Rochester Institute of Tech

NSF Awards · FY 2026 · 2026-08

Mergers of neutron stars and pairs of supermassive black holes are among the most energetic events in the universe. They can produce gravitational waves, bright flashes of light, and fast jets; neutron-star mergers can also create heavy elements found in planets and living things. Scientists use large computer simulations to understand the hot, magnetized gas around these objects, but many current simulations treat that gas as a perfect electrical conductor. This simplification makes magnetic-field reconnection, a key source of heat, light, and outflows, occur as an uncontrolled byproduct of numerical error rather than through a physically modeled process. This project develops open-source software that lets researchers model magnetic energy release with physically meaningful inputs and reproducible tests. The project advances the national interest by improving computational tools for scientific discovery, strengthening high-performance computing, and training students in computational science. It also broadens participation in science through activities that include research experiences for Deaf and Hard of Hearing students, outreach to rural K-12 students in Idaho, and public visualizations of cosmic collisions through accessible performances and outreach events. This project develops portable cyberinfrastructure for resistive general-relativistic magnetohydrodynamics (GRMHD), the computational framework used to simulate magnetized, electrically resistive fluids in strong gravity. A team of researchers extends the open-source GRHayL library with fourth-order, low-diffusion numerical methods and a resistive GRMHD module with two runtime-selectable conductivity prescriptions. The software uses a vector-potential formulation to preserve the divergence-free magnetic-field constraint, implicit-explicit time integration to handle stiff electric-field relaxation, and diagnostics that compare numerical resistivity with the prescribed physical conductivity. A human-supervised workflow uses large language models to assist code development, testing, documentation, review, and porting while leaving scientific and algorithmic decisions to human researchers. The new capabilities are integrated into the AsterX software system for production simulations and into BlackHoles@Home/GRoovy for single-node development, testing, optimization, and teaching. The project verifies the shared kernels through standard tests, cross-code comparisons, and central processing unit (CPU) and graphics processing unit (GPU) parity tests. Public deliverables include tagged software releases with digital object identifier snapshots, Apptainer containers, verification inputs and reference outputs, documentation, tutorials, and curated benchmark and diagnostic data products. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier in the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Understanding how the human brain works is essential for gaining insights into behavior, for identifying brain disorders, and for improving patient care. Intracranial electroencephalography (iEEG) is a tool for studying brain activity. Recordings from iEEG sometimes show transient events that may be correlated with brain functions and certain neurological disorders. Reliable detection of these events is important for clinical applications and advancing neuroscience. This project will develop an artificial intelligence (AI)-based tool to identify events in iEEG recordings. The tool will detect known events and enable the identification of previously unrecognized brain events. The outcomes of this project could improve treatments for patients suffering from such disorders as epilepsy and Parkinson’s disease. In addition, the project will provide training opportunities for students and clinicians. Overall, the project will result in new AI-based healthcare technologies and contribute to a skilled workforce in applied AI. This Engineering Research Initiation project will provide improved tools for automated iEEG analysis. Most existing event detectors identify only a single type of event, without knowledge of other events, which can lead to misclassifications. The majority of available iEEG data are obtained from patients and tagged by expert annotations. This process is time-intensive and prone to expert subjectivity. To address these challenges, this project will develop a deep learning-based model trained on expert-annotated iEEG data from large, multicenter human datasets. The model will be evaluated by applying a leave-one-institution-out approach. Data from a single center will be left out as the test set. The training will be performed on data from the remaining centers. Then, the model will be evaluated on the data from the left-out center. Finally, the detector will be implemented as user-friendly, open-source software. This approach will ensure dissemination, reproducibility, and long-term sustainability for the iEEG research community. By providing an accessible iEEG tool, this project has the potential to transform research and clinical applications across neurorehabilitation. 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 · 2026-05

Artificial intelligence is increasingly essential for small, power-limited devices that process information in real time, such as wearable health monitors, autonomous drones, and environmental sensor networks. However, most current computer hardware is inefficient for these "edge" applications because it relies on moving massive amounts of data between separate memory and processing units. This constant data movement creates a significant energy bottleneck and slows down operations. This project addresses this challenge by developing a new class of electronic hardware where memory and processing are combined within the same physical material. The research focuses on ionically gated transistors, which are electronic switches whose behavior is controlled by the movement of ions, or charged atoms, within a solid material. By mimicking the way biological brains process information, this "in-materia" approach enables hardware that can both respond dynamically to fast-changing signals and store learned information permanently in place. Beyond the technical innovations, the project provides a public benefit by strengthening the United States' semiconductor workforce. It integrates research with education by training graduate and undergraduate students in advanced microchip fabrication and modeling. Additionally, the project engages K–12 students and the local community through hands-on demonstrations that illustrate how new materials can enable the next generation of energy-efficient computing. Technically, the project establishes the fundamental science and engineering of a dual-mode ionically gated transistor that functions as a Co-located Adaptive Synapse (CAS). The CAS is a single device capable of operating in two distinct regimes controlled by the magnitude of the applied gate voltage. At lower voltages, the device operates as an electric double-layer transistor (EDLT), where ions accumulate at the interface of the channel to produce a volatile, fading-memory response. This regime is optimized for transient signal processing. At higher voltages, the device transitions into an electrochemical random-access memory (ECRAM) element, where ions physically enter the crystal lattice of the two-dimensional (2D) channel material to create a persistent, non-volatile change in electrical conductance. This regime is suitable for long-term analog weight storage. The research investigates how 2D channel materials, electrolyte compositions, and ionic transport kinetics govern the transition between these two regimes. The work proceeds through three integrated thrusts: (1) the fabrication and characterization of dual-mode devices with tunable response times and stable analog conductance states; (2) the development of a physics-based compact modeling framework that incorporates experimentally measured volatile and non-volatile behaviors into circuit-level simulations; and (3) the demonstration of compact adaptive computing architectures that utilize the same device array for both transient signal processing and in-place weight storage. The intellectual significance of this work lies in establishing the design principles for coupled ion-electron dynamics in low-dimensional materials and demonstrating a unified hardware primitive that reduces data movement, lowers energy consumption, and enables adaptive artificial intelligence at the hardware level. 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 · 2026-04

Machine learning models increasingly power critical systems in healthcare, finance, and national security. However, their increasing complexity introduces serious risks, as attackers can embed hidden vulnerabilities or exploit obscure failure modes. The project’s novelties are bridging the gap between powerful modern systems and classical, understandable frameworks to build machine learning models that are both secure and interpretable. Instead of treating simpler models as mere baselines, the research uses them to provide semantic validation and explanations for highly complex systems. By translating opaque machine learning behaviors into interpretable representations, defenders trace the origin of failures and repair vulnerabilities. The project's broader significance and importance are enhancing the safety, resilience, and accountability of machine learning systems deployed in high-stakes environments. Furthermore, the work empowers practitioners with tools to interpret complex systems and provides extensive educational outreach, training the next generation of students in cybersecurity-aware machine learning through hands-on labs and new curricula. The research advances a layered defense framework through four tightly integrated technical thrusts. First, the project develops scalable attribution techniques, combining gradient-based influence methods with symbolic surrogate models, such as decision trees, to identify how adversarial inputs or training data components lead to harmful outputs. Second, the work designs fast, localized model editing strategies to remove harmful behavior without full retraining, utilizing surrogate models to semantically validate that repairs are localized and verifiable. Third, the project builds automated pipelines for training data auditing, provenance tracking, and security-aware valuation to detect data poisoning and instability under distribution shifts. Fourth, the research integrates these signals into an interpretable, real-time interface where users explore suspicious outputs and interactively remediate vulnerabilities. Ultimately, this project establishes a unified approach across the adversarial threat lifecycle, ensuring that resilient, transparent, and trustworthy machine learning tools are openly available for research, education, and applied practice. 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 · 2026-04

This NSF CAREER project aims to develop machine learning systems that continuously learn from new data without forgetting prior knowledge. Today’s machine learning models are powerful but largely static, often overwriting earlier information when updated, a problem known as catastrophic forgetting that reduces reliability in evolving environments, such as healthcare monitoring, environmental sensing, and autonomous systems. The project will bring transformative change by enabling intelligent systems to recognize what they do not know, quantify uncertainty, update safely to new information, and retain prior knowledge without becoming unstable or overconfident. This will be achieved by designing learning methods that leverage uncertainty as a guiding signal to manage when models should preserve knowledge, adapt, or expand internal capacity. The intellectual merit includes advancing fundamental theory and algorithms for continual learning, uncertainty-aware decision-making, and modular neural architectures to enable stable, robust, and scalable lifelong learning. The broader impacts include advancing trustworthy AI that supports national priorities, including healthcare innovation, environmental monitoring, and intelligent infrastructure; strengthening economic competitiveness through adaptive intelligent technologies; supporting resilient sensing relevant to national defense; and integrating research with education from K-12 through graduate levels to promote scientific progress and prepare a skilled STEM workforce. This project develops a unified, theoretically grounded framework for continual learning from evolving data distributions without storing past data. The proposed research combines Bayesian uncertainty quantification, adaptive optimization, and modular neural architectures to enable continual learning in dynamic environments. Key objectives include: (1) uncertainty-guided optimization methods that regulate parameter updates across sequential tasks with theoretical generalization guarantees; (2) neural self-management masking strategies that selectively preserve or adapt parameters; (3) modular mixture-of-experts architectures with uncertainty-aware gating for scalable routing, and capacity expansion; and (4) convex-relaxed optimization formulations favoring flat minima under noise and domain shifts. The framework will be validated on benchmark and real-world sensing datasets. The integrated research-education program translates scientific advances into structured training across educational levels. The project will provide individualized mentoring and research opportunities for graduate, undergraduate, and high-school students; develop research-integrated curricula spanning foundational mathematics, robust machine learning, and adaptive sensing; and implement hands-on projects using real-world data through industry collaborators. Workforce development will be supported through workshops and tutorials, and K–12 engagement will offer experiential learning through guided projects on trustworthy and adaptive AI for environmental monitoring. These activities will help prepare the next generation of scientists and engineers to develop reliable, adaptive intelligent systems that serve societal needs. 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 · 2026-02

An aneurysm is a bulge in the wall of a blood vessel. Aneurysm rupture is a life-threatening medical emergency and constitutes a significant healthcare burden in the United States. Over the past decades, researchers have discovered that the behavior of blood flow within an aneurysm plays a substantial role in its growth and rupture. This project takes that understanding a step further by studying how human body movements, such as jogging or bicycling, may affect flow inside aneurysms. Results from the study may help medical professionals recommend specific physical exercises for patients to reduce the risk of aneurysm rupture. The experimental approach used in the project will promote interdisciplinary research in fluid dynamics and biomedical engineering. Educational activities will promote hands-on research opportunities for undergraduate students, introduce the field of bioengineering to K-12 students, and develop fluid mechanics lectures for non-engineering students, all aimed at sparking interest in future generations and highlighting the role of engineering in advancing medical innovation. This project aims to answer a fundamental physics question: What is the role of unsteady inertial forces in confined pulsatile flow? The project will employ a novel experimental platform integrating several advanced techniques, including a robotic arm, a physically relevant aneurysm flow phantom, and particle image velocimetry. Programmed motion patterns will drive the robotic arm to mimic human body movements. Simultaneously, a flow measurement system will capture changes in aneurysm flow dynamics resulting from the motion. The project will develop a scaling law that characterizes how movement affects aneurysmal flow and will verify it under realistic conditions. This knowledge could inform personalized exercise guidelines for patients with aneurysms, potentially reducing the risk of rupture. 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

Rochester Institute of Technology, Syracuse University, and the University of Rochester aim to enhance high school computing education by providing them easy to adopt, experiential educational labs. This project will adapt and evaluate the Accessible Learning Labs (ALL)—a suite of experiential educational computing modules—for effective use in grades 9–12. The investigators, in collaboration with high school educators and Science and Technology Entry Program (STEP) programs across Upstate New York, will modify and implement several existing experiential educational labs to align with secondary education contexts. By including engaging, real-world computing topics such as artificial intelligence, cybersecurity, and software development into classrooms, the project empowers students with the skills and confidence needed for STEM careers. The work supports the national interest by promoting the progress of science and broadening participation in computing through experiential learning tools. Collaborations with high schools and NY STEP programs will ensure that the project benefits a future STEM workforce. In addition to directly supporting student learning, the project contributes to research on how experiential teaching methods can improve engagement in STEM fields. This small CSforAll High School Strand project will apply experiential learning principles to teach foundational computing concepts in artificial intelligence, cybersecurity, accessible software design, and machine learning. Through iterative co-design, classroom implementation, and rigorous formative and summative evaluation, the team will assess the educational impact of these labs on both students and instructors. The effort will contribute to pedagogical knowledge on experiential computing education at the high school level, address existing gaps in accessible STEM resources, and generate scalable, open-access materials to support nationwide adoption. 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 · 2025-12

Vortex ring interactions with solid and deformable surfaces abound in nature and engineering flows. This situation is particularly relevant to the problem of replacement speech following a laryngectomy, where unsteady flow exiting a tracheoesophageal prosthesis produces pulsatile vortex rings that impinge on the curved wall of the esophagus. The resultant esophageal pressure field is responsible for successfully producing tracheoesophageal (i.e., replacement) speech. As such, understanding the mechanics that arise as vortex rings impact curved surfaces, in particular the pressure loading that is produced, could lead to improved success rates of replacement speech. This work is also more broadly applicable to both biological and engineering flows, such as cardiac hemodynamics, fluidic energy harvesting, wall-bounded turbulence, etc. The physics of these interactions will be investigated via flow visualization and both two-dimensional and tomographic particle image velocimetry. Acquisition of the velocity fields will enable determination of vorticity topologies, pressure field estimation, and identification of pressure source terms, as well as the resultant wall loading that arises during these interactions. This proposal blends the research efforts with a novel outreach plan to help high-school choral students envision how an interest in artistic expression in voice can lead to a career in science and engineering. The proposed work plan will explore the mechanics of vortex ring-surface interactions with both hemispherical and cylindrical cavities. Flow visualization and particle image velocimetry will be utilized to explore how a primary vortex ring approaching a cavity induces flow on the surface of the cavity, and subsequently causes the flow to separate and roll-up into a secondary vortex ring, and potentially develop azimuthal instabilities. This interaction will be investigated in both axisymmetric (hemispherical) and two-dimensional cavity geometries as a function of cavity radius relative to the primary vortex ring radius. The pressure loading that develops on the concave surface will also be quantified to provide insight into the fluid-structure interaction. The outcomes from this research plan will improve success rates of tracheoesophageal speech. The project will also facilitate the training and education of one graduate and multiple undergraduate students. Finally, the outreach program will inspire high-school students to pursue careers in science and engineering fields, while also providing paid summer research experiences for two of them. 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

Computational imaging technologies are increasingly required to operate in real-world applications, offering high-fidelity visual output under complex lighting environments. Among these, snapshot compressive imaging (SCI) is a promising technique that retrieves high-dimensional signals from 2D optically compressed measurements. Incorporating modern AI techniques, SCI has significantly advanced the capabilities of traditional optical sensing in various fields, including hyperspectral imaging, video compression, microscopy, and security. However, despite its promise, SCI remains sensitive and under-explored to uncertainties rooted in its hybrid structure – optical encoders and algorithmic decoders – stemming from imperfect optical hardware, algorithmic overfitting, and unpredictable environmental noise, which limits its deployment across diverse platforms and safety-critical systems. This research project aims to enhance the reliability and robustness of SCI in practical use by developing novel methods to study uncertainties at various system levels. The success of this project benefits a broad range of areas, including computational imaging, signal processing, remote sensing, AI photonics, and machine learning. Along with the proposed research, the project supports a comprehensive education and outreach agenda, encompassing cutting-edge undergraduate and graduate research activities, as well as engaging K-12 students through hands-on experiences with AI and imaging technologies. The goal of this project is to develop a versatile bilevel optimization framework to study uncertainties in hybrid models that interweave physical optics and deep learning algorithms throughout SCI systems. In collaboration with domain experts, the investigator investigates new scientific knowledge to establish computational and optical underpinnings of SCI by modeling mask, weight, and data uncertainties. The research is unfolded into three thrusts: 1) reasoning mask uncertainties with scalable hyperparameter optimization techniques through a Bayes lens to automatically calibrate models across different hardware, 2) developing new post-training quantization algorithms combined with weight uncertainty and diffusion probabilistic models to combat large model sizes and high-dimensional reconstruction, and 3) building a dual-camera spectral SCI system to capture data noise in the real imaging process. The project bridges the gap between laboratory simulation and real hardware by developing an integrated uncertainty-aware SCI toolbox and collecting a dataset that co-registers compressed measurements with reference images. 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 faculty members to teach students the computational skills they need to join the workforce, ready to take on the next generation of scientific challenges. "Big data" fields (where the amount of data is too large to be analyzed with Excel), such as bioinformatics, computational biology, protein design, and drug design, all rely on computational skills. In addition, using computers as research and information tools is recognized by the American Society of Biochemistry and Molecular Biology (ASBMB) as a necessary skill for Biochemistry and Molecular Biology (BMB) curricula. Thus, developing computational literacy in BMB students is important for the next generation of scientists to fully participate in evolving scientific fields. This project is designed to identify the computational needs of the BMB community and to develop and deliver virtual workshops and coding exercises related to BMB fields. These workshops and videos will be made freely available on sites such as GitHub and YouTube to increase the reach of this project beyond those who are able to attend the live, virtual workshops. There is currently no single source of information or exercises to train BMB faculty to teach computational literacy through coding exercises. Likewise, no tools currently exist to assess the needs and attitudes of the BMB community towards building computational literacy. The project team plans to develop a series of coding exercises and workshop materials freely accessible via GitHub and YouTube, which are designed to train BMB faculty to teach coding using Google Colab, an easy-to-use, AI-assisted coding environment. The project team plans to host three types of virtual workshops that will each be delivered twice during the project: Introductory, Learning to Code, and Teaching with Code. Workshop sessions will include topics such as working with dataframes, plotting, protein sequence analysis, and preparing to teach with live coding. This project plans to identify the evolving needs of the BMB community, provide insights into the current uses of computation in BMB curricula, and create widely accessible workshops and materials tailored to the BMB community. This will enable instructors to integrate computation effectively into their courses and will establish a foundational understanding of computational literacy in BMB curricula. Thus, this project plans to serve to advance computational literacy in BMB education and adjacent fields, such as biology and chemistry, to better prepare the next generation of scientists to tackle important biological problems using computational tools. 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

This project aims to develop a Career Interests and Decisions Assessment (CIDA) in the context of undergraduate physics education. In the United States, over 8000 students earn an undergraduate degree in physics each year. These students pursue a variety of careers in areas such as research, software development, engineering, and teaching. Many physics majors pursue careers in areas of strategic national importance, such as quantum technology, nuclear physics, semiconductor physics, and artificial intelligence. The project will investigate how students develop these specialized subfield interests and use those insights to help educators and administrators improve pathways into these crucial careers. The CIDA is built around a unique graphical format that we call a “causal map assessment.” Visually similar to a concept or mind map, causal maps allow students to tell a rich, coherent, and visual story of their experiences both before and during college. Each map will reveal how various factors, including learning experiences (e.g., courses, research opportunities, student clubs, or interactions with peers), beliefs and attitudes (e.g., confidence in programming or a desire to do hands-on work), awareness of career options, and career interests, unfold over time. This data will help us understand why students initially choose to be physics majors and how their specific career interests take shape throughout their undergraduate studies. The power of causal maps will extend beyond individual stories. The analysis will combine many students’ maps to identify broader patterns using a set of mathematical and statistical techniques known as network analysis. The collective analysis will be used to generate visualizations, tables, and reports that are useful to other researchers and to physics departments, and to provide insights regarding national trends. The CIDA is intended to help physics departments increase students’ awareness of career options, increase access to positive influences and supports, and make improvements in response to negative influences. The design of the causal map assessment will be rooted in the constructs of Social Cognitive Career Theory (SCCT). Constructing their personal map using this new assessment tool will allow students to visually articulate the interplay between their past experiences, their beliefs about their capabilities (self-efficacy), their anticipated results (outcome expectations), their emerging interests, the goals they set, and the actions they take. The graphical format is designed to capture the dynamic and reciprocal nature of SCCT, enabling a deeper understanding of students’ career development process by revealing time ordering and causation in a way that is hard to elicit in an interview or Likert-scale survey. This new assessment tool will therefore address longstanding limitations faced by researchers seeking to understand career interest development. Even a single map can be interpreted for meaningful insights, but this project will also treat causal maps as network data, which will open up the ability to aggregate and automate assessment and apply a wide range of network analysis techniques (e.g., centrality or motifs). Over three years, the project will conduct interviews and administer assessments with students from a broad range of institutions to thoroughly understand the rich array of factors influencing their interest formation; develop and test a user friendly online interface for the CIDA; gather evidence of validity and reliability; administer the CIDA to hundreds of students to collect a robust dataset; perform detailed qualitative analysis of individual causal maps; apply network analysis to identify patterns and make comparisons among causal maps; and automate the creation of graphics and tables for reports when analyzing groups of students (e.g., students in a specific undergraduate physics program). Ultimately, the outcomes of this work will not only extend research on career decision-making in physics but also provide powerful new tools for investigating complex decision processes in other STEM fields. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM workforce development, STEM learning and STEM learning 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

Manufacturing serves as a fundamental pillar of America's economic resilience and national security, exerting a critical influence across nearly every sector of the U.S. economy. To address global competition and maintain a leading role, it is essential to develop, scale, and promote the advanced manufacturing workforce by integrating advanced manufacturing into STEM education and expanding the use of emerging learning technologies and practices. As a central and strategic element of the manufacturing sector, Product Design and Manufacturing (PD&M) adds high value by bridging creative design with practical manufacturing. Proficiency in PD&M demands not only technical knowledge but also intuitive judgment shaped by extensive practical experience, sophisticated decision-making abilities, and strong adaptability to novel and unpredictable challenges. As such, human involvement remains crucial and irreplaceable in PD&M. Current educational approaches have proven insufficient in adequately preparing students for these cognitive-demanding roles. The path to these demanding manufacturing roles can be non-traditional and thus it is essential to study the construction of knowledge so that alternate educational pathways can be developed. This study aims to understand cognition of learning in an applied experiential learning environment and there is potential for the knowledge gains to impact other critical areas of STEM workforce development. The primary objective of this research is to gain a fundamental understanding of situated cognition and embodied cognition within manufacturing contexts and specifically PD&M. Understanding cognition of learning within specific work-related situations will enable us to improve knowledge acquisition towards workforce preparation. An investigation of pedagogical strategies that facilitate effective experiential knowledge acquisition specific to PD&M will also be investigated. The integration of research-informed situated cognition towards experiential learning methodologies in manufacturing education programs will foster professional-level competencies and intuitive insights among the next-generation manufacturing workforce. By collaborating with two-year Institutions of Higher Education (IHEs), K-12 educational services, and industry partners, the project will leverage the manufacturing workforce preparation and augmentation by modernizing the learning experiences of these manufacturing professionals across various educational levels. To complement the research plan, an education plan will target educational activities that will reach multiple audiences of university students, community college students, and high school students. 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's Improving Undergraduate STEM Education (IUSE) program and the Education Core Research (ECR) STEM Education (EDU) 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

Spectroscopic observations of galaxies contain critical information about their physical properties, and modern large spectroscopic surveys of galaxies are producing a plethora of data. Given the size of these datasets, traditional data analysis methods are prohibitively time consuming. The Principal Investigator (PI) will develop an artificial intelligence (AI) model called Spectroscopy Pre-trained Transformer (SpecPT) that will make use of recent advances in machine learning (ML) architectures, along with citizen science measurements, to create a general analysis package for extragalactic astronomy. The key feature of the model will be its ability to generalize across diverse spectroscopic datasets, providing rapid, accurate measurements of redshift and other physical properties of galaxies and enabling new insights into galaxy evolution. Measurements from the Redshift Wrangler citizen science project, which allows the general public to participate in measuring redshifts from extragalactic spectra, will be used as a major component of the training set for the model. The project will involve undergraduate and graduate students in the research. SpecPT is a foundational model that has been preliminarily trained using the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI), with early results showing the model can measure redshifts directly from spectroscopy with a high level of accuracy and low outlier fraction. The PI will extend SpecPT training to include the DESI EDR+DR1 dataset of over 7 million galaxies to improve the precision and accuracy of redshift measurements and use existing line fluxes to train SpecPT to measure properties of the galaxies’ interstellar medium, find active galactic nuclei, and identify outliers. She will then use transfer learning to extend SpecPT to existing ground-based spectroscopy for fainter and higher redshift galaxies, using existing redshifts and line flux measurements plus citizen science measurements to expand the training set. She will also use citizen science measurements to fine-tune an object detection algorithm to find emission lines in the spectra and integrate an interpretability mechanism into the model. The end product will be a modern, AI-powered generalized cloud-based spectroscopic analysis package that can be used by any astronomer to analyze any extragalactic spectroscopic dataset. 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

Conventional computing based on digital electronic logic faces challenges due to the rapid growth of artificial intelligence and machine learning algorithms, which need massive amounts of processing power, outpacing the rate at which computers have historically progressed (according to Moore's Law). Photonics technologies offer an attractive alternative due to several advantages, including high speed, energy efficiency, and the potential for massive parallelization of information processing. This project aims to tackle the obstacles in using integrated photonic deep neural networks for the next generation of computing platforms. Despite previous efforts, these networks still face challenges that make them impractical for real-world applications. In particular, most previous photonic neural networks are not scalable and require electronic circuits for achieving nonlinear effects, thus inevitably losing the high-speed advantages of photonics. An interdisciplinary approach is needed to tackle several problems, including designing novel neural network architectures for efficient processing of information encoded in light and integrating different materials for achieving desired functionalities, such as reconfigurability and nonlinear effects, which are essential for machine learning. This project focuses on developing a new deep learning architecture that is compatible with photonics and optoelectronics technologies and significantly reduces the size of optical neural networks compared to previous attempts. The proposed platform aims to create a scalable deep neural network, enabling real-time optical signal processing for a wide range of applications, from telecommunications, imaging, and biomedical applications to classical and quantum information processing systems. The proposed effort will create a roadmap for accelerating fundamental research and applied technology development for realizing functional photonic deep neural networks. This effort also provides a unique opportunity to train and educate students beyond their laboratory research through engagement in advanced research activities and through internships, workshops, and conferences. The participating investigators will develop short courses for two technical workshops that will involve fundamental and advanced research subjects. In addition, the team will organize conferences aiming to identify additional collaborators. Finally, internship and research rotation opportunities will be created for undergraduate students. This project brings together expertise from optical computing, integrated photonics, and hetero-integration to develop a novel deep-learning architecture that is built around the fundamental physical laws of light propagation in integrated photonic circuits. In conjunction with hetero-integration, this architecture leads to dramatic size reduction to enable the realization of large-scale photonic deep neural networks. The proposed architecture utilizes an interlacing of linear and nonlinear operations and is uniquely parametrized to facilitate integration of tens of network layers in a photonic implementation. The thrusts of this project involve (i) developing a theoretical/computational infrastructure for photonic deep learning; (ii) hetero-integration of semiconductor and other optical materials, including lithium niobate, to achieve strong and flexible nonlinear effects on a photonic chip; (iii) realizing and optimizing ultracompact phase shifters based on phase change materials; and (iv) design, fabrication, and experimental demonstration of all-photonic neural 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 · 2025-10

NON-TECHNICAL SUMMARY The objective of this project is to develop a new computational method to accelerate the design and discovery of advanced materials. Many materials with potential for transformative applications, such as clean energy and next-generation electronics, exhibit quantum properties that are too complex to simulate efficiently with existing tools, creating a bottleneck for scientific progress. This project introduces an approach that combines quantum theory with machine learning, inspired by the concept of data compression. Just as an image can be compressed by retaining only its most essential information, this method will reduce the complexity of quantum simulations by identifying and using a much smaller, representative set of quantum states. The resulting tool will enable accurate simulations of materials that were previously beyond reach. In particular, it will be applied to investigate a long-standing puzzle in the volume anomalies of heavy actinides, which may be resolved by simulating the complex interplay of spin–orbit coupling and crystal field effects, a mechanism that is currently poorly understood. Broader impacts will include training undergraduate- and graduate-student researchers, course development, and integration of the developed tools into a widely-used, publicly-available quantum-simulation toolkit. TECHNICAL SUMMARY This project aims to develop a new computational method to accelerate quantum embedding simulations for strongly correlated materials. The central idea is to apply machine learning techniques, specifically dimensionality reduction, to construct compact variational subspaces that approximate the low-energy manifold relevant to embedding calculations. These variational spaces will be learned from representative training data and used to build fast solvers for the embedding Hamiltonian, which is the main computational bottleneck in these simulations. The method will be implemented within the Ghost Gutzwiller Approximation, a variational embedding approach tailored to the simulation of correlated electron systems. By eliminating the need to repeatedly solve high-dimensional quantum problems during materials simulations, the resulting solvers will achieve accurate results at a fraction of the computational cost. The framework will be validated and applied in simulations of f- and d-electron systems, including actinides and transition metals. In particular, the project will investigate the microscopic origin of volume anomalies observed in heavy actinides, which remain poorly understood due to the interplay of spin orbit coupling and crystal field effects. The tools developed in this activity will support broader research efforts in condensed matter physics, materials science, and catalysis, where strong electron correlations are often present but computationally challenging to treat due to the complexity of many-body interactions. Broader impacts include the integration of machine learning and quantum simulation into undergraduate and graduate curricula at RIT and the public dissemination of the resulting open-source software and associated datasets. STATEMENT OF MERIT REVIEW 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

Most of the digital technology around us processes and stores information in a sequential, well-ordered manner. For example, a computer hard drive stores data bits in a structured way that allows them to be retrieved in order. Cell phone systems transmit and receive data as sequences of bits coded to allow the receiver to reconstruct the information in the same order it was sent. Recent technological advances such as DNA sequencing technologies, however, defy this ordered information paradigm. Such technologies generate data consisting of many short, out-of-order fragments. Processing this data is akin to assembling a jigsaw puzzle, where the desired information is only conveyed by the final assembled picture. Developing powerful algorithms for these tasks is important for several applications in the field of genomics and for the development of emerging molecular data storage technologies. The goal of this project is to extend techniques from the ordered digital world - codes, algorithms, and an information-theoretic framework - to these emerging out-of-order settings. This should enable new data storage paradigms to be deployed and lead to the development of new computational methods to analyze genomics data. The project will seek to extend Information Theory techniques to out-of-order information scenarios and to characterize how much information can be reliably conveyed by an unordered set of data fragments. The research will be organized along three thrusts with important practical applications. Motivated by tasks in immunogenomics and resistomics, the first thrust will focus on the problem of recovering a set of similar-looking sequences (genes, in most applications) from a set of unordered fragments (the DNA sequencing reads). This will lead to new algorithms to characterize the presence of antimicrobial resistance genes in a microbial community. The second thrust will address the problem of reordering a set of unordered fragments given a noisy reference. This has applications in reference-based genome assembly (when the genome of a related species is available) and in the problem of aligning out-of-order data across two databases. Motivated by molecular data storage and its potential for addressing ever-increasing data storage demands, the third thrust will focus on fundamental limits and coding strategies for out-of-order channels. This includes the development of near-capacity-achieving codes for molecular storage, the analysis of the combined effects of fragmentation and symbol-level noise, and the design of efficient codes that minimize synthesis costs. 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

Spectral graph theory is the study of networks from a mathematical perspective inspired by sound and vibration. Just as the shape of a musical instrument influences how it resonates and produces tones, the structure of a network determines the eigenvalues and eigenvectors of associated matrices. These spectral properties reveal hidden characteristics of the network, such as clustering, connectivity, or sparsity, which are often not immediately apparent from the graph’s visual representation. By analyzing these properties, spectral graph theory provides a powerful framework for understanding and analyzing complex systems across many domains. Applications range from social networks and transportation grids to biological systems and machine learning models, where uncovering deep structural insights enables researchers to draw general conclusions about entire classes of matrices that share a network’s structural pattern, rather than analyzing individual cases. This project investigates the fundamental relationships between the structural, combinatorial, and spectral properties of matrices associated with graphs, with particular emphasis on the inverse eigenvalue problem for graphs (IEP-G). The IEP-G aims to characterize all possible spectra of real symmetric matrices whose zero–nonzero patterns correspond to the edges of a given graph. Understanding these spectra is critical for linking a graph’s topology to its spectral behavior. A key objective is to understand how the underlying graph structure influences eigenvalue multiplicities and the geometry of their corresponding eigenspaces. This problem remains largely unresolved for many graph families and lies at the intersection of linear algebra, combinatorics, and the geometry of manifolds. Alongside advancing fundamental knowledge, the project emphasizes student training and mentorship, with a focus on broadening participation in the mathematical sciences through research opportunities, collaborative learning, and outreach. Ultimately, this work seeks to contribute both to open theoretical questions and to building an active and vibrant community of future mathematical scientists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project aims to serve the national interest by improving computer science curricula through integration of accessible technology concepts in foundational computer science courses. Providing accessibility in software applications and web sites to enable use by people with disabilities is often an afterthought, and inaccessible applications can disadvantage large numbers of people. This Level II Engaged Student Learning project intends to scale up instruction of accessible technology concepts by engaging faculty in pedagogy-focused development workshops and virtual support sessions throughout the year. Integrating instruction on accessible technology concepts into existing foundational courses has the potential to expand the number of students reached and aid in adoption since new courses will not need to be developed. This in turn has the potential to positively impact preparation of the workforce to develop technology for a wide variety of people. The workshops will be offered in both virtual and in-person formats to meet the needs of a range of faculty from different types of institutions. The project builds on prior work by the proposers where they developed accessible technology related learning outcomes and provided one-on-one professional development to help faculty develop assignments that incorporate accessible technology in their courses. An important goal of this project is to align learning objectives with industry needs through a Delphi panel of industry experts. The project team will investigate three research questions: whether the technical accessibility learning outcomes align with industry needs; the efficacy of the workshops for disseminating knowledge about, and pedagogies for, teaching technical accessibility concepts; and whether the assignments developed through their workshops achieve desired gains in student learning. This project will employ empirical evaluation to measure faculty training effectiveness and its impact on student success. 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 · 2025-09

Many advanced technologies, from self-driving cars to processing medical images, rely on machine-learning to succeed. Technologies based on deep learning or deep neural networks have proven to be especially effective at learning from vast quantities of data, and yet our theoretical understanding of these tools has lagged behind. As we rely more on neural-network systems in our daily lives, it becomes even more important that we can guarantee their safety and reliability. One failure mode of current systems is that they can be confidently incorrect, and this can cause real-world harm. It would be better if our systems "know what they don't know" by maintaining an internal representation of their own uncertainty. In recent years, progress has been made in the mathematical study of "calibration" and "multicalibration," which establish a mathematical framework for uncertainty, probability, and fairness in problems having to do with categorical prediction such as classifiers and recommender systems. The goal of this project is to port these ideas into perceptual domains such as image or video processing. This project will result in the creation of new general-purpose neural networks for image-processing based on a new mathematical principle of learning "calibrated representations" of images, resulting in general-purpose systems that effectively "know what they don't know." This will enable more robust and reliable image-processing applications across wide sectors of research and technology. Representation-learning is a challenging area of machine learning (ML) in which the goal is not to solve any particular task, but to learn – from unlabeled and minimally-structured data – to form a representation or embedding vector of the data that will turn out to be useful, robust, and generalizeable in a variety of downstream tasks. Recent progress in Self-Supervised Learning (SSL) has produced embedding models that begin to rival task-specific models in areas like vision and language-processing. However, SSL is an area where empirical results have consistently outpaced theoretical understanding, making some SSL models susceptible to surprising failure-modes. The next generation of representation-learning methods must build on the success of current methods while establishing firmer theoretical foundations. This project advances such a foundation in terms of probability and uncertainty quantification. The key idea is to draw on the recent development of "multicalibration," which provides theoretical guarantees for trustworthy and fair classifiers. However, multicalibration in its existing form is only applicable to supervised learning tasks where labels or regression targets are known. First, this project extends multicalibration to weakly-structured but unlabeled data, where it gives rise to a constrained optimization objective. This then naturally leads to a set of self-consistency constraints on the outputs of a representation-learning or embedding model. These self-consistency constraints closely resemble the kinds of heuristic learning objectives that have been empirically successful in SSL. This project therefore aims to design and train embedding models with new self-consistency constraints derived from (multi)calibration. The theoretical contribution will be to place SSL methods on more solid theoretical footing, namely by establishing a connection to probabilistic inference and representations of uncertainty. The practical contribution will be to create and release more trustworthy and robust embedding models to serve as a foundation for general downstream visual tasks. 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

Gravitational Wave Astronomy provides a revolutionary new view of the universe that can probe previously unexplored regions, including the interiors of neutron stars, collisions of black holes, which emit energy at luminosities exceeding the entire visible universe, and even remnants of the Big Bang. To gain new insights into the dynamics of the universe, gravitational waves astronomers need to be able to infer the nature of the sources from the observed signals, which can only be done using highly accurate numerical modeling of potential sources. The main goals of this project are to provide the necessary numerical models for some of the most challenging to simulate black-hole configurations, such as small mass ratios, highly spinning binaries, and highly energetic black hole collisions, as well as provide training to postdoctoral researchers and support graduate students in two interdisciplinary PhD programs at the Rochester Institute of Technolgy: Astrophysical Sciences and Technology, and Mathematical Modeling. This research will systematically produce waveforms for the LIGO-Virgo-KAGRA collaboration to assist in source parametrization and model the remnant mass, spin, and gravitational recoil from binaries with small-mass-ratios, highly precessing binaries, highly spinning binaries, high energy collisions of black holes, and multiple (3 or more) black hole interactions, to elucidate astrophysical distribution impacts on black hole growth. Key objectives include: Producing and releasing gravitational waveforms from previously undersampled regions of the binary parameter space for LIGO-Virgo-KAGRA data analysis, employing them in parameter estimation, modeling black hole binaries' extreme dynamics, and improving simulation code accuracy and efficiency. This award will significantly enhance the research efforts of the Principal Investigators, a postdoctoral researcher, PhD students, and RIT's Center for Computational Relativity and Gravitation, integrating Numerical Relativity into gravitational waves data analysis and astrophysical studies. 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 BRITE Relaunch award supports research that enables the manufacturing, modeling, and control of fiber-reinforced actuators leveraging advances in additive manufacturing and artificial intelligence, thereby promoting the progress of science, and advancing prosperity and welfare. Fiber-reinforced actuators are common in soft robotics since they closely mimic biological muscles. One challenge with fiber-reinforced actuators is that significant variability can exist between actuators if manufacturing methods are not precisely controlled. Additionally, the actuators displace nonlinearly and are difficult to control. This project will solve this challenge by utilizing 3D/4D printing, in which 3D structures are printed with electronic capabilities that allow them to sense physical phenomena, to repeatedly fabricate, characterize, and test fiber-reinforced actuators. Data obtained will inform physics-informed artificial intelligence models, which will then be integrated with advanced control strategies to enable more precise control of soft robotic actuators. The methods developed could address a fundamental gap in soft robotics related to control for applications ranging from biomedical to space systems. In addition, a multifaceted approach to generating excitement about STEM disciplines is planned that includes K-12 outreach activities, undergraduate research and teaching experiences, and development of a freely available, online workshop curriculum. The field of soft robotics offers significant potential for advancing how robots interact with humans. Fiber-reinforced, pneumatic artificial muscle (PAM) actuators and sensors could serve as transformative technologies for various applications. Control of fiber-reinforced PAMs remains challenging due to inadequate mathematical models of the nonlinear dynamics associated with the actuators. Furthermore, although many designs have been presented in the literature, each PAM is slightly different and performs differently. As a result, empirical or numerical mathematical models derived from experimental data often do not adequately capture the nonlinear dynamics of the actuators and are not broadly applicable to a variety of actuator designs. The research aims to study how advances in 3D/4D printing, additive manufacturing, strain sensing fibers, and physics-informed artificial intelligence modeling can be used to develop robust mathematical models of PAMs. The use of 3D/4D printing will lead to more reproducible fabrication and subsequently a more robust dataset that characterizes actuator performance. The data will further be used to create a physics-informed neural network model, which will then be used to develop advanced control strategies. The outcome would be a novel, data-driven approach that integrates repeatable manufacturing with AI to transform modeling and control of fiber-reinforced actuators. 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

Increased connectivity of devices and people to the Internet has created an ever-expanding security attack surface. Machine learning (ML) techniques have been used to help detect attacks and may offer a more scalable way to deal with an increasingly large attack surface. However, acquiring a large volume of high-quality labelled attack samples is both costly and time consuming. Further, the acquired data set quite often do not fully represent the true data distribution. Given the challenge of labeled data scarcity and imbalance in representation, this project's novelties are to explore new ways to build data driven cyber-attack detection systems that can learn effectively from limited or biased cyber data set in a cost-efficient manner. The project's broader significance and importance are 1) enhancing the data-driven security attack detection infrastructure that leads to more secure and trustworthy cyberspace; 2) bridging the gap between research and practice by creating open-source systems that encourage real security productions, 3) providing research opportunities to both undergraduate and graduate students in the area of AI/ML enabled cyber defense. This project unveils an insight on how limited and/or imbalanced attack samples can be used as effective training data to facilitate data-driven model construction and enable high-performance security attack detection with low cost in practice. Towards this insight, this project contains three technical approaches: (1) cross-modal adversarial reprogramming that repurposes prior trained transformer models by inserting patch-level perturbations to inputs, reducing the number of parameters needed yet still maintaining its capability for data-limited learning; (2) scalable semi-supervised learning through consistency and contrastive regularization to boost model generalization for performing pseudo-labeling tasks and to help reduce label bias; (3) leveraging labeled and unlabeled objects to extend these two learning pipelines for more effective attack detection. 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

Most stars exist in “binary systems”, where two stars orbit one another, rather than as single stars like the Sun. If the two stars in a binary system have different masses, over time the higher mass star will expand into a giant star before the lower mass star. This often results in a short phase where the lower mass star orbits within the larger star, resulting in a dramatic exchange of energy and ejection of mass. The process results in the two stars coming closer together with only the core of the high mass star remaining. The phase is poorly understood and has never been observed in real-time. For this project, the investigators will use telescopes and computer simulations to study the process. The investigators will also provide research and training opportunities for undergraduate and graduate students. The investigators will observationally determine the mapping between the initial and final conditions of common envelope (CE) evolution by characterizing a benchmark set of detached post-common envelope binaries located in stellar clusters. They recently completed a systematic search for post-CE white dwarf and main-sequence binary systems in 299 Milky Way open star clusters, yielding 52 high-probability post-CE systems, three of which they classify as confirmed. The researchers will characterize and model these 52 high-probability systems, thereby creating a unique set of benchmarks where the initial and final conditions of common envelope evolution are known. Directly relating post-CE systems to their pre-CE binary parameters will provide critical constraints on stellar evolution and population synthesis 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 · 2025-07

This project aims to serve the national interest by improving curricula in computer science education. Computing professionals need to understand the possibilities and limitations of computation in order to design efficient algorithms for problems that can be solved in practice, or to avoid large investments in attempts to implement solutions for problems which have been proven to require unreasonable amounts of time or other resources. Modeling computation is an important building block for this understanding, however, students often struggle with abstract modeling and visualization. A prior Level 1 Engaged Student Learning project resulted in a prototype tool which provides immediate feedback on the computational models designed by students. This Level 2 Engaged Student Learning project aims to add features to the tool, improve its usability and adaptability, and investigate its impact on student problem-solving at a larger scale, in different educational settings. The existing Automated Feedback for Computing Theory (AFCT) prototype tool was built on the widely used Java Formal Languages and Automata Package (JFLAP) visualization tool that aids students in learning the basic concepts of formal languages and automata theory. The enhanced tool developed in this project will initially be deployed and outcomes assessed in theoretical computer science courses at the five collaborating institutions. It will be made available under an opensource license to enable others to use and modify the software to suit their needs. The research questions are focused on understanding the impacts of the tool on students' behavior, performance, and learning of computing theory; whether students from different types of institutions are impacted in significantly different ways; and the effects of various types of feedback on students' learning. The tool's added functionality, improved usability, and availability as opensource software will encourage its adoption at other institutions and increase its educational benefits. The project, including the upgraded feedback tool and the associated research study, will provide new insights into pedagogical approaches for improving student learning and will help students to be better prepared to develop high-quality software. 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 · 2025-07

This project will address a critical need in engineering education by improving how students learn to collaborate effectively on teams—a skill essential for success in today’s complex, multidisciplinary professional contexts. Employers consistently rank teamwork as one of the most critical skills for engineering graduates; however, many new engineers feel unprepared to navigate interpersonal challenges in real-world projects. Despite widespread adoption of project-based learning (PBL) involving teamwork, instructional methods frequently emphasize evaluating final products rather than guiding the teamwork process itself, leaving students to learn vital teamwork skills through trial and error. Such limited guidance results in common challenges, including unequal participation, unresolved conflicts, and inadequate psychological safety—the belief that team members can safely take interpersonal risks without fear of negative consequences. These issues are particularly pronounced for first-year students, who often enter college with limited teamwork experience and find themselves poorly equipped to manage conflicts effectively. By investigating how students and faculty collaboratively shape psychological safety, manage conflicts, and adapt teamwork behaviors, this research aims to provide critical insights into fostering healthier, more productive team environments. The findings will directly support faculty in implementing effective instructional strategies, better preparing engineering graduates for collaborative workplaces. This work aligns closely with the National Science Foundation’s Research in the Formation of Engineers (RFE) program, advancing innovative teaching practices and developing essential professional competencies for engineers. This project will utilize a multiple-case study design involving two distinct first-year engineering courses, one each at Virginia Tech and Rochester Institute of Technology. The research will address three primary questions: (1) How do students foster psychological safety, manage conflict, and regulate team performance in first-year engineering teams? (2) How do students' perceptions of psychological safety influence their conflict management strategies and teamwork regulation? (3) How do faculty instructional and assessment practices influence students' teamwork behaviors, psychological safety, and conflict management? Employing an adapted version of Rousseau et al.’s (2006) integrative framework of teamwork behaviors, the project will collect comprehensive data in the form of student interviews, focus groups, team communication artifacts, and instructional materials. Analysis will involve inductive thematic methods and deductive framework application to identify the connections between faculty practices and student teamwork behaviors. The intellectual merit of this research lies in advancing the understanding of teamwork processes and faculty roles in supporting the adaptation of teamwork. Specifically, it contributes new knowledge on the intersection of instructional practices and student teamwork regulation, with a particular emphasis on psychological safety—an area that has been extensively studied in organizational behavior but remains under-explored in engineering education contexts. Broader impacts include enhancing engineering instructional practices, specifically improving faculty readiness to teach and manage teamwork. Beyond publications, the findings of this work will also be disseminated through workshops, which will equip faculty with actionable strategies for supporting student teamwork across engineering curricula, from introductory courses to capstone projects, ultimately contributing to more supportive and professionally effective teamwork 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.