Purdue University

NSF Awards · FY 2025 · 2025-09

Oxygen is crucial for macroscopic life, yet the causes and repercussions of its accumulation in the atmosphere are poorly understood. A key question for resolving the trajectory of planetary habitability is if chemical shifts recorded in ~2.4-2.0 billion-year-old rocks reflect global-scale oxygen changes or regional-local conditions. To answer this question, rock cores from Gabon, which hosts the best-preserved sedimentary archive across this interval, will be analyzed for possible chemical imprints of oxygenation. This project serves the national interest by promoting the progress of fundamental science that identifies how Earth became habitable. Synergistic outreach objectives include initiatives such as community tables at farmers’ markets from Northeast-Midwest USA to enhance public scientific literacy and undergraduate curriculum development to support an American STEM workforce that is globally competitive through improved education. This interdisciplinary project applies stratigraphy, paleomagnetism, geochemistry, and geochronology to assess whether extreme geochemical shifts in the wake of the Great Oxidation Event (GOE) reflect global, regional, or local-diagenetic conditions. Laterally correlative drill cores across shallow-to-deep paleoenvironments in the Francevillian sub-basins of Gabon will be applied to test the hypothesis that, in the Paleoproterozoic, there was a prolonged overshoot in O₂ coeval with a widespread perturbation of the carbon cycle. The objectives are: 1) Create a detailed stratigraphic framework and isolate primary magnetizations to examine facies and latitudinal climate-belt controls; 2) Assess a variety of isotopic and geochemical criteria in carbonates to determine if a primary “oxygen overshoot” is preserved; and 3) Apply U-Pb zircon geochronology/geochemistry and carbonate Rb-Sr isotope compositions to evaluate if geochemical shifts are of global relevance. The results will facilitate detailed tracking of the dynamics of oxygenation and concurrent environmental changes in the wake of the GOE—including extraordinary disturbances to the carbon cycle—that are key for deciphering this tipping point in the trajectory of planetary habitability. 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

The PIs research is concerned with advanced probabilistic models which have potential real-world applications in cutting-edge machine learning techniques. It aims to bring mathematical rigor and come up with new methods related to complex systems in image processing, reinforcement learning, and generative AI. Invoking recent breakthroughs in stochastic analysis as well as developing new tools, the project intends to make progress in the following directions: it introduces new ways to extract meaningful features from images, possibly enhances decision-making systems through reinforcement learning in random environments, and improves the theoretical understanding of generative models such as diffusion-based algorithms. These developments have the potential to contribute to more interpretable, robust, and effective AI systems, with applications ranging from medical imaging to autonomous driving. Beyond technical contributions, the work promotes interdisciplinary collaboration and offers strong mentorship opportunities for students and junior researchers. On a technical level, the project explores four main directions: (1) the development of 2D-signatures based on rough paths and Hopf algebra structures to extract robust features from image data; (2) the construction of new image descriptors via expansions inspired by regularity structures and nonlinear PDEs; (3) the formulation of reinforcement learning problems as relaxed control problems driven by rough paths, with entropy regularization and rigorous optimization procedures for the value function; and (4) the study of generative modeling through reversed diffusions and score-based methods, with a focus on improving theoretical guarantees and algorithmic implementations using Malliavin calculus. Each of these threads addresses fundamental challenges in modeling, analysis, and numerical approximation, and together they aim to bridge the gap between abstract mathematical theory and practical machine learning tools. 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

Large earthquakes can cause devastating damage, particularly in regions where soft sediments amplify shaking. Many major cities around the world, including those in the Pacific Northwest of the United States, are built on sedimentary basins that can trap seismic waves and greatly increase both their intensity and duration of shaking. Over 12 million people live in this region, which has the potential to produce both a megathrust great earthquake (potentially M9+) and also smaller crustal earthquakes that occur closer to population centers. This project aims to improve our understanding of how local geological structures affect earthquake ground motion amplification in the Pacific Northwest, particularly in densely populated sedimentary basins. The researchers will develop new ways to constrain the subsurface structure of Cascadia forearc basins to provide better information that can guide estimates of ground motions from seismic hazards. All data, methods, and results will be openly shared to support the broader scientific community and regional velocity model-building efforts, including collaboration with the Cascadia Region Earthquake Science Center (CRESCENT). This study will develop and apply two complementary passive seismic techniques to characterize the shallow subsurface structure of the Cascadia forearc basin, where most of the population in the Pacific Northwest lives. The research team will use particle motion analysis from teleseismic earthquakes to help define shallow shear-wave velocity structure inside and outside the basin. Then, using horizontal-to-vertical spectral ratios from local earthquakes and ambient noise, basin geometry will be constrained. The results will be incorporated into regional-scale seismic velocity models such as the Cascadia Region Earthquake Science Center (CRESCENT) Community Velocity Model (CVM). Numerical simulations will help assess how the newly defined basin structures amplify ground motion during earthquakes. This research addresses a key gap in understanding how local geology influences seismic hazard at a regional scale—an issue of critical importance for earthquake-prone areas globally. The methods developed here are globally applicable and will represent a new approach to determining seismic shaking potential in basin settings. 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 · 2025-09

Project Summary Our research is dedicated to elucidating the molecular mechanisms of RNA-guided biological systems, with a focus on CRISPR-associated transposons (CASTs) and emerging RNA-guided gene regulation functions. Leveraging our expertise in single-particle cryo-electron microscopy and a broad array of biochemical and functional assays, we aim to dissect the complex interactions and structural dynamics in these natural systems. Our recent work has advanced our understanding of diverse CRISPR-Cas systems, highlighting the structural basis for target recognition and enzyme activation. We have also made progress in studying the assembly of transpososomes that facilitate DNA transposition within CASTs. The proposed research will explore the molecular basis underlying targeted DNA insertion by CASTs, using Type I-B systems as model systems. Key questions include the structural dynamics of transpososome assembly, the mechanism of directional DNA insertion, the role of ATP hydrolysis in transposition, and a detailed understanding of target specificity. The insights gained will inform the rational design of simpler, more efficient systems. Additionally, we will investigate the structural basis of emerging RNA-guided gene regulation functions, which hold potential for developing programmable gene regulation tools. Supported by the flexibility of the MIRA mechanism, our goal is to deepen our understanding of RNA-guided systems, thereby driving the development of next-generation genome editing tools and expanding their potential applications in both therapeutic and basic research contexts.

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

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Julia Laskin at Purdue University is investigating the reactivity of well-defined transition metal complexes deposited onto self-assembled monolayer surfaces to gain fundamental insights into the stability, reactivity, and chemical degradation of complex solid interfaces. The limited molecular-level understanding of surface-mediated chemical transformations hinders progress in energy technologies, catalysis, and molecular electronics. This project addresses that gap by developing a generalizable approach that enables the controlled preparation of new interfaces for fundamental studies of interfacial processes. The work will employ sophisticated custom-built ion soft-landing instruments capable of depositing high currents of both intact ions and reactive fragments onto model surfaces with precise control over ion identity, charge state, and kinetic energy. Professor Laskin and her students will systematically examine how factors such as the gas-phase stability of precursor ions, structural rearrangements upon surface impact, and charge retention influence the reactivity of deposited species. By combining mass spectrometry with advanced spectroscopic and electrochemical techniques, the team will investigate ligand loss, charge transfer, and interfacial degradation mechanisms. Their discoveries will inform the design of stable, functional interfaces for applications in photovoltaics, spintronics, and quantum information science. The research will also contribute to the development of predictive models for surface reactivity guided by density functional theory (DFT). Broader impacts include integration of the research into undergraduate analytical chemistry education and hands-on outreach workshops, as well as a strong emphasis on workforce development. Undergraduate and graduate students will gain interdisciplinary training in surface science, mass spectrometry, and computational chemistry within a multidisciplinary, collaborative research environment—equipping them with the technical expertise and problem-solving skills needed for careers in academia, industry, and national laboratories. The technical objectives of this project are to: (1) determine how the relative gas-phase stability of fully coordinated and undercoordinated transition metal complexes affects their reactivity on surfaces; (2) assess how co-deposition of cations with weakly coordinating anions influences charge retention and structural stability; (3) probe reaction mechanisms using isotope-labeled ligands to trace ligand exchange pathways; and (4) compare surface reactivity with degradation induced by light or electrochemical stimulation. The experimental studies will focus on 3d-metal complexes (Fe, Co, Ni, Cu, Zn) ligated with bipyridine or phenanthroline, which are broadly relevant to applications in light harvesting and catalysis. The use of custom-designed ion deposition instruments will allow for precise control over experimental variables, and the resulting surface products will be characterized in situ and ex situ using a suite of spectroscopic and mass spectrometric techniques. These data will be interpreted in conjunction with DFT calculations to correlate reactivity with geometric and electronic structure. Collectively, this work will establish benchmark datasets and mechanistic frameworks to understand and control interfacial reactivity of complex ions, thereby enabling the rational design of robust functional 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

In this project the PI will study questions in algebraic geometry and commutative algebra. Algebraic geometry is the study of algebraic varieties, which are solution sets for systems of polynomial equations. For example, in the xy-plane, the solutions for y=0 consist of all points along the x-axis, while the solutions for xy=0 consist of all points along both coordinate axes. Since the tangent line at the origin (0,0) is not defined for the algebraic variety defined by xy=0, we say that this variety has a singularity at the origin (0,0). A central focus in the proposed research is that studying singularities is indispensable even when the objects of interest are smooth manifolds, which are not singular. In another direction, certain analytic objects defined using possibly divergent power series are often unavoidable as well. This contrasts with the usual situation on smooth manifolds, where all smooth functions have Taylor series expansions that converge in some neighborhood of a point. The PI will study the analogues of these situations in the field of algebraic geometry -- in particular, birational geometry -- and its interactions with other fields of mathematics, such as commutative algebra, complex geometry, and arithmetic geometry. The project will also provide research training opportunities for students. This project unifies and expands the classical boundaries of algebraic geometry and commutative algebra in multiple interconnected directions. First, the PI will expand the scope of birational geometry and the minimal model program to broader classes of singularities and categories of spaces. This work includes work on the relative minimal model program for (semi-)log canonical pairs on schemes and algebraic spaces. This work also extends to both complex and non-Archimedean analytic spaces (where functions are convergent power series) and to formal schemes (where functions are divergent power series). The PI will also extend Matsusaka's big theorem to families of varieties with normal or rational singularities, and work towards solving new cases of ACC conjecture for minimal log discrepancies, where divergent power series play a pivotal role. Second, the PI will develop new tools and techniques to construct embeddings of projective varieties in projective spaces. This work includes higher-dimensional cases of the PI's Fujita-type conjectures and a new notion of centers of F-purity, the latter of which will be applied to Iitaka's Conjecture on the subadditivity of Kodaira dimension. 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 project addresses a major challenge in next-generation autonomous systems: enabling unmanned aerial vehicles (UAVs) to physically interact with moving objects in mid-air. This capability has the potential to unlock transformative applications such as mid-air refueling, maritime rescue, and dynamic target interception. These tasks are difficult due to constantly changing environments and require high levels of precision, adaptability, and reliability. By tackling this challenge, the project supports national priorities in aviation safety, disaster response, and transportation logistics. The research will also offer hands-on learning experiences for graduate and undergraduate STEM students. The technical focus of this project is on advancing cyber-physical systems through a unified framework for aerial manipulation in dynamic environments. The research will develop: (1) a physics-informed modeling framework that improves UAV flight dynamics prediction under real-world conditions, (2) a safety-assured motion planning approach for computing adaptive, collision-free trajectories in uncertain and dynamic scenarios, and (3) a novel, energy-absorbing manipulator that enables UAVs to securely grasp and interact with moving objects. These goals will be achieved through the integration of machine learning, robotic system design, and optimization methods. The resulting algorithms and hardware will be validated through experiments and are expected to significantly expand the capabilities of UAVs for real-world operations in logistics, emergency response, and airborne transportation. 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 project examines how exposure to scholarly forecasts about artificial intelligence's (AI's) impact on labor markets shapes perceptions, decision preferences, and intended behaviors among U.S. workers, business managers, and decision makers. Research into how AI will affect the workforce is important for public debate and individual and business decisions, but it is unclear how such research influences the beliefs and decisions of key stakeholders. The findings of this study clarify how such research informs—or complicates—critical decision-making by these groups. To support broad understanding and practical use of the findings, the project includes development of a public-facing online portal with plain-language summaries of major AI forecasting studies and interactive tools, allowing users to compare labor forecasts across industries, occupations, and demographic groups. The project contributes to national priorities related to AI leadership, the future of work, and science communication, while mentoring students. The research draws on insights from labor economics, science of science, and public administration to design a set of multi-sample online survey experiments targeting three distinct stakeholder groups: workers in non-managerial roles, business managers responsible for organizational decisions, and regulatory decision makers. Participants first answer questions assessing their knowledge, beliefs, and attitudes about AI. They are then randomly exposed to brief informational vignettes based on real-world academic forecasts about AI's labor impacts. Treatments vary by predicted level of disruption, groups identified as most affected, framing of AI’s role, and whether the predictions presented are consistent or conflicting. Participants subsequently answer follow-up questions on AI-related beliefs, regulatory preferences, and stakeholder-specific outcomes. The project advances U.S. leadership in responsibly navigating AI innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project aims to advance student engagement and achievement in high-enrollment STEM courses by substantively improving and evaluating CourseMIRROR, a mobile learning environment that delivers real-time, AI-guided reflection support. Powered by state-of-the-art natural language processing, CourseMIRROR prompts learners to reflect on what interests or confuses them, provides immediate feedback that spurs deeper thinking, and compiles class-wide insights for instructors. Partnering with universities and community colleges, the project reaches hundreds of students each semester and equips faculty with scalable, evidence-based practices that require no extra grading. By expanding access to effective study strategies and informing national priorities in AI-enabled education, the goal is to have broad benefits for retention and workforce readiness in science and engineering. Guided by the Reflection-Informed Learning and Instruction model and a Self-Regulated Learning (SRL) theory of change, the research pursues three integrated aims. First, adaptive prompts, motivational nudges, and automated reflection summaries are engineered and optimized through iterative usability and feasibility tests. Second, the effects of these features on motivation, emotion, SRL processes, and course performance are explored through field experiments across multiple introductory courses at multiple institutions. Third, multimodal data, including Motivated Strategies for Learning Questionnaire subscales, fine-grained app logs, micro-analytic interviews, and graded assessments, are analyzed with multilevel models and structural-equation mediation tests to determine whether SRL gains explain achievement improvements. The multidisciplinary approach bridges natural language processing, human-computer interaction, and learning sciences, yielding transferable design principles for AI-enabled educational tools and aims to open new research directions at the intersection of emerging technologies and STEM learning. 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 2025 · 2025-09

Understanding planet formation requires knowledge of the chemical composition of the planet-forming material. The project will explore the chemistry of hot gas around protostars, where the first steps of planet formation are occurring. They will utilize high spatial resolution telescope observations along with chemical theory. The overall objective of this project is to identify key characteristics of the chemical composition of the material from which Earth-like planets form. The project will primarily support two graduate students and one postdoctoral researcher. The project will develop a series of nine astronomy lesson plans designed specifically for elementary school teachers, aimed to engage children with science at an early age. The lesson plans will also be developed in video format to be made available through portals accessible to homeschool teachers and those in classroom settings. The objective is to identify key characteristics of the chemical composition of terrestrial planet forming material, that is, material in the inner region of proto-stellar systems where the temperature is >300 K. The project will use extant ALMA data and accepted ALMA programs to directly characterize and contrast the content of both warm and hot gas. These results can be compared to existing models of bottom-up chemistry to quantify the abundance of hydrocarbons. The team will also use the ALMA data to search for distinct products of this chemistry via sophisticated line stacking and search techniques. The project will expand existing chemical networks for organic chemistry (UMIST and KIDA), with hot gas hydrocarbon chemistry and develop a 2D chemical proto-stellar disk calibrated model to identify the driving processes behind the chemistry in hot proto-stellar gas. 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 project advances the design and functionality of robotic hands by developing an integrated framework that enables more dexterous and intelligent grasping. Robotic hands are essential for enabling robots to interact with the world across industries such as manufacturing, healthcare, and service robotics. However, most current designs are limited in functionality, often focusing on a single capability such as flexibility or sensing. This award supports research activity that introduces a new approach that combines mechanically intelligent finger structures with embedded force, slip, and vision sensing based on event-driven cameras. The resulting technology will enable robotic hands to perform both delicate and power grasps while adapting to changing environments. The research will have broad impacts on industry through the development of smart robotic grippers and will promote STEM education through hands-on outreach activities with K–12 students, as well as integration into university-level robotics design curricula. These efforts aim to inspire the next generation of engineers and support the US workforce in high-tech sectors. Technically, the project introduces a novel design methodology for variable-stiffness robotic hands using a double-layered compliant mechanism structure that supports dynamic stiffness control and integrated sensing. The research team will use event-based vision and machine learning to extract force and slip information from finger deformations without the need for traditional tactile sensors. Key outcomes include (1) analytical and learning-based models for mechanism design, (2) vision-based force and slip detection algorithms, and (3) closed-loop control methods for stable, adaptive grasping. The system will be validated through hardware implementation and simulation. The integrated platform—including hardware prototypes, control software, and experimental data—will be shared with the broader research community. This work contributes to the field of compliant robotics and intelligent manipulation and lays the foundation for future developments in human-level robotic hand 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 · 2025-09

Even though safety is a critical element of engineering education and profession, the awareness about safety topics remains limited, which impacts the safety attitudes of individuals and safety culture of the organizations. The importance of safety education in engineering has been well-recognized by academic institutions, accreditation bodies, and professional societies. However, there is limited attention and time allocated to safety education in most undergraduate engineering programs, and there is limited understanding of the existing safety awareness levels among undergraduate engineering students. This project aims to understand the current safety awareness level and attitudes of undergraduate engineering technology students and the factors influencing them, develop an educational intervention to improve students’ safety knowledge, and examine the impact of this intervention on students’ safety awareness and attitudes. While traditional workplace safety interventions experience implementation challenges due to resistance from employees, educating students about safety before they enter the workforce can have a lasting influence on organizational safety culture. Engaging in the proposed theoretically grounded educational intervention will help students to develop metacognitive skills to better identify safety risks and develop strategies to mitigate those risks in their future careers. The results from this project will support the professional formation of engineers by improving their safety knowledge and attitudes by engaging them in hand-on learning environment and, in the long-term, produce engineering and technology graduates who create a safer work environment and products for society. This project’s broader impacts include cultivating a safety-conscious future workforce, providing open-source safety education modules for widespread use, and advancing the integration of safety into engineering curricula. This project will utilize a mixed-methods research design will be used to answer the following research questions: RQ1: How do engineering students’ attitudes towards safety in engineering contexts change from baseline to after an educational intervention? RQ2: What is the relationship between students’ prior exposure to safety incidents (direct, indirect or no experience) and their safety attitude? RQ3: How do students describe the changes in their attitudes towards engineering safety after completing the learning activities? RQ4: How can student attitudes toward engineering safety be categorized into distinct groups after completing the learning activities? This project will use a design-based research framework to develop the safety educational intervention and examine its effectiveness. The educational intervention will include interactive online learning module, site visit, and expert lectures, covering (a) basic safety concepts, (b) hazard identification and risk analysis (c) multi-dimensional accident causation analysis, and (d) accident prevention mechanisms. The project will be carried out in three phases. In the first phase, the educational intervention will be developed followed by a pilot study to gather student feedback to refine the materials. In the second phase, the revised module will be implemented in a senior capstone course, where students will also participate in a site visit and attend invited talks by safety experts to enhance their practical understanding. The third phase will focus on analyzing the collected data and disseminating the findings. The primary impact of this project is to foster safety-focused attitudes among students through the use of interactive modules integrated with experiential learning opportunities such as site visits and expert talks. Throughout the learning process, students will engage in structured reflection activities that deepen their understanding of safety engineering concepts and connect classroom instruction to real-world contexts. The project will provide a foundation for educators to integrate safety concepts into the undergraduate engineering and technology curricula. The findings from this project will inform which teaching mechanisms are more effective for safety education and the extent to which they can influence the safety attitudes of individuals. 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

Cancer cells crawl at slow speeds that can be challenging to detect. Even at slow speeds, cancer cells can reach blood vessels that carry them to distant locations in the body. This project will use lasers and biophotonics technology to build a sensitive cell ‘radar speed gun’ that can measure the speed of cells crawling through living tissues and find which drugs can prevent them from spreading. This approach will also be able to measure faster speeds at which immune cells move while patrolling tissues to find and eliminate wayward cancer cells. By measuring the speed and density of immune cells inside cancer tumors, this imaging system will be able to assess the benefits of immunotherapies to specific patients. This project exemplifies how fundamental principles from the science of light can deliver transformative advancements in cancer diagnosis and treatment. Biodynamic imaging will be extended to measure a broad range of frequencies to access a wider range of cellular functions, such as metastatic motion, by developing a compact common-path configuration and fast vesicle transport by implementing high-speed imaging without losing quantum efficiency. Quantitative phase imaging of thick tissues will measure the probability distribution functions of anomalous intracellular Levy transport and will explore the connections among Doppler spectral changes and drug action. Fundamental scientific studies will connect Doppler shifts to specific drug mechanisms of action, and computational models will provide predictive tools that explain spectral signatures in terms of biological mechanisms to improve assay interpretability. The long-term goal of this project is to establish biodynamic imaging as a reliable phenotyping technology for the rapid selection of personalized medical treatment when drug resistance is the principal barrier to effective treatment. 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 award supports the 2025 Midwestern Workshop on Asymptotic Analysis, held October 10-12, 2025 at Purdue University Fort Wayne. The conference advances research in the field of mathematical analysis by offering a venue for dissemination of results and building cooperation among researchers in various disciplines, especially those represented in the Midwest. An emphasis is placed on introducing graduate students, early career faculty, and other young researchers to a wide range of current problems, techniques, and results. The conference features a poster presentation to foster networking among all attendees. More information can be found on the conference webpage http://mwaa.math.indianapolis.iu.edu/. Topics covered by the conference include complex analysis, several complex variables, potential theory, approximation theory, and applications. The workshop convenes researchers in these disciplines with the goal of stimulating new mathematical interactions among them. Exposure to current work across disciplines allows for new mathematical and professional connections to be made. Presentation of cutting-edge results and building collaboration are the goals, which contribute to the professional development of all attendees and is especially valuable for early-career researchers. 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

The conduct of nearly all modern scientific research depends on software, yet the systems through which research software is developed, shared, and deployed—its supply chain—remain vulnerable to cyber threats. These Research Software Supply Chains (RSSCs) are complex networks of tools, libraries, collaborators, and institutional processes, and they form a critical foundation for the U.S. research enterprise. However, there is no shared understanding of what these supply chains look like or how to protect them. This project will initiate a coordinated planning effort, called CROSS (Community around Securing the Research Software Supply Chain), to bring together researchers, research software engineers, and government stakeholders to identify and mitigate risks to RSSC security. Through community workshops, empirical studies, and a comprehensive review of existing knowledge, this effort will produce a roadmap for securing the RSSC—helping to safeguard the integrity of scientific knowledge, promote national security, and support the development of a resilient research ecosystem. The project will also engage undergraduate students at Purdue and Loyola, supporting workforce development in cybersecurity and research software engineering. This planning project will develop foundational knowledge to guide future efforts in securing the research software supply chain. The research team will (1) conduct a systematic literature review to synthesize current knowledge into a conceptual model of the RSSC and its security threats; (2) empirically measure the security posture of real-world research software projects and their dependencies, using datasets provided by national laboratory collaborators and applying a range of software and security metrics; and (3) convene workshops with research software engineers and scientific collaborators to capture practitioner insights and build community consensus. The findings will be integrated into a unified system model and threat model, guided by the STAMP (System-Theoretic Accident Model and Process) and TOE (Technology–Organization–Environment) frameworks, and will culminate in a strategic report for the NSF’s Research on Research Security (RoRS) program. This work will support the development of new security interventions and lay the groundwork for future collaborative research to protect the software that underpins scientific innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Modern science and engineering increasingly rely on extracting meaningful information from large and noisy datasets, such as those arising in medical imaging, environmental monitoring, telecommunications, and numerous other disciplines. This project develops advanced statistical methods that improve signal recovery and noise reduction through innovative shrinkage and thresholding techniques applied in multiscale domains like wavelets. In addition to classical computational tools, the project explores emerging directions involving quantum computing simulators to prototype quantum-inspired shrinkage methods, aligning with growing national and institutional emphasis on quantum technologies. These approaches simplify complex data by selectively attenuating noise while preserving essential features, leading to more accurate and interpretable results. The project integrates education by mentoring students at multiple levels, incorporating findings into graduate and undergraduate courses, and creating open-source software tools that promote reproducible research and broad access to cutting-edge statistical techniques. This research advances the theory and application of shrinkage estimation in multiscale settings, with a particular emphasis on quantum-inspired methodologies that complement classical Bayesian and frequentist frameworks. It develops adaptive block-shrinkage procedures employing priors that capture dependence among wavelet coefficients and introduces absolutely continuous shrinkage priors that maintain computational tractability without relying on spike-and-slab or point-mass priors. The project also devises novel thresholding strategies informed by refined extreme-value approximations and Bayesian decision rules based on Bayes factors. Computational implementation includes efficient posterior simulation algorithms and exploratory shrinkage techniques using quantum computing simulators. These innovations will contribute to foundational methodology for nonparametric regression, signal processing, and scalable high-dimensional inference. 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

Mergers of ultra-compact stellar remnants called neutron stars have now been observed. They provide new tools to study general relativity, compact objects, neutron star equation of state, plasma physics, astrophysical jet physics, and nucleosynthesis. A joint US-Israeli research team will develop numerical simulations of binary mergers through their full development. The US effort is jointly led by California State University, Sacramento, Purdue University, and Northwestern University. The team will aim to construct “meter-to-parsec” models of binary mergers, which follow through the entire journey of length and time scale, by directly connecting the pre-merger state of a neutron star - neutron star (NS-NS) or neutron star - black hole (NS-BH) binary to the regions where the observed photons are produced. This project has two main broader societal impacts : (i) Development of a globally competitive STEM workforce; and (ii) Improvement of STEM education of K-12 students. The models will allow interpretation of various observables of the system by connecting them with the conditions inside the ejecta and in the pre-merger phase: (i) Generating consistent models of the jet profile will allow the researchers to connect the observed off-axis emission to the conditions at the base of the jet and help construct reliable emission models for the multi-wavelength counterparts of gravitational wave events. (ii) To date, it is still not known if the compact merger remnants powering short gamma-ray bursts (sGRB) jets are BHs or NSs. By mapping the main differences between the physical properties and emission profiles of jets powered by these two types of engines, the research will provide a method to distinguish between them, independent of the kilonova emission, which may not be detectable in typical multi-messenger events. (iii) The question of energy composition in the jet at large scales is a long standing puzzle in sGRBs. The answer can shed light on the acceleration mechanisms and emission processes responsible for the prompt gamma-rays. In addition, the Sacramento State principal investigator (PI) will partner with the Sacramento State planetarium to develop a new curriculum for K-5 audiences that visit the planetarium as part of their free school field trips. The Purdue PI will partner with the Saturday Morning Astrophysics Purdue program for middle and high-school students to develop and lead an annual session on the detection of gravitational waves and the study of compact objects in the Universe. The Northwestern PI will partner with the REACH high school program to mentor Chicago-area high school students each summer. 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 award will support US-based researchers to participate the Banff International Research Station - Casa Matemática Oaxaca (BIRS-CMO) Workshop 25w5402: Equivariant Topological Quantum Field Theory will be held from September 7th - 12th at Hotel Hacienda Los Laureles in Oaxaca City, Mexico. The first-of-its-kind event will bring together experts in a relatively small subfield of mathematical physics to concentrate efforts on making meaningful progress on open problems while training a new generation of students and early-career mathematicians to work in the area. The workshop activities are designed with the intention of developing a strong network of researchers in equivariant topological quantum field theory across the Americas. Topological quantum field theories (TQFTs) give mathematically rigorous descriptions of systems whose physics is independent of the geometry of spacetime. These theories are playing an increasingly important role in theoretical and mathematical physics, where they model the symmetries of ordinary quantum field theories as well as certain low-energy quantum systems called topological phases of matter which are influential in developing both hardware and software for quantum computers. TQFTs are also an invaluable source of inspiration in pure mathematics, where they provide a bridge between the subjects of algebra and topology. Equivariant TQFTs specifically capture TQFTs with symmetry and thus represent a critical aspect in the study of TQFTs. In spite of the relevance and importance of the topic, the BIRS-CMO Workshop 25w5402 is the first of its kind with a focus entirely on the mathematics of equivariant TQFT. A webpage for the conference website is available at https://www.birs.ca/events/2025/5-day-workshops/25w5402. 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

Elementary school students' prolonged experiences with positive numbers and operations often lead to their overgeneralizations of rules (e.g., adding always makes larger numbers, subtracting always makes smaller numbers). These overgeneralizations can make learning algebra more difficult later, particularly when students must simultaneously learn algebra, negative numbers, and operations with negative numbers. The purpose of this project is to design and develop educational games centered on negative number concepts that target students before they learn algebra in middle school. Earlier exposure to and learning about negative numbers could increase students' motivation, understanding of connections between positive and negative numbers, and preparation for algebra. In the long term, this earlier exposure could lead to later success in advanced mathematics and improve science technology engineering mathematics (STEM) education. The project team will design and develop the educational games through an iterative process of prototyping and testing. During the prototyping phase, the project team will consult with experts about how the games' mechanics and rules align with and support second- to fourth-grade students' learning of integer principles. Next, they will test the games with students. Based on students' interpretations and uses of the rules, their interaction with the materials, and their feedback, the research team will refine the materials. During the testing phase, the project team will conduct a six-session evaluation study (pretest, four gameplay sessions, posttest) to examine the refined materials. The pretests and posttests will include a set of integer knowledge measures, along with mathematics and motivation measures, so the investigators can quantitatively and qualitatively analyze students' integer knowledge gains and changes in their motivation. Further, the investigators will qualitatively analyze the sessions to track and characterize students' levels of engagement and identify those mechanics that support learning of integer principles. During the project's final year, the set of print-ready educational games will be finalized and made freely available for teachers and families. This research will contribute to a greater understanding of students' learning about negative numbers as well as the mechanics of games that support mathematical learning. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  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

A solid foundation in science, technology, engineering, and mathematics (STEM) is crucial for graduates to thrive in the workplace. However, many foundational STEM concepts are abstract, counterintuitive, and difficult to understand. Therefore, STEM learners struggle to achieve the complete and deep understanding that would benefit them the most in their careers. This project aims to develop and investigate an extended reality (XR) learning environment that integrates interactive touch technology, augmented reality, and AI-driven engagement to support undergraduate students in learning abstract STEM concepts, particularly electromagnetism. It brings together advances in XR hardware, computer vision, and learning sciences to address persistent challenges in STEM education. While the learning benefits of the XR environment will be investigated in the context of electromagnetism, the XR environment promises applicability across a range of STEM fields. The project plans to develop and validate the XR environment through a series of intertwined technology and education research activities. Grounded in principles of embodied learning design, the project will develop technology-enhanced learning experiences consisting of the XR haptics and corresponding curricular materials. XR haptics will be provided by a versatile and cost-effective haptic device that delivers physically accurate force feedback. Furthermore, the XR headset worn by the learner will both augment and diminish their view of the real world. The augmentation will include an interactive visualization of the computer simulation illustrating the learning activity. Diminished reality algorithms will erase virtually elements of the real-world scene from the field of view of the learner to reduce visual clutter, with the aim of improving learning. XR environment user traces will be leveraged to monitor the learner’s level of engagement and to provide assistance when needed by playing back pre-recorded exemplary user traces. The XR environment will be developed with feedback from formative user studies that measure the accuracy of the force feedback, the quality of eye contact, the effectiveness of learner assistance, system usability, task load, sense of presence, cybersickness levels, and subjective user preference. The project plans to build three learning activities based on XR simulations of charged particles, bar magnets, and electromagnetic fields. The activities will be used in controlled studies that investigate whether the physically accurate haptic feedback is conducive to increased learning, based on pretests, posttests, and delayed posttests, for whom, based on the learner’s individual characteristics, and at what depth, based on the learners’ post-intervention argumentation. 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 2025 · 2025-09

Many solar energy projects are installed in agricultural lands, creating land competition with crops, orchards, vineyards, and pastures. However, relatively little is known about how these solar installations affect surrounding communities, landscapes, and agriculture. Moreover, careful design and siting of these installations can yield a variety of benefits, including increasing farm income, enhancing water resources, improving plant and animal habitat, and enriching soil. To address these issues, this project will bring together an interdisciplinary team of scientists and engineers with agricultural extension specialists, landscape designers, community members, and industry and nonprofit partners. The project will focus on two questions: 1) how is solar energy affecting the landscape and surrounding communities?; and 2) how can the U.S. build a stronger, more productive, and more resilient agroenergy landscape? The project explores practices that will improve outcomes of solar energy in agricultural landscapes. To do so, the project will collect novel data at existing solar facilities and launch a first-of-its-kind scientific research facility to collect data on how solar installations affect agricultural land and communities. Using these data, the research team will study how solar facilities change soil and habitat conditions, the water cycle, crop production, economic returns, and surrounding communities. Throughout the project, an advisory team of farmers, stakeholders, policymakers, and community members will help shape the research and focus the project’s efforts on the needs of farmers, utilities, and the public. This approach will bring together new forms of biogeophysical data collection, modeling, and life cycle assessment with community co-creation. The project’s findings will be used to create decision-support tools, design new solar installations, conduct workforce training, and develop educational workshops and programs. 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 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.