SUNY at Buffalo

NSF Awards · FY 2026 · 2026-10

Biometrics, the science of recognizing individuals through physical and behavioral traits such as fingerprints, face, iris, voice, and gait, is increasingly important for securing access to devices, facilities, and services. Applications range from smartphone authentication and border security to national identification systems and financial account protection. As biometrics replaces traditional passwords and becomes the preferred authentication approach in everyday scenarios, the demand for scientists and engineers with biometric computing skills continues to grow rapidly. Despite this demand, undergraduate students have limited opportunities to gain hands-on research experience in this critical area. This Research Experiences for Undergraduates (REU) Site, hosted by the Department of Computer Science and Engineering at the University at Buffalo (UB), will address that gap by providing intensive summer research training in biometrics and authentication to undergraduate students recruited from across the nation. Through mentored research projects, hands-on workshops, seminars, field trips, professional development activities, and project demonstrations, participants will build practical research skills while exploring technologies with direct applications in national security, healthcare, and consumer electronics. The program will recruit students nationwide through broad outreach open to all eligible participants, with emphasis on reaching students from institutions with limited research infrastructure, economically disadvantaged backgrounds, and first-generation college students. The program will contribute to building a skilled cybersecurity and AI workforce prepared to meet pressing national needs. This REU Site on Frontier Technologies in Authentication and Biometrics will support ten undergraduate researchers per year for three years. Despite considerable advances, there remain unresolved challenges regarding the effectiveness and security of biometric recognition systems, including the introduction of new biometric modalities, anti-spoofing measures, system cancelability, continuous authentication, and societal acceptance. Under the long-term research vision of biometrics and authentication, this project will focus on two fundamental directions: biometric modality and biometric security. Research projects will be built on six foundational areas, including sensors and hardware, pattern recognition, machine learning, computer and network security, human-computer interaction, and usability. Specific projects will address topics such as micro-expression biometric algorithms using video and physiological data, three-dimensional finger vein imaging via near-infrared sensing, fingerprint security practice using phantom finger models, continuous biometric authentication via electrocardiography, and cancelable biometric systems based on brainwave signals. Each project will be supervised by faculty mentors and industry advisors with expertise spanning the breadth of biometric computing. Participants will receive introductory lectures, biometrics workshops led by graduate students, seminars on research methodology and professional development, and tours of local industry and border security facilities. The site leverages UB's established partnerships with industry affiliates in the biometrics field. Program effectiveness will be assessed through internal and external evaluations, and research outcomes will be disseminated through publications, open-source tools, and public demonstrations. 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

This project aims to advance AI machine-learning tools for future wireless networks by introducing a transformative paradigm called the Internet of Foundation Models (IoFM). Modern wireless networks are evolving into intelligent infrastructures that generate vast amounts of distributed data from Internet of Things (IoT) devices. This project seeks to harness this data to enable real-time decision-making, situational awareness, and autonomy in next-generation wireless systems. By leveraging multi-modal multi-task foundation models (M3T FMs), which can process diverse data types and perform multiple tasks simultaneously, IoFM envisions a globally distributed ecosystem that integrates these models across cloud, edge, and device networks. The project promises to enhance the efficiency, scalability, and privacy of AI applications in wireless systems, with potential societal benefits such as improved healthcare, smarter cities, and more sustainable technology systems. Additionally, the project includes a robust education and outreach plan to train the next generation of engineers and researchers, engage K-12 educators, and foster interdisciplinary collaboration, ensuring long-term societal and economic impact. The research of this project focuses on developing scalable, resource-efficient, and privacy-preserving methods for deploying multi-modal multi-task federated foundation models (M3T FedFMs) across heterogeneous wireless networks. The research is structured around four dimensions of heterogeneity: data, orchestration, model, and environmental variability. The project is divided into three research thrusts: (1) establishing theoretical foundations for modeling data and model heterogeneity, (2) designing scalable AI learning architectures for hierarchical and decentralized fog networks, and (3) optimizing cross-layer communication, computation, and storage resources for efficient deployment. The methods include modular AI learning algorithms, hierarchical and device-to-device (D2D) AI learning protocols, and advanced AI optimization techniques such as reinforcement learning and Lyapunov optimization. The project will also feature extensive experimental validations using simulations, testbeds, and real-world datasets. By addressing the challenges of deploying M3T FedFMs in resource-constrained environments, the project aims to advance the state of distributed machine learning, wireless network optimization, and AI applications. The outcomes will contribute to the development of intelligent wireless systems capable of supporting diverse applications, from autonomous vehicles to healthcare, while ensuring privacy and resource efficiency. 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 · 2026-07

This Faculty Early Career Development Program (CAREER) grant will contribute to advancing the nation’s leadership in data science by establishing new statistical methods for analyzing data that take the form of probability distributions rather than single numbers. In many modern scientific and engineering applications, including single cell biology, biotechnology, and advanced manufacturing, data are naturally represented by probability distributions, such as histograms or probability functions, describing variability within measurements. However, most current analytical methods reduce this rich distributional information to simple summary statistics, such as averages, thereby discarding valuable information and potentially limiting scientific insights. This project addresses the fundamental challenge of building rigorous statistical tools capable of directly modeling and drawing inferences from distribution-valued data. By preserving the full structure of the data, these methods will enable researchers and practitioners to uncover complex patterns and relationships that existing approaches may miss. The resulting tools have the potential to accelerate discoveries in areas such as perioperative medicine, biomarker identification for diagnosis and drug development, thereby strengthening the global competitiveness of the nation in data science and artificial intelligence. The educational components will provide training opportunities for undergraduate students and high school students in engineering statistics and biotechnology, while preparing graduate students to work in this emerging interdisciplinary field. This project will also engage the broader community through open-source software, accessible educational materials, and data challenge competitions. These efforts aim to broaden participation in science, technology and engineering, and support the development of a competitive workforce equipped with next-generation data analysis skills. This research will establish the statistical foundations for a distribution-in-distribution-out analytical framework that enables regression modeling and inference when both predictors and responses may be either scalar-valued or distribution-valued random variables. The project leverages differential geometry to define linear and nonlinear regression intrinsically in the metric space of probability distributions, moving beyond traditional approaches that operate exclusively in Euclidean space. Both the theory and statistical properties of these models will be developed, as well as new methods for analyzing model uncertainties. These results are expected to provide a foundational paradigm for artificial intelligence based on distributional data. The models developed will be validated through applications to single cell RNA sequencing, single cell proteomics, and perioperative medicine data, where preserving the full distributional information of protein and gene expression intensities is expected to yield more accurate and robust scientific discoveries. The project will also produce and maintain an open-source software module implementing the developed methods, making these analytical tools broadly accessible to researchers and practitioners across multiple disciplines. 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

Metal-organic frameworks (MOFs) are materials used in important industrial applications such as chemical separations, energy storage, and water harvesting. MOFs contain intricate networks of nano-sized pores. The geometry of the networks determines how molecules move through the MOF and interact with the networks’ pore walls. Computational analysis could help describe the molecular motion, but current methods are limited because they do not capture the complexity of the pore network and chemical reactions that take place inside it. This CAREER project will construct a computational scheme based on a pore graph to quantify complex pore networks and their chemistry. The pore graph will be combined with molecular modeling and machine learning to better understand molecular motions and dynamics in MOFs. The results will accelerate the design of next-generation MOF-based materials for more efficient chemical separations and energy technologies. The project will train students in artificial intelligence (AI) and machine learning (ML). A new chemical engineering course will increase AI awareness among students. Summer workshops and an online course in AI will be created for professional education. Summer camps and a partnership with a local high school will build AI literacy among pre-college students. This CAREER project will develop an integrated framework combining graph theory, molecular modeling, and ML to understand confined phenomena in MOFs where current methods fall short. The core innovation will be the development of the pore graph, a unified mathematical representation that transforms intricate pore networks and chemistry into quantifiable objects. This integrated framework will be applied to address unresolved scientific challenges in three key areas: (1) adsorption thermodynamics, to reveal why larger pores anomalously condense earlier or simultaneously with smaller ones, impacting experimental characterization for MOFs; (2) molecular transport, to elucidate how structural defects alter pore networks and chemistry in MOF membranes and thereby determine material’s performance degradation for H2/CH4 separation, bridging the gap between laboratory design and industrial deployment; and (3) confined material assembly, to enable scalable generation of interpenetrated MOFs, unlocking a new domain of stable nanoporous materials for computational exploration. A combination of interpretable ML models, topological analysis, and graph algorithms uniquely enabled by the pore graph will be developed to achieve the goal. The research efforts will be closely integrated with the educational goal to increase AI/ML literacy among chemical engineering students, engineering professionals, and high school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Cosmic radiation on the earth’s surface over long timescales creates rare forms (isotopes) of many minerals. These isotopes are known as cosmogenic nuclides. Measuring the relative abundance of these minerals provides insights into current and past processes that have shaped the earth’s surface, including erosion, tectonic processes, glaciation, and sea level changes. The scientific data on these processes is normally collected and measured by independently working groups of scientists, so having the capability to share the data in a consistent way is extremely important for reproducing scientific results, reusing data for new research questions, and (as the coverage of the collected data on the earth’s surface becomes significant) tackling large-scale or even global research problems. The Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) project enables this research by facilitating community access and engagement with the continually growing dataset of cosmogenic nuclide geochemical and field measurements used for exposure dating applications. The project expands the capabilities of prior work on ICE-D by implementing support for sophisticated surface processes such as dating now-buried surfaces, in addition to exposure dating. This project expands capabilities and trains a wider audience of geoscientists for the Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) Project. ICE-D is a computational infrastructure project aimed at facilitating synoptic data discovery and analysis of geochronologic measurements that constrain numerous Earth surface processes. Transformative components of the project - the transparent computational middle-layer and users-as-developers model - are currently enabling higher-order analyses of cosmogenic-nuclide measurements that critically underpin several fields in Earth surface processes research, namely reconstructing past contributions to sea level fluctuations from ice sheets, assessing seismic hazards along major fault systems, and constraining global climate patterns that caused past alpine glacial fluctuations. Expanded capabilities targeted in this iteration of the project will additionally aid in analyzing fluvial landscape evolution processes, biological applications such as tracking species evolution and the dispersion of ancient humans across the world during the Quaternary, among other impactful Earth surface and biological processes. By centralizing the detailed datasets of cosmogenic-nuclide measurements - including field observations and laboratory measurements - required to compute geologically meaningful parameters from samples collected in a variety of environments worldwide, ICE-D removes several bottlenecks in the community. To further increase engagement, the project undertakes a workshop program to train the community of users to contribute their data, help maintain the database and ultimately use the database for synoptic analyses. Finally, project investigators institute an undergraduate research program aimed at training undergraduates as well as fostering engagement with the geoscience community. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Earth Sciences and the Division of Research, Innovation, Synergies and Education in the Directorate of Geosciences. 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 Description: The development of solution-processable semiconductor materials has the potential to revolutionize emerging technologies in electronics and quantum engineering technologies by enabling scalable, cost-effective manufacturing methods. Among these materials, metal halide perovskite nanocrystals exhibit exceptional properties suited for advanced technological applications. However, their widespread adoption faces significant limitations due to presence of heavy metals, such as lead. This project aims to accelerate the discovery of high-performance, lead-free perovskite nanocrystals through the integration of high-throughput experimentation, artificial intelligence (AI), and advanced data-sharing strategies across multiple institutions. By establishing networked "self-driving laboratories" (SDLs) capable of autonomously exploring extensive materials synthesis parameter spaces, this research is expected to drastically shorten discovery timelines from years to weeks or months. The project's broader impacts include the development of new educational programs designed to train a skilled workforce proficient in AI-driven and autonomous scientific research methodologies, thereby promoting broad participation in innovative STEM careers. Technical Description: This research addresses the critical challenge of discovering lead-free metal halide perovskite nanocrystals by establishing distributed SDLs that integrate automated flow chemistry systems, colloidal nanoscience, and machine learning algorithms. The project aligns directly with NSF’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program and supports the objectives of the Materials Genome Initiative (MGI), aiming to create a robust, scalable framework for accelerated semiconductor materials discovery. A key technical innovation involves modular flow reactors with independently tunable reaction conditions, significantly expanding the accessible synthesis parameter space for semiconductor nanocrystals. The project will employ federated learning approaches to analyze and integrate experimental data from cloud-connected SDLs situated across multiple institutions, facilitating predictive modeling of synthesis parameters and resulting material properties. Outcomes of this project will include the establishment of a publicly accessible, AI-ready experimental database, serving as a valuable resource for the broader materials research community. Additionally, educational efforts will focus on developing innovative curricula and workshops to disseminate knowledge in autonomous experimentation and materials discovery, thus strengthening national expertise and capacity in AI-driven research and 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.

NSF Awards · FY 2025 · 2025-10

Collaborative Research: Radiation-Tolerant and Thermally Managed High-Voltage Gallium Oxide Power Diodes for High-Power Space Electronics As modern technologies in space, defense, and energy systems continue to evolve, there is a growing need for high-voltage, compact, and energy-efficient power electronics that can operate reliably in extreme environments, particularly those involving high radiation exposure and elevated temperatures. These demands are especially critical for spacecraft and satellite platforms, where power devices must endure harsh conditions while meeting strict requirements on size, weight, and power efficiency. Conventional semiconductors such as silicon (Si), silicon carbide (SiC), and gallium nitride (GaN) have enabled significant progress in power electronics, yet their performance can be compromised when exposed to intense radiation or high temperatures, often requiring additional shielding to ensure reliability. Beta-gallium oxide (β-Ga₂O₃), an emerging ultra-wide bandgap semiconductor, offers unique advantages for such applications, including the ability to sustain high voltages, resist radiation damage, and support compact device designs. However, its broader adoption remains limited by challenges in producing high-quality materials, managing heat effectively, and developing power devices that can maintain stable performance during prolonged exposure to high-radiation and high-temperature conditions. This project aims to address these limitations by advancing the synthesis of high quality β-Ga₂O₃ materials, integrating diamond layers to improve thermal performance, and developing high-voltage vertical power diode structures optimized for reliability in harsh environments. The results will support the development of next-generation power systems for space missions, defense platforms, and nuclear energy applications. In addition to its technical contributions, the project will advance national priorities in microelectronics and aerospace, creating hands-on research and training opportunities for students, developing new educational content in radiation-hardened electronics, engaging with K-12 and community college learners, and sharing research outcomes through open-access publications and partnerships with industry and national laboratories. The project will deliver a new class of vertical β-Ga₂O₃ power diodes that combine thermal management and radiation resilience through integrated innovations in materials synthesis, device design, and performance validation under extreme conditions. First, thick, low-defect β-Ga₂O₃ layers will be grown using low-pressure chemical vapor deposition, incorporating n-type dopants to investigate their effects on carrier transport, compensation, and susceptibility to radiation-induced defects. In-situ plasma-free etching technique will be used to define high-aspect-ratio fin or trench geometries without damages. Second, polycrystalline diamond layers will be deposited using microwave plasma chemical vapor deposition to enhance heat dissipation, using engineered interlayers to reduce thermal boundary resistance. Third, vertical diode structures will incorporate p-n heterojunctions and high-permittivity dielectric field-management layers to improve electric field distribution, support high breakdown voltage, and enhance radiation resilience. These devices will be subjected to radiation exposure and high-temperature electrical testing to evaluate their degradation mechanisms and overall reliability under extreme operating conditions. Modeling and device simulations will be used to guide design improvements and evaluate long-term behavior under coupled stress conditions. This work will advance fundamental understanding of dopant-defect interactions, electro-thermal transport, and radiation effects in ultra-wide bandgap β-Ga₂O₃, enabling scalable high-performance power electronics for mission-critical applications in harsh environments. 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 wireless research community continues to face major challenges in conducting rigorous, repeatable experiments to evaluate next-generation wireless networks and Internet of Things (IoT) systems. Existing testbeds are limited in availability and often fixed to specific environments, making it difficult for researchers to test how their innovations perform under different conditions. This project addresses these challenges by creating UnionLabs, a new cloud-based federation of wireless testbeds that aims to democratize access to experimental research resources. By enabling seamless remote access to testbeds distributed across multiple U.S. institutions, UnionLabs promotes wider participation in wireless research. It also helps accelerate research in areas such as Artificial Intelligence and Machine Learning (AI/ML) for autonomous systems, mobile edge computing, and spectrum sharing. Through integration into university courses, hands-on training opportunities, and public workshops, UnionLabs supports both workforce development and broader engagement. The project establishes an innovative two-tier infrastructure that combines a centralized public cloud platform with edge computing resources located at individual testbed sites. A federation plane hosted on Amazon Web Services (AWS) enables seamless integration and remote access to geographically distributed experimental facilities through a unified web-based interface. To validate and demonstrate the scalability of this platform, UnionLabs will federate testbeds across four institutions with complementary strengths including University at Buffalo (5G and UAV systems), University of Florida and University of Utah (IoT technologies), and Northeastern University (programmable 5G and beyond systems). The platform’s standardized federation APIs will support easy onboarding of additional grassroots testbeds over time, laying the foundation for a dynamic and sustainable national research infrastructure. 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

In STEM education, students' learning outcomes are highly contingent on the quality of instruction they receive. Previous research has shown that responsive teaching – instructors' efforts to elicit, attend, and respond to the substance of students' ideas and to connect those ideas with the discipline – has profound impacts on students' STEM learning. Despite the well-established role that gesture and other nonverbal forms of communication play in how students convey their ideas about STEM phenomena, research on responsive teaching in undergraduate science courses has primarily been limited to communication processes that occur in speech. This project will investigate embodied responsive teaching in science by examining how physics instructors elicit, pay attention to, and respond to nonverbal aspects of students' ideas and how these instructor-student interactions impact students' physics learning. Based on these investigations, professional development materials will be developed to educate STEM instructors on how to more effectively leverage students' use of gesture and nonverbal communication in the classroom to support their learning. By better understanding the role nonverbal communication plays in instructor-student interactions in undergraduate STEM courses, this CAREER project will contribute to more effective STEM education and STEM educator development. Drawing on an existing video corpus of instructor-guided collaborative learning activities in an undergraduate physics course, this project will use multimodal conversation analysis and the Co-Operative Action framework to investigate four research questions that focus on instructor-student interactions during discussion and laboratory sessions. The research questions include: (1) How do students use embodied communicational resources to make sense of and communicate ideas about energy in thermodynamics and mechanics in small-group and whole-class discussions? (2) How do instructors use embodied responsive teaching (ERT) to elicit, attend to, and respond to students' ideas conveyed through gesture and other embodied communicational resources? (3) How do embodied responsive teaching moves support or constrain opportunities for engagement in scientific practices? (4) How do embodied responsive teaching moves support or constrain changes in students' conceptual understanding? The project's education plan will apply the research findings to develop video-based professional development modules for pre-service secondary STEM teachers and physics teaching assistants at the University of Buffalo, SUNY. The modules will be iteratively refined and disseminated widely through the Periscope web-based platform. This project is expected to contribute to the improvement of undergraduate physics education by generating an enhanced understanding of the instructional practices that support students' learning and participation. In addition, it will develop the first widely available video-based professional development lessons to strengthen STEM educators' understanding of representational gesture in students’ scientific sensemaking. The Faculty Early Career Development (CAREER) program is a National Science Foundation (NSF)-wide activity that supports early-career faculty who have the potential to serve as academic role models in research and education. This CAREER project is supported by NSF STEM Education Directorate’s Core Research (ECR) 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.

NSF Awards · FY 2025 · 2025-10

Non-technical Description: The NeuroTronics project will create new materials that can seamlessly connect with the human nervous system, paving the way for advancements in bioelectronics. These materials, known as organic mixed ionic-electronic conductors (OMIECs), will be designed to efficiently conduct both electricity and ions and are crucial for developing improved brain-computer interfaces, therapies for neurological conditions, and more energy-efficient computing inspired by the human brain. This research could lead to breakthroughs in healthcare, human-AI interaction, computing, and robotics. The project will combine advanced computer modeling, machine learning, as well as automated and autonomous experimentation to create materials that are electronically adjustable, safe for use in the body, durable, and manufacturable at scale. A key focus will be training a new generation of scientists and engineers in AI-driven materials design through workshops and public outreach events like science museum demonstrations. By providing both fundamental knowledge and practical tools for material design, this project will overcome a major hurdle in creating reliable, mass-producible materials needed for real-world neuromorphic technologies that could eventually gain medical approval. Technical Description: This research will tackle the challenge of designing doped semiconducting polymers whose electronic properties remain stable under repeated ion insertion and mixed ionic-electronic transport. Researchers will combine sophisticated computer simulations, including density functional theory and Holstein modeling, with machine learning algorithms and automated testing systems. The work will be divided into three main areas: first, optimizing materials to achieve high carrier mobility and efficiency across different doping levels; second, designing materials that remain stable under electrochemical and thermal stress through advanced modeling and real-time monitoring; and third, developing methods for creating these materials consistently and safely using non-toxic ingredients. This integrated, closed-loop approach will accelerate development cycles and produce high-performing materials suitable for widespread deployment, directly supporting the DMREF program’s mission of revolutionizing materials innovation through data-driven, collaborative research. Next generation of materials scientists and engineers will be trained through annual workshops on FAIR data principles, AI-driven materials design, and self-driving labs. 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

Synthetic membranes facilitate the selective separation of specific molecules from mixtures. For example, membranes enable the production of clean water and safe pharmaceuticals. Unfortunately, the industrial manufacturing process used to produce membranes relies on the use of toxic solvents. In contrast, biological membranes leverage components that self-assemble to create multi-scale and hierarchical barriers that maintain unprecedented selectivity, controlling what and when molecules enter and exit cells. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to harness biology-like performance and the chemical versatility of synthetic polymers. This looks to be achieved by mixing lipids, which are the building blocks of biological membranes, with charged polymers that can be synthesized to exhibit specific chemistries or charge patterns. These mixtures will spontaneously form nanoscale, ordered structures, that can form the basis for high-precision membranes. A major challenge is to design both lipids and polymers, which can have countless variations of chemical and physical features, to yield membranes for a given application. This project aligns with DMREF and the Materials Genome Initiative by combining materials synthesis and characterization with multi-scale molecular simulation and machine learning as the experiments will inform new computational models, which will then be used to expedite materials discovery to design new membranes. This interdisciplinary effort will bring together academic researchers and scientists from the Air Force Research Laboratory (AFRL) who have combined expertise regarding the making and characterization of membranes, molecular simulation and machine learning, and the physics of lipid assembly. The effort looks to harness nanoscale structure to achieve separations capabilities seen in biology, which will benefit society and the US by establishing a versatile class of membranes with broad applications in biological, chemical, agricultural, and industrial separations. The research will also involve the interdisciplinary training of researchers with broad expertise spanning chemistry, engineering, and physics, via both student mentorship and educational outreach to K-12 students. This project seeks to establish rational design of co-assembling lipids and polyelectrolytes, using patterned polyelectrolytes and judicious choice of chemical features to target specific nanostructures that can be used in separation membranes. This effort looks to harness expertise in polymer and lipid characterization and synthesis, using sequence-defined polyelectrolytes to modulate nano-scale assembly that will be evaluated by scattering. This will be coupled with a multi-scale modeling effort that connects atomistic simulations with coarse-grained models and polymer field theory to yield predictions of charge-driven assembly. Physical insights from this combined experimental and modeling approach intend to inform machine learning tools to predict structures relevant for separation membranes, which will be tested experimentally. The overarching goal is to establish a versatile molecular design protocol capable of integrating bioinspired lipid-based assemblies with complex-forming polymers to rationally engineer permeable membranes with desired selectivity. 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 aims to serve the national interest by preparing undergraduate students for careers in advanced manufacturing through the integration of data science and digital twin practices. As the manufacturing industry becomes increasingly data-driven and intelligent, there is a growing need to ensure students not only gain technical expertise but also develop the awareness required to navigate complex real-world challenges involving privacy, security, and responsible innovation. This Level 1 Engaged Student Learning project addresses the importance of decision-making in smart manufacturing systems by creating hands-on, interdisciplinary learning experiences. The project seeks to enhance student competencies, increase workforce readiness, and foster a culture of responsibility among future engineers. The project goals include the development, implementation, and iterative improvement of a comprehensive eight-week summer research and training program hosted at the University of Georgia and the University at Buffalo. The program will engage students in applied learning through four key components: theoretical instruction, hands-on modules, professional development, and guided reflection. Students will explore issues across the digital manufacturing lifecycle, while participating in collaborative learning and research activities across both institutions. The project will use a mixed-methods assessment strategy, including pre-, mid-, and post-program evaluations, to measure student growth in reasoning and data science skills. Long-term impacts will be tracked through student career outcomes, with ongoing input from an industry advisory board. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and 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 · 2025-10

As global connectivity becomes central to several critical societal functions ranging from disaster relief and climate monitoring to defense and underwater exploration, today’s wireless infrastructure must evolve beyond isolated domains. Specifically, traditional wireless networks, confined to a specific terrestrial, aerial, or underwater environment, fall short in supporting coordinated and resilient operations across these heterogeneous domains. Addressing this challenge and motivated by the need for a unified high-performance communication architecture that spans land, air, and sea/ocean, this project aims to lay the foundation for an integrated network that leverages optical, radio-frequency (RF), and acoustic wireless technologies to connect aerial nodes (e.g., drones and high-altitude platforms), terrestrial wireless devices (e.g., mobile phones), and underwater nodes (e.g., autonomous underwater vehicles) into a cohesive system, referred to as Integrated Air-Ground-Underwater Network (IAGUN). The hybrid use of optical and RF communication modes (or optical and acoustic communication modes in underwater scenarios) offers complementary advantages, enabling faster, more secure, and more reliable communications than either alone can achieve. The project also features an educational and outreach component that supports interdisciplinary training at the intersection of optical communications, wireless networking, and machine learning. Students will gain hands-on experience in testbed development, simulation, and system-level optimization, contributing to the development of next-generation engineering workforce. Publicly released datasets and benchmarks acquired during the execution of the project will further empower the broader research community to explore and build on the project's results. Ultimately, by advancing the design of hybrid wireless systems and fostering applied research in real-world scenarios, this work aims to push the boundaries of networking technologies while training future engineers and innovators. This research pioneers in addressing the core technical challenge of developing high-performance and resilient communication infrastructures that merge RF, acoustic, and optical wireless technologies within hybrid RF/acoustic/optical IAGUNs. To this end, the project introduces six strategic pillars for enabling convergence in RF/acoustic/optical technologies: (1) modeling and optimizing communication across networks with multiple relays and source-destination pairs, (2) co-usage of RF and optical as well as acoustic communication modes, (3) incorporating link establishment overhead, (4) accounting for heterogeneity and mobility, (5) cross-layer network optimization, and (6) embedding and enabling intelligence across hybrid RF/acoustic/optical IAGUNs using distributed learning. The research is organized into three thrusts. Thrust 1 focuses on optimizing communication performance through novel system model and problem formulations that jointly improve underlying performance metrics of interest (e.g., minimizing end-to-end delay) and network establishment/configuration overhead in hybrid RF/acoustic/optical IAGUNs. These formulations are then solved using advanced optimization techniques, such as mixed-integer programming, non-convex optimization, and reinforcement learning, as well as graph-theoretic approaches that are rooted in network flow optimization. Thrust 2 addresses network resiliency of IAGUNs through redundant transmission strategies, robust optimization under adversarial attacks, and signal- and network-level defense mechanisms. Thrust 3 creates one of the first public datasets and benchmarks for hybrid IAGUNs. The interdisciplinary methods of this project, spanning wireless communications, optics, optimization, graph theory, and machine learning, address open gaps in the existing literature and promise impactful solutions for future intelligent, secure, and ubiquitous wireless networks. 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

For individuals with Type 1 diabetes, keeping blood sugar levels within a safe range is essential but often difficult due to daily changes in diet, stress, activity, and other factors. This project aims to improve diabetes care by creating virtual models of individual patients--called digital twins--that can learn from wearable health sensors and help guide real-time insulin delivery using automated medical devices. By combining personal health data with artificial intelligence (AI), the project seeks to reduce the burden of self-management, prevent life-threatening highs and lows in blood glucose concentration, and improve long-term health outcomes. A central innovation of this work is the use of mathematical methods to quantify and manage uncertainty in predictions and recommendations made by AI models, thereby improving the reliability of treatment decisions. These methods also contribute to federal efforts to advance science for medical devices, supporting the safe and effective deployment of AI-empowered healthcare technologies. The broader impacts of the project include reducing diabetes-related complications and healthcare costs, improving public trust in AI-powered systems, and fostering interdisciplinary education. It will create educational opportunities for students and early-career researchers at the intersection of mathematical science, computational engineering, and biomedical science, including hands-on workshops in computational medicine, offering early exposure to high-performance computing and digital health technologies. This project develops new mathematical methods and computational algorithms to enable safe, reliable deployment of digital twins in healthcare, with a focus on managing Type 1 diabetes through personalized insulin delivery. It tackles three critical barriers that limit the trustworthiness of deep learning-based digital twins for healthcare, including identifying reliable models capable of accurately representing individualized glucose-insulin dynamics, quantifying predictive uncertainty under data scarcity, patient variability, and sensor errors, and validating treatment recommendations made by deep learning models under physiological fluctuations and potential control system faults. To close these gaps, the research advances three integrated technical objectives. First, it introduces an iterative Bayesian model selection and validation strategy for discovering deep learning models with accurate and reliable predictions, using population-level clinical data. Second, it implements algorithms and scalable cyberinfrastructure for real-time adaptation of the digital twin to individual physiology, including risk-averse insulin control. Third, it establishes rigorous methodologies for validating treatment recommendations by the deep learning-based digital twin, using both in silico simulations and clinical datasets. Intellectual contributions include a mathematical and computational framework for decision-making under uncertainty in physiological modeling, derivation of a posteriori error bounds for deep learning forecasts, and scalable techniques for optimal control under high-dimensional uncertainty. The resulting methods provide a generalizable blueprint for constructing, evaluating, and de-risking digital twins in a wide range of biomedical applications beyond diabetes care. 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

Advances in engineering and texturing of surfaces to endow lubrication, insulation, moisture-repellence, and anti-fouling capabilities hold transformative potential for water desalination, aircraft drag reduction, self-cleaning textiles, targeted drug delivery, and other applications that profoundly impact daily life and the global economy. However, manufacturing of such surfaces relies on templating techniques that are time-, resource, and labor-intensive, or methods that have severe limitation on the structures and capabilities that can be produced. The award supports research to investigate a novel roll-to-roll assisted polymerization (RRP) process that can address the current limitations and significantly increase the efficiency of the methods to synthesize large-scale surfaces engineered to yield multifunctional properties. The outcome of the research will be integrated into the three graduate and undergraduate courses in manufacturing and data assimilation. This project involves experimental and modeling studies to elucidate the underlying process-structure-property relations and thereby establish scientific foundations for controlling the microstructures formed during the RRP process. The project will explore three components: (1) use of the light patterns generated by dynamic mask images along the projection plane to drive microstructure formation, allowing faster production of customized structures along multiple scales and dimensions, (2) exploit magnetic guiding of filler orientation to enhance mechanical strength and long-term endurance of engineered surfaces, and (3) tailor surface micropatterns to enhance surface performance in terms of mechanical, optical and hydrodynamic properties, enabling multifunctional capabilities. Outcomes from this project will inform the design of next-generation surface materials for applications across many industry sectors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Nontechnical Summary Chirality is a geometric property of objects and materials that lack mirror image symmetry. Your hands are mirror images of each other but cannot be superimposed on each other. Chirality is seen widely in nature at the molecular level. For example, amino acids in proteins are left-handed, while sugars in DNA and RNA are right-handed. Such handedness also determines how many semiconductors emit or absorb polarized light or transport electrons. While materials chirality has applications from drug design to nanoelectronics, the transfer of chirality to neighboring non-chiral materials remains poorly understood. Chirality transfer also appears in a large class of non-chiral inorganic semiconductors, known as perovskites, when they incorporate chiral organic molecules. The focus of this project is on chirality transfer from hybrid chiral perovskites to two-dimensional (2D) semiconductors with excellent optical properties. Chirality transfer in heterostructures of chiral perovskites and non-chiral 2D semiconductors will discriminate emission and absorption of polarized light as well electrons of spin up and down. The project will reveal new insights into how spin-based effects can be shared between materials without the need for magnets. This could unlock new possibilities for future quantum or optical technologies. This project includes a closely integrated educational and outreach components, benefitting students in the Buffalo area. Through summer workshops led by the investigators, students have the opportunity to explore cutting-edge topics in materials physics. Technical Summary With proximity effects, where a given material acquires properties of its neighbors, it is possible to complement the conventional materials design by doping and functionalization, as well as to overcome their limitations. This research investigates dynamics of interlayer excitons between chiral perovskites and 2D transition metal dichalcogenides (TMDs), focusing on how proximity effects influence chirality transfer across the interface. Chiral perovskites and TMDs are extensively studied and recognized for their strongly bound excitons which are expected to expand modern applications in photovoltaics, optical communications, and lasers. However, their heterostructures are less understood and are expected to be transformed by chiral proximity effects, including removing of the valley degeneracy of TMD to unlock their spin-selective properties. This implies that excitons, demonstrated to be transported in TMDs over a macroscopic distance, through chiral and spin-orbit coupling effects could acquire a nontrivial spin structure and contribute to spin-dependent transport. The project focuses on elucidating the dominant mechanism for the transfer of chirality and spin angular momentum across the interface in chiral perovskite/TMD heterostructures. Experimentally, three key mechanisms are investigated: the spin-polarized charge transfer, spin-orbital coupling proximity effect, and the effect of chiral rotation orientation. Following the growth and characterization of the heterostructures, the optical spectroscopy is employed to visualize the polarization states by mapping the photoluminescence in space and time. The experimental results are supported systematically by many-body first-principles studies of electronic structure and excitons and symmetry analysis of the effective Hamiltonian to also guide materials selection. 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

Speech technology, including artificial intelligence (AI) trained on speech data, performs poorly in cases where little or no recorded audio data exists to train the required AI models. Building better speech technology in these cases requires creating collections of speech materials and their transcriptions. However, transcription is immensely time-consuming without the assistance of existing AI technologies. This project builds a high-quality speech data set to enable phonetics and phonology research for several low-data languages, and to model an approach to ease the “transcription bottleneck” assisted by techniques in AI and natural language processing (NLP). The project jointly engages the expert perspectives of users of target languages, linguists, and computer scientists, and establishes an infrastructure for collaborative, computationally mediated language work. Other benefits to society include bridging laboratory-style research and real-world applications and providing innovative educational opportunities for trainees. This project builds a 60-hour corpus of naturalistic and read speech data recorded in the field, suitable for both AI/NLP applications and research in acoustic phonetics and phonology. Unsupervised or weakly supervised machine learning techniques are used to semi-automatically transcribe and annotate a portion of the speech corpus. This transcription and annotation process uses a novel human-in-the-loop approach making direct use of expert speaker inputs: transcripts produced for recorded audio by pretrained language models are corrected by trained language experts. These adjusted annotations are incorporated into subsequent rounds of model training and fine-tuning to further increase the accuracy of outputs. The target languages exhibit several unusual phonetic and phonological features that form the basis for exploratory phonetic and phonological research, such as complex lexical tone, stem-initial prominence with unclear acoustic correlates, vowels with consonant-like constriction features, and variable external sandhi processes. The speech corpus, annotations, and language models are available as a starting point for linguistics, NLP, and AI work on related languages with translational impact. 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

Programming concurrent and distributed systems is notoriously difficult and error prone. Since most concurrent and distributed programs are designed as a set of communicating components, incorrect communication protocols can lead to difficult-to-debug errors. Choreographic programming is a recent programming paradigm which helps developers codesign computations, thereby eliminating the possibility of errors within communication protocols. Unfortunately, choreographic programs require that all components are programmed at the same time, preventing the use of off-the-shelf software. This project develops open choreographies: choreographies that allow codesigned components to communicate with independently designed programs. This project's novelties are the invention of open choreographies and the development of a method for expressing the protocols they expect of the non-codesigned programs. This project’s impacts are allowing the development of correct distributed and concurrent systems that can integrate into standard software development workflows. This project’s educational impacts include the training of PhD researchers as well as experiential learning and research opportunities through the University at Buffalo’s experiential learning and research program for undergraduates. In particular, this project develops a new session typing discipline called scheduled session types which describe the protocols of concurrent software. This session typing discipline is then used to describe how the codesigned components of a choreography interact with other components. By proving that every concurrent program that has a session type cannot have any protocol errors, we can then know that any open choreography can interact with independently designed programs without protocol errors. By synthesizing session types from a choreography, we can make this guarantee without the programmer having to understand session types. Thus, programmers can use choreographies to develop concurrent and distributed programs without fear, and without needing to understand arcane new type-theoretic techniques. 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 critical national need for enhanced training in computational methods that support the design of advanced materials for applications in solar energy, quantum computing, sensing, and optoelectronics. The processes that determine materials’ performance—such as excitation energy and charge transfer, nonradiative relaxation of excited electronic states, photoinduced isomerization and reactions—are governed by an interplay of electrons and nuclei. The understanding of this interplay requires specialized simulations of coupled electronic and nuclear dynamics. However, researchers often lack specialized high-quality training in these complex methods as well as practical experience with the advanced software tools that implement them. This project meets that need by offering intensive training through four summer schools focused on the cyberinfrastructure (CI) for nonadiabatic quantum dynamics (NAQD) simulations and their integration with machine learning (ML) tools and excited-state electronic structure calculations. Online educational materials and a new university course will expand the project’s reach, helping to equip students and researchers across the country with the skills to use the cutting-edge computational tools for accurate modeling of broad range of quantum dynamics phenomena in complex systems. By building a stronger and better-prepared scientific workforce, by broadening participation, and by facilitating the adoption of the advanced CI for NAQD and ML, this project promotes the progress in material science, supports technological innovation and education, and enhances societal impacts and national prosperity. This implementation project will build capacity within the scientific community working on quantum-classical and quantum modeling of nonadiabatic dynamics in materials. It will do so by providing conceptual and hands-on training to approximately 100 graduate students, postdoctoral researchers, and early-career scientists through a series of four summer schools. The events will focus on advanced software packages that support NAQD, excited-state electronic structure calculations, and the application of machine learning to these problems. Sessions will be taught by leading experts, including original developers of more than 30 specialized tools and libraries. Online tutorials, input examples, and documentation will be created to promote broader adoption of these tools. The project will also contribute new educational content by developing and incorporating a "Machine Learning for Chemists" course into the undergraduate and graduate curriculum at the University at Buffalo. Additionally, the PI will develop workflows and tutorials for the comprehensive and modular Libra software package to facilitate integrated NAQD simulations. Overall, the project aims to bridge critical training gaps, advance computational capabilities, and catalyze the use of modern CI tools in chemical and materials research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Many real-world AI and big data applications, including 5G networks, autonomous systems, healthcare, finance, recommendation engines, and large foundation models, frequently involve multiple, often competing objectives arising from complex environments, conflicting goals, and vast datasets encompassing different domains and modalities. Multi-objective optimization (MOO) provides a robust theoretical framework for navigating these challenges by identifying sets of solutions that represent the best trade-offs among objectives. Despite notable efforts toward conflict-avoidant MOO approaches, algorithmic and theoretical progress in large-scale, data-driven settings remains limited. This project aims to significantly advance the theoretical and algorithmic foundations of MOO, offering provably convergent and efficient stochastic, bilevel, and fairness-aware MOO algorithms. Its outcomes hold the promise of propelling MOO research to new heights, with broad impacts on both theory and practice across wireless communication networks, multi-agent transportation and robotics systems, recommendation systems, and foundation models. The research outcomes are integrated into education and outreach activities for K-12 educators, graduate, and undergraduate students through (i) summer camp for K-12 students, (ii) student supervision, (iii) Experiential Learning and Research (ELR) undergraduate activity, (iv) CSE Colloquium and Upbeat events, and (v) course development. The research efforts are organized around three complimentary thrusts: (i) Thrust A focuses on developing new theoretical and algorithmic foundations for stochastic MOO; (ii) Thrust B focuses on proposing efficient and scalable multi-objective bilevel optimization (MOBO) algorithms, and further characterizes their convergence rates, iteration and sample complexities, and (iii) Thrust C aims to substantially advance MOO frameworks by incorporating innovative concepts of fairness that differ from existing approaches. This project aims to establish a fundamental understanding of stochastic MOO, MOBO, and fairness-aware MOO, covering aspects such as optimality, convergence guarantees, iteration requirements, and oracle complexities. The proposed methods are validated in real-world applications, including (i) fair resource allocation in communication networks, (ii) visual action prediction for multi-agent systems, and (iii) multi-task learning to rank in recommendation systems. By significantly advancing the field of MOO, this work is expected to attract interest from multiple communities, including machine learning, statistics, information science, networking, communication, robotics, and bioinformatics. Moreover, the project fosters new interdisciplinary research directions that bridge these areas. 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 Pathways to Enable Open-Source Ecosystems (POSE) project improves access, efficiency, and collaboration across the national cyberinfrastructure landscape. Data centers and high performance computing (HPC) centers support a wide array of scientific research efforts, yet the management of these complex and shared computing resources is often inefficient and inconsistent. This project addresses this challenge by building a sustainable open-source ecosystem (OSE) around a widely adopted platform designed to streamline the administration of shared computational resources. By supporting collaboration, enhancing transparency, and standardizing processes, the project has the potential to reduce overhead and operational costs for organizations, particularly those with limited staff or technical capacity. These improvements enable faster scientific discovery, better resource utilization, and better access to advanced computing infrastructure. The creation of a vibrant and sustainable OSE will help maintain U.S. leadership in research computing, promote the progress of science, and ensure that even smaller institutions can benefit from cutting-edge technologies. Ultimately, this work will serve a wide range of scientific domains and benefit academic institutions, research centers, and their broader user communities nationwide. This POSE project lays the foundation for a sustainable open-source ecosystem around a popular web-based software application used for managing resource access and allocations in high performance computing (HPC) environments. The primary goal of the project is to implement governance, development, and contribution models that will support the long-term, community-driven development of the software. Planned activities include the development of clear contribution guidelines, implementation of architecture and security policies, and piloting a continuous integration and testing pipeline to support reliable community contributions. By engaging with current users and stakeholders, the team evaluates the project’s present status, gathers feedback, and identifies the technical and organizational steps needed to support the ecosystem’s growth. The project team develops the strategic and procedural documentation required for the successful transition from a single-institution tool to a broader community-led platform. These efforts are foster more robust collaboration, improve the reliability and functionality of the software, and support the long-term viability of the ecosystem in supporting national-scale scientific research infrastructure. 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 2023 earthquake sequence in southeastern Türkiye, including the devastating magnitude 7.8 and 7.5 events, occurred along and around the East Anatolian Fault Zone. Studying this sequence provides a rare opportunity to improve understanding of how large continental earthquakes happen and how their source properties are controlled by mature active fault zones. The investigators will study the aftershock sequence in detail, with the aims to uncover the physical mechanisms that drive aftershocks, and potentially foreshocks, in this complex earthquake system. The dense seismic data previously collected will be used to create detailed images of fault zone structures, helping to identify the controlling factors that determine the rupture propagation directions and damage patterns away from the active faults. The results of this research will offer new insights into the behavior of earthquake sequences and the properties of fault zones, which are crucial for assessing seismic hazards in regions prone to large earthquakes. Thus, the findings will not only advance scientific knowledge but will also benefit society by contributing to improving earthquake risk assessments in Türkiye, a region that is highly susceptible to major earthquakes. The project will strengthen scientific collaboration between the U.S. and Türkiye and provide hands-on training opportunities for students and researchers in cutting-edge methods such as machine learning for seismic event detection, promoting a more skilled future generation of geoscientists in the U.S. while contributing to global efforts in earthquake preparedness and mitigation. The proposed project focuses on analyzing seismic data collected from an extensive deployment of ~200 nodal and 16 broadband/strong-motion seismic stations in 2023 and additional ~180 nodal deployment in 2024-2025 across the rupture zone of the 2023 Kahramanmaras earthquake sequence in southeastern Türkiye. The goal is to construct a comprehensive earthquake catalog that will provide a high-resolution understanding of the physical processes driving aftershocks and foreshocks, as well as the fault zone characteristics that influence earthquake rupture behavior. The research will focus on two primary objectives: (1) individual source parameters and collective behaviors of earthquake sequences, and (2) fault zone properties and their relationship with earthquake slip behaviors. On the first objective, by applying machine-learning and template-matching techniques, the project will relocate aftershocks and determine their focal mechanisms. This approach will shed light on the underlying mechanisms that govern aftershock sequences and foreshock triggering, and it will offer insights into rupture directivity and small earthquake behaviors. On the second objective, seismic data from ultra-dense fault zone arrays will be used to visualize internal structure of faults in the region, including its damage zone and connectivity at seismogenic depths. Three-dimensional models of seismic velocity, attenuation, and anisotropy will be inverted to identify correlations between fault zone properties and earthquake rupture velocities, specifically focusing on areas where subsidiary faults, such as the Narli Fault, where the M7.8 initiated before intersecting with the main Eastern Anatolian Fault. This detailed analysis will contribute to a deeper understanding of fault zone dynamics and offer critical data for seismic hazard assessments in a region that has experienced significant seismic activity. 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

Assembling reinforcing bar is one of the slowest and most labor-intensive tasks in reinforced concrete construction. Furthermore, when design requirements dictate complex and congested reinforcement for concrete elements, constructability and concrete quality can be compromised with potential implications on the capacity of a structure. The goal of this BRITE Pivot project is to understand the fundamental structural behavior and constructability of optimized and additively manufactured (also known as 3D printed) steel reinforcement for concrete structural elements that are prone to reinforcement congestion. This novel reinforcing steel will lead to automation, high construction quality, speed, reduced reliance on manual labor, elimination of reinforcing bar congestion, and structural efficiency. The findings of this project will enable engineers to think beyond grid-like reinforcement layouts and re-imagine reinforcement as a freeform structure tailored to function. The project will allow the principal investigator to gain knowledge on mechanical properties and processes of additively manufactured steel to transfer it to structural engineering for advancing reinforced concrete design, construction, and response. The project will study topologically optimized, free-form, additively manufactured steel reinforcement for concrete lateral load resisting elements that undergo load reversals. Shear wall coupling beams that often require challenging-to-build reinforcement in diagonal patterns are selected as the application to demonstrate the concept. The research scope includes nonlinear analyses and topology optimization to identify viable reinforcement shapes, understanding of processes that are suitable for manufacturing optimized steel for reinforced concrete applications, characterization of mechanical properties of additively manufactured steel, quantification of bond between additively manufactured steel and concrete considering inherent surface undulations of additively manufactured steel, understanding the cyclic response of shear wall coupling beams reinforced with topologically optimized steel through laboratory testing, and documenting the tradeoff between the higher cost of additive manufacturing and construction/structural efficiency. The project will initiate collaborations with engineers and researchers leading the metal additive manufacturing field in Europe and the US. In addition to training a PhD student, the project will enhance classes related to concrete structures and deliver interactive outreach activities for middle and high school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Catalysis Program in the Division of Chemistry, Professor Steven Diver of the University at Buffalo, the State University of New York, is studying new catalysts embedded in cage-like frameworks for selective catalysis. These catalysts are hybrid molecules, combining the best attributes of enzymes with the high performance and wide chemical repertoire of small molecule (molecular) catalysts. This new design will enable selective reactions, which will have impact in a number of different fields such as organic synthesis, polymer chemistry and pharmaceutical chemistry related to drug discovery. In addition to these applications, there are broader societal impacts from this research activity. The research activity will prepare students for careers in chemistry and chemistry related fields and advances sustainability concepts in synthesis. The use of these new selective catalysts has the potential to vastly improve efficiency and provide new synthetic possibilities which will streamline the synthesis of bioactive molecules. This may in the long run accelerate the drug discovery process and reduce the cost of medicines for the American public. With the support of the Chemical Catalysis Program in the Division of Chemistry, Professor Steven Diver of the University at Buffalo, the State University of New York, is studying new bimacrocyclic catalysts for size-selective reactions catalyzed by the earth abundant metals, Mn, Co, Ni and Co. The key objective is the design and modular synthesis of multidentate ligands, whose macrocyclic size can be easily and predictably adjusted. Within two ligand classes proposed, the macrocyclic design will create or enhance enantioselectivity, allowing molecular recognition in terms of both steric size and chirality. New catalysts that can manage reactivity through site selectivity will allow synthesis to be carried out differently, without protecting group manipulations to select which functional group should react. This will greatly increase efficiency and decrease costs, waste and environmental impact. Size-selectivity based on the bimacrocyclic ligand will allow members of the same functional group to be distinguishable from each other, enabling site selectivity. This new approach to chemical synthesis would change the way a synthesis can be conducted, reduce the need or reliance on protecting groups and would vastly increase efficiency. These new macrocyclic catalysts will be used to achieve selectivity in high value reactions such as alkene epoxidation, cross coupling and alkene hydrofunctionalization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive federal fellowship program. GRFP helps ensure the quality, vitality, and strength of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM), including STEM education. GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant achievements in STEM. This award supports the NSF Graduate Fellows pursuing graduate education at this awardee institution. 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.