SUNY at Buffalo

NSF Awards · FY 2025 · 2025-08

Social behaviors are integral to the lives of many species and can affect an individual’s ability to survive, thrive, and reproduce. Vasopressin is a neurochemical that influences many types of social behaviors in many species, from communication in electric fish to cooperation in humans. However, vasopressin can influence these social behaviors in conflicting ways, depending on different parameters like age and social context. For example, in birds it can increase aggression for an intruder but also decrease aggression in flocking behaviors. The proposed research will test the hypothesis that vasopressin influences social behavior through its regulation of arousal to social stimuli – that is, how exciting, interesting, or important a social stimulus is to the animal. This research will advance our understanding of how the brain regulates social behavior and provide a framework to guide the development of potential vasopressin treatments for disorders of social development such as autism spectrum disorders. The Broader Impacts of the proposal will provide students with educational and career-building opportunities. The co-PIs will establish a free, online Behavioral Neuroscience conference that will provide undergraduate students, graduate students, and postdoctoral researchers from all over the country the opportunity to present their research and network with faculty and other trainees. The co-PI’s will also establish a science exchange between the University of Kentucky (UKY) and University at Buffalo (UB). This will form a lasting connection between the UKY and UB Behavioral Neuroscience communities that will provide students from these institutions with broad scientific training and expanded academic networks. Vasopressin is among the most consistently implicated neurochemicals in social behavior. A survey of the literature, however, reveals great variability in vasopressin actions, even within the same species. Current hypotheses do not fully address this variability in vasopressin’s actions, leaving a fundamental gap in understanding of how vasopressin regulates social behavior. The proposed research tests the hypothesis that vasopressin influences social behaviors through its regulation of arousal. Arousal influences behavior in an inverted-U shaped manner. If vasopressin regulates social arousal, it could increase, decrease, or have no effect on social function depending on dose or interactions with other factors that impact arousal, like age and context. The PIs will use implantable telemetry and pharmacological manipulations to test whether vasopressin's actions on arousal predict its effects on juvenile social play. The PIs will then determine if manipulating vasopressin projections to arousal centers affects juvenile social play, using a new rat model that restricts chemogenetic manipulations to vasopressin cells. Finally, the PIs will measure autonomic, neural, and behavioral arousal responses of juvenile vasopressin-deficient Brattleboro rats to social and non-social stimuli to determine whether vasopressin’s regulation of arousal is specific to social stimuli. The proposed research may provide a unifying framework for understanding vasopressin’s varied actions on social behavior. This project is jointly funded by Neural Systems Cluster and the Established Program to Stimulate Competitive Research (EPSCoR), 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

PFAS (short for per- and polyfluoroalkyl substances) are man-made chemicals used in many products like non-stick pans, waterproof clothing, and firefighting foam. They are popular because they resist heat, water, and oil. However, that is also what makes them a potential problem — they don’t break down easily in the environment. Over time, PFAS can build up in water, soil, animals, and even people, and they may be harmful to our health. Removing PFAS from water is challenging. Most regular water treatment methods do not work well, especially when the water is full of other waste materials. This project is exploring a new way to get rid of PFAS using a low-temperature plasma — a special kind of energized gas. This plasma creates particles that can break the very strong bonds in PFAS chemicals, helping to destroy them. The research will test different plasma systems and water types to understand how PFAS break down. The goal is to create a reliable, large-scale method that can remove PFAS from water completely. This work could help solve a serious environmental problem and protect people’s health. It supports the National Science Foundation’s mission to advance science and improve life for everyone. This project investigates the mechanisms of PFAS degradation in complex aqueous matrices via non-equilibrium plasma. The central hypothesis is that non-oxidative plasma-induced mechanisms, driven by electron and photon interactions, yield transient hydrofluorocarbons that are subsequently oxidized by hydroxyl radicals to release fluoride ions. The research is organized into four tasks: optimizing plasma parameters to enhance electron and photon fluxes to the gas-liquid interface; developing analytical techniques for detecting PFAS and byproducts; redesigning reactors to improve efficiency; and applying machine learning to analyze data and guide optimization. This research will advance understanding of plasma-liquid interactions and support the development of a feedstock-agnostic plasma process capable of PFAS defluorination across diverse matrices. Broader impacts include K-12 STEM outreach, development of environmental case studies, and integration of research findings into interdisciplinary training programs. Stakeholder engagement will be promoted through public talks, workshops, and a data-sharing platform to support evidence-based policy. This project is supported by 1) Process Systems, Reaction Engineering and Molecular Thermodynamics program, 2) Environmental Engineering program and 3) the Environmental Sustainability program in response to the Dear Colleague Letter 24-130, as part of the ECosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) interdisciplinary program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project addresses critical languages for research and technology development. By leveraging advanced speech technologies, this initiative bridges the gap between linguistic research and practical application. The project develops tools including automatic speech recognition and text-to-speech systems and creates educational resources for language learners and researchers. Through collaboration with communities, the project ensures that these technologies are innovative, effective, and appropriate. Other benefits to society include a demonstration of the transformative potential of combining technology with language research. This project addresses the challenge of limited linguistic resources, which hinders the development of effective natural language processing tools and algorithms. By fine-tuning pre-existing speech technologies and applying transfer learning, the project creates accurate and scalable solutions for language documentation and analysis. Key activities include the development of benchmark datasets, the creation of comprehensive lexica, and the implementation of advanced data processing techniques such as tokenization, lemmatization, and part-of-speech tagging. These resources are made publicly available, enabling broader research and educational use. Through its interdisciplinary approach, combining computational linguistics with language documentation, the project advances linguistic research and promotes digital literacy. 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 current generation of ice-sheet models underpredicts observed rates of mass loss of the Greenland Ice Sheet, in part because these models are inherently incomplete representations of complex systems. This work aims to reduce this bias by increasing the complexity of glacier processes that are represented in the next generation of ice-sheet models. Addition and refinement of specific glacier processes to ice-sheet models will improve the accuracy of future sea-level projections, helping world communities plan for future infrastructure and societal consequences of sea-level change. However, adding specific glacier processes to ice-sheet models raises the computational demand of the already severely resource-constrained operation of those models. This project addresses this obstacle by using artificial intelligence (AI) techniques to produce high-efficiency representations of complex glacier processes that slot into ice-sheet models without substantially increasing their computational costs. The project condenses three specific glacier processes into efficient modules, produces datasets through AI-based analyses of remote-sensing products to fine-tune and evaluate those modules, and evaluates their efficacy in improving ice-sheet model predictions. The modules can be readily applied within any of the 15 plus active ice-sheet models. These outcomes are expected to improve the accuracy of forecasts of future sea-level rise. The project facilitates critical quantitative thinking about future worlds by the next generation of students to prepare them to enter the workforce. It accomplishes this through extensive contact with maturing thinkers in high schools and universities through research–education integration across Earth sciences and AI, emphasizing crucial transferrable skills across science, technology, engineering, and math (STEM). Although the glaciology community has a robust understanding of sub-grid-scale glacier processes, such as crevassing and the development of glacier hydrologic systems, there are computational barriers for their inclusion into ice-sheet models. Notably, ice-sheet models typically use a resolution on the scale of kilometers, which is considerably coarser than required to resolve, for example, the formation and evolution of crevasse fields that collect and drain surface meltwater. This work uses parameterization, deep learning, and AI to represent key glacier processes at a level for incorporation into the next generation of ice-sheet models. This work connects three specific processes – crevassing, structural glaciology, and englacial and subglacial hydrology – across the separate realms of process-scale development and large-scale ice-sheet modeling. The connection of these glacier processes into ice-sheet models ensures that resource investments into ice-sheet modeling will deliver on the promise to improve sea-level forecasts. This research capitalizes on existing process-scale models, remote sensing products, and deep-learning techniques to synthesize specific glacier processes into the ice-sheet models that make sea-level projections. This project connects deep learning and AI in Earth science to high schools and develops the STEM workforce by training university students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With the support of the Chemical Mechanism, Function, and Properties (CMFP) Program and the Chemical Catalysis Program in the Division of Chemistry, Professor Bing Gong of the State University of New York at Buffalo will be studying the function, properties, and catalytic behavior of short polyamides having spirally folded helical conformations. These slinky-like folding molecular chains, known as porous foldamers or “hollow helices”, feature an electrostatically negative, non-collapsible inner void (with a sub-nanometer diameter). These foldamers are expected to function as synthetic enzymes by accelerating chemical transformations by overcoming the energy barriers of targeted reactions within a defined pocket, similar to natural enzymes. The synthetic tunability of the hollow helices offers an adaptable structural platform for the development of enzyme-like catalysts with progressively enhanced efficiency and specificity. Broader impacts include fundamental knowledge, new catalysts, and workforce development through rigorous student training. The non-deformable inner pores of the aromatic oligoamides are strongly hydrogen-bonding and highly electronegative due to the presence of multiple inwardly oriented amide oxygen atoms. Cationic guest molecules exhibit high affinity binding to the inner pores of the hollow helices at up to 10e15/M, rivaling the tightest guest-binding systems observed in nature. This research aims to deepen our understanding of how hollow helices, with their exceptional ability to bind cationic species, can function as synthetic enzymes. By stabilizing the cationic transition states of targeted reactions, the helical foldamers are expected to facilitate the formation of acetals from aldehydes and ketones. The team will be looking at basic aspects of catalysis expected of the hollow helices. Catalytic efficiency will be tuned and enhanced by adjusting both the helical foldamers and the aldehyde/ketone substrates, along with the investigation and understanding of factors behind the observed changes in catalytic behavior. Another objective is to form mixed acetals—compounds with two distinct alkoxy groups—which are present in various medically significant natural products but continue to pose a considerable synthetic challenge. In the later stages of this project, efforts will made to develop hollow helices with biased (left or right) handedness, enabling the asymmetric synthesis of chiral mixed acetals. This project will combine the concepts and studies of molecular design, host-guest interaction, and enzyme kinetics to provide research training opportunities for undergraduate and graduate students. The research results will be publicized broadly in the scientific community and incorporated into undergraduate and graduate teaching. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This IRES project advances documentary and theoretical linguistics through the study of Creole languages, combining field-based data collection with computational and statistical methods. Each year, six U.S. undergraduate and graduate students conduct five-week fieldwork in Creole-speaking countries (Guadeloupe, Martinique, Haiti, Mauritius, and Seychelles), employing cutting-edge techniques in language documentation, experimental design, and quantitative analysis. Working with international partners, participants investigate structural properties across phonetics, morphology, and syntax while examining sociolinguistic patterns of use, creating robust datasets for theoretical analysis. The program emphasizes open science principles, with results contributing to comparative linguistic research and publicly accessible digital archives of Creole language resources. By training students in both traditional documentary methods and modern computational approaches, the project bridges disciplinary gaps in linguistic research while promoting U.S. student engagement. The international team of mentors guides students in developing statistically rigorous research protocols that address both theoretical questions and community-identified priorities. Outcomes include advancements in contact language theory, enhanced research capacity for participating institutions, and sustainable resources for Creole-speaking communities. Through conferences, workshops, and digital platforms, the project shares findings with both academic audiences and the public, fostering broader understanding of linguistic pluralism. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project investigates a new approach to quantum photonics by leveraging specialty optical fibers, originally designed for telecommunication, as compact platforms for advanced quantum information encoding. By exploring these fibers at visible wavelengths, where they behave differently than their design specifications, the project opens new possibilities for encoding quantum information using both spatial and particle properties of light. This work addresses the national interest by advancing foundational science in quantum technologies which are key areas for future secure communication, precision measurement, and quantum computing. In addition to scientific advancement, the project fosters interdisciplinary education, providing hands-on training for students in quantum optics, integrated photonics, and machine learning-based data analysis. Public outreach activities, in collaboration with the Buffalo Amateur Astronomy Association, will further broaden public understanding of quantum technologies. Technical Project Description The proposed research aims to explore the use of interacting ring-shaped multicore optical fibers (MCFs) at visible wavelengths to develop new hybrid quantum encoding schemes. Operating these telecom-grade fibers outside their design parameters at visible wavelengths enables the investigation of spatial mode structures for quantum light which is an area largely unexplored. By integrating MCFs with quantum light sources and photon-counting detectors, the project will demonstrate encoding of quantum information in high-dimensional Hilbert spaces, essential for scalable quantum communication and computing. The research involves simulations of quantum dynamics in MCFs, experimental validation of hybrid qudit-based encoding, and real-time machine learning-based photon classification. Additionally, the project will study inter-core coupling dynamics to realize quantum random walks and Boson Sampling in a fiber-based platform. These experiments aim to demonstrate new architectures for near-term quantum advantage. This exploratory effort will lay the groundwork for scalable, low-loss, fiber-integrated quantum devices, representing a paradigm shift in quantum photonic integration. 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

Excess nutrient such as nitrogen and phosphorus can cause toxic harmful algal blooms. These harmful algal blooms cause significant economic loss and public health risks across the nation. New methods to reduce nutrient pollution include using algae in wastewater treatment plants to reduce nitrogen and phosphorus before it is released to the environment. The goal of this research is to understand the types of nutrients that make algae more toxic in the environment. This goal will be achieved through experiments that grow algae on wastewater and examine different wastewater nutrient sources that cause environmental pollution. This work will be performed in collaboration with wastewater utilities as well as community groups that monitor and report harmful algal blooms. This research can be used to improve our wastewater treatment and reduce nitrogen and phosphorus pollution. These results can further inform public policy on the types of nutrients that are most important in causing toxic algal blooms. Eutrophication and nutrient management for point and non-point source pollution remain a critical challenge. While efforts to reduce eutrophication most often focus on limiting phosphorus discharges to the environment, nitrogen is an important driver in algal bloom growth and toxicity. The overall goal of this research is to improve the total nutrient recovery from algal wastewater treatment processes and to understand the role of organic and inorganic nitrogen in harmful algal bloom growth and toxicity. Specific research objectives are to (i) identify environmental and operational factors that improve total nitrogen and phosphorus removal from wastewater treatment using algae, (ii) characterize organic nitrogen effluents from both algal and traditional wastewater processes and to evaluate their potential for algal growth and toxicity, and (iii) determine the effects of organic nitrogen composition and nitrogen to phosphorus ratios on harmful algal bloom formation and dynamics. These objectives will be achieved by integrating process design with controlled lab-scale experimentation and advanced molecular and analytical chemistry methods to understand the mechanisms for nutrient conversion and recovery. This work will help advance nutrient recovery from algal wastewater systems and provide fundamental insights into the role of nitrogen in driving algal bloom dynamics and toxicity. Research will be performed in collaboration with local wastewater utilities and community groups to disseminate knowledge and inform stakeholders while broadly increasing public scientific literacy. Specific educational tasks and activities are designed to engage and excite students to improve the Nation’s STEM workforce. These include (i) the development of educational materials for community groups, (ii) an education module and workshop for high school students, and (iii) the creation of a resource-positive curriculum centered around sustainability and the environment. 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

For decades, improving computers and processing information simply relied on putting more transistor in a single microchip. However, with an explosive growth of information and communication technology, involving artificial intelligence, high-performance computing, and big data, this approach faces fundamental obstacles. Modern computers rely on so-called multicore architecture with a complex network of interconnects, establishing communication between different computer parts, including their logic and memory, for processing and storing information. The key concern in computers is their power consumption, dominated not by the transistors and information processing, but rather by interconnects and information transfer. In a simple analogy, transistors can be viewed as cars and interconnects as highways. Replacing an older car with a luxury vehicle in a traffic jam will not make much difference. Instead, it is crucial to design better (information) highways. Lasers and optical interconnects are closely intertwined, with lasers acting as the light source for high-speed data transmission and communication (interconnects) in fiber optics and other systems. They enable faster and more efficient data transfer in areas like data centers and other applications needing high bandwidth. Thus, this transformative research seeks to develop new principles for their ultrafast and energy-efficient operation. Specifically, instead of conventional lasers which transfer information by the corresponding changes in the intensity of the emitted light, this research proposes to take advantage of the much faster changes in the polarization of the emitted light. With the preliminary experimental demonstration of this principle, completed by the recent breakthrough that the polarization of the emitted light is electrically controlled at room temperature in light-emitting diodes, the proposed research envisions superior performance by seamless integration of processing, transferring, and storing information. Controlling the intensity of emitted light and charge current is the basis of transferring and processing information. In contrast, robust information storage and magnetic random-access memories are implemented using the electrons’ spins and the associated magnetization in ferromagnets. An unequal number of spins along or against the magnetization axis represents the binary information “0” and “1” which, as the magnet attached to the fridge door, can be preserved without external power. While commercial spintronic devices rely on the change of the resistance with the spin orientation--magnetoresistance, by taking electrons out of ferromagnets the spin information is quickly lost (within nanosecond) and cannot travel far (typically, up to a micrometer). However, since the light also has spin through its circular polarization or helicity, the spin information transferred from electron to light, could be carried much faster and farther. The proposed research builds on the breakthrough of switching the magnetization of a ferromagnet in light-emitting diodes (LEDs) at room temperature and no applied magnetic field. Through the conservation of the total angular momentum, switching the magnetization of the ferromagnet reverses the orientation of the injected spin and the corresponding helicity or handedness (left vs right) of the emitted light. This principle is used to predict the operation of spin-controlled LEDs and lasers, with potentially transformative implications for processing, transferring, and storing information. The device modeling will combine first-principles studies, generalized rate-equation description, and microscopic optical gain calculations. This effort will be complemented by implementation of these devices, pursued by the experimental collaborators and using commercial materials for magnetic memories. The proposed research will be supported by closely integrated educational and outreach efforts, as well as by developing resources for newcomers interested in spintronic devices beyond magnetoresistance. 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

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Jochen Autschbach of the University at Buffalo, State University of New York, is developing and applying quantum theoretical methods and computer simulations to investigate how the three-dimensional arrangement of atoms, their chemical bonding, and their motion in solutions determine a variety of properties of molecules and molecular assemblies. Properties that will be studied include nuclear magnetic resonance (NMR) parameters and natural optical activity, with potential applications in catalysis and in the design of new light-emitting materials. The accurate prediction of these properties, and discerning their exact role in targeted applications remain a key challenge in computational chemistry. Professor Autschbach and his students will investigate the optical activity of novel helicene-based systems, open-shell states of organic molecules with unpaired electrons, including helicenes with radical substituents, and how dynamics in solutions influences NMR spin relaxation. The project provides training for graduate and undergraduate students and create opportunities for summer internships of high-school students in scientific computer programming. Professor Autschbach will also create free-to-use educational scientific visualizations and open-source computational research tools. The project focuses on molecular properties that are of high practical value in informing about the structures and functions of molecules, and the dynamics of chemical systems. The theory developments and simulations will be crucial to establish and refine the sought-after structure-property relationships. The project will focus specifically on NMR relaxation phenomena driven by the underlying dynamics of molecules, and on the structural and electronic origins of optical activity. In addition, the behavior of organic radicals with an unusual inversion of the highest occupied level of molecular orbitals and the energy of the singly occupied orbital will be studied by calculations, complemented by experiments that are being carried out in the laboratories of Professor Autschbach’s scientific collaborators. NMR relaxation contains a wealth of information about the dynamics and the characteristic dynamic time scales of a chemical system. Relaxation will be predicted by ab-initio and force-field molecular dynamics simulations. The optical activity-related part of the project focuses on circularly polarized luminescence, and on the properties of metal complexes with ligands that combine N-heterocyclic carbene (NHC) and helicene moieties. 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

This project addresses the critical issue of determining the best way to incentivize electricity producers to build sufficient power plant capacity to keep the electrical grid resilient, reliable and sustainable, all this while satisfying consumers. The goal is to provide regulators with tools to promote beneficial portfolios of battery storage and electricity production technologies, while also accounting for risks, transmission limitations and unpredictable events. Other outcomes of this project are expected to include development of new classes on Electricity Markets, support of new graduate students working in this area, and industrial collaborations. A recurring question in electric power markets has been: are spot prices sufficient to incentivize enough investment in generation capacity, storage, and demand-side management in order to ensure reliable and sustainable operating conditions? In other words, are spot prices enough by themselves to stimulate efficient mixes of electricity supply and storage that ensure that the resulting grid is resilient, reliable and sustainable? This question will be addressed by a new framework that will incorporate realistic market features and explore their implications for short- and long-run equilibria, optimal capacity prices, and net benefits of market designs. These features include battery storage and variable wind and solar resources, as well as risk-aversion, incomplete markets, transmission congestion, optimal demand management, and other features. The new framework will use a unique set of analytical and computational methods to solve for Nash equilibria in market models that are unique in their combining representations of stochastic demand processes, market participant risk attitudes, and simultaneous consideration of the role of regulators, market operators, suppliers, and consumers. The proposed framework can be simplified to have tractable dynamics, and also is general enough to incorporate all the desired features of real markets, including battery storage, variable renewables, transmission limits, evolution of capacities, and optimal demand management. The goal will then be to find Nash equilibria among the supply capacities of producers, together with the regulator's optimal capacity payments that maximize net market benefits. The aim is to compare energy-only and capacity markets, find the combination of capacity payments and price caps that maximize the reliability and net benefits of the market. In the near future, the demands upon the grid are expected to dramatically increase, as more applications switch from fossil-fuels to electricity (e.g., electric vehicles), and new uses appear (e.g., AI, bitcoin mining). Therefore, it is vital to study how to keep supply reliable and affordable, and desired generation facilities financially sustainable (in a Nash equilibrium sense), while achieving social goals of renewable power by providing incentives to the producers to "do the right thing." 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

Laura Motta of the Woods Hole Oceanographic Institution and Jochen Autschbach of the University at Buffalo are supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop and implement a theoretical method to explore new types of spin-forbidden chemical reactions. The team will implement quantum-theoretical calculations of chemical reactions that would not occur without a magnetic interaction between the spins of the electrons and the spin of the nucleus of a heavy element in these molecules. This magnetic interaction is called hyperfine coupling (HFC). The resulting theoretical method developments will be used to investigate the magnetic isotope effect (MIE) in reactions of small molecules containing mercury. Phenomena such as the “magnetic compass” in migratory birds may also be facilitated by such processes, and therefore the research has the potential to solve long-standing scientific mysteries. A greater understanding of spin-forbidden 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

This I-Corps project is focused on the development of an innovative. non-invasive, diagnostic tool for viral infections. The technology is able to provide rapid, accurate, and real-time detection of influenza, respiratory syncytial virus, and COVID-19. The tool's portability ensures that it can be widely distributed, making it accessible in a variety of healthcare settings, including clinics, hospitals, and in remote areas with limited medical infrastructure. By enabling early and precise diagnosis, this tool can improve patient outcomes, reduce the spread of infectious diseases, and alleviate the burden on healthcare systems. Furthermore, its scalability and low manufacturing costs position it as a viable option for mass production and global distribution, addressing urgent public health needs. 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 method to determine multiple pathological conditions simultaneously. The solution detects the infrared signatures produced by the resonant excitation of certain molecules using a tunable source. This approach achieves a limit of detection that is orders of magnitude higher than available nanosensors. By applying machine learning techniques to analyze the nanomechanical infrared response profile, multiple pathological conditions can be identified simultaneously. This device is capable of continuous miniaturization, making it portable and affordable for widespread 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 · 2025-03

Understanding how adversaries make decisions is essential for enhancing security and protecting public spaces. This project develops new tools to help security professionals allocate resources more effectively, minimizing vulnerabilities at soft targets such as schools, public venues, and places of worship. By combining insights from human-subject experiments and surveys with advanced mathematical modeling, this research provides practical strategies for making better defensive decisions. Unlike traditional, theoretical approaches, which assume adversaries act purely rationally, this project incorporates data from human decision-making experiments and surveys to create more realistic and actionable models. The outcomes of this research extend beyond improving security as they also offer valuable insights into adversarial behavior that can inform fields like marketing and risk management. Additionally, the project supports education by involving students in cutting-edge research and developing tools that make complex mathematical models accessible to non-experts. By advancing our understanding of decision-making and optimizing resource allocation, this project promotes national safety and prosperity. This project develops a novel framework for studying adversarial decision-making by combining game-theoretic modeling with data from human-subject experiments and surveys. The primary objective is to model an attacker’s best response function based on actual decisions made in attacker-defender scenarios, rather than assuming purely rational and theoretical strategies. The research investigates key variables—reward, cost, and probability of success—and their influence on adversarial behavior. Using this data, the project derives closed-form Nash equilibria to optimize defensive resource allocation strategies across multi-layered security systems. The study incorporates mixed-effects regression and numerical optimization techniques to analyze decision-making processes and solve for equilibrium strategies. Additionally, by utilizing the research methodology of designing and conducting behavioral experiments that simulate attacker-defender games, collecting empirical data, and using advanced game-theoretic modeling, the project validates and refines prescriptive solutions displayed in Graphical User Interface toolkits for stakeholders. The results contribute to actionable resource allocation strategies while offering a scalable framework for analyzing adversarial behavior in diverse applications, including security and risk management. 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

Given the different and complementary strengths of various programming languages, it is well justified to combine multiple languages in developing a software system, which has become a norm in today's software practice. In fact, the vast majority (over 80%) of modern software systems are multi-language software. Meanwhile, software failures are known to be costly, and a common methodology for preventing these failures and reducing the cost is to reason about run-time behaviors of software (i.e., dynamic program reasoning). However, extant software-quality assurance support based on dynamic program reasoning still focuses on, and is limited to, software developed in single languages, largely dismissing what happens at and across language boundaries. As a result, cross-language behaviors are left unattended and holistic quality assurance of multi-language systems is critically lacking. This project aims to advance the state of knowledge about software-quality assurance by enabling practical dynamic program reasoning across language boundaries. The research will result in a new foundation of dynamic analysis and a series of application tools to diagnose cross-language correctness and security issues, which will help produce multi-language systems of improved quality. Moreover, this project will have an industrial impact realized through collaborations with and result disseminations via industrial partners, and will broaden the participation in computing of underrepresented minorities. The technical aims of the project are divided into three thrusts. First, multi-language code-construction mechanisms and their effects on the resulting software’s behavior and quality will be characterized. This will produce new knowledge about how modern multi-language software is constructed in terms of language selection and interoperation. Then, in the second thrust, practically scalable and cost-effective analyses for holistic cross-language dependence reasoning will be developed, as informed by the knowledge and insights gained from the characterization study. The key principle is to model and reason about cross-language code dependencies in a way that not only overcomes semantics disparities caused by language heterogeneity but also readily accommodates new/additional languages. The third thrust will build on the cross-language dependence reasoning to develop practically efficient and precise cross-language information flow reasoning, which will then provide immediate support for diagnosing program faults regarding functionality bugs and security vulnerabilities across language boundaries. This project will validate the generated new knowledge and techniques through extensive evaluation against real-world, complex multi-language systems in terms of scalability, cost-effectiveness, and capabilities in practical applications. 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

Global sea level rise and associated socioeconomic impacts will be one of the most significant challenges facing society this century. However, ice sheet models, used to predict sea level outcomes for policy making, remain under-constrained. Improvements in ice sheet modeling occur via testing simulations of ice change against known histories of ice sheets. These histories are derived from records of ice-sheet response to episodes of planetary warming in recent Earth history, which hinge on geological dating methods. The most widely applied method to accomplish this task is radiocarbon dating, where thousands of analyses (past and ongoing) are ever more precisely pinning down the timing and rates of past ice sheet changes. Radiocarbon dating, however, is an analytical method that is constantly undergoing improvements. Hence, legacy data require constant updating, and the discipline is challenged in making best use of this valuable and growing resource. To date, there is no centralized, live, maintained community resource available to maximize use of radiocarbon information. To make a leap in ice-sheet model improvement, the community must first be enabled with an easily accessible and dynamically updated source of radiocarbon data. This project will develop a transparent-middle-layer data management and analysis tool to enable synoptic applications of radiocarbon datasets for the development of accurate ice-sheet histories. The project team will work directly with the ice sheet science community to ensure community buy-in and utilization of the radiocarbon data management and analysis tools via in person community workshops and virtual tutorials, both associated with existing annual conferences, and those targeted at specific user bases. The proposed research tool seeks to sustain scientific innovation at Earth's poles and reaches across disciplinary boundaries of polar, oceanographic, and Earth science research. As such, the developed computational infrastructure is comprehensive and interoperable, and has potential to make significant impact in a broad array of disciplines. This work is guided by Findable, Accessible, Interoperable, and Reusable (FAIR) principles, with a key emphasis placed on working toward improved data accessibility, rescue, and re-use. This project will develop a transparent-middle-layer data management and analysis tool to enable synoptic applications of radiocarbon geochemistry, geochronology, paleoclimatology, and carbon-cycle research around Earth's remaining ice sheets. At present, geologic constraints on past ice sheet change derived from marine archives are scattered across decades of publications and static data repositories. The lack of cyberinfrastructure to simultaneously analyze and utilize past constraints from all environments, thus, leaves researchers to the laborious tasks of data rescue, compilation, and standardization at an individual level, ultimately limiting the research community's ability to carry out transformative research. The development of Radiocarbon Cyberinfrastructure (RAD-CI) seeks to improve scientists' ability to evaluate the changing role of the polar cryosphere in Earth's climate system, as it will offer a means by which geological constraints on past ice sheet change can be dynamically compiled, calculated, and utilized in data-model comparison efforts. RAD-CI answers the calls of the National Academies report on Future Directions for Southern Ocean and Antarctic Nearshore and Coastal Research (NASEM, 2023) and the Intergovernmental Panel on Climate Change Special Report on Ocean and Cryosphere in a Changing Climate (Meredith et al., 2019), which both urge the polar research community to employ geologic constraints on past ice sheet change to validate models that project future sea level rise. This project seeks to sustain scientific innovation at Earth's poles by catalyzing fundamental discovery of the role of existing ice sheets in Earth's changing climate system, which will help to develop tools and numerical modeling techniques to prepare, mitigate, and adapt to risks associated with climate change. This award by the Office of Advanced Cyberinfrastructure is supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

This project aims to investigate both the security and robustness of Collaborative Autonomous Driving (CoAD) to improve the safety and resiliency of connected and autonomous vehicles (CAVs). Despite being an emergent trend, CoAD systems, consisting of collaborative CAVs and Roadside Units (RSUs), are a new type of cyber-physical systems (CPS) that have received little attention in the research community, especially in terms of their security and resiliency. To conduct the proposed research, the proposers will build upon the team’s complementary expertise in a wide range of topics including vehicle security, Vehicle-to-Everything (V2X) security, adversarial attacks to the Artificial Intelligence (AI)-powered perception subsystem, formal methods and verification, robust control, and end-to-end evaluation. The project will take a systematic approach and develop a comprehensive framework when examining new attack vectors/surfaces in the CoAD systems, and propose novel mitigation and defense mechanisms. is an integrated effort by two PIs from the University at Buffalo (UB), and UC Irvine (UCI) from the US side, and two PIs from the Indian Institute of Technologies (IIT) at Kharagpur (IIT-KGP) and Jodphur (IIT-J) from the India side. The project is expected to result in joint publications as a part of dissemination efforts, joint mentoring of students by the US and India PIs, and new datasets, as well as increased public awareness of cyber-security threats and trust in the resilience of autonomous driving. In addition, new course and publicly available materials based on research results will be developed to attract and train students, including under underrepresented minority students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

One-dimensional nanostructures, such as nanorods, nanowires, and their assemblies, have numerous applications in surface modification for advanced mechanical, optical, or bioactive functions. However, established techniques for manufacturing these nanomaterials and their deposition as coatings are limited to small scales and often flat surfaces due to cost, throughput, or complexity. This grant supports fundamental research to provide needed knowledge for manufacturing multifunctional nanomaterial and composite conformal coatings at scale. The development of new manufacturing processes based on electrostatically-induced sprays of waterborne gelling polymers enables the mass production of nanowire coatings on versatile surfaces with complex shapes and three-dimensional features, which can be widely used in energy storage, tissue engineering, smart textiles, and water/air filtration. Simultaneously, the gained understanding can be translated into additional materials systems and new applications, which benefit the U.S. economy and prosperity. The broader impacts activities contribute to the research education pipeline and workforce development to secure U.S. global leadership in materials manufacturing by training students and research fellows at various levels and engaging with the broader scientific community and the general public through outreach. Electrospray deposition has shown great promise for manufacturing polymeric nanostructures. Typical morphologies of electrospray deposits are hierarchical assemblies of nanoparticles and overlaid in-plane nanofiber mats if droplet breakup is suppressed (i.e., electrospinning). The goal of this project is to enable electrospray deposition of aqueous methylcellulose solutions to produce polymer and polymer-nanoparticle composite nanowires with well-controlled dimensions on a drop-by-drop basis. This research fills the knowledge gap on the interplay between self-assembly and morphology development in multiphase droplets generated in the sprays. The research team integrates advanced experimental techniques, including X-ray characterization, microscopy, and laser strobe imaging, with mesoscale multiphysical modeling to determine the physical mechanisms of dropwise nanowire formation and deposition. New dissipative particle dynamics simulations elucidate the self-assembly dynamics of methylcellulose in nonequilibrium conditions and predict the electrohydrodynamic deformation of composite droplets. The effects of particle-polymer interaction, particle entropy, and spatial confinement are explored for controlling filler distribution and functional properties of the composite wires. The team extends the study to other materials to demonstrate the generality of nanowire formation in electrospray by rapid, homogeneous viscosity transition in the droplets. Through collaborative experimental and modeling efforts, this research advances the understanding of the electrospray of complex fluids and provides a foundation for scalable manufacturing of nanowire composites and their superstructures. 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

Minority languages across the world are endangered. One of the main reasons for language endangerment concerns the decrease of elderly population who still speak an endangered language fluently. The way elders speak is different from what younger generations speak. This difference is observed in the use of their verbs that are more complex. Verbs of elders exhibit a wide variety of semantic nuances that can be best interpreted in the cultural context where an endangered language is spoken. Furthermore, elders preserve a valuable knowledge of their language tied to their culture. However, the decrease of the elderly population and the lack of documentation and preservation of such languages across the globe is leading to a significant linguistic and cultural loss. This research project documents a morphologically complex endangered language spoken by elders with a unique linguistic background. The elders are fluent in two morphologically complex endangered languages. The goal is to document naturalistic use of one of the endangered languages used by elders and create a digital corpus. This project collects data on directional systems expressed through verbal morphology. These types of studies describing aspects of spatial communication and cognition constitute a big gap in the literature cross-linguistically. Likewise, contact between two morphologically complex languages remains an understudied area in the language sciences. This project facilitates new ways of studying language contact. Additionally, this project’s broader impacts involve training a pioneering group of speakers of endangered languages and producing a set of community-oriented materials. 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 is tied to the idea that reflection is fundamental to the educational process but recognizes that our understanding of effective forms of reflection is lacking. This is particularly true in engineering education, where students face a rigorous curriculum that requires more credit hours than any other undergraduate degree, leaving little time and space for students to reflect. Engineering education literature suggests a community belief that reflection can support engineering students’ learning in ways that contribute to their professional development. However, use of reflective interventions in the academe are disparate and it is evident that reflection is not meeting this potential for most students. This project will advance our understanding of how people become engineers, and through that understanding, lead to the development and implementation of reflection interventions that will support students in their engineering education journey. The reflection interventions developed as part of the research and design and development strands of the project have potential for immediate benefit to support undergraduate engineers at multiple institutions. These benefits will be realized in both curricular and co-curricular learning experiences. Additionally, results of the research will support institutions in the evaluation of their engineering education programs, leading to insights that support program reform. This project is motivated to understand: How can reflection support student engineers in the production of a professional engineering identity (PEI)? This project will investigate three questions as part of foundational, exploratory, and design and development research activities: RQ1) How do early career engineers story their understanding of what it means to be an engineer at different points along the student to practitioner trajectory?; RQ2) What are the most predominant factors of early career engineers’ stories that might support a conceptual model of PEI reflection?; and RQ3) How do undergraduate engineers story their understanding of what it means to be an engineer at different points along the student to practitioner trajectory, and how does that differ from early career engineers? To answer RQ1, narrative inquiry will be used to compile PEI development narratives of 30 early career engineers. Cross- case analysis of those narratives will be used to identify the most salient PEI reflection themes among these practitioners. To answer RQ2, a survey will be developed and implemented to scale findings beyond the small group of early career engineers. The survey will be deployed nationally with a minimum of 300 early career engineers. The objective is to explore the generalizability of PEI reflection themes toward developing a valid conceptual model of PEI reflection constructs. To answer RQ3, a longitudinal case study research methodology will be used to capture the PEI development narratives of 60 undergraduate engineers through reflection interventions and a repeated survey. The reflection activities will be analyzed to identify salient PEI reflection themes among students. Key outcomes of this project are expected to include: (1) a conceptual model of narrative constructs that are fundamental to development of a professional engineering identity; (2) construct validation of narrative constructs that might underly a PEI reflection framework; (3) a valid survey instrument that can be used by institutions of higher education as part of program evaluation; (4) biographies of early career engineers that engender the narrative constructs; (5) biographies of student engineers that engender these constructs as longitudinal, in-progress narratives; (6) a research methodology rooted in reflection capable of supporting additional research in engineering and beyond, (7) reflection activity development workshops with faculty from multiple institutions; and (8) reflection interventions and integration plans that will be archived as a publicly accessible resource. 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

This EArly-concept Grants for Exploratory Research (EAGER) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Software applications and workloads, especially within the domains of High-Performance Computing (HPC), Cloud computing, and large-scale Artificial Intelligence (AI) model training, exert considerable demand on computing resources, thus contributing significantly to the overall carbon footprint. Currently, the carbon emissions attributed to the software industry rival those of the aviation sector, and this trend is projected to escalate further by 2030. This project targets the creation of sustainable and environmentally friendly software by identifying and selectively restructuring energy-draining code segments (known as code smells) and addressing coding inefficiencies. This innovative approach has the potential to yield substantial savings in energy consumption, reduce carbon emissions, and make a significant contribution to the environment. The project encompasses a comprehensive education and outreach program, featuring science projects for K-12 students, the development of new undergraduate and graduate-level courses, mentoring of minority and underrepresented students, and the dissemination of the project outcomes to the wider society. This project facilitates sustainable and low-carbon software development through three significant contributions: (1) a comprehensive analysis of code smells and investigation of the impact of their refactoring on application energy consumption and carbon footprint; (2) development of novel machine-learning models tailored to intelligently and judiciously guide code smell refactoring while prioritizing energy efficiency; and (3) application of the developed models across a diverse spectrum of HPC, Cloud and AI workloads, enabling robust validation and comprehensive evaluation. The research outcomes of this project will revolutionize energy optimization in software systems by offering a holistic framework that addresses the complex challenges of code smell refactoring and energy consumption. By integrating sophisticated machine learning models and a rigorous validation process, the project will pave the way for sustainable and low-carbon software development practices, benefiting both the software industry and the environment on a broader scale. 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

Over the past two decades researchers have made significant progress in developing mathematical models and tools that are compatible with understanding the complexity of the human brain and similarly complex systems. These tools have been used to investigate how complex neural interactions underlie dynamic patterns found in processes like learning, memory formation and cognition. However, many questions on both healthy and pathological brain function remain intractable by existing mathematical approaches. That is because the system's components interact both spatially and temporally. Hence, in order to model and understand differences between healthy and pathological function in a neural circuit, one needs to simultaneously keep track of connectivity architecture in a massive network, and of its past activity. This poses a significant challenge for both analytical and computational approaches. This project aims to establish and use a tractable quantitative framework that considers both of these aspects, by employing networks of coupled equations that include time delays to capture how recent interactions between the elements of the system influence future interactions. A traditional model of neural population dynamics will be used as the building block for larger functional brain circuits, while additionally incorporating different types of time-delays. This will enable a well-studied framework to be embedded into a new mathematical environment that jointly considers the system's architecture and history. Preliminary joint work (on toy network models with selected types of time delays) has established in principle that this approach is computationally tractable, and that it can be used to contextualize transitions between healthy brain function and pathological patterns (such as those found in Parkinson's disease and emotional disorders). The project will support a vertically integrated team including a postdoctoral fellow, a co-advised Ph.D. student and five undergraduate students at a predominantly undergraduate institution, recruited from underrepresented groups. The collaboration capitalizes on the team's combined expertise in network science, delay differential equations and brain imaging techniques. The team will combine new network techniques with novel approaches to distributed time delays. The theoretical methods will be integrated with human brain data for potential clinical applications, via the collaboration with the Neurology Department at University at Buffalo. The approach will encompass three aspects that will develop simultaneously and support each other. (1) General networks under minimal assumptions on network architecture and shape of the delay kernels will be developed. (2) Numerical simulations will be used to demonstrate dynamic behaviors in specific classes of complex networks and for structured or stochastic distributions of delay kernels across the network nodes. (3) The new mathematical framework and numerical algorithms will be used to investigate how timing impacts information propagation in neural circuits that govern specific behaviors, in both computational models and in empirical data. The methods developed in this project will thus help us understand physiological mechanisms behind imaging and behavioral observations and help identify the underpinnings of pathological behaviors in neurological and psychiatric illnesses. 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

Quantum computing is an emerging computing paradigm that will revolutionize the computing and information technology with its capabilities far exceeding those of the current “conventional” computers. One fundamental bottleneck for quantum computing, however, is the small number of quantum bits (qubit) a single quantum processing unit (QPU) can hold. Despite decades of research and development, this number is limited at a few thousand qubits at most. This poses a serious obstacle to many important quantum applications (e.g., quantum machine learning, chemistry and medicine) which require tens of thousands or even millions of qubits. This project proposes to interconnect tens to hundreds of QPUs, each with limited numbers of qubits, to form a scalable quantum cluster where a virtual quantum machine (VQM) with far more qubits than a single QPU can be created. This research will investigate the fundamental qubit entanglement and mapping algorithms, optimal physical interconnections for the VQMs, and develop a simulator to evaluate the VQM designs. The project will provide a comprehensive design solution that establishes the algorithmic foundation for building efficient and scalable quantum computing systems, and facilitating the creation of future practical quantum computing applications that require large numbers of qubits. The project will also train graduate students and promote the participation of female students in quantum computing. The results will have a profound impact on the scientific and economic improvement of societies owing to the great potential of scalable quantum computing to solving many critical problems our and future generations face, from artificial intelligence, climate change, drug development, to cleaner fertilization, traffic and transportation management, logistics and manufacturing. 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

Computational tools have gain popularity across engineering curricula in the past decade. Computational notebooks, such as Jupyter, Google Colab, and Matlab Live, allow a dynamic interaction between instructors and students with immediate feedback on the concepts at hand. From programming to data visualization, computational notebooks engage students in complex engineering problems in a hands-on fashion. The design of such notebooks is still in its early stages in the context of undergraduate engineering courses. Most notebooks are adapted on a trial-and-error basis from instructor experience and student feedback. The engineering education community has been intentional in promoting the use of such notebooks in undergraduate classrooms with tutorials and templates for instructors that can be customized for the concepts of interest in their class. Nonetheless, there is very limited literature on evidence-based design and empirical evaluation of computational notebooks, and in the context of engineering curricula that objectively identify computational notebooks as better scaffolds for undergraduate learning. This project aims to provide research evidence to answer this question examining how students learn and as part of the mentoring plan for the lead researcher in formal engineering education research. The specific deliverables are in the context of undergraduate education in chemical engineering, yet the expected outcomes can be extended to other engineering disciplines. This project will provide objective analysis of computational notebooks as tools to engage and facilitate learning of statistics for undergraduate students in engineering. Data collected during the project will serve to inform the design principles of computational notebooks and produce an evaluation rubric for the notebook design in terms of learning outcomes, student interaction and performance. As a direct result of the proposed research, there will be measurable and objective improvements to an undergraduate statistics course in chemical engineering. The cognitive apprenticeship model will serve to quantify the effectiveness of the computational notebooks considering student performance and their actual learning process through questionnaires, in class interactions and observations, an online discussion board, and class assignments and examinations. The same model will be employed by the lead mentors to evaluate and adapt the training of the lead researcher. The following research questions will guide experiment design, data collection tools, and analysis in this project: (1) Does systematic scaffolding of computational notebooks for threshold concepts in undergraduate statistics facilitate student learning? (2) What research-based elements constitute effective design of computational notebooks to reinforce student learning? Upon completion of this project, the research team will contribute to the engineering community systematic, evidence-based computational notebook design guidelines to facilitate student learning in engineering. The research outcomes align with the mission of the RIEF program and the project deliverables will benefit the communities of educators and researchers affiliated with the American Society for Engineering Education, the American Institute for Chemical Engineers Education Division, and Computer Aids for Chemical Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.