University Of Notre Dame

NSF Awards · FY 2025 · 2025-08

The award supports research in pure mathematics, more precisely in Commutative Algebra. The Principal Investigators will address issues related to the structure of geometric objects that arise as solution sets of systems of polynomial equations in several variables. These objects, known as varieties, are essential in both mathematics and applications to science and engineering. A classical problem dating back to the nineteenth century is the classification of varieties, and an important tool for this classification is linkage theory. One of the goals of the project is to identify new necessary and sufficient conditions for two varieties to belong to the same linkage class. Minimal free resolutions provide a method for describing complex algebraic objects, such as modules, through a sequence of matrices. While these resolutions generally require an infinite sequence of matrices, the researchers aim to identify finite patterns in such resolutions. The implicitization problem is a classical problem in pure mathematics that is of significant interest in geometric modeling and computer-aided design. Given any variety, the goal is to find the system of polynomial equations that has the geometric object as a solution set; knowing these “implicit” equations greatly enhances the understanding of the variety. The Principal Investigators actively engage with the mathematical community by serving on editorial and scientific boards, delivering lectures, and preparing expository notes for both national and international events. They will promote scientific exchange by organizing international conferences and national online seminars. Additionally, they will provide research training opportunities for students, postdoctoral scholars, and early-career researchers. The Principal Investigators aim to identify new properties preserved by linkage, focusing on licci ideals, the ideals linked to a complete intersection in a finite number of steps. The PIs also propose sufficient conditions for an ideal to be licci, based on the interplay between shifts in free resolutions and graded Betti numbers. Additionally, they intend to prove that perfect ideals of codimension three are licci if they are strongly nonobstructed, a property that arises in deformation theory. Advances in linkage theory have applications to Hilbert schemes. The PIs seek structural results about syzygies and finite patterns in infinite resolutions. In this vein, they propose a conjecture about the direct sum decomposition of the syzygy modules of the residue field of a Golod ring. The Behrend function has been used effectively to address enumerative problems in algebraic geometry, but is challenging to compute. The investigators plan to apply their expertise on blowup algebras to compute this function for subschemes of affine space that are either zero-dimensional, or defined by monomial ideals. Additionally, they aim to tackle the classical problem of determining the implicit equations that define the graph of a rational map between projective spaces, by leveraging their recent work on Jouanolou duality and relating the Rees algebra of an ideal to its Fitting ideals. In equisingularity theory, the Principal Investigators intend to establish fiber-wise, multiplicity-based, criteria for families of analytic spaces to be Whitney equisingular by characterizing integral dependence of modules through the constancy of multiplicities. 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

SUMMARY: CAUTI is one of the most common healthcare associated infections, accounting for 40% of nosocomial infections worldwide. Recent clinical studies have estimated that during long term catheterization, up to 97% of CAUTIs are polymicrobial. Although studies involving two different pathogens have been done, trying to understand the interactions between three species, especially cross-kingdom, are few and far between. Hence, the proposed studies are intended to narrow the knowledge gap in understanding polymicrobial infections during CAUTI by three of the most prevalent uropathogens, gram negative E. coli, gram positive E. faecalis, and fungal pathogen C. albicans. Our preliminary results showed that E. coli inhibits hyphal morphogenesis as well as decrease microbial burden of C. albicans both in vitro and in vivo. In contrast, our in vivo preliminary data showed that C. albicans colonization in the bladder and on the catheter increases with E. faecalis co-infection, even in the presence of E. coli. To understand these interactions, first, we have identified that C. albicans’ transcriptional regulator, EFG1, is critical for virulence and hyphal formation in urine or during CAUTI. Therefore, we hypothesize that expression of EFG1 and/or its targets is affected by other uropathogens, impacting C. albicans’ growth, filamentation, virulence, and biofilm formation. For this R21 exploratory grant, we are proposing three independent but complementary aims to: 1) investigate E. coli’s antagonistic effect against C. albicans, specifically, on EFG1 expression and downstream genes through RNA-sequencing and validation by RT-PCR in vitro and in vivo. 2) Identify E. coli’s antagonistic factors. Our preliminary data showed that secreted small peptides of E. coli are responsible for inhibition of fungal growth and hyphal morphology. Thus, we will use a mass spectrometry-based proteomic approach to identify small peptides secreted by E. coli that may be responsible for C. albicans’ growth inhibition. 3) Characterize how E. faecalis mitigates the antagonistic effect of E. coli against C. albicans. E. faecalis highly expresses two secreted proteases, SprE and GelE, under urine conditions and during CAUTI. These proteases have been shown to target different host proteins, resulting in the modulation of host response and worsen disease outcome. We will use purified SprE and GelE to examine whether they also target E. coli’s small peptides for degradation, reducing the negative effects against C. albicans. The results from these experiments will help us gain further understanding of the complex cross- kingdom interactions during CAUTIs and eventually lead to better treatment and prevention strategies.

NIH Research Projects · FY 2025 · 2025-07

SUMMARY CD8+ T cells drive acute cellular rejection (ACR) of transplanted tissues via alloreactivity. Old paradigms about the broad degeneracy of alloreactivity are giving way to the idea that many, if not most, alloreactive T cells recognize unique peptide-allo-HLA complexes, reflecting what is referred to as allospecificity. The targets recognized by alloreactive T cells, however, remain almost completely undefined. Of the few allo-ligands identified, fewer have been validated in transplant patients. This presents a major problem as ACR remains the number one cause of allograft loss, and widely used immunosuppressive therapies for ACR are associated with serious risks and toxicities. Mimicking what has been observed in cancer, autoimmunity, and infectious disease, knowledge of the targets recognized in ACR would enable advances in predicting, monitoring, and ultimately treating ACR. Recent work has shed light on the challenge of finding allo-ligands: significant links are now known to exist between alloreactivity and anti-viral immunity. Our recent mouse model of rejection demonstrated this conclusively: compared to viral-naïve mice, viral-immune mice exhibit substantially accelerated allograft rejection, characterized by massive infiltration and activation of virus-specific memory T cells. Strikingly, these cells display substantial responsiveness to allogeneic but not syngeneic allografts. The connection between viral infection and alloreactivity extends is not limited to mouse models. Several groups have begun using “off-the- shelf” viral-specific T cells (VSTs) to treat transplant patients with active infection, and there is evidence that HLA-mismatches with VST therapy can accelerate rejection. Altogether, these observations provide a conceptual framework for re-envisioning alloreactivity through the dual lenses of allospecificity and viral cross-reactivity. We propose to develop this framework, using viral reactivity as the “hook” for identifying allo-ligands. Our driving hypothesis is that viral/allo cross-reactivity occurs predominantly through molecular mimicry at the level of peptide/MHC structure, with the surfaces of specific allo-peptide/allo-MHC complexes on donor tissue mimicking those of viral peptide/self-MHC complexes in recipients, facilitating cross-reactive binding of TCRs. Accordingly, we have developed a novel approach for identifying mimics that incorporates state-of-the-art methods for sequence analysis, structural modeling, and structural comparison, and leverages these with transcriptomic databases and other immunoinformatics tools. Our preliminary data shows our approach can successfully identify allo-mimics of viral-specific TCRs implicated in rejection. Building on this, we aim here to expand our observations and refine our approach for translation to humans. Our two aims are 1) to define the allo antigens recognized by viral-specific TCRs in mice and 2) to apply this knowledge to human cells. Our proposed work will allow immunologists to (finally!) begin identifying allo-ligands recognized by CD8+ T cells in ACR, enabling significant advances in understanding alloreactivity and ultimately predicting, monitoring, and treating rejection.

NSF Awards · FY 2025 · 2025-07

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor Brandon Ashfeld of Notre Dame University is studying the development of new chemical reactions that address critical challenges in the synthesis of complex, high value molecular targets. New synthetic tools are being evaluated based on a universal reaction design concept that enables access to functionalized oxindoles, a type of structure present in a variety of biologically relevant molecules. The new synthetic tools employ selective transition-metal catalyzed reactions to make a variety of distinct changes to a single molecular intermediate in the presence of simple building blocks. While advancing methods for organic synthesis, this research program is also being used to train graduate students in synthetic chemistry and to engage undergraduate students in teaching labs through a molecular-target-specific pedagogical approach. These activities are being used to encourage students to consider careers in research and discovery. Despite the ubiquitous presence of cycloannulated indole and oxindoles frameworks in medicinal chemistry and materials science, the number of reliable, efficient methods for diversifying these building blocks with flexible site-specific functionalization capabilities is relatively limited. Using these synthetic targets as motivating templates for reaction design, Prof. Ashfeld and his reaction team are using the difference in reactivity between PdII- and RhII-carbenoids to selectively favor [3+1]-, [3+2+1]-, and [4+3]-cycloadditions for the construction of key alkaloid frameworks. A central diazooxindole intermediate is being divergently functionalized using these methods and fundamental reactivity lessons learned about being used to expand the reactivity scope to the asymmetric synthesis of cyclobutenes, pyrans, and complex indole ring-systems, including the total synthesis of N-methylwelwitindolinone C isothiocyanate. The synthetic methods being developed also form the basis for a cohesive pedagogical approach toward laboratory instruction that will broadly impact STEM education efforts at all levels. By emphasizing target analysis, rather than traditional reaction introduction, a cohesiveness between comprehension at the conceptual level and application to the synthesis of complex molecules is being emphasized to encourage students to continue studies in STEM fields. 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 is a project in model theory, a branch of mathematical logic that studies the features of mathematical structures that can be expressed by formal languages. Model theory usually studies infinite structures, though, within model theory, there are techniques for taking a collection of finite structures--graphs, orders, groups, etc.--and producing an infinite limit structure that encodes useful information about the finite structures. For example, there are Fraïssé limits, like the Rado graph or the rational order, which allow for the study of all finite graphs and orders via their rich collection of symmetries, and ultraproducts, which are logical limits that reflect the asymptotic features of a sequence of structures detected by first order logic. Some of the most striking developments in model theory concern the 'smoothly approximable' structures, a special class of structures that can be seen simultaneously as both kinds of limit, which are built out of classical geometries over finite fields and have deep connections to combinatorics and group theory. The geometries that form their basic building blocks also can be considered over infinite fields and are important objects of study in several areas of mathematics. As with the infinite limits of finite structures, model-theoretic techniques allow for the construction of infinite-dimensional limits of these (infinite but) finite-dimensional geometries and new tools are needed to understand them. This project is focused on the development of those tools. The educational component involves organization of a summer school at University of Notre Dame and working with graduate students. In addition, the PI plans to work with students in several countries where access to high level math education is more limited. The PI's research agenda will develop a structure theory for NSOP_1 theories, pushing beyond the reaches of simplicity theory and enabling the treatment of smoothly approximable structures over infinite fields. The name NSOP_1 is an unfortunate label for a very natural class of theories, serving as a kind of 'compactification' of the simple theories and encompassing many important examples. The PI has proposed a two-part program. The first part would develop the 'geometric' aspects of the theory, by analogy with geometric stability theory, with a focus on definable groups and higher amalgamation. The second part proposes a far-reaching program to expand the reach of the existing structure theory for smoothly approximable structures developed, in its ultimate form, by Cherlin and Hrushovski. In that work, group theory, via work of Kantor, Liebeck, and Macpherson, provided a catalogue of the basic building blocks and classification theory, via the then-nascent theory of simple theories, explained how a smoothly approximable structure is assembled out of these basic pieces. For the analogue over infinite fields, there are significant questions both on the group-theoretic side, concerning the basic geometries, and the classification-theoretic side, concerning a 'geometric' theory of NSOP_1 theories. The PI aims to develop the analysis of geometries in two distinct regimes: the algebraic and the pseudo-finite. This will enable meaningful applications to algebra, representation theory, and combinatorics, particularly to groups of finite Morley rank and to the asymptotic behavior of finite permutation groups. 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

People today are faced with many privacy decisions in their daily interactions with mobile devices. In the past decade, researchers have studied the design of many tools and mechanisms, such as privacy nudges, that aim to help individuals make better privacy decisions. But just like decision support tools in other domains, these tools cannot make users perfect decision-makers. Users still make mistakes and regret their privacy decisions later. This project casts a fresh perspective on Privacy-by-Redesign by helping users revisit and rectify past privacy decisions that they may regret. In order to avoid annoying users through repetitive alerts, a focus of the project is to identify which past privacy decisions most likely trigger regrets, and to ask users to revisit only those decisions. This project has high societal importance, given that more than 75% of Americans own a smartphone today and need to make frequent privacy decisions. The broader impacts of the project also reach technology developers, policy makers, and consumers by connecting the social analysis of privacy behaviors with the technical design of privacy tools. This project is rooted in integrating substantive bodies of multidisciplinary knowledge to address the acute challenges of mobile privacy. It develops a theory on how three types of factors, cognitive appraisal, affective states, and environmental cues undercut high-effort decision making and move people toward low-effort information processing, which ultimately leads to regrettable privacy decisions. For the social analysis of privacy behaviors, this project employs a novel combination of experience sampling method and factorial vignette studies to empirically validate the theoretical framework. For the technical design of privacy tools, the project develops an expert-augmented prediction model that infers from data collectible by a mobile operating system the influential factors of cognitive appraisal, affective states, and environmental cues, so as to predict the quality of a privacy decision. The long-term vision of this project is to enable technological designs that help bridge the discrepancies between users' privacy decisions and their perceptions, especially in the context of a mobile system. 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

Polynomial equations are ubiquitous in science, describing important physical principles and serving as mathematical models for complex natural phenomena. Algebraic geometry studies geometric structures arising from solutions to systems of polynomial equations. To gain a better understanding of these structures, it is useful to study how they change when the corresponding equations are slightly perturbed. This is achieved by studying a “parameter space” for these structures. The overarching goal of this project is to use techniques from commutative algebra to tackle longstanding questions related to the Hilbert scheme, a parameter space for polynomials with fixed properties. The project’s broader impacts include developing new packages for the open-source computer algebra system Macaulay2, organizing local seminars, and organizing mathematical conferences. The investigator will focus on three areas of commutative algebra and algebraic geometry: 1) Singularities of the Hilbert scheme of points on a threefold: The main goal is to understand the singularities of the Hilbert scheme of points on a smooth threefold. In particular, the investigator will focus on determining the smooth points and explaining some of the patterns appearing in the structure of the singularities. 2) Exploring multigraded Hilbert schemes and other moduli spaces: The investigator will study the space of branch varieties, a close analogue of the Hilbert scheme, and focus on studying the projectivity of this moduli space. 3) Varieties in weighted projective spaces: The investigator will focus on developing a set of tools to extend classical theorems in projective space, such as Macaulay’s theorem on the existence of Hilbert functions and the del Pezzo-Bertini classification of varieties of minimal degree, to weighted projective spaces. 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 Myeloid cells in and around the brain are at the center of proper central nervous system (CNS) function by playing crucial roles in homeostatic surveillance and immunity1. A type of myeloid cell, microglia, are the resident immune cells of the CNS and they are vital for neuronal network architecture and injury response1. Conversely, microglia also play a key role in some diseases like autism2 and multiple sclerosis3. In some cases, microglia depletion is a promising therapy and relies on known genetic regulators of microglia production4,5. However, recent research in mice and zebrafish shows subpopulations of developmental microglia that are genetically distinguishable6,7. These data present a gap in knowledge of the complex gene regulatory networks that govern microglia production. To begin to understand the heterogeneity of microglia-producing genetic programs, we identified an undescribed cell in the brain that expresses canonical microglia markers, clears debris, and expands in injury7. These microglia are labeled with Mannose Receptor C, type 1a (mrc1a)7, a membrane receptor expressed in lymphatic and venous vasculature8. To uncover more about the genetic regulation of these cells in our supporting data, we interrogated the transcription factor sox17. Sox17 is expressed in endothelium9,10 and was recently shown to regulate the transdifferentiation of lymphatic vessels to blood vessels11. We found that genetic perturbation of sox17 significantly reduced mrc1a+ microglia abundance in the embryonic zebrafish brain. Given that sox17 functions in a vast array of embryogenic processes10,12,13, it is difficult to theorize specifically how the transcription factor could be regulating embryonic microglia production. Therefore, we carried out a CRISPR screen and identified 6 additional candidate genetic modifiers of microglia production. The aim in the F99 proposed study is to further investigate the effects of sox17 mutation on microglia and investigate genetic interactions between sox17 and other candidate genetic modifiers. I will accomplish this by using a combination of in vivo timelapse imaging, in situ hybridization of mRNA, and CRISPR gene editing. In the K00 phase of the application, the investigation of myeloid cells in and around the brain will be expanded. Aside from microglia, novel populations of other myeloid cells (monocytes and neutrophils) have also been identified in the brain14. These monocytes and neutrophils are skull bone-marrow derived, and are genetically distinct from previously-described populations of these cells14. However, it remains unknown if the skull bone marrow could be a source for CNS myeloid cells during development and if microglia are amongst these cells. The goal of the K00 proposed study is to investigate the postnatal skull bone marrow (P7, P10) as a source of myeloid cells during development. I will accomplish this by using a combination of fate mapping, cell tracking, intravital imaging, and immunofluorescence approaches. This work has the potential to inform the clinic of genes that impact myeloid cell development so that we may increase the efficacy of diagnostic testing and therapeutic strategies for treating neurodevelopmental and neurodegenerative disorders.

NSF Awards · FY 2025 · 2025-07

This project aims to develop new computational tools in the field of algebraic topology. Topology is the study of geometry where you identify one geometric object with another if one can be deformed into the other. The goal of algebraic topology is to ascribe discrete algebraic invariants to these geometric objects to distinguish their topological types. Understanding the topological type of geometric objects is a fundamental act of scientific/mathematical inquiry, comparable to the study of prime numbers, or the classification of the fundamental particles that constitute matter and carry forces. Topological computations have also been applied to solve problems in physics, and the field of topological data analysis applies the tools of algebraic topology to the qualitative study of high-dimensional data-sets. The focus of the project is on the interaction of localized and unlocalized computations of homotopy groups. Homotopy groups are the fundamental algebraic invariants which arise from geometric objects but are often very difficult to compute. These computations are made more accessible through the process of localization (inverting classes), but this process of localization loses information. The project will enhance our understanding of how to extract information about delocalized homotopy groups from these localizations. Activities in this project will also contribute to the training of the next generation of mathematicians. The specific research activities center around the recent disproof of the Telescope Conjecture by Burklund-Hahn-Levy-Schlank, which implies that the relationship between K(n)-local homotopy groups and unlocalized homotopy groups is much more subtle than previously imagined. The PI will complete our understanding of the homotopy groups of the K(2)-local sphere in the last open case of p = 2 using a new elliptic resolution, and the relationship of this resolution to the tmf-resolution will be leveraged to relate the extensive low dimensional computations of Isaksen-Wang-Xu of 2-primary stable stems to the K(2)-local computations. Generalizations of the effective slice spectral sequence to other groups will be investigated using the techniques of synthetic homotopy theory. The PI will also investigate genuine equivariant enhancements of synthetic homotopy categories. 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 · 2025-06

Total positivity studies mathematical spaces and their positive parts. For example, the positive part of a sphere is a triangle bounded by three great circles. More general spaces such as pyramids and polyhedra also arise as the positive parts of more complicated objects, and they turn out to have interesting boundary structures: vertices connected by edges, which are in turn contained in faces, etc. The boundaries of various totally positive spaces model diverse phenomena such as outcomes of a scattering experiment in particle physics or different ways to sort a list of numbers. In turn, these spaces are amenable to concrete study using techniques from combinatorics, which concerns discrete objects such as graphs and lists. The project will use the lens of total positivity to reveal the underlying combinatorial structure of spaces of polynomials and hyperplanes. It will also support K-12 outreach activities. Lorentzian polynomials were recently introduced to resolve several outstanding log-concavity conjectures. They also serve as geometric realization spaces of matroids, which are combinatorial abstractions of linear spaces. The project will determine the topology of various spaces of Lorentzian polynomials. A second direction concerns Schubert calculus, which studies intersection problems involving linear subspaces. Since the formulation of the Shapiro-Shapiro conjecture in the 1990's it has been known that total positivity can be employed to construct Schubert intersection problems whose solutions over the complex numbers are all real. This project will broaden this phenomenon with applications to real algebraic geometry. A third direction is to study the totally positive parts of Springer fibers, certain subvarieties of flag varieties which play a significant role in representation theory and algebraic combinatorics. The project will develop a new approach to studying the geometry of components of Springer fibers by relating them to Richardson varieties. 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 · 2025-06

A fundamental challenge in environmental science is applying the knowledge that scientists discover at a particular location or time to understanding phenomena occurring at other times and/or locations. In the traditional approach, environmental scientists collect field data and perform experiments at, for example, a particular river basin, and then repeat this process at a different time and/or location to see whether their conclusions generalize. This approach is rigorous, but limited because it is time-, labor-, and cost-intensive; thus there exists relatively sparse ground-collected data across the planet. Another challenge is that intensifying human activities amplify the rates of change in conditions per location and time, so knowledge discovered in the past will likely fail to predict outcomes in the future. The challenge of predicting and ameliorating the effects of environmental change disproportionately affects under-resourced communities, including those most vulnerable to environmental changes that lead to food insecurity and hence greater socioeconomic instability. Traditional Artificial Intelligence (AI) approaches cannot resolve this challenge because they require extensive human input, for example due to the need for labeling ground-collected data or other data layers, such as high-resolution satellite imagery. In this project, on-the-ground human observations and labels are replaced with AI-based discovery from abundantly available, mostly unlabeled visual data, such as that collected from a combination of satellites and other devices. This research proposes a paradigm shift that enables low-cost scaling across many types of images in order to lower the barrier to access of this scientific process. In the process, a novel AI framework is introduced that combines multiple data sources to automatically discover interpretable scientific hypotheses about the cause of ecosystem changes. Together, these approaches will accelerate the ability to identify solutions for the increasing environmental issues faced across our planet. The goal of this project is to develop and validate an AI framework that can use a broad array of image data collected using different sensing modalities (e.g., low-resolution satellite, drone, and internet-posted images) to automate and accelerate the generation of interpretable environmental scientific hypotheses at a planetary scale. An example might be correlating the spatiotemporal prevalence of certain invasive or disease-causing species with presumed causal factors present in the environment. The proposed framework integrates new techniques into foundational models for satellite imagery that can choose intelligently among sparsely-labeled data from different sensor modalities, optimizing between cost and accuracy trade-offs. By coupling this model with self-improving large language models that can both receive and provide interpretable feedback and hypotheses to researchers, this approach goes beyond black-box feature learning, the current state-of-the-art in computer vision. This proposed model will be applied and validated on the task of detecting submerged aquatic vegetation. This task poses a number of technical challenges (e.g., waves, turbidity, weak spectral signal through water) that are more difficult than detecting objects on land surface. Success in this pilot project will demonstrate that this type of model can be easily applied to the terrestrial environment and to tackle even greater grand challenges in environmental science. This award is co-funded by the Directorate for Computer and Information Science and Engineering and by the Directorate for Biological Sciences. 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 · 2025-06

PROJECT SUMMARY/ABSTRACT This project focuses on pioneering a novel approach in anti-cancer drug development through the creation and optimization of isoform-selective inhibitors targeting the 90 kDa heat shock protein (Hsp90β). Hsp90 is a crucial chaperone protein involved in the maturation of over 400 client proteins, many of which play significant roles in cancer progression. The selective inhibition of Hsp90β over other isoforms offers a promising strategy to mitigate the side effects associated with pan-Hsp90 inhibitors, such as ocular, cardio, and dose-escalating toxicities, which have hindered the clinical success of these treatments. The objectives of this research include the development of Hsp90β-selective inhibitors that demonstrate high affinity and selectivity, without inducing the heat shock response (HSR) or causing toxicity, thereby overcoming major challenges faced in clinical trials of pan-Hsp90 inhibitors. Preliminary data indicate these inhibitors not only reduce the proliferation of prostate cancer cell lines but also enhance the efficacy of immune checkpoint blockade (ICB) therapy in prostate cancer models, suggesting their potential to transform treatment- resistant cancers into manageable diseases. Novel Hsp90β-inhibitors with improved solubility were created and will be tested in the current study. The specific aims of the project are threefold: 1. To enhance the pharmacological properties of Hsp90β-selective inhibitors for effective in vivo use against cancer, focusing on solubility, permeability, and half-life while preserving specificity. 2. To evaluate the impact of Hsp90β-inhibitors on client protein profiles in comparison with non-selective Hsp90 inhibitors, aiming to elucidate mechanisms of action and identify biomarkers for efficacy and resistance. 3. To assess the efficacy and safety of Hsp90β-inhibitors in primary and metastatic castration-resistant prostate cancer (CRPC) models, examining their potential to synergize with ICB therapy and improve immunotherapy outcomes. This research aligns with the National Institutes of Health's mission to advance science and applications that improve health outcomes. By focusing on the development of selective drug candidates, this project aims to offer new therapeutic options for metastatic CRPC and potentially other cancers, addressing a significant unmet need in oncology. The outcomes of this study are expected to pave the way for clinical trials, contributing to the broader goal of enhancing cancer treatment strategies and patient survival rates.

NIH Research Projects · FY 2025 · 2025-05

PROJECT SUMMARY Characterizing the sizes, shapes, and interactions of biomolecules is a crucial prerequisite to understanding biological function on a molecular level. Analytical ultracentrifugation (AUC) has been an essential component of the biophysical tool kit for decades, exploiting the distinct optical and sedimentation properties of proteins, nucleic acids, and polysaccharides to provide information on molecular structure and its changes. For example, changes in the stoichiometry of a multimeric protein caused by small molecule binding, or the addition of a partner protein, can be quickly and accurately quantified by changes in sedimentation properties. AUC measurements are made in a matrix-free solution, typically without the need for exogenous labels, more accurately capturing biological context than many other techniques commonly used to measure binding. AUC analysis does not destroy nor dilute precious samples, making them fully recoverable for additional downstream analyses. Over the past two decades, major advancements in AUC design now enable more rapid detection of more complex molecular assemblies using a large number of wavelengths for absorbance measurements. This proposal, in response to Program Announcement PAR-22-081, requests funds to purchase a state-of-the-art Beckman Coulter Optima A/I AUC to advance the research goals of University of Notre Dame’s NIH-funded scientists and engineers. This instrument will replace Notre Dame’s current Beckman Coulter ProteomeLab XL-I AUC, the only AUC broadly available within a 90-mile radius of Notre Dame. Despite careful maintenance and support over its lifetime at Notre Dame, the ProteomeLab AUC has been plagued by numerous malfunctions over the past year. Crucially, the ProteomeLab AUC will no longer be eligible for Beckman service support after 2025 and, at 18 years old, is optically and mechanically inadequate to modern instruments. The new AUC will be housed in a recently renovated and expanded core facility; its acquisition is an important piece of a large effort currently underway to expand and improve research infrastructure at the University of Notre Dame. As such, supervision, care, and maintenance of this instrument will contribute to employment stability for core facility staff, while access to this state-of-the-art research equipment will continue to grow the scope and scale of research at Notre Dame.

NSF Awards · FY 2025 · 2025-05

Cyber-physical systems connect the real, physical world to computation, for example in the domain of autonomous vehicles. Because of the real world instantiation and potential risks, safety concerns are paramount. Safe reinforcement learning refers to machine learning that incorporates considerations of real world safety. This CAREER project focuses on enhancing the security of cyber-physical systems that are being designed using the current state of the art safe reinforcement learning methods. In general, safe learning systems focus on performance under safety constraints, however, they remain vulnerable to attacks during operation or training. Achieving safe and secure reinforcement learning protects users from systems and systems from attack. This project will develop innovations that focus on achieving these goals using precise specifications expressed in Signal Temporal Logic (STL) for studying both functional and timing vulnerabilities in these systems and eventually designing mitigation strategies. Evaluation will leverage the CARLA (CAR Learning to Act) simulator for autonomous driving research and real-world autonomous car testbeds to validate security measures, ensuring resilient CPS deployment in complex and adversarial conditions. Overall, this CAREER project will lead to improvement in the security of Cyber-Physical Systems (CPS) such as autonomous vehicles that utilize reinforcement learning in their operation. The project will lead to the discovery of potential security risks that target the learning process and real-time operation of the vehicle. The project will develop real-time detection and diagnostic tools and methods that will harden the vehicle against these risks – especially those associated with the learning and training process. By addressing these security gaps, this research will help ensure the cyber physical systems operate reliably in real-world environments, ultimately improving safety in transportation, robotics, and other critical 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 · 2025-05

Norovirus is the virus that causes the commonly known 'stomach flu'. It has been difficult to study the health risk of this virus because it does not grow in the laboratory. A new method using 'human intestinal enteroids cells,' or HIEs, now allows norovirus to be grown in the laboratory. This research project will use HIEs to measure infectious norovirus in water. The study will investigate how this virus is removed by water treatments. Ultimately, this information will help ensure safe water and potentially facilitate reuse of treated wastewater. This project will also support outreach to a local Boy's and Girl's Club to give career day talks. The project will also develop water quality learning modules for high school students and engage the public in science education through public 'Science at Sunset' and 'Science Sundays' sessions. Norovirus, a critical waterborne pathogen, is predicted to account for most infections from exposure to sewage-contaminated water. Understanding the fate of norovirus in the water environment is thus crucial for addressing pressing water management challenges, including wastewater reuse and ensuring recreational and agricultural water safety. Norovirus has historically been unculturable in the laboratory. Thus, current measurements of norovirus fate in water and wastewater treatment systems rely on molecular or surrogate methods with unknown agreement with infectious norovirus. Recent culture models based on HIEs have enabled the cultivation of norovirus. The project will use HIEs to characterize infectious norovirus fate in water and wastewater using controlled laboratory experiments and samples from full-scale systems. Examples of processes that will be evaluated include chlorine and ultraviolet disinfection, membrane treatment, and an activated sludge process. This research will also enable the assessment of the agreement of culture, molecular, and surrogate measures of infectious norovirus fate in water. Cultivation of norovirus in HIEs remains highly specialized; thus, validating molecular or surrogate approaches to understand norovirus fate would enable and support additional research in this critical arena using more accessible tools. Finally, HIE-cultured samples will be paired with metagenomic sequencing to develop a 'shotgun culturing' approach to identify the infectious virome from wastewater samples. Ultimately, this work will provide the most comprehensive evaluation of norovirus fate in water and wastewater treatment systems, informing water quality monitoring and design of wastewater reuse systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The emergence of infectious diseases continues to pose significant global health challenges, exacerbated by environmental changes and increasing human mobility. Vector-borne diseases, such as malaria, dengue, and Lyme disease, are particularly sensitive to climatic and ecological shifts, which influence vector populations and disease transmission patterns. At the same time, advances in artificial intelligence (AI) have revolutionized the ability to analyze complex datasets and predict disease outbreaks with greater accuracy. Despite the growing importance of AI and environmental gradients in infectious disease research, there remains a substantial training gap in integrating these approaches into predictive modeling. This award supports a workshop at the 22nd annual Ecology and Evolution of Infectious Diseases conference to provide graduate students and postdoctoral researchers with hands-on training at the intersection of AI, environmental science, and infectious disease modeling. By equipping early-career scientists with these interdisciplinary tools, this initiative enhances the ability to develop adaptive surveillance systems, inform public health interventions, and mitigate emerging infectious disease threats in a changing world. The workshop will offer an intensive two-and-a-half-day program featuring quantitative and applied training sessions. Participants will engage in parallel tracks, learning to apply Bayesian inference for estimating environmental response functions or utilizing AI techniques to analyze environmentally driven infectious disease patterns. Instructors with expertise in quantitative disease ecology and machine learning will guide trainees through hands-on exercises and collaborative group projects, reinforcing the application of these tools to real-world epidemiological challenges. The workshop’s structure ensures that participants develop both technical proficiency and an understanding of how to translate their research into actionable insights. To maximize accessibility, all instructional materials, including recorded sessions and coding resources, will be made publicly available. This initiative not only advances scientific knowledge but also fosters collaboration within the infectious disease research community, helping to prepare the next generation of scientists to tackle global health challenges. 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 · 2025-04

Project Summary T cell receptors (TCRs) are of increasing therapeutic interest due to the role of T cell mediated immune responses in conditions such as viral infection, cancer, cardiomyopathy, autoimmunity, and graft rejection. A T cell response begins when TCRs associate with peptide antigens presented by major histocompatibility complex (MHC) proteins on antigen-presenting cells. The formation of the TCR-peptide-MHC (TCR-pMHC) complex triggers an intracellular cascade that results in T cell activation and, for cytotoxic T cells, target cell killing. While a T cell response can be highly specific, TCRs cross-recognize multiple peptides. This feature, though biologically necessary, may cause off-target effects in therapeutic applications, as evidenced by tragic outcomes in clinical trials of T cell therapy. A challenge in developing safer T cell or TCR-based therapies thus lies in accurately predicting the cross-reactivity profile of a TCR - that is, the range and types of peptides to which it can and cannot respond. Current prediction methods are limited by a lack of high quality training data covering ranges of peptides, instead typically focusing on a single "cognate" peptide for each TCR, limiting the ability of prediction algorithms to generalize beyond what is already known. Various library-based or genetic screens have been developed, but these do not allow assessment of discrete peptides and prohibit control of relevant biologic variables. Others have tried positional scanning libraries (PSL), or X-scans, to probe the positional sensitivity of TCR recognition. While traditional PSLs overcome the limitations of other screens, they cannot probe the range of diversity needed to characterize a TCR’s cross-reactivity profile. I hypothesize that by systematically increasing the diversity of peptide libraries and integrating this data with advanced structural modeling and machine learning techniques, I can develop a more complete knowledge-base of the structural and chemical determinants of TCR cross-recognition. To test this hypothesis, I will develop an extended positional scanning library (ePSL) approach to generate more diverse peptide datasets. I will then leverage state-of-the-art protein language models and structure prediction tools to reveal the determinants of TCR specificity and cross-recognition. I will integrate our experimental and computational approaches to create robust and generalizable predictive models for TCR recognition of diverse peptides, which will be tested and refined on unknown TCRs. My approach combines sophisticated AI approaches with structural and molecular immunology, aiming to capture the intricate physicochemical features driving specificity and cross-reactivity. This research addresses a fundamental gap in the current understanding of T cell biology. By improving our knowledge of what drives TCR cross-reactivity and building more accurate predictive models, this work will further fuel efforts to develop safer therapeutics for cancer and other diseases.

NSF Awards · FY 2025 · 2025-04

The Standard Model (SM) of particle physics has been a remarkably successful description of nature on the smallest length scales. It has been validated experimentally by decades of measurements at particle colliders. Despite this success, the SM alone cannot account for measurements at larger length scales. For example, it does not explain the abundance of dark matter in galaxies. These disagreements suggest the SM is only part of some larger theory with other particles and forces that have not been discovered yet. The group led by PI Prof. Osherson is searching for such particles in proton collisions at the Large Hadron Collider (LHC) at CERN. The group focuses on discovering very light new particles that might have evaded previous searches. In particular, this project builds new tools that can probe a range of new physics scenarios with light new particles. The project also includes an educational component aimed at bringing experimental particle physics to high-school students. PI Osherson’s group works within the Compact Muon Solenoid (CMS) collaboration at the LHC. The project has two main objectives, one focused on the immediate analysis of the current CMS datasets, the other preparing for future data-taking at the LHC. The group will develop new algorithms for reconstructing the signature of two overlapping photons, a signature common to many new physics scenarios. Such signatures are also relevant to precision measurements of the Higgs. The group will use these algorithms to search specifically for axion-like particles produced at the LHC. Looking to the future, the group is working on upgrades to the CMS detector, specifically replacing the Tracker sub-detector. The new Tracker is designed to operate in the new environment of the planned High-Luminosity LHC. The group is responsible for the production of software that will operate that detector. Students from the group will also contribute to its construction. In addition to the research experience gained by students in the group, the PI will combine education and research through outreach aimed at high-school curriculum development. Specifically, as a member of QuarkNet, the PI seeks to increase participation in virtual particle physics workshops for high-school teachers by better publicizing the 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 · 2025-02

ABSTRACT Our research team seeks to elucidate the fundamental principles that govern cell communication during organ development with a long-term goal of programming cellular health and function to correct disease phenotypes. Organ size, shape, and function emerge from cell-cell interactions between heterogeneous cell types. Calcium ions (Ca2+) are essential second messengers that help coordinate each cell’s response to various interactions and stimuli. Impaired Ca2+ signaling in cells occurs in many skin diseases, Alzheimer’s, and metastatic cancer. However, much remains unknown about the functions of the spatiotemporal dynamics of Ca2+ signals in developing or regenerating organs. Our previous results have led to significant progress in answering critical questions of fundamental significance that also generate new goals for the next five years: 1. What regulatory principles govern the multicellular dynamics of Ca2+ signaling? Progress: We have mapped the signatures of Ca2+ signaling dynamics in a developing organ system, the fly wing imaginal disc, to test the hypothesis that the dynamics of Ca2+ signaling fine-tune organ size and serve as a readout of organ growth rate. Next-step Goal: How can we control organ growth, morphogenesis, or organ function by programming Ca2+ signaling dynamics? Knowledge gained from our previous research is now leading to techniques to reprogram cell physiology and function by manipulating Ca2+ signaling, enabling future applications in modeling human diseases. 2. What are the functional roles of Ca2+ signaling in regulating tissue formation and homeostasis? Progress: Ca2+ signaling ensures robust wound healing and regeneration, but we lack knowledge of how this occurs at the multicellular scale. Our computational studies provide a framework for decoding the contributions of Ca2+ signaling during tissue formation and homeostasis. Next-step Goal: How do Ca2+ dynamics drive mechanical feedback between tissues during development? Our research team will test how Ca2+ signaling regulates the proteins that drive tissue mechanics during morphogenesis with a focus on wing disc eversion. 3. Can high-content analysis of perturbations to the extended Ca2+ signaling toolkit identify new regulators of organ development? Progress: We have created high-content imaging methods to identify functional relationships between genes that regulate Ca2+ signaling and corresponding cell- and organ-level phenotypes. This imaging pipeline, combined with machine learning techniques, helped to answer many questions, including predicting mechanisms by which a new drug will act. These previous efforts have led to the identification of new hypothesized mechanisms and expansion of research goals that require new experiments, quantitative tools, and approaches. Next- step Goal: How does multicellular Ca2+ signaling mediate and contribute to endocrine-based inter-organ signaling development? This effort will lead to a whole-organ, systems perspective of how multicellular tissues coordinate physiological responses within the whole-organism context and will provide insights into critical mechanisms impacting congenital disabilities, tissue degeneration, and aging.

NSF Awards · FY 2025 · 2025-01

Ushering in a new era of spectrum sharing requires dynamic spectrum access (DSA) that natively supports both primary and legacy users, while creating new opportunities for spectrum utilization. A comprehensive blend of technical, economic, and policy-based solutions is required to realize this vision, including potential modification to existing cellular standards to ensure that future 6G standards are inherently “sharing native”. Precise, low-latency, and localized spectrum usage monitoring that is aware of and integrated with the cellular Physical (PHY) and upper layers in the networking stack is essential for facilitating effective spectrum utilization and sharing in Spectrum Era 4. However, existing spectrum sharing systems typically rely on a separate monitoring network comprising dedicated, costly, and sparsely deployed spectrum sensors, e.g., the Citizens Broadband Radio Service (CBRS) networks rely on an environmental sensing capability (ESC) sensor network deployed in coastal areas to detect transmissions from Navy vessels and radars. This project aims to realize a transformative vision for spectrum sensing in Spectrum Era 4, which supports dense and in-situ spectrum sensing with significantly enhanced sensing resolution across the temporal and spatial domains, improved energy efficiency, and cooperative sensing strategies that are aware of the cellular protocols. As such, it has the potential to revolutionize the next generation of cellular technologies (e.g., 6G and beyond) to be sharing native with significantly enhanced spectrum awareness and sensing resolution. This project targets the following scientific contributions from three interdisciplinary and interrelated research thrusts. (i) Development of ultra-efficient, single-shot, analog cross-correlators (X-Corr) capable of computing the cross-correlations between input signals and template waveforms across varying lags, enabling spectrum sensing with ultra-low latency. Using the margin computing paradigm, analog X-Corr with superior energy efficiency and (>1,000 TOPS/W) can be designed and realized in integrated circuit (IC) implementations without compromising the computing speed or precision. (ii) Design of protocol-aware configuration and adaption for X-Corr to enable fine-grained, in-band spectrum sensing. This allows for detailed sensing of spectrum occupancy and detection of interference signals at the symbol or slot level (a few to 10s of microseconds) with both known and unknown features (e.g., for airborne and ground radars) and employ diverse PHY layers (e.g., 5G New Radio and Wi-Fi). (iii) Optimized deployment and configuration of a network of densely deployed X-Corr sensors to facilitate cooperative, in-situ spectrum sensing that is aware of the communication standards. Such a network also enhances the ability to localize and track interference sources with significantly lower latency and cost. Evaluation of the proposed research includes analysis, simulations, IC implementations, circuits-system co-design and integration, as well as field experiments using local and community wireless testbeds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT ABSTRACT The accumulation of tau protein deposits is characteristic of several neurodegenerative diseases that manifest with symptoms of dementia, cognitive decline, and movement disorders. The conformational plasticity of tau allows it to play a role in many important cellular processes; however, its misfolding and self-assembly into pathological filaments results in both loss of normal function and gain of toxic function. Disease-associated filamentous tau exhibits cross-b architecture, wherein protein monomers adopt b-arch folds that stack in register. These filaments can seed the misfolding of physiological tau and propagate across neurons in a prion-like manner. Recent cryo-EM structural data demonstrate that the conformations of tau protomers within pathological protofilaments can vary by disease, even when they are comprised of the same isoform and sequence. This raises the intriguing possibility of a link between conformational fold, seeding capacity, and disease progression. Current models of tau propagation based on co-factor-induced aggregation fail to capture the structural diversity of pathological tau folds, thus limiting their relevance. Given the scarcity and variability of seed-competent patient-derived extracts, efforts to recapitulate pathological tau folds are urgently needed. The current project will address this need through the structure-based design of peptidomimetics that mimic the form and function of tauopathic filaments. At the core of our approach is the diversity-oriented synthesis of conformationally constrained peptide macrocycles that adopt stable b-arch folds. These molecules, termed "mini-taus", will serve as broadly useful chemical biology tools to probe tau seeding and to generate conformation-specific antibodies. Our overarching hypothesis is that peptide stapling will potentiate the structure and function of minimal tau epitopes and will enable the development of anti-tau therapies targeting pathological folds. In Aim 1, we will carry out the diversity-oriented synthesis of a macrocyclic b-arch peptide library based on the high-resolution structures of 4R tauopathic filaments. These macrocycles will be tested for their ability to seed endogenous inclusions of full-length tau in engineered cells and primary neurons in a macrocycle- and sequence-dependent manner. In Aim 2, we will confirm that seed-competent b-arch macrocycles self-assemble into pathological cross- b folds using a combination of CD, X-ray fiber diffraction, and high-resolution cryo-EM. In Aim 3, we will establish in vivo seeding by a lead mini-tau and generate a single-domain antibody with potent and selective immunoreactivity with patient-derived 4R tauopathic filaments. The potentiation of b-arch form and function through peptide stapling has the potential to afford unique insights into the misfolding and propagation of tau as well as other amyloids implicated in disease.

NSF Awards · FY 2024 · 2024-10

Supporting the development of language, literacy, and STEM skills is essential for children’s future academic success. This project leverages the power of emerging AI technologies to develop a novel, low-cost solution for providing evidence-based instructional support to early education teachers. Specifically, the research team is collaborating with early childhood educators to co-design an AI-powered, app-based platform that delivers targeted instructional feedback to teachers and content-based professional development designed to support children’s development of language, literacy, and STEM skills. The context for the feedback and novel professional development platform is shared book reading in early education classrooms. Shared book reading is a common educational activity in pre-school and elementary school classrooms, and quality of teacher talk during shared book reading is predictive of children’s attainment of critical early learning skills. Current approaches to evaluating and providing feedback to teachers about their shared book reading practices necessitate observational measures and human coding, posing practical challenges to providing timely feedback to teachers at scale. This project represents a transformative approach to the provision of timely feedback to teachers making use of emerging AI technologies. Specifically, the research team first aims to bypass resource-intensive human coding by developing a Natural Language Processing (NLP) pipeline in combination with machine learning (ML) models to harness the power of a large pre-trained model – capable of understanding complex language patterns and contexts – and adapt it to the specific requirements of the educational domain. Next, the project aims to implement user-centered design in collaboration with early childhood educators to develop an AI-powered app-based platform to deliver timely instructional support, conduct usability testing, and iteratively improve the platform based on educator feedback. Creation of this novel instructional support system advances the knowledge base of how innovative technology solutions can deliver individualized, timely pedagogical support towards improving early learning outcomes. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Combating the deadly opioid epidemic is a national priority. Specifically, teenagers and young adults (TYAs) are disproportionately affected by and particularly vulnerable to opioid misuse and addiction. Unfortunately, research on how to provide effective yet affordable solutions to generate and promote tailored messages for the distinct yet vulnerable TYA community against the opioid crisis is lacking. To fill this gap, the “smart and connected (S&CC) community” in this project is defined as the community of at-risk TYAs who are particularly vulnerable to opioids and connected via online social media, within which intelligent technologies will be synergistically integrated to improve their resilience and well-being against the lethal opioid epidemic. By engaging with representative community stakeholders, this project will design and develop a new AI-driven paradigm to facilitate personalized messages tailored to TYA community’s characteristics and circumstances to promote their resilience against opioid misuse and addiction, and thus help enhance national public health, safety, and welfare. The developed framework can be scaled and easily transferred to other communities in preventing and reducing corresponding harms, such as people with different types of substance misuse and adolescents at-risk for suicide. The proposed work will advance scientific theory in related research communities and benefit multidisciplinary domains, including epidemiology, economics, and social and behavioral sciences. This research will accelerate personalized interventions for the at-risk TYA community in reducing opioid misuses and overdose deaths. First, based on the large-scale data generated by TYAs on social media, the team will develop graph neural networks with novel self-supervised and prompt learning techniques to detect at-risk TYAs, by addressing the challenges of heterogeneity, multi-modality, and limited labeling of the online data. Second, to derive and interpret key factors from diverse contexts presented by at-risk TYAs, the team will develop a novel causal analysis framework by bridging knowledge graph and large language model for intricate reasoning. Third, given the detected at-risk TYAs with related causal analyses, the team will develop a safety-enhanced multi-modal learning framework aiming to generate messages tailored to at-risk TYAs in preventing opioid misuse and addiction. Fourth, the team will further develop an adaptive reinforcement learning framework enabling user-in-the-loop to facilitate an interactive process that could inform users’ feedback, calibrate the generated messages, and thus produce adaptive interventions for at-risk TYAs. This project will integrate research with education and the outcomes will be made publicly accessible and broadly distributed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Deep learning for scientific visualization has quickly emerged over the past few years as a vibrant direction in visualization research. Solutions based on well-known analytic approaches (e.g., convolutional neural networks and generative adversarial networks) have been extensively utilized to solve a range of scientific visualization problems. Visualizations science are key to helping people understand complex results and identifying issues in the analyses. This research pursues a novel direction for scientific visualization. This project will drive core visualization research, promote new methods for scientific visualization, which will impact not only computing, but other fields of study. The team will integrate research into education through special lectures, class projects, and a new course to educate students at the intersection of machine learning and data visualization. The investigator will continue attracting and recruiting undergraduate and underrepresented students through well-established institutional outreach programs and organize a conference tutorial to nourish future artificial intelligence researchers and the workforce. The research team will develop novel solutions that significantly augment the ability to synthesize, manage, explore, and communicate complex scientific data and their visualization output. The core research tasks feature visualization synthesis from sparse rendering images and expedited neural representation of visualization images. These tasks and their further possibilities cover essential topics, from visualization generation to neural compression and reconstruction. In addition, the team will perform comprehensive evaluations using multilevel metrics to assess the solution's effectiveness. The project outcomes will include neural field representation techniques for visualization synthesis and communication, comprehensive evaluations demonstrating the superiority of the proposed solutions compared with baseline and state-of-the-art solutions, and software libraries to benefit the scientific visualization community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The United States faces a severe shortage of affordable housing, particularly for communities with extremely low incomes. This crisis is compounded by the outdated infrastructure of existing housing, which results in high energy costs and inadequate living conditions. This project, BUILT2AFFORD, aims to address this dual challenge by leveraging advanced technology and strong community partnerships to enhance the energy efficiency of affordable housing. By focusing on low-cost passive design strategies, such as improved ventilation and shading, this project seeks to reduce the energy burden on low-income households and improve their living conditions. This project is significant because it tackles the pressing need for affordable, energy-efficient housing in the Midwest, particularly in South Bend, Indiana. By developing a framework to pre-identify housing units suitable for retrofits, our research will enable more targeted and effective interventions. The broader impact of this work includes reducing energy costs for low-income families, mitigating heat-related health risks, and contributing to the sustainability goals of local communities. The successful implementation of this project could serve as a model for other regions, demonstrating how affordable housing can be preserved and improved through innovative, data-driven approaches. The BUILT2AFFORD project aims to enhance the energy efficiency of affordable housing by developing, testing, and validating a tool that uses machine learning algorithms and Google Street View images. This tool will automate the identification of housing units suitable for low-cost passive retrofits. In Stage 1, we will collaborate with the City of South Bend and Near Northwest Neighborhood to conduct audits of 10-20 houses to create archetype layouts for thermal comfort simulations. We will develop computer vision algorithms to extract passive design indicators from Street View images, combining this with property data to build the BUILT2AFFORD model. In Stage 2, the model will be validated by retrofitting two testbed buildings with passive design strategies. Sensors will monitor energy usage and indoor environmental conditions over eight months. The data will refine and calibrate the model for accuracy and reliability. The project will produce the BUILT2AFFORD tool, a dashboard pre-identifying affordable housing units for retrofits. It will visualize data on design indicators, energy efficiency, and health risks, aiding homeowners, policymakers, and public health officials. This project supports energy efficiency, improved home comfort, and equitable health outcomes, contributing to broader climate resilience efforts. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.