Rochester Institute of Tech

NSF Awards · FY 2025 · 2025-06

This Research Experiences for Undergraduates (REU) Site aims to advance research in computational sensing for human-centered artificial intelligence (AI), with an emphasis on creating new knowledge to enhance interactions between human and artificial agents, using generative machine learning methods while moving away from traditional prediction. REU projects conducted over three cohorts of participants will promote human-centered AI systems that are robust in noisy conditions where humans excel, but AI typically struggles. From science, technology, engineering, and mathematics (STEM) education perspective, the REU Site also explores the effectiveness of triad-based REU student research teams. It also leverages a synergistic partnership with RIT's AWARE-AI NSF Research Traineeship (NRT) program, increasing REU participants' near-peer interaction with PhD students trained by experienced faculty in co-mentorship. The Site will offer some of its programmatic elements nationwide. The REU Site in Computational Sensing for Human-centered AI pursues four ambitious goals. First, the project will advance fundamental research in multimodal computational sensing for human-centered AI, leveraging generative machine learning techniques. Second, the investigators will formally assess the efficacy of triad-based REU research projects compared to results from previous pair-based REU projects. Third, the project will evaluate and report on a novel collaborative partnership between the undergraduate cohort-based REU Site and a graduate cohort-based NRT program. Fourth, the REU Site will disseminate findings in conferences or journals, both of the scientific outcomes of REU teams' research and of Site-based computing education research to the STEM community. Students will receive guidance for pursuing graduate careers in computing or STEM and experience public outreach enrichment that promotes the communication of research to wider audiences. 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 · 2025-06

Nontechnical Abstract This CAREER award will investigate how soft particles flow or become trapped in porous environments such as filters, tissue, and soil. Soft particles suspended in fluids are abundant in the food industry, pharmaceuticals, blood flow, and microbial ecosystems in the natural environment. The investigators will use microscopy to quantify the flow, transport, and clogging of soft particles of prescribed sizes within porous environments fabricated using a microfluidic 3D printer. These studies will reveal how particle elasticity and deformability, in conjunction with the structure of the porous medium, influence the transition from a flowing suspension of particles to a clogged state. The project will enhance student participation in career-building activities by integrating experimental skills and teamwork into freshman physics courses. Additionally, it will provide opportunities for community building and improve the retention of students through collaborative projects and teamwork. Technical Abstract This research will establish a fundamental understanding of the cooperative dynamics of deformable granular particles in porous media. It will investigate how microscale particle interactions, elasticity, and pore network connectivity influence macro-scale transport by integrating microscopic measurements and bulk permeability analysis. Two fundamental questions will be addressed: (1) How does the network of pores in a medium determine the formation of particle clusters within individual pores and enhance the long-range spatial order within clusters? (2) What is the microscopic origin of the increased apparent viscosity of a suspension of particles flowing through a porous medium at small shear stress? By connecting particle elasticity, cluster formation, and porous medium properties, this study will identify the regimes in which either single-particle properties or collective transport dominate, leading to new insights into clogging and material transport in biological and industrial applications. Results from this project will advance the physics of phase transition in a porous structure by quantifying the spatial correlation of particles within the medium. More broadly, this research will provide a foundation for developing models for anomalous shear thickening and jamming transitions based on the local dynamics of particles, applicable to various nonequilibrium systems confined to a rigid matrix. 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 · 2025-06

This award supports a collaborative research project to advance understanding of articular cartilage, the soft tissue covering the ends of bones in joints. The project will examine how cartilage responds to physical forces relevant to daily activities like walking and jumping. While much is known about how cartilage resists compression, less is understood about how it responds to shear forces, during twisting and sliding. Recent findings suggest the collagen fiber network, which provides structural support, is close to a mechanical phase transition, where small structural changes can significantly impact mechanical properties. By integrating theoretical modeling, simulations, and experiments, the researchers will study how osmotic pressure and different types of mechanical stress influence cartilage structure and function. Understanding these processes could lead to better osteoarthritis treatments, improved tissue engineering and repair strategies, and bio-inspired materials for various other applications. This project will support education and workforce development by training students in biomechanics equipping them with valuable research skills. Additionally, the project will provide mentorship opportunities through workshops and outreach programs and facilitate public outreach activities. This award supports a collaborative research project to advance our understanding of articular cartilage, a living load-bearing tissue that can endure decades of mechanical stress. While its compressive behavior is well understood, the mechanisms governing shear resistance and its interaction with compression remain unclear. Recent findings suggest that collagen fibers form a percolating network near a mechanical phase transition, where small structural changes significantly alter mechanics. Prior studies examined small strains, but cartilage undergoes large deformations, reaching strains up to 40 percent under normal and extreme conditions. This project looks to develop next-generation rigidity percolation models to investigate cartilage mechanics under physiologic and super-physiologic loading. It plans to integrate experiments, theory, and simulations in an interactive feedback loop to examine how osmotic stress, mechanical loading, and collagen network reorganization shape tissue function. Rigidity percolation models intend to capture large deformations and multi-directional loading, providing a predictive framework for mechanical phase transitions. Experimental tests seek to validate and refine these models, while imaging looks to track fiber alignment and network adaptation. This research intends to reveal how mechanical phase transitions regulate cartilage response under extreme condition, informing cartilage repair and tissue engineering. The project will train students in biomechanics, and modeling while facilitating science communication and public outreach. 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 · 2025-05

Infectious disease transmission, as highlighted by the COVID-19 pandemic, exacts a severe toll on the physical and psychological well-being, economic development, and health of the global community. To better understand how airborne diseases are spread, this project will determine how speaking generates microscopic respiratory particles that can release infectious pathogens into the surrounding air. Although actions like coughing and sneezing produce high numbers of respiratory particles, these events are not as common as speaking. In comparison, speaking produces particles continuously that results in large quantities of particles being expelled from the mouth over time. The rates at which individuals produce respiratory particles when speaking vary widely among people for unknown reasons. This project will answer this conundrum by performing experiments using both human subjects and physical models of phonation, with the goal of discovering how the mechanics of speaking influences how respiratory particles are produced. This approach will unlock new ways to understand aerosol generation in the vocal tract and how it affects airborne transmission of infectious diseases. Large-scale community outreach efforts through a university sponsored innovation fair (ImagineRIT) will transmit project findings to an interested public. The objective of this project is to elucidate the underlying mechanisms of aerosolized particle generation during phonation and their implications for the transmission of airborne pathogens. The multidisciplinary approach will utilize experimental models and human measurements to quantify the relationship between the biomechanical processes of speech and the mechanics of respiratory particle production. The project will explore how variations in the rheological properties of the respiratory tract lining fluid and the biomechanical actions at the physiological sites of particle generation contribute to the observed heterogeneity in aerosol production rates among individuals. This research is critically important due to the potential for asymptomatic speech-driven transmission of viruses, as evidenced during the COVID-19 pandemic. Findings from this work will inform the development of evidence-based strategies for mitigating infection risk and will have broad application to public health policies regarding airborne diseases. The multi-disciplinary approach of the research plan will also provide a unique educational and training environment for two graduate students and three undergraduates that will participate in the work plan. Research findings will also be incorporated into existing classroom curricula at both the undergraduate and graduate 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 2025 · 2025-05

Research funded in association with this project aims study how mechanical forces, such as changes in tissue stiffness and cyclic strain, impact viral infections. Using lung tissue as a model, it examines how physical changes from breathing or fibrosis affect virus-cell interactions. This research supports the national interest in advancing public health, as findings may guide safe activity levels during infections and suggest possible new treatments. The project also benefits society by offering research experiences for undergraduate and graduate students, with a focus on diversity. Outreach activities for K-12 students and the community will further support science education and inspire interest in research and health. The study will address two primary objectives to uncover mechanisms driving virus-host interactions under mechanical influences. Objective 1 investigates how matrix stiffness and cyclic strain affect viral infection rates and cellular responses in lung fibroblasts. Using custom-fabricated polydimethylsiloxane (PDMS) chambers with stiffness levels that mimic healthy and fibrotic lung tissue, CRL-4058 lung fibroblasts will be exposed to controlled mechanical strains that simulate breathing. Lung fibroblasts infected with vesicular stomatitis virus will be analyzed for viral infection rates (through RFU measurement, virus growth curve assays, Nucleocapsid (N) expression) and cellular behavior changes (through mRNA-seq, RT-qPCR, ELISA, Western Blot). Objective 2 examines the role of strain-activated calcium channels, such as TRP and Piezo channels in viral infection processes. By applying pharmacological inhibitors and monitoring calcium influx (Calbryte 520 AM) in response to mechanical strain, the study will first identify strain-activated calcium channels in CRL-4058 lung fibroblasts and then assess whether modulation of these identified channels affects viral replication and cell behavior. This investigation into the mechanobiology of viral infections seeks to provide new insights into the complex interplay between mechanical forces and viral pathogenesis in lung tissue. 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 · 2025-04

Additive manufacturing, often referred to as three-dimensional (3D) printing, fabricates components by selectively printing one layer of material on top of another to form a three-dimensional (3D) shape. Industrial grade additive manufacturing machines used in the aerospace and defense industries produce high performance structural metal parts. However, high costs and relatively low production speeds have greatly limited industrial use of these technologies beyond a few aerospace applications. This Future Manufacturing grant seeks to expand industrial adoption of metal additive manufacturing using an efficient molten metal droplet jetting process that is akin to high-speed inkjet printing using molten metal as the "ink". Process knowledge generated via this research helps make domestic manufacturing of metal components competitive with overseas competition in terms of both cost and speed. Three hallmarks of the process are that it intended to process both new and recycled metal as the feedstock material, there is very little scrap produced, and labor costs are minimal. This improves the business case for reshoring domestic manufacturing by lowering the costs per part. The ability to convert scrap metal such as machining chips into new parts is particularly important for commercial and military supply chain scenarios where resources are scarce. The project looks to advance education and workforce development through summer camps and certificate courses in additive manufacturing. The ambitious goal for this Future Manufacturing research is to enable additive manufacturing of metal components that are competitive with traditional casting and machining processes on the bases of cost, speed, and quality. The technology used is a novel multi-nozzle molten metal jetting process. Unlike most metal additive processes, this process melts metal prior to deposition rather than during deposition. This allows any form of metal feedstock material to be used, including ingot, rod, wire, and recycled material. Several scientific contributions are needed to realize the ambitious goals of the project. High material deposition rates are needed for this process to be competitive with traditional processes. Multi-physics modeling are used to determine the conditions under which progressively higher frequency jetting of molten metal droplets is possible. Throughput is also increased using multi-nozzle arrays. Computationally efficient thermal modeling techniques are employed to better understand the relationship between multi-nozzle jetting strategies and part quality as determined by evaluation of microstructure and porosity. Lastly, the quality of material produced from recycled metal feedstock is compared with that of virgin material, and lifecycle analysis tools are used to quantify the relative impacts associated with different recycled material usage scenarios. This Future Manufacturing award is co-funded by the Division of Materials Research (DMR) in the Directorate for Mathematical and Physical Sciences (MPS) and Divisions of CMMI, CBET and ECCS in the ENG Directorate. 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 · 2025-02

Augmented Reality (AR) is an emerging technology that overlays digital content onto a user's view of the real world in real time, creating interactive and immersive experiences. AR applications are expanding across various industries, including smart manufacturing, healthcare, navigation, education, and entertainment. Since users may rely on AR applications to directly understand and interact with the physical world, failures and errors in these systems can lead to severe consequences, including safety risks. For instance, a flawed AR-based navigation application could cause accidents or damage the surrounding physical environment. Such real-world risks underscore the critical need for testing and quality assurance practices in AR application development. Despite the demand for high-quality AR applications, their testing support remains in its early stages. The challenge of testing AR applications stems from the difficulty of handling real-world inputs and understanding their outputs blended with real-world scenes. Since real-world test environments are costly to build and difficult to control, alternative environments such as videos and Virtual Reality (VR) test scenes are adopted in practice. This project aims to develop innovative techniques to automate the testing of AR applications for higher efficiency and comprehensiveness and investigate the bug-detection effectiveness of VR test scenes. The project includes plans to engage with students from underrepresented groups in computing and to enrich the software engineering curriculum. Specifically, this project will develop an infrastructure that allows existing automatic Graphics User Interface (GUI) testing techniques to be applied to AR apps. The infrastructure will (1) automate the test scene construction by loading playback videos and configuring them at runtime, (2) automatically identify interactive areas on the screen by excluding non-interactive objects using dynamic filtering, and (3) automate GUI event triggering by inferring possible interactions of interactive areas through analysis of their event-handling functions. The project will also develop techniques to automate test oracle in AR application testing. The techniques will check inconsistencies between AR rendering and code execution, and predict the correctness of virtual object placement using models trained with labeled screenshots, video frames, and application logs. Additionally, a study will be performed to assess whether VR-based test scenes can accurately simulate real-world scenes and reveal bugs in AR apps. Pairs of real-world scenes and VR scenes will be constructed and test executions of AR apps on them will be compared based on various metrics such as code coverage, mutation scores, and user-perceived rendering difference. The project will further study the automatic revision of VR testing scenes with the guidance of code coverage. 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 · 2025-01

Artificial Intelligence (AI) is critical to the United States' economic prosperity and national security. The wide-spread application of AI in many areas dictates that such systems must be made trustworthy. In response to this pressing need, the goal of this award is to encourage ten talented undergraduate students per year to pursue graduate study and research careers in trustworthy AI by engaging them in exciting summer research projects. These research projects are rooted in the faculty mentors' existing, externally funded research in trustworthy AI areas, including robustness, fairness, explainability, privacy, and accountability. All projects feature a different topic in trustworthy AI for a team of two or three undergraduate students to explore under the guidance of the faculty mentors. This immersive research experience is expected to cultivate the participating students' interest in pursuing graduate study in trustworthy AI through their involvement in authentic research efforts. This award makes targeted efforts to recruit students underrepresented in Science, Technology, Engineering, and Mathematics (STEM) careers or who might otherwise be unable to participate in academic research. These students are selected from diverse demographic and economic backgrounds, with specific efforts to include women, underrepresented minorities, and individuals with disabilities (including Deaf/Hard of Hearing students). Faculty mentors train the students to be researchers and provide them with specialized training in the design and development of trustworthy AI systems that are motivated by highly engaging AI innovations for social good. This specialized training provides the students with highly valuable technical and analytical skills that will benefit them in future pursuits in graduate study and industry research and development. The quality of this training is further enhanced by additional professional development activities, including a machine learning crash course, invited speakers, weekly group meetings, and a mentor training workshop for participating faculty mentors. All student participants are given the opportunity to attend and present in academic conferences. The investigators will disseminate their experiences running this program in educational research publications. 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 · 2025-01

Our climate system is a complex network of interacting phenomena that impose a significant influence on society. This project aims to provide new insights into these interacting phenomena through the usage of network science, an interdisciplinary field comprising computational and mathematical tools designed to study complex networks. First, the investigator will synthesize large-scale climate records to investigate the stability of emerging network patterns associated with tipping elements, large-scale parts of the climate system that can change abruptly and irreversibly. The investigative focus will be on the Atlantic basin and its global influence, given the well-documented weakening of the Atlantic Meridional Overturning Circulation as a potential tipping element. Second, the investigator will develop hybrid algorithms that integrate machine learning techniques with network science methods to improve the seasonal forecast of climate indices like the Southern Oscillation Index and the North Atlantic Oscillation. The project will support an early-career scientist and train students. One of the most alarming potential consequences of climate change is the risk of future irreversible collapse of tipping elements, such as the Atlantic Meridional Overturning Circulation. However, it is unclear how the effects of collapsing tipping elements will spread within the climate system. This project can help us understand the cascading effects of this tipping behavior, possibly revealing previously unknown vulnerabilities in the global climate system. This project will build on existing successes of network science in the study of climate by introducing novel, low-complexity algorithms for predicting climate indices. These advancements will provide new efficient tools for climate scientists and allow for the exploration of other complex systems in nature and society. The support from the project and subsequent scientific findings will allow the investigator to promote climate science among students and faculty at the investigator’s institution which does not have an academic department focused on Earth 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 2024 · 2024-12

This project aims to serve the national interest by increasing the use of evidence-based teaching practices in undergraduate computer science curricula. One such practice is project-based learning, which gives students opportunities to learn as they work on authentic real-world problems. Project-based learning, and other active learning practices, have been shown to increase student motivation and engagement, raise exam performance, and reduce failure rates. A major barrier to implementing project-based learning in the classroom is that instructors must provide students with the support they need to be successful learners as they complete project tasks. Without that support, students can get bogged down performing unessential tasks instead of learning the targeted concepts and skills. This project aims to increase the use of project-based learning in software engineering courses. To do so, the project will design, develop, and evaluate a reference software engineering course with a repository of twelve ready-to-use projects and associated active learning activities. These resources will enable instructors to easily assign project tasks for students to complete at home. It is anticipated that these resources will increase the capacity of instructors to effectively use project-based learning in their courses. In addition, project-based learning can help students prepare for industry careers by providing learning experiences that are like the work that software engineers do. The overarching goal of this project is to increase student learning by encouraging and facilitating authentic computer science learning experiences. Data for formative evaluation of the developed project-based learning resources will be gathered through advisory board reviews, undergraduate beta testing, and in-house classroom trials. Field tests at multiple volunteer sites will assess the feasibility of implementing the project-based learning resources in college or university settings. Finally, a pilot study will be performed to assess the promise of the developed materials to improve student learning outcomes. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the IUSE 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 2024 · 2024-12

Calcium oxide is a highly abundant, inexpensive material that can be used to capture CO2 from the atmosphere and from combustion exhaust. The key drawback to using calcium oxide is that the material undergoes changes during its use, which reduces its service lifetime. This project will use computational modeling and experiments to study the fundamental chemical reaction between CO2 and calcium oxide. The results of these studies will be used to design processes that increase the amount of CO2 calcium oxide absorbs in practice and to increase calcium oxide’s service lifetime. The research team will collaborate with local middle and high school teachers to develop new educational activities and will introduce students and teachers to concepts in materials science, energy storage, and CO2 capture. This proposal integrates computational modeling with atomic-scale in situ electron microscopy to advance understanding of chemical ‘looping’ reactions between CO2 and scalable earth-abundant sorbent materials—namely CaO-based sorbents. The guiding thesis is that sorbent cycle life and CO2 uptake capacity can be extended through rational design of thermal schedules and compositions. This ‘precision temperature control’ approach is expected to thermally activate calcination (sorbent regeneration) and carbonation reactions without excessive heating known to cause sorbent deactivation by particle sintering and surface area loss. By coupling atomic-scale simulations and in situ experiments under reaction conditions, thermodynamic driving forces and key pathways governing kinetics will be elucidated, enabling design of sorbents, heat treatments, and efficient thermal processes for long sorbent life and compatibility with concentrated solar thermal energy. Research is structured in four aims. Aim 1 is to develop a multiscale computational framework to obtain atomic-level mechanistic understanding of CaO+CO2↔CaCO3 looping cycles and validate the framework experimentally using atomic-resolution and in situ gas cell (scanning) transmission electron microscopy. Aim 2 is to extend and experimentally validate the modeling framework to elucidate the role of key humidity derived calcium hydroxide (Ca(OH)2) intermediates on looping reaction mechanisms and cycling stability. Aim 3 is to extend and experimentally validate the framework to assess the role of performance-enhancing chemical additives/dopants on looping reactivity and cycling stability. Aim 4 is to extend and validate the framework to assess the role of particle-particle GB interfaces on looping reactivity and cycling stability. 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 2024 · 2024-11

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in the online fraud detection and prevention for large populations of users in different industries such as healthcare, credit card processing or retail banking business. The proposed project involves the prototyping of a novel software for online fraud detection and prevention based on behavioral biometrics. US consumers lose $5.1 billion annually due to account takeover fraud and online shopping presents the greatest fraud risk. There is strong demand for online fraud detection in sectors such as e-commerce and financial services. This project includes an entrepreneurial education and leadership development plan for the graduate students in the team, that will include customer discovery activities, mentorship from experienced entrepreneurs in the biometrics business area, and innovation and entrepreneurship activities at the Center for Identification Technology Research (CITeR), a NSF Industry University Collaborative Research Center (IUCRC). Broadening participation activities include supporting underrepresented graduate students and providing research opportunities for undergraduate students from diverse groups. The proposed project involves the prototyping of a novel software solution framework for online fraud detection and prevention that is based on behavioral biometrics. The software framework systematically integrates options for prevention via account recovery, trust assessment of universal identities, and detection via post-login authentication. The team will also explore the design and implementation of innovative features/solutions for intellectual property and user privacy protection, two deployment options, and additional post-login authentication features paving the way for integrating behavioral biometrics and post-login authentication into enterprise systems. Finally, usability considerations including end-user evaluation of behavioral biometrics in security critical scenarios that matter the most to consumers will be explored. 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 2024 · 2024-10

The Internet of Things (IoT), encompassing devices such as medical equipment, autonomous vehicles, and industrial control units, is becoming integral to modern life and is expected to reach one trillion devices by 2035. Unfortunately, malware attacks on IoT systems are increasing rapidly, exemplified by incidents like the Mirai botnet and the Colonial pipeline attack. While significant research has explored malware detection for PCs and mobile devices, these methods are not suitable for IoT systems due to their diverse operating systems and low power. Current models also struggle against sophisticated attacks that aim to evade detection. To address these challenges, the project team is developing DANGER-IoT, an approach to IoT malware detection that works across heterogeneous platforms, is efficient for low-power devices, and robust against advanced attacks. The researchers are collaborating with industry experts to ensure the project's ideas work well in real-world settings and are creating open-source tools and datasets. Spread across four universities and three countries, this project is also impacting a diverse group of students through new courses, security competitions, and international exchanges. The DANGER-IoT project focuses on developing advanced machine-learning models for IoT malware detection. The first goal is to create a generic model that can detect malware across heterogeneous IoT platforms by constructing a common embedding space for similar functions across different operating systems and architectures. The project's second aim is to ensure efficiency for low-power devices by applying model compression techniques adapted from explainable AI and model pruning. To enhance robustness, the project will explore large-language models for code-style transfer, making malware appear benign to existing classifiers, and using the results to design a novel moving-target defense. By integrating multi-task learning, behavior classification, and a comprehensive IoT malware dataset, DANGER-IoT aims to provide a scalable detection approach, robust defenses, and significant contributions to the community through shared data, benchmarks, 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 2024 · 2024-10

Non-technical Abstract: Quantum computing holds significant promise for transforming various fields, including drug development and financial modeling. However, the full potential of quantum computing is hindered by the complexities involved in the efficient routing of qubits an essential process for enabling communication between qubits in quantum hardware. This project addresses this critical challenge by developing advanced quantum compilation techniques, focusing on improving the scalability and reliability of qubit routing. Additionally, the project emphasizes diversity in the quantum computing field by promoting peer mentoring and outreach activities at different educational levels. The proposed activities aim to inspire and educate the next generation of quantum scientists, with a particular focus on increasing the participation of women and minorities in this cutting-edge area of research. Technical Abstract: The project tackles the dual problem of routing among physical qubits and composing logical qubits within the context of Quantum Error Correction (QEC) and Noisy Intermediate-Scale Quantum (NISQ) computing. It proposes innovative solutions to optimize qubit routing, which is crucial for achieving large-scale quantum entanglement. The research is divided into two main thrusts: (1) Utilizing machine learning (ML) techniques to identify and synthesize the most effective routing algorithms tailored to specific quantum devices and goals. This involves training ML models to select optimal parameters and optimization functions. (2) Exploring alternative quantum circuit representations, such as hypergraph circuit descriptions and ZX-calculus, to handle the scalability of routing problems and enhance fault tolerance. By addressing these challenges, the project aims to significantly advance quantum compiler design, making it more adaptable to the evolving demands of quantum hardware, and enhancing the overall performance and scalability of quantum computing systems. This award was jointly funded by the Directorate for Mathematical and Physical Sciences, Office of Strategic Initiatives; and the Directorate for Computer and Information Science and Engineering, Division of Computing and Communication Foundations. 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 2024 · 2024-10

Proteins are the workhorse molecules of life which participate in nearly every activity of cellular processes, including signal transduction, enzyme catalysis, structural support, bodily movement, and defense against pathogens. Interpretation of specific functional roles that each protein molecule plays in cell is thus critical for us to understand the fundamental principles of the biological processes and to design new drug treatments to regulate the processes for improving human health. The task is however highly non-trivial in modern molecular biology studies. The most accurate method to interpret protein biological functions is through structural biology and biochemistry experiments. But the cost of the experimental studies is high, and the process is too slow for large-scale application due to the involvement of manual skill and data processing. As a result, the majority of proteins in human and other important species remain unknown despite decades of efforts. The lack of genome-wide protein function information has significantly impeded the progress of system biology studies aiming at a comprehensive understanding of the life process. In this project, the investigators plan to develop advanced computational methods for automatic and yet reliable protein function annotations. The developed methods and databases will be freely released to the scientific community, which can be used for large-scale and genome-wide protein function annotation studies. The project will also provide opportunities to promote participations of underrepresented groups, including women and African Americans, in computational biology education and method developments. Built on the assumption that similar sequences have similar function, a routine approach to computational protein function annotations is comparative modeling, which deduces functions of target proteins from known homologous proteins. However, the accuracy and coverage of the approach are limited due to the diversity of gene evolution. Significant progress has been recently achieved in protein 3D structure prediction and the state-of-the-art algorithms can generate high-quality structures for distant-homology proteins with an unprecedented capacity. This project seeks to explore various new ideas to enhance the accuracy of distant-homology protein function annotations by using 3D models from the cutting-edge protein structure predictions, with a focus on ligand-protein binding interactions, gene ontology and post-translational modifications. Meanwhile, thermal motion and intrinsic disordering of protein structures are integrated in the pipelines for better function annotations. While the proposed approaches do not expect to address all the fundamental issues, like the first-principle methods, as of how and why proteins fold and function, the success of the studies should help establish a practical knowledge-based relation of structure and function that can be used for genome-scale applications with models useful for guiding new experimental design, and thus significantly enhance the impact of protein structure modeling on biological 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 2024 · 2024-10

Recognizing that AI is transforming how workers and managers learn, perform tasks, and innovate in areas such as manufacturing, production control, and other industrial domains that operate on global scales, this three-year International Research Experiences for Students (IRES) program will enable 18 US STEM IRES student scholars from Rochester Institute of Technology to experience research at University West, Sweden, under a convergent research theme focused on AI-enhanced automation, automatic control, and management for industry. The AI and industry-focused IRES theme benefits society by developing science that enhances industrial sectors, and by growing US-Swedish research collaboration. Additionally, the program aims to promote the IRES scholars’ research and AI knowledge, skills, and abilities, intercultural and international awareness, and teamwork capabilities, and to foster new educational practices for preparing the AI research workforce. The three IRES cohorts will spend eight weeks at University West’s research environment, which has advanced facilities for conducting future-of-work-centered fundamental scientific research. The research projects and professional development components are integrated across a preparatory pre-phase, the international phase, and a continuation post-phase, enabling the participants and faculty mentoring teams, which pair University West and RIT co-mentors, to develop impactful projects and disseminate research findings. The evaluation considers the outcomes of IRES students, faculty mentors, research projects, and the program. This IRES project advances fundamental scientific research on AI-enhanced automation, AI-enhanced automatic control, and AI-enhanced management for future human-centered and sustainable workplaces. The research involves innovative machine learning algorithms and architectures or new AI interface prototypes and sensor methods. In addition to IRES scholars presenting, publishing, and releasing research products with mentors, the research team will propose a journal special issue on the IRES program’s theme. The project enables the IRES scholars’ growth as future leaders through impactful research guided by faculty mentoring teams and professional development events, integrated into all three of the program’s phases. A post-workshop will be a springboard for research dissemination in publications and talks at prominent conferences, and interactions with University West-matched peers will nurture intercultural skills and strengthen networking competency. The project team will investigate the IRES model outcomes and disseminate findings to the computing education community, as well as in STEM education roundtables or workshops. 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 2024 · 2024-10

Formal methods in computer science aim to increase reliability and robustness of software and hardware designs. Unfortunately, formal methods are typically only accessible to specialized professionals. One of the reasons for this limited accessibility is the lack of exposure to formal methods in undergraduate education, even for computer science majors. Even though formal methods are used in practice to analyze the correctness of large-scale software and hardware systems, they can also be applied to algorithms and other mathematical constructs at a smaller scale. The project's novelties are developing and disseminating self-contained interactive exercises, using a web-based programming environment, to solve small-scale problems using formal methods. The project's impacts are that these small-scale problems give a taste of formal methods to different STEM communities, namely: three institutions (a research institution, a liberal-arts college, and a community college), several computing and engineering majors (computer science, bioinformatics, computer engineering, chemical engineering, and more), and several topics and levels (introductory programming, engineering design, digital system design, databases, artificial intelligence, and more). The project's impacts go beyond the investigators' institutions: The self-contained interactive exercises can be used as-is, with no work required from the instructor and no additional time needed in the course schedule since they are designed to cover standard course topics, to give a taste of formal methods to all. Formal methods are currently not part of the standard computer science undergraduate curriculum. Thus instruction using formal methods, especially outside of computer science programs and outside of research institutions, is very limited. Instructors may lack the proper experience or background to teach formal methods. Instructors may not have had the appropriate exposure, and for STEM instructors outside computer science (e.g., mechanical engineering), formal methods may be far away from their fields. Additionally, there is a lack of space in the often tightly structured undergraduate computing and engineering curricula. The project's working hypothesis is that formal methods exposure can be significantly improved by developing self-contained Jupyter notebooks (using satisfiability modulo theories solvers accessed via Python) that focus on small-scale problems. These notebooks can be used as-is, with no work required from the instructor and no additional time needed in the course schedule since they are designed to cover standard course topics. For instance, modules studying the verification of the molecular structure of a chemical compound, the links in a protein-protein interaction biological network, or the tuples retrieved by a SQL query over a relational database. The project aims to impact a wide range of students and majors. This includes computer science students at the Rochester Institute of Technology (RIT, a research institution) and the College of the Holy Cross (a liberal arts college), as well as at Monroe Community College, which has a formal transfer pathway with RIT. In addition to introducing formal methods to computer science students, the project will also introduce formal methods to several engineering programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project seeks to protect the integrity of digital content and maintain public trust. The rapid advancement of generative Artificial Intelligence (AI) has made it easier to create and manipulate digital content. Current tools for detecting AI-generated content are fragmented and challenging to use. The project team is developing an all-in-one digital content forensics platform designed to streamline the forensic analysis process. By integrating multiple tools into a single platform, it aims to empower users by providing a reliable and user-friendly platform for evaluating digital content. The project employs a user-centered design process, involving extensive qualitative interviews and user studies to understand needs and workflows. Based on the findings of these studies, the team is integrating various digital content forensic tools into a single platform, supported by a robust organization for the coherent navigation and selection of the tools. The team is also exploring explanation methods to enhance user comprehension of each tool’s outputs. Finally, to help users make the most of this platform, the team is creating novel game-based training scenarios and comprehensive ethical frameworks based on professional norms. The team is disseminating this work and other information about the project through workshops and professional networks in multiple user communities that will be able to leverage this platform to maintain the integrity of online digital content. 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 2024 · 2024-09

Sustainability is an urgent and critical need that modern engineering must address. To this end, engineering students should develop a sustainability-focused outlook. This project will contribute to the goals of the NSF Research in the Formation of Engineers Program by integrating sustainability principles into engineering courses to cultivate a generation of engineers who are mindful of their roles and equipped to fulfill their responsibilities in fostering a sustainable future. Through the development of innovative course interventions guided by the Engineering for One Planet (EOP) Framework, this project will enhance students' understanding of sustainability across social, environmental, and economic dimensions, leading to the development of a sustainability-conscious engineering identity. By focusing on sustainability-conscious engineering identities, the project will prepare students to incorporate sustainability practices into professional engineering work. The significance of this project lies in its potential to transform engineering curricula and pedagogy and prepare graduates who are ready to contribute to a sustainable and equitable society. This project will investigate how sustainability-focused course interventions help students develop a sustainability-conscious engineering identity. This will be explored in terms of students’ knowledge, attitudes, and behaviors across the social, environmental, and economic dimensions of sustainability. To achieve this goal, the project will be conducted in two phases. In the first phase, the research team will collaborate with faculty, departments, and other stakeholders to design and implement course interventions using the EOP framework. Approximately ten courses from the Rochester Institute of Technology’s (RIT) College of Engineering Technology and College of Engineering will be included in the study. The research will employ a multiple-case study design with each course serving as a case. The nature of the intervention in each course, ranging from single modules to full-term projects, will vary according to the course level, student’s background knowledge, and recommendations from the department and college curriculum committees. The second phase will involve collecting and analyzing data to explore the influence of these course interventions on students' engineering identity development using the analytical framework of Sustainability Consciousness. The research will utilize written reflections and in-person semi-structured interviews to gather qualitative data. Participants will include students enrolled in the courses in which interventions are implemented. Data will be collected from each course over a period of three years. Incremental changes will be made to the interventions based on student reflections and interviews, and course assessment data. Student reflections and interviews will be analyzed using thematic and process coding to identify shifts in students' knowledge, attitudes, and behaviors in the social, environmental, and economic dimensions of sustainability. This project will be guided by an advisory board comprising experts in STEM education, curriculum design, community engagement, and EOP implementation to ensure its rigor and relevance. Expected outcomes include insights into the mechanisms underlying students’ identity development in terms of the role of engineers in sustainable development (knowledge), viewpoints toward addressing sustainability issues (attitude), and engagement in actions to actualize sustainability-related changes (behavior). These insights will inform the design of future curricular interventions within the cases under study and beyond. In addition, effective pedagogical strategies for integrating sustainability into engineering education and the development of transferable course materials will benefit the engineering education community and subsequently contribute to developing an engineering workforce aware of and equipped to address the sustainability needs of the modern world. 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 2024 · 2024-09

This Engineering Research Initiation (ERI) grant will support research that contributes new knowledge to robotic manipulation, addresses the current bottlenecks in manipulation reliability, improves work efficiency, enhances people's well-being, and consolidates the United States' leading position in robotics. Medical, agricultural, and household robots face a common challenge: the reliable and safe manipulation of fragile objects. Overcoming this bottleneck will enable significant advancements in the application of robotics in real-world environments. However, the variety of object properties and differences in manipulation tasks in the real world hinder the application of manipulation methods that are fully validated in laboratory environments. This award supports fundamental research to provide new knowledge for reliable object manipulation. This new knowledge will lead to the development of a technology that can automatically adapt to a variety of objects and manipulation goals by understanding the relationships between object properties, manipulation tasks, and robot operations. This technology will achieve breakthroughs in the safety, reliability, and efficiency of object manipulation in robotic surgery, agricultural robots, and household robots, directly benefiting the United States' medical, economic, and social fields. This research will also provide learning and research opportunities for students with disabilities, promoting balanced societal development and comprehensive engineering education. Integrating vision and touch is considered the solution to the problem of manipulating deformable and fragile objects. Existing solutions directly integrate visual and tactile information to guide manipulation. Although simple in theory, these solutions tightly couple perception, modeling, and control issues, making it difficult to overcome the resolution differences of multimodal perception, solve the problem of sensor synchronization, and achieve universal applicability. This research project will work to establish an understanding of the complementary relationship between touch and vision and use novel deep causal learning techniques to decouple multimodal perception and the modeling of object properties. This approach will strive to realize a control method based on object properties that can simultaneously optimize the position, angle, speed, acceleration, and interaction force, thereby achieving reliable manipulation of deformable and fragile objects with wide applicability. 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 2024 · 2024-09

Advanced semiconductor technologies are essential for fast-growing needs in industry and U.S. national security in areas such as artificial intelligence (AI) and machine learning, electric vehicles, high-performance computing, augmented reality and virtual reality, and quantum technology. Despite the urgent national need for advances in this field, there is a substantial shortage in semiconductor talent, especially at the graduate level. This National Science Foundation Research Traineeship (NRT) award to Rochester Institute of Technology (RIT) will develop an innovative convergent graduate research training program in the area of next-generation complementary metal-oxide-semiconductor (CMOS) + X (X = AI, biomedical, chemical, optoelectronic, photonic, nanoelectronic, quantum, and packaging). The project anticipates training 170 graduate students including 20 funded Ph.D.s, 75 unfunded Ph.D.s, and 75 unfunded M.S. students from Microsystems Engineering, Electrical and Computer Engineering, Physics, Microelectronic Engineering, Material Science, Chemical Engineering, Biomedical Engineering, the Golisano Institute for Sustainability, and the National Technical Institute for the Deaf (NTID). RIT’s CMOS+X Semiconductor Technologies NRT aims to adchieve three goals. First, to advance interdisciplinary and inclusive semiconductor research. Four research tracks led by the NRT faculty team will address a range of themes from fundamental physics and material science to micro- and nanoelectronics, optoelectronics & photonics, and integrated circuits and packaging. Second, to equip diversified NRT cohorts with enhanced technical and professional skills. Third, to establish and sustain this convergent graduate training model to serve as a pipeline for much needed domestic interdisciplinary semiconductor workforce for industry, government, and academia. RIT’s convergent NRT program will be one of the first to offer a transformative and dedicated semiconductor graduate education training model with strong technical, professional, and broadening participation components to prepare more than 170 next-generation engineers and scientists to help close the workforce gap and strengthen the nation’s semiconductor technology leadership. Considering the extensive and wide-ranging applications of semiconductor CMOS+X technologies in AI, vehicle electrification, high-performance computing, and quantum science and engineering, as well as the importance of these technologies to U.S. national security, this project will contribute to: 1) understanding how to train diverse cohorts of semiconductor talent and 2) supplying the expertise and leadership needed for a competitive U.S. workforce. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research 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 2024 · 2024-09

This is funding to support a Doctoral Consortium (workshop) of approximately nine (9) promising graduate students from U.S. educational institutions to take part in an event that will take place on October 27, 2024, in conjunction with the ACM ASSETS 2024 Conference sponsored by the ACM Special Interest Group on Computers and Accessibility, which is the premier forum for presenting innovative research on the design and use of both mainstream and specialized assistive technologies in support of the needs associated with speech, motor, hearing, and vision impairments, cognitive limitations, emotional and learning disabilities, aging and education in computing accessibility, as well as the professionals who work with these populations, and which will be held in St. John’s, Newfoundland and Labrador on October 27–30. The Doctoral Consortium will provide doctoral students working in the field of assistive technologies and accessibility with a friendly and open forum: to present their research ideas, listen to ongoing work from peer students, and receive constructive feedback; will enable participants to develop a supportive community of scholars and a spirit of collaborative research; will provide students with relevant information about important issues for doctoral candidates and future academics; and will provide a new generation of researchers with information and advice on academic, research, The ASSETS 2024 Doctoral Consortium provides an opportunity for doctoral students to explore their research interests in an interdisciplinary and international workshop, under the guidance of a panel of distinguished experts in the field. The Doctoral Consortium will also offer discussion groups and the opportunity to learn from individuals who recently completed their PhD. Student participants will make both formal and informal presentations of their work during the Consortium and will receive feedback from the faculty panel. This feedback is designed to help students understand and articulate how their work is positioned relative to related research, whether their topics are adequately focused for thesis research projects, whether their methods are appropriately chosen and adequately applied, and whether their results are appropriately analyzed and presented. This Doctoral Consortium will also help shape ongoing and future research projects aimed at assistive technologies and universal access. A key component of building this community is engaging the next generation of researchers. The Consortium brings PhD students together from diverse backgrounds (e.g., engineering, computing, architecture, and psychology) so that they can see the broader spectrum of research and development approaches to assistive technologies and universal usability. The Consortium also provides exposure to the community in which they can pursue their endeavors. 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 2024 · 2024-09

Linguists studying sign languages experience an immense resource gap. Resources for studying visual prosody in sign languages, and its grammatical and emotional functions, are scarce. This project contributes towards closing this gap and promotes data-driven sign language research. Housed in ideal research environments, the project aims to create a large sign language corpus, inclusive of dialogues, with annotations. The project plans to release this resource for linguistic and sign language technology research and provide open access teaching modules and assignments with instructor guides for use with the corpus. This project focuses on understudied characteristics in sign languages, whose study necessitates a new corpus resource, and on their reproducible annotation representations, using an iterative process of quality measurement of inter-annotator and intra-annotator agreement. The anticipated project outcomes include: (1) a sign language corpus that captures currently understudied characteristics, (2) a tested method for representing those characteristics in the corpus, (3) best practice guidelines for continued use, and (4) research dissemination in written manuscripts and video-recorded research products. Additionally, the team aims to train students and open pathways to increase the study of sign languages in the research workforce, preparing deaf scientists with linguistic research skills, and also to release a learning module for researchers. The new annotated corpus can help develop predictive models to reduce the time and resources required to carry out annotation and accelerate scientific insights, while promoting improvements to the state of the art in sign language analysis technology. 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 2024 · 2024-09

In this project, funded by the MPS-LEAPS (Launching Early-Career Academic Pathways) Program and managed by the Broadening Participation (CHE-BP) Program in the Division of Chemistry, Professor Obioma Uche and her students at the Rochester Institute of Technology will perform studies that aim to evaluate the performance of transition metal sulfide catalysts for the production of ethylene. Although the oxidative transformation of methane with oxygen is an attractive route, it is limited by severe over-oxidation pathways. Previous research has demonstrated that sulfur can serve as a milder oxidant for the conversion of methane to ethylene over transition metal sulfide (TMS) catalysts. Professor Uche and her students will employ a combination of theoretical calculations and atomistic simulation techniques to screen for TMS catalysts which are highly effective for sulfur-assisted oxidative coupling of methane (SOCM) as well as determine the underlying mechanism. Their studies could provide new insights on the mechanistic behavior and kinetics of the SOCM reaction with the added potential for yielding increased economic advantages for the chemical industry in the United States. The project will also serve as a vehicle to introduce students from underrepresented backgrounds to computational catalysis research by utilizing student conference workshops and established K-12 partnerships. Professor Uche and her students will use key structural descriptors to identify TMS catalysts which display high activity for methane conversion and high selectivity for ethylene formation. In particular, exposed facets of the corresponding equilibrium structures for the transition metal sulfides under consideration will be examined to determine SOCM catalytic efficacy. Insights on the kinetics for sulfur-assisted oxidative coupling of methane will also be obtained from analyses of the reaction on a palladium-based sulfide catalyst. Taken together, these aims will provide a comprehensive examination of SOCM over transition metal sulfide catalysts as an alternative approach to ethylene production. Density functional theory calculations as well as molecular dynamics simulations (both ab-initio and reactive) will be used during the course of the investigation. Specific strategies for broadening participation among students from underrepresented groups will include integration of the research outcomes in the development of an interactive workshop for undergraduates and delivery of a capstone experience for high school seniors. 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 2024 · 2024-09

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Recent gravitational wave detections from binary black hole and neutron star mergers have revolutionized astrophysics, providing new insights into fields such as general relativity, cosmology, nuclear physics, and astronomy. Future observations from both Earth-based and space-based detectors will extend our understanding of these phenomena and the formation of supermassive black holes. The principal investigators plan to develop new GPU-enabled algorithms and computational tools to accurately model accreting binary black hole and neutron star mergers. These tools will utilize both Cartesian and curvilinear coordinates to model complex systems, enhancing stability and accuracy while simulating the entire merger process of binary neutron star (BNS) systems and supermassive black hole binaries (SMBBHs). Additionally, the PI's team works on "Astrodance," an artistic show with RIT's NTID performers that integrates dance, ASL, music, and digital storytelling to depict scientific visualizations of mergers. Public outreach efforts will include participating in events such as "ImagineRIT" and presentations on topics such as compact binary mergers and gravitational waves. By optimizing codes and grids within heterogeneous systems, the project aims to extend the duration of simulations for both the pre-merger and post-merger phases. This will enable enhanced long-term tracking of post-merger BNS systems and more efficient simulation of accreting SMBBHs. Additionally, the tools will calculate observables such as gravitational wave signatures, electromagnetic outputs, jet production, and nucleosynthesis-shaped outflows, thereby deepening our understanding of compact binary mergers. A public data repository will disseminate the project's findings to the scientific community. The team will also collaborate on several educational and outreach initiatives at RIT, including a multimessenger astronomy REU program and an NSF-funded PAARE project aimed at supporting hard-of-hearing and Hispanic 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.