Rensselaer Polytechnic Institute

NSF Awards · FY 2026 · 2026-07

The International Semantic Web Conference (ISWC) is the leading global conference on technologies that help computers understand and connect data. These technologies are increasingly important for building trustworthy artificial intelligence, improving scientific discovery, and enabling smarter systems in healthcare, finance, manufacturing, and national security. This project will provide travel support for approximately ten graduate and advanced undergraduate students from U.S. colleges and universities to attend ISWC 2026 in Bari, Italy. Priority will be given to students who have accepted papers and to those from institutions with limited travel resources. By enabling students to present their work, receive mentoring, and build international collaborations, this project will strengthen the U.S. workforce in artificial intelligence and data science. The award will broaden participation in international research and help ensure that the United States remains a leader in open, trustworthy, and interoperable AI technologies. This project will support U.S.-based students participating in the 25th International Semantic Web Conference, the premier venue for research on knowledge graphs, ontologies, data interoperability, logical reasoning, and explainable artificial intelligence. Students will be selected through a competitive application process that considers research quality, conference participation, and financial need. Awardees will attend technical sessions, present peer-reviewed research, and participate in the Doctoral Consortium and mentoring activities designed to provide guidance on research and career development. The project will assess outcomes through post-conference reports describing collaborations formed, feedback received, and knowledge shared at participants’ home institutions. By increasing student access to this international research community, the project will accelerate advances in knowledge-driven AI and strengthen U.S. leadership in semantic technologies and trustworthy data systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This proposal seeks to fund US-based students to attend IEEE DySPAN 2026, held in Washington, DC on May 11-14, 2026. DySPAN is a premier conference focused on enhancing spectrum innovation to address the increasing need for wireless capacity across various applications. The conference brings together researchers, engineers, policymakers, and industry professionals to discuss the latest developments, challenges, and innovations in dynamic spectrum access and related areas. DySPAN 2026 will expose selected students to cutting-edge developments in the field and enable interactions with world-leading researchers. Students will gain feedback on their ongoing work, broaden their academic perspectives, and build lasting professional connections. DySPAN 2026 will feature topics on advanced wireless technologies and applications, artificial intelligence and machine learning in spectrum access and management for 6G and emerging use cases, security and privacy issues in spectrum access and management, public safety and emergency services spectrum, and software standardization and equipment certification. This project supports students from US universities to attend the 2026 DySPAN conference in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. 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 award is made to Rensselaer Polytechnic Institute to enable the acquisition of a “partial” liquid helium recycling system. This partial system will capture nonrenewable helium gas used to maintain the high-field superconducting magnets of 600 and 500 MHz Nuclear Magnetic Resonance (NMR) instruments in the Chemistry building and have the gas liquified by the liquid helium plant located in the NMR core facility within the Center for Biotechnology and Interdisciplinary Studies building. This partial system will enable the inhouse production of over 900 liters of liquid helium annually thereby providing a reliable supply of exceptionally hard to source high-purity liquid helium that is critical for maintaining these NMR instruments that are fundamental tools in the education, hands-on training, and workforce development of undergraduate and graduate students. Campus tours, permanent poster displays, regional outreach programs, and annual “Open Houses” will also show and explain the system operation and the benefits to society from helium recycling and responsible stewardship. The helium recycling system provides the NMR instruments with a critical supply and onsite reserve of liquid helium thereby protecting the instruments from catastrophic failure due to limited liquid helium available from commercial sources. The provided security in liquid helium supply enables continuity in the research of 18 very interdisciplinary programs, representing four different Departments/Centers (Chemical and Biological Engineering, Chemistry and Chemical Biology, Biological Sciences, Materials Science and Engineering) involving more than 100 RPI investigators who depend on helium availability as well as many additional researchers in NY Capital Region and beyond. 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

The objective of this BRITE Pivot project is to support research studying computational methods to manage traffic more efficiently when disruptions occur due to accidents, cyber infrastructure failures, or sudden changes in driver behavior. These disruptions lead to nonequilibrium traffic, which is common but difficult to formulate, predict, or control, especially across large transportation networks. Traditional tools often fall short in aiding response promptly. This project seeks to integrate quantum computing with classical methods to build smarter systems that can predict and manage sudden traffic changes in real time. In addition to reducing congestion, improving road safety, and cutting emissions, the results look to advance broader goals of economic productivity and sustainability. Research conducted in association with this project tackles the growing challenge of managing traffic systems that are increasingly complex and connected. With more vehicles relying on real-time information and communication technologies, traffic is shaped by not only nearby vehicles but also signals from across the network (e.g., downstream road conditions or rerouting guidance). This invites the question, namely, how we leverage the power of quantum algorithms and machine learning to better predict traffic jams, reroute vehicles, and improve the reliability of our transportation systems. To answer this question, the project seeks to design new algorithms that can process vast amounts of information and make prompt, informed decisions under rapidly changing system dynamics. New ways to represent traffic as a complex, high-dimensional system will be explored, drawing inspiration from quantum systems. The developed tools could be extended to infrastructure management, emergency response and other applications. The project also trains students in emerging technologies and prepare the future STEM workforce for the quantum era. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Non-technical Description: Ferroelectric wurtzites show great promise for enabling advanced communications technologies and for reducing computational energy consumption, both of which are key goals of the nation and the National Science Foundation. Their commercial deployment is hindered by limited understanding of the impacts of defect populations on properties, but current state-of-the-art computational techniques rely on unrealistic dilute-limit assumptions that ignore defect interactions with one another and/or with interfaces. This research aims to rigorously capture the interactions and effects of point defects such as heterovalent substitutions (e.g., oxygen replacing nitrogen) and extended defects (e.g., structural damage from bombardment during sputter growth) on properties in wurtzite nitrides. The team includes world experts in simulation, synthesis, characterization, and testing from the U.S. and Germany, and it includes partners from the Army Research Laboratory (ARL) and an industrial advisory board (IAB) who will build on relevant findings to accelerate scale-up and deployment as appropriate. The goal is to bridge the gap between calculations requiring simplifying assumptions and real films grown using commercial techniques to accelerate deployment of these and future DMREF-developed materials. Technical Description: To-date, when charged defects are simulated computationally (particularly within the electronic nitride space), they are assumed to be dilute and non-interacting, which is invalid for substitution levels in the several- to tens of atomic percent, such as those common in ferroelectric wurtzite alloys. This research will treat defects as components of complex alloys to capture disordered configurations as well as interactions of defects with one another and, eventually, with interfaces. Such calculations will be informed and validated by high resolution electron microscopy capable of measuring not only structural and chemical but also—via electron energy loss spectroscopy (EELS)—local bonding characteristics. The rigorous mechanistic understanding will enable predictive capabilities around interacting (non-dilute) point defects including heterovalent substitutions and will advance towards quantitative predictions of coercive fields, multiscale switching dynamics, and potential degradation processes important to the very devices that the ARL and industry partners on our team will be simultaneously advancing towards deployment. 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

Despite the growing need for spectrum resources, spectrum licensed for commercial use remains largely underutilized due to lack of established market mechanisms for dynamic trading of the spectrum between cellular operators. Further, there are no protocols that implement such sharing and exchange on short time scales. Multi-operator spectrum sharing is therefore necessary not only to make best use of this critical resource (spectrum), but also to improve the profitability of spectrum service providers and reduce the cost to customers. Towards this goal, the project aims to develop new market mechanisms and protocol support for dynamic and automated spectrum sharing between cellular providers. Deriving motivation from electricity markets, the project analyzes a two-step design for spectrum markets, involving trading of both forward and spot spectrum contracts between cellular operators. Building upon the Open Radio Access Network (O-RAN) software framework, the project also analyzes designs that provide logical connectivity from different radio access networks (that might belong to different operators) to the cellular core networks of one or more of the chosen operators. This project explores a design of a two-timescale market involving a forward spectrum market (FSM) and a spot spectrum market (SSM) through which forward and spot spectrum contracts are traded between cellular providers, in addition to any bilateral settlements that may exist over longer timescales. This enables flexible sharing of radio resources between cellular operators. Further, this project utilizes network slicing within the framework of the O-RAN architecture and protocols to realize the forward and spot contracts in a secure and efficient manner. Different from traditional roaming, network slices seek to provide users of the slice an assured amount of bandwidth and latency through service-level agreements (SLAs). The project also seeks to implement prototypes of the market solution and the network slice implementation over cellular networks, where the market clearing solutions are fed to the 5G core network that supports network slicing. The broader impacts of the project are realized through continued collaboration with cellular network operators and device vendors, incorporating research insights into courses and capstone projects, and involving undergraduate student researchers in developing a Spectrum Trading Game aimed at motivating high-school students towards science and technology careers. 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

Today's quantum circuit designs are akin to classical circuits in their early stages, which were designed by hand and manually laid out, while the power of classical computing hardware was not fully unleashed until the emergence of Electronic Design Automation (EDA) in the 1950s, enabling the scalable design of integrated circuits. Although quantum computing holds great promise to dramatically speed up many chemical, financial, cryptographic, and machine-learning applications, we are witnessing that the existing quantum computing design workflow significantly relies on human designs, such as manually implementing and verifying quantum circuits on the gate level for quantum algorithms. As such, domain experts from other fields without a sufficient fundamental understanding of quantum operations can hardly leverage the power of quantum computers for their domain applications, and more importantly, they lack toolkits to test the correctness of an ad-hoc designed quantum circuit. Furthermore, since quantum computing has a fundamentally different computing scheme, which relies on superposition and entanglement, the traditional EDA techniques cannot be directly applied to quantum circuits. To close the gap between quantum hardware (in physics) and quantum algorithms (in computer science), we envision the necessity of a quantum EDA framework, which will play a role similar to that of EDA in revolutionizing classical Silicon-based hardware design. Beyond the technical impact, the fundamentals of the design automation tools can help beginners understand how a quantum system is designed and how it works, which are compiled in the education activities in this project for public access. To carry out pilot research on the quantum EDA, this project proposes to develop an automated framework, namely SPV, to efficiently synthesize, profile, and verify quantum circuits, which include a set of quantum EDA tools: (1) We develop an automated quantum circuit construction toolset to optimize quantum circuit design in modern quantum processors. The toolset supports end-to-end quantum circuit design, including both quantum state preparation and function synthesis using available quantum gates. (2) We develop both formal and simulation-based approaches to verify quantum circuits at scale. Specifically, we utilize the widely adopted ZX calculus to optimize quantum circuits for equivalence checking, and we develop a scalable, simulation-based verification methodology tailored for larger circuits. Moreover, it will comprise methodologies to verify quantum circuits in the presence of quantum error correction (QEC). And (3) we build a benchmark test platform with circuit property profiling and performance validation. To address the shortage of QEC designs in existing benchmarks for quantum verification, we integrate a set of state-of-the-art QEC code designs into the benchmark tool. After all the synthesis, profiling, verification, and benchmarking tools are developed, we integrate them into a holistic quantum design automation toolchain. With a completed toolchain, SPV can benefit researchers in deploying and testing domain-specific quantum algorithms on available quantum computers. 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 I-Corps project investigates the commercial potential of an advanced cognitive-robotics technology designed to transform general-purpose robots into trusted human teammates. The robots are to be used as members of human-robotic teams. To facilitate efficient communication, explainability and mutual trust within a team, the robots will have the ability to understand the meaning of what people communicate to them, and the ability to learn through language combined with interpreted visual perceptions. This technology has the potential to benefit society by enabling robots to take on hazardous or fatigue-inducing jobs while keeping humans “in the loop” of decision-making processes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a dual-layer framework featuring a high-level strategic cognitive layer and a low-level tactical control layer. This framework combines complex reasoning and natural language understanding with real-time sensorimotor functionality. The cognitive-robotic integration enables long-horizon planning and broadens the robot’s reasoning scope, adding metacognitive awareness and social intelligence that together support human-level explanations, essential for robots to serve as trusted teammates, not just tools, in human-robot teams. These distinctive capabilities of demonstrated explainable artificial intelligence (AI) behavior, adaptive learning, and long-horizon planning grounded in cognitively inspired decision models, hold promise for wider adoption of cognitive-robots, especially in high-risk settings where human–robot trust is critical. 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

Despite the impressive performance of modern deep learning models across various domains, they still suffer from several fundamental limitations, including high data dependency, poor generalization, and low interpretability. These limitations primarily arise from the data-driven nature of deep models and their inability to effectively leverage prior knowledge. Traditional symbolic AI models successfully incorporate different types of prior knowledge, offer interpretability, and exhibit good generalization. However, they suffer from the slow and difficult process of extracting and structuring knowledge and, therefore, do not scale up as well. To address these deficiencies and increase the applicability of deep learning models to many real-world scenarios, this project will create a hybrid AI model that systematically integrates modern deep learning with probabilistic graphical models (PGMs; a way of encoding relationships between variables to transit knowledge). Thus, the prior knowledge encoded in the PGMs works synergistically with the data encoded in the deep learning model. This approach, effectively incorporates prior knowledge into deep learning models and could greatly expand their utility to a wide range of data-scarce yet knowledge-rich applications—such as those in manufacturing, scientific discovery, medicine, and defense. However, current efforts to address these limitations tend to be heuristic, ad hoc, and narrow in scope, both in the types of domain knowledge considered and in the methods used for knowledge integration. This project proposes a systematic and unified approach to identifying, encoding, and integrating prior knowledge with data. Specifically, the project systematically categorizes and organizes diverse forms of prior knowledge—including theoretical and experiential knowledge—drawn from a broad range of sources. It employs PGMs as a unified framework to represent various types of knowledge (e.g., mathematical equations, logical rules, and knowledge graphs) and develops learning algorithms to automatically encode this knowledge into the PGM. Finally, the project introduces complementary methods for integrating PGM-encoded knowledge with data within deep learning models at multiple levels, including decision level, architectural level, and training level. The proposed framework is evaluated on both vision-based tasks, such as human nonverbal behavior analysis and recognition, and non-vision tasks in domains like scientific discovery and manufacturing, with a focus on improving data efficiency, generalization, or interpretability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Large networked systems -- ranging from communication networks and power grids to social and transportation networks -- exhibit intricate interactions and dynamic dependencies among the agents that constitute them. In many domains, decision-makers need to understand how interventions such as policy changes, infrastructural modifications, or emergency responses will impact the system before implementing the interventions. Conducting real-world interventions in complex systems can be prohibitively risky with inadvertent consequences. In such circumstances, hypothetical evaluations allow decision-makers to simulate interventions and assess their potential impacts without exposing the system to real-world risks. This is particularly important when interventions have irreversible or costly consequences, as it enables planning and preparation for adverse outcomes. The overarching goal of this project is to design a theoretically principled framework for performing accurate hypothetical interventions on complex systems and predicting their outcomes, thereby providing a reliable basis for planning and risk assessment. This is especially crucial in environments where erroneous predictions or suboptimal decisions could lead to significant performance, robustness, or safety consequences. The project consists of several educational components aimed at students at different levels (high school, undergraduate, and graduate) as well as contributions to the educational missions of the relevant technical societies. This project introduces new theoretical foundations for causal reasoning in complex systems, aiming to move beyond the limitations of traditional data analysis. Standard observational datasets primarily reveal what “did” happen, offering little insight into what “might” have happened under alternative circumstances. In contrast, causal inference empowers us to explore these counterfactual possibilities, the “what if” scenarios that are essential for anticipating how systems respond to different interventions. By simulating hypothetical changes, this framework supports systematic comparisons between competing strategies and allows us to estimate their potential outcomes. For example, identifying the most structurally influential components within a network, such as key nodes or links, enables planners to design interventions that maximize resilience, especially in the face of uncertainty or disruption. While machine learning (ML) has proven useful for uncovering statistical patterns in large-scale systems, these patterns typically reflect correlations rather than cause-and-effect relationships. Correlation implies mutual variation but does not clarify directionality or underlying mechanisms — that is, whether A causes B, B causes A, or a third factor drives both. As a result, ML approaches fall short when it comes to predicting how deliberate changes to one variable will affect others, limiting their utility in policy design or engineering control. Causal inference offers a middle ground between the interpretability of physics-based models and the flexibility of data-driven ML. It retains the ability to model directional influence and system dynamics while leveraging empirical data as ML does. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The main goal of this project is to develop and deliver remote experiments utilizing cloud-based resources aimed at educating a broad audience of students and practitioners in hardware security. In the post-COVID era, it is imperative to develop online education platforms for remote training of both students and the workforce in the field of Hardware Security. Recent advances in this field and FPGA-based cloud servers have enabled an opportunity to move related experiments to an online format that only requires a standard computer and internet connection by the students. Teaching “hardware” security in a socially distanced format poses significant challenges. Essential experiments for teaching key concepts in hardware security necessitate multiple evaluation boards and physical equipment such as voltage supplies, oscilloscopes, multimeters, and function generators. To adapt these experiments for an online platform, the project will explore innovative methods to execute or emulate them using the cloud ecosystem. This project addresses a critical gap by developing a fully online hardware security training module accessible to students and professionals worldwide. This project proposes various comprehensive experiments testing different notions in hardware security. The framework will be designed for both undergraduate and graduate students in the electrical engineering, computer engineering, and computer science departments, leveraging courses developed by the PIs in their respective institutions. The proposed infrastructure includes preparing detailed experiments for instructors with walkthrough documents and organizing student assignments for independent completion. This setup supports not only teaching but also facilitates independent research upon assignment completion. Supplemented with video instructions, these experiments will constitute a comprehensive training module, equipping participants with the necessary skills and knowledge to address complex challenges in this emerging domain, thereby instilling preparedness and confidence. This award is co-funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is further supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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 engineered products and processes are simulated using chaotic dynamical systems, Fusion reactors and aircraft are two examples. The capability to optimize these systems would positively impact multiple engineering disciplines and industries. Unfortunately, chaotic dynamical systems are notoriously difficult to optimize, particularly when the performance metric is a time-averaged quantity. Time-averages from chaotic simulations exhibit deterministic "noise," and this noise renders conventional gradient-based optimization algorithms ineffective. The failure of gradient-based methods is especially problematic for shape and topology optimization problems that have many (>100) design variables and parameters. In short, we lack the algorithmic tools to effectively optimize large-scale chaotic systems. To address these needs identified above, this research project will investigate a novel algorithm for the optimization of chaotic dynamical systems: the Linked Ensemble Aggregation Procedure, or LEAP, for short. LEAP, like other ensemble approaches, uses multiple short-time simulations instead of one long simulation in order avoid the infamous "butterfly effect" – where small changes can propagate unpredictably in later stage. However, unlike conventional ensemble methods, the simulations in LEAP are coupled sequentially, which eliminates the "noise" in time-averaged outputs and makes gradient-based optimization possible. The project's research objectives are to i) investigate LEAP's accuracy, parameters, and limitations; ii) seek to improve LEAP's robustness; and iii) generate a solution algorithm suitable for large-scale problems. To increase the broader impact of the research, the project will develop open-source software and educational resources, and will establish a workshop for the optimization of chaotic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project focuses on enhancing the safety and reliability of autonomous driving systems, making them more trustworthy in a variety of environments, including potentially hostile ones. The research will develop advanced detection and countermeasure techniques at the application, system, and hardware layers to help ensure that self-driving cars can make safe driving decisions even when faced with deliberate attempts to disrupt their operation. This involves creating robust deep-learning modules for driving decisions, enhancing the safety and security of automotive processors, and developing tools to detect and fix hardware issues in mission mode. By conducting extensive real-world testing with popular automotive benchmarks, the project aims to validate these innovations, ensuring they can be confidently adopted in everyday use. This comprehensive approach addresses current and future challenges in autonomous driving technology, paving the way for safer and more efficient transportation systems that the public can trust. The broader impact of this research extends beyond automotive technology to other critical systems such as space exploration, smart medical devices, and various Internet of Things (IoT) applications. With the deep learning market expected to grow to $24.5 billion by 2025, advancements from this project will play a crucial role in ensuring the safety and security of numerous technologies that impact daily life. The project’s findings will be widely disseminated through publications, software releases, and educational courses, contributing to the development of a skilled workforce in this rapidly evolving field. Further, the researchers will engage in educational outreach, including workshops and summer camps for students from diverse backgrounds, promoting inclusivity and inspiring future scientists and engineers. The researchers’ ongoing collaboration with leading semiconductor companies will also support translation into practice. The project not only advances technology, but contributes to societal well-being by making autonomous systems safer and more reliable, ensuring that the benefits of these innovations are widely accessible. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The widespread proliferation of computing devices embedded into everyday products has ushered in an era of ubiquitous production and dissemination of malware, computer software that has the intent to cause damage. Traditional antivirus systems to protect against malware are ineffective due to their low accuracy in identifying modern, sophisticated malware. Additionally, antivirus software incurs high overhead on resource-constrained embedded platforms. This has propelled the development of hardware-assisted malware detectors, which use the trusted underlying hardware to help detect malware. However, detection based on hardware performance counters faces several inherent pitfalls, such as high false positives in malware detection. This research proposes an end-to-end framework for developing, analyzing, and securing fine-grained design-for-security primitives, which can be incorporated into the embedded hardware. This research enhances the effectiveness of hardware-assisted security solutions, leading to lightweight and robust design-for-security primitives for resource-constrained embedded devices utilized in applications such as automotive, medical, and military. The educational plan will enhance courses at both undergraduate and graduate levels by introducing hands-on experiences in hardware security. An educational game will be designed to improve K-12 students’ understanding of malware. The project develops security-aware design principles for next-generation embedded hardware, which includes meticulously crafted design-for-security primitives comprising instruction sequences and debug-level register information. The new algorithmic approaches for trace analysis utilize time-series and explainability-based classification to improve malware detection performance. This research also investigates methods to secures the fine-grained design-for-security primitives against adversarial and snooping attacks by developing novel defense strategies based on theoretical foundations. Research findings are being integrated into undergraduate and graduate course materials, embedded hardware design camps for high-school students, and an educational prototype game to ignite the passions of future investigative minds in embedded hardware security. 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

We live in an era where smart, connected computing devices pervade many critical applications, including transportation systems, industrial automation, health and biomedical systems, etc. Naturally, these devices create, process, and exchange significant sensitive information. Unauthorized or malicious access to these assets can result in disastrous consequences, including loss of human life in the case of health monitoring systems. The goal of this project is to create a comprehensive infrastructure for information flow validation, i.e., ensuring that sensitive assets in modern System-on-Chip designs cannot be accessed or corrupted by an unauthorized or untrusted agent. The ove r-arching goal is to develop a scientific foundation and a comprehensive automated framework of integrated tools for systematically addressing the spectrum of challenges in information flow validation. The research objectives are tightly integrated into teaching and outreach activities, in the form of new curriculum development, organizing security competitions in premier conferences, recruitment of female and underrepresented minority students, and involving high-school graduates in research. The project has three technical objectives. The first objective is to develop a core foundation for information flow analysis that accounts for real-world complexities. In particular, many hard-to-detect real-world information flow violations result from interruptions of functional flow by a variety of asynchronous events, to subvert the integrity of hardware assets. The project addresses this critical issue by incorporating new, innovative approaches to specify, analyze, and integrate the role of asynchronous events and hardware-firmware interaction within the foundation. The second objective is to develop a comprehensive automated framework of integrated tools for systematically addressing the spectrum of challenges in information flow validation. The project addresses this goal through a combination of dynamic and formal analysis techniques that draw inspiration from advances in formal methods, testing, and machine learning. Third, the project targets smooth integration of the analysis infrastructure with industrial validation flows. 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

This project addresses the growing national demand for a quantum computing (QC) workforce equipped not only with theoretical knowledge but also with practical skills in programming, system design, and cyberinfrastructure (CI). By integrating AI powered tutoring, immersive visualization tools, and hands-on experience with cloud accessible quantum platforms, the project aims to promote the progress of science and strengthen national competitiveness in emerging quantum technologies and improve preparedness of learners with varied academic training. The openly shared educational resources and outreach strategy facilitated by a network of experts will support long term workforce development and broaden access to quantum education as well as ensure that the benefits extend well beyond the host institution. The long-term goal of this research team is to support a strong foundation for Quantum Computing Applications and Systems CI learning and workforce development. To achieve this goal, two investigators leverage their expertise in quantum computing, system, visualization, and cyberinfrastructure, to develop an integrated framework: 1) a modular quantum training platform that integrates a tiered curriculum, progressing from foundational quantum principles to system and application development, also couples these courses with designed engaging activities together with collaborating institutes to broaden participation. 2) an AI tutor powered by a fine tuned large language model that provides automated feedback and personalized guidance. 3) an interactive visualization framework that pairs analogy based mappings of core quantum phenomena with object based augmented reality scenes and gesture controlled interfaces. Together, these efforts deliver a reusable, scalable, and research aligned training platform that couples rigorous instruction with experiential learning and state of the art visualization, positioning participants for leadership in the national quantum workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The design and optimization of many important engineering devices, seismic exploration for oil or gas, or non-intrusive testing of aircraft parts are a few of the many applications that rely on fast computer simulation of certain so-called Helmholtz problems. Helmholtz problems are notoriously difficult to solve computationally, and there has been much research into finding better algorithms for this key and essential task. Despite this past research, there remains room for improvement. The benefit of improved algorithms could be, for example, the creation of advanced optical meta-materials that can outperform traditional optical devices used in civilian and military applications, e.g., being lighter, using less power, or operating in multiple regimes. This proposal will develop new fast algorithms for solving Helmholtz problems. The algorithms are optimal in the sense that the number of operations needed to solve the problem is proportional to the number of degrees of freedom (number of unknowns). The breakthrough is based on viewing the Helmholtz problem as the time-periodic solution of an associated wave propagation problem using the recently developed WaveHoltz algorithm. Time-periodic problems and associated eigenvalue problems arise in a wide range of applications in engineering and applied sciences involving systems exhibiting time-harmonic behavior. This proposal aims to address problems of this type by developing new and efficient high-order accurate algorithms for solving large-scale Helmholtz and eigenvalue problems for multi-domain and multi-physics applications. Numerical schemes for these problems will be based on an extended WaveHoltz algorithm for Helmholtz problems, along with a new EigenWave algorithm for eigenvalue problems. WaveHoltz computes solutions to the Helmholtz problem by filtering solutions of a related time-domain wave equation, thus avoiding the need to invert a large, indefinite matrix. The EigenWave algorithm follows a similar approach and can compute eigenvalues of an elliptic operator anywhere in the spectrum without inverting an indefinite shifted matrix. In addition to the extension of WaveHoltz to complex geometry on overset grids, the basic WaveHoltz approach will be accelerated using large-time-step implicit time-stepping at an O(N) cost per iteration (N being the number of grid points), and by deflation using eigenmodes (or approximate coarse-grid eigenmodes) computed with the new EigenWave algorithm. Dispersive (pollution) errors will be ameliorated using high-order accurate spatial approximations. WaveHoltz will also be extended to dispersive wave propagation problems as well as multi-domain problems that couple different physics or materials in different domains, coupled with high-order accurate interface conditions. For open domain problems (e.g. scattering problems) the new algorithms will be coupled to advanced radiation boundary conditions. The development of the proposed algorithms and simulation capabilities will lead to a new and transformative approach for solving large-scale Helmholtz and eigenvalue problems, and will provide high-performance open source software tools to the community. 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

This project focuses on problems arising in game theory, statistics, engineering, and machine learning. Research on minimax problems dates back almost a century, when von Neumann published his minimax theorem about zero-sum games. The past several years have witnessed tremendous research interest in solving minimax problems, motivated by training deep learning models, including generative artificial intelligence and robust machine learning. Though well-trained deep learning models can deliver high-quality performance in many tasks, they are often vulnerable to adversarial attacks. Using such models can cause serious safety and security risks; thus, improving their robustness is very important. Most existing methods for solving minimax problems require certain strong conditions that do not hold for modern applications, such as training robust deep learning models. This project will develop new optimization algorithms for solving minimax problems, which can deliver guaranteed stability and reliability under weaker and more practical conditions. Software packages will be developed and released for public use to benefit both academic and industry researchers. The results from the project will be integrated into regularly offered or topical courses at RPI for both undergraduate and graduate students. Different algorithms will be designed by leveraging the structures of the considered minimax problems, and analysis will be conducted as well to show the convergence of these algorithms and their complexity. For solving nonconvex nonsmooth minimax problems that satisfy a certain regularity condition for the dual part, a momentum-accelerated primal-dual stochastic subgradient method (PDSsG) will be investigated, and a Moreau-envelope based smoothed PDSsG, as an alternative, will also be explored. For solving nonconvex-nonconcave nonsmooth minimax problems that do not satisfy regularity conditions, new approaches will be developed by using the log-exponential smoothing function to approximate the maximization part. On solving problems that involve too-big data, new distributed methods will be designed under the setting of either a complete network or an incomplete connected network. Low-precision communication and error-compensation techniques will be used, for the first time, to solve nonconvex minimax problems, to save communication, and achieve fast convergence. These investigations are expected to invent new analysis techniques and lead to novel and efficient algorithms for solving large-scale minimax structured optimization. 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

Proteins are large, flexible macromolecules that perform a vast range of functions in all living organisms. Their functions span from copying genetic material to providing structural integrity to cells and organisms. Because of their size, flexibility, and chemistry, proteins can undergo structural changes that dictate their function and sometimes cause disease. The ability to understand and predict how the conditions that proteins experience affect their structure and conformation, and in turn their functioning in solution, is essential for science and technology. This award will be used for the development of predictive models for both fundamental physics of complex fluids and some industrial applications, including the development of first-principle models in manufacturing of biologics, pharmaceutical products composed of proteins, nucleic acids, or cells. Microgravity makes it possible to study how protein solutions flow, without complications associated with the interaction of protein solutions and solid walls. This project utilizes the microgravity environment of the International Space Station (ISS), where surface tension becomes a dominant force, this allows the study of protein solutions in the ring-sheared drop module, a container-less biochemical reactor. In this research, fluid dynamics will be used as a probe to gain fundamental insight on protein structure and function, especially at interfaces with fluidity. Simultaneously, proteins are used to gain fundamental insight into fluid dynamics. Drops of protein solutions with surfaces that are curved inward, curved outward, or not curved will be generated and studied on the ISS. The influence of flow speed and associated fluid inertia will be examined in addition to the effects of curvature on the flow. The experiments in microgravity will be augmented by experiments with analogs in the laboratory on Earth. The bulk and at the interface will also be developed and tested using novel experimental techniques for distinguishing fluid flow. Results are expected to be important not only for biomedical applications, but also for applications ranging from 3D printing to oil recovery. 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 research advances Bayesian inference for the analysis of complex and large-scale human data. Computational models are highly influential across the behavioral sciences. Many complex models, however, are beyond the reach of traditional statistical methods or demand unreasonably high computational costs. This project provides researchers with a widely applicable framework for efficient Bayesian inference. The framework enables researchers across the social and behavioral sciences to quickly develop, fit, criticize, and adapt complex mechanistic models, overcoming the constraints imposed by traditional statistical methods. Furthermore, the project makes important contributions to open science and reproducibility by providing a freely accessible and transparent interface for researchers to benchmark Bayesian methods systematically and communicate results. Finally, the project supports and trains junior researchers who will drive key methodological developments. This research upscales Bayesian methods by building on recent progress in amortized Bayesian inference (ABI). ABI repays users with instant inference of latent parameters following a lengthier training phase that relies on model simulations as training data. It can be viewed as a factory for pre-trained statistical models, akin to generative pre-trained transformers (GPTs). Building on these ideas, the project introduces qualitatively new developments that (1) generalize the scope of amortized inference by supporting multiple model families, new experimental designs, and real-time adaptation to changes in models or data; (2) ensure robustness and reliability by developing novel semi-supervised methods to mitigate model misspecification and increase accuracy on unseen real data; and (3) establish benchmarking standards by creating a comprehensive open-source framework with metrics, models, and benchmarks to evaluate and continuously improve computational tools for Bayesian inference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Over one billion tons of nutrient-rich animal waste are produced annually in the United States, yet only a small portion is utilized, primarily as fertilizer. This project addresses a challenge of modern agriculture related to the sustainable management of animal manure, which, if not properly managed, can significantly impact the environment, public health, and the economy. The team seeks to implement a novel process to optimize nutrient recovery and produce engineered carbon materials from animal manure which not only repurposes animal manure but also enhances its value as an efficient fertilizer. The project aims to improve the efficiency of resource utilization and impact in the agricultural sector. If successful, this technology will substantially alleviate the environmental, health, and economic burden associated with nutrient release into water bodies. It may also enable local farmers and others to develop new products, thereby increasing revenues and reducing environmental pollution and waste disposal costs. The project will also provide unique training opportunities for graduate and undergraduate students. This project aims to develop a novel hydrothermal carbonization (HTC) technology that efficiently recovers nutrients from animal manures while also producing value-added engineered carbons. In this pH-swing HTC process, nitrogen and phosphorus will be depleted from solid animal manure to liquid fertilizer by controlling the feedwater pH. This will also produce carbon-rich and low-impurity hydrochar, which will be further converted into engineered carbon for environmental applications. To achieve the goals of this project, the following three objectives will be pursued: 1) develop a novel pH-swing HTC technology to recover nutrients into liquid phase from wet animal manures; 2) analyze the effects of different modification methods on the physicochemical properties as well as potential applications of the hydrochar; and 3) evaluate economic viability and environmental sustainability of nutrient recovery and engineered carbon synthesis. A range of laboratory experiments and model simulations will be conducted to explore and understand the effects of pH-swing HTC processes and carbon-modification methods on the quality of liquid fertilizer and engineered carbon derived from animal manure. Technoeconomic analysis and life cycle assessment will be conducted to assess the environmental and economic benefits of nutrient recovery and engineered carbon synthesis. The success of the project will offer new insight into the chemical reactions and physical transformations occurring during the HTC of animal wastes. It will also be a significant step towards scaling-up and commercialization of the pH-swing HTC technology for sustainable circular bioeconomy. 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

Lakes can undergo rapid and sustained changes in water quality. These changes, often referred to as regime shifts, can substantially alter the lake environment, including food webs. Regime shifts often occur when conditions cross tipping points, but understanding the feedbacks and attributes that lead to regime shifts can be challenging. Increases in dissolved organic matter in lake water, termed lake browning, are occurring in many regions. Dissolved organic matter regulates water clarity, water temperature, ecosystem respiration, and many other lake attributes, suggesting potentially long-term and substantial impacts. Lake browning may stimulate lake dissolved oxygen losses, termed deoxygenation, that leads to further increases in dissolved organic matter and nutrients, ultimately resulting in sustained water quality impairment. This project seeks to understand the impacts of an anticipated lake regime shift driven by increases in dissolved organic matter and its impact on the lake overall. There may be tipping points associated with the low dissolved oxygen in lake bottom waters, driven by the increases in dissolved organic matter. Broader impacts include undergraduate student research experiences and community outreach activities. Using a sophisticated suite of sensors, manually-collected long-term data spanning multiple decades, experiments, and ecosystem models will help discern the consequences of lake browning and low dissolved oxygen availability in lakes. Studying multiple lakes that vary in dissolved organic matter, nutrients, and oxygen availability will provide the capacity to compare and assess feedbacks and tipping points resulting from rapid increases in dissolved organic matter that may push water quality conditions toward several potential future trajectories. This research will provide a foundation to understand the implications of widespread aquatic deoxygenation through research and data publications and training of early career scientists in the use of sophisticated sensors and other technologies. This project will extend a long-term data set of water quality variables sampled since the late 1980s of Lakes Lacawac and Giles in or near the Lacawac Sanctuary and Biological Field Station, located in northeastern Pennsylvania. The project will support a regional lake monitoring network in Pennsylvania and closely interface with GLEON, the Global Lake Ecological Observatory Network. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract Surgical skill is currently measured via structured checklists or composite metrics based on time and errors, which are typically assessed by experienced proctors. The need for experienced proctors for surgical skill assessment creates a time-consuming workflow and potentially subjective scoring of performance. To solve this, there has been interest in finding a method to assess surgical skill performance without the need for proctors. One approach involves the use of neuroimaging to provide performance assessment and there has been a variety of literature on the use of fMRI and EEG for surgical skill assessment. These modalities come with key drawbacks such as restrictions on head and body motion (fMRI) or poor spatial resolution (EEG). For these reasons, functional near-infrared spectroscopy (fNIRS), a modality with relatively low obstruction of body movement and high spatial resolution was selected as the means for surgical skill assessment. Currently, use of fNIRS in real-time applications is inhibited by current processing practices in the field, due to the prevalence of signal components which originate from outside of the brain. Most fNIRS studies rely on post-hoc processing to remove confounding artifacts and noise. To provide unsupervised surgical skill assessment, an online method of removing noise would be required, however, current aims to provide online noise removal, particularly noise related to superficial physiology, are reliant on short, well-characterized tasks. While data-driven approaches such as deep learning show promise, training large models requires large amounts of labeled data, which are challenging to acquire. To address this Aim 1 will focus on developing a method of generating synthetic fNIRS data using Monte Carlo simulations of photon propagation informed by amortized simulation-based inferencing to generate data. This synthetic data will be used in Aim 2.1 to train a deep learning algorithm for near real-time superficial physiological contribution removal from fNIRS data, allowing us to create an online denoising pipeline. This approach will rely on multiple open-access fNIRS datasets along with surgical skill assessment datasets to ensure the developed method is task-agnostic, allowing for its use in a wide variety of tasks. Finally, in Aim 2.2 we will provide a deep learning algorithm for predicting a widely used metric of surgical skill assessment, the FLS score (developed for the widely accredited Fundamentals of Laparoscopic Surgery program), using fNIRS recordings of surgical task completion, allowing for objective, online surgical skill assessment. Over the course of this project, we will publicly release the data generator and denoising pipeline to be freely used by the field. With Dr. Intes’ expertise in optical imaging, he will provide regular feedback and input on research progress with almost daily interactions. Similarly, weekly interactions with Dr. De regarding deep learning, Dr. Radev regarding Bayesian inferencing and Dr. Cavuoto on surgical skill assessment and neuroergonomics will assist with the development of Condell’s research skills for the proposed project and his future career as a researcher.

NSF Awards · FY 2025 · 2025-01

Abstract for NSF proposal #2430223, entitled “ENG-AI: EPCN: Small: Computationally Efficient Learning using Graph Neural Networks with Theoretical Guarantees,” PI: Wang, Meng: Associate Professor, Rensselaer Polytechnic Institute Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and processing graph-structured data. They have found applications in diverse fields such as robotics, power systems, recommendation engines, and social network analysis. Despite those success, their widespread application faces significant challenges, including high computational requirements and lack of interpretability and performance guarantees. This proposal aims to lay the groundwork for overcoming those challenges, establishing theoretical foundations and developing practical algorithms to enhance the efficiency and reliability of GNNs across various engineering applications. Key objectives include systematically analyzing how graph topology and network architecture influence performance by delving into the dynamics of learning and generalization in GNNs. Most of the existing theoretical works on GNNs focus on either analyzing the expressive power of GNNs or bounding the generalization gap between training and testing or characterizing the training convergence, disregarding the joint problem of learning dynamics and generalization. This study encompasses a range of GNN architectures, from established models like graph convolutional networks (GCNs) to emerging structures such as graph transformers (GTs) and graph mixture of experts (GMoEs). A crucial aspect of this proposal is the optimization of computational and memory resources in various aspects. Techniques such as graph data aggregation reduction, network pruning, attention sparsification, and dynamic joint sparsification methods will be explored to streamline GNN operations. These efforts are complemented by the introduction of novel GMoE architecture to further enhance efficiency. This proposal will advance the development of trustworthy AI systems applicable across societal infrastructures like social networks and power grids. Moreover, by focusing on computational efficiency, the proposal contributes to the advancement of green AI, aiming to reduce economic costs and environmental impact associated with large-scale AI models. Collaboration with IBM through the RPI-IBM AI Research Collaboration expands the project's reach and ensures real-world applicability. Additionally, an integral education and outreach plan is included, spanning from K-12 education to professional training in AI research and application. 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 essential in modern applications from smartphones to autonomous vehicles and data centers. However, the growing demand of increasingly large models and more computing resources is leading to a rapid surge in energy usage. If left unaddressed, this trend will result in significant energy waste, limiting the potential of AI technologies while creating substantial economic and environmental issues. This project aims to identify and reduce unnecessary computations and data movements to save energy by making AI computing more flexible and adaptable. The significance of this project lies in rethinking how AI hardware processes, stores, and moves data, while creating an energy-aware design approach that will be openly available. The project will integrate research activities with education initiatives to engage students through enriched curriculum and outreach programs. These educational and outreach activities will also increase participation among local communities. This project addresses the challenges of energy efficiency and scalability in large AI systems, such as serving large language models, through a co-design approach with three key objectives. The central approach is that AI models, though traditionally viewed as static, can dynamically connect essential components to form computational graphs, enabling elastic processing with comparable performance while reducing the costs of redundant components. The first objective focuses on developing methods to leverage dynamic connectivity to reduce redundancy by adapting models to specific tasks and data. The second objective involves providing architectural support for elastic processing by exploring heterogeneous architectures that integrate approximate, analog, and near-data computing. The third objective aims to enable hardware-aware model adaptation, through co-design space exploration of algorithms and architectures for greater efficiency. These objectives collectively seek to overcome the energy wall by reducing computational costs while maintaining model performance, advancing future paradigms for energy-efficient and scalable AI systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.