SUNY at Albany

NSF Awards · FY 2026 · 2026-10

Cybersecurity threats increasingly disrupt critical services such as healthcare, energy, and financial systems, affecting everyday life and national stability. These threats are no longer isolated technical problems; they involve complex interactions among digital systems, human decisions, and organizational responses. This project addresses these challenges by advancing a unified approach to understanding and improving how systems detect, respond to, and recover from cyber incidents. The project's novelties are the integration of intelligent deception techniques, automated system recovery, and human-centered decision analysis into a single, coordinated framework. The project's broader significance and importance are in strengthening the resilience of essential infrastructure, supporting national security, and expanding opportunities for undergraduate students to participate in advanced research. As a Research Experiences for Undergraduates (REU) Site hosted at the University at Albany, State University of New York, the project combines education with hands-on discovery, contributing to workforce development and promoting scientific progress that benefits society at large. The project develops an interconnected set of research activities that examine cybersecurity from multiple perspectives. It creates adaptive digital environments that interact with attackers and collect detailed behavioral data, enabling deeper understanding of evolving threats. It constructs realistic simulated attack scenarios for industrial control systems to generate high-quality datasets for experimentation and evaluation. It incorporates methods to ensure that artificial intelligence systems used in defense are reliable, transparent, and resistant to misuse. In addition, it analyzes how decisions made by organizations and leaders influence the outcomes of cyber incidents, linking technical events with broader social and strategic factors.These activities are integrated through complementary research components and consistent evaluation frameworks across the project. The project advances knowledge of how intelligent and adaptive defense systems improve resilience, while producing open datasets, reusable tools, and trained participants who contribute to a stronger and more secure digital infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project develops robust and efficient computational tools to predict how heat and fluids move through complex materials containing many small holes or pores. Such materials appear in important technologies, including water and air filtration systems, battery cooling devices, heat exchangers, and advanced manufactured materials. Accurately simulating these systems is challenging because their microscopic structures strongly influence large-scale behavior, often making conventional simulations prohibitively expensive. By enabling fast and reliable predictions without resolving every microscopic detail, the project aims to significantly reduce computational cost while maintaining accuracy. The resulting advances could improve the design of energy-efficient technologies, industrial processes, and engineered materials. The project also integrates undergraduate education by engaging students in hands-on research in scientific computing and data science, helping to train the next generation of scientists and engineers in modern computational and data-driven methods. The project focuses on developing a non-intrusive computational framework to approximate macroscopic solutions to multiscale heat transfer and fluid flow problems in perforated domains. Building on the Derivative-Free Loss Method, the approach combines a stochastic formulation based on particle trajectories with flexible function representations to capture large-scale behavior without resolving fine-scale geometry. The research extends this framework to time-dependent problems, where a central challenge lies in understanding the interaction between stochastic sampling, time discretization, and physical dynamics. The work will establish theoretical foundations for this coupling and design efficient sampling strategies that improve stability and accuracy across diverse geometries and boundary conditions. In addition, the project will develop a neural-network-free implementation to reduce computational overhead and improve scalability. Applications include the homogenization of transient heat transfer and large-scale fluid simulations in perforated materials, providing both methodological advances and validation benchmarks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Nontechnical Description: One of the central principles of quantum mechanics is wave-particle duality: quantum objects can behave both as particles and as waves. In photonic systems, this dual nature of light enables two fundamentally different ways of encoding quantum information. Information can be carried either by individual photons, corresponding to discrete quantum units, or by continuous optical fields that behave like waves. Both approaches are actively explored for the development of fault-tolerant quantum technologies, and each offers distinct advantages but also important limitations. This project explores a hybrid approach that combines these two representations using integrated silicon photonic circuits. The research will use atomic-scale defects in silicon, known as color centers, to control and prepare coherent states of light. These states behave as classical optical fields and are more robust to loss and imperfections than many fragile quantum states. By enabling new ways to generate and measure quantum states, this work could advance technologies for secure communication, precision sensing, and future quantum information systems. Quantum photonics is one of the few platforms that allows quantum phenomena to be observed at room temperature using compact chip-scale devices. The project will also develop the Quantum Photonics Education Toolkit, consisting of integrated photonic chips designed to demonstrate key quantum optics experiments. These devices will be used to train the next generation of quantum engineers and could be shared nationally as accessible educational tools for students entering the rapidly growing field of quantum technologies. Technical Description: This project investigates hybrid continuous-variable (CV) and discrete-variable (DV) quantum photonics in silicon. The objective is to determine when hybrid CV-DV circuits implemented in silicon photonics can outperform purely CV or DV approaches for generating and measuring quantum states. The research combines silicon color centers with integrated photonic circuits fabricated in a silicon photonics foundry to realize scalable spin–photon interfaces and hybrid quantum optical states. The project develops a layout-driven simulation framework, to enable co-design of quantum emitters, photonic circuits, and measurement systems. Using this framework, silicon quantum emitters will be integrated into slow-light photonic structures and characterized using hybrid balanced homodyne detection and photon-number-resolving measurements. These experiments will enable reconstruction of quantum optical states and benchmarking of single-photon fidelity. The project will also investigate remote entanglement protocols based on spin-coherent-state entanglement, enabling scalable generation of hybrid quantum correlations between localized spin qubits and propagating optical states. By combining foundry-scale fabrication, quantum emitter integration, and continuous-variable measurement techniques, the project aims to establish a scalable platform for hybrid quantum state generation and measurement in silicon photonics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This REU Site award to the State University of New York at Albany’s RNA Institute, located in Albany, NY, will support the training of 10 students for 10 weeks during the summers of 2026–2028. Students will participate in high quality, hands on research that contributes to scientific discovery in the broad field of ribonucleic acid (RNA) science and RNA based technologies. This work is important because RNA plays a central role in nearly all biological processes and advances in RNA research help transform biotechnology, medicine, and our understanding of life. The program benefits society by cultivating a skilled and diverse scientific workforce, expanding technological capacity, and encouraging innovation in fields that underpin national competitiveness. A key goal of this REU Site is to broaden participation in STEM, especially among students with limited access to research opportunities. Students will learn how research is conducted, receive mentoring from faculty and staff, and many will present their work at scientific conferences. Program assessment will include both in person and online qualitative and quantitative surveys. Students should apply to the REU Site using the NSF Education and Training Application (ETAP) at https://etap.nsf.gov. The training students will receive is aligned with the NSF priorities in Biotechnology and Artificial Intelligence. The REU in RNA focuses on how the structure and function of RNA governs cellular processes and can be leveraged to illuminate the molecular mechanisms of complex biological systems and develop new RNA based technologies. Students will work alongside faculty affiliated with the RNA Institute and a range of departments including Biological Sciences, Chemistry, Biomedical Sciences, and Bionanosciences. Each student will conduct an individual research project in an Institute laboratory under the guidance of a faculty mentor, while also participating as part of a cohort in professional development activities that introduce post graduate educational and career pathways. Training will include hands on experimental work, one on one mentoring, structured career guidance, and instruction in the ethical and responsible conduct of research. Example student projects include predicting RNA structures through analysis of RNA folding, studying transcriptional regulation by ribosomes, examining the roles of RNA modifications in cellular stress responses, designing nucleic acid nanotechnology, investigating mechanisms in developmental biology, and exploring the pathogenesis of toxic RNA. Through these research experiences, the program aims to advance fundamental RNA science, prepare the next generation of RNA researchers, and promote positive societal impacts through RNA driven innovation. 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 · 2026-03

The project supports acquisition and maintenance of an instrument that will create a state-of-the art shared facility at the University at Albany, supporting multi-institutional, interdisciplinary research in spectrum science serving the central New York region. The instrument is unique in its ability to operate in extreme bandwidths (up to 50 GHz) covering a wide range of frequencies: sub-6 GHz, X, Ku, K, Ka, D, H bands and optical frequencies. The advanced capabilities of the proposed system is anticipated to enable transformative research in electromagnetic spectrum utilization for all users including various applications, like wireless communication, sensing and imaging. The instrument will stimulate interdisciplinary, multi-institutional collaborative experimental research in spectrum science, which is currently infeasible due to lack of a testbed that can perform communication, sensing and imaging in a wide range of frequencies from lower sub-6~GHz up to Terahertz bands to exploit the unique physical characteristics of each of the bands. This equipment will facilitate novel research on problems that manifest only in experiments. This approach will bridge the gap between theory and practice and shorten the time for adoption of these novel techniques. The ability to rapidly prototype and test new algorithms will accelerate scientific discoveries. The proposed instrument will provide researchers and students in the central NY area access to a unique state-of-the-art wireless testbed. The research enabled by this testbed will facilitate efficient use of a vital national resource, electromagnetic spectrum, to enhance the quality of life for all Americans. Local colleges will play a significant role in introducing undergraduates to research and training in this instrument, leading to potentially improving the retention of students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Quantum information technologies are progressing at an ever-increasing pace. The potential for the quantum information and computing paradigm to solve complex problems exponentially faster than classical computers promises revolutionary advancements in data analysis and optimization, medicine, material science, and security and communications. This has led the government, the technology industry and the academia to invest in research towards realization of robust operations on quantum networks capable of processing quantum information in a distributed way. The QUANTINT project envisions an interconnected network of quantum devices exchanging qubits and utilizing unique quantum properties such as entanglement to enhance information processing algorithms. The project aims to design efficient universal algorithms and strategies to (i) compress distributed qubits and (ii) harness distributed entanglement in emerging tasks such as distributed learning. Achieving the above vision will amplify the performance of distributed learning and other information processing tasks by several orders of magnitude, with a great reduction of the burden on network capacity. The objective of QUANTINT is to characterize and reach the fundamental limits of quantum information and learning theory and to achieve performance breakthroughs in distributed information processing. The quantum toolbox from which the project draws includes phenomena such as superposition, entanglement and non-locality. Motivated by this, QUANTINT undertakes a comprehensive exploration of two centrally important tasks - compression and inference - in the context of quantum information. 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 · 2025-11

The growing dependence of organizations on cloud cyberinfrastructure (CI), coupled with the intrinsic on-demand and elastic nature of the cloud CI, have widened the attack surface and made it an attractive target to rapidly evolving cyber threats. The development of fairness-aware Artificial Intelligence (AI) and machine learning (ML) based security solutions can make cloud CI more resilient and trustworthy. However, a key pillar of a successful secure cloud adoption necessitates scientific research workforce training. This project aims to train the future research workforce to develop and use AI-based cloud CI cybersecurity solutions that are fair, ethical, and unbiased. In addition, the project aims to instill the ability of the workforce to adapt and evolve these AI based cybersecurity solutions for cloud CI to improve their trustworthiness and resiliency, as new adversary models are discovered. The technical innovations of this project address the growing needs for a fairness-aware AI-skilled secure cloud CI research workforce in two-fold. First, the project will develop and integrate seven advanced experiential learning modules, referred to as AI4SecureCI, for secure cloud CI using fair and explainable AI concepts into undergraduate and graduate curriculum, training around 500 diverse participants including faculty and students directly. The developed AI4SecureCI modules will include the concepts of network security, authorization and automated access control, online malware detection, classifying malware threats, adversarial attacks and defenses, bias and fairness, and explainable AI, relevant to cloud CI. These modules will include the (1) lecture materials to provide conceptual knowledge for AI4SecureCI, and (2) hands-on lab exercises to provide practical experience. To support hands-on labs and enable wider adoption of the modules, the team will utilize ready-to-use datasets created from their own cloud CI security research and public security datasets, and free-tier cloud services such as AWS Educate. Second, the project will ensure broader adoption, via student boot-camps and series of faculty workshops of developed advanced AI4SecureCI and computational data-driven methods, into underrepresented groups of CI users and contributors to foster research advancements for evolving cloud CI security threat vectors. The advances made under this project, both in terms of research, modules developed, as well as training material will be made publicly available on a project website. The team will collaborate closely with the NSF ACCESS program to enhance the dissemination of knowledge and expertise within the CI community by incorporating the AI4SecureCI modules into the ACCESS Knowledge Base. "" 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 · 2025-11

This project will examine how per- and polyfluoroalkyl substances (PFAS), known as “forever chemicals”, get into and accumulate in crops such as radish, lettuce, and soybean. It will also investigate how uptake of these chemicals by plants may reduce food quality. The research team will use advanced analytical tools measure the update of PFAS chemicals and where they accumulate in plants. Biochemical tests will be used to determine how PFAS exposure affects plants’ ability to absorb and produce nutrients. The results will fill a key knowledge gap about how PFAS move within plants and affect food quality, which will help scientists gauge potential health risks. The research will produce an approach that others can use to study new contaminants in farming systems. Outreach activities will engage students from middle school through college and offer science education events for the public. The project will promote safer agricultural practices, inform improved environmental standards, and build a more PFAS-aware community to protect public health. The goal of this project is to understand the mechanisms underlying uptake and bioaccumulation of PFAS in representative food crops and their implications for food safety and nutritional quality. The hypothesis is that the physicochemical properties of PFAS govern their interaction dynamics with plant systems. To test this hypothesis, three specific objectives will be addressed. The first objective is to determine how the physicochemical properties of eight common PFAS compounds influence their uptake and selective bioaccumulation in different organs of three crop species (radish, lettuce, and soybean) through PFAS quantification and visualization. The work will pinpoint where PFAS accumulate most within plants. The second objective is to investigate the mechanisms regulating the transport of different PFAS in plants during uptake. The relationship between protein and lipid content and PFAS accumulation will be examined. The uptake pathways for different PFAS compounds will be elucidated through inhibition tests using seven distinct protein inhibitors. The third objective is to understand how PFAS exposure affects plant nutritional quality at the molecular level. The gene expression of four key aquaporins and three key anion channel proteins will be analyzed in soybean as a model plant exposed to various PFAS. Changes in macro- and micronutrients and vitamin B9 in the seeds, which are the primary edible part of soybean, will be evaluated. The project will provide mechanistic insights into PFAS uptake, bioaccumulation, and their implications for food quality in edible crops, particularly the selected species that are widely consumed in the U.S. diet. Additionally, the project will provide experiential learning opportunities for students, promote STEM education to learners from middle school through college, engage the public and agricultural communities through science education events, and cultivate a skilled workforce equipped to address complex environmental challenges. 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

VR (Virtual Reality)-based immersive and interactive environments can be a great resource for learning and training, especially for concepts that involve safety aspects by interacting with inflammable or breakable objects. The tedious nature of developing these VR experiences continues to be a limiting factor for VR becoming mainstream. The main goal of this project is the design and development of an innovative infrastructure, SMILE (Scan to Multi-sensorial Interactive Learning Environment), focusing on: (1) nearly automated construction of VR environments that mimic real-world indoor scenes; (2) interactions with virtual objects involving multiple senses such as touch, visual, aural, and smell. The SMILE infrastructure will undergo rigorous software testing to ensure safe interactions before being deployed to support Internet-scale collaboration among users. In the exploratory phase of this project, the team will address the following: (1) Democratize the creation of VR environments for subject-matter experts that are realistic in reflecting the ambiance of a real-world scene while providing the needed interactions with objects in the scene. (2) Apply sensorial information directly to 3D objects, enabling multiple sensations during interactions with the virtual objects. (3) Internet-scale collaborations in SMILE through multi-modal inter-sender and inter-receiver skew synchronization, based on the temporal requirements of multi-sensorial interactions. This will be tested by deploying SMILE for use in the sites of the project partners: the University of Texas at Dallas, the University of Illinois at Urbana-Champaign, the New Jersey Institute of Technology, and the Dallas College. (4) A coverage-based fuzz testing module where numerous inputs can be generated and executed efficiently to test corner cases in the SMILE components, and configurable static analysis where algorithms can be flexibly tuned to check the most critical safety properties in SMILE. Virtual chemistry laboratory experiments for assessing the performance of SMILE will be designed and developed through research collaboration with the Dallas College. Through this collaboration, the SMILE project will have a significant impact on the broad undergraduate student community. SMILE could be used for different learning environments, apart from the Chemistry lab case study, promoting education involving K-16. The project will involve doctoral students, undergraduate students, and K-12 students. Open-source resources produced through the SMILE project include a multi-sensory database of material properties for sensorial displays, Unity-based VR authoring tools, and automated software testing tools for VR-based applications. These will be made available for a period of five years through the project website at: https://labs.utdallas.edu/multimedialab/. 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

Surface relative humidity (RH) has major impacts on human comfort and health, as well as droughts and wildfires. Historical data show rapid declines in surface RH over many land areas since the late 1990s. Such a RH decrease enhances atmospheric demand for moisture under rising temperatures, leading to increased risk of drought and wildfires in the western U.S. and other regions. However, the exact cause of this RH decrease is unknown. Given the broad impacts of surface RH, improved understanding of the RH decline is critically important. This project will help explain the rapid decline in recent surface RH, thereby benefiting research in human health, drought and wildfires in the U.S. and other regions. It will also help the PI train two graduate students, teach climate-related courses, and educate the public about our changing climate. A comprehensive analysis and critical assessment of the recent RH trend will be carried out to reveal its causes and assess its reliability. Utilizing the Principal Investigator's expertise in homogenizing climate records, monthly RH data series from global weather stations since 1973 will be homogenized using advanced statistical methods combined with available metadata to detect and remove discontinuities associated with instrumental changes. This will help quantify the impact of such discontinuities on the recent RH trend over land. Further, RH variations and changes will be compared with those in physically related but independently observed meteorological variables, including cloud cover and precipitation. The relationships among RH, cloud cover, precipitation, surface latent and sensible heat fluxes, and soil moisture in a reanalysis dataset (ERA5) will be examined, with a focus on whether land-air interactions have amplified the RH decrease. Finally, after removing artificial changes in the RH records, the researchers investigate the key drivers of the remaining RH trend, including a) externally-forced changes estimated from large-ensembles of climate model simulations, and b) the contribution from decreasing topsoil moisture through a series of climate model simulations with and without observed drying in topsoil forced with observed sea surface temperatures over the last four decades. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project is inspired by the challenges and opportunities that generative AI has created for K- 12 education. Students have unprecedented access to AI tools, which can streamline tasks but also shortcut authentic learning. Educators are eager to explore the potential for AI to enhance instruction and deepen student engagement. Beyond just using AI, understanding how AI works is recognized as a new form of computational thinking which is essential for everyone to learn. With guidance from the AI4K12 framework, teachers have a foundation for recognizing which ideas need to be introduced to their students, but there remains a gap in teacher preparedness to introduce these ideas effectively to all students and in authentic middle school contexts. This project will address these gaps by exploring how to embed the use of AI and understanding of AI into core middle school subjects, including science, language arts, and social studies. Results from the project will provide guidance that is applicable nationwide on effective ways to support teachers in effective use of AI and instruction on how AI works that they can bring to their students. Two school districts (one in Texas and one in New York) will work with researchers from three universities to carry out the project. University personnel will work directly with 60 teachers and academic technology specialists and instructional technology coaches across seven middle schools in the two partner districts over the two project years. This will include designing and piloting browser-based software tools and instructional materials that integrate AI concepts across the curriculum areas while developing professional learning communities among school leaders and teachers. The project will investigate three research questions: (1) What are the current understandings, policies, and practices of using and teaching AI literacy in the partner districts? (2) What are the key AI literacies every student should know in alignment with the district curricular goals? (3) How can understanding of AI be integrated into the existing curriculum to deepen learning? The student populations of the partner districts broadly represent the demographics of the United States as a whole, ensuring broad applicability of project findings to all Americans. The goal of the project is to build district capacity to provide all students with the opportunity to participate in AI and associated computational thinking education in their schools. This project is funded through the Computer Science for All: Research and RPPs 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 · 2025-09

The NSF ECR Building Capacity of STEM Education Research (BCSER) program contributes to the NSF mission 42 U.S. Code Chapter 16 by building the US workforce undertaking STEM education research. The BCSER Individual Investigator Development in STEM Education Research (IID) track supports individual investigators who are experienced STEM education researchers to develop expertise in new areas that will contribute to advancing STEM education knowledge. STEM education research generates the knowledge, theories, and understandings on which viable strategies for improving U.S. STEM education and workforce outcomes are based. This study focuses on mathematical argumentation in the elementary classroom. It involves the development of a scalable tool to analyze and assess the quality of argumentation in elementary mathematics classrooms, combining AI, psychometrics, and argumentation theory. With two focused studies, it aims to improve learning for struggling students and enable large-scale analysis of classroom discourse in elementary mathematics contexts. This BCSER IID project will allow the PI to supplement their current expertise with new research skills to design and implement cutting edge STEM education research using modern methods and tools. The project will assist the PI to develop new expertise in three intersecting areas: psychometrics, argument theory, and technologies using Large Language Models (LLM) and AI. Specifically, the principal investigator will gain new expertise in classroom-based argumentation measures and argument mining techniques to develop an AI-supported tool for measuring mathematical argumentation in elementary classrooms. This project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research. The project is also supported through a collaborative NSF activity with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. 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 Eastern Hemisphere tropics and subtropics are highly sensitive to variations in regional monsoon precipitation, which provides the majority of freshwater for the ~40% of the world population who reside there. Understanding interannual to multidecadal variations in the monsoon is critical for managing water resources. This project will compile data from cave deposits, corals and tree rings, as well as develop new records from caves in the Philippines and northern Australia, to produce a reconstruction of the behavior of the Austral-Asian monsoon for the last 1000 years. This reconstruction will guide a set of climate model simulations to identify the drivers of monsoon variability. The results will improve decadal prediction the Austral-Asian monsoon. The project will support the participation of a postdoc and undergraduate students in the research, an art-science collaboration, K-12 education, and public outreach to primary and secondary students in Iowa, California, New Mexico and Northern Australia. The goal of this project is to synthesize existing data from stalagmites, corals and tree rings with new cave records from the Philippines and northern Australia to reconstruct the Austral-Asian monsoon for the last 1000 years. The resulting multi-proxy reconstruction will guide a suite of climate model simulations, including large ensembles and isotope-enabled models, to identify drivers of monsoon variability and quantify the relative contributions of external (solar, aerosol, greenhouse gas) and internal (tropical basin interactions) forcings. The results will improve decadal prediction the Austral-Asian monsoon. The project will support the participation of a postdoc and undergraduate students in the research, an art-science collaboration, K-12 education, and public outreach to primary and secondary students in Iowa, California, New Mexico and Northern Australia. 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 Congo rainforest, the driest and second largest rainforest on Earth, has experienced a widespread and long-term drying trend, with more intense and frequent droughts over the past four decades. Extended droughts have significantly warmed and dried the land surface, reduced forest productivity, lengthened the dry season, and increased the risk of forest fires and biomass burning in the region. These changes, if continued, could alter the composition and structure of the Congolese rainforest, impact biodiversity and carbon storage, and have long-term environmental, societal, and economic implications. A surprising fact about the Congo drying trend is that the reductions in precipitation (P) have been accompanied by increases in thunderstorm activity over the region. The increase in thunderstorms is counterintuitive since most of the rainfall in the Congo comes from thunderstorms, thus one would expect thunderstorms and P to increase or decrease in tandem. The mechanisms driving this counterintuitive relationship, termed the “Congo P paradox”, remain unknown. Research conducted here follows the working hypothesis that the ultimate cause of the Congo drying trend is changes in surface temperature over the Indian and Pacific Oceans, but the intensity and duration of droughts is increased by local feedbacks between soil moisture (SM) and P. Furthermore, the opposing trends in P and thunderstorm activity occur because the increases in the most intense thunderstorms, also called Mesoscale Convective Systems (MCSs), are accompanied by reductions in weak to moderate thunderstorms. The increases in P associated with increases in MCSs are overcompensated by decreases in precipitation due to reductions in weaker thunderstorms, resulting in opposing trends for P and thunderstorm activity. The research also explores the idea that SM-P feedbacks account for the Congo P paradox due to their effects on both P and thunderstorms. The key issue here is that SM and P interact with each other in multiple ways and the mechanism through which SM and P interact to prolong drought and intensify MCSs are subtly different. The work involves analysis of observational data from satellites and other sources as well as high-resolution simulations from the Weather Research and Forecasting (WRF) model. The project supports a postdoctoral fellow, a graduate student, and an undergraduate, and various forms of outreach are conducted including engagement with researchers based in the Congo. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Drs. Jia Sheng and Ting Wang from University at Albany, State University of New York to develop nucleic acid building blocks that are responsive to light. The structures and functions of RNAs synthesized with these nucleic acids are studied to determine their abilities to serve as photoswitchable regulators of gene expression or therapeutics. In this way, when and where the functions of these light responsive RNAs are turned on or off can be controlled in ways that are noninvasive and, thus, safe to cells. The students participating in this research receive broad training in synthetic organic chemistry, physical chemistry, analytical chemistry, and nucleic acid chemistry, as well as skills in macromolecule X-ray crystallography and computational modeling of biological molecules. Such training provides highly motivated graduate and undergraduate students with skills required to enter careers in the life sciences and STEM fields in general. In addition, a set of “Preparation of Organic Chemistry” orientation sessions are established to help entering undergraduate students navigate the rigors of college-level organic chemistry. Finally, this project is integrated into an outreach science education program that provides curiosity driven hands-on experimental experiences to K-12 students. In this research project, photoswitchable nucleotide building blocks are synthesized and incorporated into RNA oligonucleotides to control their structures and functions. More specifically, a series of azobenzene-based nucleosides are designed to change conformations in response to different light wavelengths. RNA strands synthesized with these light responsive nucleotides are characterized for their ability to form stable and specific base pairs and the ability of light to drive their conformations and functions, including, RNA aptamer and ribozyme activities, and RNA-enzyme recognitions such as reverse transcription and CRISPR-Cas13-mediated RNA cleavage for genetic engineering. These photo-responsive nucleotides could also be applied to other classes of important RNAs such as microRNA, small interfering RNAs (siRNA), peptide-nucleic acids (PNA), methylene-bridged locked nucleic acids (LNA) and other non-coding RNAs for applications in biomedical research. Overall, this work advances our understanding and applications of fundamental RNA biology by creating new chemical biology toolset, which can be applied to more advanced cellular and animal studies and to develop new therapeutics and biomaterials. 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

Disasters alter the physical environment, destroy homes, and leave survivors with challenging decisions: should they return to their prior community or relocate to a new community? Past studies suggest that factors such as familial ties to an area, history in a community, age, employment opportunities, insurance coverage, and risk perceptions shape these residential decisions. Most of these studies, however, have focused on homeowners exclusively, ignoring renters, which make up 34 percent of U.S. households. Renters also encounter unique challenges in deciding where to live after disasters, since they do not decide whether to rebuild rental units after disasters. Understanding how renters make residential decisions after disasters, however, is paramount to ensuring communities recover after disasters. A wide range of businesses essential to community functioning rely heavily on renters, including restaurants, construction, farming, vegetation management, landscaping, and catering. This study addresses how renters make residential decisions after wildfires in two communities recently affected by major wildfires. Study findings support the development of disaster recovery plans that support the recovery of the housing market, the local economy, and renters after disasters. This project investigates how renters’ make residential decisions after wildfires using a modified Push-Pull Model of Migration. Previous literature, primarily focused on floods, suggests that residential decisions after disasters are shaped by several factors, including functional and emotional place attachments, hazard experiences, and risk perceptions. These factors can push individuals out of communities, pull them into others, or anchor them in place after disasters. This study uses photovoice interviews to examine how place attachment and risk perception shape the residential decisions of renters displaced by wildfires and compare how residential adjustment pathways vary between renters and homeowners. Study findings advance theory by applying a modified version of the Push Pull Model, accounting for the role of anchoring effects in residential adjustment decisions of renters and examining interdependencies between place attachments and risk perceptions among renters. Likewise, the study sheds light on the factors that shape renters’ residential adjustment decisions, supporting the development of recovery programs that account for the unique needs of renters. Study findings are relevant to other hazards as well. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award provides financial support for junior and early-career US participants in the summer school “Stability in Topological Data Analysis”, to be held at the Institute Mittag-Leffler, Stockholm, Sweden, June 30 - July 4, 2025. Topological Data Analysis (TDA) aims to develop new techniques based on topology and other disciplines in pure mathematics to understand complex data. Research questions are strongly motivated by applications from neuroscience, image recognition, biology, material science, geography, and beyond. The underlying idea is that topology helps recognize patterns within data and, therefore, turn data into compressed, useful knowledge. Besides advancing the progress of science by helping to prepare the next generation of applied topologists, this school is also designed to expand the reach of applied topology. The topic of the school is the study of general techniques to achieve stability of invariants in TDA that can be applied to different objects, from persistence modules to Reeb graphs. In particular, the focus is on questions about the stability of various TDA tools: 1) Which metrics can be introduced to ensure the stability of traditional topological invariants? 2) Which invariants can be developed to ensure stability with respect to traditional metrics? Through questions like these, TDA enriches the traditional fields of algebraic topology and geometry while at the same time striving for theoretical guarantees for practitioners in data analysis. There will be three lecturers alternating in the mornings, an introductory poster section, problem sessions in the afternoons, and a few short research talks from the participants. Ultimately, the school’s goal is to encourage students to develop their own application-motivated results in TDA by considering well-established principled strategies. Further details can be found at: https://www.mittag-leffler.se/activities/ewm-ems-summer-school-stability-in-topological-data-analysis/ 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 study addresses a longstanding issue in our understanding of hazard adjustment decisions: what pushes individuals thinking about hazard adjustment to adopt these adjustments? To address this question, the research team employs a longitudinal survey design to understand the mechanisms that lead to adjustment behaviors over time. By addressing this issue, emergency managers and other key stakeholders can create programs that reduce risk by targeting barriers to adopting hazard adjustment behaviors. The team leverages these insights by co-developing a toolkit to support local and state efforts to improve flood adjustment program design, participant experiences, and community outcomes. The project also provides an opportunity for experiential learning and graduate student training. The findings are transferable to other locations affected by natural and induced technological hazards. This project builds on Paton’s Social-Cognitive Preparation Model to examine how people develop expectations, intentions, and behaviors related to flood hazard adjustments over a 3-year period. The research team surveys households living in coastal areas within 100-year flood zones. The survey introduces an experimental intervention to measure how flood hazard adjustment behaviors change after participants receive a brochure on flood hazard protections. The study also uses advanced statistical methods such as Structure Equation Modeling and Latent Growth Curve Mediation Modeling to analyze how different factors influence people’s flood hazard adjustment decisions, as well as how behavioral intentions and actual behaviors change over six time points across 3 years. This study extends beyond typical one-time or short-term surveys by tracking how intentions to adopt flood adjustments translate into actual behaviors over time. The research model is transferable to understand drivers that affect protection behaviors across different types of hazards, cultures, and places. 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

The Cyber and Terrorism Insurance Studies (CATIS) Center addresses the growing risks posed by cyber threats and terrorism, which can cause devastating financial and operational disruptions. Despite the increasing frequency and severity of cyber incidents and terrorism-related events, the insurance industry faces challenges including inconsistent risk assessments, limited data-sharing frameworks, and a shortage of professionals equipped to manage these evolving risks. CATIS brings together leading universities, cyber and terrorism insurance carriers, reinsurers, brokers, risk modelers, data providers, insureds, cybersecurity vendors, and policymakers to develop innovative research, tools, and educational programs that can strengthen the cyber and terrorism insurance markets. By improving risk modeling and sharing critical data, the Center assists businesses and government in anticipating and managing catastrophic risks. Additionally, the CATIS Center prepares new generations of professionals through workforce development initiatives, ensuring the insurance and cybersecurity industries have the expertise needed to navigate emerging challenges. With planning grant support from the Industry-University Cooperative Research Centers (IUCRC) program, the CATIS Center is a three-site collaboration, where each site contributes expertise in cyber risk modeling, terrorism risk analysis, and insurance. The Center's research focuses on developing artificial intelligence (AI)-driven risk modeling techniques, refining definitions of cyber and terrorism-related catastrophic events for insurance and reinsurance markets, and exploring the impact of cybersecurity controls and counter-terrorism measures on risk reduction. The Center also links with industry partners to enhance data-sharing methods, standardize underwriting practices for emerging risks like AI liability, and establish guidelines for pricing and managing catastrophic cyber risks. These efforts can lead to a stronger, more resilient insurance industry, providing businesses and policymakers with better tools to assess, mitigate, and insure against cyber and terrorism-related threats. 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

The ability to culture three-dimensional groups of cells while monitoring their characteristics is a critical issue in biomanufacturing. Addressing this issue will improve the study of disease, tissue engineering, and regenerative medicine to treat the world’s aging population. This Future Manufacturing project will fund research to develop new sensing technologies for use in 3D cell cultures using cells from the nervous system. These technologies build on advances in the semiconductor and microelectronics industry. The project will train a new workforce in life sciences and engineering, with a focus on device design and fabrication. The University at Albany will partner with SUNY Polytechnic Institute’s engineering technology program and community college partners Hudson Valley Community College and Bronx Community College to develop a skilled workforce able to work in the biotechnology industry. A middle school outreach program will teach students about STEM with demonstrations of robotic cars controlled by electrical signals sent from the brain to muscles. The objective of this project is to develop manufacturable 3D mammalian cell culture systems focusing on neural organoids with incorporated sensing technologies. This project looks to address challenges related to: the limits of manufacturability of scaled-down sensing devices, the capacity to introduce photonic and electronic sensing devices in 3D culture systems, and the integration of photonic hydrogels that support cell growth and improve sensing. To address these challenges, the project has the following research objectives: (1) build and test conformal microelectrode arrays (MEAs) to interrogate neural organoids and transition these conformal MEAs from lab scale to flexible electronics manufacturing; (2) develop photonic-based sensors to detect small molecule metabolites using DNA aptamers as sensing devices; (3) optimize production of photonic hydrogels for pressure and strain sensing and integration with silicone nitride photonic devices. In parallel with these research activities, this project will engage in workforce development activities consisting of: outreach to middle school summer camps to inform students of career opportunities in manufacturing that integrate life sciences, engineering and human health; technical training combining life science manufacturing, engineering, and device design; and development of novel hands-on curricula for a Humanitarian Engineering Technology minor to benefit engineering technology students through exposure to life-science topics. This project is jointly funded by the Division of Chemical, Bioengineering, Environmental, and Transport Systems, Division of Civil, Mechanical, and Manufacturing Innovation, and Division of Engineering Education and Centers in the Directorate for Engineering, and the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Weather extremes such as drought, heavy summer rains, extreme snowfall and frost, have long plagued the central Andes region and affected local activities such as agriculture, but they also lead to loss of human life. The frequency and severity of such events is expected to change in a future climate but little is known about the causes of extreme events in this region, hence their predictability has historically been poor. Adequate planning and adaptation, however, requires successfully forecasting and predicting such events, but the development of such forecasting tools is still in its infancy. The goal of this project is thus to contribute to a better understanding and modeling of these events, thereby improving the prediction of their impacts for hydrological and agricultural systems. This will also result in better communication of the associated risks to society, thereby improving the overall preparedness. This project will employ a crop model that can simulate the effects of these processes on yields and simulate different management options. This model will be fed with output from high resolution regional climate model simulations to support decision-making by local farmers, but also aid policy- and decision-makers. The project will be developed in collaborative fashion between institutions in the United States, Switzerland and Bolivia, combining expertise in modeling and Andean climate variability with partner’s strengths in data set production, geospatial analyses and crop yield modeling. Hence this research depends on the international collaboration between researchers, as inter-dependencies related to data and expertise exist. All groups will contribute unique datasets and expertise to address this scientific challenge through an entirely integrated scientific collaboration. This project will also contribute to training of a US-based graduate student by involving them in all aspects of the project. The student will be exposed to interdisciplinary research in an international setting. The focus of this project is on extreme event analysis and impacts on crop yields in the Central Andes, but the impacts of extreme events on agriculture are equally felt in parts of the United States. Hence, while our approach here focuses on one specific vulnerable mountain region, our results will yield new insight into high-resolution modeling of extreme events that are also relevant for and applicable to other mountain areas, such as the Rocky Mountains in the United States. The crop modeling under future climate change scenarios is also relevant on a much broader scale, as many crops have limited heat and drought tolerances that may also affect future crop yields in the United States. 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

While integration of multimodal information has been widely researched, the difficulty in carrying out this integration in a domain-agnostic, generic manner has been problematic. Past results have emphasized the need for addressing multimodal integration in a domain-specific manner. For instance, cardiac rehabilitation is a diverse range of practices for restoring individual's functioning. Unfortunately, only 30% - 40% of patients report regular exercise at six months after discharge and 39%-45% of these patients suffer from at least one readmission within one year. These poor outcomes motivate the need for using technology for remote monitoring in this multi-modal system. The overarching goal of this proposal is to understand how multiple cardiac experts view multimodal data, decide on the progress, and the variations/biases among the experts' views and decisions. This understanding would help us design and build a multimodal visualization and progress-tracking system that can provide meaningful information to stakeholders. Though the proposed algorithms for multimodal data analysis are tightly tied to the biomedical domain, the derived knowledge and understanding would be useful to other domains using multimodal sensing. Results, metrics, and algorithms from this research will be published widely in high-quality academic journals and conference proceedings. Integrating, visualizing and tracking progress using multimodal data is highly domain dependent. In this project, cardiac tele-rehabilitation deployed in-home is the target domain, generating multimodal data, with their diverse data characteristics and varied timeframes. Research challenges in domain-specific multimodal integration typically include the characteristics of the multimodal data as well as the domain-specific needs. For multimodal integration for cardiac rehabilitation, the challenges include: (i) Integrating the multimodal data with diverse types of data with varying temporal characteristics to find relationships among potential adverse events; and, (ii) Possibilities for using mobile and wearable sensors to provide opportunities for personalization both in the rehabilitation and in the multimodal data integration. The proposed system, in the form of a mobile app, will democratize data acquisition. This, in turn, could lead to a better understanding of bias among experts and possible strategies for mitigating the bias and provide appropriate feedback and nudges to patients. 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

The broader impact of this I-Corps project is the development of an enhanced fluorescence-based diagnostic technology for the detection of antibodies and other biomarkers for disease diagnostics. The base technology has been demonstrated for diagnosing high profile diseases including COVID-19 and Lyme disease. For this I-Corps effort, Lyme disease has been chosen as the beachhead market due to the current diagnostic challenges, and the growing market for fast and accurate Lyme disease diagnostic technologies. The accepted standard for Lyme disease, known as standard two-tiered testing (STTT) is time consuming, requires specialists to run, and can be unreliable, especially for early stages of the disease. This technology has proven to alleviate these pain points, providing rapid and accurate Lyme disease diagnosis, especially for early Lyme disease patients. The platform has also been utilized for detecting RNA-protein and DNA-protein interactions, which potentially broadens its utility for a large number of different disease diagnostic applications, biomarker discovery, and biological / pharmaceutical research applications. The technology may have impact in several clinical and biological research fields. 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 photonics-based Lyme disease diagnostic platform. Lyme disease is the most common vector-borne disease in the United States and, despite advances, remains a considerable diagnostic challenge. Confirmatory diagnosis requires a second test, performed in series, often in batches, and at a centralized laboratory. This delay can lead to considerable morbidity in disseminated Lyme disease. This technology is a low-cost, highly sensitive, fluorescence-based platform, which provides a rapid, easy-to-use, and highly accurate Lyme test that could be used outside traditional clinical laboratories or for more rapid and accurate diagnosis within clinical laboratories. The proof-of-principle research positions the technology as a rapid (<40 minutes total test time) and reliable alternative to traditional Lyme tests, while retaining the full sophistication of a two-tiered testing system. 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

The Andes mountains provide critical freshwater resources to downstream communities, yet they are also particularly vulnerable to climate change and deemed a ‘high risk’ region for people living in this region due to the projected future water scarcity. The lack of a high-quality observational climate database or useful projections of future climate scenarios, in particular, are major roadblocks that prevent implementing proper adaptation measures in the region. Therefore, the overarching goal of the ACCORD project is to build the first multidisciplinary network of networks focused on climate change across the Andes, by taking advantage of the multidisciplinary expertise of the 10 largest international networks to produce scientific breakthroughs that have so far been elusive due to the lack of interdisciplinary and international cooperation. This project will provide a new Andean climate database shared on an accessible platform, improve understanding of Andean mountain circulation, analyze how the intensity and frequency of extreme events (droughts, storms) will change, and document how climate change will affect glaciers, snow cover and water availability going forward. The results will be displayed using advanced visualization tools, making them easy to understand for policy-makers and the public at large. In addition, this project will contribute to training the next generation of US-based scientists that are capable of addressing scientific grand challenges, by involving students and early career scientists in all aspects of the project. While the focus of these activities is on the Andes, many of the processes affecting glaciers, snow cover and extreme events also play out in other mountain regions, including in the United States. The AccelNet-ACCORD network of networks will develop a new and improved Andean hydroclimate observational database, advanced understanding of mountain circulation and dynamics such as orographic precipitation and atmospheric rivers, thoroughly constrained future extreme event characteristics (megadroughts, convective storms), and narrowed projections of future changes in the Andean cryosphere and water resources. To achieve these aims, AccelNet-ACCORD will also take advantage of new high-resolution and convection permitting model simulations. The AccelNet-ACCORD project will also prepare a globally competent scientific workforce in an international and interdisciplinary setting by: 1) Immersing them in interdisciplinary research activities in an international setting; 2) Strengthening cultural literacy of students through a joint international online seminar series; 3) Providing experiential learning opportunities for US graduate students and postdocs through two international summer schools and one WRF modeling workshop with hands-on training; and 4) Broadening participation of diverse groups in STEM by hosting Annual Climate Change Days reaching 300 undergraduate students. The many existing productive working-relationships across individual networks will facilitate the synergistic interaction and implementation of the proposed activities. AccelNet-ACCORD is poised to become a fully collaborative, interdisciplinary, international, virtual research hub that is sustainable beyond the project lifetime. 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

Based on a 2018 survey, the global industry standards association for the electronics manufacturing industry (SEMI, semi.org) discovered that more than 85% of corporate respondents identified a lack of a qualified workforce as a top strategic challenge. With rapid growth in areas such as the Internet of Things, artificial intelligence, quantum computing, autonomous vehicles, smart medical devices, and 5G, the need for a skilled technical workforce will only increase. Addressing this need requires an integrated approach to attract students to STEM careers and to provide the education and career pathways that link students, education programs, and employers as an ecosystem rather than as individual parts. To address this need, this project will develop and pilot an industry-wide Semiconductor Workforce Certification Program based on an innovative Unified Competency Model that leverages the U. S. Department of Labor competency model. A multi-level, Semiconductor Technician Certificate will be developed and offered by SEMI as part of its strategic 'SEMI Works' initiative. This program will be developed by SEMI and the Northeast Advanced Technological Education Center (NEATEC) and piloted at 16 technician education programs at two-year and four-year colleges, technical high school programs, and with newly transitioned veterans at Fort Drum, NY. The pilot program will then be expanded to Oregon and North Carolina. It is estimated that, by the conclusion of the project, nearly 400 students will have earned a SEMI Semiconductor Technician Certification. The approach is expected to create a new, intrinsically flexible and updateable competency model applicable to a wide range of technician skillsets. The project will develop: 1) a Unified Advanced Manufacturing Competency Model and a Semiconductor Manufacturing Sub-Sector Competency Model, transforming the existing U. S. Department of Labor-ETA Advanced Manufacturing Competency model by adding a proficiency and relevance scale (0-4) for each competency; 2) a SEMI Certification model for technician education programs; and 3) a Proficiency-driven Academic Alignment Program that will review partner technician education programs and translate course and program learning outcomes to a Competency Profile format for skill gap/match analysis with SEMI Technician certification programs. This effort will create a transferrable academic review and alignment process for any U.S. academic institution to obtain a SEMI Semiconductor Workforce Certification for its technician education programs. Project outcomes will include the first-ever industry-wide semiconductor technician certification program, and an automated web-based SEMI Certification portal that will be maintained and updated by SEMI. The portal will build, sustain, compare, and update the Unified Advanced Manufacturing Competency Model for certified academic programs and provide an automated pathway for certification updates. The portal will also provide tools that guide workforce strategies for employers, technician education programs, and individual job seekers. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. 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.