University of Connecticut

NSF Awards · FY 2026 · 2026-09

Urban public spaces are filling with a growing number of micromobility vehicles: examples include electric scooters and bikes, personal vehicles, and delivery robots. Understanding how these vehicles can operate safely and considerately as part of larger transportation systems requires the novel use of techniques from many research disciplines, such as human-computer interactions, robotics, remote sensing, and artificial intelligence (AI). This project creates cyberinfrastructure software, data sets, and AI models that are needed to support this emerging field of research. This project enables its research community to better understand and improve vehicle interactions in complicated, rapidly changing, real-world settings. Direct outcomes include practical solutions for mitigating micromobility-related conflicts and accidents in public spaces. The development and use of this proposed cyberinfrastructure will prepare high school and college students for the nation's future workforce. This project serves the human-centric micromobility research community via three innovative AI-based service engines. First, a sensing and perception engine garners machine, environment, and human aspects from the project team’s established testbeds to strengthen the community's Micromobility-to-Everything Interaction (MEI) data preparation. This addresses the research needs in forming holistic, comprehensive understandings of diverse interaction data and augmenting them for AI model training. Second, an MEI model engine provides the research community with the AI models and tools to expand their sensing modality studies, with self-explainable graph model support. The community benefits from the expanded capabilities in performing extensive AI model studies over multiple datasets and gains interpretable AI model insights. Third, a coalition engine assistant interacts with researchers to help them navigate the cross-domain, cross-discipline research and methods needed to understand MEIs. The project includes a variety of co-designing and workshop training activities that assess research needs and engage participants in hands-on learning and practicing micromobility AI tools. All three key elements will be integrated into a service-oriented pipeline that is used to train a wide range of researchers, practitioners, and cyberinfrastructure and AI workforce in human-centric micromobility research. 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 · 2026-09

Additive manufacturing techniques that use light, such as stereolithography and digital light processing, have transformed the fabrication of complex three-dimensional (3D) structures, enabling advances in biomedical devices, soft robotics, and advanced sensing technologies. Despite this progress, current approaches remain limited by slow printing speeds, restricted material choices, limited resolution, and reliance on complex post-processing, which hinder scalability and practical deployment. This Faculty Early Career Development Program (CAREER) award supports research that seeks to overcome these barriers by developing a new light-based manufacturing approach that directly prints multi-material, multi-scale electronic and photonic structures in a single step. By advancing fundamental understanding of how light interacts with materials to form functional conductors at micro- and sub-micron scales, the award aims to accelerate innovation while reducing material waste, chemical processing, and manufacturing complexity. This research addresses national needs in sustainable and resilient advanced manufacturing, strengthening US leadership in electronics and photonics and enabling rapid translation of scientific discoveries into real-world applications. This research investigates 3D One-step Heterogeneous Manufacturing for Integrated Circuits (3D OHMIC), a custom-built additive manufacturing platform that integrates the high throughput of digital light processing with the sub-micron resolution of stereolithography and femtosecond-laser-based photoreduction to enable one-step fabrication of multi-scale metal–polymer structures. The research aims to establish the fundamental mechanisms of light-assisted metallization by examining photoinduced excitation, transport, and metal-ion reduction in photoreactive resin systems using time-resolved spectroscopy and controlled single-beam experiments, quantifying the roles of wavelength, intensity, and repetition rate in governing metal nucleation, growth, and connectivity. Building on this mechanistic foundation, early-stage metallization dynamics will be investigated to control morphology and suppress undesired aggregation, while tailored sol–gel-based resin chemistries will be developed to couple photopolymerization and photoreduction without unnecessary chemical additives or post-processing. In situ process monitoring, validation, and data-informed feedback are integrated to ensure reproducibility, structural integrity, and electrical performance. The resulting 3D OHMIC framework enables scalable fabrication of 3D circuits, sensors, actuators, and metamaterials, delivering new mechanistic insight, validated fabrication strategies, and open-access educational resources that advance additive manufacturing of electronics and photonics while training students at multiple levels. 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 · 2026-08

Remanufacturing restores worn or damaged products to like-new performance, extending the life of high-value assets while reducing dependency on costly replacements and lowering supply chain vulnerability. Many repairs still depend on technician judgment that is difficult to document and is increasingly at risk as experienced workers retire faster than replacements can be trained. Although robots offer the potential to alleviate workforce shortages, today’s programmed automation is largely limited to repetitive operations and cannot replicate human-level reasoning and adaptability required to manage the unique geometries, uncertain damage states, and evolving conditions inherent to repair workflows. This Faculty Early Career Development (CAREER) project aims to create scientific and educational foundations for an integrated digital thread framework that enables autonomous, self-evolving cooperative robotic systems capable of additive repair. This project advances remanufacturing by moving from programmed automation toward cognitive automation, creating intelligent systems that leverage expert knowledge and continuously adapt to perform unique, customized operations across all remanufacturing steps. Further, this project will broaden participation through curriculum modules at the University of Connecticut, hands-on research and mentoring, summer programs with local schools and community colleges, and workforce development activities for manufacturers and small businesses. The overall research goal is to establish a mind-body-environment loop that integrates knowledge-based reasoning, physics-informed embodied interaction, and continuous environment-loop adaptation, to support adaptive repair actions and scalable deployment across emerging remanufacturing applications. Specific objectives include: (1) Develop a self-evolving, memory-augmented planning module to sense, diagnose, identify, and learn what processes are needed for the repair task, enabling generalizable context-aware reasoning. (2) Develop an embodied engine to decompose tasks, allocate subtasks to individual arms, optimize high degree of freedom motion plans, and execute non-planar slicing, ensure morphology-driven reconfiguration, and (3) Develop an adaptive digital twin for decision making based on multi-fidelity process data and physics-based simulation within a continuous environment loop, completing the mind-body-environment framework. Driven by the neuro-vector-symbolic architecture, this research integrates distributed sensory embeddings with structured symbolic knowledge, embodiment constraints and physics-based dynamics, and multi-fidelity simulation with experience-driven refinement. The resulting unified representation enables knowledge-driven reasoning, morphology-configured planning, and simulation-augmented adaptation. The system will be validated on multi-arm laboratory experiments and industrial case studies that include both reconstruction of damaged parts and modification to new specifications. This research advances foundational knowledge at the convergence of cognitive intelligence, embodied robotics, and advanced remanufacturing. 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

Modern scientific and engineering challenges, from understanding cell growth to predicting material failure and crack formation under stress, require complex modeling and expensive experiments. While machine learning has demonstrated remarkable potential to accelerate scientific discovery for highly complex systems and reduce costs, its adoption in scientific research remains limited by a crucial bottleneck: the shortage of labeled training data. Obtaining large quantities of labeled data for scientific problems is often itself prohibitively costly, time-consuming, and sometimes physically impossible. This data scarcity creates two additional challenges: trained models often fail when applied to new conditions or experimental contexts, and the reasoning behind their predictions remains opaque, limiting confidence in the results as well as the ability to leverage those results to develop new scientific knowledge. Solving this small data problem by taking advantage of information about how the systems change with time will unlock the potential of machine learning to achieve higher performance with limited labeled datasets. This will ultimately accelerate innovation across chemistry, materials science, biology, and engineering, advancing technologies from battery development to manufacturing innovation by reducing costs, enhancing safety, and improving performance through AI-assisted automation and discovery. This project develops a unified framework for enabling machine learning with minimal labeled data in scientific applications by considering a dynamical systems approach. The research involves three complementary algorithmic advancements: first, developing new methods to learn families of evolution equations from only a handful of dynamic trajectories; second, developing computer vision algorithms that leverage known or discovered evolution equations to learn from unlabeled or sparsely labeled experimental time series; and third, improving active learning strategies for extremely small labeling budgets that will be leveraged to enhance the first two advancements. Interpretable symbolic methods will be used throughout to model evolution, enabling extrapolation to new conditions. These methods will be validated on real scientific applications spanning multiple domains in materials science and chemical engineering to demonstrate their broad utility and generalizability across fields. The project will also support the development of educational tools and methodologies for training a workforce equipped to address the complexities of the highly interdisciplinary field of scientific machine learning. 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 · 2026-06

This Pathways to Enable Open-Source Ecosystems (POSE) project will expand access to machine learning (ML) for scientists and engineers by advancing ModularML, an open-source, research-oriented machine learning framework, into a sustainable, community-driven ecosystem. While machine learning is transforming the pace and scope of scientific discovery, many existing tools require computer science or software engineering backgrounds and are difficult to adapt to different research areas. This project addresses these gaps by strengthening ModularML’s usability, extensibility, and adoption. The new open-source ecosystem (OSE) will broaden access to machine learning tools for a wide range of domain scientists. It will help accelerate innovation in areas critical to national priorities, including energy, infrastructure, and advanced manufacturing. By providing a reproducible, transparent, and user-friendly machine learning framework, ModularML will enhance educational and training outcomes, support workforce development in computational science and engineering, and promote open science practices. Through partnerships with academic and industrial collaborators, ModularML will serve as a template for how research-focused machine learning tools can evolve into community-driven ecosystems that increase access to innovation. This POSE project will lay the foundation for transitioning ModularML into a sustainable and community-driven OSE. ModularML is a backend-agnostic framework that enables construction and execution of machine learning workflows through a graph-based architecture with modular components for data processing, feature engineering, model definition, and multi-stage training. The project team will scope ModularML’s readiness for the OSE transition by: (1) evaluating its position in the machine learning software landscape and tracking community engagement; (2) developing governance and contribution policies for sustainable growth; (3) engaging and training domain researchers and contributors through workshops, forums, and onboarding resources; and (4) formalizing a plugin system and development roadmap to encourage community-driven, domain-specific extensions. Collectively, these efforts will establish the technical and organizational foundations for a scalable OSE that enhances accessibility of machine learning tools and contributes to broader best practices in software modularity, governance, and open-source sustainability. 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

Proteins often target specific areas in the cell. Some reside exclusively inside the cell, some embed themselves in the cell wall, and some are tethered to the outside of the cell. Others are actively secreted. Designing therapeutic drugs or proteins must account for the location of the target protein. Many important targets, like viral proteins, are initially inside the cell. Developing proteins that can specifically recognize and bind to them inside a cell is challenging. The cell interior presents many competing molecular interactions due to crowding, protein aggregation, and local ionic concentrations. Therapeutics designed to target a specific protein can exhibit off-target binding. This project will use a combination of computational modeling and experiments to overcome off-target binding. Tens of millions of binder variants will be screened. The resulting datasets can be used to train AI models to design intracellular protein binders more efficiently. This project will also contribute to workforce development by integrating the technology into a new undergraduate course and by providing research opportunities for local high school students. A yeast display platform will be used to overcome the limitations of traditional protein screening methods. It will enable simultaneous screening of a single binder variant under both oxidizing (extracellular) and reducing (intracellular) environments. Using partially efficient ribosome-skipping sequences, the approach will express each binder in both soluble cytoplasmic and surface displayed forms. High-throughput fluorescence-activated cell sorting will be used to quantitatively assess binding in the extracellular environment. The intracellular binding will be monitored through Forster resonance energy transfer, taking advantage of the quantum mechanical effects of energy transfer due to dipole-dipole coupling. The approach will be applied to evolving nanobodies and improving de novo designed binders through large-scale mutant screening. The mutations will be categorized based on intracellular versus extracellular binding behavior, providing mutation-function datasets to inform future protein design. The work will address fundamental questions about trade-offs between affinity and intracellular stability, and whether increased binding affinity under non-reducing conditions will translate to higher affinity under reducing conditions. Furthermore, this screening approach may be broadly applicable to engineering proteins that function under multiple cellular contexts. 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

The Connecticut Summer School in Number Theory is a one-week event (June 1 - June 7, 2026) in two parts: First, advanced undergraduate students and beginning graduate students in mathematics who are interested in pursuing PhD work in number theory attend four series of lectures from Monday to Thursday. The topics of these lecture series are chosen to bridge the gap between standard U.S. undergraduate training and the tools and techniques used in modern-day research. Following this, on Friday afternoon, Saturday, and Sunday morning, number theorists from across the country are invited to give presentations on their research. All participants from the first part of the event are invited to stay for the research conference to witness how what they have learned during the week is used by working mathematicians. The website of the event can be found at https://ctnt-summer.math.uconn.edu, and includes information about previous editions of the event. This year, the topics for the four mini-courses offered during the Summer School are: analytic aspects of quadratic forms (taught by Keith Conrad), the Kronecker-Weber theorem and abelian extensions (taught by Lori Watson), elliptic cure cryptography (taught by Alvaro Lozano-Robledo), and Magma and the LMFDB (taught by Eran Assaf). These topics were chosen to expose students to a diversity of areas of number theory with the aim to help them choose their subfield of specialization for their PhD work. In addition to these four series of lectures, the Summer School will also feature daily invited guest lectures before lunch, and evening sessions where students will have the opportunity to learn about LaTeX and Beamer as well as work on more advanced projects related to the topics of the mini-courses. 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

Hydrothermal vents are an important source of dissolved metals to the deep ocean. Recent studies imply that changes in hydrothermal activity at mid-ocean ridges are linked with variations in sea level. The idea is that higher sea levels will increase pressure and reduce hydrothermal activity, while lower sea levels will have the opposite effect. This study will test the hypothesis linking sea level and hydrothermal activity by studying hydrothermal metal fluxes in sedimentary records at different ages in the southern Pacific. The project leverages existing sedimentary materials from NSF-funded repositories. These results will improve understanding of how seafloor processes influence ocean chemistry and the availability of metals that support marine life. The project will also provide training for students in analytical techniques and scientific research. Previous studies of sedimentary cores near several mid-ocean ridges indicate that hydrothermal metal fluxes were greater during the last deglaciation than the Last Glacial Maximum. Additionally, results from the southern East Pacific Rise indicate that fluxes of 230Th to ridge crest sediments were much higher than the water column production rate, driven by hydrothermal scavenging and lateral transport of 230Th from the ridge flanks. This study will test whether metal flux records are controlled by sea level or alternatively, by changes in oxidation rate linked to ocean ventilation and changes in ocean circulation. The focus will be on developing high-quality metal flux records along a transect spanning from 300 km to 2500 km west of the southern East Pacific Rise. Analyses include constructing foraminiferal d18O records, 14C dating and 3He-normalized sediment fluxes, and metal concentration records. The results could have broad implications for understanding the links between the fluid and solid earth. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Many engineering systems involve fluid flows that change over time, such as the flow around accelerating transport vehicles, flow control to reduce drag, and flows that remove heat in electronic devices. Time-dependent flows affect drag, heat transfer, and system reliability; however, the analysis of these situations is often limited to steady flow or flow that varies slowly in time. This project will develop new analysis tools that can accurately characterize and predict time-varying flow behavior beyond these limitations. The methods will be applied to flow scenarios with complex time dependence relevant to transport vehicles and thermal management systems. The outcomes will help enable safer aircraft operations during takeoff and landing, improve the fuel efficiency of transport vehicles, and enhance the cooling efficiency of electronic devices and data centers. Research outcomes will be integrated into multi-level education and outreach activities designed to inspire students at all levels to pursue STEM careers. This project will support the advanced manufacturing of transport vehicles. The objective of this project is to develop a suite of nonlinear analysis frameworks for time-varying wall-bounded shear flows, including nonlinear stability analysis, input-output analysis, and feedback control. These new nonlinear analysis tools are tailored to time-varying shear flows for which existing methods based on linearization or steady base flow assumptions fail to capture essential transient and nonlinear effects. The tools will enable rigorous characterization of nonlinear stability and input-output properties in time-varying shear flows based on Lyapunov theory, which will be applied in channel flows with increasing complexity of time dependence, ranging from periodic to non-periodic, and ultimately to unknown time dependence. The nonlinear frameworks will be employed to study (1) drag reduction by wall oscillations, (2) transition to turbulence in accelerating and decelerating channels relevant to aircraft takeoff and landing, and (3) liquid cooling control of electronics by designing the time-dependence of inflow velocity to enhance cooling efficiency. The research will be tightly coupled to a Fluid Learning, Outreach, and Transition education program to inspire students' interest in STEM careers through conducting outreach to a Boat Camp for 5-6th grade students, advising the undergraduate Electric Boat Club in its participation in the Promoting Electric Propulsion boat racing, mentoring high school researchers, and developing graduate course modules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Lake Tanganyika is renowned for its biodiversity, but the spectacular life in this vast and ancient ecosystem is threatened by warming temperatures in ways that are not well-understood. As one of sub-Saharan Africa’s most prolific inland fisheries, a healthy Lake Tanganyika is critically important to the nutrition of four developing nations. If global warming alters internal processes that affect the fish production in Lake Tanganyika, then the food security for millions of people will suffer. Moreover, the impacts of environmental change on the characteristics of different groups making up Lake Tanganyika’s open water and lake floor communities, as well as interactions among these groups, are unknown. This project aims to study the response of Lake Tanganyika’s food web to several different scenarios of climate change using sediments, fossils, and genetic tools. The results of the project will reveal how aquatic organisms, particularly economically valuable fish, respond to changes in temperature and precipitation within large tropical lakes. With this information, fisheries and ecosystem managers will be better equipped to safeguard food resources and biodiversity in their areas of responsibility. Finally, this project will include strong international partnership to train students, conduct workshops and develop materials for public audiences. This project will use Lake Tanganyika’s high-resolution sedimentary record to set up a series of historical experiments to track functional biodiversity lake-wide. This framework integrates geochemical, fossil, and genomic tools to assess open water and bottom-dwelling community structures and functions under different scenarios of climate change, as well as the physical and physiological responses of key organisms to these changes. Because the hydroclimatic conditions of the Holocene are underrepresented in historical data, this approach provides the opportunity to evaluate the consequences of environmental change for Lake Tanganyika’s food web in a way that was previously impossible to know. In addition, the project will identify shared and divergent responses to climatic fluctuations across the lake’s diverse fauna, and link these responses to trait-based understanding of community assembly and functioning. This work holds potential for predicting changes in biodiversity amidst severe climatic uncertainty in large tropical lakes. 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

Many safety-critical engineered systems must remain reliable and safe throughout their intended lifetime to support both public safety and economic stability. System diagnostics plays a vital role in predicting future conditions and guide decisions to prevent unexpected failures or mitigate impacts. However, the existing forecasting methods often depend on large amounts of experimental data or real-world system failure data, which are difficult to obtain, especially for safety-critical systems, where laboratory degradation experiments are time-consuming and field failure events are rare. There are two more key challenges: First, physics-based simulations of how system degrade over time can differ significantly from what actually happens in the real world, especially when a system is severely deteriorated. Second, online monitoring data from a specific system unit may be limited, showing little or no noticeable signs of damage early on. The project aims to develop algorithms and tools that enable reliable system forecasting even in small-data environments; that is, environments that are characterized by imperfect physics, scarce experimental data, and system monitoring data. By probabilistic lifetime prediction, these tools will support rapid, risk-based decisions on quality control and maintenance long before degradation becomes obvious. This project will also promote learning through open-source tools and modules and support STEM education through education and outreach activities. This project will develop a physics-informed probabilistic prognostics platform called Modular Analytics for Prognostics with Small Data, which comprises three main modules. (1) It will derive degradation models from physics-based simulations using sparse symbolic regression and recover the unmodeled degradation physics in the derived models through residual learning under uncertainty. (2) It will enable bi-level degradation model updating at the population and unit levels to achieve unit-specific, probabilistic health forecasting from early-life monitoring data. (3) It will create a degradation-aware policy optimization framework to integrate early-life health forecasts with downstream decision making. Overall, this research lays the foundation for smarter early-life health management strategies that lower life cycle costs and extend service life, and in many cases, promote sustainable practices across industries. 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

Today, society faces critical issues of poverty-driven food insecurity and the opioid epidemic: in 2023, 36.8 million Americans lived in poverty, 47.4 million experienced food insecurity, and 8.9 million misused opioids. As these crises operate synergistically, addressing poverty-driven food insecurity while mitigating opioid-related harms presents a pressing and complex societal challenge. Although efforts have been made to tackle these issues separately and to explore their interconnections, research on developing effective, integrated interventions tailored to affected populations is lacking. To bridge this gap, by harnessing the big data revolution and advancing artificial intelligence (AI) technologies, the goal of this project is to design and develop a data-driven, AI-augmented paradigm to investigate the intersection of poverty-driven food insecurity and the opioid crisis and develop integrated, personalized interventions for affected individuals to address the intertwined challenge, and thus help enhance national public health, safety, and welfare. The project outcomes (e.g., open-source code, benchmark data, models, and findings) will be made publicly accessible and broadly distributed through demos, publications, and media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities. Tackling the nexus of poverty-driven food insecurity and the opioid crisis is an urgent societal priority. To achieve this goal, this project consists of three coherent research objectives. First, although the U.S. food assistance system (with 211 food banks and 26,000 pantries) serves millions, the specific distributed foods and their nutritional value remain unclear. To address this, the team will develop an adaptive multi-agent framework powered by large language models (LLMs) to automate analysis of free food supplies and reveal their nutritional contributions. Second, a critical gap exists in understanding how poverty-driven food insecurity and opioid misuse reinforce each other, and what the specific nutritional needs of vulnerable populations are. To fill the gap, the team will build an integrated graph from multi-source data across social, food, health, and nutrition domains, and advance graph prompt learning and graph retrieval augmented generation (GraphRAG) techniques to develop a novel causal analysis method that explores their intersection and informs targeted food demand strategies. Third, with the analyzed food supplies and informed demand strategies, optimizing personalized, food-secure, and nutrition-adequate interventions for affected individuals remains a key objective. To achieve this, the team will develop a novel multi-armed bandit algorithm integrating free food access, user budgets, and nutritional needs to close the supply-demand gap and enable effective, integrated interventions. The suite of novel AI-driven techniques developed in this project will benefit research communities in information integration and informatics (III). This AI-augmented paradigm can also be adapted to other crises - such as substance abuse, educational deprivation, and suicide risk in impoverished communities - and will benefit fields including economics, epidemiology, policy, and social 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 · 2025-10

Cartilage damage is a common and difficult-to-treat condition that affects millions of people, especially those with arthritis, injuries, or chronic inflammation. Cartilage does not heal easily, and these challenges are made worse in environments such as space, where the absence of gravity weakens the musculoskeletal system. This project aims to engineer new cartilage tissue that can survive and function even under difficult conditions like inflammation or low-gravity environments. Using advanced nanotechnology, this project looks to realize a “smart” tissue that not only promotes healthy cartilage growth but also actively controls immune responses that could damage the tissue. This work aligns well with the National Science Foundation’s mission by promoting the progress of bioengineering, biomechanical and materials sciences. The outcomes could benefit people on Earth who require tissue-engineered cartilage, as well as astronauts experiencing tissue degeneration during long-term missions. Beyond scientific research, this project will provide hands-on training for undergraduate and graduate students and offer public outreach programs to high schools and broader communities. By making scientific progress accessible and impactful, this research intends to serve the national interest in science, education, and innovation. This project looks to develop immunomodulatory cartilage constructs that combine Janus base nanomatrix scaffolds with Janus base nanoparticles to promote chondrogenesis under inflammatory conditions. Unlike conventional cartilage engineering approaches that assume healthy environments, this project targets diseased and immune-activated contexts using human mesenchymal stem cells. Moreover, genetically encoded biosensors will be used to monitor inflammatory signaling and tissue response in real time. The research will test the constructs in both terrestrial and spaceflight conditions, allowing for the investigation of biomechanical and immunological interactions under mechanical unloading. By focusing on immune-cell crosstalk and microgravity effects, this study seeks to advance fundamental knowledge in mechanobiology and biomechanics. The findings could enable future scaffold designs for regenerative engineering and biomanufacturing platforms. 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

Opioid use disorder (OUD) and addiction affects approximately 3.7% of U.S. adults (9.37 million) and caused more than 70,000 deaths from fentanyl overdoses in 2023. However, we have a limited understanding of where, when, and how opioids modulate the diverse behavioral outputs of the brain. This is partly due to the limited technology available for in vivo opioid monitoring in the brain. This project aims to develop new technology to monitor fentanyl in the brain. The developed technology has a significant impact in several settings. It offers urgently needed technologies to understand with high spatiotemporal resolution how opioids modulate diverse behavioral outputs of the brain. Moreover, the underlying bioelectronic design principles and knowledge generated will be applicable to other fields, including biosensors for therapeutic drug monitoring, immune response tracking, and chronic disease management. The project will involve high school and undergraduate students. Students will receive training in experimental techniques, data analysis, and scientific writing. New course modules leveraging the results of the work will be incorporated into existing undergraduate and graduate courses at North Carolina State University and the University of Connecticut. The goal of the project is to develop and characterize a wireless bioelectronic system for high-performance fentanyl monitoring in the brain of freely moving small animal models. To achieve this goal, we will: 1) isolate, characterize, and engineer aptamers targeting fentanyl, a primary opioid associated with OUD, 2) develop an implantable nanoporous electrochemical aptamer-based (nanoEAB) fentanyl sensor, and study the structure–property relationship of a new surface coating to improve its in vivo longevity, and 3) establish and validate a wireless bioelectronic system for fentanyl monitoring in the brain of freely moving animals. The project will significantly advance the design and development of wireless bioelectronic systems for high-performance fentanyl monitoring in the brain. Additionally, the developed fentanyl sensors could serve as a technology platform for developing wearable emergency response systems for real-time opioid monitoring and closed-loop delivery of therapeutic drugs such as naloxone. Finally, due to the generalizability of the aptamer selection and nanoEAB platform, this technology will serve as a template for designing sensors for monitoring other molecules of biomedical interest by simply replacing the aptamers functionalized on the sensor surface. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by improving educational interventions and assessments of engineering students' ethical judgments. Ethical lapses in engineering practice can result in loss of life, property damage, infrastructure failures, and environmental harm. By better preparing students to make more ethical decisions, the project seeks to produce significant societal and economic benefits. This Level 2 Engaged Student Learning project integrates a game-based educational intervention that immerses students in realistic, narrative-driven scenarios requiring ethical decision-making and qualitative discussion. Additionally, the project leverages advanced machine learning and large-language model AI tools to assess student responses, creating a scalable and dynamic tool for ethics education and assessment. These innovations are expected to contribute to advances in teaching practices and the broader integration of ethical reasoning into engineering curricula. The project has two primary goals: to investigate the impact of contextualized information on student ethical judgments and to explore the affordances of large-language models (LLMs) and natural language processing (NLP) in assessing student responses. Using a game-based intervention, students engage with engineering-contextualized ethical dilemmas that provide varying contextual cues, hypothesized to promote more nuanced and situated ethical reasoning. The project team intends to code student narratives to identify themes and ethical reasoning complexity, using this data to train LLM/NLP models for categorization of responses. This approach represents a significant step toward scalable, data-driven assessment of ethical judgment. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the project supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Modern transportation systems generate massive amounts of data, including where and how vehicles and people move, traffic conditions, road conditions, and videos captured during actual trips. This includes detailed information about everyday driving behavior collected by cameras and sensors in cars and on roads. These datasets are essential for improving traffic safety, reducing congestion, and supporting the development of advanced technologies such as self-driving cars. However, they often contain sensitive personal details about individuals, making it difficult to share among traffic authorities, companies, and research institutions. This project addresses this challenge by developing secure methods for sharing transportation data while protecting individual privacy, serving the national interest by advancing transportation safety, supporting economic competitiveness in autonomous vehicle technologies, and strengthening infrastructure resilience through improved data-driven decision making. This project develops a comprehensive privacy-preserving platform for sharing diverse intelligent transportation systems data across different entities. The research targets multiple data types, including vehicle and road user information such as speed, travel times, and trajectories, as well as infrastructure data including traffic flow, control states, and videos. The project focuses particularly on naturalistic driving data collected by in-vehicle sensors and mobile devices. The research team will adapt and scale privacy-preserving techniques to support both centralized and distributed data-sharing models, ensuring secure data exchange without compromising individual privacy. The project will develop a web-based recommendation system to assist stakeholders in selecting appropriate privacy-preserving techniques for their specific datasets. Additionally, the team will create audit and compliance tools based on formal privacy guarantees and conduct user studies to ensure practical relevance. Secure cyberinfrastructure will be designed and deployed through collaboration with public and private partners. The platform will be evaluated using real-world transportation datasets to demonstrate effectiveness in enabling privacy-preserving data sharing that supports transportation research, improves traffic management, and accelerates development of data-driven mobility technologies. 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

The hardware advances of recent years have brought multicore chips and parallel computing to the mainstream. As a result, today, parallelism is not found just in the traditional scientific applications that have dominated research and development in parallel computing in past decades. We must now consider parallelism in interactive applications which are characterized by frequent interactions with users or other software systems and therefore must be responsive. This project’s aim is to develop a practical approach to interactive parallel applications. The project’s novelty, in addition to focusing on this under-studied intersection of parallelism and interaction, is that it follows an end-to-end methodology that brings together many areas of computer science and bridges theory with practice. The project has the potential to impact the design of several application areas that require large-scale interactive applications, including web services, desktop clients for CAD/CAM, games, and a variety of mobile applications. This research’s end-to-end goals require advances in type systems, programming languages, scheduling theory, and runtime systems. The research team will develop a calculus for modeling interactive parallel applications at a high level of abstraction. This calculus will equip a fully general formal programming language based on Church's Lambda Calculus with a cost semantics, making it possible 1) to express interactive parallel applications and 2) to reason about the throughput and responsiveness of the programs. A type system will ensure the absence of thorny bugs such as priority inversions that can prevent establishing responsiveness guarantees. The investigators will prove that this calculus is realizable by developing scheduling algorithms that can faithfully match the cost semantics so as to guarantee the desired performance criteria. On the practical side, the project team will extend Cilk, a C-based parallel programming language, to support interactive parallel applications. This will require developing a run-time system that faithfully implements the scheduling algorithms and optimizations that ensure practical performance. The educational component of this project, which involves teaching undergraduates parallel algorithms, will create ample opportunities to test the practical effectiveness of the proposed approach. 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

Currently, about half of the world’s population, about 4 billion people, live in areas with a risk of dengue infection, with further increased public health concerns due to recent evolutionary adaptation of dengue-transmitting mosquitoes to colder places. This project develops and uses mathematical models and computational methods, including the novel Math-model Informed Neural Networks (MINN) based on emerging mathematics in mosquito-dengue biology. Proper management guidelines, identified through data-driven models, help healthcare professionals mitigate the burdens of dengue infection, thereby improving the quality of life for dengue-infected patients and their families. The outcomes of this project not only fundamentally advance the fields of mathematical biology and quantitative biology but also have a simultaneous broad and highly positive societal impact. In addition, this project offers extensive interdisciplinary research training opportunities for undergraduate and graduate students in mathematics and biology. The project will expand research and educational opportunities to various programs for students, as well as junior and senior researchers, and will incorporate the research into an interdisciplinary mathematical biology course. This project will focus on three aims: (a) Develop Math-model Informed Neural Networks (MINN) capturing emerging mathematics in climate-dependent mosquito-dengue biology. (b) Analyze models and develop MINN-based methods to estimate epidemic thresholds. (c) Develop MINN-based user-friendly online platforms for public health policy evaluations and healthcare accessibility. The novel models, validated using data from our collaborators (biologists/environmentalists) from Nepal, will incorporate an experimentally observed mosquito life cycle and dengue transmission. The models and related MINNs will be used through a user-friendly online platform to evaluate public health policies and calculate healthcare accessibility for dengue control in spatially heterogeneous environmental conditions. This contribution will have a significant positive and practical impact on developing public health policies to prevent dengue virus infection, as well as advance the development of sophisticated mathematical and machine learning models to help explain the role of the environment and mobility in the complex biological systems of mosquito-dengue interactions. 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

Modern time series data present complexities; these complexities, along with the rapidly growing array of new statistical and machine learning (ML) methods, have driven the demand for novel solutions to emerging problems. This CAREER project is driven by three fundamental research questions: (1) How to balance interpretability and accuracy in high-dimensional time series modeling and inference? (2) How to adaptively select time series models in real time for nonstationary data, while managing uncertainty? and (3) How to efficiently combine information from time series data with varying quality? This project aims to advance the field of time series analysis by developing novel statistical models, theories, and inference methods to address these issues. The results of this research will enhance dynamic network inference, facilitate real-time decision-making, and promote the integration of diverse time series data sources. This project will achieve educational impacts by integrating our research with mentoring undergraduate and graduate students, developing courses, and high-school outreach. Additionally, an interdisciplinary time series seminar series will be organized to promote cross-disciplinary interactions and provide students and junior researchers with exposure to diverse research in time series analysis. This project will advance time series analysis on three main fronts: (1) develop Granger causality interpretable, recurrent neural network-based high-dimensional time series models to balance interpretability and accuracy; (2) develop an online, distribution-free procedure for adaptive time series model selection in nonstationary settings, addressing uncertainty via conformal miscoverage rate calibration; and (3) introduce new methods to efficiently combine time series data with different granularities and to impute data under general missing patterns. Underlying this research agenda is our overarching goal to tackle challenges due to the high-dimensionality, nonlinearity, nonstationarity, different granularity, and mixed quality and completeness of modern time series data. With an emphasis on statistical inference, we seek novel solutions by integrating existing statistical frameworks (Granger causality, model confidence sets, and factor models) with contemporary ML approaches (RNNs, model predictive control, and transfer learning). The results developed through our project will advance innovation in time series analysis, bridge the gaps between interpretability, uncertainty quantification, and black-box algorithms, and promote the use of time series data collected from diverse sources. 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

In the standard model of nuclear physics, individual protons and neutrons are described as bound assemblies of much smaller particles called quarks and gluons. While the types and numbers of quarks that belong to one proton or neutron are tightly constrained within the model, no restrictions are placed on the configuration and number of gluons that make up the bound state. One way to study gluon structure within the nucleon is through the process of deep inelastic scattering (DIS), where individual gluons are knocked out in a hard collision with a point-like probe. Experiments of this type at the CERN Large Hadron Collider (LHC) and at the future Electron Ion Collider (EIC) yield new information about the gluon structure of the nucleon. Another way to study the way gluons are arranged in stable nuclear matter is by passing high energy photons through stable nuclear matter and looking for the remnants that are produced when the photon is absorbed. While such interactions do disturb the quiescent state of the gluons inside the target, these excitations are much softer those explored in DIS experiments, and can provide complementary information about the way gluons are arranged in stable nuclear matter. One particularly interesting outcome from such spectroscopic studies would be the discovery of so-called exotic mesons in the photon remnants after the collision. The PI and students at the University of Connecticut are participating in the Gluonic Excitations Experiment (GlueX) at Jefferson Lab in Newport News, Virginia to search for a specific class of exotic mesons called hybrids that carry a unique experimental signature that allows them to be distinguished from other less exotic forms of nuclear matter. The UConn group is responsible for the source of polarized photons (gamma rays) which are produced by passing the 12 GeV electron beam from the Jefferson Lab accelerator through a carefully crafted and oriented diamond crystal. Polarized gamma rays from the source are directed onto a liquid hydrogen target where resonances are produced, whose subsequent decays are detected and identified in the GlueX spectrometer surrounding the target. The PI and students work with leaders in the diamond industry to improve the quality of the very thin sections of single-crystal diamond needed to meet the strict demands of this application. In parallel with this effort, the UConn group is also responsible for key aspects of the GlueX detector instrumentation, and for the Monte Carlo simulation that is essential to the interpretation of the experimental results. This includes improving the particle ID capability with machine learning algorithms The GlueX experiment aims at clarifying the role played by gluonic degrees of freedom in the excitation spectrum of light-quark hadrons. Data collected in pursuit of this goal also shed light on a number of additional topics in nuclear physics, including near-threshold J/ѱ photoproduction, rare decays of the (550) meson, the 2 decay width of the and ’ mesons, and the polarizability of the charged and neutral pions (CPP, NPP). GlueX has now completed 70% of its approved data collection for GlueX Phase 2, and has received approval for a third phase of running at a factor 2-3 higher intensity. The UConn group plays a critical role in quantifying and reducing systematics related to the properties of the photon beam, in addition to supporting the ongoing operation of the photon beamline and tagger. Support under this grant enables the PI and one PhD student to provide and enhance the quality of diamond radiators for Hall D experiments, maintain and operate the tagger microscope, and lead the ongoing development of the physics simulation for the GlueX experiment. In parallel with these efforts, UConn students will also carry out physics analysis in line with the primary GlueX physics program in hybrid spectroscopy and related topics. 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 provides U.S. students with an immersive international research experience focused on advancing hydrogen technologies for clean transportation. The Hydrogen Ship Technology Center (HSTC) at Pusan National University in South Korea is one of the few global research hubs with the infrastructure, industry partnerships, and technical expertise to support full-scale development and testing of liquid hydrogen (LH₂) systems for transportation. By working directly at HSTC, the program gives U.S. students access to a world-class research environment where they contribute to technologies critical to next-generation zero-emission vehicles in shipping, aviation, and heavy-duty logistics. Over ten weeks each summer, students work alongside Korean researchers and are jointly mentored by faculty from the University of Connecticut and Pusan National University, while building technical skills and gaining experience in international collaboration and scientific communication. The project focuses on both fundamental and applied research to address key challenges in LH₂-based transportation. Students investigate fuel cell systems by examining catalyst layer structure, membrane durability, and thermal and water regulation in proton exchange membrane fuel cells (PEMFCs). In the area of cryogenic storage, they evaluate insulation materials to reduce boil-off losses and study the structural integrity and thermal behavior of double-walled vacuum-insulated tanks. Research on material degradation includes assessing hydrogen embrittlement in metals and fiber-reinforced composites under cryogenic exposure through mechanical testing and microstructural evaluation. Students also explore recovery of cold energy from boil-off gas for use in vehicle-scale auxiliary cooling systems. Research activities include full-scale experimentation, material testing, thermal-fluid modeling, and finite element simulations. 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 investigator explores how galaxies, supermassive black holes (SMBHs), and the Universe's large-scale structures form and change over time. At the centers of many galaxies are SMBHs, which are billions of times heavier than our Sun and can influence their surrounding galaxies. This research team will develop computer simulations that will lead to a better understanding of galaxies, SMBHs, and the Universe itself. This research will help answer fundamental questions, like how dark matter and dark energy influence the evolution of the Universe. This research will inspire students and the public while creating new opportunities for learning. This project will also support research mentorship programs, computer programming workshops, and a public planetarium show. Through integrated educational and outreach components, this project will also significantly enhance STEM education, ensuring its benefits extend beyond the research program. The investigator will overcome the limitations of current cosmological hydrodynamic simulations of galaxy formation by focusing on two key areas: (1) advancing physically predictive models of galaxies at sub-parsec resolution and (2) developing computationally efficient, large-scale simulations that incorporate baryonic physics while satisfying observational constraints. In the first research area, the investigator will integrate detailed interstellar medium physics from the FIRE-3 model with new Lagrangian hyper-refinement techniques to explicitly resolve sub-parsec scale accretion onto SMBHs and multi-channel AGN feedback. This will allow the project to address major questions about the coevolution of SMBHs and galaxies, create synthetic observations to interpret astrophysical data across cosmic time, and develop initial conditions for super-zoom simulations that explore accretion disk properties under varying host galaxy conditions. In the second research area, the investigator will develop a hybrid simulation framework that for the first time leverages empirical galaxy-halo models to enhance the computational efficiency and flexibility of cosmological hydrodynamic simulations. This approach will produce thousands of large-volume simulations at a fraction of the computational cost of traditional methods and varying sub-grid feedback assumptions while satisfying by construction a variety of observational constraints. 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

Rising sea levels, intensifying storms, and increasing flood risks are creating unprecedented challenges for coastal communities across the northeastern United States. In this region in particular, the magnitude of projected sea level rise is among the largest of any in the world threatening millions of Americans and billions of dollars in infrastructure. Small municipalities and neighborhoods often lack the expertise and tools needed to translate cutting-edge Earth system science into practical protection strategies for their residents. This project will build a collaborative network of scientists, engineers, policy experts, and community leaders to accelerate the development and implementation of adaptation solutions at the scale where they matter most, individual properties and neighborhoods. The project aims to reduce flood risks that currently cause over $32 billion in annual damages, protect vulnerable populations from extreme heat related illness, and preserve coastal infrastructure that supports regional economies. The collaborative approach of the project is driven by community needs in the Northeast region and will provide a replicable model for environmental adaptation nationwide. This project establishes a regionally coordinated adaptation network spanning Connecticut, Maine, New York, and surrounding northeastern states. The project employs a systematic 10-step coordination plan to engage collaborators from academia, private sector, and government agencies and promote knowledge sharing between established Technical and Policy (TAP) and Municipal, Agency, and Private sector (MAP) teams. Key methods include structured stakeholder meetings, working group development, and consensus-building processes to identify and prioritize environmental challenges including coastal erosion, flood prediction, localized heat risk assessment, and regulatory barriers. The research approach integrates advanced Earth systems science with community engagement methodologies, focusing on developing practical solutions for living shoreline design, high resolution wave and flooding modeling, machine learning-enable flood alerts, and policy innovation protocols. Phase 1 deliverables include: 1) a prioritized list of regional environmental challenges, 2) co-designed solution strategies, 3) a workforce needs assessment, 4) workforce training and development plans and, 5) a sustainable organizational framework for continued collaboration. The innovation and incubation component of the project aims to ensure long-term sustainability of academia-private sector partnerships beyond the grant period, creating lasting adaptation support capacity for municipalities across the region. 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

Mountainous regions are the primary source of water for much of the western United States. Many mountain streams are sustained by groundwater, but conceptual and hydrologic models often oversimplify groundwater processes. As a result, it is challenging to predict how streamflow responds to changes in groundwater recharge and storage caused by extreme wet and dry conditions. This project is evaluating how groundwater regulates stream responses to hydrologic extremes by integrating high-resolution stream and groundwater observations with hydrologic models. The knowledge generated from this work will improve understanding of how stored groundwater impacts mountain streamflow generation, thereby enhancing streamflow predictions. Broader impact activities include an early-career workshop on data-model integration in Earth surface processes, with the goal of fostering cross-disciplinary collaboration. Additionally, the project will integrate field infrastructure and models into undergraduate coursework at three institutions to expose more students to hydrologic science. This project aims to determine the role of groundwater in regulating streamflow response to hydrologic extremes across a groundwater storage gradient using a data-model integration approach. Field observations of stream discharge, source, and age in two mountain watersheds will be integrated with an iteratively calibrated process-based hydrologic model capable of simulating groundwater-surface water interactions under future long-term and short-term hydrologic extremes and with variable subsurface structure. Study sites include two mountain watersheds with high- and low-groundwater storage settings. The project will address how the structure of the subsurface influences the source, age, and magnitude of streamflow, as well as the extent to which upstream heterogeneity affect conditions at the watershed outlet. The project will improve understanding of how groundwater storage modulates streamflow during hydrologic extremes. The project will develop a transferable data-model integration framework to address critical zone science questions. The framework will be the focus of a broader impacts workshop that will provide early-career scientists the opportunity to learn field data or modeling techniques from peers, as well as foster new collaborations and cross-disciplinary learning within the critical zone community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Michael Kienzler of the Department of Chemistry at the University of Connecticut and Professor Steven Lopez of the Department of Chemistry at Northeastern University are collaborating across synthetic and theoretical chemistry to study the reactivity of molecular photoswitches. Molecular photoswitches use light to trigger controllable changes in molecular function. In this project, the PIs are using ring strain as a second means of controlling and enhancing the photoswitching functions. Using ring-strain to increase molecule reactivity is well-established in chemistry. This proposal combines these two ideas to develop cyclized photoswitches to generate ring-strain reversibly and study the activation of functional groups in the rings for wavelength-dependent spatiotemporal control of target reactions. Results from this research project will significantly impact organic chemistry, energy storage, biophysics, and chemical biology. Furthermore, this interdisciplinary project includes a substantial educational framework for supporting STEM students from high school through graduate level chemistry. The long-term goal of this collaboration is to understand the photophysical effects of ring-strain on photoswitches and to demonstrate that the reversible generation of ring-strain can accelerate otherwise unfavorable photochemical reactions. Molecular photoswitches reversibly interconvert between isomers when irradiated with different wavelengths of light and have been a subject of fascination in the chemical community for over a century. Photoswitches like azobenzenes, fulgides, diarylethenes, and hydrazones have numerous applications in widely disparate scientific fields, from nonlinear optics to pharmacology. The broader scientific impacts include light-patterned polymerization to install photoswitches directly into the polymer backbone and to use different wavelengths of light to tune the reactivity of strain-promoted cycloadditions for bioorthogonal labeling. High throughput computations will create datasets of ground- and excited-state properties to guide experimental efforts. This collaborative work will produce structure-reactivity relationships needed to inform the discovery of new photoswitches and their reactions. 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.