Iowa State University

NSF Awards · FY 2026 · 2026-10

The SPIRE-EIT (Summer Program for Interdisciplinary Research and Education - Emerging Interface Technologies) at Iowa State University is a 10-week summer program for undergraduates integrating research and education about emerging interface technologies (EIT). The project’s novelties are hands-on research experiences using cyberinfrastructure aligned with a mini-course curriculum in computer programming, 3D graphics, extended reality (XR), and human computer interaction (HCI). In addition, focused mentoring through one-on-one interviews, team meetings, and activities elicit the uncertainties and desired skills of the interns in the program. The research projects are presented at an end-of-the summer campus-wide research symposium in the form of posters, demos, and a five-page research paper. SPIRE-EIT recruits all American undergraduate students emphasizing graduate education preparation in the interdisciplinary area of HCI as well as including instruction on ethics and research-based evaluation techniques. These are skill areas critical to maintain the U.S. economy in multiple areas such as engineered products, accessible technologies, and education and training using XR. Two major trends are driving research in EIT: 1) advances in computing technologies and 2) the use of interface technologies in many facets of everyday life. Research questions in the program focus on areas of EIT including XR, machine learning, information visualization, mobile interfaces, and intelligent software agents. These questions are explored through projects such as automating measurement of team skills in cooperative gaming, developing user interaction methods to resolve 3d printing defects, and soundscape design methods for immersive XR navigation and learning. Research projects are designed so that teams of three undergraduates make meaningful contributions over the course of the program. Faculty in Iowa State University's VRAC Research Center compete to participate in the SPIRE and cite strong benefits following each summer. Each year, SPIRE projects typically result in one to two academic conference presentations with SPIRE students as co-authors. 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 2026 · 2026-09

The International Conference on Forensic Inference and Statistics (ICFIS) will be held September 28 - October 2, 2026, on the campus of Iowa State University in Ames, the home of the Center for Statistics and Applications in Forensic Evidence. The fair administration of justice demands that evidence be correctly evaluated and interpreted. Until recently, forensic examiners relied on subjective methods to interpret evidence, mainly because data-based, scientifically solid methods were not available to them. In the past few decades, the scientific community, in collaboration with forensic and legal professionals, have begun building a toolbox for forensic practitioners using science and data. Statisticians have played a major role in this effort, aiming to provide a measure of uncertainty to findings based on the evidence at hand. ICFIS, which has been held every three years since 1990, is the main venue for statisticians, scientists, and legal and forensic professionals to gather, discuss recent advances, and find collaborators for future research projects. ICFIS will be a 5-day event with workshops, oral and poster presentations, and awards, featuring prominent forensic statisticians as well as students and early-career scientists. New, accurate technologies useful in forensic practice that may arise as a result of scientific research will have a positive impact on the justice system and contribute to the well-being of our society. The aim of ICFIS 2026 is to foster sustained growth in the field of forensic statistics by building synergistic relationships between academics and practitioners across many disciplines in forensic science, including genetics, toxicology, statistics, data science, and probability theory. With this goal in mind, the conference themes are wide-ranging. Presentations will cover timely topics including the use of artificial intelligence in the justice system, the reliability of methods to analyze low-content DNA samples, and the development of new methods for the rapid detection of illegal drugs. From a statistical perspective, the ICFIS will result in advances toward dependent data analysis, causal inference, decision theory and uncertainty quantification, and inference using image data, to name a few. These advances may be disseminated via a special collection in the Law, Probability, and Risk journal. In its totality, the ICFIS 2026 presents a singular platform for influencing forensic science reform given its positioning at the intersection of modern data science, forensic science, and the law. Website: https://forensicstats.org/icfis-conference 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 2026 · 2026-08

This Faculty Early Career Development Program (CAREER) award supports the NSF mission of securing national defense, with the aim of advancing the printability of aluminum (Al) alloys which are utilized heavily in structural applications in the naval and aerospace sectors. Printed aluminum components have the potential to enable increased fuel efficiency and enhance resistance to stress corrosion cracking compared to steel counterparts. Laser powder blown Directed Energy Deposition (L-DED) is a popular metal additive manufacturing (AM) technique due to its capabilities to repair metal components and fabricate large scale parts with high deposition rate. However, only a small percentage of alloys can be reliably manufactured using AM process which hinders widespread industrial deployment of the process. One of the major reasons behind this challenge is solidification cracking. Many high-performance alloys, including aluminum, nickel-based alloys, and refractory alloys, have high cooling rates, thermal gradients and tensile residual stress which contribute to solidification cracking during AM-based processing. The goal of this CAREER project is to first understand the crack initiation and propagation mechanisms in L-DED processed Al and establish new strategies, guided by deposition science and based on laser-material interactions, to resolve these challenges. Major difficulties related to L-DED processing of Al are solidification cracking induced by tensile residual stress, large solidification range and poor aluminum melt fluidity. Research activities will be pursued systematically to reveal the interrelationship between macro-cracks and porosities along with crack nucleation and propagation mechanisms. Strategic ‘in-situ’ alloying efforts will be pursued to advance scientific understanding about the individual effect of select alloying elements that can tune melt fluidity, solidification range and powder flowability synergistically reducing the defects in deposited parts. To address the tensile residual stress challenges, substrate preheating, optimization of scan strategy and laser shock peening (LSP) have been widely used. In this project, inter-layer UIP will be utilized, which is a flexible, cost effective and scalable process that can help induce compressive residual stress and microstructure refinement. Along with novel sample fabrication and characterization plans, high resolution strain mapping using Energy Dispersive Synchrotron X-Ray Diffraction technique will be another major scientific contribution that will identify the effect of thermal gyration of L-DED process on peening affected zone which can impact the microstructure-cracking susceptibility in sub-surface regions. 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 2026 · 2026-08

Coronary artery disease is the leading cause of death in the United States. One common treatment is the coronary bypass surgery, which is performed on about 350,000 patients in the United States each year. This surgery is considered the gold standard treatment for patients with disease in multiple coronary arteries. Vein grafts are used as bypass conduits in majority of these surgeries and have unacceptably high failure rates leading to reoperation and increased risk of complications. The underlying mechanisms of vein graft failure remain poorly understood. This Faculty Early Career Development Program (CAREER) project will use computer models of blood flow and blood vessels to understand these mechanisms of adaptations at different biological length scales. The computer models will also provide a framework for virtually testing biotechnologies and therapies for improving vein graft performance. In addition, the project includes educational programs to train students in these computer modeling tools, apply them to other blood vessel related diseases and strengthen biomedical engineering education through new educational modules and training opportunities. Coronary vein graft remodeling is a complex process influenced by multiple factors, including hemodynamic loads, inflammation and surgical trauma. Understanding how these factors interact across biological scales to determine the vein graft outcomes remains a major challenge. Recent advances in multiscale computational models of vascular adaptation can help gain insights into these adaptations; multiscale computational models allow for precise and controlled manipulation of individual contributing factors (either independently or in combination) while physics-based multiscale models can provide mechanistic insights across spatial and temporal scales. This project integrates several complementary modeling approaches to capture vein graft remodeling across biological levels. Specifically, it combines a vascular finite element model (at tissue scale) with continuum mechanics-based growth and remodeling (tissue/cellular scale), and systems cell level models. Formal data science methods will be used to inform and calibrate these models against existing experimental data. Advances in artificial intelligence will be leveraged to estimate unknown parameters, infer constitutive functions, and identify key mechanisms that govern vein graft remodeling. The resulting in silico model will be used to probe the central roles of mechanics and inflammation in vein graft remodeling and identify mechanisms that strongly influence vein graft outcomes. The multiscale model provides a powerful tool to accelerate hypothesis generation, guide experimental design, and support translational efforts. Insights will improve vein graft performance and cardiovascular health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Among all classes of phospholipids, ether phospholipids (mainly plasmalogens) have recently emerged as a novel regulator of mitochondrial membrane dynamics and animal aging. Plasmalogen deficiency has been linked to impaired mitochondrial dynamics, respiratory disorders, cardiomyopathy, and Alzheimer’s disease. The levels of plasmalogens in the circulation and animal brain decrease with age. However, the exact mechanisms underlying age-related reduction in plasmalogen biosynthesis and plasmalogen-regulated tissue homeostasis are largely unknown. Our recent studies identify the kidney as the site of plasmalogen biosynthesis and the source of circulating plasmalogens. Inhibition of kidney plasmalogen biosynthesis increases the production of inflammatory cytokines and cardiomyopathy. In contrast, plasmalogen supplements rescue cardiac defects in plasmalogen biosynthesis mutants. These findings suggest a previously unappreciated role of plasmalogen in kidney-heart communication. In this renewal application, we aim to take multi-disciplinary approaches to interrogate the novel role of kidney-derived plasmalogen ether phospholipids in inter-tissue communication and cardiac aging. Four specific aims are proposed: Aim 1. Determine the mechanisms underlying age-associated decreases in kidney plasmalogen biosynthesis. Aim 2. Determine how plasmalogens are transported from the kidney to the heart. Aim 3. Determine how kidney-derived plasmalogens regulate cardiac contractile performance.

NSF Awards · FY 2026 · 2026-06

This grant provides funds to support students for travel to the ACM International Conference on the Foundations of Software Engineering (FSE2026), which will take place in Montreal, Canada in July 2026. FSE is the one of the flagship conferences in the field of Software Engineering. A large part of the technical program is devoted to research on using artificial intelligence (AI) techniques to support software development, as well as using software engineering techniques to support AI-based software systems. The grant will provide travel and registration support for US-based students. The FSE 2026 conference also features will have a doctoral symposium and a new faculty symposium. Conference attendance is important for the technical exchange of information and research conversations/collaborations made possible by the conference, as well as advances in the field made possible by these interactions. The conference provides opportunities for education, training and mentoring to build the next generation of researchers and practitioners in the field of software engineering. The international nature of this conference helps develop a globally-aware workforce of research and educators within the US and helps build the community of researchers in the field of Software 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 2026 · 2026-06

This grant provides funds to support students for travel to the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), which will take place in Oakland, CA in October 2026. ISSTA is one of the premier conferences in the field of Software Engineering. A large part of the technical program is devoted to research on using artificial intelligence (AI) techniques to support software development, as well as using software engineering techniques to support AI-based software systems. The grant will provide travel and registration support for US-based students. The ISSTA 2026 conference also features the ACM student Research Competition. Conference attendance is important for the technical exchange of information and research conversations/collaborations made possible by the conference, as well as advances in the field made possible by these interactions. The conference provides opportunities for education, training and mentoring to build the next generation of researchers and practitioners in the field of software engineering. The international nature of this conference helps develop a globally-aware workforce of research and educators within the US and helps build the community of researchers in the field of Software 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.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Developing immunoregulatory drugs poses challenges due to the necessity for personalized treatments to address diverse immune profiles in different diseases and individuals, as well as the requirement for combination therapy to tackle the complexity of immune dysfunction. However, guidelines for designing personalized combination immunostimulatory therapeutics are lacking. To address this gap, we propose leveraging the body's natural counteracting response to disease-induced immune disorders for drug discovery in this context. Our strategy involves the intelligent utilization of the foreign body response (FBR) within a subcutaneous biomaterial scaffold implant. In health, FBR achieves homeostasis between inflammatory responses to biomaterials and anti-inflammatory biomolecules, thereby mitigating potential tissue damage. In disease, when dysfunctional immune cells infiltrate the implants and disrupt local balance, FBR will overproduce diverse biomolecules to counteract immune disorders, restoring homeostasis to some extent. Our hypothesis posits that by identifying these immunoregulatory biomolecules that assist scaffold implants in overcoming immune disorders, we can develop them into personalized combination drugs and administer them systemically to mitigate immune disorders in major organs. We will employ immunosuppression as a disease model to validate our hypothesis and seek combination drugs tailored to cancer immunosuppression and lipopolysaccharide (LPS) tolerance with varying host immunity profiles. The success of this project will offer a smart strategy to design personalized combination therapies for immune disorders across diseases.

NSF Awards · FY 2026 · 2026-04

This grant provides funding to support student participation in the 2026 Modeling, Estimation, and Control Conference (MECC 2026), which will be held 25–28 October 2026 in Phoenix, Arizona. MECC is a premier conference held annually and serves the scientific and engineering communities in the cross-disciplinary areas of modeling, estimation, and control of dynamical systems. The conference provides a platform for the dissemination and discussion of state-of-the-art research in dynamics and control and creates opportunities for networking and collaboration among researchers from academia, industry, and government laboratories. The conference features contributed sessions, invited sessions, workshops, special sessions, plenary talks, keynote speeches, student and young professional programs, industry programs, and conference awards ceremonies. Funding will empower students with high academic potential in the early stages of their research development by providing opportunities to attend the conference and engage directly with researchers at the forefront of dynamical systems and control. Such early engagement is expected to help build a strong pipeline of future researchers and leaders in modeling, estimation, and control. The award will also support educational activities for high school students by introducing them to the fields of dynamical systems, control, and robotics. Ultimately, this grant will significantly expand the horizon and impact of MECC 2026 on both the scientific community and society at large. Student participation support focuses on reaching a broadened student body by enabling high school, undergraduate, and early-stage graduate students with high academic potential to observe first-hand outcomes from state-of-the-art research and to develop professional networks with researchers and practitioners in their fields. Multiple events and activities will be supported as part of this grant, including: i) outreach activities in systems, control, and robotics for high school students, developed in collaboration with academic and industry partners; ii) professional networking lunches and panel sessions that coach participants on building professional networks and identifying pathways for academic and industry careers; and iii) special poster sessions that provide students with opportunities to present research interests and receive personalized feedback and mentoring to help shape their research trajectories and enhance their technical and professional development. Participant support is expected to strengthen students’ scientific, technical, and professional preparation. Broadening access to MECC 2026 will have a lasting impact on the future development of the modeling, estimation, and control community and will help foster a culture for future conferences that better connect education, research, and technology development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-03

Project Summary: Spinal Muscular Atrophy (SMA), the leading genetic cause of infant mortality, results from deficiency of Survival Motor Neuron (SMN) protein due to deletions of or mutations in the SMN1 gene. SMN2, a near identical copy of SMN1, fails to compensate for the loss of SMN1 due to predominant skipping of exon 7. SMN1/2 harbor unusually high numbers of Alu elements that are often associated with the generation of circular RNAs (circRNAs). Consistently, we and others have reported a huge repertoire of circRNAs produced by SMN1/2. C2A-2B-3-4, C2B-3-4 and C3-4 encompassing early exons of SMN1/2 are the most abundantly- expressed SMN1/2 circRNAs. We previously reported that C2A-2B-3-4 is downregulated in type 1 SMA patient cells, and its expression is cross-regulated by C2B-3-4 and C3-4. A recent report showed a correlation between overexpression of C2B-3-4 and improved motor outcomes in type I SMA patients treatment with Nusinersen, an antisense oligonucleotide-based drug. These findings underscore SMN1/2 circRNAs as novel candidates for diagnosis and therapy. Yet, very little is known about the functions of SMN1/2 circRNAs. We recently performed transcriptome and proteome analysis of inducible HEK293 cells stably overexpressing C2A-2B-3-4. Our results captured the altered expression of 4172 genes and 118 proteins. Surprisingly, we also observed chromosome-specific effects as expression of many genes located on chromosomes 4, 7, 10 and X were specifically impacted. In this proposal, we will perform similar transcriptome and proteome analysis of inducible HEK293 cells stably overexpressing C2B-3-4 and C3-4. We will expand this study to examine the effect of the transient overexpression of C2A-2B-3-4, C2B-3-4 and C3-4 in different cell types, including HeLa, neuronal SH-SY5Y, motor neuron-like NSC-34 cells as well as in SMA patient fibroblasts. We will also examine the effect of depletion of C2A-2B-3-4, C2B-3-4 and C3-4 on the transcriptome and proteome in different cell types. We will independently validate the findings of transcriptomic and proteomic data by qPCR and western blot, respectively. Using gel-based assays, we will validate the effects of C2A-2B-3-4, C2B-3-4 and C3-4 on splicing events. We will perform computational analysis to uncover potential miRNAs that are likely to be sponged by SMN1/2 circRNAs and affect translation of the target proteins. We will investigate if specific promoters and/or chromatin modifications are direct targets of C2A-2B-3-4, C2B-3-4 and C3-4. Findings will uncover SMN protein-independent functions of SMN1/2 in diverse cellular processes, including DNA replication, DNA repair, transcription, splicing, translation, cell signaling, macromolecular trafficking, stress granule formation, mitochondrial regeneration, and cytoskeletal dynamics. Importantly, results will reveal common as well as distinct functions of SMN1/2 circRNAs differing by one or two early exons of SMN1/2. Outcomes will significantly advance our understanding of SMA, and possibly many other diseases associated with the aberrant expression of SMN1/2 circRNAs.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Nematode parasites are present nearly every place in the world inhabited by humans. Our strategy for controlling these diseases is to use small molecule therapy, however, there has not been a new drug class on the market in 40 years. We propose that endogenous transporter systems of parasites are innate, dynamic, and play a role in the expulsion of drugs from parasites. In this study, we identify the transporter mRNAs expressed in the presence and absence of anthelmintic drugs. We will achieve an understanding of the drugs that bind a nematode transporter using a CRISPR-edited cell line that is optimized for the study of nematode transporters. In addition, we will study the innate transport properties of nematode cells and the inhibition of these transporters with competitive and allosteric inhibitors. Our results will direct future studies aimed at blocking nematode transporters selectively.

NSF Awards · FY 2026 · 2026-02

NON-TECHNICAL SUMMARY Grain boundaries in crystalline metals are very important planar defects and play a crucial role in physical and mechanical properties of metals and alloys. During thermal/mechanical processing or deformation, new grains nucleate from the matrix and coarsen as a result of grain boundary migration. But how a new grain chooses the boundary plane with the matrix and how the grain boundary migrates has remained a long-standing fundamental problem in materials science. When a grain boundary migrates, the lattice of one grain is transformed into the lattice of the neighboring grain. Numerous grain boundary models have been proposed to describe grain boundary structures and their migration mechanisms. However, the current grain boundary theories do not consider lattice transformation and are unable to account for many phenomena obtained from experimental and simulation studies. To resolve the complex mechanisms for grain boundary formation and migration in important engineering metals such as lightweight magnesium and titanium with hexagonal close-packed structures, a novel strategy that fundamentally differs from the existing framework is proposed. Computational studies on the atomic scale and advanced materials characterization are being combined to reveal the underlying physics of grain boundary formation and migration. The project is resolving the grain boundary physics with unprecedented clarity. Because grain boundaries significantly affect material mechanical behavior, the project is also impacting the processing of lightweight metals in terms of microstructure control, which is critical for improved energy efficiency. The project is also engaging students of various ages through educational outreach and curriculum development. TECHNICAL SUMMARY The existing theories such as the coincidence site lattice and the disconnection model can be described as a point-to-point matching scheme. However, experimental and simulation studies show that during grain boundary migration, an atomic plane of one grain is transformed into a corresponding plane in the neighboring grain. Thus, the grain boundary migration is achieved by a plane-to-plane lattice transformation, and a unique lattice correspondence can be established, similar to deformation twinning which linearly maps the matrix lattice into the twin lattice. The proposed research is applying the principle of lattice correspondence to resolving the fundamental physics of grain boundary formation and migration. First, the orientation distribution and structure of grain boundaries in wrought magnesium and titanium are being characterized. Electron backscatter diffraction and high-resolution transmission electron microscopy are being performed to investigate the grain boundary structure. Then the experimental results are being used as input for atomic-scale simulations in which lattice correspondence analyses are conducted. Invariant planes in lattice transformation are being identified. Grain boundary mobility and migration kinetics are being obtained from the simulation results. The outcome of the proposed research is establishing an inherent connection between deformation twinning, phase transformation and grain boundary migration, which is transformative and profoundly advancing the knowledge of grain boundary properties in low symmetry crystal structures. The proposed fundamental research well aligns with the mission of National Science Foundation, and the outcome is expected to significantly impact the materials science and engineering community. In addition, educational engagement and outreach to students of various ages are being carried out in partnership with established university programming. 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 2026 · 2026-01

Technology translation is the process of converting scientific research and technical innovations to practice. In order to move basic and use-inspired research into society most effectively, it is imperative that innovators and entrepreneurs have access to facilities that enable testing and validation of their new technologies under real world conditions. Such testing requires a safe and controlled environment to ensure the technology is robust, reliable, and ready for use. National “test beds” could include fabrication facilities and cyberinfrastructure to advance the development, operation, integration, testing, deployment, and, as appropriate, demonstration. This effort supports a workshop series facilitating conversations among critical test bed stakeholders from academia, industry, government, and non-profits. The stakeholders offer their unique perspectives on strategies and models for designing and using test beds to scale up technologies and accelerate the translation of innovations into the marketplace. This workshop series will provide opportunities for open dialogue about opportunities and challenges of bringing emergent technologies to practice using test bed facilities. Topics include lessons learned from existing test bed efforts, novel operational models, gaps in existing infrastructure, and how to expand access to physical and virtual resources, investment, and multisector collaboration. The workshop series brings together stakeholders to share community-wide perspectives in a large number of technology fields – from advanced communications to biotechnology and materials development. The workshop deliverables will include at least one white paper that will capture the conversations at the sessions. The workshop series includes virtual events and in-person sessions hosted by Iowa State University, the University of Alabama, and the University of Michigan in Spring 2026. 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 · 2025-10

The U.S. population is aging, and millions are affected by neurodegenerative disorders, yet their causes remain largely unknown. Emerging research suggests that the gut microbiota plays a role in neurodegenerative processes by influencing brain pathophysiology. However, the mechanisms behind this microbiota-gut-brain axis (MGBA) are still poorly understood. Current studies of neurodegenerative diseases rely on animal models and clinical trials, which face major limitations such as ethical concerns, high costs, low throughput, and significant time and labor demands. Hence, it is essential to develop alternative in vitro platforms to study interactions between gut microbiota and brain tissue models, thereby enabling deeper insight into the MGBA. The educational goal of this project is to integrate research with education to train both undergraduate and graduate students in interdisciplinary studies to produce next-generation bioengineers. The investigators will incorporate this work into their existing Vertically Integrated Program (VIP), Targeting Neurodegenerative Diseases Using Bioengineering Approaches. Building on years of mentorship experience, they will engage undergraduate, graduate, and K–12 students in interdisciplinary research. The aim is to equip students with strong technical knowledge and hands-on lab skills to support their future careers. The goal of this proposal is to develop a new, on-chip reductionist model to evaluate the essential role of gut microbiota and its interactions with in vitro brain models through an in vitro blood brain barrier (BBB). This platform will facilitate the studies of the interactions between the microbiota and an in vitro brain model that will permit the investigation of mechanisms of organ development, cellular interactions, and disease model progression under the influence of the microbiota within microenvironments. Specifically, the proposed efforts include (1) the development of a chip consisting of chamber arrays to mimic the bidirectional communication between the gut bacteria/microbiota and brain in vitro; and (2) the studies of the behavior of brain models under the influence of the microbiota using this chip. Major innovations of this proposed project include: (1) Using this type of chip, large-scale studies of the interactions between gut bacteria/microbiota and brain models can be performed rapidly and inexpensively; (2) Using the integrated sensors on-chip, the concentrations of the metabolites specifically the neurotransmitters produced by microbiota can be determined quantitatively in real-time; and thus (3) Using this chip will facilitate the quantitative studies of the effects of these biologically active chemicals on both healthy and diseased brain models. 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 · 2025-10

Modern transportation systems generate massive amounts of data, including where and how vehicles and people move, traffic conditions, road conditions, and videos captured during actual trips. This includes detailed information about everyday driving behavior collected by cameras and sensors in cars and on roads. These datasets are essential for improving traffic safety, reducing congestion, and supporting the development of advanced technologies such as self-driving cars. However, they often contain sensitive personal details about individuals, making it difficult to share among traffic authorities, companies, and research institutions. This project addresses this challenge by developing secure methods for sharing transportation data while protecting individual privacy, serving the national interest by advancing transportation safety, supporting economic competitiveness in autonomous vehicle technologies, and strengthening infrastructure resilience through improved data-driven decision making. This project develops a comprehensive privacy-preserving platform for sharing diverse intelligent transportation systems data across different entities. The research targets multiple data types, including vehicle and road user information such as speed, travel times, and trajectories, as well as infrastructure data including traffic flow, control states, and videos. The project focuses particularly on naturalistic driving data collected by in-vehicle sensors and mobile devices. The research team will adapt and scale privacy-preserving techniques to support both centralized and distributed data-sharing models, ensuring secure data exchange without compromising individual privacy. The project will develop a web-based recommendation system to assist stakeholders in selecting appropriate privacy-preserving techniques for their specific datasets. Additionally, the team will create audit and compliance tools based on formal privacy guarantees and conduct user studies to ensure practical relevance. Secure cyberinfrastructure will be designed and deployed through collaboration with public and private partners. The platform will be evaluated using real-world transportation datasets to demonstrate effectiveness in enabling privacy-preserving data sharing that supports transportation research, improves traffic management, and accelerates development of data-driven mobility technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

With the support from the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Matthew Panthani and Aaron Rossini from Iowa State University are studying the origin of light emission in a two-dimensional (2D) form of silicon (Si) nanomaterials, Si nanosheets. The Si nanosheets will be labelled with different types of atoms in the surface to better understand the surface chemistry of 2D silicon, developing understanding of how surface chemistry is linked to its light emitting properties. This research will have potential applications in next-generation computing and telecommunications technologies that are faster and more energy-efficient. The research will be coupled to activities that encourage engagement between academia and industry in the region, as well as contribute to the development of high school teachers and future STEM workforce participants. With the support from the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Matthew Panthani and Aaron Rossini from Iowa State University are studying structure-property relationships in two-dimensional Si derived from the layered Zintl phase, calcium disilicide. The research will use isotopic labeling to reveal structure-property relationships in 2D nanosheets. The research will include three objectives: (1) synthesizing 2D Si with precisely defined surfaces, incorporating isotopic labels, (2) characterization of the surfaces, and (3) determining relationships between the structure and optical properties. Using combination of characterization tools - including vibrational spectroscopy, solid-state nuclear magnetic resonance spectroscopy, neutron scattering, and computational modeling - will enable the researchers to develop an unprecedented understanding of the surface chemistry of 2D silicon, and insight into the origin of its light emission. 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 · 2025-10

Large scale matrix computations are at the heart of several modern technologies that are revolutionizing human life. These include, the now ubiquitous deep learning models, large language models, and scientific computing for supporting research in various domains. The sheer size of the data and models in these domains requires such computations to be performed in a distributed manner over large clusters, whereby an overall job is divided into smaller tasks. Unfortunately, these clusters often suffer from the problem of stragglers (slow or failed workers), especially when they are deployed within cloud computing platforms; this can cause an undesirable increase in the overall job execution time. The overall goal of this project is to research techniques for mitigating the effect of stragglers in the specific context of distributed matrix computations. This project will also provide training for students in the usage of cloud platforms. In addition, the project also involves outreach activities to local schools for mathematics tutoring and the creation of K-12 computer science modules. The field of coded computation uses ideas from coding theory to embed distributed matrix computation into the structure of an erasure code. Specifically, the idea is to create redundant tasks by linearly combining the input submatrices such that as long as a minimum number of workers complete their tasks, the overall job can be completed. The vast majority of prior coded matrix computation approaches are obtained by combining a large number of input submatrices. This is problematic for the practically important case of sparse input matrices as the encoding process results in dense submatrices whose product needs much higher computation time. Furthermore, much of prior work coarsely treats workers as alive or failed and does not leverage partial computations performed by slow workers. The foundational goals of this project are to investigate coded matrix computation techniques that are suitable for sparse input matrices and leverage partial work performed by slow (but not failed) workers. For dealing with sparse input matrices, the research team will adapt ideas from parity-checking in coding theory; this is however non-trivial, as the parities need to respect the computation constraints and the objectives. Moreover, the research team will design schemes that optimize a worst-case combinatorial metric for evaluating different schemes with respect to how well they leverage partial work of the slower nodes within the cluster and provide ideas on the design of schemes that address this metric. 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 · 2025-10

This project addresses a critical need in the U.S. biomanufacturing industry sector by developing flexible, modular technologies that integrate microbial conversion and chemical upgrading within a single reactor system. Such an integration is essential for producing high-performance chemicals and fuels from biobased intermediates in a cost-effective and scalable manner. The research team will design and demonstrate a novel reactor platform that supports continuous operation, efficient separation, and process intensification, which are key attributes for enabling distributed biomanufacturing. The project will also contribute to the training of students and early-career researchers in interdisciplinary areas spanning precision fermentation, biosensing, electrochemistry, and reactor engineering, thereby strengthening the future workforce in the bioeconomy. The project will develop a bio-electrocatalytic Taylor vortex reactor (BETR) system for the integrated production of 3-methylanisole (3-MA) and its upgrading into methylcyclohexane (MCH), a high-energy-density hydrocarbon. This integrated process combines microbial fermentation and electrochemical hydrogenation within a single intensified reactor platform. The team will use high-throughput microbial phenotyping, genetically encoded biosensors, and Bayesian machine learning to identify and optimize yeast strains capable of efficiently producing 3-MA from sugar-based feedstocks. A flexible microbial chassis will be engineered to enhance metabolic flux toward the desired aromatic intermediate while maintaining selectivity and robustness under aerobic fermentation conditions. In parallel, a broad library of electrocatalysts and electrolyte formulations will be screened to develop an experimental–computational framework for catalyst discovery and to establish optimal conditions for the selective hydrogenation of 3-MA to MCH. Electrocatalyst performance will be evaluated based on activity, selectivity, Faradaic efficiency, and stability under mild aqueous conditions that are compatible with upstream bioproduction. The BETR platform, leveraging Taylor–Couette flow, provides enhanced mass transfer and phase mixing that facilitate the co-location of microbial and electrochemical processes. The reactor will be engineered to support in situ product extraction to address potential toxicity issues and minimize downstream separation burdens. To guide system-level optimization and ensure economic viability, the team will use integrated machine learning models to inform technoeconomic analysis (TEA) and life cycle assessment (LCA). These tools will help identify key cost drivers, process bottlenecks, and design trade-offs early in development, supporting iterative improvements across biological, electrochemical, and reactor components. The final goal is to deliver innovative technologies and a functional, small-footprint reactor prototype that can be deployed in distributed biomanufacturing networks. Beyond the immediate application to MCH production, the approach is expected to provide generalizable insights into hybrid bio-electrocatalytic systems and enable new intensification strategies for converting sugars into high-performance products. The research outcomes will contribute foundational knowledge and technology for advancing the next generation of integrated biomanufacturing platforms. 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 · 2025-10

Non-technical Abstract: Understanding and controlling quantum behavior in materials is a key challenge that could shape the future of technology—from faster computers to advanced medical imaging and secure communication. This project brings together two powerful fields—superconductivity and terahertz nonlinear optics—to explore how fundamental quantum effects can be more precisely controlled in real superconducting materials. A cutting-edge technique called terahertz two-dimensional coherent spectroscopy allows researchers to “see” and manipulate quantum processes in ways not possible before. This work not only opens new pathways for scientific discovery but also helps train the next generation of scientists and innovators through hands-on research and educational outreach activities. By bridging fundamental science with real world practical applications and workforce development, this project has the potential to drive innovation and broaden public understanding of quantum technologies. Technical Abstract: This research explores ultrafast quantum dynamics in light-induced superconducting states using terahertz two-dimensional coherent spectroscopy. The project addresses how quantum excitations—such as Floquet bands and Leggett mode echoes—emerge and interact under strong terahertz driving fields. Processes involving interactions among such excitations are crucial for understanding and controlling non-equilibrium phases in quantum materials. The research leverages terahertz two-dimensional coherent spectroscopy to achieve correlation tomography with both temporal and spectral resolution, surpassing the capabilities of conventional time- and frequency-domain techniques. The approach enables the disentanglement of overlapping quantum pathways and reveals interactions between collective modes and quasiparticles. The research team builds on prior discoveries in iron-based and nickelate superconductors, where signatures of Higgs dynamics and unconventional order parameters were identified. Key objectives include visualizing quantum beating of Floquet bands, demonstrating coherent quantum emission from superconducting collective states, and uncovering rephasing echo signals mediated by Leggett modes. By advancing fundamental coherent dynamics of superconductivity, this activity opens new routes for dynamic manipulation of quantum materials and contributes to the broader effort to develop functional quantum 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 · 2025-10

Subsea Autonomous Systems, also called Autonomous Underwater Vehicles (AUVs) represent an important avenue for exploring and learning about our own planet, as well as others. They serve critical roles in studies of transportation, conservation, oceanography, marine biology, anthropology, and history, just to name a few. Autonomous subsea equipment has to be ultra-reliable to accomplish the given mission and avoid damaging or polluting the environment due to a system failure. They also must operate in harsh, dynamic, and unpredictable environments, more remote than space, and without the possibility of human intervention. This makes verification of AUVs extremely important. Currently, testing and analysis via ad hoc digital twins are the only standard AUV verification methods, but these fall short of the need for robust verification of AUVs. Formal methods provide proven tools and algorithms for both design-time and runtime verification of embedded systems, yet they are entirely unknown to AUV domain experts. AUV dynamic systems and control methods currently stand firmly on two legs: analysis and implementation. The project's impacts are adding a third leg of formal verification, laying the groundwork for real modernization and culture change in AUV verification education and industrial practice, and extending how formal methods are used for prognostics and digital twin technology. The project’s novelties are development and dissemination of design patterns and educational materials, based on new AUV-specific parameterized templates for formal verification, to increase the accessibility of formal methods to AUV domain experts. Since one of the biggest barriers to the adoption of formal methods in the AUV field is making formal methods tools intuitive, easy to use, and adaptable to AUV verification tasks, we contribute advances in these areas. We formulate AUV-based patterns for design-time formal verification and develop educational modules in AUV vocabulary. Since formal verification requires capturing system specifications in temporal logics, which is not an intuitive process for AUV engineers, we develop new, interactive, Graphical User Interface (GUI) representations of temporal logic specifications targeted to AUV engineers and refine them given feedback from AUV students. We also develop and release patterns for real-time (during the mission) AUV verification. To provide exciting yet practical educational experiences, we develop, teach, and release online laboratory education modules integrating this verification technology on-board real-life AUV hardware, complete with fault-injection and mitigation-triggering capabilities. Our hands-on labs using real AUVs will yield exciting yet practical experiences for AUV students and engineers (with trials at Rice University, Chevron, and Kongsberg), as well as supporting our efforts to inspire K-12 students to study 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 · 2025-10

Non-technical Abstract: The magnetic properties of materials originate from the magnetic atoms that compose them. These atoms act like tiny bar magnets. In materials such as iron, the atomic magnets align parallel to one another, much like compass needles pointing in the same direction. However, in some materials, the atomic magnets arrange themselves in non-parallel, or non-collinear, configurations. These arrangements arise from the interplay between the crystal structure and intrinsic interactions among the magnetic atoms. Certain non-collinear magnetic structures possess topological properties—unique geometric characteristics that make them robust against deformation and disruption. This topological protection holds significant promise for applications in information storage and quantum computing. This project combines experimental and theoretical approaches to study topologically protected magnetic structures, aiming to identify materials that could serve as platforms for future quantum technologies. Our research focuses on a remarkable class of compounds known as Heusler alloys—chemical combinations of several metals, including magnetic elements like iron. Recent discoveries have shown that Heusler alloys with specific crystal structures can support non-collinear magnetic arrangements. Due to their relative ease of synthesis and tunable magnetic properties, Heusler alloys offer a conducive environment for the discovery of topologically protected magnetic phases. In addition to advancing quantum science, this project will provide undergraduate and graduate students with vital experience in cutting-edge quantum research, helping train the next generation of quantum scientists and engineers. Technical Abstract: The main goal of this project is to identify the relationship between crystal structure, chemical composition, electronic band structure, and topologically protected magnetic states to design / discover novel quantum materials from the Heusler family of alloys. These materials are actively studied for practical applications such as spintronics, quantum information science and engineering, data storage, magnetic cooling, shape memory and magnetocaloric devices. Exploring topologically protected magnetic phases, such as skyrmions and antiskyrmions, as well as other forms of magnetic non-collinearity in Heusler compounds for obtaining fundamental understanding of these phenomena, which can then be applied to the development of practical device applications including novel data storage mechanisms, constitutes the main research objective of this project. The main hypothesis of this project is that Heusler materials with tetragonal crystal structure may exhibit non-collinear magnetic order, which may in certain cases result in topologically protected magnetic phases, such as skyrmions and antiskyrmions. The research team is using various experimental and theoretical techniques to perform the project, such as arc-melting, physical vapor deposition, Lorentz transmission electron microscopy (LTEM), electron-transport measurements, and density functional theory (DFT) calculations. The project aims to uncover the underlying physical principles of topological magnetic states and other forms of magnetic non-collinearity in Heusler materials. This allows the research team to identify / discover mechanisms to control these properties by intrinsic chemistry change or other forms of external stimuli (such as mechanical strain) leading to the discovery of new quantum materials exhibiting such properties. 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 · 2025-10

Farmers around the world face growing challenges from crop pests, diseases, and weeds that threaten food production and agricultural economic prosperity. These threats are becoming more severe and traditional methods of identifying and managing them often require specialized knowledge and expensive resources that many farmers cannot access. This project develops AI-based decision support tools, such as smartphone apps and a chatbot, that help farmers quickly identify agricultural problems in real-time. By taking a photo of a pest, disease, or weed, farmers receive instant identification and practical advice on how to manage the problem effectively. Technology works like having an expert crop advisor or extension agent in your pocket, making advanced pest management accessible to farmers everywhere, from small family farms to large agricultural operations. This collaborative effort between the United States and QUAD member countries ensures that the decision support tools work effectively across different crops, environmental conditions, and farming systems in the U.S. and beyond. This project addresses the critical challenge of accurate, real-time identification and management of agricultural pests, diseases, and weeds across a variety of global farming systems. The research develops an end-to-end machine learning-based pipeline with uncertainty quantification, conformal prediction, and federated learning. The artificial intelligence-based models can identify several thousand different agricultural threats, including insect pests, weeds, and crop diseases relevant to major agricultural regions. The project employs a "global-to-local" approach that trains comprehensive Artificial Intelligence (AI) models using multi-modal international pest datasets, then fine-tunes these models for specific regional conditions and pest pressures in the United States and QUAD member countries. The technical framework combines advanced computer vision, machine learning, adaptive algorithms, and natural language processing to create smartphone applications that provide real-time pest identification and integrated pest management recommendations using a chatbot for a user-friendly interface for making queries. The system includes offline functionality for areas with limited internet connectivity and incorporates federated learning techniques to protect data privacy while enabling collaborative model improvement. The project also develops multilingual chatbot interfaces that provide farmers with expert-level guidance and creates comprehensive training programs for agricultural extension workers and researchers across multiple geographies. This project also prepares an AI-versed workforce to serve the U.S. agricultural sector. 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 The Iowa State University Veterinary Diagnostic Laboratory (ISU-VDL), a globally accredited leader in veterinary diagnostics and One Health innovation, seeks to enhance its Next-Generation Sequencing (NGS) capabilities through the acquisition of an Illumina MiSeq i100 Plus system. This critical upgrade will address urgent gaps created by our aging MiSeq instrument (in service since 2013), which no longer meets the demands of our 20% annual case growth or the evolving complexity of pathogen surveillance. By modernizing our genomic infrastructure, we aim to strengthen national food safety, accelerate outbreak response, and advance antimicrobial resistance (AMR) tracking in alignment with the Veterinary Laboratory Investigation and Response Network (Vet-LIRN) and One Health priorities.

NSF Awards · FY 2025 · 2025-09

Growth of renewable energy leads to new challenges for electric power grid planning and operation. Many renewable energy resources, such as solar and wind, heavily depend on the weather conditions that are inherently uncertain. Such uncertainty is usually revealed progressively over time. Consequently, the grid planning and operation decisions need be adjusted accordingly across multiple stages to achieve optimal efficiency. The multistage decision structure calls for study on multistage grid optimization algorithms that can accommodate the discrete decisions, such as battery charging versus discharging decisions, and scale well with the number of renewable resources, which can go up to tens of thousands. Moreover, several major tripping and disturbance incidences in the past decade have underscored the heightened stability concerns of a power grid with high renewable penetration. In contrast to conventional thermal generators that have large rotating masses to stabilize themselves, renewable resources are typically power electronics-interfaced resources, which lead to lower system inertia, faster grid dynamics, more frequent disturbances, and greater control difficulty. Hence, it is increasingly essential to integrate stability considerations into grid optimization algorithms to enhance reliable power system operation. This research will include open-source implementations of the algorithms developed, which can provide a computational infrastructure and benchmark for assessing long-term energy integration plans, or for evaluating the daily operational efficiency and reliability of power grids. To address these critical challenges of uncertainty and stability, this project aims to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable grid operation. The first part of this project is focused on a class of algorithms, called stochastic dual dynamic programming, for multistage stochastic optimization models. The investigators will fundamentally advance these algorithms to handle both continuous and discrete grid decisions effectively, and to enable better statistical guarantees by exploiting the structure of grid optimization with renewable uncertainty. The second part of this project plans to directly integrate stability considerations into the objective function and constraints of grid optimization, establishing a framework of stability-augmented grid optimization. Such framework enhances conventional grid planning and operation decisions to be stability-informed and optimizes both the economic efficiency and dynamic stability performance. 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

Among cardiovascular diseases, ischemic heart disease remains a leading cause of mortality worldwide. While revascularization shows promise as an effective therapeutic method, the large patient variability in genetics, comorbidities, and response to growth factors increases the complexity of standardized regenerative therapies. This project focuses on developing a novel digital twin of blood vessel growth after cardiac injury, based on genetic information and live imaging data. This will make it possible to reverse engineer the precise genetic interventions needed to produce the desired vasculature, as well as to safely test and improve gene-editing techniques in silico. The project will have broad societal and educational applications. All software packages will be made open-source, and a web interface will be created to help in clinical settings. Immersive educational tools for students will be developed to visualize 3D simulations of vascular growth in partnership with Iowa State’s Virtual Reality Center. The project’s integration of mathematics, gene editing, and computational modeling will help train a new generation of scientists at the nexus of mathematics and medicine. This project develops a novel multiscale digital twin framework to predict and control blood vessel growth by integrating molecular signaling dynamics, cellular migration behavior, sprouting patterns, and tissue-level growth and remodeling. The research will develop (1) a multiscale molecular-to-cellular modeling framework for vascular sprouting and remodeling that integrates VEGF and Notch signaling cascades to predict the biophysical behaviors of endothelial tip and stalk cells; (2) a novel machine learning architecture for procedural volumetric T-spline models of vascular networks from 2D sprouting prediction and couple elastic deformation of tissues with growth to capture blood vessel growth; (3) a novel applied analysis framework to prevent singularity formation in the chemotaxis equations and steer angiogenesis via PDE-based optimization; and (4) a model to quantify uncertainties at each length-scale. A closed-loop control scheme uses real-time imaging feedback to guide CRISPR-based gene edits, dynamically refining the model and therapeutic interventions. The validated model will be used to recommend gene-edits (to VEGF, Notch, related pathways) that improve vascular regeneration outcomes in clinical settings such as post-infarct cardiac repair. This project is jointly funded by the Division of Mathematical Sciences and the CBET Engineering of Biomedical Systems program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.