cDC1s and Tconvs during the induction of transplantation tolerance is essential to program alloreactive Tconvs into an anergic state, as well as to maintain this state when established. This hypothesis will be tested in the context of the following Specific Aims. Specific Aim 1. To elucidate the mechanisms by which Tregs program alloreactive T cell anergy during the induction phase of transplantation tolerance. Specific Aim 2. To establish the mechanisms by which elimination of Tregs at the maintenance phase of tolerance triggers alloreactive T cell reinvigoration. This project will define a new property of Tregs (driving Tconv anergy) and establish the role of cDC subsets in this function at the initiation and maintenance of tolerance. This has implications beyond transplantation, in autoimmunity and cancer, for how to manipulate Tconv function.

NIH Research Projects · FY 2025 · 2025-07

Summary Over the past several decades, there has been a dramatic increase in incidence of human papillomavirus associated (HPV+) oropharyngeal squamous cell carcinoma (OPSCC), which is now the most common head and neck cancer in the US. As treatment of HPV+ OPSCC with chemoradiotherapy is associated with substantial treatment-related toxicity, the current clinical focus has turned to immunotherapy with antibodies (e.g. nivolumab) targeting PD-1, a T cell inhibitory receptor that functions as immune checkpoint. However, our recently completed clinical trial evaluating the addition of nivolumab to chemotherapy failed to demonstrate a significant improvement over chemotherapy alone, posing an urgent need for new therapeutic strategies for patients with HPV+ OPSCC. As checkpoint inhibitors are non-specific immunotherapies and do not leverage the tumor antigen specificity represented by HPV, further improvement in immunotherapy is contingent upon the development of new immunotherapeutic strategies specifically targeting HPV-positive OPSCC. Generation and maintenance of the HPV+ malignancy requires consistent expression of viral oncoproteins (E6 and E7). Therefore, they represent attractive targets for the development of HPV-specific immune activators. The HB-200 is a novel immunotherapy platform that contains two attenuated, replicating viral vectors based on either lymphocytic choriomeningitis virus (HB-201) or Pichinde virus (HB-202), that express the same non- oncogenic but highly immunogenic HPV E7E6 fusion protein, and infect antigen presenting cells to induce HPV- specific T cell responses. Preliminary data from our in vivo and ongoing clinical studies indicate that HB-200 viral therapy may induce stronger anti-tumor responses than immune checkpoint blockade with chemotherapy, suggesting its significant therapeutic potential for patients with HPV+ OPSCC. However, a subset of patients fail to develop deep responses to viral therapy, and the mechanisms underlying either a robust anti-tumor immune response or resistance remain unknown. In this proposal, we will use primary tumor biopsies and plasma samples collected from patients treated with HB-200, coupled with sophisticated preclinical mouse models and innovative molecular approaches to delineate the immunogenetic mechanisms that dictate patients’ clinical responses to this novel form of immunotherapy. This proposal can provide vital insights and build solid foundation for further optimizing this new form of immunotherapy, and has the potential to result a significant clinical advancement in immunotherapy for patients with not only HPV+ OPSCC, but also unveil a new path for treating other viral-associated malignancies.

NIH Research Projects · FY 2025 · 2025-07

ABSTRACT Many human autoimmune diseases are associated with the expansion of self-reactive B cells and the elevated production of autoantibodies targeting self-constituents, which can contribute to the initiation, propagation, and exacerbation of disease pathogenesis. Despite the existence of multiple layers of B cell tolerance, including clonal deletion, B cell receptor (BCR) editing, and adoption of dysfunctional states, a measurable fraction of B cells in healthy individuals express self-reactive BCRs and can participate in germinal center reactions, suggesting the need for additional layers of extrinsic control. In this proposal, we examine the hypothesis that a subset of Foxp3-expressing regulatory T (Treg) cells, which are required throughout life for the prevention of autoimmunity and the maintenance of immune homeostasis, are specialized to form direct liaisons with B cells and control the differentiation of self-reactive B cells and the production of autoantibodies. This notion is suggested by an expanding body of compelling indirect evidence, and directly supported by new foundational evidence from our lab. First, we used single cell RNA sequencing to identify two clonally related clusters of Treg cells that are significantly underrepresented in mice lacking MHC-II expression by B cells. This identified unique transcriptional signatures expressed by these B cell dependent Tregs, and identified T cell receptors expressed by these cells. Second, we conducted a TCR profiling screen to define the extent to which the clonal composition of the Treg cell repertoire is dependent on the B cell compartment, and identified numerous B cell dependent Treg cell clones that are lost in mice with restricted B cell diversity. The objectives of this proposal are to identify BCD Tregs at the clonal and polyclonal level, define their developmental requirements and peripheral functions, and identify natural self-peptide ligands recognized by these cells. We will test the central hypothesis that select Treg cell clones differentiate in the thymus following recognition of self-ligands displayed by B cells and are specialized to regulate peripheral self-specific B cells at steady state and during an immune response. In Aim 1, we will define the pathways governing the development and peripheral homeostasis of B cell dependent Treg cell clones. In Aim 2, we will determine the functional role of B cell dependent Treg cells in regulating B cell responses at steady state and following an immune response. In Aim 3, we will identify self-peptide ligands recognized by B cell dependent Treg cells. Overall, our work is expected to define direct bidirectional interactions between Treg cells and the B cell compartment, illuminating a regulatory network that is critical for maintaining B cell tolerance.

NSF Awards · FY 2025 · 2025-07

The development of three-dimensional heterogeneously integrated circuits (3D HI) marks a significant advancement in semiconductor technology, offering greater efficiency, performance, and compactness for applications ranging from consumer electronics to national defense. By enabling the vertical stacking of diverse chip components into tightly integrated systems, 3D HI unlocks new possibilities for systems built with diverse and advanced functionalities in cost efficient packages. However, despite its transformative potential, the widespread adoption of 3D HI remains limited due to challenges in research capacity and implementation, which requires significant financial investment that is typically confined to large-scale foundries. This makes it inaccessible to many academic institutions and companies. The current landscape of 3D HI mirrors that of two-dimensional (2D) Very Large Scale Integration (VLSI) in its early days, when a lack of access, standards, and trained designers posed major barriers. To unlock the full potential of 3D HI and accelerate its integration into mainstream semiconductor design, there is a pressing need to democratize access, establish standard design practices, broaden utilization across sectors, and train a skilled workforce. This project establishes the ACE-3D Chip Design Hub as a national resource to address these gaps and advance U.S. innovation and economic competitiveness in 3D HI chip design. The ACE-3D Hub will focus on advanced design challenges unique to 3D integration, such as multi-layer architectures, thermal management, and bonding technologies. The Hub will develop and support an ecosystem that enables 3D-HI chip design and advanced packaging by: (1) providing training and education for students from high school to advanced degree levels; (2) creating a user-friendly Integrated Circuit (IC) Design Ecosystem for 3D HI with pathways enabled for advanced fabrication and packaging multi-project wafers (MPWs), and (3) providing access to expert support, community involvement, and infrastructure to members with open-source methodologies, test infrastructure, and multi-level training opportunities through workshops and intensive courses in collaboration with national labs. A core impact of this Hub will be through an expert-guided ecosystem that supports users at every stage of the design process. The Hub will provide coordinated access to experienced chip designers, open-source reference methodologies, and a curated library of instructional materials (labs, tutorials, and design exercises) that are tailored to 3D HI concepts and shared through the NSF Chip Design Hub program, advancing next-generation semiconductor design and creating a pipeline of U.S. talent equipped to address future challenges in computing, communications, and sensing innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

As artificial intelligence (AI) continues to proliferate rapidly through society, there is a growing interest in incorporating core statistical principles, such as uncertainty quantification, into predictive systems to enable reliable and trustworthy AI decision-making. This research program aims to bridge the current conceptual gap between statistics and AI by ensuring that machine-assisted predictions are statistically valid, thereby supporting their safe and effective application to complex scientific challenges in data-rich domains such as imaging, personalized medicine, business analytics, marketing, and economics. The research agenda is organized around two overarching objectives, unified by a common thread: generative models in which data are viewed as stochastic outputs of computer programs. Conducting predictive inference in these models poses significant challenges due to their inherently opaque, black-box structure. The first objective is to develop a novel Bayesian inferential framework for generative models, leveraging modern machine learning tools such as deep learning and Bayesian Additive Regression Trees (BART). This work will lay the methodological and theoretical foundations for a new class of “generative Bayes” techniques that enable statistically principled inference in complex generative systems. The second objective focuses on advancing practical methodology for computerized adaptive testing (CAT), aimed at enhancing computer-human interactions through dynamically tailored questioning that adapts in real time to the respondent’s skill level with applications. The overarching goal of this research is to integrate modern machine learning tools into statistical modeling while establishing rigorous theoretical foundations that justify their practical use. The first project will develop a novel generative Bayesian framework for quantile-based learning using Bayesian Additive Regression Trees (BART). The outcome will be a flexible generative toolkit capable of simulating from a wide range of conditional distributions–core components for addressing numerous inferential tasks, including prediction. This work will chart a new path for nonparametric modeling of conditional distributions (such as posterior and posterior predictive distributions) via quantile learning under minimal assumptions. The methodological advances will be supported by a comprehensive frequentist-Bayesian theoretical analysis to assess the fidelity of distributional reconstructions. The second project will provide new theoretical insights into widely used sparsity-inducing priors, such as the horseshoe prior, by evaluating their performance from a predictive perspective. These contributions will deepen our understanding of the predictive properties of sparse Bayesian models and further enhance their applicability. The third project aims to bring machine learning techniques, particularly Q-learning, into the realm of computerized adaptive testing, thereby extending classical item response theory models to enable more responsive and individualized assessment tools. Together, these three projects will significantly advance the frontiers of nonparametric Bayesian methodology, theory, and applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Number theory is the branch of mathematics that studies phenomena related to properties of numbers, especially the relationship between integers (whole numbers) and interesting irrational numbers (such as the square root of two, which was the first irrational number to be discovered). Surprisingly, number theoretic phenomena are sometimes governed by functions arising from a different area of mathematics, known as harmonic analysis.  This is the field that studies waves and related periodic phenomena. It turns out that problems in number theory sometimes admit solutions controlled by functions known as automorphic forms, which are periodic functions, but whose group of periods is generally non-commutative. The study of this relationship between number theory and harmonic analysis is known as the "Langlands program." The Principal Investigator will study and further develop a particular aspect of the Langlands program, the so-called p-adic Langlands program. Here p stands for a prime number, and p-adic indicates that one focuses on divisibility properties with respect to this prime. The p-adic Langlands program combines the study of harmonic analysis with p-adic methods involving algebra and geometry, with the aim of making fundamental new progress in number theory. This project will also provide research training opportunities for graduate students. The goal of the project is to investigate the p-adic aspects of the Langlands program from a range of different viewpoints.  A major focus is the construction of a categorical p-adic local Langlands correspondence in contexts where it is not currently known to exist, by combining methods from representation theory, the geometry of moduli stacks, and the theory of differential graded algebras. The p-adic local Langlands correspondence has proved to be one of the most powerful tools available for establishing relationships between questions in algebraic number theory and the theory of automorphic forms, and so extending its known range of validity is one of the fundamental problems in number theory. Other aspects of the project include the p-adic interpolation of the trace formula, the study of prismatic cohomology of Hilbert modular varieties, and the study of global-to-local restriction maps between moduli stacks of Langlands parameters.  A common element in all these investigations is the combining of algebraic, geometric, and representation-theoretic techniques, and the project also serves to develop new methods in, and syntheses of, these areas of mathematics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project explores why some systems behave predictably while others react chaotically to even the slightest nudge — a question with consequences for everything from weather forecasting to data security. By uncovering the mathematical rules that govern the coexistence of order and chaos, the work strengthens the nation’s scientific knowledge base and supports future technological innovation. Findings will be shared widely through public lectures, podcasts, and a forthcoming graduate-level book, “Dynamics, Rigidity, and Geometry,” making cutting-edge ideas accessible to students, educators, and lifelong learners. The research tackles three intertwined themes in smooth dynamics: 1. Stability. Identify mechanisms that keep chaotic or regular behavior intact under small perturbations. A major target is the symplectic C1 ergodic hypothesis, with new perturbation and blender techniques expected to yield density results for stably ergodic symplectomorphisms. 2. Typicality. Determine which dynamical properties occur for “most’’ systems. Work on expanding and unstable foliations aims to prove openness and density of minimal strong foliations and to establish uniqueness of associated equilibrium (u-Gibbs) measures, with consequences for their higher statistical properties. 3. Rigidity. Classify maps that possess large symmetry groups. The project’s “affine centralizer program” links the algebraic size of a diffeomorphism’s centralizer to its long-term dynamics, producing new classifications for partially hyperbolic actions on nilmanifolds and other homogeneous spaces. Methods combine C1 perturbation theory, entropy-preserving linearization, blender and super-blender constructions, higher-rank algebraic actions, and modern measure-rigidity tools. Expected outcomes include: definitive progress on the symplectic ergodic hypothesis; a deeper understanding of how geometric structures dictate ergodic and mixing behavior; and refined criteria for when chaotic dynamics are stable or typical. Results will appear in refereed journals and preprint archives and will be integrated into the PI’s book, providing the community with a unified exposition of new techniques and discoveries while training graduate students and postdoctoral scholars in a rapidly advancing field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

PART 1: NON-TECHNICAL SUMMARY Plastics are integral to our daily lives, but most are designed for a single purpose and discarded afterward. Nature offers a different approach through the concept of pluripotency. The term pluripotent is defined as “not fixed as to developmental potentialities”. Stem cells, for instance, are pluripotent because they can differentiate into various cell types based on chemical or mechanical cues. If a diverse range of materials can be accessed from a single “stem” plastic, it could revolutionize plastic sustainability. Imagine a world where all plastics are derived from a single source material. Recycling would become simpler and more cost-effective. In this research, we design and explore materials that can be tempered, (i.e., heating of a material at a given temperature below a critical point (e.g., melting point) before rapidly quenching) to allow access to a wide range of room-temperature materials properties dictated by the tempering temperature. The proposed research is a true mix of various disciplines within the polymer field, encompassing synthesis, polymer processing, and mechanical characterization. This integrated research approach aims to provide students, regardless of their level, with a comprehensive education covering these topics. Integrated high-school and undergraduate research and outreach activities are designed to broaden the participation of all students in science and engineering. Graduate students will participate in an innovative two-year training program to enhance their engagement and effective communication skills with the general public. Through this training, they will actively participate in various outreach events, such as Junior Science Cafés, the No Small Matter Molecular Engineering Fair, and programs tailored to reach students from local Chicago public schools. Notably, this approach fosters an engaging learning environment for participants while offering unique teaching opportunities for graduate researchers. PART 2: TECHNICAL SUMMARY Recent work by the PI'sgroup revealed that polymer networks formed through room-temperature dynamic thia-Michael (tM) bonds (based on benzalcyanoacetate Michael acceptors) result in mechanically robust polymer films that can be tempered. These films exhibit two thermal transitions: a glass transition temperature and a second transition temperature associated a hard phase formation of through a dynamic reaction-induced phase separation (DRIPS) process. By tempering these films between these two transitions, a wide range of room temperature material properties can be obtained, ranging from brittle and glassy to soft and extensible, simply by changing the tempering temperature. Studies indicate that the tempering temperature controls the amount of tM adduct formed, thereby determining the extent of crosslinking in the network. Building on these findings, the objective of this proposal is to establish fundamental structure/processing/morphology/property relationships in this class of pluripotent materials. The goal is to expand the range of material properties accessible, including toughness, elasticity, and more. To achieve this, the chemistry and architecture of the networks, as well as the type of dynamic tM bonds, will be varied to gain a better understanding of the parameters that influence the properties of these materials. Additionally, photo-responsive dynamic bonds will be developed by capitalizing on the inherent trans-cis isomerism of the benzalcyanoacetate-based Michael acceptor structure. This approach will enable access to a new class of dynamic materials that can be tempered using light instead of heat. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Fishes and amphibians have a system called the lateral line, which gives these aquatic animals a special sense that land animals lack. Lateral lines allow these animals to detect changes in water currents and pressure. This sense is critical for these animals to avoid predators, to find food, and—for many fishes—to school together. This study will focus on head lateral lines, which pass between other sense organs (ears, eyes, and nostrils). The sensory lateral lines are ancient, the earliest fishes already had them, but the patterns of the lines have altered over millions of years of evolution as heads have changed in shapes and capabilities. The project will investigate changes in sensory lines over the extended time periods of evolution, how these sensory elements have specialized in different living fishes, and how lateral lines are built, as the animals grow from eggs to adults. Combining these different approaches will provide insights on how the lateral line system has changed over millions of years and how it may respond to human impacts, like undersea noise. The project will include multiple outreach and education opportunities to benefit students from primary school on up. Undergraduate and graduate students will work on the project, gaining valuable training and professional development. The lateral line system of vertebrates (excluding amniotes) includes a network of mechanosensory neuromasts that spans the head and trunk. While the single trunk (posterior) lateral line is broadly conserved in terms of position on the body, cranial (anterior) lateral lines are many and varied but with taxon-specific characteristics. Cranial lateral lines offer a unique opportunity to explore the evolutionary response of a sensory system to radical body plan change and, in certain instances, to environmental change. This project integrates paleontological, embryological, and molecular genetic approaches to reconstruct the evolutionary history and to resolve the embryonic origins of vertebrate anterior lateral lines. Using microCT datasets of fossil and extant taxa, trajectories of lateral line evolution will be tracked across vertebrate phylogeny to investigate anterior lateral line remodeling at the origin of jawed vertebrates. With embryos of cartilaginous fishes (sharks and skates), we will resolve the embryonic origin of anterior lateral lines and to test for bias in their evolutionary innovation in selected clades. Advanced imaging and genetic manipulations in zebrafish will be used to test the hypothesis that neural crest cells influence development of the anterior lateral line. Throughout the project, investigators will engage with the public and students of all ages through innovative outreach initiatives in Chicago, IL and in Woods Hole, MA. The project aims to grow the STEM workforce by mentoring and training undergraduate, graduate, and postdoctoral researchers. This project is jointly funded by the BIO-IOS-Developmental Systems Cluster, the BIO-DEB-Systematics and Biodiversity Science Cluster, and the BIO- Emerging Frontiers Division. 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.