University Of Wisconsin-Madison

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT Vocal fold (VF) scarring negatively impacts both voice quality and quality of life, and effective treatments are currently lacking. VF fibroblasts (VFF) play a crucial role in VF scarring, as they are involved in wound healing and the production and remodeling of the VF lamina propria extracellular matrix in response to injury. Recent advances in understanding VFF gene expression have identified a signaling pathway that may contribute to the scar-like phenotype of differentiated VFF, specifically myofibroblasts. This research proposal aims to identify gene targets that could prevent the differentiation of VFF into myofibroblasts, thereby reducing the overproduction of fibrous proteins and excessive cell proliferation that contribute to scarring. The central hypothesis of this proposal is that inhibiting TGF-β signaling pathways will reduce VFF differentiation and proliferation, leading to a gene expression profile and cellular behavior more characteristic of undifferentiated VFF, with a concomitant reduction in fibrosis. We plan to quantify the effects of gene editing in VFF using an in vitro model and perform functional testing to address specific hypotheses. Specifically, we will target two pathways within the TGF-β signaling cascade: SMAD-dependent pathway and PI3K/AKT SMAD-independent pathway. CRISPR/Cas9 technology will be used to knock out specific genes involved in these pathways. After gene editing, we will assess the cellular response using assays for proliferation, differentiation, and collagen contraction, and perform bulk RNA sequencing to examine gene expression changes. This research is the first to apply CRISPR/Cas9 technology to edit VFF, providing genetic control over VFF differentiation in the context of scarring. The proposed study will shed light on the role of these specific genes and pathways in myofibroblast function. Given that no reliable, minimally invasive treatments for VF scarring currently exist, this proposal may reveal novel targets for further investigation and potential therapies for VF scar remediation.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Each year 375,000 men die of prostate cancer, the majority having received an initial diagnosis of high-risk disease. The combination of radiation therapy (RT) and androgen deprivation therapy (ADT) have been the standard treatment for decades, but recently a phase III trial demonstrated that the addition of newer androgen receptor signaling inhibitors (ARSIs) to the standard therapy significantly improved metastasis-free survival. However, high cost and increased grade 3 toxicities with the addition of ARSIs suggests a need for more careful patient selection to maximize treatment efficacy while minimizing therapy side effects and reducing cost. This team has previously performed key studies validating the Decipher genomic classifier and the Artera artificial intelligence (AI)-based digital pathology tools as risk stratification approaches for patients with localized prostate cancer. This resulted in the designation of Level 1 (the strongest level of evidence) supporting the use of these classifiers in the 2023 NCCN Guidelines for Prostate Cancer Management and the use of these two classifiers independent of each other in the clinic. However, it is still unknown how they compare to each other, particularly in identifying patients who would benefit from the addition of ARSI to RT and standard ADT and whether integrating genomic and pathology AI classifiers can result in improved performance compared to each individual approach. This application will address these unmet needs by: 1) Validating genomic and pathology AI as prognostic biomarkers for high-risk prostate cancer patients; 2) Validating genomic and pathology AI as predictive biomarkers for the addition of ARSIs to RT + ADT; and 3) Developing and validating integrated genomic and pathology AI prognostic biomarkers and predictors of response to the addition of ARSI to RT + ADT. With access to pretreatment tissue samples from four international phase III trials (STAMPEDE, ENZARAD, ATLAS, and PREDICT-RT) and both the Artera AI and Decipher Genomic Classifier tools, this application is uniquely positioned to develop impactful and cost-effective prognostic and predictive clinical-grade biomarkers for management of men with high-risk prostate cancer. The CLIA-compliant platforms enable rapid clinical translation of biomarkers to personalize therapy and thus reduce cost and avoid harmful side effects among patients who might not benefit from addition of ARSI, thus providing a paradigm shift in prostate cancer treatment.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Children with Down syndrome (DS) are 56 times more likely to be diagnosed with leukemia compared to children without DS. More than 85% of all children diagnosed with cancer will survive more than 5 years but experience late effects from the cancer treatment impacting quality of life. In addition, children with DS have unique physical and psychosocial implications when reintegrating into meaningful activities. Caregivers play a critical role in advocating for their child and are essential to facilitate participation. However, the limited available reintegration research has primarily focused on children without DS, neglecting the unique social and health needs for daily participation and quality of life specific to those with intellectual and developmental disabilities. As the population of childhood cancer survivors grows, there is a critical need to move beyond describing the late effects of cancer in children with DS but to also support reintegration into daily activities in the home, school, and community environments to improve long-term developmental outcomes and quality of life for both the child and family. The goal of the F99 phase of my proposal is to understand the needs of children with DS and their families once in remission from leukemia. I will use a convergent mixed methods approach to understand the relationship between late effects of cancer (cognition, behavior, and health outcomes) in children with DS and mental health of the caregivers (n=15 family dyads) during reintegration experiences. The F99 phase will provide essential insight into the needs of children with DS and families to understand what the barriers and facilitators are to reengaging in meaningful activities once in remission. For the K00 phase of my proposal, I will develop and implement, in collaboration with stakeholders, an evidence-based and family-centered intervention toolkit, to support childhood cancer survivors, tailored to children with DS. Aim 2 will use a community-engaged approach to develop an intervention toolkit to support reintegration for children with DS and their families after leukemia back into meaningful home, school, and community activities. The intervention will be piloted on 15 families to evaluate acceptability and effectiveness using goal attainment scaling to allow families to prioritize family- centered individualized goals. The fellowship development plan will provide additional training in eight areas; a) understanding of DS and cancer survivorship; b) assessments of cognition, adaptive behavior, and health outcomes; c) qualitative analysis, d) mixed methods analysis, e) stakeholder engagement and community-based participatory research, f) mentorship and professional development; g) scientific writing and dissemination, and e) responsible conduct of research. My ultimate goal is to become a tenured professor and run an independent externally funded research lab that includes family and community partners to address the unique reintegration needs among leukemia survivors in children with Down syndrome and their families.

NSF Awards · FY 2025 · 2025-08

This project aims to enhance the development and understanding of machine learning and artificial intelligence by employing techniques from applied algebraic geometry, in the context of polynomial neural networks. The analysis of polynomial neural networks has implications both for applications and timely machine learning approaches, including generative modelling, and algebraic techniques are well-adapted for providing global insight into these neural networks. These insights about polynomial neural networks can then be used to make informed a priori design choices and to improve the learning process for a given neural network. Graduate students will participate in this research, enhancing their training at the intersection of mathematics and artificial intelligence. This award deepens recent connections between algebraic geometry and machine learning made by considering neuromanifolds of polynomial neural networks--algebraic spaces consisting of functions representable by a neural network with a fixed architecture and an algebraic activation function. By leveraging classical results in algebraic geometry and number theory, this research determines algebraic invariants of these neuromanifolds such as their dimension, learning degree, and singular locus. These invariants are then understood from the view of machine learning to better understand properties of the original neural network such as expressivity, complexity of the learning process, and limitations on gradient descent based learning algorithms. 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 Asthma is the most common chronic disease among children in the United States and is influenced by both genetic and environmental factors. Compared with White children, self-identified Black and Caribbean Hispanic children are up to three times more likely to develop childhood asthma before the age of four years (“preschool-onset asthma”). Preschool-onset asthma is associated with the highest hospitalization risk, reduced lung growth, and lower lung function. There is a critical unmet need to identify the root causes of preschool-onset asthma in minoritized children. A recent study from the CREW/ECHO consortium found that PM2.5 and NO2 averaged over the first three years of life is associated with increased odds of early-onset childhood asthma, and this association is increased among Black children. Furthermore, Black and Latinx children are exposed to more pollution from every type of emission source in the US. It is unknown how increased exposure to air pollution interacts with individual genetic risk factors to promote the development of preschool-onset asthma. This career development proposal is designed to evaluate gene-by-air pollution interactions to identify one of the root causes for increased preschool asthma incidence rates among children experiencing environmental injustice due to systemic racism. For this application, I propose the following specific aims: Aim 1: To conduct a candidate gene association study for preschool-onset asthma. Aim 2: To identify single nucleotide polymorphisms (SNPs) of preschool-onset asthma genes that interact with air pollution exposure in early life to increase asthma risk. Aim 3: To identify associations between air pollution exposure in the first year of life and the expression of preschool-onset asthma genes in nasal epithelial cells. This NIH K08 proposal is supported by an expert team of transdisciplinary mentors and collaborator with collective expertise in childhood asthma risk factors, environmental epidemiology, statistical genomics, and transcriptomics analysis. This K08 award will allow the achievement of the following training objectives: (1) Develop foundational training in genomic analyses and gene-environment interactions. (2) Increase expertise in assessing air pollution as an environmental risk (3) Obtain skills in transcriptomics analyses to investigate the association between the environment and the expression of preschool-onset asthma-related genes (4) Cultivate the professional skills to become a national and international leader in childhood asthma disparities research. The findings of this proposal will provide novel insights into the molecular mechanisms of individual-level susceptibility to air pollution among children in neighborhoods with high pollution exposure and will inform personalized asthma prevention studies among children living in high-risk environments.

NSF Awards · FY 2025 · 2025-08

Cell division is a fundamentally important process and when cell division goes awry, pathologies such as cancers, impaired healing, infertility, and abnormal development occur. This project focuses on crucial cell division events at the outer part of the cell called the cell cortex. Synthetic proteins and artificial intelligence will be used to better understand these cell division events, what goes wrong in certain pathological states, and how to artificially promote cell division when it is compromised. In addition to these research outcomes, this work will help prepare our future scientific workforce by training high school and college students in synthetic biology, molecular biology, and AI assisted protein design. The cell cortex is the primary driver of cell shape changes. Such shape changes often arise due to Rho GTPases, which self-organize into cortical patterns that direct formation of corresponding patterns of the contractile machinery--actin filaments and myosin-2 (actomyosin). A prototypical example of such self-organization is provided by cytokinesis, during which Rho waves direct the formation of actomyosin waves at the equatorial cortex, thereby driving furrowing. Ect2, a Rho activator, and RGA-3/4, a Rho inactivator, drive Rho wave formation via coupled positive and negative feedback. Normally, Rho waves are focused and amplified at the equatorial cortex by the mitotic spindle, thereby driving furrowing and, ultimately, cytokinesis. However, synthetic proteins designed to mimic Ect2 and RGA-3/4 can induce formation of actomyosin waves in nondividing cells and, remarkably, these synthetic waves spontaneously self-organize into patterns that drive cell furrowing and other shape changes. In this work, the features of the synthetic proteins that lead to self-organization of the cortical contractile waves will be determined, as will the features of Ect2 and RGA-3/4 that prevent such self-organization. Thus, this work will reveal both general and specific molecular connections between self-organized cortical patterns and the regulatory proteins that generate such patterns. This collaborative US/UK project is supported by the US National Science Foundation (NSF) and the UK Biotechnology and Biological Sciences Research Council (BBSRC), where NSF funds the US investigator and BBSRC funds the partners in the UK. 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

This mixed-methods project will develop an intervention that focuses on the impact of participation of research teams from different science, technology, engineering, and mathematics (STEM) fields. It involves a comprehensive evidence-based training program that allows for dialogue among researchers at different career stages. The program aims to improve understanding of responsible research practices, provide tools for a holistic perspective on the topic, and positively influence the dynamics and culture of the research environment. Project outcomes may include: a broader recognition of the need for an interdisciplinary, holistic approach to responsible research training; development and implementation of such training in laboratory settings; and a comparison of the efficacy of training approaches across STEM fields. The project aims to develop, implement, and study an intervention to improve research environments. The intervention includes topics such as research collaboration, mentorship, and navigating off-site scholarly activities. Participating research teams will attend an in-person interactive training intended to provide education and concrete tools to improve laboratory culture. The project team will use metrics to measure relevant changes on variables such as understanding of responsible research and collaboration. The approach will involve comparing pre- and post-intervention reports. 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 rapid end of Moore’s Law and Dennard’s Scaling has driven computing systems, from smartphones to supercomputers, to embrace heterogeneous architectures for continued efficiency gains. As specialized, compute-intensive workloads such as machine learning become increasingly prominent, there is a critical need for accurate, open-source simulation tools to model and evaluate the next generation of hardware accelerators. However, the pace of innovation in accelerator architectures, particularly graphics processing units (GPUs), has outstripped the capabilities of existing public simulation frameworks, limiting the research community’s ability to explore new ideas and validate results. This project addresses these challenges by enhancing the widely used Accel-Sim simulation infrastructure, enabling detailed, validated modeling of modern and future accelerators. The proposed enhancements will empower a broad community of researchers to advance innovations in computer architecture, improve system efficiency, and support the development of emerging applications that rely on high-performance accelerators. This award will significantly extend Accel-Sim’s capabilities through three major technical thrusts. First, the project will modernize and expand Accel-Sim’s performance and energy models to support the latest GPU architectures (including NVIDIA’s Ampere, Hopper, and Blackwell), incorporating features such as transformer engines, sparse tensor cores, and support for asynchronous execution. Second, the project will broaden the diversity of accelerators and workloads modeled by Accel-Sim, adding support for GPUs from additional vendors (such as AMD) and integrating with broader system simulation frameworks. Third, the project will develop advanced workload sampling and telescopic level-of-detail modeling to enable scalable, accurate simulation of long-running, compute-heavy workloads. These enhancements will be delivered as robust, open-source tools, accompanied by extensive documentation, community outreach, and training resources to ensure broad accessibility and long-term sustainability. Collectively, these efforts will provide the research community with essential infrastructure to drive the next decade of accelerator innovation and foster a more collaborative ecosystem for computer systems research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Networks play a central role in representing complex relationships among interconnected entities across diverse scientific domains. The increasing scale and complexity of real-world networks often make analytic tasks computationally expensive or intractable. Learning low-dimensional embeddings from high-dimensional and dependent network data has emerged as a powerful strategy, distilling essential structural information into manageable, interpretable, and computationally efficient representations. These embeddings are instrumental in facilitating downstream analyses and enhancing the practical utility of complex networks. In many data-driven scientific inquiries, researchers require not only accurate embedding estimates but also rigorous uncertainty quantification to ensure reliable inference and decision-making. Furthermore, data collected across varying conditions, time periods, or modalities often lead to the prevalence of multiple heterogeneous networks, yielding pressing needs for comparative and integrative analyses. This project will address these vital challenges by developing comprehensive methodologies for the estimation, inference, and integration of network embeddings. Additionally, it will generate broad educational impacts through research training opportunities for graduate and undergraduate students, innovations in curriculum development, and public engagement through outreach activities. This project will advance the statistical foundations of embedding learning for complex and heterogeneous networks through three core objectives. First, it will develop novel methodologies with rigorous theoretical guarantees for estimating and quantifying uncertainty in network embeddings under general models with relaxed assumptions. Second, the project will design statistically principled procedures for comparing network embeddings across distinct conditions, ensuring appropriate handling of inherent variability and effective detection of structural anomalies. Third, the project will construct an innovative framework for jointly analyzing and integrating embeddings from multiple heterogeneous networks. It will leverage the shared information across related but distinct network structures to fully exploit statistical efficiency. Collectively, these contributions will deepen the theoretical understanding of network embedding learning and produce a rigorous yet flexible toolkit that bridges the gap between statistical theory and practical network analysis in real-world 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 · 2025-08

In recent years, machine learning and artificial intelligence have been widely deployed across numerous disciplines. Much of the focus of this work has been task-driven (e.g., generating text, predicting outcomes, etc.). However, there remain important follow-up concepts that are more closely aligned with human and project interests and interpretability, such as "How accurate or reliable is the result?" or "How do we interpret this result?" and "What are the next steps?" This project will address these questions using a statistical framework that assesses interpretability and stability for any so-called "black-box" approach typically used in machine learning. The outcomes of this work also lead directly to student training at an undergraduate level, so that students are familiar not only with "how" to implement methods, but also how to interpret outcomes and take next steps in the analysis. Furthermore, all the methods developed are made scalable, applying to extremely large and complex datasets by using computational heuristics known to reduce run-time and storage. The project provides research training opportunities for graduate students. This project will address this issue by focusing on 3 specific thrusts: (1) estimating feature importance for arbitrary algorithms in a reliable and scalable way; (2) performing feature selection by developing a statistical hypothesis testing framework for these variable importance estimates; and (3) providing predictive confidence for predictions in a model-agnostic manner. Thrust (1) and (2) provide interpretability measures for black-box models, while thrust (3) addresses the issue of the reliability of predictions. The unifying theme to all 3 thrusts is providing both scalability and reliability guarantees through mathematical theory, simulations, and real data examples. Through these contributions, the project will address issues of interpretability and human confidence in machine learning algorithms. 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

Cosmological observations provide a unique window into the fundamental physics of the primordial universe. This project, led by a team at the University of Wisconsin, Madison, will make use of two major upcoming experiments, the Simons Observatory for the Cosmic Microwave Background and the Rubin Observatory for the distribution of galaxies, and combine them in a novel way. The team will cross-correlate these two data sets using an innovative method that illuminates how matter moves in the universe. This measurement can uncover the fundamental physics of cosmological inflation, the earliest known epoch in the evolution of the universe. To make this analysis possible, the team will generate very fast simulations, which are required to test the data analysis pipeline to the required precision. The project also explores the use of machine learning methods to enhance the sensitivity of the proposed analysis. As part of this project, the team will provide research experiences and develop research-based curricula for high school students and undergraduate students. The goal of this project is to develop a data analysis pipeline for kinetic Sunyaev-Zeldovich (kSZ) velocity reconstruction and apply it to Simons Observatory (SO) and Vera C. Rubin data. Before SO data becomes available, the team will apply their pipeline to existing Stage-3 data from ACT and the photometric DESI Legacy Survey. KSZ velocity reconstruction has the exciting property that it can be used to infer a map of the 3-dimensional radial density distribution of the universe, which will have lower noise than the Rubin Observatory galaxy density map itself on large scales. Due to its low noise, the reconstructed density map can be used to measure primordial non-Gaussianity very precisely and will potentially set the tightest constraint to date on local non-Gaussianity. To make this measurement possible, the team will develop a novel set of very fast approximate light-cone simulations based on perturbative matter dynamics and stochastic sampling of galaxies. These simulations will be important for correcting systematic biases in CMB-galaxy cross-correlation analyses with unprecedented precision. This research award is partially funded by a generous gift from Charles Simonyi to the NSF Astronomy division. The project includes significant contributions to Vera C. Rubin Observatory’s Legacy Survey of Space and Time. 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 Rhinoviruses (RVs), underestimated in the past as simply the cause of the common cold, are increasingly appreciated as a major cause of severe, acute respiratory illnesses involving the lower respiratory tract (LRT) across all age groups. As the most frequent cause of the common cold for both children and adults, RVs set the stage for secondary bacterial infections (acute otitis media and sinusitis) responsible for serious medical, social and economic consequences. RVs are 2nd to respiratory syncytial virus (RSV) as a cause of bronchiolitis in infants and toddlers resulting in numerous Emergency Department visits and hospitalizations. They are also recognized as the most important triggers for asthma exacerbations in children and adults, as well as chronic obstructive lung disease in adults, leading to substantial morbidity and rare mortality. RVs have long been appreciated as a cause of LRT infection in immunocompromised children and adults. However, the role of RVs as an important cause of community acquired pneumonia (CAP) in either children or adults is still a matter of debate. This is in large part because of the high frequency of subclinical RV infections which complicate the interpretation when RVs are identified in persons with serious respiratory disease. The goal of this study is to determine the burden of RVs as a cause of acute, severe, respiratory illnesses leading to hospitalization. We will create a cohort of children in Madison, Wisconsin. They will be enrolled in the 2nd year of life and followed longitudinally (when well and sick) for 36 months to identify the circulating RVs and provide samples to confirm/establish a host nasal transcriptome differentiating clinical RV infections from subclinical RV infections and clinical infections caused by other respiratory viruses. Simultaneously, we will evaluate all episodes of severe, acute respiratory infection, including CAP, admitted to the American Family Children’s Hospital to determine etiology in general and the role of RVs in particular. We will be able to determine the precise proportion of cases of lower respiratory tract illness, including CAP, that are actually caused by RV, a current gap in our clinical knowledge. We hypothesize that the nasal transcriptome will provide a signature for RV in subjects with severe, acute LRT infection that will distinguish it from asymptomatic infection and that study of host transcriptomics in severe cases of RV infection will identify immune/inflammatory defects and other targets for management. This will have vitally important implications for antibiotic stewardship and development of management strategies including vaccines and other therapeutics. Given the commanding role of RV in respiratory infections (especially as a reduction of RSV is on the clinical horizon), there must be increased efforts at both prevention of and intervention for RV infections.

NIH Research Projects · FY 2025 · 2025-08

SUMMARY Triple-negative breast cancer (TNBC), which lacks expression of the estrogen receptor, progesterone receptor, and HER2, is the most aggressive breast cancer subtype with a five-year survival rate of 12% for patients with metastatic disease. Since traditional hormonal therapies are ineffective, few therapeutic options exist for TNBC. Radiopharmaceutical therapies (RPTs) have demonstrated improved survival for several types of cancer, however, there is dearth of RPTs for TNBC. Our goal is to improve outcomes for TNBC patients by developing first-in-class targeted RPTs. One attractive target for the development of RPTs is the receptor tyrosine kinase c-MET. c-MET is overexpressed in nearly two-thirds of all TNBC cases and c-MET signaling results in disease with an aggressive phenotype. Studies have also shown that radiation induces c-MET overexpression suggesting that targeting c-MET with RPTs may result in a feed-forward mechanism where the cancer cells become lethally addicted to RPT. To develop a targeted RPT, we identified a novel Variable New Antigen Receptor (VNAR) binding domain from a nurse shark immunized with the extracellular domain of c-MET. VNARs are the antigen recognition domains of shark antibodies, and at ~11kDa they are the smallest naturally occurring variable fragment. Consisting of a single-chain, VNARs have two complementarity determining regions and two additional variable regions that allow them to adopt multiple unique conformations. Because of their usual geometries, VNARs can engage cryptic epitopes inaccessible to conventional human and camelid antibodies with high affinity. Surprisingly, VNARs also have low to no immunogenicity making them readily translatable. The VNAR we discovered, 2H4, was made into a human Fc fusion protein (2H4-Fc) to increase valency and prolong the in vivo half-life. 2H4-Fc was cross-reactive with c-MET across multiple species, specifically bound c-MET-positive cells lines by flow cytometry and was internalized on receptor binding. In a PET imaging study, [89Zr[Zr-2H4-Fc localized to a TNBC xenograft resulting in high tumor uptake with an exceptionally low background in non-target tissues. The objective of this proposal is to evaluate the ability of our novel targeting vector 2H4-Fc to deliver α- and β-emitting radionuclides (225Ac and 177Lu) for therapeutic benefit in vivo. The first specific aim is to determine the dosimetry of our RPTs and evaluate normal tissue toxicity. In the second aim, we will evaluate their therapeutic efficacy in vivo using multiple TNBC models and investigate the biological effect and mechanism of action of our RPTs. In the third aim, we will conduct studies in non-human primates to determine the immunogenicity of 2H4-Fc in addition to its pharmacokinetic and pharmacodynamic properties by PET imaging. This innovative proposal aims to provide compelling evidence for the further development of c-MET-targeted RPTs for use in TNBC.

NIH Research Projects · FY 2025 · 2025-08

OVERALL PROGRAM PROJECT SUMMARY/ABSTRACT The last decade has seen a significant increase in the number of FDA approved treatments for men with metastatic castrate resistant prostate cancer (mCRPC). Despite these advances, median survival for men with mCRPC remains less than two years and cross-resistance to therapies within the same class (e.g. Enzalutamide and Abiraterone) occurs in >95% of patients. Molecular analysis of tissue biopsies has the development of phenotypic alterations indicative of neuroendocrine prostate cancer (NEPC). These observations gave rise to the concept of “Lineage Plasticity” wherein PC, under the selective pressure of ARPIs, can undergo lineage transitions to developmental cellular prostate subtypes as a driver of resistance. However, many patients retain androgen receptor positive prostate cancer (ARPC) or even the absence of either histologic subtype (Double- negative prostate cancer or DNPC). Genomic mutations in p53 and RB associate with lineage plasticity, as well as PC invasion of different metastatic niches, such as liver and bone. However, these alterations are not exclusive to NEPC and do not explain the molecular triggers of lineage transitions. New technological innovations and biologic discoveries in our respective laboratories strongly support the overarching hypothesis that early molecular events promote lineage transitions in metastatic niches that culminate in lethal tumor phenotypes and offer new opportunities for diagnostic and therapeutic intervention. To address this hypothesis, project aims are: Project 1: Lineage Addiction in Prostate Cancer: Molecular Interactions and Translational Biomarkers. This project will study the luminal CRPC state that retains androgen dependence but is resistant to ARPIs. They will evaluate how the AR transcriptional state is regulated in distinct tumor niches, with AR mutations alone or in combination with other high-risk mutations, to promote treatment resistance. They will explore new therapeutic sensitivities and treatment combinations across distinct niches and translational biomarkers in clinical studies. Project 2: Molecular Transitions Driving AR Loss in Double Negative Prostate Cancer. This project evaluates the genetic and epigenetic alterations that lead to the DNPC lineage that is independent of androgen signaling for proliferation and invasion. They will explore why the liver metastatic niche is enriched for this unique lineage and identify biomarkers that will be studied in two prospective clinical studies. Project 3. Molecular Drivers and Therapeutic Susceptibilities in Neuroendocrine Prostate Cancer. This project builds on data from the investigative team on the terminal NEPC differentiation state that drives a rapidly proliferative disease that responds only to chemotherapy. Using perturb-seq CRISPR screens for high-risk genomic mutations, they will uncover the drivers of neuroendocrine transformation and biomarkers that will be evaluated in samples from multiple randomized clinical trials.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Early detection of cancer leads to improved prognosis and relative survival. Cell-free DNA (cfDNA) has been used to detect cancer earlier in previous studies. However, without tumor tissue available, these methods rely on screening for commonly mutated loci in cancer-associated genes, such as TP53, or examining genome- wide alterations in methylation or fragmentation. These approaches have limitations. Examining single nucleotide variants in cancer-associated genes can have lower sensitivity if the tumor does not harbor a hotspot mutation. For genome-wide alterations, the shifts noted in cfDNA fragmentation or methylation are not cancer-specific and generally a result of cellular disruption. Thus, there is a need for a signature that is both specific to cancer and sensitive enough to be assayed from cfDNA. Structural variants (SVs) are a potential biomarker for early detection. SVs are the result of errors in DNA damage repair wherein there is an aberrant joining of two distant genomic loci, which results in a derivative chimeric DNA fragment with an identifiable breakpoint. Importantly, SVs occur in most cancer types and are less likely to be PCR-derived false positives compared to single nucleotide variants. However, tailoring a cfDNA assay to SVs is not without challenges. Identifying specific SVs with high confidence and precision is informatically difficult and requires high sequencing depth (>50x). Here, we hypothesize that genome-wide locus-agnostic detection and quantification of chimeric DNA molecules can be utilized as a multi-cancer biomarker for early detection. Chimeric molecules (CMs) contain the breakpoint of the SV they are derived from. This project seeks to develop laboratory and computational methods to detect CMs in plasma DNA, identify sources of technical noise that affect CM detection, and evaluate CMs as quantitative cancer biomarker (Aim 1). Furthermore, we plan to develop laboratory methods to enrich rare chimeric molecules from abundant non-chimeric DNA fragments to improve sensitivity for cancer detection and reduce associated sequencing costs (Aim 2). Together, these aims will lead to the development of an assay for screening and earlier detection across multiple cancer types. This will be accomplished in conjunction with the University of Wisconsin–Madison’s institutional support and robust academic resources. This project will provide the applicant with a comprehensive training program that includes personalized mentorship, avenues for presenting research findings, and opportunities for career development. Through these combined efforts, the applicant will evolve into an independent, proficient translational researcher who will specialize in biomarker development for diagnostic advancements.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY This proposal requests partial funding for the second, third and fourth biennial conference of the International Society for Regenerative Biology (ISRB) to occur over a five-year period, beginning with ISRB’s second meeting scheduled for August 12-15, 2025 at the University of Wisconsin-Madison. The inaugural meeting took place in Vienna September 2-6, 2023. More than 270 investigators, including principal investigators, postdoctoral fellows, graduate and undergraduate researchers attended the inaugural meeting. The ISRB was established in 2021 with an overarching goal to formalize an inclusive and integrated community of scientists that study all aspects of tissue regeneration in invertebrate and vertebrate model organisms. These include a dominant theme of basic discovery research, while also welcoming engineering and regenerative medicine applications. ISRB membership has reached >300 individuals, with >150 faculty, >60 postdoctoral fellows, and >75 graduate students with more than half of the current ISRB membership located in the United States. With continued meetings and activities, we anticipate growing our membership. The society intends to provide highly international science experiences for US (and all) trainees. In addition to organizing its own main conference, the ISRB also hosts virtual events such as webinars, while supporting and enhancing other workshops and small meetings. ISRB will convey the importance and impact of regeneration research to the greater scientific and lay communities, highlight regenerative biologists and their discoveries, and promote regenerative biology by giving awards for achievements and service, and by advocating for research and community funding. The ISRB biennial conference is the premier opportunity for researchers in the broad field of tissue regeneration across diverse species, tissues, contexts of injury, developmental stages and disease, to meet and share data and ideas. A main goal of this meeting is to encourage interdisciplinary, cross-species and cross-tissue comparisons as a key to enhancing understanding of the commonalities and divergences in regenerative capacity and of the mechanisms regulating regeneration across paradigms – toward the creation of an integrative framework. ISRB provides an interactive forum for students, postdoctoral fellows and investigators at all stages to present their work and interact with other investigators, and the society actively supports diversity and inclusion in science. In this proposal, financial support is requested to help defray conference fees for attendees, with a goal of ensuring diversity among attendees and to enable attendance of those at early career stages.

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY Despite efforts to improve medical practices, the rate of preterm birth in the United States continues to climb; more than 1 in every 10 babies are born too soon. Many do not survive and those that do are susceptible to lifelong health problems. Our inability to predict the time of delivery reflects our poor understanding of the complex physiology of pregnancy and birth in general; while it is clear that the cervix softens, shortens and dilates in preparation for birth, that microstructural changes underlie this process, and that premature changes precede preterm delivery, precise information about how and why this remodeling occurs is lacking. As such, non-invasive, in vivo methods are desperately needed to identify cervical remodeling in pregnant patients. Cervical softening is a critical parameter to the clinician because it markedly accelerates near delivery (term or preterm). Vascular changes are clearly fundamental to the remodeling of many tissues, but have never been comprehensively explored in the cervix, in part because of technical limitations (resolution and sensitivity) of previous imaging attempts. In this proposal, we leverage our expertise in statistical image reconstruction and deep learning to develop a novel microvascular ultrasound imaging method with the potential to dramatically improve the resolution and sensitivity by jointly minimizing errors in image reconstruction and the blurring effects associated to the ultrasound beam size. We use this reconstruction approach, named Statistical icrovascular Doppler, or S-Doppler, to shine light on the microvascular changes of the cervix during pregnancy. Briefly, in aim 1 we optimize S-Doppler in calibrated phantoms to maximize resolution and contrast in microvessel imaging. In aim 2, we validate in vivo the performance of the optimized technique to detect microvascular remodeling in the gravid Sprague-Dawley rat, in which in vivo and ex vivo validation with Super- Resolution Ultrasound (using microbubbles) and micro-Computed Tomography, respectively, are feasible. In aim 3, we demonstrate the translational potential of S-Doppler through a longitudinal study in pregnant Rhesus macaques and investigate the contribution of microvasculature changes to cervical softening using an assay of quantitative imaging biomarkers implemented on the same ultrasound probe. The outcome of this proposal will be a novel, high resolution reconstruction approach to ultrasound microvascular imaging without the need of microbubble-based contrast agents. Quantitative features extracted from it will add to a growing toolkit of non-invasive in vivo imaging biomarkers for defining a personalized ultrasound “fingerprint” of cervical remodeling, either at term or preterm. Ultimately, such tools will facilitate a comprehensive understanding of specific cervical microstructural changes in a particular person’s pregnancy, leading to accurate screening and tailored interventions that will help reduce the number of preterm births. 1

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT: A significant challenge for ~4,000 new cases of childhood brain tumors every year is to minimize the risk of overtreatment or undertreatment, which is responsible for the wide disparity in patient outcomes. Despite revised molecularly informed risk-stratification, prognosis for many children with brain cancer remains poor with morbid long-term sequelae associated with aggressive chemoradiation. Given the wide availability of MRI within the clinical workflow, there is an opportunity for developing reliable, complementary image-based prognostic companion diagnostic tools for pediatric brain tumors and thus provide critically needed and clinically actionable information for (i) identifying high-risk cases who are most likely to receive added benefit from adjuvant & concomitant therapy, while (ii) enabling therapy de-escalation in low/standard-risk cases. Image-based companion prognostic models using machine-learning (ML) and deep learning (DL) have shown significant promise in adult tumors, including in brain tumors. However, biological differences and considerably limited data in pediatric brain tumors poses challenges in adopting similar ML/DL pipelines for prognostic modeling. These include: (1) ensuring “quality-controlled” cohorts that account for reduced tissue contrast, noise, and resolution issues in pediatric scans; (2) lack of expert-vetted, deeply annotated pediatric brain tumor scans; and (3) paucity of radiomics descriptors (computerized extraction of sub-visual information from routine imaging) designed to capture unique tumor manifestation in pediatric brain tumors while accounting for brain development. In this U01 project, we propose to develop, validate, disseminate the first unified, community driven pediatric brain tumor image informatics (PBTI2) toolkit, which will comprise three modules: (a) CuPed, a synergistic cohort curation tool which will allow for efficiently triaging imaging scans to meet user-specified bounds on image quality, as well as intelligently account for batch effects; (b) SegPed, an interactive human-in-the-loop segmentation tool for creating deeply annotated pediatric MRIs, and (c) RaPed, a suite of specialized radiomics descriptors that account for age and location specific morphometric differences in the growing brain structure, unique to pediatric brain tumors. Leveraging our access to the Children’s Brain Tumor Network (CBTN), the validation of PBTI2 will take place within two specific use-cases: (1) creating the largest repository of expert vetted, deeply annotated pediatric brain tumor datasets of 3000+ CBTN MRI scans, and (b) building and validating an image-based companion-prognostic model for survival stratification in medulloblastoma tumors via CBTN and a completed clinical trial cohort (N>500). With integration of PBTI2 modules into NCI platforms such as IDC, 3D Slicer, FeTS, and public release of expert-vetted segmentations and radiomic features, PBTI2 will have far-reaching implications in future diagnostic/prognostic models for improving outcomes in this underserved population.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract Cancer is the leading cause of morbidity and mortality in the US and in the world. One approach to reducing the risk and burden of cancer is to use preventive agents and interventions that are effective and safe. According to the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI), this requires the systematic development of cancer preventive agents and interventions, with three critical components; i) preclinical/toxicology studies for identification of agents through its Cancer Prevention Drug Development Program (PREVENT), ii) early phase trials of identified agents and other promising interventions through the Cancer Prevention Clinical Trials Network (CP-CTNet), and iii) late phase III trials of preventive agents and interventions that have successfully passed through early phase trials in the National Community Oncology Research Program (NCORP), in a three-legged approach. As the second leg of this three-legged approach, CP-CTNet’s overall goal is to efficiently design and conduct early phase clinical trials to assess the safety, tolerability, and cancer preventive potential of a variety of different agents or interventions. Emphasis is on novel agents and interventions that target relevant pathways important in carcinogenesis, to characterize the effects of these agents and interventions on their molecular targets, immune function, and other biological events associated with cancer development (e.g., cell proliferation, apoptosis, growth factor expression, oncogene expression, etc.) and correlate these effects with clinical endpoints, to develop further scientific insights into the mechanism of cancer prevention by the agent or strategy examined and to continue to develop novel potential markers as determinants of response and to facilitate development and conduct of cross-network trials and to speed up preventive agent development. CP-CTNet Sites will perform these early phase trials supported by DCP and the CP-CTNet Data Management, Auditing, and Statistical Center (DMASC). These trials include phase 0 (micro-dosing), phase I (dose-finding), and phase II (preliminary ef f icacy) clinical trials. To support these early phase trials, which will be conducted by CP-CTNet Sites alone or as cross-network trials, CP-CTNet DMASC will coordinate cross-network activities and provide expertise and resources in 1) centralized data management and reporting, 2) clinical trials auditing, 3) statistical support, and 4) administrative and logistical coordination, across CP-CTNet. In addition, CP-CTNet DMASC will provide an advisory role in early phase cancer prevention trial development for all CP-CTNet trials and assume the primary statistical role for supporting cross-network trials.

NIH Research Projects · FY 2025 · 2025-08

Intraneuronal inclusions of aggregated Synuclein (Syn), termed Lewy bodies (LBs), are a pathological hallmark of Parkinson’s disease (PD), PD dementia (PDD) and LB dementia (LBD). The amygdala is a preferential site for LB accumulation, suggesting that it is either highly vulnerable to the development of synucleinopathy or a primary area of αSyn ‘seeding’. Dysfunction of the amygdala has been linked to anxiety and depression, but little is known about its involvement in PD, PDD and LBD. Based de on the identification of anxiety and depression as a prodromal sign of PD, PDD and LBD, the heavy LB burden in the amygdala of these patients, and the multiple connections of the amygdala with critical brain areas affected by these disorders, the goals of this proposal are to establish a NHP model of αSyn pathology and test the primary hypothesis that αSyn pathology in the amygdala affects the regulation of anxiety and mood by altering amygdala neuronal signaling to its monosynaptic connected neurons of the ventral striatum and prefrontal cortex (e.g. Area 25, posterior OFC). To test this hypothesis, we propose three specific aims: 1) Assess time-dependent changes in anxiety- and depression-related behaviors and their associated neural correlates in rhesus macaques overexpressing αSyn in the amygdala. 2) Assess time-dependent changes in motor and non-motor behaviors and their associated neural correlates in rhesus macaques overexpressing αSyn in the SNpc. 3) Characterize αSyn burden and its distribution induced by primary targeted overexpression in the amygdala or SNpc. AAV-αSyn and AAV-mCherry (control) will be bilaterally injected into the amygdala or the SN using real-time intraoperative MRI methods. The animals’ motor and non-motor behaviors will be evaluated with a battery of validated tests, and functional brain connectivity will be assessed with fMRI; postmortem analyses will be performed 12 or 18 months after AAV surgery. This innovative proposal capitalizes on the combined expertise of the Emborg and Kalin labs to establish a primate model of αSyn pathology and investigate the role of amygdalar synucleinopathy in anxiety and depression. These highly translational results will inform future trials considering the amygdala as a target for neuroprotective strategies and for treatments against anxiety and depression in PD, PDD and LBD.

NSF Awards · FY 2025 · 2025-08

This project focuses on new ways to store hydrogen so it can be used as a clean energy source. Hydrogen is a powerful fuel, but it is hard to store on its own because it is a very light gas. One solution is to combine hydrogen with certain liquids, which makes it easier to store and transport. For this to work, scientists need new technology to create special hydrogen-storing liquids from natural sources like plants. They also need better ways to add hydrogen to these liquids and remove it when needed. This project will create new methods to turn plant materials into hydrogen-storing liquids. It will also study the chemical processes needed to store and release hydrogen more efficiently. Their work includes both laboratory experiments and computer simulations. The project will also look at how this technology could be used in the United States to improve energy systems. The project outcomes could help make the U.S. more energy independent and prepared for future energy needs. The project will also train college students in advanced science and engineering, and support educational programs to teach others about energy storage. Hydrogen is an alternative energy carrier useful for grid energy storage, transportation, and the chemical industry, but storing and transporting pure hydrogen faces major technical hurdles. Liquid hydrogen carriers (LHCs) store hydrogen for long durations in chemical bonds through catalytic (de)hydrogenation. This project will develop the fundamental knowledge to enable new technologies for the storage and transport of distributed hydrogen resources in organic molecules using an integrated experimental reaction engineering and computational process systems engineering approach. First, the researchers will design reaction processes for LHC synthesis from biomass-based carbon feedstocks, using kinetic and process modeling to identify improved conditions, catalysts, and reactor schemes. Next, they will design modular reaction processes for efficient, reversible hydrogen storage and release from LHCs, considering both LHC stability and catalyst performance. Finally, they will perform multiscale modeling, energy integration, and sensitivity analysis of reactor, process, and infrastructure-level supply chain models to evaluate and optimize the feasibility of LHC technologies for different energy demand cases. Leveraging novel reaction engineering approaches including the design of modular catalytic reactors based on alternative carbon and energy inputs, and novel process systems approaches including new mathematical tools to bridge information across length and time-scales, this project will yield fundamental new insights into the design of molecules, reactors, processes, and systems for chemical energy storage. This award is supported by the Process Systems, Reaction Engineering, and Molecular Thermodynamics program, the Environmental Sustainability program and the Catalysis program. 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 Age-related diseases are the major causes of morbidity and mortality in the US. Many elderly people suffer from multiple age-related diseases simultaneously; while the risk of almost every individual disease rises with age, they also interact, with age-related disease such as diabetes and obesity serving as additional risk factors for neurodegenerative diseases including AD. Targeting the aging process through interventions like calorie restriction (CR), which extends lifespan while delaying or preventing multiple age-related diseases simultaneously, is one plausible approach to lessen the burden of these diseases. However, reduced-calorie diets are notoriously difficult to sustain. Understanding identifying the physiological and molecular mechanisms by which CR influences metabolism, healthspan, and longevity will provide mechanistic insight into the regulation of healthy aging as well as potential therapies for AD. As typically implemented in the laboratory, CR-fed animals are subject to a period of prolonged daily fasting. We and others have found that fasting itself has beneficial effects on metabolic health and longevity, mimicking the effects of a CR diet at both the physiological and molecular levels. Here, we will use a series of distinct feeding regimens to rigorously identify the unique contributions of caloric intake and fasting to the effects of a CR diet on the lifespan, metabolic health, frailty and healthspan of wild-type mice. CR slows or prevents the development and progression of AD in mouse models, and our preliminary data suggests that fasting plays a key role in the beneficial effects of CR on cognition and AD pathology. Here, we will interrogate the ability of fasting to preserve cognition in mouse Aβ and Tau models of AD. Finally, we will use genetic mouse models with altered function of the mTORC1 protein kinase to gain insight into the role of this kinase in the metabolic response to CR. The proposed work will address long-standing questions regarding the physiological, metabolic, and molecular mechanisms by which a CR diet promotes healthy aging and slows or prevents age-related diseases, with a particular emphasis on understanding the potentially therapeutic role of fasting in AD. In the long term, this work will enable our laboratory and others to develop a mechanistic understanding of how when, how much, and what we eat regulates health and disease vulnerability, and to identify new targets for the pharmacological treatment of age-related diseases including AD, and to promote healthy aging.

NIH Research Projects · FY 2025 · 2025-08

The goal of the Molecular Biophysics Training Program (MBTP) at the University of Wisconsin-Madison is to provide predoctoral-level training in interdisciplinary quantitative research at the interface between biological and physical disciplines. The MBTP is tightly associated with the Biophysics Program (BP), which is an interdepartmental Ph.D. program consisting of 59 trainers from 14 departments and 5 colleges. The BP/MBTP faculty trainers share an interest in research at the interface between biological and physical disciplines and are engaged in a robust collaborative network that will provide an ideal ground for rigorous interdisciplinary graduate training in molecular biophysics. The mission of this training program is supported by access and technical training provided by state-of-the-art facilities in cryo-EM / cryo-ET, NMR spectroscopy, X-ray crystallography, mass spectrometry, light and super- resolution microscopy, high-throughput computing, and more. The on-site environment will provide trainees with the advanced skills and the rigorous conceptual training on the theoretical foundations of biophysics necessary to solve biological problems at the molecular level via quantitative approaches, and thus prepare them to successfully embrace careers in biomedical research. The program addresses challenges posed by the fast-paced evolution of modern biophysics and by the pressing need of combining expertise from multiple areas to solve complex biological problems. In response, the MBTP aims at recruiting cohorts of students from a variety of biological and physical majors, bringing together trainers and trainees with complementary experiences and interests and favoring communication, exchange of ideas, peer teaching/learning, sharing, and collaboration. The curriculum is designed to be flexible to support students from a wide range of educational and research backgrounds, yet it includes a fixed-core of foundational courses and educational activities. The training plan also includes important shared activities aimed at fostering participation. The plan includes robust measures to ensure the most favorable matches between students and faculty. The program will support progress and retention by monitoring students throughout their graduate careers and by providing targeted interventions to help them, whenever challenges arise. Special attention will be paid to attracting a highly talented cohort of trainees and on fostering their success via a highly collaborative and encouraging environment. The success of the training program will be monitored by assessing recruiting outcomes, scientific progress, personal growth, time to degree, number and quality of publications (including collaborative work) and career outcomes after graduation. Twelve slots to support trainees at early graduate-career stages are requested, based on the growing demand for PhD-level scientists with rigorous training in biophysics across the UW-Madison campus.

NSF Awards · FY 2025 · 2025-08

This project proposes a new class of vision systems called weak 3D cameras, which are designed to recover simplified geometric representations of the environment that are sufficient for fast decision-making but far more efficient than conventional dense 3D image reconstructions. While traditional 3D cameras aim to build detailed models of a scene, many real-world applications—such as drone navigation, robotic manipulation, and augmented reality—often require only partial or approximate geometry to function effectively. These applications also operate under tight constraints on power, compute, and latency, making dense 3D sensing impractical. This project explores the design of vision systems that can perceive their surroundings through simpler, faster, and more robust representations. The research builds on two new kinds of geometric representations—Inertial Safety Maps (ISM) and Blocks World Sets (BWS)—which can be captured directly using low-cost hardware and lightweight algorithms. The project is organized into three thrusts: building compact cameras that work reliably in challenging real-world conditions; enhancing them with additional low-cost sensors and modern geometric priors and world models to improve performance; and extending their use beyond static geometry to understand motion and semantics. The goal of the project is to create fast, affordable 3D vision for the next generation of intelligent machines, with potential uses in robotics, wearable tech, disaster response, and more. The outcome of this work will enable fast, low-power 3D perception on compact platforms, with potential impact across embedded vision, augmented reality, and assistive technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The rapid growth in data generation and collection has led to a significant proliferation of large-scale datasets in recent years. Big data has profoundly transformed scientific research and knowledge discovery. Data science integrates statistical analysis, computational algorithms, and domain-specific knowledge to extract insights from big data, enabling solutions to complex real-world problems. The increasing scale and complexity of data have driven a growing demand for more advanced computational and statistical methods—ranging from hardware to software systems—particularly for machine learning applications. Quantum computing holds the potential to revolutionize data science, especially in computational statistics and machine learning, by enabling quantum learning to meet the emerging demand. This research project aims to investigate statistical challenges in quantum learning. The investigator will develop novel statistical techniques to demonstrate the advantage of quantum approaches over classical methods for tackling difficult machine learning tasks. Additionally, the investigator will actively engage in initiatives that integrate research with workforce development (including graduate students) and apply these advancements to address complex real-world problems. A central issue in data science is the interplay between statistics and computation, with computational power being essential for developing effective methods to tackle increasingly complex challenges. Quantum computation, which involves preparing and manipulating quantum states of physical systems, offers the potential to revolutionize data science—particularly in computational statistics and machine learning—by enabling a new paradigm known as quantum learning. However, the intrinsic randomness of quantum mechanics introduces stochasticity into quantum computation, posing unique challenges. Data science, through its foundations in statistics and machine learning, is well-positioned to address these challenges by contributing to the development of quantum computing devices, algorithms, and learning techniques. This research project aims to develop statistical methodologies and theoretical foundations to address key problems in quantum learning. Specifically, it will study (i) statistical inference for the Boson sampling model, and (ii) statistical analysis for quantum state and process learning in both classical and quantum settings. The investigator will tackle emerging scientific problems through novel statistical and computational approaches and address the challenges that arise in solving complex learning tasks. The project seeks to establish rigorous, theoretically grounded statistical methodologies and computational procedures that will substantially advance our understanding of quantum learning from both statistical and computational perspectives. This award by the Division of Mathematical Sciences is jointly supported by the NSF Office of Advanced Cyberinfrastructure. 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.