Purdue University

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY This proposal introduces a novel dietary approach targeting chronic obstructive pulmonary disease (COPD) caused by air pollution. Respiratory disorders, including asthma, COPD, and lung cancer, are significant global health concerns with high mortality rates. The primary risk factor for COPD is the inhalation of air pollutant cigarette smoke, leading to chronic pulmonary inflammation, mucus hypersecretion, airway remodeling, and emphysema, all contributing to lung dysfunction. Unfortunately, there are currently no effective medicinal therapies or dietary interventions for COPD. Cigarette smoke (CS) constitutes of over 7000 chemicals with known 93 harmful and potentially harmful chemicals such as acrolein. Repeated long-term environmental exposure to CS activates reactive oxygen species, inflammatory-oxidative stress and apoptosis that leads to alveolar space enlargement and development of COPD-emphysema, as well as compromising the ability to fight infection in the lungs, ultimately resulting in respiratory failure. Therefore, novel approaches that improve mitochondrial function in the lungs of COPD patients are urgently needed. Recent studies have indicated a potential protective effect of raw parsnip root (Pastinaca sativa), in combination with celery, against acrolein-induced pulmonary damage and inflammation. Moreover, our preliminary data demonstrate that an aging process involving high-temperature post-harvesting leads to the generation of polyphenol-enriched parsnip with enhanced antioxidative capacity compared to raw parsnip. We have observed that this aged parsnip effectively reduces lung inflammation and damage in mice acutely treated with acrolein intranasally. Based on these findings, we hypothesize that aged parsnip is a safe and bioactive dietary compound that can protect lungs from the pathogenesis of CS-induced COPD. To test this hypothesis, two aims have been developed using an animal model of chronic CS inhalation and primary human normal bronchial epithelial cells. Aim 1 will define the role of aged parsnip in chronic CS exposure-induced COPD. Aim 2 will determine the mechanism(s) underlying aged parsnip-ameliorated COPD. Successful completion of these aims will enable us to define the novel function of polyphenol-enriched aged parsnip and its bioactive components in air pollution-induced COPD.

NSF Awards · FY 2025 · 2025-09

This research project seeks to develop a rigorous theoretical foundation for amortized inference, a recent and impactful paradigmatic development in machine learning, statistics, and simulation. Amortization enables efficient, real-time responses to statistical queries by learning a model-dependent mapping from data to distributions, avoiding the need for expensive computations every time new data are presented. This capability underpins modern advances in generative AI, including diffusion models and variational autoencoders, with applications also extending to scientific machine learning (SciML) and simulation-based decision-making in operations research. Despite its widespread empirical success, fundamental questions persist: When do these methods work well, and when might they fail? How robust are the mappings to properties of the underlying problem? What kinds of statistical guarantees can be made about learned mappings, embodied for instance by deep neural networks? The goals of this project are twofold: (1) to deepen our understanding of the mathematical principles that underpin amortized inference, and (2) to inform the design of improved methods with provable guarantees. The project comprises three interrelated thrusts: 1) Functional Guarantees: This thrust investigates foundational properties of mappings from data to distributions: Do they exist? Are they unique? How well can they be approximated by, for example, neural networks? These results will elucidate the stability and robustness of amortized inference under data and model perturbations, 2) Statistical Guarantees: Building on the first thrust, this will establish both large-sample and finite-sample statistical guarantees for the learned mappings. The analysis will draw on techniques from M-estimation, approximation theory and Bayesian posterior contraction theory, 3) Methodological Developments: Existing amortized inference methods largely assume independent and identically distributed (i.i.d.) data. However, many applications-e.g., those involving data generated by Markov processes-violate this assumption. This thrust will extend amortized inference to non-i.i.d. settings, and will develop novel methodologies to fill this gap in the literature. Put together, these efforts aim to lay the theoretical groundwork for amortized inference, offering both insight and innovation in how statistical inference is carried out at scale. 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 · 2025-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Dr. Jean Chmielewski of Purdue University will conduct research building unique structures on a nanoscale using peptide building blocks. In order to achieve the selectivity and diversity of structure required for critical biological functions, Nature uses specific structural groups that allow for the assembly of nanoscale building blocks into more complex structures. To gain an enhanced understanding of this assembly process, Dr. Chmielewski will study how the size and shape of the resulting structures is controlled by the surface that the peptide building blocks are built on at a molecular level. These nanostructures will provide new materials for a range of applications, including nano-batteries and self-healing materials. Through this multifaceted project, Dr. Chmielewski will train both graduate and undergraduate chemistry students at Purdue University, in an effort to develop the next generation of scientists to tackle the technological challenges of the 21st century. The Chmielewski lab seeks to fully explore the interplay between molecular level features of peptide-surface interactions, and how these will inform the higher order assembly of peptide building blocks, and the chemical modification and peptide ligation chemistry on these surfaces. The intellectual merit of the proposed studies is to expand our understanding of the mechanisms of association between the surfaces of coiled coil peptide materials and other structures such as peptide oligomers, proteins, nanoparticles, and carbon nanotubes. Improving upon our fundamental understanding of the supramolecular assembly of peptide nanomaterials and their interactions with other materials would have a broad impact on several applications, from device fabrication to self-healing materials. Ultimately, the results obtained from these proposed experiments will provide crucial information for a range of applications in biotechnology, including device fabrication, sensors, enzyme arrays, photonic barcodes and self-healing materials. 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 · 2025-09

Professional learning (PL) programs are widely used to disseminate research-based instructional principles to educators with the goal of improving classroom teaching and student outcomes. However, little empirical evidence exists regarding if and how teachers engage with and use the information they acquire once the formal training is completed. This project addresses that gap by examining teachers' experiences and decision-making processes as they interpret, adapt, implement, or disregard PL content after training concludes. The results will help improve how research-informed ideas are shared in professional learning contexts and how innovative practice can be supported in real-world school settings. This study is designed to reframe existing models of knowledge mobilization (KM) within education by centering the role of the teacher as an active agent in translating knowledge into practice. Using a narrative inquiry approach to explore how teachers make sense of and act on what they learn during PL, this study will begin with in-depth, field-based research involving a cohort of K-12 teachers who have completed the same PL program and are currently teaching in a shared regional context. Through interviews, classroom observations, and reflective activities, researchers will examine how teachers describe their understanding of the PL content, the decisions they make about whether and how to apply it, and the contextual factors that shape those decisions. The findings will inform the development of a knowledge mobilization (KM) model that positions teachers as active agents in the translation of research-based ideas into classroom practice. This model will then be explored in a broader national sample of PL providers and teacher participants to assess its relevance and applicability across varied contexts. Ultimately, the project will generate practical and theoretical insights to guide the design of future PL efforts, improve research-to-practice pathways, and inform policy discussions about supporting teachers' professional growth. This project is jointly funded by the Translation and Diffusion (TD) program that supports research that advances the science of translation and diffusion between research and practice in STEM education, and the Discovery Research preK-12 program (DRK-12), which is an applied research program that seeks to enhance the learning and teaching of preK-12 STEM. 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 · 2025-09

This IRES project prepares the next generation of U.S. environmental scientists and engineers by providing them with the scientific and engineering skills required for addressing pressing challenges of anaerobic digestion (AD), a process that utilizes microorganisms to transform wastes into natural gas, electricity, and heat. This technology has historically been used as a tool in wastewater treatment, but its use for managing agricultural wastes and food waste has increased significantly in the past two decades. Despite its growing use, several challenges remain to realize the full potential of this technology to maximize energy recovery. In this IRES project, graduate and undergraduate students from the United States participate in a 6-week research experience in Canada, with opportunities to explore, discover, decipher, develop, evaluate, and engage in the advancement of AD biotechnology through novel approaches aimed at improving utilization of waste and increasing production of energy. The mentoring team of this integrated, interdisciplinary research, education, and professional development program includes U.S. researchers (Veera Gnaneswar Gude - Purdue University Northwest; Mohammad Marufuzzaman - Mississippi State University; and Matthew Scarborough – University of Vermont) and Canadian researchers from The University of Alberta and Toronto Metropolitan University. The research experiences focus on understanding (i) the microbiological processes/key players (microbiome) responsible for successful AD operations under various chemical stressors, (ii) potential impacts of micro/ nanoplastics and antibiotic resistant genes on AD of sludge; (iii) the role of electro-assisted AD for bioproduct recovery; (iv) the impacts of micro/nano bubble aeration, mixing and operating temperature on AD performance; and (v) the potential of Artificial Intelligence for AD process monitoring and optimization; and process modeling for resource recovery and management. In addition to the immersive scientific research experience, the student participants gain global perspectives of sustainable environmental biotechnologies in different cultural, geographic, and regulatory settings, thereby preparing them for professional careers in diverse research environments and engineering practice. After the international travel, the student participants reflect and share their experiences with other graduate and undergraduate students at the U.S. institutions to catalyze motivation for the next round of research students. 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 · 2025-09

Nontechnical Description This project will focus on developing new materials that can be customized to control how light behaves, thereby advancing optical technologies for applications in communication, information technology, energy, and sensing. Conventional optical materials are not easily tuned or adapted, which limits their use in reconfigurable devices. This research will utilize MXenes, a family of two-dimensional structures engineered to reflect, absorb, or guide light in precise ways. By combining advanced material-making, experimental testing, and computer modeling, the team will establish a novel design framework for producing customizable materials with exceptional optical properties. The framework will also include new digital tools for predicting materials behavior and minimizing trial-and-error during development. In addition to the research, the project will offer interdisciplinary training for graduate and undergraduate students and contribute to public science education by developing open-access learning resources through nanoHUB.org. These initiatives will help prepare a new generation of researchers in the field of advanced photonic materials. Technical Description The project aims to develop optical materials that are customizable, dynamically tunable, scalable, and reconfigurable while exhibiting advanced light-matter interactions, such as plasmonic behavior, epsilon-near-zero (ENZ) response, hyperbolic dispersion, and strong nonlinear effects. Conventional photonic materials do not provide sufficient control or adaptability for emerging applications in photonics and optoelectronics. To tackle this challenge, the team will integrate synthesis, characterization, and computational modeling to understand and engineer how the composition, structure, and arrangement of MXenes impact their highly versatile optical and electronic properties. To create a predictive materials-by-design framework, the research will proceed with three objectives: (1) synthesize different MXene films and perform structural and optical characterization using tools such as ellipsometry, spectroscopy, and microscopy to generate a digital twin model, a physics-informed framework capable of predicting optical properties; (2) explore ordered and disordered hybrid MXene composites to achieve ENZ behavior and hyperbolic dispersion, using quantum emitters as probes for optical anisotropy; and (3) investigate all-optical and externally driven modulation through nonlinear optical measurements such as Z-scan and second-harmonic generation, incorporating these results into an expanded nonlinear digital twin. This combination of predictive modeling, experimental feedback, and dynamic control will enable the rational design of MXene-based materials for advanced optical applications. The outcomes will deepen understanding of structure-property relationships in 2D materials and establish scalable strategies for reconfigurable photonics. 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 · 2025-09

Seed plants develop specialized structures at their shoot tips called shoot apical meristems, which contain stem cells responsible for upward growth, leaf formation, and eventually, the production of flowers and fruits. Ferns, which are vascular plants but reproduce through spores instead of seeds, also develop shoot meristems; however, these structures differ significantly in their organization and morphology from those in seed plants. Additionally, ferns possess unique meristems crucial to their life cycle that are absent in seed plants, such as leaf meristems that drive prolonged vegetative growth and multicellular meristems during their reproductive gametophyte phase. This project aims to uncover the fundamental processes guiding the formation and maintenance of these diverse meristems in ferns. The research will identify key regulators that are either shared across plant lineages or unique to ferns. The outcomes will enhance the understanding of how plant developmental strategies evolved over time. Besides advancing scientific knowledge, the project will actively engage students and teachers in STEM fields. Each summer, high school science teachers will receive hands-on training through the Purdue professional summer institute workshop, enabling them to develop interacting biology labs using ferns. High school and undergraduate students will be recruited through established institutional programs, directly participating in fern research. Additionally, undergraduate students will benefit by integrating aspects of this project into their plant anatomy coursework, gaining practical experience in sample analysis and microscopic observation. The fern, Ceratopteris richardii, undergoes an alteration of generations between independently growing haploid gametophytes and diploid sporophytes, each containing distinct types of meristems. Using Ceratopteris as a model system, this project will dissect complex signaling networks mediated by key transcriptional regulators during shoot and leaf meristem development in fern sporophytes. Additionally, the project will examine the expression dynamics and functional roles of a highly conserved microRNA family in meristems across both gametophyte and sporophyte phases of the fern lifecycle. To achieve these goals, this project will integrate multiple approaches, leveraging robust Ceratopteris transgenic systems, including CRISPR-mediated genome editing, gene silencing, inducible gene activation, and fluorescence reporters for specific genes, proteins, and cell types. Additional methods will include phylogenetic analysis, confocal live imaging with quantitative image analysis, biochemical assays, histological analysis, RNA in situ hybridization, and transcriptomic profiling. These studies will provide insights into both conserved and lineage-specific regulatory mechanisms underlying meristem formation and stem cell maintenance across land plants. 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

The majority of pancreatic ductal adenocarcinoma (PDAC) patients present with late-stage, metastatic disease that is often resistant to therapeutics, resulting in the lowest survival rate of all major cancers. Metabolic rewiring in response to oncogenic signals plays a critical role in PDAC cell survival, tumor growth, and metastasis. In contrast to normal epithelial cells, these metabolic alterations make PDAC tumors dependent on glutamine for survival, highlighting a unique metabolic vulnerability that can be therapeutically exploited. However, during times of nutrient stress, PDAC cells can circumvent this vulnerability by engulfing extracellular fluids to replenish amino acids, including glutamine, in a process called, macropinocytosis. Macropinocytosis occurs downstream of oncogenic KRAS, a small GTPase that is almost universally mutated in PDAC patients. The inhibition of macropinocytosis in vivo reduces PDAC tumor growth, underscoring the importance of this pathway to cancer cell survival. However, there remain gaps in our knowledge regarding the signaling mechanisms that regulate macropinocytosis and the contribution of macropinocytic signaling to aggressive cancer phenotypes. Protein phosphatase 2A (PP2A) is a heterotrimeric complex that regulates a wide variety of cell signaling pathways, including KRAS, and is commonly deregulated in human PDAC tumors. Recently, PP2A has been implicated as an important regulator of macropinocytosis, but the mechanism by which this occurs is unknown. We have shown that the genetic loss of the specific PP2A subunit, B56a, accelerates the formation of KRAS-driven pancreatic lesions in an in vivo mouse model, implicating this subunit as a critical negative regulator of KRAS phenotypes during PDAC progression. Here, we demonstrate that PP2A-B56a activation results in attenuated glutamine signaling, a significant accumulation of macropinocytic vesicles, and cell death. Furthermore, the therapeutic combination of PP2A activators and metabolic inhibitors leads to synergistic loss of PDAC tumor growth in vivo. Based on these findings, we hypothesize that activation of PP2A-B56a sensitizes PDAC tumors to glutamine antagonists by limiting metabolic plasticity through the blockade of macropinocytosis-dependent nutrient acquisition. We will test this hypothesis in the following Specific Aims: 1) Identify the posttranslational mechanisms by which PP2A-B56a regulates macropinosome-lysosome fusion, 2) Evaluate the impact of PP2A activation on PDAC progression and macropinocytosis in vivo, and 3) Determine if PP2A activation increases the efficacy of metabolic inhibitors, in vitro and in vivo. Successful completion of these studies will significantly increase our knowledge of the posttranslational mechanisms that govern PDAC nutrient scavenging, providing novel targets for drug discovery in a highly lethal cancer.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT This R01 application proposes to use a novel telehealth-based assessment protocol to collect high quality clinical, behavioral, and electrophysiological assays to characterize early course and predictors of clinical outcomes among young children with rare neurogenetic conditions (NGCs). Characterizing the earliest development of children with NGCs, including predictors of common comorbidities, is essential to developing evidence-based standards of care, identifying clinical treatment targets, and designing effective support programs. However, the assessment tools needed to monitor the early development of NGC patients are severely lacking, particularly for young children with severe sensorimotor or cognitive impairments. To address these gaps, PI Kelleher developed PANDABox (Parent Assisted Neurodevelopmental Assessment; K23MH111955), an open science, telehealth-based assessment protocol for collecting high quality, integrated clinical, behavioral, and spectral assays (e.g. heart activity, vocal patterns) from children with rare disorders. In contrast to standard neuropsychological assessments, PANDABox includes a series of classic “laboratory tasks” that are appropriate for a wide spectrum of abilities and are administered by caregivers in the family home. Now, what is lacking is a large corpus of natural history data, collected using PANDABox, that can be harvested to identify the most valid, reliable, and feasible assays for clinical monitoring of NGCs. To address this need, the present proposal will deploy PANDABox in a large, cross-syndrome, longitudinal cohort of young children with NGCs to generate a large corpus of natural history data that will be used to identify reliable and valid assays that distinguish early developmental profiles in 6-42 month old children with and without rare NGCs, and that predict variability in community-prioritized clinical outcomes. If funded, this project will improve the human condition by deploying accessible, patient-centered telehealth assays that have potential to radically shift the status quo for developmental surveillance in young NGC patients, including those who are geographically distributed or marginalized, setting the stage for improved clinical outcomes for children with NGCs and their families.

NSF Awards · FY 2025 · 2025-08

This project focuses on the study of qualitative reconstruction methods for nondestructive testing in thin elastic plates. Nondestructive testing is ubiquitous in engineering applications and medical imaging, and scattering in thin elastic plates has significant applications in the area of detecting geomagnetic anomalies and medical imaging of the brain. In recent years, qualitative reconstruction methods have been shown to provide quick and accurate reconstructions when applied to acoustic and electromagnetic scattering. Therefore, the primary goal of this project is to study the applicability of these methods to the thin elastic plate model. This would allow for computationally simple yet analytically rigorous algorithms for recovering defects in thin elastic materials. This project will also involve the training of graduate students who will contribute to this project. The proposed research has two main components. The first component aims to study the direct and inverse scattering problems of a thin elastic plate. This is given by a biharmonic wave equation that is derived from the Kirchhoff-Love infinite plate problem in an infinite domain, which will be studied in the frequency domain. Questions such as the well-posedness of many direct scattering problems will be addressed and well-known qualitative reconstruction methods, such as the linear sampling and factorization methods, will be applied to these scattering problems. These methods are advantageous for shape reconstruction problems since little a priori information is required for their implementation. One disadvantage of the aforementioned reconstruction methods is that they are not valid at frequencies that correspond to an associated eigenvalue problem. This leads to the second component of the research, which is to study the associated transmission eigenvalue problems. In general, these eigenvalue problems are non-self-adjoint and nonlinear, which makes their investigation mathematically interesting. Preliminary calculations indicate that these eigenvalues can be recovered from the scattering data and depend monotonically on the material parameters. This project will aim to provide an analytical and computational study of these new eigenvalue problems. 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

Topology is classically a qualitative study of the shape of spaces, which can help understand the shape of the universe, robotics, and paths of vehicles. It is helpful to probe the space in question using low-dimensional objects such as surfaces. Relevant to this idea, this project aims to advance a more modern theme in topology: Studying quantitative and optimization problems regarding surfaces in spaces to obtain deeper insights about the ambient space. In terms of broader impacts, the PI will continue co-organizing a regional workshop and sectional meetings, mentor graduate students, and supervise the directed reading program at Purdue University. In more technical terms, the goal of this research is to better understand the minimal complexity of surface maps into various spaces under different constraints, where the complexity is measured using negative Euler characteristics. One setting is about the existence of optimizers for the stable commutator length (scl) and the Gromov norm in negatively curved 2-complexes, such as the presentation complex of hyperbolic one-relator groups. This is related to Gromov’s surface subgroup problem and unfolds into two new attempts: Reducing the problem to understanding immersed surfaces, or studying the optimization problem from a dual point of view. Another setting is about sharp estimates of a geometric-degree analog of scl, which is well-connected to various fundamental open problems in topology and group theory such as the cabling conjecture, the Kervaire-Laudenbach conjectures, and the Wiegold problem. 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

Advanced materials such as composites, metamaterials, soft materials, and architected materials are inherently heterogeneous and multiscale in nature. Currently, multiscale modeling serves as the most effective approach for analyzing and designing these materials. However, the growing complexity of microstructural features and macroscopic structural configurations presents significant challenges to achieving both computational efficiency and predictive accuracy. While emerging machine learning (ML) models offer a cost-effective alternative, their effectiveness is often limited by the lack of high-quality training data in many real-world engineering applications. Moreover, advanced ML techniques are still not routinely incorporated into traditional mechanics or materials engineering curricula. To address these challenges and support both research and education in multiscale material and structural modeling, this project supports research that develops a cloud-based cyberinfrastructure that integrates new multiscale modeling theory with multi-fidelity ML models. This platform seeks to provide open-access tools, curated datasets, and comprehensive training resources to advance materials science, enable efficient structural analysis and design, and support workforce development in ML-assisted material and structural modeling. The goal of this project is to develop a cloud-based, open-source multiscale modeling software called OpenMSG, providing an ultra-efficient prediction toolkit for mechanical and multiphysics behaviors of highly heterogeneous materials and structures. To achieve the goal, this project first develops new multiscale models based on mechanics of structure genome (MSG), which can discretize analysis domains using efficient beam and shell elements while still considering strong material heterogeneity and anisotropy. The new models will provide an unprecedented combination of computational efficiency and accuracy and generate highly correlated multi-fidelity data. Building on these multiscale models, the project then develops a framework using multi-fidelity neural networks (NNs) for ML-assisted multiscale modeling. This hybrid approach further reduces the computational burden while preserving model accuracy across design spaces. The models and framework are demonstrated using additively manufactured functionally graded materials and composite blade designs, which also showcase OpenMSG’s capabilities in advancing the fundamental understanding of heterogeneous material behavior and facilitating the efficient design of complex engineering structures. OpenMSG will be developed, tested, and maintained on the widely recognized Composites Design and Manufacturing HUB (cdmHUB) with the support of CI experts from HUBzero. Leveraging established user bases and infrastructure on cdmHUB, this project delivers not only a cutting-edge multiscale modeling tool but also fosters a sustainable global user community dedicated to data-driven multiscale materials and structural modeling. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil Mechanical and Manufacturing Innovation within the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This research will advance the state-of-the-art of industrial material removal processes for high-temperature refractory metals through a recently uncovered chemical effect (local embrittlement) in surface plasticity, referred to as Organic Monolayer Embrittlement (OME), arising from nanoscale organic films. It is well known that high-strength metal alloys, e.g., hard steels, are difficult to machine. What is much less well recognized is that relatively soft, refractory metals like tantalum and niobium are equally challenging to cut, grind and comminute, with high forces and surface quality problems, earning them the moniker "gummy." The gummy behavior is due to the high malleability of these metals, with non-homogeneous deformation and intense energy dissipation. This award supports research that seeks to solve the gumminess challenge via scientific understanding of the nanoscale OME phenomenon and its implementation in manufacturing processes. The research project will test the hypothesis that if the gumminess can be eliminated by local embrittlement, using benign organic media that induce a surface stress in the metal, then material removal will occur by fracture, with low forces/energy, improved surface quality and increased productivity. A suite of high-performance chemomechanical manufacturing processes should emerge, advancing refractory metal applications in areas including aerospace, hypersonics, nuclear energy and electronics. Complementing the research is an education program involving undergraduate researchers in creating a video gallery of plastic flow and fracture phenomena for manufacturing, and scientific collaborations with companies and universities. The research combining high-speed in situ observations of deformation, chemistry/material interactions and surface science will explain how nanoscale organic films influence (a) large-strain deformation and material removal in refractory metals via surface stress and (b) forces, deformation, and fracture, which are all manifest at the macroscale. A fully instrumented plane-strain cutting system will impose controlled large-strain deformation typical of material removal processes. The basis of the chemical effect in plasticity will be established by (a) integrating surface molecular probes and high-resolution in situ imaging of deformation, with ex situ materials characterization, (b) multiscale modeling of materials behavior and chemical effects in plasticity/fracture, and (c) characterizing media effects on process attributes such as forces, energy, and workpiece surface quality (finish, metallurgy). The study will investigate model material systems, including tantalum and nickel alloys, selected for their deformation response and technological interest. The findings will impact areas such as manufacturing, wear, and environmentally assisted cracking, wherein interactive effects of chemistry, plasticity and fracture often play a key role. 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 award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. The IMPHRESS project seeks to help communities address challenges around extreme heat events. Extreme heat is a serious global public health challenge and impacts of heat in areas with large populations are poorly understood. While there has been an increasing amount of research into extreme heat it has been challenging to mount a systemic response to the heat-health challenge due to the lack of research that addresses all aspects of this systems problem. Working with partners from India, the research team will seek to measure heat exposure and the frequency and intensity of extreme heat events to help develop physical activity and energy expenditure models for occupations with high heat exposure. These models will be combined with weather forecasts to develop a prototype early warning system that provides targeted heat-health advisories. The project will also develop a suite of heat stress metrics and develop multi-dimensional estimates of chronic heat stress and loss in labor productivity and engage with community and institutional actors to facilitate development of heat-health policies. 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 Faculty Early Career Development Program (CAREER) grant will contribute to the progress of science and the advancement of national prosperity and welfare by supporting research to study efficient scalable methods for solving large-scale network optimization problems. These problems, which appear in train scheduling, information infrastructure and telecommunication network design, often involve complex structures that make their optimal solution exceedingly challenging for realistic problem sizes. Recent advances in multi-core and cluster computing create exciting opportunities to develop new approaches that can exploit these developments to enhance the scalability of optimization methods. This award pursues a fundamental understanding of the network elements that can be modeled more effectively through novel parallelization frameworks, leading to the design of cost-efficient and structurally robust networks. The accompanying educational plan aims to boost the engagement of the new generation of students in STEM education by developing new gaming tools aligned with optimization concepts and disseminating them through appropriate K-12 and college-level venues, while providing opportunities for underrepresented students. This research will develop a new graph-based parallelization framework to solve large-scale network optimization problems with combinatorial requirements. The methodology exploits the structure of decision diagrams, that provide a compact graphical representation of the network problem, to design an effective parallelization strategy with balanced workloads and low communication overheads between parallel cores, mitigating a formidable computational challenge for conventional mixed-integer programming algorithms. This research will also expand the application of decision diagrams from the traditional discrete optimization to include continuous decision variables through novel decomposition and outer approximation frameworks. The developed methods will be applied to the unsplittable network flow problem that arises in unit train scheduling, load balancing, bandwidth allocation, and survivable network models to improve their solution time and quality when compared to the outcome of modern solvers and state-of-the-art methods. 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

Awards are made to Carnegie Mellon University and Purdue University to enable the development of a cyberinfrastructure that supports the analysis of cryo-electron tomography (cryo-ET) data. Cryo-ET is a cutting-edge imaging technology for revealing the structures and spatial organizations of subcellular components, in particular, macromolecular complexes, inside cells. This project will build an open-access, annotated database of cryo-ET images—both simulated and experimentally obtained—alongside a robust toolbox of computational methods for their analysis. The resulting resources will lower the entry barrier for new researchers, promote collaboration, and accelerate scientific discoveries across the life sciences. Educational outreach includes training Ph.D., graduate, and undergraduate students through interdisciplinary coursework, hands-on research, and workshops at both institutions. Workshops will also be held for the broader research community, including educators and students at the high school level. Beyond biology, the tools developed will support innovations in medical imaging, and materials science, ultimately contributing to workforce development in data-driven scientific fields. The intellectual merit of this project lies in establishing a foundational infrastructure for cryo-ET data analysis that addresses a critical gap in the field: the lack of well-curated, annotated datasets and standardized computational tools. By developing realistic simulated datasets, manually and semi-automatically annotated experimental data, and a benchmark database, the project supports rigorous method development and validation. The project also integrates state-of-the-art machine learning and computer vision algorithms, including novel simulation methods and segmentation frameworks. Together, these innovations will catalyze the development of new computational techniques and deepen our understanding of the structures and spatial organizations of subcellular components within cells, advancing the frontiers of structural and cell biology. 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.

NIH Research Projects · FY 2025 · 2025-08

The purpose of the International Neurotoxicity Association (INA) is to: (1) foster the science of identifying neurotoxic agents that pose a risk to human and environmental health and determining the neural mechanisms and behavioral consequences of toxicant exposure; and (2) encourage international collaborations in neurotoxicology research. The bi-annual INA meeting (this will be the 19th INA convention) enables the global neurotoxicology community to communicate their latest research findings to their colleagues for critical evaluation and continuing education, and to provide a forum for discussion of future directions for the field of neurotoxicology. We will build upon the most recent INA meetings in Israel (2009), China (2011), The Netherlands (2013), Canada (2015), Brazil (2017), Germany (2019) and Durham, North Carolina, USA (2022). The Arctic Hotel in Norway has experience bringing in people from around the globe to participate in meetings and conferences with a unique coastal scenery, top technical equipment, activities for all seasons, local food and local fishing village culture dating back nearly 1000 years. This venue provides a great opportunity for INA to engage both established neurotoxicology scientists from diverse government, academic and industry centers, as well as students. The theme of the meeting is “Integration Across Biological and Scientific Domains in Neurotoxicology.” Dr. Jason Cannon is counselor and US-based treasurer for INA. Dr. Pamela Lein is president of INA and past-chair of the Scientific Organizing Committee for the 2022 convention. Dr. Aaron Bowman, INA President-elect, is chair of the Scientific Program Committee. Symposia at the INA convention will include: “Challenges and opportunities for using new approach methods data on developmental neurotoxicity for risk assessment and decision making”, “Leveraging functional assays and microphysiological systems for neurotoxicity screening”, “Unveiling the impact: How plastics affect the human brain”, as well as special symposium for postdoctoral and graduate student trainees. At least 11 other symposia are under development across a variety of topics relevant to chronic and acute neurological diseases with environmental etiologies. Two poster sessions are planned, and Dr. Amy Kind, University Wisconsin School of Medicine and Public Health has agreed to provide the plenary Jacob Hoosima presentation, entitled “The power of social exposome research to catalyze real-world change: The case of Milwaukee Water Works lead pipe replacement plan”. Dr. Heidi Aase, head of Child Health and Development Department of the Norwegian Institute of Public Health (NIPH), in Oslo, Norway, has been selected as the Pioneer in Neurotoxicology awardee and will present the other keynote talk. Funding is needed to support travel and accommodations for graduate students and postdoctoral trainees to participate in the meeting. A write-up of the proceedings of the meeting will be submitted to the peer reviewed journal, NeuroToxicology, a leading journal in the field.

NSF Awards · FY 2025 · 2025-08

This project is aimed at problems in the subject of nematic liquid crystals that are not only important and challenging mathematically, but also have close connections to other fields, such as material sciences and fluid mechanics, and have found profound applications to the liquid crystal device (LCD) industry. The rigorous analysis of certain types of solutions to the governing equations for nematic liquid crystals in either static or dynamic situations can predict the formation and structure of defects in the materials, allow better understandings of turbulence phenomena, and justify both experimental and computational studies by applied scientists. This, in turn, can advance the design and control of optical features in display devices. The sharp phase transition problems in the vectorial valued setting have their origins in applied sciences and have already found important applications in many chemical reaction-diffusion problems and isotropic-nematic transition phenomena in liquid crystal materials. The questions will be integrated into the training of Ph.D. students, and the findings will be disseminated through a research monograph and other means. The project consists of three parts: 1) The hydrodynamics of nematic liquid crystals modeled by the Ericksen-Leslie system; 2) Phase transition problems between isotropic-nematic phases and the structure of line defects in the framework of Landau - de Gennes Q-tensor theory; and 3) the heat flow of s-harmonic maps into manifolds. The goal of the first part is to establish the existence of global Leray-Hopf weak solutions in dimension three for any finite energy initial data; in particular, the investigator is focused on the existence of global axisymmetric solutions with swirls, the existence of forward self-similar solutions with sole singularity at the origin, and the Liouville problem for steady Ericksen-Leslie system in three spaces. The second part of the project is devoted to continuing the study of the sharp interface limit problem of minimizers to a singular perturbed Landau - de Gennes energy functional by the Gamma convergence theory and the geometric description of the defect set of Q-tensor minimizers around its negative uniaxial set. In the third part of the project, the investigator aims to establish the global existence of partially smooth solutions of the heat flow of s-harmonic maps. 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 advancements in neuroscience, robotics, and computer systems have underscored the vital interactions between mechanical and computational systems in shaping behavior. In natural systems, such as those found in animals, the brain and body must collaborate effectively for the successful navigation of a complex environment. The brain contributes computational intelligence, while the body provides mechanical intelligence. Integrating these elements—computational and mechanical intelligence—into the concept of mechano-computation represents a frontier in both robotics and neuroscience research. Progress in this field necessitates interdisciplinary communication and collaboration across various scientific domains. To propel this promising field forward, the Mechano-computation for Expanding Scientific Horizons (MESH) Network aims to unite diverse researchers from robotics, mechanics, materials science, neuroscience, information theory, biology, engineering design, and applied mathematics. Through workshops, travel grants, and the facilitation of collaborative projects, this network seeks to stimulate interdisciplinary dialogue, develop rigorous metrics for assessing autonomous systems, train the next generation of researchers, and push the boundaries of research in all areas of mechano-computation. By establishing a centralized resource for sharing findings, benchmarks, and methodologies, this network of researchers can accelerate innovation and position the United States as a leader in this transformative field, laying the groundwork for enhanced robotic systems in healthcare, agriculture, forestry, national security, and beyond. It may be argued that the full potential of robotics will not be realized until an intelligent physical body is purposefully designed from the outset, with careful consideration of both the available computational intelligence and the affordances the body can provide—affordances that, if appropriately leveraged, can offload and simplify computational demands by enabling efficient, embodied solutions to complex tasks. The Mechano-computation for Expanding Scientific Horizons (MESH) Network will bring together leading experts to tackle these critical challenges in autonomous systems through the integration of mechanical and computational intelligence. Creating intentional mechano-computation will enhance the design and control of autonomous systems, making them more efficient and explainable, and it will contribute to the development of innovative materials, mechanisms, and control strategies, pushing the boundaries of current research. We anticipate five key outcomes as a result of the formation of the MESH Network: (1) A comprehensive theoretical framework and standardized metrics for mechano-computation; (2) Improved interdisciplinary collaboration and communication among researchers; (3) Long-term interactions among network members and early-career researchers, including nurturing graduate students trained at the intersection of disciplines; (4) Sharing of innovative materials, mechanisms, and control strategies; (5) Practical demonstrations by network participants of mechano-computation systems addressing societal and environmental challenges. The network will accomplish these outcomes through tasks that build online repositories of network critical technical and organizational information, in-person events to broaden discussion and collaboration, online communities, and targeted support for bringing in new collaborative research areas. This project is supported by the Dynamics, Control, and System Diagnostics (DCSD), the Engineering Design and Systems Engineering (EDSE) and the Mechanics of Materials (MoMs) programs of the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) in the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This grant provides travel support funds for undergraduate and graduate students and early career faculty to attend the 2025 ASME International Mechanical Engineering Congress and Exposition (ASME-IMECE) in Memphis, Tennessee; 16-20 November 2025. The travel funds will support 30 undergraduate and graduate students to present at a special poster session and 15 Assistant Professors to attend the ASME-IMECE and participate in the inaugural Mechanical Engineering Rising Star Celebration, expected to attract hundreds of participants. Both events will be open to all eligible conference attendees. The travel award selection will consider the full breadth of institutions represented by the students and faculty, as well as the variety of programs within engineering. This grant aims to benefit the nation by educating a skilled engineering workforce prepared to provide transformative solutions to the challenges in their fields. Participant support is expected to enhance students' professional, scientific, and technical development as they present their NSF-funded research projects at the largest mechanical engineering conference in the nation. Students are expected to improve their communication skills through discussions of their work with top researchers. Participants will also have the chance to attend various technical presentations, keynote and plenary sessions featuring technological pioneers, and network with potential mentors, colleagues, and employers. For faculty, particularly untenured members, the conference provides a unique opportunity to network and form crucial connections with Rising Stars in Mechanical Engineering, offering long-term career benefits. This project is funded by programs within the Division of Civil, Mechanical, and Manufacturing Innovations (CMMI) and the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET). 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

Biomolecules, proteins and nucleic acids, carry out almost all tasks inside living cells. To understand how such molecules work and how to design new drugs that bind to them, their three-dimensional (3D) structures are crucial. Recently, cryogenic electron microscopy (cryo-EM) has allowed scientists to visualize these biomolecules as 3D volume data, but its resolution is not high enough to capture all the details. Converting those cryo-EM data into precise 3D atomic models remains slow, expensive, and challenging, especially for large complexes. This project aims to develop new artificial-intelligence (AI) tools that will automatically interpret cryo-EM data into accurate 3D molecular structures and check those structures for mistakes. By releasing the software as freely available web computing services and expanding an open database of quality assessments, many academic laboratories, biotech companies, and pharmaceutical companies can enhance their research and development. By this project, hands-on workshops on developed tools and outreach activities will be conducted. Faster, more reliable cryo-EM modeling will accelerate drug discovery, enhance numerous structural biology research efforts, and lead to new applications of AI in the biological field. This project will pursue three closely related objectives: (1) it will develop a deep learning-based method capable of constructing protein-DNA/RNA complex structures. The proposed architecture will integrate advanced deep learning architectures, making the process more accurate and scalable for large protein-DNA/RNA complexes. Additionally, a novel method will be developed to identify unknown proteins and nucleic acids within cryo-EM maps with high accuracy. (2) The model quality assessment score framework will be expanded in two directions: The score will assess backbone and side-chain errors in high resolution EM maps and also scores that evaluate molecules other than proteins will be developed. (3) For challenging medium to low-resolution cryo-EM maps, a new biomolecular modeling and assembly method will be developed. It will sharpen density with a diffusion model, convert it to a backbone point cloud, and fit the local structure of AlphaFold models using advanced point-cloud registration techniques, followed by clustering and real-space refinement. Expected outcomes include open-source software, a publicly accessible online computing web service, and an expanded quality-assessment database. These outcomes will overcome the current limitations in biomolecular modeling for cryo-EM data. 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 CAREER project will explore how engineering students develop the skills necessary to function in a globalized engineering work environment. Addressing the complex and global issues that our world faces often requires the collaboration of teams of engineers from across cultural and national boundaries. Working in international and intercultural teams can enhance innovation, but only when engineers have the skills necessary to support such collaboration. This project will study the processes by which students develop the skills needed for success in a global engineering workplace. The project will characterize how this learning takes place in many contexts including programs where engineering students travel abroad (e.g., study abroad, internship abroad) and experiences that do not involve travel (e.g., global courses, domestic internships). Understanding this learning process will support the development of curricular and co-curricular experiences that help all engineering students develop these essential skills, regardless of whether they are able to travel abroad. The project will also produce two online educational modules that can be used to enhance learning in experiential programs and provide workshop and webinar training to educators in how to implement these modules. The research and educational activities in this project support National Science Foundation’s mission to advance national health, prosperity, and welfare by supporting the professional formation of engineers through the development of an understanding as to how students become global engineers and providing tools to facilitate this process. This project will characterize the process of global engineering competency (GEC) development by studying travel and non-travel global programs for engineers. The project will address the following research questions: RQ1) What significant events can initiate the process of global formation for engineering students? RQ2) How do engineering students interpret and respond to significant events in their intercultural experiences? RQ3) How do personal, program, contact, and context factors inform engineering students’ processes of global formation during intercultural experiences? The project will use a longitudinal video reflection method to collect weekly video reflections from 50 engineering students participating in four categories of experiences: study abroad, global courses at home, internship abroad, and internship at home. These reflections will be analyzed using critical incident analysis (RQ1), a hybrid coding approach (RQ2), and framework analysis (RQ3) to develop a framework describing the process of GEC development. The framework analysis will be grounded in existing theoretical frameworks of GEC and intercultural competence development. The educational activities for the project will focus on the development of two online training modules: 1) Reflecting on Experiential Learning and 2) Connecting Culture and Engineering. All participants in the research study will complete both training modules before participating in their intercultural experiences, and the video reflection data from the research study will be used to evaluate and refine the modules. Two workshops and a webinar will be presented to various faculty audiences to disseminate these modules, which will also be published online as a publicly accessible resource. The key contributions of this project are expected to include: (1) developing a framework describing the process of GEC development; (2) expanding our understanding of the relationship between GEC and intercultural competence; (3) refining the video reflection data collection technique; (4) disseminating two online educational modules; (5) training an estimated 150 educators to use the modules through workshops/webinars; (6) training 50 students in critical reflection using the educational modules. Strengthening global and experiential learning for engineers connects to ABET criteria and NAE goals focused on educating engineers who can work on teams to address the global challenges facing the world today. 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

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Cryo-electron microscopy (cryo-EM) has become a widely used technique in structural biology for determining 3D structures of biological macromolecules. Despite an increasing number of structures being deposited in public databases like PDB and EMDB, many maps are still determined at 3 Å or worse resolution, posing challenges for structure modeling. Thus, there is a strong need for computational tools to assist in structure modeling and validation, given the increasing use of cryo-EM and cryo-electron tomography (cryo-ET) by scientists. Computational modeling is an integral and indispensable component in structural biology. Similar to the situation with microscope, superior modeling methods have the capacity to extract more accurate structural information from otherwise less informative data from cryo-EM and cryo-ET and provide new insights and opens up new research strategies. The goal of this project is to develop and apply computational methods for biomolecular structural modeling for cryo-electron microscopy. In this project I will substantially expand and enhance the capabilities of structure modeling methods to meet new demands and to improve accuracy and efficiency. This will be achieved by developing a deep learning-based approach that can consider key aspects in structure modeling altogether, including structure heterogeneity, atom detection in the density, structure prediction, interaction between proteins and nucleic acids. For lower-resolution maps, we will introduce a novel approach designed to identify and enhance key structural features within the map, which will significantly improve the accuracy of model building. This approach will also be applied to structure fitting and identification for cryo-ET. Additionally, a method for detecting and modeling small-molecule ligand structures in medium-high resolution EM maps will be developed, which is needed for drug discovery. In the process of building model structures, model validation is of crucial importance. To pinpoint modeling errors in PDB, we have devised a pioneering deep-learning-based quality assessment score, known as DAQ. We intend to expand DAQ's capabilities to identify atom-level errors in protein and nucleic acid structures, while also offering suggested corrections for flawed structure models within the DAQ-Score Database, which presently delivers model validation reports.

NSF Awards · FY 2025 · 2025-08

This project is focused on inverse problems, where one seeks to recover the parameters of unknown media from remote measurements. First, the investigator will study the recovery of a Lorentzian metric, up to a gauge group of transformations, from measurements of the way light or positive mass particles propagate as observed on a timelike boundary. In cosmology, this means recovery of spacetime from remote observations. It has applications to probing moving media with acoustic waves as well. The measurements could be either arrival times and directions of rays or more generally, the arriving wave itself. The second problem is to recover the underlying Riemannian geometry in a bounded domain, say in three dimensions, from the area of the minimal surfaces attached at various loops on the boundary. This problem arises in relativity, precisely in the anti-de Sitter/conformal field theory correspondence, sometimes called the holographic duality in physics. The stability of the recovery will be studied as well, i.e., not so sensitive to small errors in the data. The third type of problem to be studied is recovery of the nonlinear parameters of media from the way light or sound, etc., propagate. In particular, the investigator will show that one can achieve a two-wave interaction in nonlinear wave propagation, which does not fit within the conventional framework. Graduate students and postdoctoral researchers will be mentored and trained as part of the project. The project is primarily in the area of inverse problems in Lorentzian geometry, the inverse problem for minimal surfaces, and in nonlinear wave propagation and related inverse problems. More concretely, the investigator studies the recovery of a Lorentzian metric, up to a gauge group of transformations, from measurements of the way light or positive mass particles propagate as observed on a timelike boundary. This is called lens/scattering rigidity. The linearization is a tensorial X-ray transform restricted to lightlike or timelike geodesics. What makes the nonlinear and the linear problems fundamentally different from their Riemannian version is that the linear one loses the ellipticity of the Riemannian case. In particular, stability is lost. A version of this problem when the whole wave is observed, is studied as well using the hyperbolic Dirichlet-to-Neumann map as data. The minimal surfaces inverse problem on a compact Riemannian manifold with boundary asks whether one can recover a Riemannian metric from the knowledge of the areas of the minimal surfaces with prescribed boundary intersections (say, 1D loops in 3D). The investigator plans to investigate stability as well. Finally, it will be shown that one can force a two-wave interaction in nonlinear wave propagation. This interaction is used to recover the nonlinear parameters and the geometry, locally, involved in nonlinear hyperbolic partial differential equations. 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.