Trustees of Boston University

NSF Awards · FY 2026 · 2026-09

Vision foundation models (VFMs) are artificial intelligence systems for "all-purpose" understanding of images and videos. They are currently extremely expensive to create. This high cost restricts their creation to a few highly resourced institutions and leaves independent researchers and the public unable to fully explore how these systems learn. This project seeks to democratize this research by creating a highly efficient training method inspired by how human infants learn. A human child acquires foundational visual skills from a limited number of waking hours compared to the massive amount of data used by current VFMs. By using longitudinal video and audio recorded from the viewpoint of infants, this project develops a training process that is affordable for university budgets. Innovating and understanding how to train these systems efficiently using this infant-inspired approach will increase accessibility to artificial intelligence research for the broader public. Furthermore, the project provides unique educational opportunities for students and offers insights that can be transferred to specialized industries, such as medical imaging and vocational training, where data is often limited. Expanding community involvement in building these models will ultimately promote artificial intelligence safety, enhance transparency, and build public trust. The technical goal of this project is to formalize a developmentally plausible, data-efficient pretraining framework for VFMs. First, the team of researchers will establish a core framework by curating longitudinal, egocentric audiovisual recordings of human infants and designing a suite of evaluation benchmarks strictly aligned with early cognitive milestones. Second, the project bridges inherent sensory and temporal gaps in the recordings. This involves employing model ensembling to simulate tactile and gustatory senses from audiovisual cues and utilizing a meta-learning formulation to optimally mix heterogeneous data sources. Third, the investigators will design novel model architectures and pretraining algorithms tailored for a continuous "baby learning" paradigm. To achieve this, the research incorporates continuous-state Hopfield networks to serve as an expansive associative memory module, which mitigates catastrophic forgetting. Moreover, the project introduces a monotonic neural network for non-linear uncertainty calibration without sacrificing the accuracy of the pretext tasks. By integrating these three thrusts, the project will yield open-source baseline models, developmental benchmarks, and algorithms that enable the broader scientific community to investigate highly efficient pretraining methodologies. 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

Computational fabrication via technologies such as 3D printing and computational knitting are key methods that form a significant part of modern advanced manufacturing, allowing for production of state-of-the-art composites, ceramics, medical grafts, and architectural formworks in complex geometries. Furthermore, recent advances have allowed for curved layer fabrication, producing objects and materials that have superior strength and quality characteristics due to control over build direction. Underlying many of these technologies is the fundamental problem of constructing a surface or volume from a single continuous curve, representing a toolpath or fiber path. To maximize utilization of these technologies, the research team will produce mathematical design frameworks for solving this problem under various fabrication modalities. The frameworks will be tailored to achieve domain-specific performance goals and accommodate domain-specific user design constraints. All resulting tools will be released as open-source implementations for use and further development by industry and academic researchers. Parts of the research will also be incorporated into coursework on geometry processing and graphics, and into graduate- and undergraduate-level research projects, via theses and summer research programs. The research effort will be divided into three thrusts. First, the team will build upon prior work understanding the global topology of vector field flows on surfaces and construct an appropriate discretization and optimization framework that achieves the necessary path continuity and spacing constraints crucial to the fabrication modalities at hand. Second, the work will more closely explore application of the general optimization framework to the specific use case of computational knitting, where the space-filling curve follows the path of stitches that are produced by the machine. In this setting, the team will explore optimal geometric shaping, and incorporation of user design constraints as communicated by industry partners, who are using these methods to produce garments and curved surface composites. Thirdly, the team will look to extend the topological understanding and optimization frameworks to the challenging volumetric setting. This will target the nascent fabrication methodology of curved-layer 3D printing. The global topologies for volumetric fields are much more complex, and not yet fully understood from the theoretical perspective. Incorporation of structural and manufacturing constraints into layer design will also be considered, in collaboration with engineering colleagues. 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

Data-driven personalized decision-making has become increasingly important across many fields, such as health sciences where tailoring treatments to individual patients can improve effectiveness and reduce adverse effects. Achieving reliable personalized decisions requires understanding cause-and-effect relationships between actions and outcomes. However, most real-world data sources, such as electronic medical records, health surveys, and social media data, are observational rather than randomized, making causal relationships difficult to establish. In these settings, hidden or unmeasured factors may influence both the actions individuals take and the outcomes they experience, leading to biased conclusions and unreliable recommendations if not properly addressed. This project will address this fundamental challenge by developing new statistical methods for learning optimal personalized decision rules from observational data when important confounding factors are not fully observed. The project will consider both single-stage and sequential decisions, with particular attention to continuous treatments such as medication dosages. A motivating application is kidney transplantation, where optimizing immunosuppressive therapy over time is essential to reduce the risk of graft failure while minimizing harmful side effects. By enabling more reliable individualized decision-making, this project will advance statistical science, machine learning, and artificial intelligence, support the training of students in modern data science, and contribute to improved health outcomes and broader societal well-being. This project aims to develop novel Bayesian causal methods for estimating treatment effects and optimizing individualized decision rules from observational data with unmeasured confounding. A Bayesian joint modeling framework will be introduced for treatment, outcome, observed covariates, and latent confounders, leveraging mild distributional assumptions to enable causal identification without relying on additional data sources required by many existing approaches, such as instrumental or proxy variables. The project will also develop a dynamic Bayesian causal modeling framework for longitudinal data, where treatment decisions and unmeasured confounders evolve over time. This framework will support the estimation of adaptive treatment regimes that respond to an individual’s evolving treatment history, outcomes, and characteristics. In addition, the project will design optimization methods for both single-stage and sequential decision-making, using posterior uncertainty to improve robustness in finite and unbalanced observational data settings. The methods will be evaluated through simulation studies and applied to large-scale real-world kidney transplantation data for studying optimal personalized and dynamic immunosuppressive dosing strategies. To facilitate broad dissemination, open-source software will be developed for implementation. The resulting framework and tools will provide a general approach to reliable personalized decision-making in biomedicine and other fields that rely on complex observational data. 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 project introduces a unified software stack for secure computation that integrates cryptographic and hardware-based techniques, each with its own unique strengths, challenges, and performance characteristics. The project’s novelties include (i) software abstractions and intermediate representations that allow reusing functionality across technologies and workloads, (ii) a distributed and fault-tolerant system runtime for secure data analysis pipelines, and (iii) a versatile performance modeling and optimization framework that integrates diverse cost metrics to efficiently deploy secure data pipelines in heterogeneous environments. The project’s broader significance is the potential to enable secure analytics in a scalable fashion; an ability that will have implications on how modern society protects privacy and intellectual property while extracting value from data. The project includes three complementary thrusts that focus on software abstractions, scalable workload distribution, and cost-based optimization. The project designs a unified software architecture for secure analytics that supports diverse technologies (fully homomorphic encryption, secure multiparty computation, trusted execution environments) and workloads (machine learning, relational analytics, time series computations) on top of the same oblivious execution engine. Second, the project develops a novel parallel and distributed system runtime that scales secure computation within and across machines, leveraging heterogeneous resources and ensuring transparent fault tolerance. Finally, the project introduces original cost-based optimization techniques that incorporate performance objectives, threat models, and monetary budgets to enable automated planning of secure data pipelines. The project aims to make privacy-enhancing technologies a core component of the computer science education and to lay the foundation for a new generation of secure computing systems by rethinking the entire analytics stack: from the programming abstractions all the way down to the hardware. 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 Boston University, Boston, MA, will support the training of 10 students for ten weeks during the summers of 2026-2028. Students will learn how scientific research in Biology is conducted, and many trainees will present the results of their work at scientific conferences or be included on scientific publications. The program will provide advanced scientific training to undergraduate researchers and enhance the biological research of faculty members at Boston University. A primary benefit to society is that the program will provide early career training in cellular and molecular biology, leading to an enhanced US workforce in key areas of science. Assessment of this program will be done through program surveys, exit interviews, and career tracking after completion of the program. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). Student success will be assessed by scientific publications, further training in graduate school, and career outcomes. The training students will receive is aligned with NSF priorities in Artificial Intelligence and Biotechnology. The main objectives of this REU program are to 1) provide students with a research experience that focuses on modern biological approaches in the area of gene regulation for the control of biological processes; 2) train students in research methods, ethics, and scientific culture; and 3) contribute to scientific expertise in the US by giving experiences and knowledge that can enable students to pursue molecular biology-related STEM careers. Students will be matched to research teams consisting of a faculty mentor and a graduate student. Close monitoring of each student’s progress will enhance the likelihood that a student becomes participatory and independent. Students will give three oral scientific presentations during the summer and will likely return to present their research at BU’s Undergraduate Research Symposium. An understanding of how changes in gene expression lead to given biological outcomes is a key component of modern molecular biology, and is used in many biotechnology applications for the understanding of fundamental biological processes, normal and disease physiologies, as well as diagnostic, monitoring, and therapeutic developments. Sample projects may include studying changes in gene expression during development in model organisms, epigenetic and gene network changes in the control of gene expression, and AI-based approaches to understanding the activity of gene regulatory proteins. 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

This award supports US-based participants at two conferences at the Centre de Recherches Mathématiques (CRM) in Montréal, Canada in the spring of 2026: Arithmetic Statistics (March 2–6, 2026) and Probability in Number Theory (June 29–July 3, 2026): https://www.crmath.ca/en/activities/#/type/activity/id/3952. These are two important inter-related topics that have been the focus of a great deal of high-level research over the last few years. These events will be part of a larger thematic program on "Universal Statistics in Number Theory," that will be held at the CRM from March to July 2026. This program will give early-career US-based researchers the opportunity to interact with the leading researchers in their field, to present their work in front of these leaders, and to develop new working relationships. The main goal of these conferences is the dissemination of the most recent developments in the area of number theory and statistics, including statistics of L-functions, non-vanishing of L-functions, counting integral points, Selmer groups, and probabilistic number theory. The statistical side of the program has important links with homology, topology, and algebraic geometry, and the probabilistic side with cryptography, dynamical systems and ergodic theory, and quantum chaos in mathematical physics. 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

PART 1: NON-TECHNICAL SUMMARY Cells in the body use chemical signals, called chemokines, to guide immune cells to specific locations. This guidance is essential to influence their behavior and help fight infections or maintain normal health processes. These chemical signals often take the form of gradients, meaning the concentration of the chemical signal changes across a region. Immune cells sense these gradients and follow them like a map. Thus, the ability to precisely program these gradients would enable a powerful means to instruct cell behavior. Hydrogels - soft, stretchable materials used in medical products like contact lenses - are currently the standard tool that serves as a passive reservoir of these chemical signals, releasing them over time. However, these simple release mechanisms do not match complex biological timing. Moreover, they cannot schedule release of multiple signals unless leveraging differences in signal size or solubility, and fail to protect the signals against biological stresses that can degrade them. This underscores the need for innovative hybrid biomaterials that can mimic the body's natural way of forming these signaling gradients, and advances the field of biotechnology. This research develops platforms that can release these chemical signals to guide immune cells in predictable ways in three-dimensional environments. It leverages computational modeling-informed design and rationally structured combinations of porous nanoparticles (that can encapsulate and protect chemokines) with hydrogel chemistry. Design features such as nanoparticle pore size, nanoparticle degradation rate, and chemical interactions linking nanoparticles to the hydrogel, can control chemokine protection and release, and shape resulting gradient formation. By uncovering these foundational structure-function relationships, this research will define design principles that map how cells move, forming the basis for future innovations across biotechnology sectors in immune therapies and regenerative medicine. This project also integrates a significant educational component to broaden engagement and participation in STEM. Local high school students will learn about biotechnology and nanomaterials through hands-on activities, veterans will participate in facilitated two-way discussions to strengthen the research-community nexus, and curriculum development will train students for biotechnology-based STEM careers. PART 2: TECHNICAL SUMMARY This CAREER project will rationally design hybrid materials comprising metal-organic framework (MOF) nanoparticles embedded in hydrogels to program chemokine gradients and resulting immune cell chemotaxis. Chemical signaling gradients are crucial for guiding cell behavior; chemokine spatial distribution influences and biases cell movement. Thus, precisely programming these gradients can instruct cell behavior. Hydrogels have served as passive cargo reservoirs to release these types of cues, but they traditionally exhibit release dictated by a single degradation process, which may not match complex biological timing. Moreover, they cannot schedule multi-cargo release unless leveraging differences in cargo size or solubility - insufficient to program complex responses - and these systems cannot protect chemokines against biological stresses and achieve precise spatiotemporal tuning without cargo conjugation to the network, which risks altering function or structure. Holistically, this underscores the need for innovative biomaterials. By leveraging the tunable degradation, pore size, protective stability, and connective chemical interactions of MOF integrated into hydrogels, this project will program chemokine release and instruct immune activity through 4 research objectives: (i): determine how MOF nanostructure encodes chemokine release; (ii) alter hybrid interactions between MOF nanostructure and hydrogel chemistry; (iii) evaluate how the interplay of local MOF chemokine release and global hydrogel environment affects chemokine gradient formation; and (iv) define biointerface dynamics by employing cell migration and activation as readouts. Collectively, this will deepen understanding of design principles that govern hybrid materials tailored for dynamic and precise chemical signaling. Insights gained can inform biotechnological interventions to program cell movement and interactions, and enable therapeutic breakthroughs in immunotherapy and regenerative medicine that depend on immune activity. The broader impacts of this work integrate with educational objectives: (i) increase participation in biotechnology and hydrogel materials for young trainees; (ii) increase biomaterials exposure and communication with veterans; and (iii) curriculum development to bridge biomaterials design with immunology and enhance critical analysis skills to prepare students for biotechnologically-focused STEM careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Models used to simulate weather and climate rely on sophisticated algorithms to represent the physics of the atmosphere, ocean, land surface, and cryosphere. These models have been quite successful but they have two important shortcomings: first, they are computationally intensive, typically running on world-class supercomputers and generating terabytes of data which are challenging to host and serve. Second, they do not take advantage of the large amounts of observational data collected over decades using satellites, weather balloons, ocean moorings, and other observing systems. A new approach addresses these shortcomings by developing "climate emulators" which use machine learning to extract statistical relationships from observations and various types of physics-based computer simulations. Climate emulators have tremendous potential but it is unclear how well they capture the underlying physics of weather and climate and are thus able to generalize beyond their training sets. For instance an emulator which has learned statistical relationships in a cold climate might not perform well in a warmer climate or vice versa. Work performed under this award uses multiple emulators to simulate the response of the climate system to patches of warmer surface temperatures in different regions. The patch methodology is well established and thus allows evaluation of emulators against traditional physics-based climate models. The work also addresses long-standing questions in coupled climate dynamics, such as the effect of surface temperature fluctuations in one region on surface temperature in other widely separated regions, the effect of regional surface temperature variations on the global energy balance, and the extent to which precipitation in the Southwestern US can be predicted from knowledge of surface temperature variations over the tropical Pacific Ocean. A key issue in the work is the ability of climate emulators to conserve energy, as energy conservation would dramatically increase their value and adoption for both scientific and practical applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

Over fifty percent of the world’s reef-building corals have experienced mortality since the 1970s. Conserving what remains of coral reefs is vital given the importance of these ecosystems to ecological stability and human society. For example, coral reefs are a valuable economic resource, protecting coastlines from damaging waves and supporting fish and other species that are important food resources across the globe. However, our ability to sustain healthy coral reefs is limited by our lack of knowledge surrounding how corals respond to environmental conditions, and in particular combinations of stressors. Through experiments conducted in Bermuda, the lead researcher will investigate how seawater heat, low oxygen, and the combination of these factors impacts corals as adults and early in their life cycle. Given the importance of coral reefs for national health, prosperity, welfare, and defense, this research will serve the national interest in line with NSF’s mission. Additionally, this research contributes to our understanding of how the ocean impacts and is impacted by humans, in line with the mission of the NSF Division of Ocean Sciences. The lead investigator will also involve students in the research process to integrate youth into coral reef research and conservation efforts while strengthening the public understanding of the importance of coral reefs. This project will characterize the effects of heat, hypoxia, and their combination on a reef-building coral across life history stages (larvae to adult). First, the investigator will profile abiotic conditions at a reef in Bermuda and perform a mesocosm experiment to characterize coral physiological and molecular (e.g., gene expression) responses to heat and/or hypoxia. Next, coral larvae will be exposed to heat and/or hypoxia and their physiological and molecular responses characterized, followed by being outplanted on the reef as juveniles for later sampling to quantify carryover effects. This work will expand our understanding of how corals respond to multiple environmental stressors across life stages, with implications for population projection models and conservation efforts. In Boston and Bermuda, the investigator will work with students at all levels, including through an educational module that will give students hands-on research experience while expanding public awareness of the global importance of coral reef ecosystems. Overall, this work will benefit society by advancing our ability to conserve coral reefs while also providing foundational education to the next generation of scientists. 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

Microbial fermentation is essential for producing a wide range of products, including medicines, biofuels, and food ingredients. The microbial behavior during these complex processes must be monitored closely. This project will develop a new class of wireless, free-floating biosensors that operate directly inside fermentation tanks. These sensors will continuously track microbial health and activity. The sensors combine specially engineered microbes that emit light in response to cellular stress with embedded electronics that detect both chemical and optical signals. The resulting system provides a non-invasive way to observe changes in fermentation and improve control of biomanufacturing processes. The interdisciplinary project will train students in cutting-edge techniques across synthetic biology, semiconductor technology, and biomanufacturing. All designs and data will be shared through open-access platforms. Through partnerships with industry, the technology will also be validated in real-world production environments, supporting a stronger economy. This project develops a novel class of wireless, free-floating biosensors that integrate electronic and biological sensing elements to provide in situ, real-time monitoring of microbial dynamics within industrial fermentation environments. The sensing platform combines miniaturized, complementary metal–oxide–semiconductor-based electrochemical and optical sensor arrays with engineered whole-cell biosensors in the yeast Yarrowia lipolytica, which has been genetically modified to emit bioluminescent signals in response to intracellular stress and metabolite levels. These hybrid sensors enable continuous, spatiotemporal measurements of key fermentation parameters, including redox state, media composition, and cellular metabolic activity, without intrusive sampling or fixed probes. The NSF-supported work focuses on engineering and characterizing auto-bioluminescent Y. lipolytica strains, developing calibration frameworks for interpreting complex biological signals, and building embedded sensor nodes that operate autonomously and communicate wirelessly. These efforts advance core research areas in microbial signal transduction, bio-electronic interfacing, and systems-level bioprocess modeling, laying the groundwork for Artificial Intelligence-driven fermentation control strategies. Through collaboration with Capra Biosciences and complementary translational support from BioMADE, the platform will be further miniaturized, industrially validated, and deployed in real-world production settings. This research contributes to national bioeconomy goals by enabling tools that improve the efficiency and resilience of microbial bioproduction systems. It will provide interdisciplinary research training for undergraduate and graduate students in synthetic biology, integrated electronics, and bioprocess engineering. The project includes a partnership with Boston University’s Science, Technology, Engineering, and Mathematics Pathways program to engage students in hands-on research and offers industry-facing experience through collaboration with Capra Biosciences. All microbial reporter strains, protocols, and sensor datasets will be shared through open-access repositories to ensure broad dissemination and reuse. In addition, the team will host both virtual and in-person technical workshops, covering topics such as whole-cell biosensor design, low-power sensing technologies, and bioinstrumentation in synthetic biology, to promote broader adoption and community engagement across academic and industrial institutions. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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

Hydrilla verticillata is an invasive aquatic plant that is rapidly spreading in freshwaters across the eastern United States, including the lower Connecticut River. With large environmental and economic impacts, hydrilla is one of the most problematic invasive aquatic plants in the United States. Hydrilla forms dense submerged mats or canopies, which impact native habitats, hydrology, carbon cycling, and recreation. To control the spread of hydrilla, experimental herbicides will be applied to affected areas in the lower Connecticut River in summer 2024. This RAPID project will leverage these herbicide applications as a unique plant-removal experiment to better understand the effects of hydrilla on freshwater ecosystems. The overarching goal of the project is to understand how this invasive aquatic plant can alter carbon cycling and greenhouse gas emission in inland waters. This research is important for understanding the full impacts of invasive aquatic plants on ecosystem function and potential linkages between climate change and invasive aquatic plants via greenhouse gases. Findings will inform aquatic plant management. This research will explore how carbon concentrating mechanisms that help invasives like hydrilla outcompete native species can shift ecosystem-scale primary productivity from using only carbon dioxide to also using bicarbonate, a non-gaseous form of inorganic carbon. Bicarbonate uptake by hydrilla has the potential to transfer carbon from the slow-cycling geologic pool (bicarbonate has geologic sources) to the faster-cycling biologic pool (carbon dioxide in inland waters largely comes from respiration). This study will use oxygen and carbon dioxide sensors and grab samples to compare ecosystem metabolism and carbon cycling in treated and untreated embayments. Weekly samples will be used to assess greenhouse gas emissions. Analyses will determine the extent of bicarbonate uptake by hydrilla and the cascading impacts on carbon cycling processes. 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

Earth’s ionized outer atmosphere has a significant impact on radio-frequency (RF) signals used for communication, radar, and Global Navigation Satellite Systems (GNSS) such as GPS. These effects intensify and become less predictable during elevated solar activity, particularly at higher latitudes where auroral phenomena produce complex and transient ionospheric structures that cause GNSS signal scintillations and tracking failures. At high magnetic latitudes, Earth’s convergent magnetic field acts as a lens, channeling electromagnetic energy derived from the solar wind into a narrow latitudinal region. Most of this energy is dissipated in the lower ionosphere (<200km) through a complex interplay of particle precipitation, plasma heating, and turbulent transport. This multi-scale dynamic is poorly understood and challenging to observe. This project develops innovative AI-driven methodologies to enhance our understanding of these structuring processes and their implications for technologies we rely on for convenience, safety, and national security. The research will facilitate new approaches for monitoring and mitigating ionospheric effects on RF signals and will guide the creation of next-generation "smart sensors" that incorporate a hybrid suite of sensors alongside on-sensor generation of ionospheric models. This project will support graduate and undergraduate student training. This project addresses this challenge through a methodology that leverages the complementary nature of GNSS and optical data. Wide-field imaging of select emissions in the aurora and airglow spectrum provide quantitative information about plasma production and loss rates, while dual-frequency GNSS receivers offer precise measurements of path-integrated plasma density, referred to as Total Electron Content (TEC). These measurements are connected through established physics-based models. The objective is to identify ionospheric parameters—such as density, temperature, and ion composition—that are consistent with both observational data and established physical principles. Science questions to be addressed are (1) What are the important multi-scale plasma density patterns that define the electrical load seen by the magnetosphere? and (2) How do variations in latent parameters impact the ionospheric response? This work is ideally suited to a specialized form of AI known as Physics Informed Machine Learning (PIML), which incorporates known physics to uncover hidden states of the ionosphere, providing both detailed ionospheric representations and insights into unobserved parameters. The accuracy of PIML models improves with the amount of data applied. Thus, citizen scientists can play a significant role in this research, as GNSS and optical sensors embedded in consumer smartphones continue to improve. 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

Humans and machines make sense of their surroundings first and foremost using vision, and vision is limited when there is a lack of light in the familiar visible spectrum, with wavelengths from 380 nm (violet) to 750 nm (red). However, light is often plentiful in situations when visible light is lacking (such as at night), but most of it is at wavelengths far beyond the red end of the visible spectrum. This long-wave infrared (LWIR) light is responsible for the images produced by common thermal cameras, which are used in search-and-rescue operations, law enforcement, building inspections, and wildlife observation. Those uses are based on sensing only temperature or heat. Broadly, the goal of this project is to do more with LWIR sensing, such as three-dimensional imaging and remote sensing of air composition and temperature. These new capabilities may contribute to improved security, navigation, pollution monitoring, and aeronautical safety. Additional broader impact aspects of the work include workforce training at the high-school, undergraduate, and graduate levels. This project is focused on formulating and solving new inverse problems based on well-studied aspects of radiative transport. The central challenges come from these inverse problems being underdetermined because the degrees of freedom in emissivity, transmittance, and reflected light increase with the number of spectral channels measured. The project will combine physics-based approaches such as parametric modeling of transmittance with learning-based exploitation of scene structure. The specific goals include improving passive ranging and demonstrating the measurement of air temperature and gas concentrations at a distance. 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

Graphs are used to model transportation networks, social networks, the internet, and many other systems of major significance. A number of important computational problems naturally arise on these graphs. For example, one may wish to find the shortest route visiting all points – in other words, to solve the traveling salesperson problem. Unfortunately, this problem and many others like it are believed to be computationally intractable, requiring lifetimes of computation to solve exactly. However, in many cases, it is computationally tractable to find an approximate solution, such that the cost of the proposed solution is guaranteed to be not much worse than the best cost achievable. The guarantee is usually that the ratio of the cost of the generated solution to the cost of the best solution is at most some quantity, called the approximation factor. This proposal aims to find fast algorithms with low approximation factors, obtaining high-quality approximate solutions to such problems. The project also aims to increase participation in mathematics and computing via undergraduate research, an educational website, and an interdisciplinary course. More specifically, the goal of this research is to develop new tools for approximating NP-hard graph problems and to improve our understanding of powerful techniques such as max entropy sampling and iterative rounding. Through studying these methods, this project will deepen the connections between approximation algorithms and other areas of computer science and math like graph theory, graph sparsification, and the geometry of polynomials. To pursue these goals, the investigator will focus on finding better approximation algorithms for several time-tested, fundamental graph problems including the traveling salesperson problem, its asymmetric variant, and a generalization of the minimum spanning tree problem known as k-edge-connected spanning subgraph (k-ECSS). All the problems considered are related to long-standing conjectures in mathematics and combinatorial optimization which will be examined alongside the algorithmic questions. 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 is focused on critical infrastructures such as power systems, transportation networks, and emerging computing platforms. These systems are evolving into far more dynamic networks due to advancements in technology that enable greater participation and responsiveness from users. For example, in power grids, increasing numbers of customers can now react in real time to prices, incentives, and new service offerings, leading to unpredictable and highly variable power consumption patterns; increased volatility is also emerging due to data centers and electrified heating. These changing dynamics and volatility introduce complex uncertainties into the management of these infrastructures, severely limiting the effectiveness of traditional models and optimization strategies that were designed for more predictable conditions. There is a critical need to develop new methods capable of managing these new forms of uncertainty so that infrastructure operators can maintain reliable, cost-effective, and resilient service. The intellectual merit of this project includes mathematical foundations and algorithms to advance control strategies that are essential for maintaining the resilience and reliability of critical infrastructures. The broader impacts include fostering technological innovation in energy, artificial intelligence, and autonomous systems, as well as contributing to workforce development by providing mentoring, research opportunities, and new course offerings. This project seeks to develop and validate a suite of mathematical and algorithmic innovations for modeling and optimizing complex, networked infrastructures operating in environments with uncertainty. Central to the project is a novel integration of classical feedback control and incentive design with modern advances in online optimization and stochastic modeling that accounts for decision-dependent uncertainty. The technical objectives include: (i) developing real-time optimization algorithms that leverage up-to-date system measurements and stochastic gradient techniques to compute control actions and user incentives; (ii) formulating and solving multi-agent, constrained optimization problems that capture the interplay between multiple service providers competing through incentives for user participation, all while ensuring system-wide reliability and operational security; and (iii) designing adaptive methods that harness real-time or data-driven estimates of how users respond to incentives, so optimization remains robust in the face of persistent uncertainty. The project is grounded in the formalism of time-varying stochastic optimization with decision-dependent data and aims to establish general principles and practical approaches for resilient, data-driven infrastructure management. The outcome will be new strategies and algorithms for power grids and similar systems, positively impacting their reliability, resilience, and economic efficiency. 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

Three-dimensional representations of scenes are of growing importance. For example, robots and autonomous vehicles rely on 3D mapping for path planning and safe navigation, and augmented reality systems use 3D scene models when they create visual overlays with useful information. These representations are also used in many other fields, such as surgical planning, architecture, and manufacturing. The prevalent technologies to create 3D representations emit laser light toward a scene and detect reflections from the scene. Among these technologies, frequency-modulated continuous-wave (FMCW) lidar is notable for achieving very good distance accuracy while also giving the instantaneous velocities of scene objects. The challenges with FMCW lidar, however, are mostly related to the complexity and cost of the hardware. One usually needs precise control of the laser’s frequency and very fast electronics at the detector. Broadly, the goal of this project is to introduce new concepts and algorithms that improve the performance of FMCW lidar or maintain current performance while loosening the requirements on the hardware. These developments may contribute to improved safety and lower cost for robots and autonomous vehicles. The basic conventional signal processing in FMCW lidar is undoubtedly clever. It reduces the estimation of distance and velocity to frequency estimation problems, and these can be solved by finding the position of the maximum magnitude of a discrete Fourier transform. In emphasizing simplicity, this processing neglects both robustness to phase noise of the laser source and the cost of having a high sampling rate at the receiver. While some methods have been developed to mitigate phase noise, the role of the sampling rate and the reduction to a pair of frequency estimation problems has been essentially unquestioned. This project examines FMCW lidar signal processing from first principles. Coherent detection produces a certain continuous-time complex interference signal, and its entire digitally sampled version is informative about the delay and Doppler shift—not only the segments that have constant frequencies. This project seeks methods to use the whole signal to estimate delay and Doppler shift as well as possible. Preliminary results demonstrate that aliasing in the digital sampling need not be disastrous; this leads to an increase in unambiguous range. Furthermore, setting the proper end goal of delay and Doppler estimation—as opposed to the arguably misguided intermediate goal of constant beat frequency estimation—provides improved robustness to noise. The specific goals include the introduction of estimation methods with good performance at moderate computational complexity and methods for use with nonlinear frequency modulation. 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

Nontechnical Description The rapid advancement of deep neural networks (DNNs) and large language models (LLMs) is transforming many facets of modern society. These AI models are trained and deployed in data centers powered by specialized hardware such as graphics processing units (GPUs), resulting in significant energy demands and raising critical concerns around sustainability and energy security. This project aims to explore the use of light for performing neural network computations, enabling the development of energy-efficient AI hardware. Specifically, the project will leverage the integration of thin-film lithium niobate (TFLN) — a high-performance electro-optic material — with silicon photonic chip platforms to fabricate analog optical modulators that offer significantly lower loss and higher speed compared to traditional silicon-based devices. In addition, the project will design new architectures and circuit techniques to achieve high-resolution AI computation using low-precision building blocks, optimizing both efficiency and accuracy. The educational component of this project will train students in both photonic and advanced electronic chip design, equipping them with the skills essential for next-generation AI hardware development. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and AI. The industry sponsor will be actively engaged as a strategic partner to help transition the technology from research prototypes to real-world deployment. Technical Description The heterogeneously-integrated electronic-photonic AI accelerator (HIEPAA) project features cross-layer innovations from device design to integrated circuits, to wafer-scale architecture to achieve significant improvements in throughput and energy efficiency of AI accelerators. By combining co-packaged electronic-photonic ICs (EPICs) with bonded TFLN modulators promising above 50 GHz bandwidth and extremely low loss, this architecture will enable space-time multiplexed computations, delivering over 2 Tera operations per second (TOPS) per tile with 2 TOPS/W energy-efficiency and scaling to 1 ExaOPS performance at the wafer scale with 200 TOPS/W energy-efficiency. Architectural innovations will solve the long-standing challenge associated with the precision and energy consumption tradeoff of data converters and devices used in the accelerator tile by investigating residue number system (RNS)-based photonic VMM architecture. The EPIC photonic core will support coherent vector-matrix multiplication (VMM) at up to 60 GS/s symbol rates. The space-time multiplexed architecture will enable flexible VMM operations with vector lengths ranging over 1000s to perform inference on transformer-based LLM models. Fabricated PICs and EICs will be independently verified, packaged, and integrated into a system, with a packaged printed circuit board (PCB) prototype with a field-programmable gate array (FPGA)-based digital backend to validate the HIEPAA tile's performance on the state-of-the-art LLM models, which will guide wafer-scale architectural performance benchmarking. A comprehensive education and workforce development plan will focus on building expertise in electro-optic AI accelerator architecture, photonic and electronic chip design, and AI and Machine Learning. A key emphasis is to fast-track the training of students on newer FinFET CMOS nodes through a complete revamp of analog IC design courses and developing structured training material with a focus on photonics IC design. New undergraduate research opportunities will be introduced to sustain the tradition of involving undergraduates in the PIs' labs through summer scholar programs and NSF-sponsored REU initiatives. 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

Throughout the United States, elementary classrooms include students with a range of communicative practices and strengths, including strengths in speaking one or more languages, and strengths in generating and understanding different types of representations. Although an emerging body of research has begun to explore how individual teachers can productively leverage these communicative strengths toward enhanced science learning and further develop language through science, there is currently little research on how larger-scale district infrastructures can be designed to support science learning that leverages and supports language development. This project will address this critical gap by developing a process through which school districts can design comprehensive infrastructures that leverage a broad range of linguistic and communicative practices for enhanced science learning among elementary students. Specifically, researchers will partner with district experts in multiple roles, including supervisors, specialists, coaches, principals, and educators. Together, they will envision, co-design, implement, and refine materials that foster the use of research-based practices in science learning, such as using multiple forms of language and communication in the context of standards-aligned tasks in which students explain phenomena or use engineering design processes to develop solutions to problems. Research will explore whether and how different components of the district infrastructure influence elementary educators' science teaching. This project will result in a framework and processes that school districts can adapt as they develop their own instructional visions and systems of teacher support that improve science learning for all elementary students. In this exploratory project, researchers will partner with school districts to co-develop coherent infrastructures that support science learning and language development among elementary students. These infrastructures include a shared instructional vision; a tailored needs assessment; coherent professional learning materials; routines that shape teachers' daily practice; and additional tools as identified during the co-design process. Design-based implementation research will be used to develop and study infrastructure, including investigating how the elementary educators interpret the resulting recommended teaching practices, and whether and how components of the district infrastructure influence their science instruction. Research will also explore how specific envisioning and co-design processes support or constrain the design of district infrastructures and how instructional leaders in different roles learn to support science instruction that leverages the communicative strengths of elementary learners. To achieve these research purposes, the project team will use qualitative and quantitative methods to analyze a range of data sources, such as transcripts from classroom observations and interviews; artifacts from district meetings (e.g., transcripts of audio-recordings, field notes, and participant-generated products); and validated surveys. After initial co-design work with one school district, five districts, each of which includes a significant population of multilingual elementary students, will hold inter-district implementation meetings in which they iteratively discuss, evaluate, and refine the infrastructural materials, prior to their dissemination on a national level. Ultimately, this project will result in empirically-based materials that school districts can use to build coherent systems that support elementary students' science learning, forming the foundation for future success in science pathways for students across the nation who engage in a variety of linguistic and communicative practices. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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

Research using observational data and natural experiments relies on statistical analysis to provide reliable results. This project develops new methods to help data analysts test hypotheses about the causes of observed outcomes. The team improves statistical methods in a practical way that can be widely adopted by researchers, business analysts, policy analysts, and others who want to isolate the effects of changes in business and/or government methods, policies, and regulations. This award funds development of (a) computationally simple methods for sharp identification of causal parameters, (b) good estimators for the bounds on partially identified parameters, (c) computationally reliable methods to derive identifying restrictions, and (d) translational research through a publicly available code library that implements the methods and makes these advances available to the broad community that uses statistical tools to conduct program evaluation. The research advances knowledge by developing a unified framework for identification, counterfactual prediction, and specification analyses for potential outcome models through two subprojects. The first subproject uses a new approach, based on random set theory, to bound counterfactuals of interest in a class of potential outcome models. Crucially, this approach avoids computing the sharp identified set for the joint distribution of potential quantities, which is often intractable. The team obtains simple closed-form solutions in several well-studied settings where the bounds have previously been expressed through high dimensional linear programs or intractable optimization problems. The second subproject derives sharp testable implications of the modeling assumptions in a class of potential outcome models. So far, such testable implications have been studied case-by-case in a limited set of models. Using a novel graph-based representation of the model, the team provides a systematic way of deriving sharp testable implications of commonly used identifying assumptions. The research achieves broader impacts through those who conduct empirical research and program evaluation via a translational research component. The team provides practitioners with an accessible “guided tour” of the existing results, focusing on implementation. The guide discusses which of the available approaches (moment inequalities, support functions, linear programs) leads to the most tractable description of the identified set and provide guidance on estimation and inference procedures. Furthermore, the PIs develop a Python library associated with the guided-tour paper and the subprojects described above. The library is accompanied by “hands-on” tutorials hosted on a GitHub repository. 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 expands our understanding of what modern artificial intelligence (AI) systems know, and our ability to precisely control them. Large language models (LLMs), a type of AI, have proven useful on a wide variety of tasks, yet their internal decision-making processes remain largely a mystery. Public understanding of these systems is often based on observing their successes and failures, and there is often an assumption that LLMs represent concepts and reason with them in human-like ways. This project challenges that assumption by looking inside the models themselves. While current research has identified simple, binary concepts, or features that are either “on” or “off”, most real-world concepts are more complex. They can be multi-valued, like educational attainment, which can take values high school, college, professional degree, or among others. Concepts can also exist on a continuous spectrum, like measurements of distance or time. By developing methods to find and understand these more sophisticated internal representations, this work aims to ensure that AI systems are reliable, safe, and can be controlled when needed. The first goal of this project is to move beyond binary concepts when interpreting the inner workings of AI systems, and to directly search for and characterize these multi-valued and continuous concepts. Leveraging this understanding, the second goal of this project will then be to develop tools for applying this understanding to improve AI. For example, if we can find out which parts of an LLM perform logical reasoning, we can directly update them without harming the rest of the model, or directly control them to change how the LLM reasons. To achieve its goals, this award supports research in three main stages. First, the project will develop and test methods for discovering multi-dimensional and continuous features within LLMs. This involves using techniques like sparse dictionary learning and causal analysis to move beyond individual neurons and identify the underlying concepts they represent. Second, the project will characterize the geometric structure of these discovered features to understand how a model organizes related values. For example, are the values of an ordinal variable organized along a line, within a more complex, low-dimensional subspace, or neither? Finally, the project will leverage this structural understanding to create new techniques for precise model control. Instead of simply turning features on or off, these methods will allow for nuanced interventions, such as adjusting a concept’s influence on a model’s reasoning process. The developed tools and insights can enable developers and scientists to fix AI flaws without costly retraining. This democratizes AI research by enabling labs with smaller computational resources to participate in model improvement. The project will also support education by demystifying how AI systems work, fostering more practical and scientifically grounded conversations about their capabilities and limitations. 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

Artificial intelligence (AI) tools are increasingly used in healthcare systems to help diagnose diseases from medical images such as Computerized Tomography (CT) scans and mammograms. While these systems can be highly accurate, they often learn unintended patterns, such as utilizing hospital-specific markings rather than markers of disease. This can lead to uneven or unsafe performance. Compounding this problem, most AI models are “black boxes,” offering little insight into how decisions are made or why mistakes occur. Identifying the source of mistakes is challenging for AI developers due to the knowledge gap between AI scientists and clinicians, and rectifying those mistakes is difficult for doctors because of the inherent complexity of the AI systems. This project develops new methods to make these systems more transparent and adjustable, allowing clinicians and researchers to understand, diagnose, and correct AI errors without needing to rebuild the models entirely. For instance, if a breast cancer risk model performs better on one group of patients than another, the new tools can help identify the cause and allow clinicians to intervene. In addition to improving fairness and reliability in medical AI, this project will also advance education and workforce development by involving students in interdisciplinary research at the intersection of medicine, computer science, and engineering. To reach these objectives, this project utilizes and enhances innovative computational methods, drawing on recent advancements in AI. The investigator will employ a generative model designed to synthesize new images that illustrate the disease's progression as perceived by a black box AI model. This kind of visualization is more comprehensible for clinicians, such as radiologists, aiding in the identification of error sources. The investigator uses large language models, similar to commercial chatbots, as a "translator” between clinicians, AI scientists, and the black box AI models. This award supports the development of a new framework that leverages vision language models (VLLMs) to improve the interpretability and steerability of domain specific models (DSMs) in medical imaging. The project will integrate large-scale, multimodal foundation models with symbolic reasoning techniques to extract verifiable, human-understandable rules from deep neural networks. In Aim 1, the investigators will construct an anatomically aware VLLM capable of encoding and generating 3D medical images and radiology reports. Developing an anatomically aware generative model is essential to reduce the chance of “hallucination,” a common problem in generative AI. In Aim 2, this model will be used to break down existing medical AI systems into understandable components—symbolic rules and programs—that reflect how the model makes decisions. These components will help clinicians and AI researchers identify errors and guide the system’s behavior. The premise of Aim 2 is that symbolic models, including computer programs and logical expressions, are more comprehensible and verifiable. This leads to AI models that are more trustworthy and steerable AI models, which is crucial in the healthcare domain. The approach will be evaluated using largescale data on breast cancer and chronic lung disease, with the goal of improving fairness and reliability in medical AI. The research will be validated on real-world tasks involving breast cancer risk prediction and chronic lung disease, aiming to improve model robustness across diverse patient populations. The framework holds the potential to transform current practices in clinical AI by embedding clinicians more directly in the model development and deployment cycle. 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

Biological systems use complex networks of sensors and regulatory pathways to respond to and change their surrounding environments. In recent years, synthetic biologists strived to modify and repurpose these networks to change what organisms can do, and to use the modified or synthetic organisms for biotechnology, biomedicine and agriculture applications. However, our lack of knowledge about network function and organization often made it difficult to rationally design and successfully repurpose these networks, which in turn made it difficult to modify organisms in a predicable way. This project aims to define some of the underlying principles behind the function of such networks, and ultimately to use this knowledge to create synthetic organisms with particular downstream applications. Additionally, the project includes support for educational outreach activities, including a multi-day workshop for high school students that introduces cutting-edge concepts from synthetic biology, electrical circuit design, and data analysis techniques. By training young students and supporting undergraduate researchers, the project contributes to developing the next generation of scientific talent. This research project will identify the fidelity with which downstream genes can be controlled as a function of regulatory network architecture. Signal propagation will be tested in synthetic networks designed to systematically assess the impact of network architecture, and in the endogenous PhoP stress response network. The project uses an optogenetic strategy, where light will be used to drive time-varying expression of a transcription factor that regulates downstream target genes. First, the researchers will use well-defined synthetic networks to test how dynamic input signals are propagated within regulatory networks with increasing levels of complexity. The synthetic circuits will encompass common motifs from biological networks including multi-layer regulatory cascades and networks involving negative and positive feedback. Second, the project will test the extent to which it is possible for an upstream time-varying input to precisely drive the expression of a downstream target from which it may be distantly removed. The project will establish which downstream dynamics are possible to achieve as a function of network architecture. Third, signal propagation and control will be tested in the context of the endogenous PhoP stress response network. This network is subject to additional regulation and plays a critical role in regulating acid stress resistance, and these studies will be used to probe natural network dynamics and their phenotypic consequences. Overall, this project capitalizes on optogenetic methods for driving gene expression in single cells, using programmed light signals, deep learning models, and feedback control to precisely test how dynamic biological signals are utilized within regulatory networks. 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

Nitrogen (N) is essential for life, yet most of it exists as inert atmospheric N2, inaccessible to most living organisms. Only a small fraction of N – reactive N (Nr)– is biologically available and directly usable by plants and microbes. Bioavailable forms of N are exchanged between the atmosphere and terrestrial environments primarily through emission and precipitation, or wet deposition. Understandings of deposition are shifting, from a general focus on inorganic acidic solutes such as nitrate to a more complex and dynamic picture inclusive of dissolved organic N which encompasses any molecule with an organic carbon (C) backbone that also contains N. The chemical composition of precipitation can reveal landscape-scale emission sources of organic C, and by association N, to the atmosphere and the degree to which these complex organic molecules may participate in C and/or N cycles once deposited into ecosystems. The goal of this postdoctoral fellowship project is to advance the understanding of how organic C and N in precipitation originate, transform, and impact ecosystems. The research will explicitly test whether events such as forest fires, transpiration, and agricultural soil decomposition may individually or collectively drive the trends in organic matter deposition. The broader impacts of this research encompass quantifying biogeochemical links between the atmosphere and biosphere while also achieving stakeholder engagement, interdisciplinary collaboration, and teaching and mentoring of graduate and undergraduate students. The overarching research goal is to determine the contribution of organic matter in wet deposition, quantify how it impacts forested ecosystems, and identify the role of emission sources. During this fellowship, new atmospheric deposition samples will be collected and an archive of existing deposition samples will be leveraged, precipitation bioavailability and airmass origin will be evaluated, high-resolution molecular analyses will be conducted, and an Earth system model will be utilized to address several research questions. (1) What is the concentration, flux, and relative abundance of bioavailable organic compounds in wet deposition? (2) How does incorporating organic N into total deposition fluxes alter deposition gradients and the extent of area exceeding an ecosystems capacity to process N? (3) How responsive are wet deposition organic matter fluxes to landscape emissions from biogenic and anthropogenic sources? This interdisciplinary approach integrates atmospheric science, Earth systems science, and ecology by examining how climate dynamics, land use, and wildfire activity shape wet deposition chemistry and the cycling of essential elements. 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 RET Site: Integrated Nanomanufacturing (INM) will offer transformative research experiences in nanotechnology and nanoengineering to 30 local high school teachers and community college faculty. This area of research underpins a broad spectrum of application, including biotechnology and healthcare, semiconductor electronics, computing, artificial intelligence, and energy. Participants will engage in mentored laboratory research in thriving academic engineering research laboratories. They will translate their research into informative classroom experiences at their schools and integrate these lessons into curriculum enhancements that align with educational frameworks. Participants will engage in comprehensive, hands-on laboratory experiences in a six-week summer experience, including group training sessions on safety and advanced manufacturing techniques. In addition, workshops to foster educator networking, technical communication, and research ethics will be featured. The project aims to strengthen K-12 STEM education in support of building a robust STEM workforce to meet strong regional demand in nanomanufacturing. Participants will acquire skills and expertise in INM and engineering to bring to their classrooms, with follow-up support from BU faculty and graduate student mentors. The primary objectives are to deepen participants’ understanding of nanomanufacturing research, support the translation and integration of engineering concepts into their classroom teaching, and build sustainable partnerships with RET participants and schools to inspire and prepare their students for STEM degrees and careers. Comprehensive, hands-on laboratory experiences and expert mentorship will empower teachers to engage in authentic research within integrated nanomanufacturing (INM). Participants will engage in group training sessions on photolithography, 3D printing, electron microscopy, and nanofabrication. Research activities will be translated into standards-aligned curriculum, which will be shared with other educators via the TeachEngineering digital library and presented at professional conferences. 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

Understanding the integral and rational solutions to polynomial equations has remained a question of fundamental importance for centuries. For instance, a problem formulated by Diophantus in the third century, when translated into the language of a polynomial equation, amounts to determining the rational solutions (x, y) to the equation y^2 = x^6 + x^2 + 1. This problem remained unsolved until the work of Wetherell in 1997. Studying this algebraic equation from the point of view of geometry yields a curve, and more precisely, a curve of genus 2. While curves of genus 2 or more are known to have finitely many rational points by the work of Faltings in 1983, as of yet there is no algorithm to determine these finite sets in general. In particular, when the curve’s Jacobian rank is equal to or larger than the genus, there are many challenges that remain. In this project, the PI will study methods for explicitly determining the finite set of rational points on curves of genus 2 or more. The PI will also organize educational activities to build the mathematical pipeline and mentor students and postdoctoral researchers. One promising approach for determining the finite set of rational points on curves of genus 2 or more, regardless of Jacobian rank, is through Kim's nonabelian Chabauty program and the computation of Selmer sets. Kim has conjectured that Selmer sets in depth n are finite for n sufficiently large, and that moreover, that these Selmer sets eventually precisely cut out the set of rational points. Several components of the first nonabelian part of this program (i.e., in depth 2) have been made algorithmic by the PI and her collaborators. In this project, the PI proposes a careful study of Selmer sets in depth 2 and 3 for new classes of curves. She will do this by carrying out explicit computations that are motivated by Kim’s conjecture. Additionally, she will compute rational points on a new database of curves in the L-functions and Modular Forms Database (LMFDB), using all available Diophantine tools at scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.